Nonlinear large-scale optimization with WORHP
NASA Astrophysics Data System (ADS)
Nikolayzik, Tim; Büskens, Christof; Gerdts, Matthias
Nonlinear optimization has grown to a key technology in many areas of aerospace industry, e.g. satellite control, shape-optimization, aerodynamamics, trajectory planning, reentry prob-lems, interplanetary flights. One of the most extensive areas is the optimization of trajectories for aerospace applications. These problems typically are discretized optimal control problems, which leads to large sparse nonlinear optimization problems. In the end all these different problems from different areas can be described in the general formulation as a nonlinear opti-mization problem. WORHP is designed to solve nonlinear optimization problems with more then one million variables and one million constraints. WORHP uses a lot of different advanced techniques, e.g. reverse communication, to organize the optimization process as efficient and controllable by the user as possible. The solver has nine different interfaces, e.g. to MAT-LAB/SIMULINK and AMPL. Tests of WORHP had shown that WORHP is a very robust and promising solver. Several examples from space applications will be presented.
Comparative study of large-scale nonlinear optimization methods
Alemzadeh, S.A.
1987-01-01
Solving large-scale nonlinear optimization problems has been one of the active research areas for the last twenty years. Several heuristic algorithms with codes have been developed and implemented since 1966. This study explores the motivation and basic mathematical ideas leading to the development of MINOS-1.0, GRG-2,and MINOS-5.0 algorithms and their codes. The reliability, accuracy, and complexity of the algorithms and software depend upon their use of the gradient, Jacobian, and the Hessian. MINOS-1.0 and GRG-2 incorporate all of the input and output features, while MINOS-1.0 is not able to handle the nonlinearly constrained problems, and GRG-2 is not able to handle large-scale problems, MINOS-5.0 is a robust and an efficient software that incorporates all input, output features.
Large scale nonlinear programming for the optimization of spacecraft trajectories
NASA Astrophysics Data System (ADS)
Arrieta-Camacho, Juan Jose
. Future research directions are identified, involving the automatic scheduling and optimization of trajectory correction maneuvers. The sensitivity information provided by the methodology is expected to be invaluable in such research pursuit. The collocation scheme and nonlinear programming algorithm presented in this work, complement other existing methodologies by providing reliable and efficient numerical methods able to handle large scale, nonlinear dynamic models.
Developing and Understanding Methods for Large-Scale Nonlinear Optimization
2006-07-24
algorithms for large-scale uncon- strained and constrained optimization problems, including limited-memory methods for problems with -2- many thousands...34Published in peer-reviewed journals" E. Eskow, B. Bader, R. Byrd, S. Crivelli, T. Head-Gordon, V. Lamberti and R. Schnabel, "An optimization approach to the
Developing and Understanding Methods for Large Scale Nonlinear Optimization
2001-12-01
development of new algorithms for large-scale uncon- strained and constrained optimization problems, including limited-memory methods for problems with...analysis of tensor and SQP methods for singular con- strained optimization", to appear in SIAM Journal on Optimization. Published in peer-reviewed...Mathematica, Vol III, Journal der Deutschen Mathematiker-Vereinigung, 1998. S. Crivelli, B. Bader, R. Byrd, E. Eskow, V. Lamberti , R.Schnabel and T
Large Scale Nonlinear Programming.
1978-06-15
KEY WORDS (Conhinu. as, t.n.t.. aid. if nic••iary aid ld.ntify by block n,a,b.r) L. In,~~~ IP!CIE LARGE SCALE OPTIMIZATION APPLICATIONS OF NONLINEAR ... NONLINEAR PROGRAMMING by Garth P. McCormick 1. Introduction The general mathematical programming ( optimization ) problem can be stated in the following form...because the difficulty in solving a general nonlinear optimization problem has a~ much to do with the nature of the functions involved as it does with the
Fuzzy Adaptive Decentralized Optimal Control for Strict Feedback Nonlinear Large-Scale Systems.
Sun, Kangkang; Sui, Shuai; Tong, Shaocheng
2017-05-16
This paper considers the optimal decentralized fuzzy adaptive control design problem for a class of interconnected large-scale nonlinear systems in strict feedback form and with unknown nonlinear functions. The fuzzy logic systems are introduced to learn the unknown dynamics and cost functions, respectively, and a state estimator is developed. By applying the state estimator and the backstepping recursive design algorithm, a decentralized feedforward controller is established. By using the backstepping decentralized feedforward control scheme, the considered interconnected large-scale nonlinear system in strict feedback form is changed into an equivalent affine large-scale nonlinear system. Subsequently, an optimal decentralized fuzzy adaptive control scheme is constructed. The whole optimal decentralized fuzzy adaptive controller is composed of a decentralized feedforward control and an optimal decentralized control. It is proved that the developed optimal decentralized controller can ensure that all the variables of the control system are uniformly ultimately bounded, and the cost functions are the smallest. Two simulation examples are provided to illustrate the validity of the developed optimal decentralized fuzzy adaptive control scheme.
Large scale nonlinear numerical optimal control for finite element models of flexible structures
NASA Technical Reports Server (NTRS)
Shoemaker, Christine A.; Liao, Li-Zhi
1990-01-01
This paper discusses the development of large scale numerical optimal control algorithms for nonlinear systems and their application to finite element models of structures. This work is based on our expansion of the optimal control algorithm (DDP) in the following steps: improvement of convergence for initial policies in non-convex regions, development of a numerically accurate penalty function method approach for constrained DDP problems, and parallel processing on supercomputers. The expanded constrained DDP algorithm was applied to the control of a four-bay, two dimensional truss with 12 soft members, which generates geometric nonlinearities. Using an explicit finite element model to describe the structural system requires 32 state variables and 10,000 time steps. Our numerical results indicate that for constrained or unconstrained structural problems with nonlinear dynamics, the results obtained by our expanded constrained DDP are significantly better than those obtained using linear-quadratic feedback control.
On large-scale nonlinear programming techniques for solving optimal control problems
Faco, J.L.D.
1994-12-31
The formulation of decision problems by Optimal Control Theory allows the consideration of their dynamic structure and parameters estimation. This paper deals with techniques for choosing directions in the iterative solution of discrete-time optimal control problems. A unified formulation incorporates nonlinear performance criteria and dynamic equations, time delays, bounded state and control variables, free planning horizon and variable initial state vector. In general they are characterized by a large number of variables, mostly when arising from discretization of continuous-time optimal control or calculus of variations problems. In a GRG context the staircase structure of the jacobian matrix of the dynamic equations is exploited in the choice of basic and super basic variables and when changes of basis occur along the process. The search directions of the bound constrained nonlinear programming problem in the reduced space of the super basic variables are computed by large-scale NLP techniques. A modified Polak-Ribiere conjugate gradient method and a limited storage quasi-Newton BFGS method are analyzed and modifications to deal with the bounds on the variables are suggested based on projected gradient devices with specific linesearches. Some practical models are presented for electric generation planning and fishery management, and the application of the code GRECO - Gradient REduit pour la Commande Optimale - is discussed.
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.
Large-scale structural optimization
NASA Technical Reports Server (NTRS)
Sobieszczanski-Sobieski, J.
1983-01-01
Problems encountered by aerospace designers in attempting to optimize whole aircraft are discussed, along with possible solutions. Large scale optimization, as opposed to component-by-component optimization, is hindered by computational costs, software inflexibility, concentration on a single, rather than trade-off, design methodology and the incompatibility of large-scale optimization with single program, single computer methods. The software problem can be approached by placing the full analysis outside of the optimization loop. Full analysis is then performed only periodically. Problem-dependent software can be removed from the generic code using a systems programming technique, and then embody the definitions of design variables, objective function and design constraints. Trade-off algorithms can be used at the design points to obtain quantitative answers. Finally, decomposing the large-scale problem into independent subproblems allows systematic optimization of the problems by an organization of people and machines.
Carlberg, Kevin Thomas; Drohmann, Martin; Tuminaro, Raymond S.; Boggs, Paul T.; Ray, Jaideep; van Bloemen Waanders, Bart Gustaaf
2014-10-01
Model reduction for dynamical systems is a promising approach for reducing the computational cost of large-scale physics-based simulations to enable high-fidelity models to be used in many- query (e.g., Bayesian inference) and near-real-time (e.g., fast-turnaround simulation) contexts. While model reduction works well for specialized problems such as linear time-invariant systems, it is much more difficult to obtain accurate, stable, and efficient reduced-order models (ROMs) for systems with general nonlinearities. This report describes several advances that enable nonlinear reduced-order models (ROMs) to be deployed in a variety of time-critical settings. First, we present an error bound for the Gauss-Newton with Approximated Tensors (GNAT) nonlinear model reduction technique. This bound allows the state-space error for the GNAT method to be quantified when applied with the backward Euler time-integration scheme. Second, we present a methodology for preserving classical Lagrangian structure in nonlinear model reduction. This technique guarantees that important properties--such as energy conservation and symplectic time-evolution maps--are preserved when performing model reduction for models described by a Lagrangian formalism (e.g., molecular dynamics, structural dynamics). Third, we present a novel technique for decreasing the temporal complexity --defined as the number of Newton-like iterations performed over the course of the simulation--by exploiting time-domain data. Fourth, we describe a novel method for refining projection-based reduced-order models a posteriori using a goal-oriented framework similar to mesh-adaptive h -refinement in finite elements. The technique allows the ROM to generate arbitrarily accurate solutions, thereby providing the ROM with a 'failsafe' mechanism in the event of insufficient training data. Finally, we present the reduced-order model error surrogate (ROMES) method for statistically quantifying reduced- order-model errors. This
NASA Astrophysics Data System (ADS)
Mohamed, Nur Syarafina; Mamat, Mustafa; Rivaie, Mohd
2016-11-01
Conjugate gradient (CG) methods are one of the tools in optimization. Due to its low computational memory requirement, this method is used in solving several of nonlinear unconstrained optimization problems from designs, economics, physics and engineering. In this paper, a new modification of CG family coefficient (βk) is proposed and posses global convergence under exact line search direction. Numerical experimental results based on the number of iterations and central processing unit (CPU) time show that the new βk performs better than some other well known CG methods under some standard test functions.
Distributed Coordinated Control of Large-Scale Nonlinear Networks
Kundu, Soumya; Anghel, Marian
2015-11-08
We provide a distributed coordinated approach to the stability analysis and control design of largescale nonlinear dynamical systems by using a vector Lyapunov functions approach. In this formulation the large-scale system is decomposed into a network of interacting subsystems and the stability of the system is analyzed through a comparison system. However finding such comparison system is not trivial. In this work, we propose a sum-of-squares based completely decentralized approach for computing the comparison systems for networks of nonlinear systems. Moreover, based on the comparison systems, we introduce a distributed optimal control strategy in which the individual subsystems (agents) coordinatemore » with their immediate neighbors to design local control policies that can exponentially stabilize the full system under initial disturbances.We illustrate the control algorithm on a network of interacting Van der Pol systems.« less
Distributed Coordinated Control of Large-Scale Nonlinear Networks
Kundu, Soumya; Anghel, Marian
2015-11-08
We provide a distributed coordinated approach to the stability analysis and control design of largescale nonlinear dynamical systems by using a vector Lyapunov functions approach. In this formulation the large-scale system is decomposed into a network of interacting subsystems and the stability of the system is analyzed through a comparison system. However finding such comparison system is not trivial. In this work, we propose a sum-of-squares based completely decentralized approach for computing the comparison systems for networks of nonlinear systems. Moreover, based on the comparison systems, we introduce a distributed optimal control strategy in which the individual subsystems (agents) coordinate with their immediate neighbors to design local control policies that can exponentially stabilize the full system under initial disturbances.We illustrate the control algorithm on a network of interacting Van der Pol systems.
Robust large-scale parallel nonlinear solvers for simulations.
Bader, Brett William; Pawlowski, Roger Patrick; Kolda, Tamara Gibson
2005-11-01
This report documents research to develop robust and efficient solution techniques for solving large-scale systems of nonlinear equations. The most widely used method for solving systems of nonlinear equations is Newton's method. While much research has been devoted to augmenting Newton-based solvers (usually with globalization techniques), little has been devoted to exploring the application of different models. Our research has been directed at evaluating techniques using different models than Newton's method: a lower order model, Broyden's method, and a higher order model, the tensor method. We have developed large-scale versions of each of these models and have demonstrated their use in important applications at Sandia. Broyden's method replaces the Jacobian with an approximation, allowing codes that cannot evaluate a Jacobian or have an inaccurate Jacobian to converge to a solution. Limited-memory methods, which have been successful in optimization, allow us to extend this approach to large-scale problems. We compare the robustness and efficiency of Newton's method, modified Newton's method, Jacobian-free Newton-Krylov method, and our limited-memory Broyden method. Comparisons are carried out for large-scale applications of fluid flow simulations and electronic circuit simulations. Results show that, in cases where the Jacobian was inaccurate or could not be computed, Broyden's method converged in some cases where Newton's method failed to converge. We identify conditions where Broyden's method can be more efficient than Newton's method. We also present modifications to a large-scale tensor method, originally proposed by Bouaricha, for greater efficiency, better robustness, and wider applicability. Tensor methods are an alternative to Newton-based methods and are based on computing a step based on a local quadratic model rather than a linear model. The advantage of Bouaricha's method is that it can use any existing linear solver, which makes it simple to write
Large-scale optimization of neuron arbors
NASA Astrophysics Data System (ADS)
Cherniak, Christopher; Changizi, Mark; Won Kang, Du
1999-05-01
At the global as well as local scales, some of the geometry of types of neuron arbors-both dendrites and axons-appears to be self-organizing: Their morphogenesis behaves like flowing water, that is, fluid dynamically; waterflow in branching networks in turn acts like a tree composed of cords under tension, that is, vector mechanically. Branch diameters and angles and junction sites conform significantly to this model. The result is that such neuron tree samples globally minimize their total volume-rather than, for example, surface area or branch length. In addition, the arbors perform well at generating the cheapest topology interconnecting their terminals: their large-scale layouts are among the best of all such possible connecting patterns, approaching 5% of optimum. This model also applies comparably to arterial and river networks.
Optimal management of large scale aquifers under uncertainty
NASA Astrophysics Data System (ADS)
Ghorbanidehno, H.; Kokkinaki, A.; Kitanidis, P. K.; Darve, E. F.
2016-12-01
Water resources systems, and especially groundwater reservoirs, are a valuable resource that is often being endangered by contamination and over-exploitation. Optimal control techniques can be applied for groundwater management to ensure the long-term sustainability of this vulnerable resource. Linear Quadratic Gaussian (LQG) control is an optimal control method that combines a Kalman filter for real time estimation with a linear quadratic regulator for dynamic optimization. The LQG controller can be used to determine the optimal controls (e.g. pumping schedule) upon receiving feedback about the system from incomplete noisy measurements. However, applying LQG control for systems of large dimension is computationally expensive. This work presents the Spectral Linear Quadratic Gaussian (SpecLQG) control, a new fast LQG controller that can be used for large scale problems. SpecLQG control combines the Spectral Kalman filter, which is a fast Kalman filter algorithm, with an efficient low rank LQR, and provides a practical approach for combined monitoring, parameter estimation, uncertainty quantification and optimal control for linear and weakly non-linear systems. The computational cost of SpecLQG controller scales linearly with the number of unknowns, a great improvement compared to the quadratic cost of basic LQG. We demonstrate the accuracy and computational efficiency of SpecLQG control using two applications: first, a linear validation case for pumping schedule management in a small homogeneous confined aquifer; and second, a larger scale nonlinear case with unknown heterogeneities in aquifer properties and boundary conditions.
The workshop on iterative methods for large scale nonlinear problems
Walker, H.F.; Pernice, M.
1995-12-01
The aim of the workshop was to bring together researchers working on large scale applications with numerical specialists of various kinds. Applications that were addressed included reactive flows (combustion and other chemically reacting flows, tokamak modeling), porous media flows, cardiac modeling, chemical vapor deposition, image restoration, macromolecular modeling, and population dynamics. Numerical areas included Newton iterative (truncated Newton) methods, Krylov subspace methods, domain decomposition and other preconditioning methods, large scale optimization and optimal control, and parallel implementations and software. This report offers a brief summary of workshop activities and information about the participants. Interested readers are encouraged to look into an online proceedings available at http://www.usi.utah.edu/logan.proceedings. In this, the material offered here is augmented with hypertext abstracts that include links to locations such as speakers` home pages, PostScript copies of talks and papers, cross-references to related talks, and other information about topics addresses at the workshop.
Global smoothing and continuation for large-scale molecular optimization
More, J.J.; Wu, Zhijun
1995-10-01
We discuss the formulation of optimization problems that arise in the study of distance geometry, ionic systems, and molecular clusters. We show that continuation techniques based on global smoothing are applicable to these molecular optimization problems, and we outline the issues that must be resolved in the solution of large-scale molecular optimization problems.
Large-Scale Optimization for Bayesian Inference in Complex Systems
Willcox, Karen; Marzouk, Youssef
2013-11-12
The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimization) Project focused on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimization and inversion methods. The project was a collaborative effort among MIT, the University of Texas at Austin, Georgia Institute of Technology, and Sandia National Laboratories. The research was directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. The MIT--Sandia component of the SAGUARO Project addressed the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas--Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to-observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as ``reduce then sample'' and ``sample then reduce.'' In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their
Geospatial Optimization of Siting Large-Scale Solar Projects
Macknick, J.; Quinby, T.; Caulfield, E.; Gerritsen, M.; Diffendorfer, J.; Haines, S.
2014-03-01
Recent policy and economic conditions have encouraged a renewed interest in developing large-scale solar projects in the U.S. Southwest. However, siting large-scale solar projects is complex. In addition to the quality of the solar resource, solar developers must take into consideration many environmental, social, and economic factors when evaluating a potential site. This report describes a proof-of-concept, Web-based Geographical Information Systems (GIS) tool that evaluates multiple user-defined criteria in an optimization algorithm to inform discussions and decisions regarding the locations of utility-scale solar projects. Existing siting recommendations for large-scale solar projects from governmental and non-governmental organizations are not consistent with each other, are often not transparent in methods, and do not take into consideration the differing priorities of stakeholders. The siting assistance GIS tool we have developed improves upon the existing siting guidelines by being user-driven, transparent, interactive, capable of incorporating multiple criteria, and flexible. This work provides the foundation for a dynamic siting assistance tool that can greatly facilitate siting decisions among multiple stakeholders.
Efficient multiobjective optimization scheme for large scale structures
NASA Astrophysics Data System (ADS)
Grandhi, Ramana V.; Bharatram, Geetha; Venkayya, V. B.
1992-09-01
This paper presents a multiobjective optimization algorithm for an efficient design of large scale structures. The algorithm is based on generalized compound scaling techniques to reach the intersection of multiple functions. Multiple objective functions are treated similar to behavior constraints. Thus, any number of objectives can be handled in the formulation. Pseudo targets on objectives are generated at each iteration in computing the scale factors. The algorithm develops a partial Pareto set. This method is computationally efficient due to the fact that it does not solve many single objective optimization problems in reaching the Pareto set. The computational efficiency is compared with other multiobjective optimization methods, such as the weighting method and the global criterion method. Trusses, plate, and wing structure design cases with stress and frequency considerations are presented to demonstrate the effectiveness of the method.
The GRG approach for large-scale optimization
Drud, A.
1994-12-31
The Generalized Reduced Gradient (GRG) algorithm for general Nonlinear Programming (NLP) has been used successfully for over 25 years. The ideas of the original GRG algorithm have been modified and have absorbed developments in unconstrained optimization, linear programming, sparse matrix techniques, etc. The talk will review the essential aspects of the GRG approach and will discuss current development trends, especially related to very large models. Examples will be based on the CONOPT implementation.
Optimal Wind Energy Integration in Large-Scale Electric Grids
NASA Astrophysics Data System (ADS)
Albaijat, Mohammad H.
The major concern in electric grid operation is operating under the most economical and reliable fashion to ensure affordability and continuity of electricity supply. This dissertation investigates the effects of such challenges, which affect electric grid reliability and economic operations. These challenges are: 1. Congestion of transmission lines, 2. Transmission lines expansion, 3. Large-scale wind energy integration, and 4. Phaser Measurement Units (PMUs) optimal placement for highest electric grid observability. Performing congestion analysis aids in evaluating the required increase of transmission line capacity in electric grids. However, it is necessary to evaluate expansion of transmission line capacity on methods to ensure optimal electric grid operation. Therefore, the expansion of transmission line capacity must enable grid operators to provide low-cost electricity while maintaining reliable operation of the electric grid. Because congestion affects the reliability of delivering power and increases its cost, the congestion analysis in electric grid networks is an important subject. Consequently, next-generation electric grids require novel methodologies for studying and managing congestion in electric grids. We suggest a novel method of long-term congestion management in large-scale electric grids. Owing to the complication and size of transmission line systems and the competitive nature of current grid operation, it is important for electric grid operators to determine how many transmission lines capacity to add. Traditional questions requiring answers are "Where" to add, "How much of transmission line capacity" to add, and "Which voltage level". Because of electric grid deregulation, transmission lines expansion is more complicated as it is now open to investors, whose main interest is to generate revenue, to build new transmission lines. Adding a new transmission capacity will help the system to relieve the transmission system congestion, create
Cloud-based large-scale air traffic flow optimization
NASA Astrophysics Data System (ADS)
Cao, Yi
The ever-increasing traffic demand makes the efficient use of airspace an imperative mission, and this paper presents an effort in response to this call. Firstly, a new aggregate model, called Link Transmission Model (LTM), is proposed, which models the nationwide traffic as a network of flight routes identified by origin-destination pairs. The traversal time of a flight route is assumed to be the mode of distribution of historical flight records, and the mode is estimated by using Kernel Density Estimation. As this simplification abstracts away physical trajectory details, the complexity of modeling is drastically decreased, resulting in efficient traffic forecasting. The predicative capability of LTM is validated against recorded traffic data. Secondly, a nationwide traffic flow optimization problem with airport and en route capacity constraints is formulated based on LTM. The optimization problem aims at alleviating traffic congestions with minimal global delays. This problem is intractable due to millions of variables. A dual decomposition method is applied to decompose the large-scale problem such that the subproblems are solvable. However, the whole problem is still computational expensive to solve since each subproblem is an smaller integer programming problem that pursues integer solutions. Solving an integer programing problem is known to be far more time-consuming than solving its linear relaxation. In addition, sequential execution on a standalone computer leads to linear runtime increase when the problem size increases. To address the computational efficiency problem, a parallel computing framework is designed which accommodates concurrent executions via multithreading programming. The multithreaded version is compared with its monolithic version to show decreased runtime. Finally, an open-source cloud computing framework, Hadoop MapReduce, is employed for better scalability and reliability. This framework is an "off-the-shelf" parallel computing model
Nonlinear Generation of shear flows and large scale magnetic fields by small scale
NASA Astrophysics Data System (ADS)
Aburjania, G.
2009-04-01
EGU2009-233 Nonlinear Generation of shear flows and large scale magnetic fields by small scale turbulence in the ionosphere by G. Aburjania Contact: George Aburjania, g.aburjania@gmail.com,aburj@mymail.ge
Wang, Jian; Wang, Xiaolong; Jiang, Aipeng; Jiangzhou, Shu; Li, Ping
2014-01-01
A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality.
Wang, Xiaolong; Jiang, Aipeng; Jiangzhou, Shu; Li, Ping
2014-01-01
A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality. PMID:24701180
Non-linear shrinkage estimation of large-scale structure covariance
NASA Astrophysics Data System (ADS)
Joachimi, Benjamin
2017-03-01
In many astrophysical settings, covariance matrices of large data sets have to be determined empirically from a finite number of mock realizations. The resulting noise degrades inference and precludes it completely if there are fewer realizations than data points. This work applies a recently proposed non-linear shrinkage estimator of covariance to a realistic example from large-scale structure cosmology. After optimizing its performance for the usage in likelihood expressions, the shrinkage estimator yields subdominant bias and variance comparable to that of the standard estimator with a factor of ∼50 less realizations. This is achieved without any prior information on the properties of the data or the structure of the covariance matrix, at a negligible computational cost.
Nonlinear modulation of the HI power spectrum on ultra-large scales. I
Umeh, Obinna; Maartens, Roy; Santos, Mario E-mail: roy.maartens@gmail.com
2016-03-01
Intensity mapping of the neutral hydrogen brightness temperature promises to provide a three-dimensional view of the universe on very large scales. Nonlinear effects are typically thought to alter only the small-scale power, but we show how they may bias the extraction of cosmological information contained in the power spectrum on ultra-large scales. For linear perturbations to remain valid on large scales, we need to renormalize perturbations at higher order. In the case of intensity mapping, the second-order contribution to clustering from weak lensing dominates the nonlinear contribution at high redshift. Renormalization modifies the mean brightness temperature and therefore the evolution bias. It also introduces a term that mimics white noise. These effects may influence forecasting analysis on ultra-large scales.
Imprint of non-linear effects on HI intensity mapping on large scales
NASA Astrophysics Data System (ADS)
Umeh, Obinna
2017-06-01
Intensity mapping of the HI brightness temperature provides a unique way of tracing large-scale structures of the Universe up to the largest possible scales. This is achieved by using a low angular resolution radio telescopes to detect emission line from cosmic neutral Hydrogen in the post-reionization Universe. We use general relativistic perturbation theory techniques to derive for the first time the full expression for the HI brightness temperature up to third order in perturbation theory without making any plane-parallel approximation. We use this result and the renormalization prescription for biased tracers to study the impact of nonlinear effects on the power spectrum of HI brightness temperature both in real and redshift space. We show how mode coupling at nonlinear order due to nonlinear bias parameters and redshift space distortion terms modulate the power spectrum on large scales. The large scale modulation may be understood to be due to the effective bias parameter and effective shot noise.
Adaptive Fault-Tolerant Control of Uncertain Nonlinear Large-Scale Systems With Unknown Dead Zone.
Chen, Mou; Tao, Gang
2016-08-01
In this paper, an adaptive neural fault-tolerant control scheme is proposed and analyzed for a class of uncertain nonlinear large-scale systems with unknown dead zone and external disturbances. To tackle the unknown nonlinear interaction functions in the large-scale system, the radial basis function neural network (RBFNN) is employed to approximate them. To further handle the unknown approximation errors and the effects of the unknown dead zone and external disturbances, integrated as the compounded disturbances, the corresponding disturbance observers are developed for their estimations. Based on the outputs of the RBFNN and the disturbance observer, the adaptive neural fault-tolerant control scheme is designed for uncertain nonlinear large-scale systems by using a decentralized backstepping technique. The closed-loop stability of the adaptive control system is rigorously proved via Lyapunov analysis and the satisfactory tracking performance is achieved under the integrated effects of unknown dead zone, actuator fault, and unknown external disturbances. Simulation results of a mass-spring-damper system are given to illustrate the effectiveness of the proposed adaptive neural fault-tolerant control scheme for uncertain nonlinear large-scale systems.
Recent developments in large-scale structural optimization
NASA Technical Reports Server (NTRS)
Venkayya, Vipperla B.
1989-01-01
A brief discussion is given of mathematical optimization and the motivation for the development of more recent numerical search procedures. A review of recent developments and issues in multidisciplinary optimization is also presented. These developments are discussed in the context of the preliminary design of aircraft structures. A capability description of programs FASTOP, TSO, STARS, LAGRANGE, ELFINI and ASTROS is included.
NASA Astrophysics Data System (ADS)
Cheng, Wanyou; Xiao, Yunhai; Hu, Qing-Jie
2009-02-01
In this paper, we propose a family of derivative-free conjugate gradient methods for large-scale nonlinear systems of equations. They come from two modified conjugate gradient methods [W.Y. Cheng, A two term PRP based descent Method, Numer. Funct. Anal. Optim. 28 (2007) 1217-1230; L. Zhang, W.J. Zhou, D.H. Li, A descent modified Polak-Ribiére-Polyak conjugate gradient method and its global convergence, IMA J. Numer. Anal. 26 (2006) 629-640] recently proposed for unconstrained optimization problems. Under appropriate conditions, the global convergence of the proposed method is established. Preliminary numerical results show that the proposed method is promising.
Large-scale spherical fixed bed reactors: Modeling and optimization
Hartig, F.; Keil, F.J. )
1993-03-01
Iterative dynamic programming (IDP) according to Luus was used for the optimization of the methanol production in a cascade of spherical reactors. The system of three spherical reactors was compared to an externally cooled tubular reactor and a quench reactor. The reactors were modeled by the pseudohomogeneous and heterogeneous approach. The effectiveness factors of the heterogeneous model were calculated by the dusty gas model. The IDP method was compared with sequential quadratic programming (SQP) and the Box complex method. The optimized distributions of catalyst volume with the pseudohomogeneous and heterogeneous model lead to different results. The IDP method finds the global optimum with high probability. A combination of IDP and SQP provides a reliable optimization procedure that needs minimum computing time.
Solving Large Scale Nonlinear Eigenvalue Problem in Next-Generation Accelerator Design
Liao, Ben-Shan; Bai, Zhaojun; Lee, Lie-Quan; Ko, Kwok; /SLAC
2006-09-28
A number of numerical methods, including inverse iteration, method of successive linear problem and nonlinear Arnoldi algorithm, are studied in this paper to solve a large scale nonlinear eigenvalue problem arising from finite element analysis of resonant frequencies and external Q{sub e} values of a waveguide loaded cavity in the next-generation accelerator design. They present a nonlinear Rayleigh-Ritz iterative projection algorithm, NRRIT in short and demonstrate that it is the most promising approach for a model scale cavity design. The NRRIT algorithm is an extension of the nonlinear Arnoldi algorithm due to Voss. Computational challenges of solving such a nonlinear eigenvalue problem for a full scale cavity design are outlined.
Segment-Based Predominant Learning Swarm Optimizer for Large-Scale Optimization.
Yang, Qiang; Chen, Wei-Neng; Gu, Tianlong; Zhang, Huaxiang; Deng, Jeremiah D; Li, Yun; Zhang, Jun
2016-10-24
Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is proposed to randomly divide the whole dimensions into segments. During update, variables in different segments are evolved by learning from different exemplars while the ones in the same segment are evolved by the same exemplar. Second, to accelerate search speed and enhance search diversity, a predominant learning strategy is also proposed, which lets several predominant particles guide the update of a particle with each predominant particle responsible for one segment of dimensions. By combining these two learning strategies together, SPLSO evolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities. Extensive experiments are conducted on two large-scale benchmark function sets to investigate the influence of each algorithmic component and comparisons with several state-of-the-art meta-heuristic algorithms dealing with large-scale problems demonstrate the competitive efficiency and effectiveness of the proposed optimizer. Further the scalability of the optimizer to solve problems with dimensionality up to 2000 is also verified.
LM-CMA: An Alternative to L-BFGS for Large-Scale Black Box Optimization.
Loshchilov, Ilya
2017-01-01
Limited-memory BFGS (L-BFGS; Liu and Nocedal, 1989 ) is often considered to be the method of choice for continuous optimization when first- or second-order information is available. However, the use of L-BFGS can be complicated in a black box scenario where gradient information is not available and therefore should be numerically estimated. The accuracy of this estimation, obtained by finite difference methods, is often problem-dependent and may lead to premature convergence of the algorithm. This article demonstrates an alternative to L-BFGS, the limited memory covariance matrix adaptation evolution strategy (LM-CMA) proposed by Loshchilov ( 2014 ). LM-CMA is a stochastic derivative-free algorithm for numerical optimization of nonlinear, nonconvex optimization problems. Inspired by L-BFGS, LM-CMA samples candidate solutions according to a covariance matrix reproduced from m direction vectors selected during the optimization process. The decomposition of the covariance matrix into Cholesky factors allows reducing the memory complexity to [Formula: see text], where n is the number of decision variables. The time complexity of sampling one candidate solution is also [Formula: see text] but scales as only about 25 scalar-vector multiplications in practice. The algorithm has an important property of invariance with respect to strictly increasing transformations of the objective function; such transformations do not compromise its ability to approach the optimum. LM-CMA outperforms the original CMA-ES and its large-scale versions on nonseparable ill-conditioned problems with a factor increasing with problem dimension. Invariance properties of the algorithm do not prevent it from demonstrating a comparable performance to L-BFGS on nontrivial large-scale smooth and nonsmooth optimization problems.
Limitations of Parallel Global Optimization for Large-Scale Human Movement Problems
Koh, Byung-Il; Reinbolt, Jeffrey A.; George, Alan D.; Haftka, Raphael T.; Fregly, Benjamin J.
2009-01-01
Global optimization algorithms (e.g., simulated annealing, genetic, and particle swarm) have been gaining popularity in biomechanics research, in part due to advances in parallel computing. To date, such algorithms have only been applied to small- or medium-scale optimization problems (< 100 design variables). This study evaluates the applicability of a parallel particle swarm global optimization algorithm to large-scale human movement problems. The evaluation was performed using two large-scale (660 design variables) optimization problems that utilized a dynamic, 27 degree-of-freedom, full-body gait model to predict new gait motions from a nominal gait motion. Both cost functions minimized a quantity that reduced the knee adduction torque. The first one minimized foot path errors corresponding to an increased toe out angle of 15 deg, while the second one minimized the knee adduction torque directly without changing the foot path. Constraints on allowable changes in trunk orientation, joint angles, joint torques, centers of pressure, and ground reactions were handled using a penalty method. For both problems, a single run with a gradient-based nonlinear least squares algorithm found a significantly better solution than did 10 runs with the global particle swarm algorithm. Due to the penalty terms, the physically-realistic gradient-based solutions were located within a narrow “channel” in design space that was difficult to enter without gradient information. Researchers should exercise caution when extrapolating the performance of parallel global optimizers to human movement problems with hundreds of design variables, especially when penalty terms are included in the cost function. PMID:19036629
Adaptive Optimization Techniques for Large-Scale Stochastic Planning
2011-06-28
cannot be kept longer than a few weeks. The decision maker must decide on blood - type substitutions that minimize the chance of future shortage. Because...optimal blood - type substitution is a large stochastic problem. Another application is managing water reservoirs. In this domain, an operator needs to decide...compatibility constraints among blood types , blood inventory management does not fit well the standard inventory control framework. In reservoir management
New Methods for Large Scale Local and Global Optimization
1994-07-08
investigators together with Jorge Nocedal of Northwestern University was completed during this research period has been accepted for publication by...easier to implement for a particular application. We have written a paper based on this work with Jorge Nocedal . In addition we have developed and...Liu, D., and J. Nocedal , "On the behavior of Broyden’s class of quasi-Newton methods," SlAM Journal on Optimization 2, 1992, pp. 533-557. (2) R. H
Small parametric model for nonlinear dynamics of large scale cyclogenesis with wind speed variations
NASA Astrophysics Data System (ADS)
Erokhin, Nikolay; Shkevov, Rumen; Zolnikova, Nadezhda; Mikhailovskaya, Ludmila
2016-07-01
It is performed a numerical investigation of a self consistent small parametric model (SPM) for large scale cyclogenesis (RLSC) by usage of connected nonlinear equations for mean wind speed and ocean surface temperature in the tropical cyclone (TC). These equations may describe the different scenario of temporal dynamics of a powerful atmospheric vortex during its full life cycle. The numerical calculations have shown that relevant choice of SPMTs incoming parameters allows to describe the seasonal behavior of regional large scale cyclogenesis dynamics for a given number of TC during the active season. It is shown that SPM allows describe also the variable wind speed variations inside the TC. Thus by usage of the nonlinear small parametric model it is possible to study the features of RLSCTs temporal dynamics during the active season in the region given and to analyze the relationship between regional cyclogenesis parameters and different external factors like the space weather including the solar activity level and cosmic rays variations.
Optimization algorithms for large-scale multireservoir hydropower systems
Hiew, K.L.
1987-01-01
Five optimization algorithms were vigorously evaluated based on applications on a hypothetical five-reservoir hydropower system. These algorithms are incremental dynamic programming (IDP), successive linear programing (SLP), feasible direction method (FDM), optimal control theory (OCT) and objective-space dynamic programming (OSDP). The performance of these algorithms were comparatively evaluated using unbiased, objective criteria which include accuracy of results, rate of convergence, smoothness of resulting storage and release trajectories, computer time and memory requirements, robustness and other pertinent secondary considerations. Results have shown that all the algorithms, with the exception of OSDP converge to optimum objective values within 1.0% difference from one another. The highest objective value is obtained by IDP, followed closely by OCT. Computer time required by these algorithms, however, differ by more than two orders of magnitude, ranging from 10 seconds in the case of OCT to a maximum of about 2000 seconds for IDP. With a well-designed penalty scheme to deal with state-space constraints, OCT proves to be the most-efficient algorithm based on its overall performance. SLP, FDM, and OCT were applied to the case study of Mahaweli project, a ten-powerplant system in Sri Lanka.
On the importance of nonlinear couplings in large-scale neutrino streams
Dupuy, Hélène; Bernardeau, Francis E-mail: francis.bernardeau@iap.fr
2015-08-01
We propose a procedure to evaluate the impact of nonlinear couplings on the evolution of massive neutrino streams in the context of large-scale structure growth. Such streams can be described by general nonlinear conservation equations, derived from a multiple-flow perspective, which generalize the conservation equations of non-relativistic pressureless fluids. The relevance of the nonlinear couplings is quantified with the help of the eikonal approximation applied to the subhorizon limit of this system. It highlights the role played by the relative displacements of different cosmic streams and it specifies, for each flow, the spatial scales at which the growth of structure is affected by nonlinear couplings. We found that, at redshift zero, such couplings can be significant for wavenumbers as small as k=0.2 h/Mpc for most of the neutrino streams.
Wildfire Emission, injection height: Development, Optimization, and Large Scale Impact
NASA Astrophysics Data System (ADS)
Paugam, R.; Wooster, M.; Atherton, J.; Beevers, S.; Kitwiroon, N.; Kaiser, J. W.; Remy, S.; Freitas, S. R.
2013-12-01
Evaluation of wildfire emissions in global chemistry transport model is still a subject of debate in the atmospheric community, though some inventory like GFAS and GFED are already available. In particular none of those approaches are currently dealing with height induced by buoyant plumes. In this work we aim to set-up a 3-dimensional wildfire emission inventory. Our approach is based on the Fire Radiative Power product (FRP) evaluated at a cluster level coupled with the plume rise model (PRM) originally developed by Saulo Freitas. PRM was developed to take into account effects of atmospheric stability and latent heat in plume updraft. Here, the original version is modified: (i) the input data of convective heat flux and Active Fire area are directly force from FRP data derived from a modified version of the Dozier algorithm applied to the MOD12 product, (ii) and the dynamical core of the plume model is modified with a new entrainment scheme inspired from latest results in shallow convection parametrization. The new parameters introduced are then defined via an optimization procedure based on (i) fire plume characteristics of single fire events extracted from the official MISR plume height project and (ii) atmospheric profile derived from the ECMWF analysis. Calibration of the new version of PRM is made for Europe and North America. For each geographic zone, fire events are selected out of the MISR data set. In particular, it is shown that the only information extracted from Terra overpass is not enough to guaranty that the injection height of the plume is linked to the FRP measured at the same time. The plume is a dynamical system, and a time delay (related to the atmospheric state) is necessary to adjust change in FRP to the plume behaviour. Therefore, multiple overpasses of the same fire from Terra and Aqua are used here to determine fire and plume behaviours and system in a steady state at the time of MISR (central scan of Terra) overpass are selected for the
Parallel processing for large-scale nonlinear control experiments in economics
Amman, H.M. ); Kendrick, D.A. . Dept. of Economics)
1991-01-01
In general, the econometric models relevant for purposes of evaluating economic policy contain a large number of nonlinear equations. Therefore, in applying optimal control techniques, computational difficulties are encountered. This paper presents the most common algorithm for computing nonlinear control problems and investigates the degree to which vector processing and parallel processing can facilitate optimal control experiments.
Large-Scale Optimal Control of Interconnected Natural Gas and Electrical Transmission Systems
Chiang, Nai-Yuan; Zavala, Victor M.
2016-04-15
We present a detailed optimal control model that captures spatiotemporal interactions between gas and electric transmission networks. We use the model to study flexibility and economic opportunities provided by coordination. A large-scale case study in the Illinois system reveals that coordination can enable the delivery of significantly larger amounts of natural gas to the power grid. In particular, under a coordinated setting, gas-fired generators act as distributed demand response resources that can be controlled by the gas pipeline operator. This enables more efficient control of pressures and flows in space and time and overcomes delivery bottlenecks. We demonstrate that the additional flexibility not only can benefit the gas operator but can also lead to more efficient power grid operations and results in increased revenue for gas-fired power plants. We also use the optimal control model to analyze computational issues arising in these complex models. We demonstrate that the interconnected Illinois system with full physical resolution gives rise to a highly nonlinear optimal control problem with 4400 differential and algebraic equations and 1040 controls that can be solved with a state-of-the-art sparse optimization solver. (C) 2016 Elsevier Ltd. All rights reserved.
2016-08-10
AFRL-AFOSR-JP-TR-2016-0073 Large-scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation...2016 4. TITLE AND SUBTITLE Large-scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation 5a...performances on various machine learning tasks and it naturally lends itself to fast parallel implementations. Despite this, very little work has been
NASA Astrophysics Data System (ADS)
Li, Judith Yue; Kokkinaki, Amalia; Ghorbanidehno, Hojat; Darve, Eric F.; Kitanidis, Peter K.
2015-12-01
Reservoir monitoring aims to provide snapshots of reservoir conditions and their uncertainties to assist operation management and risk analysis. These snapshots may contain millions of state variables, e.g., pressures and saturations, which can be estimated by assimilating data in real time using the Kalman filter (KF). However, the KF has a computational cost that scales quadratically with the number of unknowns, m, due to the cost of computing and storing the covariance and Jacobian matrices, along with their products. The compressed state Kalman filter (CSKF) adapts the KF for solving large-scale monitoring problems. The CSKF uses N preselected orthogonal bases to compute an accurate rank-N approximation of the covariance that is close to the optimal spectral approximation given by SVD. The CSKF has a computational cost that scales linearly in m and uses an efficient matrix-free approach that propagates uncertainties using N + 1 forward model evaluations, where N≪m. Here we present a generalized CSKF algorithm for nonlinear state estimation problems such as CO2 monitoring. For simultaneous estimation of multiple types of state variables, the algorithm allows selecting bases that represent the variability of each state type. Through synthetic numerical experiments of CO2 monitoring, we show that the CSKF can reproduce the Kalman gain accurately even for large compression ratios (m/N). For a given computational cost, the CSKF uses a robust and flexible compression scheme that gives more reliable uncertainty estimates than the ensemble Kalman filter, which may display loss of ensemble variability leading to suboptimal uncertainty estimates.
NASA Technical Reports Server (NTRS)
Dahl, Milo D.; Hixon, Ray; Mankbadi, Reda R.
2003-01-01
An approximate technique is presented for the prediction of the large-scale turbulent structure sound source in a supersonic jet. A linearized Euler equations code is used to solve for the flow disturbances within and near a jet with a given mean flow. Assuming a normal mode composition for the wave-like disturbances, the linear radial profiles are used in an integration of the Navier-Stokes equations. This results in a set of ordinary differential equations representing the weakly nonlinear self-interactions of the modes along with their interaction with the mean flow. Solutions are then used to correct the amplitude of the disturbances that represent the source of large-scale turbulent structure sound in the jet.
An inertia-free filter line-search algorithm for large-scale nonlinear programming
Chiang, Nai-Yuan; Zavala, Victor M.
2016-02-15
We present a filter line-search algorithm that does not require inertia information of the linear system. This feature enables the use of a wide range of linear algebra strategies and libraries, which is essential to tackle large-scale problems on modern computing architectures. The proposed approach performs curvature tests along the search step to detect negative curvature and to trigger convexification. We prove that the approach is globally convergent and we implement the approach within a parallel interior-point framework to solve large-scale and highly nonlinear problems. Our numerical tests demonstrate that the inertia-free approach is as efficient as inertia detection via symmetric indefinite factorizations. We also demonstrate that the inertia-free approach can lead to reductions in solution time because it reduces the amount of convexification needed.
NASA Astrophysics Data System (ADS)
Tong, Shaocheng; Xu, Yinyin; Li, Yongming
2015-06-01
This paper is concerned with the problem of adaptive fuzzy decentralised output-feedback control for a class of uncertain stochastic nonlinear pure-feedback large-scale systems with completely unknown functions, the mismatched interconnections and without requiring the states being available for controller design. With the help of fuzzy logic systems approximating the unknown nonlinear functions, a fuzzy state observer is designed estimating the unmeasured states. Therefore, the nonlinear filtered signals are incorporated into the backstepping recursive design, and an adaptive fuzzy decentralised output-feedback control scheme is developed. It is proved that the filter system converges to a small neighbourhood of the origin based on appropriate choice of the design parameters. Simulation studies are included illustrating the effectiveness of the proposed approach.
Real-time, large scale optimization of water network systems using a subdomain approach.
van Bloemen Waanders, Bart Gustaaf; Biegler, Lorenz T.; Laird, Carl Damon
2005-03-01
Certain classes of dynamic network problems can be modeled by a set of hyperbolic partial differential equations describing behavior along network edges and a set of differential and algebraic equations describing behavior at network nodes. In this paper, we demonstrate real-time performance for optimization problems in drinking water networks. While optimization problems subject to partial differential, differential, and algebraic equations can be solved with a variety of techniques, efficient solutions are difficult for large network problems with many degrees of freedom and variable bounds. Sequential optimization strategies can be inefficient for this problem due to the high cost of computing derivatives with respect to many degrees of freedom. Simultaneous techniques can be more efficient, but are difficult because of the need to solve a large nonlinear program; a program that may be too large for current solver. This study describes a dynamic optimization formulation for estimating contaminant sources in drinking water networks, given concentration measurements at various network nodes. We achieve real-time performance by combining an efficient large-scale nonlinear programming algorithm with two problem reduction techniques. D Alembert's principle can be applied to the partial differential equations governing behavior along the network edges (distribution pipes). This allows us to approximate the time-delay relationships between network nodes, removing the need to discretize along the length of the pipes. The efficiency of this approach alone, however, is still dependent on the size of the network and does not scale indefinitely to larger network models. We further reduce the problem size with a subdomain approach and solve smaller inversion problems using a geographic window around the area of contamination. We illustrate the effectiveness of this overall approach and these reduction techniques on an actual metropolitan water network model.
An efficient multigrid strategy for large-scale molecular mechanics optimization
NASA Astrophysics Data System (ADS)
Chen, Jingrun; García-Cervera, Carlos J.
2017-08-01
Static mechanical properties of materials require large-scale nonlinear optimization of the molecular mechanics model under various controls. This paper presents an efficient multigrid strategy to solve such problems. This strategy approximates solutions on grids in a quasi-atomistic and inexact manner, transfers solutions on grids following a coarse-to-fine (oneway) schedule, and finds physically relevant minimizers with linear scaling complexity. Compared to the full multigrid method which has the same complexity, the prefactor of this strategy is orders of magnitude smaller. Consequently, the required CPU time of this strategy is orders of magnitude smaller than that of the full multigrid method, and is smaller than that of the brute-force optimization for systems with more than 200 , 000 atoms. Considerable savings are found if the number of atoms becomes even larger due to the super-linear scaling complexity of the brute-force optimization. For systems with 1 , 000 , 000 atoms (over three million degrees of freedom), on average a more than 70% reduction of CPU time is observed regardless of the type of defects, including vacancies, dislocations, and cracks. In addition, linear scalability of the proposed strategy is tested in the presence of a dislocation pair for systems with more than 100 million atoms (over 400 million degrees of freedom).
CMB lensing bispectrum from nonlinear growth of the large scale structure
NASA Astrophysics Data System (ADS)
Namikawa, Toshiya
2016-06-01
We discuss detectability of the nonlinear growth of the large-scale structure in the cosmic microwave background (CMB) lensing. The lensing signals involved in the CMB fluctuations have been measured from multiple CMB experiments, such as Atacama Cosmology Telescope (ACT), Planck, POLARBEAR, and South Pole Telescope (SPT). The reconstructed CMB lensing signals are useful to constrain cosmology via their angular power spectrum, while detectability and cosmological application of their bispectrum induced by the nonlinear evolution are not well studied. Extending the analytic estimate of the galaxy lensing bispectrum presented by Takada and Jain (2004) to the CMB case, we show that even near term CMB experiments such as Advanced ACT, Simons Array and SPT3G could detect the CMB lensing bispectrum induced by the nonlinear growth of the large-scale structure. In the case of the CMB Stage-IV, we find that the lensing bispectrum is detectable at ≳50 σ statistical significance. This precisely measured lensing bispectrum has rich cosmological information, and could be used to constrain cosmology, e.g., the sum of the neutrino masses and the dark-energy properties.
Carey, G.F.; Young, D.M.
1993-12-31
The program outlined here is directed to research on methods, algorithms, and software for distributed parallel supercomputers. Of particular interest are finite element methods and finite difference methods together with sparse iterative solution schemes for scientific and engineering computations of very large-scale systems. Both linear and nonlinear problems will be investigated. In the nonlinear case, applications with bifurcation to multiple solutions will be considered using continuation strategies. The parallelizable numerical methods of particular interest are a family of partitioning schemes embracing domain decomposition, element-by-element strategies, and multi-level techniques. The methods will be further developed incorporating parallel iterative solution algorithms with associated preconditioners in parallel computer software. The schemes will be implemented on distributed memory parallel architectures such as the CRAY MPP, Intel Paragon, the NCUBE3, and the Connection Machine. We will also consider other new architectures such as the Kendall-Square (KSQ) and proposed machines such as the TERA. The applications will focus on large-scale three-dimensional nonlinear flow and reservoir problems with strong convective transport contributions. These are legitimate grand challenge class computational fluid dynamics (CFD) problems of significant practical interest to DOE. The methods developed and algorithms will, however, be of wider interest.
Classification of large-scale stellar spectra based on the non-linearly assembling learning machine
NASA Astrophysics Data System (ADS)
Liu, Zhongbao; Song, Lipeng; Zhao, Wenjuan
2016-02-01
An important problem to be solved of traditional classification methods is they cannot deal with large-scale classification because of very high time complexity. In order to solve above problem, inspired by the thinking of collaborative management, the non-linearly assembling learning machine (NALM) is proposed and used in the large-scale stellar spectral classification. In NALM, the large-scale dataset is firstly divided into several subsets, and then the traditional classifiers such as support vector machine (SVM) runs on the subset, finally, the classification results on each subset are assembled and the overall classification decision is obtained. In comparative experiments, we investigate the performance of NALM in the stellar spectral subclasses classification compared with SVM. We apply SVM and NALM respectively to classify the four subclasses of K-type spectra, three subclasses of F-type spectra and three subclasses of G-type spectra from Sloan Digital Sky Survey (SDSS). The comparative experiment results show that the performance of NALM is much better than SVM in view of the classification accuracy and the computation time.
Destruction of large-scale magnetic field in non-linear simulations of the shear dynamo
NASA Astrophysics Data System (ADS)
Teed, Robert J.; Proctor, Michael R. E.
2016-05-01
The Sun's magnetic field exhibits coherence in space and time on much larger scales than the turbulent convection that ultimately powers the dynamo. In the past the α-effect (mean-field) concept has been used to model the solar cycle, but recent work has cast doubt on the validity of the mean-field ansatz under solar conditions. This indicates that one should seek an alternative mechanism for generating large-scale structure. One possibility is the recently proposed `shear dynamo' mechanism where large-scale magnetic fields are generated in the presence of a simple shear. Further investigation of this proposition is required, however, because work has been focused on the linear regime with a uniform shear profile thus far. In this paper we report results of the extension of the original shear dynamo model into the non-linear regime. We find that whilst large-scale structure can initially persist into the saturated regime, in several of our simulations it is destroyed via large increase in kinetic energy. This result casts doubt on the ability of the simple uniform shear dynamo mechanism to act as an alternative to the α-effect in solar conditions.
Nonlinear random response of large-scale sparse finite element plate bending problems
NASA Astrophysics Data System (ADS)
Chokshi, Swati
Acoustic fatigue is one of the major design considerations for skin panels exposed to high levels of random pressure at subsonic/supersonic/hypersonic speeds. The nonlinear large deflection random response of the single-bay panels aerospace structures subjected to random excitations at various sound pressure levels (SPLs) is investigated. The nonlinear responses of plate analyses are limited to determine the root-mean-square displacement under uniformly distributed pressure random loads. Efficient computational technologies like sparse storage schemes and parallel computation are proposed and incorporated to solve large-scale, nonlinear large deflection random vibration problems for both types of loading cases: (1) synchronized in time and (2) unsynchronized and statistically uncorrelated in time. For the first time, large scale plate bending problems subjected to unsynchronized load are solved using parallel computing capabilities to account for computational burden due to the simulation of the unsynchronized random pressure fluctuations. The main focus of the research work is placed upon computational issues involved in the nonlinear modal methodologies. A nonlinear FEM method in time domain is incorporated with the Monte Carlo simulation and sparse computational technologies, including the efficient sparse Subspace Eigen-solutions are presented and applied to accurately determine the random response with a refined, large finite element mesh for the first time. Sparse equation solver and sparse matrix operations embedded inside the subspace Eigen-solution algorithms are also exploited. The approach uses the von-Karman nonlinear strain-displacement relations and the classical plate theory. In the proposed methodologies, the solution for a small number (say less than 100) of lowest linear, sparse Eigen-pairs need to be solved for only once, in order to transform nonlinear large displacements from the conventional structural degree-of-freedom (dof) into the modal
THREE-POINT PHASE CORRELATIONS: A NEW MEASURE OF NONLINEAR LARGE-SCALE STRUCTURE
Wolstenhulme, Richard; Bonvin, Camille; Obreschkow, Danail
2015-05-10
We derive an analytical expression for a novel large-scale structure observable: the line correlation function. The line correlation function, which is constructed from the three-point correlation function of the phase of the density field, is a robust statistical measure allowing the extraction of information in the nonlinear and non-Gaussian regime. We show that, in perturbation theory, the line correlation is sensitive to the coupling kernel F{sub 2}, which governs the nonlinear gravitational evolution of the density field. We compare our analytical expression with results from numerical simulations and find a 1σ agreement for separations r ≳ 30 h{sup −1} Mpc. Fitting formulae for the power spectrum and the nonlinear coupling kernel at small scales allow us to extend our prediction into the strongly nonlinear regime, where we find a 1σ agreement with the simulations for r ≳ 2 h{sup −1} Mpc. We discuss the advantages of the line correlation relative to standard statistical measures like the bispectrum. Unlike the latter, the line correlation is independent of the bias, in the regime where the bias is local and linear. Furthermore, the variance of the line correlation is independent of the Gaussian variance on the modulus of the density field. This suggests that the line correlation can probe more precisely the nonlinear regime of gravity, with less contamination from the power spectrum variance.
Peloso, Marco; Pietroni, Massimo E-mail: pietroni@pd.infn.it
2013-05-01
We discuss the constraints imposed on the nonlinear evolution of the Large Scale Structure (LSS) of the universe by galilean invariance, the symmetry relevant on subhorizon scales. Using Ward identities associated to the invariance, we derive fully nonlinear consistency relations between statistical correlators of the density and velocity perturbations, such as the power spectrum and the bispectrum. These relations are valid up to O(f{sub NL}{sup 2}) corrections. We then show that most of the semi-analytic methods proposed so far to resum the perturbative expansion of the LSS dynamics fail to fulfill the constraints imposed by galilean invariance, and are therefore susceptible to non-physical infrared effects. Finally, we identify and discuss a nonperturbative semi-analytical scheme which is manifestly galilean invariant at any order of its expansion.
Toward Optimal and Scalable Dimension Reduction Methods for large-scale Bayesian Inversions
NASA Astrophysics Data System (ADS)
Bousserez, N.; Henze, D. K.
2015-12-01
Many inverse problems in geophysics are solved within the Bayesian framework, in which a prior probability density function of a quantity of interest is optimally updated using newly available observations. A maximum likelihood of the posterior probability density function is estimated using a model of the physics that relates the variables to be optimized to the observations. However, in many practical situations the number of observations is much smaller than the number of variables estimated, which leads to an ill-posed problem. In practice, this means that the data are informative only in a subspace of the initial space. It is both of theoretical and practical interest to characterize this "data-informed" subspace, since it allows a simple interpretation of the inverse solution and its uncertainty, but can also dramatically reduce the computational cost of the optimization by reducing the size of the problem. In this presentation the formalism of dimension reduction in Bayesian methods will be introduced, and different optimality criteria will be discussed (e.g., minimum error variances, maximum degree of freedom for signal). For each criterion, an optimal design for the reduced Bayesian problem will be proposed and compared with other suboptimal approaches. A significant advantage of our method is its high scalability owing to an efficient parallel implementation, making it very attractive for large-scale inverse problems. Numerical results from an Observation Simulation System Experiment (OSSE) consisting of a high spatial resolution (0.5°x0.7°) source inversion of methane over North America using observations from the Greenhouse gases Observing SATellite (GOSAT) instrument and the GEOS-Chem chemistry-transport model will illustrate the computational efficiency of our approach. Although only linear models are considered in this study, possible extensions to the non-linear case will also be discussed
Nonlinear Seismic Correlation Analysis of the JNES/NUPEC Large-Scale Piping System Tests.
Nie,J.; DeGrassi, G.; Hofmayer, C.; Ali, S.
2008-06-01
The Japan Nuclear Energy Safety Organization/Nuclear Power Engineering Corporation (JNES/NUPEC) large-scale piping test program has provided valuable new test data on high level seismic elasto-plastic behavior and failure modes for typical nuclear power plant piping systems. The component and piping system tests demonstrated the strain ratcheting behavior that is expected to occur when a pressurized pipe is subjected to cyclic seismic loading. Under a collaboration agreement between the US and Japan on seismic issues, the US Nuclear Regulatory Commission (NRC)/Brookhaven National Laboratory (BNL) performed a correlation analysis of the large-scale piping system tests using derailed state-of-the-art nonlinear finite element models. Techniques are introduced to develop material models that can closely match the test data. The shaking table motions are examined. The analytical results are assessed in terms of the overall system responses and the strain ratcheting behavior at an elbow. The paper concludes with the insights about the accuracy of the analytical methods for use in performance assessments of highly nonlinear piping systems under large seismic motions.
From Self-consistency to SOAR: Solving Large Scale NonlinearEigenvalue Problems
Bai, Zhaojun; Yang, Chao
2006-02-01
What is common among electronic structure calculation, design of MEMS devices, vibrational analysis of high speed railways, and simulation of the electromagnetic field of a particle accelerator? The answer: they all require solving large scale nonlinear eigenvalue problems. In fact, these are just a handful of examples in which solving nonlinear eigenvalue problems accurately and efficiently is becoming increasingly important. Recognizing the importance of this class of problems, an invited minisymposium dedicated to nonlinear eigenvalue problems was held at the 2005 SIAM Annual Meeting. The purpose of the minisymposium was to bring together numerical analysts and application scientists to showcase some of the cutting edge results from both communities and to discuss the challenges they are still facing. The minisymposium consisted of eight talks divided into two sessions. The first three talks focused on a type of nonlinear eigenvalue problem arising from electronic structure calculations. In this type of problem, the matrix Hamiltonian H depends, in a non-trivial way, on the set of eigenvectors X to be computed. The invariant subspace spanned by these eigenvectors also minimizes a total energy function that is highly nonlinear with respect to X on a manifold defined by a set of orthonormality constraints. In other applications, the nonlinearity of the matrix eigenvalue problem is restricted to the dependency of the matrix on the eigenvalues to be computed. These problems are often called polynomial or rational eigenvalue problems In the second session, Christian Mehl from Technical University of Berlin described numerical techniques for solving a special type of polynomial eigenvalue problem arising from vibration analysis of rail tracks excited by high-speed trains.
Das, B; Meirovitch, H; Navon, I M
2003-07-30
Energy minimization plays an important role in structure determination and analysis of proteins, peptides, and other organic molecules; therefore, development of efficient minimization algorithms is important. Recently, Morales and Nocedal developed hybrid methods for large-scale unconstrained optimization that interlace iterations of the limited-memory BFGS method (L-BFGS) and the Hessian-free Newton method (Computat Opt Appl 2002, 21, 143-154). We test the performance of this approach as compared to those of the L-BFGS algorithm of Liu and Nocedal and the truncated Newton (TN) with automatic preconditioner of Nash, as applied to the protein bovine pancreatic trypsin inhibitor (BPTI) and a loop of the protein ribonuclease A. These systems are described by the all-atom AMBER force field with a dielectric constant epsilon = 1 and a distance-dependent dielectric function epsilon = 2r, where r is the distance between two atoms. It is shown that for the optimal parameters the hybrid approach is typically two times more efficient in terms of CPU time and function/gradient calculations than the two other methods. The advantage of the hybrid approach increases as the electrostatic interactions become stronger, that is, in going from epsilon = 2r to epsilon = 1, which leads to a more rugged and probably more nonlinear potential energy surface. However, no general rule that defines the optimal parameters has been found and their determination requires a relatively large number of trial-and-error calculations for each problem. Copyright 2003 Wiley Periodicals, Inc. J Comput Chem 24: 1222-1231, 2003
Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger
2017-01-01
Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.
Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger
2017-01-01
Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods. PMID:28166542
Fault-Tolerant Tracker for Interconnected Large-Scale Nonlinear Systems with Input Constraint
NASA Astrophysics Data System (ADS)
Shiu, Y. C.; Tsai, J. S. H.; Guo, S. M.; Shieh, L. S.; Han, Z.
This paper presents the decentralized fault-tolerant tracker based on the model predictive control (MPC) for a class of unknown interconnected large-scale sampled-data nonlinear systems. Due to the computational requirements of MPC and the system information is unknown, the observer/Kalman filter identification (OKID) method is utilized to determine decentralized appropriate (low-) order discrete-time linear models. Then, to overcome the effect of modeling error on the identified linear model of each subsystem, the improved observers with the high-gain property based on the digital redesign approach will be presented. Once fault is detected in each decentralized controller, one of the backup control configurations in each decentralized subsystem is switched to using the soft switching approach. Thus, the decentralized fault-tolerant control with the closed-loop decoupling property can be achieved through the above approach with high-gain property decentralized observer/tracker.
A New Large-Scale Global Optimization Method and Its Application to Lennard-Jones Problems
1992-11-01
stochastic methods. Computational results on Lennard - Jones problems show that the new method is considerably more successful than any other method that...our method does not find as good a solution as has been found by the best special purpose methods for Lennard - Jones problems. This illustrates the inherent difficulty of large scale global optimization.
NASA Astrophysics Data System (ADS)
Ling, Mei Mei; Leong, Wah June
2014-12-01
In this paper, we make a modification to the standard conjugate gradient method so that its search direction satisfies the sufficient descent condition. We prove that the modified conjugate gradient method is globally convergent under Armijo line search. Numerical results show that the proposed conjugate gradient method is efficient compared to some of its standard counterparts for large-scale unconstrained optimization.
NASA Astrophysics Data System (ADS)
Liu, Wen-Jeng
2011-03-01
Although the state feedback approach is quite popular in control engineering, it cannot be used while the system states cannot be measured. The state observer approach may be used to overcome such a shortcoming. Also, most control systems have become larger and more complicated; therefore, based on the variable structure control theory, a new decentralised variable structure observer (DVSO) for a class of nonlinear large-scale systems with mismatched uncertainties will be considered in this article. The switching surface function is determined such that the equivalent system will have the desired behaviour once the system reaches the switching surface. And then a new DVSO is designed such that the estimated states will approach the system states. Using the Lyapunov stability theory and using the generalised matrix inverse concept, the uncertain nonlinear error system trajectories can be driven onto the sliding manifold and then the existence of a sliding mode and the attractiveness to the sliding surface is ensured. With the proposed DVSO, the estimation errors asymptotically tend to zero if the matching condition is satisfied, and the effects of the mismatched parts can be uniformly ultimately bounded if the matching condition is not satisfied. Finally, a numerical example with a succession of computer simulations is given to demonstrate the effectiveness of the proposed approach.
Ceroici, Chris; Zemp, Roger J
2017-07-01
Capacitive micromachined ultrasonic transducers (CMUTs) promise many advantages over traditional piezoelectric transducers such as the potential to construct large, cost-effective 2-D arrays. To avoid wiring congestion issues associated with fully wired arrays, top-orthogonal-to-bottom electrode (TOBE) CMUT array architectures have proven to be a more practical alternative, using only 2N wires for an N ×N array. Optimally designing a TOBE CMUT array is a significant challenge due to the range of parameters from the device level up to the operating conditions of the entire array. Since testing many design variations can be prohibitively expensive, a simulation approach accounting for both the small and large-scale array characteristics of TOBE arrays is essential. In this paper, we demonstrate large-scale TOBE CMUT array simulations using a nonlinear CMUT lumped-circuit model. We investigate the performance of the array with different CMUT design parameters and array operating conditions. These simulated results are then compared with measurements of TOBE arrays fabricated using a sacrificial release process.
Deschaine, L.M.
2008-07-01
The global impact to human health and the environment from large scale chemical / radionuclide releases is well documented. Examples are the wide spread release of radionuclides from the Chernobyl nuclear reactors, the mobilization of arsenic in Bangladesh, the formation of Environmental Protection Agencies in the United States, Canada and Europe, and the like. The fiscal costs of addressing and remediating these issues on a global scale are astronomical, but then so are the fiscal and human health costs of ignoring them. An integrated methodology for optimizing the response(s) to these issues is needed. This work addresses development of optimal policy design for large scale, complex, environmental issues. It discusses the development, capabilities, and application of a hybrid system of algorithms that optimizes the environmental response. It is important to note that 'optimization' does not singularly refer to cost minimization, but to the effective and efficient balance of cost, performance, risk, management, and societal priorities along with uncertainty analysis. This tool integrates all of these elements into a single decision framework. It provides a consistent approach to designing optimal solutions that are tractable, traceable, and defensible. The system is modular and scalable. It can be applied either as individual components or in total. By developing the approach in a complex systems framework, a solution methodology represents a significant improvement over the non-optimal 'trial and error' approach to environmental response(s). Subsurface environmental processes are represented by linear and non-linear, elliptic and parabolic equations. The state equations solved using numerical methods include multi-phase flow (water, soil gas, NAPL), and multicomponent transport (radionuclides, heavy metals, volatile organics, explosives, etc.). Genetic programming is used to generate the simulators either when simulation models do not exist, or to extend the
Li, Yong; Yuan, Gonglin; Wei, Zengxin
2015-01-01
In this paper, a trust-region algorithm is proposed for large-scale nonlinear equations, where the limited-memory BFGS (L-M-BFGS) update matrix is used in the trust-region subproblem to improve the effectiveness of the algorithm for large-scale problems. The global convergence of the presented method is established under suitable conditions. The numerical results of the test problems show that the method is competitive with the norm method. PMID:25950725
NASA Astrophysics Data System (ADS)
Wang, J.; Cai, X.
2007-12-01
A water resources system can be defined as a large-scale spatial system, within which distributed ecological system interacts with the stream network and ground water system. Water resources management, the causative factors and hence the solutions to be developed have a significant spatial dimension. This motivates a modeling analysis of water resources management within a spatial analytical framework, where data is usually geo- referenced and in the form of a map. One of the important functions of Geographic information systems (GIS) is to identify spatial patterns of environmental variables. The role of spatial patterns in water resources management has been well established in the literature particularly regarding how to design better spatial patterns for satisfying the designated objectives of water resources management. Evolutionary algorithms (EA) have been demonstrated to be successful in solving complex optimization models for water resources management due to its flexibility to incorporate complex simulation models in the optimal search procedure. The idea of combining GIS and EA motivates the development and application of spatial evolutionary algorithms (SEA). SEA assimilates spatial information into EA, and even changes the representation and operators of EA. In an EA used for water resources management, the mathematical optimization model should be modified to account the spatial patterns; however, spatial patterns are usually implicit, and it is difficult to impose appropriate patterns to spatial data. Also it is difficult to express complex spatial patterns by explicit constraints included in the EA. The GIS can help identify the spatial linkages and correlations based on the spatial knowledge of the problem. These linkages are incorporated in the fitness function for the preference of the compatible vegetation distribution. Unlike a regular GA for spatial models, the SEA employs a special hierarchical hyper-population and spatial genetic operators
Test Problems for Large-Scale Multiobjective and Many-Objective Optimization.
Cheng, Ran; Jin, Yaochu; Olhofer, Markus; Sendhoff, Bernhard
2016-08-26
The interests in multiobjective and many-objective optimization have been rapidly increasing in the evolutionary computation community. However, most studies on multiobjective and many-objective optimization are limited to small-scale problems, despite the fact that many real-world multiobjective and many-objective optimization problems may involve a large number of decision variables. As has been evident in the history of evolutionary optimization, the development of evolutionary algorithms (EAs) for solving a particular type of optimization problems has undergone a co-evolution with the development of test problems. To promote the research on large-scale multiobjective and many-objective optimization, we propose a set of generic test problems based on design principles widely used in the literature of multiobjective and many-objective optimization. In order for the test problems to be able to reflect challenges in real-world applications, we consider mixed separability between decision variables and nonuniform correlation between decision variables and objective functions. To assess the proposed test problems, six representative evolutionary multiobjective and many-objective EAs are tested on the proposed test problems. Our empirical results indicate that although the compared algorithms exhibit slightly different capabilities in dealing with the challenges in the test problems, none of them are able to efficiently solve these optimization problems, calling for the need for developing new EAs dedicated to large-scale multiobjective and many-objective optimization.
Computational Advances in Large-Scale Nonlinear Optimization.
1981-09-01
collected from a variety of sources. The first six problems are listed in Himmelblau [Ref. 26: pp. 395-425] and the original source author/developer for...Problem 1 ( Himmelblau 6) This problem [Ref. 303] is an example of determining the chemical composition of a complex mixture under conditions of chemical... Himmelblau 4A) This is also a chemical equilibrium problem which had been redefined in the Himmelblau study from a problem originally formulated and
Decentralized Adaptive Neural Output-Feedback DSC for Switched Large-Scale Nonlinear Systems.
Long, Lijun; Zhao, Jun
2016-03-08
In this paper, for a class of switched large-scale uncertain nonlinear systems with unknown control coefficients and unmeasurable states, a switched-dynamic-surface-based decentralized adaptive neural output-feedback control approach is developed. The approach proposed extends the classical dynamic surface control (DSC) technique for nonswitched version to switched version by designing switched first-order filters, which overcomes the problem of multiple ``explosion of complexity.'' Also, a dual common coordinates transformation of all subsystems is exploited to avoid individual coordinate transformations for subsystems that are required when applying the backstepping recursive design scheme. Nussbaum-type functions are utilized to handle the unknown control coefficients, and a switched neural network observer is constructed to estimate the unmeasurable states. Combining with the average dwell time method and backstepping and the DSC technique, decentralized adaptive neural controllers of subsystems are explicitly designed. It is proved that the approach provided can guarantee the semiglobal uniformly ultimately boundedness for all the signals in the closed-loop system under a class of switching signals with average dwell time, and the tracking errors to a small neighborhood of the origin. A two inverted pendulums system is provided to demonstrate the effectiveness of the method proposed.
Friedman, A.
1996-12-01
The summer program in Large Scale Optimization concentrated largely on process engineering, aerospace engineering, inverse problems and optimal design, and molecular structure and protein folding. The program brought together application people, optimizers, and mathematicians with interest in learning about these topics. Three proceedings volumes are being prepared. The year in Materials Sciences deals with disordered media and percolation, phase transformations, composite materials, microstructure; topological and geometric methods as well as statistical mechanics approach to polymers (included were Monte Carlo simulation for polymers); miscellaneous other topics such as nonlinear optical material, particulate flow, and thin film. All these activities saw strong interaction among material scientists, mathematicians, physicists, and engineers. About 8 proceedings volumes are being prepared.
Optimization of large-scale heterogeneous system-of-systems models.
Parekh, Ojas; Watson, Jean-Paul; Phillips, Cynthia Ann; Siirola, John; Swiler, Laura Painton; Hough, Patricia Diane; Lee, Herbert K. H.; Hart, William Eugene; Gray, Genetha Anne; Woodruff, David L.
2012-01-01
Decision makers increasingly rely on large-scale computational models to simulate and analyze complex man-made systems. For example, computational models of national infrastructures are being used to inform government policy, assess economic and national security risks, evaluate infrastructure interdependencies, and plan for the growth and evolution of infrastructure capabilities. A major challenge for decision makers is the analysis of national-scale models that are composed of interacting systems: effective integration of system models is difficult, there are many parameters to analyze in these systems, and fundamental modeling uncertainties complicate analysis. This project is developing optimization methods to effectively represent and analyze large-scale heterogeneous system of systems (HSoS) models, which have emerged as a promising approach for describing such complex man-made systems. These optimization methods enable decision makers to predict future system behavior, manage system risk, assess tradeoffs between system criteria, and identify critical modeling uncertainties.
Ge, Hongwei; Sun, Liang; Tan, Guozhen; Chen, Zheng; Chen, C L Philip
2017-09-01
Large scale optimization problems arise in diverse fields. Decomposing the large scale problem into small scale subproblems regarding the variable interactions and optimizing them cooperatively are critical steps in an optimization algorithm. To explore the variable interactions and perform the problem decomposition tasks, we develop a two stage variable interaction reconstruction algorithm. A learning model is proposed to explore part of the variable interactions as prior knowledge. A marginalized denoising model is proposed to construct the overall variable interactions using the prior knowledge, with which the problem is decomposed into small scale modules. To optimize the subproblems and relieve premature convergence, we propose a cooperative hierarchical particle swarm optimization framework, where the operators of contingency leadership, interactional cognition, and self-directed exploitation are designed. Finally, we conduct theoretical analysis for further understanding of the proposed algorithm. The analysis shows that the proposed algorithm can guarantee converging to the global optimal solutions if the problems are correctly decomposed. Experiments are conducted on the CEC2008 and CEC2010 benchmarks. The results demonstrate the effectiveness, convergence, and usefulness of the proposed algorithm.
A large scale application of an optimal deterministic hydrothermal scheduling algorithm
Carneiro, A.A.F.M.; Soares, S. ); Bond, P.S. )
1990-02-01
This paper presents an application of a deterministic optimization algorithm in the hydrothermal scheduling of the large scale Brazilian south-southeast interconnected system, composed of 51 hydro and 12 thermal plants, corresponding to 45 GW of installed capacity. The application considers the system operational conditions according to the 1986 operational plan coordinated by the Brazilian electric holding company. The employed algorithm is based on a network flow approach especially developed for hydrothermal scheduling. For the south-southeast interconnected system the problem formulation suggests a primal decomposition optimization approach.
Efficient Interpretation of Large-Scale Real Data by Static Inverse Optimization
NASA Astrophysics Data System (ADS)
Zhang, Hong; Ishikawa, Masumi
We have already proposed a methodology for static inverse optimization to interpret real data from a viewpoint of optimization. In this paper we propose a method for efficiently generating constraints by divide-and-conquer to interpret large-scale data by static inverse optimization. It radically decreases computational cost of generating constraints by deleting non-Pareto optimal data from given data. To evaluate the effectiveness of the proposed method, simulation experiments using 3-D artifical data are carried out. As an application to real data, criterion functions underlying decision making of about 5, 000 tenants living along Yamanote line and Soubu-Chuo line in Tokyo are estimated, providing interpretation of rented housing data from a viewpoint of optimization.
NASA Astrophysics Data System (ADS)
Hasegawa, Mikio; Tran, Ha Nguyen; Miyamoto, Goh; Murata, Yoshitoshi; Harada, Hiroshi; Kato, Shuzo
We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.
Ten key considerations for the successful optimization of large-scale health information technology.
Cresswell, Kathrin M; Bates, David W; Sheikh, Aziz
2017-01-01
Implementation and adoption of complex health information technology (HIT) is gaining momentum internationally. This is underpinned by the drive to improve the safety, quality, and efficiency of care. Although most of the benefits associated with HIT will only be realized through optimization of these systems, relatively few health care organizations currently have the expertise or experience needed to undertake this. It is extremely important to have systems working before embarking on HIT optimization, which, much like implementation, is an ongoing, difficult, and often expensive process. We discuss some key organization-level activities that are important in optimizing large-scale HIT systems. These include considerations relating to leadership, strategy, vision, and continuous cycles of improvement. Although these alone are not sufficient to fully optimize complex HIT, they provide a starting point for conceptualizing this important area.
Final Report: Large-Scale Optimization for Bayesian Inference in Complex Systems
Ghattas, Omar
2013-10-15
The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimiza- tion) Project focuses on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimiza- tion and inversion methods. Our research is directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. Our efforts are integrated in the context of a challenging testbed problem that considers subsurface reacting flow and transport. The MIT component of the SAGUARO Project addresses the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas-Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to- observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as "reduce then sample" and "sample then reduce." In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.
Using Agent Base Models to Optimize Large Scale Network for Large System Inventories
NASA Technical Reports Server (NTRS)
Shameldin, Ramez Ahmed; Bowling, Shannon R.
2010-01-01
The aim of this paper is to use Agent Base Models (ABM) to optimize large scale network handling capabilities for large system inventories and to implement strategies for the purpose of reducing capital expenses. The models used in this paper either use computational algorithms or procedure implementations developed by Matlab to simulate agent based models in a principal programming language and mathematical theory using clusters, these clusters work as a high performance computational performance to run the program in parallel computational. In both cases, a model is defined as compilation of a set of structures and processes assumed to underlie the behavior of a network system.
Integration of Large-Scale Optimization and Game Theory for Sustainable Water Quality Management
NASA Astrophysics Data System (ADS)
Tsao, J.; Li, J.; Chou, C.; Tung, C.
2009-12-01
Sustainable water quality management requires total mass control in pollutant discharge based on both the principles of not exceeding assimilative capacity in a river and equity among generations. The stream assimilative capacity is the carrying capacity of a river for the maximum waste load without violating the water quality standard and the spirit of total mass control is to optimize the waste load allocation in subregions. For the goal of sustainable watershed development, this study will use large-scale optimization theory to optimize the profit, and find the marginal values of loadings as reference of the fair price and then the best way to get the equilibrium by water quality trading for the whole of watershed will be found. On the other hand, game theory plays an important role to maximize both individual and entire profits. This study proves the water quality trading market is available in some situation, and also makes the whole participants get a better outcome.
Large scale test simulations using the Virtual Environment for Test Optimization (VETO)
Klenke, S.E.; Heffelfinger, S.R.; Bell, H.J.; Shierling, C.L.
1997-10-01
The Virtual Environment for Test Optimization (VETO) is a set of simulation tools under development at Sandia to enable test engineers to do computer simulations of tests. The tool set utilizes analysis codes and test information to optimize design parameters and to provide an accurate model of the test environment which aides in the maximization of test performance, training, and safety. Previous VETO effort has included the development of two structural dynamics simulation modules that provide design and optimization tools for modal and vibration testing. These modules have allowed test engineers to model and simulate complex laboratory testing, to evaluate dynamic response behavior, and to investigate system testability. Further development of the VETO tool set will address the accurate modeling of large scale field test environments at Sandia. These field test environments provide weapon system certification capabilities and have different simulation requirements than those of laboratory testing.
Negrete, Alejandro; Esteban, Geoffrey; Kotin, Robert M
2007-09-01
A well-characterized manufacturing process for the large-scale production of recombinant adeno-associated vectors (rAAV) for gene therapy applications is required to meet current and future demands for pre-clinical and clinical studies and potential commercialization. Economic considerations argue in favor of suspension culture-based production. Currently, the only feasible method for large-scale rAAV production utilizes baculovirus expression vectors and insect cells in suspension cultures. To maximize yields and achieve reproducibility between batches, online monitoring of various metabolic and physical parameters is useful for characterizing early stages of baculovirus-infected insect cells. In this study, rAAVs were produced at 40-l scale yielding ~1 x 10(15) particles. During the process, dielectric spectroscopy was performed by real time scanning in radio frequencies between 300 kHz and 10 MHz. The corresponding permittivity values were correlated with the rAAV production. Both infected and uninfected reached a maximum value; however, only infected cell cultures permittivity profile reached a second maximum value. This effect was correlated with the optimal harvest time for rAAV production. Analysis of rAAV indicated the harvesting time around 48 h post-infection (hpi), and 72 hpi produced similar quantities of biologically active rAAV. Thus, if operated continuously, the 24-h reduction in the production process of rAAV gives sufficient time for additional 18 runs a year corresponding to an extra production of ~2 x 10(16) particles. As part of large-scale optimization studies, this new finding will facilitate the bioprocessing scale-up of rAAV and other bioproducts.
Maximum-entropy large-scale structures of Boolean networks optimized for criticality
NASA Astrophysics Data System (ADS)
Möller, Marco; Peixoto, Tiago P.
2015-04-01
We construct statistical ensembles of modular Boolean networks that are constrained to lie at the critical line between frozen and chaotic dynamic regimes. The ensembles are maximally random given the imposed constraints, and thus represent null models of critical networks. By varying the network density and the entropic cost associated with biased Boolean functions, the ensembles undergo several phase transitions. The observed structures range from fully random to several ordered ones, including a prominent core-periphery-like structure, and an 'attenuated' two-group structure, where the network is divided in two groups of nodes, and one of them has Boolean functions with very low sensitivity. This shows that such simple large-scale structures are the most likely to occur when optimizing for criticality, in the absence of any other constraint or competing optimization criteria.
Optimally amplified large-scale streaks and drag reduction in turbulent pipe flow.
Willis, Ashley P; Hwang, Yongyun; Cossu, Carlo
2010-09-01
The optimal amplifications of small coherent perturbations within turbulent pipe flow are computed for Reynolds numbers up to one million. Three standard frameworks are considered: the optimal growth of an initial condition, the response to harmonic forcing and the Karhunen-Loève (proper orthogonal decomposition) analysis of the response to stochastic forcing. Similar to analyses of the turbulent plane channel flow and boundary layer, it is found that streaks elongated in the streamwise direction can be greatly amplified from quasistreamwise vortices, despite linear stability of the mean flow profile. The most responsive perturbations are streamwise uniform and, for sufficiently large Reynolds number, the most responsive azimuthal mode is of wave number m=1 . The response of this mode increases with the Reynolds number. A secondary peak, where m corresponds to azimuthal wavelengths λ_{θ}^{+}≈70-90 in wall units, also exists in the amplification of initial conditions and in premultiplied response curves for the forced problems. Direct numerical simulations at Re=5300 confirm that the forcing of m=1,2 and m=4 optimal structures results in the large response of coherent large-scale streaks. For moderate amplitudes of the forcing, low-speed streaks become narrower and more energetic, whereas high-speed streaks become more spread. It is further shown that drag reduction can be achieved by forcing steady large-scale structures, as anticipated from earlier investigations. Here the energy balance is calculated. At Re=5300 it is shown that, due to the small power required by the forcing of optimal structures, a net power saving of the order of 10% can be achieved following this approach, which could be relevant for practical applications.
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
Li, Yongming; Ma, Zhiyao; Tong, Shaocheng
2017-09-01
The problem of adaptive fuzzy output-constrained tracking fault-tolerant control (FTC) is investigated for the large-scale stochastic nonlinear systems of pure-feedback form. The nonlinear systems considered in this paper possess the unstructured uncertainties, unknown interconnected terms and unknown nonaffine nonlinear faults. The fuzzy logic systems are employed to identify the unknown lumped nonlinear functions so that the problems of structured uncertainties can be solved. An adaptive fuzzy state observer is designed to solve the nonmeasurable state problem. By combining the barrier Lyapunov function theory, adaptive decentralized and stochastic control principles, a novel fuzzy adaptive output-constrained FTC approach is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.
Fast Bound Methods for Large Scale Simulation with Application for Engineering Optimization
NASA Technical Reports Server (NTRS)
Patera, Anthony T.; Peraire, Jaime; Zang, Thomas A. (Technical Monitor)
2002-01-01
In this work, we have focused on fast bound methods for large scale simulation with application for engineering optimization. The emphasis is on the development of techniques that provide both very fast turnaround and a certificate of Fidelity; these attributes ensure that the results are indeed relevant to - and trustworthy within - the engineering context. The bound methodology which underlies this work has many different instantiations: finite element approximation; iterative solution techniques; and reduced-basis (parameter) approximation. In this grant we have, in fact, treated all three, but most of our effort has been concentrated on the first and third. We describe these below briefly - but with a pointer to an Appendix which describes, in some detail, the current "state of the art."
Rakshit, Sourav; Ananthasuresh, G K
2010-02-07
We present a new computationally efficient method for large-scale polypeptide folding using coarse-grained elastic networks and gradient-based continuous optimization techniques. The folding is governed by minimization of energy based on Miyazawa-Jernigan contact potentials. Using this method we are able to substantially reduce the computation time on ordinary desktop computers for simulation of polypeptide folding starting from a fully unfolded state. We compare our results with available native state structures from Protein Data Bank (PDB) for a few de-novo proteins and two natural proteins, Ubiquitin and Lysozyme. Based on our simulations we are able to draw the energy landscape for a small de-novo protein, Chignolin. We also use two well known protein structure prediction software, MODELLER and GROMACS to compare our results. In the end, we show how a modification of normal elastic network model can lead to higher accuracy and lower time required for simulation.
NASA Astrophysics Data System (ADS)
Schneider, T.; Walker, C. C.
2004-12-01
It is generally held that atmospheric macroturbulence can be strongly nonlinear. Yet weakly nonlinear models successfully account for the length scales ( ˜4000~km), time scales ( ˜2~days), and for aspects of the structure of the energy-containing baroclinic eddies in the extratropics of the Earth atmosphere. Here we present theoretical arguments and simulations that suggest that the historic successes of weakly nonlinear models of atmospheric macroturbulence are not a coincidence but a result of self-organization of atmospheric macroturbulence into critical states of weak nonlinearity. A negative feedback between the extra\\-tro\\-pi\\-cal thermal stratification and atmospheric macroturbulence limits nonlinear eddy--eddy interactions and the concomitant inverse cascade of eddy energy from the length scales of baroclinic instability to larger scales. The theory and simulations point to fundamental constraints on the climate of Earth and other planets.
Asymptotically Optimal Transmission Policies for Large-Scale Low-Power Wireless Sensor Networks
I. Ch. Paschalidis; W. Lai; D. Starobinski
2007-02-01
We consider wireless sensor networks with multiple gateways and multiple classes of traffic carrying data generated by different sensory inputs. The objective is to devise joint routing, power control and transmission scheduling policies in order to gather data in the most efficient manner while respecting the needs of different sensing tasks (fairness). We formulate the problem as maximizing the utility of transmissions subject to explicit fairness constraints and propose an efficient decomposition algorithm drawing upon large-scale decomposition ideas in mathematical programming. We show that our algorithm terminates in a finite number of iterations and produces a policy that is asymptotically optimal at low transmission power levels. Furthermore, we establish that the utility maximization problem we consider can, in principle, be solved in polynomial time. Numerical results show that our policy is near-optimal, even at high power levels, and far superior to the best known heuristics at low power levels. We also demonstrate how to adapt our algorithm to accommodate energy constraints and node failures. The approach we introduce can efficiently determine near-optimal transmission policies for dramatically larger problem instances than an alternative enumeration approach.
NASA Astrophysics Data System (ADS)
Xiao, Jinyou; Zhou, Hang; Zhang, Chuanzeng; Xu, Chao
2017-02-01
This paper focuses on the development and engineering applications of a new resolvent sampling based Rayleigh-Ritz method (RSRR) for solving large-scale nonlinear eigenvalue problems (NEPs) in finite element analysis. There are three contributions. First, to generate reliable eigenspaces the resolvent sampling scheme is derived from Keldysh's theorem for holomorphic matrix functions following a more concise and insightful algebraic framework. Second, based on the new derivation a two-stage solution strategy is proposed for solving large-scale NEPs, which can greatly enhance the computational cost and accuracy of the RSRR. The effects of the user-defined parameters are studied, which provides a useful guide for real applications. Finally, the RSRR and the two-stage scheme is applied to solve two NEPs in the FE analysis of viscoelastic damping structures with up to 1 million degrees of freedom. The method is versatile, robust and suitable for parallelization, and can be easily implemented into other packages.
NASA Astrophysics Data System (ADS)
Xiao, Jinyou; Zhou, Hang; Zhang, Chuanzeng; Xu, Chao
2016-11-01
This paper focuses on the development and engineering applications of a new resolvent sampling based Rayleigh-Ritz method (RSRR) for solving large-scale nonlinear eigenvalue problems (NEPs) in finite element analysis. There are three contributions. First, to generate reliable eigenspaces the resolvent sampling scheme is derived from Keldysh's theorem for holomorphic matrix functions following a more concise and insightful algebraic framework. Second, based on the new derivation a two-stage solution strategy is proposed for solving large-scale NEPs, which can greatly enhance the computational cost and accuracy of the RSRR. The effects of the user-defined parameters are studied, which provides a useful guide for real applications. Finally, the RSRR and the two-stage scheme is applied to solve two NEPs in the FE analysis of viscoelastic damping structures with up to 1 million degrees of freedom. The method is versatile, robust and suitable for parallelization, and can be easily implemented into other packages.
a Stochastic Approach to Multiobjective Optimization of Large-Scale Water Reservoir Networks
NASA Astrophysics Data System (ADS)
Bottacin-Busolin, A.; Worman, A. L.
2013-12-01
A main challenge for the planning and management of water resources is the development of multiobjective strategies for operation of large-scale water reservoir networks. The optimal sequence of water releases from multiple reservoirs depends on the stochastic variability of correlated hydrologic inflows and on various processes that affect water demand and energy prices. Although several methods have been suggested, large-scale optimization problems arising in water resources management are still plagued by the high dimensional state space and by the stochastic nature of the hydrologic inflows. In this work, the optimization of reservoir operation is approached using approximate dynamic programming (ADP) with policy iteration and function approximators. The method is based on an off-line learning process in which operating policies are evaluated for a number of stochastic inflow scenarios, and the resulting value functions are used to design new, improved policies until convergence is attained. A case study is presented of a multi-reservoir system in the Dalälven River, Sweden, which includes 13 interconnected reservoirs and 36 power stations. Depending on the late spring and summer peak discharges, the lowlands adjacent to Dalälven can often be flooded during the summer period, and the presence of stagnating floodwater during the hottest months of the year is the cause of a large proliferation of mosquitos, which is a major problem for the people living in the surroundings. Chemical pesticides are currently being used as a preventive countermeasure, which do not provide an effective solution to the problem and have adverse environmental impacts. In this study, ADP was used to analyze the feasibility of alternative operating policies for reducing the flood risk at a reasonable economic cost for the hydropower companies. To this end, mid-term operating policies were derived by combining flood risk reduction with hydropower production objectives. The performance
Optimization and large scale computation of an entropy-based moment closure
Hauck, Cory D.; Hill, Judith C.; Garrett, C. Kristopher
2015-09-10
We present computational advances and results in the implementation of an entropy-based moment closure, M_{N}, in the context of linear kinetic equations, with an emphasis on heterogeneous and large-scale computing platforms. Entropy-based closures are known in several cases to yield more accurate results than closures based on standard spectral approximations, such as P_{N}, but the computational cost is generally much higher and often prohibitive. Several optimizations are introduced to improve the performance of entropy-based algorithms over previous implementations. These optimizations include the use of GPU acceleration and the exploitation of the mathematical properties of spherical harmonics, which are used as test functions in the moment formulation. To test the emerging high-performance computing paradigm of communication bound simulations, we present timing results at the largest computational scales currently available. Lastly, these results show, in particular, load balancing issues in scaling the M_{N} algorithm that do not appear for the P_{N} algorithm. We also observe that in weak scaling tests, the ratio in time to solution of M_{N} to P_{N} decreases.
Optimization and large scale computation of an entropy-based moment closure
Hauck, Cory D.; Hill, Judith C.; Garrett, C. Kristopher
2015-09-10
We present computational advances and results in the implementation of an entropy-based moment closure, MN, in the context of linear kinetic equations, with an emphasis on heterogeneous and large-scale computing platforms. Entropy-based closures are known in several cases to yield more accurate results than closures based on standard spectral approximations, such as PN, but the computational cost is generally much higher and often prohibitive. Several optimizations are introduced to improve the performance of entropy-based algorithms over previous implementations. These optimizations include the use of GPU acceleration and the exploitation of the mathematical properties of spherical harmonics, which are used asmore » test functions in the moment formulation. To test the emerging high-performance computing paradigm of communication bound simulations, we present timing results at the largest computational scales currently available. Lastly, these results show, in particular, load balancing issues in scaling the MN algorithm that do not appear for the PN algorithm. We also observe that in weak scaling tests, the ratio in time to solution of MN to PN decreases.« less
Optimization and large scale computation of an entropy-based moment closure
NASA Astrophysics Data System (ADS)
Kristopher Garrett, C.; Hauck, Cory; Hill, Judith
2015-12-01
We present computational advances and results in the implementation of an entropy-based moment closure, MN, in the context of linear kinetic equations, with an emphasis on heterogeneous and large-scale computing platforms. Entropy-based closures are known in several cases to yield more accurate results than closures based on standard spectral approximations, such as PN, but the computational cost is generally much higher and often prohibitive. Several optimizations are introduced to improve the performance of entropy-based algorithms over previous implementations. These optimizations include the use of GPU acceleration and the exploitation of the mathematical properties of spherical harmonics, which are used as test functions in the moment formulation. To test the emerging high-performance computing paradigm of communication bound simulations, we present timing results at the largest computational scales currently available. These results show, in particular, load balancing issues in scaling the MN algorithm that do not appear for the PN algorithm. We also observe that in weak scaling tests, the ratio in time to solution of MN to PN decreases.
Cooperative Co-evolution with Formula-based Variable Grouping for Large-Scale Global Optimization.
Wang, Yuping; Liu, Haiyan; Wei, Fei; Zong, Tingting; Li, Xiaodong
2017-08-09
For a large-scale global optimization (LSGO) problem, divide-and-conquer is usually considered as an effective strategy to decompose the problem into smaller subproblems, each of which can be then solved individually. Among these decomposition methods, variable grouping is shown to be promising in recent years. Existing variable grouping methods usually assume the problem to be black-box (i.e., assuming that an analytical model of the objective function is unknown), and they attempt to learn appropriate variable grouping that would allow for a better decomposition of the problem. In such cases, these variable grouping methods do not make a direct use of the formula of the objective function. However, it can be argued that many real world problems are white-box problems, i.e., the formulas of objective functions are often known a priori. These formulas of the objective functions provide rich information which can be then used to design an effective variable group method. In this paper, a formulabased grouping strategy (FBG) for white-box problems is first proposed. It groups variables directly via the formula of an objective function which usually consists of a finite number of operations (i.e., four arithmetic operations " + ", " - ", " × ", " ÷ " and composite operations of basic elementary functions). In FBG, the operations are classified into two classes: one resulting in non-separable variables, and the other resulting in separable variables. In FBG, variables can be automatically grouped into a suitable number of non-interacting subcomponents, with variables in each subcomponent being inter-dependent. FBG can be applied to any white-box problem easily and can be integrated into a cooperative co-evolution framework. Based on FBG, a novel cooperative co-evolution algorithm with formula-based variable grouping (so-called CCF) is proposed in this paper for decomposing a large-scale white-box problem into several smaller sub-problems and optimizing them
NASA Astrophysics Data System (ADS)
Bandyopadhyay, Saptarshi
Multi-agent systems are widely used for constructing a desired formation shape, exploring an area, surveillance, coverage, and other cooperative tasks. This dissertation introduces novel algorithms in the three main areas of shape formation, distributed estimation, and attitude control of large-scale multi-agent systems. In the first part of this dissertation, we address the problem of shape formation for thousands to millions of agents. Here, we present two novel algorithms for guiding a large-scale swarm of robotic systems into a desired formation shape in a distributed and scalable manner. These probabilistic swarm guidance algorithms adopt an Eulerian framework, where the physical space is partitioned into bins and the swarm's density distribution over each bin is controlled using tunable Markov chains. In the first algorithm - Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC) - each agent determines its bin transition probabilities using a time-inhomogeneous Markov chain that is constructed in real-time using feedback from the current swarm distribution. This PSG-IMC algorithm minimizes the expected cost of the transitions required to achieve and maintain the desired formation shape, even when agents are added to or removed from the swarm. The algorithm scales well with a large number of agents and complex formation shapes, and can also be adapted for area exploration applications. In the second algorithm - Probabilistic Swarm Guidance using Optimal Transport (PSG-OT) - each agent determines its bin transition probabilities by solving an optimal transport problem, which is recast as a linear program. In the presence of perfect feedback of the current swarm distribution, this algorithm minimizes the given cost function, guarantees faster convergence, reduces the number of transitions for achieving the desired formation, and is robust to disturbances or damages to the formation. We demonstrate the effectiveness of these two proposed swarm
Huang, Zhipeng; Wang, Ruxue; Jia, Ding; Maoying, Li; Humphrey, Mark G; Zhang, Chi
2012-03-01
A facile method for the low-cost and large-scale production of silicon nanowires has been developed. Silicon powders were subjected to sequential metal plating and metal-assisted chemical etching, resulting in well-defined silicon nanowires. The morphology and structure of the silicon nanowires were investigated, revealing that single-crystal silicon nanowires with average diameters of 79 ± 35 nm and length more than 10 μm can be fabricated. The silicon nanowires show excellent third-order nonlinear optical properties, with a third-order susceptibility much larger than that of bulk silicon, porous silicon, and silicon nanocrystals embedded in SiO(2).
Characterizing the nonlinear growth of large-scale structure in the Universe
Coles; Chiang
2000-07-27
The local Universe displays a rich hierarchical pattern of galaxy clusters and superclusters. The early Universe, however, was almost smooth, with only slight 'ripples' as seen in the cosmic microwave background radiation. Models of the evolution of cosmic structure link these observations through the effect of gravity, because the small initially overdense fluctuations are predicted to attract additional mass as the Universe expands. During the early stages of this expansion, the ripples evolve independently, like linear waves on the surface of deep water. As the structures grow in mass, they interact with each other in nonlinear ways, more like waves breaking in shallow water. We have recently shown how cosmic structure can be characterized by phase correlations associated with these nonlinear interactions, but it was not clear how to use that information to obtain quantitative insights into the growth of structures. Here we report a method of revealing phase information, and show quantitatively how this relates to the formation of filaments, sheets and clusters of galaxies by nonlinear collapse. We develop a statistical method based on information entropy to separate linear from nonlinear effects, and thereby are able to disentangle those aspects of galaxy clustering that arise from initial conditions (the ripples) from the subsequent dynamical evolution.
Hu, Eric Y.; Bouteiller, Jean-Marie C.; Song, Dong; Baudry, Michel; Berger, Theodore W.
2015-01-01
Chemical synapses are comprised of a wide collection of intricate signaling pathways involving complex dynamics. These mechanisms are often reduced to simple spikes or exponential representations in order to enable computer simulations at higher spatial levels of complexity. However, these representations cannot capture important nonlinear dynamics found in synaptic transmission. Here, we propose an input-output (IO) synapse model capable of generating complex nonlinear dynamics while maintaining low computational complexity. This IO synapse model is an extension of a detailed mechanistic glutamatergic synapse model capable of capturing the input-output relationships of the mechanistic model using the Volterra functional power series. We demonstrate that the IO synapse model is able to successfully track the nonlinear dynamics of the synapse up to the third order with high accuracy. We also evaluate the accuracy of the IO synapse model at different input frequencies and compared its performance with that of kinetic models in compartmental neuron models. Our results demonstrate that the IO synapse model is capable of efficiently replicating complex nonlinear dynamics that were represented in the original mechanistic model and provide a method to replicate complex and diverse synaptic transmission within neuron network simulations. PMID:26441622
NASA Astrophysics Data System (ADS)
Weerasinghe, Harshi; Schneider, Uwe A.
2010-05-01
Assessment of economically optimal water management and geospatial potential for large-scale water storage Weerasinghe, Harshi; Schneider, Uwe A Water is an essential but limited and vulnerable resource for all socio-economic development and for maintaining healthy ecosystems. Water scarcity accelerated due to population expansion, improved living standards, and rapid growth in economic activities, has profound environmental and social implications. These include severe environmental degradation, declining groundwater levels, and increasing problems of water conflicts. Water scarcity is predicted to be one of the key factors limiting development in the 21st century. Climate scientists have projected spatial and temporal changes in precipitation and changes in the probability of intense floods and droughts in the future. As scarcity of accessible and usable water increases, demand for efficient water management and adaptation strategies increases as well. Addressing water scarcity requires an intersectoral and multidisciplinary approach in managing water resources. This would in return safeguard the social welfare and the economical benefit to be at their optimal balance without compromising the sustainability of ecosystems. This paper presents a geographically explicit method to assess the potential for water storage with reservoirs and a dynamic model that identifies the dimensions and material requirements under an economically optimal water management plan. The methodology is applied to the Elbe and Nile river basins. Input data for geospatial analysis at watershed level are taken from global data repositories and include data on elevation, rainfall, soil texture, soil depth, drainage, land use and land cover; which are then downscaled to 1km spatial resolution. Runoff potential for different combinations of land use and hydraulic soil groups and for mean annual precipitation levels are derived by the SCS-CN method. Using the overlay and decision tree algorithms
NASA Astrophysics Data System (ADS)
Kitaura, F. S.; Enßlin, T. A.
2008-09-01
We address the inverse problem of cosmic large-scale structure reconstruction from a Bayesian perspective. For a linear data model, a number of known and novel reconstruction schemes, which differ in terms of the underlying signal prior, data likelihood and numerical inverse extraregularization schemes are derived and classified. The Bayesian methodology presented in this paper tries to unify and extend the following methods: Wiener filtering, Tikhonov regularization, ridge regression, maximum entropy and inverse regularization techniques. The inverse techniques considered here are the asymptotic regularization, the Jacobi, Steepest Descent, Newton-Raphson, Landweber-Fridman and both linear and non-linear Krylov methods based on Fletcher-Reeves, Polak-Ribière and Hestenes-Stiefel conjugate gradients. The structures of the up-to-date highest performing algorithms are presented, based on an operator scheme, which permits one to exploit the power of fast Fourier transforms. Using such an implementation of the generalized Wiener filter in the novel ARGO software package, the different numerical schemes are benchmarked with one-, two- and three-dimensional problems including structured white and Poissonian noise, data windowing and blurring effects. A novel numerical Krylov scheme is shown to be superior in terms of performance and fidelity. These fast inverse methods ultimately will enable the application of sampling techniques to explore complex joint posterior distributions. We outline how the space of the dark matter density field, the peculiar velocity field and the power spectrum can jointly be investigated by a Gibbs-sampling process. Such a method can be applied for the redshift distortions correction of the observed galaxies and for time-reversal reconstructions of the initial density field.
Optimizing Cluster Heads for Energy Efficiency in Large-Scale Heterogeneous Wireless Sensor Networks
Gu, Yi; Wu, Qishi; Rao, Nageswara S. V.
2010-01-01
Many complex sensor network applications require deploying a large number of inexpensive and small sensors in a vast geographical region to achieve quality through quantity. Hierarchical clustering is generally considered as an efficient and scalable way to facilitate the management and operation of such large-scale networks and minimize the total energy consumption for prolonged lifetime. Judicious selection of cluster heads for data integration and communication is critical to the success of applications based on hierarchical sensor networks organized as layered clusters. We investigate the problem of selecting sensor nodes in a predeployed sensor network to be the cluster headsmore » to minimize the total energy needed for data gathering. We rigorously derive an analytical formula to optimize the number of cluster heads in sensor networks under uniform node distribution, and propose a Distance-based Crowdedness Clustering algorithm to determine the cluster heads in sensor networks under general node distribution. The results from an extensive set of experiments on a large number of simulated sensor networks illustrate the performance superiority of the proposed solution over the clustering schemes based on k -means algorithm.« less
Robust nonlinear controller design to improve the stability of a large scale photovoltaic system
NASA Astrophysics Data System (ADS)
Islam, Gazi Md. Saeedul
Recently interest in photovoltaic (PV) power generation systems is increasing rapidly and the installation of large PV systems or large groups of PV systems that are interconnected with the utility grid is accelerating despite their high cost and low efficiency due to environmental issues and depletions of fossil fuels. Most of the photovoltaic (PV) applications are grid connected. Existing power systems may face the stability problems because of the high penetration of PV systems to the grid. Therefore, more stringent grid codes are being imposed by the energy regulatory bodies for grid integration of PV plants. Recent grid codes dictate that PV plants need to stay connected with the power grid during the network faults because of their increased power penetration level. This requires the system to have large disturbance rejection capability to protect the system and provide dynamic grid support. This thesis presents a new control method to enhance the steady-state and transient stabilities of a grid connected large scale photovoltaic (PV) system. A new control coordination scheme is also presented to reduce the power mismatch during the fault condition in order to limit the fault currents, which is one of the salient features of this study. The performance of the overall system is analyzed using laboratory standard power system simulation software PSCAD/EMTDC.
Zhang, Yong-Feng; Chiang, Hsiao-Dong
2016-06-20
A novel three-stage methodology, termed the "consensus-based particle swarm optimization (PSO)-assisted Trust-Tech methodology," to find global optimal solutions for nonlinear optimization problems is presented. It is composed of Trust-Tech methods, consensus-based PSO, and local optimization methods that are integrated to compute a set of high-quality local optimal solutions that can contain the global optimal solution. The proposed methodology compares very favorably with several recently developed PSO algorithms based on a set of small-dimension benchmark optimization problems and 20 large-dimension test functions from the CEC 2010 competition. The analytical basis for the proposed methodology is also provided. Experimental results demonstrate that the proposed methodology can rapidly obtain high-quality optimal solutions that can contain the global optimal solution. The scalability of the proposed methodology is promising.
The Modified HZ Conjugate Gradient Algorithm for Large-Scale Nonsmooth Optimization
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). PMID:27780245
The Modified HZ Conjugate Gradient Algorithm for Large-Scale Nonsmooth Optimization.
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).
Design optimization studies for large-scale contoured beam deployable satellite antennas
NASA Astrophysics Data System (ADS)
Tanaka, Hiroaki
2006-05-01
Satellite communications systems over the past two decades have become more sophisticated and evolved new applications that require much higher flux densities. These new requirements to provide high data rate services to very small user terminals have in turn led to the need for large aperture space antenna systems with higher gain. Conventional parabolic reflectors constructed of metal have become, over time, too massive to support these new missions in a cost effective manner and also have posed problems of fitting within the constrained volume of launch vehicles. Designers of new space antenna systems have thus begun to explore new design options. These design options for advanced space communications networks include such alternatives as inflatable antennas using polyimide materials, antennas constructed of piezo-electric materials, phased array antenna systems (especially in the EHF bands) and deployable antenna systems constructed of wire mesh or cabling systems. This article updates studies being conducted in Japan of such deployable space antenna systems [H. Tanaka, M.C. Natori, Shape control of space antennas consisting of cable networks, Acta Astronautica 55 (2004) 519-527]. In particular, this study shows how the design of such large-scale deployable antenna systems can be optimized based on various factors including the frequency bands to be employed with such innovative reflector design. In particular, this study investigates how contoured beam space antennas can be effective by constructed out of so-called cable networks or mesh-like reflectors. This design can be accomplished via "plane wave synthesis" and by the "force density method" and then to iterate the design to achieve the optimum solution. We have concluded that the best design is achieved by plane wave synthesis. Further, we demonstrate that the nodes on the reflector are best determined by a pseudo-inverse calculation of the matrix that can be interpolated so as to achieve the minimum
Characteristic-based non-linear simulation of large-scale standing-wave thermoacoustic engine.
Abd El-Rahman, Ahmed I; Abdel-Rahman, Ehab
2014-08-01
A few linear theories [Swift, J. Acoust. Soc. Am. 84(4), 1145-1180 (1988); Swift, J. Acoust. Soc. Am. 92(3), 1551-1563 (1992); Olson and Swift, J. Acoust. Soc. Am. 95(3), 1405-1412 (1994)] and numerical models, based on low-Mach number analysis [Worlikar and Knio, J. Comput. Phys. 127(2), 424-451 (1996); Worlikar et al., J. Comput. Phys. 144(2), 199-324 (1996); Hireche et al., Canadian Acoust. 36(3), 164-165 (2008)], describe the flow dynamics of standing-wave thermoacoustic engines, but almost no simulation results are available that enable the prediction of the behavior of practical engines experiencing significant temperature gradient between the stack ends and thus producing large-amplitude oscillations. Here, a one-dimensional non-linear numerical simulation based on the method of characteristics to solve the unsteady compressible Euler equations is reported. Formulation of the governing equations, implementation of the numerical method, and application of the appropriate boundary conditions are presented. The calculation uses explicit time integration along with deduced relationships, expressing the friction coefficient and the Stanton number for oscillating flow inside circular ducts. Helium, a mixture of Helium and Argon, and Neon are used for system operation at mean pressures of 13.8, 9.9, and 7.0 bars, respectively. The self-induced pressure oscillations are accurately captured in the time domain, and then transferred into the frequency domain, distinguishing the pressure signals into fundamental and harmonic responses. The results obtained are compared with reported experimental works [Swift, J. Acoust. Soc. Am. 92(3), 1551-1563 (1992); Olson and Swift, J. Acoust. Soc. Am. 95(3), 1405-1412 (1994)] and the linear theory, showing better agreement with the measured values, particularly in the non-linear regime of the dynamic pressure response.
Huang, Yi-Shao; Liu, Wel-Ping; Wu, Min; Wang, Zheng-Wu
2014-09-01
This paper presents a novel observer-based decentralized hybrid adaptive fuzzy control scheme for a class of large-scale continuous-time multiple-input multiple-output (MIMO) uncertain nonlinear systems whose state variables are unmeasurable. The scheme integrates fuzzy logic systems, state observers, and strictly positive real conditions to deal with three issues in the control of a large-scale MIMO uncertain nonlinear system: algorithm design, controller singularity, and transient response. Then, the design of the hybrid adaptive fuzzy controller is extended to address a general large-scale uncertain nonlinear system. It is shown that the resultant closed-loop large-scale system keeps asymptotically stable and the tracking error converges to zero. The better characteristics of our scheme are demonstrated by simulations.
2014-05-01
global convergence and further show its linear convergence under a variety of scenarios, which cover a wide range of applications . The derived rate of...global convergence and further show its linear convergence under a variety of scenarios, which cover a wide range of applications . The derived rate of...efficiency, flexibility and applicability for large-scale and distributed op- timization problems. We also make important extensions to the convergence
Identifiability of large-scale non-linear dynamic network models applied to the ADM1-case study.
Nimmegeers, Philippe; Lauwers, Joost; Telen, Dries; Logist, Filip; Impe, Jan Van
2017-06-01
In this work, both the structural and practical identifiability of the Anaerobic Digestion Model no. 1 (ADM1) is investigated, which serves as a relevant case study of large non-linear dynamic network models. The structural identifiability is investigated using the probabilistic algorithm, adapted to deal with the specifics of the case study (i.e., a large-scale non-linear dynamic system of differential and algebraic equations). The practical identifiability is analyzed using a Monte Carlo parameter estimation procedure for a 'non-informative' and 'informative' experiment, which are heuristically designed. The model structure of ADM1 has been modified by replacing parameters by parameter combinations, to provide a generally locally structurally identifiable version of ADM1. This means that in an idealized theoretical situation, the parameters can be estimated accurately. Furthermore, the generally positive structural identifiability results can be explained from the large number of interconnections between the states in the network structure. This interconnectivity, however, is also observed in the parameter estimates, making uncorrelated parameter estimations in practice difficult. Copyright © 2017. Published by Elsevier Inc.
Parallel Optimization of Polynomials for Large-scale Problems in Stability and Control
NASA Astrophysics Data System (ADS)
Kamyar, Reza
In this thesis, we focus on some of the NP-hard problems in control theory. Thanks to the converse Lyapunov theory, these problems can often be modeled as optimization over polynomials. To avoid the problem of intractability, we establish a trade off between accuracy and complexity. In particular, we develop a sequence of tractable optimization problems --- in the form of Linear Programs (LPs) and/or Semi-Definite Programs (SDPs) --- whose solutions converge to the exact solution of the NP-hard problem. However, the computational and memory complexity of these LPs and SDPs grow exponentially with the progress of the sequence - meaning that improving the accuracy of the solutions requires solving SDPs with tens of thousands of decision variables and constraints. Setting up and solving such problems is a significant challenge. The existing optimization algorithms and software are only designed to use desktop computers or small cluster computers --- machines which do not have sufficient memory for solving such large SDPs. Moreover, the speed-up of these algorithms does not scale beyond dozens of processors. This in fact is the reason we seek parallel algorithms for setting-up and solving large SDPs on large cluster- and/or super-computers. We propose parallel algorithms for stability analysis of two classes of systems: 1) Linear systems with a large number of uncertain parameters; 2) Nonlinear systems defined by polynomial vector fields. First, we develop a distributed parallel algorithm which applies Polya's and/or Handelman's theorems to some variants of parameter-dependent Lyapunov inequalities with parameters defined over the standard simplex. The result is a sequence of SDPs which possess a block-diagonal structure. We then develop a parallel SDP solver which exploits this structure in order to map the computation, memory and communication to a distributed parallel environment. Numerical tests on a supercomputer demonstrate the ability of the algorithm to
NASA Astrophysics Data System (ADS)
Nikitenkova, S.; Singh, N.; Stepanyants, Y.
2015-12-01
In this paper, we revisit the problem of modulation stability of quasi-monochromatic wave-trains propagating in a media with the double dispersion occurring both at small and large wavenumbers. We start with the shallow-water equations derived by Shrira [Izv., Acad. Sci., USSR, Atmos. Ocean. Phys. (Engl. Transl.) 17, 55-59 (1981)] which describes both surface and internal long waves in a rotating fluid. The small-scale (Boussinesq-type) dispersion is assumed to be weak, whereas the large-scale (Coriolis-type) dispersion is considered as without any restriction. For unidirectional waves propagating in one direction, only the considered set of equations reduces to the Gardner-Ostrovsky equation which is applicable only within a finite range of wavenumbers. We derive the nonlinear Schrödinger equation (NLSE) which describes the evolution of narrow-band wave-trains and show that within a more general bi-directional equation the wave-trains, similar to that derived from the Ostrovsky equation, are also modulationally stable at relatively small wavenumbers k < kc and unstable at k > kc, where kc is some critical wavenumber. The NLSE derived here has a wider range of applicability: it is valid for arbitrarily small wavenumbers. We present the analysis of coefficients of the NLSE for different signs of coefficients of the governing equation and compare them with those derived from the Ostrovsky equation. The analysis shows that for weakly dispersive waves in the range of parameters where the Gardner-Ostrovsky equation is valid, the cubic nonlinearity does not contribute to the nonlinear coefficient of NLSE; therefore, the NLSE can be correctly derived from the Ostrovsky equation.
Nikitenkova, S; Singh, N; Stepanyants, Y
2015-12-01
In this paper, we revisit the problem of modulation stability of quasi-monochromatic wave-trains propagating in a media with the double dispersion occurring both at small and large wavenumbers. We start with the shallow-water equations derived by Shrira [Izv., Acad. Sci., USSR, Atmos. Ocean. Phys. (Engl. Transl.) 17, 55-59 (1981)] which describes both surface and internal long waves in a rotating fluid. The small-scale (Boussinesq-type) dispersion is assumed to be weak, whereas the large-scale (Coriolis-type) dispersion is considered as without any restriction. For unidirectional waves propagating in one direction, only the considered set of equations reduces to the Gardner-Ostrovsky equation which is applicable only within a finite range of wavenumbers. We derive the nonlinear Schrödinger equation (NLSE) which describes the evolution of narrow-band wave-trains and show that within a more general bi-directional equation the wave-trains, similar to that derived from the Ostrovsky equation, are also modulationally stable at relatively small wavenumbers k < kc and unstable at k > kc, where kc is some critical wavenumber. The NLSE derived here has a wider range of applicability: it is valid for arbitrarily small wavenumbers. We present the analysis of coefficients of the NLSE for different signs of coefficients of the governing equation and compare them with those derived from the Ostrovsky equation. The analysis shows that for weakly dispersive waves in the range of parameters where the Gardner-Ostrovsky equation is valid, the cubic nonlinearity does not contribute to the nonlinear coefficient of NLSE; therefore, the NLSE can be correctly derived from the Ostrovsky equation.
Improved tomographic reconstruction of large-scale real-world data by filter optimization.
Pelt, Daniël M; De Andrade, Vincent
2017-01-01
In advanced tomographic experiments, large detector sizes and large numbers of acquired datasets can make it difficult to process the data in a reasonable time. At the same time, the acquired projections are often limited in some way, for example having a low number of projections or a low signal-to-noise ratio. Direct analytical reconstruction methods are able to produce reconstructions in very little time, even for large-scale data, but the quality of these reconstructions can be insufficient for further analysis in cases with limited data. Iterative reconstruction methods typically produce more accurate reconstructions, but take significantly more time to compute, which limits their usefulness in practice. In this paper, we present the application of the SIRT-FBP method to large-scale real-world tomographic data. The SIRT-FBP method is able to accurately approximate the simultaneous iterative reconstruction technique (SIRT) method by the computationally efficient filtered backprojection (FBP) method, using precomputed experiment-specific filters. We specifically focus on the many implementation details that are important for application on large-scale real-world data, and give solutions to common problems that occur with experimental data. We show that SIRT-FBP filters can be computed in reasonable time, even for large problem sizes, and that precomputed filters can be reused for future experiments. Reconstruction results are given for three different experiments, and are compared with results of popular existing methods. The results show that the SIRT-FBP method is able to accurately approximate iterative reconstructions of experimental data. Furthermore, they show that, in practice, the SIRT-FBP method can produce more accurate reconstructions than standard direct analytical reconstructions with popular filters, without increasing the required computation time.
Chen, Hanbo; Liu, Tao; Zhao, Yu; Zhang, Tuo; Li, Yujie; Li, Meng; Zhang, Hongmiao; Kuang, Hui; Guo, Lei; Tsien, Joe Z; Liu, Tianming
2015-07-15
Tractography based on diffusion tensor imaging (DTI) data has been used as a tool by a large number of recent studies to investigate structural connectome. Despite its great success in offering unique 3D neuroanatomy information, DTI is an indirect observation with limited resolution and accuracy and its reliability is still unclear. Thus, it is essential to answer this fundamental question: how reliable is DTI tractography in constructing large-scale connectome? To answer this question, we employed neuron tracing data of 1772 experiments on the mouse brain released by the Allen Mouse Brain Connectivity Atlas (AMCA) as the ground-truth to assess the performance of DTI tractography in inferring white matter fiber pathways and inter-regional connections. For the first time in the neuroimaging field, the performance of whole brain DTI tractography in constructing a large-scale connectome has been evaluated by comparison with tracing data. Our results suggested that only with the optimized tractography parameters and the appropriate scale of brain parcellation scheme, can DTI produce relatively reliable fiber pathways and a large-scale connectome. Meanwhile, a considerable amount of errors were also identified in optimized DTI tractography results, which we believe could be potentially alleviated by efforts in developing better DTI tractography approaches. In this scenario, our framework could serve as a reliable and quantitative test bed to identify errors in tractography results which will facilitate the development of such novel tractography algorithms and the selection of optimal parameters. Copyright © 2015 Elsevier Inc. All rights reserved.
NASA Astrophysics Data System (ADS)
Shi, Huaitao; Liu, Jianchang; Wu, Yuhou; Zhang, Ke; Zhang, Lixiu; Xue, Peng
2016-04-01
It is pretty significant for fault diagnosis timely and accurately to improve the dependability of industrial processes. In this study, fault diagnosis of nonlinear and large-scale processes by variable-weighted kernel Fisher discriminant analysis (KFDA) based on improved biogeography-based optimisation (IBBO) is proposed, referred to as IBBO-KFDA, where IBBO is used to determine the parameters of variable-weighted KFDA, and variable-weighted KFDA is used to solve the multi-classification overlapping problem. The main contributions of this work are four-fold to further improve the performance of KFDA for fault diagnosis. First, a nonlinear fault diagnosis approach with variable-weighted KFDA is developed for maximising separation between the overlapping fault samples. Second, kernel parameters and features selection of variable-weighted KFDA are simultaneously optimised using IBBO. Finally, a single fitness function that combines erroneous diagnosis rate with feature cost is created, a novel mixed kernel function is introduced to improve the classification capability in the feature space and diagnosis accuracy of the IBBO-KFDA, and serves as the target function in the optimisation problem. Moreover, an IBBO approach is developed to obtain the better quality of solution and faster convergence speed. On the one hand, the proposed IBBO-KFDA method is first used on Tennessee Eastman process benchmark data sets to validate the feasibility and efficiency. On the other hand, IBBO-KFDA is applied to diagnose faults of automation gauge control system. Simulation results demonstrate that IBBO-KFDA can obtain better kernel parameters and feature vectors with a lower computing cost, higher diagnosis accuracy and a better real-time capacity.
NASA Astrophysics Data System (ADS)
Wierschem, Nicholas E.; Hubbard, Sean A.; Luo, Jie; Fahnestock, Larry A.; Spencer, Billie F.; McFarland, D. Michael; Quinn, D. Dane; Vakakis, Alexander F.; Bergman, Lawrence A.
2017-02-01
Limiting peak stresses and strains in a structure subjected to high-energy, short-duration transient loadings, such as blasts, is a challenging problem, largely due to the well-known insensitivity of the first few cycles of the structural response to damping. Linear isolation, while a potential solution, requires a very low fundamental natural frequency to be effective, resulting in large nearly-rigid body displacement of the structure, while linear vibration absorbers have little or no effect on the early-time response where relative motions, and thus stresses and strains, are at their highest levels. The problem has become increasingly important in recent years with the expectation of blast-resistance as a design requirement in new construction. In this paper, the problem is examined experimentally and computationally in the context of offset-blast loading applied to a custom-built nine story steel frame structure. A fully-passive response mitigation system consisting of six lightweight, essentially nonlinear vibration absorbers (termed nonlinear energy sinks - NESs) is optimized and deployed on the upper two floors of this structure. Two NESs have vibro-impact nonlinearities and the other four possess smooth but essentially nonlinear stiffnesses. Results of the computational and experimental study demonstrate the efficacy of the proposed passive nonlinear mitigation system to rapidly and efficiently attenuate the global structural response, even at early time (i.e., starting at the first response cycle), thus minimizing the peak demand on the structure. This is achieved by nonlinear redistribution of the blast energy within the modal space through low-to-high energy scattering due to the action of the NESs. The experimental results validate the theoretical predictions.
Strategic optimization of large-scale vertical closed-loop shallow geothermal systems
NASA Astrophysics Data System (ADS)
Hecht-Méndez, J.; de Paly, M.; Beck, M.; Blum, P.; Bayer, P.
2012-04-01
Vertical closed-loop geothermal systems or ground source heat pump (GSHP) systems with multiple vertical borehole heat exchangers (BHEs) are attractive technologies that provide heating and cooling to large facilities such as hotels, schools, big office buildings or district heating systems. Currently, the worldwide number of installed systems shows a recurrent increase. By running arrays of multiple BHEs, the energy demand of a given facility is fulfilled by exchanging heat with the ground. Due to practical and technical reasons, square arrays of the BHEs are commonly used and the total energy extraction from the subsurface is accomplished by an equal operation of each BHE. Moreover, standard designing practices disregard the presence of groundwater flow. We present a simulation-optimization approach that is able to regulate the individual operation of multiple BHEs, depending on the given hydro-geothermal conditions. The developed approach optimizes the overall performance of the geothermal system while mitigating the environmental impact. As an example, a synthetic case with a geothermal system using 25 BHEs for supplying a seasonal heating energy demand is defined. The optimization approach is evaluated for finding optimal energy extractions for 15 scenarios with different specific constant groundwater flow velocities. Ground temperature development is simulated using the optimal energy extractions and contrasted against standard application. It is demonstrated that optimized systems always level the ground temperature distribution and generate smaller subsurface temperature changes than non-optimized ones. Mean underground temperature changes within the studied BHE field are between 13% and 24% smaller when the optimized system is used. By applying the optimized energy extraction patterns, the temperature of the heat carrier fluid in the BHE, which controls the overall performance of the system, can also be raised by more than 1 °C.
NASA Astrophysics Data System (ADS)
Bechtold, M.; Tiemeyer, B.; Laggner, A.; Leppelt, T.; Frahm, E.; Belting, S.
2014-04-01
Fluxes of the three main greenhouse gases (GHG) CO2, CH4 and N2O from peat and other organic soils are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new dataset comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip well specific long-term annual mean water level (WL) as well as a transformed form of it (WLt). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WLt is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45% of WLt variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WLt and the observed parameter interactions provide insights into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WLt variance cannot be explained by nationally available predictor variables and that predictors with
Optimized Large-scale CMB Likelihood and Quadratic Maximum Likelihood Power Spectrum Estimation
NASA Astrophysics Data System (ADS)
Gjerløw, E.; Colombo, L. P. L.; Eriksen, H. K.; Górski, K. M.; Gruppuso, A.; Jewell, J. B.; Plaszczynski, S.; Wehus, I. K.
2015-11-01
We revisit the problem of exact cosmic microwave background (CMB) likelihood and power spectrum estimation with the goal of minimizing computational costs through linear compression. This idea was originally proposed for CMB purposes by Tegmark et al., and here we develop it into a fully functioning computational framework for large-scale polarization analysis, adopting WMAP as a working example. We compare five different linear bases (pixel space, harmonic space, noise covariance eigenvectors, signal-to-noise covariance eigenvectors, and signal-plus-noise covariance eigenvectors) in terms of compression efficiency, and find that the computationally most efficient basis is the signal-to-noise eigenvector basis, which is closely related to the Karhunen-Loeve and Principal Component transforms, in agreement with previous suggestions. For this basis, the information in 6836 unmasked WMAP sky map pixels can be compressed into a smaller set of 3102 modes, with a maximum error increase of any single multipole of 3.8% at ℓ ≤ 32 and a maximum shift in the mean values of a joint distribution of an amplitude-tilt model of 0.006σ. This compression reduces the computational cost of a single likelihood evaluation by a factor of 5, from 38 to 7.5 CPU seconds, and it also results in a more robust likelihood by implicitly regularizing nearly degenerate modes. Finally, we use the same compression framework to formulate a numerically stable and computationally efficient variation of the Quadratic Maximum Likelihood implementation, which requires less than 3 GB of memory and 2 CPU minutes per iteration for ℓ ≤ 32, rendering low-ℓ QML CMB power spectrum analysis fully tractable on a standard laptop.
Large-Scale Multi-Objective Optimization for the Management of Seawater Intrusion, Santa Barbara, CA
NASA Astrophysics Data System (ADS)
Stanko, Z. P.; Nishikawa, T.; Paulinski, S. R.
2015-12-01
The City of Santa Barbara, located in coastal southern California, is concerned that excessive groundwater pumping will lead to chloride (Cl) contamination of its groundwater system from seawater intrusion (SWI). In addition, the city wishes to estimate the effect of continued pumping on the groundwater basin under a variety of initial and climatic conditions. A SEAWAT-based groundwater-flow and solute-transport model of the Santa Barbara groundwater basin was optimized to produce optimal pumping schedules assuming 5 different scenarios. Borg, a multi-objective genetic algorithm, was coupled with the SEAWAT model to identify optimal management strategies. The optimization problems were formulated as multi-objective so that the tradeoffs between maximizing pumping, minimizing SWI, and minimizing drawdowns can be examined by the city. Decisions can then be made on a pumping schedule in light of current preferences and climatic conditions. Borg was used to produce Pareto optimal results for all 5 scenarios, which vary in their initial conditions (high water levels, low water levels, or current basin state), simulated climate (normal or drought conditions), and problem formulation (objective equations and decision-variable aggregation). Results show mostly well-defined Pareto surfaces with a few singularities. Furthermore, the results identify the precise pumping schedule per well that was suitable given the desired restriction on drawdown and Cl concentrations. A system of decision-making is then possible based on various observations of the basin's hydrologic states and climatic trends without having to run any further optimizations. In addition, an assessment of selected Pareto-optimal solutions was analyzed with sensitivity information using the simulation model alone. A wide range of possible groundwater pumping scenarios is available and depends heavily on the future climate scenarios and the Pareto-optimal solution selected while managing the pumping wells.
Multilevel Algorithms for Nonlinear Optimization
1994-06-01
NASA Contractor Report 194940 ICASE Report No. 94-53 AD-A284 318 * ICASE MULTILEVEL ALGORITHMSDDTIC FOR NONLINEAR OPTIMIZATION ELECTESEP 1 4 1994 F...Association SOperated b MULTILEVEL ALGORITHMS FOR NONLINEAR OPTIMIZATION Natalia Alexandrov Accesion For ICASE C Mail Stop 132C NTIS CRA&ID C TAB 1Q...ABSTRACT Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that
NASA Astrophysics Data System (ADS)
Bechtold, M.; Tiemeyer, B.; Laggner, A.; Leppelt, T.; Frahm, E.; Belting, S.
2014-09-01
Fluxes of the three main greenhouse gases (GHG) CO2, CH4 and N2O from peat and other soils with high organic carbon contents are strongly controlled by water table depth. Information about the spatial distribution of water level is thus a crucial input parameter when upscaling GHG emissions to large scales. Here, we investigate the potential of statistical modeling for the regionalization of water levels in organic soils when data covers only a small fraction of the peatlands of the final map. Our study area is Germany. Phreatic water level data from 53 peatlands in Germany were compiled in a new data set comprising 1094 dip wells and 7155 years of data. For each dip well, numerous possible predictor variables were determined using nationally available data sources, which included information about land cover, ditch network, protected areas, topography, peatland characteristics and climatic boundary conditions. We applied boosted regression trees to identify dependencies between predictor variables and dip-well-specific long-term annual mean water level (WL) as well as a transformed form (WLt). The latter was obtained by assuming a hypothetical GHG transfer function and is linearly related to GHG emissions. Our results demonstrate that model calibration on WLt is superior. It increases the explained variance of the water level in the sensitive range for GHG emissions and avoids model bias in subsequent GHG upscaling. The final model explained 45% of WLt variance and was built on nine predictor variables that are based on information about land cover, peatland characteristics, drainage network, topography and climatic boundary conditions. Their individual effects on WLt and the observed parameter interactions provide insight into natural and anthropogenic boundary conditions that control water levels in organic soils. Our study also demonstrates that a large fraction of the observed WLt variance cannot be explained by nationally available predictor variables and
h2-norm optimal model reduction for large scale discrete dynamical MIMO systems
NASA Astrophysics Data System (ADS)
Bunse-Gerstner, A.; Kubalinska, D.; Vossen, G.; Wilczek, D.
2010-01-01
Modeling strategies often result in dynamical systems of very high dimension. It is then desirable to find systems of the same form but of lower complexity, whose input-output behavior approximates the behavior of the original system. Here we consider linear time-invariant discrete-time dynamical systems. The cornerstone of this paper is a relation between optimal model reduction in the h2-norm and (tangential) rational Hermite interpolation. First order necessary conditions for h2-optimal model reduction are presented for discrete Multiple-Input-Multiple-Output (MIMO) systems. These conditions suggest a specific choice of interpolation data and a novel algorithm aiming for anh2-optimal model reduction for MIMO systems. It is also shown that the conditions are equivalent to two known gramian-based first order necessary conditions. Numerical experiments demonstrate the approximation quality of the method.
Large scale scientific computing
Deuflhard, P. ); Engquist, B. )
1987-01-01
This book presents papers on large scale scientific computing. It includes: Initial value problems of ODE's and parabolic PDE's; Boundary value problems of ODE's and elliptic PDE's; Hyperbolic PDE's; Inverse problems; Optimization and optimal control problems; and Algorithm adaptation on supercomputers.
Merkx, E P J; Ten Kate, O M; van der Kolk, E
2017-06-12
The phenomenon of self-absorption is by far the largest influential factor in the efficiency of luminescent solar concentrators (LSCs), but also the most challenging one to capture computationally. In this work we present a model using a multiple-generation light transport (MGLT) approach to quantify light transport through single-layer luminescent solar concentrators of arbitrary shape and size. We demonstrate that MGLT offers a significant speed increase over Monte Carlo (raytracing) when optimizing the luminophore concentration in large LSCs and more insight into light transport processes. Our results show that optimizing luminophore concentration in a lab-scale device does not yield an optimal optical efficiency after scaling up to realistically sized windows. Each differently sized LSC therefore has to be optimized individually to obtain maximal efficiency. We show that, for strongly self-absorbing LSCs with a high quantum yield, parasitic self-absorption can turn into a positive effect at very high absorption coefficients. This is due to a combination of increased light trapping and stronger absorption of the incoming sunlight. We conclude that, except for scattering losses, MGLT can compute all aspects in light transport through an LSC accurately and can be used as a design tool for building-integrated photovoltaic elements. This design tool is therefore used to calculate many building-integrated LSC power conversion efficiencies.
Optimization of a Large-scale Microseismic Monitoring Network in Northern Switzerland
NASA Astrophysics Data System (ADS)
Kraft, T.; Husen, S.; Mignan, A.; Bethmann, F.
2011-12-01
We have performed a computer aided network optimization for a regional scale microseismic network in northeastern Switzerland. The goal of the optimization was to find the geometry and size of the network that assures a location precision of 0.5 km in the epicenter and 2.0 km in focal depth for earthquakes of magnitude ML>= 1.0, by taking into account 67 existing stations in Switzerland, Germany and Austria, and the expected detectability of Ml 1 earthquakes in the study area. The optimization was based on the simulated annealing approach by Hardt and Scherbaum (1993), that aims to minimize the volume of the error ellipsoid of the linearized earthquake location problem (D-criterion). We have extended their algorithm: to calculate traveltimes of seismic body waves using a finite differences raytracer and the three-dimensional velocity model of Switzerland, to calculate seismic body waves amplitudes at arbitrary stations assuming Brune source model and using scaling relations recently derived for Switzerland, and to estimate the noise level at arbitrary locations within Switzerland using a first order ambient seismic noise model based on 14 land-use classes defined by the EU-project CORINE and open GIS data. Considering 67 existing stations in Switzerland, Germany and Austria, optimizations for networks of 10 to 35 new stations were calculated with respect to 2240 synthetic earthquakes of magnitudes between ML=0.8-1.1. We incorporated the case of non-detections by considering only earthquake-station pairs with an expected signal-to-noise ratio larger than 10 for the considered body wave. Station noise levels were derived from measured ground motion for existing stations and from the first order ambient noise model for new sites. The stability of the optimization result was tested by repeated optimization runs with changing initial conditions. Due to the highly non linear nature and size of the problem, station locations in the individual solutions show small
Large scale structural optimization of trimetallic Cu-Au-Pt clusters up to 147 atoms
NASA Astrophysics Data System (ADS)
Wu, Genhua; Sun, Yan; Wu, Xia; Chen, Run; Wang, Yan
2017-10-01
The stable structures of Cu-Au-Pt clusters up to 147 atoms are optimized by using an improved adaptive immune optimization algorithm (AIOA-IC method), in which several motifs, such as decahedron, icosahedron, face centered cubic, sixfold pancake, and Leary tetrahedron, are randomly selected as the inner cores of the starting structures. The structures of Cu8AunPt30-n (n = 1-29), Cu8AunPt47-n (n = 1-46), and partial 75-, 79-, 100-, and 147-atom clusters are analyzed. Cu12Au93Pt42 cluster has onion-like Mackay icosahedral motif. The segregation phenomena of Cu, Au and Pt in clusters are explained by the atomic radius, surface energy, and cohesive energy.
Model-Constrained Optimization Methods for Reduction of Parameterized Large-Scale Systems
2007-05-01
colorful with his stereo karaoke system. Anh Hai, thanks for helping me move my furnitures many times, and for all the beers too! To all Vietnamese...visit them. My trips to Springfield would have been very boring if Anh Tung (+ Thao) and Anh Danh (+ Thuy) had not turn on their super stereo karaoke ...expensive to solve, e.g. for applications such as optimal design or probabilistic analyses. Model order reduction is a powerful tool that permits the
Large Scale Multi-area Static/Dynamic Economic Dispatch using Nature Inspired Optimization
NASA Astrophysics Data System (ADS)
Pandit, Manjaree; Jain, Kalpana; Dubey, Hari Mohan; Singh, Rameshwar
2016-07-01
Economic dispatch (ED) ensures that the generation allocation to the power units is carried out such that the total fuel cost is minimized and all the operating equality/inequality constraints are satisfied. Classical ED does not take transmission constraints into consideration, but in the present restructured power systems the tie-line limits play a very important role in deciding operational policies. ED is a dynamic problem which is performed on-line in the central load dispatch centre with changing load scenarios. The dynamic multi-area ED (MAED) problem is more complex due to the additional tie-line, ramp-rate and area-wise power balance constraints. Nature inspired (NI) heuristic optimization methods are gaining popularity over the traditional methods for complex problems. This work presents the modified particle swarm optimization (PSO) based techniques where parameter automation is effectively used for improving the search efficiency by avoiding stagnation to a sub-optimal result. This work validates the performance of the PSO variants with traditional solver GAMS for single as well as multi-area economic dispatch (MAED) on three test cases of a large 140-unit standard test system having complex constraints.
Large Scale Multi-area Static/Dynamic Economic Dispatch using Nature Inspired Optimization
NASA Astrophysics Data System (ADS)
Pandit, Manjaree; Jain, Kalpana; Dubey, Hari Mohan; Singh, Rameshwar
2017-04-01
Economic dispatch (ED) ensures that the generation allocation to the power units is carried out such that the total fuel cost is minimized and all the operating equality/inequality constraints are satisfied. Classical ED does not take transmission constraints into consideration, but in the present restructured power systems the tie-line limits play a very important role in deciding operational policies. ED is a dynamic problem which is performed on-line in the central load dispatch centre with changing load scenarios. The dynamic multi-area ED (MAED) problem is more complex due to the additional tie-line, ramp-rate and area-wise power balance constraints. Nature inspired (NI) heuristic optimization methods are gaining popularity over the traditional methods for complex problems. This work presents the modified particle swarm optimization (PSO) based techniques where parameter automation is effectively used for improving the search efficiency by avoiding stagnation to a sub-optimal result. This work validates the performance of the PSO variants with traditional solver GAMS for single as well as multi-area economic dispatch (MAED) on three test cases of a large 140-unit standard test system having complex constraints.
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.
Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.
2016-07-26
It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, used for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.
Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.
2016-07-26
It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, usedmore » for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.« less
Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.
2016-07-26
It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, used for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.
fast_protein_cluster: parallel and optimized clustering of large-scale protein modeling data
Hung, Ling-Hong; Samudrala, Ram
2014-01-01
Motivation: fast_protein_cluster is a fast, parallel and memory efficient package used to cluster 60 000 sets of protein models (with up to 550 000 models per set) generated by the Nutritious Rice for the World project. Results: fast_protein_cluster is an optimized and extensible toolkit that supports Root Mean Square Deviation after optimal superposition (RMSD) and Template Modeling score (TM-score) as metrics. RMSD calculations using a laptop CPU are 60× faster than qcprot and 3× faster than current graphics processing unit (GPU) implementations. New GPU code further increases the speed of RMSD and TM-score calculations. fast_protein_cluster provides novel k-means and hierarchical clustering methods that are up to 250× and 2000× faster, respectively, than Clusco, and identify significantly more accurate models than Spicker and Clusco. Availability and implementation: fast_protein_cluster is written in C++ using OpenMP for multi-threading support. Custom streaming Single Instruction Multiple Data (SIMD) extensions and advanced vector extension intrinsics code accelerate CPU calculations, and OpenCL kernels support AMD and Nvidia GPUs. fast_protein_cluster is available under the M.I.T. license. (http://software.compbio.washington.edu/fast_protein_cluster) Contact: lhhung@compbio.washington.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24532722
fast_protein_cluster: parallel and optimized clustering of large-scale protein modeling data.
Hung, Ling-Hong; Samudrala, Ram
2014-06-15
fast_protein_cluster is a fast, parallel and memory efficient package used to cluster 60 000 sets of protein models (with up to 550 000 models per set) generated by the Nutritious Rice for the World project. fast_protein_cluster is an optimized and extensible toolkit that supports Root Mean Square Deviation after optimal superposition (RMSD) and Template Modeling score (TM-score) as metrics. RMSD calculations using a laptop CPU are 60× faster than qcprot and 3× faster than current graphics processing unit (GPU) implementations. New GPU code further increases the speed of RMSD and TM-score calculations. fast_protein_cluster provides novel k-means and hierarchical clustering methods that are up to 250× and 2000× faster, respectively, than Clusco, and identify significantly more accurate models than Spicker and Clusco. fast_protein_cluster is written in C++ using OpenMP for multi-threading support. Custom streaming Single Instruction Multiple Data (SIMD) extensions and advanced vector extension intrinsics code accelerate CPU calculations, and OpenCL kernels support AMD and Nvidia GPUs. fast_protein_cluster is available under the M.I.T. license. (http://software.compbio.washington.edu/fast_protein_cluster) © The Author 2014. Published by Oxford University Press.
Optimization of Nanoparticle-Based SERS Substrates through Large-Scale Realistic Simulations
2016-01-01
Surface-enhanced Raman scattering (SERS) has become a widely used spectroscopic technique for chemical identification, providing unbeaten sensitivity down to the single-molecule level. The amplification of the optical near field produced by collective electron excitations —plasmons— in nanostructured metal surfaces gives rise to a dramatic increase by many orders of magnitude in the Raman scattering intensities from neighboring molecules. This effect strongly depends on the detailed geometry and composition of the plasmon-supporting metallic structures. However, the search for optimized SERS substrates has largely relied on empirical data, due in part to the complexity of the structures, whose simulation becomes prohibitively demanding. In this work, we use state-of-the-art electromagnetic computation techniques to produce predictive simulations for a wide range of nanoparticle-based SERS substrates, including realistic configurations consisting of random arrangements of hundreds of nanoparticles with various morphologies. This allows us to derive rules of thumb for the influence of particle anisotropy and substrate coverage on the obtained SERS enhancement and optimum spectral ranges of operation. Our results provide a solid background to understand and design optimized SERS substrates. PMID:28239616
Optimization of Nanoparticle-Based SERS Substrates through Large-Scale Realistic Simulations.
Solís, Diego M; Taboada, José M; Obelleiro, Fernando; Liz-Marzán, Luis M; García de Abajo, F Javier
2017-02-15
Surface-enhanced Raman scattering (SERS) has become a widely used spectroscopic technique for chemical identification, providing unbeaten sensitivity down to the single-molecule level. The amplification of the optical near field produced by collective electron excitations -plasmons- in nanostructured metal surfaces gives rise to a dramatic increase by many orders of magnitude in the Raman scattering intensities from neighboring molecules. This effect strongly depends on the detailed geometry and composition of the plasmon-supporting metallic structures. However, the search for optimized SERS substrates has largely relied on empirical data, due in part to the complexity of the structures, whose simulation becomes prohibitively demanding. In this work, we use state-of-the-art electromagnetic computation techniques to produce predictive simulations for a wide range of nanoparticle-based SERS substrates, including realistic configurations consisting of random arrangements of hundreds of nanoparticles with various morphologies. This allows us to derive rules of thumb for the influence of particle anisotropy and substrate coverage on the obtained SERS enhancement and optimum spectral ranges of operation. Our results provide a solid background to understand and design optimized SERS substrates.
Sdika, Michaël
2008-02-01
This paper presents a new nonrigid monomodality image registration algorithm based on B-splines. The deformation is described by a cubic B-spline field and found by minimizing the energy between a reference image and a deformed version of a floating image. To penalize noninvertible transformation, we propose two different constraints on the Jacobian of the transformation and its derivatives. The problem is modeled by an inequality constrained optimization problem which is efficiently solved by a combination of the multipliers method and the L-BFGS algorithm to handle the large number of variables and constraints of the registration of 3-D images. Numerical experiments are presented on magnetic resonance images using synthetic deformations and atlas based segmentation.
An optimization approach for large scale simulations of discrete fracture network flows
NASA Astrophysics Data System (ADS)
Berrone, Stefano; Pieraccini, Sandra; Scialò, Stefano
2014-01-01
In recent papers [1,2] the authors introduced a new method for simulating subsurface flow in a system of fractures based on a PDE-constrained optimization reformulation, removing all difficulties related to mesh generation and providing an easily parallel approach to the problem. In this paper we further improve the method removing the constraint of having on each fracture a non-empty portion of the boundary with Dirichlet boundary conditions. This way, Dirichlet boundary conditions are prescribed only on a possibly small portion of DFN boundary. The proposed generalization of the method in [1,2] relies on a modified definition of control variables ensuring the non-singularity of the operator on each fracture. A conjugate gradient method is also introduced in order to speed up the minimization process.
Framework to trade optimality for local processing in large-scale wavefront reconstruction problems.
Haber, Aleksandar; Verhaegen, Michel
2016-11-15
We show that the minimum variance wavefront estimation problems permit localized approximate solutions, in the sense that the wavefront value at a point (excluding unobservable modes, such as the piston mode) can be approximated by a linear combination of the wavefront slope measurements in the point's neighborhood. This enables us to efficiently compute a wavefront estimate by performing a single sparse matrix-vector multiplication. Moreover, our results open the possibility for the development of wavefront estimators that can be easily implemented in a decentralized/distributed manner, and in which the estimate optimality can be easily traded for computational efficiency. We numerically validate our approach on Hudgin wavefront sensor geometries, and the results can be easily generalized to Fried geometries.
SWAP-Assembler 2: Optimization of De Novo Genome Assembler at Large Scale
Meng, Jintao; Seo, Sangmin; Balaji, Pavan; Wei, Yanjie; Wang, Bingqiang; Feng, Shengzhong
2016-01-01
In this paper, we analyze and optimize the most time-consuming steps of the SWAP-Assembler, a parallel genome assembler, so that it can scale to a large number of cores for huge genomes with the size of sequencing data ranging from terabyes to petabytes. According to the performance analysis results, the most time-consuming steps are input parallelization, k-mer graph construction, and graph simplification (edge merging). For the input parallelization, the input data is divided into virtual fragments with nearly equal size, and the start position and end position of each fragment are automatically separated at the beginning of the reads. In k-mer graph construction, in order to improve the communication efficiency, the message size is kept constant between any two processes by proportionally increasing the number of nucleotides to the number of processes in the input parallelization step for each round. The memory usage is also decreased because only a small part of the input data is processed in each round. With graph simplification, the communication protocol reduces the number of communication loops from four to two loops and decreases the idle communication time. The optimized assembler is denoted as SWAP-Assembler 2 (SWAP2). In our experiments using a 1000 Genomes project dataset of 4 terabytes (the largest dataset ever used for assembling) on the supercomputer Mira, the results show that SWAP2 scales to 131,072 cores with an efficiency of 40%. We also compared our work with both the HipMER assembler and the SWAP-Assembler. On the Yanhuang dataset of 300 gigabytes, SWAP2 shows a 3X speedup and 4X better scalability compared with the HipMer assembler and is 45 times faster than the SWAP-Assembler. The SWAP2 software is available at https://sourceforge.net/projects/swapassembler.
SWAP-Assembler 2: Optimization of De Novo Genome Assembler at Large Scale
Meng, Jintao; Seo, Sangmin; Balaji, Pavan; Wei, Yanjie; Wang, Bingqiang; Feng, Shengzhong
2016-08-16
In this paper, we analyze and optimize the most time-consuming steps of the SWAP-Assembler, a parallel genome assembler, so that it can scale to a large number of cores for huge genomes with the size of sequencing data ranging from terabyes to petabytes. According to the performance analysis results, the most time-consuming steps are input parallelization, k-mer graph construction, and graph simplification (edge merging). For the input parallelization, the input data is divided into virtual fragments with nearly equal size, and the start position and end position of each fragment are automatically separated at the beginning of the reads. In k-mer graph construction, in order to improve the communication efficiency, the message size is kept constant between any two processes by proportionally increasing the number of nucleotides to the number of processes in the input parallelization step for each round. The memory usage is also decreased because only a small part of the input data is processed in each round. With graph simplification, the communication protocol reduces the number of communication loops from four to two loops and decreases the idle communication time. The optimized assembler is denoted as SWAP-Assembler 2 (SWAP2). In our experiments using a 1000 Genomes project dataset of 4 terabytes (the largest dataset ever used for assembling) on the supercomputer Mira, the results show that SWAP2 scales to 131,072 cores with an efficiency of 40%. We also compared our work with both the HipMER assembler and the SWAP-Assembler. On the Yanhuang dataset of 300 gigabytes, SWAP2 shows a 3X speedup and 4X better scalability compared with the HipMer assembler and is 45 times faster than the SWAP-Assembler. The SWAP2 software is available at https://sourceforge.net/projects/swapassembler.
Optimization of culture media for large-scale lutein production by heterotrophic Chlorella vulgaris.
Jeon, Jin Young; Kwon, Ji-Sue; Kang, Soon Tae; Kim, Bo-Ra; Jung, Yuchul; Han, Jae Gap; Park, Joon Hyun; Hwang, Jae Kwan
2014-01-01
Lutein is a carotenoid with a purported role in protecting eyes from oxidative stress, particularly the high-energy photons of blue light. Statistical optimization was performed to growth media that supports a higher production of lutein by heterotrophically cultivated Chlorella vulgaris. The effect of media composition of C. vulgaris on lutein was examined using fractional factorial design (FFD) and central composite design (CCD). The results indicated that the presence of magnesium sulfate, EDTA-2Na, and trace metal solution significantly affected lutein production. The optimum concentrations for lutein production were found to be 0.34 g/L, 0.06 g/L, and 0.4 mL/L for MgSO4 ·7H2 O, EDTA-2Na, and trace metal solution, respectively. These values were validated using a 5-L jar fermenter. Lutein concentration was increased by almost 80% (139.64 ± 12.88 mg/L to 252.75 ± 12.92 mg/L) after 4 days. Moreover, the lutein concentration was not reduced as the cultivation was scaled up to 25,000 L (260.55 ± 3.23 mg/L) and 240,000 L (263.13 ± 2.72 mg/L). These observations suggest C. vulgaris as a potential lutein source.
NASA Astrophysics Data System (ADS)
Chaves-González, José M.; Vega-Rodríguez, Miguel A.; Gómez-Pulido, Juan A.; Sánchez-Pérez, Juan M.
2011-08-01
This article analyses the use of a novel parallel evolutionary strategy to solve complex optimization problems. The work developed here has been focused on a relevant real-world problem from the telecommunication domain to verify the effectiveness of the approach. The problem, known as frequency assignment problem (FAP), basically consists of assigning a very small number of frequencies to a very large set of transceivers used in a cellular phone network. Real data FAP instances are very difficult to solve due to the NP-hard nature of the problem, therefore using an efficient parallel approach which makes the most of different evolutionary strategies can be considered as a good way to obtain high-quality solutions in short periods of time. Specifically, a parallel hyper-heuristic based on several meta-heuristics has been developed. After a complete experimental evaluation, results prove that the proposed approach obtains very high-quality solutions for the FAP and beats any other result published.
Large-Scale Statistics for Threshold Optimization of Optically Pumped Nanowire Lasers.
Alanis, Juan Arturo; Saxena, Dhruv; Mokkapati, Sudha; Jiang, Nian; Peng, Kun; Tang, Xiaoyan; Fu, Lan; Tan, Hark Hoe; Jagadish, Chennupati; Parkinson, Patrick
2017-08-09
Single nanowire lasers based on bottom-up III-V materials have been shown to exhibit room-temperature near-infrared lasing, making them highly promising for use as nanoscale, silicon-integrable, and coherent light sources. While lasing behavior is reproducible, small variations in growth conditions across a substrate arising from the use of bottom-up growth techniques can introduce interwire disorder, either through geometric or material inhomogeneity. Nanolasers critically depend on both high material quality and tight dimensional tolerances, and as such, lasing threshold is both sensitive to and a sensitive probe of such inhomogeneity. We present an all-optical characterization technique coupled to statistical analysis to correlate geometrical and material parameters with lasing threshold. For these multiple-quantum-well nanolasers, it is found that low threshold is closely linked to longer lasing wavelength caused by losses in the core, providing a route to optimized future low-threshold devices. A best-in-group room temperature lasing threshold of ∼43 μJ cm(-2) under pulsed excitation was found, and overall device yields in excess of 50% are measured, demonstrating a promising future for the nanolaser architecture.
Weighted modularity optimization for crisp and fuzzy community detection in large-scale networks
NASA Astrophysics Data System (ADS)
Cao, Jie; Bu, Zhan; Gao, Guangliang; Tao, Haicheng
2016-11-01
Community detection is a classic and very difficult task in the field of complex network analysis, principally for its applications in domains such as social or biological networks analysis. One of the most widely used technologies for community detection in networks is the maximization of the quality function known as modularity. However, existing work has proved that modularity maximization algorithms for community detection may fail to resolve communities in small size. Here we present a new community detection method, which is able to find crisp and fuzzy communities in undirected and unweighted networks by maximizing weighted modularity. The algorithm derives new edge weights using the cosine similarity in order to go around the resolution limit problem. Then a new local moving heuristic based on weighted modularity optimization is proposed to cluster the updated network. Finally, the set of potentially attractive clusters for each node is computed, to further uncover the crisply fuzzy partition of the network. We give demonstrative applications of the algorithm to a set of synthetic benchmark networks and six real-world networks and find that it outperforms the current state of the art proposals (even those aimed at finding overlapping communities) in terms of quality and scalability.
Hydro-economic Modeling: Reducing the Gap between Large Scale Simulation and Optimization Models
NASA Astrophysics Data System (ADS)
Forni, L.; Medellin-Azuara, J.; Purkey, D.; Joyce, B. A.; Sieber, J.; Howitt, R.
2012-12-01
The integration of hydrological and socio economic components into hydro-economic models has become essential for water resources policy and planning analysis. In this study we integrate the economic value of water in irrigated agricultural production using SWAP (a StateWide Agricultural Production Model for California), and WEAP (Water Evaluation and Planning System) a climate driven hydrological model. The integration of the models is performed using a step function approximation of water demand curves from SWAP, and by relating the demand tranches to the priority scheme in WEAP. In order to do so, a modified version of SWAP was developed called SWEAP that has the Planning Area delimitations of WEAP, a Maximum Entropy Model to estimate evenly sized steps (tranches) of water derived demand functions, and the translation of water tranches into crop land. In addition, a modified version of WEAP was created called ECONWEAP with minor structural changes for the incorporation of land decisions from SWEAP and series of iterations run via an external VBA script. This paper shows the validity of this integration by comparing revenues from WEAP vs. ECONWEAP as well as an assessment of the approximation of tranches. Results show a significant increase in the resulting agricultural revenues for our case study in California's Central Valley using ECONWEAP while maintaining the same hydrology and regional water flows. These results highlight the gains from allocating water based on its economic compared to priority-based water allocation systems. Furthermore, this work shows the potential of integrating optimization and simulation-based hydrologic models like ECONWEAP.ercentage difference in total agricultural revenues (EconWEAP versus WEAP).
NASA Astrophysics Data System (ADS)
Manfredi, Sabato
2016-06-01
Large-scale dynamic systems are becoming highly pervasive in their occurrence with applications ranging from system biology, environment monitoring, sensor networks, and power systems. They are characterised by high dimensionality, complexity, and uncertainty in the node dynamic/interactions that require more and more computational demanding methods for their analysis and control design, as well as the network size and node system/interaction complexity increase. Therefore, it is a challenging problem to find scalable computational method for distributed control design of large-scale networks. In this paper, we investigate the robust distributed stabilisation problem of large-scale nonlinear multi-agent systems (briefly MASs) composed of non-identical (heterogeneous) linear dynamical systems coupled by uncertain nonlinear time-varying interconnections. By employing Lyapunov stability theory and linear matrix inequality (LMI) technique, new conditions are given for the distributed control design of large-scale MASs that can be easily solved by the toolbox of MATLAB. The stabilisability of each node dynamic is a sufficient assumption to design a global stabilising distributed control. The proposed approach improves some of the existing LMI-based results on MAS by both overcoming their computational limits and extending the applicative scenario to large-scale nonlinear heterogeneous MASs. Additionally, the proposed LMI conditions are further reduced in terms of computational requirement in the case of weakly heterogeneous MASs, which is a common scenario in real application where the network nodes and links are affected by parameter uncertainties. One of the main advantages of the proposed approach is to allow to move from a centralised towards a distributed computing architecture so that the expensive computation workload spent to solve LMIs may be shared among processors located at the networked nodes, thus increasing the scalability of the approach than the network
2017-01-01
Large-scale metabolic profiling requires the development of novel economical high-throughput analytical methods to facilitate characterization of systemic metabolic variation in population phenotypes. We report a fit-for-purpose direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) method with time-of-flight detection for rapid targeted parallel analysis of over 40 urinary metabolites. The newly developed 2 min infusion method requires <10 μL of urine sample and generates high-resolution MS profiles in both positive and negative polarities, enabling further data mining and relative quantification of hundreds of metabolites. Here we present optimization of the DI-nESI-HRMS method in a detailed step-by-step guide and provide a workflow with rigorous quality assessment for large-scale studies. We demonstrate for the first time the application of the method for urinary metabolic profiling in human epidemiological investigations. Implementation of the presented DI-nESI-HRMS method enabled cost-efficient analysis of >10 000 24 h urine samples from the INTERMAP study in 12 weeks and >2200 spot urine samples from the ARIC study in <3 weeks with the required sensitivity and accuracy. We illustrate the application of the technique by characterizing the differences in metabolic phenotypes of the USA and Japanese population from the INTERMAP study. PMID:28245357
Kang, Chao; Wen, Ting-Chi; Kang, Ji-Chuan; Meng, Ze-Bing; Li, Guang-Rong; Hyde, Kevin D.
2014-01-01
Cordycepin is one of the most important bioactive compounds produced by species of Cordyceps sensu lato, but it is hard to produce large amounts of this substance in industrial production. In this work, single factor design, Plackett-Burman design, and central composite design were employed to establish the key factors and identify optimal culture conditions which improved cordycepin production. Using these culture conditions, a maximum production of cordycepin was 2008.48 mg/L for 700 mL working volume in the 1000 mL glass jars and total content of cordycepin reached 1405.94 mg/bottle. This method provides an effective way for increasing the cordycepin production at a large scale. The strategies used in this study could have a wide application in other fermentation processes. PMID:25054182
Tsvetkov, S V; Petreev, I V; Greben'kov, S V
2011-09-01
The article contains results of fulfilled studies that allowed finding main features of radiology safety, working out academic and research recommendations to perfect radiology safety in treatment-and-preventive institutions (TPI) and creating a method of calculation of authorized staffing needed by radiological safety services. It was established that the following actions are least fulfilled: radiation control, organization of radiation safety education, authorization for work with ionizing radiation both for military men and civil staff, maintenance of documentation. We suggest that promising direction of optimization of providing radiological safety in large-scale TPI is the following: allotment of special structure that will provide comprehensive fulfillment of regulatory documents demands, it may be, e. g. radiological safety service.
Decentralized nonlinear optimal excitation control
Lu, Q.; Sun, Y.; Xu, Z.; Mochizuki, T.
1996-11-01
A design method to lay emphasis on differential geometric approach for decentralized nonlinear optimal excitation control of multimachine systems is suggested in this paper. The control law achieved is implemented via purely local measurements. Moreover, it is independent of the parameters of power networks. Simulations are performed on a six-machine system. It has been demonstrated that the nonlinear optimal excitation control could adapt to the conditions under large disturbances. Besides, this paper has verified that the optimal control in the sense of LQR principle for the linearized system is equivalent to an optimal control in the sense of a quasi-quadratic performance index for the primitive nonlinear control system.
NASA Astrophysics Data System (ADS)
Weitnauer, C.; Beck, C.; Jacobeit, J.
2013-12-01
In the last decades the critical increase of the emission of air pollutants like nitrogen dioxide, sulfur oxides and particulate matter especially in urban areas has become a problem for the environment as well as human health. Several studies confirm a risk of high concentration episodes of particulate matter with an aerodynamic diameter < 10 μm (PM10) for the respiratory tract or cardiovascular diseases. Furthermore it is known that local meteorological and large scale atmospheric conditions are important influencing factors on local PM10 concentrations. With climate changing rapidly, these connections need to be better understood in order to provide estimates of climate change related consequences for air quality management purposes. For quantifying the link between large-scale atmospheric conditions and local PM10 concentrations circulation- and weather type classifications are used in a number of studies by using different statistical approaches. Thus far only few systematic attempts have been made to modify consisting or to develop new weather- and circulation type classifications in order to improve their ability to resolve local PM10 concentrations. In this contribution existing weather- and circulation type classifications, performed on daily 2.5 x 2.5 gridded parameters of the NCEP/NCAR reanalysis data set, are optimized with regard to their discriminative power for local PM10 concentrations at 49 Bavarian measurement sites for the period 1980 to 2011. Most of the PM10 stations are situated in urban areas covering urban background, traffic and industry related pollution regimes. The range of regimes is extended by a few rural background stations. To characterize the correspondence between the PM10 measurements of the different stations by spatial patterns, a regionalization by an s-mode principal component analysis is realized on the high-pass filtered data. The optimization of the circulation- and weather types is implemented using two representative
NASA Astrophysics Data System (ADS)
Tyralis, Hristos; Karakatsanis, Georgios; Tzouka, Katerina; Mamassis, Nikos
2015-04-01
The Greek electricity system is examined for the period 2002-2014. The demand load data are analysed at various time scales (hourly, daily, seasonal and annual) and they are related to the mean daily temperature and the gross domestic product (GDP) of Greece for the same time period. The prediction of energy demand, a product of the Greek Independent Power Transmission Operator, is also compared with the demand load. Interesting results about the change of the electricity demand scheme after the year 2010 are derived. This change is related to the decrease of the GDP, during the period 2010-2014. The results of the analysis will be used in the development of an energy forecasting system which will be a part of a framework for optimal planning of a large-scale hybrid renewable energy system in which hydropower plays the dominant role. Acknowledgement: This research was funded by the Greek General Secretariat for Research and Technology through the research project Combined REnewable Systems for Sustainable ENergy DevelOpment (CRESSENDO; grant number 5145)
NASA Astrophysics Data System (ADS)
Corbin, Charles D.
Demand management is an important component of the emerging Smart Grid, and a potential solution to the supply-demand imbalance occurring increasingly as intermittent renewable electricity is added to the generation mix. Model predictive control (MPC) has shown great promise for controlling HVAC demand in commercial buildings, making it an ideal solution to this problem. MPC is believed to hold similar promise for residential applications, yet very few examples exist in the literature despite a growing interest in residential demand management. This work explores the potential for residential buildings to shape electric demand at the distribution feeder level in order to reduce peak demand, reduce system ramping, and increase load factor using detailed sub-hourly simulations of thousands of buildings coupled to distribution power flow software. More generally, this work develops a methodology for the directed optimization of residential HVAC operation using a distributed but directed MPC scheme that can be applied to today's programmable thermostat technologies to address the increasing variability in electric supply and demand. Case studies incorporating varying levels of renewable energy generation demonstrate the approach and highlight important considerations for large-scale residential model predictive control.
Zhang, Jun; Koo, Imhoi; Wang, Bing; Gao, Qing-Wei; Zheng, Chun-Hou; Zhang, Xiang
2012-01-01
Retention index (RI) is useful for metabolite identification. However, when RI is integrated with mass spectral similarity for metabolite identification, many controversial RI threshold setup are reported in literatures. In this study, a large scale test dataset of 5844 compounds with both mass spectra and RI information were created from National Institute of Standards and Technology (NIST) repetitive mass spectra (MS) and RI library. Three MS similarity measures: NIST composite measure, the real part of Discrete Fourier Transform (DFT.R) and the detail of Discrete Wavelet Transform (DWT.D) were used to investigate the accuracy of compound identification using the test dataset. To imitate real identification experiments, NIST MS main library was employed as reference library and the test dataset was used as search data. Our study shows that the optimal RI thresholds are 22, 15, and 15 i.u. for the NIST composite, DFT.R and DWT.D measures, respectively, when the RI and mass spectral similarity are integrated for compound identification. Compared to the mass spectrum matching, using both RI and mass spectral matching can improve the identification accuracy by 1.7%, 3.5%, and 3.5% for the three mass spectral similarity measures, respectively. It is concluded that the improvement of RI matching for compound identification heavily depends on the method of MS spectral similarity measure and the accuracy of RI data. PMID:22771253
Kozica, Samantha L; Teede, Helena J; Harrison, Cheryce L; Klein, Ruth; Lombard, Catherine B
2016-01-01
The prevalence of obesity in rural and remote areas is elevated in comparison to urban populations, highlighting the need for interventions targeting obesity prevention in these settings. Implementing evidence-based obesity prevention programs is challenging. This study aimed to investigate factors influencing the implementation of obesity prevention programs, including adoption, program delivery, community uptake, and continuation, specifically within rural settings. Nested within a large-scale randomized controlled trial, a qualitative exploratory approach was adopted, with purposive sampling techniques utilized, to recruit stakeholders from 41 small rural towns in Australia. In-depth semistructured interviews were conducted with clinical health professionals, health service managers, and local government employees. Open coding was completed independently by 2 investigators and thematic analysis undertaken. In-depth interviews revealed that obesity prevention programs were valued by the rural workforce. Program implementation is influenced by interrelated factors across: (1) contextual factors and (2) organizational capacity. Key recommendations to manage the challenges of implementing evidence-based programs focused on reducing program delivery costs, aided by the provision of a suite of implementation and evaluation resources. Informing the scale-up of future prevention programs, stakeholders highlighted the need to build local rural capacity through developing supportive university partnerships, generating local program ownership and promoting active feedback to all program partners. We demonstrate that the rural workforce places a high value on obesity prevention programs. Our results inform the future scale-up of obesity prevention programs, providing an improved understanding of strategies to optimize implementation of evidence-based prevention programs. © 2015 National Rural Health Association.
NASA Astrophysics Data System (ADS)
Martin, Elly; Treeby, Bradley E.
2015-10-01
To increase the effectiveness of high intensity focused ultrasound (HIFU) treatments, prediction of ultrasound propagation in biological tissues is essential, particularly where bones are present in the field. This requires complex full-wave computational models which account for nonlinearity, absorption, and heterogeneity. These models must be properly validated but there is a lack of analytical solutions which apply in these conditions. Experimental validation of the models is therefore essential. However, accurate measurement of HIFU fields is not trivial. Our aim is to establish rigorous methods for obtaining reference data sets with which to validate tissue realistic simulations of ultrasound propagation. Here, we present preliminary measurements which form an initial validation of simulations performed using the k-Wave MATLAB toolbox. Acoustic pressure was measured on a plane in the field of a focused ultrasound transducer in free field conditions to be used as a Dirichlet boundary condition for simulations. Rectangular and wedge shaped olive oil scatterers were placed in the field and further pressure measurements were made in the far field for comparison with simulations. Good qualitative agreement was observed between the measured and simulated nonlinear pressure fields.
NASA Astrophysics Data System (ADS)
Chatterjee, Renuka Gonella
2000-10-01
Reliable delivery of electric power is a major concern in both regulated and deregulated energy markets. Power transfers are limited due to voltage limit violations, thermal limits on transmission lines and instability. Voltage collapse is a catastrophic instability leading to cascaded tripping of network and generation equipment eventually causing blackouts. Most importantly, contingencies can trigger voltage collapse. The traditional tool for determining the distance to collapse is the repeated power flow technique. Power flow takes about 3 minutes for a case with over 18,000 buses. On an average it takes about 10 power flow solutions to determine the distance to collapse requiring 30 minutes of computation time. An attractive alternative is continuation, which takes approximately 15 minutes to compute the entire trajectory and the exact distance to collapse. Using a continuation method to compute the distance to collapse for 1336 contingencies would take about 14 days. Thus faster methods of contingency analysis for voltage collapse are required for planning and operating studies. Three new methodologies, lambda/MVA sensitivity, Nonlinear sensitivity and the 2n+1 method are presented for fast and accurate voltage collapse contingency analysis. Linear sensitivity techniques with admittance parameterization give poor distance to collapse predictions for large admittance branches. A new lambda/MVA sensitivity technique with branch MVA parameterization was developed to correct this error. The lambda/MVA algorithm can estimate 6689 single branch outage contingency bifurcation points of a 3493 bus power system with less than 3% relative error, except for two branches within 7%, in less than 4 minutes on a Pentium Pro 180 MHz PC. To facilitate analysis of multi-terminal branch outages and generator contingencies, the Nonlinear sensitivity method was developed. This method can rank 1336 multi-terminal contingencies of a 18,000 bus case with a speedup of 112 compared to
NASA Technical Reports Server (NTRS)
Nash, Stephen G.; Polyak, R.; Sofer, Ariela
1994-01-01
When a classical barrier method is applied to the solution of a nonlinear programming problem with inequality constraints, the Hessian matrix of the barrier function becomes increasingly ill-conditioned as the solution is approached. As a result, it may be desirable to consider alternative numerical algorithms. We compare the performance of two methods motivated by barrier functions. The first is a stabilized form of the classical barrier method, where a numerically stable approximation to the Newton direction is used when the barrier parameter is small. The second is a modified barrier method where a barrier function is applied to a shifted form of the problem, and the resulting barrier terms are scaled by estimates of the optimal Lagrange multipliers. The condition number of the Hessian matrix of the resulting modified barrier function remains bounded as the solution to the constrained optimization problem is approached. Both of these techniques can be used in the context of a truncated-Newton method, and hence can be applied to large problems, as well as on parallel computers. In this paper, both techniques are applied to problems with bound constraints and we compare their practical behavior.
NASA Astrophysics Data System (ADS)
Molina, J. M.; Zaitchik, B. F.
2016-12-01
Recent findings considering high CO2 emission scenarios (RCP8.5) suggest that the tropical Andes may experience a massive warming and a significant precipitation increase (decrease) during the wet (dry) seasons by the end of the 21st century. Variations on rainfall-streamflow relationships and seasonal crop yields significantly affect human development in this region and make local communities highly vulnerable to climate change and variability. We developed an expert-informed empirical statistical downscaling (ESD) algorithm to explore and construct robust global climate predictors to perform skillful RCP8.5 projections of in-situ March-May (MAM) precipitation required for impact modeling and adaptation studies. We applied our framework to a topographically-complex region of the Colombian Andes where a number of previous studies have reported El Niño-Southern Oscillation (ENSO) as the main driver of climate variability. Supervised machine learning algorithms were trained with customized and bias-corrected predictors from NCEP reanalysis, and a cross-validation approach was implemented to assess both predictive skill and model selection. We found weak and not significant teleconnections between precipitation and lagged seasonal surface temperatures over El Niño3.4 domain, which suggests that ENSO fails to explain MAM rainfall variability in the study region. In contrast, series of Sea Level Pressure (SLP) over American Samoa -likely associated with the South Pacific Convergence Zone (SPCZ)- explains more than 65% of the precipitation variance. The best prediction skill was obtained with Selected Generalized Additive Models (SGAM) given their ability to capture linear/nonlinear relationships present in the data. While SPCZ-related series exhibited a positive linear effect in the rainfall response, SLP predictors in the north Atlantic and central equatorial Pacific showed nonlinear effects. A multimodel (MIROC, CanESM2 and CCSM) ensemble of ESD projections revealed
NASA Astrophysics Data System (ADS)
Pedinotti, V.; Boone, A.; Ricci, S.; Biancamaria, S.; Mognard, N.
2014-04-01
During the last few decades, satellite measurements have been widely used to study the continental water cycle, especially in regions where in situ measurements are not readily available. The future Surface Water and Ocean Topography (SWOT) satellite mission will deliver maps of water surface elevation (WSE) with an unprecedented resolution and provide observation of rivers wider than 100 m and water surface areas greater than approximately 250 m × 250 m over continental surfaces between 78° S and 78° N. This study aims to investigate the potential of SWOT data for parameter optimization for large scale river routing models which are typically employed in Land Surface Models (LSM) for global scale applications. The method consists in applying a data assimilation approach, the Extended Kalman Filter (EKF) algorithm, to correct the Manning roughness coefficients of the ISBA-TRIP Continental Hydrologic System. Indeed, parameters such as the Manning coefficient, used within such models to describe water basin characteristics, are generally derived from geomorphological relationships, which might have locally significant errors. The current study focuses on the Niger basin, a trans-boundary river, which is the main source of fresh water for all the riparian countries. In addition, geopolitical issues in this region can restrict the exchange of hydrological data, so that SWOT should help improve this situation by making hydrological data freely available. In a previous study, the model was first evaluated against in-situ and satellite derived data sets within the framework of the international African Monsoon Multi-disciplinary Analysis (AMMA) project. Since the SWOT observations are not available yet and also to assess the proposed assimilation method, the study is carried out under the framework of an Observing System Simulation Experiment (OSSE). It is assumed that modeling errors are only due to uncertainties in the Manning coefficient. The true Manning
Gain optimization with nonlinear controls
NASA Technical Reports Server (NTRS)
Slater, G. L.; Kandadai, R. D.
1982-01-01
An algorithm has been developed for the analysis and design of controls for nonlinear systems. The technical approach is to use statistical linearization to model the nonlinear dynamics of a system. A covariance analysis is performed to determine the behavior of the dynamical system and a quadratic cost function. Expressions for the cost function and its derivatives are determined so that numerical optimization techniques can be applied to determine optimal feedback laws. The primary application for this report is centered about the design of controls for nominally linear systems but where the controls are saturated or limited by fixed constraints. The analysis is general however and numerical computation requires only that the specific nonlinearity be considered in the analysis.
Gain optimization with nonlinear controls
NASA Technical Reports Server (NTRS)
Slater, G. L.; Kandadai, R. D.
1982-01-01
An algorithm has been developed for the analysis and design of controls for nonlinear systems. The technical approach is to use statistical linearization to model the nonlinear dynamics of a system. A covariance analysis is performed to determine the behavior of the dynamical system and a quadratic cost function. Expressions for the cost function and its derivatives are determined so that numerical optimization techniques can be applied to determine optimal feedback laws. The primary application for this report is centered about the design of controls for nominally linear systems but where the controls are saturated or limited by fixed constraints. The analysis is general however and numerical computation requires only that the specific nonlinearity be considered in the analysis.
Multilevel algorithms for nonlinear optimization
NASA Technical Reports Server (NTRS)
Alexandrov, Natalia; Dennis, J. E., Jr.
1994-01-01
Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.
Interactive graphics nonlinear constrained optimization
NASA Technical Reports Server (NTRS)
Saouma, V. E.; Sikiotis, E. S.
1984-01-01
An interactive computer graphics environment was used for nonlinear constrained optimization analysis. It is found that by combining the power of a digital computer with the subtlety of engineering judgment during program execution, final results can be substantially better than the ones achieved by the numerical algorithm by itself.
Jiang, Chaowei; Wu, S. T.; Hu, Qiang; Feng, Xueshang E-mail: wus@uah.edu E-mail: fengx@spaceweather.ac.cn
2014-05-10
Solar filaments are commonly thought to be supported in magnetic dips, in particular, in those of magnetic flux ropes (FRs). In this Letter, based on the observed photospheric vector magnetogram, we implement a nonlinear force-free field (NLFFF) extrapolation of a coronal magnetic FR that supports a large-scale intermediate filament between an active region and a weak polarity region. This result is a first, in the sense that current NLFFF extrapolations including the presence of FRs are limited to relatively small-scale filaments that are close to sunspots and along main polarity inversion lines (PILs) with strong transverse field and magnetic shear, and the existence of an FR is usually predictable. In contrast, the present filament lies along the weak-field region (photospheric field strength ≲ 100 G), where the PIL is very fragmented due to small parasitic polarities on both sides of the PIL and the transverse field has a low signal-to-noise ratio. Thus, extrapolating a large-scale FR in such a case represents a far more difficult challenge. We demonstrate that our CESE-MHD-NLFFF code is sufficient for the challenge. The numerically reproduced magnetic dips of the extrapolated FR match observations of the filament and its barbs very well, which strongly supports the FR-dip model for filaments. The filament is stably sustained because the FR is weakly twisted and strongly confined by the overlying closed arcades.
Mauch, H; Kümel, G; Hammer, H J
1980-01-01
for the preparation of gram amounts of IgM from human sera sedimentation at 100,000 g or treatment with ZnSO4 of the redissolved "euglobulin"-precipitate was compared to direct precipitation from the clarified serum by boric acid. Three alternative large scale purification procedures were developed, leading to an IgM-sample characterized as pure by various criteria. Inclusion of protein A chromatography proved to enhance the yield very considerably.
NASA Astrophysics Data System (ADS)
Uritsky, V. M.; Davila, J. M.; Jones, S. I.
2014-12-01
Solar Probe Plus and Solar Orbiter will provide detailed measurements in the inner heliosphere magnetically connected with the topologically complex and eruptive solar corona. Interpretation of these measurements will require accurate reconstruction of the large-scale coronal magnetic field. In a related presentation by S. Jones et al., we argue that such reconstruction can be performed using photospheric extrapolation methods constrained by white-light coronagraph images. Here, we present the image-processing component of this project dealing with an automated segmentation of fan-like coronal loop structures. In contrast to the existing segmentation codes designed for detecting small-scale closed loops in the vicinity of active regions, we focus on the large-scale geometry of the open-field coronal features observed at significant radial distances from the solar surface. The coronagraph images used for the loop segmentation are transformed into a polar coordinate system and undergo radial detrending and initial noise reduction. The preprocessed images are subject to an adaptive second order differentiation combining radial and azimuthal directions. An adjustable thresholding technique is applied to identify candidate coronagraph features associated with the large-scale coronal field. A blob detection algorithm is used to extract valid features and discard noisy data pixels. The obtained features are interpolated using higher-order polynomials which are used to derive empirical directional constraints for magnetic field extrapolation procedures based on photospheric magnetograms.
Practical Aspects of Nonlinear Optimization.
1981-06-19
14. E. Levitan and B . Polyak, "Constrained Minimization Methods", USSR Comp. Math. and Math. Physics 6, 1, (1966). 15. J. May, "Solving Nonlinear...AD-AIO 858 MASSACHUSETTS INST OF TECH LEXINGTON LINCOLN LAB F/G 12/1 PRACTICAL ASPECTS OF NONLINEAR OPTIMIZATION.U) JUN 81 R B HOLMES, J W TOLLESON...dj, l<j< m , (2) with the understanding the Q so defined has a non-empty interior (is "solid"). No qualitative assumptions on the objective - i
Tong, Shaocheng; Liu, Changliang; Li, Yongming; Zhang, Huaguang
2011-04-01
In this paper, an adaptive fuzzy decentralized robust output feedback control approach is proposed for a class of large-scale strict-feedback nonlinear systems without the measurements of the states. The nonlinear systems in this paper are assumed to possess unstructured uncertainties, time-varying delays, and unknown high-frequency gain sign. Fuzzy logic systems are used to approximate the unstructured uncertainties, K-filters are designed to estimate the unmeasured states, and a special Nussbaum gain function is introduced to solve the problem of unknown high-frequency gain sign. Combining the backstepping technique with adaptive fuzzy control theory, an adaptive fuzzy decentralized robust output feedback control scheme is developed. In order to obtain the stability of the closed-loop system, a new lemma is given and proved. Based on this lemma and Lyapunov-Krasovskii functions, it is proved that all the signals in the closed-loop system are uniformly ultimately bounded and that the tracking errors can converge to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated from simulation results.
Newton Methods for Large Scale Problems in Machine Learning
ERIC Educational Resources Information Center
Hansen, Samantha Leigh
2014-01-01
The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…
Newton Methods for Large Scale Problems in Machine Learning
ERIC Educational Resources Information Center
Hansen, Samantha Leigh
2014-01-01
The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…
NASA Astrophysics Data System (ADS)
Zhang, Jingwen; Wang, Xu; Liu, Pan; Lei, Xiaohui; Li, Zejun; Gong, Wei; Duan, Qingyun; Wang, Hao
2017-01-01
The optimization of large-scale reservoir system is time-consuming due to its intrinsic characteristics of non-commensurable objectives and high dimensionality. One way to solve the problem is to employ an efficient multi-objective optimization algorithm in the derivation of large-scale reservoir operating rules. In this study, the Weighted Multi-Objective Adaptive Surrogate Model Optimization (WMO-ASMO) algorithm is used. It consists of three steps: (1) simplifying the large-scale reservoir operating rules by the aggregation-decomposition model, (2) identifying the most sensitive parameters through multivariate adaptive regression splines (MARS) for dimensional reduction, and (3) reducing computational cost and speeding the searching process by WMO-ASMO, embedded with weighted non-dominated sorting genetic algorithm II (WNSGAII). The intercomparison of non-dominated sorting genetic algorithm (NSGAII), WNSGAII and WMO-ASMO are conducted in the large-scale reservoir system of Xijiang river basin in China. Results indicate that: (1) WNSGAII surpasses NSGAII in the median of annual power generation, increased by 1.03% (from 523.29 to 528.67 billion kW h), and the median of ecological index, optimized by 3.87% (from 1.879 to 1.809) with 500 simulations, because of the weighted crowding distance and (2) WMO-ASMO outperforms NSGAII and WNSGAII in terms of better solutions (annual power generation (530.032 billion kW h) and ecological index (1.675)) with 1000 simulations and computational time reduced by 25% (from 10 h to 8 h) with 500 simulations. Therefore, the proposed method is proved to be more efficient and could provide better Pareto frontier.
Ramamurthy, Byravamurthy
2014-05-05
In this project, developed scheduling frameworks for dynamic bandwidth demands for large-scale science applications. In particular, we developed scheduling algorithms for dynamic bandwidth demands in this project. Apart from theoretical approaches such as Integer Linear Programming, Tabu Search and Genetic Algorithm heuristics, we have utilized practical data from ESnet OSCARS project (from our DOE lab partners) to conduct realistic simulations of our approaches. We have disseminated our work through conference paper presentations and journal papers and a book chapter. In this project we addressed the problem of scheduling of lightpaths over optical wavelength division multiplexed (WDM) networks. We published several conference papers and journal papers on this topic. We also addressed the problems of joint allocation of computing, storage and networking resources in Grid/Cloud networks and proposed energy-efficient mechanisms for operatin optical WDM networks.
Lin, Ya-Ching; Wu, Sheng-Chi; Yang, Ming-Yu; Chen, Guan-Ting; Li, Tzung-Han; Liau, Ming-Yi
2013-11-12
Due to the non-cytopathogenic replication of classical swine fever virus (CSFV) in cell culture, large-scale production of CSFV using bioreactor system remains the problem of monitoring the time of maximum virus production for optimal harvest. In this study, we proposed the application of real-time quantitative PCR assay to monitoring the progress of CSFV infection and yield determination in large scale. The region of NS5B of CSFV responsible for CSFV genome replication was used for the designation of primers and probe. Viral titers determined by the real-time quantitative PCR assay were compared with the conventional cell-culture based method of immunofluorescent staining. Results from large scale production show that a similar profile of CSFV production was successfully outlined by real-time quantitative PCR and virus yields were comparable to the results from immunofluorescent staining assay. By using this method, an optimal harvesting time of the production could be rapidly and precisely determined leading to an improvement in virus harvest. Copyright © 2013 Elsevier Ltd. All rights reserved.
Large-scale circuit simulation
NASA Astrophysics Data System (ADS)
Wei, Y. P.
1982-12-01
The simulation of VLSI (Very Large Scale Integration) circuits falls beyond the capabilities of conventional circuit simulators like SPICE. On the other hand, conventional logic simulators can only give the results of logic levels 1 and 0 with the attendent loss of detail in the waveforms. The aim of developing large-scale circuit simulation is to bridge the gap between conventional circuit simulation and logic simulation. This research is to investigate new approaches for fast and relatively accurate time-domain simulation of MOS (Metal Oxide Semiconductors), LSI (Large Scale Integration) and VLSI circuits. New techniques and new algorithms are studied in the following areas: (1) analysis sequencing (2) nonlinear iteration (3) modified Gauss-Seidel method (4) latency criteria and timestep control scheme. The developed methods have been implemented into a simulation program PREMOS which could be used as a design verification tool for MOS circuits.
Harder, Nathalie; Batra, Richa; Diessl, Nicolle; Gogolin, Sina; Eils, Roland; Westermann, Frank; König, Rainer; Rohr, Karl
2015-06-01
Computational approaches for automatic analysis of image-based high-throughput and high-content screens are gaining increased importance to cope with the large amounts of data generated by automated microscopy systems. Typically, automatic image analysis is used to extract phenotypic information once all images of a screen have been acquired. However, also in earlier stages of large-scale experiments image analysis is important, in particular, to support and accelerate the tedious and time-consuming optimization of the experimental conditions and technical settings. We here present a novel approach for automatic, large-scale analysis and experimental optimization with application to a screen on neuroblastoma cell lines. Our approach consists of cell segmentation, tracking, feature extraction, classification, and model-based error correction. The approach can be used for experimental optimization by extracting quantitative information which allows experimentalists to optimally choose and to verify the experimental parameters. This involves systematically studying the global cell movement and proliferation behavior. Moreover, we performed a comprehensive phenotypic analysis of a large-scale neuroblastoma screen including the detection of rare division events such as multi-polar divisions. Major challenges of the analyzed high-throughput data are the relatively low spatio-temporal resolution in conjunction with densely growing cells as well as the high variability of the data. To account for the data variability we optimized feature extraction and classification, and introduced a gray value normalization technique as well as a novel approach for automatic model-based correction of classification errors. In total, we analyzed 4,400 real image sequences, covering observation periods of around 120 h each. We performed an extensive quantitative evaluation, which showed that our approach yields high accuracies of 92.2% for segmentation, 98.2% for tracking, and 86.5% for
Vercelloni, Julie; Caley, M. Julian; Kayal, Mohsen; Low-Choy, Samantha; Mengersen, Kerrie
2014-01-01
Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making. PMID:25364915
Vercelloni, Julie; Caley, M Julian; Kayal, Mohsen; Low-Choy, Samantha; Mengersen, Kerrie
2014-01-01
Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making.
Bhanu Prakash, G V S; Padmaja, V; Siva Kiran, R R
2008-04-01
Optimization of conidial production was achieved by response surface methodology (RSM), a powerful mathematical approach widely applied in the optimization of fermentation process, using the three substrates; rice, barley and sorghum at variable pH, moisture content and yeast extract concentrations. These three factors were found to be important, affecting Metarhizium anisopliae spore production. A 2(3) full factorial central composite design and RSM were applied to determine the optimal concentration of each variable. A second-order polynomial was determined by the multiple regression analysis of the experimental data. Moisture content of 75.68% for sorghum, 73.21% for barley and 22.34% for rice produced optimal results. Maximal conidial yield was recorded for rice at a pH of 7.01; at 7.06 for sorghum and at 6.76 for barley.
Guo, Xiwang; Liu, Shixin; Zhou, MengChu; Tian, Guangdong
2016-11-01
Disassembly modeling and planning are meaningful and important to the reuse, recovery, and recycling of obsolete and discarded products. However, the existing methods pay little or no attention to resources constraints, e.g., disassembly operators and tools. Thus a resulting plan when being executed may be ineffective in actual product disassembly. This paper proposes to model and optimize selective disassembly sequences subject to multiresource constraints to maximize disassembly profit. Moreover, two scatter search algorithms with different combination operators, namely one with precedence preserved crossover combination operator and another with path-relink combination operator, are designed to solve the proposed model. Their validity is shown by comparing them with the optimization results from well-known optimization software CPLEX for different cases. The experimental results illustrate the effectiveness of the proposed method.
NASA Technical Reports Server (NTRS)
Doolin, B. F.
1975-01-01
Classes of large scale dynamic systems were discussed in the context of modern control theory. Specific examples discussed were in the technical fields of aeronautics, water resources and electric power.
Large-scale sequential quadratic programming algorithms
Eldersveld, S.K.
1992-09-01
The problem addressed is the general nonlinear programming problem: finding a local minimizer for a nonlinear function subject to a mixture of nonlinear equality and inequality constraints. The methods studied are in the class of sequential quadratic programming (SQP) algorithms, which have previously proved successful for problems of moderate size. Our goal is to devise an SQP algorithm that is applicable to large-scale optimization problems, using sparse data structures and storing less curvature information but maintaining the property of superlinear convergence. The main features are: 1. The use of a quasi-Newton approximation to the reduced Hessian of the Lagrangian function. Only an estimate of the reduced Hessian matrix is required by our algorithm. The impact of not having available the full Hessian approximation is studied and alternative estimates are constructed. 2. The use of a transformation matrix Q. This allows the QP gradient to be computed easily when only the reduced Hessian approximation is maintained. 3. The use of a reduced-gradient form of the basis for the null space of the working set. This choice of basis is more practical than an orthogonal null-space basis for large-scale problems. The continuity condition for this choice is proven. 4. The use of incomplete solutions of quadratic programming subproblems. Certain iterates generated by an active-set method for the QP subproblem are used in place of the QP minimizer to define the search direction for the nonlinear problem. An implementation of the new algorithm has been obtained by modifying the code MINOS. Results and comparisons with MINOS and NPSOL are given for the new algorithm on a set of 92 test problems.
You, Shutang; Hadley, Stanton W.; Shankar, Mallikarjun; ...
2016-01-12
This paper studies the generation and transmission expansion co-optimization problem with a high wind power penetration rate in the US Eastern Interconnection (EI) power grid. In this paper, the generation and transmission expansion problem for the EI system is modeled as a mixed-integer programming (MIP) problem. Our paper also analyzed a time series generation method to capture the variation and correlation of both load and wind power across regions. The obtained series can be easily introduced into the expansion planning problem and then solved through existing MIP solvers. Simulation results show that the proposed planning model and series generation methodmore » can improve the expansion result significantly through modeling more detailed information of wind and load variation among regions in the US EI system. Moreover, the improved expansion plan that combines generation and transmission will aid system planners and policy makers to maximize the social welfare in large-scale power grids.« less
You, Shutang; Hadley, Stanton W.; Shankar, Mallikarjun; Liu, Yilu
2016-01-12
This paper studies the generation and transmission expansion co-optimization problem with a high wind power penetration rate in the US Eastern Interconnection (EI) power grid. In this paper, the generation and transmission expansion problem for the EI system is modeled as a mixed-integer programming (MIP) problem. Our paper also analyzed a time series generation method to capture the variation and correlation of both load and wind power across regions. The obtained series can be easily introduced into the expansion planning problem and then solved through existing MIP solvers. Simulation results show that the proposed planning model and series generation method can improve the expansion result significantly through modeling more detailed information of wind and load variation among regions in the US EI system. Moreover, the improved expansion plan that combines generation and transmission will aid system planners and policy makers to maximize the social welfare in large-scale power grids.
Solving nonlinear equality constrained multiobjective optimization problems using neural networks.
Mestari, Mohammed; Benzirar, Mohammed; Saber, Nadia; Khouil, Meryem
2015-10-01
This paper develops a neural network architecture and a new processing method for solving in real time, the nonlinear equality constrained multiobjective optimization problem (NECMOP), where several nonlinear objective functions must be optimized in a conflicting situation. In this processing method, the NECMOP is converted to an equivalent scalar optimization problem (SOP). The SOP is then decomposed into several-separable subproblems processable in parallel and in a reasonable time by multiplexing switched capacitor circuits. The approach which we propose makes use of a decomposition-coordination principle that allows nonlinearity to be treated at a local level and where coordination is achieved through the use of Lagrange multipliers. The modularity and the regularity of the neural networks architecture herein proposed make it suitable for very large scale integration implementation. An application to the resolution of a physical problem is given to show that the approach used here possesses some advantages of the point of algorithmic view, and provides processes of resolution often simpler than the usual techniques.
NASA Astrophysics Data System (ADS)
Gad-El-Hak, Mohamed
"Extreme" events - including climatic events, such as hurricanes, tornadoes, and drought - can cause massive disruption to society, including large death tolls and property damage in the billions of dollars. Events in recent years have shown the importance of being prepared and that countries need to work together to help alleviate the resulting pain and suffering. This volume presents a review of the broad research field of large-scale disasters. It establishes a common framework for predicting, controlling and managing both manmade and natural disasters. There is a particular focus on events caused by weather and climate change. Other topics include air pollution, tsunamis, disaster modeling, the use of remote sensing and the logistics of disaster management. It will appeal to scientists, engineers, first responders and health-care professionals, in addition to graduate students and researchers who have an interest in the prediction, prevention or mitigation of large-scale disasters.
Chen, Qingkui; Zhao, Deyu; Wang, Jingjuan
2017-01-01
This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services. PMID:28777325
Fang, Yuling; Chen, Qingkui; Xiong, Neal N; Zhao, Deyu; Wang, Jingjuan
2017-08-04
This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes' diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.
Structural optimization for nonlinear dynamic response.
Dou, Suguang; Strachan, B Scott; Shaw, Steven W; Jensen, Jakob S
2015-09-28
Much is known about the nonlinear resonant response of mechanical systems, but methods for the systematic design of structures that optimize aspects of these responses have received little attention. Progress in this area is particularly important in the area of micro-systems, where nonlinear resonant behaviour is being used for a variety of applications in sensing and signal conditioning. In this work, we describe a computational method that provides a systematic means for manipulating and optimizing features of nonlinear resonant responses of mechanical structures that are described by a single vibrating mode, or by a pair of internally resonant modes. The approach combines techniques from nonlinear dynamics, computational mechanics and optimization, and it allows one to relate the geometric and material properties of structural elements to terms in the normal form for a given resonance condition, thereby providing a means for tailoring its nonlinear response. The method is applied to the fundamental nonlinear resonance of a clamped-clamped beam and to the coupled mode response of a frame structure, and the results show that one can modify essential normal form coefficients by an order of magnitude by relatively simple changes in the shape of these elements. We expect the proposed approach, and its extensions, to be useful for the design of systems used for fundamental studies of nonlinear behaviour as well as for the development of commercial devices that exploit nonlinear behaviour.
NASA Astrophysics Data System (ADS)
Rafiee, Mohammad; Barrau, Axel; Bayen, Alexandre M.
2013-06-01
This article investigates the performance of Monte Carlo-based estimation methods for estimation of flow state in large-scale open channel networks. After constructing a state space model of the flow based on the Saint-Venant equations, we implement the optimal sampling importance resampling filter to perform state estimation in a case in which measurements are available at every time step. Considering a case in which measurements become available intermittently, a random-map implementation of the implicit particle filter is applied to estimate the state trajectory in the interval between the measurements. Finally, some heuristics are proposed, which are shown to improve the estimation results and lower the computational cost. In the first heuristics, considering the case in which measurements are available at every time step, we apply the implicit particle filter over time intervals of a desired size while incorporating all the available measurements over the corresponding time interval. As a second heuristic method, we introduce a maximum a posteriori (MAP) method, which does not require sampling. It will be seen, through implementation, that the MAP method provides more accurate results in the case of our application while having a smaller computational cost. All estimation methods are tested on a network of 19 tidally forced subchannels and 1 reservoir, Clifton Court Forebay, in Sacramento-San Joaquin Delta in California, and numerical results are presented.
Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems
NASA Astrophysics Data System (ADS)
Xu, Yuechun; Cui, Zhihua; Zeng, Jianchao
Nonlinear programming problem is one important branch in operational research, and has been successfully applied to various real-life problems. In this paper, a new approach called Social emotional optimization algorithm (SEOA) is used to solve this problem which is a new swarm intelligent technique by simulating the human behavior guided by emotion. Simulation results show that the social emotional optimization algorithm proposed in this paper is effective and efficiency for the nonlinear constrained programming problems.
Nonlinear optimization for stochastic simulations.
Johnson, Michael M.; Yoshimura, Ann S.; Hough, Patricia Diane; Ammerlahn, Heidi R.
2003-12-01
This report describes research targeting development of stochastic optimization algorithms and their application to mission-critical optimization problems in which uncertainty arises. The first section of this report covers the enhancement of the Trust Region Parallel Direct Search (TRPDS) algorithm to address stochastic responses and the incorporation of the algorithm into the OPT++ optimization library. The second section describes the Weapons of Mass Destruction Decision Analysis Center (WMD-DAC) suite of systems analysis tools and motivates the use of stochastic optimization techniques in such non-deterministic simulations. The third section details a batch programming interface designed to facilitate criteria-based or algorithm-driven execution of system-of-system simulations. The fourth section outlines the use of the enhanced OPT++ library and batch execution mechanism to perform systems analysis and technology trade-off studies in the WMD detection and response problem domain.
Mojarrad Moghanloo, Gol Mohammad; Khatami, Maryam; Javidanbardan, Amin; Hosseini, Seyed Nezamedin
2018-01-01
In biopharmaceutical science, ion-exchange chromatography (IEC) is a well-known purification technique to separate the impurities such as host cell proteins from recombinant proteins. However, IEC is one of the limiting steps in the purification process of recombinant hepatitis B surface antigen (rHBsAg), due to its low recovery rate (<50%). In the current study, we hypothesized that ionic strengths of IEC buffers are easy-to-control parameters which can play a major role in optimizing the process and increasing the recovery. Thus, we investigated the effects of ionic strengths of buffers on rHBsAg recovery via adjusting Tris-HCl and NaCl concentrations. Increasing the conductivity of equilibration (Eq.), washing (Wash.) and elution (Elut.) buffers from their initial values of 1.6 mS/cm, 1.6 mS/cm, and 7.0 mS/cm to 1.6 mS/cm, 7 mS/cm and 50 mS/cm, respectively yielded an average recovery rate of 82% in both lab-scale and large-scale weak anion-exchange chromatography without any harsh effect on the purity percentage of rHBsAg. The recovery enhancement via increasing the conductivity of Eq. and Wash. buffers can be explained by their roles in reducing the binding strength and aggregation of retained particles in the column. Moreover, further increase in the salt concentration of Elut. Buffer could substantially promote the ion exchange process and the elution of retained rHBsAg. Copyright © 2017 Elsevier Inc. All rights reserved.
Large scale tracking algorithms
Hansen, Ross L.; Love, Joshua Alan; Melgaard, David Kennett; Karelitz, David B.; Pitts, Todd Alan; Zollweg, Joshua David; Anderson, Dylan Z.; Nandy, Prabal; Whitlow, Gary L.; Bender, Daniel A.; Byrne, Raymond Harry
2015-01-01
Low signal-to-noise data processing algorithms for improved detection, tracking, discrimination and situational threat assessment are a key research challenge. As sensor technologies progress, the number of pixels will increase signi cantly. This will result in increased resolution, which could improve object discrimination, but unfortunately, will also result in a significant increase in the number of potential targets to track. Many tracking techniques, like multi-hypothesis trackers, suffer from a combinatorial explosion as the number of potential targets increase. As the resolution increases, the phenomenology applied towards detection algorithms also changes. For low resolution sensors, "blob" tracking is the norm. For higher resolution data, additional information may be employed in the detection and classfication steps. The most challenging scenarios are those where the targets cannot be fully resolved, yet must be tracked and distinguished for neighboring closely spaced objects. Tracking vehicles in an urban environment is an example of such a challenging scenario. This report evaluates several potential tracking algorithms for large-scale tracking in an urban environment.
Large scale traffic simulations
Nagel, K.; Barrett, C.L. |; Rickert, M. |
1997-04-01
Large scale microscopic (i.e. vehicle-based) traffic simulations pose high demands on computational speed in at least two application areas: (i) real-time traffic forecasting, and (ii) long-term planning applications (where repeated {open_quotes}looping{close_quotes} between the microsimulation and the simulated planning of individual person`s behavior is necessary). As a rough number, a real-time simulation of an area such as Los Angeles (ca. 1 million travellers) will need a computational speed of much higher than 1 million {open_quotes}particle{close_quotes} (= vehicle) updates per second. This paper reviews how this problem is approached in different projects and how these approaches are dependent both on the specific questions and on the prospective user community. The approaches reach from highly parallel and vectorizable, single-bit implementations on parallel supercomputers for Statistical Physics questions, via more realistic implementations on coupled workstations, to more complicated driving dynamics implemented again on parallel supercomputers. 45 refs., 9 figs., 1 tab.
Particle swarm optimization for complex nonlinear optimization problems
NASA Astrophysics Data System (ADS)
Alexandridis, Alex; Famelis, Ioannis Th.; Tsitouras, Charalambos
2016-06-01
This work presents the application of a technique belonging to evolutionary computation, namely particle swarm optimization (PSO), to complex nonlinear optimization problems. To be more specific, a PSO optimizer is setup and applied to the derivation of Runge-Kutta pairs for the numerical solution of initial value problems. The effect of critical PSO operational parameters on the performance of the proposed scheme is thoroughly investigated.
New Methods for Nonlinear Optimization.
1988-05-11
been described in a paper by Richard Byrd, Jorge Nocedal and Ya-Xiang Yuan published in SIAM Journal on Numerical Analysis. odes t~"(t JI*. 7, c’K -3- 3...Schnabel and Gerald Shultz has appeared in Mathematical Programming. The techniques used by Byrd and Nocedal in analyzing the convergence of constrained...optimiza- tion methods are of substantial interest in unconstrained optimization as well. A paper by Byrd and Nocedal discussing some of these issues has
Optimization of nonlinear aeroelastic tailoring criteria
NASA Technical Reports Server (NTRS)
Abdi, F.; Ide, H.; Shankar, V. J.; Sobieszczanski-Sobieski, J.
1988-01-01
A static flexible fighter aircraft wing configuration is presently addressed by a multilevel optimization technique, based on both a full-potential concept and a rapid structural optimization program, which can be applied to such aircraft-design problems as maneuver load control, aileron reversal, and lift effectiveness. It is found that nonlinearities are important in the design of an aircraft whose flight envelope encompasses the transonic regime, and that the present structural suboptimization produces a significantly lighter wing by reducing ply thicknesses.
Gain optimization with non-linear controls
NASA Technical Reports Server (NTRS)
Slater, G. L.; Kandadai, R. D.
1984-01-01
An algorithm has been developed for the analysis and design of controls for non-linear systems. The technical approach is to use statistical linearization to model the non-linear dynamics of a system by a quasi-Gaussian model. A covariance analysis is performed to determine the behavior of the dynamical system and a quadratic cost function. Expressions for the cost function and its derivatives are determined so that numerical optimization techniques can be applied to determine optimal feedback laws. The primary application for this paper is centered about the design of controls for nominally linear systems but where the controls are saturated or limited by fixed constraints. The analysis is general, however, and numerical computation requires only that the specific non-linearity be considered in the analysis.
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...
Optimal singular control for nonlinear semistabilisation
NASA Astrophysics Data System (ADS)
L'Afflitto, Andrea; Haddad, Wassim M.
2016-06-01
The singular optimal control problem for asymptotic stabilisation has been extensively studied in the literature. In this paper, the optimal singular control problem is extended to address a weaker version of closed-loop stability, namely, semistability, which is of paramount importance for consensus control of network dynamical systems. Three approaches are presented to address the nonlinear semistable singular control problem. Namely, a singular perturbation method is presented to construct a state-feedback singular controller that guarantees closed-loop semistability for nonlinear systems. In this approach, we show that for a non-negative cost-to-go function the minimum cost of a nonlinear semistabilising singular controller is lower than the minimum cost of a singular controller that guarantees asymptotic stability of the closed-loop system. In the second approach, we solve the nonlinear semistable singular control problem by using the cost-to-go function to cancel the singularities in the corresponding Hamilton-Jacobi-Bellman equation. For this case, we show that the minimum value of the singular performance measure is zero. Finally, we provide a framework based on the concepts of state-feedback linearisation and feedback equivalence to solve the singular control problem for semistabilisation of nonlinear dynamical systems. For this approach, we also show that the minimum value of the singular performance measure is zero. Three numerical examples are presented to demonstrate the efficacy of the proposed singular semistabilisation frameworks.
On decentralized control of large-scale systems
NASA Technical Reports Server (NTRS)
Siljak, D. D.
1978-01-01
A scheme is presented for decentralized control of large-scale linear systems which are composed of a number of interconnected subsystems. By ignoring the interconnections, local feedback controls are chosen to optimize each decoupled subsystem. Conditions are provided to establish compatibility of the individual local controllers and achieve stability of the overall system. Besides computational simplifications, the scheme is attractive because of its structural features and the fact that it produces a robust decentralized regulator for large dynamic systems, which can tolerate a wide range of nonlinearities and perturbations among the subsystems.
Optimized spectral estimation for nonlinear synchronizing systems
NASA Astrophysics Data System (ADS)
Sommerlade, Linda; Mader, Malenka; Mader, Wolfgang; Timmer, Jens; Thiel, Marco; Grebogi, Celso; Schelter, Björn
2014-03-01
In many fields of research nonlinear dynamical systems are investigated. When more than one process is measured, besides the distinct properties of the individual processes, their interactions are of interest. Often linear methods such as coherence are used for the analysis. The estimation of coherence can lead to false conclusions when applied without fulfilling several key assumptions. We introduce a data driven method to optimize the choice of the parameters for spectral estimation. Its applicability is demonstrated based on analytical calculations and exemplified in a simulation study. We complete our investigation with an application to nonlinear tremor signals in Parkinson's disease. In particular, we analyze electroencephalogram and electromyogram data.
Optimized spectral estimation for nonlinear synchronizing systems.
Sommerlade, Linda; Mader, Malenka; Mader, Wolfgang; Timmer, Jens; Thiel, Marco; Grebogi, Celso; Schelter, Björn
2014-03-01
In many fields of research nonlinear dynamical systems are investigated. When more than one process is measured, besides the distinct properties of the individual processes, their interactions are of interest. Often linear methods such as coherence are used for the analysis. The estimation of coherence can lead to false conclusions when applied without fulfilling several key assumptions. We introduce a data driven method to optimize the choice of the parameters for spectral estimation. Its applicability is demonstrated based on analytical calculations and exemplified in a simulation study. We complete our investigation with an application to nonlinear tremor signals in Parkinson's disease. In particular, we analyze electroencephalogram and electromyogram data.
Nonlinear Brightness Optimization in Compton Scattering
NASA Astrophysics Data System (ADS)
Hartemann, Fred V.; Wu, Sheldon S. Q.
2013-07-01
In Compton scattering light sources, a laser pulse is scattered by a relativistic electron beam to generate tunable x and gamma rays. Because of the inhomogeneous nature of the incident radiation, the relativistic Lorentz boost of the electrons is modulated by the ponderomotive force during the interaction, leading to intrinsic spectral broadening and brightness limitations. These effects are discussed, along with an optimization strategy to properly balance the laser bandwidth, diffraction, and nonlinear ponderomotive force.
Nonlinear Brightness Optimization in Compton Scattering
Hartemann, Fred V.; Wu, Sheldon S. Q.
2013-07-26
In Compton scattering light sources, a laser pulse is scattered by a relativistic electron beam to generate tunable x and gamma rays. Because of the inhomogeneous nature of the incident radiation, the relativistic Lorentz boost of the electrons is modulated by the ponderomotive force during the interaction, leading to intrinsic spectral broadening and brightness limitations. We discuss these effects, along with an optimization strategy to properly balance the laser bandwidth, diffraction, and nonlinear ponderomotive force.
Nonlinear brightness optimization in compton scattering.
Hartemann, Fred V; Wu, Sheldon S Q
2013-07-26
In Compton scattering light sources, a laser pulse is scattered by a relativistic electron beam to generate tunable x and gamma rays. Because of the inhomogeneous nature of the incident radiation, the relativistic Lorentz boost of the electrons is modulated by the ponderomotive force during the interaction, leading to intrinsic spectral broadening and brightness limitations. These effects are discussed, along with an optimization strategy to properly balance the laser bandwidth, diffraction, and nonlinear ponderomotive force.
Dark energy from large-scale structure lensing information
Lu Tingting; Pen Ueli; Dore, Oliver
2010-06-15
Wide area large-scale structure (LSS) surveys are planning to map a substantial fraction of the visible Universe to quantify dark energy through baryon acoustic oscillations. At increasing redshift, for example, that probed by proposed 21-cm intensity mapping surveys, gravitational lensing potentially limits the fidelity (Hui et al., 2007) because it distorts the apparent matter distribution. In this paper we show that these distortions can be reconstructed, and actually used to map the distribution of intervening dark matter. The lensing information for sources at z=1-3 allows accurate reconstruction of the gravitational potential on large scales, l < or approx. 100, which is well matched for integrated Sachs-Wolfe effect measurements of dark energy and its sound speed, and a strong constraint for modified gravity models of dark energy. We built an optimal quadratic lensing estimator for non-Gaussian sources, which is necessary for LSS. The phenomenon of 'information saturation' (Rimes and Hamilton, 2005) saturates reconstruction at mildly nonlinear scales, where the linear source power spectrum {Delta}{sup 2{approx}}0.2-0.5, depending on power spectrum slope. Naive Gaussian estimators with nonlinear cutoff can be tuned to reproduce the optimal non-Gaussian errors within a factor of 2. We compute the effective number densities of independent lensing sources for LSS lensing, and find that they increase rapidly with redshifts. For LSS/21-cm sources at z{approx}2-4, the lensing reconstruction is limited by cosmic variance at l < or approx. 100.
Sensitivity technologies for large scale simulation.
Collis, Samuel Scott; Bartlett, Roscoe Ainsworth; Smith, Thomas Michael; Heinkenschloss, Matthias; Wilcox, Lucas C.; Hill, Judith C.; Ghattas, Omar; Berggren, Martin Olof; Akcelik, Volkan; Ober, Curtis Curry; van Bloemen Waanders, Bart Gustaaf; Keiter, Eric Richard
2005-01-01
order approximation of the Euler equations and used as a preconditioner. In comparison to other methods, the AD preconditioner showed better convergence behavior. Our ultimate target is to perform shape optimization and hp adaptivity using adjoint formulations in the Premo compressible fluid flow simulator. A mathematical formulation for mixed-level simulation algorithms has been developed where different physics interact at potentially different spatial resolutions in a single domain. To minimize the implementation effort, explicit solution methods can be considered, however, implicit methods are preferred if computational efficiency is of high priority. We present the use of a partial elimination nonlinear solver technique to solve these mixed level problems and show how these formulation are closely coupled to intrusive optimization approaches and sensitivity analyses. Production codes are typically not designed for sensitivity analysis or large scale optimization. The implementation of our optimization libraries into multiple production simulation codes in which each code has their own linear algebra interface becomes an intractable problem. In an attempt to streamline this task, we have developed a standard interface between the numerical algorithm (such as optimization) and the underlying linear algebra. These interfaces (TSFCore and TSFCoreNonlin) have been adopted by the Trilinos framework and the goal is to promote the use of these interfaces especially with new developments. Finally, an adjoint based a posteriori error estimator has been developed for discontinuous Galerkin discretization of Poisson's equation. The goal is to investigate other ways to leverage the adjoint calculations and we show how the convergence of the forward problem can be improved by adapting the grid using adjoint-based error estimates. Error estimation is usually conducted with continuous adjoints but if discrete adjoints are available it may be possible to reuse the discrete version
A class of finite dimensional optimal nonlinear estimators
NASA Technical Reports Server (NTRS)
Marcus, S. I.; Willsky, A. S.
1974-01-01
Finite dimensional optimal nonlinear state estimators are derived for bilinear systems evolving on nilpotent and solvable Lie groups. These results are extended to other classes of systems involving polynomial nonlinearities. The concepts of exact differentials and path-independent integrals are used to derive optimal finite dimensional estimators for a further class of nonlinear systems.
Optimal measurement precision of a nonlinear interferometer
NASA Astrophysics Data System (ADS)
Javanainen, Juha; Chen, Han
2012-06-01
We study the best attainable measurement precision when a double-well trap with bosons inside acts as an interferometer to measure the energy difference of the atoms on the two sides of the trap. We introduce time-independent perturbation theory as the main tool in both analytical arguments and numerical computations. Nonlinearity from atom-atom interactions will not indirectly allow the interferometer to beat the Heisenberg limit, but in many regimes of the operation the Heisenberg limit scaling of measurement precision is preserved in spite of added tunneling of the atoms and atom-atom interactions, often even with the optimal prefactor.
Nonlinear simulations to optimize magnetic nanoparticle hyperthermia
Reeves, Daniel B. Weaver, John B.
2014-03-10
Magnetic nanoparticle hyperthermia is an attractive emerging cancer treatment, but the acting microscopic energy deposition mechanisms are not well understood and optimization suffers. We describe several approximate forms for the characteristic time of Néel rotations with varying properties and external influences. We then present stochastic simulations that show agreement between the approximate expressions and the micromagnetic model. The simulations show nonlinear imaginary responses and associated relaxational hysteresis due to the field and frequency dependencies of the magnetization. This suggests that efficient heating is possible by matching fields to particles instead of resorting to maximizing the power of the applied magnetic fields.
Large-scale instabilities of helical flows
NASA Astrophysics Data System (ADS)
Cameron, Alexandre; Alexakis, Alexandros; Brachet, Marc-Étienne
2016-10-01
Large-scale hydrodynamic instabilities of periodic helical flows of a given wave number K are investigated using three-dimensional Floquet numerical computations. In the Floquet formalism the unstable field is expanded in modes of different spacial periodicity. This allows us (i) to clearly distinguish large from small scale instabilities and (ii) to study modes of wave number q of arbitrarily large-scale separation q ≪K . Different flows are examined including flows that exhibit small-scale turbulence. The growth rate σ of the most unstable mode is measured as a function of the scale separation q /K ≪1 and the Reynolds number Re. It is shown that the growth rate follows the scaling σ ∝q if an AKA effect [Frisch et al., Physica D: Nonlinear Phenomena 28, 382 (1987), 10.1016/0167-2789(87)90026-1] is present or a negative eddy viscosity scaling σ ∝q2 in its absence. This holds both for the Re≪1 regime where previously derived asymptotic results are verified but also for Re=O (1 ) that is beyond their range of validity. Furthermore, for values of Re above a critical value ReSc beyond which small-scale instabilities are present, the growth rate becomes independent of q and the energy of the perturbation at large scales decreases with scale separation. The nonlinear behavior of these large-scale instabilities is also examined in the nonlinear regime where the largest scales of the system are found to be the most dominant energetically. These results are interpreted by low-order models.
Nonlinear optimization simplified by hypersurface deformation
Stillinger, F.H.; Weber, T.A.
1988-09-01
A general strategy is advanced for simplifying nonlinear optimization problems, the ant-lion method. This approach exploits shape modifications of the cost-function hypersurface which distend basins surrounding low-lying minima (including global minima). By intertwining hypersurface deformations with steepest-descent displacements, the search is concentrated on a small relevant subset of all minima. Specific calculations demonstrating the value of this method are reported for the partitioning of two classes of irregular but nonrandom graphs, the prime-factor graphs and the pi graphs. We also indicate how this approach can be applied to the traveling salesman problem and to design layout optimization, and that it may be useful in combination with simulated annealing strategies.
Inverting magnetic meridian data using nonlinear optimization
NASA Astrophysics Data System (ADS)
Connors, Martin; Rostoker, Gordon
2015-09-01
A nonlinear optimization algorithm coupled with a model of auroral current systems allows derivation of physical parameters from data and is the basis of a new inversion technique. We refer to this technique as automated forward modeling (AFM), with the variant used here being automated meridian modeling (AMM). AFM is applicable on scales from regional to global, yielding simple and easily understood output, and using only magnetic data with no assumptions about electrodynamic parameters. We have found the most useful output parameters to be the total current and the boundaries of the auroral electrojet on a meridian densely populated with magnetometers, as derived by AMM. Here, we describe application of AFM nonlinear optimization to magnetic data and then describe the use of AMM to study substorms with magnetic data from ground meridian chains as input. AMM inversion results are compared to optical data, results from other inversion methods, and field-aligned current data from AMPERE. AMM yields physical parameters meaningful in describing local electrodynamics and is suitable for ongoing monitoring of activity. The relation of AMM model parameters to equivalent currents is discussed, and the two are found to compare well if the field-aligned currents are far from the inversion meridian.
Vazquez-Anderson, Jorge; Mihailovic, Mia K; Baldridge, Kevin C; Reyes, Kristofer G; Haning, Katie; Cho, Seung Hee; Amador, Paul; Powell, Warren B; Contreras, Lydia M
2017-05-19
Current approaches to design efficient antisense RNAs (asRNAs) rely primarily on a thermodynamic understanding of RNA-RNA interactions. However, these approaches depend on structure predictions and have limited accuracy, arguably due to overlooking important cellular environment factors. In this work, we develop a biophysical model to describe asRNA-RNA hybridization that incorporates in vivo factors using large-scale experimental hybridization data for three model RNAs: a group I intron, CsrB and a tRNA. A unique element of our model is the estimation of the availability of the target region to interact with a given asRNA using a differential entropic consideration of suboptimal structures. We showcase the utility of this model by evaluating its prediction capabilities in four additional RNAs: a group II intron, Spinach II, 2-MS2 binding domain and glgC 5΄ UTR. Additionally, we demonstrate the applicability of this approach to other bacterial species by predicting sRNA-mRNA binding regions in two newly discovered, though uncharacterized, regulatory RNAs. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Vazquez-Anderson, Jorge; Mihailovic, Mia K.; Baldridge, Kevin C.; Reyes, Kristofer G.; Haning, Katie; Cho, Seung Hee; Amador, Paul; Powell, Warren B.
2017-01-01
Abstract Current approaches to design efficient antisense RNAs (asRNAs) rely primarily on a thermodynamic understanding of RNA–RNA interactions. However, these approaches depend on structure predictions and have limited accuracy, arguably due to overlooking important cellular environment factors. In this work, we develop a biophysical model to describe asRNA–RNA hybridization that incorporates in vivo factors using large-scale experimental hybridization data for three model RNAs: a group I intron, CsrB and a tRNA. A unique element of our model is the estimation of the availability of the target region to interact with a given asRNA using a differential entropic consideration of suboptimal structures. We showcase the utility of this model by evaluating its prediction capabilities in four additional RNAs: a group II intron, Spinach II, 2-MS2 binding domain and glgC 5΄ UTR. Additionally, we demonstrate the applicability of this approach to other bacterial species by predicting sRNA–mRNA binding regions in two newly discovered, though uncharacterized, regulatory RNAs. PMID:28334800
Survey of optimization techniques for nonlinear spacecraft trajectory searches
NASA Technical Reports Server (NTRS)
Wang, Tseng-Chan; Stanford, Richard H.; Sunseri, Richard F.; Breckheimer, Peter J.
1988-01-01
Mathematical analysis of the optimal search of a nonlinear spacecraft trajectory to arrive at a set of desired targets is presented. A high precision integrated trajectory program and several optimization software libraries are used to search for a converged nonlinear spacecraft trajectory. Several examples for the Galileo Jupiter Orbiter and the Ocean Topography Experiment (TOPEX) are presented that illustrate a variety of the optimization methods used in nonlinear spacecraft trajectory searches.
NASA Astrophysics Data System (ADS)
Vishniac, Ethan T.
2015-01-01
We show that a differentially rotating conducting fluid automatically creates a magnetic helicity flux with components along the rotation axis and in the direction of the local vorticity. This drives a rapid growth in the local density of current helicity, which in turn drives a large scale dynamo. The dynamo growth rate derived from this process is not constant, but depends inversely on the large scale magnetic field strength. This dynamo saturates when buoyant losses of magnetic flux compete with the large scale dynamo, providing a simple prediction for magnetic field strength as a function of Rossby number in stars. Increasing anisotropy in the turbulence produces a decreasing magnetic helicity flux, which explains the flattening of the B/Rossby number relation at low Rossby numbers. We also show that the kinetic helicity is always a subdominant effect. There is no kinematic dynamo in real stars.
Very Large Scale Integration (VLSI).
ERIC Educational Resources Information Center
Yeaman, Andrew R. J.
Very Large Scale Integration (VLSI), the state-of-the-art production techniques for computer chips, promises such powerful, inexpensive computing that, in the future, people will be able to communicate with computer devices in natural language or even speech. However, before full-scale VLSI implementation can occur, certain salient factors must be…
Galaxy clustering on large scales.
Efstathiou, G
1993-01-01
I describe some recent observations of large-scale structure in the galaxy distribution. The best constraints come from two-dimensional galaxy surveys and studies of angular correlation functions. Results from galaxy redshift surveys are much less precise but are consistent with the angular correlations, provided the distortions in mapping between real-space and redshift-space are relatively weak. The galaxy two-point correlation function, rich-cluster two-point correlation function, and galaxy-cluster cross-correlation function are all well described on large scales ( greater, similar 20h-1 Mpc, where the Hubble constant, H0 = 100h km.s-1.Mpc; 1 pc = 3.09 x 10(16) m) by the power spectrum of an initially scale-invariant, adiabatic, cold-dark-matter Universe with Gamma = Omegah approximately 0.2. I discuss how this fits in with the Cosmic Background Explorer (COBE) satellite detection of large-scale anisotropies in the microwave background radiation and other measures of large-scale structure in the Universe. PMID:11607400
Nonlinear optimization approach for Fourier ptychographic microscopy.
Zhang, Yongbing; Jiang, Weixin; Dai, Qionghai
2015-12-28
Fourier ptychographic microscopy (FPM) is recently proposed as a computational imaging method to bypass the limitation of the space-bandwidth product of the traditional optical system. It employs a sequence of low-resolution images captured under angularly varying illumination and applies the phase retrieval algorithm to iteratively reconstruct a wide-field, high-resolution image. In current FPM imaging system, system uncertainties, such as the pupil aberration of the employed optics, may significantly degrade the quality of the reconstruction. In this paper, we develop and test a nonlinear optimization algorithm to improve the robustness of the FPM imaging system by simultaneously considering the reconstruction and the system imperfections. Analytical expressions for the gradient of a squared-error metric with respect to the object and illumination allow joint optimization of the object and system parameters. The algorithm achieves superior reconstructions when the system parameters are inaccurately known or in the presence of noise and corrects the pupil aberrations simultaneously. Experiments on both synthetic and real captured data validate the effectiveness of the proposed method.
Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor
Ye, Lin; Yang, Ming; Xu, Liang; Zhuang, Xiaoqi; Dong, Zhaopeng; Li, Shiyang
2014-01-01
Using the finite element method (FEM) and particle swarm optimization (PSO), a nonlinearity analysis based on parameter optimization is proposed to design an inductive angle sensor. Due to the structure complexity of the sensor, understanding the influences of structure parameters on the nonlinearity errors is a critical step in designing an effective sensor. Key parameters are selected for the design based on the parameters' effects on the nonlinearity errors. The finite element method and particle swarm optimization are combined for the sensor design to get the minimal nonlinearity error. In the simulation, the nonlinearity error of the optimized sensor is 0.053% in the angle range from −60° to 60°. A prototype sensor is manufactured and measured experimentally, and the experimental nonlinearity error is 0.081% in the angle range from −60° to 60°. PMID:24590353
Constrained optimization for image restoration using nonlinear programming
NASA Technical Reports Server (NTRS)
Yeh, C.-L.; Chin, R. T.
1985-01-01
The constrained optimization problem for image restoration, utilizing incomplete information and partial constraints, is formulated using nonlinear proramming techniques. This method restores a distorted image by optimizing a chosen object function subject to available constraints. The penalty function method of nonlinear programming is used. Both linear or nonlinear object function, and linear or nonlinear constraint functions can be incorporated in the formulation. This formulation provides a generalized approach to solve constrained optimization problems for image restoration. Experiments using this scheme have been performed. The results are compared with those obtained from other restoration methods and the comparative study is presented.
Constrained optimization for image restoration using nonlinear programming
NASA Technical Reports Server (NTRS)
Yeh, C.-L.; Chin, R. T.
1985-01-01
The constrained optimization problem for image restoration, utilizing incomplete information and partial constraints, is formulated using nonlinear proramming techniques. This method restores a distorted image by optimizing a chosen object function subject to available constraints. The penalty function method of nonlinear programming is used. Both linear or nonlinear object function, and linear or nonlinear constraint functions can be incorporated in the formulation. This formulation provides a generalized approach to solve constrained optimization problems for image restoration. Experiments using this scheme have been performed. The results are compared with those obtained from other restoration methods and the comparative study is presented.
Li, Ji-Qing; Zhang, Yu-Shan; Ji, Chang-Ming; Wang, Ai-Jing; Lund, Jay R
2013-01-01
This paper examines long-term optimal operation using dynamic programming for a large hydropower system of 10 reservoirs in Northeast China. Besides considering flow and hydraulic head, the optimization explicitly includes time-varying electricity market prices to maximize benefit. Two techniques are used to reduce the 'curse of dimensionality' of dynamic programming with many reservoirs. Discrete differential dynamic programming (DDDP) reduces the search space and computer memory needed. Object-oriented programming (OOP) and the ability to dynamically allocate and release memory with the C++ language greatly reduces the cumulative effect of computer memory for solving multi-dimensional dynamic programming models. The case study shows that the model can reduce the 'curse of dimensionality' and achieve satisfactory results.
Nonlinear Multidimensional Assignment Problems Efficient Conic Optimization Methods and Applications
2015-06-24
AFRL-AFOSR-VA-TR-2015-0281 Nonlinear Multidimensional Assignment Problems Efficient Conic Optimization Methods and Applications Hans Mittelmann...2012 - March 2015 4. TITLE AND SUBTITLE Nonlinear Multidimensional Assignment Problems Efficient Conic Optimization Methods and Applications 5a...problems. The size 16 three-dimensional quadratic assignment problem Q3AP from wireless communications was solved using a sophisticated approach
Matching trajectory optimization and nonlinear tracking control for HALE
NASA Astrophysics Data System (ADS)
Lee, Sangjong; Jang, Jieun; Ryu, Hyeok; Lee, Kyun Ho
2014-11-01
This paper concerns optimal trajectory generation and nonlinear tracking control for stratospheric airship platform of VIA-200. To compensate for the mismatch between the point-mass model of optimal trajectory and the 6-DOF model of the nonlinear tracking problem, a new matching trajectory optimization approach is proposed. The proposed idea reduces the dissimilarity of both problems and reduces the uncertainties in the nonlinear equations of motion for stratospheric airship. In addition, its refined optimal trajectories yield better results under jet stream conditions during flight. The resultant optimal trajectories of VIA-200 are full three-dimensional ascent flight trajectories reflecting the realistic constraints of flight conditions and airship performance with and without a jet stream. Finally, 6-DOF nonlinear equations of motion are derived, including a moving wind field, and the vectorial backstepping approach is applied. The desirable tracking performance is demonstrated that application of the proposed matching optimization method enables the smooth linkage of trajectory optimization to tracking control problems.
Schwarz, Christopher G; Senjem, Matthew L; Gunter, Jeffrey L; Tosakulwong, Nirubol; Weigand, Stephen D; Kemp, Bradley J; Spychalla, Anthony J; Vemuri, Prashanthi; Petersen, Ronald C; Lowe, Val J; Jack, Clifford R
2017-01-01
Quantitative measurements of change in β-amyloid load from Positron Emission Tomography (PET) images play a critical role in clinical trials and longitudinal observational studies of Alzheimer's disease. These measurements are strongly affected by methodological differences between implementations, including choice of reference region and use of partial volume correction, but there is a lack of consensus for an optimal method. Previous works have examined some relevant variables under varying criteria, but interactions between them prevent choosing a method via combined meta-analysis. In this work, we present a thorough comparison of methods to measure change in β-amyloid over time using Pittsburgh Compound B (PiB) PET imaging.
A method for nonlinear optimization with discrete design variables
NASA Technical Reports Server (NTRS)
Olsen, Gregory R.; Vanderplaats, Garret N.
1987-01-01
A numerical method is presented for the solution of nonlinear discrete optimization problems. The applicability of discrete optimization to engineering design is discussed, and several standard structural optimization problems are solved using discrete design variables. The method uses approximation techniques to create subproblems suitable for linear mixed-integer programming methods. The method employs existing software for continuous optimization and integer programming.
A method for nonlinear optimization with discrete design variables
NASA Technical Reports Server (NTRS)
Olsen, Gregory R.; Vanderplaats, Garret N.
1987-01-01
A numerical method is presented for the solution of nonlinear discrete optimization problems. The applicability of discrete optimization to engineering design is discussed, and several standard structural optimization problems are solved using discrete design variables. The method uses approximation techniques to create subproblems suitable for linear mixed-integer programming methods. The method employs existing software for continuous optimization and integer programming.
Optimal second order sliding mode control for nonlinear uncertain systems.
Das, Madhulika; Mahanta, Chitralekha
2014-07-01
In this paper, a chattering free optimal second order sliding mode control (OSOSMC) method is proposed to stabilize nonlinear systems affected by uncertainties. The nonlinear optimal control strategy is based on the control Lyapunov function (CLF). For ensuring robustness of the optimal controller in the presence of parametric uncertainty and external disturbances, a sliding mode control scheme is realized by combining an integral and a terminal sliding surface. The resulting second order sliding mode can effectively reduce chattering in the control input. Simulation results confirm the supremacy of the proposed optimal second order sliding mode control over some existing sliding mode controllers in controlling nonlinear systems affected by uncertainty.
Optimal linear estimation under unknown nonlinear transform
Yi, Xinyang; Wang, Zhaoran; Caramanis, Constantine; Liu, Han
2016-01-01
Linear regression studies the problem of estimating a model parameter β* ∈ℝp, from n observations {(yi,xi)}i=1n from linear model yi = 〈xi, β*〉 + εi. We consider a significant generalization in which the relationship between 〈xi, β*〉 and yi is noisy, quantized to a single bit, potentially nonlinear, noninvertible, as well as unknown. This model is known as the single-index model in statistics, and, among other things, it represents a significant generalization of one-bit compressed sensing. We propose a novel spectral-based estimation procedure and show that we can recover β* in settings (i.e., classes of link function f) where previous algorithms fail. In general, our algorithm requires only very mild restrictions on the (unknown) functional relationship between yi and 〈xi, β*〉. We also consider the high dimensional setting where β* is sparse, and introduce a two-stage nonconvex framework that addresses estimation challenges in high dimensional regimes where p ≫ n. For a broad class of link functions between 〈xi, β*〉 and yi, we establish minimax lower bounds that demonstrate the optimality of our estimators in both the classical and high dimensional regimes.
An Adaptive Multiscale Finite Element Method for Large Scale Simulations
2015-09-28
the method . Using the above definitions , the weak statement of the non-linear local problem at the kth 4 DISTRIBUTION A: Distribution approved for...AFRL-AFOSR-VA-TR-2015-0305 An Adaptive Multiscale Finite Element Method for Large Scale Simulations Carlos Duarte UNIVERSITY OF ILLINOIS CHAMPAIGN...14-07-2015 4. TITLE AND SUBTITLE An Adaptive Multiscale Generalized Finite Element Method for Large Scale Simulations 5a. CONTRACT NUMBER 5b
Economically viable large-scale hydrogen liquefaction
NASA Astrophysics Data System (ADS)
Cardella, U.; Decker, L.; Klein, H.
2017-02-01
The liquid hydrogen demand, particularly driven by clean energy applications, will rise in the near future. As industrial large scale liquefiers will play a major role within the hydrogen supply chain, production capacity will have to increase by a multiple of today’s typical sizes. The main goal is to reduce the total cost of ownership for these plants by increasing energy efficiency with innovative and simple process designs, optimized in capital expenditure. New concepts must ensure a manageable plant complexity and flexible operability. In the phase of process development and selection, a dimensioning of key equipment for large scale liquefiers, such as turbines and compressors as well as heat exchangers, must be performed iteratively to ensure technological feasibility and maturity. Further critical aspects related to hydrogen liquefaction, e.g. fluid properties, ortho-para hydrogen conversion, and coldbox configuration, must be analysed in detail. This paper provides an overview on the approach, challenges and preliminary results in the development of efficient as well as economically viable concepts for large-scale hydrogen liquefaction.
Guaranteed robustness properties of multivariable, nonlinear, stochastic optimal regulators
NASA Technical Reports Server (NTRS)
Tsitsiklis, J. N.; Athans, M.
1983-01-01
The robustness of optimal regulators for nonlinear, deterministic and stochastic, multi-input dynamical systems is studied under the assumption that all state variables can be measured. It is shown that, under mild assumptions, such nonlinear regulators have a guaranteed infinite gain margin; moreover, they have a guaranteed 50 percent gain reduction margin and a 60 degree phase margin, in each feedback channel, provided that the system is linear in the control and the penalty to the control is quadratic, thus extending the well-known properties of LQ regulators to nonlinear optimal designs. These results are also valid for infinite horizon, average cost, stochastic optimal control problems.
EINSTEIN'S SIGNATURE IN COSMOLOGICAL LARGE-SCALE STRUCTURE
Bruni, Marco; Hidalgo, Juan Carlos; Wands, David
2014-10-10
We show how the nonlinearity of general relativity generates a characteristic nonGaussian signal in cosmological large-scale structure that we calculate at all perturbative orders in a large-scale limit. Newtonian gravity and general relativity provide complementary theoretical frameworks for modeling large-scale structure in ΛCDM cosmology; a relativistic approach is essential to determine initial conditions, which can then be used in Newtonian simulations studying the nonlinear evolution of the matter density. Most inflationary models in the very early universe predict an almost Gaussian distribution for the primordial metric perturbation, ζ. However, we argue that it is the Ricci curvature of comoving-orthogonal spatial hypersurfaces, R, that drives structure formation at large scales. We show how the nonlinear relation between the spatial curvature, R, and the metric perturbation, ζ, translates into a specific nonGaussian contribution to the initial comoving matter density that we calculate for the simple case of an initially Gaussian ζ. Our analysis shows the nonlinear signature of Einstein's gravity in large-scale structure.
On a Highly Nonlinear Self-Obstacle Optimal Control Problem
Di Donato, Daniela; Mugnai, Dimitri
2015-10-15
We consider a non-quadratic optimal control problem associated to a nonlinear elliptic variational inequality, where the obstacle is the control itself. We show that, fixed a desired profile, there exists an optimal solution which is not far from it. Detailed characterizations of the optimal solution are given, also in terms of approximating problems.
Colloquium: Large scale simulations on GPU clusters
NASA Astrophysics Data System (ADS)
Bernaschi, Massimo; Bisson, Mauro; Fatica, Massimiliano
2015-06-01
Graphics processing units (GPU) are currently used as a cost-effective platform for computer simulations and big-data processing. Large scale applications require that multiple GPUs work together but the efficiency obtained with cluster of GPUs is, at times, sub-optimal because the GPU features are not exploited at their best. We describe how it is possible to achieve an excellent efficiency for applications in statistical mechanics, particle dynamics and networks analysis by using suitable memory access patterns and mechanisms like CUDA streams, profiling tools, etc. Similar concepts and techniques may be applied also to other problems like the solution of Partial Differential Equations.
Lyapunov optimal feedback control of a nonlinear inverted pendulum
NASA Technical Reports Server (NTRS)
Grantham, W. J.; Anderson, M. J.
1989-01-01
Liapunov optimal feedback control is applied to a nonlinear inverted pendulum in which the control torque was constrained to be less than the nonlinear gravity torque in the model. This necessitates a control algorithm which 'rocks' the pendulum out of its potential wells, in order to stabilize it at a unique vertical position. Simulation results indicate that a preliminary Liapunov feedback controller can successfully overcome the nonlinearity and bring almost all trajectories to the target.
Cycle 2 Nonlinear Design Optimization Analytical Cross Checks
NASA Technical Reports Server (NTRS)
Bencze, Dan
1999-01-01
The objectives of the Cycle 2 Nonlinear Design Optimization Anlaytical Cross Checks are to: 1) Understand the variability in the predicted performance levels of the nonlinear designs arising from the use of different inviscid (full potential/Euler) and viscous (Navier-Stokes) analysis methods; and 2) Provide the information required to allow the performance levels of all three designs to be validated using the data from the NCV (nonlinear Cruise Validation) model test.
Dynamic Factorization in Large-Scale Optimization
1989-06-01
1196;i primal partitioning method as examples of partitioning met hods. Perhaps the first example of what we consider factorization is the treatment ...structured manner in their treatment of general- ized upper bounds (GUB). In a problem with p GUB constraints and m structural constraints, their... treatment of the two which establishes the theoretical importance of the algorithm and justifies its use as the foundation of our specializa- tions. The
Cosmology with Large Scale Structure
NASA Astrophysics Data System (ADS)
Ho, Shirley; Cuesta, A.; Ross, A.; Seo, H.; DePutter, R.; Padmanabhan, N.; White, M.; Myers, A.; Bovy, J.; Blanton, M.; Hernandez, C.; Mena, O.; Percival, W.; Prada, F.; Ross, N. P.; Saito, S.; Schneider, D.; Skibba, R.; Smith, K.; Slosar, A.; Strauss, M.; Verde, L.; Weinberg, D.; Bachall, N.; Brinkmann, J.; da Costa, L. A.
2012-01-01
The Sloan Digital Sky Survey I-III surveyed 14,000 square degrees, and delivered over a trillion pixels of imaging data. I present cosmological results from this unprecedented data set which contains over a million galaxies distributed between redshift of 0.45 to 0.70. With such a large volume of data set, high precision cosmological constraints can be obtained given a careful control and understanding of observational systematics. I present a novel treatment of observational systematics and its application to the clustering signals from the data set. I will present cosmological constraints on dark components of the Universe and tightest constraints of the non-gaussianity of early Universe to date utilizing Large Scale Structure.
Large scale biomimetic membrane arrays.
Hansen, Jesper S; Perry, Mark; Vogel, Jörg; Groth, Jesper S; Vissing, Thomas; Larsen, Marianne S; Geschke, Oliver; Emneús, Jenny; Bohr, Henrik; Nielsen, Claus H
2009-10-01
To establish planar biomimetic membranes across large scale partition aperture arrays, we created a disposable single-use horizontal chamber design that supports combined optical-electrical measurements. Functional lipid bilayers could easily and efficiently be established across CO(2) laser micro-structured 8 x 8 aperture partition arrays with average aperture diameters of 301 +/- 5 microm. We addressed the electro-physical properties of the lipid bilayers established across the micro-structured scaffold arrays by controllable reconstitution of biotechnological and physiological relevant membrane peptides and proteins. Next, we tested the scalability of the biomimetic membrane design by establishing lipid bilayers in rectangular 24 x 24 and hexagonal 24 x 27 aperture arrays, respectively. The results presented show that the design is suitable for further developments of sensitive biosensor assays, and furthermore demonstrate that the design can conveniently be scaled up to support planar lipid bilayers in large square-centimeter partition arrays.
Challenges for Large Scale Simulations
NASA Astrophysics Data System (ADS)
Troyer, Matthias
2010-03-01
With computational approaches becoming ubiquitous the growing impact of large scale computing on research influences both theoretical and experimental work. I will review a few examples in condensed matter physics and quantum optics, including the impact of computer simulations in the search for supersolidity, thermometry in ultracold quantum gases, and the challenging search for novel phases in strongly correlated electron systems. While only a decade ago such simulations needed the fastest supercomputers, many simulations can now be performed on small workstation clusters or even a laptop: what was previously restricted to a few experts can now potentially be used by many. Only part of the gain in computational capabilities is due to Moore's law and improvement in hardware. Equally impressive is the performance gain due to new algorithms - as I will illustrate using some recently developed algorithms. At the same time modern peta-scale supercomputers offer unprecedented computational power and allow us to tackle new problems and address questions that were impossible to solve numerically only a few years ago. While there is a roadmap for future hardware developments to exascale and beyond, the main challenges are on the algorithmic and software infrastructure side. Among the problems that face the computational physicist are: the development of new algorithms that scale to thousands of cores and beyond, a software infrastructure that lifts code development to a higher level and speeds up the development of new simulation programs for large scale computing machines, tools to analyze the large volume of data obtained from such simulations, and as an emerging field provenance-aware software that aims for reproducibility of the complete computational workflow from model parameters to the final figures. Interdisciplinary collaborations and collective efforts will be required, in contrast to the cottage-industry culture currently present in many areas of computational
Nonlinear dynamic analysis and optimal trajectory planning of a high-speed macro-micro manipulator
NASA Astrophysics Data System (ADS)
Yang, Yi-ling; Wei, Yan-ding; Lou, Jun-qiang; Fu, Lei; Zhao, Xiao-wei
2017-09-01
This paper reports the nonlinear dynamic modeling and the optimal trajectory planning for a flexure-based macro-micro manipulator, which is dedicated to the large-scale and high-speed tasks. In particular, a macro- micro manipulator composed of a servo motor, a rigid arm and a compliant microgripper is focused. Moreover, both flexure hinges and flexible beams are considered. By combining the pseudorigid-body-model method, the assumed mode method and the Lagrange equation, the overall dynamic model is derived. Then, the rigid-flexible-coupling characteristics are analyzed by numerical simulations. After that, the microscopic scale vibration excited by the large-scale motion is reduced through the trajectory planning approach. Especially, a fitness function regards the comprehensive excitation torque of the compliant microgripper is proposed. The reference curve and the interpolation curve using the quintic polynomial trajectories are adopted. Afterwards, an improved genetic algorithm is used to identify the optimal trajectory by minimizing the fitness function. Finally, the numerical simulations and experiments validate the feasibility and the effectiveness of the established dynamic model and the trajectory planning approach. The amplitude of the residual vibration reduces approximately 54.9%, and the settling time decreases 57.1%. Therefore, the operation efficiency and manipulation stability are significantly improved.
Aircraft nonlinear optimal control using fuzzy gain scheduling
NASA Astrophysics Data System (ADS)
Nusyirwan, I. F.; Kung, Z. Y.
2016-10-01
Fuzzy gain scheduling is a common solution for nonlinear flight control. The highly nonlinear region of flight dynamics is determined throughout the examination of eigenvalues and the irregular pattern of root locus plots that show the nonlinear characteristic. By using the optimal control for command tracking, the pitch rate stability augmented system is constructed and the longitudinal flight control system is established. The outputs of optimal control for 21 linear systems are fed into the fuzzy gain scheduler. This research explores the capability in using both optimal control and fuzzy gain scheduling to improve the efficiency in finding the optimal control gains and to achieve Level 1 flying qualities. The numerical simulation work is carried out to determine the effectiveness and performance of the entire flight control system. The simulation results show that the fuzzy gain scheduling technique is able to perform in real time to find near optimal control law in various flying conditions.
Limitations and tradeoffs in synchronization of large-scale networks with uncertain links
Diwadkar, Amit; Vaidya, Umesh
2016-01-01
The synchronization of nonlinear systems connected over large-scale networks has gained popularity in a variety of applications, such as power grids, sensor networks, and biology. Stochastic uncertainty in the interconnections is a ubiquitous phenomenon observed in these physical and biological networks. We provide a size-independent network sufficient condition for the synchronization of scalar nonlinear systems with stochastic linear interactions over large-scale networks. This sufficient condition, expressed in terms of nonlinear dynamics, the Laplacian eigenvalues of the nominal interconnections, and the variance and location of the stochastic uncertainty, allows us to define a synchronization margin. We provide an analytical characterization of important trade-offs between the internal nonlinear dynamics, network topology, and uncertainty in synchronization. For nearest neighbour networks, the existence of an optimal number of neighbours with a maximum synchronization margin is demonstrated. An analytical formula for the optimal gain that produces the maximum synchronization margin allows us to compare the synchronization properties of various complex network topologies. PMID:27067994
Limitations and tradeoffs in synchronization of large-scale networks with uncertain links.
Diwadkar, Amit; Vaidya, Umesh
2016-04-12
The synchronization of nonlinear systems connected over large-scale networks has gained popularity in a variety of applications, such as power grids, sensor networks, and biology. Stochastic uncertainty in the interconnections is a ubiquitous phenomenon observed in these physical and biological networks. We provide a size-independent network sufficient condition for the synchronization of scalar nonlinear systems with stochastic linear interactions over large-scale networks. This sufficient condition, expressed in terms of nonlinear dynamics, the Laplacian eigenvalues of the nominal interconnections, and the variance and location of the stochastic uncertainty, allows us to define a synchronization margin. We provide an analytical characterization of important trade-offs between the internal nonlinear dynamics, network topology, and uncertainty in synchronization. For nearest neighbour networks, the existence of an optimal number of neighbours with a maximum synchronization margin is demonstrated. An analytical formula for the optimal gain that produces the maximum synchronization margin allows us to compare the synchronization properties of various complex network topologies.
Limitations and tradeoffs in synchronization of large-scale networks with uncertain links
NASA Astrophysics Data System (ADS)
Diwadkar, Amit; Vaidya, Umesh
2016-04-01
The synchronization of nonlinear systems connected over large-scale networks has gained popularity in a variety of applications, such as power grids, sensor networks, and biology. Stochastic uncertainty in the interconnections is a ubiquitous phenomenon observed in these physical and biological networks. We provide a size-independent network sufficient condition for the synchronization of scalar nonlinear systems with stochastic linear interactions over large-scale networks. This sufficient condition, expressed in terms of nonlinear dynamics, the Laplacian eigenvalues of the nominal interconnections, and the variance and location of the stochastic uncertainty, allows us to define a synchronization margin. We provide an analytical characterization of important trade-offs between the internal nonlinear dynamics, network topology, and uncertainty in synchronization. For nearest neighbour networks, the existence of an optimal number of neighbours with a maximum synchronization margin is demonstrated. An analytical formula for the optimal gain that produces the maximum synchronization margin allows us to compare the synchronization properties of various complex network topologies.
Grid sensitivity capability for large scale structures
NASA Technical Reports Server (NTRS)
Nagendra, Gopal K.; Wallerstein, David V.
1989-01-01
The considerations and the resultant approach used to implement design sensitivity capability for grids into a large scale, general purpose finite element system (MSC/NASTRAN) are presented. The design variables are grid perturbations with a rather general linking capability. Moreover, shape and sizing variables may be linked together. The design is general enough to facilitate geometric modeling techniques for generating design variable linking schemes in an easy and straightforward manner. Test cases have been run and validated by comparison with the overall finite difference method. The linking of a design sensitivity capability for shape variables in MSC/NASTRAN with an optimizer would give designers a powerful, automated tool to carry out practical optimization design of real life, complicated structures.
Large-scale PACS implementation.
Carrino, J A; Unkel, P J; Miller, I D; Bowser, C L; Freckleton, M W; Johnson, T G
1998-08-01
The transition to filmless radiology is a much more formidable task than making the request for proposal to purchase a (Picture Archiving and Communications System) PACS. The Department of Defense and the Veterans Administration have been pioneers in the transformation of medical diagnostic imaging to the electronic environment. Many civilian sites are expected to implement large-scale PACS in the next five to ten years. This presentation will related the empirical insights gleaned at our institution from a large-scale PACS implementation. Our PACS integration was introduced into a fully operational department (not a new hospital) in which work flow had to continue with minimal impact. Impediments to user acceptance will be addressed. The critical components of this enormous task will be discussed. The topics covered during this session will include issues such as phased implementation, DICOM (digital imaging and communications in medicine) standard-based interaction of devices, hospital information system (HIS)/radiology information system (RIS) interface, user approval, networking, workstation deployment and backup procedures. The presentation will make specific suggestions regarding the implementation team, operating instructions, quality control (QC), training and education. The concept of identifying key functional areas is relevant to transitioning the facility to be entirely on line. Special attention must be paid to specific functional areas such as the operating rooms and trauma rooms where the clinical requirements may not match the PACS capabilities. The printing of films may be necessary for certain circumstances. The integration of teleradiology and remote clinics into a PACS is a salient topic with respect to the overall role of the radiologists providing rapid consultation. A Web-based server allows a clinician to review images and reports on a desk-top (personal) computer and thus reduce the number of dedicated PACS review workstations. This session
Large scale cluster computing workshop
Dane Skow; Alan Silverman
2002-12-23
Recent revolutions in computer hardware and software technologies have paved the way for the large-scale deployment of clusters of commodity computers to address problems heretofore the domain of tightly coupled SMP processors. Near term projects within High Energy Physics and other computing communities will deploy clusters of scale 1000s of processors and be used by 100s to 1000s of independent users. This will expand the reach in both dimensions by an order of magnitude from the current successful production facilities. The goals of this workshop were: (1) to determine what tools exist which can scale up to the cluster sizes foreseen for the next generation of HENP experiments (several thousand nodes) and by implication to identify areas where some investment of money or effort is likely to be needed. (2) To compare and record experimences gained with such tools. (3) To produce a practical guide to all stages of planning, installing, building and operating a large computing cluster in HENP. (4) To identify and connect groups with similar interest within HENP and the larger clustering community.
Large-Scale Sequence Comparison.
Lal, Devi; Verma, Mansi
2017-01-01
There are millions of sequences deposited in genomic databases, and it is an important task to categorize them according to their structural and functional roles. Sequence comparison is a prerequisite for proper categorization of both DNA and protein sequences, and helps in assigning a putative or hypothetical structure and function to a given sequence. There are various methods available for comparing sequences, alignment being first and foremost for sequences with a small number of base pairs as well as for large-scale genome comparison. Various tools are available for performing pairwise large sequence comparison. The best known tools either perform global alignment or generate local alignments between the two sequences. In this chapter we first provide basic information regarding sequence comparison. This is followed by the description of the PAM and BLOSUM matrices that form the basis of sequence comparison. We also give a practical overview of currently available methods such as BLAST and FASTA, followed by a description and overview of tools available for genome comparison including LAGAN, MumMER, BLASTZ, and AVID.
Large Scale Homing in Honeybees
Pahl, Mario; Zhu, Hong; Tautz, Jürgen; Zhang, Shaowu
2011-01-01
Honeybee foragers frequently fly several kilometres to and from vital resources, and communicate those locations to their nest mates by a symbolic dance language. Research has shown that they achieve this feat by memorizing landmarks and the skyline panorama, using the sun and polarized skylight as compasses and by integrating their outbound flight paths. In order to investigate the capacity of the honeybees' homing abilities, we artificially displaced foragers to novel release spots at various distances up to 13 km in the four cardinal directions. Returning bees were individually registered by a radio frequency identification (RFID) system at the hive entrance. We found that homing rate, homing speed and the maximum homing distance depend on the release direction. Bees released in the east were more likely to find their way back home, and returned faster than bees released in any other direction, due to the familiarity of global landmarks seen from the hive. Our findings suggest that such large scale homing is facilitated by global landmarks acting as beacons, and possibly the entire skyline panorama. PMID:21602920
Large Scale Magnetostrictive Valve Actuator
NASA Technical Reports Server (NTRS)
Richard, James A.; Holleman, Elizabeth; Eddleman, David
2008-01-01
Marshall Space Flight Center's Valves, Actuators and Ducts Design and Development Branch developed a large scale magnetostrictive valve actuator. The potential advantages of this technology are faster, more efficient valve actuators that consume less power and provide precise position control and deliver higher flow rates than conventional solenoid valves. Magnetostrictive materials change dimensions when a magnetic field is applied; this property is referred to as magnetostriction. Magnetostriction is caused by the alignment of the magnetic domains in the material s crystalline structure and the applied magnetic field lines. Typically, the material changes shape by elongating in the axial direction and constricting in the radial direction, resulting in no net change in volume. All hardware and testing is complete. This paper will discuss: the potential applications of the technology; overview of the as built actuator design; discuss problems that were uncovered during the development testing; review test data and evaluate weaknesses of the design; and discuss areas for improvement for future work. This actuator holds promises of a low power, high load, proportionally controlled actuator for valves requiring 440 to 1500 newtons load.
Algorithm studies on how to obtain a conditional nonlinear optimal perturbation (CNOP)
NASA Astrophysics Data System (ADS)
Sun, Guodong; Mu, Mu; Zhang, Yale
2010-11-01
The conditional nonlinear optimal perturbation (CNOP), which is a nonlinear generalization of the linear singular vector (LSV), is applied in important problems of atmospheric and oceanic sciences, including ENSO predictability, targeted observations, and ensemble forecast. In this study, we investigate the computational cost of obtaining the CNOP by several methods. Differences and similarities, in terms of the computational error and cost in obtaining the CNOP, are compared among the sequential quadratic programming (SQP) algorithm, the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, and the spectral projected gradients (SPG2) algorithm. A theoretical grassland ecosystem model and the classical Lorenz model are used as examples. Numerical results demonstrate that the computational error is acceptable with all three algorithms. The computational cost to obtain the CNOP is reduced by using the SQP algorithm. The experimental results also reveal that the L-BFGS algorithm is the most effective algorithm among the three optimization algorithms for obtaining the CNOP. The numerical results suggest a new approach and algorithm for obtaining the CNOP for a large-scale optimization problem.
Large-scale Intelligent Transporation Systems simulation
Ewing, T.; Canfield, T.; Hannebutte, U.; Levine, D.; Tentner, A.
1995-06-01
A prototype computer system has been developed which defines a high-level architecture for a large-scale, comprehensive, scalable simulation of an Intelligent Transportation System (ITS) capable of running on massively parallel computers and distributed (networked) computer systems. The prototype includes the modelling of instrumented ``smart`` vehicles with in-vehicle navigation units capable of optimal route planning and Traffic Management Centers (TMC). The TMC has probe vehicle tracking capabilities (display position and attributes of instrumented vehicles), and can provide 2-way interaction with traffic to provide advisories and link times. Both the in-vehicle navigation module and the TMC feature detailed graphical user interfaces to support human-factors studies. The prototype has been developed on a distributed system of networked UNIX computers but is designed to run on ANL`s IBM SP-X parallel computer system for large scale problems. A novel feature of our design is that vehicles will be represented by autonomus computer processes, each with a behavior model which performs independent route selection and reacts to external traffic events much like real vehicles. With this approach, one will be able to take advantage of emerging massively parallel processor (MPP) systems.
Methane emissions on large scales
NASA Astrophysics Data System (ADS)
Beswick, K. M.; Simpson, T. W.; Fowler, D.; Choularton, T. W.; Gallagher, M. W.; Hargreaves, K. J.; Sutton, M. A.; Kaye, A.
with previous results from the area, indicating that this method of data analysis provided good estimates of large scale methane emissions.
Large Scale Nanolaminate Deformable Mirror
Papavasiliou, A; Olivier, S; Barbee, T; Miles, R; Chang, K
2005-11-30
This work concerns the development of a technology that uses Nanolaminate foils to form light-weight, deformable mirrors that are scalable over a wide range of mirror sizes. While MEMS-based deformable mirrors and spatial light modulators have considerably reduced the cost and increased the capabilities of adaptive optic systems, there has not been a way to utilize the advantages of lithography and batch-fabrication to produce large-scale deformable mirrors. This technology is made scalable by using fabrication techniques and lithography that are not limited to the sizes of conventional MEMS devices. Like many MEMS devices, these mirrors use parallel plate electrostatic actuators. This technology replicates that functionality by suspending a horizontal piece of nanolaminate foil over an electrode by electroplated nickel posts. This actuator is attached, with another post, to another nanolaminate foil that acts as the mirror surface. Most MEMS devices are produced with integrated circuit lithography techniques that are capable of very small line widths, but are not scalable to large sizes. This technology is very tolerant of lithography errors and can use coarser, printed circuit board lithography techniques that can be scaled to very large sizes. These mirrors use small, lithographically defined actuators and thin nanolaminate foils allowing them to produce deformations over a large area while minimizing weight. This paper will describe a staged program to develop this technology. First-principles models were developed to determine design parameters. Three stages of fabrication will be described starting with a 3 x 3 device using conventional metal foils and epoxy to a 10-across all-metal device with nanolaminate mirror surfaces.
Large-Scale Information Systems
D. M. Nicol; H. R. Ammerlahn; M. E. Goldsby; M. M. Johnson; D. E. Rhodes; A. S. Yoshimura
2000-12-01
Large enterprises are ever more dependent on their Large-Scale Information Systems (LSLS), computer systems that are distinguished architecturally by distributed components--data sources, networks, computing engines, simulations, human-in-the-loop control and remote access stations. These systems provide such capabilities as workflow, data fusion and distributed database access. The Nuclear Weapons Complex (NWC) contains many examples of LSIS components, a fact that motivates this research. However, most LSIS in use grew up from collections of separate subsystems that were not designed to be components of an integrated system. For this reason, they are often difficult to analyze and control. The problem is made more difficult by the size of a typical system, its diversity of information sources, and the institutional complexities associated with its geographic distribution across the enterprise. Moreover, there is no integrated approach for analyzing or managing such systems. Indeed, integrated development of LSIS is an active area of academic research. This work developed such an approach by simulating the various components of the LSIS and allowing the simulated components to interact with real LSIS subsystems. This research demonstrated two benefits. First, applying it to a particular LSIS provided a thorough understanding of the interfaces between the system's components. Second, it demonstrated how more rapid and detailed answers could be obtained to questions significant to the enterprise by interacting with the relevant LSIS subsystems through simulated components designed with those questions in mind. In a final, added phase of the project, investigations were made on extending this research to wireless communication networks in support of telemetry applications.
NASA Astrophysics Data System (ADS)
Zou, Rui; Riverson, John; Liu, Yong; Murphy, Ryan; Sim, Youn
2015-03-01
Integrated continuous simulation-optimization models can be effective predictors of a process-based responses for cost-benefit optimization of best management practices (BMPs) selection and placement. However, practical application of simulation-optimization model is computationally prohibitive for large-scale systems. This study proposes an enhanced Nonlinearity Interval Mapping Scheme (NIMS) to solve large-scale watershed simulation-optimization problems several orders of magnitude faster than other commonly used algorithms. An efficient interval response coefficient (IRC) derivation method was incorporated into the NIMS framework to overcome a computational bottleneck. The proposed algorithm was evaluated using a case study watershed in the Los Angeles County Flood Control District. Using a continuous simulation watershed/stream-transport model, Loading Simulation Program in C++ (LSPC), three nested in-stream compliance points (CP)—each with multiple Total Maximum Daily Loads (TMDL) targets—were selected to derive optimal treatment levels for each of the 28 subwatersheds, so that the TMDL targets at all the CP were met with the lowest possible BMP implementation cost. Genetic Algorithm (GA) and NIMS were both applied and compared. The results showed that the NIMS took 11 iterations (about 11 min) to complete with the resulting optimal solution having a total cost of 67.2 million, while each of the multiple GA executions took 21-38 days to reach near optimal solutions. The best solution obtained among all the GA executions compared had a minimized cost of 67.7 million—marginally higher, but approximately equal to that of the NIMS solution. The results highlight the utility for decision making in large-scale watershed simulation-optimization formulations.
Nonlinear model predictive control based on collective neurodynamic optimization.
Yan, Zheng; Wang, Jun
2015-04-01
In general, nonlinear model predictive control (NMPC) entails solving a sequential global optimization problem with a nonconvex cost function or constraints. This paper presents a novel collective neurodynamic optimization approach to NMPC without linearization. Utilizing a group of recurrent neural networks (RNNs), the proposed collective neurodynamic optimization approach searches for optimal solutions to global optimization problems by emulating brainstorming. Each RNN is guaranteed to converge to a candidate solution by performing constrained local search. By exchanging information and iteratively improving the starting and restarting points of each RNN using the information of local and global best known solutions in a framework of particle swarm optimization, the group of RNNs is able to reach global optimal solutions to global optimization problems. The essence of the proposed collective neurodynamic optimization approach lies in the integration of capabilities of global search and precise local search. The simulation results of many cases are discussed to substantiate the effectiveness and the characteristics of the proposed approach.
Optimization under uncertainty of parallel nonlinear energy sinks
NASA Astrophysics Data System (ADS)
Boroson, Ethan; Missoum, Samy; Mattei, Pierre-Olivier; Vergez, Christophe
2017-04-01
Nonlinear Energy Sinks (NESs) are a promising technique for passively reducing the amplitude of vibrations. Through nonlinear stiffness properties, a NES is able to passively and irreversibly absorb energy. Unlike the traditional Tuned Mass Damper (TMD), NESs do not require a specific tuning and absorb energy over a wider range of frequencies. Nevertheless, they are still only efficient over a limited range of excitations. In order to mitigate this limitation and maximize the efficiency range, this work investigates the optimization of multiple NESs configured in parallel. It is well known that the efficiency of a NES is extremely sensitive to small perturbations in loading conditions or design parameters. In fact, the efficiency of a NES has been shown to be nearly discontinuous in the neighborhood of its activation threshold. For this reason, uncertainties must be taken into account in the design optimization of NESs. In addition, the discontinuities require a specific treatment during the optimization process. In this work, the objective of the optimization is to maximize the expected value of the efficiency of NESs in parallel. The optimization algorithm is able to tackle design variables with uncertainty (e.g., nonlinear stiffness coefficients) as well as aleatory variables such as the initial velocity of the main system. The optimal design of several parallel NES configurations for maximum mean efficiency is investigated. Specifically, NES nonlinear stiffness properties, considered random design variables, are optimized for cases with 1, 2, 3, 4, 5, and 10 NESs in parallel. The distributions of efficiency for the optimal parallel configurations are compared to distributions of efficiencies of non-optimized NESs. It is observed that the optimization enables a sharp increase in the mean value of efficiency while reducing the corresponding variance, thus leading to more robust NES designs.
Statistical Measures of Large-Scale Structure
NASA Astrophysics Data System (ADS)
Vogeley, Michael; Geller, Margaret; Huchra, John; Park, Changbom; Gott, J. Richard
1993-12-01
\\inv Mpc} To quantify clustering in the large-scale distribution of galaxies and to test theories for the formation of structure in the universe, we apply statistical measures to the CfA Redshift Survey. This survey is complete to m_{B(0)}=15.5 over two contiguous regions which cover one-quarter of the sky and include ~ 11,000 galaxies. The salient features of these data are voids with diameter 30-50\\hmpc and coherent dense structures with a scale ~ 100\\hmpc. Comparison with N-body simulations rules out the ``standard" CDM model (Omega =1, b=1.5, sigma_8 =1) at the 99% confidence level because this model has insufficient power on scales lambda >30\\hmpc. An unbiased open universe CDM model (Omega h =0.2) and a biased CDM model with non-zero cosmological constant (Omega h =0.24, lambda_0 =0.6) match the observed power spectrum. The amplitude of the power spectrum depends on the luminosity of galaxies in the sample; bright (L>L(*) ) galaxies are more strongly clustered than faint galaxies. The paucity of bright galaxies in low-density regions may explain this dependence. To measure the topology of large-scale structure, we compute the genus of isodensity surfaces of the smoothed density field. On scales in the ``non-linear" regime, <= 10\\hmpc, the high- and low-density regions are multiply-connected over a broad range of density threshold, as in a filamentary net. On smoothing scales >10\\hmpc, the topology is consistent with statistics of a Gaussian random field. Simulations of CDM models fail to produce the observed coherence of structure on non-linear scales (>95% confidence level). The underdensity probability (the frequency of regions with density contrast delta rho //lineρ=-0.8) depends strongly on the luminosity of galaxies; underdense regions are significantly more common (>2sigma ) in bright (L>L(*) ) galaxy samples than in samples which include fainter galaxies.
Online optimization of storage ring nonlinear beam dynamics
Huang, Xiaobiao; Safranek, James
2015-08-01
We propose to optimize the nonlinear beam dynamics of existing and future storage rings with direct online optimization techniques. This approach may have crucial importance for the implementation of diffraction limited storage rings. In this paper considerations and algorithms for the online optimization approach are discussed. We have applied this approach to experimentally improve the dynamic aperture of the SPEAR3 storage ring with the robust conjugate direction search method and the particle swarm optimization method. The dynamic aperture was improved by more than 5 mm within a short period of time. Experimental setup and results are presented.
Asynchronous parallel pattern search for nonlinear optimization
P. D. Hough; T. G. Kolda; V. J. Torczon
2000-01-01
Parallel pattern search (PPS) can be quite useful for engineering optimization problems characterized by a small number of variables (say 10--50) and by expensive objective function evaluations such as complex simulations that take from minutes to hours to run. However, PPS, which was originally designed for execution on homogeneous and tightly-coupled parallel machine, is not well suited to the more heterogeneous, loosely-coupled, and even fault-prone parallel systems available today. Specifically, PPS is hindered by synchronization penalties and cannot recover in the event of a failure. The authors introduce a new asynchronous and fault tolerant parallel pattern search (AAPS) method and demonstrate its effectiveness on both simple test problems as well as some engineering optimization problems
Optimal ignition placement using nonlinear adjoint looping
NASA Astrophysics Data System (ADS)
Qadri, Ubaid; Schmid, Peter; Magri, Luca; Ihme, Matthias
2016-11-01
Spark ignition of a turbulent mixture of fuel and oxidizer is a highly sensitive process. Traditionally, a large number of parametric studies are used to determine the effects of different factors on ignition and this can be quite tedious. In contrast, we treat ignition as an initial value problem and seek to find the initial condition that maximizes a given cost function. We use direct numerical simulation of the low Mach number equations with finite rate one-step chemistry, and of the corresponding adjoint equations, to study an axisymmetric jet diffusion flame. We find the L - 2 norm of the temperature field integrated over a short time to be a suitable cost function. We find that the adjoint fields localize around the flame front, identifying the most sensitive region of the flow. The adjoint fields provide gradient information that we use as part of an optimization loop to converge to a local optimal ignition location. We find that the optimal locations correspond with the stoichiometric surface downstream of the jet inlet plane. The methods and results of this study can be easily applied to more complex flow geometries.
Large-scale tides in general relativity
NASA Astrophysics Data System (ADS)
Ip, Hiu Yan; Schmidt, Fabian
2017-02-01
Density perturbations in cosmology, i.e. spherically symmetric adiabatic perturbations of a Friedmann-Lemaȋtre-Robertson-Walker (FLRW) spacetime, are locally exactly equivalent to a different FLRW solution, as long as their wavelength is much larger than the sound horizon of all fluid components. This fact is known as the "separate universe" paradigm. However, no such relation is known for anisotropic adiabatic perturbations, which correspond to an FLRW spacetime with large-scale tidal fields. Here, we provide a closed, fully relativistic set of evolutionary equations for the nonlinear evolution of such modes, based on the conformal Fermi (CFC) frame. We show explicitly that the tidal effects are encoded by the Weyl tensor, and are hence entirely different from an anisotropic Bianchi I spacetime, where the anisotropy is sourced by the Ricci tensor. In order to close the system, certain higher derivative terms have to be dropped. We show that this approximation is equivalent to the local tidal approximation of Hui and Bertschinger [1]. We also show that this very simple set of equations matches the exact evolution of the density field at second order, but fails at third and higher order. This provides a useful, easy-to-use framework for computing the fully relativistic growth of structure at second order.
A relativistic signature in large-scale structure
NASA Astrophysics Data System (ADS)
Bartolo, Nicola; Bertacca, Daniele; Bruni, Marco; Koyama, Kazuya; Maartens, Roy; Matarrese, Sabino; Sasaki, Misao; Verde, Licia; Wands, David
2016-09-01
In General Relativity, the constraint equation relating metric and density perturbations is inherently nonlinear, leading to an effective non-Gaussianity in the dark matter density field on large scales-even if the primordial metric perturbation is Gaussian. Intrinsic non-Gaussianity in the large-scale dark matter overdensity in GR is real and physical. However, the variance smoothed on a local physical scale is not correlated with the large-scale curvature perturbation, so that there is no relativistic signature in the galaxy bias when using the simplest model of bias. It is an open question whether the observable mass proxies such as luminosity or weak lensing correspond directly to the physical mass in the simple halo bias model. If not, there may be observables that encode this relativistic signature.
Optimal state discrimination and unstructured search in nonlinear quantum mechanics
NASA Astrophysics Data System (ADS)
Childs, Andrew M.; Young, Joshua
2016-02-01
Nonlinear variants of quantum mechanics can solve tasks that are impossible in standard quantum theory, such as perfectly distinguishing nonorthogonal states. Here we derive the optimal protocol for distinguishing two states of a qubit using the Gross-Pitaevskii equation, a model of nonlinear quantum mechanics that arises as an effective description of Bose-Einstein condensates. Using this protocol, we present an algorithm for unstructured search in the Gross-Pitaevskii model, obtaining an exponential improvement over a previous algorithm of Meyer and Wong. This result establishes a limitation on the effectiveness of the Gross-Pitaevskii approximation. More generally, we demonstrate similar behavior under a family of related nonlinearities, giving evidence that the ability to quickly discriminate nonorthogonal states and thereby solve unstructured search is a generic feature of nonlinear quantum mechanics.
Supporting large-scale computational science
Musick, R., LLNL
1998-02-19
Business needs have driven the development of commercial database systems since their inception. As a result, there has been a strong focus on supporting many users, minimizing the potential corruption or loss of data, and maximizing performance metrics like transactions per second, or TPC-C and TPC-D results. It turns out that these optimizations have little to do with the needs of the scientific community, and in particular have little impact on improving the management and use of large-scale high-dimensional data. At the same time, there is an unanswered need in the scientific community for many of the benefits offered by a robust DBMS. For example, tying an ad-hoc query language such as SQL together with a visualization toolkit would be a powerful enhancement to current capabilities. Unfortunately, there has been little emphasis or discussion in the VLDB community on this mismatch over the last decade. The goal of the paper is to identify the specific issues that need to be resolved before large-scale scientific applications can make use of DBMS products. This topic is addressed in the context of an evaluation of commercial DBMS technology applied to the exploration of data generated by the Department of Energy`s Accelerated Strategic Computing Initiative (ASCI). The paper describes the data being generated for ASCI as well as current capabilities for interacting with and exploring this data. The attraction of applying standard DBMS technology to this domain is discussed, as well as the technical and business issues that currently make this an infeasible solution.
Optimal Variational Asymptotic Method for Nonlinear Fractional Partial Differential Equations.
Baranwal, Vipul K; Pandey, Ram K; Singh, Om P
2014-01-01
We propose optimal variational asymptotic method to solve time fractional nonlinear partial differential equations. In the proposed method, an arbitrary number of auxiliary parameters γ 0, γ 1, γ 2,… and auxiliary functions H 0(x), H 1(x), H 2(x),… are introduced in the correction functional of the standard variational iteration method. The optimal values of these parameters are obtained by minimizing the square residual error. To test the method, we apply it to solve two important classes of nonlinear partial differential equations: (1) the fractional advection-diffusion equation with nonlinear source term and (2) the fractional Swift-Hohenberg equation. Only few iterations are required to achieve fairly accurate solutions of both the first and second problems.
Nonlinear optimization with linear constraints using a projection method
NASA Technical Reports Server (NTRS)
Fox, T.
1982-01-01
Nonlinear optimization problems that are encountered in science and industry are examined. A method of projecting the gradient vector onto a set of linear contraints is developed, and a program that uses this method is presented. The algorithm that generates this projection matrix is based on the Gram-Schmidt method and overcomes some of the objections to the Rosen projection method.
Special section on analysis, design and optimization of nonlinear circuits
NASA Astrophysics Data System (ADS)
Okumura, Kohshi
Nonlinear theory plays an indispensable role in analysis, design and optimization of electric/electronic circuits because almost all circuits in the real world are modeled by nonlinear systems. Also, as the scale and complexity of circuits increase, more effective and systematic methods for the analysis, design and optimization are desired. The goal of this special section is to bring together research results from a variety of perspectives and academic disciplines related to nonlinear electric/electronic circuits.This special section includes three invited papers and six regular papers. The first invited paper by Kennedy entitled “Recent advances in the analysis, design and optimization of digital delta-sigma modulators” gives an overview of digital delta-sigma modulators and some techniques for improving their efficiency. The second invited paper by Trajkovic entitled “DC operating points of transistor circuits” surveys main theoretical results on the analysis of DC operating points of transistor circuits and discusses numerical methods for calculating them. The third invited paper by Nishi et al. entitled “Some properties of solution curves of a class of nonlinear equations and the number of solutions” gives several new theorems concerning solution curves of a class of nonlinear equations which is closely related to DC operating point analysis of nonlinear circuits. The six regular papers cover a wide range of areas such as memristors, chaos circuits, filters, sigma-delta modulators, energy harvesting systems and analog circuits for solving optimization problems.The guest editor would like to express his sincere thanks to the authors who submitted their papers to this special section. He also thanks the reviewers and the editorial committee members of this special section for their support during the review process. Last, but not least, he would also like to acknowledge the editorial staff of the NOLTA journal for their continuous support of this
Utility of coupling nonlinear optimization methods with numerical modeling software
Murphy, M.J.
1996-08-05
Results of using GLO (Global Local Optimizer), a general purpose nonlinear optimization software package for investigating multi-parameter problems in science and engineering is discussed. The package consists of the modular optimization control system (GLO), a graphical user interface (GLO-GUI), a pre-processor (GLO-PUT), a post-processor (GLO-GET), and nonlinear optimization software modules, GLOBAL & LOCAL. GLO is designed for controlling and easy coupling to any scientific software application. GLO runs the optimization module and scientific software application in an iterative loop. At each iteration, the optimization module defines new values for the set of parameters being optimized. GLO-PUT inserts the new parameter values into the input file of the scientific application. GLO runs the application with the new parameter values. GLO-GET determines the value of the objective function by extracting the results of the analysis and comparing to the desired result. GLO continues to run the scientific application over and over until it finds the ``best`` set of parameters by minimizing (or maximizing) the objective function. An example problem showing the optimization of material model is presented (Taylor cylinder impact test).
Fully localised nonlinear energy growth optimals in pipe flow
Pringle, Chris C. T.; Willis, Ashley P.; Kerswell, Rich R.
2015-06-15
A new, fully localised, energy growth optimal is found over large times and in long pipe domains at a given mass flow rate. This optimal emerges at a threshold disturbance energy below which a nonlinear version of the known (streamwise-independent) linear optimal [P. J. Schmid and D. S. Henningson, “Optimal energy density growth in Hagen-Poiseuille flow,” J. Fluid Mech. 277, 192–225 (1994)] is selected and appears to remain the optimal up until the critical energy at which transition is triggered. The form of this optimal is similar to that found in short pipes [Pringle et al., “Minimal seeds for shear flow turbulence: Using nonlinear transient growth to touch the edge of chaos,” J. Fluid Mech. 702, 415–443 (2012)], but now with full localisation in the streamwise direction. This fully localised optimal perturbation represents the best approximation yet of the minimal seed (the smallest perturbation which is arbitrarily close to states capable of triggering a turbulent episode) for “real” (laboratory) pipe flows. Dependence of the optimal with respect to several parameters has been computed and establishes that the structure is robust.
Route Monopolie and Optimal Nonlinear Pricing
NASA Technical Reports Server (NTRS)
Tournut, Jacques
2003-01-01
To cope with air traffic growth and congested airports, two solutions are apparent on the supply side: 1) use larger aircraft in the hub and spoke system; or 2) develop new routes through secondary airports. An enlarged route system through secondary airports may increase the proportion of route monopolies in the air transport market.The monopoly optimal non linear pricing policy is well known in the case of one dimension (one instrument, one characteristic) but not in the case of several dimensions. This paper explores the robustness of the one dimensional screening model with respect to increasing the number of instruments and the number of characteristics. The objective of this paper is then to link and fill the gap in both literatures. One of the merits of the screening model has been to show that a great varieD" of economic questions (non linear pricing, product line choice, auction design, income taxation, regulation...) could be handled within the same framework.VCe study a case of non linear pricing (2 instruments (2 routes on which the airline pro_ddes customers with services), 2 characteristics (demand of services on these routes) and two values per characteristic (low and high demand of services on these routes)) and we show that none of the conclusions of the one dimensional analysis remain valid. In particular, upward incentive compatibility constraint may be binding at the optimum. As a consequence, they may be distortion at the top of the distribution. In addition to this, we show that the optimal solution often requires a kind of form of bundling, we explain explicitly distortions and show that it is sometimes optimal for the monopolist to only produce one good (instead of two) or to exclude some buyers from the market. Actually, this means that the monopolist cannot fully apply his monopoly power and is better off selling both goods independently.We then define all the possible solutions in the case of a quadratic cost function for a uniform
Optimization of optical nonlinearities in quantum cascade lasers
NASA Astrophysics Data System (ADS)
Bai, Jing
Nonlinearities in quantum cascade lasers (QCL's) have wide applications in wavelength tunability and ultra-short pulse generation. In this thesis, optical nonlinearities in InGaAs/AlInAs-based mid-infrared (MIR) QCL's with quadruple resonant levels are investigated. Design optimization for the second-harmonic generation (SHG) of the device is presented. Performance characteristics associated with the third-order nonlinearities are also analyzed. The design optimization for SHG efficiency is obtained utilizing techniques from supersymmetric quantum mechanics (SUSYQM) with both material-dependent effective mass and band nonparabolicity. Current flow and power output of the structure are analyzed by self-consistently solving rate equations for the carriers and photons. Nonunity pumping efficiency from one period of the QCL to the next is taken into account by including all relevant electron-electron (e-e) and longitudinal (LO) phonon scattering mechanisms between the injector/collector and active regions. Two-photon absorption processes are analyzed for the resonant cascading triple levels designed for enhancing SHG. Both sequential and simultaneous two-photon absorption processes are included in the rate-equation model. The current output characteristics for both the original and optimized structures are analyzed and compared. Stronger resonant tunneling in the optimized structure is manifested by enhanced negative differential resistance. Current-dependent linear optical output power is derived based on the steady-state photon populations in the active region. The second-harmonic (SH) power is derived from the Maxwell equations with the phase mismatch included. Due to stronger coupling between lasing levels, the optimized structure has both higher linear and nonlinear output powers. Phase mismatch effects are significant for both structures leading to a substantial reduction of the linear-to-nonlinear conversion efficiency. The optimized structure can be fabricated
Wu, Zong-Sheng; Fu, Wei-Ping; Xue, Ru
2015-01-01
Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well. PMID:26421005
Wu, Zong-Sheng; Fu, Wei-Ping; Xue, Ru
2015-01-01
Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.
Generalization of norm optimal ILC for nonlinear systems with constraints
NASA Astrophysics Data System (ADS)
Volckaert, Marnix; Diehl, Moritz; Swevers, Jan
2013-08-01
This paper discusses a generalization of norm optimal iterative learning control (ILC) for nonlinear systems with constraints. The conventional norm optimal ILC for linear time invariant systems formulates an update equation as a closed form solution of the minimization of a quadratic cost function. In this cost function the next trial's tracking error is approximated by implicitly adding a correction to the model. The proposed approach makes two adaptations to the conventional approach: the model correction is explicitly estimated, and the cost function is minimized using a direct optimal control approach resulting in nonlinear programming problems. An efficient solution strategy for such problems is developed, using a sparse implementation of an interior point method, such that long data records can be efficiently processed. The proposed approach is validated experimentally.
NASA Technical Reports Server (NTRS)
Nguyen, Duc T.
1990-01-01
Practical engineering application can often be formulated in the form of a constrained optimization problem. There are several solution algorithms for solving a constrained optimization problem. One approach is to convert a constrained problem into a series of unconstrained problems. Furthermore, unconstrained solution algorithms can be used as part of the constrained solution algorithms. Structural optimization is an iterative process where one starts with an initial design, a finite element structure analysis is then performed to calculate the response of the system (such as displacements, stresses, eigenvalues, etc.). Based upon the sensitivity information on the objective and constraint functions, an optimizer such as ADS or IDESIGN, can be used to find the new, improved design. For the structural analysis phase, the equation solver for the system of simultaneous, linear equations plays a key role since it is needed for either static, or eigenvalue, or dynamic analysis. For practical, large-scale structural analysis-synthesis applications, computational time can be excessively large. Thus, it is necessary to have a new structural analysis-synthesis code which employs new solution algorithms to exploit both parallel and vector capabilities offered by modern, high performance computers such as the Convex, Cray-2 and Cray-YMP computers. The objective of this research project is, therefore, to incorporate the latest development in the parallel-vector equation solver, PVSOLVE into the widely popular finite-element production code, such as the SAP-4. Furthermore, several nonlinear unconstrained optimization subroutines have also been developed and tested under a parallel computer environment. The unconstrained optimization subroutines are not only useful in their own right, but they can also be incorporated into a more popular constrained optimization code, such as ADS.
Optimal bipedal interactions with dynamic terrain: synthesis and analysis via nonlinear programming
NASA Astrophysics Data System (ADS)
Hubicki, Christian; Goldman, Daniel; Ames, Aaron
In terrestrial locomotion, gait dynamics and motor control behaviors are tuned to interact efficiently and stably with the dynamics of the terrain (i.e. terradynamics). This controlled interaction must be particularly thoughtful in bipeds, as their reduced contact points render them highly susceptible to falls. While bipedalism under rigid terrain assumptions is well-studied, insights for two-legged locomotion on soft terrain, such as sand and dirt, are comparatively sparse. We seek an understanding of how biological bipeds stably and economically negotiate granular media, with an eye toward imbuing those abilities in bipedal robots. We present a trajectory optimization method for controlled systems subject to granular intrusion. By formulating a large-scale nonlinear program (NLP) with reduced-order resistive force theory (RFT) models and jamming cone dynamics, the optimized motions are informed and shaped by the dynamics of the terrain. Using a variant of direct collocation methods, we can express all optimization objectives and constraints in closed-form, resulting in rapid solving by standard NLP solvers, such as IPOPT. We employ this tool to analyze emergent features of bipedal locomotion in granular media, with an eye toward robotic implementation.
Nonlinear Rescaling and Proximal-Like Methods in Convex Optimization
NASA Technical Reports Server (NTRS)
Polyak, Roman; Teboulle, Marc
1997-01-01
The nonlinear rescaling principle (NRP) consists of transforming the objective function and/or the constraints of a given constrained optimization problem into another problem which is equivalent to the original one in the sense that their optimal set of solutions coincides. A nonlinear transformation parameterized by a positive scalar parameter and based on a smooth scaling function is used to transform the constraints. The methods based on NRP consist of sequential unconstrained minimization of the classical Lagrangian for the equivalent problem, followed by an explicit formula updating the Lagrange multipliers. We first show that the NRP leads naturally to proximal methods with an entropy-like kernel, which is defined by the conjugate of the scaling function, and establish that the two methods are dually equivalent for convex constrained minimization problems. We then study the convergence properties of the nonlinear rescaling algorithm and the corresponding entropy-like proximal methods for convex constrained optimization problems. Special cases of the nonlinear resealing algorithm are presented. In particular a new class of exponential penalty-modified barrier functions methods is introduced.
Nonlinear Rescaling and Proximal-Like Methods in Convex Optimization
NASA Technical Reports Server (NTRS)
Polyak, Roman; Teboulle, Marc
1997-01-01
The nonlinear rescaling principle (NRP) consists of transforming the objective function and/or the constraints of a given constrained optimization problem into another problem which is equivalent to the original one in the sense that their optimal set of solutions coincides. A nonlinear transformation parameterized by a positive scalar parameter and based on a smooth scaling function is used to transform the constraints. The methods based on NRP consist of sequential unconstrained minimization of the classical Lagrangian for the equivalent problem, followed by an explicit formula updating the Lagrange multipliers. We first show that the NRP leads naturally to proximal methods with an entropy-like kernel, which is defined by the conjugate of the scaling function, and establish that the two methods are dually equivalent for convex constrained minimization problems. We then study the convergence properties of the nonlinear rescaling algorithm and the corresponding entropy-like proximal methods for convex constrained optimization problems. Special cases of the nonlinear resealing algorithm are presented. In particular a new class of exponential penalty-modified barrier functions methods is introduced.
Lagrangian space consistency relation for large scale structure
Horn, Bart; Hui, Lam; Xiao, Xiao E-mail: lh399@columbia.edu
2015-09-01
Consistency relations, which relate the squeezed limit of an (N+1)-point correlation function to an N-point function, are non-perturbative symmetry statements that hold even if the associated high momentum modes are deep in the nonlinear regime and astrophysically complex. Recently, Kehagias and Riotto and Peloso and Pietroni discovered a consistency relation applicable to large scale structure. We show that this can be recast into a simple physical statement in Lagrangian space: that the squeezed correlation function (suitably normalized) vanishes. This holds regardless of whether the correlation observables are at the same time or not, and regardless of whether multiple-streaming is present. The simplicity of this statement suggests that an analytic understanding of large scale structure in the nonlinear regime may be particularly promising in Lagrangian space.
Large-Scale Reform Comes of Age
ERIC Educational Resources Information Center
Fullan, Michael
2009-01-01
This article reviews the history of large-scale education reform and makes the case that large-scale or whole system reform policies and strategies are becoming increasingly evident. The review briefly addresses the pre 1997 period concluding that while the pressure for reform was mounting that there were very few examples of deliberate or…
Automating large-scale reactor systems
Kisner, R.A.
1985-01-01
This paper conveys a philosophy for developing automated large-scale control systems that behave in an integrated, intelligent, flexible manner. Methods for operating large-scale systems under varying degrees of equipment degradation are discussed, and a design approach that separates the effort into phases is suggested. 5 refs., 1 fig.
Optimal spacecraft attitude control using collocation and nonlinear programming
NASA Astrophysics Data System (ADS)
Herman, A. L.; Conway, B. A.
1992-10-01
Direct collocation with nonlinear programming (DCNLP) is employed to find the optimal open-loop control histories for detumbling a disabled satellite. The controls are torques and forces applied to the docking arm and joint and torques applied about the body axes of the OMV. Solutions are obtained for cases in which various constraints are placed on the controls and in which the number of controls is reduced or increased from that considered in Conway and Widhalm (1986). DCLNP works well when applied to the optimal control problem of satellite attitude control. The formulation is straightforward and produces good results in a relatively small amount of time on a Cray X/MP with no a priori information about the optimal solution. The addition of joint acceleration to the controls significantly reduces the control magnitudes and optimal cost. In all cases, the torques and acclerations are modest and the optimal cost is very modest.
Design of Life Extending Controls Using Nonlinear Parameter Optimization
NASA Technical Reports Server (NTRS)
Lorenzo, Carl F.; Holmes, Michael S.; Ray, Asok
1998-01-01
This report presents the conceptual development of a life extending control system where the objective is to achieve high performance and structural durability of the plant. A life extending controller is designed for a reusable rocket engine via damage mitigation in both the fuel and oxidizer turbines while achieving high performance for transient responses of the combustion chamber pressure and the O2/H2 mixture ratio. This design approach makes use of a combination of linear and nonlinear controller synthesis techniques and also allows adaptation of the life extending controller module to augment a conventional performance controller of a rocket engine. The nonlinear aspect of the design is achieved using nonlinear parameter optimization of a prescribed control structure.
Large scale mechanical metamaterials as seismic shields
NASA Astrophysics Data System (ADS)
Miniaci, Marco; Krushynska, Anastasiia; Bosia, Federico; Pugno, Nicola M.
2016-08-01
Earthquakes represent one of the most catastrophic natural events affecting mankind. At present, a universally accepted risk mitigation strategy for seismic events remains to be proposed. Most approaches are based on vibration isolation of structures rather than on the remote shielding of incoming waves. In this work, we propose a novel approach to the problem and discuss the feasibility of a passive isolation strategy for seismic waves based on large-scale mechanical metamaterials, including for the first time numerical analysis of both surface and guided waves, soil dissipation effects, and adopting a full 3D simulations. The study focuses on realistic structures that can be effective in frequency ranges of interest for seismic waves, and optimal design criteria are provided, exploring different metamaterial configurations, combining phononic crystals and locally resonant structures and different ranges of mechanical properties. Dispersion analysis and full-scale 3D transient wave transmission simulations are carried out on finite size systems to assess the seismic wave amplitude attenuation in realistic conditions. Results reveal that both surface and bulk seismic waves can be considerably attenuated, making this strategy viable for the protection of civil structures against seismic risk. The proposed remote shielding approach could open up new perspectives in the field of seismology and in related areas of low-frequency vibration damping or blast protection.
[Issues of large scale tissue culture of medicinal plant].
Lv, Dong-Mei; Yuan, Yuan; Zhan, Zhi-Lai
2014-09-01
In order to increase the yield and quality of the medicinal plant and enhance the competitive power of industry of medicinal plant in our country, this paper analyzed the status, problem and countermeasure of the tissue culture of medicinal plant on large scale. Although the biotechnology is one of the most efficient and promising means in production of medicinal plant, it still has problems such as stability of the material, safety of the transgenic medicinal plant and optimization of cultured condition. Establishing perfect evaluation system according to the characteristic of the medicinal plant is the key measures to assure the sustainable development of the tissue culture of medicinal plant on large scale.
Continuation and bifurcation analysis of large-scale dynamical systems with LOCA.
Salinger, Andrew Gerhard; Phipps, Eric Todd; Pawlowski, Roger Patrick
2010-06-01
Dynamical systems theory provides a powerful framework for understanding the behavior of complex evolving systems. However applying these ideas to large-scale dynamical systems such as discretizations of multi-dimensional PDEs is challenging. Such systems can easily give rise to problems with billions of dynamical variables, requiring specialized numerical algorithms implemented on high performance computing architectures with thousands of processors. This talk will describe LOCA, the Library of Continuation Algorithms, a suite of scalable continuation and bifurcation tools optimized for these types of systems that is part of the Trilinos software collection. In particular, we will describe continuation and bifurcation analysis techniques designed for large-scale dynamical systems that are based on specialized parallel linear algebra methods for solving augmented linear systems. We will also discuss several other Trilinos tools providing nonlinear solvers (NOX), eigensolvers (Anasazi), iterative linear solvers (AztecOO and Belos), preconditioners (Ifpack, ML, Amesos) and parallel linear algebra data structures (Epetra and Tpetra) that LOCA can leverage for efficient and scalable analysis of large-scale dynamical systems.
Constrained nonlinear optimization approaches to color-signal separation.
Chang, P R; Hsieh, T H
1995-01-01
Separating a color signal into illumination and surface reflectance components is a fundamental issue in color reproduction and constancy. This can be carried out by minimizing the error in the least squares (LS) fit of the product of the illumination and the surface spectral reflectance to the actual color signal. When taking in account the physical realizability constraints on the surface reflectance and illumination, the feasible solutions to the nonlinear LS problem should satisfy a number of linear inequalities. Four distinct novel optimization algorithms are presented to employ these constraints to minimize the nonlinear LS fitting error. The first approach, which is based on Ritter's superlinear convergent method (Luengerger, 1980), provides a computationally superior algorithm to find the minimum solution to the nonlinear LS error problem subject to linear inequality constraints. Unfortunately, this gradient-like algorithm may sometimes be trapped at a local minimum or become unstable when the parameters involved in the algorithm are not tuned properly. The remaining three methods are based on the stable and promising global minimizer called simulated annealing. The annealing algorithm can always find the global minimum solution with probability one, but its convergence is slow. To tackle this, a cost-effective variable-separable formulation based on the concept of Golub and Pereyra (1973) is adopted to reduce the nonlinear LS problem to be a small-scale nonlinear LS problem. The computational efficiency can be further improved when the original Boltzman generating distribution of the classical annealing is replaced by the Cauchy distribution.
Passive and Active Vibrations Allow Self-Organization in Large-Scale Electromechanical Systems
NASA Astrophysics Data System (ADS)
Buscarino, Arturo; Fortuna, Carlo Famoso Luigi; Frasca, Mattia
2016-06-01
In this paper, the role of passive and active vibrations for the control of nonlinear large-scale electromechanical systems is investigated. The mathematical model of the system is discussed and detailed experimental results are shown in order to prove that coupling the effects of feedback and vibrations elicited by proper control signals makes possible to regularize imperfect uncertain large-scale systems.
Optimizing Nonlinear Beam Coupling in Low-Symmetry Crystals (Postprint)
2014-10-02
AFRL-RX-WP-JA-2016-0242 OPTIMIZING NONLINEAR BEAM COUPLING IN LOW- SYMMETRY CRYSTALS (POSTPRINT) A. Shumelyuk, A. Volkov, and S...BEAM COUPLING IN LOW- SYMMETRY CRYSTALS (POSTPRINT) 5a. CONTRACT NUMBER FA8650-09-D-5434-0011 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER...experimentally with Sn2P2S6. 15. SUBJECT TERMS Low- symmetry photorefractive crystals, two-beam coupling, transmission space-charge gratings 16. SECURITY
OPT++: An object-oriented class library for nonlinear optimization
Meza, J.C.
1994-03-01
Object-oriented programming is becoming a popular way of developing new software. The promise of this new programming paradigm is that software developed through these concepts will be more reliable and easier to re-use, thereby decreasing the time and cost of the software development cycle. This report describes the development of a C++ class library for nonlinear optimization. Using object-oriented techniques, this new library was designed so that the interface is easy to use while being general enough so that new optimization algorithms can be added easily to the existing framework.
Global nonlinear optimization of spacecraft protective structures design
NASA Technical Reports Server (NTRS)
Mog, R. A.; Lovett, J. N., Jr.; Avans, S. L.
1990-01-01
The global optimization of protective structural designs for spacecraft subject to hypervelocity meteoroid and space debris impacts is presented. This nonlinear problem is first formulated for weight minimization of the space station core module configuration using the Nysmith impact predictor. Next, the equivalence and uniqueness of local and global optima is shown using properties of convexity. This analysis results in a new feasibility condition for this problem. The solution existence is then shown, followed by a comparison of optimization techniques. Finally, a sensitivity analysis is presented to determine the effects of variations in the systemic parameters on optimal design. The results show that global optimization of this problem is unique and may be achieved by a number of methods, provided the feasibility condition is satisfied. Furthermore, module structural design thicknesses and weight increase with increasing projectile velocity and diameter and decrease with increasing separation between bumper and wall for the Nysmith predictor.
Robust optimization of nonlinear impulsive rendezvous with uncertainty
NASA Astrophysics Data System (ADS)
Luo, YaZhong; Yang, Zhen; Li, HengNian
2014-04-01
The optimal rendezvous trajectory designs in many current research efforts do not incorporate the practical uncertainties into the closed loop of the design. A robust optimization design method for a nonlinear rendezvous trajectory with uncertainty is proposed in this paper. One performance index related to the variances of the terminal state error is termed the robustness performance index, and a two-objective optimization model (including the minimum characteristic velocity and the minimum robustness performance index) is formulated on the basis of the Lambert algorithm. A multi-objective, non-dominated sorting genetic algorithm is employed to obtain the Pareto optimal solution set. It is shown that the proposed approach can be used to quickly obtain several inherent principles of the rendezvous trajectory by taking practical errors into account. Furthermore, this approach can identify the most preferable design space in which a specific solution for the actual application of the rendezvous control should be chosen.
Optimal experimental design for a nonlinear response in environmental toxicology.
Wright, Stephen E; Bailer, A John
2006-09-01
A start-stop experiment in environmental toxicology provides a backdrop for this design discussion. The basic problem is to decide when to sample a nonlinear response in order to minimize the generalized variance of the estimated parameters. An easily coded heuristic optimization strategy can be applied to this problem to obtain optimal or nearly optimal designs. The efficiency of the heuristic approach allows a straightforward exploration of the sensitivity of the suggested design with respect to such problem-specific concerns as variance heterogeneity, time-grid resolution, design criteria, and interval specification of planning values for parameters. A second illustration of design optimization is briefly presented in the context of concentration spacing for a reproductive toxicity study.
Optimal design for nonlinear estimation of the hemodynamic response function.
Maus, Bärbel; van Breukelen, Gerard J P; Goebel, Rainer; Berger, Martijn P F
2012-06-01
Subject-specific hemodynamic response functions (HRFs) have been recommended to capture variation in the form of the hemodynamic response between subjects (Aguirre et al., [ 1998]: Neuroimage 8:360-369). The purpose of this article is to find optimal designs for estimation of subject-specific parameters for the double gamma HRF. As the double gamma function is a nonlinear function of its parameters, optimal design theory for nonlinear models is employed in this article. The double gamma function is linearized by a Taylor approximation and the maximin criterion is used to handle dependency of the D-optimal design on the expansion point of the Taylor approximation. A realistic range of double gamma HRF parameters is used for the expansion point of the Taylor approximation. Furthermore, a genetic algorithm (GA) (Kao et al., [ 2009]: Neuroimage 44:849-856) is applied to find locally optimal designs for the different expansion points and the maximin design chosen from the locally optimal designs is compared to maximin designs obtained by m-sequences, blocked designs, designs with constant interstimulus interval (ISI) and random event-related designs. The maximin design obtained by the GA is most efficient. Random event-related designs chosen from several generated designs and m-sequences have a high efficiency, while blocked designs and designs with a constant ISI have a low efficiency compared to the maximin GA design.
Feres, Fausto; Costa, Ricardo A; Bhatt, Deepak L; Leon, Martin B; Botelho, Roberto V; King, Spencer B; de Paula, J Eduardo T; Mangione, José A; Salvadori, Décio; Gusmão, Marcos O; Castello, Hélio; Nicolela, Eduardo; Perin, Marco A; Devito, Fernando S; Marin-Neto, J Antônio; Abizaid, Alexandre
2012-12-01
Current recommendations for antithrombotic therapy after drug-eluting stent (DES) implantation include prolonged dual antiplatelet therapy (DAPT) with aspirin and clopidogrel ≥12 months. However, the impact of such a regimen for all patients receiving any DES system remains unclear based on scientific evidence available to date. Also, several other shortcomings have been identified with prolonged DAPT, including bleeding complications, compliance, and cost. The second-generation Endeavor zotarolimus-eluting stent (E-ZES) has demonstrated efficacy and safety, despite short duration DAPT (3 months) in the majority of studies. Still, the safety and clinical impact of short-term DAPT with E-ZES in the real world is yet to be determined. The OPTIMIZE trial is a large, prospective, multicenter, randomized (1:1) non-inferiority clinical evaluation of short-term (3 months) vs long-term (12-months) DAPT in patients undergoing E-ZES implantation in daily clinical practice. Overall, 3,120 patients were enrolled at 33 clinical sites in Brazil. The primary composite endpoint is death (any cause), myocardial infarction, cerebral vascular accident, and major bleeding at 12-month clinical follow-up post-index procedure. The OPTIMIZE clinical trial will determine the clinical implications of DAPT duration with the second generation E-ZES in real-world patients undergoing percutaneous coronary intervention. Copyright © 2012 Mosby, Inc. All rights reserved.
Large Scale Metal Additive Techniques Review
Nycz, Andrzej; Adediran, Adeola I; Noakes, Mark W; Love, Lonnie J
2016-01-01
In recent years additive manufacturing made long strides toward becoming a main stream production technology. Particularly strong progress has been made in large-scale polymer deposition. However, large scale metal additive has not yet reached parity with large scale polymer. This paper is a review study of the metal additive techniques in the context of building large structures. Current commercial devices are capable of printing metal parts on the order of several cubic feet compared to hundreds of cubic feet for the polymer side. In order to follow the polymer progress path several factors are considered: potential to scale, economy, environment friendliness, material properties, feedstock availability, robustness of the process, quality and accuracy, potential for defects, and post processing as well as potential applications. This paper focuses on current state of art of large scale metal additive technology with a focus on expanding the geometric limits.
Large-scale regions of antimatter
Grobov, A. V. Rubin, S. G.
2015-07-15
Amodified mechanism of the formation of large-scale antimatter regions is proposed. Antimatter appears owing to fluctuations of a complex scalar field that carries a baryon charge in the inflation era.
The Large -scale Distribution of Galaxies
NASA Astrophysics Data System (ADS)
Flin, Piotr
A review of the Large-scale structure of the Universe is given. A connection is made with the titanic work by Johannes Kepler in many areas of astronomy and cosmology. A special concern is made to spatial distribution of Galaxies, voids and walls (cellular structure of the Universe). Finaly, the author is concluding that the large scale structure of the Universe can be observed in much greater scale that it was thought twenty years ago.
Structural Optimization for Reliability Using Nonlinear Goal Programming
NASA Technical Reports Server (NTRS)
El-Sayed, Mohamed E.
1999-01-01
This report details the development of a reliability based multi-objective design tool for solving structural optimization problems. Based on two different optimization techniques, namely sequential unconstrained minimization and nonlinear goal programming, the developed design method has the capability to take into account the effects of variability on the proposed design through a user specified reliability design criterion. In its sequential unconstrained minimization mode, the developed design tool uses a composite objective function, in conjunction with weight ordered design objectives, in order to take into account conflicting and multiple design criteria. Multiple design criteria of interest including structural weight, load induced stress and deflection, and mechanical reliability. The nonlinear goal programming mode, on the other hand, provides for a design method that eliminates the difficulty of having to define an objective function and constraints, while at the same time has the capability of handling rank ordered design objectives or goals. For simulation purposes the design of a pressure vessel cover plate was undertaken as a test bed for the newly developed design tool. The formulation of this structural optimization problem into sequential unconstrained minimization and goal programming form is presented. The resulting optimization problem was solved using: (i) the linear extended interior penalty function method algorithm; and (ii) Powell's conjugate directions method. Both single and multi-objective numerical test cases are included demonstrating the design tool's capabilities as it applies to this design problem.
Noise and Nonlinear Estimation with Optimal Schemes in DTI
Özcan, Alpay
2010-01-01
In general, the estimation of the diffusion properties for diffusion tensor experiments (DTI) is accomplished via least squares estimation (LSE). The technique requires applying the logarithm to the measurements, which causes bad propagation of errors. Moreover, the way noise is injected to the equations invalidates the least squares estimate as the best linear unbiased estimate. Nonlinear estimation (NE), despite its longer computation time, does not possess any of these problems. However, all of the conditions and optimization methods developed in the past are based on the coefficient matrix obtained in a LSE setup. In this manuscript, nonlinear estimation for DTI is analyzed to demonstrate that any result obtained relatively easily in a linear algebra setup about the coefficient matrix can be applied to the more complicated NE framework. The data, obtained earlier using non–optimal and optimized diffusion gradient schemes, are processed with NE. In comparison with LSE, the results show significant improvements, especially for the optimization criterion. However, NE does not resolve the existing conflicts and ambiguities displayed with LSE methods. PMID:20655681
Optimization of nonlinear quarter car suspension-seat-driver model.
Nagarkar, Mahesh P; Vikhe Patil, Gahininath J; Zaware Patil, Rahul N
2016-11-01
In this paper a nonlinear quarter car suspension-seat-driver model was implemented for optimum design. A nonlinear quarter car model comprising of quadratic tyre stiffness and cubic stiffness in suspension spring, frame, and seat cushion with 4 degrees of freedom (DoF) driver model was presented for optimization and analysis. Suspension system was aimed to optimize the comfort and health criterion comprising of Vibration Dose Value (VDV) at head, frequency weighted RMS head acceleration, crest factor, amplitude ratio of head RMS acceleration to seat RMS acceleration and amplitude ratio of upper torso RMS acceleration to seat RMS acceleration along with stability criterion comprising of suspension space deflection and dynamic tyre force. ISO 2631-1 standard was adopted to assess ride and health criterions. Suspension spring stiffness and damping and seat cushion stiffness and damping are the design variables. Non-dominated Sort Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization - Crowding Distance (MOPSO-CD) algorithm are implemented for optimization. Simulation result shows that optimum design improves ride comfort and health criterion over classical design variables.
The new discussion of a neutrino mass and issues in the formation of large-scale structure
NASA Technical Reports Server (NTRS)
Melott, Adrian L.
1991-01-01
It is argued that the discrepancy between the large-scale structure predicted by cosmological models with neutrino mass (hot dark matter) do not differ drastically from the observed structure. Evidence from the correlation amplitude, nonlinearity and the onset of galaxy formation, large-scale streaming velocities, and the topology of large-scale structure is considered. Hot dark matter models seem to be as accurate predictors of the large-scale structure as are cold dark matter models.
Nonlinear Analysis and Optimal Design of Dynamic Mechanical Systems for Spacecraft Application.
1986-02-01
Mechanisms, vibrational analysis, optimization , geometric nonlinearity , material nonlinearity 20. AUSTRACT (C..,I.,.. 01 ".Id*If oO...p .,d Id.MII( by... nonlinear finite element analysis procedure for three-dimensional mechanisms. A niew optimization algorithm has also been developed based on the Gauss DD I...1986 NONLINEAR ANALYSIS AND OPTIMAL DESIGN OF DYNAMIC MECHANICAL SYSTEMS FOR SPACECRAFT APPLICATION Air Force Office of Scientific Research Grant No
Spin glasses and nonlinear constraints in portfolio optimization
NASA Astrophysics Data System (ADS)
Andrecut, M.
2014-01-01
We discuss the portfolio optimization problem with the obligatory deposits constraint. Recently it has been shown that as a consequence of this nonlinear constraint, the solution consists of an exponentially large number of optimal portfolios, completely different from each other, and extremely sensitive to any changes in the input parameters of the problem, making the concept of rational decision making questionable. Here we reformulate the problem using a quadratic obligatory deposits constraint, and we show that from the physics point of view, finding an optimal portfolio amounts to calculating the mean-field magnetizations of a random Ising model with the constraint of a constant magnetization norm. We show that the model reduces to an eigenproblem, with 2N solutions, where N is the number of assets defining the portfolio. Also, in order to illustrate our results, we present a detailed numerical example of a portfolio of several risky common stocks traded on the Nasdaq Market.
A hybrid nonlinear programming method for design optimization
NASA Technical Reports Server (NTRS)
Rajan, S. D.
1986-01-01
Solutions to engineering design problems formulated as nonlinear programming (NLP) problems usually require the use of more than one optimization technique. Moreover, the interaction between the user (analysis/synthesis) program and the NLP system can lead to interface, scaling, or convergence problems. An NLP solution system is presented that seeks to solve these problems by providing a programming system to ease the user-system interface. A simple set of rules is used to select an optimization technique or to switch from one technique to another in an attempt to detect, diagnose, and solve some potential problems. Numerical examples involving finite element based optimal design of space trusses and rotor bearing systems are used to illustrate the applicability of the proposed methodology.
A hybrid nonlinear programming method for design optimization
NASA Technical Reports Server (NTRS)
Rajan, S. D.
1986-01-01
Solutions to engineering design problems formulated as nonlinear programming (NLP) problems usually require the use of more than one optimization technique. Moreover, the interaction between the user (analysis/synthesis) program and the NLP system can lead to interface, scaling, or convergence problems. An NLP solution system is presented that seeks to solve these problems by providing a programming system to ease the user-system interface. A simple set of rules is used to select an optimization technique or to switch from one technique to another in an attempt to detect, diagnose, and solve some potential problems. Numerical examples involving finite element based optimal design of space trusses and rotor bearing systems are used to illustrate the applicability of the proposed methodology.
Minimax Techniques For Optimizing Non-Linear Image Algebra Transforms
NASA Astrophysics Data System (ADS)
Davidson, Jennifer L.
1989-08-01
It has been well established that the Air Force Armament Technical Laboratory (AFATL) image algebra is capable of expressing all linear transformations [7]. The embedding of the linear algebra in the image algebra makes this possible. In this paper we show a relation of the image algebra to another algebraic system called the minimax algebra. This system is used extensively in economics and operations research, but until now has not been investigated for applications to image processing. The relationship is exploited to develop new optimization methods for a class of non-linear image processing transforms. In particular, a general decomposition technique for templates in this non-linear domain is presented. Template decomposition techniques are an important tool in mapping algorithms efficiently to both sequential and massively parallel architectures.
Fitting Nonlinear Curves by use of Optimization Techniques
NASA Technical Reports Server (NTRS)
Hill, Scott A.
2005-01-01
MULTIVAR is a FORTRAN 77 computer program that fits one of the members of a set of six multivariable mathematical models (five of which are nonlinear) to a multivariable set of data. The inputs to MULTIVAR include the data for the independent and dependent variables plus the user s choice of one of the models, one of the three optimization engines, and convergence criteria. By use of the chosen optimization engine, MULTIVAR finds values for the parameters of the chosen model so as to minimize the sum of squares of the residuals. One of the optimization engines implements a routine, developed in 1982, that utilizes the Broydon-Fletcher-Goldfarb-Shanno (BFGS) variable-metric method for unconstrained minimization in conjunction with a one-dimensional search technique that finds the minimum of an unconstrained function by polynomial interpolation and extrapolation without first finding bounds on the solution. The second optimization engine is a faster and more robust commercially available code, denoted Design Optimization Tool, that also uses the BFGS method. The third optimization engine is a robust and relatively fast routine that implements the Levenberg-Marquardt algorithm.
Large-scale cortical networks and cognition.
Bressler, S L
1995-03-01
The well-known parcellation of the mammalian cerebral cortex into a large number of functionally distinct cytoarchitectonic areas presents a problem for understanding the complex cortical integrative functions that underlie cognition. How do cortical areas having unique individual functional properties cooperate to accomplish these complex operations? Do neurons distributed throughout the cerebral cortex act together in large-scale functional assemblages? This review examines the substantial body of evidence supporting the view that complex integrative functions are carried out by large-scale networks of cortical areas. Pathway tracing studies in non-human primates have revealed widely distributed networks of interconnected cortical areas, providing an anatomical substrate for large-scale parallel processing of information in the cerebral cortex. Functional coactivation of multiple cortical areas has been demonstrated by neurophysiological studies in non-human primates and several different cognitive functions have been shown to depend on multiple distributed areas by human neuropsychological studies. Electrophysiological studies on interareal synchronization have provided evidence that active neurons in different cortical areas may become not only coactive, but also functionally interdependent. The computational advantages of synchronization between cortical areas in large-scale networks have been elucidated by studies using artificial neural network models. Recent observations of time-varying multi-areal cortical synchronization suggest that the functional topology of a large-scale cortical network is dynamically reorganized during visuomotor behavior.
An hp symplectic pseudospectral method for nonlinear optimal control
NASA Astrophysics Data System (ADS)
Peng, Haijun; Wang, Xinwei; Li, Mingwu; Chen, Biaosong
2017-01-01
An adaptive symplectic pseudospectral method based on the dual variational principle is proposed and is successfully applied to solving nonlinear optimal control problems in this paper. The proposed method satisfies the first order necessary conditions of continuous optimal control problems, also the symplectic property of the original continuous Hamiltonian system is preserved. The original optimal control problem is transferred into a set of nonlinear equations which can be solved easily by Newton-Raphson iterations, and the Jacobian matrix is found to be sparse and symmetric. The proposed method, on one hand, exhibits exponent convergence rates when the number of collocation points are increasing with the fixed number of sub-intervals; on the other hand, exhibits linear convergence rates when the number of sub-intervals is increasing with the fixed number of collocation points. Furthermore, combining with the hp method based on the residual error of dynamic constraints, the proposed method can achieve given precisions in a few iterations. Five examples highlight the high precision and high computational efficiency of the proposed method.
Survey on large scale system control methods
NASA Technical Reports Server (NTRS)
Mercadal, Mathieu
1987-01-01
The problem inherent to large scale systems such as power network, communication network and economic or ecological systems were studied. The increase in size and flexibility of future spacecraft has put those dynamical systems into the category of large scale systems, and tools specific to the class of large systems are being sought to design control systems that can guarantee more stability and better performance. Among several survey papers, reference was found to a thorough investigation on decentralized control methods. Especially helpful was the classification made of the different existing approaches to deal with large scale systems. A very similar classification is used, even though the papers surveyed are somehow different from the ones reviewed in other papers. Special attention is brought to the applicability of the existing methods to controlling large mechanical systems like large space structures. Some recent developments are added to this survey.
Large-scale nanophotonic phased array.
Sun, Jie; Timurdogan, Erman; Yaacobi, Ami; Hosseini, Ehsan Shah; Watts, Michael R
2013-01-10
Electromagnetic phased arrays at radio frequencies are well known and have enabled applications ranging from communications to radar, broadcasting and astronomy. The ability to generate arbitrary radiation patterns with large-scale phased arrays has long been pursued. Although it is extremely expensive and cumbersome to deploy large-scale radiofrequency phased arrays, optical phased arrays have a unique advantage in that the much shorter optical wavelength holds promise for large-scale integration. However, the short optical wavelength also imposes stringent requirements on fabrication. As a consequence, although optical phased arrays have been studied with various platforms and recently with chip-scale nanophotonics, all of the demonstrations so far are restricted to one-dimensional or small-scale two-dimensional arrays. Here we report the demonstration of a large-scale two-dimensional nanophotonic phased array (NPA), in which 64 × 64 (4,096) optical nanoantennas are densely integrated on a silicon chip within a footprint of 576 μm × 576 μm with all of the nanoantennas precisely balanced in power and aligned in phase to generate a designed, sophisticated radiation pattern in the far field. We also show that active phase tunability can be realized in the proposed NPA by demonstrating dynamic beam steering and shaping with an 8 × 8 array. This work demonstrates that a robust design, together with state-of-the-art complementary metal-oxide-semiconductor technology, allows large-scale NPAs to be implemented on compact and inexpensive nanophotonic chips. In turn, this enables arbitrary radiation pattern generation using NPAs and therefore extends the functionalities of phased arrays beyond conventional beam focusing and steering, opening up possibilities for large-scale deployment in applications such as communication, laser detection and ranging, three-dimensional holography and biomedical sciences, to name just a few.
The large-scale distribution of galaxies
NASA Technical Reports Server (NTRS)
Geller, Margaret J.
1989-01-01
The spatial distribution of galaxies in the universe is characterized on the basis of the six completed strips of the Harvard-Smithsonian Center for Astrophysics redshift-survey extension. The design of the survey is briefly reviewed, and the results are presented graphically. Vast low-density voids similar to the void in Bootes are found, almost completely surrounded by thin sheets of galaxies. Also discussed are the implications of the results for the survey sampling problem, the two-point correlation function of the galaxy distribution, the possibility of detecting large-scale coherent flows, theoretical models of large-scale structure, and the identification of groups and clusters of galaxies.
US National Large-scale City Orthoimage Standard Initiative
Zhou, G.; Song, C.; Benjamin, S.; Schickler, W.
2003-01-01
The early procedures and algorithms for National digital orthophoto generation in National Digital Orthophoto Program (NDOP) were based on earlier USGS mapping operations, such as field control, aerotriangulation (derived in the early 1920's), the quarter-quadrangle-centered (3.75 minutes of longitude and latitude in geographic extent), 1:40,000 aerial photographs, and 2.5 D digital elevation models. However, large-scale city orthophotos using early procedures have disclosed many shortcomings, e.g., ghost image, occlusion, shadow. Thus, to provide the technical base (algorithms, procedure) and experience needed for city large-scale digital orthophoto creation is essential for the near future national large-scale digital orthophoto deployment and the revision of the Standards for National Large-scale City Digital Orthophoto in National Digital Orthophoto Program (NDOP). This paper will report our initial research results as follows: (1) High-precision 3D city DSM generation through LIDAR data processing, (2) Spatial objects/features extraction through surface material information and high-accuracy 3D DSM data, (3) 3D city model development, (4) Algorithm development for generation of DTM-based orthophoto, and DBM-based orthophoto, (5) True orthophoto generation by merging DBM-based orthophoto and DTM-based orthophoto, and (6) Automatic mosaic by optimizing and combining imagery from many perspectives.
Moon-based Earth Observation for Large Scale Geoscience Phenomena
NASA Astrophysics Data System (ADS)
Guo, Huadong; Liu, Guang; Ding, Yixing
2016-07-01
The capability of Earth observation for large-global-scale natural phenomena needs to be improved and new observing platform are expected. We have studied the concept of Moon as an Earth observation in these years. Comparing with manmade satellite platform, Moon-based Earth observation can obtain multi-spherical, full-band, active and passive information,which is of following advantages: large observation range, variable view angle, long-term continuous observation, extra-long life cycle, with the characteristics of longevity ,consistency, integrity, stability and uniqueness. Moon-based Earth observation is suitable for monitoring the large scale geoscience phenomena including large scale atmosphere change, large scale ocean change,large scale land surface dynamic change,solid earth dynamic change,etc. For the purpose of establishing a Moon-based Earth observation platform, we already have a plan to study the five aspects as follows: mechanism and models of moon-based observing earth sciences macroscopic phenomena; sensors' parameters optimization and methods of moon-based Earth observation; site selection and environment of moon-based Earth observation; Moon-based Earth observation platform; and Moon-based Earth observation fundamental scientific framework.
Nonlinear optimization of acoustic energy harvesting using piezoelectric devices.
Lallart, Mickaeël; Guyomar, Daniel; Richard, Claude; Petit, Lionel
2010-11-01
In the first part of the paper, a single degree-of-freedom model of a vibrating membrane with piezoelectric inserts is introduced and is initially applied to the case when a plane wave is incident with frequency close to one of the resonance frequencies. The model is a prototype of a device which converts ambient acoustical energy to electrical energy with the use of piezoelectric devices. The paper then proposes an enhancement of the energy harvesting process using a nonlinear processing of the output voltage of piezoelectric actuators, and suggests that this improves the energy conversion and reduces the sensitivity to frequency drifts. A theoretical discussion is given for the electrical power that can be expected making use of various models. This and supporting experimental results suggest that a nonlinear optimization approach allows a gain of up to 10 in harvested energy and a doubling of the bandwidth. A model is introduced in the latter part of the paper for predicting the behavior of the energy-harvesting device with changes in acoustic frequency, this model taking into account the damping effect and the frequency changes introduced by the nonlinear processes in the device.
Safe microburst penetration techniques: A deterministic, nonlinear, optimal control approach
NASA Technical Reports Server (NTRS)
Psiaki, Mark L.
1987-01-01
A relatively large amount of computer time was used for the calculation of a optimal trajectory, but it is subject to reduction with moderate effort. The Deterministic, Nonlinear, Optimal Control algorithm yielded excellent aircraft performance in trajectory tracking for the given microburst. It did so by varying the angle of attack to counteract the lift effects of microburst induced airspeed variations. Throttle saturation and aerodynamic stall limits were not a problem for the case considered, proving that the aircraft's performance capabilities were not violated by the given wind field. All closed loop control laws previously considered performed very poorly in comparison, and therefore do not come near to taking full advantage of aircraft performance.
Phase retrieval with transverse translation diversity: a nonlinear optimization approach.
Guizar-Sicairos, Manuel; Fienup, James R
2008-05-12
We develop and test a nonlinear optimization algorithm for solving the problem of phase retrieval with transverse translation diversity, where the diverse far-field intensity measurements are taken after translating the object relative to a known illumination pattern. Analytical expressions for the gradient of a squared-error metric with respect to the object, illumination and translations allow joint optimization of the object and system parameters. This approach achieves superior reconstructions, with respect to a previously reported technique [H. M. L. Faulkner and J. M. Rodenburg, Phys. Rev. Lett. 93, 023903 (2004)], when the system parameters are inaccurately known or in the presence of noise. Applicability of this method for samples that are smaller than the illumination pattern is explored.
A forward method for optimal stochastic nonlinear and adaptive control
NASA Technical Reports Server (NTRS)
Bayard, David S.
1988-01-01
A computational approach is taken to solve the optimal nonlinear stochastic control problem. The approach is to systematically solve the stochastic dynamic programming equations forward in time, using a nested stochastic approximation technique. Although computationally intensive, this provides a straightforward numerical solution for this class of problems and provides an alternative to the usual dimensionality problem associated with solving the dynamic programming equations backward in time. It is shown that the cost degrades monotonically as the complexity of the algorithm is reduced. This provides a strategy for suboptimal control with clear performance/computation tradeoffs. A numerical study focusing on a generic optimal stochastic adaptive control example is included to demonstrate the feasibility of the method.
Solving Large-scale Eigenvalue Problems in SciDACApplications
Yang, Chao
2005-06-29
Large-scale eigenvalue problems arise in a number of DOE applications. This paper provides an overview of the recent development of eigenvalue computation in the context of two SciDAC applications. We emphasize the importance of Krylov subspace methods, and point out its limitations. We discuss the value of alternative approaches that are more amenable to the use of preconditioners, and report the progression using the multi-level algebraic sub-structuring techniques to speed up eigenvalue calculation. In addition to methods for linear eigenvalue problems, we also examine new approaches to solving two types of non-linear eigenvalue problems arising from SciDAC applications.
A cooperative strategy for parameter estimation in large scale systems biology models
2012-01-01
Background Mathematical models play a key role in systems biology: they summarize the currently available knowledge in a way that allows to make experimentally verifiable predictions. Model calibration consists of finding the parameters that give the best fit to a set of experimental data, which entails minimizing a cost function that measures the goodness of this fit. Most mathematical models in systems biology present three characteristics which make this problem very difficult to solve: they are highly non-linear, they have a large number of parameters to be estimated, and the information content of the available experimental data is frequently scarce. Hence, there is a need for global optimization methods capable of solving this problem efficiently. Results A new approach for parameter estimation of large scale models, called Cooperative Enhanced Scatter Search (CeSS), is presented. Its key feature is the cooperation between different programs (“threads”) that run in parallel in different processors. Each thread implements a state of the art metaheuristic, the enhanced Scatter Search algorithm (eSS). Cooperation, meaning information sharing between threads, modifies the systemic properties of the algorithm and allows to speed up performance. Two parameter estimation problems involving models related with the central carbon metabolism of E. coli which include different regulatory levels (metabolic and transcriptional) are used as case studies. The performance and capabilities of the method are also evaluated using benchmark problems of large-scale global optimization, with excellent results. Conclusions The cooperative CeSS strategy is a general purpose technique that can be applied to any model calibration problem. Its capability has been demonstrated by calibrating two large-scale models of different characteristics, improving the performance of previously existing methods in both cases. The cooperative metaheuristic presented here can be easily extended
Management of large-scale technology
NASA Technical Reports Server (NTRS)
Levine, A.
1985-01-01
Two major themes are addressed in this assessment of the management of large-scale NASA programs: (1) how a high technology agency was a decade marked by a rapid expansion of funds and manpower in the first half and almost as rapid contraction in the second; and (2) how NASA combined central planning and control with decentralized project execution.
Large-scale multimedia modeling applications
Droppo, J.G. Jr.; Buck, J.W.; Whelan, G.; Strenge, D.L.; Castleton, K.J.; Gelston, G.M.
1995-08-01
Over the past decade, the US Department of Energy (DOE) and other agencies have faced increasing scrutiny for a wide range of environmental issues related to past and current practices. A number of large-scale applications have been undertaken that required analysis of large numbers of potential environmental issues over a wide range of environmental conditions and contaminants. Several of these applications, referred to here as large-scale applications, have addressed long-term public health risks using a holistic approach for assessing impacts from potential waterborne and airborne transport pathways. Multimedia models such as the Multimedia Environmental Pollutant Assessment System (MEPAS) were designed for use in such applications. MEPAS integrates radioactive and hazardous contaminants impact computations for major exposure routes via air, surface water, ground water, and overland flow transport. A number of large-scale applications of MEPAS have been conducted to assess various endpoints for environmental and human health impacts. These applications are described in terms of lessons learned in the development of an effective approach for large-scale applications.
Evaluating Large-Scale Interactive Radio Programmes
ERIC Educational Resources Information Center
Potter, Charles; Naidoo, Gordon
2009-01-01
This article focuses on the challenges involved in conducting evaluations of interactive radio programmes in South Africa with large numbers of schools, teachers, and learners. It focuses on the role such large-scale evaluation has played during the South African radio learning programme's development stage, as well as during its subsequent…
Evaluating Large-Scale Interactive Radio Programmes
ERIC Educational Resources Information Center
Potter, Charles; Naidoo, Gordon
2009-01-01
This article focuses on the challenges involved in conducting evaluations of interactive radio programmes in South Africa with large numbers of schools, teachers, and learners. It focuses on the role such large-scale evaluation has played during the South African radio learning programme's development stage, as well as during its subsequent…
Handling inequality constraints in continuous nonlinear global optimization
Wang, Tao; Wah, B.W.
1996-12-31
In this paper, we present a new method to handle inequality constraints and apply it in NOVEL (Nonlinear Optimization via External Lead), a system we have developed for solving constrained continuous nonlinear optimization problems. In general, in applying Lagrange-multiplier methods to solve these problems, inequality constraints are first converted into equivalent equality constraints. One such conversion method adds a slack variable to each inequality constraint in order to convert it into an equality constraint. The disadvantage of this conversion is that when the search is inside a feasible region, some satisfied constraints may still pose a non-zero weight in the Lagrangian function, leading to possible oscillations and divergence when a local optimum lies on the boundary of a feasible region. We propose a new conversion method called the MaxQ method such that all satisfied constraints in a feasible region always carry zero weight in the Lagrange function; hence, minimizing the Lagrange function in a feasible region always leads to local minima of the objective function. We demonstrate that oscillations do not happen in our method. We also propose methods to speed up convergence when a local optimum lies on the boundary of a feasible region. Finally, we show improved experimental results in applying our proposed method in NOVEL on some existing benchmark problems and compare them to those obtained by applying the method based on slack variables.
Optimizing BAO measurements with non-linear transformations of the Lyman-α forest
Wang, Xinkang; Font-Ribera, Andreu; Seljak, Uroš E-mail: afont@lbl.gov
2015-04-01
We explore the effect of applying a non-linear transformation to the Lyman-α forest transmitted flux F=e{sup −τ} and the ability of analytic models to predict the resulting clustering amplitude. Both the large-scale bias of the transformed field (signal) and the amplitude of small scale fluctuations (noise) can be arbitrarily modified, but we were unable to find a transformation that increases significantly the signal-to-noise ratio on large scales using Taylor expansion up to the third order. In particular, however, we achieve a 33% improvement in signal to noise for Gaussianized field in transverse direction. On the other hand, we explore an analytic model for the large-scale biasing of the Lyα forest, and present an extension of this model to describe the biasing of the transformed fields. Using hydrodynamic simulations we show that the model works best to describe the biasing with respect to velocity gradients, but is less successful in predicting the biasing with respect to large-scale density fluctuations, especially for very nonlinear transformations.
Time-optimal quantum control of nonlinear two-level systems
NASA Astrophysics Data System (ADS)
Chen, Xi; Ban, Yue; Hegerfeldt, Gerhard C.
2016-08-01
Nonlinear two-level Landau-Zener type equations for systems with relevance for Bose-Einstein condensates and nonlinear optics are considered and the minimal time Tmin to drive an initial state to a given target state is investigated. Surprisingly, the nonlinearity may be canceled by a time-optimal unconstrained driving and Tmin becomes independent of the nonlinearity. For constrained and unconstrained driving explicit expressions are derived for Tmin, the optimal driving, and the protocol.
Large scale structure in universes dominated by cold dark matter
NASA Technical Reports Server (NTRS)
Bond, J. Richard
1986-01-01
The theory of Gaussian random density field peaks is applied to a numerical study of the large-scale structure developing from adiabatic fluctuations in models of biased galaxy formation in universes with Omega = 1, h = 0.5 dominated by cold dark matter (CDM). The angular anisotropy of the cross-correlation function demonstrates that the far-field regions of cluster-scale peaks are asymmetric, as recent observations indicate. These regions will generate pancakes or filaments upon collapse. One-dimensional singularities in the large-scale bulk flow should arise in these CDM models, appearing as pancakes in position space. They are too rare to explain the CfA bubble walls, but pancakes that are just turning around now are sufficiently abundant and would appear to be thin walls normal to the line of sight in redshift space. Large scale streaming velocities are significantly smaller than recent observations indicate. To explain the reported 700 km/s coherent motions, mass must be significantly more clustered than galaxies with a biasing factor of less than 0.4 and a nonlinear redshift at cluster scales greater than one for both massive neutrino and cold models.
Robust and fast nonlinear optimization of diffusion MRI microstructure models.
Harms, R L; Fritz, F J; Tobisch, A; Goebel, R; Roebroeck, A
2017-07-15
Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of
Optimal spatiotemporal reduced order modeling for nonlinear dynamical systems
NASA Astrophysics Data System (ADS)
LaBryer, Allen
Proposed in this dissertation is a novel reduced order modeling (ROM) framework called optimal spatiotemporal reduced order modeling (OPSTROM) for nonlinear dynamical systems. The OPSTROM approach is a data-driven methodology for the synthesis of multiscale reduced order models (ROMs) which can be used to enhance the efficiency and reliability of under-resolved simulations for nonlinear dynamical systems. In the context of nonlinear continuum dynamics, the OPSTROM approach relies on the concept of embedding subgrid-scale models into the governing equations in order to account for the effects due to unresolved spatial and temporal scales. Traditional ROMs neglect these effects, whereas most other multiscale ROMs account for these effects in ways that are inconsistent with the underlying spatiotemporal statistical structure of the nonlinear dynamical system. The OPSTROM framework presented in this dissertation begins with a general system of partial differential equations, which are modified for an under-resolved simulation in space and time with an arbitrary discretization scheme. Basic filtering concepts are used to demonstrate the manner in which residual terms, representing subgrid-scale dynamics, arise with a coarse computational grid. Models for these residual terms are then developed by accounting for the underlying spatiotemporal statistical structure in a consistent manner. These subgrid-scale models are designed to provide closure by accounting for the dynamic interactions between spatiotemporal macroscales and microscales which are otherwise neglected in a ROM. For a given resolution, the predictions obtained with the modified system of equations are optimal (in a mean-square sense) as the subgrid-scale models are based upon principles of mean-square error minimization, conditional expectations and stochastic estimation. Methods are suggested for efficient model construction, appraisal, error measure, and implementation with a couple of well-known time
Condition Monitoring of Large-Scale Facilities
NASA Technical Reports Server (NTRS)
Hall, David L.
1999-01-01
This document provides a summary of the research conducted for the NASA Ames Research Center under grant NAG2-1182 (Condition-Based Monitoring of Large-Scale Facilities). The information includes copies of view graphs presented at NASA Ames in the final Workshop (held during December of 1998), as well as a copy of a technical report provided to the COTR (Dr. Anne Patterson-Hine) subsequent to the workshop. The material describes the experimental design, collection of data, and analysis results associated with monitoring the health of large-scale facilities. In addition to this material, a copy of the Pennsylvania State University Applied Research Laboratory data fusion visual programming tool kit was also provided to NASA Ames researchers.
Large-scale Advanced Propfan (LAP) program
NASA Technical Reports Server (NTRS)
Sagerser, D. A.; Ludemann, S. G.
1985-01-01
The propfan is an advanced propeller concept which maintains the high efficiencies traditionally associated with conventional propellers at the higher aircraft cruise speeds associated with jet transports. The large-scale advanced propfan (LAP) program extends the research done on 2 ft diameter propfan models to a 9 ft diameter article. The program includes design, fabrication, and testing of both an eight bladed, 9 ft diameter propfan, designated SR-7L, and a 2 ft diameter aeroelastically scaled model, SR-7A. The LAP program is complemented by the propfan test assessment (PTA) program, which takes the large-scale propfan and mates it with a gas generator and gearbox to form a propfan propulsion system and then flight tests this system on the wing of a Gulfstream 2 testbed aircraft.
Nonlinear Burn Control and Operating Point Optimization in ITER
NASA Astrophysics Data System (ADS)
Boyer, Mark; Schuster, Eugenio
2013-10-01
Control of the fusion power through regulation of the plasma density and temperature will be essential for achieving and maintaining desired operating points in fusion reactors and burning plasma experiments like ITER. In this work, a volume averaged model for the evolution of the density of energy, deuterium and tritium fuel ions, alpha-particles, and impurity ions is used to synthesize a multi-input multi-output nonlinear feedback controller for stabilizing and modulating the burn condition. Adaptive control techniques are used to account for uncertainty in model parameters, including particle confinement times and recycling rates. The control approach makes use of the different possible methods for altering the fusion power, including adjusting the temperature through auxiliary heating, modulating the density and isotopic mix through fueling, and altering the impurity density through impurity injection. Furthermore, a model-based optimization scheme is proposed to drive the system as close as possible to desired fusion power and temperature references. Constraints are considered in the optimization scheme to ensure that, for example, density and beta limits are avoided, and that optimal operation is achieved even when actuators reach saturation. Supported by the NSF CAREER award program (ECCS-0645086).
Design optimization of a twist compliant mechanism with nonlinear stiffness
NASA Astrophysics Data System (ADS)
Tummala, Y.; Frecker, M. I.; Wissa, A. A.; Hubbard, J. E., Jr.
2014-10-01
A contact-aided compliant mechanism called a twist compliant mechanism (TCM) is presented in this paper. This mechanism has nonlinear stiffness when it is twisted in both directions along its axis. The inner core of the mechanism is primarily responsible for its flexibility in one twisting direction. The contact surfaces of the cross-members and compliant sectors are primarily responsible for its high stiffness in the opposite direction. A desired twist angle in a given direction can be achieved by tailoring the stiffness of a TCM. The stiffness of a compliant twist mechanism can be tailored by varying thickness of its cross-members, thickness of the core and thickness of its sectors. A multi-objective optimization problem with three objective functions is proposed in this paper, and used to design an optimal TCM with desired twist angle. The objective functions are to minimize the mass and maximum von-Mises stress observed, while minimizing or maximizing the twist angles under specific loading conditions. The multi-objective optimization problem proposed in this paper is solved for an ornithopter flight research platform as a case study, with the goal of using the TCM to achieve passive twisting of the wing during upstroke, while keeping the wing fully extended and rigid during the downstroke. Prototype TCMs have been fabricated using 3D printing and tested. Testing results are also presented in this paper.
Automatic threshold optimization in nonlinear energy operator based spike detection.
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.
Large-scale fibre-array multiplexing
Cheremiskin, I V; Chekhlova, T K
2001-05-31
The possibility of creating a fibre multiplexer/demultiplexer with large-scale multiplexing without any basic restrictions on the number of channels and the spectral spacing between them is shown. The operating capacity of a fibre multiplexer based on a four-fibre array ensuring a spectral spacing of 0.7 pm ({approx} 10 GHz) between channels is demonstrated. (laser applications and other topics in quantum electronics)
Modeling Human Behavior at a Large Scale
2012-01-01
Discerning intentions in dynamic human action. Trends in Cognitive Sciences , 5(4):171 – 178, 2001. Shirli Bar-David, Israel Bar-David, Paul C. Cross, Sadie...Limits of predictability in human mobility. Science , 327(5968):1018, 2010. S.A. Stouffer. Intervening opportunities: a theory relating mobility and...Modeling Human Behavior at a Large Scale by Adam Sadilek Submitted in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy
Large-Scale Aerosol Modeling and Analysis
2008-09-30
aerosol species up to six days in advance anywhere on the globe. NAAPS and COAMPS are particularly useful for forecasts of dust storms in areas...impact cloud processes globally. With increasing dust storms due to climate change and land use changes in desert regions, the impact of the...bacteria in large-scale dust storms is expected to significantly impact warm ice cloud formation, human health, and ecosystems globally. In Niemi et al
Large-Scale Visual Data Analysis
NASA Astrophysics Data System (ADS)
Johnson, Chris
2014-04-01
Modern high performance computers have speeds measured in petaflops and handle data set sizes measured in terabytes and petabytes. Although these machines offer enormous potential for solving very large-scale realistic computational problems, their effectiveness will hinge upon the ability of human experts to interact with their simulation results and extract useful information. One of the greatest scientific challenges of the 21st century is to effectively understand and make use of the vast amount of information being produced. Visual data analysis will be among our most most important tools in helping to understand such large-scale information. Our research at the Scientific Computing and Imaging (SCI) Institute at the University of Utah has focused on innovative, scalable techniques for large-scale 3D visual data analysis. In this talk, I will present state- of-the-art visualization techniques, including scalable visualization algorithms and software, cluster-based visualization methods and innovate visualization techniques applied to problems in computational science, engineering, and medicine. I will conclude with an outline for a future high performance visualization research challenges and opportunities.
Large-scale neuromorphic computing systems
NASA Astrophysics Data System (ADS)
Furber, Steve
2016-10-01
Neuromorphic computing covers a diverse range of approaches to information processing all of which demonstrate some degree of neurobiological inspiration that differentiates them from mainstream conventional computing systems. The philosophy behind neuromorphic computing has its origins in the seminal work carried out by Carver Mead at Caltech in the late 1980s. This early work influenced others to carry developments forward, and advances in VLSI technology supported steady growth in the scale and capability of neuromorphic devices. Recently, a number of large-scale neuromorphic projects have emerged, taking the approach to unprecedented scales and capabilities. These large-scale projects are associated with major new funding initiatives for brain-related research, creating a sense that the time and circumstances are right for progress in our understanding of information processing in the brain. In this review we present a brief history of neuromorphic engineering then focus on some of the principal current large-scale projects, their main features, how their approaches are complementary and distinct, their advantages and drawbacks, and highlight the sorts of capabilities that each can deliver to neural modellers.
Optimization of microscopic and macroscopic second order optical nonlinearities
NASA Technical Reports Server (NTRS)
Marder, Seth R.; Perry, Joseph W.
1993-01-01
Nonlinear optical materials (NLO) can be used to extend the useful frequency range of lasers. Frequency generation is important for laser-based remote sensing and optical data storage. Another NLO effect, the electro-optic effect, can be used to modulate the amplitude, phase, or polarization state of an optical beam. Applications of this effect in telecommunications and in integrated optics include the impression of information on an optical carrier signal or routing of optical signals between fiber optic channels. In order to utilize these effects most effectively, it is necessary to synthesize materials which respond to applied fields very efficiently. In this talk, it will be shown how the development of a fundamental understanding of the science of nonlinear optics can lead to a rational approach to organic molecules and materials with optimized properties. In some cases, figures of merit for newly developed materials are more than an order of magnitude higher than those of currently employed materials. Some of these materials are being examined for phased-array radar and other electro-optic switching applications.
Multigrid Equation Solvers for Large Scale Nonlinear Finite Element Simulations
1999-01-01
algorithm in three dimensions. In Proc. Second Ann. ACM Symp. Comp. Geom., 1986. [39] J. Fish, V. Belsky , and S. Gomma. Unstructured multigrid method...for shells. International Journal for Numerical Methods in Engineering, 39:1181{1197, 1996. [40] J. Fish, M. Pandheeradi, and V. Belsky . An ecient
Optimization of a rubidium magnetometer based on nonlinear optical rotation
NASA Astrophysics Data System (ADS)
Chan, Lok Fai; Jacome, L. R.; Guttikonda, Srikanth; Bahr, Eric; Kimball, Derek
2009-11-01
Atomic spin polarization of alkali atoms in the ground state can survive thousands of collisions with paraffin-coated cell walls. The resulting long spin-relaxation times achieved in evacuated, paraffin-coated cells enable precise measurement of atomic spin precession and energy shifts of ground-state Zeeman sublevels. In the present work, nonlinear magneto-optical rotation with frequency-modulated light (FM NMOR) is used to measure magnetic-field-induced spin precession for rubidium atoms contained in a paraffin-coated cell. We discuss optimization of the shot-noise-projected magnetometer sensitivity and practical implementation of the Rb magnetometer. The magnetometer will be applied to searches for anomalous spin-dependent interactions of the proton.
Optimal operating points of oscillators using nonlinear resonators.
Kenig, Eyal; Cross, M C; Villanueva, L G; Karabalin, R B; Matheny, M H; Lifshitz, Ron; Roukes, M L
2012-11-01
We demonstrate an analytical method for calculating the phase sensitivity of a class of oscillators whose phase does not affect the time evolution of the other dynamic variables. We show that such oscillators possess the possibility for complete phase noise elimination. We apply the method to a feedback oscillator which employs a high Q weakly nonlinear resonator and provide explicit parameter values for which the feedback phase noise is completely eliminated and others for which there is no amplitude-phase noise conversion. We then establish an operational mode of the oscillator which optimizes its performance by diminishing the feedback noise in both quadratures, thermal noise, and quality factor fluctuations. We also study the spectrum of the oscillator and provide specific results for the case of 1/f noise sources.
Optimal operating points of oscillators using nonlinear resonators
Kenig, Eyal; Cross, M. C.; Villanueva, L. G.; Karabalin, R. B.; Matheny, M. H.; Lifshitz, Ron; Roukes, M. L.
2013-01-01
We demonstrate an analytical method for calculating the phase sensitivity of a class of oscillators whose phase does not affect the time evolution of the other dynamic variables. We show that such oscillators possess the possibility for complete phase noise elimination. We apply the method to a feedback oscillator which employs a high Q weakly nonlinear resonator and provide explicit parameter values for which the feedback phase noise is completely eliminated and others for which there is no amplitude-phase noise conversion. We then establish an operational mode of the oscillator which optimizes its performance by diminishing the feedback noise in both quadratures, thermal noise, and quality factor fluctuations. We also study the spectrum of the oscillator and provide specific results for the case of 1/f noise sources. PMID:23214857
NASA Astrophysics Data System (ADS)
Nogaret, Alain; Meliza, C. Daniel; Margoliash, Daniel; Abarbanel, Henry D. I.
2016-09-01
We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20–50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight.
Nogaret, Alain; Meliza, C. Daniel; Margoliash, Daniel; Abarbanel, Henry D. I.
2016-01-01
We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20–50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight. PMID:27605157
Bhat, Pallavi; Ebrahimi, Fatima; Blackman, Eric G.
2016-07-06
Here, we study the dynamo generation (exponential growth) of large-scale (planar averaged) fields in unstratified shearing box simulations of the magnetorotational instability (MRI). In contrast to previous studies restricted to horizontal (x–y) averaging, we also demonstrate the presence of large-scale fields when vertical (y–z) averaging is employed instead. By computing space–time planar averaged fields and power spectra, we find large-scale dynamo action in the early MRI growth phase – a previously unidentified feature. Non-axisymmetric linear MRI modes with low horizontal wavenumbers and vertical wavenumbers near that of expected maximal growth, amplify the large-scale fields exponentially before turbulence and high wavenumbermore » fluctuations arise. Thus the large-scale dynamo requires only linear fluctuations but not non-linear turbulence (as defined by mode–mode coupling). Vertical averaging also allows for monitoring the evolution of the large-scale vertical field and we find that a feedback from horizontal low wavenumber MRI modes provides a clue as to why the large-scale vertical field sustains against turbulent diffusion in the non-linear saturation regime. We compute the terms in the mean field equations to identify the individual contributions to large-scale field growth for both types of averaging. The large-scale fields obtained from vertical averaging are found to compare well with global simulations and quasi-linear analytical analysis from a previous study by Ebrahimi & Blackman. We discuss the potential implications of these new results for understanding the large-scale MRI dynamo saturation and turbulence.« less
Bhat, Pallavi; Ebrahimi, Fatima; Blackman, Eric G.
2016-07-06
Here, we study the dynamo generation (exponential growth) of large-scale (planar averaged) fields in unstratified shearing box simulations of the magnetorotational instability (MRI). In contrast to previous studies restricted to horizontal (x–y) averaging, we also demonstrate the presence of large-scale fields when vertical (y–z) averaging is employed instead. By computing space–time planar averaged fields and power spectra, we find large-scale dynamo action in the early MRI growth phase – a previously unidentified feature. Non-axisymmetric linear MRI modes with low horizontal wavenumbers and vertical wavenumbers near that of expected maximal growth, amplify the large-scale fields exponentially before turbulence and high wavenumber fluctuations arise. Thus the large-scale dynamo requires only linear fluctuations but not non-linear turbulence (as defined by mode–mode coupling). Vertical averaging also allows for monitoring the evolution of the large-scale vertical field and we find that a feedback from horizontal low wavenumber MRI modes provides a clue as to why the large-scale vertical field sustains against turbulent diffusion in the non-linear saturation regime. We compute the terms in the mean field equations to identify the individual contributions to large-scale field growth for both types of averaging. The large-scale fields obtained from vertical averaging are found to compare well with global simulations and quasi-linear analytical analysis from a previous study by Ebrahimi & Blackman. We discuss the potential implications of these new results for understanding the large-scale MRI dynamo saturation and turbulence.
NASA Astrophysics Data System (ADS)
Bhat, Pallavi; Ebrahimi, Fatima; Blackman, Eric G.
2016-10-01
We study the dynamo generation (exponential growth) of large-scale (planar averaged) fields in unstratified shearing box simulations of the magnetorotational instability (MRI). In contrast to previous studies restricted to horizontal (x-y) averaging, we also demonstrate the presence of large-scale fields when vertical (y-z) averaging is employed instead. By computing space-time planar averaged fields and power spectra, we find large-scale dynamo action in the early MRI growth phase - a previously unidentified feature. Non-axisymmetric linear MRI modes with low horizontal wavenumbers and vertical wavenumbers near that of expected maximal growth, amplify the large-scale fields exponentially before turbulence and high wavenumber fluctuations arise. Thus the large-scale dynamo requires only linear fluctuations but not non-linear turbulence (as defined by mode-mode coupling). Vertical averaging also allows for monitoring the evolution of the large-scale vertical field and we find that a feedback from horizontal low wavenumber MRI modes provides a clue as to why the large-scale vertical field sustains against turbulent diffusion in the non-linear saturation regime. We compute the terms in the mean field equations to identify the individual contributions to large-scale field growth for both types of averaging. The large-scale fields obtained from vertical averaging are found to compare well with global simulations and quasi-linear analytical analysis from a previous study by Ebrahimi & Blackman. We discuss the potential implications of these new results for understanding the large-scale MRI dynamo saturation and turbulence.
Large Scale Bacterial Colony Screening of Diversified FRET Biosensors
Litzlbauer, Julia; Schifferer, Martina; Ng, David; Fabritius, Arne; Thestrup, Thomas; Griesbeck, Oliver
2015-01-01
Biosensors based on Förster Resonance Energy Transfer (FRET) between fluorescent protein mutants have started to revolutionize physiology and biochemistry. However, many types of FRET biosensors show relatively small FRET changes, making measurements with these probes challenging when used under sub-optimal experimental conditions. Thus, a major effort in the field currently lies in designing new optimization strategies for these types of sensors. Here we describe procedures for optimizing FRET changes by large scale screening of mutant biosensor libraries in bacterial colonies. We describe optimization of biosensor expression, permeabilization of bacteria, software tools for analysis, and screening conditions. The procedures reported here may help in improving FRET changes in multiple suitable classes of biosensors. PMID:26061878
NASA Astrophysics Data System (ADS)
Cai, Lanlan; Li, Peng; Luo, Qi; Zhai, Pengcheng; Zhang, Qingjie
2017-01-01
As no single thermoelectric material has presented a high figure-of-merit (ZT) over a very wide temperature range, segmented thermoelectric generators (STEGs), where the p- and n-legs are formed of different thermoelectric material segments joined in series, have been developed to improve the performance of thermoelectric generators. A crucial but difficult problem in a STEG design is to determine the optimal values of the geometrical parameters, like the relative lengths of each segment and the cross-sectional area ratio of the n- and p-legs. Herein, a multi-parameter and nonlinear optimization method, based on the Improved Powell Algorithm in conjunction with the discrete numerical model, was implemented to solve the STEG's geometrical optimization problem. The multi-parameter optimal results were validated by comparison with the optimal outcomes obtained from the single-parameter optimization method. Finally, the effect of the hot- and cold-junction temperatures on the geometry optimization was investigated. Results show that the optimal geometry parameters for maximizing the specific output power of a STEG are different from those for maximizing the conversion efficiency. Data also suggest that the optimal geometry parameters and the interfacial temperatures of the adjacent segments optimized for maximum specific output power or conversion efficiency vary with changing hot- and cold-junction temperatures. Through the geometry optimization, the CoSb3/Bi2Te3-based STEG can obtain a maximum specific output power up to 1725.3 W/kg and a maximum efficiency of 13.4% when operating at a hot-junction temperature of 823 K and a cold-junction temperature of 298 K.
NASA Astrophysics Data System (ADS)
Cai, Lanlan; Li, Peng; Luo, Qi; Zhai, Pengcheng; Zhang, Qingjie
2017-03-01
As no single thermoelectric material has presented a high figure-of-merit (ZT) over a very wide temperature range, segmented thermoelectric generators (STEGs), where the p- and n-legs are formed of different thermoelectric material segments joined in series, have been developed to improve the performance of thermoelectric generators. A crucial but difficult problem in a STEG design is to determine the optimal values of the geometrical parameters, like the relative lengths of each segment and the cross-sectional area ratio of the n- and p-legs. Herein, a multi-parameter and nonlinear optimization method, based on the Improved Powell Algorithm in conjunction with the discrete numerical model, was implemented to solve the STEG's geometrical optimization problem. The multi-parameter optimal results were validated by comparison with the optimal outcomes obtained from the single-parameter optimization method. Finally, the effect of the hot- and cold-junction temperatures on the geometry optimization was investigated. Results show that the optimal geometry parameters for maximizing the specific output power of a STEG are different from those for maximizing the conversion efficiency. Data also suggest that the optimal geometry parameters and the interfacial temperatures of the adjacent segments optimized for maximum specific output power or conversion efficiency vary with changing hot- and cold-junction temperatures. Through the geometry optimization, the CoSb3/Bi2Te3-based STEG can obtain a maximum specific output power up to 1725.3 W/kg and a maximum efficiency of 13.4% when operating at a hot-junction temperature of 823 K and a cold-junction temperature of 298 K.
A Nonlinear Fuel Optimal Reaction Jet Control Law
Breitfeller, E.; Ng, L.C.
2002-06-30
We derive a nonlinear fuel optimal attitude control system (ACS) that drives the final state to the desired state according to a cost function that weights the final state angular error relative to the angular rate error. Control is achieved by allowing the pulse-width-modulated (PWM) commands to begin and end anywhere within a control cycle, achieving a pulse width pulse time (PWPT) control. We show through a MATLAB{reg_sign} Simulink model that this steady-state condition may be accomplished, in the absence of sensor noise or model uncertainties, with the theoretical minimum number of actuator cycles. The ability to analytically achieve near-zero drift rates is particularly important in applications such as station-keeping and sensor imaging. Consideration is also given to the fact that, for relatively small sensor and model errors, the controller requires significantly fewer actuator cycles to reach the final state error than a traditional proportional-integral-derivative (PID) controller. The optimal PWPT attitude controller may be applicable for a high performance kinetic energy kill vehicle.
[A large-scale accident in Alpine terrain].
Wildner, M; Paal, P
2015-02-01
Due to the geographical conditions, large-scale accidents amounting to mass casualty incidents (MCI) in Alpine terrain regularly present rescue teams with huge challenges. Using an example incident, specific conditions and typical problems associated with such a situation are presented. The first rescue team members to arrive have the elementary tasks of qualified triage and communication to the control room, which is required to dispatch the necessary additional support. Only with a clear "concept", to which all have to adhere, can the subsequent chaos phase be limited. In this respect, a time factor confounded by adverse weather conditions or darkness represents enormous pressure. Additional hazards are frostbite and hypothermia. If priorities can be established in terms of urgency, then treatment and procedure algorithms have proven successful. For evacuation of causalities, a helicopter should be strived for. Due to the low density of hospitals in Alpine regions, it is often necessary to distribute the patients over a wide area. Rescue operations in Alpine terrain have to be performed according to the particular conditions and require rescue teams to have specific knowledge and expertise. The possibility of a large-scale accident should be considered when planning events. With respect to optimization of rescue measures, regular training and exercises are rational, as is the analysis of previous large-scale Alpine accidents.
Reliability assessment for components of large scale photovoltaic systems
NASA Astrophysics Data System (ADS)
Ahadi, Amir; Ghadimi, Noradin; Mirabbasi, Davar
2014-10-01
Photovoltaic (PV) systems have significantly shifted from independent power generation systems to a large-scale grid-connected generation systems in recent years. The power output of PV systems is affected by the reliability of various components in the system. This study proposes an analytical approach to evaluate the reliability of large-scale, grid-connected PV systems. The fault tree method with an exponential probability distribution function is used to analyze the components of large-scale PV systems. The system is considered in the various sequential and parallel fault combinations in order to find all realistic ways in which the top or undesired events can occur. Additionally, it can identify areas that the planned maintenance should focus on. By monitoring the critical components of a PV system, it is possible not only to improve the reliability of the system, but also to optimize the maintenance costs. The latter is achieved by informing the operators about the system component's status. This approach can be used to ensure secure operation of the system by its flexibility in monitoring system applications. The implementation demonstrates that the proposed method is effective and efficient and can conveniently incorporate more system maintenance plans and diagnostic strategies.
NASA Astrophysics Data System (ADS)
Bhat, Pallavi; Ebrahimi, Fatima; Blackman, Eric G.
2016-10-01
We study dynamo generation (exponential growth) of large scale (planar averaged) fields in the in shearing box simulations of magnetorotational instability (MRI). By computing space-time planar averaged fields and power spectra, we find large scale dynamo action in early MRI growth phase, a previously unidentified feature. Non-axisymmetric linear MRI modes with low horizontal wavenumbers and vertical wavenumbers near that of expected maximal growth, amplify the large scale fields exponentially before turbulence and high wavenumber fluctuations arise. Thus the large scale dynamo requires only linear fluctuations but not nonlinear turbulence (or mode-mode coupling). In contrast to previous studies restricted to horizontal (x- y) averaging, we also show the presence of large scale fields when vertical (y- z) averaging is employed instead. We compute the terms in the mean field equations to identify the contributions to large scale field growth in both types of averaging. The large scale fields obtained from vertical averaging are found to match well with global simulations and quasilinear analytical analysis from a previous study by Ebrahimi & Blackman. We discuss implications of our new results for understanding large scale MRI dynamo saturation and turbulence. Work supported by DOE DE-SC0012467.
Experimental Simulations of Large-Scale Collisions
NASA Technical Reports Server (NTRS)
Housen, Kevin R.
2002-01-01
This report summarizes research on the effects of target porosity on the mechanics of impact cratering. Impact experiments conducted on a centrifuge provide direct simulations of large-scale cratering on porous asteroids. The experiments show that large craters in porous materials form mostly by compaction, with essentially no deposition of material into the ejecta blanket that is a signature of cratering in less-porous materials. The ratio of ejecta mass to crater mass is shown to decrease with increasing crater size or target porosity. These results are consistent with the observation that large closely-packed craters on asteroid Mathilde appear to have formed without degradation to earlier craters.
NASA Astrophysics Data System (ADS)
Nigro, G.; Pongkitiwanichakul, P.; Cattaneo, F.; Tobias, S. M.
2017-01-01
We consider kinematic dynamo action in a sheared helical flow at moderate to high values of the magnetic Reynolds number (Rm). We find exponentially growing solutions which, for large enough shear, take the form of a coherent part embedded in incoherent fluctuations. We argue that at large Rm large-scale dynamo action should be identified by the presence of structures coherent in time, rather than those at large spatial scales. We further argue that although the growth rate is determined by small-scale processes, the period of the coherent structures is set by mean-field considerations.
Large-scale brightenings associated with flares
NASA Technical Reports Server (NTRS)
Mandrini, Cristina H.; Machado, Marcos E.
1992-01-01
It is shown that large-scale brightenings (LSBs) associated with solar flares, similar to the 'giant arches' discovered by Svestka et al. (1982) in images obtained by the SSM HXIS hours after the onset of two-ribbon flares, can also occur in association with confined flares in complex active regions. For these events, a clear link between the LSB and the underlying flare is clearly evident from the active-region magnetic field topology. The implications of these findings are discussed within the framework of the interacting loops of flares and the giant arch phenomenology.
Large scale phononic metamaterials for seismic isolation
Aravantinos-Zafiris, N.; Sigalas, M. M.
2015-08-14
In this work, we numerically examine structures that could be characterized as large scale phononic metamaterials. These novel structures could have band gaps in the frequency spectrum of seismic waves when their dimensions are chosen appropriately, thus raising the belief that they could be serious candidates for seismic isolation structures. Different and easy to fabricate structures were examined made from construction materials such as concrete and steel. The well-known finite difference time domain method is used in our calculations in order to calculate the band structures of the proposed metamaterials.
Large-scale planar lightwave circuits
NASA Astrophysics Data System (ADS)
Bidnyk, Serge; Zhang, Hua; Pearson, Matt; Balakrishnan, Ashok
2011-01-01
By leveraging advanced wafer processing and flip-chip bonding techniques, we have succeeded in hybrid integrating a myriad of active optical components, including photodetectors and laser diodes, with our planar lightwave circuit (PLC) platform. We have combined hybrid integration of active components with monolithic integration of other critical functions, such as diffraction gratings, on-chip mirrors, mode-converters, and thermo-optic elements. Further process development has led to the integration of polarization controlling functionality. Most recently, all these technological advancements have been combined to create large-scale planar lightwave circuits that comprise hundreds of optical elements integrated on chips less than a square inch in size.
Large-Scale PV Integration Study
Lu, Shuai; Etingov, Pavel V.; Diao, Ruisheng; Ma, Jian; Samaan, Nader A.; Makarov, Yuri V.; Guo, Xinxin; Hafen, Ryan P.; Jin, Chunlian; Kirkham, Harold; Shlatz, Eugene; Frantzis, Lisa; McClive, Timothy; Karlson, Gregory; Acharya, Dhruv; Ellis, Abraham; Stein, Joshua; Hansen, Clifford; Chadliev, Vladimir; Smart, Michael; Salgo, Richard; Sorensen, Rahn; Allen, Barbara; Idelchik, Boris
2011-07-29
This research effort evaluates the impact of large-scale photovoltaic (PV) and distributed generation (DG) output on NV Energy’s electric grid system in southern Nevada. It analyzes the ability of NV Energy’s generation to accommodate increasing amounts of utility-scale PV and DG, and the resulting cost of integrating variable renewable resources. The study was jointly funded by the United States Department of Energy and NV Energy, and conducted by a project team comprised of industry experts and research scientists from Navigant Consulting Inc., Sandia National Laboratories, Pacific Northwest National Laboratory and NV Energy.
Neutrinos and large-scale structure
Eisenstein, Daniel J.
2015-07-15
I review the use of cosmological large-scale structure to measure properties of neutrinos and other relic populations of light relativistic particles. With experiments to measure the anisotropies of the cosmic microwave anisotropies and the clustering of matter at low redshift, we now have securely measured a relativistic background with density appropriate to the cosmic neutrino background. Our limits on the mass of the neutrino continue to shrink. Experiments coming in the next decade will greatly improve the available precision on searches for the energy density of novel relativistic backgrounds and the mass of neutrinos.
Large-scale Heterogeneous Network Data Analysis
2012-07-31
Data for Multi-Player Influence Maximization on Social Networks.” KDD 2012 (Demo). Po-Tzu Chang , Yen-Chieh Huang, Cheng-Lun Yang, Shou-De Lin, Pu...Jen Cheng. “Learning-Based Time-Sensitive Re-Ranking for Web Search.” SIGIR 2012 (poster) Hung -Che Lai, Cheng-Te Li, Yi-Chen Lo, and Shou-De Lin...Exploiting and Evaluating MapReduce for Large-Scale Graph Mining.” ASONAM 2012 (Full, 16% acceptance ratio). Hsun-Ping Hsieh , Cheng-Te Li, and Shou
Design and Testing of a Generalized Reduced Gradient Code for Nonlinear Optimization
1975-03-01
Case of Nonlinear Constraints" in Optimization , R. Fletcher, Ed., Academic Press, 1969, pp. 37-47 3. J. Abadie, "Application of the GRG Algorithm to...139130 DESIGN AND TESTING OF A GENE .WLIZED REDUCED GRADIENT CODE U IFOR NONLINEAR OPTIMIZATION ) :I I J by Leon S. Lasdon Allan D.Waren Arvind Jain...Methods are algorithms for solving nonlinear programs of general structure. An earlier paper 1--’ discussed the basic principles of GRG and
Large-Scale Numerical Simulations of Human Motion
NASA Technical Reports Server (NTRS)
Anderson, Frank C.; Ziegler, James M.; Pandy, Marcus G.; Whalen, Robert T.
1994-01-01
This paper examines the feasibility of using massively-parallel and vector-processing supercomputers to solve large-scale optimal control problems for human movement. Specifically, we compare the computational expense of determining the optimal controls for the single support phase of walking using a conventional serial machine (a Silicon Graphics Personal Iris 4D25 workstation), a MIMD parallel machine (an Intel iPSC/860 comprising 128 processors), and a parallel-vector-processing machine (a Cray Y-MP 8/864). With the human body modeled as a 14 degree-of-freedom linkage actuated by 46 musculotendinous units, computation of the optimal controls for walking could take up to 3 months of CPU time on the Iris. Both the Cray Y-MP and the Intel iPSC/860 are able to reduce this time to practical levels. The optimal control solution for walking can be found with about 77 hours of CPU time on the Cray, and with about 88 hours of CPU time on the Intel. Although the overall speeds of the Cray and the Intel were found to be similar, the unique capabilities of each machine are best suited to different parts of the optimal control algorithm used. The Intel performed best in the calculation of the derivatives of the performance criterion and the constraints. In contrast, the Cray performed best during parameter optimization of the controls. These results suggest that the ideal computer architecture for solving very large-scale optimal control problems is a hybrid system in which a vector-processing machine is integrated into the communication network of a MIMD parallel machine.
Application of a Nonlinear Optimal Control Algorithm to Spacecraft and Airship Control
NASA Astrophysics Data System (ADS)
Fujii, Hironori A.; Kusagaya, Tairo; Watanabe, Takeo; An, Andrew
This paper presents a synthetic method that is based on both the algorithm of the geometry nonlinear feedback and nonlinear system optimal control of hierarchical differential feedback regulation. This method enables us to solve optimal feedback control problems without solving the Riccati Equations or adjoint vectors. Also, the method takes into consideration the avoidance of conjugate points, which is a important aspect of research in optimal control of nonlinear system. The present method is applied to two examples, one is a nonlinear attitude maneuver of spacecraft and the other is an airship optimal feedback tracking control. These applications have been studied numerically in order to show the performance of the present method applied to nonlinear optimal control for aerospace application.
Internationalization Measures in Large Scale Research Projects
NASA Astrophysics Data System (ADS)
Soeding, Emanuel; Smith, Nancy
2017-04-01
Internationalization measures in Large Scale Research Projects Large scale research projects (LSRP) often serve as flagships used by universities or research institutions to demonstrate their performance and capability to stakeholders and other interested parties. As the global competition among universities for the recruitment of the brightest brains has increased, effective internationalization measures have become hot topics for universities and LSRP alike. Nevertheless, most projects and universities are challenged with little experience on how to conduct these measures and make internationalization an cost efficient and useful activity. Furthermore, those undertakings permanently have to be justified with the Project PIs as important, valuable tools to improve the capacity of the project and the research location. There are a variety of measures, suited to support universities in international recruitment. These include e.g. institutional partnerships, research marketing, a welcome culture, support for science mobility and an effective alumni strategy. These activities, although often conducted by different university entities, are interlocked and can be very powerful measures if interfaced in an effective way. On this poster we display a number of internationalization measures for various target groups, identify interfaces between project management, university administration, researchers and international partners to work together, exchange information and improve processes in order to be able to recruit, support and keep the brightest heads to your project.
Local gravity and large-scale structure
NASA Technical Reports Server (NTRS)
Juszkiewicz, Roman; Vittorio, Nicola; Wyse, Rosemary F. G.
1990-01-01
The magnitude and direction of the observed dipole anisotropy of the galaxy distribution can in principle constrain the amount of large-scale power present in the spectrum of primordial density fluctuations. This paper confronts the data, provided by a recent redshift survey of galaxies detected by the IRAS satellite, with the predictions of two cosmological models with very different levels of large-scale power: the biased Cold Dark Matter dominated model (CDM) and a baryon-dominated model (BDM) with isocurvature initial conditions. Model predictions are investigated for the Local Group peculiar velocity, v(R), induced by mass inhomogeneities distributed out to a given radius, R, for R less than about 10,000 km/s. Several convergence measures for v(R) are developed, which can become powerful cosmological tests when deep enough samples become available. For the present data sets, the CDM and BDM predictions are indistinguishable at the 2 sigma level and both are consistent with observations. A promising discriminant between cosmological models is the misalignment angle between v(R) and the apex of the dipole anisotropy of the microwave background.
Local gravity and large-scale structure
NASA Technical Reports Server (NTRS)
Juszkiewicz, Roman; Vittorio, Nicola; Wyse, Rosemary F. G.
1990-01-01
The magnitude and direction of the observed dipole anisotropy of the galaxy distribution can in principle constrain the amount of large-scale power present in the spectrum of primordial density fluctuations. This paper confronts the data, provided by a recent redshift survey of galaxies detected by the IRAS satellite, with the predictions of two cosmological models with very different levels of large-scale power: the biased Cold Dark Matter dominated model (CDM) and a baryon-dominated model (BDM) with isocurvature initial conditions. Model predictions are investigated for the Local Group peculiar velocity, v(R), induced by mass inhomogeneities distributed out to a given radius, R, for R less than about 10,000 km/s. Several convergence measures for v(R) are developed, which can become powerful cosmological tests when deep enough samples become available. For the present data sets, the CDM and BDM predictions are indistinguishable at the 2 sigma level and both are consistent with observations. A promising discriminant between cosmological models is the misalignment angle between v(R) and the apex of the dipole anisotropy of the microwave background.
Large-scale Globally Propagating Coronal Waves.
Warmuth, Alexander
Large-scale, globally propagating wave-like disturbances have been observed in the solar chromosphere and by inference in the corona since the 1960s. However, detailed analysis of these phenomena has only been conducted since the late 1990s. This was prompted by the availability of high-cadence coronal imaging data from numerous spaced-based instruments, which routinely show spectacular globally propagating bright fronts. Coronal waves, as these perturbations are usually referred to, have now been observed in a wide range of spectral channels, yielding a wealth of information. Many findings have supported the "classical" interpretation of the disturbances: fast-mode MHD waves or shocks that are propagating in the solar corona. However, observations that seemed inconsistent with this picture have stimulated the development of alternative models in which "pseudo waves" are generated by magnetic reconfiguration in the framework of an expanding coronal mass ejection. This has resulted in a vigorous debate on the physical nature of these disturbances. This review focuses on demonstrating how the numerous observational findings of the last one and a half decades can be used to constrain our models of large-scale coronal waves, and how a coherent physical understanding of these disturbances is finally emerging.
Penas, David R; González, Patricia; Egea, Jose A; Doallo, Ramón; Banga, Julio R
2017-01-21
The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies. The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used. The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.
Systematic Optimization of Second Order Nonlinear Optical Materials
1994-06-14
nonlinear optical materials and 2) to develop advanced electrooptic and photonic materials for enhanced...Nonlinear Optics, Val Thorens, (France), January 9-13, 1994. 7. Marder. S.R." A Chemists View of the Science and Technology of Organic Nonlinear Optical Materials ." Presented...DC, August 22-26, 1994. (Invited Lecture). 5. Marder. S. R." Nonlinear Optical Materials Design Criteria" To be presented at American Chemical
Zhang, Songchuan; Xia, Youshen
2016-12-28
Much research has been devoted to complex-variable optimization problems due to their engineering applications. However, the complex-valued optimization method for solving complex-variable optimization problems is still an active research area. This paper proposes two efficient complex-valued optimization methods for solving constrained nonlinear optimization problems of real functions in complex variables, respectively. One solves the complex-valued nonlinear programming problem with linear equality constraints. Another solves the complex-valued nonlinear programming problem with both linear equality constraints and an ℓ₁-norm constraint. Theoretically, we prove the global convergence of the proposed two complex-valued optimization algorithms under mild conditions. The proposed two algorithms can solve the complex-valued optimization problem completely in the complex domain and significantly extend existing complex-valued optimization algorithms. Numerical results further show that the proposed two algorithms have a faster speed than several conventional real-valued optimization algorithms.
Statistical analysis of large-scale neuronal recording data
Reed, Jamie L.; Kaas, Jon H.
2010-01-01
Relating stimulus properties to the response properties of individual neurons and neuronal networks is a major goal of sensory research. Many investigators implant electrode arrays in multiple brain areas and record from chronically implanted electrodes over time to answer a variety of questions. Technical challenges related to analyzing large-scale neuronal recording data are not trivial. Several analysis methods traditionally used by neurophysiologists do not account for dependencies in the data that are inherent in multi-electrode recordings. In addition, when neurophysiological data are not best modeled by the normal distribution and when the variables of interest may not be linearly related, extensions of the linear modeling techniques are recommended. A variety of methods exist to analyze correlated data, even when data are not normally distributed and the relationships are nonlinear. Here we review expansions of the Generalized Linear Model designed to address these data properties. Such methods are used in other research fields, and the application to large-scale neuronal recording data will enable investigators to determine the variable properties that convincingly contribute to the variances in the observed neuronal measures. Standard measures of neuron properties such as response magnitudes can be analyzed using these methods, and measures of neuronal network activity such as spike timing correlations can be analyzed as well. We have done just that in recordings from 100-electrode arrays implanted in the primary somatosensory cortex of owl monkeys. Here we illustrate how one example method, Generalized Estimating Equations analysis, is a useful method to apply to large-scale neuronal recordings. PMID:20472395
The influence of the large scale circulation on an eastern boundary current
NASA Astrophysics Data System (ADS)
Wang, J.; Rizzoli, P. M.; Spall, M. A.
2010-12-01
The wind-driven gyre circulation in the ocean interior varies across large temporal and spatial scales, while the current along the eastern boundary is concentrated in a narrow jet with smaller temporal and spatial scales. These boundary currents are often hydrodynamically unstable and generate mesoscale and sub-mesoscale variability. In this study, we investigate the influence of the large scale circulation on an unstable eastern boundary current. One example is the influence of the Pacific subtropical gyre on the California current system. We study the problem using both a linear stability analysis and a nonlinear numerical model in a barotropic and quasi-geostrophic framework. The large scale circulation and the boundary current are specified in the linear analysis and are generated by an Ekman forcing in the numerical model. The linear stability analysis shows that to the lowest order the eastward (westward) flow of the large scale circulation stabilizes (destabilizes) the boundary current. Additionally, the meridional flow contributed by the large scale circulation accelerates or decelerates the originally parallel boundary current and modifies the stability of the current through the Doppler effect. Unstable perturbations which can be represented by normal modes for a parallel current then develop streamwise spatial structures. In the nonlinear numerical simulations, the streamwise nonuniformity of the boundary current influenced by the large scale circulation is clearly shown in the eddy kinetic energy. The location of the maximum eddy kinetic energy depends on the relative strength of the large scale circulation and the boundary current. The meridionally nonuniform eddy activities are important in offshore tracer transport. The nonlinear numerical simulation is forced by a wind curl field which generates a southward eastern boundary current and a large scale circulation with double gyres (white contours). The mean eddy kinetic energy of the boundary current
NASA Astrophysics Data System (ADS)
Swaidan, Waleeda; Hussin, Amran
2015-10-01
Most direct methods solve finite time horizon optimal control problems with nonlinear programming solver. In this paper, we propose a numerical method for solving nonlinear optimal control problem with state and control inequality constraints. This method used quasilinearization technique and Haar wavelet operational matrix to convert the nonlinear optimal control problem into a quadratic programming problem. The linear inequality constraints for trajectories variables are converted to quadratic programming constraint by using Haar wavelet collocation method. The proposed method has been applied to solve Optimal Control of Multi-Item Inventory Model. The accuracy of the states, controls and cost can be improved by increasing the Haar wavelet resolution.
NASA Technical Reports Server (NTRS)
Storaasli, Olaf O. (Editor); Housner, Jerrold M. (Editor)
1993-01-01
Computing speed is leaping forward by several orders of magnitude each decade. Engineers and scientists gathered at a NASA Langley symposium to discuss these exciting trends as they apply to parallel computational methods for large-scale structural analysis and design. Among the topics discussed were: large-scale static analysis; dynamic, transient, and thermal analysis; domain decomposition (substructuring); and nonlinear and numerical methods.
Ridzal, Danis
2007-03-01
Aristos is a Trilinos package for nonlinear continuous optimization, based on full-space sequential quadratic programming (SQP) methods. Aristos is specifically designed for the solution of large-scale constrained optimization problems in which the linearized constraint equations require iterative (i.e. inexact) linear solver techniques. Aristos' unique feature is an efficient handling of inexactness in linear system solves. Aristos currently supports the solution of equality-constrained convex and nonconvex optimization problems. It has been used successfully in the area of PDE-constrained optimization, for the solution of nonlinear optimal control, optimal design, and inverse problems.
Large Scale Deformation Monitoring and Atmospheric Removal in Mexico City
NASA Astrophysics Data System (ADS)
McCardle, Adrian; McCardel, Jim; Ramos, Fernanda Ledo G.
2010-03-01
Large scale, accurate measurement of non-linear ground movement is required for monitoring applications pertaining to groundwater extraction, oil and gas production, and carbon capture and storage. Mexico City experiences severe subsidence as high as 35 centimeters per year due to continued exploitation of groundwater. Such extreme ground deformation has caused damage to infrastructure and many areas of the city are now subjected to periodic flooding. Furthermore, subsidence rates change seasonally creating a non-linear deformation signature manifesting over an area larger than 30 x 30 kilometers. The geographical location and climate of Mexico City, coupled with aforementioned subsidence characteristics create unique challenges for repeat-pass InSAR processing: Firstly, Mexico City is a tropical highland and experiences an oceanic climate that leads to significant temporal de-correlation. Secondly, the large magnitude subsidence leads to phase aliasing over coherent targets, particularly for interferograms with large temporal separation. Lastly, the expansive deformation is spatially correlated on scales similar to the long-range atmosphere, complicating the separation of the two signals. This paper discusses the results from the application of traditional DInSAR techniques combined with Multi-temporal InSAR Network Analysis processing algorithms to accurately identify and measure displacement, specifically in light of the challenges peculiar to Mexico City. Multi-temporal InSAR Network Analysis techniques are used to identify non-linear displacement and remove atmospheric noise from 38 ENVISAT images that were acquired over Mexico City from 2002 to 2007.
The dynamics of large-scale arrays of coupled resonators
NASA Astrophysics Data System (ADS)
Borra, Chaitanya; Pyles, Conor S.; Wetherton, Blake A.; Quinn, D. Dane; Rhoads, Jeffrey F.
2017-03-01
This work describes an analytical framework suitable for the analysis of large-scale arrays of coupled resonators, including those which feature amplitude and phase dynamics, inherent element-level parameter variation, nonlinearity, and/or noise. In particular, this analysis allows for the consideration of coupled systems in which the number of individual resonators is large, extending as far as the continuum limit corresponding to an infinite number of resonators. Moreover, this framework permits analytical predictions for the amplitude and phase dynamics of such systems. The utility of this analytical methodology is explored through the analysis of a system of N non-identical resonators with global coupling, including both reactive and dissipative components, physically motivated by an electromagnetically-transduced microresonator array. In addition to the amplitude and phase dynamics, the behavior of the system as the number of resonators varies is investigated and the convergence of the discrete system to the infinite-N limit is characterized.
Statistics of Caustics in Large-Scale Structure Formation
NASA Astrophysics Data System (ADS)
Feldbrugge, Job L.; Hidding, Johan; van de Weygaert, Rien
2016-10-01
The cosmic web is a complex spatial pattern of walls, filaments, cluster nodes and underdense void regions. It emerged through gravitational amplification from the Gaussian primordial density field. Here we infer analytical expressions for the spatial statistics of caustics in the evolving large-scale mass distribution. In our analysis, following the quasi-linear Zel'dovich formalism and confined to the 1D and 2D situation, we compute number density and correlation properties of caustics in cosmic density fields that evolve from Gaussian primordial conditions. The analysis can be straightforwardly extended to the 3D situation. We moreover, are currently extending the approach to the non-linear regime of structure formation by including higher order Lagrangian approximations and Lagrangian effective field theory.
Large-scale structure non-Gaussianities with modal methods
NASA Astrophysics Data System (ADS)
Schmittfull, Marcel
2016-10-01
Relying on a separable modal expansion of the bispectrum, the implementation of a fast estimator for the full bispectrum of a 3d particle distribution is presented. The computational cost of accurate bispectrum estimation is negligible relative to simulation evolution, so the bispectrum can be used as a standard diagnostic whenever the power spectrum is evaluated. As an application, the time evolution of gravitational and primordial dark matter bispectra was measured in a large suite of N-body simulations. The bispectrum shape changes characteristically when the cosmic web becomes dominated by filaments and halos, therefore providing a quantitative probe of 3d structure formation. Our measured bispectra are determined by ~ 50 coefficients, which can be used as fitting formulae in the nonlinear regime and for non-Gaussian initial conditions. We also compare the measured bispectra with predictions from the Effective Field Theory of Large Scale Structures (EFTofLSS).
Efficient, large scale separation of coal macerals
Dyrkacz, G.R.; Bloomquist, C.A.A.
1988-01-01
The authors believe that the separation of macerals by continuous flow centrifugation offers a simple technique for the large scale separation of macerals. With relatively little cost (/approximately/ $10K), it provides an opportunity for obtaining quite pure maceral fractions. Although they have not completely worked out all the nuances of this separation system, they believe that the problems they have indicated can be minimized to pose only minor inconvenience. It cannot be said that this system completely bypasses the disagreeable tedium or time involved in separating macerals, nor will it by itself overcome the mental inertia required to make maceral separation an accepted necessary fact in fundamental coal science. However, they find their particular brand of continuous flow centrifugation is considerably faster than sink/float separation, can provide a good quality product with even one separation cycle, and permits the handling of more material than a conventional sink/float centrifuge separation.
Primer design for large scale sequencing.
Haas, S; Vingron, M; Poustka, A; Wiemann, S
1998-01-01
We have developed PRIDE, a primer design program that automatically designs primers in single contigs or whole sequencing projects to extend the already known sequence and to double strand single-stranded regions. The program is fully integrated into the Staden package (GAP4) and accessible with a graphical user interface. PRIDE uses a fuzzy logic-based system to calculate primer qualities. The computational performance of PRIDE is enhanced by using suffix trees to store the huge amount of data being produced. A test set of 110 sequencing primers and 11 PCR primer pairs has been designed on genomic templates, cDNAs and sequences containing repetitive elements to analyze PRIDE's success rate. The high performance of PRIDE, combined with its minimal requirement of user interaction and its fast algorithm, make this program useful for the large scale design of primers, especially in large sequencing projects. PMID:9611248
Large-Scale Organization of Glycosylation Networks
NASA Astrophysics Data System (ADS)
Kim, Pan-Jun; Lee, Dong-Yup; Jeong, Hawoong
2009-03-01
Glycosylation is a highly complex process to produce a diverse repertoire of cellular glycans that are frequently attached to proteins and lipids. Glycans participate in fundamental biological processes including molecular trafficking and clearance, cell proliferation and apoptosis, developmental biology, immune response, and pathogenesis. N-linked glycans found on proteins are formed by sequential attachments of monosaccharides with the help of a relatively small number of enzymes. Many of these enzymes can accept multiple N-linked glycans as substrates, thus generating a large number of glycan intermediates and their intermingled pathways. Motivated by the quantitative methods developed in complex network research, we investigate the large-scale organization of such N-glycosylation pathways in a mammalian cell. The uncovered results give the experimentally-testable predictions for glycosylation process, and can be applied to the engineering of therapeutic glycoproteins.
Engineering management of large scale systems
NASA Technical Reports Server (NTRS)
Sanders, Serita; Gill, Tepper L.; Paul, Arthur S.
1989-01-01
The organization of high technology and engineering problem solving, has given rise to an emerging concept. Reasoning principles for integrating traditional engineering problem solving with system theory, management sciences, behavioral decision theory, and planning and design approaches can be incorporated into a methodological approach to solving problems with a long range perspective. Long range planning has a great potential to improve productivity by using a systematic and organized approach. Thus, efficiency and cost effectiveness are the driving forces in promoting the organization of engineering problems. Aspects of systems engineering that provide an understanding of management of large scale systems are broadly covered here. Due to the focus and application of research, other significant factors (e.g., human behavior, decision making, etc.) are not emphasized but are considered.
Large scale cryogenic fluid systems testing
NASA Technical Reports Server (NTRS)
1992-01-01
NASA Lewis Research Center's Cryogenic Fluid Systems Branch (CFSB) within the Space Propulsion Technology Division (SPTD) has the ultimate goal of enabling the long term storage and in-space fueling/resupply operations for spacecraft and reusable vehicles in support of space exploration. Using analytical modeling, ground based testing, and on-orbit experimentation, the CFSB is studying three primary categories of fluid technology: storage, supply, and transfer. The CFSB is also investigating fluid handling, advanced instrumentation, and tank structures and materials. Ground based testing of large-scale systems is done using liquid hydrogen as a test fluid at the Cryogenic Propellant Tank Facility (K-site) at Lewis' Plum Brook Station in Sandusky, Ohio. A general overview of tests involving liquid transfer, thermal control, pressure control, and pressurization is given.
Large scale preparation of pure phycobiliproteins.
Padgett, M P; Krogmann, D W
1987-01-01
This paper describes simple procedures for the purification of large amounts of phycocyanin and allophycocyanin from the cyanobacterium Microcystis aeruginosa. A homogeneous natural bloom of this organism provided hundreds of kilograms of cells. Large samples of cells were broken by freezing and thawing. Repeated extraction of the broken cells with distilled water released phycocyanin first, then allophycocyanin, and provides supporting evidence for the current models of phycobilisome structure. The very low ionic strength of the aqueous extracts allowed allophycocyanin release in a particulate form so that this protein could be easily concentrated by centrifugation. Other proteins in the extract were enriched and concentrated by large scale membrane filtration. The biliproteins were purified to homogeneity by chromatography on DEAE cellulose. Purity was established by HPLC and by N-terminal amino acid sequence analysis. The proteins were examined for stability at various pHs and exposures to visible light.
Primer design for large scale sequencing.
Haas, S; Vingron, M; Poustka, A; Wiemann, S
1998-06-15
We have developed PRIDE, a primer design program that automatically designs primers in single contigs or whole sequencing projects to extend the already known sequence and to double strand single-stranded regions. The program is fully integrated into the Staden package (GAP4) and accessible with a graphical user interface. PRIDE uses a fuzzy logic-based system to calculate primer qualities. The computational performance of PRIDE is enhanced by using suffix trees to store the huge amount of data being produced. A test set of 110 sequencing primers and 11 PCR primer pairs has been designed on genomic templates, cDNAs and sequences containing repetitive elements to analyze PRIDE's success rate. The high performance of PRIDE, combined with its minimal requirement of user interaction and its fast algorithm, make this program useful for the large scale design of primers, especially in large sequencing projects.
Large-scale synthesis of peptides.
Andersson, L; Blomberg, L; Flegel, M; Lepsa, L; Nilsson, B; Verlander, M
2000-01-01
Recent advances in the areas of formulation and delivery have rekindled the interest of the pharmaceutical community in peptides as drug candidates, which, in turn, has provided a challenge to the peptide industry to develop efficient methods for the manufacture of relatively complex peptides on scales of up to metric tons per year. This article focuses on chemical synthesis approaches for peptides, and presents an overview of the methods available and in use currently, together with a discussion of scale-up strategies. Examples of the different methods are discussed, together with solutions to some specific problems encountered during scale-up development. Finally, an overview is presented of issues common to all manufacturing methods, i.e., methods used for the large-scale purification and isolation of final bulk products and regulatory considerations to be addressed during scale-up of processes to commercial levels. Copyright 2000 John Wiley & Sons, Inc. Biopolymers (Pept Sci) 55: 227-250, 2000
Large Scale Quantum Simulations of Nuclear Pasta
NASA Astrophysics Data System (ADS)
Fattoyev, Farrukh J.; Horowitz, Charles J.; Schuetrumpf, Bastian
2016-03-01
Complex and exotic nuclear geometries collectively referred to as ``nuclear pasta'' are expected to naturally exist in the crust of neutron stars and in supernovae matter. Using a set of self-consistent microscopic nuclear energy density functionals we present the first results of large scale quantum simulations of pasta phases at baryon densities 0 . 03 < ρ < 0 . 10 fm-3, proton fractions 0 . 05
Jovian large-scale stratospheric circulation
NASA Technical Reports Server (NTRS)
West, R. A.; Friedson, A. J.; Appleby, J. F.
1992-01-01
An attempt is made to diagnose the annual-average mean meridional residual Jovian large-scale stratospheric circulation from observations of the temperature and reflected sunlight that reveal the morphology of the aerosol heating. The annual mean solar heating, total radiative flux divergence, mass stream function, and Eliassen-Palm flux divergence are shown. The stratospheric radiative flux divergence is dominated the high latitudes by aerosol absorption. Between the 270 and 100 mbar pressure levels, where there is no aerosol heating in the model, the structure of the circulation at low- to midlatitudes is governed by the meridional variation of infrared cooling in association with the variation of zonal mean temperatures observed by IRIS. The principal features of the vertical velocity profile found by Gierasch et al. (1986) are recovered in the present calculation.
Large-scale parametric survival analysis.
Mittal, Sushil; Madigan, David; Cheng, Jerry Q; Burd, Randall S
2013-10-15
Survival analysis has been a topic of active statistical research in the past few decades with applications spread across several areas. Traditional applications usually consider data with only a small numbers of predictors with a few hundreds or thousands of observations. Recent advances in data acquisition techniques and computation power have led to considerable interest in analyzing very-high-dimensional data where the number of predictor variables and the number of observations range between 10(4) and 10(6). In this paper, we present a tool for performing large-scale regularized parametric survival analysis using a variant of the cyclic coordinate descent method. Through our experiments on two real data sets, we show that application of regularized models to high-dimensional data avoids overfitting and can provide improved predictive performance and calibration over corresponding low-dimensional models.
Large-Scale Parametric Survival Analysis†
Mittal, Sushil; Madigan, David; Cheng, Jerry; Burd, Randall S.
2013-01-01
Survival analysis has been a topic of active statistical research in the past few decades with applications spread across several areas. Traditional applications usually consider data with only small numbers of predictors with a few hundreds or thousands of observations. Recent advances in data acquisition techniques and computation power has led to considerable interest in analyzing very high-dimensional data where the number of predictor variables and the number of observations range between 104 – 106. In this paper, we present a tool for performing large-scale regularized parametric survival analysis using a variant of cyclic coordinate descent method. Through our experiments on two real data sets, we show that application of regularized models to high-dimensional data avoids overfitting and can provide improved predictive performance and calibration over corresponding low-dimensional models. PMID:23625862
Large scale study of tooth enamel
Bodart, F.; Deconninck, G.; Martin, M.Th.
1981-04-01
Human tooth enamel contains traces of foreign elements. The presence of these elements is related to the history and the environment of the human body and can be considered as the signature of perturbations which occur during the growth of a tooth. A map of the distribution of these traces on a large scale sample of the population will constitute a reference for further investigations of environmental effects. One hundred eighty samples of teeth were first analysed using PIXE, backscattering and nuclear reaction techniques. The results were analysed using statistical methods. Correlations between O, F, Na, P, Ca, Mn, Fe, Cu, Zn, Pb and Sr were observed and cluster analysis was in progress. The techniques described in the present work have been developed in order to establish a method for the exploration of very large samples of the Belgian population.
The challenge of large-scale structure
NASA Astrophysics Data System (ADS)
Gregory, S. A.
1996-03-01
The tasks that I have assumed for myself in this presentation include three separate parts. The first, appropriate to the particular setting of this meeting, is to review the basic work of the founding of this field; the appropriateness comes from the fact that W. G. Tifft made immense contributions that are not often realized by the astronomical community. The second task is to outline the general tone of the observational evidence for large scale structures. (Here, in particular, I cannot claim to be complete. I beg forgiveness from any workers who are left out by my oversight for lack of space and time.) The third task is to point out some of the major aspects of the field that may represent the clues by which some brilliant sleuth will ultimately figure out how galaxies formed.
Modeling the Internet's large-scale topology
Yook, Soon-Hyung; Jeong, Hawoong; Barabási, Albert-László
2002-01-01
Network generators that capture the Internet's large-scale topology are crucial for the development of efficient routing protocols and modeling Internet traffic. Our ability to design realistic generators is limited by the incomplete understanding of the fundamental driving forces that affect the Internet's evolution. By combining several independent databases capturing the time evolution, topology, and physical layout of the Internet, we identify the universal mechanisms that shape the Internet's router and autonomous system level topology. We find that the physical layout of nodes form a fractal set, determined by population density patterns around the globe. The placement of links is driven by competition between preferential attachment and linear distance dependence, a marked departure from the currently used exponential laws. The universal parameters that we extract significantly restrict the class of potentially correct Internet models and indicate that the networks created by all available topology generators are fundamentally different from the current Internet. PMID:12368484
Improving Recent Large-Scale Pulsar Surveys
NASA Astrophysics Data System (ADS)
Cardoso, Rogerio Fernando; Ransom, S.
2011-01-01
Pulsars are unique in that they act as celestial laboratories for precise tests of gravity and other extreme physics (Kramer 2004). There are approximately 2000 known pulsars today, which is less than ten percent of pulsars in the Milky Way according to theoretical models (Lorimer 2004). Out of these 2000 known pulsars, approximately ten percent are known millisecond pulsars, objects used for their period stability for detailed physics tests and searches for gravitational radiation (Lorimer 2008). As the field and instrumentation progress, pulsar astronomers attempt to overcome observational biases and detect new pulsars, consequently discovering new millisecond pulsars. We attempt to improve large scale pulsar surveys by examining three recent pulsar surveys. The first, the Green Bank Telescope 350MHz Drift Scan, a low frequency isotropic survey of the northern sky, has yielded a large number of candidates that were visually inspected and identified, resulting in over 34.000 thousands candidates viewed, dozens of detections of known pulsars, and the discovery of a new low-flux pulsar, PSRJ1911+22. The second, the PALFA survey, is a high frequency survey of the galactic plane with the Arecibo telescope. We created a processing pipeline for the PALFA survey at the National Radio Astronomy Observatory in Charlottesville- VA, in addition to making needed modifications upon advice from the PALFA consortium. The third survey examined is a new GBT 820MHz survey devoted to find new millisecond pulsars by observing the target-rich environment of unidentified sources in the FERMI LAT catalogue. By approaching these three pulsar surveys at different stages, we seek to improve the success rates of large scale surveys, and hence the possibility for ground-breaking work in both basic physics and astrophysics.
Introducing Large-Scale Innovation in Schools
NASA Astrophysics Data System (ADS)
Sotiriou, Sofoklis; Riviou, Katherina; Cherouvis, Stephanos; Chelioti, Eleni; Bogner, Franz X.
2016-08-01
Education reform initiatives tend to promise higher effectiveness in classrooms especially when emphasis is given to e-learning and digital resources. Practical changes in classroom realities or school organization, however, are lacking. A major European initiative entitled Open Discovery Space (ODS) examined the challenge of modernizing school education via a large-scale implementation of an open-scale methodology in using technology-supported innovation. The present paper describes this innovation scheme which involved schools and teachers all over Europe, embedded technology-enhanced learning into wider school environments and provided training to teachers. Our implementation scheme consisted of three phases: (1) stimulating interest, (2) incorporating the innovation into school settings and (3) accelerating the implementation of the innovation. The scheme's impact was monitored for a school year using five indicators: leadership and vision building, ICT in the curriculum, development of ICT culture, professional development support, and school resources and infrastructure. Based on about 400 schools, our study produced four results: (1) The growth in digital maturity was substantial, even for previously high scoring schools. This was even more important for indicators such as vision and leadership" and "professional development." (2) The evolution of networking is presented graphically, showing the gradual growth of connections achieved. (3) These communities became core nodes, involving numerous teachers in sharing educational content and experiences: One out of three registered users (36 %) has shared his/her educational resources in at least one community. (4) Satisfaction scores ranged from 76 % (offer of useful support through teacher academies) to 87 % (good environment to exchange best practices). Initiatives such as ODS add substantial value to schools on a large scale.
Supporting large-scale computational science
Musick, R
1998-10-01
A study has been carried out to determine the feasibility of using commercial database management systems (DBMSs) to support large-scale computational science. Conventional wisdom in the past has been that DBMSs are too slow for such data. Several events over the past few years have muddied the clarity of this mindset: 1. 2. 3. 4. Several commercial DBMS systems have demonstrated storage and ad-hoc quer access to Terabyte data sets. Several large-scale science teams, such as EOSDIS [NAS91], high energy physics [MM97] and human genome [Kin93] have adopted (or make frequent use of) commercial DBMS systems as the central part of their data management scheme. Several major DBMS vendors have introduced their first object-relational products (ORDBMSs), which have the potential to support large, array-oriented data. In some cases, performance is a moot issue. This is true in particular if the performance of legacy applications is not reduced while new, albeit slow, capabilities are added to the system. The basic assessment is still that DBMSs do not scale to large computational data. However, many of the reasons have changed, and there is an expiration date attached to that prognosis. This document expands on this conclusion, identifies the advantages and disadvantages of various commercial approaches, and describes the studies carried out in exploring this area. The document is meant to be brief, technical and informative, rather than a motivational pitch. The conclusions within are very likely to become outdated within the next 5-7 years, as market forces will have a significant impact on the state of the art in scientific data management over the next decade.
Voids in the Large-Scale Structure
NASA Astrophysics Data System (ADS)
El-Ad, Hagai; Piran, Tsvi
1997-12-01
Voids are the most prominent feature of the large-scale structure of the universe. Still, their incorporation into quantitative analysis of it has been relatively recent, owing essentially to the lack of an objective tool to identify the voids and to quantify them. To overcome this, we present here the VOID FINDER algorithm, a novel tool for objectively quantifying voids in the galaxy distribution. The algorithm first classifies galaxies as either wall galaxies or field galaxies. Then, it identifies voids in the wall-galaxy distribution. Voids are defined as continuous volumes that do not contain any wall galaxies. The voids must be thicker than an adjustable limit, which is refined in successive iterations. In this way, we identify the same regions that would be recognized as voids by the eye. Small breaches in the walls are ignored, avoiding artificial connections between neighboring voids. We test the algorithm using Voronoi tesselations. By appropriate scaling of the parameters with the selection function, we apply it to two redshift surveys, the dense SSRS2 and the full-sky IRAS 1.2 Jy. Both surveys show similar properties: ~50% of the volume is filled by voids. The voids have a scale of at least 40 h-1 Mpc and an average -0.9 underdensity. Faint galaxies do not fill the voids, but they do populate them more than bright ones. These results suggest that both optically and IRAS-selected galaxies delineate the same large-scale structure. Comparison with the recovered mass distribution further suggests that the observed voids in the galaxy distribution correspond well to underdense regions in the mass distribution. This confirms the gravitational origin of the voids.
NASA Technical Reports Server (NTRS)
Pavarini, C.
1974-01-01
Work in two somewhat distinct areas is presented. First, the optimal system design problem for a Mars-roving vehicle is attacked by creating static system models and a system evaluation function and optimizing via nonlinear programming techniques. The second area concerns the problem of perturbed-optimal solutions. Given an initial perturbation in an element of the solution to a nonlinear programming problem, a linear method is determined to approximate the optimal readjustments of the other elements of the solution. Then, the sensitivity of the Mars rover designs is described by application of this method.
Large-scale Ising spin network based on degenerate optical parametric oscillators
NASA Astrophysics Data System (ADS)
Inagaki, Takahiro; Inaba, Kensuke; Hamerly, Ryan; Inoue, Kyo; Yamamoto, Yoshihisa; Takesue, Hiroki
2016-06-01
Solving combinatorial optimization problems is becoming increasingly important in modern society, where the analysis and optimization of unprecedentedly complex systems are required. Many such problems can be mapped onto the ground-state-search problem of the Ising Hamiltonian, and simulating the Ising spins with physical systems is now emerging as a promising approach for tackling such problems. Here, we report a large-scale network of artificial spins based on degenerate optical parametric oscillators (DOPOs), paving the way towards a photonic Ising machine capable of solving difficult combinatorial optimization problems. We generate >10,000 time-division-multiplexed DOPOs using dual-pump four-wave mixing in a highly nonlinear fibre placed in a cavity. Using those DOPOs, a one-dimensional Ising model is simulated by introducing nearest-neighbour optical coupling. We observe the formation of spin domains and find that the domain size diverges near the DOPO threshold, which suggests that the DOPO network can simulate the behaviour of low-temperature Ising spins.
Scalable analysis of nonlinear systems using convex optimization
NASA Astrophysics Data System (ADS)
Papachristodoulou, Antonis
In this thesis, we investigate how convex optimization can be used to analyze different classes of nonlinear systems at various scales algorithmically. The methodology is based on the construction of appropriate Lyapunov-type certificates using sum of squares techniques. After a brief introduction on the mathematical tools that we will be using, we turn our attention to robust stability and performance analysis of systems described by Ordinary Differential Equations. A general framework for constrained systems analysis is developed, under which stability of systems with polynomial, non-polynomial vector fields and switching systems, as well estimating the region of attraction and the L2 gain can be treated in a unified manner. We apply our results to examples from biology and aerospace. We then consider systems described by Functional Differential Equations (FDEs), i.e., time-delay systems. Their main characteristic is that they are infinite dimensional, which complicates their analysis. We first show how the complete Lyapunov-Krasovskii functional can be constructed algorithmically for linear time-delay systems. Then, we concentrate on delay-independent and delay-dependent stability analysis of nonlinear FDEs using sum of squares techniques. An example from ecology is given. The scalable stability analysis of congestion control algorithms for the Internet is investigated next. The models we use result in an arbitrary interconnection of FDE subsystems, for which we require that stability holds for arbitrary delays, network topologies and link capacities. Through a constructive proof, we develop a Lyapunov functional for FAST---a recently developed network congestion control scheme---so that the Lyapunov stability properties scale with the system size. We also show how other network congestion control schemes can be analyzed in the same way. Finally, we concentrate on systems described by Partial Differential Equations. We show that axially constant perturbations of
Reconstructing Information in Large-Scale Structure via Logarithmic Mapping
NASA Astrophysics Data System (ADS)
Szapudi, Istvan
We propose to develop a new method to extract information from large-scale structure data combining two-point statistics and non-linear transformations; before, this information was available only with substantially more complex higher-order statistical methods. Initially, most of the cosmological information in large-scale structure lies in two-point statistics. With non- linear evolution, some of that useful information leaks into higher-order statistics. The PI and group has shown in a series of theoretical investigations how that leakage occurs, and explained the Fisher information plateau at smaller scales. This plateau means that even as more modes are added to the measurement of the power spectrum, the total cumulative information (loosely speaking the inverse errorbar) is not increasing. Recently we have shown in Neyrinck et al. (2009, 2010) that a logarithmic (and a related Gaussianization or Box-Cox) transformation on the non-linear Dark Matter or galaxy field reconstructs a surprisingly large fraction of this missing Fisher information of the initial conditions. This was predicted by the earlier wave mechanical formulation of gravitational dynamics by Szapudi & Kaiser (2003). The present proposal is focused on working out the theoretical underpinning of the method to a point that it can be used in practice to analyze data. In particular, one needs to deal with the usual real-life issues of galaxy surveys, such as complex geometry, discrete sam- pling (Poisson or sub-Poisson noise), bias (linear, or non-linear, deterministic, or stochastic), redshift distortions, pro jection effects for 2D samples, and the effects of photometric redshift errors. We will develop methods for weak lensing and Sunyaev-Zeldovich power spectra as well, the latter specifically targetting Planck. In addition, we plan to investigate the question of residual higher- order information after the non-linear mapping, and possible applications for cosmology. Our aim will be to work out
Optimization-based calculation of optical nonlinear processes in a micro-resonator.
Klemens, Guy; Fainman, Yeshaiahu
2006-10-16
We present a new method of calculating the performance of nonlinear processes in a resonator. An optimization-based approach, conceptually similar to techniques used in nonlinear circuit analysis, is formulated and used to find the wave magnitudes that satisfy all of the boundary conditions and account for nonlinear optical effects. Unlike previous solution methods, this technique is applicable to any nonlinear process (second-order, third-order, etc.) and multiple coupled resonators, maintains the phase relations between the waves, and is exact. Examples are given for second-order nonlinear processes in a one-dimensional resonator.
Schmidt hammer rebound data for estimation of large scale in situ coal strength
Sheorey, P.R.
1984-02-01
The paper reports an investigation to determine whether a correlation exists between the Schmidt hammer rebound and the in situ large-scale strength. The results showed a reasonable correlation between the large-scale in situ crushing strength of 0.3 m cubes of coal and the lower mean of rebound values obtained. The regression is seen to be linear within the range 2.75 - 13.14 MPa of in situ strength. In situ large-scale testing procedures are cumbersome and expensive, and the Schmidt hammer rebound method can offer a quicker and cheaper means of estimating this strength. Laboratory 25 mm cube coal strength shows a greater scatter with rebound values than the large-scale in situ strength, and gives a non-linear regression.
NASA Astrophysics Data System (ADS)
Keselman, J. A.; Nusser, A.
2017-05-01
No Action Method (NoAM) is a framework for reconstructing the past orbits of observed tracers of the large-scale mass density field. It seeks exact solutions of the equations of motion (EoM), satisfying initial homogeneity and the final observed particle (tracer) positions. The solutions are found iteratively reaching a specified tolerance defined as the RMS of the distance between reconstructed and observed positions. Starting from a guess for the initial conditions, NoAM advances particles using standard N-body techniques for solving the EoM. Alternatively, the EoM can be replaced by any approximation such as Zel'dovich and second-order perturbation theory (2LPT). NoAM is suitable for billions of particles and can easily handle non-regular volumes, redshift space and other constraints. We implement NoAM to systematically compare Zel'dovich, 2LPT, and N-body dynamics over diverse configurations ranging from an idealized high-res periodic simulation box to realistic galaxy mocks. Our findings are: (i) non-linear reconstructions with Zel'dovich, 2LPT, and full dynamics perform better than linear theory only for idealized catalogues in real space. For realistic catalogues, linear theory is the optimal choice for reconstructing velocity fields smoothed on scales ≳ 5 h- 1 Mpc; (ii) all non-linear back-in-time reconstructions tested here produce comparable enhancement of the baryonic oscillation signal in the correlation function.
NASA Astrophysics Data System (ADS)
Keselman, J. A.; Nusser, A.
2017-01-01
NoAM for "No Action Method" is a framework for reconstructing the past orbits of observed tracers of the large scale mass density field. It seeks exact solutions of the equations of motion (EoM), satisfying initial homogeneity and the final observed particle (tracer) positions. The solutions are found iteratively reaching a specified tolerance defined as the RMS of the distance between reconstructed and observed positions. Starting from a guess for the initial conditions, NoAM advances particles using standard N-body techniques for solving the EoM. Alternatively, the EoM can be replaced by any approximation such as Zel'dovich and second order perturbation theory (2LPT). NoAM is suitable for billions of particles and can easily handle non-regular volumes, redshift space, and other constraints. We implement NoAM to systematically compare Zel'dovich, 2LPT, and N-body dynamics over diverse configurations ranging from idealized high-res periodic simulation box to realistic galaxy mocks. Our findings are (i) Non-linear reconstructions with Zel'dovich, 2LPT, and full dynamics perform better than linear theory only for idealized catalogs in real space. For realistic catalogs, linear theory is the optimal choice for reconstructing velocity fields smoothed on scales {buildrel > over {˜}} 5 h^{-1}{Mpc}.(ii) all non-linear back-in-time reconstructions tested here, produce comparable enhancement of the baryonic oscillation signal in the correlation function.
Gálvez, Akemi; Iglesias, Andrés
2013-01-01
Fitting spline curves to data points is a very important issue in many applied fields. It is also challenging, because these curves typically depend on many continuous variables in a highly interrelated nonlinear way. In general, it is not possible to compute these parameters analytically, so the problem is formulated as a continuous nonlinear optimization problem, for which traditional optimization techniques usually fail. This paper presents a new bioinspired method to tackle this issue. In this method, optimization is performed through a combination of two techniques. Firstly, we apply the indirect approach to the knots, in which they are not initially the subject of optimization but precomputed with a coarse approximation scheme. Secondly, a powerful bioinspired metaheuristic technique, the firefly algorithm, is applied to optimization of data parameterization; then, the knot vector is refined by using De Boor's method, thus yielding a better approximation to the optimal knot vector. This scheme converts the original nonlinear continuous optimization problem into a convex optimization problem, solved by singular value decomposition. Our method is applied to some illustrative real-world examples from the CAD/CAM field. Our experimental results show that the proposed scheme can solve the original continuous nonlinear optimization problem very efficiently. PMID:24376380
Gálvez, Akemi; Iglesias, Andrés
2013-01-01
Fitting spline curves to data points is a very important issue in many applied fields. It is also challenging, because these curves typically depend on many continuous variables in a highly interrelated nonlinear way. In general, it is not possible to compute these parameters analytically, so the problem is formulated as a continuous nonlinear optimization problem, for which traditional optimization techniques usually fail. This paper presents a new bioinspired method to tackle this issue. In this method, optimization is performed through a combination of two techniques. Firstly, we apply the indirect approach to the knots, in which they are not initially the subject of optimization but precomputed with a coarse approximation scheme. Secondly, a powerful bioinspired metaheuristic technique, the firefly algorithm, is applied to optimization of data parameterization; then, the knot vector is refined by using De Boor's method, thus yielding a better approximation to the optimal knot vector. This scheme converts the original nonlinear continuous optimization problem into a convex optimization problem, solved by singular value decomposition. Our method is applied to some illustrative real-world examples from the CAD/CAM field. Our experimental results show that the proposed scheme can solve the original continuous nonlinear optimization problem very efficiently.
Solution of transient optimization problems by using an algorithm based on nonlinear programming
NASA Technical Reports Server (NTRS)
Teren, F.
1977-01-01
A new algorithm is presented for solution of dynamic optimization problems which are nonlinear in the state variables and linear in the control variables. It is shown that the optimal control is bang-bang. A nominal bang-bang solution is found which satisfies the system equations and constraints, and influence functions are generated which check the optimality of the solution. Nonlinear optimization (gradient search) techniques are used to find the optimal solution. The algorithm is used to find a minimum time acceleration for a turbofan engine.
Solution of transient optimization problems by using an algorithm based on nonlinear programming
NASA Technical Reports Server (NTRS)
Teren, F.
1977-01-01
An algorithm is presented for solution of dynamic optimization problems which are nonlinear in the state variables and linear in the control variables. It is shown that the optimal control is bang-bang. A nominal bang-bang solution is found which satisfies the system equations and constraints, and influence functions are generated which check the optimality of the solution. Nonlinear optimization (gradient search) techniques are used to find the optimal solution. The algorithm is used to find a minimum time acceleration for a turbofan engine.
Solution of transient optimization problems by using an algorithm based on nonlinear programming
NASA Technical Reports Server (NTRS)
Teren, F.
1977-01-01
A new algorithm is presented for solution of dynamic optimization problems which are nonlinear in the state variables and linear in the control variables. It is shown that the optimal control is bang-bang. A nominal bang-bang solution is found which satisfies the system equations and constraints, and influence functions are generated which check the optimality of the solution. Nonlinear optimization (gradient search) techniques are used to find the optimal solution. The algorithm is used to find a minimum time acceleration for a turbofan engine.
Optimal design of a class of nonlinear networks.
NASA Technical Reports Server (NTRS)
Peikari, B.
1972-01-01
The problem of synthesizing nth order nonlinear nonautonomous networks with a prescribed small signal behavior is considered. It is shown that, in the absence of coupling elements, the solution of this problem reduces to synthesizing a set of first order nonlinear characteristics. These characteristics can then be determined using a recently developed generalized steepest descent criterion.
Management of large-scale multimedia conferencing
NASA Astrophysics Data System (ADS)
Cidon, Israel; Nachum, Youval
1998-12-01
The goal of this work is to explore management strategies and algorithms for large-scale multimedia conferencing over a communication network. Since the use of multimedia conferencing is still limited, the management of such systems has not yet been studied in depth. A well organized and human friendly multimedia conference management should utilize efficiently and fairly its limited resources as well as take into account the requirements of the conference participants. The ability of the management to enforce fair policies and to quickly take into account the participants preferences may even lead to a conference environment that is more pleasant and more effective than a similar face to face meeting. We suggest several principles for defining and solving resource sharing problems in this context. The conference resources which are addressed in this paper are the bandwidth (conference network capacity), time (participants' scheduling) and limitations of audio and visual equipment. The participants' requirements for these resources are defined and translated in terms of Quality of Service requirements and the fairness criteria.
Large-scale wind turbine structures
NASA Technical Reports Server (NTRS)
Spera, David A.
1988-01-01
The purpose of this presentation is to show how structural technology was applied in the design of modern wind turbines, which were recently brought to an advanced stage of development as sources of renewable power. Wind turbine structures present many difficult problems because they are relatively slender and flexible; subject to vibration and aeroelastic instabilities; acted upon by loads which are often nondeterministic; operated continuously with little maintenance in all weather; and dominated by life-cycle cost considerations. Progress in horizontal-axis wind turbines (HAWT) development was paced by progress in the understanding of structural loads, modeling of structural dynamic response, and designing of innovative structural response. During the past 15 years a series of large HAWTs was developed. This has culminated in the recent completion of the world's largest operating wind turbine, the 3.2 MW Mod-5B power plane installed on the island of Oahu, Hawaii. Some of the applications of structures technology to wind turbine will be illustrated by referring to the Mod-5B design. First, a video overview will be presented to provide familiarization with the Mod-5B project and the important components of the wind turbine system. Next, the structural requirements for large-scale wind turbines will be discussed, emphasizing the difficult fatigue-life requirements. Finally, the procedures used to design the structure will be presented, including the use of the fracture mechanics approach for determining allowable fatigue stresses.
Food appropriation through large scale land acquisitions
NASA Astrophysics Data System (ADS)
Rulli, Maria Cristina; D'Odorico, Paolo
2014-05-01
The increasing demand for agricultural products and the uncertainty of international food markets has recently drawn the attention of governments and agribusiness firms toward investments in productive agricultural land, mostly in the developing world. The targeted countries are typically located in regions that have remained only marginally utilized because of lack of modern technology. It is expected that in the long run large scale land acquisitions (LSLAs) for commercial farming will bring the technology required to close the existing crops yield gaps. While the extent of the acquired land and the associated appropriation of freshwater resources have been investigated in detail, the amount of food this land can produce and the number of people it could feed still need to be quantified. Here we use a unique dataset of land deals to provide a global quantitative assessment of the rates of crop and food appropriation potentially associated with LSLAs. We show how up to 300-550 million people could be fed by crops grown in the acquired land, should these investments in agriculture improve crop production and close the yield gap. In contrast, about 190-370 million people could be supported by this land without closing of the yield gap. These numbers raise some concern because the food produced in the acquired land is typically exported to other regions, while the target countries exhibit high levels of malnourishment. Conversely, if used for domestic consumption, the crops harvested in the acquired land could ensure food security to the local populations.
Large scale structure of the sun's corona
NASA Astrophysics Data System (ADS)
Kundu, Mukul R.
Results concerning the large-scale structure of the solar corona obtained by observations at meter-decameter wavelengths are reviewed. Coronal holes observed on the disk at multiple frequencies show the radial and azimuthal geometry of the hole. At the base of the hole there is good correspondence to the chromospheric signature in He I 10,830 A, but at greater heights the hole may show departures from symmetry. Two-dimensional imaging of weak-type III bursts simultaneously with the HAO SMM coronagraph/polarimeter measurements indicate that these bursts occur along elongated features emanating from the quiet sun, corresponding in position angle to the bright coronal streamers. It is shown that the densest regions of streamers and the regions of maximum intensity of type II bursts coincide closely. Non-flare-associated type II/type IV bursts associated with coronal streamer disruption events are studied along with correlated type II burst emissions originating from distant centers on the sun.
Large-scale carbon fiber tests
NASA Technical Reports Server (NTRS)
Pride, R. A.
1980-01-01
A realistic release of carbon fibers was established by burning a minimum of 45 kg of carbon fiber composite aircraft structural components in each of five large scale, outdoor aviation jet fuel fire tests. This release was quantified by several independent assessments with various instruments developed specifically for these tests. The most likely values for the mass of single carbon fibers released ranged from 0.2 percent of the initial mass of carbon fiber for the source tests (zero wind velocity) to a maximum of 0.6 percent of the initial carbon fiber mass for dissemination tests (5 to 6 m/s wind velocity). Mean fiber lengths for fibers greater than 1 mm in length ranged from 2.5 to 3.5 mm. Mean diameters ranged from 3.6 to 5.3 micrometers which was indicative of significant oxidation. Footprints of downwind dissemination of the fire released fibers were measured to 19.1 km from the fire.
Large-scale clustering of cosmic voids
NASA Astrophysics Data System (ADS)
Chan, Kwan Chuen; Hamaus, Nico; Desjacques, Vincent
2014-11-01
We study the clustering of voids using N -body simulations and simple theoretical models. The excursion-set formalism describes fairly well the abundance of voids identified with the watershed algorithm, although the void formation threshold required is quite different from the spherical collapse value. The void cross bias bc is measured and its large-scale value is found to be consistent with the peak background split results. A simple fitting formula for bc is found. We model the void auto-power spectrum taking into account the void biasing and exclusion effect. A good fit to the simulation data is obtained for voids with radii ≳30 Mpc h-1 , especially when the void biasing model is extended to 1-loop order. However, the best-fit bias parameters do not agree well with the peak-background results. Being able to fit the void auto-power spectrum is particularly important not only because it is the direct observable in galaxy surveys, but also our method enables us to treat the bias parameters as nuisance parameters, which are sensitive to the techniques used to identify voids.
Large-scale autostereoscopic outdoor display
NASA Astrophysics Data System (ADS)
Reitterer, Jörg; Fidler, Franz; Saint Julien-Wallsee, Ferdinand; Schmid, Gerhard; Gartner, Wolfgang; Leeb, Walter; Schmid, Ulrich
2013-03-01
State-of-the-art autostereoscopic displays are often limited in size, effective brightness, number of 3D viewing zones, and maximum 3D viewing distances, all of which are mandatory requirements for large-scale outdoor displays. Conventional autostereoscopic indoor concepts like lenticular lenses or parallax barriers cannot simply be adapted for these screens due to the inherent loss of effective resolution and brightness, which would reduce both image quality and sunlight readability. We have developed a modular autostereoscopic multi-view laser display concept with sunlight readable effective brightness, theoretically up to several thousand 3D viewing zones, and maximum 3D viewing distances of up to 60 meters. For proof-of-concept purposes a prototype display with two pixels was realized. Due to various manufacturing tolerances each individual pixel has slightly different optical properties, and hence the 3D image quality of the display has to be calculated stochastically. In this paper we present the corresponding stochastic model, we evaluate the simulation and measurement results of the prototype display, and we calculate the achievable autostereoscopic image quality to be expected for our concept.
Large Scale EOF Analysis of Climate Data
NASA Astrophysics Data System (ADS)
Prabhat, M.; Gittens, A.; Kashinath, K.; Cavanaugh, N. R.; Mahoney, M.
2016-12-01
We present a distributed approach towards extracting EOFs from 3D climate data. We implement the method in Apache Spark, and process multi-TB sized datasets on O(1000-10,000) cores. We apply this method to latitude-weighted ocean temperature data from CSFR, a 2.2 terabyte-sized data set comprising ocean and subsurface reanalysis measurements collected at 41 levels in the ocean, at 6 hour intervals over 31 years. We extract the first 100 EOFs of this full data set and compare to the EOFs computed simply on the surface temperature field. Our analyses provide evidence of Kelvin and Rossy waves and components of large-scale modes of oscillation including the ENSO and PDO that are not visible in the usual SST EOFs. Further, they provide information on the the most influential parts of the ocean, such as the thermocline, that exist below the surface. Work is ongoing to understand the factors determining the depth-varying spatial patterns observed in the EOFs. We will experiment with weighting schemes to appropriately account for the differing depths of the observations. We also plan to apply the same distributed approach to analysis of analysis of 3D atmospheric climatic data sets, including multiple variables. Because the atmosphere changes on a quicker time-scale than the ocean, we expect that the results will demonstrate an even greater advantage to computing 3D EOFs in lieu of 2D EOFs.
Numerical Modeling for Large Scale Hydrothermal System
NASA Astrophysics Data System (ADS)
Sohrabi, Reza; Jansen, Gunnar; Malvoisin, Benjamin; Mazzini, Adriano; Miller, Stephen A.
2017-04-01
Moderate-to-high enthalpy systems are driven by multiphase and multicomponent processes, fluid and rock mechanics, and heat transport processes, all of which present challenges in developing realistic numerical models of the underlying physics. The objective of this work is to present an approach, and some initial results, for modeling and understanding dynamics of the birth of large scale hydrothermal systems. Numerical modeling of such complex systems must take into account a variety of coupled thermal, hydraulic, mechanical and chemical processes, which is numerically challenging. To provide first estimates of the behavior of this deep complex systems, geological structures must be constrained, and the fluid dynamics, mechanics and the heat transport need to be investigated in three dimensions. Modeling these processes numerically at adequate resolution and reasonable computation times requires a suite of tools that we are developing and/or utilizing to investigate such systems. Our long-term goal is to develop 3D numerical models, based on a geological models, which couples mechanics with the hydraulics and thermal processes driving hydrothermal system. Our first results from the Lusi hydrothermal system in East Java, Indonesia provide a basis for more sophisticated studies, eventually in 3D, and we introduce a workflow necessary to achieve these objectives. Future work focuses with the aim and parallelization suitable for High Performance Computing (HPC). Such developments are necessary to achieve high-resolution simulations to more fully understand the complex dynamics of hydrothermal systems.
Large scale digital atlases in neuroscience
NASA Astrophysics Data System (ADS)
Hawrylycz, M.; Feng, D.; Lau, C.; Kuan, C.; Miller, J.; Dang, C.; Ng, L.
2014-03-01
Imaging in neuroscience has revolutionized our current understanding of brain structure, architecture and increasingly its function. Many characteristics of morphology, cell type, and neuronal circuitry have been elucidated through methods of neuroimaging. Combining this data in a meaningful, standardized, and accessible manner is the scope and goal of the digital brain atlas. Digital brain atlases are used today in neuroscience to characterize the spatial organization of neuronal structures, for planning and guidance during neurosurgery, and as a reference for interpreting other data modalities such as gene expression and connectivity data. The field of digital atlases is extensive and in addition to atlases of the human includes high quality brain atlases of the mouse, rat, rhesus macaque, and other model organisms. Using techniques based on histology, structural and functional magnetic resonance imaging as well as gene expression data, modern digital atlases use probabilistic and multimodal techniques, as well as sophisticated visualization software to form an integrated product. Toward this goal, brain atlases form a common coordinate framework for summarizing, accessing, and organizing this knowledge and will undoubtedly remain a key technology in neuroscience in the future. Since the development of its flagship project of a genome wide image-based atlas of the mouse brain, the Allen Institute for Brain Science has used imaging as a primary data modality for many of its large scale atlas projects. We present an overview of Allen Institute digital atlases in neuroscience, with a focus on the challenges and opportunities for image processing and computation.
A Nonlinear Physics-Based Optimal Control Method for Magnetostrictive Actuators
NASA Technical Reports Server (NTRS)
Smith, Ralph C.
1998-01-01
This paper addresses the development of a nonlinear optimal control methodology for magnetostrictive actuators. At moderate to high drive levels, the output from these actuators is highly nonlinear and contains significant magnetic and magnetomechanical hysteresis. These dynamics must be accommodated by models and control laws to utilize the full capabilities of the actuators. A characterization based upon ferromagnetic mean field theory provides a model which accurately quantifies both transient and steady state actuator dynamics under a variety of operating conditions. The control method consists of a linear perturbation feedback law used in combination with an optimal open loop nonlinear control. The nonlinear control incorporates the hysteresis and nonlinearities inherent to the transducer and can be computed offline. The feedback control is constructed through linearization of the perturbed system about the optimal system and is efficient for online implementation. As demonstrated through numerical examples, the combined hybrid control is robust and can be readily implemented in linear PDE-based structural models.
Spatial solitons in photonic lattices with large-scale defects
NASA Astrophysics Data System (ADS)
Yang, Xiao-Yu; Zheng, Jiang-Bo; Dong, Liang-Wei
2011-03-01
We address the existence, stability and propagation dynamics of solitons supported by large-scale defects surrounded by the harmonic photonic lattices imprinted in the defocusing saturable nonlinear medium. Several families of soliton solutions, including flat-topped, dipole-like, and multipole-like solitons, can be supported by the defected lattices with different heights of defects. The width of existence domain of solitons is determined solely by the saturable parameter. The existence domains of various types of solitons can be shifted by the variations of defect size, lattice depth and soliton order. Solitons in the model are stable in a wide parameter window, provided that the propagation constant exceeds a critical value, which is in sharp contrast to the case where the soliton trains is supported by periodic lattices imprinted in defocusing saturable nonlinear medium. We also find stable solitons in the semi-infinite gap which rarely occur in the defocusing media. Project supported by the National Natural Science Foundation of China (Grant No. 10704067) and the Natural Science Foundation of Zhejiang Province, China (Grant No. Y6100381).
Bentley, Jason; Sloan, Charlotte; Kawajiri, Yoshiaki
2013-03-08
This work demonstrates a systematic prediction-correction (PC) method for simultaneously modeling and optimizing nonlinear simulated moving bed (SMB) chromatography. The PC method uses model-based optimization, SMB startup data, isotherm model selection, and parameter estimation to iteratively refine model parameters and find optimal operating conditions in a matter of hours to ensure high purity constraints and achieve optimal productivity. The PC algorithm proceeds until the SMB process is optimized without manual tuning. In case studies, it is shown that a nonlinear isotherm model and parameter values are determined reliably using SMB startup data. In one case study, a nonlinear SMB system is optimized after only two changes of operating conditions following the PC algorithm. The refined isotherm models are validated by frontal analysis and perturbation analysis. Copyright © 2013 Elsevier B.V. All rights reserved.
NASA Astrophysics Data System (ADS)
Peng, Haijun; Wang, Xinwei; Zhang, Sheng; Chen, Biaosong
2017-07-01
Nonlinear state-delayed optimal control problems have complex nonlinear characters. To solve this complex nonlinear problem, an iterative symplectic pseudospectral method based on quasilinearization techniques, the dual variational principle and pseudospectral methods is proposed in this paper. First, the proposed method transforms the original nonlinear optimal control problem into a series of linear quadratic optimal control problems. Then, a symplectic pseudospectral method is developed to solve these converted linear quadratic state-delayed optimal control problems. Coefficient matrices in the proposed method are sparse and symmetric since the dual variational principle is used, which makes the proposed method highly efficient. Converged numerical solutions with high precision can be obtained after a few iterations due to the benefit of the local pseudospectral method and quasilinearization techniques. In the numerical simulations, other numerical methods were used for comparisons. The numerical simulation results show that the proposed method is highly accurate, efficient and robust.
Pulov, Vladimir I.
2011-04-07
This paper is devoted to finding an optimal system of one-dimensional subalgebras of an eight-dimensional Lie algebra of point symmetry transformations, admitted by a system of two coupled nonlinear Schroedinger equations.
Maestro: an orchestration framework for large-scale WSN simulations.
Riliskis, Laurynas; Osipov, Evgeny
2014-03-18
Contemporary wireless sensor networks (WSNs) have evolved into large and complex systems and are one of the main technologies used in cyber-physical systems and the Internet of Things. Extensive research on WSNs has led to the development of diverse solutions at all levels of software architecture, including protocol stacks for communications. This multitude of solutions is due to the limited computational power and restrictions on energy consumption that must be accounted for when designing typical WSN systems. It is therefore challenging to develop, test and validate even small WSN applications, and this process can easily consume significant resources. Simulations are inexpensive tools for testing, verifying and generally experimenting with new technologies in a repeatable fashion. Consequently, as the size of the systems to be tested increases, so does the need for large-scale simulations. This article describes a tool called Maestro for the automation of large-scale simulation and investigates the feasibility of using cloud computing facilities for such task. Using tools that are built into Maestro, we demonstrate a feasible approach for benchmarking cloud infrastructure in order to identify cloud Virtual Machine (VM)instances that provide an optimal balance of performance and cost for a given simulation.
Maestro: An Orchestration Framework for Large-Scale WSN Simulations
Riliskis, Laurynas; Osipov, Evgeny
2014-01-01
Contemporary wireless sensor networks (WSNs) have evolved into large and complex systems and are one of the main technologies used in cyber-physical systems and the Internet of Things. Extensive research on WSNs has led to the development of diverse solutions at all levels of software architecture, including protocol stacks for communications. This multitude of solutions is due to the limited computational power and restrictions on energy consumption that must be accounted for when designing typical WSN systems. It is therefore challenging to develop, test and validate even small WSN applications, and this process can easily consume significant resources. Simulations are inexpensive tools for testing, verifying and generally experimenting with new technologies in a repeatable fashion. Consequently, as the size of the systems to be tested increases, so does the need for large-scale simulations. This article describes a tool called Maestro for the automation of large-scale simulation and investigates the feasibility of using cloud computing facilities for such task. Using tools that are built into Maestro, we demonstrate a feasible approach for benchmarking cloud infrastructure in order to identify cloud Virtual Machine (VM)instances that provide an optimal balance of performance and cost for a given simulation. PMID:24647123
Large Scale Flame Spread Environmental Characterization Testing
NASA Technical Reports Server (NTRS)
Clayman, Lauren K.; Olson, Sandra L.; Gokoghi, Suleyman A.; Brooker, John E.; Ferkul, Paul V.; Kacher, Henry F.
2013-01-01
Under the Advanced Exploration Systems (AES) Spacecraft Fire Safety Demonstration Project (SFSDP), as a risk mitigation activity in support of the development of a large-scale fire demonstration experiment in microgravity, flame-spread tests were conducted in normal gravity on thin, cellulose-based fuels in a sealed chamber. The primary objective of the tests was to measure pressure rise in a chamber as sample material, burning direction (upward/downward), total heat release, heat release rate, and heat loss mechanisms were varied between tests. A Design of Experiments (DOE) method was imposed to produce an array of tests from a fixed set of constraints and a coupled response model was developed. Supplementary tests were run without experimental design to additionally vary select parameters such as initial chamber pressure. The starting chamber pressure for each test was set below atmospheric to prevent chamber overpressure. Bottom ignition, or upward propagating burns, produced rapid acceleratory turbulent flame spread. Pressure rise in the chamber increases as the amount of fuel burned increases mainly because of the larger amount of heat generation and, to a much smaller extent, due to the increase in gaseous number of moles. Top ignition, or downward propagating burns, produced a steady flame spread with a very small flat flame across the burning edge. Steady-state pressure is achieved during downward flame spread as the pressure rises and plateaus. This indicates that the heat generation by the flame matches the heat loss to surroundings during the longer, slower downward burns. One heat loss mechanism included mounting a heat exchanger directly above the burning sample in the path of the plume to act as a heat sink and more efficiently dissipate the heat due to the combustion event. This proved an effective means for chamber overpressure mitigation for those tests producing the most total heat release and thusly was determined to be a feasible mitigation
Optimal control of a satellite-robot system using direct collocation with non-linear programming
NASA Astrophysics Data System (ADS)
Coverstone-Carroll, V. L.; Wilkey, N. M.
1995-08-01
The non-holonomic behavior of a satellite-robot system is used to develop the system's equations of motion. The resulting non-linear differential equations are transformed into a non-linear programming problem using direct collocation. The link rates of the robot are minimized along optimal reorientations. Optimal solutions to several maneuvers are obtained and the results are interpreted to gain an understanding of the satellite-robot dynamics.
Synchronization of coupled large-scale Boolean networks
Li, Fangfei
2014-03-15
This paper investigates the complete synchronization and partial synchronization of two large-scale Boolean networks. First, the aggregation algorithm towards large-scale Boolean network is reviewed. Second, the aggregation algorithm is applied to study the complete synchronization and partial synchronization of large-scale Boolean networks. Finally, an illustrative example is presented to show the efficiency of the proposed results.
The School Principal's Role in Large-Scale Assessment
ERIC Educational Resources Information Center
Newton, Paul; Tunison, Scott; Viczko, Melody
2010-01-01
This paper reports on an interpretive study in which 25 elementary principals were asked about their assessment knowledge, the use of large-scale assessments in their schools, and principals' perceptions on their roles with respect to large-scale assessments. Principals in this study suggested that the current context of large-scale assessment and…
Synchronization of coupled large-scale Boolean networks
NASA Astrophysics Data System (ADS)
Li, Fangfei
2014-03-01
This paper investigates the complete synchronization and partial synchronization of two large-scale Boolean networks. First, the aggregation algorithm towards large-scale Boolean network is reviewed. Second, the aggregation algorithm is applied to study the complete synchronization and partial synchronization of large-scale Boolean networks. Finally, an illustrative example is presented to show the efficiency of the proposed results.
Li, Hancao; Haddad, Wassim M
2012-01-01
We develop optimal respiratory airflow patterns using a nonlinear multicompartment model for a lung mechanics system. Specifically, we use classical calculus of variations minimization techniques to derive an optimal airflow pattern for inspiratory and expiratory breathing cycles. The physiological interpretation of the optimality criteria used involves the minimization of work of breathing and lung volume acceleration for the inspiratory phase, and the minimization of the elastic potential energy and rapid airflow rate changes for the expiratory phase. Finally, we numerically integrate the resulting nonlinear two-point boundary value problems to determine the optimal airflow patterns over the inspiratory and expiratory breathing cycles.
Li, Hancao; Haddad, Wassim M.
2012-01-01
We develop optimal respiratory airflow patterns using a nonlinear multicompartment model for a lung mechanics system. Specifically, we use classical calculus of variations minimization techniques to derive an optimal airflow pattern for inspiratory and expiratory breathing cycles. The physiological interpretation of the optimality criteria used involves the minimization of work of breathing and lung volume acceleration for the inspiratory phase, and the minimization of the elastic potential energy and rapid airflow rate changes for the expiratory phase. Finally, we numerically integrate the resulting nonlinear two-point boundary value problems to determine the optimal airflow patterns over the inspiratory and expiratory breathing cycles. PMID:22719793
Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection.
Wang, Xinghu; Hong, Yiguang; Ji, Haibo
2016-07-01
The paper studies the distributed optimization problem for a class of nonlinear multiagent systems in the presence of external disturbances. To solve the problem, we need to achieve the optimal multiagent consensus based on local cost function information and neighboring information and meanwhile to reject local disturbance signals modeled by an exogenous system. With convex analysis and the internal model approach, we propose a distributed optimization controller for heterogeneous and nonlinear agents in the form of continuous-time minimum-phase systems with unity relative degree. We prove that the proposed design can solve the exact optimization problem with rejecting disturbances.
Sheltering in buildings from large-scale outdoor releases
Chan, W.R.; Price, P.N.; Gadgil, A.J.
2004-06-01
Intentional or accidental large-scale airborne toxic release (e.g. terrorist attacks or industrial accidents) can cause severe harm to nearby communities. Under these circumstances, taking shelter in buildings can be an effective emergency response strategy. Some examples where shelter-in-place was successful at preventing injuries and casualties have been documented [1, 2]. As public education and preparedness are vital to ensure the success of an emergency response, many agencies have prepared documents advising the public on what to do during and after sheltering [3, 4, 5]. In this document, we will focus on the role buildings play in providing protection to occupants. The conclusions to this article are: (1) Under most circumstances, shelter-in-place is an effective response against large-scale outdoor releases. This is particularly true for release of short duration (a few hours or less) and chemicals that exhibit non-linear dose-response characteristics. (2) The building envelope not only restricts the outdoor-indoor air exchange, but can also filter some biological or even chemical agents. Once indoors, the toxic materials can deposit or sorb onto indoor surfaces. All these processes contribute to the effectiveness of shelter-in-place. (3) Tightening of building envelope and improved filtration can enhance the protection offered by buildings. Common mechanical ventilation system present in most commercial buildings, however, should be turned off and dampers closed when sheltering from an outdoor release. (4) After the passing of the outdoor plume, some residuals will remain indoors. It is therefore important to terminate shelter-in-place to minimize exposure to the toxic materials.
Large-scale quantum photonic circuits in silicon
NASA Astrophysics Data System (ADS)
Harris, Nicholas C.; Bunandar, Darius; Pant, Mihir; Steinbrecher, Greg R.; Mower, Jacob; Prabhu, Mihika; Baehr-Jones, Tom; Hochberg, Michael; Englund, Dirk
2016-08-01
Quantum information science offers inherently more powerful methods for communication, computation, and precision measurement that take advantage of quantum superposition and entanglement. In recent years, theoretical and experimental advances in quantum computing and simulation with photons have spurred great interest in developing large photonic entangled states that challenge today's classical computers. As experiments have increased in complexity, there has been an increasing need to transition bulk optics experiments to integrated photonics platforms to control more spatial modes with higher fidelity and phase stability. The silicon-on-insulator (SOI) nanophotonics platform offers new possibilities for quantum optics, including the integration of bright, nonclassical light sources, based on the large third-order nonlinearity (χ(3)) of silicon, alongside quantum state manipulation circuits with thousands of optical elements, all on a single phase-stable chip. How large do these photonic systems need to be? Recent theoretical work on Boson Sampling suggests that even the problem of sampling from e30 identical photons, having passed through an interferometer of hundreds of modes, becomes challenging for classical computers. While experiments of this size are still challenging, the SOI platform has the required component density to enable low-loss and programmable interferometers for manipulating hundreds of spatial modes. Here, we discuss the SOI nanophotonics platform for quantum photonic circuits with hundreds-to-thousands of optical elements and the associated challenges. We compare SOI to competing technologies in terms of requirements for quantum optical systems. We review recent results on large-scale quantum state evolution circuits and strategies for realizing high-fidelity heralded gates with imperfect, practical systems. Next, we review recent results on silicon photonics-based photon-pair sources and device architectures, and we discuss a path towards
Simulating the large-scale structure of HI intensity maps
Seehars, Sebastian; Paranjape, Aseem; Witzemann, Amadeus; Refregier, Alexandre; Amara, Adam; Akeret, Joel E-mail: aseem@iucaa.in E-mail: alexandre.refregier@phys.ethz.ch E-mail: joel.akeret@phys.ethz.ch
2016-03-01
Intensity mapping of neutral hydrogen (HI) is a promising observational probe of cosmology and large-scale structure. We present wide field simulations of HI intensity maps based on N-body simulations of a 2.6 Gpc / h box with 2048{sup 3} particles (particle mass 1.6 × 10{sup 11} M{sub ⊙} / h). Using a conditional mass function to populate the simulated dark matter density field with halos below the mass resolution of the simulation (10{sup 8} M{sub ⊙} / h < M{sub halo} < 10{sup 13} M{sub ⊙} / h), we assign HI to those halos according to a phenomenological halo to HI mass relation. The simulations span a redshift range of 0.35 ∼< z ∼< 0.9 in redshift bins of width Δ z ≈ 0.05 and cover a quarter of the sky at an angular resolution of about 7'. We use the simulated intensity maps to study the impact of non-linear effects and redshift space distortions on the angular clustering of HI. Focusing on the autocorrelations of the maps, we apply and compare several estimators for the angular power spectrum and its covariance. We verify that these estimators agree with analytic predictions on large scales and study the validity of approximations based on Gaussian random fields, particularly in the context of the covariance. We discuss how our results and the simulated maps can be useful for planning and interpreting future HI intensity mapping surveys.
Aircraft design for mission performance using nonlinear multiobjective optimization methods
NASA Technical Reports Server (NTRS)
Dovi, Augustine R.; Wrenn, Gregory A.
1990-01-01
A new technique which converts a constrained optimization problem to an unconstrained one where conflicting figures of merit may be simultaneously considered was combined with a complex mission analysis system. The method is compared with existing single and multiobjective optimization methods. A primary benefit from this new method for multiobjective optimization is the elimination of separate optimizations for each objective, which is required by some optimization methods. A typical wide body transport aircraft is used for the comparative studies.
Modelling large-scale halo bias using the bispectrum
NASA Astrophysics Data System (ADS)
Pollack, Jennifer E.; Smith, Robert E.; Porciani, Cristiano
2012-03-01
We study the relation between the density distribution of tracers for large-scale structure and the underlying matter distribution - commonly termed bias - in the Λ cold dark matter framework. In particular, we examine the validity of the local model of biasing at quadratic order in the matter density. This model is characterized by parameters b1 and b2. Using an ensemble of N-body simulations, we apply several statistical methods to estimate the parameters. We measure halo and matter fluctuations smoothed on various scales. We find that, whilst the fits are reasonably good, the parameters vary with smoothing scale. We argue that, for real-space measurements, owing to the mixing of wavemodes, no smoothing scale can be found for which the parameters are independent of smoothing. However, this is not the case in Fourier space. We measure halo and halo-mass power spectra and from these construct estimates of the effective large-scale bias as a guide for b1. We measure the configuration dependence of the halo bispectra Bhhh and reduced bispectra Qhhh for very large-scale k-space triangles. From these data, we constrain b1 and b2, taking into account the full bispectrum covariance matrix. Using the lowest order perturbation theory, we find that for Bhhh the best-fitting parameters are in reasonable agreement with one another as the triangle scale is varied, although the fits become poor as smaller scales are included. The same is true for Qhhh. The best-fitting values were found to depend on the discreteness correction. This led us to consider halo-mass cross-bispectra. The results from these statistics supported our earlier findings. We then developed a test to explore whether the inconsistency in the recovered bias parameters could be attributed to missing higher order corrections in the models. We prove that low-order expansions are not sufficiently accurate to model the data, even on scales k1˜ 0.04 h Mpc-1. If robust inferences concerning bias are to be drawn
CSOLNP: Numerical Optimization Engine for Solving Non-linearly Constrained Problems.
Zahery, Mahsa; Maes, Hermine H; Neale, Michael C
2017-08-01
We introduce the optimizer CSOLNP, which is a C++ implementation of the R package RSOLNP (Ghalanos & Theussl, 2012, Rsolnp: General non-linear optimization using augmented Lagrange multiplier method. R package version, 1) alongside some improvements. CSOLNP solves non-linearly constrained optimization problems using a Sequential Quadratic Programming (SQP) algorithm. CSOLNP, NPSOL (a very popular implementation of SQP method in FORTRAN (Gill et al., 1986, User's guide for NPSOL (version 4.0): A Fortran package for nonlinear programming (No. SOL-86-2). Stanford, CA: Stanford University Systems Optimization Laboratory), and SLSQP (another SQP implementation available as part of the NLOPT collection (Johnson, 2014, The NLopt nonlinear-optimization package. Retrieved from http://ab-initio.mit.edu/nlopt)) are three optimizers available in OpenMx package. These optimizers are compared in terms of runtimes, final objective values, and memory consumption. A Monte Carlo analysis of the performance of the optimizers was performed on ordinal and continuous models with five variables and one or two factors. While the relative difference between the objective values is less than 0.5%, CSOLNP is in general faster than NPSOL and SLSQP for ordinal analysis. As for continuous data, none of the optimizers performs consistently faster than the others. In terms of memory usage, we used Valgrind's heap profiler tool, called Massif, on one-factor threshold models. CSOLNP and NPSOL consume the same amount of memory, while SLSQP uses 71 MB more memory than the other two optimizers.
Large scale structure from viscous dark matter
Blas, Diego; Floerchinger, Stefan; Garny, Mathias; Tetradis, Nikolaos; Wiedemann, Urs Achim E-mail: stefan.floerchinger@cern.ch E-mail: ntetrad@phys.uoa.gr
2015-11-01
Cosmological perturbations of sufficiently long wavelength admit a fluid dynamic description. We consider modes with wavevectors below a scale k{sub m} for which the dynamics is only mildly non-linear. The leading effect of modes above that scale can be accounted for by effective non-equilibrium viscosity and pressure terms. For mildly non-linear scales, these mainly arise from momentum transport within the ideal and cold but inhomogeneous fluid, while momentum transport due to more microscopic degrees of freedom is suppressed. As a consequence, concrete expressions with no free parameters, except the matching scale k{sub m}, can be derived from matching evolution equations to standard cosmological perturbation theory. Two-loop calculations of the matter power spectrum in the viscous theory lead to excellent agreement with N-body simulations up to scales k=0.2 h/Mpc. The convergence properties in the ultraviolet are better than for standard perturbation theory and the results are robust with respect to variations of the matching scale.
Large scale structure from viscous dark matter
NASA Astrophysics Data System (ADS)
Blas, Diego; Floerchinger, Stefan; Garny, Mathias; Tetradis, Nikolaos; Wiedemann, Urs Achim
2015-11-01
Cosmological perturbations of sufficiently long wavelength admit a fluid dynamic description. We consider modes with wavevectors below a scale km for which the dynamics is only mildly non-linear. The leading effect of modes above that scale can be accounted for by effective non-equilibrium viscosity and pressure terms. For mildly non-linear scales, these mainly arise from momentum transport within the ideal and cold but inhomogeneous fluid, while momentum transport due to more microscopic degrees of freedom is suppressed. As a consequence, concrete expressions with no free parameters, except the matching scale km, can be derived from matching evolution equations to standard cosmological perturbation theory. Two-loop calculations of the matter power spectrum in the viscous theory lead to excellent agreement with N-body simulations up to scales k=0.2 h/Mpc. The convergence properties in the ultraviolet are better than for standard perturbation theory and the results are robust with respect to variations of the matching scale.
Large scale dynamics of protoplanetary discs
NASA Astrophysics Data System (ADS)
BÃ©thune, William
2017-08-01
Planets form in the gaseous and dusty disks orbiting young stars. These protoplanetary disks are dispersed in a few million years, being accreted onto the central star or evaporated into the interstellar medium. To explain the observed accretion rates, it is commonly assumed that matter is transported through the disk by turbulence, although the mechanism sustaining turbulence is uncertain. On the other side, irradiation by the central star could heat up the disk surface and trigger a photoevaporative wind, but thermal effects cannot account for the observed acceleration and collimation of the wind into a narrow jet perpendicular to the disk plane. Both issues can be solved if the disk is sensitive to magnetic fields. Weak fields lead to the magnetorotational instability, whose outcome is a state of sustained turbulence. Strong fields can slow down the disk, causing it to accrete while launching a collimated wind. However, the coupling between the disk and the neutral gas is done via electric charges, each of which is outnumbered by several billion neutral molecules. The imperfect coupling between the magnetic field and the neutral gas is described in terms of "non-ideal" effects, introducing new dynamical behaviors. This thesis is devoted to the transport processes happening inside weakly ionized and weakly magnetized accretion disks; the role of microphysical effects on the large-scale dynamics of the disk is of primary importance. As a first step, I exclude the wind and examine the impact of non-ideal effects on the turbulent properties near the disk midplane. I show that the flow can spontaneously organize itself if the ionization fraction is low enough; in this case, accretion is halted and the disk exhibits axisymmetric structures, with possible consequences on planetary formation. As a second step, I study the launching of disk winds via a global model of stratified disk embedded in a warm atmosphere. This model is the first to compute non-ideal effects from
Large-Scale Spacecraft Fire Safety Tests
NASA Technical Reports Server (NTRS)
Urban, David; Ruff, Gary A.; Ferkul, Paul V.; Olson, Sandra; Fernandez-Pello, A. Carlos; T'ien, James S.; Torero, Jose L.; Cowlard, Adam J.; Rouvreau, Sebastien; Minster, Olivier;
2014-01-01
An international collaborative program is underway to address open issues in spacecraft fire safety. Because of limited access to long-term low-gravity conditions and the small volume generally allotted for these experiments, there have been relatively few experiments that directly study spacecraft fire safety under low-gravity conditions. Furthermore, none of these experiments have studied sample sizes and environment conditions typical of those expected in a spacecraft fire. The major constraint has been the size of the sample, with prior experiments limited to samples of the order of 10 cm in length and width or smaller. This lack of experimental data forces spacecraft designers to base their designs and safety precautions on 1-g understanding of flame spread, fire detection, and suppression. However, low-gravity combustion research has demonstrated substantial differences in flame behavior in low-gravity. This, combined with the differences caused by the confined spacecraft environment, necessitates practical scale spacecraft fire safety research to mitigate risks for future space missions. To address this issue, a large-scale spacecraft fire experiment is under development by NASA and an international team of investigators. This poster presents the objectives, status, and concept of this collaborative international project (Saffire). The project plan is to conduct fire safety experiments on three sequential flights of an unmanned ISS re-supply spacecraft (the Orbital Cygnus vehicle) after they have completed their delivery of cargo to the ISS and have begun their return journeys to earth. On two flights (Saffire-1 and Saffire-3), the experiment will consist of a flame spread test involving a meter-scale sample ignited in the pressurized volume of the spacecraft and allowed to burn to completion while measurements are made. On one of the flights (Saffire-2), 9 smaller (5 x 30 cm) samples will be tested to evaluate NASAs material flammability screening tests
Large scale simulations of Brownian suspensions
NASA Astrophysics Data System (ADS)
Viera, Marc Nathaniel
Particle suspensions occur in a wide variety of natural and engineering materials. Some examples are colloids, polymers, paints, and slurries. These materials exhibit complex behavior owing to the forces which act among the particles and are transmitted through the fluid medium. Depending on the application, particle sizes range from large macroscopic molecules of 100mum to smaller colloidal particles in the range of 10nm to 1mum. Particles of this size interact though interparticle forces such as electrostatic and van der Waals, as well as hydrodynamic forces transmitted through the fluid medium. Additionally, the particles are subjected to random thermal fluctuations in the fluid giving rise to Brownian motion. The central objective of our research is to develop efficient numerical algorithms for the large scale dynamic simulation of particle suspensions. While previous methods have incurred a computational cost of O(N3), where N is the number of particles, we have developed a novel algorithm capable of solving this problem in O(N ln N) operations. This has allowed us to perform dynamic simulations with up to 64,000 particles and Monte Carlo realizations of up to 1 million particles. Our algorithm follows a Stokesian dynamics formulation by evaluating many-body hydrodynamic interactions using a far-field multipole expansion combined with a near-field lubrication correction. The breakthrough O(N ln N) scaling is obtained by employing a Particle-Mesh-Ewald (PME) approach whereby near-field interactions are evaluated directly and far-field interactions are evaluated using a grid based velocity computed with FFT's. This approach is readily extended to include the effects of Brownian motion. For interacting particles, the fluctuation-dissipation theorem requires that the individual Brownian forces satisfy a correlation based on the N body resistance tensor R. The accurate modeling of these forces requires the computation of a matrix square root R 1/2 for matrices up
NASA Astrophysics Data System (ADS)
Farano, Mirko; Cherubini, Stefania; Robinet, Jean-Christophe; De Palma, Pietro
2016-12-01
Subcritical transition in plane Poiseuille flow is investigated by means of a Lagrange-multiplier direct-adjoint optimization procedure with the aim of finding localized three-dimensional perturbations optimally growing in a given time interval (target time). Space localization of these optimal perturbations (OPs) is achieved by choosing as objective function either a p-norm (with p\\gg 1) of the perturbation energy density in a linear framework; or the classical (1-norm) perturbation energy, including nonlinear effects. This work aims at analyzing the structure of linear and nonlinear localized OPs for Poiseuille flow, and comparing their transition thresholds and scenarios. The nonlinear optimization approach provides three types of solutions: a weakly nonlinear, a hairpin-like and a highly nonlinear optimal perturbation, depending on the value of the initial energy and the target time. The former shows localization only in the wall-normal direction, whereas the latter appears much more localized and breaks the spanwise symmetry found at lower target times. Both solutions show spanwise inclined vortices and large values of the streamwise component of velocity already at the initial time. On the other hand, p-norm optimal perturbations, although being strongly localized in space, keep a shape similar to linear 1-norm optimal perturbations, showing streamwise-aligned vortices characterized by low values of the streamwise velocity component. When used for initializing direct numerical simulations, in most of the cases nonlinear OPs provide the most efficient route to transition in terms of time to transition and initial energy, even when they are less localized in space than the p-norm OP. The p-norm OP follows a transition path similar to the oblique transition scenario, with slightly oscillating streaks which saturate and eventually experience secondary instability. On the other hand, the nonlinear OP rapidly forms large-amplitude bent streaks and skips the phases
Exploiting large-scale correlations to detect continuous gravitational waves.
Pletsch, Holger J; Allen, Bruce
2009-10-30
Fully coherent searches (over realistic ranges of parameter space and year-long observation times) for unknown sources of continuous gravitational waves are computationally prohibitive. Less expensive hierarchical searches divide the data into shorter segments which are analyzed coherently, then detection statistics from different segments are combined incoherently. The novel method presented here solves the long-standing problem of how best to do the incoherent combination. The optimal solution exploits large-scale parameter-space correlations in the coherent detection statistic. Application to simulated data shows dramatic sensitivity improvements compared with previously available (ad hoc) methods, increasing the spatial volume probed by more than 2 orders of magnitude at lower computational cost.
Biohazards Assessment in Large-Scale Zonal Centrifugation
Baldwin, C. L.; Lemp, J. F.; Barbeito, M. S.
1975-01-01
A study was conducted to determine the biohazards associated with use of the large-scale zonal centrifuge for purification of moderate risk oncogenic viruses. To safely and conveniently assess the hazard, coliphage T3 was substituted for the virus in a typical processing procedure performed in a National Cancer Institute contract laboratory. Risk of personnel exposure was found to be minimal during optimal operation but definite potential for virus release from a number of centrifuge components during mechanical malfunction was shown by assay of surface, liquid, and air samples collected during the processing. High concentration of phage was detected in the turbine air exhaust and the seal coolant system when faulty seals were employed. The simulant virus was also found on both centrifuge chamber interior and rotor surfaces. Images PMID:1124921
Biohazards assessment in large-scale zonal centrifugation.
Baldwin, C L; Lemp, J F; Barbeito, M S
1975-04-01
A study was conducted to determine the biohazards associated with use of the large-scale zonal centrifuge for purification of moderate risk oncogenic viruses. To safely and conveniently assess the hazard, coliphage T3 was substituted for the virus in a typical processing procedure performed in a National Cancer Institute contract laboratory. Risk of personnel exposure was found to be minimal during optimal operation but definite potential for virus release from a number of centrifuge components during mechanical malfunction was shown by assay of surface, liquid, and air samples collected during the processing. High concentration of phage was detected in the turbine air exhaust and the seal coolant system when faulty seals were employed. The simulant virus was also found on both the centrifuge chamber interior and rotor surfaces.
Atypical Behavior Identification in Large Scale Network Traffic
Best, Daniel M.; Hafen, Ryan P.; Olsen, Bryan K.; Pike, William A.
2011-10-23
Cyber analysts are faced with the daunting challenge of identifying exploits and threats within potentially billions of daily records of network traffic. Enterprise-wide cyber traffic involves hundreds of millions of distinct IP addresses and results in data sets ranging from terabytes to petabytes of raw data. Creating behavioral models and identifying trends based on those models requires data intensive architectures and techniques that can scale as data volume increases. Analysts need scalable visualization methods that foster interactive exploration of data and enable identification of behavioral anomalies. Developers must carefully consider application design, storage, processing, and display to provide usability and interactivity with large-scale data. We present an application that highlights atypical behavior in enterprise network flow records. This is accomplished by utilizing data intensive architectures to store the data, aggregation techniques to optimize data access, statistical techniques to characterize behavior, and a visual analytic environment to render the behavioral trends, highlight atypical activity, and allow for exploration.
A mini review: photobioreactors for large scale algal cultivation.
Gupta, Prabuddha L; Lee, Seung-Mok; Choi, Hee-Jeong
2015-09-01
Microalgae cultivation has gained much interest in terms of the production of foods, biofuels, and bioactive compounds and offers a great potential option for cleaning the environment through CO2 sequestration and wastewater treatment. Although open pond cultivation is most affordable option, there tends to be insufficient control on growth conditions and the risk of contamination. In contrast, while providing minimal risk of contamination, closed photobioreactors offer better control on culture conditions, such as: CO2 supply, water supply, optimal temperatures, efficient exposure to light, culture density, pH levels, and mixing rates. For a large scale production of biomass, efficient photobioreactors are required. This review paper describes general design considerations pertaining to photobioreactor systems, in order to cultivate microalgae for biomass production. It also discusses the current challenges in designing of photobioreactors for the production of low-cost biomass.
Xia, Youshen; Feng, Gang; Wang, Jun
2008-08-01
This paper presents a novel recurrent neural network for solving nonlinear optimization problems with inequality constraints. Under the condition that the Hessian matrix of the associated Lagrangian function is positive semidefinite, it is shown that the proposed neural network is stable at a Karush-Kuhn-Tucker point in the sense of Lyapunov and its output trajectory is globally convergent to a minimum solution. Compared with variety of the existing projection neural networks, including their extensions and modification, for solving such nonlinearly constrained optimization problems, it is shown that the proposed neural network can solve constrained convex optimization problems and a class of constrained nonconvex optimization problems and there is no restriction on the initial point. Simulation results show the effectiveness of the proposed neural network in solving nonlinearly constrained optimization problems.
NASA Astrophysics Data System (ADS)
Royston, T. J.; Singh, R.
1996-07-01
While significant non-linear behavior has been observed in many vibration mounting applications, most design studies are typically based on the concept of linear system theory in terms of force or motion transmissibility. In this paper, an improved analytical strategy is presented for the design optimization of complex, active of passive, non-linear mounting systems. This strategy is built upon the computational Galerkin method of weighted residuals, and incorporates order reduction and numerical continuation in an iterative optimization scheme. The overall dynamic characteristics of the mounting system are considered and vibratory power transmission is minimized via adjustment of mount parameters by using both passive and active means. The method is first applied through a computational example case to the optimization of basic passive and active, non-linear isolation configurations. It is found that either active control or intentionally introduced non-linearity can improve the mount's performance; but a combination of both produces the greatest benefit. Next, a novel experimental, active, non-linear isolation system is studied. The effect of non-linearity on vibratory power transmission and active control are assessed via experimental measurements and the enhanced Galerkin method. Results show how harmonic excitation can result in multiharmonic vibratory power transmission. The proposed optimization strategy offers designers some flexibility in utilizing both passive and active means in combination with linear and non-linear components for improved vibration mounts.
NASA Astrophysics Data System (ADS)
Koyuncu, A.; Cigeroglu, E.; Özgüven, H. N.
2017-10-01
In this study, a new approach is proposed for identification of structural nonlinearities by employing cascaded optimization and neural networks. Linear finite element model of the system and frequency response functions measured at arbitrary locations of the system are used in this approach. Using the finite element model, a training data set is created, which appropriately spans the possible nonlinear configurations space of the system. A classification neural network trained on these data sets then localizes and determines the types of all nonlinearities associated with the nonlinear degrees of freedom in the system. A new training data set spanning the parametric space associated with the determined nonlinearities is created to facilitate parametric identification. Utilizing this data set, initially, a feed forward regression neural network is trained, which parametrically identifies the classified nonlinearities. Then, the results obtained are further improved by carrying out an optimization which uses network identified values as starting points. Unlike identification methods available in literature, the proposed approach does not require data collection from the degrees of freedoms where nonlinear elements are attached, and furthermore, it is sufficiently accurate even in the presence of measurement noise. The application of the proposed approach is demonstrated on an example system with nonlinear elements and on a real life experimental setup with a local nonlinearity.
Large-scale sparse singular value computations
NASA Technical Reports Server (NTRS)
Berry, Michael W.
1992-01-01
Four numerical methods for computing the singular value decomposition (SVD) of large sparse matrices on a multiprocessor architecture are presented. Lanczos and subspace iteration-based methods for determining several of the largest singular triplets (singular values and corresponding left and right-singular vectors) for sparse matrices arising from two practical applications: information retrieval and seismic reflection tomography are emphasized. The target architectures for implementations are the CRAY-2S/4-128 and Alliant FX/80. The sparse SVD problem is well motivated by recent information-retrieval techniques in which dominant singular values and their corresponding singular vectors of large sparse term-document matrices are desired, and by nonlinear inverse problems from seismic tomography applications which require approximate pseudo-inverses of large sparse Jacobian matrices.
"Cosmological Parameters from Large Scale Structure"
NASA Technical Reports Server (NTRS)
Hamilton, A. J. S.
2005-01-01
This grant has provided primary support for graduate student Mark Neyrinck, and some support for the PI and for colleague Nick Gnedin, who helped co-supervise Neyrinck. This award had two major goals. First, to continue to develop and apply methods for measuring galaxy power spectra on large, linear scales, with a view to constraining cosmological parameters. And second, to begin try to understand galaxy clustering at smaller. nonlinear scales well enough to constrain cosmology from those scales also. Under this grant, the PI and collaborators, notably Max Tegmark. continued to improve their technology for measuring power spectra from galaxy surveys at large, linear scales. and to apply the technology to surveys as the data become available. We believe that our methods are best in the world. These measurements become the foundation from which we and other groups measure cosmological parameters.
Dynamics of large-scale instabilities in conductors electrically exploded in strong magnetic fields
NASA Astrophysics Data System (ADS)
Datsko, I. M.; Chaikovsky, S. A.; Labetskaya, N. A.; Oreshkin, V. I.; Ratakhin, N. A.
2014-11-01
The growth of large-scale instabilities during the propagation of a nonlinear magnetic diffusion wave through a conductor was studied experimentally. The experiment was carried out using the MIG terawatt pulsed power generator at a peak current up to 2.5 MA with 100 ns rise time. It was observed that instabilities with a wavelength of 150 μm developed on the surface of the conductor hollow part within 160 ns after the onset of current flow, whereas the surface of the solid rod remained almost unperturbed. A system of equations describing the propagation of a nonlinear diffusion wave through a conductor and the growth of thermal instabilities has been solved numerically. It has been revealed that the development of large- scale instabilities is obviously related to the propagation of a nonlinear magnetic diffusion wave.
Zhong, Xiangnan; He, Haibo; Zhang, Huaguang; Wang, Zhanshan
2014-12-01
In this paper, we develop and analyze an optimal control method for a class of discrete-time nonlinear Markov jump systems (MJSs) with unknown system dynamics. Specifically, an identifier is established for the unknown systems to approximate system states, and an optimal control approach for nonlinear MJSs is developed to solve the Hamilton-Jacobi-Bellman equation based on the adaptive dynamic programming technique. We also develop detailed stability analysis of the control approach, including the convergence of the performance index function for nonlinear MJSs and the existence of the corresponding admissible control. Neural network techniques are used to approximate the proposed performance index function and the control law. To demonstrate the effectiveness of our approach, three simulation studies, one linear case, one nonlinear case, and one single link robot arm case, are used to validate the performance of the proposed optimal control method.
Geospatial optimization of siting large-scale solar projects
Macknick, Jordan; Quinby, Ted; Caulfield, Emmet; Gerritsen, Margot; Diffendorfer, James E.; Haines, Seth S.
2014-01-01
guidelines by being user-driven, transparent, interactive, capable of incorporating multiple criteria, and flexible. This work provides the foundation for a dynamic siting assistance tool that can greatly facilitate siting decisions among multiple stakeholders.
Structural Optimization and Other Large-Scale Processes.
1984-09-15
Denelcor HEP multiprocessing computer at the Argonne National Lab. This is joint work with A. Sameh in the Computer Science Department at the...may be submitted to Linear Algebra and Its Applications (with A Sameh ). V. PROFESSIONAL PERSONNEL ASSOCIATED WITH THE RESEARCH EFFORT R. J. Plemmons
Solution Procedures for Large-Scale Combinatorial Optimization
1993-08-31
and that incorporate related advances of the mathematical theory into, a general approach called "branch-and- cut ". The term "branch-and- cut " and the...OSL (an cod6 marketing by IBM) and CpleX (a code marketed by the CpleX corporation) both incorporate cutting plane ideas and use the term "branch-and... cut " in their marketing literature. DTIC " "’"L,. .,.J 3 17. Sj 2 j f CA SiC ON . C RiTY CLASS)FýC-47,CN $Ec. : .LA> .: -" .... UNCLASSIFIED
Optimization of Large Scale HEP Data Analysis in LHCb
NASA Astrophysics Data System (ADS)
Remenska, Daniela; Aaij, Roel; Raven, Gerhard; Merk, Marcel; Templon, Jeff; Bril, Reinder J.; LHCb Collaboration
2011-12-01
Observation has lead to a conclusion that the physics analysis jobs run by LHCb physicists on a local computing farm (i.e. non-grid) require more efficient access to the data which resides on the Grid. Our experiments have shown that the I/O bound nature of the analysis jobs in combination with the latency due to the remote access protocols (e.g. rfio, dcap) cause a low CPU efficiency of these jobs. In addition to causing a low CPU efficiency, the remote access protocols give rise to high overhead (in terms of amount of data transferred). This paper gives an overview of the concept of pre-fetching and caching of input files in the proximity of the processing resources, which is exploited to cope with the I/O bound analysis jobs. The files are copied from Grid storage elements (using GridFTP), while concurrently performing computations, inspired from a similar idea used in the ATLAS experiment. The results illustrate that this file staging approach is relatively insensitive to the original location of the data, and a significant improvement can be achieved in terms of the CPU efficiency of an analysis job. Dealing with scalability of such a solution on the Grid environment is discussed briefly.
Manifestations of dynamo driven large-scale magnetic field in accretion disks of compact objects
NASA Technical Reports Server (NTRS)
Chagelishvili, G. D.; Chanishvili, R. G.; Lominadze, J. G.; Sokhadze, Z. A.
1991-01-01
A turbulent dynamo nonlinear theory of turbulence was developed that shows that in the compact objects of accretion disks, the generated large-scale magnetic field (when the generation takes place) has a practically toroidal configuration. Its energy density can be much higher than turbulent pulsations energy density, and it becomes comparable with the thermal energy density of the medium. On this basis, the manifestations to which the large-scale magnetic field can lead at the accretion onto black holes and gravimagnetic rotators, respectively, are presented.
Population generation for large-scale simulation
NASA Astrophysics Data System (ADS)
Hannon, Andrew C.; King, Gary; Morrison, Clayton; Galstyan, Aram; Cohen, Paul
2005-05-01
Computer simulation is used to research phenomena ranging from the structure of the space-time continuum to population genetics and future combat.1-3 Multi-agent simulations in particular are now commonplace in many fields.4, 5 By modeling populations whose complex behavior emerges from individual interactions, these simulations help to answer questions about effects where closed form solutions are difficult to solve or impossible to derive.6 To be useful, simulations must accurately model the relevant aspects of the underlying domain. In multi-agent simulation, this means that the modeling must include both the agents and their relationships. Typically, each agent can be modeled as a set of attributes drawn from various distributions (e.g., height, morale, intelligence and so forth). Though these can interact - for example, agent height is related to agent weight - they are usually independent. Modeling relations between agents, on the other hand, adds a new layer of complexity, and tools from graph theory and social network analysis are finding increasing application.7, 8 Recognizing the role and proper use of these techniques, however, remains the subject of ongoing research. We recently encountered these complexities while building large scale social simulations.9-11 One of these, the Hats Simulator, is designed to be a lightweight proxy for intelligence analysis problems. Hats models a "society in a box" consisting of many simple agents, called hats. Hats gets its name from the classic spaghetti western, in which the heroes and villains are known by the color of the hats they wear. The Hats society also has its heroes and villains, but the challenge is to identify which color hat they should be wearing based on how they behave. There are three types of hats: benign hats, known terrorists, and covert terrorists. Covert terrorists look just like benign hats but act like terrorists. Population structure can make covert hat identification significantly more
Large-scale Fractal Motion of Clouds
2017-09-27
waters surrounding the island.) The “swallowed” gulps of clear island air get carried along within the vortices, but these are soon mixed into the surrounding clouds. Landsat is unique in its ability to image both the small-scale eddies that mix clear and cloudy air, down to the 30 meter pixel size of Landsat, but also having a wide enough field-of-view, 180 km, to reveal the connection of the turbulence to large-scale flows such as the subtropical oceanic gyres. Landsat 7, with its new onboard digital recorder, has extended this capability away from the few Landsat ground stations to remote areas such as Alejandro Island, and thus is gradually providing a global dynamic picture of evolving human-scale phenomena. For more details on von Karman vortices, refer to climate.gsfc.nasa.gov/~cahalan. Image and caption courtesy Bob Cahalan, NASA GSFC Instrument: Landsat 7 - ETM+ Credit: NASA/GSFC/Landsat NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Join us on Facebook
Large-scale assembly of colloidal particles
NASA Astrophysics Data System (ADS)
Yang, Hongta
This study reports a simple, roll-to-roll compatible coating technology for producing three-dimensional highly ordered colloidal crystal-polymer composites, colloidal crystals, and macroporous polymer membranes. A vertically beveled doctor blade is utilized to shear align silica microsphere-monomer suspensions to form large-area composites in a single step. The polymer matrix and the silica microspheres can be selectively removed to create colloidal crystals and self-standing macroporous polymer membranes. The thickness of the shear-aligned crystal is correlated with the viscosity of the colloidal suspension and the coating speed, and the correlations can be qualitatively explained by adapting the mechanisms developed for conventional doctor blade coating. Five important research topics related to the application of large-scale three-dimensional highly ordered macroporous films by doctor blade coating are covered in this study. The first topic describes the invention in large area and low cost color reflective displays. This invention is inspired by the heat pipe technology. The self-standing macroporous polymer films exhibit brilliant colors which originate from the Bragg diffractive of visible light form the three-dimensional highly ordered air cavities. The colors can be easily changed by tuning the size of the air cavities to cover the whole visible spectrum. When the air cavities are filled with a solvent which has the same refractive index as that of the polymer, the macroporous polymer films become completely transparent due to the index matching. When the solvent trapped in the cavities is evaporated by in-situ heating, the sample color changes back to brilliant color. This process is highly reversible and reproducible for thousands of cycles. The second topic reports the achievement of rapid and reversible vapor detection by using 3-D macroporous photonic crystals. Capillary condensation of a condensable vapor in the interconnected macropores leads to the