Design Method of Digital Optimal Control Scheme and Multiple Paralleled Bridge Type Current Amplifier for Generating Gradient Magnetic Fields in MRI Systems

NASA Astrophysics Data System (ADS)

Watanabe, Shuji; Takano, Hiroshi; Fukuda, Hiroya; Hiraki, Eiji; Nakaoka, Mutsuo

This paper deals with a digital control scheme of multiple paralleled high frequency switching current amplifier with four-quadrant chopper for generating gradient magnetic fields in MRI (Magnetic Resonance Imaging) systems. In order to track high precise current pattern in Gradient Coils (GC), the proposal current amplifier cancels the switching current ripples in GC with each other and designed optimum switching gate pulse patterns without influences of the large filter current ripple amplitude. The optimal control implementation and the linear control theory in GC current amplifiers have affinity to each other with excellent characteristics. The digital control system can be realized easily through the digital control implementation, DSPs or microprocessors. Multiple-parallel operational microprocessors realize two or higher paralleled GC current pattern tracking amplifier with optimal control design and excellent results are given for improving the image quality of MRI systems.

Selectivity optimization in green chromatography by gradient stationary phase optimized selectivity liquid chromatography.

PubMed

Chen, Kai; Lynen, Frédéric; De Beer, Maarten; Hitzel, Laure; Ferguson, Paul; Hanna-Brown, Melissa; Sandra, Pat

2010-11-12

Stationary phase optimized selectivity liquid chromatography (SOSLC) is a promising technique to optimize the selectivity of a given separation by using a combination of different stationary phases. Previous work has shown that SOSLC offers excellent possibilities for method development, especially after the recent modification towards linear gradient SOSLC. The present work is aimed at developing and extending the SOSLC approach towards selectivity optimization and method development for green chromatography. Contrary to current LC practices, a green mobile phase (water/ethanol/formic acid) is hereby preselected and the composition of the stationary phase is optimized under a given gradient profile to obtain baseline resolution of all target solutes in the shortest possible analysis time. With the algorithm adapted to the high viscosity property of ethanol, the principle is illustrated with a fast, full baseline resolution for a randomly selected mixture composed of sulphonamides, xanthine alkaloids and steroids. Copyright © 2010 Elsevier B.V. All rights reserved.

High-Lift Optimization Design Using Neural Networks on a Multi-Element Airfoil

NASA Technical Reports Server (NTRS)

Greenman, Roxana M.; Roth, Karlin R.; Smith, Charles A. (Technical Monitor)

1998-01-01

The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag, and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural networks were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 83% compared with traditional gradient-based optimization procedures for multiple optimization runs.

Gradient Material Strategies for Hydrogel Optimization in Tissue Engineering Applications

PubMed Central

2018-01-01

Although a number of combinatorial/high-throughput approaches have been developed for biomaterial hydrogel optimization, a gradient sample approach is particularly well suited to identify hydrogel property thresholds that alter cellular behavior in response to interacting with the hydrogel due to reduced variation in material preparation and the ability to screen biological response over a range instead of discrete samples each containing only one condition. This review highlights recent work on cell–hydrogel interactions using a gradient material sample approach. Fabrication strategies for composition, material and mechanical property, and bioactive signaling gradient hydrogels that can be used to examine cell–hydrogel interactions will be discussed. The effects of gradients in hydrogel samples on cellular adhesion, migration, proliferation, and differentiation will then be examined, providing an assessment of the current state of the field and the potential of wider use of the gradient sample approach to accelerate our understanding of matrices on cellular behavior. PMID:29485612

Gradient and shim technologies for ultra high field MRI

PubMed Central

Winkler, Simone A.; Schmitt, Franz; Landes, Hermann; DeBever, Josh; Wade, Trevor; Alejski, Andrew

2017-01-01

Ultra High Field (UHF) MRI requires improved gradient and shim performance to fully realize the promised gains (SNR as well as spatial, spectral, diffusion resolution) that higher main magnetic fields offer. Both the more challenging UHF environment by itself, as well as the higher currents used in high performance coils, require a deeper understanding combined with sophisticated engineering modeling and construction, to optimize gradient and shim hardware for safe operation and for highest image quality. This review summarizes the basics of gradient and shim technologies, and outlines a number of UHF-related challenges and solutions. In particular, Lorentz forces, vibroacoustics, eddy currents, and peripheral nerve stimulation are discussed. Several promising UHF-relevant gradient concepts are described, including insertable gradient coils aimed at higher performance neuroimaging. PMID:27915120

Gradient design for liquid chromatography using multi-scale optimization.

PubMed

López-Ureña, S; Torres-Lapasió, J R; Donat, R; García-Alvarez-Coque, M C

2018-01-26

In reversed phase-liquid chromatography, the usual solution to the "general elution problem" is the application of gradient elution with programmed changes of organic solvent (or other properties). A correct quantification of chromatographic peaks in liquid chromatography requires well resolved signals in a proper analysis time. When the complexity of the sample is high, the gradient program should be accommodated to the local resolution needs of each analyte. This makes the optimization of such situations rather troublesome, since enhancing the resolution for a given analyte may imply a collateral worsening of the resolution of other analytes. The aim of this work is to design multi-linear gradients that maximize the resolution, while fulfilling some restrictions: all peaks should be eluted before a given maximal time, the gradient should be flat or increasing, and sudden changes close to eluting peaks are penalized. Consequently, an equilibrated baseline resolution for all compounds is sought. This goal is achieved by splitting the optimization problem in a multi-scale framework. In each scale κ, an optimization problem is solved with N κ  ≈ 2 κ variables that are used to build the gradients. The N κ variables define cubic splines written in terms of a B-spline basis. This allows expressing gradients as polygonals of M points approximating the splines. The cubic splines are built using subdivision schemes, a technique of fast generation of smooth curves, compatible with the multi-scale framework. Owing to the nature of the problem and the presence of multiple local maxima, the algorithm used in the optimization problem of each scale κ should be "global", such as the pattern-search algorithm. The multi-scale optimization approach is successfully applied to find the best multi-linear gradient for resolving a mixture of amino acid derivatives. Copyright © 2017 Elsevier B.V. All rights reserved.

Conjugate-Gradient Neural Networks in Classification of Multisource and Very-High-Dimensional Remote Sensing Data

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Swain, P. H.; Ersoy, O. K.

1993-01-01

Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data, but do not compare as well with statistical methods in classification of very-high-dimentional data.

Improving the FLORIS wind plant model for compatibility with gradient-based optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomas, Jared J.; Gebraad, Pieter MO; Ning, Andrew

The FLORIS (FLOw Redirection and Induction in Steady-state) model, a parametric wind turbine wake model that predicts steady-state wake characteristics based on wind turbine position and yaw angle, was developed for optimization of control settings and turbine locations. This article provides details on changes made to the FLORIS model to make the model more suitable for gradient-based optimization. Changes to the FLORIS model were made to remove discontinuities and add curvature to regions of non-physical zero gradient. Exact gradients for the FLORIS model were obtained using algorithmic differentiation. A set of three case studies demonstrate that using exact gradients withmore » gradient-based optimization reduces the number of function calls by several orders of magnitude. The case studies also show that adding curvature improves convergence behavior, allowing gradient-based optimization algorithms used with the FLORIS model to more reliably find better solutions to wind farm optimization problems.« less

Combinatorial techniques to efficiently investigate and optimize organic thin film processing and properties.

PubMed

Wieberger, Florian; Kolb, Tristan; Neuber, Christian; Ober, Christopher K; Schmidt, Hans-Werner

2013-04-08

In this article we present several developed and improved combinatorial techniques to optimize processing conditions and material properties of organic thin films. The combinatorial approach allows investigations of multi-variable dependencies and is the perfect tool to investigate organic thin films regarding their high performance purposes. In this context we develop and establish the reliable preparation of gradients of material composition, temperature, exposure, and immersion time. Furthermore we demonstrate the smart application of combinations of composition and processing gradients to create combinatorial libraries. First a binary combinatorial library is created by applying two gradients perpendicular to each other. A third gradient is carried out in very small areas and arranged matrix-like over the entire binary combinatorial library resulting in a ternary combinatorial library. Ternary combinatorial libraries allow identifying precise trends for the optimization of multi-variable dependent processes which is demonstrated on the lithographic patterning process. Here we verify conclusively the strong interaction and thus the interdependency of variables in the preparation and properties of complex organic thin film systems. The established gradient preparation techniques are not limited to lithographic patterning. It is possible to utilize and transfer the reported combinatorial techniques to other multi-variable dependent processes and to investigate and optimize thin film layers and devices for optical, electro-optical, and electronic applications.

Imprinting high-gradient topographical structures onto optical surfaces using magnetorheological finishing: manufacturing corrective optical elements for high-power laser applications.

PubMed

Menapace, Joseph A; Ehrmann, Paul E; Bayramian, Andrew J; Bullington, Amber; Di Nicola, Jean-Michel G; Haefner, Constantin; Jarboe, Jeffrey; Marshall, Christopher; Schaffers, Kathleen I; Smith, Cal

2016-07-01

Corrective optical elements form an important part of high-precision optical systems. We have developed a method to manufacture high-gradient corrective optical elements for high-power laser systems using deterministic magnetorheological finishing (MRF) imprinting technology. Several process factors need to be considered for polishing ultraprecise topographical structures onto optical surfaces using MRF. They include proper selection of MRF removal function and wheel sizes, detailed MRF tool and interferometry alignment, and optimized MRF polishing schedules. Dependable interferometry also is a key factor in high-gradient component manufacture. A wavefront attenuating cell, which enables reliable measurement of gradients beyond what is attainable using conventional interferometry, is discussed. The results of MRF imprinting a 23 μm deep structure containing gradients over 1.6 μm / mm onto a fused-silica window are presented as an example of the technique's capabilities. This high-gradient element serves as a thermal correction plate in the high-repetition-rate advanced petawatt laser system currently being built at Lawrence Livermore National Laboratory.

Imprinting high-gradient topographical structures onto optical surfaces using magnetorheological finishing: Manufacturing corrective optical elements for high-power laser applications

DOE PAGES

Menapace, Joseph A.; Ehrmann, Paul E.; Bayramian, Andrew J.; ...

2016-03-15

Corrective optical elements form an important part of high-precision optical systems. We have developed a method to manufacture high-gradient corrective optical elements for high-power laser systems using deterministic magnetorheological finishing (MRF) imprinting technology. Several process factors need to be considered for polishing ultraprecise topographical structures onto optical surfaces using MRF. They include proper selection of MRF removal function and wheel sizes, detailed MRF tool and interferometry alignment, and optimized MRF polishing schedules. Dependable interferometry also is a key factor in high-gradient component manufacture. A wavefront attenuating cell, which enables reliable measurement of gradients beyond what is attainable using conventional interferometry,more » is discussed. The results of MRF imprinting a 23 μm deep structure containing gradients over 1.6 μm / mm onto a fused-silica window are presented as an example of the technique’s capabilities. As a result, this high-gradient element serves as a thermal correction plate in the high-repetition-rate advanced petawatt laser system currently being built at Lawrence Livermore National Laboratory.« less

Microfluidic Synthesis of Composite Cross-Gradient Materials for Investigating Cell–Biomaterial Interactions

PubMed Central

He, Jiankang; Du, Yanan; Guo, Yuqi; Hancock, Matthew J.; Wang, Ben; Shin, Hyeongho; Wu, Jinhui; Li, Dichen; Khademhosseini, Ali

2010-01-01

Combinatorial material synthesis is a powerful approach for creating composite material libraries for the high-throughput screening of cell–material interactions. Although current combinatorial screening platforms have been tremendously successful in identifying target (termed “hit”) materials from composite material libraries, new material synthesis approaches are needed to further optimize the concentrations and blending ratios of the component materials. Here we employed a microfluidic platform to rapidly synthesize composite materials containing cross-gradients of gelatin and chitosan for investigating cell–biomaterial interactions. The microfluidic synthesis of the cross-gradient was optimized experimentally and theoretically to produce quantitatively controllable variations in the concentrations and blending ratios of the two components. The anisotropic chemical compositions of the gelatin/chitosan cross-gradients were characterized by Fourier transform infrared spectrometry and X-ray photoelectron spectrometry. The three-dimensional (3D) porous gelatin/chitosan cross-gradient materials were shown to regulate the cellular morphology and proliferation of smooth muscle cells (SMCs) in a gradient-dependent manner. We envision that our microfluidic cross-gradient platform may accelerate the material development processes involved in a wide range of biomedical applications. PMID:20721897

Structural optimization with approximate sensitivities

NASA Technical Reports Server (NTRS)

Patnaik, S. N.; Hopkins, D. A.; Coroneos, R.

1994-01-01

Computational efficiency in structural optimization can be enhanced if the intensive computations associated with the calculation of the sensitivities, that is, gradients of the behavior constraints, are reduced. Approximation to gradients of the behavior constraints that can be generated with small amount of numerical calculations is proposed. Structural optimization with these approximate sensitivities produced correct optimum solution. Approximate gradients performed well for different nonlinear programming methods, such as the sequence of unconstrained minimization technique, method of feasible directions, sequence of quadratic programming, and sequence of linear programming. Structural optimization with approximate gradients can reduce by one third the CPU time that would otherwise be required to solve the problem with explicit closed-form gradients. The proposed gradient approximation shows potential to reduce intensive computation that has been associated with traditional structural optimization.

Two-Dimensional High-Lift Aerodynamic Optimization Using Neural Networks

NASA Technical Reports Server (NTRS)

Greenman, Roxana M.

1998-01-01

The high-lift performance of a multi-element airfoil was optimized by using neural-net predictions that were trained using a computational data set. The numerical data was generated using a two-dimensional, incompressible, Navier-Stokes algorithm with the Spalart-Allmaras turbulence model. Because it is difficult to predict maximum lift for high-lift systems, an empirically-based maximum lift criteria was used in this study to determine both the maximum lift and the angle at which it occurs. The 'pressure difference rule,' which states that the maximum lift condition corresponds to a certain pressure difference between the peak suction pressure and the pressure at the trailing edge of the element, was applied and verified with experimental observations for this configuration. Multiple input, single output networks were trained using the NASA Ames variation of the Levenberg-Marquardt algorithm for each of the aerodynamic coefficients (lift, drag and moment). The artificial neural networks were integrated with a gradient-based optimizer. Using independent numerical simulations and experimental data for this high-lift configuration, it was shown that this design process successfully optimized flap deflection, gap, overlap, and angle of attack to maximize lift. Once the neural nets were trained and integrated with the optimizer, minimal additional computer resources were required to perform optimization runs with different initial conditions and parameters. Applying the neural networks within the high-lift rigging optimization process reduced the amount of computational time and resources by 44% compared with traditional gradient-based optimization procedures for multiple optimization runs.

Automated correction of improperly rotated diffusion gradient orientations in diffusion weighted MRI.

PubMed

Jeurissen, Ben; Leemans, Alexander; Sijbers, Jan

2014-10-01

Ensuring one is using the correct gradient orientations in a diffusion MRI study can be a challenging task. As different scanners, file formats and processing tools use different coordinate frame conventions, in practice, users can end up with improperly oriented gradient orientations. Using such wrongly oriented gradient orientations for subsequent diffusion parameter estimation will invalidate all rotationally variant parameters and fiber tractography results. While large misalignments can be detected by visual inspection, small rotations of the gradient table (e.g. due to angulation of the acquisition plane), are much more difficult to detect. In this work, we propose an automated method to align the coordinate frame of the gradient orientations with that of the corresponding diffusion weighted images, using a metric based on whole brain fiber tractography. By transforming the gradient table and measuring the average fiber trajectory length, we search for the transformation that results in the best global 'connectivity'. To ensure a fast calculation of the metric we included a range of algorithmic optimizations in our tractography routine. To make the optimization routine robust to spurious local maxima, we use a stochastic optimization routine that selects a random set of seed points on each evaluation. Using simulations, we show that our method can recover the correct gradient orientations with high accuracy and precision. In addition, we demonstrate that our technique can successfully recover rotated gradient tables on a wide range of clinically realistic data sets. As such, our method provides a practical and robust solution to an often overlooked pitfall in the processing of diffusion MRI. Copyright © 2014 Elsevier B.V. All rights reserved.

Monoplane 3D-2D registration of cerebral angiograms based on multi-objective stratified optimization

NASA Astrophysics Data System (ADS)

Aksoy, T.; Špiclin, Ž.; Pernuš, F.; Unal, G.

2017-12-01

Registration of 3D pre-interventional to 2D intra-interventional medical images has an increasingly important role in surgical planning, navigation and treatment, because it enables the physician to co-locate depth information given by pre-interventional 3D images with the live information in intra-interventional 2D images such as x-ray. Most tasks during image-guided interventions are carried out under a monoplane x-ray, which is a highly ill-posed problem for state-of-the-art 3D to 2D registration methods. To address the problem of rigid 3D-2D monoplane registration we propose a novel multi-objective stratified parameter optimization, wherein a small set of high-magnitude intensity gradients are matched between the 3D and 2D images. The stratified parameter optimization matches rotation templates to depth templates, first sampled from projected 3D gradients and second from the 2D image gradients, so as to recover 3D rigid-body rotations and out-of-plane translation. The objective for matching was the gradient magnitude correlation coefficient, which is invariant to in-plane translation. The in-plane translations are then found by locating the maximum of the gradient phase correlation between the best matching pair of rotation and depth templates. On twenty pairs of 3D and 2D images of ten patients undergoing cerebral endovascular image-guided intervention the 3D to monoplane 2D registration experiments were setup with a rather high range of initial mean target registration error from 0 to 100 mm. The proposed method effectively reduced the registration error to below 2 mm, which was further refined by a fast iterative method and resulted in a high final registration accuracy (0.40 mm) and high success rate (> 96%). Taking into account a fast execution time below 10 s, the observed performance of the proposed method shows a high potential for application into clinical image-guidance systems.

Optimal Design of Gradient Materials and Bi-Level Optimization of Topology Using Targets (BOTT)

NASA Astrophysics Data System (ADS)

Garland, Anthony

The objective of this research is to understand the fundamental relationships necessary to develop a method to optimize both the topology and the internal gradient material distribution of a single object while meeting constraints and conflicting objectives. Functionally gradient material (FGM) objects possess continuous varying material properties throughout the object, and they allow an engineer to tailor individual regions of an object to have specific mechanical properties by locally modifying the internal material composition. A variety of techniques exists for topology optimization, and several methods exist for FGM optimization, but combining the two together is difficult. Understanding the relationship between topology and material gradient optimization enables the selection of an appropriate model and the development of algorithms, which allow engineers to design high-performance parts that better meet design objectives than optimized homogeneous material objects. For this research effort, topology optimization means finding the optimal connected structure with an optimal shape. FGM optimization means finding the optimal macroscopic material properties within an object. Tailoring the material constitutive matrix as a function of position results in gradient properties. Once, the target macroscopic properties are known, a mesostructure or a particular material nanostructure can be found which gives the target material properties at each macroscopic point. This research demonstrates that topology and gradient materials can both be optimized together for a single part. The algorithms use a discretized model of the domain and gradient based optimization algorithms. In addition, when considering two conflicting objectives the algorithms in this research generate clear 'features' within a single part. This tailoring of material properties within different areas of a single part (automated design of 'features') using computational design tools is a novel benefit of gradient material designs. A macroscopic gradient can be achieved by varying the microstructure or the mesostructures of an object. The mesostructure interpretation allows for more design freedom since the mesostructures can be tuned to have non-isotropic material properties. A new algorithm called Bi-level Optimization of Topology using Targets (BOTT) seeks to find the best distribution of mesostructure designs throughout a single object in order to minimize an objective value. On the macro level, the BOTT algorithm optimizes the macro topology and gradient material properties within the object. The BOTT algorithm optimizes the material gradient by finding the best constitutive matrix at each location with the object. In order to enhance the likelihood that a mesostructure can be generated with the same equivalent constitutive matrix, the variability of the constitutive matrix is constrained to be an orthotropic material. The stiffness in the X and Y directions (of the base coordinate system) can change in addition to rotating the orthotropic material to align with the loading at each region. Second, the BOTT algorithm designs mesostructures with macroscopic properties equal to the target properties found in step one while at the same time the algorithm seeks to minimize material usage in each mesostructure. The mesostructure algorithm maximizes the strain energy of the mesostructures unit cell when a pseudo strain is applied to the cell. A set of experiments reveals the fundamental relationship between target cell density and the strain (or pseudo strain) applied to a unit cell and the output effective properties of the mesostructure. At low density, a few mesostructure unit cell design are possible, while at higher density the mesostructure unit cell designs have many possibilities. Therefore, at low densities the effective properties of the mesostructure are a step function of the applied pseudo strain. At high densities, the effective properties of the mesostructure are continuous function of the applied pseudo strain. Finally, the macro and mesostructure designs are coordinated so that the macro and meso levels agree on the material properties at each macro region. In addition, a coordination effort seeks to coordinate the boundaries of adjacent mesostructure designs so that the macro load path is transmitted from one mesostructure design to its neighbors. The BOTT algorithm has several advantages over existing algorithms within the literature. First, the BOTT algorithm significantly reduces the computational power required to run the algorithm. Second, the BOTT algorithm indirectly enforces a minimum mesostructure density constraint which increases the manufacturability of the final design. Third, the BOTT algorithm seeks to transfer the load from one mesostructure to its neighbors by coordinating the boundaries of adjacent mesostructure designs. However, the BOTT algorithm can still be improved since it may have difficulty converging due to the step function nature of the mesostructure design problem at low density.

Preliminary Structural Design Using Topology Optimization with a Comparison of Results from Gradient and Genetic Algorithm Methods

NASA Technical Reports Server (NTRS)

Burt, Adam O.; Tinker, Michael L.

2014-01-01

In this paper, genetic algorithm based and gradient-based topology optimization is presented in application to a real hardware design problem. Preliminary design of a planetary lander mockup structure is accomplished using these methods that prove to provide major weight savings by addressing the structural efficiency during the design cycle. This paper presents two alternative formulations of the topology optimization problem. The first is the widely-used gradient-based implementation using commercially available algorithms. The second is formulated using genetic algorithms and internally developed capabilities. These two approaches are applied to a practical design problem for hardware that has been built, tested and proven to be functional. Both formulations converged on similar solutions and therefore were proven to be equally valid implementations of the process. This paper discusses both of these formulations at a high level.

Data-driven gradient algorithm for high-precision quantum control

NASA Astrophysics Data System (ADS)

Wu, Re-Bing; Chu, Bing; Owens, David H.; Rabitz, Herschel

2018-04-01

In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.

Investigations into dual-grating THz-driven accelerators

NASA Astrophysics Data System (ADS)

Wei, Y.; Ischebeck, R.; Dehler, M.; Ferrari, E.; Hiller, N.; Jamison, S.; Xia, G.; Hanahoe, K.; Li, Y.; Smith, J. D. A.; Welsch, C. P.

2018-01-01

Advanced acceleration technologies are receiving considerable interest in order to miniaturize future particle accelerators. One such technology is the dual-grating dielectric structures, which can support accelerating fields one to two orders of magnitude higher than the metal RF cavities in conventional accelerators. This opens up the possibility of enabling high accelerating gradients of up to several GV/m. This paper investigates numerically a quartz dual-grating structure which is driven by THz pulses to accelerate electrons. Geometry optimizations are carried out to achieve the trade-offs between accelerating gradient and vacuum channel gap. A realistic electron bunch available from the future Compact Linear Accelerator for Research and Applications (CLARA) is loaded into an optimized 100-period dual-grating structure for a detailed wakefield study. A THz pulse is then employed to interact with this CLARA bunch in the optimized structure. The computed beam quality is analyzed in terms of emittance, energy spread and loaded accelerating gradient. The simulations show that an accelerating gradient of 348 ± 12 MV/m with an emittance growth of 3.0% can be obtained.

Dai-Kou type conjugate gradient methods with a line search only using gradient.

PubMed

Huang, Yuanyuan; Liu, Changhe

2017-01-01

In this paper, the Dai-Kou type conjugate gradient methods are developed to solve the optimality condition of an unconstrained optimization, they only utilize gradient information and have broader application scope. Under suitable conditions, the developed methods are globally convergent. Numerical tests and comparisons with the PRP+ conjugate gradient method only using gradient show that the methods are efficient.

Tunable, Flexible, and Efficient Optimization of Control Pulses for Practical Qubits

NASA Astrophysics Data System (ADS)

Machnes, Shai; Assémat, Elie; Tannor, David; Wilhelm, Frank K.

2018-04-01

Quantum computation places very stringent demands on gate fidelities, and experimental implementations require both the controls and the resultant dynamics to conform to hardware-specific constraints. Superconducting qubits present the additional requirement that pulses must have simple parameterizations, so they can be further calibrated in the experiment, to compensate for uncertainties in system parameters. Other quantum technologies, such as sensing, require extremely high fidelities. We present a novel, conceptually simple and easy-to-implement gradient-based optimal control technique named gradient optimization of analytic controls (GOAT), which satisfies all the above requirements, unlike previous approaches. To demonstrate GOAT's capabilities, with emphasis on flexibility and ease of subsequent calibration, we optimize fast coherence-limited pulses for two leading superconducting qubits architectures—flux-tunable transmons and fixed-frequency transmons with tunable couplers.

Study of genetic direct search algorithms for function optimization

NASA Technical Reports Server (NTRS)

Zeigler, B. P.

1974-01-01

The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.

Design optimization using adjoint of Long-time LES for the trailing edge of a transonic turbine vane

NASA Astrophysics Data System (ADS)

Talnikar, Chaitanya; Wang, Qiqi

2017-11-01

Adjoint-based design optimization methods have been applied to low-fidelity simulation methods like Reynolds Averaged Navier-Stokes (RANS) and are useful for designing fluid machinery components. But to reliably capture the complex flow phenomena involved in turbomachinery, high fidelity simulations like large eddy simulation (LES) are required. Unfortunately due to the chaotic dynamics of turbulence, the unsteady adjoint method for LES diverges and produces incorrect gradients. Using a viscosity stabilized unsteady adjoint method developed for LES, the gradient can be obtained with reasonable accuracy. In this paper, design of the trailing edge of a gas turbine inlet guide vane is performed with the objective to reduce stagnation pressure loss and heat transfer over the surface of the vane. Slight changes in the shape of trailing edge can significantly impact these quantities by altering the boundary layer development process and separation points. The trailing edge is parameterized using a linear combination of 5 convex designs. Bayesian optimization is used as a global optimizer with the objective function evaluated from the LES and gradients obtained using the viscosity adjoint method. Results from the optimization, performed on the supercomputer Mira, are presented.

Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm.

PubMed

Chang, Joshua; Paydarfar, David

2014-12-01

Inducing a switch in neuronal state using energy optimal stimuli is relevant to a variety of problems in neuroscience. Analytical techniques from optimal control theory can identify such stimuli; however, solutions to the optimization problem using indirect variational approaches can be elusive in models that describe neuronal behavior. Here we develop and apply a direct gradient-based optimization algorithm to find stimulus waveforms that elicit a change in neuronal state while minimizing energy usage. We analyze standard models of neuronal behavior, the Hodgkin-Huxley and FitzHugh-Nagumo models, to show that the gradient-based algorithm: (1) enables automated exploration of a wide solution space, using stochastically generated initial waveforms that converge to multiple locally optimal solutions; and (2) finds optimal stimulus waveforms that achieve a physiological outcome condition, without a priori knowledge of the optimal terminal condition of all state variables. Analysis of biological systems using stochastically-seeded gradient methods can reveal salient dynamical mechanisms underlying the optimal control of system behavior. The gradient algorithm may also have practical applications in future work, for example, finding energy optimal waveforms for therapeutic neural stimulation that minimizes power usage and diminishes off-target effects and damage to neighboring tissue.

Higher Nucleoporin-Importinβ Affinity at the Nuclear Basket Increases Nucleocytoplasmic Import

PubMed Central

Azimi, Mohammad; Mofrad, Mohammad R. K.

2013-01-01

Several in vitro studies have shown the presence of an affinity gradient in nuclear pore complex proteins for the import receptor Importinβ, at least partially contributing to nucleocytoplasmic transport, while others have historically argued against the presence of such a gradient. Nonetheless, the existence of an affinity gradient has remained an uncharacterized contributing factor. To shed light on the affinity gradient theory and better characterize how the existence of such an affinity gradient between the nuclear pore and the import receptor may influence the nucleocytoplasmic traffic, we have developed a general-purpose agent based modeling (ABM) framework that features a new method for relating rate constants to molecular binding and unbinding probabilities, and used our ABM approach to quantify the effects of a wide range of forward and reverse nucleoporin-Importinβ affinity gradients. Our results indicate that transport through the nuclear pore complex is maximized with an effective macroscopic affinity gradient of 2000 µM, 200 µM and 10 µM in the cytoplasmic, central channel and nuclear basket respectively. The transport rate at this gradient is approximately 10% higher than the transport rate for a comparable pore lacking any affinity gradient, which has a peak transport rate when all nucleoporins have an affinity of 200 µM for Importinβ. Furthermore, this optimal ratio of affinity gradients is representative of the ratio of affinities reported for the yeast nuclear pore complex – suggesting that the affinity gradient seen in vitro is highly optimized. PMID:24282617

Stationary-phase optimized selectivity liquid chromatography: development of a linear gradient prediction algorithm.

PubMed

De Beer, Maarten; Lynen, Fréderic; Chen, Kai; Ferguson, Paul; Hanna-Brown, Melissa; Sandra, Pat

2010-03-01

Stationary-phase optimized selectivity liquid chromatography (SOS-LC) is a tool in reversed-phase LC (RP-LC) to optimize the selectivity for a given separation by combining stationary phases in a multisegment column. The presently (commercially) available SOS-LC optimization procedure and algorithm are only applicable to isocratic analyses. Step gradient SOS-LC has been developed, but this is still not very elegant for the analysis of complex mixtures composed of components covering a broad hydrophobicity range. A linear gradient prediction algorithm has been developed allowing one to apply SOS-LC as a generic RP-LC optimization method. The algorithm allows operation in isocratic, stepwise, and linear gradient run modes. The features of SOS-LC in the linear gradient mode are demonstrated by means of a mixture of 13 steroids, whereby baseline separation is predicted and experimentally demonstrated.

Cost Optimal Design of a Power Inductor by Sequential Gradient Search

NASA Astrophysics Data System (ADS)

Basak, Raju; Das, Arabinda; Sanyal, Amarnath

2018-05-01

Power inductors are used for compensating VAR generated by long EHV transmission lines and in electronic circuits. For the EHV-lines, the rating of the inductor is decided upon by techno-economic considerations on the basis of the line-susceptance. It is a high voltage high current device, absorbing little active power and large reactive power. The cost is quite high- hence the design should be made cost-optimally. The 3-phase power inductor is similar in construction to a 3-phase core-type transformer with the exception that it has only one winding per phase and each limb is provided with an air-gap, the length of which is decided upon by the inductance required. In this paper, a design methodology based on sequential gradient search technique and the corresponding algorithm leading to cost-optimal design of a 3-phase EHV power inductor has been presented. The case-study has been made on a 220 kV long line of NHPC running from Chukha HPS to Birpara of Coochbihar.

Whole brain inhomogeneous magnetization transfer (ihMT) imaging: Sensitivity enhancement within a steady-state gradient echo sequence.

PubMed

Mchinda, Samira; Varma, Gopal; Prevost, Valentin H; Le Troter, Arnaud; Rapacchi, Stanislas; Guye, Maxime; Pelletier, Jean; Ranjeva, Jean-Philippe; Alsop, David C; Duhamel, Guillaume; Girard, Olivier M

2018-05-01

To implement, characterize, and optimize an interleaved inhomogeneous magnetization transfer (ihMT) gradient echo sequence allowing for whole-brain imaging within a clinically compatible scan time. A general framework for ihMT modelling was developed based on the Provotorov theory of radiofrequency saturation, which accounts for the dipolar order underpinning the ihMT effect. Experimental studies and numerical simulations were performed to characterize and optimize the ihMT-gradient echo dependency with sequence timings, saturation power, and offset frequency. The protocol was optimized in terms of maximum signal intensity and the reproducibility assessed for a nominal resolution of 1.5 mm isotropic. All experiments were performed on healthy volunteers at 1.5T. An important mechanism driving signal optimization and leading to strong ihMT signal enhancement that relies on the dynamics of radiofrequency energy deposition has been identified. By taking advantage of the delay allowed for readout between ihMT pulse bursts, it was possible to boost the ihMT signal by almost 2-fold compared to previous implementation. Reproducibility of the optimal protocol was very good, with an intra-individual error < 2%. The proposed sensitivity-boosted and time-efficient steady-state ihMT-gradient echo sequence, implemented and optimized at 1.5T, allowed robust high-resolution 3D ihMT imaging of the whole brain within a clinically compatible scan time. Magn Reson Med 79:2607-2619, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

PubMed Central

2017-01-01

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. PMID:29236718

High-throughput growth temperature optimization of ferroelectric SrxBa1-xNb2O6 epitaxial thin films using a temperature gradient method

NASA Astrophysics Data System (ADS)

Ohkubo, I.; Christen, H. M.; Kalinin, Sergei V.; Jellison, G. E.; Rouleau, C. M.; Lowndes, D. H.

2004-02-01

We have developed a multisample film growth method on a temperature-gradient substrate holder to quickly optimize the film growth temperature in pulsed-laser deposition. A smooth temperature gradient is achieved, covering a range of temperatures from 200 to 830 °C. In a single growth run, the optimal growth temperature for SrxBa1-xNb2O6 thin films on MgO(001) substrates was determined to be 750 °C, based on results from ellipsometry and piezoresponse force microscopy. Variations in optical properties and ferroelectric domains structures were clearly observed as function of growth temperature, and these physical properties can be related to their different crystalline quality. Piezoresponse force microscopy indicated the formation of uniform ferroelectric film for deposition temperatures above 750 °C. At 660 °C, isolated micron-sized ferroelectric islands were observed, while samples deposited below 550 °C did not exhibit clear piezoelectric contrast.

Performance evaluation of matrix gradient coils.

PubMed

Jia, Feng; Schultz, Gerrit; Testud, Frederik; Welz, Anna Masako; Weber, Hans; Littin, Sebastian; Yu, Huijun; Hennig, Jürgen; Zaitsev, Maxim

2016-02-01

In this paper, we present a new performance measure of a matrix coil (also known as multi-coil) from the perspective of efficient, local, non-linear encoding without explicitly considering target encoding fields. An optimization problem based on a joint optimization for the non-linear encoding fields is formulated. Based on the derived objective function, a figure of merit of a matrix coil is defined, which is a generalization of a previously known resistive figure of merit for traditional gradient coils. A cylindrical matrix coil design with a high number of elements is used to illustrate the proposed performance measure. The results are analyzed to reveal novel features of matrix coil designs, which allowed us to optimize coil parameters, such as number of coil elements. A comparison to a scaled, existing multi-coil is also provided to demonstrate the use of the proposed performance parameter. The assessment of a matrix gradient coil profits from using a single performance parameter that takes the local encoding performance of the coil into account in relation to the dissipated power.

Performance optimization of dye-sensitized solar cells by multilayer gradient scattering architecture of TiO2 microspheres.

PubMed

Li, Mingyue; Li, Meiya; Liu, Xiaolian; Bai, Lihua; Luoshan, Mengdai; Lei, Wen; Wang, Zhen; Zhu, Yongdan; Zhao, Xingzhong

2017-01-20

TiO 2 microspheres (TMSs) with unique hierarchical structure and unusual high specific surface area are synthesized and incorporated into a photoanode in various TMS multilayer gradient architectures to form novel photoanodes and dye-sensitized solar cells (DSSCs). Significant influences of these architectures on the photoelectric properties of DSSCs are obtained. The DSSC with the optimal TMS gradient-ascent architecture of M036 has the largest amounts of dye absorption, strongest light absorption, longest electron lifetime and lowest electron recombination, and thus exhibits the maximum short circuit current density (J sc ) of 16.49 mA cm -2 and photoelectric conversion efficiency (η) of 7.01%, notably higher than those of conventional DSSCs by 21% and 22%, respectively. These notable improvements in the properties of DSSCs can be attributed to the TMS gradient-ascent architecture of M036 which can most effectively increase dye absorption and localize incident light within the photoanode by the light scattering of TMSs, and thus utilize the incident light thoroughly. This study provides an optimized and universal configuration for the scattering microspheres incorporated in the hybrid photoanode, which can significantly improve the performance of DSSCs.

Fourier ptychographic reconstruction using Poisson maximum likelihood and truncated Wirtinger gradient.

PubMed

Bian, Liheng; Suo, Jinli; Chung, Jaebum; Ou, Xiaoze; Yang, Changhuei; Chen, Feng; Dai, Qionghai

2016-06-10

Fourier ptychographic microscopy (FPM) is a novel computational coherent imaging technique for high space-bandwidth product imaging. Mathematically, Fourier ptychographic (FP) reconstruction can be implemented as a phase retrieval optimization process, in which we only obtain low resolution intensity images corresponding to the sub-bands of the sample's high resolution (HR) spatial spectrum, and aim to retrieve the complex HR spectrum. In real setups, the measurements always suffer from various degenerations such as Gaussian noise, Poisson noise, speckle noise and pupil location error, which would largely degrade the reconstruction. To efficiently address these degenerations, we propose a novel FP reconstruction method under a gradient descent optimization framework in this paper. The technique utilizes Poisson maximum likelihood for better signal modeling, and truncated Wirtinger gradient for effective error removal. Results on both simulated data and real data captured using our laser-illuminated FPM setup show that the proposed method outperforms other state-of-the-art algorithms. Also, we have released our source code for non-commercial use.

Gradient-Based Optimization of Wind Farms with Different Turbine Heights: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stanley, Andrew P. J.; Thomas, Jared; Ning, Andrew

Turbine wakes reduce power production in a wind farm. Current wind farms are generally built with turbines that are all the same height, but if wind farms included turbines with different tower heights, the cost of energy (COE) may be reduced. We used gradient-based optimization to demonstrate a method to optimize wind farms with varied hub heights. Our study includes a modified version of the FLORIS wake model that accommodates three-dimensional wakes integrated with a tower structural model. Our purpose was to design a process to minimize the COE of a wind farm through layout optimization and varying turbine hubmore » heights. Results indicate that when a farm is optimized for layout and height with two separate height groups, COE can be lowered by as much as 5%-9%, compared to a similar layout and height optimization where all the towers are the same. The COE has the best improvement in farms with high turbine density and a low wind shear exponent.« less

Gradient-Based Optimization of Wind Farms with Different Turbine Heights

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stanley, Andrew P. J.; Thomas, Jared; Ning, Andrew

Turbine wakes reduce power production in a wind farm. Current wind farms are generally built with turbines that are all the same height, but if wind farms included turbines with different tower heights, the cost of energy (COE) may be reduced. We used gradient-based optimization to demonstrate a method to optimize wind farms with varied hub heights. Our study includes a modified version of the FLORIS wake model that accommodates three-dimensional wakes integrated with a tower structural model. Our purpose was to design a process to minimize the COE of a wind farm through layout optimization and varying turbine hubmore » heights. Results indicate that when a farm is optimized for layout and height with two separate height groups, COE can be lowered by as much as 5%-9%, compared to a similar layout and height optimization where all the towers are the same. The COE has the best improvement in farms with high turbine density and a low wind shear exponent.« less

B1 transmit phase gradient coil for single-axis TRASE RF encoding.

PubMed

Deng, Qunli; King, Scott B; Volotovskyy, Vyacheslav; Tomanek, Boguslaw; Sharp, Jonathan C

2013-07-01

TRASE (Transmit Array Spatial Encoding) MRI uses RF transmit phase gradients instead of B0 field gradients for k-space traversal and high-resolution MR image formation. Transmit coil performance is a key determinant of TRASE image quality. The purpose of this work is to design an optimized RF transmit phase gradient array for spatial encoding in a transverse direction (x- or y- axis) for a 0.2T vertical B0 field MRI system, using a single transmitter channel. This requires the generation of two transmit B1 RF fields with uniform amplitude and positive and negative linear phase gradients respectively over the imaging volume. A two-element array consisting of a double Maxwell-type coil and a Helmholtz-type coil was designed using 3D field simulations. The phase gradient polarity is set by the relative phase of the RF signals driving the simultaneously energized elements. Field mapping and 1D TRASE imaging experiments confirmed that the constructed coil produced the fields and operated as designed. A substantially larger imaging volume relative to that obtainable from a non-optimized Maxwell-Helmholtz design was achieved. The Maxwell (sine)-Helmholtz (cosine) approach has proven successful for a horizontal phase gradient coil. A similar approach may be useful for other phase-gradient coil designs. Copyright © 2013 Elsevier Inc. All rights reserved.

In vitro reconstruction of branched tubular structures from lung epithelial cells in high cell concentration gradient environment.

PubMed

Hagiwara, Masaya; Peng, Fei; Ho, Chih-Ming

2015-01-27

We have succeeded in developing hollow branching structure in vitro commonly observed in lung airway using primary lung airway epithelial cells. Cell concentration gradient is the key factor that determines production of the branching cellular structures, as optimization of this component removes the need for heterotypic culture. The higher cell concentration leads to the more production of morphogens and increases the growth rate of cells. However, homogeneous high cell concentration does not make a branching structure. Branching requires sufficient space in which cells can grow from a high concentration toward a low concentration. Simulation performed using a reaction-diffusion model revealed that long-range inhibition prevents cells from branching when they are homogeneously spread in culture environments, while short-range activation from neighboring cells leads to positive feedback. Thus, a high cell concentration gradient is required to make branching structures. Spatial distributions of morphogens, such as BMP-4, play important roles in the pattern formation. This simple yet robust system provides an optimal platform for the further study and understanding of branching mechanisms in the lung airway, and will facilitate chemical and genetic studies of lung morphogenesis programs.

Optimization with artificial neural network systems - A mapping principle and a comparison to gradient based methods

NASA Technical Reports Server (NTRS)

Leong, Harrison Monfook

1988-01-01

General formulae for mapping optimization problems into systems of ordinary differential equations associated with artificial neural networks are presented. A comparison is made to optimization using gradient-search methods. The performance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient-search. Settling time was investigated for a more complicated optimization problem using computer simulations. The problem was a simplified version of a problem in medical imaging: determining loci of cerebral activity from electromagnetic measurements at the scalp. The simulations showed that gradient based systems typically settled 50 to 100 times faster than systems based on current neural network optimization methods.

Optimal disturbances in boundary layers subject to streamwise pressure gradient

NASA Technical Reports Server (NTRS)

Ashpis, David E.; Tumin, Anatoli

2003-01-01

An analysis of the optimal non-modal growth of perturbations in a boundary layer in the presence of a streamwise pressure gradient is presented. The analysis is based on PSE equations for an incompressible fluid. Examples with Falkner-Scan profiles indicate that a favorable pressure gradient decreases the non-modal growth, while an unfavorable pressure gradient leads to an increase of the amplification. It is suggested that the transient growth mechanism be utilized to choose optimal parameters of tripping elements on a low-pressure turbine (LPT) airfoil. As an example, a boundary layer flow with a streamwise pressure gradient corresponding to the pressure distribution over a LPT airfoil is considered. It is shown that there is an optimal spacing of the tripping elements and that the transient growth effect depends on the starting point.

Exploring the complexity of quantum control optimization trajectories.

PubMed

Nanduri, Arun; Shir, Ofer M; Donovan, Ashley; Ho, Tak-San; Rabitz, Herschel

2015-01-07

The control of quantum system dynamics is generally performed by seeking a suitable applied field. The physical objective as a functional of the field forms the quantum control landscape, whose topology, under certain conditions, has been shown to contain no critical point suboptimal traps, thereby enabling effective searches for fields that give the global maximum of the objective. This paper addresses the structure of the landscape as a complement to topological critical point features. Recent work showed that landscape structure is highly favorable for optimization of state-to-state transition probabilities, in that gradient-based control trajectories to the global maximum value are nearly straight paths. The landscape structure is codified in the metric R ≥ 1.0, defined as the ratio of the length of the control trajectory to the Euclidean distance between the initial and optimal controls. A value of R = 1 would indicate an exactly straight trajectory to the optimal observable value. This paper extends the state-to-state transition probability results to the quantum ensemble and unitary transformation control landscapes. Again, nearly straight trajectories predominate, and we demonstrate that R can take values approaching 1.0 with high precision. However, the interplay of optimization trajectories with critical saddle submanifolds is found to influence landscape structure. A fundamental relationship necessary for perfectly straight gradient-based control trajectories is derived, wherein the gradient on the quantum control landscape must be an eigenfunction of the Hessian. This relation is an indicator of landscape structure and may provide a means to identify physical conditions when control trajectories can achieve perfect linearity. The collective favorable landscape topology and structure provide a foundation to understand why optimal quantum control can be readily achieved.

A comparison of two closely-related approaches to aerodynamic design optimization

NASA Technical Reports Server (NTRS)

Shubin, G. R.; Frank, P. D.

1991-01-01

Two related methods for aerodynamic design optimization are compared. The methods, called the implicit gradient approach and the variational (or optimal control) approach, both attempt to obtain gradients necessary for numerical optimization at a cost significantly less than that of the usual black-box approach that employs finite difference gradients. While the two methods are seemingly quite different, they are shown to differ (essentially) in that the order of discretizing the continuous problem, and of applying calculus, is interchanged. Under certain circumstances, the two methods turn out to be identical. We explore the relationship between these methods by applying them to a model problem for duct flow that has many features in common with transonic flow over an airfoil. We find that the gradients computed by the variational method can sometimes be sufficiently inaccurate to cause the optimization to fail.

Systematic Optimization of Long Gradient Chromatography Mass Spectrometry for Deep Analysis of Brain Proteome

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Hong; Yang, Yanling; Li, Yuxin

2015-02-06

Development of high resolution liquid chromatography (LC) is essential for improving the sensitivity and throughput of mass spectrometry (MS)-based proteomics. Here we present systematic optimization of a long gradient LC-MS/MS platform to enhance protein identification from a complex mixture. The platform employed an in-house fabricated, reverse phase column (100 μm x 150 cm) coupled with Q Exactive MS. The column was capable of achieving a peak capacity of approximately 700 in a 720 min gradient of 10-45% acetonitrile. The optimal loading level was about 6 micrograms of peptides, although the column allowed loading as many as 20 micrograms. Gas phasemore » fractionation of peptide ions further increased the number of peptide identification by ~10%. Moreover, the combination of basic pH LC pre-fractionation with the long gradient LC-MS/MS platform enabled the identification of 96,127 peptides and 10,544 proteins at 1% protein false discovery rate in a postmortem brain sample of Alzheimer’s disease. As deep RNA sequencing of the same specimen suggested that ~16,000 genes were expressed, current analysis covered more than 60% of the expressed proteome. Further improvement strategies of the LC/LC-MS/MS platform were also discussed.« less

Sea surface velocities from visible and infrared multispectral atmospheric mapping sensor imagery

NASA Technical Reports Server (NTRS)

Pope, P. A.; Emery, W. J.; Radebaugh, M.

1992-01-01

High resolution (100 m), sequential Multispectral Atmospheric Mapping Sensor (MAMS) images were used in a study to calculate advective surface velocities using the Maximum Cross Correlation (MCC) technique. Radiance and brightness temperature gradient magnitude images were formed from visible (0.48 microns) and infrared (11.12 microns) image pairs, respectively, of Chandeleur Sound, which is a shallow body of water northeast of the Mississippi delta, at 145546 GMT and 170701 GMT on 30 Mar. 1989. The gradient magnitude images enhanced the surface water feature boundaries, and a lower cutoff on the gradient magnitudes calculated allowed the undesirable sunglare and backscatter gradients in the visible images, and the water vapor absorption gradients in the infrared images, to be reduced in strength. Requiring high (greater than 0.4) maximum cross correlation coefficients and spatial coherence of the vector field aided in the selection of an optimal template size of 10 x 10 pixels (first image) and search limit of 20 pixels (second image) to use in the MCC technique. Use of these optimum input parameters to the MCC algorithm, and high correlation and spatial coherence filtering of the resulting velocity field from the MCC calculation yielded a clustered velocity distribution over the visible and infrared gradient images. The velocity field calculated from the visible gradient image pair agreed well with a subjective analysis of the motion, but the velocity field from the infrared gradient image pair did not. This was attributed to the changing shapes of the gradient features, their nonuniqueness, and large displacements relative to the mean distance between them. These problems implied a lower repeat time for the imagery was needed in order to improve the velocity field derived from gradient imagery. Suggestions are given for optimizing the repeat time of sequential imagery when using the MCC method for motion studies. Applying the MCC method to the infrared brightness temperature imagery yielded a velocity field which did agree with the subjective analysis of the motion and that derived from the visible gradient imagery. Differences between the visible and infrared derived velocities were 14.9 cm/s in speed and 56.7 degrees in direction. Both of these velocity fields also agreed well with the motion expected from considerations of the ocean bottom topography and wind and tidal forcing in the study area during the 2.175 hour time interval.

Design keys for paper-based concentration gradient generators.

PubMed

Schaumburg, Federico; Urteaga, Raúl; Kler, Pablo A; Berli, Claudio L A

2018-08-03

The generation of concentration gradients is an essential operation for several analytical processes implemented on microfluidic paper-based analytical devices. The dynamic gradient formation is based on the transverse dispersion of chemical species across co-flowing streams. In paper channels, this transverse flux of molecules is dominated by mechanical dispersion, which is substantially different than molecular diffusion, which is the mechanism acting in conventional microchannels. Therefore, the design of gradient generators on paper requires strategies different from those used in traditional microfluidics. This work considers the foundations of transverse dispersion in porous substrates to investigate the optimal design of microfluidic paper-based concentration gradient generators (μPGGs) by computer simulations. A set of novel and versatile μPGGs were designed in the format of numerical prototypes, and virtual experiments were run to explore the ranges of operation and the overall performance of such devices. Then physical prototypes were fabricated and experimentally tested in our lab. Finally, some basic rules for the design of optimized μPGGs are proposed. Apart from improving the efficiency of mixers, diluters and μPGGs, the results of this investigation are relevant to attain highly controlled concentration fields on paper-based devices. Copyright © 2018 Elsevier B.V. All rights reserved.

Particle Swarm Optimization of Low-Thrust, Geocentric-to-Halo-Orbit Transfers

NASA Astrophysics Data System (ADS)

Abraham, Andrew J.

Missions to Lagrange points are becoming increasingly popular amongst spacecraft mission planners. Lagrange points are locations in space where the gravity force from two bodies, and the centrifugal force acting on a third body, cancel. To date, all spacecraft that have visited a Lagrange point have done so using high-thrust, chemical propulsion. Due to the increasing availability of low-thrust (high efficiency) propulsive devices, and their increasing capability in terms of fuel efficiency and instantaneous thrust, it has now become possible for a spacecraft to reach a Lagrange point orbit without the aid of chemical propellant. While at any given time there are many paths for a low-thrust trajectory to take, only one is optimal. The traditional approach to spacecraft trajectory optimization utilizes some form of gradient-based algorithm. While these algorithms offer numerous advantages, they also have a few significant shortcomings. The three most significant shortcomings are: (1) the fact that an initial guess solution is required to initialize the algorithm, (2) the radius of convergence can be quite small and can allow the algorithm to become trapped in local minima, and (3) gradient information is not always assessable nor always trustworthy for a given problem. To avoid these problems, this dissertation is focused on optimizing a low-thrust transfer trajectory from a geocentric orbit to an Earth-Moon, L1, Lagrange point orbit using the method of Particle Swarm Optimization (PSO). The PSO method is an evolutionary heuristic that was originally written to model birds swarming to locate hidden food sources. This PSO method will enable the exploration of the invariant stable manifold of the target Lagrange point orbit in an effort to optimize the spacecraft's low-thrust trajectory. Examples of these optimized trajectories are presented and contrasted with those found using traditional, gradient-based approaches. In summary, the results of this dissertation find that the PSO method does, indeed, successfully optimize the low-thrust trajectory transfer problem without the need for initial guessing. Furthermore, a two-degree-of-freedom PSO problem formulation significantly outperformed a one-degree-of-freedom formulation by at least an order of magnitude, in terms of CPU time. Finally, the PSO method is also used to solve a traditional, two-burn, impulsive transfer to a Lagrange point orbit using a hybrid optimization algorithm that incorporates a gradient-based shooting algorithm as a pre-optimizer. Surprisingly, the results of this study show that "fast" transfers outperform "slow" transfers in terms of both Deltav and time of flight.

Optimal trajectories for aeroassisted orbital transfer

NASA Technical Reports Server (NTRS)

Miele, A.; Venkataraman, P.

1983-01-01

Consideration is given to classical and minimax problems involved in aeroassisted transfer from high earth orbit (HEO) to low earth orbit (LEO). The transfer is restricted to coplanar operation, with trajectory control effected by means of lift modulation. The performance of the maneuver is indexed to the energy expenditure or, alternatively, the time integral of the heating rate. Firist-order optimality conditions are defined for the classical approach, as are a sequential gradient-restoration algorithm and a combined gradient-restoration algorithm. Minimization techniques are presented for the aeroassisted transfer energy consumption and time-delay integral of the heating rate, as well as minimization of the pressure. It is shown that the eigenvalues of the Jacobian matrix of the differential system is both stiff and unstable, implying that the sequential gradient restoration algorithm in its present version is unsuitable. A new method, involving a multipoint approach to the two-poing boundary value problem, is recommended.

Analytical instrumentation infrastructure for combinatorial and high-throughput development of formulated discrete and gradient polymeric sensor materials arrays

NASA Astrophysics Data System (ADS)

Potyrailo, Radislav A.; Hassib, Lamyaa

2005-06-01

Multicomponent polymer-based formulations of optical sensor materials are difficult and time consuming to optimize using conventional approaches. To address these challenges, our long-term goal is to determine relationships between sensor formulation and sensor response parameters using new scientific methodologies. As the first step, we have designed and implemented an automated analytical instrumentation infrastructure for combinatorial and high-throughput development of polymeric sensor materials for optical sensors. Our approach is based on the fabrication and performance screening of discrete and gradient sensor arrays. Simultaneous formation of multiple sensor coatings into discrete 4×6, 6×8, and 8×12 element arrays (3-15μL volume per element) and their screening provides not only a well-recognized acceleration in the screening rate, but also considerably reduces or even eliminates sources of variability, which are randomly affecting sensors response during a conventional one-at-a-time sensor coating evaluation. The application of gradient sensor arrays provides additional capabilities for rapid finding of the optimal formulation parameters.

High-gradient SRF R&D for ILC at Jefferson Lab

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geng, Rongli; Crawford, Anthony; Ciovati, Gianluigi

2008-10-01

Jefferson Lab plays an active role in high-gradient SRF R&D in the frame work of the internationally coordinated ILC S0 program. The S0 aim is to push the yield at 35 MV/m in 9-cell cavities. So far, twelve cavities have been electropolishing (EP) processed and RF tested by using the state-of-the-art recipes at JLab, in close collaboration with FNAL and KEK. Seven of them reached a best gradient of over 31.5 MV/m. Understanding gradient limiting mechanisms in real 9-cell cavities is an important component of our studies. Thermometry and high-resolution optical inspection are used to locate and understand the sourcemore » of gradient limits. Experimenting with selective cavities is still a necessary method for process optimization. One example is the first demonstration of 35 MV/m without detectable Bremsstrahlung X-ray after a light EP is applied to a previously heavy BCP etched 7-cell cavity. Some new understanding has been gained with regard to quench behaviors, field emission behaviors as« less

A modular approach to large-scale design optimization of aerospace systems

NASA Astrophysics Data System (ADS)

Hwang, John T.

Gradient-based optimization and the adjoint method form a synergistic combination that enables the efficient solution of large-scale optimization problems. Though the gradient-based approach struggles with non-smooth or multi-modal problems, the capability to efficiently optimize up to tens of thousands of design variables provides a valuable design tool for exploring complex tradeoffs and finding unintuitive designs. However, the widespread adoption of gradient-based optimization is limited by the implementation challenges for computing derivatives efficiently and accurately, particularly in multidisciplinary and shape design problems. This thesis addresses these difficulties in two ways. First, to deal with the heterogeneity and integration challenges of multidisciplinary problems, this thesis presents a computational modeling framework that solves multidisciplinary systems and computes their derivatives in a semi-automated fashion. This framework is built upon a new mathematical formulation developed in this thesis that expresses any computational model as a system of algebraic equations and unifies all methods for computing derivatives using a single equation. The framework is applied to two engineering problems: the optimization of a nanosatellite with 7 disciplines and over 25,000 design variables; and simultaneous allocation and mission optimization for commercial aircraft involving 330 design variables, 12 of which are integer variables handled using the branch-and-bound method. In both cases, the framework makes large-scale optimization possible by reducing the implementation effort and code complexity. The second half of this thesis presents a differentiable parametrization of aircraft geometries and structures for high-fidelity shape optimization. Existing geometry parametrizations are not differentiable, or they are limited in the types of shape changes they allow. This is addressed by a novel parametrization that smoothly interpolates aircraft components, providing differentiability. An unstructured quadrilateral mesh generation algorithm is also developed to automate the creation of detailed meshes for aircraft structures, and a mesh convergence study is performed to verify that the quality of the mesh is maintained as it is refined. As a demonstration, high-fidelity aerostructural analysis is performed for two unconventional configurations with detailed structures included, and aerodynamic shape optimization is applied to the truss-braced wing, which finds and eliminates a shock in the region bounded by the struts and the wing.

Multi-sensor image fusion algorithm based on multi-objective particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Xie, Xia-zhu; Xu, Ya-wei

2017-11-01

On the basis of DT-CWT (Dual-Tree Complex Wavelet Transform - DT-CWT) theory, an approach based on MOPSO (Multi-objective Particle Swarm Optimization Algorithm) was proposed to objectively choose the fused weights of low frequency sub-bands. High and low frequency sub-bands were produced by DT-CWT. Absolute value of coefficients was adopted as fusion rule to fuse high frequency sub-bands. Fusion weights in low frequency sub-bands were used as particles in MOPSO. Spatial Frequency and Average Gradient were adopted as two kinds of fitness functions in MOPSO. The experimental result shows that the proposed approach performances better than Average Fusion and fusion methods based on local variance and local energy respectively in brightness, clarity and quantitative evaluation which includes Entropy, Spatial Frequency, Average Gradient and QAB/F.

Effect of RF Gradient upon the Performance of the Wisconsin SRF Electron Gun

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bosch, Robert; Legg, Robert A.

2013-12-01

The performance of the Wisconsin 200-MHz SRF electron gun is simulated for several values of the RF gradient. Bunches with charge of 200 pC are modeled for the case where emittance compensation is completed during post-acceleration to 85 MeV in a TESLA module. We first perform simulations in which the initial bunch radius is optimal for the design gradient of 41 MV/m. We then optimize the radius as a function of RF gradient to improve the performance for low gradients.

Development of Simultaneous Corrosion Barrier and Optimized Microstructure in FeCrAl Heat-Resistant Alloy for Energy Applications. Part II: The Optimized Creep-Resistant Microstructure

NASA Astrophysics Data System (ADS)

Pimentel, G.; Aranda, M. M.; Chao, J.; González-Carrasco, J. L.; Capdevila, C.

2015-09-01

The first part of this two-part study reported the possibility of simultaneously generating a dense, self-healing α-alumina layer by thermal oxidation and a coarse-grained microstructure with a potential goodness for high-temperature creep resistance in a FeCrAl oxide dispersion-strengthened ferritic alloy that was cold deformed after hot rolling and extrusion. In this second part, the factors affecting the formation of the coarse-grained microstructure such as strain gradients induced during the rolling process are analyzed. It is concluded that larger strain gradients lead to more refined and more isotropic grain structures.

A new smoothing modified three-term conjugate gradient method for [Formula: see text]-norm minimization problem.

PubMed

Du, Shouqiang; Chen, Miao

2018-01-01

We consider a kind of nonsmooth optimization problems with [Formula: see text]-norm minimization, which has many applications in compressed sensing, signal reconstruction, and the related engineering problems. Using smoothing approximate techniques, this kind of nonsmooth optimization problem can be transformed into a general unconstrained optimization problem, which can be solved by the proposed smoothing modified three-term conjugate gradient method. The smoothing modified three-term conjugate gradient method is based on Polak-Ribière-Polyak conjugate gradient method. For the Polak-Ribière-Polyak conjugate gradient method has good numerical properties, the proposed method possesses the sufficient descent property without any line searches, and it is also proved to be globally convergent. Finally, the numerical experiments show the efficiency of the proposed method.

Rapid analysis of charge variants of monoclonal antibodies using non-linear salt gradient in cation-exchange high performance liquid chromatography.

PubMed

Joshi, Varsha; Kumar, Vijesh; Rathore, Anurag S

2015-08-07

A method is proposed for rapid development of a short, analytical cation exchange high performance liquid chromatography method for analysis of charge heterogeneity in monoclonal antibody products. The parameters investigated and optimized include pH, shape of elution gradient and length of the column. It is found that the most important parameter for development of a shorter method is the choice of the shape of elution gradient. In this paper, we propose a step by step approach to develop a non-linear sigmoidal shape gradient for analysis of charge heterogeneity for two different monoclonal antibody products. The use of this gradient not only decreases the run time of the method to 4min against the conventional method that takes more than 40min but also the resolution is retained. Superiority of the phosphate gradient over sodium chloride gradient for elution of mAbs is also observed. The method has been successfully evaluated for specificity, sensitivity, linearity, limit of detection, and limit of quantification. Application of this method as a potential at-line process analytical technology tool has been suggested. Copyright © 2015 Elsevier B.V. All rights reserved.

Evolutionary Optimization of Centrifugal Nozzles for Organic Vapours

NASA Astrophysics Data System (ADS)

Persico, Giacomo

2017-03-01

This paper discusses the shape-optimization of non-conventional centrifugal turbine nozzles for Organic Rankine Cycle applications. The optimal aerodynamic design is supported by the use of a non-intrusive, gradient-free technique specifically developed for shape optimization of turbomachinery profiles. The method is constructed as a combination of a geometrical parametrization technique based on B-Splines, a high-fidelity and experimentally validated Computational Fluid Dynamic solver, and a surrogate-based evolutionary algorithm. The non-ideal gas behaviour featuring the flow of organic fluids in the cascades of interest is introduced via a look-up-table approach, which is rigorously applied throughout the whole optimization process. Two transonic centrifugal nozzles are considered, featuring very different loading and radial extension. The use of a systematic and automatic design method to such a non-conventional configuration highlights the character of centrifugal cascades; the blades require a specific and non-trivial definition of the shape, especially in the rear part, to avoid the onset of shock waves. It is shown that the optimization acts in similar way for the two cascades, identifying an optimal curvature of the blade that both provides a relevant increase of cascade performance and a reduction of downstream gradients.

An optimization-based framework for anisotropic simplex mesh adaptation

NASA Astrophysics Data System (ADS)

Yano, Masayuki; Darmofal, David L.

2012-09-01

We present a general framework for anisotropic h-adaptation of simplex meshes. Given a discretization and any element-wise, localizable error estimate, our adaptive method iterates toward a mesh that minimizes error for a given degrees of freedom. Utilizing mesh-metric duality, we consider a continuous optimization problem of the Riemannian metric tensor field that provides an anisotropic description of element sizes. First, our method performs a series of local solves to survey the behavior of the local error function. This information is then synthesized using an affine-invariant tensor manipulation framework to reconstruct an approximate gradient of the error function with respect to the metric tensor field. Finally, we perform gradient descent in the metric space to drive the mesh toward optimality. The method is first demonstrated to produce optimal anisotropic meshes minimizing the L2 projection error for a pair of canonical problems containing a singularity and a singular perturbation. The effectiveness of the framework is then demonstrated in the context of output-based adaptation for the advection-diffusion equation using a high-order discontinuous Galerkin discretization and the dual-weighted residual (DWR) error estimate. The method presented provides a unified framework for optimizing both the element size and anisotropy distribution using an a posteriori error estimate and enables efficient adaptation of anisotropic simplex meshes for high-order discretizations.

Application of modified Rosenbrock's method for optimization of nutrient media used in microorganism culturing.

PubMed

Votruba, J; Pilát, P; Prokop, A

1975-12-01

The Rosenbrock's procedure has been modified for optimization of nutrient medium composition and has been found to be less tedious than the Box-Wilson method, especially for larger numbers of optimized parameters. Its merits are particularly obvious with multiparameter optimization where the gradient method, so far the only one employed in microbiology from a variety of optimization methods (e.g., refs, 9 and 10), becomes impractical because of the excessive number of experiments required. The method suggested is also more stable during optimization than the gradient methods which are very sensitive to the selection of steps in the direction of the gradient and may thus easily shoot out of the optimized region. It is also anticipated that other direct search methods, particularly simplex design, may be easily adapted for optimization of medium composition. It is obvious that direct search methods may find an application in process improvement in antibiotic and related industries.

Multidisciplinary design optimization using genetic algorithms

NASA Technical Reports Server (NTRS)

Unal, Resit

1994-01-01

Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing. The strengths and limitations of GA for launch vehicle design optimization is studied.

Multigrid one shot methods for optimal control problems: Infinite dimensional control

NASA Technical Reports Server (NTRS)

Arian, Eyal; Taasan, Shlomo

1994-01-01

The multigrid one shot method for optimal control problems, governed by elliptic systems, is introduced for the infinite dimensional control space. ln this case, the control variable is a function whose discrete representation involves_an increasing number of variables with grid refinement. The minimization algorithm uses Lagrange multipliers to calculate sensitivity gradients. A preconditioned gradient descent algorithm is accelerated by a set of coarse grids. It optimizes for different scales in the representation of the control variable on different discretization levels. An analysis which reduces the problem to the boundary is introduced. It is used to approximate the two level asymptotic convergence rate, to determine the amplitude of the minimization steps, and the choice of a high pass filter to be used when necessary. The effectiveness of the method is demonstrated on a series of test problems. The new method enables the solutions of optimal control problems at the same cost of solving the corresponding analysis problems just a few times.

Ability of polymorphonuclear leukocytes to orient in gradients of chemotactic factors

PubMed Central

1977-01-01

Polymorphonuclear leukocyte (PMN) chemotaxis has been examined under conditions which allow phase microscope observations of cells responding to controlled gradients of chemotactic factors. With this visual assay, PMNs can be seen to orient rapidly and reversibly to gradients of N-formylmethionyl peptides. The level of orientation depends upon the mean concentration of peptide present as well as the concentration gradient. The response allows an estimation of the binding constant of the peptide to the cell. In optimal gradients, PMNs can detect a 1% difference in the concentration of peptide. At high cell densities, PMNs incubated with active peptides orient their locomotion away from the center of the cell population. This orientation appears to be due to inactivation of the peptides by the cells. Such inactivation in vivo could help to limit an inflammatory response. PMID:264125

Turbopump Performance Improved by Evolutionary Algorithms

NASA Technical Reports Server (NTRS)

Oyama, Akira; Liou, Meng-Sing

2002-01-01

The development of design optimization technology for turbomachinery has been initiated using the multiobjective evolutionary algorithm under NASA's Intelligent Synthesis Environment and Revolutionary Aeropropulsion Concepts programs. As an alternative to the traditional gradient-based methods, evolutionary algorithms (EA's) are emergent design-optimization algorithms modeled after the mechanisms found in natural evolution. EA's search from multiple points, instead of moving from a single point. In addition, they require no derivatives or gradients of the objective function, leading to robustness and simplicity in coupling any evaluation codes. Parallel efficiency also becomes very high by using a simple master-slave concept for function evaluations, since such evaluations often consume the most CPU time, such as computational fluid dynamics. Application of EA's to multiobjective design problems is also straightforward because EA's maintain a population of design candidates in parallel. Because of these advantages, EA's are a unique and attractive approach to real-world design optimization problems.

A flatter gallium profile for high-efficiency Cu(In,Ga)(Se,S)2 solar cell and improved robustness against sulfur-gradient variation

NASA Astrophysics Data System (ADS)

Huang, Chien-Yao; Lee, Wen-Chin; Lin, Albert

2016-09-01

Co-optimization of the gallium and sulfur profiles in penternary Cu(In,Ga)(Se,S)2 thin film solar cell and its impacts on device performance and variability are investigated in this work. An absorber formation method to modulate the gallium profiling under low sulfur-incorporation is disclosed, which solves the problem of Ga-segregation in selenization. Flatter Ga-profiles, which lack of experimental investigations to date, are explored and an optimal Ga-profile achieving 17.1% conversion efficiency on a 30 cm × 30 cm sub-module without anti-reflection coating is presented. Flatter Ga-profile gives rise to the higher Voc × Jsc by improved bandgap matching to solar spectrum, which is hard to be achieved by the case of Ga-accumulation. However, voltage-induced carrier collection loss is found, as evident from the measured voltage-dependent photocurrent characteristics based on a small-signal circuit model. The simulation results reveal that the loss is attributed to the synergistic effect of the detrimental gallium and sulfur gradients, which can deteriorate the carrier collection especially in quasi-neutral region (QNR). Furthermore, the underlying physics is presented, and it provides a clear physical picture to the empirical trends of device performance, I-V characteristics, and voltage-dependent photocurrent, which cannot be explained by the standard solar circuit model. The parameter "FGa" and front sulfur-gradient are found to play critical roles on the trade-off between space charge region (SCR) recombination and QNR carrier collection. The co-optimized gallium and sulfur gradients are investigated, and the corresponding process modification for further efficiency-enhancement is proposed. In addition, the performance impact of sulfur-gradient variation is studied, and a gallium design for suppressing the sulfur-induced variability is proposed. Device performances of varied Ga-profiles with front sulfur-gradients are simulated based on a compact device model. Finally, an exploratory path toward 20% high-efficiency Ga-profile with robustness against sulfur-induced performance variability is presented.

Four-body trajectory optimization

NASA Technical Reports Server (NTRS)

Pu, C. L.; Edelbaum, T. N.

1974-01-01

A comprehensive optimization program has been developed for computing fuel-optimal trajectories between the earth and a point in the sun-earth-moon system. It presents methods for generating fuel optimal two-impulse trajectories which may originate at the earth or a point in space and fuel optimal three-impulse trajectories between two points in space. The extrapolation of the state vector and the computation of the state transition matrix are accomplished by the Stumpff-Weiss method. The cost and constraint gradients are computed analytically in terms of the terminal state and the state transition matrix. The 4-body Lambert problem is solved by using the Newton-Raphson method. An accelerated gradient projection method is used to optimize a 2-impulse trajectory with terminal constraint. The Davidon's Variance Method is used both in the accelerated gradient projection method and the outer loop of a 3-impulse trajectory optimization problem.

Multi-Innovation Gradient Iterative Locally Weighted Learning Identification for A Nonlinear Ship Maneuvering System

NASA Astrophysics Data System (ADS)

Bai, Wei-wei; Ren, Jun-sheng; Li, Tie-shan

2018-06-01

This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative (MIGI) approach is proposed to optimize the distance metric of locally weighted learning (LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method's advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or underlearning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.

Longitudinal gradient coil optimization in the presence of transient eddy currents.

PubMed

Trakic, A; Liu, F; Lopez, H Sanchez; Wang, H; Crozier, S

2007-06-01

The switching of magnetic field gradient coils in magnetic resonance imaging (MRI) inevitably induces transient eddy currents in conducting system components, such as the cryostat vessel. These secondary currents degrade the spatial and temporal performance of the gradient coils, and compensation methods are commonly employed to correct for these distortions. This theoretical study shows that by incorporating the eddy currents into the coil optimization process, it is possible to modify a gradient coil design so that the fields created by the coil and the eddy currents combine together to generate a spatially homogeneous gradient that follows the input pulse. Shielded and unshielded longitudinal gradient coils are used to exemplify this novel approach. To assist in the evaluation of transient eddy currents induced within a realistic cryostat vessel, a low-frequency finite-difference time-domain (FDTD) method using the total-field scattered-field (TFSF) scheme was performed. The simulations demonstrate the effectiveness of the proposed method for optimizing longitudinal gradient fields while taking into account the spatial and temporal behavior of the eddy currents.

Solar collector parameter identification from unsteady data by a discrete-gradient optimization algorithm

NASA Technical Reports Server (NTRS)

Hotchkiss, G. B.; Burmeister, L. C.; Bishop, K. A.

1980-01-01

A discrete-gradient optimization algorithm is used to identify the parameters in a one-node and a two-node capacitance model of a flat-plate collector. Collector parameters are first obtained by a linear-least-squares fit to steady state data. These parameters, together with the collector heat capacitances, are then determined from unsteady data by use of the discrete-gradient optimization algorithm with less than 10 percent deviation from the steady state determination. All data were obtained in the indoor solar simulator at the NASA Lewis Research Center.

Performance of Nonlinear Finite-Difference Poisson-Boltzmann Solvers

PubMed Central

Cai, Qin; Hsieh, Meng-Juei; Wang, Jun; Luo, Ray

2014-01-01

We implemented and optimized seven finite-difference solvers for the full nonlinear Poisson-Boltzmann equation in biomolecular applications, including four relaxation methods, one conjugate gradient method, and two inexact Newton methods. The performance of the seven solvers was extensively evaluated with a large number of nucleic acids and proteins. Worth noting is the inexact Newton method in our analysis. We investigated the role of linear solvers in its performance by incorporating the incomplete Cholesky conjugate gradient and the geometric multigrid into its inner linear loop. We tailored and optimized both linear solvers for faster convergence rate. In addition, we explored strategies to optimize the successive over-relaxation method to reduce its convergence failures without too much sacrifice in its convergence rate. Specifically we attempted to adaptively change the relaxation parameter and to utilize the damping strategy from the inexact Newton method to improve the successive over-relaxation method. Our analysis shows that the nonlinear methods accompanied with a functional-assisted strategy, such as the conjugate gradient method and the inexact Newton method, can guarantee convergence in the tested molecules. Especially the inexact Newton method exhibits impressive performance when it is combined with highly efficient linear solvers that are tailored for its special requirement. PMID:24723843

Gradient optimization of finite projected entangled pair states

NASA Astrophysics Data System (ADS)

Liu, Wen-Yuan; Dong, Shao-Jun; Han, Yong-Jian; Guo, Guang-Can; He, Lixin

2017-05-01

Projected entangled pair states (PEPS) methods have been proven to be powerful tools to solve strongly correlated quantum many-body problems in two dimensions. However, due to the high computational scaling with the virtual bond dimension D , in a practical application, PEPS are often limited to rather small bond dimensions, which may not be large enough for some highly entangled systems, for instance, frustrated systems. Optimization of the ground state using the imaginary time evolution method with a simple update scheme may go to a larger bond dimension. However, the accuracy of the rough approximation to the environment of the local tensors is questionable. Here, we demonstrate that by combining the imaginary time evolution method with a simple update, Monte Carlo sampling techniques and gradient optimization will offer an efficient method to calculate the PEPS ground state. By taking advantage of massive parallel computing, we can study quantum systems with larger bond dimensions up to D =10 without resorting to any symmetry. Benchmark tests of the method on the J1-J2 model give impressive accuracy compared with exact results.

Urban Forest Ecosystem Service Optimization, Tradeoffs, and Disparities

NASA Astrophysics Data System (ADS)

Bodnaruk, E.; Kroll, C. N.; Endreny, T. A.; Hirabayashi, S.; Yang, Y.

2014-12-01

Urban land area and the proportion of humanity living in cities is growing, leading to increased urban air pollution, temperature, and stormwater runoff. These changes can exacerbate respiratory and heat-related illnesses and affect ecosystem functioning. Urban trees can help mitigate these threats by removing air pollutants, mitigating urban heat island effects, and infiltrating and filtering stormwater. The urban environment is highly heterogeneous, and there is no tool to determine optimal locations to plant or protect trees. Using spatially explicit land cover, weather, and demographic data within biophysical ecosystem service models, this research expands upon the iTree urban forest tools to produce a new decision support tool (iTree-DST) that will explore the development and impacts of optimal tree planting. It will also heighten awareness of environmental justice by incorporating the Atkinson Index to quantify disparities in health risks and ecosystem services across vulnerable and susceptible populations. The study area is Baltimore City, a location whose urban forest and environmental justice concerns have been studied extensively. The iTree-DST is run at the US Census block group level and utilizes a local gradient approach to calculate the change in ecosystem services with changing tree cover across the study area. Empirical fits provide ecosystem service gradients for possible tree cover scenarios, greatly increasing the speed and efficiency of the optimization procedure. Initial results include an evaluation of the performance of the gradient method, optimal planting schemes for individual ecosystem services, and an analysis of tradeoffs and synergies between competing objectives.

Optimization of flexible substrate by gradient elastic modulus design for performance improvement of flexible electronic devices

NASA Astrophysics Data System (ADS)

Xia, Minggang; Liang, Chunping; Hu, Ruixue; Cheng, Zhaofang; Liu, Shiru; Zhang, Shengli

2018-05-01

It is imperative and highly desirable to buffer the stress in flexible electronic devices. In this study, we designed and fabricated lamellate poly(dimethylsiloxane) (PDMS) samples with gradient elastic moduli, motivated by the protection of the pomelo pulp by its skin, followed by the measurements of their elastic moduli. We demonstrated that the electrical and fatigue performances of a Ag-nanowire thin film device on the PDMS substrate with a gradient elastic modulus are significantly better than those of a device on a substrate with a monolayer PDMS. This study provides a robust scheme to effectively protect flexible electronic devices.

Panel flutter optimization by gradient projection

NASA Technical Reports Server (NTRS)

Pierson, B. L.

1975-01-01

A gradient projection optimal control algorithm incorporating conjugate gradient directions of search is described and applied to several minimum weight panel design problems subject to a flutter speed constraint. New numerical solutions are obtained for both simply-supported and clamped homogeneous panels of infinite span for various levels of inplane loading and minimum thickness. The minimum thickness inequality constraint is enforced by a simple transformation of variables.

Multi-Mode Cavity Accelerator Structure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Yong; Hirshfield, Jay Leonard

2016-11-10

This project aimed to develop a prototype for a novel accelerator structure comprising coupled cavities that are tuned to support modes with harmonically-related eigenfrequencies, with the goal of reaching an acceleration gradient >200 MeV/m and a breakdown rate <10 -7/pulse/meter. Phase I involved computations, design, and preliminary engineering of a prototype multi-harmonic cavity accelerator structure; plus tests of a bimodal cavity. A computational procedure was used to design an optimized profile for a bimodal cavity with high shunt impedance and low surface fields to maximize the reduction in temperature rise ΔT. This cavity supports the TM010 mode and its 2ndmore » harmonic TM011 mode. Its fundamental frequency is at 12 GHz, to benchmark against the empirical criteria proposed within the worldwide High Gradient collaboration for X-band copper structures; namely, a surface electric field E sur max< 260 MV/m and pulsed surface heating ΔT max< 56 °K. With optimized geometry, amplitude and relative phase of the two modes, reductions are found in surface pulsed heating, modified Poynting vector, and total RF power—as compared with operation at the same acceleration gradient using only the fundamental mode.« less

Aspects of the "Design Space" in high pressure liquid chromatography method development.

PubMed

Molnár, I; Rieger, H-J; Monks, K E

2010-05-07

The present paper describes a multifactorial optimization of 4 critical HPLC method parameters, i.e. gradient time (t(G)), temperature (T), pH and ternary composition (B(1):B(2)) based on 36 experiments. The effect of these experimental variables on critical resolution and selectivity was carried out in such a way as to systematically vary all four factors simultaneously. The basic element is a gradient time-temperature (t(G)-T) plane, which is repeated at three different pH's of the eluent A and at three different ternary compositions of eluent B between methanol and acetonitrile. The so-defined volume enables the investigation of the critical resolution for a part of the Design Space of a given sample. Further improvement of the analysis time, with conservation of the previously optimized selectivity, was possible by reducing the gradient time and increasing the flow rate. Multidimensional robust regions were successfully defined and graphically depicted. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks

PubMed Central

Chen, Jianhui; Liu, Ji; Ye, Jieping

2013-01-01

We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem. We also present two projected gradient algorithms and analyze their rates of convergence in details. In addition, we illustrate the use of the presented projected gradient algorithms for the proposed multi-task learning formulation using the least squares loss. Experimental results on a collection of real-world data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient algorithms. PMID:24077658

Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks.

PubMed

Chen, Jianhui; Liu, Ji; Ye, Jieping

2012-02-01

We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the sparse and low-rank patterns are induced by a cardinality regularization term and a low-rank constraint, respectively. This formulation is non-convex; we convert it into its convex surrogate, which can be routinely solved via semidefinite programming for small-size problems. We propose to employ the general projected gradient scheme to efficiently solve such a convex surrogate; however, in the optimization formulation, the objective function is non-differentiable and the feasible domain is non-trivial. We present the procedures for computing the projected gradient and ensuring the global convergence of the projected gradient scheme. The computation of projected gradient involves a constrained optimization problem; we show that the optimal solution to such a problem can be obtained via solving an unconstrained optimization subproblem and an Euclidean projection subproblem. We also present two projected gradient algorithms and analyze their rates of convergence in details. In addition, we illustrate the use of the presented projected gradient algorithms for the proposed multi-task learning formulation using the least squares loss. Experimental results on a collection of real-world data sets demonstrate the effectiveness of the proposed multi-task learning formulation and the efficiency of the proposed projected gradient algorithms.

Intrinsic diffusion sensitivity of the balanced steady-state free precession (bSSFP) imaging sequence.

PubMed

Bär, Sébastien; Weigel, Matthias; von Elverfeldt, Dominik; Hennig, Jürgen; Leupold, Jochen

2015-11-01

The purpose of this work was to analyze the intrinsic diffusion sensitivity of the balanced steady-state free precession (bSSFP) imaging sequence, meaning the observation of diffusion-induced attenuation of the bSSFP steady-state signal due to the imaging gradients. Although these diffusion effects are usually neglected for most clinical gradient systems, such strong gradient systems are employed for high resolution imaging of small animals or MR Microscopy. The impact on the bSSFP signal of the imaging gradients characterized by their b-values was analyzed with simulations and experiments at a 7T animal scanner using a gradient system with maximum gradient amplitude of approx. 700 mT/m. It was found that the readout gradients have a stronger impact on the attenuation than the phase encoding gradients. Also, as the PE gradients are varying with each repetition interval, the diffusion effects induce strong modulations of the bSSFP signal over the sequence repetition cycles depending on the phase encoding gradient table. It is shown that a signal gain can be obtained through a change of flip angle as a new optimal flip angle maximizing the signal can be defined. The dependency of the diffusion effects on relaxation times and b-values were explored with simulations. The attenuation increases with T2. In conclusion, diffusion attenuation of the bSSFP signal becomes significant for high resolution imaging voxel size (roughly < 100 μm) of long T2 substances. Copyright © 2015 John Wiley & Sons, Ltd.

A modified form of conjugate gradient method for unconstrained optimization problems

NASA Astrophysics Data System (ADS)

Ghani, Nur Hamizah Abdul; Rivaie, Mohd.; Mamat, Mustafa

2016-06-01

Conjugate gradient (CG) methods have been recognized as an interesting technique to solve optimization problems, due to the numerical efficiency, simplicity and low memory requirements. In this paper, we propose a new CG method based on the study of Rivaie et al. [7] (Comparative study of conjugate gradient coefficient for unconstrained Optimization, Aus. J. Bas. Appl. Sci. 5(2011) 947-951). Then, we show that our method satisfies sufficient descent condition and converges globally with exact line search. Numerical results show that our proposed method is efficient for given standard test problems, compare to other existing CG methods.

Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms.

PubMed

Xu, Dongpo; Xia, Yili; Mandic, Danilo P

2016-02-01

The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address this issue, we propose new definitions of quaternion gradient and Hessian, based on the novel generalized Hamilton-real (GHR) calculus, thus making a possible efficient derivation of general optimization algorithms directly in the quaternion field, rather than using the isomorphism with the real domain, as is current practice. In addition, unlike the existing quaternion gradients, the GHR calculus allows for the product and chain rule, and for a one-to-one correspondence of the novel quaternion gradient and Hessian with their real counterparts. Properties of the quaternion gradient and Hessian relevant to numerical applications are also introduced, opening a new avenue of research in quaternion optimization and greatly simplified the derivations of learning algorithms. The proposed GHR calculus is shown to yield the same generic algorithm forms as the corresponding real- and complex-valued algorithms. Advantages of the proposed framework are illuminated over illustrative simulations in quaternion signal processing and neural networks.

Airfoil Design and Optimization by the One-Shot Method

NASA Technical Reports Server (NTRS)

Kuruvila, G.; Taasan, Shlomo; Salas, M. D.

1995-01-01

An efficient numerical approach for the design of optimal aerodynamic shapes is presented in this paper. The objective of any optimization problem is to find the optimum of a cost function subject to a certain state equation (governing equation of the flow field) and certain side constraints. As in classical optimal control methods, the present approach introduces a costate variable (Lagrange multiplier) to evaluate the gradient of the cost function. High efficiency in reaching the optimum solution is achieved by using a multigrid technique and updating the shape in a hierarchical manner such that smooth (low-frequency) changes are done separately from high-frequency changes. Thus, the design variables are changed on a grid where their changes produce nonsmooth (high-frequency) perturbations that can be damped efficiently by the multigrid. The cost of solving the optimization problem is approximately two to three times the cost of the equivalent analysis problem.

Airfoil optimization by the one-shot method

NASA Technical Reports Server (NTRS)

Kuruvila, G.; Taasan, Shlomo; Salas, M. D.

1994-01-01

An efficient numerical approach for the design of optimal aerodynamic shapes is presented in this paper. The objective of any optimization problem is to find the optimum of a cost function subject to a certain state equation (Governing equation of the flow field) and certain side constraints. As in classical optimal control methods, the present approach introduces a costate variable (Language multiplier) to evaluate the gradient of the cost function. High efficiency in reaching the optimum solution is achieved by using a multigrid technique and updating the shape in a hierarchical manner such that smooth (low-frequency) changes are done separately from high-frequency changes. Thus, the design variables are changed on a grid where their changes produce nonsmooth (high-frequency) perturbations that can be damped efficiently by the multigrid. The cost of solving the optimization problem is approximately two to three times the cost of the equivalent analysis problem.

SU-E-T-295: Simultaneous Beam Sampling and Aperture Shape Optimization for Station Parameter Optimized Radiation Therapy (SPORT)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zarepisheh, M; Li, R; Xing, L

Purpose: Station Parameter Optimized Radiation Therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital LINACs, in which the station parameters of a delivery system, (such as aperture shape and weight, couch position/angle, gantry/collimator angle) are optimized altogether. SPORT promises to deliver unprecedented radiation dose distributions efficiently, yet there does not exist any optimization algorithm to implement it. The purpose of this work is to propose an optimization algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: We build a mathematical model whose variables are beam angles (including non-coplanar and/or even nonisocentric beams) andmore » aperture shapes. To solve the resulting large scale optimization problem, we devise an exact, convergent and fast optimization algorithm by integrating three advanced optimization techniques named column generation, gradient method, and pattern search. Column generation is used to find a good set of aperture shapes as an initial solution by adding apertures sequentially. Then we apply the gradient method to iteratively improve the current solution by reshaping the aperture shapes and updating the beam angles toward the gradient. Algorithm continues by pattern search method to explore the part of the search space that cannot be reached by the gradient method. Results: The proposed technique is applied to a series of patient cases and significantly improves the plan quality. In a head-and-neck case, for example, the left parotid gland mean-dose, brainstem max-dose, spinal cord max-dose, and mandible mean-dose are reduced by 10%, 7%, 24% and 12% respectively, compared to the conventional VMAT plan while maintaining the same PTV coverage. Conclusion: Combined use of column generation, gradient search and pattern search algorithms provide an effective way to optimize simultaneously the large collection of station parameters and significantly improves quality of resultant treatment plans as compared with conventional VMAT or IMRT treatments.« less

An historical survey of computational methods in optimal control.

NASA Technical Reports Server (NTRS)

Polak, E.

1973-01-01

Review of some of the salient theoretical developments in the specific area of optimal control algorithms. The first algorithms for optimal control were aimed at unconstrained problems and were derived by using first- and second-variation methods of the calculus of variations. These methods have subsequently been recognized as gradient, Newton-Raphson, or Gauss-Newton methods in function space. A much more recent addition to the arsenal of unconstrained optimal control algorithms are several variations of conjugate-gradient methods. At first, constrained optimal control problems could only be solved by exterior penalty function methods. Later algorithms specifically designed for constrained problems have appeared. Among these are methods for solving the unconstrained linear quadratic regulator problem, as well as certain constrained minimum-time and minimum-energy problems. Differential-dynamic programming was developed from dynamic programming considerations. The conditional-gradient method, the gradient-projection method, and a couple of feasible directions methods were obtained as extensions or adaptations of related algorithms for finite-dimensional problems. Finally, the so-called epsilon-methods combine the Ritz method with penalty function techniques.

Integrated Design of Downwind Land-Based Wind Turbines using Analytic Gradients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ning, Andrew; Petch, Derek

2016-12-01

Wind turbines are complex systems where component-level changes can have significant system-level effects. Effective wind turbine optimization generally requires an integrated analysis approach with a large number of design variables. Optimizing across large variable sets is orders of magnitude more efficient with gradient-based methods as compared with gradient-free method, particularly when using exact gradients. We have developed a wind turbine analysis set of over 100 components where 90% of the models provide numerically exact gradients through symbolic differentiation, automatic differentiation, and adjoint methods. This framework is applied to a specific design study focused on downwind land-based wind turbines. Downwind machinesmore » are of potential interest for large wind turbines where the blades are often constrained by the stiffness required to prevent a tower strike. The mass of these rotor blades may be reduced by utilizing a downwind configuration where the constraints on tower strike are less restrictive. The large turbines of this study range in power rating from 5-7MW and in diameter from 105m to 175m. The changes in blade mass and power production have important effects on the rest of the system, and thus the nacelle and tower systems are also optimized. For high-speed wind sites, downwind configurations do not appear advantageous. The decrease in blade mass (10%) is offset by increases in tower mass caused by the bending moment from the rotor-nacelle-assembly. For low-wind speed sites, the decrease in blade mass is more significant (25-30%) and shows potential for modest decreases in overall cost of energy (around 1-2%).« less

Method for computationally efficient design of dielectric laser accelerator structures

DOE PAGES

Hughes, Tyler; Veronis, Georgios; Wootton, Kent P.; ...

2017-06-22

Here, dielectric microstructures have generated much interest in recent years as a means of accelerating charged particles when powered by solid state lasers. The acceleration gradient (or particle energy gain per unit length) is an important figure of merit. To design structures with high acceleration gradients, we explore the adjoint variable method, a highly efficient technique used to compute the sensitivity of an objective with respect to a large number of parameters. With this formalism, the sensitivity of the acceleration gradient of a dielectric structure with respect to its entire spatial permittivity distribution is calculated by the use of onlymore » two full-field electromagnetic simulations, the original and ‘adjoint’. The adjoint simulation corresponds physically to the reciprocal situation of a point charge moving through the accelerator gap and radiating. Using this formalism, we perform numerical optimizations aimed at maximizing acceleration gradients, which generate fabricable structures of greatly improved performance in comparison to previously examined geometries.« less

A gradient system solution to Potts mean field equations and its electronic implementation.

PubMed

Urahama, K; Ueno, S

1993-03-01

A gradient system solution method is presented for solving Potts mean field equations for combinatorial optimization problems subject to winner-take-all constraints. In the proposed solution method the optimum solution is searched by using gradient descent differential equations whose trajectory is confined within the feasible solution space of optimization problems. This gradient system is proven theoretically to always produce a legal local optimum solution of combinatorial optimization problems. An elementary analog electronic circuit implementing the presented method is designed on the basis of current-mode subthreshold MOS technologies. The core constituent of the circuit is the winner-take-all circuit developed by Lazzaro et al. Correct functioning of the presented circuit is exemplified with simulations of the circuits implementing the scheme for solving the shortest path problems.

Improved optimization of polycyclic aromatic hydrocarbons (PAHs) mixtures resolution in reversed-phase high-performance liquid chromatography by using factorial design and response surface methodology.

PubMed

Andrade-Eiroa, Auréa; Diévart, Pascal; Dagaut, Philippe

2010-04-15

A new procedure for optimizing PAHs separation in very complex mixtures by reverse phase high performance (RPLC) is proposed. It is based on changing gradually the experimental conditions all along the chromatographic procedure as a function of the physical properties of the compounds eluted. The temperature and speed flow gradients allowed obtaining the optimum resolution in large chromatographic determinations where PAHs with very different medium polarizability have to be separated. Whereas optimization procedures of RPLC methodologies had always been accomplished regardless of the physico-chemical properties of the target analytes, we found that resolution is highly dependent on the physico-chemical properties of the target analytes. Based on resolution criterion, optimization process for a 16 EPA PAHs mixture was performed on three sets of difficult-to-separate PAHs pairs: acenaphthene-fluorene (for the optimization procedure in the first part of the chromatogram where light PAHs elute), benzo[g,h,i]perylene-dibenzo[a,h]anthracene and benzo[g,h,i]perylene-indeno[1,2,3-cd]pyrene (for the optimization procedure of the second part of the chromatogram where the heavier PAHs elute). Two-level full factorial designs were applied to detect interactions among variables to be optimized: speed flow, temperature of column oven and mobile-phase gradient in the two parts of the studied chromatogram. Experimental data were fitted by multivariate nonlinear regression models and optimum values of speed flow and temperature were obtained through mathematical analysis of the constructed models. An HPLC system equipped with a reversed phase 5 microm C18, 250 mm x 4.6mm column (with acetonitrile/water mobile phase), a column oven, a binary pump, a photodiode array detector (PDA), and a fluorimetric detector were used in this work. Optimum resolution was achieved operating at 1.0 mL/min in the first part of the chromatogram (until 45 min) and 0.5 mL/min in the second one (from 45 min to the end) and by applying programmed temperature gradient (15 degrees C until 30 min and progressively increasing temperature until reaching 40 degrees C at 45 min). (c) 2009 Elsevier B.V. All rights reserved.

Conjugate gradient optimization programs for shuttle reentry

NASA Technical Reports Server (NTRS)

Powers, W. F.; Jacobson, R. A.; Leonard, D. A.

1972-01-01

Two computer programs for shuttle reentry trajectory optimization are listed and described. Both programs use the conjugate gradient method as the optimization procedure. The Phase 1 Program is developed in cartesian coordinates for a rotating spherical earth, and crossrange, downrange, maximum deceleration, total heating, and terminal speed, altitude, and flight path angle are included in the performance index. The programs make extensive use of subroutines so that they may be easily adapted to other atmospheric trajectory optimization problems.

Optimization of Turbine Engine Cycle Analysis with Analytic Derivatives

NASA Technical Reports Server (NTRS)

Hearn, Tristan; Hendricks, Eric; Chin, Jeffrey; Gray, Justin; Moore, Kenneth T.

2016-01-01

A new engine cycle analysis tool, called Pycycle, was built using the OpenMDAO framework. Pycycle provides analytic derivatives allowing for an efficient use of gradient-based optimization methods on engine cycle models, without requiring the use of finite difference derivative approximation methods. To demonstrate this, a gradient-based design optimization was performed on a turbofan engine model. Results demonstrate very favorable performance compared to an optimization of an identical model using finite-difference approximated derivatives.

Concurrent Monte Carlo transport and fluence optimization with fluence adjusting scalable transport Monte Carlo

PubMed Central

Svatos, M.; Zankowski, C.; Bednarz, B.

2016-01-01

Purpose: The future of radiation therapy will require advanced inverse planning solutions to support single-arc, multiple-arc, and “4π” delivery modes, which present unique challenges in finding an optimal treatment plan over a vast search space, while still preserving dosimetric accuracy. The successful clinical implementation of such methods would benefit from Monte Carlo (MC) based dose calculation methods, which can offer improvements in dosimetric accuracy when compared to deterministic methods. The standard method for MC based treatment planning optimization leverages the accuracy of the MC dose calculation and efficiency of well-developed optimization methods, by precalculating the fluence to dose relationship within a patient with MC methods and subsequently optimizing the fluence weights. However, the sequential nature of this implementation is computationally time consuming and memory intensive. Methods to reduce the overhead of the MC precalculation have been explored in the past, demonstrating promising reductions of computational time overhead, but with limited impact on the memory overhead due to the sequential nature of the dose calculation and fluence optimization. The authors propose an entirely new form of “concurrent” Monte Carlo treat plan optimization: a platform which optimizes the fluence during the dose calculation, reduces wasted computation time being spent on beamlets that weakly contribute to the final dose distribution, and requires only a low memory footprint to function. In this initial investigation, the authors explore the key theoretical and practical considerations of optimizing fluence in such a manner. Methods: The authors present a novel derivation and implementation of a gradient descent algorithm that allows for optimization during MC particle transport, based on highly stochastic information generated through particle transport of very few histories. A gradient rescaling and renormalization algorithm, and the concept of momentum from stochastic gradient descent were used to address obstacles unique to performing gradient descent fluence optimization during MC particle transport. The authors have applied their method to two simple geometrical phantoms, and one clinical patient geometry to examine the capability of this platform to generate conformal plans as well as assess its computational scaling and efficiency, respectively. Results: The authors obtain a reduction of at least 50% in total histories transported in their investigation compared to a theoretical unweighted beamlet calculation and subsequent fluence optimization method, and observe a roughly fixed optimization time overhead consisting of ∼10% of the total computation time in all cases. Finally, the authors demonstrate a negligible increase in memory overhead of ∼7–8 MB to allow for optimization of a clinical patient geometry surrounded by 36 beams using their platform. Conclusions: This study demonstrates a fluence optimization approach, which could significantly improve the development of next generation radiation therapy solutions while incurring minimal additional computational overhead. PMID:27277051

Highly improved passivation of c-Si surfaces using a gradient i a-Si:H layer

NASA Astrophysics Data System (ADS)

Lee, Soonil; Ahn, Jaehyun; Mathew, Leo; Rao, Rajesh; Zhang, Zhongjian; Kim, Jae Hyun; Banerjee, Sanjay K.; Yu, Edward T.

2018-04-01

Surface passivation using intrinsic a-Si:H (i a-Si:H) films plays a key role in high efficiency c-Si heterojunction solar cells. In this study, we demonstrate improved passivation quality using i a-Si:H films with a gradient-layered structure consisting of interfacial, transition, and capping layers deposited on c-Si surfaces. The H2 dilution ratio (R) during deposition was optimized individually for the interfacial and capping layers, which were separated by a transition layer for which R changed gradually between its values for the interfacial and capping layers. This approach yielded a significant reduction in surface carrier recombination, resulting in improvement of the minority carrier lifetime from 1480 μs for mono-layered i a-Si:H passivation to 2550 μs for the gradient-layered passivation approach.

Development and implementation of an 84-channel matrix gradient coil.

PubMed

Littin, Sebastian; Jia, Feng; Layton, Kelvin J; Kroboth, Stefan; Yu, Huijun; Hennig, Jürgen; Zaitsev, Maxim

2018-02-01

Design, implement, integrate, and characterize a customized coil system that allows for generating spatial encoding magnetic fields (SEMs) in a highly-flexible fashion. A gradient coil with a high number of individual elements was designed. Dimensions of the coil were chosen to mimic a whole-body gradient system, scaled down to a head insert. Mechanical shape and wire layout of each element were optimized to increase the local gradient strength while minimizing eddy current effects and simultaneously considering manufacturing constraints. Resulting wire layout and mechanical design is presented. A prototype matrix gradient coil with 12 × 7 = 84 elements consisting of two element types was realized and characterized. Measured eddy currents are <1% of the original field. The coil is shown to be capable of creating nonlinear, and linear SEMs. In a DSV of 0.22 m gradient strengths between 24 mT∕m and 78 mT∕m could be realized locally with maximum currents of 150 A. Initial proof-of-concept imaging experiments using linear and nonlinear encoding fields are demonstrated. A shielded matrix gradient coil setup capable of generating encoding fields in a highly-flexible manner was designed and implemented. The presented setup is expected to serve as a basis for validating novel imaging techniques that rely on nonlinear spatial encoding fields. Magn Reson Med 79:1181-1191, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Fast three-dimensional inner volume excitations using parallel transmission and optimized k-space trajectories.

PubMed

Davids, Mathias; Schad, Lothar R; Wald, Lawrence L; Guérin, Bastien

2016-10-01

To design short parallel transmission (pTx) pulses for excitation of arbitrary three-dimensional (3D) magnetization patterns. We propose a joint optimization of the pTx radiofrequency (RF) and gradient waveforms for excitation of arbitrary 3D magnetization patterns. Our optimization of the gradient waveforms is based on the parameterization of k-space trajectories (3D shells, stack-of-spirals, and cross) using a small number of shape parameters that are well-suited for optimization. The resulting trajectories are smooth and sample k-space efficiently with few turns while using the gradient system at maximum performance. Within each iteration of the k-space trajectory optimization, we solve a small tip angle least-squares RF pulse design problem. Our RF pulse optimization framework was evaluated both in Bloch simulations and experiments on a 7T scanner with eight transmit channels. Using an optimized 3D cross (shells) trajectory, we were able to excite a cube shape (brain shape) with 3.4% (6.2%) normalized root-mean-square error in less than 5 ms using eight pTx channels and a clinical gradient system (Gmax  = 40 mT/m, Smax  = 150 T/m/s). This compared with 4.7% (41.2%) error for the unoptimized 3D cross (shells) trajectory. Incorporation of B0 robustness in the pulse design significantly altered the k-space trajectory solutions. Our joint gradient and RF optimization approach yields excellent excitation of 3D cube and brain shapes in less than 5 ms, which can be used for reduced field of view imaging and fat suppression in spectroscopy by excitation of the brain only. Magn Reson Med 76:1170-1182, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

A PDE Sensitivity Equation Method for Optimal Aerodynamic Design

NASA Technical Reports Server (NTRS)

Borggaard, Jeff; Burns, John

1996-01-01

The use of gradient based optimization algorithms in inverse design is well established as a practical approach to aerodynamic design. A typical procedure uses a simulation scheme to evaluate the objective function (from the approximate states) and its gradient, then passes this information to an optimization algorithm. Once the simulation scheme (CFD flow solver) has been selected and used to provide approximate function evaluations, there are several possible approaches to the problem of computing gradients. One popular method is to differentiate the simulation scheme and compute design sensitivities that are then used to obtain gradients. Although this black-box approach has many advantages in shape optimization problems, one must compute mesh sensitivities in order to compute the design sensitivity. In this paper, we present an alternative approach using the PDE sensitivity equation to develop algorithms for computing gradients. This approach has the advantage that mesh sensitivities need not be computed. Moreover, when it is possible to use the CFD scheme for both the forward problem and the sensitivity equation, then there are computational advantages. An apparent disadvantage of this approach is that it does not always produce consistent derivatives. However, for a proper combination of discretization schemes, one can show asymptotic consistency under mesh refinement, which is often sufficient to guarantee convergence of the optimal design algorithm. In particular, we show that when asymptotically consistent schemes are combined with a trust-region optimization algorithm, the resulting optimal design method converges. We denote this approach as the sensitivity equation method. The sensitivity equation method is presented, convergence results are given and the approach is illustrated on two optimal design problems involving shocks.

Speed and convergence properties of gradient algorithms for optimization of IMRT.

PubMed

Zhang, Xiaodong; Liu, Helen; Wang, Xiaochun; Dong, Lei; Wu, Qiuwen; Mohan, Radhe

2004-05-01

Gradient algorithms are the most commonly employed search methods in the routine optimization of IMRT plans. It is well known that local minima can exist for dose-volume-based and biology-based objective functions. The purpose of this paper is to compare the relative speed of different gradient algorithms, to investigate the strategies for accelerating the optimization process, to assess the validity of these strategies, and to study the convergence properties of these algorithms for dose-volume and biological objective functions. With these aims in mind, we implemented Newton's, conjugate gradient (CG), and the steepest decent (SD) algorithms for dose-volume- and EUD-based objective functions. Our implementation of Newton's algorithm approximates the second derivative matrix (Hessian) by its diagonal. The standard SD algorithm and the CG algorithm with "line minimization" were also implemented. In addition, we investigated the use of a variation of the CG algorithm, called the "scaled conjugate gradient" (SCG) algorithm. To accelerate the optimization process, we investigated the validity of the use of a "hybrid optimization" strategy, in which approximations to calculated dose distributions are used during most of the iterations. Published studies have indicated that getting trapped in local minima is not a significant problem. To investigate this issue further, we first obtained, by trial and error, and starting with uniform intensity distributions, the parameters of the dose-volume- or EUD-based objective functions which produced IMRT plans that satisfied the clinical requirements. Using the resulting optimized intensity distributions as the initial guess, we investigated the possibility of getting trapped in a local minimum. For most of the results presented, we used a lung cancer case. To illustrate the generality of our methods, the results for a prostate case are also presented. For both dose-volume and EUD based objective functions, Newton's method far outperforms other algorithms in terms of speed. The SCG algorithm, which avoids expensive "line minimization," can speed up the standard CG algorithm by at least a factor of 2. For the same initial conditions, all algorithms converge essentially to the same plan. However, we demonstrate that for any of the algorithms studied, starting with previously optimized intensity distributions as the initial guess but for different objective function parameters, the solution frequently gets trapped in local minima. We found that the initial intensity distribution obtained from IMRT optimization utilizing objective function parameters, which favor a specific anatomic structure, would lead to a local minimum corresponding to that structure. Our results indicate that from among the gradient algorithms tested, Newton's method appears to be the fastest by far. Different gradient algorithms have the same convergence properties for dose-volume- and EUD-based objective functions. The hybrid dose calculation strategy is valid and can significantly accelerate the optimization process. The degree of acceleration achieved depends on the type of optimization problem being addressed (e.g., IMRT optimization, intensity modulated beam configuration optimization, or objective function parameter optimization). Under special conditions, gradient algorithms will get trapped in local minima, and reoptimization, starting with the results of previous optimization, will lead to solutions that are generally not significantly different from the local minimum.

Performance of local optimization in single-plane fluoroscopic analysis for total knee arthroplasty.

PubMed

Prins, A H; Kaptein, B L; Stoel, B C; Lahaye, D J P; Valstar, E R

2015-11-05

Fluoroscopy-derived joint kinematics plays an important role in the evaluation of knee prostheses. Fluoroscopic analysis requires estimation of the 3D prosthesis pose from its 2D silhouette in the fluoroscopic image, by optimizing a dissimilarity measure. Currently, extensive user-interaction is needed, which makes analysis labor-intensive and operator-dependent. The aim of this study was to review five optimization methods for 3D pose estimation and to assess their performance in finding the correct solution. Two derivative-free optimizers (DHSAnn and IIPM) and three gradient-based optimizers (LevMar, DoNLP2 and IpOpt) were evaluated. For the latter three optimizers two different implementations were evaluated: one with a numerically approximated gradient and one with an analytically derived gradient for computational efficiency. On phantom data, all methods were able to find the 3D pose within 1mm and 1° in more than 85% of cases. IpOpt had the highest success-rate: 97%. On clinical data, the success rates were higher than 85% for the in-plane positions, but not for the rotations. IpOpt was the most expensive method and the application of an analytically derived gradients accelerated the gradient-based methods by a factor 3-4 without any differences in success rate. In conclusion, 85% of the frames can be analyzed automatically in clinical data and only 15% of the frames require manual supervision. The optimal success-rate on phantom data (97% with IpOpt) on phantom data indicates that even less supervision may become feasible. Copyright © 2015 Elsevier Ltd. All rights reserved.

Investigation on the parameter optimization and performance of laser cladding a gradient composite coating by a mixed powder of Co50 and Ni/WC on 20CrMnTi low carbon alloy steel

NASA Astrophysics Data System (ADS)

Shi, Yan; Li, Yunfeng; Liu, Jia; Yuan, Zhenyu

2018-02-01

In this study, a gradient composite coating was manufactured on 20CrMnTi alloy steel by laser cladding. The laser power, cladding scan velocity and powder flow rate were selected as influencing factors of the orthogonal cladding experiments. The influencing factors were optimized by the comprehensive analysis of Taguchi OA and TOPSIS method. The high significant parameters and the predicted results were confirmed by the ANOVA method. The macromorphology and microstructures are characterized by using laser microscope, SEM, XRD and microhardness tester. Comparison tests of wear resistance of gradient composite coating, 20CrMnTi cemented quenching sample and the 20CrMnTi sample were conducted on the friction-wear tester. The results show that the phases are γ-Co solid solution, Co3B, M23C6 and etc. The interlayers and wear-resisting layer also contain new hard phases as WC, W2C. The microhardness of the gradient coating was increased to 3 times as compared with that of the 20CrMnTi substrate. The wear resistance of the gradient composite coating and 20CrMnTi cemented quenching sample was enhanced to 36.4 and 15.9 times as compared with that of the 20CrMnTi.

Optimization of elution salt concentration in stepwise elution of protein chromatography using linear gradient elution data. Reducing residual protein A by cation-exchange chromatography in monoclonal antibody purification.

PubMed

Ishihara, Takashi; Kadoya, Toshihiko; Endo, Naomi; Yamamoto, Shuichi

2006-05-05

Our simple method for optimization of the elution salt concentration in stepwise elution was applied to the actual protein separation system, which involves several difficulties such as detection of the target. As a model separation system, reducing residual protein A by cation-exchange chromatography in human monoclonal antibody (hMab) purification was chosen. We carried out linear gradient elution experiments and obtained the data for the peak salt concentration of hMab and residual protein A, respectively. An enzyme-linked immunosorbent assay was applied to the measurement of the residual protein A. From these data, we calculated the distribution coefficient of the hMab and the residual protein A as a function of salt concentration. The optimal salt concentration of stepwise elution to reduce the residual protein A from the hMab was determined based on the relationship between the distribution coefficient and the salt concentration. Using the optimized condition, we successfully performed the separation, resulting in high recovery of hMab and the elimination of residual protein A.

Digital robust control law synthesis using constrained optimization

NASA Technical Reports Server (NTRS)

Mukhopadhyay, Vivekananda

1989-01-01

Development of digital robust control laws for active control of high performance flexible aircraft and large space structures is a research area of significant practical importance. The flexible system is typically modeled by a large order state space system of equations in order to accurately represent the dynamics. The active control law must satisy multiple conflicting design requirements and maintain certain stability margins, yet should be simple enough to be implementable on an onboard digital computer. Described here is an application of a generic digital control law synthesis procedure for such a system, using optimal control theory and constrained optimization technique. A linear quadratic Gaussian type cost function is minimized by updating the free parameters of the digital control law, while trying to satisfy a set of constraints on the design loads, responses and stability margins. Analytical expressions for the gradients of the cost function and the constraints with respect to the control law design variables are used to facilitate rapid numerical convergence. These gradients can be used for sensitivity study and may be integrated into a simultaneous structure and control optimization scheme.

Adaptive hybrid optimal quantum control for imprecisely characterized systems.

PubMed

Egger, D J; Wilhelm, F K

2014-06-20

Optimal quantum control theory carries a huge promise for quantum technology. Its experimental application, however, is often hindered by imprecise knowledge of the input variables, the quantum system's parameters. We show how to overcome this by adaptive hybrid optimal control, using a protocol named Ad-HOC. This protocol combines open- and closed-loop optimal control by first performing a gradient search towards a near-optimal control pulse and then an experimental fidelity estimation with a gradient-free method. For typical settings in solid-state quantum information processing, adaptive hybrid optimal control enhances gate fidelities by an order of magnitude, making optimal control theory applicable and useful.

Generalized gradient algorithm for trajectory optimization

NASA Technical Reports Server (NTRS)

Zhao, Yiyuan; Bryson, A. E.; Slattery, R.

1990-01-01

The generalized gradient algorithm presented and verified as a basis for the solution of trajectory optimization problems improves the performance index while reducing path equality constraints, and terminal equality constraints. The algorithm is conveniently divided into two phases, of which the first, 'feasibility' phase yields a solution satisfying both path and terminal constraints, while the second, 'optimization' phase uses the results of the first phase as initial guesses.

Simulation and Optimization of an Airfoil with Leading Edge Slat

NASA Astrophysics Data System (ADS)

Schramm, Matthias; Stoevesandt, Bernhard; Peinke, Joachim

2016-09-01

A gradient-based optimization is used in order to improve the shape of a leading edge slat upstream of a DU 91-W2-250 airfoil. The simulations are performed by solving the Reynolds-Averaged Navier-Stokes equations (RANS) using the open source CFD code OpenFOAM. Gradients are computed via the adjoint approach, which is suitable to deal with many design parameters, but keeping the computational costs low. The implementation is verified by comparing the gradients from the adjoint method with gradients obtained by finite differences for a NACA 0012 airfoil. The simulations of the leading edge slat are validated against measurements from the acoustic wind tunnel of Oldenburg University at a Reynolds number of Re = 6 • 105. The shape of the slat is optimized using the adjoint approach resulting in a drag reduction of 2%. Although the optimization is done for Re = 6 • 105, the improvements also hold for a higher Reynolds number of Re = 7.9 • 106, which is more realistic at modern wind turbines.

Optimal read/write memory system components

NASA Technical Reports Server (NTRS)

Kozma, A.; Vander Lugt, A.; Klinger, D.

1972-01-01

Two holographic data storage and display systems, voltage gradient ionization system, and linear strain manipulation system are discussed in terms of creating fast, high bit density, storage device. Components described include: novel mounting fixture for photoplastic arrays; corona discharge device; and block data composer.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Y. M., E-mail: ymingy@gmail.com; Bednarz, B.; Svatos, M.

Purpose: The future of radiation therapy will require advanced inverse planning solutions to support single-arc, multiple-arc, and “4π” delivery modes, which present unique challenges in finding an optimal treatment plan over a vast search space, while still preserving dosimetric accuracy. The successful clinical implementation of such methods would benefit from Monte Carlo (MC) based dose calculation methods, which can offer improvements in dosimetric accuracy when compared to deterministic methods. The standard method for MC based treatment planning optimization leverages the accuracy of the MC dose calculation and efficiency of well-developed optimization methods, by precalculating the fluence to dose relationship withinmore » a patient with MC methods and subsequently optimizing the fluence weights. However, the sequential nature of this implementation is computationally time consuming and memory intensive. Methods to reduce the overhead of the MC precalculation have been explored in the past, demonstrating promising reductions of computational time overhead, but with limited impact on the memory overhead due to the sequential nature of the dose calculation and fluence optimization. The authors propose an entirely new form of “concurrent” Monte Carlo treat plan optimization: a platform which optimizes the fluence during the dose calculation, reduces wasted computation time being spent on beamlets that weakly contribute to the final dose distribution, and requires only a low memory footprint to function. In this initial investigation, the authors explore the key theoretical and practical considerations of optimizing fluence in such a manner. Methods: The authors present a novel derivation and implementation of a gradient descent algorithm that allows for optimization during MC particle transport, based on highly stochastic information generated through particle transport of very few histories. A gradient rescaling and renormalization algorithm, and the concept of momentum from stochastic gradient descent were used to address obstacles unique to performing gradient descent fluence optimization during MC particle transport. The authors have applied their method to two simple geometrical phantoms, and one clinical patient geometry to examine the capability of this platform to generate conformal plans as well as assess its computational scaling and efficiency, respectively. Results: The authors obtain a reduction of at least 50% in total histories transported in their investigation compared to a theoretical unweighted beamlet calculation and subsequent fluence optimization method, and observe a roughly fixed optimization time overhead consisting of ∼10% of the total computation time in all cases. Finally, the authors demonstrate a negligible increase in memory overhead of ∼7–8 MB to allow for optimization of a clinical patient geometry surrounded by 36 beams using their platform. Conclusions: This study demonstrates a fluence optimization approach, which could significantly improve the development of next generation radiation therapy solutions while incurring minimal additional computational overhead.« less

Optimal Disturbances in Boundary Layers Subject to Streamwise Pressure Gradient

NASA Technical Reports Server (NTRS)

Ashpis, David E.; Tumin, Anatoli

2003-01-01

An analysis of the non-modal growth of perturbations in a boundary layer in the presence of a streamwise pressure gradient is presented. The analysis is based on PSE equations for an incompressible fluid. Examples with Falkner- Skan profiles indicate that a favorable pressure gradient decreases the non-modal growth while an unfavorable pressure gradient leads to an increase of the amplification. It is suggested that the transient growth mechanism be utilized to choose optimal parameters of tripping elements on a low-pressure turbine (LPT) airfoil. As an example, a boundary-layer flow with a streamwise pressure gradient corresponding to the pressure distribution over a LPT airfoil is considered. It is shown that there is an optimal spacing of the tripping elements and that the transient growth effect depends on the starting point. The amplification is found to be small at the LPT s very low Reynolds numbers, but there is a possibility to enhance the transient energy growth by means of wall cooling.

The use of singular value gradients and optimization techniques to design robust controllers for multiloop systems

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two-input/two-output drone flight control system.

The use of singular value gradients and optimization techniques to design robust controllers for multiloop systems

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two output drone flight control system.

Optimization for high-dose-rate brachytherapy of cervical cancer with adaptive simulated annealing and gradient descent.

PubMed

Yao, Rui; Templeton, Alistair K; Liao, Yixiang; Turian, Julius V; Kiel, Krystyna D; Chu, James C H

2014-01-01

To validate an in-house optimization program that uses adaptive simulated annealing (ASA) and gradient descent (GD) algorithms and investigate features of physical dose and generalized equivalent uniform dose (gEUD)-based objective functions in high-dose-rate (HDR) brachytherapy for cervical cancer. Eight Syed/Neblett template-based cervical cancer HDR interstitial brachytherapy cases were used for this study. Brachytherapy treatment plans were first generated using inverse planning simulated annealing (IPSA). Using the same dwell positions designated in IPSA, plans were then optimized with both physical dose and gEUD-based objective functions, using both ASA and GD algorithms. Comparisons were made between plans both qualitatively and based on dose-volume parameters, evaluating each optimization method and objective function. A hybrid objective function was also designed and implemented in the in-house program. The ASA plans are higher on bladder V75% and D2cc (p=0.034) and lower on rectum V75% and D2cc (p=0.034) than the IPSA plans. The ASA and GD plans are not significantly different. The gEUD-based plans have higher homogeneity index (p=0.034), lower overdose index (p=0.005), and lower rectum gEUD and normal tissue complication probability (p=0.005) than the physical dose-based plans. The hybrid function can produce a plan with dosimetric parameters between the physical dose-based and gEUD-based plans. The optimized plans with the same objective value and dose-volume histogram could have different dose distributions. Our optimization program based on ASA and GD algorithms is flexible on objective functions, optimization parameters, and can generate optimized plans comparable with IPSA. Copyright © 2014 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

Parallelization of Program to Optimize Simulated Trajectories (POST3D)

NASA Technical Reports Server (NTRS)

Hammond, Dana P.; Korte, John J. (Technical Monitor)

2001-01-01

This paper describes the parallelization of the Program to Optimize Simulated Trajectories (POST3D). POST3D uses a gradient-based optimization algorithm that reaches an optimum design point by moving from one design point to the next. The gradient calculations required to complete the optimization process, dominate the computational time and have been parallelized using a Single Program Multiple Data (SPMD) on a distributed memory NUMA (non-uniform memory access) architecture. The Origin2000 was used for the tests presented.

Did recent world record marathon runners employ optimal pacing strategies?

PubMed

Angus, Simon D

2014-01-01

We apply statistical analysis of high frequency (1 km) split data for the most recent two world-record marathon runs: Run 1 (2:03:59, 28 September 2008) and Run 2 (2:03:38, 25 September 2011). Based on studies in the endurance cycling literature, we develop two principles to approximate 'optimal' pacing in the field marathon. By utilising GPS and weather data, we test, and then de-trend, for each athlete's field response to gradient and headwind on course, recovering standardised proxies for power-based pacing traces. The resultant traces were analysed to ascertain if either runner followed optimal pacing principles; and characterise any deviations from optimality. Whereas gradient was insignificant, headwind was a significant factor in running speed variability for both runners, with Runner 2 targeting the (optimal) parallel variation principle, whilst Runner 1 did not. After adjusting for these responses, neither runner followed the (optimal) 'even' power pacing principle, with Runner 2's macro-pacing strategy fitting a sinusoidal oscillator with exponentially expanding envelope whilst Runner 1 followed a U-shaped, quadratic form. The study suggests that: (a) better pacing strategy could provide elite marathon runners with an economical pathway to significant performance improvements at world-record level; and (b) the data and analysis herein is consistent with a complex-adaptive model of power regulation.

Multimaterial topology optimization of contact problems using phase field regularization

NASA Astrophysics Data System (ADS)

Myśliński, Andrzej

2018-01-01

The numerical method to solve multimaterial topology optimization problems for elastic bodies in unilateral contact with Tresca friction is developed in the paper. The displacement of the elastic body in contact is governed by elliptic equation with inequality boundary conditions. The body is assumed to consists from more than two distinct isotropic elastic materials. The materials distribution function is chosen as the design variable. Since high contact stress appears during the contact phenomenon the aim of the structural optimization problem is to find such topology of the domain occupied by the body that the normal contact stress along the boundary of the body is minimized. The original cost functional is regularized using the multiphase volume constrained Ginzburg-Landau energy functional rather than the perimeter functional. The first order necessary optimality condition is recalled and used to formulate the generalized gradient flow equations of Allen-Cahn type. The optimal topology is obtained as the steady state of the phase transition governed by the generalized Allen-Cahn equation. As the interface width parameter tends to zero the transition of the phase field model to the level set model is studied. The optimization problem is solved numerically using the operator splitting approach combined with the projection gradient method. Numerical examples confirming the applicability of the proposed method are provided and discussed.

Wing-section optimization for supersonic viscous flow

NASA Technical Reports Server (NTRS)

Item, Cem C.; Baysal, Oktay (Editor)

1995-01-01

To improve the shape of a supersonic wing, an automated method that also includes higher fidelity to the flow physics is desirable. With this impetus, an aerodynamic optimization methodology incorporating thin-layer Navier-Stokes equations and sensitivity analysis had been previously developed. Prior to embarking upon the wind design task, the present investigation concentrated on testing the feasibility of the methodology, and the identification of adequate problem formulations, by defining two-dimensional, cost-effective test cases. Starting with two distinctly different initial airfoils, two independent shape optimizations resulted in shapes with similar features: slightly cambered, parabolic profiles with sharp leading- and trailing-edges. Secondly, the normal section to the subsonic portion of the leading edge, which had a high normal angle-of-attack, was considered. The optimization resulted in a shape with twist and camber which eliminated the adverse pressure gradient, hence, exploiting the leading-edge thrust. The wing section shapes obtained in all the test cases had the features predicted by previous studies. Therefore, it was concluded that the flowfield analyses and sensitivity coefficients were computed and fed to the present gradient-based optimizer correctly. Also, as a result of the present two-dimensional study, suggestions were made for the problem formulations which should contribute to an effective wing shape optimization.

Gradient stationary phase optimized selectivity liquid chromatography with conventional columns.

PubMed

Chen, Kai; Lynen, Frédéric; Szucs, Roman; Hanna-Brown, Melissa; Sandra, Pat

2013-05-21

Stationary phase optimized selectivity liquid chromatography (SOSLC) is a promising technique to optimize the selectivity of a given separation. By combination of different stationary phases, SOSLC offers excellent possibilities for method development under both isocratic and gradient conditions. The so far available commercial SOSLC protocol utilizes dedicated column cartridges and corresponding cartridge holders to build up the combined column of different stationary phases. The present work is aimed at developing and extending the gradient SOSLC approach towards coupling conventional columns. Generic tubing was used to connect short commercially available LC columns. Fast and base-line separation of a mixture of 12 compounds containing phenones, benzoic acids and hydroxybenzoates under both isocratic and linear gradient conditions was selected to demonstrate the potential of SOSLC. The influence of the connecting tubing on the deviation of predictions is also discussed.

Blind Compressed Image Watermarking for Noisy Communication Channels

DTIC Science & Technology

2015-10-26

Lenna test image [11] for our simulations, and gradient projection for sparse recon- struction (GPSR) [12] to solve the convex optimization prob- lem...E. Candes, J. Romberg , and T. Tao, “Robust uncertainty prin- ciples: exact signal reconstruction from highly incomplete fre- quency information,” IEEE...Images - Requirements and Guidelines,” ITU-T Recommen- dation T.81, 1992. [6] M. Gkizeli, D. Pados, and M. Medley, “Optimal signature de - sign for

A three-term conjugate gradient method under the strong-Wolfe line search

NASA Astrophysics Data System (ADS)

Khadijah, Wan; Rivaie, Mohd; Mamat, Mustafa

2017-08-01

Recently, numerous studies have been concerned in conjugate gradient methods for solving large-scale unconstrained optimization method. In this paper, a three-term conjugate gradient method is proposed for unconstrained optimization which always satisfies sufficient descent direction and namely as Three-Term Rivaie-Mustafa-Ismail-Leong (TTRMIL). Under standard conditions, TTRMIL method is proved to be globally convergent under strong-Wolfe line search. Finally, numerical results are provided for the purpose of comparison.

Monte Carlo Study on Carbon-Gradient-Doped Silica Aerogel Insulation.

PubMed

Zhao, Y; Tang, G H

2015-04-01

Silica aerogel is almost transparent for wavelengths below 8 µm where significant energy is transferred by thermal radiation. The radiative heat transfer can be restricted at high temperature if doped with carbon powder in silica aerogel. However, different particle sizes of carbon powder doping have different spectral extinction coefficients and the doped carbon powder will increase the solid conduction of silica aerogel. This paper presents a theoretical method for determining the optimal carbon doping in silica aerogel to minimize the energy transfer. Firstly we determine the optimal particle size by combining the spectral extinction coefficient with blackbody radiation and then evaluate the optimal doping amount between heat conduction and radiation. Secondly we develop the Monte Carlo numerical method to study radiative properties of carbon-gradient-doped silica aerogel to decrease the radiative heat transfer further. The results indicate that the carbon powder is able to block infrared radiation and thus improve the thermal insulating performance of silica aerogel effectively.

Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L

2017-10-01

The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.

A Gradient-Based Multistart Algorithm for Multimodal Aerodynamic Shape Optimization Problems Based on Free-Form Deformation

NASA Astrophysics Data System (ADS)

Streuber, Gregg Mitchell

Environmental and economic factors motivate the pursuit of more fuel-efficient aircraft designs. Aerodynamic shape optimization is a powerful tool in this effort, but is hampered by the presence of multimodality in many design spaces. Gradient-based multistart optimization uses a sampling algorithm and multiple parallel optimizations to reliably apply fast gradient-based optimization to moderately multimodal problems. Ensuring that the sampled geometries remain physically realizable requires manually developing specialized linear constraints for each class of problem. Utilizing free-form deformation geometry control allows these linear constraints to be written in a geometry-independent fashion, greatly easing the process of applying the algorithm to new problems. This algorithm was used to assess the presence of multimodality when optimizing a wing in subsonic and transonic flows, under inviscid and viscous conditions, and a blended wing-body under transonic, viscous conditions. Multimodality was present in every wing case, while the blended wing-body was found to be generally unimodal.

Fast Gradient Elution Reversed-Phase HPLC with Diode-Array Detection as a High Throughput Screening Method for Drugs of Abuse

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peter W. Carr; K.M. Fuller; D.R. Stoll

A new approach has been developed by modifying a conventional gradient elution liquid chromatograph for the high throughput screening of biological samples to detect the presence of regulated intoxicants. The goal of this work was to improve the speed of a gradient elution screening method over current approaches by optimizing the operational parameters of both the column and the instrument without compromising the reproducibility of the retention times, which are the basis for the identification. Most importantly, the novel instrument configuration substantially reduces the time needed to re-equilibrate the column between gradient runs, thereby reducing the total time for eachmore » analysis. The total analysis time for each gradient elution run is only 2.8 minutes, including 0.3 minutes for column reequilibration between analyses. Retention times standard calibration solutes are reproducible to better than 0.002 minutes in consecutive runs. A corrected retention index was adopted to account for day-to-day and column-to-column variations in retention time. The discriminating power and mean list length were calculated for a library of 47 intoxicants and compared with previous work from other laboratories to evaluate fast gradient elution HPLC as a screening tool.« less

A New Artificial Neural Network Enhanced by the Shuffled Complex Evolution Optimization with Principal Component Analysis (SP-UCI) for Water Resources Management

NASA Astrophysics Data System (ADS)

Hayatbini, N.; Faridzad, M.; Yang, T.; Akbari Asanjan, A.; Gao, X.; Sorooshian, S.

2016-12-01

The Artificial Neural Networks (ANNs) are useful in many fields, including water resources engineering and management. However, due to the non-linear and chaotic characteristics associated with natural processes and human decision making, the use of ANNs in real-world applications is still limited, and its performance needs to be further improved for a broader practical use. The commonly used Back-Propagation (BP) scheme and gradient-based optimization in training the ANNs have already found to be problematic in some cases. The BP scheme and gradient-based optimization methods are associated with the risk of premature convergence, stuck in local optimums, and the searching is highly dependent on initial conditions. Therefore, as an alternative to BP and gradient-based searching scheme, we propose an effective and efficient global searching method, termed the Shuffled Complex Evolutionary Global optimization algorithm with Principal Component Analysis (SP-UCI), to train the ANN connectivity weights. Large number of real-world datasets are tested with the SP-UCI-based ANN, as well as various popular Evolutionary Algorithms (EAs)-enhanced ANNs, i.e., Particle Swarm Optimization (PSO)-, Genetic Algorithm (GA)-, Simulated Annealing (SA)-, and Differential Evolution (DE)-enhanced ANNs. Results show that SP-UCI-enhanced ANN is generally superior over other EA-enhanced ANNs with regard to the convergence and computational performance. In addition, we carried out a case study for hydropower scheduling in the Trinity Lake in the western U.S. In this case study, multiple climate indices are used as predictors for the SP-UCI-enhanced ANN. The reservoir inflows and hydropower releases are predicted up to sub-seasonal to seasonal scale. Results show that SP-UCI-enhanced ANN is able to achieve better statistics than other EAs-based ANN, which implies the usefulness and powerfulness of proposed SP-UCI-enhanced ANN for reservoir operation, water resources engineering and management. The SP-UCI-enhanced ANN is universally applicable to many other regression and prediction problems, and it has a good potential to be an alternative to the classical BP scheme and gradient-based optimization methods.

A nano ultra-performance liquid chromatography-high resolution mass spectrometry approach for global metabolomic profiling and case study on drug-resistant multiple myeloma.

PubMed

Jones, Drew R; Wu, Zhiping; Chauhan, Dharminder; Anderson, Kenneth C; Peng, Junmin

2014-04-01

Global metabolomics relies on highly reproducible and sensitive detection of a wide range of metabolites in biological samples. Here we report the optimization of metabolome analysis by nanoflow ultraperformance liquid chromatography coupled to high-resolution orbitrap mass spectrometry. Reliable peak features were extracted from the LC-MS runs based on mandatory detection in duplicates and additional noise filtering according to blank injections. The run-to-run variation in peak area showed a median of 14%, and the false discovery rate during a mock comparison was evaluated. To maximize the number of peak features identified, we systematically characterized the effect of sample loading amount, gradient length, and MS resolution. The number of features initially rose and later reached a plateau as a function of sample amount, fitting a hyperbolic curve. Longer gradients improved unique feature detection in part by time-resolving isobaric species. Increasing the MS resolution up to 120000 also aided in the differentiation of near isobaric metabolites, but higher MS resolution reduced the data acquisition rate and conferred no benefits, as predicted from a theoretical simulation of possible metabolites. Moreover, a biphasic LC gradient allowed even distribution of peak features across the elution, yielding markedly more peak features than the linear gradient. Using this robust nUPLC-HRMS platform, we were able to consistently analyze ~6500 metabolite features in a single 60 min gradient from 2 mg of yeast, equivalent to ~50 million cells. We applied this optimized method in a case study of drug (bortezomib) resistant and drug-sensitive multiple myeloma cells. Overall, 18% of metabolite features were matched to KEGG identifiers, enabling pathway enrichment analysis. Principal component analysis and heat map data correctly clustered isogenic phenotypes, highlighting the potential for hundreds of small molecule biomarkers of cancer drug resistance.

FusionArc optimization: a hybrid volumetric modulated arc therapy (VMAT) and intensity modulated radiation therapy (IMRT) planning strategy.

PubMed

Matuszak, Martha M; Steers, Jennifer M; Long, Troy; McShan, Daniel L; Fraass, Benedick A; Romeijn, H Edwin; Ten Haken, Randall K

2013-07-01

To introduce a hybrid volumetric modulated arc therapy/intensity modulated radiation therapy (VMAT/IMRT) optimization strategy called FusionArc that combines the delivery efficiency of single-arc VMAT with the potentially desirable intensity modulation possible with IMRT. A beamlet-based inverse planning system was enhanced to combine the advantages of VMAT and IMRT into one comprehensive technique. In the hybrid strategy, baseline single-arc VMAT plans are optimized and then the current cost function gradients with respect to the beamlets are used to define a metric for predicting which beam angles would benefit from further intensity modulation. Beams with the highest metric values (called the gradient factor) are converted from VMAT apertures to IMRT fluence, and the optimization proceeds with the mixed variable set until convergence or until additional beams are selected for conversion. One phantom and two clinical cases were used to validate the gradient factor and characterize the FusionArc strategy. Comparisons were made between standard IMRT, single-arc VMAT, and FusionArc plans with one to five IMRT∕hybrid beams. The gradient factor was found to be highly predictive of the VMAT angles that would benefit plan quality the most from beam modulation. Over the three cases studied, a FusionArc plan with three converted beams achieved superior dosimetric quality with reductions in final cost ranging from 26.4% to 48.1% compared to single-arc VMAT. Additionally, the three beam FusionArc plans required 22.4%-43.7% fewer MU∕Gy than a seven beam IMRT plan. While the FusionArc plans with five converted beams offer larger reductions in final cost--32.9%-55.2% compared to single-arc VMAT--the decrease in MU∕Gy compared to IMRT was noticeably smaller at 12.2%-18.5%, when compared to IMRT. A hybrid VMAT∕IMRT strategy was implemented to find a high quality compromise between gantry-angle and intensity-based degrees of freedom. This optimization method will allow patients to be simultaneously planned for dosimetric quality and delivery efficiency without switching between delivery techniques. Example phantom and clinical cases suggest that the conversion of only three VMAT segments to modulated beams may result in a good combination of quality and efficiency.

Gradient descent for robust kernel-based regression

NASA Astrophysics Data System (ADS)

Guo, Zheng-Chu; Hu, Ting; Shi, Lei

2018-06-01

In this paper, we study the gradient descent algorithm generated by a robust loss function over a reproducing kernel Hilbert space (RKHS). The loss function is defined by a windowing function G and a scale parameter σ, which can include a wide range of commonly used robust losses for regression. There is still a gap between theoretical analysis and optimization process of empirical risk minimization based on loss: the estimator needs to be global optimal in the theoretical analysis while the optimization method can not ensure the global optimality of its solutions. In this paper, we aim to fill this gap by developing a novel theoretical analysis on the performance of estimators generated by the gradient descent algorithm. We demonstrate that with an appropriately chosen scale parameter σ, the gradient update with early stopping rules can approximate the regression function. Our elegant error analysis can lead to convergence in the standard L 2 norm and the strong RKHS norm, both of which are optimal in the mini-max sense. We show that the scale parameter σ plays an important role in providing robustness as well as fast convergence. The numerical experiments implemented on synthetic examples and real data set also support our theoretical results.

Conjugate gradient heat bath for ill-conditioned actions.

PubMed

Ceriotti, Michele; Bussi, Giovanni; Parrinello, Michele

2007-08-01

We present a method for performing sampling from a Boltzmann distribution of an ill-conditioned quadratic action. This method is based on heat-bath thermalization along a set of conjugate directions, generated via a conjugate-gradient procedure. The resulting scheme outperforms local updates for matrices with very high condition number, since it avoids the slowing down of modes with lower eigenvalue, and has some advantages over the global heat-bath approach, compared to which it is more stable and allows for more freedom in devising case-specific optimizations.

Gradient-Modulated SWIFT

PubMed Central

Zhang, Jinjin; Idiyatullin, Djaudat; Corum, Curtis A.; Kobayashi, Naoharu; Garwood, Michael

2017-01-01

Purpose Methods designed to image fast-relaxing spins, such as sweep imaging with Fourier transformation (SWIFT), often utilize high excitation bandwidth and duty cycle, and in some applications the optimal flip angle cannot be used without exceeding safe specific absorption rate (SAR) levels. The aim is to reduce SAR and increase the flexibility of SWIFT by applying time-varying gradient-modulation (GM). The modified sequence is called GM-SWIFT. Theory and Methods The method known as gradient-modulated offset independent adiabaticity was used to modulate the radiofrequency (RF) pulse and gradients. An expanded correlation algorithm was developed for GM-SWIFT to correct the phase and scale effects. Simulations and phantom and in vivo human experiments were performed to verify the correlation algorithm and to evaluate imaging performance. Results GM-SWIFT reduces SAR, RF amplitude, and acquisition time by up to 90%, 70%, and 45%, respectively, while maintaining image quality. The choice of GM parameter influences the lower limit of short T2* sensitivity, which can be exploited to suppress unwanted image haze from unresolvable ultrashort T2* signals originating from plastic materials in the coil housing and fixatives. Conclusions GM-SWIFT reduces peak and total RF power requirements and provides additional flexibility for optimizing SAR, RF amplitude, scan time, and image quality. PMID:25800547

Retention prediction and separation optimization under multilinear gradient elution in liquid chromatography with Microsoft Excel macros.

PubMed

Fasoula, S; Zisi, Ch; Gika, H; Pappa-Louisi, A; Nikitas, P

2015-05-22

A package of Excel VBA macros have been developed for modeling multilinear gradient retention data obtained in single or double gradient elution mode by changing organic modifier(s) content and/or eluent pH. For this purpose, ten chromatographic models were used and four methods were adopted for their application. The methods were based on (a) the analytical expression of the retention time, provided that this expression is available, (b) the retention times estimated using the Nikitas-Pappa approach, (c) the stepwise approximation, and (d) a simple numerical approximation involving the trapezoid rule for integration of the fundamental equation for gradient elution. For all these methods, Excel VBA macros have been written and implemented using two different platforms; the fitting and the optimization platform. The fitting platform calculates not only the adjustable parameters of the chromatographic models, but also the significance of these parameters and furthermore predicts the analyte elution times. The optimization platform determines the gradient conditions that lead to the optimum separation of a mixture of analytes by using the Solver evolutionary mode, provided that proper constraints are set in order to obtain the optimum gradient profile in the minimum gradient time. The performance of the two platforms was tested using experimental and artificial data. It was found that using the proposed spreadsheets, fitting, prediction, and optimization can be performed easily and effectively under all conditions. Overall, the best performance is exhibited by the analytical and Nikitas-Pappa's methods, although the former cannot be used under all circumstances. Copyright © 2015 Elsevier B.V. All rights reserved.

A conjugate gradients/trust regions algorithms for training multilayer perceptrons for nonlinear mapping

NASA Technical Reports Server (NTRS)

Madyastha, Raghavendra K.; Aazhang, Behnaam; Henson, Troy F.; Huxhold, Wendy L.

1992-01-01

This paper addresses the issue of applying a globally convergent optimization algorithm to the training of multilayer perceptrons, a class of Artificial Neural Networks. The multilayer perceptrons are trained towards the solution of two highly nonlinear problems: (1) signal detection in a multi-user communication network, and (2) solving the inverse kinematics for a robotic manipulator. The research is motivated by the fact that a multilayer perceptron is theoretically capable of approximating any nonlinear function to within a specified accuracy. The algorithm that has been employed in this study combines the merits of two well known optimization algorithms, the Conjugate Gradients and the Trust Regions Algorithms. The performance is compared to a widely used algorithm, the Backpropagation Algorithm, that is basically a gradient-based algorithm, and hence, slow in converging. The performances of the two algorithms are compared with the convergence rate. Furthermore, in the case of the signal detection problem, performances are also benchmarked by the decision boundaries drawn as well as the probability of error obtained in either case.

Reentry-Vehicle Shape Optimization Using a Cartesian Adjoint Method and CAD Geometry

NASA Technical Reports Server (NTRS)

Nemec, Marian; Aftosmis, Michael J.

2006-01-01

A DJOINT solutions of the governing flow equations are becoming increasingly important for the development of efficient analysis and optimization algorithms. A well-known use of the adjoint method is gradient-based shape. Given an objective function that defines some measure of performance, such as the lift and drag functionals, its gradient is computed at a cost that is essentially independent of the number of design variables (e.g., geometric parameters that control the shape). Classic aerodynamic applications of gradient-based optimization include the design of cruise configurations for transonic and supersonic flow, as well as the design of high-lift systems. are perhaps the most promising approach for addressing the issues of flow solution automation for aerodynamic design problems. In these methods, the discretization of the wetted surface is decoupled from that of the volume mesh. This not only enables fast and robust mesh generation for geometry of arbitrary complexity, but also facilitates access to geometry modeling and manipulation using parametric computer-aided design (CAD). In previous work on Cartesian adjoint solvers, Melvin et al. developed an adjoint formulation for the TRANAIR code, which is based on the full-potential equation with viscous corrections. More recently, Dadone and Grossman presented an adjoint formulation for the two-dimensional Euler equations using a ghost-cell method to enforce the wall boundary conditions. In Refs. 18 and 19, we presented an accurate and efficient algorithm for the solution of the adjoint Euler equations discretized on Cartesian meshes with embedded, cut-cell boundaries. Novel aspects of the algorithm were the computation of surface shape sensitivities for triangulations based on parametric-CAD models and the linearization of the coupling between the surface triangulation and the cut-cells. The accuracy of the gradient computation was verified using several three-dimensional test cases, which included design variables such as the free stream parameters and the planform shape of an isolated wing. The objective of the present work is to extend our adjoint formulation to problems involving general shape changes. Factors under consideration include the computation of mesh sensitivities that provide a reliable approximation of the objective function gradient, as well as the computation of surface shape sensitivities based on a direct-CAD interface. We present detailed gradient verification studies and then focus on a shape optimization problem for an Apollo-like reentry vehicle. The goal of the optimization is to enhance the lift-to-drag ratio of the capsule by modifying the shape of its heat-shield in conjunction with a center-of-gravity (c.g.) offset. This multipoint and multi-objective optimization problem is used to demonstrate the overall effectiveness of the Cartesian adjoint method for addressing the issues of complex aerodynamic design.

High-Fidelity Aerodynamic Shape Optimization for Natural Laminar Flow

NASA Astrophysics Data System (ADS)

Rashad, Ramy

To ensure the long-term sustainability of aviation, serious effort is underway to mitigate the escalating economic, environmental, and social concerns of the industry. Significant improvement to the energy efficiency of air transportation is required through the research and development of advanced and unconventional airframe and engine technologies. In the quest to reduce airframe drag, this thesis is concerned with the development and demonstration of an effective design tool for improving the aerodynamic efficiency of subsonic and transonic airfoils. The objective is to advance the state-of-the-art in high-fidelity aerodynamic shape optimization by incorporating and exploiting the phenomenon of laminar-turbulent transition in an efficient manner. A framework for the design and optimization of Natural Laminar Flow (NLF) airfoils is developed and demonstrated with transition prediction capable of accounting for the effects of Reynolds number, freestream turbulence intensity, Mach number, and pressure gradients. First, a two-dimensional Reynolds-averaged Navier-Stokes (RANS) flow solver has been extended to incorporate an iterative laminar-turbulent transition prediction methodology. The natural transition locations due to Tollmien-Schlichting instabilities are predicted using the simplified eN envelope method of Drela and Giles or, alternatively, the compressible form of the Arnal-Habiballah-Delcourt criterion. The boundary-layer properties are obtained directly from the Navier-Stokes flow solution, and the transition to turbulent flow is modeled using an intermittency function in conjunction with the Spalart-Allmaras turbulence model. The RANS solver is subsequently employed in a gradient-based sequential quadratic programming shape optimization framework. The laminar-turbulent transition criteria are tightly coupled into the objective and gradient evaluations. The gradients are obtained using a new augmented discrete-adjoint formulation for non-local transition criteria. Using the eN transition criterion, the proposed framework is applied to the single and multipoint optimization of subsonic and transonic airfoils, leading to robust NLF designs. The aerodynamic design requirements over a range of cruise flight conditions are cast into a multipoint optimization problem through a composite objective defined using a weighted integral of the operating points. To study and quantify off-design performance, a Pareto front is formed using a weighted objective combining free-transition and fully-turbulent operating conditions. Next we examine the sensitivity of NLF design to the freestream disturbance environment, highlighting the on- and off-design performance at different critical N-factors. Finally, we propose and demonstrate a technique to enable the design of airfoils with robust performance over a range of critical N-factors.

Research on particle swarm optimization algorithm based on optimal movement probability

NASA Astrophysics Data System (ADS)

Ma, Jianhong; Zhang, Han; He, Baofeng

2017-01-01

The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.

Optimization of Turbine Engine Cycle Analysis with Analytic Derivatives

NASA Technical Reports Server (NTRS)

Hearn, Tristan; Hendricks, Eric; Chin, Jeffrey; Gray, Justin; Moore, Kenneth T.

2016-01-01

A new engine cycle analysis tool, called Pycycle, was recently built using the OpenMDAO framework. This tool uses equilibrium chemistry based thermodynamics, and provides analytic derivatives. This allows for stable and efficient use of gradient-based optimization and sensitivity analysis methods on engine cycle models, without requiring the use of finite difference derivative approximation methods. To demonstrate this, a gradient-based design optimization was performed on a multi-point turbofan engine model. Results demonstrate very favorable performance compared to an optimization of an identical model using finite-difference approximated derivatives.

Optimization of Coil Element Configurations for a Matrix Gradient Coil.

PubMed

Kroboth, Stefan; Layton, Kelvin J; Jia, Feng; Littin, Sebastian; Yu, Huijun; Hennig, Jurgen; Zaitsev, Maxim

2018-01-01

Recently, matrix gradient coils (also termed multi-coils or multi-coil arrays) were introduced for imaging and B 0 shimming with 24, 48, and even 84 coil elements. However, in imaging applications, providing one amplifier per coil element is not always feasible due to high cost and technical complexity. In this simulation study, we show that an 84-channel matrix gradient coil (head insert for brain imaging) is able to create a wide variety of field shapes even if the number of amplifiers is reduced. An optimization algorithm was implemented that obtains groups of coil elements, such that a desired target field can be created by driving each group with an amplifier. This limits the number of amplifiers to the number of coil element groups. Simulated annealing is used due to the NP-hard combinatorial nature of the given problem. A spherical harmonic basis set up to the full third order within a sphere of 20-cm diameter in the center of the coil was investigated as target fields. We show that the median normalized least squares error for all target fields is below approximately 5% for 12 or more amplifiers. At the same time, the dissipated power stays within reasonable limits. With a relatively small set of amplifiers, switches can be used to sequentially generate spherical harmonics up to third order. The costs associated with a matrix gradient coil can be lowered, which increases the practical utility of matrix gradient coils.

Evaluation of differences between dual salt-pH gradient elution and mono gradient elution using a thermodynamic model: Simultaneous separation of six monoclonal antibody charge and size variants on preparative-scale ion exchange chromatographic resin.

PubMed

Lee, Yi Feng; Jöhnck, Matthias; Frech, Christian

2018-02-21

The efficiencies of mono gradient elution and dual salt-pH gradient elution for separation of six mAb charge and size variants on a preparative-scale ion exchange chromatographic resin are compared in this study. Results showed that opposite dual salt-pH gradient elution with increasing pH gradient and simultaneously decreasing salt gradient is best suited for the separation of these mAb charge and size variants on Eshmuno ® CPX. Besides giving high binding capacity, this type of opposite dual salt-pH gradient also provides better resolved mAb variant peaks and lower conductivity in the elution pools compared to single pH or salt gradients. To have a mechanistic understanding of the differences in mAb variants retention behaviors of mono pH gradient, parallel dual salt-pH gradient, and opposite dual salt-pH gradient, a linear gradient elution model was used. After determining the model parameters using the linear gradient elution model, 2D plots were used to show the pH and salt dependencies of the reciprocals of distribution coefficient, equilibrium constant, and effective ionic capacity of the mAb variants in these gradient elution systems. Comparison of the 2D plots indicated that the advantage of opposite dual salt-pH gradient system with increasing pH gradient and simultaneously decreasing salt gradient is the noncontinuous increased acceleration of protein migration. Furthermore, the fitted model parameters can be used for the prediction and optimization of mAb variants separation in dual salt-pH gradient and step elution. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 2018. © 2018 American Institute of Chemical Engineers.

Zero- to low-field MRI with averaging of concomitant gradient fields.

PubMed

Meriles, Carlos A; Sakellariou, Dimitris; Trabesinger, Andreas H; Demas, Vasiliki; Pines, Alexander

2005-02-08

Magnetic resonance imaging (MRI) encounters fundamental limits in circumstances in which the static magnetic field is not sufficiently strong to truncate unwanted, so-called concomitant components of the gradient field. This limitation affects the attainable optimal image fidelity and resolution most prominently in low-field imaging. In this article, we introduce the use of pulsed magnetic-field averaging toward relaxing these constraints. It is found that the image of an object can be retrieved by pulsed low fields in the presence of the full spatial variation of the imaging encoding gradient field even in the absence of the typical uniform high-field time-independent contribution. In addition, error-compensation schemes can be introduced through the application of symmetrized pulse sequences. Such schemes substantially mitigate artifacts related to evolution in strong magnetic-field gradients, magnetic fields that vary in direction and orientation, and imperfections of the applied field pulses.

Counterbalancing hydrodynamic sample distortion effects increases resolution of free-flow zone electrophoresis.

PubMed

Weber, G; Bauer, J

1998-06-01

On fractionation of highly heterogeneous protein mixtures, optimal resolution was achieved by forcing proteins to migrate through a preestablished pH gradient, until they entered a medium with a pH similar but not equal to their pIs. For this purpose, up to seven different media were pumped through the electrophoresis chamber so that they were flowing adjacently to each other, forming a pH gradient declining stepwise from the cathode to the anode. This gradient had a sufficiently strong band-focusing effect to counterbalance sample distortion effects of the flowing medium as proteins approached their isoelectric medium closer than 0.5 pH units. Continuous free-flow zone electrophoresis (FFZE) with high throughput capability was applicable if proteins did not precipitate or aggregate in these media. If components of heterogeneous protein mixtures had already started to precipitate or aggregate, in a medium with a pH exceeding their pI by more than 0.5 pH units, the application of interval modus and media forming flat pH gradients appeared advantageous.

Optimization and quantization in gradient symbol systems: a framework for integrating the continuous and the discrete in cognition.

PubMed

Smolensky, Paul; Goldrick, Matthew; Mathis, Donald

2014-08-01

Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization-Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance. Copyright © 2013 Cognitive Science Society, Inc.

The q-G method : A q-version of the Steepest Descent method for global optimization.

PubMed

Soterroni, Aline C; Galski, Roberto L; Scarabello, Marluce C; Ramos, Fernando M

2015-01-01

In this work, the q-Gradient (q-G) method, a q-version of the Steepest Descent method, is presented. The main idea behind the q-G method is the use of the negative of the q-gradient vector of the objective function as the search direction. The q-gradient vector, or simply the q-gradient, is a generalization of the classical gradient vector based on the concept of Jackson's derivative from the q-calculus. Its use provides the algorithm an effective mechanism for escaping from local minima. The q-G method reduces to the Steepest Descent method when the parameter q tends to 1. The algorithm has three free parameters and it is implemented so that the search process gradually shifts from global exploration in the beginning to local exploitation in the end. We evaluated the q-G method on 34 test functions, and compared its performance with 34 optimization algorithms, including derivative-free algorithms and the Steepest Descent method. Our results show that the q-G method is competitive and has a great potential for solving multimodal optimization problems.

Compressed Sensing for fMRI: Feasibility Study on the Acceleration of Non-EPI fMRI at 9.4T

PubMed Central

Kim, Seong-Gi; Ye, Jong Chul

2015-01-01

Conventional functional magnetic resonance imaging (fMRI) technique known as gradient-recalled echo (GRE) echo-planar imaging (EPI) is sensitive to image distortion and degradation caused by local magnetic field inhomogeneity at high magnetic fields. Non-EPI sequences such as spoiled gradient echo and balanced steady-state free precession (bSSFP) have been proposed as an alternative high-resolution fMRI technique; however, the temporal resolution of these sequences is lower than the typically used GRE-EPI fMRI. One potential approach to improve the temporal resolution is to use compressed sensing (CS). In this study, we tested the feasibility of k-t FOCUSS—one of the high performance CS algorithms for dynamic MRI—for non-EPI fMRI at 9.4T using the model of rat somatosensory stimulation. To optimize the performance of CS reconstruction, different sampling patterns and k-t FOCUSS variations were investigated. Experimental results show that an optimized k-t FOCUSS algorithm with acceleration by a factor of 4 works well for non-EPI fMRI at high field under various statistical criteria, which confirms that a combination of CS and a non-EPI sequence may be a good solution for high-resolution fMRI at high fields. PMID:26413503

Design of a high power TM01 mode launcher optimized for manufacturing by milling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dal Forno, Massimo

2016-12-15

Recent research on high-gradient rf acceleration found that hard metals, such as hard copper and hard copper-silver, have lower breakdown rate than soft metals. Traditional high-gradient accelerating structures are manufactured with parts joined by high-temperature brazing. The high temperature used in brazing makes the metal soft; therefore, this process cannot be used to manufacture structures out of hard metal alloys. In order to build the structure with hard metals, the components must be designed for joining without high-temperature brazing. One method is to build the accelerating structures out of two halves, and join them by using a low-temperature technique, atmore » the symmetry plane along the beam axis. The structure has input and output rf power couplers. We use a TM01 mode launcher as a rf power coupler, which was introduced during the Next Linear Collider (NLC) work. The part of the mode launcher will be built in each half of the structure. This paper presents a novel geometry of a mode launcher, optimized for manufacturing by milling. The coupler was designed for the CERN CLIC working frequency f = 11.9942 GHz; the same geometry can be scaled to any other frequency.« less

3-D phononic crystals with ultra-wide band gaps

PubMed Central

Lu, Yan; Yang, Yang; Guest, James K.; Srivastava, Ankit

2017-01-01

In this paper gradient based topology optimization (TO) is used to discover 3-D phononic structures that exhibit ultra-wide normalized all-angle all-mode band gaps. The challenging computational task of repeated 3-D phononic band-structure evaluations is accomplished by a combination of a fast mixed variational eigenvalue solver and distributed Graphic Processing Unit (GPU) parallel computations. The TO algorithm utilizes the material distribution-based approach and a gradient-based optimizer. The design sensitivity for the mixed variational eigenvalue problem is derived using the adjoint method and is implemented through highly efficient vectorization techniques. We present optimized results for two-material simple cubic (SC), body centered cubic (BCC), and face centered cubic (FCC) crystal structures and show that in each of these cases different initial designs converge to single inclusion network topologies within their corresponding primitive cells. The optimized results show that large phononic stop bands for bulk wave propagation can be achieved at lower than close packed spherical configurations leading to lighter unit cells. For tungsten carbide - epoxy crystals we identify all angle all mode normalized stop bands exceeding 100%, which is larger than what is possible with only spherical inclusions. PMID:28233812

3-D phononic crystals with ultra-wide band gaps.

PubMed

Lu, Yan; Yang, Yang; Guest, James K; Srivastava, Ankit

2017-02-24

In this paper gradient based topology optimization (TO) is used to discover 3-D phononic structures that exhibit ultra-wide normalized all-angle all-mode band gaps. The challenging computational task of repeated 3-D phononic band-structure evaluations is accomplished by a combination of a fast mixed variational eigenvalue solver and distributed Graphic Processing Unit (GPU) parallel computations. The TO algorithm utilizes the material distribution-based approach and a gradient-based optimizer. The design sensitivity for the mixed variational eigenvalue problem is derived using the adjoint method and is implemented through highly efficient vectorization techniques. We present optimized results for two-material simple cubic (SC), body centered cubic (BCC), and face centered cubic (FCC) crystal structures and show that in each of these cases different initial designs converge to single inclusion network topologies within their corresponding primitive cells. The optimized results show that large phononic stop bands for bulk wave propagation can be achieved at lower than close packed spherical configurations leading to lighter unit cells. For tungsten carbide - epoxy crystals we identify all angle all mode normalized stop bands exceeding 100%, which is larger than what is possible with only spherical inclusions.

Optimization of wind plant layouts using an adjoint approach

DOE PAGES

King, Ryan N.; Dykes, Katherine; Graf, Peter; ...

2017-03-10

Using adjoint optimization and three-dimensional steady-state Reynolds-averaged Navier–Stokes (RANS) simulations, we present a new gradient-based approach for optimally siting wind turbines within utility-scale wind plants. By solving the adjoint equations of the flow model, the gradients needed for optimization are found at a cost that is independent of the number of control variables, thereby permitting optimization of large wind plants with many turbine locations. Moreover, compared to the common approach of superimposing prescribed wake deficits onto linearized flow models, the computational efficiency of the adjoint approach allows the use of higher-fidelity RANS flow models which can capture nonlinear turbulent flowmore » physics within a wind plant. The steady-state RANS flow model is implemented in the Python finite-element package FEniCS and the derivation and solution of the discrete adjoint equations are automated within the dolfin-adjoint framework. Gradient-based optimization of wind turbine locations is demonstrated for idealized test cases that reveal new optimization heuristics such as rotational symmetry, local speedups, and nonlinear wake curvature effects. Layout optimization is also demonstrated on more complex wind rose shapes, including a full annual energy production (AEP) layout optimization over 36 inflow directions and 5 wind speed bins.« less

Optimization of wind plant layouts using an adjoint approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, Ryan N.; Dykes, Katherine; Graf, Peter

Using adjoint optimization and three-dimensional steady-state Reynolds-averaged Navier–Stokes (RANS) simulations, we present a new gradient-based approach for optimally siting wind turbines within utility-scale wind plants. By solving the adjoint equations of the flow model, the gradients needed for optimization are found at a cost that is independent of the number of control variables, thereby permitting optimization of large wind plants with many turbine locations. Moreover, compared to the common approach of superimposing prescribed wake deficits onto linearized flow models, the computational efficiency of the adjoint approach allows the use of higher-fidelity RANS flow models which can capture nonlinear turbulent flowmore » physics within a wind plant. The steady-state RANS flow model is implemented in the Python finite-element package FEniCS and the derivation and solution of the discrete adjoint equations are automated within the dolfin-adjoint framework. Gradient-based optimization of wind turbine locations is demonstrated for idealized test cases that reveal new optimization heuristics such as rotational symmetry, local speedups, and nonlinear wake curvature effects. Layout optimization is also demonstrated on more complex wind rose shapes, including a full annual energy production (AEP) layout optimization over 36 inflow directions and 5 wind speed bins.« less

An optimized resistor pattern for temperature gradient control in microfluidics

NASA Astrophysics Data System (ADS)

Selva, Bertrand; Marchalot, Julien; Jullien, Marie-Caroline

2009-06-01

In this paper, we demonstrate the possibility of generating high-temperature gradients with a linear temperature profile when heating is provided in situ. Thanks to improved optimization algorithms, the shape of resistors, which constitute the heating source, is optimized by applying the genetic algorithm NSGA-II (acronym for the non-dominated sorting genetic algorithm) (Deb et al 2002 IEEE Trans. Evol. Comput. 6 2). Experimental validation of the linear temperature profile within the cavity is carried out using a thermally sensitive fluorophore, called Rhodamine B (Ross et al 2001 Anal. Chem. 73 4117-23, Erickson et al 2003 Lab Chip 3 141-9). The high level of agreement obtained between experimental and numerical results serves to validate the accuracy of this method for generating highly controlled temperature profiles. In the field of actuation, such a device is of potential interest since it allows for controlling bubbles or droplets moving by means of thermocapillary effects (Baroud et al 2007 Phys. Rev. E 75 046302). Digital microfluidics is a critical area in the field of microfluidics (Dreyfus et al 2003 Phys. Rev. Lett. 90 14) as well as in the so-called lab-on-a-chip technology. Through an example, the large application potential of such a technique is demonstrated, which entails handling a single bubble driven along a cavity using simple and tunable embedded resistors.

Parameter Optimization for Turbulent Reacting Flows Using Adjoints

NASA Astrophysics Data System (ADS)

Lapointe, Caelan; Hamlington, Peter E.

2017-11-01

The formulation of a new adjoint solver for topology optimization of turbulent reacting flows is presented. This solver provides novel configurations (e.g., geometries and operating conditions) based on desired system outcomes (i.e., objective functions) for complex reacting flow problems of practical interest. For many such problems, it would be desirable to know optimal values of design parameters (e.g., physical dimensions, fuel-oxidizer ratios, and inflow-outflow conditions) prior to real-world manufacture and testing, which can be expensive, time-consuming, and dangerous. However, computational optimization of these problems is made difficult by the complexity of most reacting flows, necessitating the use of gradient-based optimization techniques in order to explore a wide design space at manageable computational cost. The adjoint method is an attractive way to obtain the required gradients, because the cost of the method is determined by the dimension of the objective function rather than the size of the design space. Here, the formulation of a novel solver is outlined that enables gradient-based parameter optimization of turbulent reacting flows using the discrete adjoint method. Initial results and an outlook for future research directions are provided.

Gradient-based adaptation of general gaussian kernels.

PubMed

Glasmachers, Tobias; Igel, Christian

2005-10-01

Gradient-based optimizing of gaussian kernel functions is considered. The gradient for the adaptation of scaling and rotation of the input space is computed to achieve invariance against linear transformations. This is done by using the exponential map as a parameterization of the kernel parameter manifold. By restricting the optimization to a constant trace subspace, the kernel size can be controlled. This is, for example, useful to prevent overfitting when minimizing radius-margin generalization performance measures. The concepts are demonstrated by training hard margin support vector machines on toy data.

Computational methods for aerodynamic design using numerical optimization

NASA Technical Reports Server (NTRS)

Peeters, M. F.

1983-01-01

Five methods to increase the computational efficiency of aerodynamic design using numerical optimization, by reducing the computer time required to perform gradient calculations, are examined. The most promising method consists of drastically reducing the size of the computational domain on which aerodynamic calculations are made during gradient calculations. Since a gradient calculation requires the solution of the flow about an airfoil whose geometry was slightly perturbed from a base airfoil, the flow about the base airfoil is used to determine boundary conditions on the reduced computational domain. This method worked well in subcritical flow.

A mesh gradient technique for numerical optimization

NASA Technical Reports Server (NTRS)

Willis, E. A., Jr.

1973-01-01

A class of successive-improvement optimization methods in which directions of descent are defined in the state space along each trial trajectory are considered. The given problem is first decomposed into two discrete levels by imposing mesh points. Level 1 consists of running optimal subarcs between each successive pair of mesh points. For normal systems, these optimal two-point boundary value problems can be solved by following a routine prescription if the mesh spacing is sufficiently close. A spacing criterion is given. Under appropriate conditions, the criterion value depends only on the coordinates of the mesh points, and its gradient with respect to those coordinates may be defined by interpreting the adjoint variables as partial derivatives of the criterion value function. In level 2, the gradient data is used to generate improvement steps or search directions in the state space which satisfy the boundary values and constraints of the given problem.

Systematic interpolation method predicts protein chromatographic elution with salt gradients, pH gradients and combined salt/pH gradients.

PubMed

Creasy, Arch; Barker, Gregory; Carta, Giorgio

2017-03-01

A methodology is presented to predict protein elution behavior from an ion exchange column using both individual or combined pH and salt gradients based on high-throughput batch isotherm data. The buffer compositions are first optimized to generate linear pH gradients from pH 5.5 to 7 with defined concentrations of sodium chloride. Next, high-throughput batch isotherm data are collected for a monoclonal antibody on the cation exchange resin POROS XS over a range of protein concentrations, salt concentrations, and solution pH. Finally, a previously developed empirical interpolation (EI) method is extended to describe protein binding as a function of the protein and salt concentration and solution pH without using an explicit isotherm model. The interpolated isotherm data are then used with a lumped kinetic model to predict the protein elution behavior. Experimental results obtained for laboratory scale columns show excellent agreement with the predicted elution curves for both individual or combined pH and salt gradients at protein loads up to 45 mg/mL of column. Numerical studies show that the model predictions are robust as long as the isotherm data cover the range of mobile phase compositions where the protein actually elutes from the column. Copyright © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Solving the optimal attention allocation problem in manual control

NASA Technical Reports Server (NTRS)

Kleinman, D. L.

1976-01-01

Within the context of the optimal control model of human response, analytic expressions for the gradients of closed-loop performance metrics with respect to human operator attention allocation are derived. These derivatives serve as the basis for a gradient algorithm that determines the optimal attention that a human should allocate among several display indicators in a steady-state manual control task. Application of the human modeling techniques are made to study the hover control task for a CH-46 VTOL flight tested by NASA.

Momentum-weighted conjugate gradient descent algorithm for gradient coil optimization.

PubMed

Lu, Hanbing; Jesmanowicz, Andrzej; Li, Shi-Jiang; Hyde, James S

2004-01-01

MRI gradient coil design is a type of nonlinear constrained optimization. A practical problem in transverse gradient coil design using the conjugate gradient descent (CGD) method is that wire elements move at different rates along orthogonal directions (r, phi, z), and tend to cross, breaking the constraints. A momentum-weighted conjugate gradient descent (MW-CGD) method is presented to overcome this problem. This method takes advantage of the efficiency of the CGD method combined with momentum weighting, which is also an intrinsic property of the Levenberg-Marquardt algorithm, to adjust step sizes along the three orthogonal directions. A water-cooled, 12.8 cm inner diameter, three axis torque-balanced gradient coil for rat imaging was developed based on this method, with an efficiency of 2.13, 2.08, and 4.12 mT.m(-1).A(-1) along X, Y, and Z, respectively. Experimental data demonstrate that this method can improve efficiency by 40% and field uniformity by 27%. This method has also been applied to the design of a gradient coil for the human brain, employing remote current return paths. The benefits of this design include improved gradient field uniformity and efficiency, with a shorter length than gradient coil designs using coaxial return paths. Copyright 2003 Wiley-Liss, Inc.

Gradient ascent pulse engineering approach to CNOT gates in donor electron spin quantum computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsai, D.-B.; Goan, H.-S.

2008-11-07

In this paper, we demonstrate how gradient ascent pulse engineering (GRAPE) optimal control methods can be implemented on donor electron spin qubits in semiconductors with an architecture complementary to the original Kane's proposal. We focus on the high fidelity controlled-NOT (CNOT) gate and we explicitly find the digitized control sequences for a controlled-NOT gate by optimizing its fidelity using the effective, reduced donor electron spin Hamiltonian with external controls over the hyperfine A and exchange J interactions. We then simulate the CNOT-gate sequence with the full spin Hamiltonian and find that it has an error of 10{sup -6} that ismore » below the error threshold of 10{sup -4} required for fault-tolerant quantum computation. Also the CNOT gate operation time of 100 ns is 3 times faster than 297 ns of the proposed global control scheme.« less

Geodesic regression on orientation distribution functions with its application to an aging study.

PubMed

Du, Jia; Goh, Alvina; Kushnarev, Sergey; Qiu, Anqi

2014-02-15

In this paper, we treat orientation distribution functions (ODFs) derived from high angular resolution diffusion imaging (HARDI) as elements of a Riemannian manifold and present a method for geodesic regression on this manifold. In order to find the optimal regression model, we pose this as a least-squares problem involving the sum-of-squared geodesic distances between observed ODFs and their model fitted data. We derive the appropriate gradient terms and employ gradient descent to find the minimizer of this least-squares optimization problem. In addition, we show how to perform statistical testing for determining the significance of the relationship between the manifold-valued regressors and the real-valued regressands. Experiments on both synthetic and real human data are presented. In particular, we examine aging effects on HARDI via geodesic regression of ODFs in normal adults aged 22 years old and above. © 2013 Elsevier Inc. All rights reserved.

Automated Calibration For Numerical Models Of Riverflow

NASA Astrophysics Data System (ADS)

Fernandez, Betsaida; Kopmann, Rebekka; Oladyshkin, Sergey

2017-04-01

Calibration of numerical models is fundamental since the beginning of all types of hydro system modeling, to approximate the parameters that can mimic the overall system behavior. Thus, an assessment of different deterministic and stochastic optimization methods is undertaken to compare their robustness, computational feasibility, and global search capacity. Also, the uncertainty of the most suitable methods is analyzed. These optimization methods minimize the objective function that comprises synthetic measurements and simulated data. Synthetic measurement data replace the observed data set to guarantee an existing parameter solution. The input data for the objective function derivate from a hydro-morphological dynamics numerical model which represents an 180-degree bend channel. The hydro- morphological numerical model shows a high level of ill-posedness in the mathematical problem. The minimization of the objective function by different candidate methods for optimization indicates a failure in some of the gradient-based methods as Newton Conjugated and BFGS. Others reveal partial convergence, such as Nelder-Mead, Polak und Ribieri, L-BFGS-B, Truncated Newton Conjugated, and Trust-Region Newton Conjugated Gradient. Further ones indicate parameter solutions that range outside the physical limits, such as Levenberg-Marquardt and LeastSquareRoot. Moreover, there is a significant computational demand for genetic optimization methods, such as Differential Evolution and Basin-Hopping, as well as for Brute Force methods. The Deterministic Sequential Least Square Programming and the scholastic Bayes Inference theory methods present the optimal optimization results. keywords: Automated calibration of hydro-morphological dynamic numerical model, Bayesian inference theory, deterministic optimization methods.

Optimal structural design of the midship of a VLCC based on the strategy integrating SVM and GA

NASA Astrophysics Data System (ADS)

Sun, Li; Wang, Deyu

2012-03-01

In this paper a hybrid process of modeling and optimization, which integrates a support vector machine (SVM) and genetic algorithm (GA), was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier (VLCC) ship was carried out according to the direct strength assessment method in common structural rules (CSR), which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.

Quantitative structure-retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans.

PubMed

Golubović, Jelena; Protić, Ana; Otašević, Biljana; Zečević, Mira

2016-04-01

QSRR are mathematically derived relationships between the chromatographic parameters determined for a representative series of analytes in given separation systems and the molecular descriptors accounting for the structural differences among the investigated analytes. Artificial neural network is a technique of data analysis, which sets out to emulate the human brain's way of working. The aim of the present work was to optimize separation of six angiotensin receptor antagonists, so-called sartans: losartan, valsartan, irbesartan, telmisartan, candesartan cilexetil and eprosartan in a gradient-elution HPLC method. For this purpose, ANN as a mathematical tool was used for establishing a QSRR model based on molecular descriptors of sartans and varied instrumental conditions. The optimized model can be further used for prediction of an external congener of sartans and analysis of the influence of the analyte structure, represented through molecular descriptors, on retention behaviour. Molecular descriptors included in modelling were electrostatic, geometrical and quantum-chemical descriptors: connolly solvent excluded volume non-1,4 van der Waals energy, octanol/water distribution coefficient, polarizability, number of proton-donor sites and number of proton-acceptor sites. Varied instrumental conditions were gradient time, buffer pH and buffer molarity. High prediction ability of the optimized network enabled complete separation of the analytes within the run time of 15.5 min under following conditions: gradient time of 12.5 min, buffer pH of 3.95 and buffer molarity of 25 mM. Applied methodology showed the potential to predict retention behaviour of an external analyte with the properties within the training space. Connolly solvent excluded volume, polarizability and number of proton-acceptor sites appeared to be most influential paramateres on retention behaviour of the sartans. Copyright © 2015 Elsevier B.V. All rights reserved.

Optimality and inference in hydrology from entropy production considerations: synthetic hillslope numerical experiments

NASA Astrophysics Data System (ADS)

Kollet, S. J.

2015-05-01

In this study, entropy production optimization and inference principles are applied to a synthetic semi-arid hillslope in high-resolution, physics-based simulations. The results suggest that entropy or power is indeed maximized, because of the strong nonlinearity of variably saturated flow and competing processes related to soil moisture fluxes, the depletion of gradients, and the movement of a free water table. Thus, it appears that the maximum entropy production (MEP) principle may indeed be applicable to hydrologic systems. In the application to hydrologic system, the free water table constitutes an important degree of freedom in the optimization of entropy production and may also relate the theory to actual observations. In an ensuing analysis, an attempt is made to transfer the complex, "microscopic" hillslope model into a macroscopic model of reduced complexity using the MEP principle as an interference tool to obtain effective conductance coefficients and forces/gradients. The results demonstrate a new approach for the application of MEP to hydrologic systems and may form the basis for fruitful discussions and research in future.

Towards computational materials design from first principles using alchemical changes and derivatives.

DOE Office of Scientific and Technical Information (OSTI.GOV)

von Lilienfeld-Toal, Otto Anatole

2010-11-01

The design of new materials with specific physical, chemical, or biological properties is a central goal of much research in materials and medicinal sciences. Except for the simplest and most restricted cases brute-force computational screening of all possible compounds for interesting properties is beyond any current capacity due to the combinatorial nature of chemical compound space (set of stoichiometries and configurations). Consequently, when it comes to computationally optimizing more complex systems, reliable optimization algorithms must not only trade-off sufficient accuracy and computational speed of the models involved, they must also aim for rapid convergence in terms of number of compoundsmore » 'visited'. I will give an overview on recent progress on alchemical first principles paths and gradients in compound space that appear to be promising ingredients for more efficient property optimizations. Specifically, based on molecular grand canonical density functional theory an approach will be presented for the construction of high-dimensional yet analytical property gradients in chemical compound space. Thereafter, applications to molecular HOMO eigenvalues, catalyst design, and other problems and systems shall be discussed.« less

Transient Growth Theory Prediction of Optimal Placing of Passive and Active Flow Control Devices for Separation Delay in LPT Airfoils

NASA Technical Reports Server (NTRS)

Tumin, Anatoli; Ashpis, David E.

2003-01-01

An analysis of the non-modal growth of perturbations in a boundary layer in the presence of a streamwise pressure gradient is presented. The analysis is based on PSE equations for an incompressible fluid. Examples with Falkner-Skan profiles indicate that a favorable pressure gradient decreases the non-modal growth while an unfavorable pressure gradient leads to an increase of the amplification. It is suggested that the transient growth mechanism be utilized to choose optimal parameters of tripping elements on a low-pressure turbine (LPT) airfoil. As an example, a boundary layer flow with a streamwise pressure gradient corresponding to the pressure distribution over a LPT airfoil is considered. It is shown that there is an optimal spacing of the tripping elements and that the transient growth effect depends on the starting point. At very low Reynolds numbers, there is a possibility to enhance the transient energy growth by means of wall cooling.

Gradient-Based Aerodynamic Shape Optimization Using ADI Method for Large-Scale Problems

NASA Technical Reports Server (NTRS)

Pandya, Mohagna J.; Baysal, Oktay

1997-01-01

A gradient-based shape optimization methodology, that is intended for practical three-dimensional aerodynamic applications, has been developed. It is based on the quasi-analytical sensitivities. The flow analysis is rendered by a fully implicit, finite volume formulation of the Euler equations.The aerodynamic sensitivity equation is solved using the alternating-direction-implicit (ADI) algorithm for memory efficiency. A flexible wing geometry model, that is based on surface parameterization and platform schedules, is utilized. The present methodology and its components have been tested via several comparisons. Initially, the flow analysis for for a wing is compared with those obtained using an unfactored, preconditioned conjugate gradient approach (PCG), and an extensively validated CFD code. Then, the sensitivities computed with the present method have been compared with those obtained using the finite-difference and the PCG approaches. Effects of grid refinement and convergence tolerance on the analysis and shape optimization have been explored. Finally the new procedure has been demonstrated in the design of a cranked arrow wing at Mach 2.4. Despite the expected increase in the computational time, the results indicate that shape optimization, which require large numbers of grid points can be resolved with a gradient-based approach.

Contribution to the optimal shape design of two-dimensional internal flows with embedded shocks

NASA Technical Reports Server (NTRS)

Iollo, Angelo; Salas, Manuel D.

1995-01-01

We explore the practicability of optimal shape design for flows modeled by the Euler equations. We define a functional whose minimum represents the optimality condition. The gradient of the functional with respect to the geometry is calculated with the Lagrange multipliers, which are determined by solving a co-state equation. The optimization problem is then examined by comparing the performance of several gradient-based optimization algorithms. In this formulation, the flow field can be computed to an arbitrary order of accuracy. Finally, some results for internal flows with embedded shocks are presented, including a case for which the solution to the inverse problem does not belong to the design space.

SIRF: Simultaneous Satellite Image Registration and Fusion in a Unified Framework.

PubMed

Chen, Chen; Li, Yeqing; Liu, Wei; Huang, Junzhou

2015-11-01

In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral (Ms) image at the same geographical location. The fusion is formulated as a convex optimization problem which minimizes a linear combination of a least-squares fitting term and a dynamic gradient sparsity regularizer. The former is to preserve accurate spectral information of the Ms image, while the latter is to keep sharp edges of the high-resolution panchromatic image. We further propose to simultaneously register the two images during the fusing process, which is naturally achieved by virtue of the dynamic gradient sparsity property. An efficient algorithm is then devised to solve the optimization problem, accomplishing a linear computational complexity in the size of the output image in each iteration. We compare our method against six state-of-the-art image fusion methods on Ms image data sets from four satellites. Extensive experimental results demonstrate that the proposed method substantially outperforms the others in terms of both spatial and spectral qualities. We also show that our method can provide high-quality products from coarsely registered real-world IKONOS data sets. Finally, a MATLAB implementation is provided to facilitate future research.

Simulations of Flame Acceleration and DDT in Mixture Composition Gradients

NASA Astrophysics Data System (ADS)

Zheng, Weilin; Kaplan, Carolyn; Houim, Ryan; Oran, Elaine

2017-11-01

Unsteady, multidimensional, fully compressible numerical simulations of methane-air in an obstructed channel with spatial gradients in equivalence ratios have been carried to determine the effects of the gradients on flame acceleration and transition to detonation. Results for gradients perpendicular to the propagation direction were considered here. A calibrated, optimized chemical-diffusive model that reproduces correct flame and detonation properties for methane-air over a range of equivalence ratios was derived from a combination of a genetic algorithm with a Nelder-Mead optimization scheme. Inhomogeneous mixtures of methane-air resulted in slower flame acceleration and longer distance to DDT. Detonations were more likely to decouple into a flame and a shock under sharper concentration gradients. Detailed analyses of temperature and equivalence ratio illustrated that vertical gradients can greatly affect the formation of hot spots that initiate detonation by changing the strength of leading shock wave and local equivalence ratio near the base of obstacles. This work is supported by the Alpha Foundation (Grant No. AFC215-20).

Optimized operation of dielectric laser accelerators: Single bunch

NASA Astrophysics Data System (ADS)

Hanuka, Adi; Schächter, Levi

2018-05-01

We introduce a general approach to determine the optimal charge, efficiency and gradient for laser driven accelerators in a self-consistent way. We propose a way to enhance the operational gradient of dielectric laser accelerators by leverage of beam-loading effect. While the latter may be detrimental from the perspective of the effective gradient experienced by the particles, it can be beneficial as the effective field experienced by the accelerating structure, is weaker. As a result, the constraint imposed by the damage threshold fluence is accordingly weakened and our self-consistent approach predicts permissible gradients of ˜10 GV /m , one order of magnitude higher than previously reported experimental results—with unbunched pulse of electrons. Our approach leads to maximum efficiency to occur for higher gradients as compared with a scenario in which the beam-loading effect on the material is ignored. In any case, maximum gradient does not occur for the same conditions that maximum efficiency does—a trade-off set of parameters is suggested.

Three-Dimensional Viscous Alternating Direction Implicit Algorithm and Strategies for Shape Optimization

NASA Technical Reports Server (NTRS)

Pandya, Mohagna J.; Baysal, Oktay

1997-01-01

A gradient-based shape optimization based on quasi-analytical sensitivities has been extended for practical three-dimensional aerodynamic applications. The flow analysis has been rendered by a fully implicit, finite-volume formulation of the Euler and Thin-Layer Navier-Stokes (TLNS) equations. Initially, the viscous laminar flow analysis for a wing has been compared with an independent computational fluid dynamics (CFD) code which has been extensively validated. The new procedure has been demonstrated in the design of a cranked arrow wing at Mach 2.4 with coarse- and fine-grid based computations performed with Euler and TLNS equations. The influence of the initial constraints on the geometry and aerodynamics of the optimized shape has been explored. Various final shapes generated for an identical initial problem formulation but with different optimization path options (coarse or fine grid, Euler or TLNS), have been aerodynamically evaluated via a common fine-grid TLNS-based analysis. The initial constraint conditions show significant bearing on the optimization results. Also, the results demonstrate that to produce an aerodynamically efficient design, it is imperative to include the viscous physics in the optimization procedure with the proper resolution. Based upon the present results, to better utilize the scarce computational resources, it is recommended that, a number of viscous coarse grid cases using either a preconditioned bi-conjugate gradient (PbCG) or an alternating-direction-implicit (ADI) method, should initially be employed to improve the optimization problem definition, the design space and initial shape. Optimized shapes should subsequently be analyzed using a high fidelity (viscous with fine-grid resolution) flow analysis to evaluate their true performance potential. Finally, a viscous fine-grid-based shape optimization should be conducted, using an ADI method, to accurately obtain the final optimized shape.

Topology optimization of hyperelastic structures using a level set method

NASA Astrophysics Data System (ADS)

Chen, Feifei; Wang, Yiqiang; Wang, Michael Yu; Zhang, Y. F.

2017-12-01

Soft rubberlike materials, due to their inherent compliance, are finding widespread implementation in a variety of applications ranging from assistive wearable technologies to soft material robots. Structural design of such soft and rubbery materials necessitates the consideration of large nonlinear deformations and hyperelastic material models to accurately predict their mechanical behaviour. In this paper, we present an effective level set-based topology optimization method for the design of hyperelastic structures that undergo large deformations. The method incorporates both geometric and material nonlinearities where the strain and stress measures are defined within the total Lagrange framework and the hyperelasticity is characterized by the widely-adopted Mooney-Rivlin material model. A shape sensitivity analysis is carried out, in the strict sense of the material derivative, where the high-order terms involving the displacement gradient are retained to ensure the descent direction. As the design velocity enters into the shape derivative in terms of its gradient and divergence terms, we develop a discrete velocity selection strategy. The whole optimization implementation undergoes a two-step process, where the linear optimization is first performed and its optimized solution serves as the initial design for the subsequent nonlinear optimization. It turns out that this operation could efficiently alleviate the numerical instability and facilitate the optimization process. To demonstrate the validity and effectiveness of the proposed method, three compliance minimization problems are studied and their optimized solutions present significant mechanical benefits of incorporating the nonlinearities, in terms of remarkable enhancement in not only the structural stiffness but also the critical buckling load.

Aircraft symmetric flight optimization. [gradient techniques for supersonic aircraft control

NASA Technical Reports Server (NTRS)

Falco, M.; Kelley, H. J.

1973-01-01

Review of the development of gradient techniques and their application to aircraft optimal performance computations in the vertical plane of flight. Results obtained using the method of gradients are presented for attitude- and throttle-control programs which extremize the fuel, range, and time performance indices subject to various trajectory and control constraints, including boundedness of engine throttle control. A penalty function treatment of state inequality constraints which generally appear in aircraft performance problems is outlined. Numerical results for maximum-range, minimum-fuel, and minimum-time climb paths for a hypothetical supersonic turbojet interceptor are presented and discussed. In addition, minimum-fuel climb paths subject to various levels of ground overpressure intensity constraint are indicated for a representative supersonic transport. A variant of the Gel'fand-Tsetlin 'method of ravines' is reviewed, and two possibilities for further development of continuous gradient processes are cited - namely, a projection version of conjugate gradients and a curvilinear search.

[Simultaneous determination of five cold medicine ingredients in paracetamol triprolidine hydrochloride and pseudoephedrine hydrochloride tablets by pH/organic solvent double-gradient high performance liquid chromatography].

PubMed

Xuan, Xueyi; Huang, Lina; Pan, Xiaoling; Li, Ning

2013-02-01

A pH/organic solvent double-gradient mode in reversed-phase high performance liquid chromatography (HPLC) has been established as a new approach to the simultaneous determination of acetaminophen, caffeine, salicylamide, pseudoephedrine hydrochloride and triprolidine hydrochloride in paracetamol triprolidine hydrochloride and pseudoephedrine hydrochloride tablets. Through the optimization of the organic solvent gradient mode and pH/organic solvent double-gradient mode, the optimum double-gradient HPLC system of the five cold medicine ingredients has been built. The determination was carried out on a Diamonsiol C18 column (250 mm x 4.6 mm, 5 microm). The mobile phase consisted of methanol, 0.05 mol/L ammonium acetate solution and 0.08 mol/L acetic acid solution. The column temperature was set at 30 degrees C. The flow rate was 1.0 mL/min. The sample was measured at multiple wavelengths: 0-6 min, 280 nm; 6-7 min, 257 nm; 7-14 min, 280 nm; 14 min, 233 nm. The separation of the five cold medicine ingredients in the tablets was achieved in 25.5 min. The linear ranges of acetaminophen, pseudoephedrine hydrochloride, caffeine, salicylamide and triprolidine hydrochloride were 0.055 -0.998 g/L, 0.053-0.946 g/L, 0.007-0.129 g/L, 0.035-0.622 g/L and 0.002-0.039 g/L, respectively, with their correlation coefficients greater than 0.999 0. The detection limits (S/N = 3) were 0.09, 6, 0.02, 0.128 and 0.02 mg/L, respectively. Their mean recoveries were 97.9%-102.8%. The advantage of the method is the simultaneous determination of acidic, neutral and basic compounds. It also can improve the column efficiency of the analyte, compress the half-peak width and reduce the trailing. The optimized and validated method can be used for the simultaneous determination of the five cold medicine ingredients in the tablets.

Demonstration of Automatically-Generated Adjoint Code for Use in Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Green, Lawrence; Carle, Alan; Fagan, Mike

1999-01-01

Gradient-based optimization requires accurate derivatives of the objective function and constraints. These gradients may have previously been obtained by manual differentiation of analysis codes, symbolic manipulators, finite-difference approximations, or existing automatic differentiation (AD) tools such as ADIFOR (Automatic Differentiation in FORTRAN). Each of these methods has certain deficiencies, particularly when applied to complex, coupled analyses with many design variables. Recently, a new AD tool called ADJIFOR (Automatic Adjoint Generation in FORTRAN), based upon ADIFOR, was developed and demonstrated. Whereas ADIFOR implements forward-mode (direct) differentiation throughout an analysis program to obtain exact derivatives via the chain rule of calculus, ADJIFOR implements the reverse-mode counterpart of the chain rule to obtain exact adjoint form derivatives from FORTRAN code. Automatically-generated adjoint versions of the widely-used CFL3D computational fluid dynamics (CFD) code and an algebraic wing grid generation code were obtained with just a few hours processing time using the ADJIFOR tool. The codes were verified for accuracy and were shown to compute the exact gradient of the wing lift-to-drag ratio, with respect to any number of shape parameters, in about the time required for 7 to 20 function evaluations. The codes have now been executed on various computers with typical memory and disk space for problems with up to 129 x 65 x 33 grid points, and for hundreds to thousands of independent variables. These adjoint codes are now used in a gradient-based aerodynamic shape optimization problem for a swept, tapered wing. For each design iteration, the optimization package constructs an approximate, linear optimization problem, based upon the current objective function, constraints, and gradient values. The optimizer subroutines are called within a design loop employing the approximate linear problem until an optimum shape is found, the design loop limit is reached, or no further design improvement is possible due to active design variable bounds and/or constraints. The resulting shape parameters are then used by the grid generation code to define a new wing surface and computational grid. The lift-to-drag ratio and its gradient are computed for the new design by the automatically-generated adjoint codes. Several optimization iterations may be required to find an optimum wing shape. Results from two sample cases will be discussed. The reader should note that this work primarily represents a demonstration of use of automatically- generated adjoint code within an aerodynamic shape optimization. As such, little significance is placed upon the actual optimization results, relative to the method for obtaining the results.

A feasible DY conjugate gradient method for linear equality constraints

NASA Astrophysics Data System (ADS)

LI, Can

2017-09-01

In this paper, we propose a feasible conjugate gradient method for solving linear equality constrained optimization problem. The method is an extension of the Dai-Yuan conjugate gradient method proposed by Dai and Yuan to linear equality constrained optimization problem. It can be applied to solve large linear equality constrained problem due to lower storage requirement. An attractive property of the method is that the generated direction is always feasible and descent direction. Under mild conditions, the global convergence of the proposed method with exact line search is established. Numerical experiments are also given which show the efficiency of the method.

A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Jinglai, E-mail: jinglaili@sjtu.edu.cn; Lin, Guang, E-mail: lin491@purdue.edu; Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, WA 99352

2015-09-01

In this paper, we propose a frozen Gaussian approximation (FGA)-based multi-level particle swarm optimization (MLPSO) method for seismic inversion of high-frequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of high-frequency of seismic waves requires a large number of grid points in direct computational methods, and thus renders an extremely high computational demand on the simulation of each sample in MLPSO. We overcome this difficulty by threemore » steps: First, we use FGA to compute high-frequency wave propagation based on asymptotic analysis on phase plane; Then we design a constrained full waveform inversion problem to prevent the optimization search getting into regions of velocity where FGA is not accurate; Last, we solve the constrained optimization problem by MLPSO that employs FGA solvers with different fidelity. The performance of the proposed method is demonstrated by a two-dimensional full-waveform inversion example of the smoothed Marmousi model.« less

Truncated Conjugate Gradient: An Optimal Strategy for the Analytical Evaluation of the Many-Body Polarization Energy and Forces in Molecular Simulations.

PubMed

Aviat, Félix; Levitt, Antoine; Stamm, Benjamin; Maday, Yvon; Ren, Pengyu; Ponder, Jay W; Lagardère, Louis; Piquemal, Jean-Philip

2017-01-10

We introduce a new class of methods, denoted as Truncated Conjugate Gradient(TCG), to solve the many-body polarization energy and its associated forces in molecular simulations (i.e. molecular dynamics (MD) and Monte Carlo). The method consists in a fixed number of Conjugate Gradient (CG) iterations. TCG approaches provide a scalable solution to the polarization problem at a user-chosen cost and a corresponding optimal accuracy. The optimality of the CG-method guarantees that the number of the required matrix-vector products are reduced to a minimum compared to other iterative methods. This family of methods is non-empirical, fully adaptive, and provides analytical gradients, avoiding therefore any energy drift in MD as compared to popular iterative solvers. Besides speed, one great advantage of this class of approximate methods is that their accuracy is systematically improvable. Indeed, as the CG-method is a Krylov subspace method, the associated error is monotonically reduced at each iteration. On top of that, two improvements can be proposed at virtually no cost: (i) the use of preconditioners can be employed, which leads to the Truncated Preconditioned Conjugate Gradient (TPCG); (ii) since the residual of the final step of the CG-method is available, one additional Picard fixed point iteration ("peek"), equivalent to one step of Jacobi Over Relaxation (JOR) with relaxation parameter ω, can be made at almost no cost. This method is denoted by TCG-n(ω). Black-box adaptive methods to find good choices of ω are provided and discussed. Results show that TPCG-3(ω) is converged to high accuracy (a few kcal/mol) for various types of systems including proteins and highly charged systems at the fixed cost of four matrix-vector products: three CG iterations plus the initial CG descent direction. Alternatively, T(P)CG-2(ω) provides robust results at a reduced cost (three matrix-vector products) and offers new perspectives for long polarizable MD as a production algorithm. The T(P)CG-1(ω) level provides less accurate solutions for inhomogeneous systems, but its applicability to well-conditioned problems such as water is remarkable, with only two matrix-vector product evaluations.

Truncated Conjugate Gradient: An Optimal Strategy for the Analytical Evaluation of the Many-Body Polarization Energy and Forces in Molecular Simulations

PubMed Central

2016-01-01

We introduce a new class of methods, denoted as Truncated Conjugate Gradient(TCG), to solve the many-body polarization energy and its associated forces in molecular simulations (i.e. molecular dynamics (MD) and Monte Carlo). The method consists in a fixed number of Conjugate Gradient (CG) iterations. TCG approaches provide a scalable solution to the polarization problem at a user-chosen cost and a corresponding optimal accuracy. The optimality of the CG-method guarantees that the number of the required matrix-vector products are reduced to a minimum compared to other iterative methods. This family of methods is non-empirical, fully adaptive, and provides analytical gradients, avoiding therefore any energy drift in MD as compared to popular iterative solvers. Besides speed, one great advantage of this class of approximate methods is that their accuracy is systematically improvable. Indeed, as the CG-method is a Krylov subspace method, the associated error is monotonically reduced at each iteration. On top of that, two improvements can be proposed at virtually no cost: (i) the use of preconditioners can be employed, which leads to the Truncated Preconditioned Conjugate Gradient (TPCG); (ii) since the residual of the final step of the CG-method is available, one additional Picard fixed point iteration (“peek”), equivalent to one step of Jacobi Over Relaxation (JOR) with relaxation parameter ω, can be made at almost no cost. This method is denoted by TCG-n(ω). Black-box adaptive methods to find good choices of ω are provided and discussed. Results show that TPCG-3(ω) is converged to high accuracy (a few kcal/mol) for various types of systems including proteins and highly charged systems at the fixed cost of four matrix-vector products: three CG iterations plus the initial CG descent direction. Alternatively, T(P)CG-2(ω) provides robust results at a reduced cost (three matrix-vector products) and offers new perspectives for long polarizable MD as a production algorithm. The T(P)CG-1(ω) level provides less accurate solutions for inhomogeneous systems, but its applicability to well-conditioned problems such as water is remarkable, with only two matrix-vector product evaluations. PMID:28068773

A Requirements-Driven Optimization Method for Acoustic Liners Using Analytic Derivatives

NASA Technical Reports Server (NTRS)

Berton, Jeffrey J.; Lopes, Leonard V.

2017-01-01

More than ever, there is flexibility and freedom in acoustic liner design. Subject to practical considerations, liner design variables may be manipulated to achieve a target attenuation spectrum. But characteristics of the ideal attenuation spectrum can be difficult to know. Many multidisciplinary system effects govern how engine noise sources contribute to community noise. Given a hardwall fan noise source to be suppressed, and using an analytical certification noise model to compute a community noise measure of merit, the optimal attenuation spectrum can be derived using multidisciplinary systems analysis methods. In a previous paper on this subject, a method deriving the ideal target attenuation spectrum that minimizes noise perceived by observers on the ground was described. A simple code-wrapping approach was used to evaluate a community noise objective function for an external optimizer. Gradients were evaluated using a finite difference formula. The subject of this paper is an application of analytic derivatives that supply precise gradients to an optimization process. Analytic derivatives improve the efficiency and accuracy of gradient-based optimization methods and allow consideration of more design variables. In addition, the benefit of variable impedance liners is explored using a multi-objective optimization.

Chiral stationary phase optimized selectivity liquid chromatography: A strategy for the separation of chiral isomers.

PubMed

Hegade, Ravindra Suryakant; De Beer, Maarten; Lynen, Frederic

2017-09-15

Chiral Stationary-Phase Optimized Selectivity Liquid Chromatography (SOSLC) is proposed as a tool to optimally separate mixtures of enantiomers on a set of commercially available coupled chiral columns. This approach allows for the prediction of the separation profiles on any possible combination of the chiral stationary phases based on a limited number of preliminary analyses, followed by automated selection of the optimal column combination. Both the isocratic and gradient SOSLC approach were implemented for prediction of the retention times for a mixture of 4 chiral pairs on all possible combinations of the 5 commercial chiral columns. Predictions in isocratic and gradient mode were performed with a commercially available and with an in-house developed Microsoft visual basic algorithm, respectively. Optimal predictions in the isocratic mode required the coupling of 4 columns whereby relative deviations between the predicted and experimental retention times ranged between 2 and 7%. Gradient predictions led to the coupling of 3 chiral columns allowing baseline separation of all solutes, whereby differences between predictions and experiments ranged between 0 and 12%. The methodology is a novel tool allowing optimizing the separation of mixtures of optical isomers. Copyright © 2017 Elsevier B.V. All rights reserved.

Fast engineering optimization: A novel highly effective control parameterization approach for industrial dynamic processes.

PubMed

Liu, Ping; Li, Guodong; Liu, Xinggao

2015-09-01

Control vector parameterization (CVP) is an important approach of the engineering optimization for the industrial dynamic processes. However, its major defect, the low optimization efficiency caused by calculating the relevant differential equations in the generated nonlinear programming (NLP) problem repeatedly, limits its wide application in the engineering optimization for the industrial dynamic processes. A novel highly effective control parameterization approach, fast-CVP, is first proposed to improve the optimization efficiency for industrial dynamic processes, where the costate gradient formulae is employed and a fast approximate scheme is presented to solve the differential equations in dynamic process simulation. Three well-known engineering optimization benchmark problems of the industrial dynamic processes are demonstrated as illustration. The research results show that the proposed fast approach achieves a fine performance that at least 90% of the computation time can be saved in contrast to the traditional CVP method, which reveals the effectiveness of the proposed fast engineering optimization approach for the industrial dynamic processes. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Model of separation performance of bilinear gradients in scanning format counter-flow gradient electrofocusing techniques.

PubMed

Shameli, Seyed Mostafa; Glawdel, Tomasz; Ren, Carolyn L

2015-03-01

Counter-flow gradient electrofocusing allows the simultaneous concentration and separation of analytes by generating a gradient in the total velocity of each analyte that is the sum of its electrophoretic velocity and the bulk counter-flow velocity. In the scanning format, the bulk counter-flow velocity is varying with time so that a number of analytes with large differences in electrophoretic mobility can be sequentially focused and passed by a single detection point. Studies have shown that nonlinear (such as a bilinear) velocity gradients along the separation channel can improve both peak capacity and separation resolution simultaneously, which cannot be realized by using a single linear gradient. Developing an effective separation system based on the scanning counter-flow nonlinear gradient electrofocusing technique usually requires extensive experimental and numerical efforts, which can be reduced significantly with the help of analytical models for design optimization and guiding experimental studies. Therefore, this study focuses on developing an analytical model to evaluate the separation performance of scanning counter-flow bilinear gradient electrofocusing methods. In particular, this model allows a bilinear gradient and a scanning rate to be optimized for the desired separation performance. The results based on this model indicate that any bilinear gradient provides a higher separation resolution (up to 100%) compared to the linear case. This model is validated by numerical studies. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Method for optimizing channelized quadratic observers for binary classification of large-dimensional image datasets

PubMed Central

Kupinski, M. K.; Clarkson, E.

2015-01-01

We present a new method for computing optimized channels for channelized quadratic observers (CQO) that is feasible for high-dimensional image data. The method for calculating channels is applicable in general and optimal for Gaussian distributed image data. Gradient-based algorithms for determining the channels are presented for five different information-based figures of merit (FOMs). Analytic solutions for the optimum channels for each of the five FOMs are derived for the case of equal mean data for both classes. The optimum channels for three of the FOMs under the equal mean condition are shown to be the same. This result is critical since some of the FOMs are much easier to compute. Implementing the CQO requires a set of channels and the first- and second-order statistics of channelized image data from both classes. The dimensionality reduction from M measurements to L channels is a critical advantage of CQO since estimating image statistics from channelized data requires smaller sample sizes and inverting a smaller covariance matrix is easier. In a simulation study we compare the performance of ideal and Hotelling observers to CQO. The optimal CQO channels are calculated using both eigenanalysis and a new gradient-based algorithm for maximizing Jeffrey's divergence (J). Optimal channel selection without eigenanalysis makes the J-CQO on large-dimensional image data feasible. PMID:26366764

Brain vascular image enhancement based on gradient adjust with split Bregman

NASA Astrophysics Data System (ADS)

Liang, Xiao; Dong, Di; Hui, Hui; Zhang, Liwen; Fang, Mengjie; Tian, Jie

2016-04-01

Light Sheet Microscopy is a high-resolution fluorescence microscopic technique which enables to observe the mouse brain vascular network clearly with immunostaining. However, micro-vessels are stained with few fluorescence antibodies and their signals are much weaker than large vessels, which make micro-vessels unclear in LSM images. In this work, we developed a vascular image enhancement method to enhance micro-vessel details which should be useful for vessel statistics analysis. Since gradient describes the edge information of the vessel, the main idea of our method is to increase the gradient values of the enhanced image to improve the micro-vessels contrast. Our method contained two steps: 1) calculate the gradient image of LSM image, and then amplify high gradient values of the original image to enhance the vessel edge and suppress low gradient values to remove noises. Then we formulated a new L1-norm regularization optimization problem to find an image with the expected gradient while keeping the main structure information of the original image. 2) The split Bregman iteration method was used to deal with the L1-norm regularization problem and generate the final enhanced image. The main advantage of the split Bregman method is that it has both fast convergence and low memory cost. In order to verify the effectiveness of our method, we applied our method to a series of mouse brain vascular images acquired from a commercial LSM system in our lab. The experimental results showed that our method could greatly enhance micro-vessel edges which were unclear in the original images.

Space mapping method for the design of passive shields

NASA Astrophysics Data System (ADS)

Sergeant, Peter; Dupré, Luc; Melkebeek, Jan

2006-04-01

The aim of the paper is to find the optimal geometry of a passive shield for the reduction of the magnetic stray field of an axisymmetric induction heater. For the optimization, a space mapping algorithm is used that requires two models. The first is an accurate model with a high computational effort as it contains finite element models. The second is less accurate, but it has a low computational effort as it uses an analytical model: the shield is replaced by a number of mutually coupled coils. The currents in the shield are found by solving an electrical circuit. Space mapping combines both models to obtain the optimal passive shield fast and accurately. The presented optimization technique is compared with gradient, simplex, and genetic algorithms.

Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation

NASA Astrophysics Data System (ADS)

Schmitz, Gunnar; Christiansen, Ove

2018-06-01

We study how with means of Gaussian Process Regression (GPR) geometry optimizations, which rely on numerical gradients, can be accelerated. The GPR interpolates a local potential energy surface on which the structure is optimized. It is found to be efficient to combine results on a low computational level (HF or MP2) with the GPR-calculated gradient of the difference between the low level method and the target method, which is a variant of explicitly correlated Coupled Cluster Singles and Doubles with perturbative Triples correction CCSD(F12*)(T) in this study. Overall convergence is achieved if both the potential and the geometry are converged. Compared to numerical gradient-based algorithms, the number of required single point calculations is reduced. Although introducing an error due to the interpolation, the optimized structures are sufficiently close to the minimum of the target level of theory meaning that the reference and predicted minimum only vary energetically in the μEh regime.

Morphing Wings: A Study Using High-Fidelity Aerodynamic Shape Optimization

NASA Astrophysics Data System (ADS)

Curiale, Nathanael J.

With the aviation industry under pressure to reduce fuel consumption, morphing wings have the capacity to improve aircraft performance, thereby making a significant contribution to reversing climate change. Through high-fidelity aerodynamic shape optimization, various forms of morphing wings are assessed for a hypothetical regional-class aircraft. The framework used solves the Reynolds-averaged Navier-Stokes equations and utilizes a gradient-based optimization algorithm. Baseline geometries are developed through multipoint optimization, where the average drag coefficient is minimized over a range of flight conditions with additional dive constraints. Morphing optimizations are then performed, beginning with these baseline shapes. Five distinct types of morphing are investigated and compared. Overall, a theoretical fully adaptable wing produces roughly a 2% improvement in average performance, whereas trailing-edge morphing with a 27-point multipoint baseline results in just over a 1% improvement in average performance. Trailing-edge morphing proves to be more beneficial than leading-edge morphing, upper-surface morphing, and a conventional flap.

Direct Electrospray Printing of Gradient Refractive Index Chalcogenide Glass Films.

PubMed

Novak, Spencer; Lin, Pao Tai; Li, Cheng; Lumdee, Chatdanai; Hu, Juejun; Agarwal, Anuradha; Kik, Pieter G; Deng, Weiwei; Richardson, Kathleen

2017-08-16

A spatially varying effective refractive index gradient using chalcogenide glass layers is printed on a silicon wafer using an optimized electrospray (ES) deposition process. Using solution-derived glass precursors, IR-transparent Ge 23 Sb 7 S 70 and As 40 S 60 glass films of programmed thickness are fabricated to yield a bilayer structure, resulting in an effective gradient refractive index (GRIN) film. Optical and compositional analysis tools confirm the optical and physical nature of the gradient in the resulting high-optical-quality films, demonstrating the power of direct printing of multimaterial structures compatible with planar photonic fabrication protocols. The potential application of such tailorable materials and structures as they relate to the enhancement of sensitivity in chalcogenide glass based planar chemical sensor device design is presented. This method, applicable to a broad cross section of glass compositions, shows promise in directly depositing GRIN films with tunable refractive index profiles for bulk and planar optical components and devices.

Development of a Novel, Bicombinatorial Approach to Alloy Development, and Application to Rapid Screening of Creep Resistant Titanium Alloys

NASA Astrophysics Data System (ADS)

Martin, Brian

Combinatorial approaches have proven useful for rapid alloy fabrication and optimization. A new method of producing controlled isothermal gradients using the Gleeble Thermomechanical simulator has been developed, and demonstrated on the metastable beta-Ti alloy beta-21S, achieving a thermal gradient of 525-700 °C. This thermal gradient method has subsequently been coupled with existing combinatorial methods of producing composition gradients using the LENS(TM) additive manufacturing system, through the use of elemental blended powders. This has been demonstrated with a binary Ti-(0-15) wt% Cr build, which has subsequently been characterized with optical and electron microscopy, with special attention to the precipitate of TiCr2 Laves phases. The TiCr2 phase has been explored for its high temperature mechanical properties in a new oxidation resistant beta-Ti alloy, which serves as a demonstration of the new bicombinatorial methods developed as applied to a multicomponent alloy system.

Statistics of vacuum breakdown in the high-gradient and low-rate regime

NASA Astrophysics Data System (ADS)

Wuensch, Walter; Degiovanni, Alberto; Calatroni, Sergio; Korsbäck, Anders; Djurabekova, Flyura; Rajamäki, Robin; Giner-Navarro, Jorge

2017-01-01

In an increasing number of high-gradient linear accelerator applications, accelerating structures must operate with both high surface electric fields and low breakdown rates. Understanding the statistical properties of breakdown occurrence in such a regime is of practical importance for optimizing accelerator conditioning and operation algorithms, as well as of interest for efforts to understand the physical processes which underlie the breakdown phenomenon. Experimental data of breakdown has been collected in two distinct high-gradient experimental set-ups: A prototype linear accelerating structure operated in the Compact Linear Collider Xbox 12 GHz test stands, and a parallel plate electrode system operated with pulsed DC in the kV range. Collected data is presented, analyzed and compared. The two systems show similar, distinctive, two-part distributions of number of pulses between breakdowns, with each part corresponding to a specific, constant event rate. The correlation between distance and number of pulses between breakdown indicates that the two parts of the distribution, and their corresponding event rates, represent independent primary and induced follow-up breakdowns. The similarity of results from pulsed DC to 12 GHz rf indicates a similar vacuum arc triggering mechanism over the range of conditions covered by the experiments.

Energetic constraints, size gradients, and size limits in benthic marine invertebrates.

PubMed

Sebens, Kenneth P

2002-08-01

Populations of marine benthic organisms occupy habitats with a range of physical and biological characteristics. In the intertidal zone, energetic costs increase with temperature and aerial exposure, and prey intake increases with immersion time, generating size gradients with small individuals often found at upper limits of distribution. Wave action can have similar effects, limiting feeding time or success, although certain species benefit from wave dislodgment of their prey; this also results in gradients of size and morphology. The difference between energy intake and metabolic (and/or behavioral) costs can be used to determine an energetic optimal size for individuals in such populations. Comparisons of the energetic optimal size to the maximum predicted size based on mechanical constraints, and the ensuing mortality schedule, provides a mechanism to study and explain organism size gradients in intertidal and subtidal habitats. For species where the energetic optimal size is well below the maximum size that could persist under a certain set of wave/flow conditions, it is probable that energetic constraints dominate. When the opposite is true, populations of small individuals can dominate habitats with strong dislodgment or damage probability. When the maximum size of individuals is far below either energetic optima or mechanical limits, other sources of mortality (e.g., predation) may favor energy allocation to early reproduction rather than to continued growth. Predictions based on optimal size models have been tested for a variety of intertidal and subtidal invertebrates including sea anemones, corals, and octocorals. This paper provides a review of the optimal size concept, and employs a combination of the optimal energetic size model and life history modeling approach to explore energy allocation to growth or reproduction as the optimal size is approached.

Subpixel displacement measurement method based on the combination of particle swarm optimization and gradient algorithm

NASA Astrophysics Data System (ADS)

Guang, Chen; Qibo, Feng; Keqin, Ding; Zhan, Gao

2017-10-01

A subpixel displacement measurement method based on the combination of particle swarm optimization (PSO) and gradient algorithm (GA) was proposed for accuracy and speed optimization in GA, which is a subpixel displacement measurement method better applied in engineering practice. An initial integer-pixel value was obtained according to the global searching ability of PSO, and then gradient operators were adopted for a subpixel displacement search. A comparison was made between this method and GA by simulated speckle images and rigid-body displacement in metal specimens. The results showed that the computational accuracy of the combination of PSO and GA method reached 0.1 pixel in the simulated speckle images, or even 0.01 pixels in the metal specimen. Also, computational efficiency and the antinoise performance of the improved method were markedly enhanced.

High-Fidelity Multidisciplinary Design Optimization of Aircraft Configurations

NASA Technical Reports Server (NTRS)

Martins, Joaquim R. R. A.; Kenway, Gaetan K. W.; Burdette, David; Jonsson, Eirikur; Kennedy, Graeme J.

2017-01-01

To evaluate new airframe technologies we need design tools based on high-fidelity models that consider multidisciplinary interactions early in the design process. The overarching goal of this NRA is to develop tools that enable high-fidelity multidisciplinary design optimization of aircraft configurations, and to apply these tools to the design of high aspect ratio flexible wings. We develop a geometry engine that is capable of quickly generating conventional and unconventional aircraft configurations including the internal structure. This geometry engine features adjoint derivative computation for efficient gradient-based optimization. We also added overset capability to a computational fluid dynamics solver, complete with an adjoint implementation and semiautomatic mesh generation. We also developed an approach to constraining buffet and started the development of an approach for constraining utter. On the applications side, we developed a new common high-fidelity model for aeroelastic studies of high aspect ratio wings. We performed optimal design trade-o s between fuel burn and aircraft weight for metal, conventional composite, and carbon nanotube composite wings. We also assessed a continuous morphing trailing edge technology applied to high aspect ratio wings. This research resulted in the publication of 26 manuscripts so far, and the developed methodologies were used in two other NRAs. 1

Consequences of flight height and line spacing on airborne (helicopter) gravity gradient resolution in the Great Sand Dunes National Park and Preserve, Colorado

USGS Publications Warehouse

Kass, M. Andy

2013-01-01

Line spacing and flight height are critical parameters in airborne gravity gradient surveys; the optimal trade-off between survey costs and desired resolution, however, is different for every situation. This article investigates the additional benefit of reducing the flight height and line spacing though a study of a survey conducted over the Great Sand Dunes National Park and Preserve, which is the highest-resolution public-domain airborne gravity gradient data set available, with overlapping high- and lower-resolution surveys. By using Fourier analysis and matched filtering, it is shown that while the lower-resolution survey delineates the target body, reducing the flight height from 80 m to 40 m and the line spacing from 100 m to 50 m improves the recoverable resolution even at basement depths.

Optimization of neural network architecture for classification of radar jamming FM signals

NASA Astrophysics Data System (ADS)

Soto, Alberto; Mendoza, Ariadna; Flores, Benjamin C.

2017-05-01

The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

D-Optimal Experimental Design for Contaminant Source Identification

NASA Astrophysics Data System (ADS)

Sai Baba, A. K.; Alexanderian, A.

2016-12-01

Contaminant source identification seeks to estimate the release history of a conservative solute given point concentration measurements at some time after the release. This can be mathematically expressed as an inverse problem, with a linear observation operator or a parameter-to-observation map, which we tackle using a Bayesian approach. Acquisition of experimental data can be laborious and expensive. The goal is to control the experimental parameters - in our case, the sparsity of the sensors, to maximize the information gain subject to some physical or budget constraints. This is known as optimal experimental design (OED). D-optimal experimental design seeks to maximize the expected information gain, and has long been considered the gold standard in the statistics community. Our goal is to develop scalable methods for D-optimal experimental designs involving large-scale PDE constrained problems with high-dimensional parameter fields. A major challenge for the OED, is that a nonlinear optimization algorithm for the D-optimality criterion requires repeated evaluation of objective function and gradient involving the determinant of large and dense matrices - this cost can be prohibitively expensive for applications of interest. We propose novel randomized matrix techniques that bring down the computational costs of the objective function and gradient evaluations by several orders of magnitude compared to the naive approach. The effect of randomized estimators on the accuracy and the convergence of the optimization solver will be discussed. The features and benefits of our new approach will be demonstrated on a challenging model problem from contaminant source identification involving the inference of the initial condition from spatio-temporal observations in a time-dependent advection-diffusion problem.

A Class of Prediction-Correction Methods for Time-Varying Convex Optimization

NASA Astrophysics Data System (ADS)

Simonetto, Andrea; Mokhtari, Aryan; Koppel, Alec; Leus, Geert; Ribeiro, Alejandro

2016-09-01

This paper considers unconstrained convex optimization problems with time-varying objective functions. We propose algorithms with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and correction steps, while sampling the problem data at a constant rate of $1/h$, where $h$ is the length of the sampling interval. The prediction step is derived by analyzing the iso-residual dynamics of the optimality conditions. The correction step adjusts for the distance between the current prediction and the optimizer at each time step, and consists either of one or multiple gradient steps or Newton steps, which respectively correspond to the gradient trajectory tracking (GTT) or Newton trajectory tracking (NTT) algorithms. Under suitable conditions, we establish that the asymptotic error incurred by both proposed methods behaves as $O(h^2)$, and in some cases as $O(h^4)$, which outperforms the state-of-the-art error bound of $O(h)$ for correction-only methods in the gradient-correction step. Moreover, when the characteristics of the objective function variation are not available, we propose approximate gradient and Newton tracking algorithms (AGT and ANT, respectively) that still attain these asymptotical error bounds. Numerical simulations demonstrate the practical utility of the proposed methods and that they improve upon existing techniques by several orders of magnitude.

Multiple-Point Temperature Gradient Algorithm for Ring Laser Gyroscope Bias Compensation

PubMed Central

Li, Geng; Zhang, Pengfei; Wei, Guo; Xie, Yuanping; Yu, Xudong; Long, Xingwu

2015-01-01

To further improve ring laser gyroscope (RLG) bias stability, a multiple-point temperature gradient algorithm is proposed for RLG bias compensation in this paper. Based on the multiple-point temperature measurement system, a complete thermo-image of the RLG block is developed. Combined with the multiple-point temperature gradients between different points of the RLG block, the particle swarm optimization algorithm is used to tune the support vector machine (SVM) parameters, and an optimized design for selecting the thermometer locations is also discussed. The experimental results validate the superiority of the introduced method and enhance the precision and generalizability in the RLG bias compensation model. PMID:26633401

Neural Stem Cell Differentiation Using Microfluidic Device-Generated Growth Factor Gradient.

PubMed

Kim, Ji Hyeon; Sim, Jiyeon; Kim, Hyun-Jung

2018-04-11

Neural stem cells (NSCs) have the ability to self-renew and differentiate into multiple nervous system cell types. During embryonic development, the concentrations of soluble biological molecules have a critical role in controlling cell proliferation, migration, differentiation and apoptosis. In an effort to find optimal culture conditions for the generation of desired cell types in vitro , we used a microfluidic chip-generated growth factor gradient system. In the current study, NSCs in the microfluidic device remained healthy during the entire period of cell culture, and proliferated and differentiated in response to the concentration gradient of growth factors (epithermal growth factor and basic fibroblast growth factor). We also showed that overexpression of ASCL1 in NSCs increased neuronal differentiation depending on the concentration gradient of growth factors generated in the microfluidic gradient chip. The microfluidic system allowed us to study concentration-dependent effects of growth factors within a single device, while a traditional system requires multiple independent cultures using fixed growth factor concentrations. Our study suggests that the microfluidic gradient-generating chip is a powerful tool for determining the optimal culture conditions.

Optimized phase gradient measurements and phase-amplitude interplay in optical coherence elastography

NASA Astrophysics Data System (ADS)

Zaitsev, Vladimir Y.; Matveyev, Alexander L.; Matveev, Lev A.; Gelikonov, Grigory V.; Sovetsky, Aleksandr A.; Vitkin, Alex

2016-11-01

In compressional optical coherence elastography, phase-variation gradients are used for estimating quasistatic strains created in tissue. Using reference and deformed optical coherence tomography (OCT) scans, one typically compares phases from pixels with the same coordinates in both scans. Usually, this limits the allowable strains to fairly small values < to 10-3, with the caveat that such weak phase gradients may become corrupted by stronger measurement noises. Here, we extend the OCT phase-resolved elastographic methodology by (1) showing that an order of magnitude greater strains can significantly increase the accuracy of derived phase-gradient differences, while also avoiding error-phone phase-unwrapping procedures and minimizing the influence of decorrelation noise caused by suprapixel displacements, (2) discussing the appearance of artifactual stiff inclusions in resultant OCT elastograms in the vicinity of bright scatterers due to the amplitude-phase interplay in phase-variation measurements, and (3) deriving/evaluating methods of phase-gradient estimation that can outperform conventionally used least-square gradient fitting. We present analytical arguments, numerical simulations, and experimental examples to demonstrate the advantages of the proposed optimized phase-variation methodology.

The effect of model uncertainty on some optimal routing problems

NASA Technical Reports Server (NTRS)

Mohanty, Bibhu; Cassandras, Christos G.

1991-01-01

The effect of model uncertainties on optimal routing in a system of parallel queues is examined. The uncertainty arises in modeling the service time distribution for the customers (jobs, packets) to be served. For a Poisson arrival process and Bernoulli routing, the optimal mean system delay generally depends on the variance of this distribution. However, as the input traffic load approaches the system capacity the optimal routing assignment and corresponding mean system delay are shown to converge to a variance-invariant point. The implications of these results are examined in the context of gradient-based routing algorithms. An example of a model-independent algorithm using online gradient estimation is also included.

SEEK: A FORTRAN optimization program using a feasible directions gradient search

NASA Technical Reports Server (NTRS)

Savage, M.

1995-01-01

This report describes the use of computer program 'SEEK' which works in conjunction with two user-written subroutines and an input data file to perform an optimization procedure on a user's problem. The optimization method uses a modified feasible directions gradient technique. SEEK is written in ANSI standard Fortran 77, has an object size of about 46K bytes, and can be used on a personal computer running DOS. This report describes the use of the program and discusses the optimizing method. The program use is illustrated with four example problems: a bushing design, a helical coil spring design, a gear mesh design, and a two-parameter Weibull life-reliability curve fit.

Optimizing Irregular Applications for Energy and Performance on the Tilera Many-core Architecture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chavarría-Miranda, Daniel; Panyala, Ajay R.; Halappanavar, Mahantesh

Optimizing applications simultaneously for energy and performance is a complex problem. High performance, parallel, irregular applications are notoriously hard to optimize due to their data-dependent memory accesses, lack of structured locality and complex data structures and code patterns. Irregular kernels are growing in importance in applications such as machine learning, graph analytics and combinatorial scientific computing. Performance- and energy-efficient implementation of these kernels on modern, energy efficient, multicore and many-core platforms is therefore an important and challenging problem. We present results from optimizing two irregular applications { the Louvain method for community detection (Grappolo), and high-performance conjugate gradient (HPCCG) {more » on the Tilera many-core system. We have significantly extended MIT's OpenTuner auto-tuning framework to conduct a detailed study of platform-independent and platform-specific optimizations to improve performance as well as reduce total energy consumption. We explore the optimization design space along three dimensions: memory layout schemes, compiler-based code transformations, and optimization of parallel loop schedules. Using auto-tuning, we demonstrate whole node energy savings of up to 41% relative to a baseline instantiation, and up to 31% relative to manually optimized variants.« less

Modularization of gradient-index optical design using wavefront matching enabled optimization.

PubMed

Nagar, Jogender; Brocker, Donovan E; Campbell, Sawyer D; Easum, John A; Werner, Douglas H

2016-05-02

This paper proposes a new design paradigm which allows for a modular approach to replacing a homogeneous optical lens system with a higher-performance GRadient-INdex (GRIN) lens system using a WaveFront Matching (WFM) method. In multi-lens GRIN systems, a full-system-optimization approach can be challenging due to the large number of design variables. The proposed WFM design paradigm enables optimization of each component independently by explicitly matching the WaveFront Error (WFE) of the original homogeneous component at the exit pupil, resulting in an efficient design procedure for complex multi-lens systems.

Constrained Burn Optimization for the International Space Station

NASA Technical Reports Server (NTRS)

Brown, Aaron J.; Jones, Brandon A.

2017-01-01

In long-term trajectory planning for the International Space Station (ISS), translational burns are currently targeted sequentially to meet the immediate trajectory constraints, rather than simultaneously to meet all constraints, do not employ gradient-based search techniques, and are not optimized for a minimum total deltav (v) solution. An analytic formulation of the constraint gradients is developed and used in an optimization solver to overcome these obstacles. Two trajectory examples are explored, highlighting the advantage of the proposed method over the current approach, as well as the potential v and propellant savings in the event of propellant shortages.

Gradient gravitational search: An efficient metaheuristic algorithm for global optimization.

PubMed

Dash, Tirtharaj; Sahu, Prabhat K

2015-05-30

The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient-based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two-dimensional and three-dimensional off-lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost. © 2015 Wiley Periodicals, Inc.

Efficient Gradient-Based Shape Optimization Methodology Using Inviscid/Viscous CFD

NASA Technical Reports Server (NTRS)

Baysal, Oktay

1997-01-01

The formerly developed preconditioned-biconjugate-gradient (PBCG) solvers for the analysis and the sensitivity equations had resulted in very large error reductions per iteration; quadratic convergence was achieved whenever the solution entered the domain of attraction to the root. Its memory requirement was also lower as compared to a direct inversion solver. However, this memory requirement was high enough to preclude the realistic, high grid-density design of a practical 3D geometry. This limitation served as the impetus to the first-year activity (March 9, 1995 to March 8, 1996). Therefore, the major activity for this period was the development of the low-memory methodology for the discrete-sensitivity-based shape optimization. This was accomplished by solving all the resulting sets of equations using an alternating-direction-implicit (ADI) approach. The results indicated that shape optimization problems which required large numbers of grid points could be resolved with a gradient-based approach. Therefore, to better utilize the computational resources, it was recommended that a number of coarse grid cases, using the PBCG method, should initially be conducted to better define the optimization problem and the design space, and obtain an improved initial shape. Subsequently, a fine grid shape optimization, which necessitates using the ADI method, should be conducted to accurately obtain the final optimized shape. The other activity during this period was the interaction with the members of the Aerodynamic and Aeroacoustic Methods Branch of Langley Research Center during one stage of their investigation to develop an adjoint-variable sensitivity method using the viscous flow equations. This method had algorithmic similarities to the variational sensitivity methods and the control-theory approach. However, unlike the prior studies, it was considered for the three-dimensional, viscous flow equations. The major accomplishment in the second period of this project (March 9, 1996 to March 8, 1997) was the extension of the shape optimization methodology for the Thin-Layer Navier-Stokes equations. Both the Euler-based and the TLNS-based analyses compared with the analyses obtained using the CFL3D code. The sensitivities, again from both levels of the flow equations, also compared very well with the finite-differenced sensitivities. A fairly large set of shape optimization cases were conducted to study a number of issues previously not well understood. The testbed for these cases was the shaping of an arrow wing in Mach 2.4 flow. All the final shapes, obtained either from a coarse-grid-based or a fine-grid-based optimization, using either a Euler-based or a TLNS-based analysis, were all re-analyzed using a fine-grid, TLNS solution for their function evaluations. This allowed for a more fair comparison of their relative merits. From the aerodynamic performance standpoint, the fine-grid TLNS-based optimization produced the best shape, and the fine-grid Euler-based optimization produced the lowest cruise efficiency.

Implementation of quantum logic gates using polar molecules in pendular states.

PubMed

Zhu, Jing; Kais, Sabre; Wei, Qi; Herschbach, Dudley; Friedrich, Bretislav

2013-01-14

We present a systematic approach to implementation of basic quantum logic gates operating on polar molecules in pendular states as qubits for a quantum computer. A static electric field prevents quenching of the dipole moments by rotation, thereby creating the pendular states; also, the field gradient enables distinguishing among qubit sites. Multi-target optimal control theory is used as a means of optimizing the initial-to-target transition probability via a laser field. We give detailed calculations for the SrO molecule, a favorite candidate for proposed quantum computers. Our simulation results indicate that NOT, Hadamard and CNOT gates can be realized with high fidelity, as high as 0.985, for such pendular qubit states.

Tunable, Flexible and Efficient Optimization of Control Pulses for Superconducting Qubits, part I - Theory

NASA Astrophysics Data System (ADS)

Machnes, Shai; AsséMat, Elie; Tannor, David; Wilhelm, Frank

Quantum computation places very stringent demands on gate fidelities, and experimental implementations require both the controls and the resultant dynamics to conform to hardware-specific ansatzes and constraints. Superconducting qubits present the additional requirement that pulses have simple parametrizations, so they can be further calibrated in the experiment, to compensate for uncertainties in system characterization. We present a novel, conceptually simple and easy-to-implement gradient-based optimal control algorithm, GOAT, which satisfies all the above requirements. In part II we shall demonstrate the algorithm's capabilities, by using GOAT to optimize fast high-accuracy pulses for two leading superconducting qubits architectures - Xmons and IBM's flux-tunable couplers.

Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models.

PubMed

Yuan, Gonglin; Duan, Xiabin; Liu, Wenjie; Wang, Xiaoliang; Cui, Zengru; Sheng, Zhou

2015-01-01

Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1) βk ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations.

DOE Office of Scientific and Technical Information (OSTI.GOV)

H.E. Mynick, N. Pomphrey and P. Xanthopoulos

Recent progress in reducing turbulent transport in stellarators and tokamaks by 3D shaping using a stellarator optimization code in conjunction with a gyrokinetic code is presented. The original applications of the method focussed on ion temperature gradient transport in a quasi-axisymmetric stellarator design. Here, an examination of both other turbulence channels and other starting configurations is initiated. It is found that the designs evolved for transport from ion temperature gradient turbulence also display reduced transport from other transport channels whose modes are also stabilized by improved curvature, such as electron temperature gradient and ballooning modes. The optimizer is also appliedmore » to evolving from a tokamak, finding appreciable turbulence reduction for these devices as well. From these studies, improved understanding is obtained of why the deformations found by the optimizer are beneficial, and these deformations are related to earlier theoretical work in both stellarators and tokamaks.« less

Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models

PubMed Central

Yuan, Gonglin; Duan, Xiabin; Liu, Wenjie; Wang, Xiaoliang; Cui, Zengru; Sheng, Zhou

2015-01-01

Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1)β k ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations. PMID:26502409

An Optimal Control Modification to Model-Reference Adaptive Control for Fast Adaptation

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Krishnakumar, Kalmanje; Boskovic, Jovan

2008-01-01

This paper presents a method that can achieve fast adaptation for a class of model-reference adaptive control. It is well-known that standard model-reference adaptive control exhibits high-gain control behaviors when a large adaptive gain is used to achieve fast adaptation in order to reduce tracking error rapidly. High gain control creates high-frequency oscillations that can excite unmodeled dynamics and can lead to instability. The fast adaptation approach is based on the minimization of the squares of the tracking error, which is formulated as an optimal control problem. The necessary condition of optimality is used to derive an adaptive law using the gradient method. This adaptive law is shown to result in uniform boundedness of the tracking error by means of the Lyapunov s direct method. Furthermore, this adaptive law allows a large adaptive gain to be used without causing undesired high-gain control effects. The method is shown to be more robust than standard model-reference adaptive control. Simulations demonstrate the effectiveness of the proposed method.

Three-dimensional desirability spaces for quality-by-design-based HPLC development.

PubMed

Mokhtar, Hatem I; Abdel-Salam, Randa A; Hadad, Ghada M

2015-04-01

In this study, three-dimensional desirability spaces were introduced as a graphical representation method of design space. This was illustrated in the context of application of quality-by-design concepts on development of a stability indicating gradient reversed-phase high-performance liquid chromatography method for the determination of vinpocetine and α-tocopheryl acetate in a capsule dosage form. A mechanistic retention model to optimize gradient time, initial organic solvent concentration and ternary solvent ratio was constructed for each compound from six experimental runs. Then, desirability function of each optimized criterion and subsequently the global desirability function were calculated throughout the knowledge space. The three-dimensional desirability spaces were plotted as zones exceeding a threshold value of desirability index in space defined by the three optimized method parameters. Probabilistic mapping of desirability index aided selection of design space within the potential desirability subspaces. Three-dimensional desirability spaces offered better visualization and potential design spaces for the method as a function of three method parameters with ability to assign priorities to this critical quality as compared with the corresponding resolution spaces. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Comparison of genetic algorithms with conjugate gradient methods

NASA Technical Reports Server (NTRS)

Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.

1972-01-01

Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.

Novel Scalable 3-D MT Inverse Solver

NASA Astrophysics Data System (ADS)

Kuvshinov, A. V.; Kruglyakov, M.; Geraskin, A.

2016-12-01

We present a new, robust and fast, three-dimensional (3-D) magnetotelluric (MT) inverse solver. As a forward modelling engine a highly-scalable solver extrEMe [1] is used. The (regularized) inversion is based on an iterative gradient-type optimization (quasi-Newton method) and exploits adjoint sources approach for fast calculation of the gradient of the misfit. The inverse solver is able to deal with highly detailed and contrasting models, allows for working (separately or jointly) with any type of MT (single-site and/or inter-site) responses, and supports massive parallelization. Different parallelization strategies implemented in the code allow for optimal usage of available computational resources for a given problem set up. To parameterize an inverse domain a mask approach is implemented, which means that one can merge any subset of forward modelling cells in order to account for (usually) irregular distribution of observation sites. We report results of 3-D numerical experiments aimed at analysing the robustness, performance and scalability of the code. In particular, our computational experiments carried out at different platforms ranging from modern laptops to high-performance clusters demonstrate practically linear scalability of the code up to thousands of nodes. 1. Kruglyakov, M., A. Geraskin, A. Kuvshinov, 2016. Novel accurate and scalable 3-D MT forward solver based on a contracting integral equation method, Computers and Geosciences, in press.

X-ray transparent microfluidic chips for high-throughput screening and optimization of in meso membrane protein crystallization

PubMed Central

Schieferstein, Jeremy M.; Pawate, Ashtamurthy S.; Wan, Frank; Sheraden, Paige N.; Broecker, Jana; Ernst, Oliver P.; Gennis, Robert B.

2017-01-01

Elucidating and clarifying the function of membrane proteins ultimately requires atomic resolution structures as determined most commonly by X-ray crystallography. Many high impact membrane protein structures have resulted from advanced techniques such as in meso crystallization that present technical difficulties for the set-up and scale-out of high-throughput crystallization experiments. In prior work, we designed a novel, low-throughput X-ray transparent microfluidic device that automated the mixing of protein and lipid by diffusion for in meso crystallization trials. Here, we report X-ray transparent microfluidic devices for high-throughput crystallization screening and optimization that overcome the limitations of scale and demonstrate their application to the crystallization of several membrane proteins. Two complementary chips are presented: (1) a high-throughput screening chip to test 192 crystallization conditions in parallel using as little as 8 nl of membrane protein per well and (2) a crystallization optimization chip to rapidly optimize preliminary crystallization hits through fine-gradient re-screening. We screened three membrane proteins for new in meso crystallization conditions, identifying several preliminary hits that we tested for X-ray diffraction quality. Further, we identified and optimized the crystallization condition for a photosynthetic reaction center mutant and solved its structure to a resolution of 3.5 Å. PMID:28469762

Spectral edge: gradient-preserving spectral mapping for image fusion.

PubMed

Connah, David; Drew, Mark S; Finlayson, Graham D

2015-12-01

This paper describes a novel approach to image fusion for color display. Our goal is to generate an output image whose gradient matches that of the input as closely as possible. We achieve this using a constrained contrast mapping paradigm in the gradient domain, where the structure tensor of a high-dimensional gradient representation is mapped exactly to that of a low-dimensional gradient field which is then reintegrated to form an output. Constraints on output colors are provided by an initial RGB rendering. Initially, we motivate our solution with a simple "ansatz" (educated guess) for projecting higher-D contrast onto color gradients, which we expand to a more rigorous theorem to incorporate color constraints. The solution to these constrained optimizations is closed-form, allowing for simple and hence fast and efficient algorithms. The approach can map any N-D image data to any M-D output and can be used in a variety of applications using the same basic algorithm. In this paper, we focus on the problem of mapping N-D inputs to 3D color outputs. We present results in five applications: hyperspectral remote sensing, fusion of color and near-infrared or clear-filter images, multilighting imaging, dark flash, and color visualization of magnetic resonance imaging diffusion-tensor imaging.

Performance optimization in electric field gradient focusing.

PubMed

Sun, Xuefei; Farnsworth, Paul B; Tolley, H Dennis; Warnick, Karl F; Woolley, Adam T; Lee, Milton L

2009-01-02

Electric field gradient focusing (EFGF) is a technique used to simultaneously separate and concentrate biomacromolecules, such as proteins, based on the opposing forces of an electric field gradient and a hydrodynamic flow. Recently, we reported EFGF devices fabricated completely from copolymers functionalized with poly(ethylene glycol), which display excellent resistance to protein adsorption. However, the previous devices did not provide the predicted linear electric field gradient and stable current. To improve performance, Tris-HCl buffer that was previously doped in the hydrogel was replaced with a phosphate buffer containing a salt (i.e., potassium chloride, KCl) with high mobility ions. The new devices exhibited stable current, good reproducibility, and a linear electric field distribution in agreement with the shaped gradient region design due to improved ion transport in the hydrogel. The field gradient was calculated based on theory to be approximately 5.76 V/cm(2) for R-phycoerythrin when the applied voltage was 500 V. The effect of EFGF separation channel dimensions was also investigated; a narrower focused band was achieved in a smaller diameter channel. The relationship between the bandwidth and channel diameter is consistent with theory. Three model proteins were resolved in an EFGF channel of this design. The improved device demonstrated 14,000-fold concentration of a protein sample (from 2 ng/mL to 27 microg/mL).

The plug-based nanovolume Microcapillary Protein Crystallization System (MPCS)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gerdts, Cory J.; Elliott, Mark; Lovell, Scott

2012-02-08

The Microcapillary Protein Crystallization System (MPCS) embodies a new semi-automated plug-based crystallization technology which enables nanolitre-volume screening of crystallization conditions in a plasticware format that allows crystals to be easily removed for traditional cryoprotection and X-ray diffraction data collection. Protein crystals grown in these plastic devices can be directly subjected to in situ X-ray diffraction studies. The MPCS integrates the formulation of crystallization cocktails with the preparation of the crystallization experiments. Within microfluidic Teflon tubing or the microfluidic circuitry of a plastic CrystalCard, {approx}10-20 nl volume droplets are generated, each representing a microbatch-style crystallization experiment with a different chemical composition.more » The entire protein sample is utilized in crystallization experiments. Sparse-matrix screening and chemical gradient screening can be combined in one comprehensive 'hybrid' crystallization trial. The technology lends itself well to optimization by high-granularity gradient screening using optimization reagents such as precipitation agents, ligands or cryoprotectants.« less

Joint design of large-tip-angle parallel RF pulses and blipped gradient trajectories.

PubMed

Cao, Zhipeng; Donahue, Manus J; Ma, Jun; Grissom, William A

2016-03-01

To design multichannel large-tip-angle kT-points and spokes radiofrequency (RF) pulses and gradient waveforms for transmit field inhomogeneity compensation in high field magnetic resonance imaging. An algorithm to design RF subpulse weights and gradient blip areas is proposed to minimize a magnitude least-squares cost function that measures the difference between realized and desired state parameters in the spin domain, and penalizes integrated RF power. The minimization problem is solved iteratively with interleaved target phase updates, RF subpulse weights updates using the conjugate gradient method with optimal control-based derivatives, and gradient blip area updates using the conjugate gradient method. Two-channel parallel transmit simulations and experiments were conducted in phantoms and human subjects at 7 T to demonstrate the method and compare it to small-tip-angle-designed pulses and circularly polarized excitations. The proposed algorithm designed more homogeneous and accurate 180° inversion and refocusing pulses than other methods. It also designed large-tip-angle pulses on multiple frequency bands with independent and joint phase relaxation. Pulses designed by the method improved specificity and contrast-to-noise ratio in a finger-tapping spin echo blood oxygen level dependent functional magnetic resonance imaging study, compared with circularly polarized mode refocusing. A joint RF and gradient waveform design algorithm was proposed and validated to improve large-tip-angle inversion and refocusing at ultrahigh field. © 2015 Wiley Periodicals, Inc.

Reflectance analysis of porosity gradient in nanostructured silicon layers

NASA Astrophysics Data System (ADS)

Jurečka, Stanislav; Imamura, Kentaro; Matsumoto, Taketoshi; Kobayashi, Hikaru

2017-12-01

In this work we study optical properties of nanostructured layers formed on silicon surface. Nanostructured layers on Si are formed in order to reach high suppression of the light reflectance. Low spectral reflectance is important for improvement of the conversion efficiency of solar cells and for other optoelectronic applications. Effective method of forming nanostructured layers with ultralow reflectance in a broad interval of wavelengths is in our approach based on metal assisted etching of Si. Si surface immersed in HF and H2O2 solution is etched in contact with the Pt mesh roller and the structure of the mesh is transferred on the etched surface. During this etching procedure the layer density evolves gradually and the spectral reflectance decreases exponentially with the depth in porous layer. We analyzed properties of the layer porosity by incorporating the porosity gradient into construction of the layer spectral reflectance theoretical model. Analyzed layer is splitted into 20 sublayers in our approach. Complex dielectric function in each sublayer is computed by using Bruggeman effective media theory and the theoretical spectral reflectance of modelled multilayer system is computed by using Abeles matrix formalism. Porosity gradient is extracted from the theoretical reflectance model optimized in comparison to the experimental values. Resulting values of the structure porosity development provide important information for optimization of the technological treatment operations.

Control-enhanced multiparameter quantum estimation

NASA Astrophysics Data System (ADS)

Liu, Jing; Yuan, Haidong

2017-10-01

Most studies in multiparameter estimation assume the dynamics is fixed and focus on identifying the optimal probe state and the optimal measurements. In practice, however, controls are usually available to alter the dynamics, which provides another degree of freedom. In this paper we employ optimal control methods, particularly the gradient ascent pulse engineering (GRAPE), to design optimal controls for the improvement of the precision limit in multiparameter estimation. We show that the controlled schemes are not only capable to provide a higher precision limit, but also have a higher stability to the inaccuracy of the time point performing the measurements. This high time stability will benefit the practical metrology, where it is hard to perform the measurement at a very accurate time point due to the response time of the measurement apparatus.

Salinity Gradient Energy from Expansion and Contraction of Poly(allylamine hydrochloride) Hydrogels.

PubMed

Bui, Tri Quang; Cao, Vinh Duy; Do, Nu Bich Duyen; Christoffersen, Trine Eker; Wang, Wei; Kjøniksen, Anna-Lena

2018-06-22

Salinity gradients exhibit a great potential for production of renewable energy. Several techniques such as pressure-retarded osmosis and reverse electrodialysis have been employed to extract this energy. Unfortunately, these techniques are restricted by the high costs of membranes and problems with membrane fouling. However, the expansion and contraction of hydrogels can be a new and cheaper way to harvest energy from salinity gradients since the hydrogels swell in freshwater and shrink in saltwater. We have examined the effect of cross-linker concentration and different external loads on the energy recovered for this type of energy-producing systems. Poly(allylamine hydrochloride) hydrogels were cross-linked with glutaraldehyde to produce hydrogels with excellent expansion and contraction properties. Increasing the cross-linker concentration markedly improved the energy that could be recovered from the hydrogels, especially at high external loads. A swollen hydrogel of 60 g could recover more than 1800 mJ when utilizing a high cross-linker concentration, and the maximum amount of energy produced per gram of polymer was 3.4 J/g. Although more energy is recovered at high cross-linking densities, the maximum amount of energy produced per gram of polymer is highest at an intermediate cross-linking concentration. Energy recovery was reduced when the salt concentration was increased for the low-concentration saline solution. The results illustrate that hydrogels are promising for salinity gradient energy recovery, and that optimizing the systems significantly increases the amount of energy that can be recovered.

Tropical forest soil microbial communities couple iron and carbon biogeochemistry

Treesearch

Eric A. Dubinsky; Whendee L. Silver; Mary K. Firestone

2010-01-01

We report that iron-reducing bacteria are primary mediators of anaerobic carbon oxidation in upland tropical soils spanning a rainfall gradient (3500–5000 mm/yr) in northeast Puerto Rico. The abundant rainfall and high net primary productivity of these tropical forests provide optimal soil habitat for iron-reducing and iron-oxidizing bacteria. Spatially and temporally...

Aerodynamic shape optimization using preconditioned conjugate gradient methods

NASA Technical Reports Server (NTRS)

Burgreen, Greg W.; Baysal, Oktay

1993-01-01

In an effort to further improve upon the latest advancements made in aerodynamic shape optimization procedures, a systematic study is performed to examine several current solution methodologies as applied to various aspects of the optimization procedure. It is demonstrated that preconditioned conjugate gradient-like methodologies dramatically decrease the computational efforts required for such procedures. The design problem investigated is the shape optimization of the upper and lower surfaces of an initially symmetric (NACA-012) airfoil in inviscid transonic flow and at zero degree angle-of-attack. The complete surface shape is represented using a Bezier-Bernstein polynomial. The present optimization method then automatically obtains supercritical airfoil shapes over a variety of freestream Mach numbers. Furthermore, the best optimization strategy examined resulted in a factor of 8 decrease in computational time as well as a factor of 4 decrease in memory over the most efficient strategies in current use.

Accelerating IMRT optimization by voxel sampling

NASA Astrophysics Data System (ADS)

Martin, Benjamin C.; Bortfeld, Thomas R.; Castañon, David A.

2007-12-01

This paper presents a new method for accelerating intensity-modulated radiation therapy (IMRT) optimization using voxel sampling. Rather than calculating the dose to the entire patient at each step in the optimization, the dose is only calculated for some randomly selected voxels. Those voxels are then used to calculate estimates of the objective and gradient which are used in a randomized version of a steepest descent algorithm. By selecting different voxels on each step, we are able to find an optimal solution to the full problem. We also present an algorithm to automatically choose the best sampling rate for each structure within the patient during the optimization. Seeking further improvements, we experimented with several other gradient-based optimization algorithms and found that the delta-bar-delta algorithm performs well despite the randomness. Overall, we were able to achieve approximately an order of magnitude speedup on our test case as compared to steepest descent.

A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization

NASA Astrophysics Data System (ADS)

Sessarego, M.; Dixon, K. R.; Rival, D. E.; Wood, D. H.

2015-08-01

A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.

Topology optimization of finite strain viscoplastic systems under transient loads [Dynamic topology optimization based on finite strain visco-plasticity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ivarsson, Niklas; Wallin, Mathias; Tortorelli, Daniel

In this paper, a transient finite strain viscoplastic model is implemented in a gradient-based topology optimization framework to design impact mitigating structures. The model's kinematics relies on the multiplicative split of the deformation gradient, and the constitutive response is based on isotropic hardening viscoplasticity. To solve the mechanical balance laws, the implicit Newmark-beta method is used together with a total Lagrangian finite element formulation. The optimization problem is regularized using a partial differential equation filter and solved using the method of moving asymptotes. Sensitivities required to solve the optimization problem are derived using the adjoint method. To demonstrate the capabilitymore » of the algorithm, several protective systems are designed, in which the absorbed viscoplastic energy is maximized. Finally, the numerical examples demonstrate that transient finite strain viscoplastic effects can successfully be combined with topology optimization.« less

Topology optimization of finite strain viscoplastic systems under transient loads [Dynamic topology optimization based on finite strain visco-plasticity

DOE PAGES

Ivarsson, Niklas; Wallin, Mathias; Tortorelli, Daniel

2018-02-08

In this paper, a transient finite strain viscoplastic model is implemented in a gradient-based topology optimization framework to design impact mitigating structures. The model's kinematics relies on the multiplicative split of the deformation gradient, and the constitutive response is based on isotropic hardening viscoplasticity. To solve the mechanical balance laws, the implicit Newmark-beta method is used together with a total Lagrangian finite element formulation. The optimization problem is regularized using a partial differential equation filter and solved using the method of moving asymptotes. Sensitivities required to solve the optimization problem are derived using the adjoint method. To demonstrate the capabilitymore » of the algorithm, several protective systems are designed, in which the absorbed viscoplastic energy is maximized. Finally, the numerical examples demonstrate that transient finite strain viscoplastic effects can successfully be combined with topology optimization.« less

Acceleration for 2D time-domain elastic full waveform inversion using a single GPU card

NASA Astrophysics Data System (ADS)

Jiang, Jinpeng; Zhu, Peimin

2018-05-01

Full waveform inversion (FWI) is a challenging procedure due to the high computational cost related to the modeling, especially for the elastic case. The graphics processing unit (GPU) has become a popular device for the high-performance computing (HPC). To reduce the long computation time, we design and implement the GPU-based 2D elastic FWI (EFWI) in time domain using a single GPU card. We parallelize the forward modeling and gradient calculations using the CUDA programming language. To overcome the limitation of relatively small global memory on GPU, the boundary saving strategy is exploited to reconstruct the forward wavefield. Moreover, the L-BFGS optimization method used in the inversion increases the convergence of the misfit function. A multiscale inversion strategy is performed in the workflow to obtain the accurate inversion results. In our tests, the GPU-based implementations using a single GPU device achieve >15 times speedup in forward modeling, and about 12 times speedup in gradient calculation, compared with the eight-core CPU implementations optimized by OpenMP. The test results from the GPU implementations are verified to have enough accuracy by comparing the results obtained from the CPU implementations.

Subpixel edge estimation with lens aberrations compensation based on the iterative image approximation for high-precision thermal expansion measurements of solids

NASA Astrophysics Data System (ADS)

Inochkin, F. M.; Kruglov, S. K.; Bronshtein, I. G.; Kompan, T. A.; Kondratjev, S. V.; Korenev, A. S.; Pukhov, N. F.

2017-06-01

A new method for precise subpixel edge estimation is presented. The principle of the method is the iterative image approximation in 2D with subpixel accuracy until the appropriate simulated is found, matching the simulated and acquired images. A numerical image model is presented consisting of three parts: an edge model, object and background brightness distribution model, lens aberrations model including diffraction. The optimal values of model parameters are determined by means of conjugate-gradient numerical optimization of a merit function corresponding to the L2 distance between acquired and simulated images. Computationally-effective procedure for the merit function calculation along with sufficient gradient approximation is described. Subpixel-accuracy image simulation is performed in a Fourier domain with theoretically unlimited precision of edge points location. The method is capable of compensating lens aberrations and obtaining the edge information with increased resolution. Experimental method verification with digital micromirror device applied to physically simulate an object with known edge geometry is shown. Experimental results for various high-temperature materials within the temperature range of 1000°C..2400°C are presented.

SU-E-T-453: Optimization of Dose Gradient for Gamma Knife Radiosurgery.

PubMed

Sheth, N; Chen, Y; Yang, J

2012-06-01

The goals of stereotactic radiosurgery (SRS) are the ablation of target tissue and sparing of critical normal tissue. We develop tools to aid in the selection of collimation and prescription (Rx) isodose line to optimize the dose gradient for single isocenter intracranial stereotactic radiosurgery (SRS) with GammaKnife 4C utilizing the updated physics data in GammaPlan v10.1. Single isocenter intracranial SRS plans were created to treat the center of a solid water anthropomorphism head phantom for each GammaKnife collimator (4 mm, 8 mm, 14 mm, and 18 mm). The dose gradient, defined as the difference of effective radii of spheres equal to half and full Rx volumes, and Rx treatment volume was analyzed for isodoses from 99% to 20% of Rx. The dosimetric data on Rx volume and dose gradient vs. Rx isodose for each collimator was compiled into an easy to read nomogram as well as plotted graphically. The 4, 8, 14, and 18 mm collimators have the sharpest dose gradient at the 64%, 70%, 76%, and 77% Rx isodose lines, respectively. This corresponds to treating 4.77 mm, 8.86 mm, 14.78 mm, and 18.77 mm diameter targets with dose gradients radii of 1.06 mm, 1.63 mm, 2.54 mm, and 3.17 mm, respectively. We analyzed the dosimetric data for the most recent version of GammaPlan treatment planning software to develop tools that when applied clinically will aid in the selection of a collimator and Rx isodose line for optimal dose gradient and target coverage for single isocenter intracranial SRS with GammaKnife 4C. © 2012 American Association of Physicists in Medicine.

Gradient optimization and nonlinear control

NASA Technical Reports Server (NTRS)

Hasdorff, L.

1976-01-01

The book represents an introduction to computation in control by an iterative, gradient, numerical method, where linearity is not assumed. The general language and approach used are those of elementary functional analysis. The particular gradient method that is emphasized and used is conjugate gradient descent, a well known method exhibiting quadratic convergence while requiring very little more computation than simple steepest descent. Constraints are not dealt with directly, but rather the approach is to introduce them as penalty terms in the criterion. General conjugate gradient descent methods are developed and applied to problems in control.

Optimum concentration gradient of the electrocatalyst, Nafion® and poly(tetrafluoroethylene) in a membrane-electrode-assembly for enhanced performance of direct methanol fuel cells.

PubMed

Liu, Jing Hua; Jeon, Min Ku; Lee, Ki Rak; Woo, Seong Ihl

2010-12-14

A combinatorial library of membrane-electrode-assemblies (MEAs) which consisted of 27 different compositions was fabricated to optimize the multilayer structure of direct methanol fuel cells. Each spot consisted of three layers of ink and a gradient was generated by employing different concentrations of the three components (Pt catalyst, Nafion® and polytetrafluoroethylene (PTFE)) of each layer. For quick evaluation of the library, a high-throughput optical screening technique was employed for methanol electro-oxidation reaction (MOR) activity. The screening results revealed that gradient layers could lead to higher MOR activity than uniform layers. It was found that the MOR activity was higher when the concentrations of Pt catalyst and Nafion ionomer decreased downward from the top layer to the bottom layer. On the other hand, higher MOR activity was observed when PTFE concentration increased downward from the top to the bottom layer.

Development of a shear stress-free microfluidic gradient generator capable of quantitatively analyzing single-cell morphology.

PubMed

Barata, David; Spennati, Giulia; Correia, Cristina; Ribeiro, Nelson; Harink, Björn; van Blitterswijk, Clemens; Habibovic, Pamela; van Rijt, Sabine

2017-09-07

Microfluidics, the science of engineering fluid streams at the micrometer scale, offers unique tools for creating and controlling gradients of soluble compounds. Gradient generation can be used to recreate complex physiological microenvironments, but is also useful for screening purposes. For example, in a single experiment, adherent cells can be exposed to a range of concentrations of the compound of interest, enabling high-content analysis of cell behaviour and enhancing throughput. In this study, we present the development of a microfluidic screening platform where, by means of diffusion, gradients of soluble compounds can be generated and sustained. This platform enables the culture of adherent cells under shear stress-free conditions, and their exposure to a soluble compound in a concentration gradient-wise manner. The platform consists of five serial cell culture chambers, all coupled to two lateral fluid supply channels that are used for gradient generation through a source-sink mechanism. Furthermore, an additional inlet and outlet are used for cell seeding inside the chambers. Finite element modeling was used for the optimization of the design of the platform and for validation of the dynamics of gradient generation. Then, as a proof-of-concept, human osteosarcoma MG-63 cells were cultured inside the platform and exposed to a gradient of Cytochalasin D, an actin polymerization inhibitor. This set-up allowed us to analyze cell morphological changes over time, including cell area and eccentricity measurements, as a function of Cytochalasin D concentration by using fluorescence image-based cytometry.

Performance comparison of a new hybrid conjugate gradient method under exact and inexact line searches

NASA Astrophysics Data System (ADS)

Ghani, N. H. A.; Mohamed, N. S.; Zull, N.; Shoid, S.; Rivaie, M.; Mamat, M.

2017-09-01

Conjugate gradient (CG) method is one of iterative techniques prominently used in solving unconstrained optimization problems due to its simplicity, low memory storage, and good convergence analysis. This paper presents a new hybrid conjugate gradient method, named NRM1 method. The method is analyzed under the exact and inexact line searches in given conditions. Theoretically, proofs show that the NRM1 method satisfies the sufficient descent condition with both line searches. The computational result indicates that NRM1 method is capable in solving the standard unconstrained optimization problems used. On the other hand, the NRM1 method performs better under inexact line search compared with exact line search.

ANOTHER LOOK AT THE FAST ITERATIVE SHRINKAGE/THRESHOLDING ALGORITHM (FISTA)*

PubMed Central

Kim, Donghwan; Fessler, Jeffrey A.

2017-01-01

This paper provides a new way of developing the “Fast Iterative Shrinkage/Thresholding Algorithm (FISTA)” [3] that is widely used for minimizing composite convex functions with a nonsmooth term such as the ℓ1 regularizer. In particular, this paper shows that FISTA corresponds to an optimized approach to accelerating the proximal gradient method with respect to a worst-case bound of the cost function. This paper then proposes a new algorithm that is derived by instead optimizing the step coefficients of the proximal gradient method with respect to a worst-case bound of the composite gradient mapping. The proof is based on the worst-case analysis called Performance Estimation Problem in [11]. PMID:29805242

Implementation of a dose gradient method into optimization of dose distribution in prostate cancer 3D-CRT plans

PubMed Central

Giżyńska, Marta K.; Kukołowicz, Paweł F.; Kordowski, Paweł

2014-01-01

Aim The aim of this work is to present a method of beam weight and wedge angle optimization for patients with prostate cancer. Background 3D-CRT is usually realized with forward planning based on a trial and error method. Several authors have published a few methods of beam weight optimization applicable to the 3D-CRT. Still, none on these methods is in common use. Materials and methods Optimization is based on the assumption that the best plan is achieved if dose gradient at ICRU point is equal to zero. Our optimization algorithm requires beam quality index, depth of maximum dose, profiles of wedged fields and maximum dose to femoral heads. The method was tested for 10 patients with prostate cancer, treated with the 3-field technique. Optimized plans were compared with plans prepared by 12 experienced planners. Dose standard deviation in target volume, and minimum and maximum doses were analyzed. Results The quality of plans obtained with the proposed optimization algorithms was comparable to that prepared by experienced planners. Mean difference in target dose standard deviation was 0.1% in favor of the plans prepared by planners for optimization of beam weights and wedge angles. Introducing a correction factor for patient body outline for dose gradient at ICRU point improved dose distribution homogeneity. On average, a 0.1% lower standard deviation was achieved with the optimization algorithm. No significant difference in mean dose–volume histogram for the rectum was observed. Conclusions Optimization shortens very much time planning. The average planning time was 5 min and less than a minute for forward and computer optimization, respectively. PMID:25337411

Optimal trajectories of aircraft and spacecraft

NASA Technical Reports Server (NTRS)

Miele, A.

1990-01-01

Work done on algorithms for the numerical solutions of optimal control problems and their application to the computation of optimal flight trajectories of aircraft and spacecraft is summarized. General considerations on calculus of variations, optimal control, numerical algorithms, and applications of these algorithms to real-world problems are presented. The sequential gradient-restoration algorithm (SGRA) is examined for the numerical solution of optimal control problems of the Bolza type. Both the primal formulation and the dual formulation are discussed. Aircraft trajectories, in particular, the application of the dual sequential gradient-restoration algorithm (DSGRA) to the determination of optimal flight trajectories in the presence of windshear are described. Both take-off trajectories and abort landing trajectories are discussed. Take-off trajectories are optimized by minimizing the peak deviation of the absolute path inclination from a reference value. Abort landing trajectories are optimized by minimizing the peak drop of altitude from a reference value. Abort landing trajectories are optimized by minimizing the peak drop of altitude from a reference value. The survival capability of an aircraft in a severe windshear is discussed, and the optimal trajectories are found to be superior to both constant pitch trajectories and maximum angle of attack trajectories. Spacecraft trajectories, in particular, the application of the primal sequential gradient-restoration algorithm (PSGRA) to the determination of optimal flight trajectories for aeroassisted orbital transfer are examined. Both the coplanar case and the noncoplanar case are discussed within the frame of three problems: minimization of the total characteristic velocity; minimization of the time integral of the square of the path inclination; and minimization of the peak heating rate. The solution of the second problem is called nearly-grazing solution, and its merits are pointed out as a useful engineering compromise between energy requirements and aerodynamics heating requirements.

Energy minimization in medical image analysis: Methodologies and applications.

PubMed

Zhao, Feng; Xie, Xianghua

2016-02-01

Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree-reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, for example, image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well. Copyright © 2015 John Wiley & Sons, Ltd.

A new family of Polak-Ribiere-Polyak conjugate gradient method with the strong-Wolfe line search

NASA Astrophysics Data System (ADS)

Ghani, Nur Hamizah Abdul; Mamat, Mustafa; Rivaie, Mohd

2017-08-01

Conjugate gradient (CG) method is an important technique in unconstrained optimization, due to its effectiveness and low memory requirements. The focus of this paper is to introduce a new CG method for solving large scale unconstrained optimization. Theoretical proofs show that the new method fulfills sufficient descent condition if strong Wolfe-Powell inexact line search is used. Besides, computational results show that our proposed method outperforms to other existing CG methods.

Efficient robust conditional random fields.

PubMed

Song, Dongjin; Liu, Wei; Zhou, Tianyi; Tao, Dacheng; Meyer, David A

2015-10-01

Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features. Moreover, conventional optimization methods often converge slowly in solving the training procedure of CRFs, and will degrade significantly for tasks with a large number of samples and features. In this paper, we propose robust CRFs (RCRFs) to simultaneously select relevant features. An optimal gradient method (OGM) is further designed to train RCRFs efficiently. Specifically, the proposed RCRFs employ the l1 norm of the model parameters to regularize the objective used by traditional CRFs, therefore enabling discovery of the relevant unary features and pairwise features of CRFs. In each iteration of OGM, the gradient direction is determined jointly by the current gradient together with the historical gradients, and the Lipschitz constant is leveraged to specify the proper step size. We show that an OGM can tackle the RCRF model training very efficiently, achieving the optimal convergence rate [Formula: see text] (where k is the number of iterations). This convergence rate is theoretically superior to the convergence rate O(1/k) of previous first-order optimization methods. Extensive experiments performed on three practical image segmentation tasks demonstrate the efficacy of OGM in training our proposed RCRFs.

MR-based field-of-view extension in MR/PET: B0 homogenization using gradient enhancement (HUGE).

PubMed

Blumhagen, Jan O; Ladebeck, Ralf; Fenchel, Matthias; Scheffler, Klaus

2013-10-01

In whole-body MR/PET, the human attenuation correction can be based on the MR data. However, an MR-based field-of-view (FoV) is limited due to physical restrictions such as B0 inhomogeneities and gradient nonlinearities. Therefore, for large patients, the MR image and the attenuation map might be truncated and the attenuation correction might be biased. The aim of this work is to explore extending the MR FoV through B0 homogenization using gradient enhancement in which an optimal readout gradient field is determined to locally compensate B0 inhomogeneities and gradient nonlinearities. A spin-echo-based sequence was developed that computes an optimal gradient for certain regions of interest, for example, the patient's arms. A significant distortion reduction was achieved outside the normal MR-based FoV. This FoV extension was achieved without any hardware modifications. In-plane distortions in a transaxially extended FoV of up to 600 mm were analyzed in phantom studies. In vivo measurements of the patient's arms lying outside the normal specified FoV were compared with and without the use of B0 homogenization using gradient enhancement. In summary, we designed a sequence that provides data for reducing the image distortions due to B0 inhomogeneities and gradient nonlinearities and used the data to extend the MR FoV. Copyright © 2011 Wiley Periodicals, Inc.

The influence of installation angle of GGIs on full-tensor gravity gradient measurement

NASA Astrophysics Data System (ADS)

Wei, Hongwei; Wu, Meiping

2018-03-01

Gravity gradient plays an important role in many disciplines as a fundamental signal to reflect the information of the earth. Full-tensor gravity gradient measurement (FGGM) is an effective way to obtain the gravity gradient signal. In this paper, the installation mode of GGIs in FGGM is studied. It is expected that the accuracy of FGGM will be improved by optimizing the installation mode of GGIs. In addition, we analysed the relationship between GGIs’ installation angle and FGGM by establishing the measurement model of FGGM. Then the following conclusions was proved that there was no relationship between GGIs’ installation angle and the measurement result. This conclusion showed that there was no optimal angle for the GGIs’ installation in FGGM, and the installation angle only need to satisfy the relationship shown in the conclusion section of this paper. Finally, this conclusion was demonstrated by computer simulations.

Gradient-based Optimization for Poroelastic and Viscoelastic MR Elastography

PubMed Central

Tan, Likun; McGarry, Matthew D.J.; Van Houten, Elijah E.W.; Ji, Ming; Solamen, Ligin; Weaver, John B.

2017-01-01

We describe an efficient gradient computation for solving inverse problems arising in magnetic resonance elastography (MRE). The algorithm can be considered as a generalized ‘adjoint method’ based on a Lagrangian formulation. One requirement for the classic adjoint method is assurance of the self-adjoint property of the stiffness matrix in the elasticity problem. In this paper, we show this property is no longer a necessary condition in our algorithm, but the computational performance can be as efficient as the classic method, which involves only two forward solutions and is independent of the number of parameters to be estimated. The algorithm is developed and implemented in material property reconstructions using poroelastic and viscoelastic modeling. Various gradient- and Hessian-based optimization techniques have been tested on simulation, phantom and in vivo brain data. The numerical results show the feasibility and the efficiency of the proposed scheme for gradient calculation. PMID:27608454

Topology optimization of finite strain viscoplastic systems under transient loads

DOE PAGES

Ivarsson, Niklas; Wallin, Mathias; Tortorelli, Daniel

2018-02-08

In this paper, a transient finite strain viscoplastic model is implemented in a gradient-based topology optimization framework to design impact mitigating structures. The model's kinematics relies on the multiplicative split of the deformation gradient, and the constitutive response is based on isotropic hardening viscoplasticity. To solve the mechanical balance laws, the implicit Newmark-beta method is used together with a total Lagrangian finite element formulation. The optimization problem is regularized using a partial differential equation filter and solved using the method of moving asymptotes. Sensitivities required to solve the optimization problem are derived using the adjoint method. To demonstrate the capabilitymore » of the algorithm, several protective systems are designed, in which the absorbed viscoplastic energy is maximized. Finally, the numerical examples demonstrate that transient finite strain viscoplastic effects can successfully be combined with topology optimization.« less

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization.

PubMed

Nishio, Mizuho; Nishizawa, Mitsuo; Sugiyama, Osamu; Kojima, Ryosuke; Yakami, Masahiro; Kuroda, Tomohiro; Togashi, Kaori

2018-01-01

We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.

A deep belief network with PLSR for nonlinear system modeling.

PubMed

Qiao, Junfei; Wang, Gongming; Li, Wenjing; Li, Xiaoli

2018-08-01

Nonlinear system modeling plays an important role in practical engineering, and deep learning-based deep belief network (DBN) is now popular in nonlinear system modeling and identification because of the strong learning ability. However, the existing weights optimization for DBN is based on gradient, which always leads to a local optimum and a poor training result. In this paper, a DBN with partial least square regression (PLSR-DBN) is proposed for nonlinear system modeling, which focuses on the problem of weights optimization for DBN using PLSR. Firstly, unsupervised contrastive divergence (CD) algorithm is used in weights initialization. Secondly, initial weights derived from CD algorithm are optimized through layer-by-layer PLSR modeling from top layer to bottom layer. Instead of gradient method, PLSR-DBN can determine the optimal weights using several PLSR models, so that a better performance of PLSR-DBN is achieved. Then, the analysis of convergence is theoretically given to guarantee the effectiveness of the proposed PLSR-DBN model. Finally, the proposed PLSR-DBN is tested on two benchmark nonlinear systems and an actual wastewater treatment system as well as a handwritten digit recognition (nonlinear mapping and modeling) with high-dimension input data. The experiment results show that the proposed PLSR-DBN has better performances of time and accuracy on nonlinear system modeling than that of other methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Systematical Optimization of Reverse-phase Chromatography for Shotgun Proteomics

PubMed Central

Xu, Ping; Duong, Duc M.; Peng, Junmin

2009-01-01

Summary We report the optimization of a common LC/MS/MS platform to maximize the number of proteins identified from a complex biological sample. The platform uses digested yeast lysate on a 75 μm internal diameter × 12 cm reverse-phase column that is combined with an LTQ-Orbitrap mass spectrometer. We first generated a yeast peptide mix that was quantified by multiple methods including the strategy of stable isotope labeling with amino acids in cell culture (SILAC). The peptide mix was analyzed on a highly reproducible, automated nanoLC/MS/MS system with systematic adjustment of loading amount, flow rate, elution gradient range and length. Interestingly, the column was found to be almost saturated by loading ~1 μg of the sample. Whereas the optimal flow rate (~0.2 μl/min) and elution buffer range (13–32% of acetonitrile) appeared to be independent of the loading amount, the best gradient length varied according to the amount of samples: 160 min for 1 μg of the peptide mix, but 40 min for 10 ng of the same sample. The effect of these parameters on elution peptide peak width is evaluated. After full optimization, 1,012 proteins (clustered in 806 groups) with an estimated protein false discovery rate of ~3% were identified in 1 μg of yeast lysate in a single 160-min LC/MS/MS run. PMID:19566079

A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm

NASA Technical Reports Server (NTRS)

Ortiz, Francisco

2004-01-01

COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.

Finite Volume Scheme for Double Convection-Diffusion Exchange of Solutes in Bicarbonate High-Flux Hollow-Fiber Dialyzer Therapy

PubMed Central

Annan, Kodwo

2012-01-01

The efficiency of a high-flux dialyzer in terms of buffering and toxic solute removal largely depends on the ability to use convection-diffusion mechanism inside the membrane. A two-dimensional transient convection-diffusion model coupled with acid-base correction term was developed. A finite volume technique was used to discretize the model and to numerically simulate it using MATLAB software tool. We observed that small solute concentration gradients peaked and were large enough to activate solute diffusion process in the membrane. While CO2 concentration gradients diminished from their maxima and shifted toward the end of the membrane, HCO3 − concentration gradients peaked at the same position. Also, CO2 concentration decreased rapidly within the first 47 minutes while optimal HCO3 − concentration was achieved within 30 minutes of the therapy. Abnormally high diffusion fluxes were observed near the blood-membrane interface that increased diffusion driving force and enhanced the overall diffusive process. While convective flux dominated total flux during the dialysis session, there was a continuous interference between convection and diffusion fluxes that call for the need to seek minimal interference between these two mechanisms. This is critical for the effective design and operation of high-flux dialyzers. PMID:23197994

Aerodynamic optimization of supersonic compressor cascade using differential evolution on GPU

NASA Astrophysics Data System (ADS)

Aissa, Mohamed Hasanine; Verstraete, Tom; Vuik, Cornelis

2016-06-01

Differential Evolution (DE) is a powerful stochastic optimization method. Compared to gradient-based algorithms, DE is able to avoid local minima but requires at the same time more function evaluations. In turbomachinery applications, function evaluations are performed with time-consuming CFD simulation, which results in a long, non affordable, design cycle. Modern High Performance Computing systems, especially Graphic Processing Units (GPUs), are able to alleviate this inconvenience by accelerating the design evaluation itself. In this work we present a validated CFD Solver running on GPUs, able to accelerate the design evaluation and thus the entire design process. An achieved speedup of 20x to 30x enabled the DE algorithm to run on a high-end computer instead of a costly large cluster. The GPU-enhanced DE was used to optimize the aerodynamics of a supersonic compressor cascade, achieving an aerodynamic loss minimization of 20%.

Meta-Modeling-Based Groundwater Remediation Optimization under Flexibility in Environmental Standard.

PubMed

He, Li; Xu, Zongda; Fan, Xing; Li, Jing; Lu, Hongwei

2017-05-01

  This study develops a meta-modeling based mathematical programming approach with flexibility in environmental standards. It integrates numerical simulation, meta-modeling analysis, and fuzzy programming within a general framework. A set of models between remediation strategies and remediation performance can well guarantee the mitigation in computational efforts in the simulation and optimization process. In order to prevent the occurrence of over-optimistic and pessimistic optimization strategies, a high satisfaction level resulting from the implementation of a flexible standard can indicate the degree to which the environmental standard is satisfied. The proposed approach is applied to a naphthalene-contaminated site in China. Results show that a longer remediation period corresponds to a lower total pumping rate and a stringent risk standard implies a high total pumping rate. The wells located near or in the down-gradient direction to the contaminant sources have the most significant efficiency among all of remediation schemes.

Aerodynamic optimization of supersonic compressor cascade using differential evolution on GPU

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aissa, Mohamed Hasanine; Verstraete, Tom; Vuik, Cornelis

Differential Evolution (DE) is a powerful stochastic optimization method. Compared to gradient-based algorithms, DE is able to avoid local minima but requires at the same time more function evaluations. In turbomachinery applications, function evaluations are performed with time-consuming CFD simulation, which results in a long, non affordable, design cycle. Modern High Performance Computing systems, especially Graphic Processing Units (GPUs), are able to alleviate this inconvenience by accelerating the design evaluation itself. In this work we present a validated CFD Solver running on GPUs, able to accelerate the design evaluation and thus the entire design process. An achieved speedup of 20xmore » to 30x enabled the DE algorithm to run on a high-end computer instead of a costly large cluster. The GPU-enhanced DE was used to optimize the aerodynamics of a supersonic compressor cascade, achieving an aerodynamic loss minimization of 20%.« less

Use of the preconditioned conjugate gradient algorithm as a generic solver for mixed-model equations in animal breeding applications.

PubMed

Tsuruta, S; Misztal, I; Strandén, I

2001-05-01

Utility of the preconditioned conjugate gradient algorithm with a diagonal preconditioner for solving mixed-model equations in animal breeding applications was evaluated with 16 test problems. The problems included single- and multiple-trait analyses, with data on beef, dairy, and swine ranging from small examples to national data sets. Multiple-trait models considered low and high genetic correlations. Convergence was based on relative differences between left- and right-hand sides. The ordering of equations was fixed effects followed by random effects, with no special ordering within random effects. The preconditioned conjugate gradient program implemented with double precision converged for all models. However, when implemented in single precision, the preconditioned conjugate gradient algorithm did not converge for seven large models. The preconditioned conjugate gradient and successive overrelaxation algorithms were subsequently compared for 13 of the test problems. The preconditioned conjugate gradient algorithm was easy to implement with the iteration on data for general models. However, successive overrelaxation requires specific programming for each set of models. On average, the preconditioned conjugate gradient algorithm converged in three times fewer rounds of iteration than successive overrelaxation. With straightforward implementations, programs using the preconditioned conjugate gradient algorithm may be two or more times faster than those using successive overrelaxation. However, programs using the preconditioned conjugate gradient algorithm would use more memory than would comparable implementations using successive overrelaxation. Extensive optimization of either algorithm can influence rankings. The preconditioned conjugate gradient implemented with iteration on data, a diagonal preconditioner, and in double precision may be the algorithm of choice for solving mixed-model equations when sufficient memory is available and ease of implementation is essential.

Groundwater resource vulnerability and spatial variability of nitrate contamination: Insights from high density tubewell monitoring in a hard rock aquifer.

PubMed

Buvaneshwari, Sriramulu; Riotte, Jean; Sekhar, M; Mohan Kumar, M S; Sharma, Amit Kumar; Duprey, Jean Louis; Audry, Stephane; Giriraja, P R; Praveenkumarreddy, Yerabham; Moger, Hemanth; Durand, Patrick; Braun, Jean-Jacques; Ruiz, Laurent

2017-02-01

Agriculture has been increasingly relying on groundwater irrigation for the last decades, leading to severe groundwater depletion and/or nitrate contamination. Understanding the links between nitrate concentration and groundwater resource is a prerequisite for assessing the sustainability of irrigated systems. The Berambadi catchment (ORE-BVET/Kabini Critical Zone Observatory) in Southern India is a typical example of intensive irrigated agriculture and then an ideal site to study the relative influences of land use, management practices and aquifer properties on NO 3 spatial distribution in groundwater. The monitoring of >200 tube wells revealed nitrate concentrations from 1 to 360mg/L. Three configurations of groundwater level and elevation gradient were identified: i) NO 3 hot spots associated to deep groundwater levels (30-60m) and low groundwater elevation gradient suggest small groundwater reserve with absence of lateral flow, then degradation of groundwater quality due to recycling through pumping and return flow; ii) high groundwater elevation gradient, moderate NO 3 concentrations suggest that significant lateral flow prevented NO 3 enrichment; iii) low NO 3 concentrations, low groundwater elevation gradient and shallow groundwater indicate a large reserve. We propose that mapping groundwater level and gradient could be used to delineate zones vulnerable to agriculture intensification in catchments where groundwater from low-yielding aquifers is the only source of irrigation. Then, wells located in low groundwater elevation gradient zones are likely to be suitable for assessing the impacts of local agricultural systems, while wells located in zones with high elevation gradient would reflect the average groundwater quality of the catchment, and hence should be used for regional mapping of groundwater quality. Irrigation with NO 3 concentrated groundwater induces a "hidden" input of nitrogen to the crop which can reach 200kgN/ha/yr in hotspot areas, enhancing groundwater contamination. Such fluxes, once taken into account in fertilizer management, would allow optimizing fertilizer consumption and mitigate high nitrate concentrations in groundwater. Copyright © 2016 Elsevier B.V. All rights reserved.

Large scale micro-photometry for high resolution pH-characterization during electro-osmotic pumping and modular micro-swimming

NASA Astrophysics Data System (ADS)

Niu, Ran; Khodorov, Stanislav; Weber, Julian; Reinmüller, Alexander; Palberg, Thomas

2017-11-01

Micro-fluidic pumps as well as artificial micro-swimmers are conveniently realized exploiting phoretic solvent flows based on local gradients of temperature, electrolyte concentration or pH. We here present a facile micro-photometric method for monitoring pH gradients and demonstrate its performance and scope on different experimental situations including an electro-osmotic pump and modular micro-swimmers assembled from ion exchange resin beads and polystyrene colloids. In combination with the present microscope and DSLR camera our method offers a 2 μm spatial resolution at video frame rate over a field of view of 3920 × 2602 μm2. Under optimal conditions we achieve a pH-resolution of 0.05 with about equal contributions from statistical and systematical uncertainties. Our quantitative micro-photometric characterization of pH gradients which develop in time and reach out several mm is anticipated to provide valuable input for reliable modeling and simulations of a large variety of complex flow situations involving pH-gradients including artificial micro-swimmers, microfluidic pumping or even electro-convection.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albert, F.; Hartemann, F. V.; Anderson, S. G.

Tunable, high precision gamma-ray sources are under development to enable nuclear photonics, an emerging field of research. This paper focuses on the technological and theoretical challenges related to precision Compton scattering gamma-ray sources. In this scheme, incident laser photons are scattered and Doppler upshifted by a high brightness electron beam to generate tunable and highly collimated gamma-ray pulses. The electron and laser beam parameters can be optimized to achieve the spectral brightness and narrow bandwidth required by nuclear photonics applications. A description of the design of the next generation precision gamma-ray source currently under construction at Lawrence Livermore National Laboratorymore » is presented, along with the underlying motivations. Within this context, high-gradient X-band technology, used in conjunction with fiber-based photocathode drive laser and diode pumped solid-state interaction laser technologies, will be shown to offer optimal performance for high gamma-ray spectral flux, narrow bandwidth applications.« less

Designing optimal nanofocusing with a gradient hyperlens

NASA Astrophysics Data System (ADS)

Shen, Lian; Prokopeva, Ludmila J.; Chen, Hongsheng; Kildishev, Alexander V.

2017-11-01

We report the design of a high-throughput gradient hyperbolic lenslet built with real-life materials and capable of focusing a beam into a deep sub-wavelength spot of λ/23. This efficient design is achieved through high-order transformation optics and circular effective-medium theory (CEMT), which are used to engineer the radially varying anisotropic artificial material based on the thin alternating cylindrical metal and dielectric layers. The radial gradient of the effective anisotropic optical constants allows for matching the impedances at the input and output interfaces, drastically improving the throughput of the lenslet. However, it is the use of the zeroth-order CEMT that enables the practical realization of a gradient hyperlens with realistic materials. To illustrate the importance of using the CEMT versus the conventional planar effective-medium theory (PEMT) for cylindrical anisotropic systems, such as our hyperlens, both the CEMT and PEMT are adopted to design gradient hyperlenses with the same materials and order of elemental layers. The CEMT- and PEMT-based designs show similar performance if the number of metal-dielectric binary layers is sufficiently large (9+ pairs) and if the layers are sufficiently thin. However, for the manufacturable lenses with realistic numbers of layers (e.g. five pairs) and thicknesses, the performance of the CEMT design continues to be practical, whereas the PEMT-based design stops working altogether. The accurate design of transformation optics-based layered cylindrical devices enabled by CEMT allow for a new class of robustly manufacturable nanophotonic systems, even with relatively thick layers of real-life materials.

A blind deconvolution method based on L1/L2 regularization prior in the gradient space

NASA Astrophysics Data System (ADS)

Cai, Ying; Shi, Yu; Hua, Xia

2018-02-01

In the process of image restoration, the result of image restoration is very different from the real image because of the existence of noise, in order to solve the ill posed problem in image restoration, a blind deconvolution method based on L1/L2 regularization prior to gradient domain is proposed. The method presented in this paper first adds a function to the prior knowledge, which is the ratio of the L1 norm to the L2 norm, and takes the function as the penalty term in the high frequency domain of the image. Then, the function is iteratively updated, and the iterative shrinkage threshold algorithm is applied to solve the high frequency image. In this paper, it is considered that the information in the gradient domain is better for the estimation of blur kernel, so the blur kernel is estimated in the gradient domain. This problem can be quickly implemented in the frequency domain by fast Fast Fourier Transform. In addition, in order to improve the effectiveness of the algorithm, we have added a multi-scale iterative optimization method. This paper proposes the blind deconvolution method based on L1/L2 regularization priors in the gradient space can obtain the unique and stable solution in the process of image restoration, which not only keeps the edges and details of the image, but also ensures the accuracy of the results.

Fast optimal wavefront reconstruction for multi-conjugate adaptive optics using the Fourier domain preconditioned conjugate gradient algorithm.

PubMed

Vogel, Curtis R; Yang, Qiang

2006-08-21

We present two different implementations of the Fourier domain preconditioned conjugate gradient algorithm (FD-PCG) to efficiently solve the large structured linear systems that arise in optimal volume turbulence estimation, or tomography, for multi-conjugate adaptive optics (MCAO). We describe how to deal with several critical technical issues, including the cone coordinate transformation problem and sensor subaperture grid spacing. We also extend the FD-PCG approach to handle the deformable mirror fitting problem for MCAO.

Mitigation of Engine Inlet Distortion Through Adjoint-Based Design

NASA Technical Reports Server (NTRS)

Ordaz, Irian; Rallabhandi, Sriram; Nielsen, Eric J.; Diskin, Boris

2017-01-01

The adjoint-based design capability in FUN3D is extended to allow efficient gradient- based optimization and design of concepts with highly integrated aero-propulsive systems. A circumferential distortion calculation, along with the derivatives needed to perform adjoint-based design, have been implemented in FUN3D. This newly implemented distortion calculation can be used not only for design but also to drive the existing mesh adaptation process and reduce the error associated with the fan distortion calculation. The design capability is demonstrated by the shape optimization of an in-house aircraft concept equipped with an aft fuselage propulsor. The optimization objective is the minimization of flow distortion at the aerodynamic interface plane of this aft fuselage propulsor.

Aeroassisted orbital maneuvering using Lyapunov optimal feedback control

NASA Technical Reports Server (NTRS)

Grantham, Walter J.; Lee, Byoung-Soo

1987-01-01

A Liapunov optimal feedback controller incorporating a preferred direction of motion at each state of the system which is opposite to the gradient of a specified descent function is developed for aeroassisted orbital transfer from high-earth orbit to LEO. The performances of the Liapunov controller and a calculus-of-variations open-loop minimum-fuel controller, both of which are based on the 1962 U.S. Standard Atmosphere, are simulated using both the 1962 U.S. Standard Atmosphere and an atmosphere corresponding to the STS-6 Space Shuttle flight. In the STS-6 atmosphere, the calculus-of-variations open-loop controller fails to exit the atmosphere, while the Liapunov controller achieves the optimal minimum-fuel conditions, despite the + or - 40 percent fluctuations in the STS-6 atmosphere.

Trajectory optimization for an asymmetric launch vehicle. M.S. Thesis - MIT

NASA Technical Reports Server (NTRS)

Sullivan, Jeanne Marie

1990-01-01

A numerical optimization technique is used to fully automate the trajectory design process for an symmetric configuration of the proposed Advanced Launch System (ALS). The objective of the ALS trajectory design process is the maximization of the vehicle mass when it reaches the desired orbit. The trajectories used were based on a simple shape that could be described by a small set of parameters. The use of a simple trajectory model can significantly reduce the computation time required for trajectory optimization. A predictive simulation was developed to determine the on-orbit mass given an initial vehicle state, wind information, and a set of trajectory parameters. This simulation utilizes an idealized control system to speed computation by increasing the integration time step. The conjugate gradient method is used for the numerical optimization of on-orbit mass. The method requires only the evaluation of the on-orbit mass function using the predictive simulation, and the gradient of the on-orbit mass function with respect to the trajectory parameters. The gradient is approximated with finite differencing. Prelaunch trajectory designs were carried out using the optimization procedure. The predictive simulation is used in flight to redesign the trajectory to account for trajectory deviations produced by off-nominal conditions, e.g., stronger than expected head winds.

Applications of thermal-gradients method for the optimization of α-amylase crystallization conditions based on dynamic and static light scattering data

NASA Astrophysics Data System (ADS)

Delboni, L. F.; Iulek, J.; Burger, R.; da Silva, A. C. R.; Moreno, A.

2002-02-01

The expression, purification, crystallization, and characterization by X-ray diffraction of α-amylase are described here. Dynamic and static light scattering methods with a temperature controller was used to optimize the crystallization conditions of α-amylase from Bacillus stearothermophilus an important enzyme in many fields of industrial activity. After applying thermal gradients for growing crystals, X-ray cryo-crystallographic methods were employed for the data collection. Crystals grown by these thermal-gradients diffracted up to a maximum resolution of 3.8 Å, which allowed the determination of the unit cell constants as follows: a=61.7 Å, b=86.7 Å, c=92.2 Å and space group C222 (or C222 1).

Optimized operation of dielectric laser accelerators: Multibunch

NASA Astrophysics Data System (ADS)

Hanuka, Adi; Schächter, Levi

2018-06-01

We present a self-consistent analysis to determine the optimal charge, gradient, and efficiency for laser driven accelerators operating with a train of microbunches. Specifically, we account for the beam loading reduction on the material occurring at the dielectric-vacuum interface. In the case of a train of microbunches, such beam loading effect could be detrimental due to energy spread, however this may be compensated by a tapered laser pulse. We ultimately propose an optimization procedure with an analytical solution for group velocity which equals to half the speed of light. This optimization results in a maximum efficiency 20% lower than the single bunch case, and a total accelerated charge of 1 06 electrons in the train. The approach holds promise for improving operations of dielectric laser accelerators and may have an impact on emerging laser accelerators driven by high-power optical lasers.

Redundant interferometric calibration as a complex optimization problem

NASA Astrophysics Data System (ADS)

Grobler, T. L.; Bernardi, G.; Kenyon, J. S.; Parsons, A. R.; Smirnov, O. M.

2018-05-01

Observations of the redshifted 21 cm line from the epoch of reionization have recently motivated the construction of low-frequency radio arrays with highly redundant configurations. These configurations provide an alternative calibration strategy - `redundant calibration' - and boost sensitivity on specific spatial scales. In this paper, we formulate calibration of redundant interferometric arrays as a complex optimization problem. We solve this optimization problem via the Levenberg-Marquardt algorithm. This calibration approach is more robust to initial conditions than current algorithms and, by leveraging an approximate matrix inversion, allows for further optimization and an efficient implementation (`redundant STEFCAL'). We also investigated using the preconditioned conjugate gradient method as an alternative to the approximate matrix inverse, but found that its computational performance is not competitive with respect to `redundant STEFCAL'. The efficient implementation of this new algorithm is made publicly available.

Formulation for Simultaneous Aerodynamic Analysis and Design Optimization

NASA Technical Reports Server (NTRS)

Hou, G. W.; Taylor, A. C., III; Mani, S. V.; Newman, P. A.

1993-01-01

An efficient approach for simultaneous aerodynamic analysis and design optimization is presented. This approach does not require the performance of many flow analyses at each design optimization step, which can be an expensive procedure. Thus, this approach brings us one step closer to meeting the challenge of incorporating computational fluid dynamic codes into gradient-based optimization techniques for aerodynamic design. An adjoint-variable method is introduced to nullify the effect of the increased number of design variables in the problem formulation. The method has been successfully tested on one-dimensional nozzle flow problems, including a sample problem with a normal shock. Implementations of the above algorithm are also presented that incorporate Newton iterations to secure a high-quality flow solution at the end of the design process. Implementations with iterative flow solvers are possible and will be required for large, multidimensional flow problems.

Artificial Bee Colony Optimization of Capping Potentials for Hybrid Quantum Mechanical/Molecular Mechanical Calculations.

PubMed

Schiffmann, Christoph; Sebastiani, Daniel

2011-05-10

We present an algorithmic extension of a numerical optimization scheme for analytic capping potentials for use in mixed quantum-classical (quantum mechanical/molecular mechanical, QM/MM) ab initio calculations. Our goal is to minimize bond-cleavage-induced perturbations in the electronic structure, measured by means of a suitable penalty functional. The optimization algorithm-a variant of the artificial bee colony (ABC) algorithm, which relies on swarm intelligence-couples deterministic (downhill gradient) and stochastic elements to avoid local minimum trapping. The ABC algorithm outperforms the conventional downhill gradient approach, if the penalty hypersurface exhibits wiggles that prevent a straight minimization pathway. We characterize the optimized capping potentials by computing NMR chemical shifts. This approach will increase the accuracy of QM/MM calculations of complex biomolecules.

Multifidelity Analysis and Optimization for Supersonic Design

NASA Technical Reports Server (NTRS)

Kroo, Ilan; Willcox, Karen; March, Andrew; Haas, Alex; Rajnarayan, Dev; Kays, Cory

2010-01-01

Supersonic aircraft design is a computationally expensive optimization problem and multifidelity approaches over a significant opportunity to reduce design time and computational cost. This report presents tools developed to improve supersonic aircraft design capabilities including: aerodynamic tools for supersonic aircraft configurations; a systematic way to manage model uncertainty; and multifidelity model management concepts that incorporate uncertainty. The aerodynamic analysis tools developed are appropriate for use in a multifidelity optimization framework, and include four analysis routines to estimate the lift and drag of a supersonic airfoil, a multifidelity supersonic drag code that estimates the drag of aircraft configurations with three different methods: an area rule method, a panel method, and an Euler solver. In addition, five multifidelity optimization methods are developed, which include local and global methods as well as gradient-based and gradient-free techniques.

Feedback-Based Projected-Gradient Method for Real-Time Optimization of Aggregations of Energy Resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Bernstein, Andrey; Simonetto, Andrea

This paper develops an online optimization method to maximize operational objectives of distribution-level distributed energy resources (DERs), while adjusting the aggregate power generated (or consumed) in response to services requested by grid operators. The design of the online algorithm is based on a projected-gradient method, suitably modified to accommodate appropriate measurements from the distribution network and the DERs. By virtue of this approach, the resultant algorithm can cope with inaccuracies in the representation of the AC power flows, it avoids pervasive metering to gather the state of noncontrollable resources, and it naturally lends itself to a distributed implementation. Optimality claimsmore » are established in terms of tracking of the solution of a well-posed time-varying convex optimization problem.« less

Feedback-Based Projected-Gradient Method For Real-Time Optimization of Aggregations of Energy Resources: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Bernstein, Andrey; Simonetto, Andrea

This paper develops an online optimization method to maximize the operational objectives of distribution-level distributed energy resources (DERs) while adjusting the aggregate power generated (or consumed) in response to services requested by grid operators. The design of the online algorithm is based on a projected-gradient method, suitably modified to accommodate appropriate measurements from the distribution network and the DERs. By virtue of this approach, the resultant algorithm can cope with inaccuracies in the representation of the AC power, it avoids pervasive metering to gather the state of noncontrollable resources, and it naturally lends itself to a distributed implementation. Optimality claimsmore » are established in terms of tracking of the solution of a well-posed time-varying optimization problem.« less

A Simulation-Optimization Model for the Management of Seawater Intrusion

NASA Astrophysics Data System (ADS)

Stanko, Z.; Nishikawa, T.

2012-12-01

Seawater intrusion is a common problem in coastal aquifers where excessive groundwater pumping can lead to chloride contamination of a freshwater resource. Simulation-optimization techniques have been developed to determine optimal management strategies while mitigating seawater intrusion. The simulation models are often density-independent groundwater-flow models that may assume a sharp interface and/or use equivalent freshwater heads. The optimization methods are often linear-programming (LP) based techniques that that require simplifications of the real-world system. However, seawater intrusion is a highly nonlinear, density-dependent flow and transport problem, which requires the use of nonlinear-programming (NLP) or global-optimization (GO) techniques. NLP approaches are difficult because of the need for gradient information; therefore, we have chosen a GO technique for this study. Specifically, we have coupled a multi-objective genetic algorithm (GA) with a density-dependent groundwater-flow and transport model to simulate and identify strategies that optimally manage seawater intrusion. GA is a heuristic approach, often chosen when seeking optimal solutions to highly complex and nonlinear problems where LP or NLP methods cannot be applied. The GA utilized in this study is the Epsilon-Nondominated Sorted Genetic Algorithm II (ɛ-NSGAII), which can approximate a pareto-optimal front between competing objectives. This algorithm has several key features: real and/or binary variable capabilities; an efficient sorting scheme; preservation and diversity of good solutions; dynamic population sizing; constraint handling; parallelizable implementation; and user controlled precision for each objective. The simulation model is SEAWAT, the USGS model that couples MODFLOW with MT3DMS for variable-density flow and transport. ɛ-NSGAII and SEAWAT were efficiently linked together through a C-Fortran interface. The simulation-optimization model was first tested by using a published density-independent flow model test case that was originally solved using a sequential LP method with the USGS's Ground-Water Management Process (GWM). For the problem formulation, the objective is to maximize net groundwater extraction, subject to head and head-gradient constraints. The decision variables are pumping rates at fixed wells and the system's state is represented with freshwater hydraulic head. The results of the proposed algorithm were similar to the published results (within 1%); discrepancies may be attributed to differences in the simulators and inherent differences between LP and GA. The GWM test case was then extended to a density-dependent flow and transport version. As formulated, the optimization problem is infeasible because of the density effects on hydraulic head. Therefore, the sum of the squared constraint violation (SSC) was used as a second objective. The result is a pareto curve showing optimal pumping rates versus the SSC. Analysis of this curve indicates that a similar net-extraction rate to the test case can be obtained with a minor violation in vertical head-gradient constraints. This study shows that a coupled ɛ-NSGAII/SEAWAT model can be used for the management of groundwater seawater intrusion. In the future, the proposed methodology will be applied to a real-world seawater intrusion and resource management problem for Santa Barbara, CA.

Demonstration of a strategy for product purification by high-gradient magnetic fishing: recovery of superoxide dismutase from unconditioned whey.

PubMed

Meyer, Andrea; Hansen, Dennis B; Gomes, Cláudia S G; Hobley, Timothy J; Thomas, Owen R T; Franzreb, Matthias

2005-01-01

A systematic approach for the design of a bioproduct recovery process employing magnetic supports and the technique of high-gradient magnetic fishing (HGMF) is described. The approach is illustrated for the separation of superoxide dismutase (SOD), an antioxidant protein present in low concentrations (ca. 0.15-0.6 mg L(-1)) in whey. The first part of the process design consisted of ligand screening in which metal chelate supports charged with copper(II) ions were found to be the most suitable. The second stage involved systematic and sequential optimization of conditions for the following steps: product adsorption, support washing, and product elution. Next, the capacity of a novel high-gradient magnetic separator (designed for biotechnological applications) for trapping and holding magnetic supports was determined. Finally, all of the above elements were assembled to deliver a HGMF process for the isolation of SOD from crude sweet whey, which consisted of (i) binding SOD using Cu2+ -charged magnetic metal chelator particles in a batch reactor with whey; (ii) recovery of the "SOD-loaded" supports by high-gradient magnetic separation (HGMS); (iii) washing out loosely bound and entrained proteins and solids; (iv) elution of the target protein; and (v) recovery of the eluted supports from the HGMF rig. Efficient recovery of SOD was demonstrated at approximately 50-fold increased scale (cf magnetic rack studies) in three separate HGMF experiments, and in the best of these (run 3) an SOD yield of >85% and purification factor of approximately 21 were obtained.

The effect of parking orbit constraints on the optimization of ballistic planetary trajectories

NASA Technical Reports Server (NTRS)

Sauer, C. G., Jr.

1984-01-01

The optimization of ballistic planetary trajectories is developed which includes constraints on departure parking orbit inclination and node. This problem is formulated to result in a minimum total Delta V where the entire constrained injection Delta V is included in the optimization. An additional Delta V is also defined to allow for possible optimization of parking orbit inclination when the launch vehicle orbit capability varies as a function of parking orbit inclination. The optimization problem is formulated using primer vector theory to derive partial derivatives of total Delta V with respect to possible free parameters. Minimization of total Delta V is accomplished using a quasi-Newton gradient search routine. The analysis is applied to an Eros rendezvous mission whose transfer trajectories are characterized by high values of launch asymptote declination during particular launch opportunities. Comparisons in performance are made between trajectories where parking orbit constraints are included in the optimization and trajectories where the constraints are not included.

Multi-disciplinary optimization of aeroservoelastic systems

NASA Technical Reports Server (NTRS)

Karpel, Mordechay

1990-01-01

Efficient analytical and computational tools for simultaneous optimal design of the structural and control components of aeroservoelastic systems are presented. The optimization objective is to achieve aircraft performance requirements and sufficient flutter and control stability margins with a minimal weight penalty and without violating the design constraints. Analytical sensitivity derivatives facilitate an efficient optimization process which allows a relatively large number of design variables. Standard finite element and unsteady aerodynamic routines are used to construct a modal data base. Minimum State aerodynamic approximations and dynamic residualization methods are used to construct a high accuracy, low order aeroservoelastic model. Sensitivity derivatives of flutter dynamic pressure, control stability margins and control effectiveness with respect to structural and control design variables are presented. The performance requirements are utilized by equality constraints which affect the sensitivity derivatives. A gradient-based optimization algorithm is used to minimize an overall cost function. A realistic numerical example of a composite wing with four controls is used to demonstrate the modeling technique, the optimization process, and their accuracy and efficiency.

Multidisciplinary optimization of aeroservoelastic systems using reduced-size models

NASA Technical Reports Server (NTRS)

Karpel, Mordechay

1992-01-01

Efficient analytical and computational tools for simultaneous optimal design of the structural and control components of aeroservoelastic systems are presented. The optimization objective is to achieve aircraft performance requirements and sufficient flutter and control stability margins with a minimal weight penalty and without violating the design constraints. Analytical sensitivity derivatives facilitate an efficient optimization process which allows a relatively large number of design variables. Standard finite element and unsteady aerodynamic routines are used to construct a modal data base. Minimum State aerodynamic approximations and dynamic residualization methods are used to construct a high accuracy, low order aeroservoelastic model. Sensitivity derivatives of flutter dynamic pressure, control stability margins and control effectiveness with respect to structural and control design variables are presented. The performance requirements are utilized by equality constraints which affect the sensitivity derivatives. A gradient-based optimization algorithm is used to minimize an overall cost function. A realistic numerical example of a composite wing with four controls is used to demonstrate the modeling technique, the optimization process, and their accuracy and efficiency.

A modified conjugate gradient coefficient with inexact line search for unconstrained optimization

NASA Astrophysics Data System (ADS)

Aini, Nurul; Rivaie, Mohd; Mamat, Mustafa

2016-11-01

Conjugate gradient (CG) method is a line search algorithm mostly known for its wide application in solving unconstrained optimization problems. Its low memory requirements and global convergence properties makes it one of the most preferred method in real life application such as in engineering and business. In this paper, we present a new CG method based on AMR* and CD method for solving unconstrained optimization functions. The resulting algorithm is proven to have both the sufficient descent and global convergence properties under inexact line search. Numerical tests are conducted to assess the effectiveness of the new method in comparison to some previous CG methods. The results obtained indicate that our method is indeed superior.

Solving traveling salesman problems with DNA molecules encoding numerical values.

PubMed

Lee, Ji Youn; Shin, Soo-Yong; Park, Tai Hyun; Zhang, Byoung-Tak

2004-12-01

We introduce a DNA encoding method to represent numerical values and a biased molecular algorithm based on the thermodynamic properties of DNA. DNA strands are designed to encode real values by variation of their melting temperatures. The thermodynamic properties of DNA are used for effective local search of optimal solutions using biochemical techniques, such as denaturation temperature gradient polymerase chain reaction and temperature gradient gel electrophoresis. The proposed method was successfully applied to the traveling salesman problem, an instance of optimization problems on weighted graphs. This work extends the capability of DNA computing to solving numerical optimization problems, which is contrasted with other DNA computing methods focusing on logical problem solving.

Proposed Drill Sites

DOE Data Explorer

Lane, Michael

2013-06-28

Proposed drill sites for intermediate depth temperature gradient holes and/or deep resource confirmation wells. Temperature gradient contours based on shallow TG program and faults interpreted from seismic reflection survey are shown, as are two faults interpreted by seismic contractor Optim but not by Oski Energy, LLC.

Numerical simulations on active shielding methods comparison and wrapped angle optimization for gradient coil design in MRI with enhanced shielding effect

NASA Astrophysics Data System (ADS)

Wang, Yaohui; Xin, Xuegang; Guo, Lei; Chen, Zhifeng; Liu, Feng

2018-05-01

The switching of a gradient coil current in magnetic resonance imaging will induce an eddy current in the surrounding conducting structures while the secondary magnetic field produced by the eddy current is harmful for the imaging. To minimize the eddy current effects, the stray field shielding in the gradient coil design is usually realized by minimizing the magnetic fields on the cryostat surface or the secondary magnetic fields over the imaging region. In this work, we explicitly compared these two active shielding design methods. Both the stray field and eddy current on the cryostat inner surface were quantitatively discussed by setting the stray field constraint with an ultra-low maximum intensity of 2 G and setting the secondary field constraint with an extreme small shielding ratio of 0.000 001. The investigation revealed that the secondary magnetic field control strategy can produce coils with a better performance. However, the former (minimizing the magnetic fields) is preferable when designing a gradient coil with an ultra-low eddy current that can also strictly control the stray field leakage at the edge of the cryostat inner surface. A wrapped-edge gradient coil design scheme was then optimized for a more effective control of the stray fields. The numerical simulation on the wrapped-edge coil design shows that the optimized wrapping angles for the x and z coils in terms of our coil dimensions are 40° and 90°, respectively.

Broadband metamaterial lens antennas with special properties by controlling both refractive-index distribution and feed directivity

NASA Astrophysics Data System (ADS)

Ma, Qian; Shi, Chuan Bo; Chen, Tian Yi; Qing Qi, Mei; Li, Yun Bo; Cui, Tie Jun

2018-04-01

A new method is proposed to design gradient refractive-index metamaterial lens antennas by optimizing both the refractive-index distribution of the lens and the feed directivity. Comparing to the conventional design methods, source optimization provides a new degree of freedom to control aperture fields effectively. To demonstrate this method, two lenses with special properties based on this method are designed, to emit high-efficiency plane waves and fan-shaped beams, respectively. Both lenses have good performance and wide frequency band from 12 to 18 GHz, verifying the validity of the proposed method. The plane-wave emitting lens realized a high aperture efficiency of 75%, and the fan-beam lens achieved a high gain of 15 dB over board bandwidth. The experimental results have good agreement with the design targets and full-wave simulations.

Strategies and Applications for Incorporating Physical and Chemical Signal Gradients in Tissue Engineering

PubMed Central

Singh, Milind; Berkland, Cory

2008-01-01

From embryonic development to wound repair, concentration gradients of bioactive signaling molecules guide tissue formation and regeneration. Moreover, gradients in cellular and extracellular architecture as well as in mechanical properties are readily apparent in native tissues. Perhaps tissue engineers can take a cue from nature in attempting to regenerate tissues by incorporating gradients into engineering design strategies. Indeed, gradient-based approaches are an emerging trend in tissue engineering, standing in contrast to traditional approaches of homogeneous delivery of cells and/or growth factors using isotropic scaffolds. Gradients in tissue engineering lie at the intersection of three major paradigms in the field—biomimetic, interfacial, and functional tissue engineering—by combining physical (via biomaterial design) and chemical (with growth/differentiation factors and cell adhesion molecules) signal delivery to achieve a continuous transition in both structure and function. This review consolidates several key methodologies to generate gradients, some of which have never been employed in a tissue engineering application, and discusses strategies for incorporating these methods into tissue engineering and implant design. A key finding of this review was that two-dimensional physicochemical gradient substrates, which serve as excellent high-throughput screening tools for optimizing desired biomaterial properties, can be enhanced in the future by transitioning from two dimensions to three dimensions, which would enable studies of cell–protein–biomaterial interactions in a more native tissue–like environment. In addition, biomimetic tissue regeneration via combined delivery of graded physical and chemical signals appears to be a promising strategy for the regeneration of heterogeneous tissues and tissue interfaces. In the future, in vivo applications will shed more light on the performance of gradient-based mechanical integrity and signal delivery strategies compared to traditional tissue engineering approaches. PMID:18803499

Strategies and applications for incorporating physical and chemical signal gradients in tissue engineering.

PubMed

Singh, Milind; Berkland, Cory; Detamore, Michael S

2008-12-01

From embryonic development to wound repair, concentration gradients of bioactive signaling molecules guide tissue formation and regeneration. Moreover, gradients in cellular and extracellular architecture as well as in mechanical properties are readily apparent in native tissues. Perhaps tissue engineers can take a cue from nature in attempting to regenerate tissues by incorporating gradients into engineering design strategies. Indeed, gradient-based approaches are an emerging trend in tissue engineering, standing in contrast to traditional approaches of homogeneous delivery of cells and/or growth factors using isotropic scaffolds. Gradients in tissue engineering lie at the intersection of three major paradigms in the field-biomimetic, interfacial, and functional tissue engineering-by combining physical (via biomaterial design) and chemical (with growth/differentiation factors and cell adhesion molecules) signal delivery to achieve a continuous transition in both structure and function. This review consolidates several key methodologies to generate gradients, some of which have never been employed in a tissue engineering application, and discusses strategies for incorporating these methods into tissue engineering and implant design. A key finding of this review was that two-dimensional physicochemical gradient substrates, which serve as excellent high-throughput screening tools for optimizing desired biomaterial properties, can be enhanced in the future by transitioning from two dimensions to three dimensions, which would enable studies of cell-protein-biomaterial interactions in a more native tissue-like environment. In addition, biomimetic tissue regeneration via combined delivery of graded physical and chemical signals appears to be a promising strategy for the regeneration of heterogeneous tissues and tissue interfaces. In the future, in vivo applications will shed more light on the performance of gradient-based mechanical integrity and signal delivery strategies compared to traditional tissue engineering approaches.

Carbon and nutrient use efficiencies optimally balance stoichiometric imbalances

NASA Astrophysics Data System (ADS)

Manzoni, Stefano; Čapek, Petr; Lindahl, Björn; Mooshammer, Maria; Richter, Andreas; Šantrůčková, Hana

2016-04-01

Decomposer organisms face large stoichiometric imbalances because their food is generally poor in nutrients compared to the decomposer cellular composition. The presence of excess carbon (C) requires adaptations to utilize nutrients effectively while disposing of or investing excess C. As food composition changes, these adaptations lead to variable C- and nutrient-use efficiencies (defined as the ratios of C and nutrients used for growth over the amounts consumed). For organisms to be ecologically competitive, these changes in efficiencies with resource stoichiometry have to balance advantages and disadvantages in an optimal way. We hypothesize that efficiencies are varied so that community growth rate is optimized along stoichiometric gradients of their resources. Building from previous theories, we predict that maximum growth is achieved when C and nutrients are co-limiting, so that the maximum C-use efficiency is reached, and nutrient release is minimized. This optimality principle is expected to be applicable across terrestrial-aquatic borders, to various elements, and at different trophic levels. While the growth rate maximization hypothesis has been evaluated for consumers and predators, in this contribution we test it for terrestrial and aquatic decomposers degrading resources across wide stoichiometry gradients. The optimality hypothesis predicts constant efficiencies at low substrate C:N and C:P, whereas above a stoichiometric threshold, C-use efficiency declines and nitrogen- and phosphorus-use efficiencies increase up to one. Thus, high resource C:N and C:P lead to low C-use efficiency, but effective retention of nitrogen and phosphorus. Predictions are broadly consistent with efficiency trends in decomposer communities across terrestrial and aquatic ecosystems.

Transient Growth Analysis of Compressible Boundary Layers with Parabolized Stability Equations

NASA Technical Reports Server (NTRS)

Paredes, Pedro; Choudhari, Meelan M.; Li, Fei; Chang, Chau-Lyan

2016-01-01

The linear form of parabolized linear stability equations (PSE) is used in a variational approach to extend the previous body of results for the optimal, non-modal disturbance growth in boundary layer flows. This methodology includes the non-parallel effects associated with the spatial development of boundary layer flows. As noted in literature, the optimal initial disturbances correspond to steady counter-rotating stream-wise vortices, which subsequently lead to the formation of stream-wise-elongated structures, i.e., streaks, via a lift-up effect. The parameter space for optimal growth is extended to the hypersonic Mach number regime without any high enthalpy effects, and the effect of wall cooling is studied with particular emphasis on the role of the initial disturbance location and the value of the span-wise wavenumber that leads to the maximum energy growth up to a specified location. Unlike previous predictions that used a basic state obtained from a self-similar solution to the boundary layer equations, mean flow solutions based on the full Navier-Stokes (NS) equations are used in select cases to help account for the viscous-inviscid interaction near the leading edge of the plate and also for the weak shock wave emanating from that region. These differences in the base flow lead to an increasing reduction with Mach number in the magnitude of optimal growth relative to the predictions based on self-similar mean-flow approximation. Finally, the maximum optimal energy gain for the favorable pressure gradient boundary layer near a planar stagnation point is found to be substantially weaker than that in a zero pressure gradient Blasius boundary layer.

X-Band RF Gun Development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vlieks, Arnold; Dolgashev, Valery; Tantawi, Sami

In support of the MEGa-ray program at LLNL and the High Gradient research program at SLAC, a new X-band multi-cell RF gun is being developed. This gun, similar to earlier guns developed at SLAC for Compton X-ray source program, will be a standing wave structure made of 5.5 cells operating in the pi mode with copper cathode. This gun was designed following criteria used to build SLAC X-band high gradient accelerating structures. It is anticipated that this gun will operate with surface electric fields on the cathode of 200 MeV/m with low breakdown rate. RF will be coupled into themore » structure through a final cell with symmetric duel feeds and with a shape optimized to minimize quadrupole field components. In addition, geometry changes to the original gun, operated with Compton X-ray source, will include a wider RF mode separation, reduced surface electric and magnetic fields.« less

3D theory of a high-gain free-electron laser based on a transverse gradient undulator

NASA Astrophysics Data System (ADS)

Baxevanis, Panagiotis; Ding, Yuantao; Huang, Zhirong; Ruth, Ronald

2014-02-01

The performance of a free-electron laser (FEL) depends significantly on the various parameters of the driving electron beam. In particular, a large energy spread in the beam results in a substantial reduction of the FEL gain, an effect which is especially relevant when one considers FELs driven by plasma accelerators or ultimate storage rings. For such cases, one possible solution is to use a transverse gradient undulator (TGU). In this concept, the energy spread problem is mitigated by properly dispersing the electron beam and introducing a linear, transverse field dependence in the undulator. This paper presents a self-consistent theoretical analysis of a TGU-based, high-gain FEL which takes into account three-dimensional (3D) effects, including beam size variations along the undulator. The results of our theory compare favorably with simulation and are used in fast optimization studies of various x-ray FEL configurations.

Performance seeking control excitation mode

NASA Technical Reports Server (NTRS)

Schkolnik, Gerard

1995-01-01

Flight testing of the performance seeking control (PSC) excitation mode was successfully completed at NASA Dryden on the F-15 highly integrated digital electronic control (HIDEC) aircraft. Although the excitation mode was not one of the original objectives of the PSC program, it was rapidly prototyped and implemented into the architecture of the PSC algorithm, allowing valuable and timely research data to be gathered. The primary flight test objective was to investigate the feasibility of a future measurement-based performance optimization algorithm. This future algorithm, called AdAPT, which stands for adaptive aircraft performance technology, generates and applies excitation inputs to selected control effectors. Fourier transformations are used to convert measured response and control effector data into frequency domain models which are mapped into state space models using multiterm frequency matching. Formal optimization principles are applied to produce an integrated, performance optimal effector suite. The key technical challenge of the measurement-based approach is the identification of the gradient of the performance index to the selected control effector. This concern was addressed by the excitation mode flight test. The AdAPT feasibility study utilized the PSC excitation mode to apply separate sinusoidal excitation trims to the controls - one aircraft, inlet first ramp (cowl), and one engine, throat area. Aircraft control and response data were recorded using on-board instrumentation and analyzed post-flight. Sensor noise characteristics, axial acceleration performance gradients, and repeatability were determined. Results were compared to pilot comments to assess the ride quality. Flight test results indicate that performance gradients were identified at all flight conditions, sensor noise levels were acceptable at the frequencies of interest, and excitations were generally not sensed by the pilot.

An approach to multiobjective optimization of rotational therapy. II. Pareto optimal surfaces and linear combinations of modulated blocked arcs for a prostate geometry.

PubMed

Pardo-Montero, Juan; Fenwick, John D

2010-06-01

The purpose of this work is twofold: To further develop an approach to multiobjective optimization of rotational therapy treatments recently introduced by the authors [J. Pardo-Montero and J. D. Fenwick, "An approach to multiobjective optimization of rotational therapy," Med. Phys. 36, 3292-3303 (2009)], especially regarding its application to realistic geometries, and to study the quality (Pareto optimality) of plans obtained using such an approach by comparing them with Pareto optimal plans obtained through inverse planning. In the previous work of the authors, a methodology is proposed for constructing a large number of plans, with different compromises between the objectives involved, from a small number of geometrically based arcs, each arc prioritizing different objectives. Here, this method has been further developed and studied. Two different techniques for constructing these arcs are investigated, one based on image-reconstruction algorithms and the other based on more common gradient-descent algorithms. The difficulty of dealing with organs abutting the target, briefly reported in previous work of the authors, has been investigated using partial OAR unblocking. Optimality of the solutions has been investigated by comparison with a Pareto front obtained from inverse planning. A relative Euclidean distance has been used to measure the distance of these plans to the Pareto front, and dose volume histogram comparisons have been used to gauge the clinical impact of these distances. A prostate geometry has been used for the study. For geometries where a blocked OAR abuts the target, moderate OAR unblocking can substantially improve target dose distribution and minimize hot spots while not overly compromising dose sparing of the organ. Image-reconstruction type and gradient-descent blocked-arc computations generate similar results. The Pareto front for the prostate geometry, reconstructed using a large number of inverse plans, presents a hockey-stick shape comprising two regions: One where the dose to the target is close to prescription and trade-offs can be made between doses to the organs at risk and (small) changes in target dose, and one where very substantial rectal sparing is achieved at the cost of large target underdosage. Plans computed following the approach using a conformal arc and four blocked arcs generally lie close to the Pareto front, although distances of some plans from high gradient regions of the Pareto front can be greater. Only around 12% of plans lie a relative Euclidean distance of 0.15 or greater from the Pareto front. Using the alternative distance measure of Craft ["Calculating and controlling the error of discrete representations of Pareto surfaces in convex multi-criteria optimization," Phys. Medica (to be published)], around 2/5 of plans lie more than 0.05 from the front. Computation of blocked arcs is quite fast, the algorithms requiring 35%-80% of the running time per iteration needed for conventional inverse plan computation. The geometry-based arc approach to multicriteria optimization of rotational therapy allows solutions to be obtained that lie close to the Pareto front. Both the image-reconstruction type and gradient-descent algorithms produce similar modulated arcs, the latter one perhaps being preferred because it is more easily implementable in standard treatment planning systems. Moderate unblocking provides a good way of dealing with OARs which abut the PTV. Optimization of geometry-based arcs is faster than usual inverse optimization of treatment plans, making this approach more rapid than an inverse-based Pareto front reconstruction.

Remote-loading labeling of liposomes with (99m)Tc-BMEDA and its stability evaluation: effects of lipid formulation and pH/chemical gradient.

PubMed

Li, Shihong; Goins, Beth; Phillips, William T; Bao, Ande

2011-03-01

Efficient, convenient, and stable radiolabeling plays a critical role for the monitoring of liposome behavior via either blood sampling, organ distribution, or noninvasive nuclear imaging. The direct labeling of liposome-carrying drugs without any prior modification undoubtedly is convenient and optimal for liposomal drug testing. In this article, we investigated the effect of various lipid formulations and pH/chemical gradients on the radiolabeling efficiency and entrapment stability of technetium-99m ((99m)Tc) remotely loaded into liposomes, using (99m)Tc-N,N-bis(2-mercaptoethyl)-N',N'-diethyl-ethylenediamine ((99m)Tc-BMEDA) complex. The tested liposomes either contained unsaturated lipid or possessed various surface charges. (99m)Tc could be efficiently loaded into various premanufactured liposomes containing either an ammonium sulfate pH, citrate pH, or glutathione (GSH) chemical gradient. (99m)Tc-entrapment stabilities of these liposomes in phosphate-buffered saline (PBS; pH 7.4) buffer at 25°C were mainly dependent on the pH/chemical gradient, but not lipid formulation. Stability sequence was ammonium sulfate pH-gradient>citrate pH-gradient>GSH-gradient. Stabilities of (99m)Tc-liposomes in 50% fetal bovine serum (FBS)/PBS (pH 7.4) buffer at 37°C are dependent on both lipid formulation and pH/chemical gradient. Specifically, (99m)Tc labeling of the ammonium sulfate pH-gradient liposomes were less stable in 50% FBS/PBS than in PBS, whereas noncationic liposomes with citrate pH- or GSH-gradient displayed higher stability, except that anionic citrate pH-gradient liposomes showed no stability difference in these two media. Cationic liposomes aggregated in 50% FBS/PBS, forming a new discrete fraction with larger particle sizes. These in vitro characterization results have indicated the optimism of using (99m)Tc-BMEDA for labeling pH/GSH gradient liposomes without the requirement of modifying lipid formulation for liposomal therapeutic-agent development.

Teaching Simulation and Computer-Aided Separation Optimization in Liquid Chromatography by Means of Illustrative Microsoft Excel Spreadsheets

ERIC Educational Resources Information Center

Fasoula, S.; Nikitas, P.; Pappa-Louisi, A.

2017-01-01

A series of Microsoft Excel spreadsheets were developed to simulate the process of separation optimization under isocratic and simple gradient conditions. The optimization procedure is performed in a stepwise fashion using simple macros for an automatic application of this approach. The proposed optimization approach involves modeling of the peak…

Induction Heating Model of Cermet Fuel Element Environmental Test (CFEET)

NASA Technical Reports Server (NTRS)

Gomez, Carlos F.; Bradley, D. E.; Cavender, D. P.; Mireles, O. R.; Hickman, R. R.; Trent, D.; Stewart, E.

2013-01-01

Deep space missions with large payloads require high specific impulse and relatively high thrust to achieve mission goals in reasonable time frames. Nuclear Thermal Rockets (NTR) are capable of producing a high specific impulse by employing heat produced by a fission reactor to heat and therefore accelerate hydrogen through a rocket nozzle providing thrust. Fuel element temperatures are very high (up to 3000 K) and hydrogen is highly reactive with most materials at high temperatures. Data covering the effects of high-temperature hydrogen exposure on fuel elements are limited. The primary concern is the mechanical failure of fuel elements due to large thermal gradients; therefore, high-melting-point ceramics-metallic matrix composites (cermets) are one of the fuels under consideration as part of the Nuclear Cryogenic Propulsion Stage (NCPS) Advance Exploration System (AES) technology project at the Marshall Space Flight Center. The purpose of testing and analytical modeling is to determine their ability to survive and maintain thermal performance in a prototypical NTR reactor environment of exposure to hydrogen at very high temperatures and obtain data to assess the properties of the non-nuclear support materials. The fission process and the resulting heating performance are well known and do not require that active fissile material to be integrated in this testing. A small-scale test bed; Compact Fuel Element Environmental Tester (CFEET), designed to heat fuel element samples via induction heating and expose samples to hydrogen is being developed at MSFC to assist in optimal material and manufacturing process selection without utilizing fissile material. This paper details the analytical approach to help design and optimize the test bed using COMSOL Multiphysics for predicting thermal gradients induced by electromagnetic heating (Induction heating) and Thermal Desktop for radiation calculations.

Hybrid Quantum-Classical Approach to Quantum Optimal Control.

PubMed

Li, Jun; Yang, Xiaodong; Peng, Xinhua; Sun, Chang-Pu

2017-04-14

A central challenge in quantum computing is to identify more computational problems for which utilization of quantum resources can offer significant speedup. Here, we propose a hybrid quantum-classical scheme to tackle the quantum optimal control problem. We show that the most computationally demanding part of gradient-based algorithms, namely, computing the fitness function and its gradient for a control input, can be accomplished by the process of evolution and measurement on a quantum simulator. By posing queries to and receiving answers from the quantum simulator, classical computing devices update the control parameters until an optimal control solution is found. To demonstrate the quantum-classical scheme in experiment, we use a seven-qubit nuclear magnetic resonance system, on which we have succeeded in optimizing state preparation without involving classical computation of the large Hilbert space evolution.

Pronounced gradients of light, photosynthesis and O2 consumption in the tissue of the brown alga Fucus serratus.

PubMed

Lichtenberg, Mads; Kühl, Michael

2015-08-01

Macroalgae live in an ever-changing light environment affected by wave motion, self-shading and light-scattering effects, and on the thallus scale, gradients of light and chemical parameters influence algal photosynthesis. However, the thallus microenvironment and internal gradients remain underexplored. In this study, microsensors were used to quantify gradients of light, O2 concentration, variable chlorophyll fluorescence, photosynthesis and O2 consumption as a function of irradiance in the cortex and medulla layers of Fucus serratus. The two cortex layers showed more efficient light utilization compared to the medulla, calculated both from electron transport rates through photosystem II and from photosynthesis-irradiance curves. At moderate irradiance, the upper cortex exhibited onset of photosynthetic saturation, whereas lower thallus layers exhibited net O2 consumption. O2 consumption rates in light varied with depth and irradiance and were more than two-fold higher than dark respiration. We show that the thallus microenvironment of F. serratus exhibits a highly stratified balance of production and consumption of O2 , and when the frond was held in a fixed position, high incident irradiance levels on the upper cortex did not saturate photosynthesis in the lower thallus layers. We discuss possible photoadaptive responses and consequences for optimizing photosynthetic activity on the basis of vertical differences in light attenuation coefficients. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Hybrid DFP-CG method for solving unconstrained optimization problems

NASA Astrophysics Data System (ADS)

Osman, Wan Farah Hanan Wan; Asrul Hery Ibrahim, Mohd; Mamat, Mustafa

2017-09-01

The conjugate gradient (CG) method and quasi-Newton method are both well known method for solving unconstrained optimization method. In this paper, we proposed a new method by combining the search direction between conjugate gradient method and quasi-Newton method based on BFGS-CG method developed by Ibrahim et al. The Davidon-Fletcher-Powell (DFP) update formula is used as an approximation of Hessian for this new hybrid algorithm. Numerical result showed that the new algorithm perform well than the ordinary DFP method and proven to posses both sufficient descent and global convergence properties.

Geometrical optics analysis of the structural imperfection of retroreflection corner cubes with a nonlinear conjugate gradient method.

PubMed

Kim, Hwi; Min, Sung-Wook; Lee, Byoungho

2008-12-01

Geometrical optics analysis of the structural imperfection of retroreflection corner cubes is described. In the analysis, a geometrical optics model of six-beam reflection patterns generated by an imperfect retroreflection corner cube is developed, and its structural error extraction is formulated as a nonlinear optimization problem. The nonlinear conjugate gradient method is employed for solving the nonlinear optimization problem, and its detailed implementation is described. The proposed method of analysis is a mathematical basis for the nondestructive optical inspection of imperfectly fabricated retroreflection corner cubes.

A Smoothed Eclipse Model for Solar Electric Propulsion Trajectory Optimization

NASA Technical Reports Server (NTRS)

Aziz, Jonathan D.; Scheeres, Daniel J.; Parker, Jeffrey S.; Englander, Jacob A.

2017-01-01

Solar electric propulsion (SEP) is the dominant design option for employing low-thrust propulsion on a space mission. Spacecraft solar arrays power the SEP system but are subject to blackout periods during solar eclipse conditions. Discontinuity in power available to the spacecraft must be accounted for in trajectory optimization, but gradient-based methods require a differentiable power model. This work presents a power model that smooths the eclipse transition from total eclipse to total sunlight with a logistic function. Example trajectories are computed with differential dynamic programming, a second-order gradient-based method.

Microfluidic platform for optimization of crystallization conditions

NASA Astrophysics Data System (ADS)

Zhang, Shuheng; Gerard, Charline J. J.; Ikni, Aziza; Ferry, Gilles; Vuillard, Laurent M.; Boutin, Jean A.; Ferte, Nathalie; Grossier, Romain; Candoni, Nadine; Veesler, Stéphane

2017-08-01

We describe a universal, high-throughput droplet-based microfluidic platform for crystallization. It is suitable for a multitude of applications, due to its flexibility, ease of use, compatibility with all solvents and low cost. The platform offers four modular functions: droplet formation, on-line characterization, incubation and observation. We use it to generate droplet arrays with a concentration gradient in continuous long tubing, without using surfactant. We control droplet properties (size, frequency and spacing) in long tubing by using hydrodynamic empirical relations. We measure droplet chemical composition using both an off-line and a real-time on-line method. Applying this platform to a complicated chemical environment, membrane proteins, we successfully handle crystallization, suggesting that the platform is likely to perform well in other circumstances. We validate the platform for fine-gradient screening and optimization of crystallization conditions. Additional on-line detection methods may well be integrated into this platform in the future, for instance, an on-line diffraction technique. We believe this method could find applications in fields such as fluid interaction engineering, live cell study and enzyme kinetics.

[Effect of topographical factors on podophyllotoxin content in Sinopodophyllum hexandrum and study on ecological suitability].

PubMed

Wu, Ao-lin; Li, Min; Zhang, Shou-wen; Zhao, Ji-feng; Liu, Xiang; Wang, Chang-hua; Wang, Xiao-yun; Zhong, Guo-yue

2015-06-01

In order to find the optimal topographical factor for regionslization, the content of cimetidine in 116 Sinopodophyllum hexandrum sample collected from Sichuan, Qinghai, Gansu, Tibet, Yunnan and Shaanxi provinces, was determined. Using mathematical statistics and geographical spatial analysis of GIS analysis, the relationship between content of podophyllotoxin and influencing factors including altitude gradient and gradient position was analyzed. It is found that the optimal altitude was 2 800 m to 3 600 m, the aspect of slope north or northeast and northwest and the slope 12 degrees to 65 degrees with a high suitability degree. Considering the artificial planting, the suitable planting area for S. hexandrum is comfirmed. The topographical factor is important for S. hexandrum regionalization, but has hardly effect on podophyllotoxin content. The results of the study provide an important scientific basis for S. hexandrum production development. But there are many factors which affect suitability index and podophyllotoxin content of S. hexandrum, it is necessary to consider other factors like climate and soil while exploitation and protection of S. hexandrum.

Numerical Simulation and Performance Optimization of a Magnetophoretic Bio-separation chip

NASA Astrophysics Data System (ADS)

Golozar, Matin; Darabi, Jeff; Molki, Majid

Separation of micro/nanoparticles is important in biomedicine and biotechnology. This research presents the modeling and optimization of a magnetophoretic bio-separation chip for the isolation of biomaterials, such as circulating tumor cells (CTCs) from the peripheral blood. The chip consists of a continuous flow through microfluidic channels that contains locally engineered magnetic field gradients. The high gradient magnetic field produced by the magnets is spatially non-uniform and gives rise to an attractive force on magnetic particles that move through the flow channel. The computational model takes into account the magnetic and fluidic forces as well as the effect of the volume fraction of particles on the continuous phase. The model is used to investigate the effect of two-way particle-fluid coupling on both the capture efficiency and the flow pattern in the separation chip. The results show that the microfluidic device has the capability of separating CTCs from their native environment. Additionally, a parametric study is performed to investigate the effects of the channel height, substrate thickness, magnetic bead size, bioparticle size, and the number of beads per cell on the cell separation performance.

Nonseparable exchange–correlation functional for molecules, including homogeneous catalysis involving transition metals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Haoyu S.; Zhang, Wenjing; Verma, Pragya

2015-01-01

The goal of this work is to develop a gradient approximation to the exchange–correlation functional of Kohn–Sham density functional theory for treating molecular problems with a special emphasis on the prediction of quantities important for homogeneous catalysis and other molecular energetics. Our training and validation of exchange–correlation functionals is organized in terms of databases and subdatabases. The key properties required for homogeneous catalysis are main group bond energies (database MGBE137), transition metal bond energies (database TMBE32), reaction barrier heights (database BH76), and molecular structures (database MS10). We also consider 26 other databases, most of which are subdatabases of a newlymore » extended broad database called Database 2015, which is presented in the present article and in its ESI. Based on the mathematical form of a nonseparable gradient approximation (NGA), as first employed in the N12 functional, we design a new functional by using Database 2015 and by adding smoothness constraints to the optimization of the functional. The resulting functional is called the gradient approximation for molecules, or GAM. The GAM functional gives better results for MGBE137, TMBE32, and BH76 than any available generalized gradient approximation (GGA) or than N12. The GAM functional also gives reasonable results for MS10 with an MUE of 0.018 Å. The GAM functional provides good results both within the training sets and outside the training sets. The convergence tests and the smooth curves of exchange–correlation enhancement factor as a function of the reduced density gradient show that the GAM functional is a smooth functional that should not lead to extra expense or instability in optimizations. NGAs, like GGAs, have the advantage over meta-GGAs and hybrid GGAs of respectively smaller grid-size requirements for integrations and lower costs for extended systems. These computational advantages combined with the relatively high accuracy for all the key properties needed for molecular catalysis make the GAM functional very promising for future applications.« less

Optimal inverse functions created via population-based optimization.

PubMed

Jennings, Alan L; Ordóñez, Raúl

2014-06-01

Finding optimal inputs for a multiple-input, single-output system is taxing for a system operator. Population-based optimization is used to create sets of functions that produce a locally optimal input based on a desired output. An operator or higher level planner could use one of the functions in real time. For the optimization, each agent in the population uses the cost and output gradients to take steps lowering the cost while maintaining their current output. When an agent reaches an optimal input for its current output, additional agents are generated in the output gradient directions. The new agents then settle to the local optima for the new output values. The set of associated optimal points forms an inverse function, via spline interpolation, from a desired output to an optimal input. In this manner, multiple locally optimal functions can be created. These functions are naturally clustered in input and output spaces allowing for a continuous inverse function. The operator selects the best cluster over the anticipated range of desired outputs and adjusts the set point (desired output) while maintaining optimality. This reduces the demand from controlling multiple inputs, to controlling a single set point with no loss in performance. Results are demonstrated on a sample set of functions and on a robot control problem.

Optimal control of a variable spin speed CMG system for space vehicles. [Control Moment Gyros

NASA Technical Reports Server (NTRS)

Liu, T. C.; Chubb, W. B.; Seltzer, S. M.; Thompson, Z.

1973-01-01

Many future NASA programs require very high accurate pointing stability. These pointing requirements are well beyond anything attempted to date. This paper suggests a control system which has the capability of meeting these requirements. An optimal control law for the suggested system is specified. However, since no direct method of solution is known for this complicated system, a computation technique using successive approximations is used to develop the required solution. The method of calculus of variations is applied for estimating the changes of index of performance as well as those constraints of inequality of state variables and terminal conditions. Thus, an algorithm is obtained by the steepest descent method and/or conjugate gradient method. Numerical examples are given to show the optimal controls.

Detection of buried magnetic objects by a SQUID gradiometer system

NASA Astrophysics Data System (ADS)

Meyer, Hans-Georg; Hartung, Konrad; Linzen, Sven; Schneider, Michael; Stolz, Ronny; Fried, Wolfgang; Hauspurg, Sebastian

2009-05-01

We present a magnetic detection system based on superconducting gradiometric sensors (SQUID gradiometers). The system provides a unique fast mapping of large areas with a high resolution of the magnetic field gradient as well as the local position. A main part of this work is the localization and classification of magnetic objects in the ground by automatic interpretation of geomagnetic field gradients, measured by the SQUID system. In accordance with specific features the field is decomposed into segments, which allow inferences to possible objects in the ground. The global consideration of object describing properties and their optimization using error minimization methods allows the reconstruction of superimposed features and detection of buried objects. The analysis system of measured geomagnetic fields works fully automatically. By a given surface of area-measured gradients the algorithm determines within numerical limits the absolute position of objects including depth with sub-pixel accuracy and allows an arbitrary position and attitude of sources. Several SQUID gradiometer data sets were used to show the applicability of the analysis algorithm.

Optimization of ceramic strength using elastic gradients

PubMed Central

Zhang, Yu; Ma, Li

2009-01-01

We present a new concept for strengthening ceamics by utilizing a graded structure with a low elastic modulus at both top and bottom surfaces sandwiching a high-modulus interior. Closed-form equations have been developed for stress analysis of simply supported graded sandwich beams subject to transverse center loads. Theory predicts that suitable modulus gradients at the ceramic surface can effectively reduce and spread the maximum bending stress from the surface into the interior. The magnitude of such stress dissipation is governed by the thickness ratio of the beam to the graded layers. We test our concept by infiltrating both top and bottom surfaces of a strong class of zirconia ceramic with an in-house prepared glass of similar coefficient of thermal expansion and Poisson’s ratio to zirconia, producing a controlled modulus gradient at the surface without significant long-range residual stresses. The resultant graded glass/zirconia/glass composite exhibits significantly higher load-bearing capacity than homogeneous zirconia. PMID:20161019

Airfoil optimization for unsteady flows with application to high-lift noise reduction

NASA Astrophysics Data System (ADS)

Rumpfkeil, Markus Peer

The use of steady-state aerodynamic optimization methods in the computational fluid dynamic (CFD) community is fairly well established. In particular, the use of adjoint methods has proven to be very beneficial because their cost is independent of the number of design variables. The application of numerical optimization to airframe-generated noise, however, has not received as much attention, but with the significant quieting of modern engines, airframe noise now competes with engine noise. Optimal control techniques for unsteady flows are needed in order to be able to reduce airframe-generated noise. In this thesis, a general framework is formulated to calculate the gradient of a cost function in a nonlinear unsteady flow environment via the discrete adjoint method. The unsteady optimization algorithm developed in this work utilizes a Newton-Krylov approach since the gradient-based optimizer uses the quasi-Newton method BFGS, Newton's method is applied to the nonlinear flow problem, GMRES is used to solve the resulting linear problem inexactly, and last but not least the linear adjoint problem is solved using Bi-CGSTAB. The flow is governed by the unsteady two-dimensional compressible Navier-Stokes equations in conjunction with a one-equation turbulence model, which are discretized using structured grids and a finite difference approach. The effectiveness of the unsteady optimization algorithm is demonstrated by applying it to several problems of interest including shocktubes, pulses in converging-diverging nozzles, rotating cylinders, transonic buffeting, and an unsteady trailing-edge flow. In order to address radiated far-field noise, an acoustic wave propagation program based on the Ffowcs Williams and Hawkings (FW-H) formulation is implemented and validated. The general framework is then used to derive the adjoint equations for a novel hybrid URANS/FW-H optimization algorithm in order to be able to optimize the shape of airfoils based on their calculated far-field pressure fluctuations. Validation and application results for this novel hybrid URANS/FW-H optimization algorithm show that it is possible to optimize the shape of an airfoil in an unsteady flow environment to minimize its radiated far-field noise while maintaining good aerodynamic performance.

Adjoint Algorithm for CAD-Based Shape Optimization Using a Cartesian Method

NASA Technical Reports Server (NTRS)

Nemec, Marian; Aftosmis, Michael J.

2004-01-01

Adjoint solutions of the governing flow equations are becoming increasingly important for the development of efficient analysis and optimization algorithms. A well-known use of the adjoint method is gradient-based shape optimization. Given an objective function that defines some measure of performance, such as the lift and drag functionals, its gradient is computed at a cost that is essentially independent of the number of design variables (geometric parameters that control the shape). More recently, emerging adjoint applications focus on the analysis problem, where the adjoint solution is used to drive mesh adaptation, as well as to provide estimates of functional error bounds and corrections. The attractive feature of this approach is that the mesh-adaptation procedure targets a specific functional, thereby localizing the mesh refinement and reducing computational cost. Our focus is on the development of adjoint-based optimization techniques for a Cartesian method with embedded boundaries.12 In contrast t o implementations on structured and unstructured grids, Cartesian methods decouple the surface discretization from the volume mesh. This feature makes Cartesian methods well suited for the automated analysis of complex geometry problems, and consequently a promising approach to aerodynamic optimization. Melvin et developed an adjoint formulation for the TRANAIR code, which is based on the full-potential equation with viscous corrections. More recently, Dadone and Grossman presented an adjoint formulation for the Euler equations. In both approaches, a boundary condition is introduced to approximate the effects of the evolving surface shape that results in accurate gradient computation. Central to automated shape optimization algorithms is the issue of geometry modeling and control. The need to optimize complex, "real-life" geometry provides a strong incentive for the use of parametric-CAD systems within the optimization procedure. In previous work, we presented an effective optimization framework that incorporates a direct-CAD interface. In this work, we enhance the capabilities of this framework with efficient gradient computations using the discrete adjoint method. We present details of the adjoint numerical implementation, which reuses the domain decomposition, multigrid, and time-marching schemes of the flow solver. Furthermore, we explain and demonstrate the use of CAD in conjunction with the Cartesian adjoint approach. The final paper will contain a number of complex geometry, industrially relevant examples with many design variables to demonstrate the effectiveness of the adjoint method on Cartesian meshes.

Fast exploration of an optimal path on the multidimensional free energy surface

PubMed Central

Chen, Changjun

2017-01-01

In a reaction, determination of an optimal path with a high reaction rate (or a low free energy barrier) is important for the study of the reaction mechanism. This is a complicated problem that involves lots of degrees of freedom. For simple models, one can build an initial path in the collective variable space by the interpolation method first and then update the whole path constantly in the optimization. However, such interpolation method could be risky in the high dimensional space for large molecules. On the path, steric clashes between neighboring atoms could cause extremely high energy barriers and thus fail the optimization. Moreover, performing simulations for all the snapshots on the path is also time-consuming. In this paper, we build and optimize the path by a growing method on the free energy surface. The method grows a path from the reactant and extends its length in the collective variable space step by step. The growing direction is determined by both the free energy gradient at the end of the path and the direction vector pointing at the product. With fewer snapshots on the path, this strategy can let the path avoid the high energy states in the growing process and save the precious simulation time at each iteration step. Applications show that the presented method is efficient enough to produce optimal paths on either the two-dimensional or the twelve-dimensional free energy surfaces of different small molecules. PMID:28542475

Spatio-Temporal Analyses of Symbiodinium Physiology of the Coral Pocillopora verrucosa along Large-Scale Nutrient and Temperature Gradients in the Red Sea

PubMed Central

Sawall, Yvonne; Al-Sofyani, Abdulmohsin; Banguera-Hinestroza, Eulalia; Voolstra, Christian R.

2014-01-01

Algal symbionts (zooxanthellae, genus Symbiodinium) of scleractinian corals respond strongly to temperature, nutrient and light changes. These factors vary greatly along the north-south gradient in the Red Sea and include conditions, which are outside of those typically considered optimal for coral growth. Nevertheless, coral communities thrive throughout the Red Sea, suggesting that zooxanthellae have successfully acclimatized or adapted to the harsh conditions they experience particularly in the south (high temperatures and high nutrient supply). As such, the Red Sea is a region, which may help to better understand how zooxanthellae and their coral hosts successfully acclimatize or adapt to environmental change (e.g. increased temperatures and localized eutrophication). To gain further insight into the physiology of coral symbionts in the Red Sea, we examined the abundance of dominant Symbiodinium types associated with the coral Pocillopora verrucosa, and measured Symbiodinium physiological characteristics (i.e. photosynthetic processes, cell density, pigmentation, and protein composition) along the latitudinal gradient of the Red Sea in summer and winter. Despite the strong environmental gradients from north to south, our results demonstrate that Symbiodinium microadriaticum (type A1) was the predominant species in P. verrucosa along the latitudinal gradient. Furthermore, measured physiological characteristics were found to vary more with prevailing seasonal environmental conditions than with region-specific differences, although the measured environmental parameters displayed much higher spatial than temporal variability. We conclude that our findings might present the result of long-term acclimatization or adaptation of S. microadriaticum to regionally specific conditions within the Red Sea. Of additional note, high nutrients in the South correlated with high zooxanthellae density indicating a compensation for a temperature-driven loss of photosynthetic performance, which may prove promising for the resilience of these corals under increase of temperature increase and eutrophication. PMID:25137123

Spatio-temporal analyses of Symbiodinium physiology of the coral Pocillopora verrucosa along large-scale nutrient and temperature gradients in the Red Sea.

PubMed

Sawall, Yvonne; Al-Sofyani, Abdulmohsin; Banguera-Hinestroza, Eulalia; Voolstra, Christian R

2014-01-01

Algal symbionts (zooxanthellae, genus Symbiodinium) of scleractinian corals respond strongly to temperature, nutrient and light changes. These factors vary greatly along the north-south gradient in the Red Sea and include conditions, which are outside of those typically considered optimal for coral growth. Nevertheless, coral communities thrive throughout the Red Sea, suggesting that zooxanthellae have successfully acclimatized or adapted to the harsh conditions they experience particularly in the south (high temperatures and high nutrient supply). As such, the Red Sea is a region, which may help to better understand how zooxanthellae and their coral hosts successfully acclimatize or adapt to environmental change (e.g. increased temperatures and localized eutrophication). To gain further insight into the physiology of coral symbionts in the Red Sea, we examined the abundance of dominant Symbiodinium types associated with the coral Pocillopora verrucosa, and measured Symbiodinium physiological characteristics (i.e. photosynthetic processes, cell density, pigmentation, and protein composition) along the latitudinal gradient of the Red Sea in summer and winter. Despite the strong environmental gradients from north to south, our results demonstrate that Symbiodinium microadriaticum (type A1) was the predominant species in P. verrucosa along the latitudinal gradient. Furthermore, measured physiological characteristics were found to vary more with prevailing seasonal environmental conditions than with region-specific differences, although the measured environmental parameters displayed much higher spatial than temporal variability. We conclude that our findings might present the result of long-term acclimatization or adaptation of S. microadriaticum to regionally specific conditions within the Red Sea. Of additional note, high nutrients in the South correlated with high zooxanthellae density indicating a compensation for a temperature-driven loss of photosynthetic performance, which may prove promising for the resilience of these corals under increase of temperature increase and eutrophication.

Optimal control of switching time in switched stochastic systems with multi-switching times and different costs

NASA Astrophysics Data System (ADS)

Liu, Xiaomei; Li, Shengtao; Zhang, Kanjian

2017-08-01

In this paper, we solve an optimal control problem for a class of time-invariant switched stochastic systems with multi-switching times, where the objective is to minimise a cost functional with different costs defined on the states. In particular, we focus on problems in which a pre-specified sequence of active subsystems is given and the switching times are the only control variables. Based on the calculus of variation, we derive the gradient of the cost functional with respect to the switching times on an especially simple form, which can be directly used in gradient descent algorithms to locate the optimal switching instants. Finally, a numerical example is given, highlighting the validity of the proposed methodology.

Robust Optimal Adaptive Control Method with Large Adaptive Gain

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2009-01-01

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.

Efficient Geometry Minimization and Transition Structure Optimization Using Interpolated Potential Energy Surfaces and Iteratively Updated Hessians.

PubMed

Zheng, Jingjing; Frisch, Michael J

2017-12-12

An efficient geometry optimization algorithm based on interpolated potential energy surfaces with iteratively updated Hessians is presented in this work. At each step of geometry optimization (including both minimization and transition structure search), an interpolated potential energy surface is properly constructed by using the previously calculated information (energies, gradients, and Hessians/updated Hessians), and Hessians of the two latest geometries are updated in an iterative manner. The optimized minimum or transition structure on the interpolated surface is used for the starting geometry of the next geometry optimization step. The cost of searching the minimum or transition structure on the interpolated surface and iteratively updating Hessians is usually negligible compared with most electronic structure single gradient calculations. These interpolated potential energy surfaces are often better representations of the true potential energy surface in a broader range than a local quadratic approximation that is usually used in most geometry optimization algorithms. Tests on a series of large and floppy molecules and transition structures both in gas phase and in solutions show that the new algorithm can significantly improve the optimization efficiency by using the iteratively updated Hessians and optimizations on interpolated surfaces.

Simultaneous multislice refocusing via time optimal control.

PubMed

Rund, Armin; Aigner, Christoph Stefan; Kunisch, Karl; Stollberger, Rudolf

2018-02-09

Joint design of minimum duration RF pulses and slice-selective gradient shapes for MRI via time optimal control with strict physical constraints, and its application to simultaneous multislice imaging. The minimization of the pulse duration is cast as a time optimal control problem with inequality constraints describing the refocusing quality and physical constraints. It is solved with a bilevel method, where the pulse length is minimized in the upper level, and the constraints are satisfied in the lower level. To address the inherent nonconvexity of the optimization problem, the upper level is enhanced with new heuristics for finding a near global optimizer based on a second optimization problem. A large set of optimized examples shows an average temporal reduction of 87.1% for double diffusion and 74% for turbo spin echo pulses compared to power independent number of slices pulses. The optimized results are validated on a 3T scanner with phantom measurements. The presented design method computes minimum duration RF pulse and slice-selective gradient shapes subject to physical constraints. The shorter pulse duration can be used to decrease the effective echo time in existing echo-planar imaging or echo spacing in turbo spin echo sequences. © 2018 International Society for Magnetic Resonance in Medicine.

A penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography.

PubMed

Shang, Shang; Bai, Jing; Song, Xiaolei; Wang, Hongkai; Lau, Jaclyn

2007-01-01

Conjugate gradient method is verified to be efficient for nonlinear optimization problems of large-dimension data. In this paper, a penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography (FMT) is presented. The algorithm combines the linear conjugate gradient method and the nonlinear conjugate gradient method together based on a restart strategy, in order to take advantage of the two kinds of conjugate gradient methods and compensate for the disadvantages. A quadratic penalty method is adopted to gain a nonnegative constraint and reduce the illposedness of the problem. Simulation studies show that the presented algorithm is accurate, stable, and fast. It has a better performance than the conventional conjugate gradient-based reconstruction algorithms. It offers an effective approach to reconstruct fluorochrome information for FMT.

ASTRID© - Advanced Solar Tubular ReceIver Design: A powerful tool for receiver design and optimization

NASA Astrophysics Data System (ADS)

Frantz, Cathy; Fritsch, Andreas; Uhlig, Ralf

2017-06-01

In solar tower power plants the receiver is one of the critical components. It converts the solar radiation into heat and must withstand high heat flux densities and high daily or even hourly gradients (due to passage of clouds). For this reason, the challenge during receiver design is to find a reasonable compromise between receiver efficiency, reliability, lifetime and cost. There is a strong interaction between the heliostat field, the receiver and the heat transfer fluid. Therefore, a proper receiver design needs to consider these components within the receiver optimization. There are several design and optimization tools for receivers, but most of them focus only on the receiver, ignoring the heliostat field and other parts of the plant. During the last years DLR developed the ASTRIDcode for tubular receiver concept simulation. The code comprises both a high and a low-detail model. The low-detail model utilizes a number of simplifications which allow the user to screen a high number of receiver concepts for optimization purposes. The high-detail model uses a FE model and is able to compute local absorber and salt temperatures with high accuracy. One key strength of the ASTRIDcode is its interface to a ray tracing software which simulates a realistic heat flux distributions on the receiver surface. The results generated by the ASTRIDcode have been validated by CFD simulations and measurement data.

Manipulating the Gradient

ERIC Educational Resources Information Center

Gaze, Eric C.

2005-01-01

We introduce a cooperative learning, group lab for a Calculus III course to facilitate comprehension of the gradient vector and directional derivative concepts. The lab is a hands-on experience allowing students to manipulate a tangent plane and empirically measure the effect of partial derivatives on the direction of optimal ascent. (Contains 7…

Multi-objective optimization of piezoelectric circuitry network for mode delocalization and suppression of bladed disk

NASA Astrophysics Data System (ADS)

Yoo, David; Tang, J.

2017-04-01

Since weakly-coupled bladed disks are highly sensitive to the presence of uncertainties, they can easily undergo vibration localization. When vibration localization occurs, vibration modes of bladed disk become dramatically different from those under the perfectly periodic condition, and the dynamic response under engine-order excitation is drastically amplified. In previous studies, it is investigated that amplified vibration response can be suppressed by connecting piezoelectric circuitry into individual blades to induce the damped absorber effect, and localized vibration modes can be alleviated by integrating piezoelectric circuitry network. Delocalization of vibration modes and vibration suppression of bladed disk, however, require different optimal set of circuit parameters. In this research, multi-objective optimization approach is developed to enable finding the best circuit parameters, simultaneously achieving both objectives. In this way, the robustness and reliability in bladed disk can be ensured. Gradient-based optimizations are individually developed for mode delocalization and vibration suppression, which are then integrated into multi-objective optimization framework.

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, Brian M.; Ebeida, Mohamed Salah; Eldred, Michael S.

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components requiredmore » for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.« less

Optimization of OT-MACH Filter Generation for Target Recognition

NASA Technical Reports Server (NTRS)

Johnson, Oliver C.; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

2009-01-01

An automatic Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter generator for use in a gray-scale optical correlator (GOC) has been developed for improved target detection at JPL. While the OT-MACH filter has been shown to be an optimal filter for target detection, actually solving for the optimum is too computationally intensive for multiple targets. Instead, an adaptive step gradient descent method was tested to iteratively optimize the three OT-MACH parameters, alpha, beta, and gamma. The feedback for the gradient descent method was a composite of the performance measures, correlation peak height and peak to side lobe ratio. The automated method generated and tested multiple filters in order to approach the optimal filter quicker and more reliably than the current manual method. Initial usage and testing has shown preliminary success at finding an approximation of the optimal filter, in terms of alpha, beta, gamma values. This corresponded to a substantial improvement in detection performance where the true positive rate increased for the same average false positives per image.

A mathematical model for the generation and control of a pH gradient in an immobilized enzyme system involving acid generation.

PubMed

Chen, G; Fournier, R L; Varanasi, S

1998-02-20

An optimal pH control technique has been developed for multistep enzymatic synthesis reactions where the optimal pH differs by several units for each step. This technique separates an acidic environment from a basic environment by the hydrolysis of urea within a thin layer of immobilized urease. With this technique, a two-step enzymatic reaction can take place simultaneously, in proximity to each other, and at their respective optimal pH. Because a reaction system involving an acid generation represents a more challenging test of this pH control technique, a number of factors that affect the generation of such a pH gradient are considered in this study. The mathematical model proposed is based on several simplifying assumptions and represents a first attempt to provide an analysis of this complex problem. The results show that, by choosing appropriate parameters, the pH control technique still can generate the desired pH gradient even if there is an acid-generating reaction in the system. Copyright 1998 John Wiley & Sons, Inc.

An adjoint method for gradient-based optimization of stellarator coil shapes

NASA Astrophysics Data System (ADS)

Paul, E. J.; Landreman, M.; Bader, A.; Dorland, W.

2018-07-01

We present a method for stellarator coil design via gradient-based optimization of the coil-winding surface. The REGCOIL (Landreman 2017 Nucl. Fusion 57 046003) approach is used to obtain the coil shapes on the winding surface using a continuous current potential. We apply the adjoint method to calculate derivatives of the objective function, allowing for efficient computation of analytic gradients while eliminating the numerical noise of approximate derivatives. We are able to improve engineering properties of the coils by targeting the root-mean-squared current density in the objective function. We obtain winding surfaces for W7-X and HSX which simultaneously decrease the normal magnetic field on the plasma surface and increase the surface-averaged distance between the coils and the plasma in comparison with the actual winding surfaces. The coils computed on the optimized surfaces feature a smaller toroidal extent and curvature and increased inter-coil spacing. A technique for computation of the local sensitivity of figures of merit to normal displacements of the winding surface is presented, with potential applications for understanding engineering tolerances.

Quiet echo planar imaging for functional and diffusion MRI

PubMed Central

Price, Anthony N.; Cordero‐Grande, Lucilio; Malik, Shaihan; Ferrazzi, Giulio; Gaspar, Andreia; Hughes, Emer J.; Christiaens, Daan; McCabe, Laura; Schneider, Torben; Rutherford, Mary A.; Hajnal, Joseph V.

2017-01-01

Purpose To develop a purpose‐built quiet echo planar imaging capability for fetal functional and diffusion scans, for which acoustic considerations often compromise efficiency and resolution as well as angular/temporal coverage. Methods The gradient waveforms in multiband‐accelerated single‐shot echo planar imaging sequences have been redesigned to minimize spectral content. This includes a sinusoidal read‐out with a single fundamental frequency, a constant phase encoding gradient, overlapping smoothed CAIPIRINHA blips, and a novel strategy to merge the crushers in diffusion MRI. These changes are then tuned in conjunction with the gradient system frequency response function. Results Maintained image quality, SNR, and quantitative diffusion values while reducing acoustic noise up to 12 dB (A) is illustrated in two adult experiments. Fetal experiments in 10 subjects covering a range of parameters depict the adaptability and increased efficiency of quiet echo planar imaging. Conclusion Purpose‐built for highly efficient multiband fetal echo planar imaging studies, the presented framework reduces acoustic noise for all echo planar imaging‐based sequences. Full optimization by tuning to the gradient frequency response functions allows for a maximally time‐efficient scan within safe limits. This allows ambitious in‐utero studies such as functional brain imaging with high spatial/temporal resolution and diffusion scans with high angular/spatial resolution to be run in a highly efficient manner at acceptable sound levels. Magn Reson Med 79:1447–1459, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMID:28653363

Complexity in congestive heart failure: A time-frequency approach

NASA Astrophysics Data System (ADS)

Banerjee, Santo; Palit, Sanjay K.; Mukherjee, Sayan; Ariffin, MRK; Rondoni, Lamberto

2016-03-01

Reconstruction of phase space is an effective method to quantify the dynamics of a signal or a time series. Various phase space reconstruction techniques have been investigated. However, there are some issues on the optimal reconstructions and the best possible choice of the reconstruction parameters. This research introduces the idea of gradient cross recurrence (GCR) and mean gradient cross recurrence density which shows that reconstructions in time frequency domain preserve more information about the dynamics than the optimal reconstructions in time domain. This analysis is further extended to ECG signals of normal and congestive heart failure patients. By using another newly introduced measure—gradient cross recurrence period density entropy, two classes of aforesaid ECG signals can be classified with a proper threshold. This analysis can be applied to quantifying and distinguishing biomedical and other nonlinear signals.

Design of LED projector based on gradient-index lens

NASA Astrophysics Data System (ADS)

Qian, Liyong; Zhu, Xiangbing; Cui, Haitian; Wang, Yuanhang

2018-01-01

In this study, a new type of projector light path is designed to eliminate the deficits of existing projection systems, such as complex structure and low collection efficiency. Using a three-color LED array as the lighting source, by means of the special optical properties of a gradient-index lens, the complex structure of the traditional projector is simplified. Traditional components, such as the color wheel, relay lens, and mirror, become unnecessary. In this way, traditional problems, such as low utilization of light energy and loss of light energy, are solved. With the help of Zemax software, the projection lens is optimized. The optimized projection lens, LED, gradient-index lens, and digital micromirror device are imported into Tracepro. The ray tracing results show that both the utilization of light energy and the uniformity are improved significantly.

Structural optimization of framed structures using generalized optimality criteria

NASA Technical Reports Server (NTRS)

Kolonay, R. M.; Venkayya, Vipperla B.; Tischler, V. A.; Canfield, R. A.

1989-01-01

The application of a generalized optimality criteria to framed structures is presented. The optimality conditions, Lagrangian multipliers, resizing algorithm, and scaling procedures are all represented as a function of the objective and constraint functions along with their respective gradients. The optimization of two plane frames under multiple loading conditions subject to stress, displacement, generalized stiffness, and side constraints is presented. These results are compared to those found by optimizing the frames using a nonlinear mathematical programming technique.

Directed transport by surface chemical potential gradients for enhancing analyte collection in nanoscale sensors.

PubMed

Sitt, Amit; Hess, Henry

2015-05-13

Nanoscale detectors hold great promise for single molecule detection and the analysis of small volumes of dilute samples. However, the probability of an analyte reaching the nanosensor in a dilute solution is extremely low due to the sensor's small size. Here, we examine the use of a chemical potential gradient along a surface to accelerate analyte capture by nanoscale sensors. Utilizing a simple model for transport induced by surface binding energy gradients, we study the effect of the gradient on the efficiency of collecting nanoparticles and single and double stranded DNA. The results indicate that chemical potential gradients along a surface can lead to an acceleration of analyte capture by several orders of magnitude compared to direct collection from the solution. The improvement in collection is limited to a relatively narrow window of gradient slopes, and its extent strongly depends on the size of the gradient patch. Our model allows the optimization of gradient layouts and sheds light on the fundamental characteristics of chemical potential gradient induced transport.

Tailoring magnetic field gradient design to magnet cryostat geometry.

PubMed

Trakic, A; Liu, F; Lopez, H S; Wang, H; Crozier, S

2006-01-01

Eddy currents induced within a magnetic resonance imaging (MRI) cryostat bore during pulsing of gradient coils can be applied constructively together with the gradient currents that generate them, to obtain good quality gradient uniformities within a specified imaging volume over time. This can be achieved by simultaneously optimizing the spatial distribution and temporal pre-emphasis of the gradient coil current, to account for the spatial and temporal variation of the secondary magnetic fields due to the induced eddy currents. This method allows the tailored design of gradient coil/magnet configurations and consequent engineering trade-offs. To compute the transient eddy currents within a realistic cryostat vessel, a low-frequency finite-difference time-domain (FDTD) method using total-field scattered-field (TFSF) scheme has been performed and validated.

Construction of bionic tissue engineering cartilage scaffold based on three-dimensional printing and oriented frozen technology.

PubMed

Xu, Yuanyuan; Guo, Xiao; Yang, Shuaitao; Li, Long; Zhang, Peng; Sun, Wei; Liu, Changyong; Mi, Shengli

2018-06-01

Articular cartilage (AC) has gradient features in both mechanics and histology as well as a poor regeneration ability. The repair of AC poses difficulties in both research and the clinic. In this paper, a gradient scaffold based on poly(lactic-co-glycolic acid) (PLGA)-extracellular matrix was proposed. Cartilage scaffolds with a three-layer gradient structure were fabricated by PLGA through three-dimensional printing, and the microstructure orientation and pore fabrication were made by decellularized extracellular matrix injection and directional freezing. The manufactured scaffold has a mechanical strength close to that of real cartilage. A quantitative optimization of the Young's modulus and shear modulus was achieved by material mechanics formulas, which achieved a more accurate mechanical bionic and a more stable interface performance because of the one-time molding process. At the same time, the scaffolds have a bionic and gradient microstructure orientation and pore size, and the stratification ratio can be quantitatively optimized by design of the freeze box and temperature simulation. In general, this paper provides a method to optimize AC scaffolds by both mechanics and histology as well as a bionic multimaterial scaffold. This paper is of significance for cell culture and clinical transplantation experiments. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 1664-1676, 2018. © 2018 Wiley Periodicals, Inc.

Conjugate-gradient optimization method for orbital-free density functional calculations.

PubMed

Jiang, Hong; Yang, Weitao

2004-08-01

Orbital-free density functional theory as an extension of traditional Thomas-Fermi theory has attracted a lot of interest in the past decade because of developments in both more accurate kinetic energy functionals and highly efficient numerical methodology. In this paper, we developed a conjugate-gradient method for the numerical solution of spin-dependent extended Thomas-Fermi equation by incorporating techniques previously used in Kohn-Sham calculations. The key ingredient of the method is an approximate line-search scheme and a collective treatment of two spin densities in the case of spin-dependent extended Thomas-Fermi problem. Test calculations for a quartic two-dimensional quantum dot system and a three-dimensional sodium cluster Na216 with a local pseudopotential demonstrate that the method is accurate and efficient. (c) 2004 American Institute of Physics.

Optimal climate for large trees at high elevations drives patterns of biomass in remote forests of Papua New Guinea.

PubMed

Venter, Michelle; Dwyer, John; Dieleman, Wouter; Ramachandra, Anurag; Gillieson, David; Laurance, Susan; Cernusak, Lucas A; Beehler, Bruce; Jensen, Rigel; Bird, Michael I

2017-11-01

Our ability to model global carbon fluxes depends on understanding how terrestrial carbon stocks respond to varying environmental conditions. Tropical forests contain the bulk of the biosphere's carbon. However, there is a lack of consensus as to how gradients in environmental conditions affect tropical forest carbon. Papua New Guinea (PNG) lies within one of the largest areas of contiguous tropical forest and is characterized by environmental gradients driven by altitude; yet, the region has been grossly understudied. Here, we present the first field assessment of aboveground biomass (AGB) across three main forest types of PNG using 193 plots stratified across 3,100-m elevation gradient. Unexpectedly, AGB had no direct relationship to rainfall, temperature, soil, or topography. Instead, natural disturbances explained most variation in AGB. While large trees (diameter at breast height > 50 cm) drove altitudinal patterns of AGB, resulting in a major peak in AGB (2,200-3,100 m) and some of the most carbon-rich forests at these altitudes anywhere. Large trees were correlated to a set of climatic variables following a hump-shaped curve. The set of "optimal" climatic conditions found in montane cloud forests is similar to that of maritime temperate areas that harbor the largest trees in the world: high ratio of precipitation to evapotranspiration (2.8), moderate mean annual temperature (13.7°C), and low intra-annual temperature range (7.5°C). At extreme altitudes (2,800-3,100 m), where tree diversity elsewhere is usually low and large trees are generally rare or absent, specimens from 18 families had girths >70 cm diameter and maximum heights 20-41 m. These findings indicate that simple AGB-climate-edaphic models may not be suitable for estimating carbon storage in forests where optimal climate niches exist. Our study, conducted in a very remote area, suggests that tropical montane forests may contain greater AGB than previously thought and the importance of securing their future under a changing climate is therefore enhanced. © 2017 John Wiley & Sons Ltd.

MILC Code Performance on High End CPU and GPU Supercomputer Clusters

NASA Astrophysics Data System (ADS)

DeTar, Carleton; Gottlieb, Steven; Li, Ruizi; Toussaint, Doug

2018-03-01

With recent developments in parallel supercomputing architecture, many core, multi-core, and GPU processors are now commonplace, resulting in more levels of parallelism, memory hierarchy, and programming complexity. It has been necessary to adapt the MILC code to these new processors starting with NVIDIA GPUs, and more recently, the Intel Xeon Phi processors. We report on our efforts to port and optimize our code for the Intel Knights Landing architecture. We consider performance of the MILC code with MPI and OpenMP, and optimizations with QOPQDP and QPhiX. For the latter approach, we concentrate on the staggered conjugate gradient and gauge force. We also consider performance on recent NVIDIA GPUs using the QUDA library.

DAKOTA Design Analysis Kit for Optimization and Terascale

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, Brian M.; Dalbey, Keith R.; Eldred, Michael S.

2010-02-24

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes (computational models) and iterative analysis methods. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and analysis of computational models on high performance computers.A user provides a set of DAKOTA commands in an input file and launches DAKOTA. DAKOTA invokes instances of the computational models, collects their results, and performs systems analyses. DAKOTA contains algorithms for optimization with gradient and nongradient-basedmore » methods; uncertainty quantification with sampling, reliability, polynomial chaos, stochastic collocation, and epistemic methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as hybrid optimization, surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. Services for parallel computing, simulation interfacing, approximation modeling, fault tolerance, restart, and graphics are also included.« less

Optimization and Quantization in Gradient Symbol Systems: A Framework for Integrating the Continuous and the Discrete in Cognition

ERIC Educational Resources Information Center

Smolensky, Paul; Goldrick, Matthew; Mathis, Donald

2014-01-01

Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The…

Primer vector theory and applications

NASA Technical Reports Server (NTRS)

Jezewski, D. J.

1975-01-01

A method developed to compute two-body, optimal, N-impulse trajectories was presented. The necessary conditions established define the gradient structure of the primer vector and its derivative for any set of boundary conditions and any number of impulses. Inequality constraints, a conjugate gradient iterator technique, and the use of a penalty function were also discussed.

Improved alternating gradient transport and focusing of neutral molecules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kalnins, Juris; Lambertson, Glen; Gould, Harvey

2001-12-02

Polar molecules, in strong-field seeking states, can be transported and focused by an alternating sequence of electric field gradients that focus in one transverse direction while defocusing in the other. We show by calculation and numerical simulation, how one may greatly improve the alternating gradient transport and focusing of molecules. We use a new optimized multipole lens design, a FODO lattice beam transport line, and lenses to match the beam transport line to the beam source and the final focus. We derive analytic expressions for the potentials, fields, and gradients that may be used to design these lenses. We describemore » a simple lens optimization procedure and derive the equations of motion for tracking molecules through a beam transport line. As an example, we model a straight beamline that transports a 560 m/s jet-source beam of methyl fluoride molecules 15 m from its source and focuses it to 2 mm diameter. We calculate the beam transport line acceptance and transmission, for a beam with velocity spread, and estimate the transmitted intensity for specified source conditions. Possible applications are discussed.« less

Proteomic profiling of an undefined microbial consortium cultured in fermented dairy manure: Methods development.

PubMed

Hanson, Andrea J; Paszczynski, Andrzej J; Coats, Erik R

2016-03-01

The production of polyhydroxyalkanoates (PHA; bioplastics) from waste or surplus feedstocks using mixed microbial consortia (MMC) and aerobic dynamic feeding (ADF) is a growing field within mixed culture biotechnology. This study aimed to optimize a 2DE workflow to investigate the proteome dynamics of an MMC synthesizing PHA from fermented dairy manure. To mitigate the challenges posed to effective 2DE by this complex sample matrix, the bacterial biomass was purified using Accudenz gradient centrifugation (AGC) before protein extraction. The optimized 2DE method yielded high-quality gels suitable for quantitative comparative analysis and subsequent protein identification by LC-MS/MS. The optimized 2DE method could be adapted to other proteomic investigations involving MMC in complex organic or environmental matrices. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Transfer-printing of active layers to achieve high quality interfaces in sequentially deposited multilayer inverted polymer solar cells fabricated in air

PubMed Central

Vohra, Varun; Anzai, Takuya; Inaba, Shusei; Porzio, William; Barba, Luisa

2016-01-01

Abstract Polymer solar cells (PSCs) are greatly influenced by both the vertical concentration gradient in the active layer and the quality of the various interfaces. To achieve vertical concentration gradients in inverted PSCs, a sequential deposition approach is necessary. However, a direct approach to sequential deposition by spin-coating results in partial dissolution of the underlying layers which decreases the control over the process and results in not well-defined interfaces. Here, we demonstrate that by using a transfer-printing process based on polydimethylsiloxane (PDMS) stamps we can obtain increased control over the thickness of the various layers while at the same time increasing the quality of the interfaces and the overall concentration gradient within the active layer of PSCs prepared in air. To optimize the process and understand the influence of various interlayers, our approach is based on surface free energy, spreading parameters and work of adhesion calculations. The key parameter presented here is the insertion of high quality hole transporting and electron transporting layers, respectively above and underneath the active layer of the inverted structure PSC which not only facilitates the transfer process but also induces the adequate vertical concentration gradient in the device to facilitate charge extraction. The resulting non-encapsulated devices (active layer prepared in air) demonstrate over 40% increase in power conversion efficiency with respect to the reference spin-coated inverted PSCs. PMID:27877901

Modelling Schumann resonances from ELF measurements using non-linear optimization methods

NASA Astrophysics Data System (ADS)

Castro, Francisco; Toledo-Redondo, Sergio; Fornieles, Jesús; Salinas, Alfonso; Portí, Jorge; Navarro, Enrique; Sierra, Pablo

2017-04-01

Schumann resonances (SR) can be found in planetary atmospheres, inside the cavity formed by the conducting surface of the planet and the lower ionosphere. They are a powerful tool to investigate both the electric processes that occur in the atmosphere and the characteristics of the surface and the lower ionosphere. In this study, the measurements are obtained in the ELF (Extremely Low Frequency) Juan Antonio Morente station located in the national park of Sierra Nevada. The three first modes, contained in the frequency band between 6 to 25 Hz, will be considered. For each time series recorded by the station, the amplitude spectrum was estimated by using Bartlett averaging. Then, the central frequencies and amplitudes of the SRs were obtained by fitting the spectrum with non-linear functions. In the poster, a study of nonlinear unconstrained optimization methods applied to the estimation of the Schumann Resonances will be presented. Non-linear fit, also known as optimization process, is the procedure followed in obtaining Schumann Resonances from the natural electromagnetic noise. The optimization methods that have been analysed are: Levenberg-Marquardt, Conjugate Gradient, Gradient, Newton and Quasi-Newton. The functions that the different methods fit to data are three lorentzian curves plus a straight line. Gaussian curves have also been considered. The conclusions of this study are outlined in the following paragraphs: i) Natural electromagnetic noise is better fitted using Lorentzian functions; ii) the measurement bandwidth can accelerate the convergence of the optimization method; iii) Gradient method has less convergence and has a highest mean squared error (MSE) between measurement and the fitted function, whereas Levenberg-Marquad, Gradient conjugate method and Cuasi-Newton method give similar results (Newton method presents higher MSE); v) There are differences in the MSE between the parameters that define the fit function, and an interval from 1% to 5% has been found.

Noise-shaping gradient descent-based online adaptation algorithms for digital calibration of analog circuits.

PubMed

Chakrabartty, Shantanu; Shaga, Ravi K; Aono, Kenji

2013-04-01

Analog circuits that are calibrated using digital-to-analog converters (DACs) use a digital signal processor-based algorithm for real-time adaptation and programming of system parameters. In this paper, we first show that this conventional framework for adaptation yields suboptimal calibration properties because of artifacts introduced by quantization noise. We then propose a novel online stochastic optimization algorithm called noise-shaping or ΣΔ gradient descent, which can shape the quantization noise out of the frequency regions spanning the parameter adaptation trajectories. As a result, the proposed algorithms demonstrate superior parameter search properties compared to floating-point gradient methods and better convergence properties than conventional quantized gradient-methods. In the second part of this paper, we apply the ΣΔ gradient descent algorithm to two examples of real-time digital calibration: 1) balancing and tracking of bias currents, and 2) frequency calibration of a band-pass Gm-C biquad filter biased in weak inversion. For each of these examples, the circuits have been prototyped in a 0.5-μm complementary metal-oxide-semiconductor process, and we demonstrate that the proposed algorithm is able to find the optimal solution even in the presence of spurious local minima, which are introduced by the nonlinear and non-monotonic response of calibration DACs.

Optimizing the sequence of diameter distributions and selection harvests for uneven-aged stand management

Treesearch

Robert G. Haight; J. Douglas Brodie; Darius M. Adams

1985-01-01

The determination of an optimal sequence of diameter distributions and selection harvests for uneven-aged stand management is formulated as a discrete-time optimal-control problem with bounded control variables and free-terminal point. An efficient programming technique utilizing gradients provides solutions that are stable and interpretable on the basis of economic...

Virus purification by CsCl density gradient using general centrifugation.

PubMed

Nasukawa, Tadahiro; Uchiyama, Jumpei; Taharaguchi, Satoshi; Ota, Sumire; Ujihara, Takako; Matsuzaki, Shigenobu; Murakami, Hironobu; Mizukami, Keijirou; Sakaguchi, Masahiro

2017-11-01

Virus purification by cesium chloride (CsCl) density gradient, which generally requires an expensive ultracentrifuge, is an essential technique in virology. Here, we optimized virus purification by CsCl density gradient using general centrifugation (40,000 × g, 2 h, 4 °C), which showed almost the same purification ability as conventional CsCl density gradient ultracentrifugation (100,000 × g, 1 h, 4 °C) using phages S13' and φEF24C. Moreover, adenovirus strain JM1/1 was also successfully purified by this method. We suggest that general centrifugation can become a less costly alternative to ultracentrifugation for virus purification by CsCl densiy gradient and will thus encourage research in virology.

Oscillator strengths, first-order properties, and nuclear gradients for local ADC(2).

PubMed

Schütz, Martin

2015-06-07

We describe theory and implementation of oscillator strengths, orbital-relaxed first-order properties, and nuclear gradients for the local algebraic diagrammatic construction scheme through second order. The formalism is derived via time-dependent linear response theory based on a second-order unitary coupled cluster model. The implementation presented here is a modification of our previously developed algorithms for Laplace transform based local time-dependent coupled cluster linear response (CC2LR); the local approximations thus are state specific and adaptive. The symmetry of the Jacobian leads to considerable simplifications relative to the local CC2LR method; as a result, a gradient evaluation is about four times less expensive. Test calculations show that in geometry optimizations, usually very similar geometries are obtained as with the local CC2LR method (provided that a second-order method is applicable). As an exemplary application, we performed geometry optimizations on the low-lying singlet states of chlorophyllide a.

Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

NASA Technical Reports Server (NTRS)

Kopasakis, George

1997-01-01

Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

Recent developments of axial flow compressors under transonic flow conditions

NASA Astrophysics Data System (ADS)

Srinivas, G.; Raghunandana, K.; Satish Shenoy, B.

2017-05-01

The objective of this paper is to give a holistic view of the most advanced technology and procedures that are practiced in the field of turbomachinery design. Compressor flow solver is the turbulence model used in the CFD to solve viscous problems. The popular techniques like Jameson’s rotated difference scheme was used to solve potential flow equation in transonic condition for two dimensional aero foils and later three dimensional wings. The gradient base method is also a popular method especially for compressor blade shape optimization. Various other types of optimization techniques available are Evolutionary algorithms (EAs) and Response surface methodology (RSM). It is observed that in order to improve compressor flow solver and to get agreeable results careful attention need to be paid towards viscous relations, grid resolution, turbulent modeling and artificial viscosity, in CFD. The advanced techniques like Jameson’s rotated difference had most substantial impact on wing design and aero foil. For compressor blade shape optimization, Evolutionary algorithm is quite simple than gradient based technique because it can solve the parameters simultaneously by searching from multiple points in the given design space. Response surface methodology (RSM) is a method basically used to design empirical models of the response that were observed and to study systematically the experimental data. This methodology analyses the correct relationship between expected responses (output) and design variables (input). RSM solves the function systematically in a series of mathematical and statistical processes. For turbomachinery blade optimization recently RSM has been implemented successfully. The well-designed high performance axial flow compressors finds its application in any air-breathing jet engines.

Aerodynamic Shape Sensitivity Analysis and Design Optimization of Complex Configurations Using Unstructured Grids

NASA Technical Reports Server (NTRS)

Taylor, Arthur C., III; Newman, James C., III; Barnwell, Richard W.

1997-01-01

A three-dimensional unstructured grid approach to aerodynamic shape sensitivity analysis and design optimization has been developed and is extended to model geometrically complex configurations. The advantage of unstructured grids (when compared with a structured-grid approach) is their inherent ability to discretize irregularly shaped domains with greater efficiency and less effort. Hence, this approach is ideally suited for geometrically complex configurations of practical interest. In this work the nonlinear Euler equations are solved using an upwind, cell-centered, finite-volume scheme. The discrete, linearized systems which result from this scheme are solved iteratively by a preconditioned conjugate-gradient-like algorithm known as GMRES for the two-dimensional geometry and a Gauss-Seidel algorithm for the three-dimensional; similar procedures are used to solve the accompanying linear aerodynamic sensitivity equations in incremental iterative form. As shown, this particular form of the sensitivity equation makes large-scale gradient-based aerodynamic optimization possible by taking advantage of memory efficient methods to construct exact Jacobian matrix-vector products. Simple parameterization techniques are utilized for demonstrative purposes. Once the surface has been deformed, the unstructured grid is adapted by considering the mesh as a system of interconnected springs. Grid sensitivities are obtained by differentiating the surface parameterization and the grid adaptation algorithms with ADIFOR (which is an advanced automatic-differentiation software tool). To demonstrate the ability of this procedure to analyze and design complex configurations of practical interest, the sensitivity analysis and shape optimization has been performed for a two-dimensional high-lift multielement airfoil and for a three-dimensional Boeing 747-200 aircraft.

Optimal mixing rate in linear solvent strength gradient liquid chromatography. Balanced mixing program.

PubMed

Blumberg, Leonid M; Desmet, Gert

2016-12-09

The mixing rate (R ϕ ) is the temporal rate of increase in the solvent strength in gradient LC. The optimal R ϕ (R ϕ ,Opt ) is the one at which a required peak capacity of gradient LC analysis is obtained in the shortest time. The balanced mixing program is a one where, for better separation of early eluting solutes, the mixing ramp is preceded by a balanced isocratic hold of the duration depending on R ϕ . The improvement in the separation of the earlier eluites due to the balanced programming has been evaluated. The value of R ϕ ,Opt depends on the solvent composition range covered by the mixing ramp and on the column pressure conditions. The R ϕ ,Opt for a column operating at maximum instrumental pressure is different from R ϕ ,Opt for a column operating below the instrumental pressure limit. On the other hand, it has been shown that the difference in the R ϕ ,Opt values under different conditions is not very large so that a single default R ϕ previously recommended for gradient analyses without the isocratic hold also yields a good approximation to the shortest analysis time for all conditions in the balanced analyses. With or without the initial balance isocratic hold, the recommended default R ϕ is about 5%/t 0 (5% increase in the solvent strength per each t 0 -long increment in time) for small-molecule samples, and about an order of magnitude slower (0.5%/t 0 ) for protein samples. A discussion illustrating the use of the optimization criteria employed here for the techniques other than LSS gradient LC is included. Copyright © 2016 Elsevier B.V. All rights reserved.

The novel high-performance 3-D MT inverse solver

NASA Astrophysics Data System (ADS)

Kruglyakov, Mikhail; Geraskin, Alexey; Kuvshinov, Alexey

2016-04-01

We present novel, robust, scalable, and fast 3-D magnetotelluric (MT) inverse solver. The solver is written in multi-language paradigm to make it as efficient, readable and maintainable as possible. Separation of concerns and single responsibility concepts go through implementation of the solver. As a forward modelling engine a modern scalable solver extrEMe, based on contracting integral equation approach, is used. Iterative gradient-type (quasi-Newton) optimization scheme is invoked to search for (regularized) inverse problem solution, and adjoint source approach is used to calculate efficiently the gradient of the misfit. The inverse solver is able to deal with highly detailed and contrasting models, allows for working (separately or jointly) with any type of MT responses, and supports massive parallelization. Moreover, different parallelization strategies implemented in the code allow optimal usage of available computational resources for a given problem statement. To parameterize an inverse domain the so-called mask parameterization is implemented, which means that one can merge any subset of forward modelling cells in order to account for (usually) irregular distribution of observation sites. We report results of 3-D numerical experiments aimed at analysing the robustness, performance and scalability of the code. In particular, our computational experiments carried out at different platforms ranging from modern laptops to HPC Piz Daint (6th supercomputer in the world) demonstrate practically linear scalability of the code up to thousands of nodes.

Gel-based coloration technique for the submillimeter-scale imaging of labile phosphorus in sediments and soils with diffusive gradients in thin films.

PubMed

Ding, Shiming; Wang, Yan; Xu, Di; Zhu, Chungang; Zhang, Chaosheng

2013-07-16

We report a highly promising technique for the high-resolution imaging of labile phosphorus (P) in sediments and soils in combination with the diffusive gradients in thin films (DGT). This technique was based on the surface coloration of the Zr-oxide binding gel using the conventional molybdenum blue method following the DGT uptake of P to this gel. The accumulated mass of the P in the gel was then measured according to the grayscale intensity on the gel surface using computer-imaging densitometry. A pretreatment of the gel in hot water (85 °C) for 5 d was required to immobilize the phosphate and the formed blue complex in the gel during the color development. The optimal time required for a complete color development was determined to be 45 min. The appropriate volume of the coloring reagent added was 200 times of that of the gel. A calibration equation was established under the optimized conditions, based on which a quantitative measurement of P was obtained when the concentration of P in solutions ranged from 0.04 mg L(-1) to 4.1 mg L(-1) for a 24 h deployment of typical DGT devices at 25 °C. The suitability of the coloration technique was well demonstrated by the observation of small, discrete spots with elevated P concentrations in a sediment profile.

Distributed Optimization Design of Continuous-Time Multiagent Systems With Unknown-Frequency Disturbances.

PubMed

Wang, Xinghu; Hong, Yiguang; Yi, Peng; Ji, Haibo; Kang, Yu

2017-05-24

In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters.

Aerothermoelastic Topology Optimization with Flutter and Buckling Metrics (Postprint)

DTIC Science & Technology

2013-07-01

topologies of an unheated panel, thermal buckling-optimal topologies, and flutter- optimality of a heated panel (where the latter case presents a...topological compromise between the former two). The effect of various constraint boundaries, temperature gradients, and (for the flutter of the heated panel...optimality of a heated panel (where the latter case presents a topological compromise between the former two). The effect of various constraint boundaries

Searching for quantum optimal controls under severe constraints

DOE PAGES

Riviello, Gregory; Tibbetts, Katharine Moore; Brif, Constantin; ...

2015-04-06

The success of quantum optimal control for both experimental and theoretical objectives is connected to the topology of the corresponding control landscapes, which are free from local traps if three conditions are met: (1) the quantum system is controllable, (2) the Jacobian of the map from the control field to the evolution operator is of full rank, and (3) there are no constraints on the control field. This paper investigates how the violation of assumption (3) affects gradient searches for globally optimal control fields. The satisfaction of assumptions (1) and (2) ensures that the control landscape lacks fundamental traps, butmore » certain control constraints can still prevent successful optimization of the objective. Using optimal control simulations, we show that the most severe field constraints are those that limit essential control resources, such as the number of control variables, the control duration, and the field strength. Proper management of these resources is an issue of great practical importance for optimization in the laboratory. For each resource, we show that constraints exceeding quantifiable limits can introduce artificial traps to the control landscape and prevent gradient searches from reaching a globally optimal solution. These results demonstrate that careful choice of relevant control parameters helps to eliminate artificial traps and facilitate successful optimization.« less

Sail Plan Configuration Optimization for a Modern Clipper Ship

NASA Astrophysics Data System (ADS)

Gerritsen, Margot; Doyle, Tyler; Iaccarino, Gianluca; Moin, Parviz

2002-11-01

We investigate the use of gradient-based and evolutionary algorithms for sail shape optimization. We present preliminary results for the optimization of sheeting angles for the rig of the future three-masted clipper yacht Maltese Falcon. This yacht will be equipped with square-rigged masts made up of yards of circular arc cross sections. This design is especially attractive for megayachts because it provides a large sail area while maintaining aerodynamic and structural efficiency. The rig remains almost rigid in a large range of wind conditions and therefore a simple geometrical model can be constructed without accounting for the true flying shape. The sheeting angle optimization studies are performed using both gradient-based cost function minimization and evolutionary algorithms. The fluid flow is modeled by the Reynolds-averaged Navier-Stokes equations with the Spallart-Allmaras turbulence model. Unstructured non-conforming grids are used to increase robustness and computational efficiency. The optimization process is automated by integrating the system components (geometry construction, grid generation, flow solver, force calculator, optimization). We compare the optimization results to those done previously by user-controlled parametric studies using simple cost functions and user intuition. We also investigate the effectiveness of various cost functions in the optimization (driving force maximization, ratio of driving force to heeling force maximization).

Development of pseudo-linear gradient elution for high-throughput resin selectivity screening in RoboColumn® Format.

PubMed

Kiesewetter, André; Menstell, Peter; Peeck, Lars H; Stein, Andreas

2016-11-01

Rapid development of chromatographic processes relies on effective high-throughput screening (HTS) methods. This article describes the development of pseudo-linear gradient elution for resin selectivity screening using RoboColumns ® . It gives guidelines for the implementation of this HTS method on a Tecan Freedom EVO ® robotic platform, addressing fundamental aspects of scale down and liquid handling. The creation of a flexible script for buffer preparation and column operation plus efficient data processing provided the basis for this work. Based on the concept of discretization, linear gradient elution was transformed into multistep gradients. The impact of column size, flow rate, multistep gradient design, and fractionation scheme on separation efficiency was systematically investigated, using a ternary model protein mixture. We identified key parameters and defined optimal settings for effective column performance. For proof of concept, we examined the selectivity of several cation exchange resins using various buffer conditions. The final protocol enabled a clear differentiation of resin selectivity on miniature chromatography column (MCC) scale. Distinct differences in separation behavior of individual resins and the influence of buffer conditions could be demonstrated. Results obtained with the robotic platform were representative and consistent with data generated on a conventional chromatography system. A study on antibody monomer/high molecular weight separation comparing MCC and lab scale under higher loading conditions provided evidence of the applicability of the miniaturized approach to practically relevant feedstocks with challenging separation tasks as well as of the predictive quality for larger scale. A comparison of varying degrees of robotic method complexity with corresponding effort (analysis time and labware consumption) and output quality highlights tradeoffs to select a method appropriate for a given separation challenge or analytical constraints. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1503-1519, 2016. © 2016 American Institute of Chemical Engineers.

The tensor distribution function.

PubMed

Leow, A D; Zhu, S; Zhan, L; McMahon, K; de Zubicaray, G I; Meredith, M; Wright, M J; Toga, A W; Thompson, P M

2009-01-01

Diffusion weighted magnetic resonance imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of six directions, second-order tensors (represented by three-by-three positive definite matrices) can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve more complicated white matter configurations, e.g., crossing fiber tracts. Recently, a number of high-angular resolution schemes with more than six gradient directions have been employed to address this issue. In this article, we introduce the tensor distribution function (TDF), a probability function defined on the space of symmetric positive definite matrices. Using the calculus of variations, we solve the TDF that optimally describes the observed data. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function. Moreover, a tensor orientation distribution function (TOD) may also be derived from the TDF, allowing for the estimation of principal fiber directions and their corresponding eigenvalues.

Phospholipid hydrolysis in a pharmaceutical emulsion assessed by physicochemical parameters and a new analytical method.

PubMed

Rabinovich-Guilatt, Laura; Dubernet, Catherine; Gaudin, Karen; Lambert, Gregory; Couvreur, Patrick; Chaminade, Pierre

2005-09-01

The aim of this work was to develop a simple high-performance liquid chromatography (HPLC) technique with evaporative light scattering detection (ELSD) for the separation and quantification of the major phospholipid (PL) and lysophospholipid (LPL) classes contained in a pharmaceutical phospholipid-based emulsion. In the established method, phosphatidylcholine (PC), phosphatidylethanolamine (PE), sphingomyeline (SM), lysophosphatidylcholine (LPC) and lysophosphatidylethanolamine (LPE) were separated with a PVA-Sil stationary phase and a binary gradient from pure chloroform to methanol:water (94:6 v/v) at 3.4%/min. The ELSD detection was enhanced using 0.1% triethylamine and formic acid in each gradient mobile phases. Factors such as stationary phase and ELSD drift tube temperature were optimized, concluding in optimal temperatures of 25 degrees C for separation and 50 degrees C for evaporation. This HPLC-ELSD method was then applied to a PL-emulsion exposed to autoclaving and accelerated thermal conditions at 50 degrees C. Hydrolysis of PC and PE followed first-order kinetics, representing only 45% of the total lipid mass after 3 months. The chemical stability was correlated to commonly measured formulation physical and physico-chemical parameters such as droplet size, emulsion pH and zeta-potential.

Higher Temperature at Lower Elevation Sites Fails to Promote Acclimation or Adaptation to Heat Stress During Pollen Germination.

PubMed

Flores-Rentería, Lluvia; Whipple, Amy V; Benally, Gilbert J; Patterson, Adair; Canyon, Brandon; Gehring, Catherine A

2018-01-01

High temperatures associated with climate change are expected to be detrimental for aspects of plant reproduction, such as pollen viability. We hypothesized that (1) higher peak temperatures predicted with climate change would have a minimal effect on pollen viability, while high temperatures during pollen germination would negatively affect pollen viability, (2) high temperatures during pollen dispersal would facilitate acclimation to high temperatures during pollen germination, and (3) pollen from populations at sites with warmer average temperatures would be better adapted to high temperature peaks. We tested these hypotheses in Pinus edulis , a species with demonstrated sensitivity to climate change, using populations along an elevational gradient. We tested for acclimation to high temperatures by measuring pollen viability during dispersal and germination stages in pollen subjected to 30, 35, and 40°C in a factorial design. We also characterized pollen phenology and measured pollen heat tolerance using trees from nine sites along a 200 m elevational gradient that varied 4°C in temperature. We demonstrated that this gradient is biologically meaningful by evaluating variation in vegetation composition and P. edulis performance. Male reproduction was negatively affected by high temperatures, with stronger effects during pollen germination than pollen dispersal. Populations along the elevational gradient varied in pollen phenology, vegetation composition, plant water stress, nutrient availability, and plant growth. In contrast to our hypothesis, pollen viability was highest in pinyons from mid-elevation sites rather than from lower elevation sites. We found no evidence of acclimation or adaptation of pollen to high temperatures. Maximal plant performance as measured by growth did not occur at the same elevation as maximal pollen viability. These results indicate that periods of high temperature negatively affected sexual reproduction, such that even high pollen production may not result in successful fertilization due to low germination. Acquired thermotolerance might not limit these impacts, but pinyon could avoid heat stress by phenological adjustment of pollen development. Higher pollen viability at the core of the distribution could be explained by an optimal combination of biotic and abiotic environmental factors. The disconnect between measures of growth and pollen production suggests that vigor metrics may not accurately estimate reproduction.

Adjoint-based Sensitivity of Jet Noise to Near-nozzle Forcing

NASA Astrophysics Data System (ADS)

Chung, Seung Whan; Vishnampet, Ramanathan; Bodony, Daniel; Freund, Jonathan

2017-11-01

Past efforts have used optimal control theory, based on the numerical solution of the adjoint flow equations, to perturb turbulent jets in order to reduce their radiated sound. These efforts have been successful in that sound is reduced, with concomitant changes to the large-scale turbulence structures in the flow. However, they have also been inconclusive, in that the ultimate level of reduction seemed to depend upon the accuracy of the adjoint-based gradient rather than a physical limitation of the flow. The chaotic dynamics of the turbulence can degrade the smoothness of cost functional in the control-parameter space, which is necessary for gradient-based optimization. We introduce a route to overcoming this challenge, in part by leveraging the regularity and accuracy with a dual-consistent, discrete-exact adjoint formulation. We confirm its properties and use it to study the sensitivity and controllability of the acoustic radiation from a simulation of a M = 1.3 turbulent jet, whose statistics matches data. The smoothness of the cost functional over time is quantified by a minimum optimization step size beyond which the gradient cannot have a certain degree of accuracy. Based on this, we achieve a moderate level of sound reduction in the first few optimization steps. This material is based [in part] upon work supported by the Department of Energy, National Nuclear Security Administration, under Award Number DE-NA0002374.

Comparing, optimizing, and benchmarking quantum-control algorithms in a unifying programming framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Machnes, S.; Institute for Theoretical Physics, University of Ulm, D-89069 Ulm; Sander, U.

2011-08-15

For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions aremore » pointed out. Moreover, we introduce a unifying algorithmic framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.« less

Transition-state optimization by the free energy gradient method: Application to aqueous-phase Menshutkin reaction between ammonia and methyl chloride

NASA Astrophysics Data System (ADS)

Hirao, Hajime; Nagae, Yukihiko; Nagaoka, Masataka

2001-11-01

The transition state (TS) for the Menshutkin reaction H 3N+CH 3Cl→H 3NCH 3++Cl - in aqueous solution was located on the free energy surface (FES) by the free energy gradient (FEG) method. The solute-solvent system was described by a hybrid quantum mechanical and molecular mechanical (QM/MM) method. The reaction path in water was found to deviate largely from that in the gas phase. It was concluded that, in such a reaction including charge separation, TS structure optimization on an FES is inevitable for obtaining valid information about a TS in solution.

Optimization of Pulse Sequences in MRI Scheme

NASA Astrophysics Data System (ADS)

Roy, Subhankar; Hu, Jianping; Ummal Momeen, M.

2018-04-01

Magnetic resonance imaging (MRI) has a wide range of applications towards imaging the human body. In this work we have solved the Bloch equations for different magnetic field gradients along the transverse direction. We have modified the magnetic field components based on the relaxation terms and solved the field gradient as well as the field components for both off –pulse and on -pulse configurations. In particular we focus on different pulse sequences and optimize them to realize the best possible output. We have analyzed the field components along transverse direction because the rotation of the object to form the image by emitting signal is along the xy plane.

Dual optimization method of radiofrequency and quasistatic field simulations for reduction of eddy currents generated on 7T radiofrequency coil shielding.

PubMed

Zhao, Yujuan; Zhao, Tiejun; Raval, Shailesh B; Krishnamurthy, Narayanan; Zheng, Hai; Harris, Chad T; Handler, William B; Chronik, Blaine A; Ibrahim, Tamer S

2015-11-01

To optimize the design of radiofrequency (RF) shielding of transmit coils at 7T and reduce eddy currents generated on the RF shielding when imaging with rapid gradient waveforms. One set of a four-element, 2 × 2 Tic-Tac-Toe head coil structure was selected and constructed to study eddy currents on the RF coil shielding. The generated eddy currents were quantitatively studied in the time and frequency domains. The RF characteristics were studied using the finite difference time domain method. Five different kinds of RF shielding were tested on a 7T MRI scanner with phantoms and in vivo human subjects. The eddy current simulation method was verified by the measurement results. Eddy currents induced by solid/intact and simple-structured slotted RF shielding significantly distorted the gradient fields. Echo-planar images, B1+ maps, and S matrix measurements verified that the proposed slot pattern suppressed the eddy currents while maintaining the RF characteristics of the transmit coil. The presented dual-optimization method could be used to design RF shielding and reduce the gradient field-induced eddy currents while maintaining the RF characteristics of the transmit coil. © 2014 Wiley Periodicals, Inc.

An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem

PubMed Central

Yu, Yi; Hu, Binqi; Liu, Xinglong

2018-01-01

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm’s performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms. PMID:29324743

An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem.

PubMed

Yu, Yi; Wu, Yonggang; Hu, Binqi; Liu, Xinglong

2018-01-01

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm's performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms.

Time‐efficient and flexible design of optimized multishell HARDI diffusion

PubMed Central

Tournier, J. Donald; Price, Anthony N.; Cordero‐Grande, Lucilio; Hughes, Emer J.; Malik, Shaihan; Steinweg, Johannes; Bastiani, Matteo; Sotiropoulos, Stamatios N.; Jbabdi, Saad; Andersson, Jesper; Edwards, A. David; Hajnal, Joseph V.

2017-01-01

Purpose Advanced diffusion magnetic resonance imaging benefits from collecting as much data as is feasible but is highly sensitive to subject motion and the risk of data loss increases with longer acquisition times. Our purpose was to create a maximally time‐efficient and flexible diffusion acquisition capability with built‐in robustness to partially acquired or interrupted scans. Our framework has been developed for the developing Human Connectome Project, but different application domains are equally possible. Methods Complete flexibility in the sampling of diffusion space combined with free choice of phase‐encode‐direction and the temporal ordering of the sampling scheme was developed taking into account motion robustness, internal consistency, and hardware limits. A split‐diffusion‐gradient preparation, multiband acceleration, and a restart capacity were added. Results The framework was used to explore different parameters choices for the desired high angular resolution diffusion imaging diffusion sampling. For the developing Human Connectome Project, a high‐angular resolution, maximally time‐efficient (20 min) multishell protocol with 300 diffusion‐weighted volumes was acquired in >400 neonates. An optimal design of a high‐resolution (1.2 × 1.2 mm2) two‐shell acquisition with 54 diffusion weighted volumes was obtained using a split‐gradient design. Conclusion The presented framework provides flexibility to generate time‐efficient and motion‐robust diffusion magnetic resonance imaging acquisitions taking into account hardware constraints that might otherwise result in sub‐optimal choices. Magn Reson Med 79:1276–1292, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMID:28557055

Analysis of flavonoids from lotus (Nelumbo nucifera) leaves using high performance liquid chromatography/photodiode array detector tandem electrospray ionization mass spectrometry and an extraction method optimized by orthogonal design.

PubMed

Chen, Sha; Wu, Ben-Hong; Fang, Jin-Bao; Liu, Yan-Ling; Zhang, Hao-Hao; Fang, Lin-Chuan; Guan, Le; Li, Shao-Hua

2012-03-02

The extraction protocol of flavonoids from lotus (Nelumbo nucifera) leaves was optimized through an orthogonal design. The solvent was the most important factor comparing solvent, solvent:tissue ratio, extraction time, and temperature. The highest yield of flavonoids was achieved with 70% methanol-water and a solvent:tissue ratio of 30:1 at 4 °C for 36 h. The optimized analytical method for HPLC was a multi-step gradient elution using 0.5% formic acid (A) and CH₃CN containing 0.1% formic acid (B), at a flow rate of 0.6 mL/min. Using this optimized method, thirteen flavonoids were simultaneously separated and identified by high performance liquid chromatography coupled with photodiode array detection/electrospray ionization mass spectrometry (HPLC/DAD/ESI-MS(n)). Five of the bioactive compounds are reported in lotus leaves for the first time. The flavonoid content of the leaves of three representative cultivars was assessed under the optimized extraction and HPLC analytical conditions, and the seed-producing cultivar 'Baijianlian' had the highest flavonoid content compared with rhizome-producing 'Zhimahuoulian' and wild floral cultivar 'Honglian'. Copyright © 2012 Elsevier B.V. All rights reserved.

Adaptive Flight Control Design with Optimal Control Modification on an F-18 Aircraft Model

NASA Technical Reports Server (NTRS)

Burken, John J.; Nguyen, Nhan T.; Griffin, Brian J.

2010-01-01

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly; however, a large adaptive gain can lead to high-frequency oscillations which can adversely affect the robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model.

Magnetic field gradient driven self-assembly of superparamagnetic nanoparticles using programmable magnetically-recorded templates

NASA Astrophysics Data System (ADS)

Ye, L.; Qi, B.; Lawton, T. G.; Mefford, O. T.; Rinaldi, C.; Garzon, S.; Crawford, T. M.

2013-03-01

Using the enormous magnetic field gradients (100 MT/m @ z =20 nm) present near the surface of magnetic recording media, we demonstrate the fabrication of diffraction gratings with lines consisting entirely of magnetic nanoparticles assembled from a colloidal fluid onto a disk drive medium, followed by transfer to a flexible and transparent polymer thin film. These nanomanufactured gratings have line spacings programmed with commercial magnetic recording and are inherently concave with radii of curvature controlled by varying the polymer film thickness. The diffracted intensity increases non-monotonically with the length of time the colloidal fluid remains on the disk surface. In addition to comparing longitudinal and perpendicular magnetic recording, a combination of spectral diffraction efficiency measurements, magnetometry, scanning electron microscopy and inductively coupled plasma atomic emmission spectroscopy of these gratings are employed to understand colloidal nanoparticle dynamics in this extreme gradient limit. Such experiments are necessary to optimize nanoparticle assembly and obtain uniform patterned features. This low-cost and sustainable approach to nanomanufacturing could enable low-cost, high-quality diffraction gratings as well as more complex polymer nanocomposite materials assembled with single-nanometer precision.

Four Bed Molecular Sieve - Exploration (4BMS-X) Virtual Heater Design and Optimization

NASA Technical Reports Server (NTRS)

Schunk, R. Gregory; Peters, Warren T.; Thomas, John T., Jr.

2017-01-01

A 4BMS-X (Four Bed Molecular Sieve - Exploration) design and heater optimization study for CO2 sorbent beds in proposed exploration system architectures is presented. The primary objectives of the study are to reduce heater power and thermal gradients within the CO2 sorbent beds while minimizing channeling effects. Some of the notable changes from the ISS (International Space Station) CDRA (Carbon Dioxide Removal Assembly) to the proposed exploration system architecture include cylindrical beds, alternate sorbents and an improved heater core. Results from both 2D and 3D sorbent bed thermal models with integrated heaters are presented. The 2D sorbent bed models are used to optimize heater power and fin geometry while the 3D models address end effects in the beds for more realistic thermal gradient and heater power predictions.

Separation of the principal HDL subclasses by iodixanol ultracentrifugation

PubMed Central

Harman, Nicola L.; Griffin, Bruce A.; Davies, Ian G.

2013-01-01

HDL subclasses detection, in cardiovascular risk, has been limited due to the time-consuming nature of current techniques. We have developed a time-saving and reliable separation of the principal HDL subclasses employing iodixanol density gradient ultracentrifugation (IxDGUC) combined with digital photography. HDL subclasses were separated in 2.5 h from prestained plasma on a three-step iodixanol gradient. HDL subclass profiles were generated by digital photography and gel scan software. Plasma samples (n = 46) were used to optimize the gradient for the resolution of HDL heterogeneity and to compare profiles generated by IxDGUC with gradient gel electrophoresis (GGE); further characterization from participants (n = 548) with a range of lipid profiles was also performed. HDL subclass profiles generated by IxDGUC were comparable to those separated by GGE as indicated by a significant association between areas under the curve for both HDL2 and HDL3 (HDL2, r = 0.896, P < 0.01; HDL3, r = 0.894, P < 0.01). The method was highly reproducible, with intra- and interassay coefficient of variation percentage < 5 for percentage area under the curve HDL2 and HDL3, and < 1% for peak Rf and peak density. The method provides time-saving and cost-effective detection and preparation of the principal HDL subclasses. PMID:23690506

The Researches on Reasonable Well Spacing of Gas Wells in Deep and low Permeability Gas Reservoirs

NASA Astrophysics Data System (ADS)

Bei, Yu Bei; Hui, Li; Lin, Li Dong

2018-06-01

This Gs64 gas reservoir is a condensate gas reservoir which is relatively integrated with low porosity and low permeability found in Dagang Oilfield in recent years. The condensate content is as high as 610g/m3. At present, there are few reports about the well spacing of similar gas reservoirs at home and abroad. Therefore, determining the reasonable well spacing of the gas reservoir is important for ensuring the optimal development effect and economic benefit of the gas field development. This paper discusses the reasonable well spacing of the deep and low permeability gas reservoir from the aspects of percolation mechanics, gas reservoir engineering and numerical simulation. considering there exist the start-up pressure gradient in percolation process of low permeability gas reservoir, this paper combined with productivity equation under starting pressure gradient, established the formula of gas well spacing with the formation pressure and start-up pressure gradient. The calculation formula of starting pressure gradient and well spacing of gas wells. Adopting various methods to calculate values of gas reservoir spacing are close to well testing' radius, so the calculation method is reliable, which is very important for the determination of reasonable well spacing in low permeability gas reservoirs.

Optimizing zonal advection of the Advanced Research WRF (ARW) dynamics for Intel MIC

NASA Astrophysics Data System (ADS)

Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.

2014-10-01

The Weather Research and Forecast (WRF) model is the most widely used community weather forecast and research model in the world. There are two distinct varieties of WRF. The Advanced Research WRF (ARW) is an experimental, advanced research version featuring very high resolution. The WRF Nonhydrostatic Mesoscale Model (WRF-NMM) has been designed for forecasting operations. WRF consists of dynamics code and several physics modules. The WRF-ARW core is based on an Eulerian solver for the fully compressible nonhydrostatic equations. In the paper, we will use Intel Intel Many Integrated Core (MIC) architecture to substantially increase the performance of a zonal advection subroutine for optimization. It is of the most time consuming routines in the ARW dynamics core. Advection advances the explicit perturbation horizontal momentum equations by adding in the large-timestep tendency along with the small timestep pressure gradient tendency. We will describe the challenges we met during the development of a high-speed dynamics code subroutine for MIC architecture. Furthermore, lessons learned from the code optimization process will be discussed. The results show that the optimizations improved performance of the original code on Xeon Phi 5110P by a factor of 2.4x.

Optimizing meridional advection of the Advanced Research WRF (ARW) dynamics for Intel Xeon Phi coprocessor

NASA Astrophysics Data System (ADS)

Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.-L.

2015-05-01

The most widely used community weather forecast and research model in the world is the Weather Research and Forecast (WRF) model. Two distinct varieties of WRF exist. The one we are interested is the Advanced Research WRF (ARW) is an experimental, advanced research version featuring very high resolution. The WRF Nonhydrostatic Mesoscale Model (WRF-NMM) has been designed for forecasting operations. WRF consists of dynamics code and several physics modules. The WRF-ARW core is based on an Eulerian solver for the fully compressible nonhydrostatic equations. In the paper, we optimize a meridional (north-south direction) advection subroutine for Intel Xeon Phi coprocessor. Advection is of the most time consuming routines in the ARW dynamics core. It advances the explicit perturbation horizontal momentum equations by adding in the large-timestep tendency along with the small timestep pressure gradient tendency. We will describe the challenges we met during the development of a high-speed dynamics code subroutine for MIC architecture. Furthermore, lessons learned from the code optimization process will be discussed. The results show that the optimizations improved performance of the original code on Xeon Phi 7120P by a factor of 1.2x.

Dynamics of hepatitis C under optimal therapy and sampling based analysis

NASA Astrophysics Data System (ADS)

Pachpute, Gaurav; Chakrabarty, Siddhartha P.

2013-08-01

We examine two models for hepatitis C viral (HCV) dynamics, one for monotherapy with interferon (IFN) and the other for combination therapy with IFN and ribavirin. Optimal therapy for both the models is determined using the steepest gradient method, by defining an objective functional which minimizes infected hepatocyte levels, virion population and side-effects of the drug(s). The optimal therapies for both the models show an initial period of high efficacy, followed by a gradual decline. The period of high efficacy coincides with a significant decrease in the viral load, whereas the efficacy drops after hepatocyte levels are restored. We use the Latin hypercube sampling technique to randomly generate a large number of patient scenarios and study the dynamics of each set under the optimal therapy already determined. Results show an increase in the percentage of responders (indicated by drop in viral load below detection levels) in case of combination therapy (72%) as compared to monotherapy (57%). Statistical tests performed to study correlations between sample parameters and time required for the viral load to fall below detection level, show a strong monotonic correlation with the death rate of infected hepatocytes, identifying it to be an important factor in deciding individual drug regimens.

Surface-plasmon enhanced photoemission of a silver nano-patterned photocathode

NASA Astrophysics Data System (ADS)

Zhang, Z.; Li, R.; To, H.; Andonian, G.; Pirez, E.; Meade, D.; Maxson, J.; Musumeci, P.

2017-09-01

Nano-patterned photocathodes (NPC) take advantage of plasmonic effects to resonantly increase absorption of light and localize electromagnetic field intensity on metal surfaces leading to surface-plasmon enhanced photoemission. In this paper, we report the status of NPC research at UCLA including in particular the optimization of the dimensions of a nanohole array on a silver wafer to enhance plasmonic response at 800 nm light, the development of a spectrally-resolved reflectivity measurement setup for quick nanopattern validation, and of a novel cathode plug to enable high power tests of NPCs on single crystal substrates in a high gradient radiofrequency gun.

Algorithms for the optimization of RBE-weighted dose in particle therapy.

PubMed

Horcicka, M; Meyer, C; Buschbacher, A; Durante, M; Krämer, M

2013-01-21

We report on various algorithms used for the nonlinear optimization of RBE-weighted dose in particle therapy. Concerning the dose calculation carbon ions are considered and biological effects are calculated by the Local Effect Model. Taking biological effects fully into account requires iterative methods to solve the optimization problem. We implemented several additional algorithms into GSI's treatment planning system TRiP98, like the BFGS-algorithm and the method of conjugated gradients, in order to investigate their computational performance. We modified textbook iteration procedures to improve the convergence speed. The performance of the algorithms is presented by convergence in terms of iterations and computation time. We found that the Fletcher-Reeves variant of the method of conjugated gradients is the algorithm with the best computational performance. With this algorithm we could speed up computation times by a factor of 4 compared to the method of steepest descent, which was used before. With our new methods it is possible to optimize complex treatment plans in a few minutes leading to good dose distributions. At the end we discuss future goals concerning dose optimization issues in particle therapy which might benefit from fast optimization solvers.

Algorithms for the optimization of RBE-weighted dose in particle therapy

NASA Astrophysics Data System (ADS)

Horcicka, M.; Meyer, C.; Buschbacher, A.; Durante, M.; Krämer, M.

2013-01-01

We report on various algorithms used for the nonlinear optimization of RBE-weighted dose in particle therapy. Concerning the dose calculation carbon ions are considered and biological effects are calculated by the Local Effect Model. Taking biological effects fully into account requires iterative methods to solve the optimization problem. We implemented several additional algorithms into GSI's treatment planning system TRiP98, like the BFGS-algorithm and the method of conjugated gradients, in order to investigate their computational performance. We modified textbook iteration procedures to improve the convergence speed. The performance of the algorithms is presented by convergence in terms of iterations and computation time. We found that the Fletcher-Reeves variant of the method of conjugated gradients is the algorithm with the best computational performance. With this algorithm we could speed up computation times by a factor of 4 compared to the method of steepest descent, which was used before. With our new methods it is possible to optimize complex treatment plans in a few minutes leading to good dose distributions. At the end we discuss future goals concerning dose optimization issues in particle therapy which might benefit from fast optimization solvers.

Automatic threshold optimization in nonlinear energy operator based spike detection.

PubMed

Malik, Muhammad H; Saeed, Maryam; Kamboh, Awais M

2016-08-01

In neural spike sorting systems, the performance of the spike detector has to be maximized because it affects the performance of all subsequent blocks. Non-linear energy operator (NEO), is a popular spike detector due to its detection accuracy and its hardware friendly architecture. However, it involves a thresholding stage, whose value is usually approximated and is thus not optimal. This approximation deteriorates the performance in real-time systems where signal to noise ratio (SNR) estimation is a challenge, especially at lower SNRs. In this paper, we propose an automatic and robust threshold calculation method using an empirical gradient technique. The method is tested on two different datasets. The results show that our optimized threshold improves the detection accuracy in both high SNR and low SNR signals. Boxplots are presented that provide a statistical analysis of improvements in accuracy, for instance, the 75th percentile was at 98.7% and 93.5% for the optimized NEO threshold and traditional NEO threshold, respectively.

A multi-resolution approach for optimal mass transport

NASA Astrophysics Data System (ADS)

Dominitz, Ayelet; Angenent, Sigurd; Tannenbaum, Allen

2007-09-01

Optimal mass transport is an important technique with numerous applications in econometrics, fluid dynamics, automatic control, statistical physics, shape optimization, expert systems, and meteorology. Motivated by certain problems in image registration and medical image visualization, in this note, we describe a simple gradient descent methodology for computing the optimal L2 transport mapping which may be easily implemented using a multiresolution scheme. We also indicate how the optimal transport map may be computed on the sphere. A numerical example is presented illustrating our ideas.

Optimal quantum control of Bose-Einstein condensates in magnetic microtraps: Comparison of gradient-ascent-pulse-engineering and Krotov optimization schemes

NASA Astrophysics Data System (ADS)

Jäger, Georg; Reich, Daniel M.; Goerz, Michael H.; Koch, Christiane P.; Hohenester, Ulrich

2014-09-01

We study optimal quantum control of the dynamics of trapped Bose-Einstein condensates: The targets are to split a condensate, residing initially in a single well, into a double well, without inducing excitation, and to excite a condensate from the ground state to the first-excited state of a single well. The condensate is described in the mean-field approximation of the Gross-Pitaevskii equation. We compare two optimization approaches in terms of their performance and ease of use; namely, gradient-ascent pulse engineering (GRAPE) and Krotov's method. Both approaches are derived from the variational principle but differ in the way the control is updated, additional costs are accounted for, and second-order-derivative information can be included. We find that GRAPE produces smoother control fields and works in a black-box manner, whereas Krotov with a suitably chosen step-size parameter converges faster but can produce sharp features in the control fields.

Influence of measuring algorithm on shape accuracy in the compensating turning of high gradient thin-wall parts

NASA Astrophysics Data System (ADS)

Wang, Tao; Wang, Guilin; Zhu, Dengchao; Li, Shengyi

2015-02-01

In order to meet the requirement of aerodynamics, the infrared domes or windows with conformal and thin-wall structure becomes the development trend of high-speed aircrafts in the future. But these parts usually have low stiffness, the cutting force will change along with the axial position, and it is very difficult to meet the requirement of shape accuracy by single machining. Therefore, on-machine measurement and compensating turning are used to control the shape errors caused by the fluctuation of cutting force and the change of stiffness. In this paper, on the basis of ultra precision diamond lathe, a contact measuring system with five DOFs is developed to achieve on-machine measurement of conformal thin-wall parts with high accuracy. According to high gradient surface, the optimizing algorithm is designed on the distribution of measuring points by using the data screening method. The influence rule of sampling frequency is analyzed on measuring errors, the best sampling frequency is found out based on planning algorithm, the effect of environmental factors and the fitting errors are controlled within lower range, and the measuring accuracy of conformal dome is greatly improved in the process of on-machine measurement. According to MgF2 conformal dome with high gradient, the compensating turning is implemented by using the designed on-machine measuring algorithm. The shape error is less than PV 0.8μm, greatly superior compared with PV 3μm before compensating turning, which verifies the correctness of measuring algorithm.

A new sparse optimization scheme for simultaneous beam angle and fluence map optimization in radiotherapy planning

NASA Astrophysics Data System (ADS)

Liu, Hongcheng; Dong, Peng; Xing, Lei

2017-08-01

{{\\ell }2,1} -minimization-based sparse optimization was employed to solve the beam angle optimization (BAO) in intensity-modulated radiation therapy (IMRT) planning. The technique approximates the exact BAO formulation with efficiently computable convex surrogates, leading to plans that are inferior to those attainable with recently proposed gradient-based greedy schemes. In this paper, we alleviate/reduce the nontrivial inconsistencies between the {{\\ell }2,1} -based formulations and the exact BAO model by proposing a new sparse optimization framework based on the most recent developments in group variable selection. We propose the incorporation of the group-folded concave penalty (gFCP) as a substitution to the {{\\ell }2,1} -minimization framework. The new formulation is then solved by a variation of an existing gradient method. The performance of the proposed scheme is evaluated by both plan quality and the computational efficiency using three IMRT cases: a coplanar prostate case, a coplanar head-and-neck case, and a noncoplanar liver case. Involved in the evaluation are two alternative schemes: the {{\\ell }2,1} -minimization approach and the gradient norm method (GNM). The gFCP-based scheme outperforms both counterpart approaches. In particular, gFCP generates better plans than those obtained using the {{\\ell }2,1} -minimization for all three cases with a comparable computation time. As compared to the GNM, the gFCP improves both the plan quality and computational efficiency. The proposed gFCP-based scheme provides a promising framework for BAO and promises to improve both planning time and plan quality.

A new sparse optimization scheme for simultaneous beam angle and fluence map optimization in radiotherapy planning.

PubMed

Liu, Hongcheng; Dong, Peng; Xing, Lei

2017-07-20

[Formula: see text]-minimization-based sparse optimization was employed to solve the beam angle optimization (BAO) in intensity-modulated radiation therapy (IMRT) planning. The technique approximates the exact BAO formulation with efficiently computable convex surrogates, leading to plans that are inferior to those attainable with recently proposed gradient-based greedy schemes. In this paper, we alleviate/reduce the nontrivial inconsistencies between the [Formula: see text]-based formulations and the exact BAO model by proposing a new sparse optimization framework based on the most recent developments in group variable selection. We propose the incorporation of the group-folded concave penalty (gFCP) as a substitution to the [Formula: see text]-minimization framework. The new formulation is then solved by a variation of an existing gradient method. The performance of the proposed scheme is evaluated by both plan quality and the computational efficiency using three IMRT cases: a coplanar prostate case, a coplanar head-and-neck case, and a noncoplanar liver case. Involved in the evaluation are two alternative schemes: the [Formula: see text]-minimization approach and the gradient norm method (GNM). The gFCP-based scheme outperforms both counterpart approaches. In particular, gFCP generates better plans than those obtained using the [Formula: see text]-minimization for all three cases with a comparable computation time. As compared to the GNM, the gFCP improves both the plan quality and computational efficiency. The proposed gFCP-based scheme provides a promising framework for BAO and promises to improve both planning time and plan quality.

Reliable numerical computation in an optimal output-feedback design

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1991-01-01

A reliable algorithm is presented for the evaluation of a quadratic performance index and its gradients with respect to the controller design parameters. The algorithm is a part of a design algorithm for optimal linear dynamic output-feedback controller that minimizes a finite-time quadratic performance index. The numerical scheme is particularly robust when it is applied to the control-law synthesis for systems with densely packed modes and where there is a high likelihood of encountering degeneracies in the closed-loop eigensystem. This approach through the use of an accurate Pade series approximation does not require the closed-loop system matrix to be diagonalizable. The algorithm was included in a control design package for optimal robust low-order controllers. Usefulness of the proposed numerical algorithm was demonstrated using numerous practical design cases where degeneracies occur frequently in the closed-loop system under an arbitrary controller design initialization and during the numerical search.

Advanced rotorcraft control using parameter optimization

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1991-01-01

A reliable algorithm for the evaluation of a quadratic performance index and its gradients with respect to the controller design parameters is presented. The algorithm is part of a design algorithm for an optimal linear dynamic output feedback controller that minimizes a finite time quadratic performance index. The numerical scheme is particularly robust when it is applied to the control law synthesis for systems with densely packed modes and where there is a high likelihood of encountering degeneracies in the closed loop eigensystem. This approach through the use of a accurate Pade series approximation does not require the closed loop system matrix to be diagonalizable. The algorithm has been included in a control design package for optimal robust low order controllers. Usefulness of the proposed numerical algorithm has been demonstrated using numerous practical design cases where degeneracies occur frequently in the closed loop system under an arbitrary controller design initialization and during the numerical search.

Gradient maintenance: A new algorithm for fast online replanning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ahunbay, Ergun E., E-mail: eahunbay@mcw.edu; Li, X. Allen

2015-06-15

Purpose: Clinical use of online adaptive replanning has been hampered by the unpractically long time required to delineate volumes based on the image of the day. The authors propose a new replanning algorithm, named gradient maintenance (GM), which does not require the delineation of organs at risk (OARs), and can enhance automation, drastically reducing planning time and improving consistency and throughput of online replanning. Methods: The proposed GM algorithm is based on the hypothesis that if the dose gradient toward each OAR in daily anatomy can be maintained the same as that in the original plan, the intended plan qualitymore » of the original plan would be preserved in the adaptive plan. The algorithm requires a series of partial concentric rings (PCRs) to be automatically generated around the target toward each OAR on the planning and the daily images. The PCRs are used in the daily optimization objective function. The PCR dose constraints are generated with dose–volume data extracted from the original plan. To demonstrate this idea, GM plans generated using daily images acquired using an in-room CT were compared to regular optimization and image guided radiation therapy repositioning plans for representative prostate and pancreatic cancer cases. Results: The adaptive replanning using the GM algorithm, requiring only the target contour from the CT of the day, can be completed within 5 min without using high-power hardware. The obtained adaptive plans were almost as good as the regular optimization plans and were better than the repositioning plans for the cases studied. Conclusions: The newly proposed GM replanning algorithm, requiring only target delineation, not full delineation of OARs, substantially increased planning speed for online adaptive replanning. The preliminary results indicate that the GM algorithm may be a solution to improve the ability for automation and may be especially suitable for sites with small-to-medium size targets surrounded by several critical structures.« less

A parameter optimization approach to controller partitioning for integrated flight/propulsion control application

NASA Technical Reports Server (NTRS)

Schmidt, Phillip; Garg, Sanjay; Holowecky, Brian

1992-01-01

A parameter optimization framework is presented to solve the problem of partitioning a centralized controller into a decentralized hierarchical structure suitable for integrated flight/propulsion control implementation. The controller partitioning problem is briefly discussed and a cost function to be minimized is formulated, such that the resulting 'optimal' partitioned subsystem controllers will closely match the performance (including robustness) properties of the closed-loop system with the centralized controller while maintaining the desired controller partitioning structure. The cost function is written in terms of parameters in a state-space representation of the partitioned sub-controllers. Analytical expressions are obtained for the gradient of this cost function with respect to parameters, and an optimization algorithm is developed using modern computer-aided control design and analysis software. The capabilities of the algorithm are demonstrated by application to partitioned integrated flight/propulsion control design for a modern fighter aircraft in the short approach to landing task. The partitioning optimization is shown to lead to reduced-order subcontrollers that match the closed-loop command tracking and decoupling performance achieved by a high-order centralized controller.

A parameter optimization approach to controller partitioning for integrated flight/propulsion control application

NASA Technical Reports Server (NTRS)

Schmidt, Phillip H.; Garg, Sanjay; Holowecky, Brian R.

1993-01-01

A parameter optimization framework is presented to solve the problem of partitioning a centralized controller into a decentralized hierarchical structure suitable for integrated flight/propulsion control implementation. The controller partitioning problem is briefly discussed and a cost function to be minimized is formulated, such that the resulting 'optimal' partitioned subsystem controllers will closely match the performance (including robustness) properties of the closed-loop system with the centralized controller while maintaining the desired controller partitioning structure. The cost function is written in terms of parameters in a state-space representation of the partitioned sub-controllers. Analytical expressions are obtained for the gradient of this cost function with respect to parameters, and an optimization algorithm is developed using modern computer-aided control design and analysis software. The capabilities of the algorithm are demonstrated by application to partitioned integrated flight/propulsion control design for a modern fighter aircraft in the short approach to landing task. The partitioning optimization is shown to lead to reduced-order subcontrollers that match the closed-loop command tracking and decoupling performance achieved by a high-order centralized controller.

Design optimization of a radial functionally graded dental implant.

PubMed

Ichim, Paul I; Hu, Xiaozhi; Bazen, Jennifer J; Yi, Wei

2016-01-01

In this work, we use FEA to test the hypothesis that a low-modulus coating of a cylindrical zirconia dental implant would reduce the stresses in the peri-implant bone and we use design optimization and the rule of mixture to estimate the elastic modulus and the porosity of the coating that provides optimal stress shielding. We show that a low-modulus coating of a dental implant significantly reduces the maximum stresses in the peri-implant bone without affecting the average stresses thus creating a potentially favorable biomechanical environment. Our results suggest that a resilient coating is capable of reducing the maximum compressive and tensile stresses in the peri-implant bone by up to 50% and the average stresses in the peri-implant bone by up to 15%. We further show that a transitional gradient between the high-modulus core and the low-modulus coating is not necessary and for a considered zirconia/HA composite the optimal thickness of the coating is 100 µ with its optimal elastic at the lowest value considered of 45 GPa. © 2015 Wiley Periodicals, Inc.

Multidisciplinary optimization of an HSCT wing using a response surface methodology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Giunta, A.A.; Grossman, B.; Mason, W.H.

1994-12-31

Aerospace vehicle design is traditionally divided into three phases: conceptual, preliminary, and detailed. Each of these design phases entails a particular level of accuracy and computational expense. While there are several computer programs which perform inexpensive conceptual-level aircraft multidisciplinary design optimization (MDO), aircraft MDO remains prohibitively expensive using preliminary- and detailed-level analysis tools. This occurs due to the expense of computational analyses and because gradient-based optimization requires the analysis of hundreds or thousands of aircraft configurations to estimate design sensitivity information. A further hindrance to aircraft MDO is the problem of numerical noise which occurs frequently in engineering computations. Computermore » models produce numerical noise as a result of the incomplete convergence of iterative processes, round-off errors, and modeling errors. Such numerical noise is typically manifested as a high frequency, low amplitude variation in the results obtained from the computer models. Optimization attempted using noisy computer models may result in the erroneous calculation of design sensitivities and may slow or prevent convergence to an optimal design.« less

Application of augmented-Lagrangian methods in meteorology: Comparison of different conjugate-gradient codes for large-scale minimization

NASA Technical Reports Server (NTRS)

Navon, I. M.

1984-01-01

A Lagrange multiplier method using techniques developed by Bertsekas (1982) was applied to solving the problem of enforcing simultaneous conservation of the nonlinear integral invariants of the shallow water equations on a limited area domain. This application of nonlinear constrained optimization is of the large dimensional type and the conjugate gradient method was found to be the only computationally viable method for the unconstrained minimization. Several conjugate-gradient codes were tested and compared for increasing accuracy requirements. Robustness and computational efficiency were the principal criteria.

Superconducting multi-cell trapped mode deflecting cavity

DOEpatents

Lunin, Andrei; Khabiboulline, Timergali; Gonin, Ivan; Yakovlev, Vyacheslav; Zholents, Alexander

2017-10-10

A method and system for beam deflection. The method and system for beam deflection comprises a compact superconducting RF cavity further comprising a waveguide comprising an open ended resonator volume configured to operate as a trapped dipole mode; a plurality of cells configured to provide a high operating gradient; at least two pairs of protrusions configured for lowering surface electric and magnetic fields; and a main power coupler positioned to optimize necessary coupling for an operating mode and damping lower dipole modes simultaneously.

Role of spatial averaging in multicellular gradient sensing.

PubMed

Smith, Tyler; Fancher, Sean; Levchenko, Andre; Nemenman, Ilya; Mugler, Andrew

2016-05-20

Gradient sensing underlies important biological processes including morphogenesis, polarization, and cell migration. The precision of gradient sensing increases with the length of a detector (a cell or group of cells) in the gradient direction, since a longer detector spans a larger range of concentration values. Intuition from studies of concentration sensing suggests that precision should also increase with detector length in the direction transverse to the gradient, since then spatial averaging should reduce the noise. However, here we show that, unlike for concentration sensing, the precision of gradient sensing decreases with transverse length for the simplest gradient sensing model, local excitation-global inhibition. The reason is that gradient sensing ultimately relies on a subtraction of measured concentration values. While spatial averaging indeed reduces the noise in these measurements, which increases precision, it also reduces the covariance between the measurements, which results in the net decrease in precision. We demonstrate how a recently introduced gradient sensing mechanism, regional excitation-global inhibition (REGI), overcomes this effect and recovers the benefit of transverse averaging. Using a REGI-based model, we compute the optimal two- and three-dimensional detector shapes, and argue that they are consistent with the shapes of naturally occurring gradient-sensing cell populations.

Role of spatial averaging in multicellular gradient sensing

NASA Astrophysics Data System (ADS)

Smith, Tyler; Fancher, Sean; Levchenko, Andre; Nemenman, Ilya; Mugler, Andrew

2016-06-01

Gradient sensing underlies important biological processes including morphogenesis, polarization, and cell migration. The precision of gradient sensing increases with the length of a detector (a cell or group of cells) in the gradient direction, since a longer detector spans a larger range of concentration values. Intuition from studies of concentration sensing suggests that precision should also increase with detector length in the direction transverse to the gradient, since then spatial averaging should reduce the noise. However, here we show that, unlike for concentration sensing, the precision of gradient sensing decreases with transverse length for the simplest gradient sensing model, local excitation-global inhibition. The reason is that gradient sensing ultimately relies on a subtraction of measured concentration values. While spatial averaging indeed reduces the noise in these measurements, which increases precision, it also reduces the covariance between the measurements, which results in the net decrease in precision. We demonstrate how a recently introduced gradient sensing mechanism, regional excitation-global inhibition (REGI), overcomes this effect and recovers the benefit of transverse averaging. Using a REGI-based model, we compute the optimal two- and three-dimensional detector shapes, and argue that they are consistent with the shapes of naturally occurring gradient-sensing cell populations.

Different elution modes and field programming in gravitational field-flow fractionation. III. Field programming by flow-rate gradient generated by a programmable pump.

PubMed

Plocková, J; Chmelík, J

2001-05-25

Gravitational field-flow fractionation (GFFF) utilizes the Earth's gravitational field as an external force that causes the settlement of particles towards the channel accumulation wall. Hydrodynamic lift forces oppose this action by elevating particles away from the channel accumulation wall. These two counteracting forces enable modulation of the resulting force field acting on particles in GFFF. In this work, force-field programming based on modulating the magnitude of hydrodynamic lift forces was implemented via changes of flow-rate, which was accomplished by a programmable pump. Several flow-rate gradients (step gradients, linear gradients, parabolic, and combined gradients) were tested and evaluated as tools for optimization of the separation of a silica gel particle mixture. The influence of increasing amount of sample injected on the peak resolution under flow-rate gradient conditions was also investigated. This is the first time that flow-rate gradients have been implemented for programming of the resulting force field acting on particles in GFFF.

Separation of Bacteria, Protozoa and Carbon Nanotubes by Density Gradient Centrifugation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mortimer, Monika; Petersen, Elijah; Buchholz, Bruce

Sustainable production and use of carbon nanotube (CNT)-enabled materials require efficient assessment of CNT environmental hazards, including the potential for CNT bioaccumulation and biomagnification in environmental receptors. Microbes, as abundant organisms responsible for nutrient cycling in soil and water, are important ecological receptors for studying the effects of CNTs. Quantification of CNT association with microbial cells requires efficient separation of CNT-associated cells from individually dispersed CNTs and CNT agglomerates. Here in this paper, we designed, optimized, and demonstrated procedures for separating bacteria (Pseudomonas aeruginosa) from unbound multiwall carbon nanotubes (MWCNTs) and MWCNT agglomerates using sucrose density gradient centrifugation. We demonstratemore » separation of protozoa (Tetrahymena thermophila) from MWCNTs, bacterial agglomerates, and protozoan fecal pellets by centrifugation in an iodixanol solution. The presence of MWCNTs in the density gradients after centrifugation was determined by quantification of 14C-labeled MWCNTs; the recovery of microbes from the density gradient media was confirmed by optical microscopy. Protozoan intracellular contents of MWCNTs and of bacteria were also unaffected by the designed separation process. Lastly, the optimized methods contribute to improved efficiency and accuracy in quantifying MWCNT association with bacteria and MWCNT accumulation in protozoan cells, thus supporting improved assessment of CNT bioaccumulation.« less

Separation of Bacteria, Protozoa and Carbon Nanotubes by Density Gradient Centrifugation

DOE PAGES

Mortimer, Monika; Petersen, Elijah; Buchholz, Bruce; ...

2016-10-12

Sustainable production and use of carbon nanotube (CNT)-enabled materials require efficient assessment of CNT environmental hazards, including the potential for CNT bioaccumulation and biomagnification in environmental receptors. Microbes, as abundant organisms responsible for nutrient cycling in soil and water, are important ecological receptors for studying the effects of CNTs. Quantification of CNT association with microbial cells requires efficient separation of CNT-associated cells from individually dispersed CNTs and CNT agglomerates. Here in this paper, we designed, optimized, and demonstrated procedures for separating bacteria (Pseudomonas aeruginosa) from unbound multiwall carbon nanotubes (MWCNTs) and MWCNT agglomerates using sucrose density gradient centrifugation. We demonstratemore » separation of protozoa (Tetrahymena thermophila) from MWCNTs, bacterial agglomerates, and protozoan fecal pellets by centrifugation in an iodixanol solution. The presence of MWCNTs in the density gradients after centrifugation was determined by quantification of 14C-labeled MWCNTs; the recovery of microbes from the density gradient media was confirmed by optical microscopy. Protozoan intracellular contents of MWCNTs and of bacteria were also unaffected by the designed separation process. Lastly, the optimized methods contribute to improved efficiency and accuracy in quantifying MWCNT association with bacteria and MWCNT accumulation in protozoan cells, thus supporting improved assessment of CNT bioaccumulation.« less

Separation of Bacteria, Protozoa and Carbon Nanotubes by Density Gradient Centrifugation

PubMed Central

Mortimer, Monika; Petersen, Elijah J.; Buchholz, Bruce A.; Holden, Patricia A.

2016-01-01

Sustainable production and use of carbon nanotube (CNT)-enabled materials require efficient assessment of CNT environmental hazards, including the potential for CNT bioaccumulation and biomagnification in environmental receptors. Microbes, as abundant organisms responsible for nutrient cycling in soil and water, are important ecological receptors for studying the effects of CNTs. Quantification of CNT association with microbial cells requires efficient separation of CNT-associated cells from individually dispersed CNTs and CNT agglomerates. Here, we designed, optimized, and demonstrated procedures for separating bacteria (Pseudomonas aeruginosa) from unbound multiwall carbon nanotubes (MWCNTs) and MWCNT agglomerates using sucrose density gradient centrifugation. We demonstrate separation of protozoa (Tetrahymena thermophila) from MWCNTs, bacterial agglomerates, and protozoan fecal pellets by centrifugation in an iodixanol solution. The presence of MWCNTs in the density gradients after centrifugation was determined by quantification of 14C-labeled MWCNTs; the recovery of microbes from the density gradient media was confirmed by optical microscopy. Protozoan intracellular contents of MWCNTs and of bacteria were also unaffected by the designed separation process. The optimized methods contribute to improved efficiency and accuracy in quantifying MWCNT association with bacteria and MWCNT accumulation in protozoan cells, thus supporting improved assessment of CNT bioaccumulation. PMID:27917301

Photosystem II efficiency of the palisade and spongy mesophyll in Quercus coccifera using adaxial/abaxial illumination and excitation light sources with wavelengths varying in penetration into the leaf tissue.

PubMed

Peguero-Pina, José Javier; Gil-Pelegrín, Eustaquio; Morales, Fermín

2009-01-01

The existence of major vertical gradients within the leaf is often overlooked in studies of photosynthesis. These gradients, which involve light heterogeneity, cell composition, and CO(2) concentration across the mesophyll, can generate differences in the maximum potential PSII efficiency (F (V)/F (M) or F (V)/F (P)) of the different cell layers. Evidence is presented for a step gradient of F (V)/F (P) ratios across the mesophyll, from the adaxial (palisade parenchyma, optimal efficiencies) to the abaxial (spongy parenchyma, sub-optimal efficiencies) side of Quercus coccifera leaves. For this purpose, light sources with different wavelengths that penetrate more or less deep within the leaf were employed, and measurements from the adaxial and abaxial sides were performed. To our knowledge, this is the first report where a low photosynthetic performance in the abaxial side of leaves is accompanied by impaired F (V)/F (P) ratios. This low photosynthetic efficiency of the abaxial side could be related to the occurrence of bundle sheath extensions, which facilitates the penetration of high light intensities deep within the mesophyll. Also, leaf morphology (twisted in shape) and orientation (with a marked angle from the horizontal plane) imply direct sunlight illumination of the abaxial side. The existence of cell layers within leaves with different photosynthetic efficiencies makes appropriate the evaluation of how light penetrates within the mesophyll when using Chl fluorescence or gas exchange techniques that use different wavelengths for excitation and/or for driving photosynthesis.

Development of novel microfluidic platforms for neural stem cell research

NASA Astrophysics Data System (ADS)

Chung, Bonggeun

This dissertation describes the development and characterization of novel microfluidic platforms to study proliferation, differentiation, migration, and apoptosis of neural stem cells (NSCs). NSCs hold tremendous promise for fundamental biological studies and cell-based therapies in human disorders. NSCs are defined as cells that can self-renew yet maintain the ability to generate the three principal cell types of the central nervous system such as neurons, astrocytes, and oligodendrocytes. NSCs therefore have therapeutic possibilities in multiple neurodevelopmental and neurodegenerative diseases. Despite their promise, cell-based therapies are limited by the inability to precisely control their behavior in culture. Compared to traditional culture tools, microfluidic platforms can provide much greater control over cell microenvironments and optimize proliferation and differentiation conditions of cells exposed to combinatorial mixtures of growth factors. Human NSCs were cultured for more than 1 week in the microfluidic device while constantly exposed to a continuous gradient of a growth factor mixture. NSCs proliferated and differentiated in a graded and proportional fashion that varied directly with growth factor concentration. In parallel to the study of growth and differentiation of NSCs, we are interested in proliferation and apoptosis of mouse NSCs exposed to morphogen gradients. Morphogen gradients are fundamental to animal brain development. Nonetheless, much controversy remains about the mechanisms by which morphogen gradients act on the developing brain. To overcome limitations of in-vitro models of gradients, we have developed a hybrid microfluidic platform that can mimic morphogen gradient profiles. Bone morphogenetic protein (BMP) activity in the developing cortex is graded and cortical NSC responses to BMPs are highly dependent on concentration and gradient slope of BMPs. To make novel microfluidic devices integrated with multiple functions, we have also developed a microfluidic multi-injector (MMI) that can generate temporal and spatial concentration gradients. MMI consists of fluidic channels and control channels with pneumatically actuated on-chip barrier valves. Repetitive actuations of on-chip valves control pulsatile release of solution that establishes microscopic chemical gradients. The development of novel gradient-generating microfluidic platforms will help in advancing our understanding of brain development and provide a versatile tool with basic and applied studies in stem cell biology.

Möbius domain-wall fermions on gradient-flowed dynamical HISQ ensembles

NASA Astrophysics Data System (ADS)

Berkowitz, Evan; Bouchard, Chris; Chang, Chia Cheng; Clark, M. A.; Joó, Bálint; Kurth, Thorsten; Monahan, Christopher; Nicholson, Amy; Orginos, Kostas; Rinaldi, Enrico; Vranas, Pavlos; Walker-Loud, André

2017-09-01

We report on salient features of a mixed lattice QCD action using valence Möbius domain-wall fermions solved on the dynamical Nf=2 +1 +1 highly improved staggered quark sea-quark ensembles generated by the MILC Collaboration. The approximate chiral symmetry properties of the valence fermions are shown to be significantly improved by utilizing the gradient-flow scheme to first smear the highly improved staggered quark configurations. The greater numerical cost of the Möbius domain-wall inversions is mitigated by the highly efficient QUDA library optimized for NVIDIA GPU accelerated compute nodes. We have created an interface to this optimized QUDA solver in Chroma. We provide tuned parameters of the action and performance of QUDA using ensembles with the lattice spacings a ≃{0.15 ,0.12 ,0.09 } fm and pion masses mπ≃{310 ,220 ,130 } MeV . We have additionally generated two new ensembles with a ˜0.12 fm and mπ˜{400 ,350 } MeV . With a fixed flow time of tg f=1 in lattice units, the residual chiral symmetry breaking of the valence fermions is kept below 10% of the light quark mass on all ensembles, mres≲0.1 ×ml , with moderate values of the fifth dimension L5 and a domain-wall height M5≤1.3 . As a benchmark calculation, we perform a continuum, infinite volume, physical pion and kaon mass extrapolation of FK±/Fπ± and demonstrate our results are independent of flow time and consistent with the FLAG determination of this quantity at the level of less than one standard deviation.

Magnetic resonance imaging protocols for examination of the neurocranium at 3 T.

PubMed

Schwindt, W; Kugel, H; Bachmann, R; Kloska, S; Allkemper, T; Maintz, D; Pfleiderer, B; Tombach, B; Heindel, W

2003-09-01

The increasing availability of high-field (3 T) MR scanners requires adapting and optimizing clinical imaging protocols to exploit the theoretically higher signal-to-noise ratio (SNR) of the higher field strength. Our aim was to establish reliable and stable protocols meeting the clinical demands for imaging the neurocranium at 3 T. Two hundred patients with a broad range of indications received an examination of the neurocranium with an appropriate assortment of imaging techniques at 3 T. Several imaging parameters were optimized. Keeping scan times comparable to those at 1.5 T we increased spatial resolution. Contrast-enhanced and non-enhanced T1-weighted imaging was best applying gradient-echo and inversion recovery (rather than spin-echo) techniques, respectively. For fluid-attenuated inversion recovery (FLAIR) imaging a TE of 120 ms yielded optimum contrast-to-noise ratio (CNR). High-resolution isotropic 3D data sets were acquired within reasonable scan times. Some artifacts were pronounced, but generally imaging profited from the higher SNR. We present a set of optimized examination protocols for neuroimaging at 3 T, which proved to be reliable in a clinical routine setting.

Constrained growth flips the direction of optimal phenological responses among annual plants.

PubMed

Lindh, Magnus; Johansson, Jacob; Bolmgren, Kjell; Lundström, Niklas L P; Brännström, Åke; Jonzén, Niclas

2016-03-01

Phenological changes among plants due to climate change are well documented, but often hard to interpret. In order to assess the adaptive value of observed changes, we study how annual plants with and without growth constraints should optimize their flowering time when productivity and season length changes. We consider growth constraints that depend on the plant's vegetative mass: self-shading, costs for nonphotosynthetic structural tissue and sibling competition. We derive the optimal flowering time from a dynamic energy allocation model using optimal control theory. We prove that an immediate switch (bang-bang control) from vegetative to reproductive growth is optimal with constrained growth and constant mortality. Increasing mean productivity, while keeping season length constant and growth unconstrained, delayed the optimal flowering time. When growth was constrained and productivity was relatively high, the optimal flowering time advanced instead. When the growth season was extended equally at both ends, the optimal flowering time was advanced under constrained growth and delayed under unconstrained growth. Our results suggests that growth constraints are key factors to consider when interpreting phenological flowering responses. It can help to explain phenological patterns along productivity gradients, and links empirical observations made on calendar scales with life-history theory. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hartemann, F V; Albert, F; Anderson, S G

Nuclear photonics is an emerging field of research requiring new tools, including high spectral brightness, tunable gamma-ray sources; high photon energy, ultrahigh-resolution crystal spectrometers; and novel detectors. This presentation focuses on the precision linac technology required for Compton scattering gamma-ray light sources, and on the optimization of the laser and electron beam pulse format to achieve unprecedented spectral brightness. Within this context, high-gradient X-band technology will be shown to offer optimal performance in a compact package, when used in conjunction with the appropriate pulse format, and photocathode illumination and interaction laser technologies. The nascent field of nuclear photonics is enabledmore » by the recent maturation of new technologies, including high-gradient X-band electron acceleration, robust fiber laser systems, and hyper-dispersion CPA. Recent work has been performed at LLNL to demonstrate isotope-specific detection of shielded materials via NRF using a tunable, quasi-monochromatic Compton scattering gamma-ray source operating between 0.2 MeV and 0.9 MeV photon energy. This technique is called Fluorescence Imaging in the Nuclear Domain with Energetic Radiation (or FINDER). This work has, among other things, demonstrated the detection of {sup 7}Li shielded by Pb, utilizing gamma rays generated by a linac-driven, laser-based Compton scattering gamma-ray source developed at LLNL. Within this context, a new facility is currently under construction at LLNL, with the goal of generating tunable {gamma}-rays in the 0.5-2.5 MeV photon energy range, at a repetition rate of 120 Hz, and with a peak brightness in the 10{sup 20} photons/(s x mm{sup 2} x mrad{sup 2} x 0.1% bw).« less

Algorithms for Mathematical Programming with Emphasis on Bi-level Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goldfarb, Donald; Iyengar, Garud

2014-05-22

The research supported by this grant was focused primarily on first-order methods for solving large scale and structured convex optimization problems and convex relaxations of nonconvex problems. These include optimal gradient methods, operator and variable splitting methods, alternating direction augmented Lagrangian methods, and block coordinate descent methods.

Seamless image stitching by homography refinement and structure deformation using optimal seam pair detection

NASA Astrophysics Data System (ADS)

Lee, Daeho; Lee, Seohyung

2017-11-01

We propose an image stitching method that can remove ghost effects and realign the structure misalignments that occur in common image stitching methods. To reduce the artifacts caused by different parallaxes, an optimal seam pair is selected by comparing the cross correlations from multiple seams detected by variable cost weights. Along the optimal seam pair, a histogram of oriented gradients is calculated, and feature points for matching are detected. The homography is refined using the matching points, and the remaining misalignment is eliminated using the propagation of deformation vectors calculated from matching points. In multiband blending, the overlapping regions are determined from a distance between the matching points to remove overlapping artifacts. The experimental results show that the proposed method more robustly eliminates misalignments and overlapping artifacts than the existing method that uses single seam detection and gradient features.

Microbial decomposers not constrained by climate history along a Mediterranean climate gradient in southern California.

PubMed

Baker, Nameer R; Khalili, Banafshe; Martiny, Jennifer B H; Allison, Steven D

2018-06-01

Microbial decomposers mediate the return of CO 2 to the atmosphere by producing extracellular enzymes to degrade complex plant polymers, making plant carbon available for metabolism. Determining if and how these decomposer communities are constrained in their ability to degrade plant litter is necessary for predicting how carbon cycling will be affected by future climate change. We analyzed mass loss, litter chemistry, microbial biomass, extracellular enzyme activities, and enzyme temperature sensitivities in grassland litter transplanted along a Mediterranean climate gradient in southern California. Microbial community composition was manipulated by caging litter within bags made of nylon membrane that prevent microbial immigration. To test whether grassland microbes were constrained by climate history, half of the bags were inoculated with local microbial communities native to each gradient site. We determined that temperature and precipitation likely interact to limit microbial decomposition in the extreme sites along our gradient. Despite their unique climate history, grassland microbial communities were not restricted in their ability to decompose litter under different climate conditions across the gradient, although microbial communities across our gradient may be restricted in their ability to degrade different types of litter. We did find some evidence that local microbial communities were optimized based on climate, but local microbial taxa that proliferated after inoculation into litterbags did not enhance litter decomposition. Our results suggest that microbial community composition does not constrain C-cycling rates under climate change in our system, but optimization to particular resource environments may act as more general constraints on microbial communities. © 2018 by the Ecological Society of America.

Towards adjoint-based inversion for rheological parameters in nonlinear viscous mantle flow

NASA Astrophysics Data System (ADS)

Worthen, Jennifer; Stadler, Georg; Petra, Noemi; Gurnis, Michael; Ghattas, Omar

2014-09-01

We address the problem of inferring mantle rheological parameter fields from surface velocity observations and instantaneous nonlinear mantle flow models. We formulate this inverse problem as an infinite-dimensional nonlinear least squares optimization problem governed by nonlinear Stokes equations. We provide expressions for the gradient of the cost functional of this optimization problem with respect to two spatially-varying rheological parameter fields: the viscosity prefactor and the exponent of the second invariant of the strain rate tensor. Adjoint (linearized) Stokes equations, which are characterized by a 4th order anisotropic viscosity tensor, facilitates efficient computation of the gradient. A quasi-Newton method for the solution of this optimization problem is presented, which requires the repeated solution of both nonlinear forward Stokes and linearized adjoint Stokes equations. For the solution of the nonlinear Stokes equations, we find that Newton’s method is significantly more efficient than a Picard fixed point method. Spectral analysis of the inverse operator given by the Hessian of the optimization problem reveals that the numerical eigenvalues collapse rapidly to zero, suggesting a high degree of ill-posedness of the inverse problem. To overcome this ill-posedness, we employ Tikhonov regularization (favoring smooth parameter fields) or total variation (TV) regularization (favoring piecewise-smooth parameter fields). Solution of two- and three-dimensional finite element-based model inverse problems show that a constant parameter in the constitutive law can be recovered well from surface velocity observations. Inverting for a spatially-varying parameter field leads to its reasonable recovery, in particular close to the surface. When inferring two spatially varying parameter fields, only an effective viscosity field and the total viscous dissipation are recoverable. Finally, a model of a subducting plate shows that a localized weak zone at the plate boundary can be partially recovered, especially with TV regularization.

Conjugate gradient determination of optimal plane changes for a class of three-impulse transfers between noncoplanar circular orbits

NASA Technical Reports Server (NTRS)

Burrows, R. R.

1972-01-01

A particular type of three-impulse transfer between two circular orbits is analyzed. The possibility of three plane changes is recognized, and the problem is to optimally distribute these plane changes to minimize the sum of the individual impulses. Numerical difficulties and their solution are discussed. Numerical results obtained from a conjugate gradient technique are presented for both the case where the individual plane changes are unconstrained and for the case where they are constrained. Possibly not unexpectedly, multiple minima are found. The techniques presented could be extended to the finite burn case, but primarily the contents are addressed to preliminary mission design and vehicle sizing.

Static and Dynamic Model Update of an Inflatable/Rigidizable Torus Structure

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, mercedes C.

2006-01-01

The present work addresses the development of an experimental and computational procedure for validating finite element models. A torus structure, part of an inflatable/rigidizable Hexapod, is used to demonstrate the approach. Because of fabrication, materials, and geometric uncertainties, a statistical approach combined with optimization is used to modify key model parameters. Static test results are used to update stiffness parameters and dynamic test results are used to update the mass distribution. Updated parameters are computed using gradient and non-gradient based optimization algorithms. Results show significant improvements in model predictions after parameters are updated. Lessons learned in the areas of test procedures, modeling approaches, and uncertainties quantification are presented.

Convex Optimization over Classes of Multiparticle Entanglement

NASA Astrophysics Data System (ADS)

Shang, Jiangwei; Gühne, Otfried

2018-02-01

A well-known strategy to characterize multiparticle entanglement utilizes the notion of stochastic local operations and classical communication (SLOCC), but characterizing the resulting entanglement classes is difficult. Given a multiparticle quantum state, we first show that Gilbert's algorithm can be adapted to prove separability or membership in a certain entanglement class. We then present two algorithms for convex optimization over SLOCC classes. The first algorithm uses a simple gradient approach, while the other one employs the accelerated projected-gradient method. For demonstration, the algorithms are applied to the likelihood-ratio test using experimental data on bound entanglement of a noisy four-photon Smolin state [Phys. Rev. Lett. 105, 130501 (2010), 10.1103/PhysRevLett.105.130501].

Implementation and verification of global optimization benchmark problems

NASA Astrophysics Data System (ADS)

Posypkin, Mikhail; Usov, Alexander

2017-12-01

The paper considers the implementation and verification of a test suite containing 150 benchmarks for global deterministic box-constrained optimization. A C++ library for describing standard mathematical expressions was developed for this purpose. The library automate the process of generating the value of a function and its' gradient at a given point and the interval estimates of a function and its' gradient on a given box using a single description. Based on this functionality, we have developed a collection of tests for an automatic verification of the proposed benchmarks. The verification has shown that literary sources contain mistakes in the benchmarks description. The library and the test suite are available for download and can be used freely.

On the convergence of a linesearch based proximal-gradient method for nonconvex optimization

NASA Astrophysics Data System (ADS)

Bonettini, S.; Loris, I.; Porta, F.; Prato, M.; Rebegoldi, S.

2017-05-01

We consider a variable metric linesearch based proximal gradient method for the minimization of the sum of a smooth, possibly nonconvex function plus a convex, possibly nonsmooth term. We prove convergence of this iterative algorithm to a critical point if the objective function satisfies the Kurdyka-Łojasiewicz property at each point of its domain, under the assumption that a limit point exists. The proposed method is applied to a wide collection of image processing problems and our numerical tests show that our algorithm results to be flexible, robust and competitive when compared to recently proposed approaches able to address the optimization problems arising in the considered applications.

Guiding automated left ventricular chamber segmentation in cardiac imaging using the concept of conserved myocardial volume.

PubMed

Garson, Christopher D; Li, Bing; Acton, Scott T; Hossack, John A

2008-06-01

The active surface technique using gradient vector flow allows semi-automated segmentation of ventricular borders. The accuracy of the algorithm depends on the optimal selection of several key parameters. We investigated the use of conservation of myocardial volume for quantitative assessment of each of these parameters using synthetic and in vivo data. We predicted that for a given set of model parameters, strong conservation of volume would correlate with accurate segmentation. The metric was most useful when applied to the gradient vector field weighting and temporal step-size parameters, but less effective in guiding an optimal choice of the active surface tension and rigidity parameters.

A hybrid linear/nonlinear training algorithm for feedforward neural networks.

PubMed

McLoone, S; Brown, M D; Irwin, G; Lightbody, A

1998-01-01

This paper presents a new hybrid optimization strategy for training feedforward neural networks. The algorithm combines gradient-based optimization of nonlinear weights with singular value decomposition (SVD) computation of linear weights in one integrated routine. It is described for the multilayer perceptron (MLP) and radial basis function (RBF) networks and then extended to the local model network (LMN), a new feedforward structure in which a global nonlinear model is constructed from a set of locally valid submodels. Simulation results are presented demonstrating the superiority of the new hybrid training scheme compared to second-order gradient methods. It is particularly effective for the LMN architecture where the linear to nonlinear parameter ratio is large.

Thermal dependence of sprint performance in the lizard Psammodromus algirus along a 2200-meter elevational gradient: Cold-habitat lizards do not perform better at low temperatures.

PubMed

Zamora-Camacho, Francisco Javier; Rubiño-Hispán, María Virtudes; Reguera, Senda; Moreno-Rueda, Gregorio

2015-08-01

Sprint speed has a capital relevance in most animals' fitness, mainly for fleeing from predators. Sprint performance is maximal within a certain range of body temperatures in ectotherms, whose thermal upkeep relies on exogenous thermal sources. Ectotherms can respond to diverse thermal environments either by shifting their thermal preferences or maintaining them through different adaptive mechanisms. Here, we tested whether maximum sprint speed of a lizard that shows conservative thermal ecology along a 2200-meter elevational gradient differs with body temperature in lizards from different elevations. Lizards ran faster at optimum than at suboptimum body temperature. Notably, high-elevation lizards were not faster than mid- and low-elevation lizards at suboptimum body temperature, despite their low-quality thermal environment. This result suggests that both preferred body temperature and thermal dependence of speed performance are co-adapted along the elevational gradient. High-elevation lizards display a number of thermoregulatory strategies that allow them to achieve high optimum body temperatures in a low thermal-quality habitat and thus maximize speed performance. As for reproductive condition, we did not find any effect of it on sprint speed, or any significant interaction with elevation or body temperature. However, strikingly, gravid females were significantly slower than males and non-gravid females at suboptimum temperature, but performed similarly well at optimal temperature. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quality by Design in the development of hydrophilic interaction liquid chromatography method with gradient elution for the analysis of olanzapine.

PubMed

Tumpa, Anja; Stajić, Ana; Jančić-Stojanović, Biljana; Medenica, Mirjana

2017-02-05

This paper deals with the development of hydrophilic interaction liquid chromatography (HILIC) method with gradient elution, in accordance with Analytical Quality by Design (AQbD) methodology, for the first time. The method is developed for olanzapine and its seven related substances. Following step by step AQbD methodology, firstly as critical process parameters (CPPs) temperature, starting content of aqueous phase and duration of linear gradient are recognized, and as critical quality attributes (CQAs) separation criterion S of critical pairs of substances are investigated. Rechtschaffen design is used for the creation of models that describe the dependence between CPPs and CQAs. The design space that is obtained at the end is used for choosing the optimal conditions (set point). The method is fully validated at the end to verify the adequacy of the chosen optimal conditions and applied to real samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Power Control and Optimization of Photovoltaic and Wind Energy Conversion Systems

NASA Astrophysics Data System (ADS)

Ghaffari, Azad

Power map and Maximum Power Point (MPP) of Photovoltaic (PV) and Wind Energy Conversion Systems (WECS) highly depend on system dynamics and environmental parameters, e.g., solar irradiance, temperature, and wind speed. Power optimization algorithms for PV systems and WECS are collectively known as Maximum Power Point Tracking (MPPT) algorithm. Gradient-based Extremum Seeking (ES), as a non-model-based MPPT algorithm, governs the system to its peak point on the steepest descent curve regardless of changes of the system dynamics and variations of the environmental parameters. Since the power map shape defines the gradient vector, then a close estimate of the power map shape is needed to create user assignable transients in the MPPT algorithm. The Hessian gives a precise estimate of the power map in a neighborhood around the MPP. The estimate of the inverse of the Hessian in combination with the estimate of the gradient vector are the key parts to implement the Newton-based ES algorithm. Hence, we generate an estimate of the Hessian using our proposed perturbation matrix. Also, we introduce a dynamic estimator to calculate the inverse of the Hessian which is an essential part of our algorithm. We present various simulations and experiments on the micro-converter PV systems to verify the validity of our proposed algorithm. The ES scheme can also be used in combination with other control algorithms to achieve desired closed-loop performance. The WECS dynamics is slow which causes even slower response time for the MPPT based on the ES. Hence, we present a control scheme, extended from Field-Oriented Control (FOC), in combination with feedback linearization to reduce the convergence time of the closed-loop system. Furthermore, the nonlinear control prevents magnetic saturation of the stator of the Induction Generator (IG). The proposed control algorithm in combination with the ES guarantees the closed-loop system robustness with respect to high level parameter uncertainty in the IG dynamics. The simulation results verify the effectiveness of the proposed algorithm.

Aerodynamic Optimization of Rocket Control Surface Geometry Using Cartesian Methods and CAD Geometry

NASA Technical Reports Server (NTRS)

Nelson, Andrea; Aftosmis, Michael J.; Nemec, Marian; Pulliam, Thomas H.

2004-01-01

Aerodynamic design is an iterative process involving geometry manipulation and complex computational analysis subject to physical constraints and aerodynamic objectives. A design cycle consists of first establishing the performance of a baseline design, which is usually created with low-fidelity engineering tools, and then progressively optimizing the design to maximize its performance. Optimization techniques have evolved from relying exclusively on designer intuition and insight in traditional trial and error methods, to sophisticated local and global search methods. Recent attempts at automating the search through a large design space with formal optimization methods include both database driven and direct evaluation schemes. Databases are being used in conjunction with surrogate and neural network models as a basis on which to run optimization algorithms. Optimization algorithms are also being driven by the direct evaluation of objectives and constraints using high-fidelity simulations. Surrogate methods use data points obtained from simulations, and possibly gradients evaluated at the data points, to create mathematical approximations of a database. Neural network models work in a similar fashion, using a number of high-fidelity database calculations as training iterations to create a database model. Optimal designs are obtained by coupling an optimization algorithm to the database model. Evaluation of the current best design then gives either a new local optima and/or increases the fidelity of the approximation model for the next iteration. Surrogate methods have also been developed that iterate on the selection of data points to decrease the uncertainty of the approximation model prior to searching for an optimal design. The database approximation models for each of these cases, however, become computationally expensive with increase in dimensionality. Thus the method of using optimization algorithms to search a database model becomes problematic as the number of design variables is increased.

Placement of implantable cardioverter-defibrillators in paediatric and congenital heart defect patients: a pipeline for model generation and simulation prediction of optimal configurations

PubMed Central

Rantner, Lukas J; Vadakkumpadan, Fijoy; Spevak, Philip J; Crosson, Jane E; Trayanova, Natalia A

2013-01-01

There is currently no reliable way of predicting the optimal implantable cardioverter-defibrillator (ICD) placement in paediatric and congenital heart defect (CHD) patients. This study aimed to: (1) develop a new image processing pipeline for constructing patient-specific heart–torso models from clinical magnetic resonance images (MRIs); (2) use the pipeline to determine the optimal ICD configuration in a paediatric tricuspid valve atresia patient; (3) establish whether the widely used criterion of shock-induced extracellular potential (Φe) gradients ≥5 V cm−1 in ≥95% of ventricular volume predicts defibrillation success. A biophysically detailed heart–torso model was generated from patient MRIs. Because transvenous access was impossible, three subcutaneous and three epicardial lead placement sites were identified along with five ICD scan locations. Ventricular fibrillation was induced, and defibrillation shocks were applied from 11 ICD configurations to determine defibrillation thresholds (DFTs). Two configurations with epicardial leads resulted in the lowest DFTs overall and were thus considered optimal. Three configurations shared the lowest DFT among subcutaneous lead ICDs. The Φe gradient criterion was an inadequate predictor of defibrillation success, as defibrillation failed in numerous instances even when 100% of the myocardium experienced such gradients. In conclusion, we have developed a new image processing pipeline and applied it to a CHD patient to construct the first active heart–torso model from clinical MRIs. PMID:23798492

Hydraulic constraints modify optimal photosynthetic profiles in giant sequoia trees.

PubMed

Ambrose, Anthony R; Baxter, Wendy L; Wong, Christopher S; Burgess, Stephen S O; Williams, Cameron B; Næsborg, Rikke R; Koch, George W; Dawson, Todd E

2016-11-01

Optimality theory states that whole-tree carbon gain is maximized when leaf N and photosynthetic capacity profiles are distributed along vertical light gradients such that the marginal gain of nitrogen investment is identical among leaves. However, observed photosynthetic N gradients in trees do not follow this prediction, and the causes for this apparent discrepancy remain uncertain. Our objective was to evaluate how hydraulic limitations potentially modify crown-level optimization in Sequoiadendron giganteum (giant sequoia) trees up to 90 m tall. Leaf water potential (Ψ l ) and branch sap flow closely followed diurnal patterns of solar radiation throughout each tree crown. Minimum leaf water potential correlated negatively with height above ground, while leaf mass per area (LMA), shoot mass per area (SMA), leaf nitrogen content (%N), and bulk leaf stable carbon isotope ratios (δ(13)C) correlated positively with height. We found no significant vertical trends in maximum leaf photosynthesis (A), stomatal conductance (g s), and intrinsic water-use efficiency (A/g s), nor in branch-averaged transpiration (E L), stomatal conductance (G S), and hydraulic conductance (K L). Adjustments in hydraulic architecture appear to partially compensate for increasing hydraulic limitations with height in giant sequoia, allowing them to sustain global maximum summer water use rates exceeding 2000 kg day(-1). However, we found that leaf N and photosynthetic capacity do not follow the vertical light gradient, supporting the hypothesis that increasing limitations on water transport capacity with height modify photosynthetic optimization in tall trees.

Foraging behavior by Daphnia in stoichiometric gradients of food quality.

PubMed

Schatz, Greg S; McCauley, Edward

2007-10-01

Mismatches in the elemental composition of herbivores and their resources can impact herbivore growth and reproduction. In aquatic systems, the ratio of elements, such as C, P, and N, is used to characterize the food quality of algal prey. For example, large increases in the C:P ratio of edible algae can decrease rates of growth and reproduction in Daphnia. Current theory emphasizes that Daphnia utilize only assimilation and respiration processes to maintain an optimal elemental composition, yet studies of terrestrial herbivores implicate behavioral processes in coping with local variation in food quality. We tested the ability of juvenile and adult Daphnia to locate regions of high-quality food within a spatial gradient of algal prey differing in C:P ratio, while holding food density constant over space. Both juveniles and adults demonstrated similar behavior by quickly locating (i.e., <10 min) the region of high food quality. Foraging paths were centred on regions of high food quality and these differed significantly from paths of individuals exposed to a homogeneous environment of both food density and food quality. Ingestion rate experiments on algal prey of differing stoichiometric ratio show that individuals can adjust their intake rate over fast behavioral time-scales, and we use these data to examine how individuals choose foraging locations when presented with a spatial gradient that trades off food quality and food quantity. Daphnia reared under low food quality conditions chose to forage in regions of high food quality even though they could attain the same C ingestion rate elsewhere along a spatial gradient. We argue that these aspects of foraging behavior by Daphnia have important implications for how these herbivores manage their elemental composition and our understanding of the dynamics of these herbivore-plant systems in lakes and ponds where spatial variation in food quality is present.

Global patterns of protection of elevational gradients in mountain ranges.

PubMed

Elsen, Paul R; Monahan, William B; Merenlender, Adina M

2018-05-21

Protected areas (PAs) that span elevational gradients enhance protection for taxonomic and phylogenetic diversity and facilitate species range shifts under climate change. We quantified the global protection of elevational gradients by analyzing the elevational distributions of 44,155 PAs in 1,010 mountain ranges using the highest resolution digital elevation models available. We show that, on average, mountain ranges in Africa and Asia have the lowest elevational protection, ranges in Europe and South America have intermediate elevational protection, and ranges in North America and Oceania have the highest elevational protection. We use the Convention on Biological Diversity's Aichi Target 11 to assess the proportion of elevational gradients meeting the 17% suggested minimum target and examine how different protection categories contribute to elevational protection. When considering only strict PAs [International Union for Conservation of Nature (IUCN) categories I-IV, n = 24,706], nearly 40% of ranges do not contain any PAs, roughly half fail to meet the 17% target at any elevation, and ∼75% fail to meet the target throughout ≥50% of the elevational gradient. Observed elevational protection is well below optimal, and frequently below a null model of elevational protection. Including less stringent PAs (IUCN categories V-VI and nondesignated PAs, n = 19,449) significantly enhances elevational protection for most continents, but several highly biodiverse ranges require new or expanded PAs to increase elevational protection. Ensuring conservation outcomes for PAs with lower IUCN designations as well as strategically placing PAs to better represent and connect elevational gradients will enhance ecological representation and facilitate species range shifts under climate change. Copyright © 2018 the Author(s). Published by PNAS.

Quality-by-design-based ultra high performance liquid chromatography related substances method development by establishing the proficient design space for sumatriptan and naproxen combination.

PubMed

Patel, Prinesh N; Karakam, Vijaya Saradhi; Samanthula, Gananadhamu; Ragampeta, Srinivas

2015-10-01

Quality-by-design-based methods hold greater level of confidence for variations and greater success in method transfer. A quality-by-design-based ultra high performance liquid chromatography method was developed for the simultaneous assay of sumatriptan and naproxen along with their related substances. The first screening was performed by fractional factorial design comprising 44 experiments for reversed-phase stationary phases, pH, and organic modifiers. The results of screening design experiments suggested phenyl hexyl column and acetonitrile were the best combination. The method was further optimized for flow rate, temperature, and gradient time by experimental design of 20 experiments and the knowledge space was generated for effect of variable on response (number of peaks ≥ 1.50 - resolution). Proficient design space was generated from knowledge space by applying Monte Carlo simulation to successfully integrate quantitative robustness metrics during optimization stage itself. The final method provided the robust performance which was verified and validated. Final conditions comprised Waters® Acquity phenyl hexyl column with gradient elution using ammonium acetate (pH 4.12, 0.02 M) buffer and acetonitrile at 0.355 mL/min flow rate and 30°C. The developed method separates all 13 analytes within a 15 min run time with fewer experiments compared to the traditional quality-by-testing approach. ©2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Accelerated gradient methods for the x-ray imaging of solar flares

NASA Astrophysics Data System (ADS)

Bonettini, S.; Prato, M.

2014-05-01

In this paper we present new optimization strategies for the reconstruction of x-ray images of solar flares by means of the data collected by the Reuven Ramaty high energy solar spectroscopic imager. The imaging concept of the satellite is based on rotating modulation collimator instruments, which allow the use of both Fourier imaging approaches and reconstruction techniques based on the straightforward inversion of the modulated count profiles. Although in the last decade, greater attention has been devoted to the former strategies due to their very limited computational cost, here we consider the latter model and investigate the effectiveness of different accelerated gradient methods for the solution of the corresponding constrained minimization problem. Moreover, regularization is introduced through either an early stopping of the iterative procedure, or a Tikhonov term added to the discrepancy function by means of a discrepancy principle accounting for the Poisson nature of the noise affecting the data.

Discrete adjoint of fractional step Navier-Stokes solver in generalized coordinates

NASA Astrophysics Data System (ADS)

Wang, Mengze; Mons, Vincent; Zaki, Tamer

2017-11-01

Optimization and control in transitional and turbulent flows require evaluation of gradients of the flow state with respect to the problem parameters. Using adjoint approaches, these high-dimensional gradients can be evaluated with a similar computational cost as the forward Navier-Stokes simulations. The adjoint algorithm can be obtained by discretizing the continuous adjoint Navier-Stokes equations or by deriving the adjoint to the discretized Navier-Stokes equations directly. The latter algorithm is necessary when the forward-adjoint relations must be satisfied to machine precision. In this work, our forward model is the fractional step solution to the Navier-Stokes equations in generalized coordinates, proposed by Rosenfeld, Kwak & Vinokur. We derive the corresponding discrete adjoint equations. We also demonstrate the accuracy of the combined forward-adjoint model, and its application to unsteady wall-bounded flows. This work has been partially funded by the Office of Naval Research (Grant N00014-16-1-2542).

Online learning in optical tomography: a stochastic approach

NASA Astrophysics Data System (ADS)

Chen, Ke; Li, Qin; Liu, Jian-Guo

2018-07-01

We study the inverse problem of radiative transfer equation (RTE) using stochastic gradient descent method (SGD) in this paper. Mathematically, optical tomography amounts to recovering the optical parameters in RTE using the incoming–outgoing pair of light intensity. We formulate it as a PDE-constraint optimization problem, where the mismatch of computed and measured outgoing data is minimized with same initial data and RTE constraint. The memory and computation cost it requires, however, is typically prohibitive, especially in high dimensional space. Smart iterative solvers that only use partial information in each step is called for thereafter. Stochastic gradient descent method is an online learning algorithm that randomly selects data for minimizing the mismatch. It requires minimum memory and computation, and advances fast, therefore perfectly serves the purpose. In this paper we formulate the problem, in both nonlinear and its linearized setting, apply SGD algorithm and analyze the convergence performance.

Effects of Fuel Distribution on Detonation Tube Performance

NASA Technical Reports Server (NTRS)

Perkins, H. Douglas; Sung, Chih-Jen

2003-01-01

A pulse detonation engine uses a series of high frequency intermittent detonation tubes to generate thrust. The process of filling the detonation tube with fuel and air for each cycle may yield non-uniform mixtures. Uniform mixing is commonly assumed when calculating detonation tube thrust performance. In this study, detonation cycles featuring idealized non-uniform Hz/air mixtures were analyzed using a two-dimensional Navier-Stokes computational fluid dynamics code with detailed chemistry. Mixture non-uniformities examined included axial equivalence ratio gradients, transverse equivalence ratio gradients, and partially fueled tubes. Three different average test section equivalence ratios were studied; one stoichiometric, one fuel lean, and one fuel rich. All mixtures were detonable throughout the detonation tube. Various mixtures representing the same average test section equivalence ratio were shown to have specific impulses within 1% of each other, indicating that good fuel/air mixing is not a prerequisite for optimal detonation tube performance under conditions investigated.

High-sensitivity silicon ultraviolet p+-i-n avalanche photodiode using ultra-shallow boron gradient doping

DOE PAGES

Xia, Zhenyang; Zang, Kai; Liu, Dong; ...

2017-08-21

Photo detection of ultraviolet (UV) light remains a challenge since the penetration depth of UV light is limited to the nanometer scale. Therefore, the doping profile and electric field in the top nanometer range of the photo detection devices become critical. Traditional UV photodetectors usually use a constant doping profile near the semiconductor surface, resulting in a negligible electric field, which limits the photo-generated carrier collection efficiency of the photodetector. Here, we demonstrate, via the use of an optimized gradient boron doping technique, that the carrier collection efficiency and photo responsivity under the UV wavelength region have been enhanced. Moreover,more » the ultrathin p+-i-n junction shows an avalanche gain of 2800 and an ultra-low junction capacitance (sub pico-farad), indicating potential applications in the low timing jitter single photon detection area.« less

High-sensitivity silicon ultraviolet p+-i-n avalanche photodiode using ultra-shallow boron gradient doping

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xia, Zhenyang; Zang, Kai; Liu, Dong

Photo detection of ultraviolet (UV) light remains a challenge since the penetration depth of UV light is limited to the nanometer scale. Therefore, the doping profile and electric field in the top nanometer range of the photo detection devices become critical. Traditional UV photodetectors usually use a constant doping profile near the semiconductor surface, resulting in a negligible electric field, which limits the photo-generated carrier collection efficiency of the photodetector. Here, we demonstrate, via the use of an optimized gradient boron doping technique, that the carrier collection efficiency and photo responsivity under the UV wavelength region have been enhanced. Moreover,more » the ultrathin p+-i-n junction shows an avalanche gain of 2800 and an ultra-low junction capacitance (sub pico-farad), indicating potential applications in the low timing jitter single photon detection area.« less

Selection considerations between ZERODUR® and silicon carbide for dimensionally-stable spaceborne optical telescopes in two-earth-orbits

NASA Astrophysics Data System (ADS)

Hull, Tony; Westerhoff, Thomas; Weidmann, Gunter

2015-09-01

A key consideration in defining a space telescope mission is definition of the optical materials. This selection defines both the performance of the system and system complexity and cost. Optimal material selection for system stability must consider the thermal environment and its variation. Via numerical simulations, we compare the thermal and structural-mechanical behavior of ZERODUR® and SiC as mirror substrates for telescope assemblies in space. SiC has significantly larger CTE values then ZERODUR®, but also its thermal diffusivity k/(ρcp) is larger, and that helps to homogenize thermal gradients in the mirror. Therefore it is not obvious at first glance which material performs with better dimensional stability under realistic unsteady, inhomogeneous thermal loads. We specifically examine the telescope response to transient, gradient driving, thermal environments representative of low- and high-earth- orbits.

Estimating cellular parameters through optimization procedures: elementary principles and applications.

PubMed

Kimura, Akatsuki; Celani, Antonio; Nagao, Hiromichi; Stasevich, Timothy; Nakamura, Kazuyuki

2015-01-01

Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE) in a prediction or to maximize likelihood. A (local) maximum of likelihood or (local) minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.

Medial-based deformable models in nonconvex shape-spaces for medical image segmentation.

PubMed

McIntosh, Chris; Hamarneh, Ghassan

2012-01-01

We explore the application of genetic algorithms (GA) to deformable models through the proposition of a novel method for medical image segmentation that combines GA with nonconvex, localized, medial-based shape statistics. We replace the more typical gradient descent optimizer used in deformable models with GA, and the convex, implicit, global shape statistics with nonconvex, explicit, localized ones. Specifically, we propose GA to reduce typical deformable model weaknesses pertaining to model initialization, pose estimation and local minima, through the simultaneous evolution of a large number of models. Furthermore, we constrain the evolution, and thus reduce the size of the search-space, by using statistically-based deformable models whose deformations are intuitive (stretch, bulge, bend) and are driven in terms of localized principal modes of variation, instead of modes of variation across the entire shape that often fail to capture localized shape changes. Although GA are not guaranteed to achieve the global optima, our method compares favorably to the prevalent optimization techniques, convex/nonconvex gradient-based optimizers and to globally optimal graph-theoretic combinatorial optimization techniques, when applied to the task of corpus callosum segmentation in 50 mid-sagittal brain magnetic resonance images.

Aircraft Trajectories Computation-Prediction-Control. Volume 1 (La Trajectoire de l’Avion Calcul-Prediction-Controle)

DTIC Science & Technology

1990-03-01

knowledge covering problems of this type is called calculus of variations or optimal control theory (Refs. 1-8). As stated before, appli - cations occur...to the optimality conditions and the feasibility equations of Problem (GP), respectively. Clearly, after the transformation (26) is applied , the...trajectories, the primal sequential gradient-restoration algorithm (PSGRA) is applied to compute optimal trajectories for aeroassisted orbital transfer

A 3D correction method for predicting the readings of a PinPoint chamber on the CyberKnife® M6™ machine

NASA Astrophysics Data System (ADS)

Zhang, Yongqian; Brandner, Edward; Ozhasoglu, Cihat; Lalonde, Ron; Heron, Dwight E.; Saiful Huq, M.

2018-02-01

The use of small fields in radiation therapy techniques has increased substantially in particular in stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT). However, as field size reduces further still, the response of the detector changes more rapidly with field size, and the effects of measurement uncertainties become increasingly significant due to the lack of lateral charged particle equilibrium, spectral changes as a function of field size, detector choice, and subsequent perturbations of the charged particle fluence. This work presents a novel 3D dose volume-to-point correction method to predict the readings of a 0.015 cc PinPoint chamber (PTW 31014) for both small static-fields and composite-field dosimetry formed by fixed cones on the CyberKnife® M6™ machine. A 3D correction matrix is introduced to link the 3D dose distribution to the response of the PinPoint chamber in water. The parameters of the correction matrix are determined by modeling its 3D dose response in circular fields created using the 12 fixed cones (5 mm-60 mm) on a CyberKnife® M6™ machine. A penalized least-square optimization problem is defined by fitting the calculated detector reading to the experimental measurement data to generate the optimal correction matrix; the simulated annealing algorithm is used to solve the inverse optimization problem. All the experimental measurements are acquired for every 2 mm chamber shift in the horizontal planes for each field size. The 3D dose distributions for the measurements are calculated using the Monte Carlo calculation with the MultiPlan® treatment planning system (Accuray Inc., Sunnyvale, CA, USA). The performance evaluation of the 3D conversion matrix is carried out by comparing the predictions of the output factors (OFs), off-axis ratios (OARs) and percentage depth dose (PDD) data to the experimental measurement data. The discrepancy of the measurement and the prediction data for composite fields is also performed for clinical SRS plans. The optimization algorithm used for generating the optimal correction factors is stable, and the resulting correction factors were smooth in the spatial domain. The measurement and prediction of OFs agree closely with percentage differences of less than 1.9% for all the 12 cones. The discrepancies between the prediction and the measurement PDD readings at 50 mm and 80 mm depth are 1.7% and 1.9%, respectively. The percentage differences of OARs between measurement and prediction data are less than 2% in the low dose gradient region, and 2%/1 mm discrepancies are observed within the high dose gradient regions. The differences between the measurement and prediction data for all the CyberKnife based SRS plans are less than 1%. These results demonstrate the existence and efficiency of the novel 3D correction method for small field dosimetry. The 3D correction matrix links the 3D dose distribution and the reading of the PinPoint chamber. The comparison between the predicted reading and the measurement data for static small fields (OFs, OARs and PDDs) yield discrepancies within 2% for low dose gradient regions and 2%/1 mm for high dose gradient regions; the discrepancies between the predicted and the measurement data are less than 1% for all the SRS plans. The 3D correction method provides an access to evaluate the clinical measurement data and can be applied to non-standard composite fields intensity modulated radiation therapy point dose verification.

Spherical gradient-index lenses as perfect imaging and maximum power transfer devices.

PubMed

Gordon, J M

2000-08-01

Gradient-index lenses can be viewed from the perspectives of both imaging and nonimaging optics, that is, in terms of both image fidelity and achievable flux concentration. The simple class of gradient-index lenses with spherical symmetry, often referred to as modified Luneburg lenses, is revisited. An alternative derivation for established solutions is offered; the method of Fermat's strings and the principle of skewness conservation are invoked. Then these nominally perfect imaging devices are examined from the additional vantage point of power transfer, and the degree to which they realize the thermodynamic limit to flux concentration is determined. Finally, the spherical gradient-index lens of the fish eye is considered as a modified Luneburg lens optimized subject to material constraints.

Edge remap for solids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kamm, James R.; Love, Edward; Robinson, Allen C.

We review the edge element formulation for describing the kinematics of hyperelastic solids. This approach is used to frame the problem of remapping the inverse deformation gradient for Arbitrary Lagrangian-Eulerian (ALE) simulations of solid dynamics. For hyperelastic materials, the stress state is completely determined by the deformation gradient, so remapping this quantity effectively updates the stress state of the material. A method, inspired by the constrained transport remap in electromagnetics, is reviewed, according to which the zero-curl constraint on the inverse deformation gradient is implicitly satisfied. Open issues related to the accuracy of this approach are identified. An optimization-based approachmore » is implemented to enforce positivity of the determinant of the deformation gradient. The efficacy of this approach is illustrated with numerical examples.« less

Hybrid intelligent optimization methods for engineering problems

NASA Astrophysics Data System (ADS)

Pehlivanoglu, Yasin Volkan

The purpose of optimization is to obtain the best solution under certain conditions. There are numerous optimization methods because different problems need different solution methodologies; therefore, it is difficult to construct patterns. Also mathematical modeling of a natural phenomenon is almost based on differentials. Differential equations are constructed with relative increments among the factors related to yield. Therefore, the gradients of these increments are essential to search the yield space. However, the landscape of yield is not a simple one and mostly multi-modal. Another issue is differentiability. Engineering design problems are usually nonlinear and they sometimes exhibit discontinuous derivatives for the objective and constraint functions. Due to these difficulties, non-gradient-based algorithms have become more popular in recent decades. Genetic algorithms (GA) and particle swarm optimization (PSO) algorithms are popular, non-gradient based algorithms. Both are population-based search algorithms and have multiple points for initiation. A significant difference from a gradient-based method is the nature of the search methodologies. For example, randomness is essential for the search in GA or PSO. Hence, they are also called stochastic optimization methods. These algorithms are simple, robust, and have high fidelity. However, they suffer from similar defects, such as, premature convergence, less accuracy, or large computational time. The premature convergence is sometimes inevitable due to the lack of diversity. As the generations of particles or individuals in the population evolve, they may lose their diversity and become similar to each other. To overcome this issue, we studied the diversity concept in GA and PSO algorithms. Diversity is essential for a healthy search, and mutations are the basic operators to provide the necessary variety within a population. After having a close scrutiny of the diversity concept based on qualification and quantification studies, we improved new mutation strategies and operators to provide beneficial diversity within the population. We called this new approach as multi-frequency vibrational GA or PSO. They were applied to different aeronautical engineering problems in order to study the efficiency of these new approaches. These implementations were: applications to selected benchmark test functions, inverse design of two-dimensional (2D) airfoil in subsonic flow, optimization of 2D airfoil in transonic flow, path planning problems of autonomous unmanned aerial vehicle (UAV) over a 3D terrain environment, 3D radar cross section minimization problem for a 3D air vehicle, and active flow control over a 2D airfoil. As demonstrated by these test cases, we observed that new algorithms outperform the current popular algorithms. The principal role of this multi-frequency approach was to determine which individuals or particles should be mutated, when they should be mutated, and which ones should be merged into the population. The new mutation operators, when combined with a mutation strategy and an artificial intelligent method, such as, neural networks or fuzzy logic process, they provided local and global diversities during the reproduction phases of the generations. Additionally, the new approach also introduced random and controlled diversity. Due to still being population-based techniques, these methods were as robust as the plain GA or PSO algorithms. Based on the results obtained, it was concluded that the variants of the present multi-frequency vibrational GA and PSO were efficient algorithms, since they successfully avoided all local optima within relatively short optimization cycles.

Eye model for the ground squirrel

NASA Astrophysics Data System (ADS)

Sussman, Dafna; Chou, B. Ralph; Lakshminarayanan, Vasudevan

2011-11-01

This paper presents an anatomically-correct eye model for the ground squirrel, a diurnal, highly-developed mammal with high visual acuity. This model can assist in understanding the relationship between ocular structural development and its corresponding function. The eye model is constructed based on anatomical measurements of thicknesses and indices of refraction of the various ocular media. The model then derives the gradient index distribution of the crystalline lens using a ray tracing method with a Monte Carlo optimization. Results indicate a diffraction-limited ocular behaviour, implying the visual acuity of the ground squirrel is more likely to be limited by photoreceptor density and diffraction effects, than by ocular geometry.

Surface-plasmon enhanced photoemission of a silver nano-patterned photocathode

DOE PAGES

Zhang, Z.; Li, R.; To, H.; ...

2016-11-22

Here, nano-patterned photocathodes (NPC) take advantage of plasmonic effects to resonantly increase absorption of light and localize electromagnetic field intensity on metal surfaces leading to surface-plasmon enhanced photoemission. In this paper, we report the status of NPC research at UCLA including in particular the optimization of the dimensions of a nanohole array on a silver wafer to enhance plasmonic response at 800 nm light, the development of a spectrally-resolved reflectivity measurement setup for quick nanopattern validation, and of a novel cathode plug to enable high power tests of NPCs on single crystal substrates in a high gradient radiofrequency gun.

Surface-plasmon enhanced photoemission of a silver nano-patterned photocathode

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Z.; Li, R.; To, H.

Here, nano-patterned photocathodes (NPC) take advantage of plasmonic effects to resonantly increase absorption of light and localize electromagnetic field intensity on metal surfaces leading to surface-plasmon enhanced photoemission. In this paper, we report the status of NPC research at UCLA including in particular the optimization of the dimensions of a nanohole array on a silver wafer to enhance plasmonic response at 800 nm light, the development of a spectrally-resolved reflectivity measurement setup for quick nanopattern validation, and of a novel cathode plug to enable high power tests of NPCs on single crystal substrates in a high gradient radiofrequency gun.

Classical Optimal Control for Energy Minimization Based On Diffeomorphic Modulation under Observable-Response-Preserving Homotopy.

PubMed

Soley, Micheline B; Markmann, Andreas; Batista, Victor S

2018-06-12

We introduce the so-called "Classical Optimal Control Optimization" (COCO) method for global energy minimization based on the implementation of the diffeomorphic modulation under observable-response-preserving homotopy (DMORPH) gradient algorithm. A probe particle with time-dependent mass m( t;β) and dipole μ( r, t;β) is evolved classically on the potential energy surface V( r) coupled to an electric field E( t;β), as described by the time-dependent density of states represented on a grid, or otherwise as a linear combination of Gaussians generated by the k-means clustering algorithm. Control parameters β defining m( t;β), μ( r, t;β), and E( t;β) are optimized by following the gradients of the energy with respect to β, adapting them to steer the particle toward the global minimum energy configuration. We find that the resulting COCO algorithm is capable of resolving near-degenerate states separated by large energy barriers and successfully locates the global minima of golf potentials on flat and rugged surfaces, previously explored for testing quantum annealing methodologies and the quantum optimal control optimization (QuOCO) method. Preliminary results show successful energy minimization of multidimensional Lennard-Jones clusters. Beyond the analysis of energy minimization in the specific model systems investigated, we anticipate COCO should be valuable for solving minimization problems in general, including optimization of parameters in applications to machine learning and molecular structure determination.

Pixel-based OPC optimization based on conjugate gradients.

PubMed

Ma, Xu; Arce, Gonzalo R

2011-01-31

Optical proximity correction (OPC) methods are resolution enhancement techniques (RET) used extensively in the semiconductor industry to improve the resolution and pattern fidelity of optical lithography. In pixel-based OPC (PBOPC), the mask is divided into small pixels, each of which is modified during the optimization process. Two critical issues in PBOPC are the required computational complexity of the optimization process, and the manufacturability of the optimized mask. Most current OPC optimization methods apply the steepest descent (SD) algorithm to improve image fidelity augmented by regularization penalties to reduce the complexity of the mask. Although simple to implement, the SD algorithm converges slowly. The existing regularization penalties, however, fall short in meeting the mask rule check (MRC) requirements often used in semiconductor manufacturing. This paper focuses on developing OPC optimization algorithms based on the conjugate gradient (CG) method which exhibits much faster convergence than the SD algorithm. The imaging formation process is represented by the Fourier series expansion model which approximates the partially coherent system as a sum of coherent systems. In order to obtain more desirable manufacturability properties of the mask pattern, a MRC penalty is proposed to enlarge the linear size of the sub-resolution assistant features (SRAFs), as well as the distances between the SRAFs and the main body of the mask. Finally, a projection method is developed to further reduce the complexity of the optimized mask pattern.

Dynamic Aberration Correction for Conformal Window of High-Speed Aircraft Using Optimized Model-Based Wavefront Sensorless Adaptive Optics.

PubMed

Dong, Bing; Li, Yan; Han, Xin-Li; Hu, Bin

2016-09-02

For high-speed aircraft, a conformal window is used to optimize the aerodynamic performance. However, the local shape of the conformal window leads to large amounts of dynamic aberrations varying with look angle. In this paper, deformable mirror (DM) and model-based wavefront sensorless adaptive optics (WSLAO) are used for dynamic aberration correction of an infrared remote sensor equipped with a conformal window and scanning mirror. In model-based WSLAO, aberration is captured using Lukosz mode, and we use the low spatial frequency content of the image spectral density as the metric function. Simulations show that aberrations induced by the conformal window are dominated by some low-order Lukosz modes. To optimize the dynamic correction, we can only correct dominant Lukosz modes and the image size can be minimized to reduce the time required to compute the metric function. In our experiment, a 37-channel DM is used to mimic the dynamic aberration of conformal window with scanning rate of 10 degrees per second. A 52-channel DM is used for correction. For a 128 × 128 image, the mean value of image sharpness during dynamic correction is 1.436 × 10(-5) in optimized correction and is 1.427 × 10(-5) in un-optimized correction. We also demonstrated that model-based WSLAO can achieve convergence two times faster than traditional stochastic parallel gradient descent (SPGD) method.

A homotopy algorithm for digital optimal projection control GASD-HADOC

NASA Technical Reports Server (NTRS)

Collins, Emmanuel G., Jr.; Richter, Stephen; Davis, Lawrence D.

1993-01-01

The linear-quadratic-gaussian (LQG) compensator was developed to facilitate the design of control laws for multi-input, multi-output (MIMO) systems. The compensator is computed by solving two algebraic equations for which standard closed-loop solutions exist. Unfortunately, the minimal dimension of an LQG compensator is almost always equal to the dimension of the plant and can thus often violate practical implementation constraints on controller order. This deficiency is especially highlighted when considering control-design for high-order systems such as flexible space structures. This deficiency motivated the development of techniques that enable the design of optimal controllers whose dimension is less than that of the design plant. A homotopy approach based on the optimal projection equations that characterize the necessary conditions for optimal reduced-order control. Homotopy algorithms have global convergence properties and hence do not require that the initializing reduced-order controller be close to the optimal reduced-order controller to guarantee convergence. However, the homotopy algorithm previously developed for solving the optimal projection equations has sublinear convergence properties and the convergence slows at higher authority levels and may fail. A new homotopy algorithm for synthesizing optimal reduced-order controllers for discrete-time systems is described. Unlike the previous homotopy approach, the new algorithm is a gradient-based, parameter optimization formulation and was implemented in MATLAB. The results reported may offer the foundation for a reliable approach to optimal, reduced-order controller design.

Optimal and fast E/B separation with a dual messenger field

NASA Astrophysics Data System (ADS)

Kodi Ramanah, Doogesh; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-05-01

We adapt our recently proposed dual messenger algorithm for spin field reconstruction and showcase its efficiency and effectiveness in Wiener filtering polarized cosmic microwave background (CMB) maps. Unlike conventional preconditioned conjugate gradient (PCG) solvers, our preconditioner-free technique can deal with high-resolution joint temperature and polarization maps with inhomogeneous noise distributions and arbitrary mask geometries with relative ease. Various convergence diagnostics illustrate the high quality of the dual messenger reconstruction. In contrast, the PCG implementation fails to converge to a reasonable solution for the specific problem considered. The implementation of the dual messenger method is straightforward and guarantees numerical stability and convergence. We show how the algorithm can be modified to generate fluctuation maps, which, combined with the Wiener filter solution, yield unbiased constrained signal realizations, consistent with observed data. This algorithm presents a pathway to exact global analyses of high-resolution and high-sensitivity CMB data for a statistically optimal separation of E and B modes. It is therefore relevant for current and next-generation CMB experiments, in the quest for the elusive primordial B-mode signal.

Key parameters controlling the performance of catalytic motors.

PubMed

Esplandiu, Maria J; Afshar Farniya, Ali; Reguera, David

2016-03-28

The development of autonomous micro/nanomotors driven by self-generated chemical gradients is a topic of high interest given their potential impact in medicine and environmental remediation. Although impressive functionalities of these devices have been demonstrated, a detailed understanding of the propulsion mechanism is still lacking. In this work, we perform a comprehensive numerical analysis of the key parameters governing the actuation of bimetallic catalytic micropumps. We show that the fluid motion is driven by self-generated electro-osmosis where the electric field originates by a proton current rather than by a lateral charge asymmetry inside the double layer. Hence, the surface potential and the electric field are the key parameters for setting the pumping strength and directionality. The proton flux that generates the electric field stems from the proton gradient induced by the electrochemical reactions taken place at the pump. Surprisingly the electric field and consequently the fluid flow are mainly controlled by the ionic strength and not by the conductivity of the solution, as one could have expected. We have also analyzed the influence of the chemical fuel concentration, electrochemical reaction rates, and size of the metallic structures for an optimized pump performance. Our findings cast light on the complex chemomechanical actuation of catalytic motors and provide important clues for the search, design, and optimization of novel catalytic actuators.

Plasmonic optical nanotweezers

NASA Astrophysics Data System (ADS)

Kotb, Rehab; El Maklizi, Mahmoud; Ismail, Yehea; Swillam, Mohamed A.

2017-02-01

Plasmonic grating structures can be used in many applications such as nanolithography and optical trapping. In this paper, we used plasmonic grating as optical tweezers to trap and manipulate dielectric nano-particles. Different plasmonic grating structures with single, double, and triple slits have been investigated and analyzed. The three configurations are optimized and compared to find the best candidate to trap and manipulate nanoparticles. The three optimized structures results in capability to super focusing and beaming the light effectively beyond the diffraction limit. A high transverse gradient optical force is obtained using the triple slit configuration that managed to significantly enhance the field and its gradient. Therefore, it has been chosen as an efficient optical tweezers. This structure managed to trap sub10nm particles efficiently. The resultant 50KT potential well traps the nano particles stably. The proposed structure is used also to manipulate the nano-particles by simply changing the angle of the incident light. We managed to control the movement of nano particle over an area of (5μm x 5μm) precisely. The proposed structure has the advantage of trapping and manipulating the particles outside the structure (not inside the structure such as the most proposed optical tweezers). As a result, it can be used in many applications such as drug delivery and biomedical analysis.

Ultrastructure and wear patterns of the ventral epidermis of four snake species (Squamata, Serpentes).

PubMed

Klein, Marie-Christin G; Gorb, Stanislav N

2014-10-01

Snakes are limbless tetrapods highly specialized for sliding locomotion. This locomotion leads to the skin being exposed to friction loads, especially on the ventral body side, which leads to wear. It is presumed that snakes therefore have specific optimizations for minimizing abrasion. Scales from snakes with habitat, locomotor and/or behavior specializations have specific gradients in material properties that may be due to different epidermal architecture. To approach this issue we examined the skin of Lampropeltis getula californiae (terrestrial), Epicrates cenchria cenchria (generalist), Morelia viridis (arboreal), and Gongylophis colubrinus (burrowing) with a focus on (i) the ultrastructure of the ventral epidermis and (ii) the qualitative abrasion pattern of the ventral scales. Scanning and transmission electron microscopy revealed variations in the structure, thickness, layering, and material composition of the epidermis between the species. Furthermore, SEM and white light interferometer images of the scale surface showed that the abrasion patterns differed, even when the snakes were reared on the same substrate. These data support the idea that (i) a specific gradient in material properties may be due to a variation in epidermis architecture (thickness/ultrastructure) and (ii) this variation may be an optimization of material properties for specific ways of life. Copyright © 2014 Elsevier GmbH. All rights reserved.

Fixed and equilibrium endpoint problems in uneven-aged stand management

Treesearch

Robert G. Haight; Wayne M. Getz

1987-01-01

Studies in uneven-aged management have concentrated on the determination of optimal steady-state diameter distribution harvest policies for single and mixed species stands. To find optimal transition harvests for irregular stands, either fixed endpoint or equilibrium endpoint constraints can be imposed after finite transition periods. Penalty function and gradient...

Real time on-chip sequential adaptive principal component analysis for data feature extraction and image compression

NASA Technical Reports Server (NTRS)

Duong, T. A.

2004-01-01

In this paper, we present a new, simple, and optimized hardware architecture sequential learning technique for adaptive Principle Component Analysis (PCA) which will help optimize the hardware implementation in VLSI and to overcome the difficulties of the traditional gradient descent in learning convergence and hardware implementation.

Impact of the Z potential technique on reducing the sperm DNA fragmentation index, fertilization rate and embryo development.

PubMed

Duarte, Carlos; Núñez, Víctor; Wong, Yat; Vivar, Carlos; Benites, Elder; Rodriguez, Urso; Vergara, Carlos; Ponce, Jorge

2017-12-01

In assisted reproduction procedures, we need to develop and enhance new protocols to optimize sperm selection. The aim of this study is to evaluate the ability of the Z potential technique to select sperm with intact DNA in non-normospermic patients and evaluate the impact of this selection on embryonic development. We analyzed a total of 174 human seminal samples with at least one altered parameter. We measured basal, post density gradients, and post density gradients + Z potential DNA fragmentation index. To evaluate the impact of this technique on embryo development, 54 cases were selected. The embryo development parameters evaluated were fertilization rate, cleavage rate, top quality embryos at the third day and blastocysts rate. We found significant differences in the study groups when we compared the sperm fragmentation index by adding the Z potential technique to density gradient selection vs. density gradients alone. Furthermore, there was no significant difference in the embryo development parameters between the low sperm fragmentation index group vs. the moderate and high sperm fragmentation index groups, when selecting sperms with this new technique. The Z potential technique is a very useful tool for sperm selection; it significantly reduces the DNA fragmentation index and improves the parameters of embryo development. This technique could be considered routine for its simplicity and low cost.

Very Large Scale Optimization

NASA Technical Reports Server (NTRS)

Vanderplaats, Garrett; Townsend, James C. (Technical Monitor)

2002-01-01

The purpose of this research under the NASA Small Business Innovative Research program was to develop algorithms and associated software to solve very large nonlinear, constrained optimization tasks. Key issues included efficiency, reliability, memory, and gradient calculation requirements. This report describes the general optimization problem, ten candidate methods, and detailed evaluations of four candidates. The algorithm chosen for final development is a modern recreation of a 1960s external penalty function method that uses very limited computer memory and computational time. Although of lower efficiency, the new method can solve problems orders of magnitude larger than current methods. The resulting BIGDOT software has been demonstrated on problems with 50,000 variables and about 50,000 active constraints. For unconstrained optimization, it has solved a problem in excess of 135,000 variables. The method includes a technique for solving discrete variable problems that finds a "good" design, although a theoretical optimum cannot be guaranteed. It is very scalable in that the number of function and gradient evaluations does not change significantly with increased problem size. Test cases are provided to demonstrate the efficiency and reliability of the methods and software.

An optimization approach for fitting canonical tensor decompositions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dunlavy, Daniel M.; Acar, Evrim; Kolda, Tamara Gibson

Tensor decompositions are higher-order analogues of matrix decompositions and have proven to be powerful tools for data analysis. In particular, we are interested in the canonical tensor decomposition, otherwise known as the CANDECOMP/PARAFAC decomposition (CPD), which expresses a tensor as the sum of component rank-one tensors and is used in a multitude of applications such as chemometrics, signal processing, neuroscience, and web analysis. The task of computing the CPD, however, can be difficult. The typical approach is based on alternating least squares (ALS) optimization, which can be remarkably fast but is not very accurate. Previously, nonlinear least squares (NLS) methodsmore » have also been recommended; existing NLS methods are accurate but slow. In this paper, we propose the use of gradient-based optimization methods. We discuss the mathematical calculation of the derivatives and further show that they can be computed efficiently, at the same cost as one iteration of ALS. Computational experiments demonstrate that the gradient-based optimization methods are much more accurate than ALS and orders of magnitude faster than NLS.« less

Contact-force distribution optimization and control for quadruped robots using both gradient and adaptive neural networks.

PubMed

Li, Zhijun; Ge, Shuzhi Sam; Liu, Sibang

2014-08-01

This paper investigates optimal feet forces' distribution and control of quadruped robots under external disturbance forces. First, we formulate a constrained dynamics of quadruped robots and derive a reduced-order dynamical model of motion/force. Consider an external wrench on quadruped robots; the distribution of required forces and moments on the supporting legs of a quadruped robot is handled as a tip-point force distribution and used to equilibrate the external wrench. Then, a gradient neural network is adopted to deal with the optimized objective function formulated as to minimize this quadratic objective function subjected to linear equality and inequality constraints. For the obtained optimized tip-point force and the motion of legs, we propose the hybrid motion/force control based on an adaptive neural network to compensate for the perturbations in the environment and approximate feedforward force and impedance of the leg joints. The proposed control can confront the uncertainties including approximation error and external perturbation. The verification of the proposed control is conducted using a simulation.

Neural Network and Regression Approximations in High Speed Civil Transport Aircraft Design Optimization

NASA Technical Reports Server (NTRS)

Patniak, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.

1998-01-01

Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, can make the process computational intensive. The computational burden can be greatly reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high speed civil transport aircraft is the subject of this paper. The Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and a regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the Lewis Research Center's CometBoards test bed to provide the optimization capability. With the combined software, both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. On the other hand, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the input-output pairs and to train the approximating analyzers was seven times that required for solution of the problem.

Optimized and validated high-performance liquid chromatography method for the determination of deoxynivalenol and aflatoxins in cereals.

PubMed

Skendi, Adriana; Irakli, Maria N; Papageorgiou, Maria D

2016-04-01

A simple, sensitive and accurate analytical method was optimized and developed for the determination of deoxynivalenol and aflatoxins in cereals intended for human consumption using high-performance liquid chromatography with diode array and fluorescence detection and a photochemical reactor for enhanced detection. A response surface methodology, using a fractional central composite design, was carried out for optimization of the water percentage at the beginning of the run (X1, 80-90%), the level of acetonitrile at the end of gradient system (X2, 10-20%) with the water percentage fixed at 60%, and the flow rate (X3, 0.8-1.2 mL/min). The studied responses were the chromatographic peak area, the resolution factor and the time of analysis. Optimal chromatographic conditions were: X1 = 80%, X2 = 10%, and X3 = 1 mL/min. Following a double sample extraction with water and a mixture of methanol/water, mycotoxins were rapidly purified by an optimized solid-phase extraction protocol. The optimized method was further validated with respect to linearity (R(2) >0.9991), sensitivity, precision, and recovery (90-112%). The application to 23 commercial cereal samples from Greece showed contamination levels below the legally set limits, except for one maize sample. The main advantages of the developed method are the simplicity of operation and the low cost. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimization of non-linear gradient in hydrophobic interaction chromatography for the analytical characterization of antibody-drug conjugates.

PubMed

Bobály, Balázs; Randazzo, Giuseppe Marco; Rudaz, Serge; Guillarme, Davy; Fekete, Szabolcs

2017-01-20

The goal of this work was to evaluate the potential of non-linear gradients in hydrophobic interaction chromatography (HIC), to improve the separation between the different homologous species (drug-to-antibody, DAR) of commercial antibody-drug conjugates (ADC). The selectivities between Brentuximab Vedotin species were measured using three different gradient profiles, namely linear, power function based and logarithmic ones. The logarithmic gradient provides the most equidistant retention distribution for the DAR species and offers the best overall separation of cysteine linked ADC in HIC. Another important advantage of the logarithmic gradient, is its peak focusing effect for the DAR0 species, which is particularly useful to improve the quantitation limit of DAR0. Finally, the logarithmic behavior of DAR species of ADC in HIC was modelled using two different approaches, based on i) the linear solvent strength theory (LSS) and two scouting linear gradients and ii) a new derived equation and two logarithmic scouting gradients. In both cases, the retention predictions were excellent and systematically below 3% compared to the experimental values. Copyright © 2016 Elsevier B.V. All rights reserved.

Design of two-channel filter bank using nature inspired optimization based fractional derivative constraints.

PubMed

Kuldeep, B; Singh, V K; Kumar, A; Singh, G K

2015-01-01

In this article, a novel approach for 2-channel linear phase quadrature mirror filter (QMF) bank design based on a hybrid of gradient based optimization and optimization of fractional derivative constraints is introduced. For the purpose of this work, recently proposed nature inspired optimization techniques such as cuckoo search (CS), modified cuckoo search (MCS) and wind driven optimization (WDO) are explored for the design of QMF bank. 2-Channel QMF is also designed with particle swarm optimization (PSO) and artificial bee colony (ABC) nature inspired optimization techniques. The design problem is formulated in frequency domain as sum of L2 norm of error in passband, stopband and transition band at quadrature frequency. The contribution of this work is the novel hybrid combination of gradient based optimization (Lagrange multiplier method) and nature inspired optimization (CS, MCS, WDO, PSO and ABC) and its usage for optimizing the design problem. Performance of the proposed method is evaluated by passband error (ϕp), stopband error (ϕs), transition band error (ϕt), peak reconstruction error (PRE), stopband attenuation (As) and computational time. The design examples illustrate the ingenuity of the proposed method. Results are also compared with the other existing algorithms, and it was found that the proposed method gives best result in terms of peak reconstruction error and transition band error while it is comparable in terms of passband and stopband error. Results show that the proposed method is successful for both lower and higher order 2-channel QMF bank design. A comparative study of various nature inspired optimization techniques is also presented, and the study singles out CS as a best QMF optimization technique. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

High-resolution comparative modeling with RosettaCM.

PubMed

Song, Yifan; DiMaio, Frank; Wang, Ray Yu-Ruei; Kim, David; Miles, Chris; Brunette, Tj; Thompson, James; Baker, David

2013-10-08

We describe an improved method for comparative modeling, RosettaCM, which optimizes a physically realistic all-atom energy function over the conformational space defined by homologous structures. Given a set of sequence alignments, RosettaCM assembles topologies by recombining aligned segments in Cartesian space and building unaligned regions de novo in torsion space. The junctions between segments are regularized using a loop closure method combining fragment superposition with gradient-based minimization. The energies of the resulting models are optimized by all-atom refinement, and the most representative low-energy model is selected. The CASP10 experiment suggests that RosettaCM yields models with more accurate side-chain and backbone conformations than other methods when the sequence identity to the templates is greater than ∼15%. Copyright © 2013 Elsevier Ltd. All rights reserved.

Graded High-Strength Spring-Steels by a Special Inductive Heat T reatment

NASA Astrophysics Data System (ADS)

Tump, A.; Brandt, R.

2016-03-01

A method for effective lightweight design is the use of materials with high specific strength. As materials e.g. titanium are very expensive, steel is still the most important material for manufacturing automotive components. Steel is cost efficient, easy to recycle and its tensile strength easily exceeds 2,000 MPa by means of modern QT-technology (Quenched and Tempered). Therefore, lightweight design is still feasible in spite of the high density of steel. However, a further increase of tensile strength is limited, especially due to an increasing notch sensitivity and exposure to a corrosive environment. One solution is a special QT-process for steel, which creates a hardness gradient from the surface to the core of the material. This type of tailored material possesses a softer layer, which improves material properties such as fracture toughness and notch sensitivity. This leads to a better resistance to stress corrosion cracking and corrosion fatigue. Due to this optimization, a weight reduction is feasible without the use of expensive alloying elements. To understand the damage mechanism a comprehensive testing procedure was performed on homogeneous and gradient steels. Some results regarding the fracture mechanic behavior of such steels will be discussed.

UHPLC-UV method for the determination of flavonoids in dietary supplements and for evaluation of their antioxidant activities.

PubMed

Magiera, Sylwia; Baranowska, Irena; Lautenszleger, Anna

2015-01-01

A simple and rapid ultra-high performance liquid chromatographic (UHPLC) method coupled with an ultraviolet detector (UV) has been developed and validated for the separation and determination of 14 major flavonoids ((±)-catechin, (-)-epicatechin, glycitin, (-)-epicatechin gallate, rutin, quercitrin, hesperidine, neohesperidine, daidzein, glycitein, quercetin, genistein, hesperetin, and biochanin A) in herbal dietary supplements. The flavonoids have been separated on a Chromolith Fast Gradient Monolithic RP-18e column utilizing a mobile phase composed of 0.05% trifluoroacetic acid in water and acetonitrile in gradient elution mode. Under these conditions, flavonoids were separated in a 5 min run. The selectivity of the developed UHPLC-UV method was confirmed by comparison with ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) analysis. The validation parameters such as linearity, sensitivity, precision, and accuracy were found to be highly satisfactory. The optimized method was applied to determination of flavonoids in different dietary supplements. Additionally, the developed HPLC-UV method combined with 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) assay was used in the evaluation of antioxidant activity of the selected flavonoids. Copyright © 2014 Elsevier B.V. All rights reserved.

Advanced laser diagnostics for diamond deposition research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kruger, C.H.; Owano, T.G.; Wahl, E.H.

Chemical Vapor Deposition (CVD) using thermal plasmas is attractive for diamond synthesis applications due to the inherently high reactant densities and throughput, but the associated high gas-phase collision rates in the boundary layer above the substrate produce steep thermal and species gradients which can drive the complex plasma chemistry away from optimal conditions. To understand and control these environments, accurate measurements of temperature and species concentrations within the reacting boundary layer are needed. This is challenging in atmospheric pressure reactors due to the highly luminous environment, steep thermal and species gradients, and small spatial scales. The applicability of degenerate four-wavemore » mixing (DFWM) as a spectroscopic probe of atmospheric pressure reacting plasmas has been investigated. This powerful, nonlinear technique has been applied to the measurement of temperature and radical species concentrations in the boundary layer of a diamond growth substrate immersed in a flowing atmospheric pressure plasma. In-situ measurements of CH and C{sub 2} radicals have been performed to determine spatially resolved profiles of vibrational temperature, rotational temperature, and species concentration. Results of these measurements are compared with the predictions of a detailed numerical simulation.« less

Semiconductor apparatus utilizing gradient freeze and liquid-solid techniques

NASA Technical Reports Server (NTRS)

Fleurial, Jean-Pierre (Inventor); Caillat, Thierry F. (Inventor); Borshchevsky, Alexander (Inventor)

1998-01-01

Transition metals of Group VIII (Co, Rh and Ir) have been prepared as semiconductor compounds with the general formula TSb.sub.3. The skutterudite-type crystal lattice structure of these semiconductor compounds and their enhanced thermoelectric properties results in semiconductor materials which may be used in the fabrication of thermoelectric elements to substantially improve the efficiency of the resulting thermoelectric device. Semiconductor materials having the desired skutterudite-type crystal lattice structure may be prepared in accordance with the present invention by using vertical gradient freezing techniques and/or liquid phase sintering techniques. Measurements of electrical and thermal transport properties of selected semiconductor materials prepared in accordance with the present invention, demonstrated high Hall mobilities (up to 1200 cm.sup.2.V.sup.-1.s.sup.-1) and good Seebeck coefficients (up to 150 .mu.VK.sup.-1 between 300.degree. C. and 700.degree. C.). Optimizing the transport properties of semiconductor materials prepared from elemental mixtures Co, Rh, Ir and Sb resulted in a substantial increase in the thermoelectric figure of merit (ZT) at temperatures as high as 400.degree. C. for thermoelectric elements fabricated from such semiconductor materials.

A Scalable, Parallel Approach for Multi-Point, High-Fidelity Aerostructural Optimization of Aircraft Configurations

NASA Astrophysics Data System (ADS)

Kenway, Gaetan K. W.

This thesis presents new tools and techniques developed to address the challenging problem of high-fidelity aerostructural optimization with respect to large numbers of design variables. A new mesh-movement scheme is developed that is both computationally efficient and sufficiently robust to accommodate large geometric design changes and aerostructural deformations. A fully coupled Newton-Krylov method is presented that accelerates the convergence of aerostructural systems and provides a 20% performance improvement over the traditional nonlinear block Gauss-Seidel approach and can handle more exible structures. A coupled adjoint method is used that efficiently computes derivatives for a gradient-based optimization algorithm. The implementation uses only machine accurate derivative techniques and is verified to yield fully consistent derivatives by comparing against the complex step method. The fully-coupled large-scale coupled adjoint solution method is shown to have 30% better performance than the segregated approach. The parallel scalability of the coupled adjoint technique is demonstrated on an Euler Computational Fluid Dynamics (CFD) model with more than 80 million state variables coupled to a detailed structural finite-element model of the wing with more than 1 million degrees of freedom. Multi-point high-fidelity aerostructural optimizations of a long-range wide-body, transonic transport aircraft configuration are performed using the developed techniques. The aerostructural analysis employs Euler CFD with a 2 million cell mesh and a structural finite element model with 300 000 DOF. Two design optimization problems are solved: one where takeoff gross weight is minimized, and another where fuel burn is minimized. Each optimization uses a multi-point formulation with 5 cruise conditions and 2 maneuver conditions. The optimization problems have 476 design variables are optimal results are obtained within 36 hours of wall time using 435 processors. The TOGW minimization results in a 4.2% reduction in TOGW with a 6.6% fuel burn reduction, while the fuel burn optimization resulted in a 11.2% fuel burn reduction with no change to the takeoff gross weight.

The Modified HZ Conjugate Gradient Algorithm for Large-Scale Nonsmooth Optimization.

PubMed

Yuan, Gonglin; Sheng, Zhou; Liu, Wenjie

2016-01-01

In this paper, the Hager and Zhang (HZ) conjugate gradient (CG) method and the modified HZ (MHZ) CG method are presented for large-scale nonsmooth convex minimization. Under some mild conditions, convergent results of the proposed methods are established. Numerical results show that the presented methods can be better efficiency for large-scale nonsmooth problems, and several problems are tested (with the maximum dimensions to 100,000 variables).

Technical Note: Dose gradients and prescription isodose in orthovoltage stereotactic radiosurgery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fagerstrom, Jessica M., E-mail: fagerstrom@wisc.edu; Bender, Edward T.; Culberson, Wesley S.

Purpose: The purpose of this work is to examine the trade-off between prescription isodose and dose gradients in orthovoltage stereotactic radiosurgery. Methods: Point energy deposition kernels (EDKs) describing photon and electron transport were calculated using Monte Carlo methods. EDKs were generated from 10  to 250 keV, in 10 keV increments. The EDKs were converted to pencil beam kernels and used to calculate dose profiles through isocenter from a 4π isotropic delivery from all angles of circularly collimated beams. Monoenergetic beams and an orthovoltage polyenergetic spectrum were analyzed. The dose gradient index (DGI) is the ratio of the 50% prescription isodosemore » volume to the 100% prescription isodose volume and represents a metric by which dose gradients in stereotactic radiosurgery (SRS) may be evaluated. Results: Using the 4π dose profiles calculated using pencil beam kernels, the relationship between DGI and prescription isodose was examined for circular cones ranging from 4 to 18 mm in diameter and monoenergetic photon beams with energies ranging from 20 to 250 keV. Values were found to exist for prescription isodose that optimize DGI. Conclusions: The relationship between DGI and prescription isodose was found to be dependent on both field size and energy. Examining this trade-off is an important consideration for designing optimal SRS systems.« less

Development path and current status of the NANIVID: a new device for cancer cell studies

NASA Astrophysics Data System (ADS)

Raja, Waseem Khan; Padgen, Michael R.; Williams, James K.; Wyckoff, Jeffrey; Condeelis, John; Castracane, James

2011-02-01

Cancer cells create a unique microenvironment in vivo which enables migration to distant organs. To better understand the tumor microenvironment, special tools and devices are required to monitor the interactions between different cell types and the effects of particular chemical gradients. This study presents the design and optimization of a new, versatile chemotaxis device called the NANIVID (NANo IntraVital Device). The device is fabricated using BioMEMS techniques and consists of etched and bonded Pyrex substrates, a soluble factor reservoir, fluorescent tracking beads and a microelectrode array for cell quantification. The reservoir contains a customized hydrogel blend loaded with EGF which diffuses out of the hydrogel to create a chemotactic gradient. This reservoir sustains a steady release of growth factor into the surrounding environment for many hours and establishes a concentration gradient that attracts specific cells to the device. In addition to a cell collection tool, the NANIVID can be modified to act as a delivery vehicle for the local generation of alternate soluble factor gradients to initiate controlled changes to the microenvironment such as hypoxia, ECM stiffness and etc. The focus of this study is to design and optimize the new device for wide ranging studies of breast cancer cell dynamics in vitro and ultimately, implantation for in vivo work.

Microstructure and Property Modifications of Cold Rolled IF Steel by Local Laser Annealing

NASA Astrophysics Data System (ADS)

Hallberg, Håkan; Adamski, Frédéric; Baïz, Sarah; Castelnau, Olivier

2017-10-01

Laser annealing experiments are performed on cold rolled IF steel whereby highly localized microstructure and property modification are achieved. The microstructure is seen to develop by strongly heterogeneous recrystallization to provide steep gradients, across the submillimeter scale, of grain size and crystallographic texture. Hardness mapping by microindentation is used to reveal the corresponding gradients in macroscopic properties. A 2D level set model of the microstructure development is established as a tool to further optimize the method and to investigate, for example, the development of grain size variations due to the strong and transient thermal gradient. Particular focus is given to the evolution of the beneficial γ-fiber texture during laser annealing. The simulations indicate that the influence of selective growth based on anisotropic grain boundary properties only has a minor effect on texture evolution compared to heterogeneous stored energy, temperature variations, and nucleation conditions. It is also shown that although the α-fiber has an initial frequency advantage, the higher probability of γ-nucleation, in combination with a higher stored energy driving force in this fiber, promotes a stronger presence of the γ-fiber as also observed in experiments.

Energy Neutral Wireless Bolt for Safety Critical Fastening

PubMed Central

Seyoum, Biruk B.

2017-01-01

Thermoelectric generators (TEGs) are now capable of powering the abundant low power electronics from very small (just a few degrees Celsius) temperature gradients. This factor along with the continuously lowering cost and size of TEGs, has contributed to the growing number of miniaturized battery-free sensor modules powered by TEGs. In this article, we present the design of an ambient-powered wireless bolt for high-end electro-mechanical systems. The bolt is equipped with a temperature sensor and a low power RF chip powered from a TEG. A DC-DC converter interfacing the TEG with the RF chip is used to step-up the low TEG voltage. The work includes the characterizations of different TEGs and DC-DC converters to determine the optimal design based on the amount of power that can be generated from a TEG under different loads and at temperature gradients typical of industrial environments. A prototype system was implemented and the power consumption of this system under different conditions was also measured. Results demonstrate that the power generated by the TEG at very low temperature gradients is sufficient to guarantee continuous wireless monitoring of the critical fasteners in critical systems such as avionics, motorsport and aerospace. PMID:28954432

Energy Neutral Wireless Bolt for Safety Critical Fastening.

PubMed

Seyoum, Biruk B; Rossi, Maurizio; Brunelli, Davide

2017-09-26

Thermoelectric generators (TEGs) are now capable of powering the abundant low power electronics from very small (just a few degrees Celsius) temperature gradients. This factor along with the continuously lowering cost and size of TEGs, has contributed to the growing number of miniaturized battery-free sensor modules powered by TEGs. In this article, we present the design of an ambient-powered wireless bolt for high-end electro-mechanical systems. The bolt is equipped with a temperature sensor and a low power RF chip powered from a TEG. A DC-DC converter interfacing the TEG with the RF chip is used to step-up the low TEG voltage. The work includes the characterizations of different TEGs and DC-DC converters to determine the optimal design based on the amount of power that can be generated from a TEG under different loads and at temperature gradients typical of industrial environments. A prototype system was implemented and the power consumption of this system under different conditions was also measured. Results demonstrate that the power generated by the TEG at very low temperature gradients is sufficient to guarantee continuous wireless monitoring of the critical fasteners in critical systems such as avionics, motorsport and aerospace.

A microfluidic multi-injector for gradient generation.

PubMed

Chung, Bong Geun; Lin, Francis; Jeon, Noo Li

2006-06-01

This paper describes a microfluidic multi-injector (MMI) that can generate temporal and spatial concentration gradients of soluble molecules. Compared to conventional glass micropipette-based methods that generate a single gradient, the MMI exploits microfluidic integration and actuation of multiple pulsatile injectors to generate arbitrary overlapping gradients that have not previously been possible. The MMI device is fabricated in poly(dimethylsiloxane) (PDMS) using multi-layer soft lithography and consists of fluidic channels and control channels with pneumatically actuated on-chip barrier valves. Repetitive actuation of on-chip valves control pulsatile release of solution that establishes microscopic chemical gradients around the orifice. The volume of solution released per actuation cycle ranged from 30 picolitres to several hundred picolitres and increased linearly with the duration of valve opening. The shape of the measured gradient profile agreed closely with the simulated diffusion profile from a point source. Steady state gradient profiles could be attained within 10 minutes, or less with an optimized pulse sequence. Overlapping gradients from 2 injectors were generated and characterized to highlight the advantages of MMI over conventional micropipette assays. The MMI platform should be useful for a wide range of basic and applied studies on chemotaxis and axon guidance.

Simulation and analysis of the interactions between split gradient coils and a split magnet cryostat in an MRI-PET system.

PubMed

Liu, Limei; Sanchez-Lopez, Hector; Poole, Michael; Liu, Feng; Crozier, Stuart

2012-09-01

Splitting a magnetic resonance imaging (MRI) magnet into two halves can provide a central region to accommodate other modalities, such as positron emission tomography (PET). This approach, however, produces challenges in the design of the gradient coils in terms of gradient performance and fabrication. In this paper, the impact of a central gap in a split MRI system was theoretically studied by analysing the performance of split, actively-shielded transverse gradient coils. In addition, the effects of the eddy currents induced in the cryostat on power loss, mechanical vibration and magnetic field harmonics were also investigated. It was found, as expected, that the gradient performance tended to decrease as the central gap increased. Furthermore, the effects of the eddy currents were heightened as a consequence of splitting the gradient assembly into two halves. An optimal central gap size was found, such that the split gradient coils designed with this central gap size could produce an engineering solution with an acceptable trade-off between gradient performance and eddy current effects. These investigations provide useful information on the inherent trade-offs in hybrid MRI imaging systems. Copyright © 2012 Elsevier Inc. All rights reserved.

A Motor-Gradient and Clustering Model of the Centripetal Motility of MTOCs in Meiosis I of Mouse Oocytes

PubMed Central

2016-01-01

Asters nucleated by Microtubule (MT) organizing centers (MTOCs) converge on chromosomes during spindle assembly in mouse oocytes undergoing meiosis I. Time-lapse imaging suggests that this centripetal motion is driven by a biased ‘search-and-capture’ mechanism. Here, we develop a model of a random walk in a drift field to test the nature of the bias and the spatio-temporal dynamics of the search process. The model is used to optimize the spatial field of drift in simulations, by comparison to experimental motility statistics. In a second step, this optimized gradient is used to determine the location of immobilized dynein motors and MT polymerization parameters, since these are hypothesized to generate the gradient of forces needed to move MTOCs. We compare these scenarios to self-organized mechanisms by which asters have been hypothesized to find the cell-center- MT pushing at the cell-boundary and clustering motor complexes. By minimizing the error between simulation outputs and experiments, we find a model of “pulling” by a gradient of dynein motors alone can drive the centripetal motility. Interestingly, models of passive MT based “pushing” at the cortex, clustering by cross-linking motors and MT-dynamic instability gradients alone, by themselves do not result in the observed motility. The model predicts the sensitivity of the results to motor density and stall force, but not MTs per aster. A hybrid model combining a chromatin-centered immobilized dynein gradient, diffusible minus-end directed clustering motors and pushing at the cell cortex, is required to comprehensively explain the available data. The model makes experimentally testable predictions of a spatial bias and self-organized mechanisms by which MT asters can find the center of a large cell. PMID:27706163

A Motor-Gradient and Clustering Model of the Centripetal Motility of MTOCs in Meiosis I of Mouse Oocytes.

PubMed

Khetan, Neha; Athale, Chaitanya A

2016-10-01

Asters nucleated by Microtubule (MT) organizing centers (MTOCs) converge on chromosomes during spindle assembly in mouse oocytes undergoing meiosis I. Time-lapse imaging suggests that this centripetal motion is driven by a biased 'search-and-capture' mechanism. Here, we develop a model of a random walk in a drift field to test the nature of the bias and the spatio-temporal dynamics of the search process. The model is used to optimize the spatial field of drift in simulations, by comparison to experimental motility statistics. In a second step, this optimized gradient is used to determine the location of immobilized dynein motors and MT polymerization parameters, since these are hypothesized to generate the gradient of forces needed to move MTOCs. We compare these scenarios to self-organized mechanisms by which asters have been hypothesized to find the cell-center- MT pushing at the cell-boundary and clustering motor complexes. By minimizing the error between simulation outputs and experiments, we find a model of "pulling" by a gradient of dynein motors alone can drive the centripetal motility. Interestingly, models of passive MT based "pushing" at the cortex, clustering by cross-linking motors and MT-dynamic instability gradients alone, by themselves do not result in the observed motility. The model predicts the sensitivity of the results to motor density and stall force, but not MTs per aster. A hybrid model combining a chromatin-centered immobilized dynein gradient, diffusible minus-end directed clustering motors and pushing at the cell cortex, is required to comprehensively explain the available data. The model makes experimentally testable predictions of a spatial bias and self-organized mechanisms by which MT asters can find the center of a large cell.

Seafloor Topography Estimation from Gravity Gradient Using Simulated Annealing

NASA Astrophysics Data System (ADS)

Yang, J.; Jekeli, C.; Liu, L.

2017-12-01

Inferring seafloor topography from gravimetry is an indirect yet proven and efficient means to map the ocean floor. Standard techniques rely on an approximate, linear relationship (Parker's formula) between topography and gravity. It has been reported that in the very rugged areas the discrepancies between prediction and ship soundings are very large, partly because the linear term of Parker's infinite series is dominant only in areas where the local topography is small compared with the regional topography. The validity of the linear approximation is therefore in need of analysis. In this study the nonlinear effects caused by terrain are quantified by both numerical tests and an algorithmic approach called coherency. It is shown that the nonlinear effects are more significant at higher frequencies, which suggests that estimation algorithms with nonlinear approximation in the modeled relationship between gravity gradient and topography should be developed in preparation for future high-resolution gravity gradient missions. The simulated annealing (SA) method is such an optimization technique that can process nonlinear inverse problems, and is used to estimate the seafloor topography parameters in a forward model by minimizing the difference between the observed and forward-computed vertical gravity gradients. Careful treatments like choosing suitable truncation distance, padding the vicinity of the study area with a known topography model, and using the relative cost function, are considered to improve the estimation accuracy. This study uses the gravity gradient, which is more sensitive to topography at short wavelengths than gravity anomaly. The gravity gradient data are derived from satellite altimetry, but the SA has no restrictions on data distribution, as required in Parker's infinite series model, thus enabling the use of airborne gravity gradient data, whose survey trajectories are irregular. The SA method is tested in an area of Guyots (E 156°-158° in longitude, N 20°-22° in latitude). Comparison between the estimation and ship sounding shows that half of the discrepancy is within 110 m, which improves the result from standard techniques by 32%.

Optimizing wavefront-guided corrections for highly aberrated eyes in the presence of registration uncertainty

PubMed Central

Shi, Yue; Queener, Hope M.; Marsack, Jason D.; Ravikumar, Ayeswarya; Bedell, Harold E.; Applegate, Raymond A.

2013-01-01

Dynamic registration uncertainty of a wavefront-guided correction with respect to underlying wavefront error (WFE) inevitably decreases retinal image quality. A partial correction may improve average retinal image quality and visual acuity in the presence of registration uncertainties. The purpose of this paper is to (a) develop an algorithm to optimize wavefront-guided correction that improves visual acuity given registration uncertainty and (b) test the hypothesis that these corrections provide improved visual performance in the presence of these uncertainties as compared to a full-magnitude correction or a correction by Guirao, Cox, and Williams (2002). A stochastic parallel gradient descent (SPGD) algorithm was used to optimize the partial-magnitude correction for three keratoconic eyes based on measured scleral contact lens movement. Given its high correlation with logMAR acuity, the retinal image quality metric log visual Strehl was used as a predictor of visual acuity. Predicted values of visual acuity with the optimized corrections were validated by regressing measured acuity loss against predicted loss. Measured loss was obtained from normal subjects viewing acuity charts that were degraded by the residual aberrations generated by the movement of the full-magnitude correction, the correction by Guirao, and optimized SPGD correction. Partial-magnitude corrections optimized with an SPGD algorithm provide at least one line improvement of average visual acuity over the full magnitude and the correction by Guirao given the registration uncertainty. This study demonstrates that it is possible to improve the average visual acuity by optimizing wavefront-guided correction in the presence of registration uncertainty. PMID:23757512

A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations

NASA Technical Reports Server (NTRS)

Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw

2005-01-01

A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.

Optimal coherent control of dissipative N -level systems

NASA Astrophysics Data System (ADS)

Jirari, H.; Pötz, W.

2005-07-01

General optimal coherent control of dissipative N -level systems in the Markovian time regime is formulated within Pointryagin’s principle and the Lindblad equation. In the present paper, we study feasibility and limitations of steering of dissipative two-, three-, and four-level systems from a given initial pure or mixed state into a desired final state under the influence of an external electric field. The time evolution of the system is computed within the Lindblad equation and a conjugate gradient method is used to identify optimal control fields. The influence of both field-independent population and polarization decay on achieving the objective is investigated in systematic fashion. It is shown that, for realistic dephasing times, optimum control fields can be identified which drive the system into the target state with very high success rate and in economical fashion, even when starting from a poor initial guess. Furthermore, the optimal fields obtained give insight into the system dynamics. However, if decay rates of the system cannot be subjected to electromagnetic control, the dissipative system cannot be maintained in a specific pure or mixed state, in general.

Optimization of CO2 Surface Flux using GOSAT Total Column CO2: First Results for 2009-2010

NASA Astrophysics Data System (ADS)

Basu, S.; Houweling, S.

2011-12-01

Constraining surface flux estimates of CO2 using satellite measurements has been one of the long-standing goals of the atmospheric inverse modeling community. We present the first results of inverting GOSAT total column CO2 measurements for obtaining global monthly CO2 flux maps over one year (June 2009 to May 2010). We use the SRON RemoTeC retrieval of CO2 for our inversions. The SRON retrieval has been shown to have no bias when compared to TCCON total column measurements, and latitudinal gradients of the retrieved CO2 are consistent with gradients deduced from the surface flask network [Butz et al, 2011]. This makes this retrieval an ideal candidate for atmospheric inversions, which are highly sensitive to spurious gradients. Our inversion system is analogous to the CarbonTracker (CT) data assimilation system; it is initialized with the prior CO2 fluxes of CT, and uses the same atmospheric transport model, i.e., TM5. The two major differences are (a) we add GOSAT CO2 data to the inversion in addition to flask data, and (b) we use a 4DVAR optimization system instead of a Kalman filter. We compare inversions using (a) only GOSAT total column CO2 measurements, (b) only surface flask CO2 measurements, and (c) the joint data set of GOSAT and surface flask measurements. We validate GOSAT-only inversions against the NOAA surface flask network and joint inversions against CONTRAIL and other aircraft campaigns. We see that inverted fluxes from a GOSAT-only inversion are consistent with fluxes from a stations-only inversion, reaffirming the low biases in SRON retrievals. From the joint inversion, we estimate the amount of added constraints upon adding GOSAT total column measurements to existing surface layer measurements.

Topology optimization of natural convection: Flow in a differentially heated cavity

NASA Astrophysics Data System (ADS)

Saglietti, Clio; Schlatter, Philipp; Berggren, Martin; Henningson, Dan

2017-11-01

The goal of the present work is to develop methods for optimization of the design of natural convection cooled heat sinks, using resolved simulation of both fluid flow and heat transfer. We rely on mathematical programming techniques combined with direct numerical simulations in order to iteratively update the topology of a solid structure towards optimality, i.e. until the design yielding the best performance is found, while satisfying a specific set of constraints. The investigated test case is a two-dimensional differentially heated cavity, in which the two vertical walls are held at different temperatures. The buoyancy force induces a swirling convective flow around a solid structure, whose topology is optimized to maximize the heat flux through the cavity. We rely on the spectral-element code Nek5000 to compute a high-order accurate solution of the natural convection flow arising from the conjugate heat transfer in the cavity. The laminar, steady-state solution of the problem is evaluated with a time-marching scheme that has an increased convergence rate; the actual iterative optimization is obtained using a steepest-decent algorithm, and the gradients are conveniently computed using the continuous adjoint equations for convective heat transfer.

Rat Bone Marrow-Derived Schwann-Like Cells Differentiated by the Optimal Inducers Combination on Microfluidic Chip and Their Functional Performance

PubMed Central

Lv, Decheng

2012-01-01

Numerous researches demonstrated the possibility of derivation of Schwann-like (SC-like) cells in vitro from bone marrow stromal cells (BMSCs). However, the concentration of the induce factors were different in those studies, especially for the critical factors forskolin (FSK) and β-heregulin (HRG). Here, we used a new and useful method to build an integrated microfluidic chip for rapid analyses of the optimal combination between the induce factors FSK and HRG. The microfluidic device was mainly composed of an upstream concentration gradient generator (CGG) and a downstream cell culture module. Rat BMSCs were cultured in the cell chambers for 11 days at the different concentrations of induce factors generated by CGG. The result of immunofluorescence staining on-chip showed that the group of 4.00 µM FSK and 250.00 ng/ml HRG presented an optimal effect to promote the derivation of SC-like cells. Moreover, the optimal SC-like cells obtained on-chip were further tested using DRG co-culture and ELISA to detect their functional performance. Our findings demonstrate that SC-like cells could be obtained with high efficiency and functional performance in the optimal inducers combination. PMID:22880114

Rat bone marrow-derived Schwann-like cells differentiated by the optimal inducers combination on microfluidic chip and their functional performance.

PubMed

Tian, Xiliang; Wang, Shouyu; Zhang, Zhen; Lv, Decheng

2012-01-01

Numerous researches demonstrated the possibility of derivation of Schwann-like (SC-like) cells in vitro from bone marrow stromal cells (BMSCs). However, the concentration of the induce factors were different in those studies, especially for the critical factors forskolin (FSK) and β-heregulin (HRG). Here, we used a new and useful method to build an integrated microfluidic chip for rapid analyses of the optimal combination between the induce factors FSK and HRG. The microfluidic device was mainly composed of an upstream concentration gradient generator (CGG) and a downstream cell culture module. Rat BMSCs were cultured in the cell chambers for 11 days at the different concentrations of induce factors generated by CGG. The result of immunofluorescence staining on-chip showed that the group of 4.00 µM FSK and 250.00 ng/ml HRG presented an optimal effect to promote the derivation of SC-like cells. Moreover, the optimal SC-like cells obtained on-chip were further tested using DRG co-culture and ELISA to detect their functional performance. Our findings demonstrate that SC-like cells could be obtained with high efficiency and functional performance in the optimal inducers combination.