Evaluation of Frameworks for HSCT Design Optimization
NASA Technical Reports Server (NTRS)
Krishnan, Ramki
1998-01-01
This report is an evaluation of engineering frameworks that could be used to augment, supplement, or replace the existing FIDO 3.5 (Framework for Interdisciplinary Design and Optimization Version 3.5) framework. The report begins with the motivation for this effort, followed by a description of an "ideal" multidisciplinary design and optimization (MDO) framework. The discussion then turns to how each candidate framework stacks up against this ideal. This report ends with recommendations as to the "best" frameworks that should be down-selected for detailed review.
Deterministic Design Optimization of Structures in OpenMDAO Framework
NASA Technical Reports Server (NTRS)
Coroneos, Rula M.; Pai, Shantaram S.
2012-01-01
Nonlinear programming algorithms play an important role in structural design optimization. Several such algorithms have been implemented in OpenMDAO framework developed at NASA Glenn Research Center (GRC). OpenMDAO is an open source engineering analysis framework, written in Python, for analyzing and solving Multi-Disciplinary Analysis and Optimization (MDAO) problems. It provides a number of solvers and optimizers, referred to as components and drivers, which users can leverage to build new tools and processes quickly and efficiently. Users may download, use, modify, and distribute the OpenMDAO software at no cost. This paper summarizes the process involved in analyzing and optimizing structural components by utilizing the framework s structural solvers and several gradient based optimizers along with a multi-objective genetic algorithm. For comparison purposes, the same structural components were analyzed and optimized using CometBoards, a NASA GRC developed code. The reliability and efficiency of the OpenMDAO framework was compared and reported in this report.
A FRAMEWORK TO DESIGN AND OPTIMIZE CHEMICAL FLOODING PROCESSES
Mojdeh Delshad; Gary A. Pope; Kamy Sepehrnoori
2005-07-01
The goal of this proposed research is to provide an efficient and user friendly simulation framework for screening and optimizing chemical/microbial enhanced oil recovery processes. The framework will include (1) a user friendly interface to identify the variables that have the most impact on oil recovery using the concept of experimental design and response surface maps, (2) UTCHEM reservoir simulator to perform the numerical simulations, and (3) an economic model that automatically imports the simulation production data to evaluate the profitability of a particular design. Such a reservoir simulation framework is not currently available to the oil industry. The objectives of Task 1 are to develop three primary modules representing reservoir, chemical, and well data. The modules will be interfaced with an already available experimental design model. The objective of the Task 2 is to incorporate UTCHEM reservoir simulator and the modules with the strategic variables and developing the response surface maps to identify the significant variables from each module. The objective of the Task 3 is to develop the economic model designed specifically for the chemical processes targeted in this proposal and interface the economic model with UTCHEM production output. Task 4 is on the validation of the framework and performing simulations of oil reservoirs to screen, design and optimize the chemical processes.
A Framework to Design and Optimize Chemical Flooding Processes
Mojdeh Delshad; Gary A. Pope Kamy Sepehrnoori
2006-08-31
The goal of this proposed research is to provide an efficient and user friendly simulation framework for screening and optimizing chemical/microbial enhanced oil recovery processes. The framework will include (1) a user friendly interface to identify the variables that have the most impact on oil recovery using the concept of experimental design and response surface maps, (2) UTCHEM reservoir simulator to perform the numerical simulations, and (3) an economic model that automatically imports the simulation production data to evaluate the profitability of a particular design. Such a reservoir simulation framework is not currently available to the oil industry. The objectives of Task 1 are to develop three primary modules representing reservoir, chemical, and well data. The modules will be interfaced with an already available experimental design model. The objective of the Task 2 is to incorporate UTCHEM reservoir simulator and the modules with the strategic variables and developing the response surface maps to identify the significant variables from each module. The objective of the Task 3 is to develop the economic model designed specifically for the chemical processes targeted in this proposal and interface the economic model with UTCHEM production output. Task 4 is on the validation of the framework and performing simulations of oil reservoirs to screen, design and optimize the chemical processes.
An framework for robust flight control design using constrained optimization
NASA Technical Reports Server (NTRS)
Palazoglu, A.; Yousefpor, M.; Hess, R. A.
1992-01-01
An analytical framework is described for the design of feedback control systems to meet specified performance criteria in the presence of structured and unstructured uncertainty. Attention is focused upon the linear time invariant, single-input, single-output problem for the purposes of exposition. The framework provides for control of the degree of the stabilizing compensator or controller.
ROSE: The Design of a General Tool for the Independent Optimization of Object-Oriented Frameworks
Davis, K.; Philip, B.; Quinlan, D.
1999-05-18
ROSE represents a programmable preprocessor for the highly aggressive optimization of C++ object-oriented frameworks. A fundamental feature of ROSE is that it preserves the semantics, the implicit meaning, of the object-oriented framework's abstractions throughout the optimization process, permitting the framework's abstractions to be recognized and optimizations to capitalize upon the added value of the framework's true meaning. In contrast, a C++ compiler only sees the semantics of the C++ language and thus is severely limited in what optimizations it can introduce. The use of the semantics of the framework's abstractions avoids program analysis that would be incapable of recapturing the framework's full semantics from those of the C++ language implementation of the application or framework. Just as no level of program analysis within the C++ compiler would not be expected to recognize the use of adaptive mesh refinement and introduce optimizations based upon such information. Since ROSE is programmable, additional specialized program analysis is possible which then compliments the semantics of the framework's abstractions. Enabling an optimization mechanism to use the high level semantics of the framework's abstractions together with a programmable level of program analysis (e.g. dependence analysis), at the level of the framework's abstractions, allows for the design of high performance object-oriented frameworks with uniquely tailored sophisticated optimizations far beyond the limits of contemporary serial F0RTRAN 77, C or C++ language compiler technology. In short, faster, more highly aggressive optimizations are possible. The resulting optimizations are literally driven by the framework's definition of its abstractions. Since the abstractions within a framework are of third party design the optimizations are similarly of third party design, specifically independent of the compiler and the applications that use the framework. The interface to ROSE is
NASA Astrophysics Data System (ADS)
Pavese, Christian; Tibaldi, Carlo; Larsen, Torben J.; Kim, Taeseong; Thomsen, Kenneth
2016-09-01
The aim is to provide a fast and reliable approach to estimate ultimate blade loads for a multidisciplinary design optimization (MDO) framework. For blade design purposes, the standards require a large amount of computationally expensive simulations, which cannot be efficiently run each cost function evaluation of an MDO process. This work describes a method that allows integrating the calculation of the blade load envelopes inside an MDO loop. Ultimate blade load envelopes are calculated for a baseline design and a design obtained after an iteration of an MDO. These envelopes are computed for a full standard design load basis (DLB) and a deterministic reduced DLB. Ultimate loads extracted from the two DLBs with the two blade designs each are compared and analyzed. Although the reduced DLB supplies ultimate loads of different magnitude, the shape of the estimated envelopes are similar to the one computed using the full DLB. This observation is used to propose a scheme that is computationally cheap, and that can be integrated inside an MDO framework, providing a sufficiently reliable estimation of the blade ultimate loading. The latter aspect is of key importance when design variables implementing passive control methodologies are included in the formulation of the optimization problem. An MDO of a 10 MW wind turbine blade is presented as an applied case study to show the efficacy of the reduced DLB concept.
A multiobjective optimization framework for multicontaminant industrial water network design.
Boix, Marianne; Montastruc, Ludovic; Pibouleau, Luc; Azzaro-Pantel, Catherine; Domenech, Serge
2011-07-01
The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view.
A Systematic Approach for Quantitative Analysis of Multidisciplinary Design Optimization Framework
NASA Astrophysics Data System (ADS)
Kim, Sangho; Park, Jungkeun; Lee, Jeong-Oog; Lee, Jae-Woo
An efficient Multidisciplinary Design and Optimization (MDO) framework for an aerospace engineering system should use and integrate distributed resources such as various analysis codes, optimization codes, Computer Aided Design (CAD) tools, Data Base Management Systems (DBMS), etc. in a heterogeneous environment, and need to provide user-friendly graphical user interfaces. In this paper, we propose a systematic approach for determining a reference MDO framework and for evaluating MDO frameworks. The proposed approach incorporates two well-known methods, Analytic Hierarchy Process (AHP) and Quality Function Deployment (QFD), in order to provide a quantitative analysis of the qualitative criteria of MDO frameworks. Identification and hierarchy of the framework requirements and the corresponding solutions for the reference MDO frameworks, the general one and the aircraft oriented one were carefully investigated. The reference frameworks were also quantitatively identified using AHP and QFD. An assessment of three in-house frameworks was then performed. The results produced clear and useful guidelines for improvement of the in-house MDO frameworks and showed the feasibility of the proposed approach for evaluating an MDO framework without a human interference.
A KBE-enabled design framework for cost/weight optimization study of aircraft composite structures
NASA Astrophysics Data System (ADS)
Wang, H.; La Rocca, G.; van Tooren, M. J. L.
2014-10-01
Traditionally, minimum weight is the objective when optimizing airframe structures. This optimization, however, does not consider the manufacturing cost which actually determines the profit of the airframe manufacturer. To this purpose, a design framework has been developed able to perform cost/weight multi-objective optimization of an aircraft component, including large topology variations of the structural configuration. The key element of the proposed framework is a dedicated knowledge based engineering (KBE) application, called multi-model generator, which enables modelling very different product configurations and variants and extract all data required to feed the weight and cost estimation modules, in a fully automated fashion. The weight estimation method developed in this research work uses Finite Element Analysis to calculate the internal stresses of the structural elements and an analytical composite plate sizing method to determine their minimum required thicknesses. The manufacturing cost estimation module was developed on the basis of a cost model available in literature. The capability of the framework was successfully demonstrated by designing and optimizing the composite structure of a business jet rudder. The study case indicates the design framework is able to find the Pareto optimal set for minimum structural weight and manufacturing costin a very quick way. Based on the Pareto set, the rudder manufacturer is in conditions to conduct both internal trade-off studies between minimum weight and minimum cost solutions, as well as to offer the OEM a full set of optimized options to choose, rather than one feasible design.
A Hybrid Optimization Framework with POD-based Order Reduction and Design-Space Evolution Scheme
NASA Astrophysics Data System (ADS)
Ghoman, Satyajit S.
The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness. The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M 3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M 3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE. The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M 3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of
OpenMDAO: Framework for Flexible Multidisciplinary Design, Analysis and Optimization Methods
NASA Technical Reports Server (NTRS)
Heath, Christopher M.; Gray, Justin S.
2012-01-01
The OpenMDAO project is underway at NASA to develop a framework which simplifies the implementation of state-of-the-art tools and methods for multidisciplinary design, analysis and optimization. Foremost, OpenMDAO has been designed to handle variable problem formulations, encourage reconfigurability, and promote model reuse. This work demonstrates the concept of iteration hierarchies in OpenMDAO to achieve a flexible environment for supporting advanced optimization methods which include adaptive sampling and surrogate modeling techniques. In this effort, two efficient global optimization methods were applied to solve a constrained, single-objective and constrained, multiobjective version of a joint aircraft/engine sizing problem. The aircraft model, NASA's nextgeneration advanced single-aisle civil transport, is being studied as part of the Subsonic Fixed Wing project to help meet simultaneous program goals for reduced fuel burn, emissions, and noise. This analysis serves as a realistic test problem to demonstrate the flexibility and reconfigurability offered by OpenMDAO.
Framework for Multidisciplinary Analysis, Design, and Optimization with High-Fidelity Analysis Tools
NASA Technical Reports Server (NTRS)
Orr, Stanley A.; Narducci, Robert P.
2009-01-01
A plan is presented for the development of a high fidelity multidisciplinary optimization process for rotorcraft. The plan formulates individual disciplinary design problems, identifies practical high-fidelity tools and processes that can be incorporated in an automated optimization environment, and establishes statements of the multidisciplinary design problem including objectives, constraints, design variables, and cross-disciplinary dependencies. Five key disciplinary areas are selected in the development plan. These are rotor aerodynamics, rotor structures and dynamics, fuselage aerodynamics, fuselage structures, and propulsion / drive system. Flying qualities and noise are included as ancillary areas. Consistency across engineering disciplines is maintained with a central geometry engine that supports all multidisciplinary analysis. The multidisciplinary optimization process targets the preliminary design cycle where gross elements of the helicopter have been defined. These might include number of rotors and rotor configuration (tandem, coaxial, etc.). It is at this stage that sufficient configuration information is defined to perform high-fidelity analysis. At the same time there is enough design freedom to influence a design. The rotorcraft multidisciplinary optimization tool is built and substantiated throughout its development cycle in a staged approach by incorporating disciplines sequentially.
Accounting for Proof Test Data in a Reliability Based Design Optimization Framework
NASA Technical Reports Server (NTRS)
Ventor, Gerharad; Scotti, Stephen J.
2012-01-01
This paper investigates the use of proof (or acceptance) test data during the reliability based design optimization of structural components. It is assumed that every component will be proof tested and that the component will only enter into service if it passes the proof test. The goal is to reduce the component weight, while maintaining high reliability, by exploiting the proof test results during the design process. The proposed procedure results in the simultaneous design of the structural component and the proof test itself and provides the designer with direct control over the probability of failing the proof test. The procedure is illustrated using two analytical example problems and the results indicate that significant weight savings are possible when exploiting the proof test results during the design process.
Lorenz, Romy; Monti, Ricardo Pio; Violante, Inês R.; Anagnostopoulos, Christoforos; Faisal, Aldo A.; Montana, Giovanni; Leech, Robert
2016-01-01
Functional neuroimaging typically explores how a particular task activates a set of brain regions. Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system. Here, we propose and validate an alternative framework, the Automatic Neuroscientist, which turns the standard fMRI approach on its head. We use real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state. In this work, we present two proof-of-principle studies involving perceptual stimuli. In both studies optimization algorithms of varying complexity were employed; the first involved a stochastic approximation method while the second incorporated a more sophisticated Bayesian optimization technique. In the first study, we achieved convergence for the hypothesized optimum in 11 out of 14 runs in less than 10 min. Results of the second study showed how our closed-loop framework accurately and with high efficiency estimated the underlying relationship between stimuli and neural responses for each subject in one to two runs: with each run lasting 6.3 min. Moreover, we demonstrate that using only the first run produced a reliable solution at a group-level. Supporting simulation analyses provided evidence on the robustness of the Bayesian optimization approach for scenarios with low contrast-to-noise ratio. This framework is generalizable to numerous applications, ranging from optimizing stimuli in neuroimaging pilot studies to tailoring clinical rehabilitation therapy to patients and can be used with multiple imaging modalities in humans and animals. PMID:26804778
NASA Astrophysics Data System (ADS)
Babbar-Sebens, M.; Minsker, B. S.
2006-12-01
In the water resources management field, decision making encompasses many kinds of engineering, social, and economic constraints and objectives. Representing all of these problem dependant criteria through models (analytical or numerical) and various formulations (e.g., objectives, constraints, etc.) within an optimization- simulation system can be a very non-trivial issue. Most models and formulations utilized for discerning desirable traits in a solution can only approximate the decision maker's (DM) true preference criteria, and they often fail to consider important qualitative and incomputable phenomena related to the management problem. In our research, we have proposed novel decision support frameworks that allow DMs to actively participate in the optimization process. The DMs explicitly indicate their true preferences based on their subjective criteria and the results of various simulation models and formulations. The feedback from the DMs is then used to guide the search process towards solutions that are "all-rounders" from the perspective of the DM. The two main research questions explored in this work are: a) Does interaction between the optimization algorithm and a DM assist the system in searching for groundwater monitoring designs that are robust from the DM's perspective?, and b) How can an interactive search process be made more effective when human factors, such as human fatigue and cognitive learning processes, affect the performance of the algorithm? The application of these frameworks on a real-world groundwater long-term monitoring (LTM) case study in Michigan highlighted the following salient advantages: a) in contrast to the non-interactive optimization methodology, the proposed interactive frameworks were able to identify low cost monitoring designs whose interpolation maps respected the expected spatial distribution of the contaminants, b) for many same-cost designs, the interactive methodologies were able to propose multiple alternatives
Multi-Disciplinary Analysis and Optimization Frameworks
NASA Technical Reports Server (NTRS)
Naiman, Cynthia Gutierrez
2009-01-01
Since July 2008, the Multidisciplinary Analysis & Optimization Working Group (MDAO WG) of the Systems Analysis Design & Optimization (SAD&O) discipline in the Fundamental Aeronautics Program s Subsonic Fixed Wing (SFW) project completed one major milestone, Define Architecture & Interfaces for Next Generation Open Source MDAO Framework Milestone (9/30/08), and is completing the Generation 1 Framework validation milestone, which is due December 2008. Included in the presentation are: details of progress on developing the Open MDAO framework, modeling and testing the Generation 1 Framework, progress toward establishing partnerships with external parties, and discussion of additional potential collaborations
A Virtual Reality Framework to Optimize Design, Operation and Refueling of GEN-IV Reactors.
Rizwan-uddin; Nick Karancevic; Stefano Markidis; Joel Dixon; Cheng Luo; Jared Reynolds
2008-04-23
many GEN-IV candidate designs are currently under investigation. Technical issues related to material, safety and economics are being addressed at research laboratories, industry and in academia. After safety, economic feasibility is likely to be the most important crterion in the success of GEN-IV design(s). Lessons learned from the designers and operators of GEN-II (and GEN-III) reactors must play a vital role in achieving both safety and economic feasibility goals.
NASA Technical Reports Server (NTRS)
Karman, Steve L., Jr.
2011-01-01
The Aeronautics Research Mission Directorate (ARMD) sent out an NASA Research Announcement (NRA) for proposals soliciting research and technical development. The proposed research program was aimed at addressing the desired milestones and outcomes of ROA (ROA-2006) Subtopic A.4.1.1 Advanced Computational Methods. The second milestone, SUP.1.06.02 Robust, validated mesh adaptation and error quantification for near field Computational Fluid Dynamics (CFD), was addressed by the proposed research. Additional research utilizing the direct links to geometry through a CAD interface enabled by this work will allow for geometric constraints to be applied and address the final milestone, SUP2.07.06 Constrained low-drag supersonic aerodynamic design capability. The original product of the proposed research program was an integrated system of tools that can be used for the mesh mechanics required for rapid high fidelity analysis and for design of supersonic cruise vehicles. These Euler and Navier-Stokes volume grid manipulation tools were proposed to efficiently use parallel processing. The mesh adaptation provides a systematic approach for achieving demonstrated levels of accuracy in the solutions. NASA chose to fund only the mesh generation/adaptation portion of the proposal. So this report describes the completion of the proposed tasks for mesh creation, manipulation and adaptation as it pertains to sonic boom prediction of supersonic configurations.
Eldred, Michael Scott; Vigil, Dena M.; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Lefantzi, Sophia; Hough, Patricia Diane; Eddy, John P.
2011-12-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the DAKOTA software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of DAKOTA-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of DAKOTA's iterative analysis capabilities.
ERIC Educational Resources Information Center
Konstantopoulos, Spyros
2013-01-01
Large-scale experiments that involve nested structures may assign treatment conditions either to subgroups such as classrooms or to individuals such as students within subgroups. Key aspects of the design of such experiments include knowledge of the variance structure in higher levels and the sample sizes necessary to reach sufficient power to…
Adams, Brian M.; Ebeida, Mohamed Salah; Eldred, Michael S.; Jakeman, John Davis; Swiler, Laura Painton; Stephens, John Adam; Vigil, Dena M.; Wildey, Timothy Michael; Bohnhoff, William J.; Eddy, John P.; Hu, Kenneth T.; Dalbey, Keith R.; Bauman, Lara E; Hough, Patricia Diane
2014-05-01
The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
An Optimization Framework for Driver Feedback Systems
Malikopoulos, Andreas; Aguilar, Juan P.
2013-01-01
Modern vehicles have sophisticated electronic control units that can control engine operation with discretion to balance fuel economy, emissions, and power. These control units are designed for specific driving conditions (e.g., different speed profiles for highway and city driving). However, individual driving styles are different and rarely match the specific driving conditions for which the units were designed. In the research reported here, we investigate driving-style factors that have a major impact on fuel economy and construct an optimization framework to optimize individual driving styles with respect to these driving factors. In this context, we construct a set of polynomial metamodels to reflect the responses produced in fuel economy by changing the driving factors. Then, we compare the optimized driving styles to the original driving styles and evaluate the effectiveness of the optimization framework. Finally, we use this proposed framework to develop a real-time feedback system, including visual instructions, to enable drivers to alter their driving styles in response to actual driving conditions to improve fuel efficiency.
Conceptual design optimization study
NASA Technical Reports Server (NTRS)
Hollowell, S. J.; Beeman, E. R., II; Hiyama, R. M.
1990-01-01
The feasibility of applying multilevel functional decomposition and optimization techniques to conceptual design of advanced fighter aircraft was investigated. Applying the functional decomposition techniques to the conceptual design phase appears to be feasible. The initial implementation of the modified design process will optimize wing design variables. A hybrid approach, combining functional decomposition techniques for generation of aerodynamic and mass properties linear sensitivity derivatives with existing techniques for sizing mission performance and optimization, is proposed.
NASA Astrophysics Data System (ADS)
Furniss, S. G.
1989-10-01
While an SSTO with airbreathing propulsion for initial acceleration may greatly reduce future payload launch costs, such vehicles exhibit extreme sensitivity to design assumptions; the process of vehicle optimization is, accordingly, a difficult one. Attention is presently given to the role in optimization of the design mission, fuselage geometry, and the means employed to furnish adequate pitch and directional control. The requirements influencing wing design and scaling are also discussed. The Saenger and Hotol designs are the illustrative cases noted in this generalizing consideration of the SSTO-optimization process.
Structural Analysis in a Conceptual Design Framework
NASA Technical Reports Server (NTRS)
Padula, Sharon L.; Robinson, Jay H.; Eldred, Lloyd B.
2012-01-01
Supersonic aircraft designers must shape the outer mold line of the aircraft to improve multiple objectives, such as mission performance, cruise efficiency, and sonic-boom signatures. Conceptual designers have demonstrated an ability to assess these objectives for a large number of candidate designs. Other critical objectives and constraints, such as weight, fuel volume, aeroelastic effects, and structural soundness, are more difficult to address during the conceptual design process. The present research adds both static structural analysis and sizing to an existing conceptual design framework. The ultimate goal is to include structural analysis in the multidisciplinary optimization of a supersonic aircraft. Progress towards that goal is discussed and demonstrated.
Design Activity Framework for Visualization Design.
McKenna, Sean; Mazur, Dominika; Agutter, James; Meyer, Miriah
2014-12-01
An important aspect in visualization design is the connection between what a designer does and the decisions the designer makes. Existing design process models, however, do not explicitly link back to models for visualization design decisions. We bridge this gap by introducing the design activity framework, a process model that explicitly connects to the nested model, a well-known visualization design decision model. The framework includes four overlapping activities that characterize the design process, with each activity explicating outcomes related to the nested model. Additionally, we describe and characterize a list of exemplar methods and how they overlap among these activities. The design activity framework is the result of reflective discussions from a collaboration on a visualization redesign project, the details of which we describe to ground the framework in a real-world design process. Lastly, from this redesign project we provide several research outcomes in the domain of cybersecurity, including an extended data abstraction and rich opportunities for future visualization research. PMID:26356933
Design Activity Framework for Visualization Design.
McKenna, Sean; Mazur, Dominika; Agutter, James; Meyer, Miriah
2014-12-01
An important aspect in visualization design is the connection between what a designer does and the decisions the designer makes. Existing design process models, however, do not explicitly link back to models for visualization design decisions. We bridge this gap by introducing the design activity framework, a process model that explicitly connects to the nested model, a well-known visualization design decision model. The framework includes four overlapping activities that characterize the design process, with each activity explicating outcomes related to the nested model. Additionally, we describe and characterize a list of exemplar methods and how they overlap among these activities. The design activity framework is the result of reflective discussions from a collaboration on a visualization redesign project, the details of which we describe to ground the framework in a real-world design process. Lastly, from this redesign project we provide several research outcomes in the domain of cybersecurity, including an extended data abstraction and rich opportunities for future visualization research.
An Optimization Framework for Dynamic Hybrid Energy Systems
Wenbo Du; Humberto E Garcia; Christiaan J.J. Paredis
2014-03-01
A computational framework for the efficient analysis and optimization of dynamic hybrid energy systems (HES) is developed. A microgrid system with multiple inputs and multiple outputs (MIMO) is modeled using the Modelica language in the Dymola environment. The optimization loop is implemented in MATLAB, with the FMI Toolbox serving as the interface between the computational platforms. Two characteristic optimization problems are selected to demonstrate the methodology and gain insight into the system performance. The first is an unconstrained optimization problem that optimizes the dynamic properties of the battery, reactor and generator to minimize variability in the HES. The second problem takes operating and capital costs into consideration by imposing linear and nonlinear constraints on the design variables. The preliminary optimization results obtained in this study provide an essential step towards the development of a comprehensive framework for designing HES.
NASA Technical Reports Server (NTRS)
Townsend, James C.; Weston, Robert P.; Eidson, Thomas M.
1993-01-01
The Framework for Interdisciplinary Design Optimization (FIDO) is a general programming environment for automating the distribution of complex computing tasks over a networked system of heterogeneous computers. For example, instead of manually passing a complex design problem between its diverse specialty disciplines, the FIDO system provides for automatic interactions between the discipline tasks and facilitates their communications. The FIDO system networks all the computers involved into a distributed heterogeneous computing system, so they have access to centralized data and can work on their parts of the total computation simultaneously in parallel whenever possible. Thus, each computational task can be done by the most appropriate computer. Results can be viewed as they are produced and variables changed manually for steering the process. The software is modular in order to ease migration to new problems: different codes can be substituted for each of the current code modules with little or no effect on the others. The potential for commercial use of FIDO rests in the capability it provides for automatically coordinating diverse computations on a networked system of workstations and computers. For example, FIDO could provide the coordination required for the design of vehicles or electronics or for modeling complex systems.
Optimization of digital designs
NASA Technical Reports Server (NTRS)
Whitaker, Sterling R. (Inventor); Miles, Lowell H. (Inventor)
2009-01-01
An application specific integrated circuit is optimized by translating a first representation of its digital design to a second representation. The second representation includes multiple syntactic expressions that admit a representation of a higher-order function of base Boolean values. The syntactic expressions are manipulated to form a third representation of the digital design.
Optimizations for parallel object oriented frameworks
Basetti, F; Davis, K; Quinlan, D
1998-09-22
Application codes reliably under perform the advertised performance of existing architectures, compilers have only limited mechanisms with which to effect sophisticated transformations to arrest this trend. Compilers are forced to work within the broad semantics of the complete language specification and thus can not guarantee correctness of more sophisticated transformations. Object-oriented frameworks provide a level of tailoring of the C++ language to specific, albeit often restricted contexts. But such frameworks traditionally rely upon the compiler for most performance level optimization, often with disappointing results since the compiler must work within the context of the full language rather than the restricted semantics of abstractions introduced within the class library. No mechanism exists to express the restricted semantics of a class library to the compiler and effect correspondingly more sophisticated optimizations. In this paper, the authors explore both a family of transformations/optimizations appropriate to object-oriented frameworks for scientific computing and present a preprocessor mechanism, ROSE, which delivers the more sophisticated transformations automatically from the use of abstractions represented within high level object-oriented frameworks. They have found that these optimizations permit improved performance over FORTRAN 77 by factors of three to four, sufficiently interesting to suggest that higher level abstractions can contain greater semantics and that the greater semantics can be used to drive more sophisticated optimizations than are possible within lower level languages.
Hydraulic fracture design optimization
Lee, Tae-Soo; Advani, S.H.
1992-01-01
This research and development investigation, sponsored by US DOE and the oil and gas industry, extends previously developed hydraulic fracture geometry models and applied energy related characteristic time concepts towards the optimal design and control of hydraulic fracture geometries. The primary objective of this program is to develop rational criteria, by examining the associated energy rate components during the hydraulic fracture evolution, for the formulation of stimulation treatment design along with real-time fracture configuration interpretation and control.
Hydraulic fracture design optimization
Lee, Tae-Soo; Advani, S.H.
1992-06-01
This research and development investigation, sponsored by US DOE and the oil and gas industry, extends previously developed hydraulic fracture geometry models and applied energy related characteristic time concepts towards the optimal design and control of hydraulic fracture geometries. The primary objective of this program is to develop rational criteria, by examining the associated energy rate components during the hydraulic fracture evolution, for the formulation of stimulation treatment design along with real-time fracture configuration interpretation and control.
An Algorithmic Framework for Multiobjective Optimization
Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.
2013-01-01
Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795
Griffin, Joshua D. (Sandai National Labs, Livermore, CA); Eldred, Michael Scott; Martinez-Canales, Monica L.; Watson, Jean-Paul; Kolda, Tamara Gibson; Adams, Brian M.; Swiler, Laura Painton; Williams, Pamela J.; Hough, Patricia Diane; Gay, David M.; Dunlavy, Daniel M.; Eddy, John P.; Hart, William Eugene; Guinta, Anthony A.; Brown, Shannon L.
2006-10-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications.
Eldred, Michael Scott; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Hough, Patricia Diane; Gay, David M.; Eddy, John P.; Haskell, Karen H.
2010-05-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications.
Topology Optimization for Architected Materials Design
NASA Astrophysics Data System (ADS)
Osanov, Mikhail; Guest, James K.
2016-07-01
Advanced manufacturing processes provide a tremendous opportunity to fabricate materials with precisely defined architectures. To fully leverage these capabilities, however, materials architectures must be optimally designed according to the target application, base material used, and specifics of the fabrication process. Computational topology optimization offers a systematic, mathematically driven framework for navigating this new design challenge. The design problem is posed and solved formally as an optimization problem with unit cell and upscaling mechanics embedded within this formulation. This article briefly reviews the key requirements to apply topology optimization to materials architecture design and discusses several fundamental findings related to optimization of elastic, thermal, and fluidic properties in periodic materials. Emerging areas related to topology optimization for manufacturability and manufacturing variations, nonlinear mechanics, and multiscale design are also discussed.
A Rigorous Framework for Optimization of Expensive Functions by Surrogates
NASA Technical Reports Server (NTRS)
Booker, Andrew J.; Dennis, J. E., Jr.; Frank, Paul D.; Serafini, David B.; Torczon, Virginia; Trosset, Michael W.
1998-01-01
The goal of the research reported here is to develop rigorous optimization algorithms to apply to some engineering design problems for which design application of traditional optimization approaches is not practical. This paper presents and analyzes a framework for generating a sequence of approximations to the objective function and managing the use of these approximations as surrogates for optimization. The result is to obtain convergence to a minimizer of an expensive objective function subject to simple constraints. The approach is widely applicable because it does not require, or even explicitly approximate, derivatives of the objective. Numerical results are presented for a 31-variable helicopter rotor blade design example and for a standard optimization test example.
Gandolfi, F; Malleret, L; Sergent, M; Doumenq, P
2015-08-01
The water framework directives (WFD 2000/60/EC and 2013/39/EU) force European countries to monitor the quality of their aquatic environment. Among the priority hazardous substances targeted by the WFD, short chain chlorinated paraffins C10-C13 (SCCPs), still represent an analytical challenge, because few laboratories are nowadays able to analyze them. Moreover, an annual average quality standards as low as 0.4μgL(-1) was set for SCCPs in surface water. Therefore, to test for compliance, the implementation of sensitive and reliable analysis method of SCCPs in water are required. The aim of this work was to address this issue by evaluating automated solid phase micro-extraction (SPME) combined on line with gas chromatography-electron capture negative ionization mass spectrometry (GC/ECNI-MS). Fiber polymer, extraction mode, ionic strength, extraction temperature and time were the most significant thermodynamic and kinetic parameters studied. To determine the suitable factors working ranges, the study of the extraction conditions was first carried out by using a classical one factor-at-a-time approach. Then a mixed level factorial 3×2(3) design was performed, in order to give rise to the most influent parameters and to estimate potential interactions effects between them. The most influent factors, i.e. extraction temperature and duration, were optimized by using a second experimental design, in order to maximize the chromatographic response. At the close of the study, a method involving headspace SPME (HS-SPME) coupled to GC/ECNI-MS is proposed. The optimum extraction conditions were sample temperature 90°C, extraction time 80min, with the PDMS 100μm fiber and desorption at 250°C during 2min. Linear response from 0.2ngmL(-1) to 10ngmL(-1) with r(2)=0.99 and limits of detection and quantification, respectively of 4pgmL(-1) and 120pgmL(-1) in MilliQ water, were achieved. The method proved to be applicable in different types of waters and show key advantages, such
Gandolfi, F; Malleret, L; Sergent, M; Doumenq, P
2015-08-01
The water framework directives (WFD 2000/60/EC and 2013/39/EU) force European countries to monitor the quality of their aquatic environment. Among the priority hazardous substances targeted by the WFD, short chain chlorinated paraffins C10-C13 (SCCPs), still represent an analytical challenge, because few laboratories are nowadays able to analyze them. Moreover, an annual average quality standards as low as 0.4μgL(-1) was set for SCCPs in surface water. Therefore, to test for compliance, the implementation of sensitive and reliable analysis method of SCCPs in water are required. The aim of this work was to address this issue by evaluating automated solid phase micro-extraction (SPME) combined on line with gas chromatography-electron capture negative ionization mass spectrometry (GC/ECNI-MS). Fiber polymer, extraction mode, ionic strength, extraction temperature and time were the most significant thermodynamic and kinetic parameters studied. To determine the suitable factors working ranges, the study of the extraction conditions was first carried out by using a classical one factor-at-a-time approach. Then a mixed level factorial 3×2(3) design was performed, in order to give rise to the most influent parameters and to estimate potential interactions effects between them. The most influent factors, i.e. extraction temperature and duration, were optimized by using a second experimental design, in order to maximize the chromatographic response. At the close of the study, a method involving headspace SPME (HS-SPME) coupled to GC/ECNI-MS is proposed. The optimum extraction conditions were sample temperature 90°C, extraction time 80min, with the PDMS 100μm fiber and desorption at 250°C during 2min. Linear response from 0.2ngmL(-1) to 10ngmL(-1) with r(2)=0.99 and limits of detection and quantification, respectively of 4pgmL(-1) and 120pgmL(-1) in MilliQ water, were achieved. The method proved to be applicable in different types of waters and show key advantages, such
OPTIMAL NETWORK TOPOLOGY DESIGN
NASA Technical Reports Server (NTRS)
Yuen, J. H.
1994-01-01
This program was developed as part of a research study on the topology design and performance analysis for the Space Station Information System (SSIS) network. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. It is intended that this new design technique consider all important performance measures explicitly and take into account the constraints due to various technical feasibilities. In the current program, technical constraints are taken care of by the user properly forming the starting set of candidate components (e.g. nonfeasible links are not included). As subsets are generated, they are tested to see if they form an acceptable network by checking that all requirements are satisfied. Thus the first acceptable subset encountered gives the cost-optimal topology satisfying all given constraints. The user must sort the set of "feasible" link elements in increasing order of their costs. The program prompts the user for the following information for each link: 1) cost, 2) connectivity (number of stations connected by the link), and 3) the stations connected by that link. Unless instructed to stop, the program generates all possible acceptable networks in increasing order of their total costs. The program is written only to generate topologies that are simply connected. Tests on reliability, delay, and other performance measures are discussed in the documentation, but have not been incorporated into the program. This program is written in PASCAL for interactive execution and has been implemented on an IBM PC series computer operating under PC DOS. The disk contains source code only. This program was developed in 1985.
Griffin, Joshua D. (Sandia National lababoratory, Livermore, CA); Eldred, Michael Scott; Martinez-Canales, Monica L.; Watson, Jean-Paul; Kolda, Tamara Gibson (Sandia National lababoratory, Livermore, CA); Giunta, Anthony Andrew; Adams, Brian M.; Swiler, Laura Painton; Williams, Pamela J.; Hough, Patricia Diane (Sandia National lababoratory, Livermore, CA); Gay, David M.; Dunlavy, Daniel M.; Eddy, John P.; Hart, William Eugene; Brown, Shannon L.
2006-10-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a developers manual for the DAKOTA software and describes the DAKOTA class hierarchies and their interrelationships. It derives directly from annotation of the actual source code and provides detailed class documentation, including all member functions and attributes.
Griffin, Joshua D. (Sandai National Labs, Livermore, CA); Eldred, Michael Scott; Martinez-Canales, Monica L.; Watson, Jean-Paul; Kolda, Tamara Gibson; Giunta, Anthony Andrew; Adams, Brian M.; Swiler, Laura Painton; Williams, Pamela J. (Sandai National Labs, Livermore, CA); Hough, Patricia Diane (Sandai National Labs, Livermore, CA); Gay, David M.; Dunlavy, Daniel M.; Eddy, John P.; Hart, William Eugene; Brown, Shannon L.
2006-10-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the DAKOTA software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
Eldred, Michael Scott; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Hough, Patricia Diane; Gay, David M.; Eddy, John P.; Haskell, Karen H.
2010-05-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a developers manual for the DAKOTA software and describes the DAKOTA class hierarchies and their interrelationships. It derives directly from annotation of the actual source code and provides detailed class documentation, including all member functions and attributes.
Eldred, Michael Scott; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Hough, Patricia Diane; Gay, David M.; Eddy, John P.; Haskell, Karen H.
2010-05-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the DAKOTA software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
Aerodynamic design using numerical optimization
NASA Technical Reports Server (NTRS)
Murman, E. M.; Chapman, G. T.
1983-01-01
The procedure of using numerical optimization methods coupled with computational fluid dynamic (CFD) codes for the development of an aerodynamic design is examined. Several approaches that replace wind tunnel tests, develop pressure distributions and derive designs, or fulfill preset design criteria are presented. The method of Aerodynamic Design by Numerical Optimization (ADNO) is described and illustrated with examples.
Structural Optimization in automotive design
NASA Technical Reports Server (NTRS)
Bennett, J. A.; Botkin, M. E.
1984-01-01
Although mathematical structural optimization has been an active research area for twenty years, there has been relatively little penetration into the design process. Experience indicates that often this is due to the traditional layout-analysis design process. In many cases, optimization efforts have been outgrowths of analysis groups which are themselves appendages to the traditional design process. As a result, optimization is often introduced into the design process too late to have a significant effect because many potential design variables have already been fixed. A series of examples are given to indicate how structural optimization has been effectively integrated into the design process.
ELPSA as a Lesson Design Framework
ERIC Educational Resources Information Center
Lowrie, Tom; Patahuddin, Sitti Maesuri
2015-01-01
This paper offers a framework for a mathematics lesson design that is consistent with the way we learn about, and discover, most things in life. In addition, the framework provides a structure for identifying how mathematical concepts and understanding are acquired and developed. This framework is called ELPSA and represents five learning…
NASA Technical Reports Server (NTRS)
Axdahl, Erik L.
2015-01-01
Removing human interaction from design processes by using automation may lead to gains in both productivity and design precision. This memorandum describes efforts to incorporate high fidelity numerical analysis tools into an automated framework and applying that framework to applications of practical interest. The purpose of this effort was to integrate VULCAN-CFD into an automated, DAKOTA-enabled framework with a proof-of-concept application being the optimization of supersonic test facility nozzles. It was shown that the optimization framework could be deployed on a high performance computing cluster with the flow of information handled effectively to guide the optimization process. Furthermore, the application of the framework to supersonic test facility nozzle flowpath design and optimization was demonstrated using multiple optimization algorithms.
Integrated controls design optimization
Lou, Xinsheng; Neuschaefer, Carl H.
2015-09-01
A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.
Hansborough, L.; Hamm, R.; Stovall, J.; Swenson, D.
1980-01-01
PIGMI (Pion Generator for Medical Irradiations) is a compact linear proton accelerator design, optimized for pion production and cancer treatment use in a hospital environment. Technology developed during a four-year PIGMI Prototype experimental program allows the design of smaller, less expensive, and more reliable proton linacs. A new type of low-energy accelerating structure, the radio-frequency quadrupole (RFQ) has been tested; it produces an exceptionally good-quality beam and allows the use of a simple 30-kV injector. Average axial electric-field gradients of over 9 MV/m have been demonstrated in a drift-tube linac (DTL) structure. Experimental work is underway to test the disk-and-washer (DAW) structure, another new type of accelerating structure for use in the high-energy coupled-cavity linac (CCL). Sufficient experimental and developmental progress has been made to closely define an actual PIGMI. It will consist of a 30-kV injector, and RFQ linac to a proton energy of 2.5 MeV, a DTL linac to 125 MeV, and a CCL linac to the final energy of 650 MeV. The total length of the accelerator is 133 meters. The RFQ and DTL will be driven by a single 440-MHz klystron; the CCL will be driven by six 1320-MHz klystrons. The peak beam current is 28 mA. The beam pulse length is 60 ..mu..s at a 60-Hz repetition rate, resulting in a 100-..mu..A average beam current. The total cost of the accelerator is estimated to be approx. $10 million.
Design Optimization Toolkit: Users' Manual
Aguilo Valentin, Miguel Alejandro
2014-07-01
The Design Optimization Toolkit (DOTk) is a stand-alone C++ software package intended to solve complex design optimization problems. DOTk software package provides a range of solution methods that are suited for gradient/nongradient-based optimization, large scale constrained optimization, and topology optimization. DOTk was design to have a flexible user interface to allow easy access to DOTk solution methods from external engineering software packages. This inherent flexibility makes DOTk barely intrusive to other engineering software packages. As part of this inherent flexibility, DOTk software package provides an easy-to-use MATLAB interface that enables users to call DOTk solution methods directly from the MATLAB command window.
Cold Climates Heat Pump Design Optimization
Abdelaziz, Omar; Shen, Bo
2012-01-01
Heat pumps provide an efficient heating method; however they suffer from sever capacity and performance degradation at low ambient conditions. This has deterred market penetration in cold climates. There is a continuing effort to find an efficient air source cold climate heat pump that maintains acceptable capacity and performance at low ambient conditions. Systematic optimization techniques provide a reliable approach for the design of such systems. This paper presents a step-by-step approach for the design optimization of cold climate heat pumps. We first start by describing the optimization problem: objective function, constraints, and design space. Then we illustrate how to perform this design optimization using an open source publically available optimization toolbox. The response of the heat pump design was evaluated using a validated component based vapor compression model. This model was treated as a black box model within the optimization framework. Optimum designs for different system configurations are presented. These optimum results were further analyzed to understand the performance tradeoff and selection criteria. The paper ends with a discussion on the use of systematic optimization for the cold climate heat pump design.
Thompson, Kimberly M; Duintjer Tebbens, Radboud J
2016-07-01
Managing the dynamics of vaccine supply and demand represents a significant challenge with very high stakes. Insufficient vaccine supplies can necessitate rationing, lead to preventable adverse health outcomes, delay the achievements of elimination or eradication goals, and/or pose reputation risks for public health authorities and/or manufacturers. This article explores the dynamics of global vaccine supply and demand to consider the opportunities to develop and maintain optimal global vaccine stockpiles for universal vaccines, characterized by large global demand (for which we use measles vaccines as an example), and nonuniversal (including new and niche) vaccines (for which we use oral cholera vaccine as an example). We contrast our approach with other vaccine stockpile optimization frameworks previously developed for the United States pediatric vaccine stockpile to address disruptions in supply and global emergency response vaccine stockpiles to provide on-demand vaccines for use in outbreaks. For measles vaccine, we explore the complexity that arises due to different formulations and presentations of vaccines, consideration of rubella, and the context of regional elimination goals. We conclude that global health policy leaders and stakeholders should procure and maintain appropriate global vaccine rotating stocks for measles and rubella vaccine now to support current regional elimination goals, and should probably also do so for other vaccines to help prevent and control endemic or epidemic diseases. This work suggests the need to better model global vaccine supplies to improve efficiency in the vaccine supply chain, ensure adequate supplies to support elimination and eradication initiatives, and support progress toward the goals of the Global Vaccine Action Plan. PMID:25109229
Thompson, Kimberly M; Duintjer Tebbens, Radboud J
2016-07-01
Managing the dynamics of vaccine supply and demand represents a significant challenge with very high stakes. Insufficient vaccine supplies can necessitate rationing, lead to preventable adverse health outcomes, delay the achievements of elimination or eradication goals, and/or pose reputation risks for public health authorities and/or manufacturers. This article explores the dynamics of global vaccine supply and demand to consider the opportunities to develop and maintain optimal global vaccine stockpiles for universal vaccines, characterized by large global demand (for which we use measles vaccines as an example), and nonuniversal (including new and niche) vaccines (for which we use oral cholera vaccine as an example). We contrast our approach with other vaccine stockpile optimization frameworks previously developed for the United States pediatric vaccine stockpile to address disruptions in supply and global emergency response vaccine stockpiles to provide on-demand vaccines for use in outbreaks. For measles vaccine, we explore the complexity that arises due to different formulations and presentations of vaccines, consideration of rubella, and the context of regional elimination goals. We conclude that global health policy leaders and stakeholders should procure and maintain appropriate global vaccine rotating stocks for measles and rubella vaccine now to support current regional elimination goals, and should probably also do so for other vaccines to help prevent and control endemic or epidemic diseases. This work suggests the need to better model global vaccine supplies to improve efficiency in the vaccine supply chain, ensure adequate supplies to support elimination and eradication initiatives, and support progress toward the goals of the Global Vaccine Action Plan.
Multidisciplinary Design Optimization on Conceptual Design of Aero-engine
NASA Astrophysics Data System (ADS)
Zhang, Xiao-bo; Wang, Zhan-xue; Zhou, Li; Liu, Zeng-wen
2016-06-01
In order to obtain better integrated performance of aero-engine during the conceptual design stage, multiple disciplines such as aerodynamics, structure, weight, and aircraft mission are required. Unfortunately, the couplings between these disciplines make it difficult to model or solve by conventional method. MDO (Multidisciplinary Design Optimization) methodology which can well deal with couplings of disciplines is considered to solve this coupled problem. Approximation method, optimization method, coordination method, and modeling method for MDO framework are deeply analyzed. For obtaining the more efficient MDO framework, an improved CSSO (Concurrent Subspace Optimization) strategy which is based on DOE (Design Of Experiment) and RSM (Response Surface Model) methods is proposed in this paper; and an improved DE (Differential Evolution) algorithm is recommended to solve the system-level and discipline-level optimization problems in MDO framework. The improved CSSO strategy and DE algorithm are evaluated by utilizing the numerical test problem. The result shows that the efficiency of improved methods proposed by this paper is significantly increased. The coupled problem of VCE (Variable Cycle Engine) conceptual design is solved by utilizing improved CSSO strategy, and the design parameter given by improved CSSO strategy is better than the original one. The integrated performance of VCE is significantly improved.
Coupling between a multi-physics workflow engine and an optimization framework
NASA Astrophysics Data System (ADS)
Di Gallo, L.; Reux, C.; Imbeaux, F.; Artaud, J.-F.; Owsiak, M.; Saoutic, B.; Aiello, G.; Bernardi, P.; Ciraolo, G.; Bucalossi, J.; Duchateau, J.-L.; Fausser, C.; Galassi, D.; Hertout, P.; Jaboulay, J.-C.; Li-Puma, A.; Zani, L.
2016-03-01
A generic coupling method between a multi-physics workflow engine and an optimization framework is presented in this paper. The coupling architecture has been developed in order to preserve the integrity of the two frameworks. The objective is to provide the possibility to replace a framework, a workflow or an optimizer by another one without changing the whole coupling procedure or modifying the main content in each framework. The coupling is achieved by using a socket-based communication library for exchanging data between the two frameworks. Among a number of algorithms provided by optimization frameworks, Genetic Algorithms (GAs) have demonstrated their efficiency on single and multiple criteria optimization. Additionally to their robustness, GAs can handle non-valid data which may appear during the optimization. Consequently GAs work on most general cases. A parallelized framework has been developed to reduce the time spent for optimizations and evaluation of large samples. A test has shown a good scaling efficiency of this parallelized framework. This coupling method has been applied to the case of SYCOMORE (SYstem COde for MOdeling tokamak REactor) which is a system code developed in form of a modular workflow for designing magnetic fusion reactors. The coupling of SYCOMORE with the optimization platform URANIE enables design optimization along various figures of merit and constraints.
Information optimal compressive sensing: static measurement design.
Ashok, Amit; Huang, Liang-Chih; Neifeld, Mark A
2013-05-01
The compressive sensing paradigm exploits the inherent sparsity/compressibility of signals to reduce the number of measurements required for reliable reconstruction/recovery. In many applications additional prior information beyond signal sparsity, such as structure in sparsity, is available, and current efforts are mainly limited to exploiting that information exclusively in the signal reconstruction problem. In this work, we describe an information-theoretic framework that incorporates the additional prior information as well as appropriate measurement constraints in the design of compressive measurements. Using a Gaussian binomial mixture prior we design and analyze the performance of optimized projections relative to random projections under two specific design constraints and different operating measurement signal-to-noise ratio (SNR) regimes. We find that the information-optimized designs yield significant, in some cases nearly an order of magnitude, improvements in the reconstruction performance with respect to the random projections. These improvements are especially notable in the low measurement SNR regime where the energy-efficient design of optimized projections is most advantageous. In such cases, the optimized projection design departs significantly from random projections in terms of their incoherence with the representation basis. In fact, we find that the maximizing incoherence of projections with the representation basis is not necessarily optimal in the presence of additional prior information and finite measurement noise/error. We also apply the information-optimized projections to the compressive image formation problem for natural scenes, and the improved visual quality of reconstructed images with respect to random projections and other compressive measurement design affirms the overall effectiveness of the information-theoretic design framework.
NASA Astrophysics Data System (ADS)
Mikhalchenko, V. V.; Rubanik, Yu T.
2016-10-01
The work is devoted to the problem of cost-effective adaptation of coal mines to the volatile and uncertain market conditions. Conceptually it can be achieved through alignment of the dynamic characteristics of the coal mining system and power spectrum of market demand for coal product. In practical terms, this ensures the viability and competitiveness of coal mines. Transformation of dynamic characteristics is to be done by changing the structure of production system as well as corporate, logistics and management processes. The proposed methods and algorithms of control are aimed at the development of the theoretical foundations of adaptive optimization as basic methodology for coal mine enterprise management in conditions of high variability and uncertainty of economic and natural environment. Implementation of the proposed methodology requires a revision of the basic principles of open coal mining enterprises design.
Design optimization of transonic airfoils
NASA Technical Reports Server (NTRS)
Joh, C.-Y.; Grossman, B.; Haftka, R. T.
1991-01-01
Numerical optimization procedures were considered for the design of airfoils in transonic flow based on the transonic small disturbance (TSD) and Euler equations. A sequential approximation optimization technique was implemented with an accurate approximation of the wave drag based on the Nixon's coordinate straining approach. A modification of the Euler surface boundary conditions was implemented in order to efficiently compute design sensitivities without remeshing the grid. Two effective design procedures producing converged designs in approximately 10 global iterations were developed: interchanging the role of the objective function and constraint and the direct lift maximization with move limits which were fixed absolute values of the design variables.
Multifidelity Analysis and Optimization for Supersonic Design
NASA Technical Reports Server (NTRS)
Kroo, Ilan; Willcox, Karen; March, Andrew; Haas, Alex; Rajnarayan, Dev; Kays, Cory
2010-01-01
Supersonic aircraft design is a computationally expensive optimization problem and multifidelity approaches over a significant opportunity to reduce design time and computational cost. This report presents tools developed to improve supersonic aircraft design capabilities including: aerodynamic tools for supersonic aircraft configurations; a systematic way to manage model uncertainty; and multifidelity model management concepts that incorporate uncertainty. The aerodynamic analysis tools developed are appropriate for use in a multifidelity optimization framework, and include four analysis routines to estimate the lift and drag of a supersonic airfoil, a multifidelity supersonic drag code that estimates the drag of aircraft configurations with three different methods: an area rule method, a panel method, and an Euler solver. In addition, five multifidelity optimization methods are developed, which include local and global methods as well as gradient-based and gradient-free techniques.
Dynamic optimization and adaptive controller design
NASA Astrophysics Data System (ADS)
Inamdar, S. R.
2010-10-01
In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.
A visualization framework for design and evaluation
NASA Astrophysics Data System (ADS)
Blundell, Benjamin J.; Ng, Gary; Pettifer, Steve
2006-01-01
The creation of compelling visualisation paradigms is a craft often dominated by intuition and issues of aesthetics, with relatively few models to support good design. The majority of problem cases are approached by simply applying a previously evaluated visualisation technique. A large body of work exists covering the individual aspects of visualisation design such as the human cognition aspects visualisation methods for specific problem areas, psychology studies and so forth, yet most frameworks regarding visualisation are applied after-the-fact as an evaluation measure. We present an extensible framework for visualisation aimed at structuring the design process, increasing decision traceability and delineating the notions of function, aesthetics and usability. The framework can be used to derive a set of requirements for good visualisation design and evaluating existing visualisations, presenting possible improvements. Our framework achieves this by being both broad and general, built on top of existing works, with hooks for extensions and customizations. This paper shows how existing theories of information visualisation fit into the scheme, presents our experience in the application of this framework on several designs, and offers our evaluation of the framework and the designs studied.
Habitat Design Optimization and Analysis
NASA Technical Reports Server (NTRS)
SanSoucie, Michael P.; Hull, Patrick V.; Tinker, Michael L.
2006-01-01
Long-duration surface missions to the Moon and Mars will require habitats for the astronauts. The materials chosen for the habitat walls play a direct role in the protection against the harsh environments found on the surface. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Advanced optimization techniques are necessary for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat design optimization tool utilizing genetic algorithms has been developed. Genetic algorithms use a "survival of the fittest" philosophy, where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multi-objective formulation of structural analysis, heat loss, radiation protection, and meteoroid protection. This paper presents the research and development of this tool.
Initial Multidisciplinary Design and Analysis Framework
NASA Technical Reports Server (NTRS)
Ozoroski, L. P.; Geiselhart, K. A.; Padula, S. L.; Li, W.; Olson, E. D.; Campbell, R. L.; Shields, E. W.; Berton, J. J.; Gray, J. S.; Jones, S. M.; Naiman, C. G.; Seidel, J. A.; Moore, K. T.; Naylor, B. A.; Townsend, S.
2010-01-01
Within the Supersonics (SUP) Project of the Fundamental Aeronautics Program (FAP), an initial multidisciplinary design & analysis framework has been developed. A set of low- and intermediate-fidelity discipline design and analysis codes were integrated within a multidisciplinary design and analysis framework and demonstrated on two challenging test cases. The first test case demonstrates an initial capability to design for low boom and performance. The second test case demonstrates rapid assessment of a well-characterized design. The current system has been shown to greatly increase the design and analysis speed and capability, and many future areas for development were identified. This work has established a state-of-the-art capability for immediate use by supersonic concept designers and systems analysts at NASA, while also providing a strong base to build upon for future releases as more multifidelity capabilities are developed and integrated.
Optimally designing games for behavioural research.
Rafferty, Anna N; Zaharia, Matei; Griffiths, Thomas L
2014-07-01
Computer games can be motivating and engaging experiences that facilitate learning, leading to their increasing use in education and behavioural experiments. For these applications, it is often important to make inferences about the knowledge and cognitive processes of players based on their behaviour. However, designing games that provide useful behavioural data are a difficult task that typically requires significant trial and error. We address this issue by creating a new formal framework that extends optimal experiment design, used in statistics, to apply to game design. In this framework, we use Markov decision processes to model players' actions within a game, and then make inferences about the parameters of a cognitive model from these actions. Using a variety of concept learning games, we show that in practice, this method can predict which games will result in better estimates of the parameters of interest. The best games require only half as many players to attain the same level of precision.
Optimally designing games for behavioural research
Rafferty, Anna N.; Zaharia, Matei; Griffiths, Thomas L.
2014-01-01
Computer games can be motivating and engaging experiences that facilitate learning, leading to their increasing use in education and behavioural experiments. For these applications, it is often important to make inferences about the knowledge and cognitive processes of players based on their behaviour. However, designing games that provide useful behavioural data are a difficult task that typically requires significant trial and error. We address this issue by creating a new formal framework that extends optimal experiment design, used in statistics, to apply to game design. In this framework, we use Markov decision processes to model players' actions within a game, and then make inferences about the parameters of a cognitive model from these actions. Using a variety of concept learning games, we show that in practice, this method can predict which games will result in better estimates of the parameters of interest. The best games require only half as many players to attain the same level of precision. PMID:25002821
The hyper-cube framework for ant colony optimization.
Blum, Christian; Dorigo, Marco
2004-04-01
Ant colony optimization is a metaheuristic approach belonging to the class of model-based search algorithms. In this paper, we propose a new framework for implementing ant colony optimization algorithms called the hyper-cube framework for ant colony optimization. In contrast to the usual way of implementing ant colony optimization algorithms, this framework limits the pheromone values to the interval [0,1]. This is obtained by introducing changes in the pheromone value update rule. These changes can in general be applied to any pheromone value update rule used in ant colony optimization. We discuss the benefits coming with this new framework. The benefits are twofold. On the theoretical side, the new framework allows us to prove that in Ant System, the ancestor of all ant colony optimization algorithms, the average quality of the solutions produced increases in expectation over time when applied to unconstrained problems. On the practical side, the new framework automatically handles the scaling of the objective function values. We experimentally show that this leads on average to a more robust behavior of ant colony optimization algorithms. PMID:15376861
An optimization framework for interdependent planning goals
NASA Technical Reports Server (NTRS)
Estlin, T. A.; Gaines, D. M.
2002-01-01
This paper describes an approach for optimizing over interdependent planning goals. We have implemented a methodology for representing and utilizing information about interdependent goals and their related utilities using the ASPEN planning and scheduling system.
Design of optimal systolic arrays
Li, G.J.; Wah, B.W.
1985-01-01
Conventional design of systolic arrays is based on the mapping of an algorithm onto an interconnection of processing elements in a VLSI chip. This mapping is done in an ad hoc manner, and the resulting configuration usually represents a feasible but suboptimal design. In this paper, systolic arrays are characterized by three classes of parameters: the velocities of data flows, the spatial distributions of data, and the periods of computation. By relating these parameters in constraint equations that govern the correctness of the design, the design is formulated into an optimization problem. The size of the search space is a polynomial of the problem size, and a methodology to systematically search and reduce this space and to obtain the optimal design is proposed. Some examples of applying the method, including matrix multiplication, finite impulse response filtering, deconvolution, and triangular-matrix inversion, are given. 30 references.
Optimal designs for copula models
Perrone, E.; Müller, W.G.
2016-01-01
Copula modelling has in the past decade become a standard tool in many areas of applied statistics. However, a largely neglected aspect concerns the design of related experiments. Particularly the issue of whether the estimation of copula parameters can be enhanced by optimizing experimental conditions and how robust all the parameter estimates for the model are with respect to the type of copula employed. In this paper an equivalence theorem for (bivariate) copula models is provided that allows formulation of efficient design algorithms and quick checks of whether designs are optimal or at least efficient. Some examples illustrate that in practical situations considerable gains in design efficiency can be achieved. A natural comparison between different copula models with respect to design efficiency is provided as well. PMID:27453616
Comparison of optimal design methods in inverse problems
NASA Astrophysics Data System (ADS)
Banks, H. T.; Holm, K.; Kappel, F.
2011-07-01
Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric-based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher information matrix. A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criterion with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst-Pearl logistic population model (Banks H T and Tran H T 2009 Mathematical and Experimental Modeling of Physical and Biological Processes (Boca Raton, FL: Chapman and Hall/CRC)), the standard harmonic oscillator model (Banks H T and Tran H T 2009) and a popular glucose regulation model (Bergman R N, Ider Y Z, Bowden C R and Cobelli C 1979 Am. J. Physiol. 236 E667-77 De Gaetano A and Arino O 2000 J. Math. Biol. 40 136-68 Toffolo G, Bergman R N, Finegood D T, Bowden C R and Cobelli C 1980 Diabetes 29 979-90).
Intelligent and robust optimization frameworks for smart grids
NASA Astrophysics Data System (ADS)
Dhansri, Naren Reddy
A smart grid implies a cyberspace real-time distributed power control system to optimally deliver electricity based on varying consumer characteristics. Although smart grids solve many of the contemporary problems, they give rise to new control and optimization problems with the growing role of renewable energy sources such as wind or solar energy. Under highly dynamic nature of distributed power generation and the varying consumer demand and cost requirements, the total power output of the grid should be controlled such that the load demand is met by giving a higher priority to renewable energy sources. Hence, the power generated from renewable energy sources should be optimized while minimizing the generation from non renewable energy sources. This research develops a demand-based automatic generation control and optimization framework for real-time smart grid operations by integrating conventional and renewable energy sources under varying consumer demand and cost requirements. Focusing on the renewable energy sources, the intelligent and robust control frameworks optimize the power generation by tracking the consumer demand in a closed-loop control framework, yielding superior economic and ecological benefits and circumvent nonlinear model complexities and handles uncertainties for superior real-time operations. The proposed intelligent system framework optimizes the smart grid power generation for maximum economical and ecological benefits under an uncertain renewable wind energy source. The numerical results demonstrate that the proposed framework is a viable approach to integrate various energy sources for real-time smart grid implementations. The robust optimization framework results demonstrate the effectiveness of the robust controllers under bounded power plant model uncertainties and exogenous wind input excitation while maximizing economical and ecological performance objectives. Therefore, the proposed framework offers a new worst-case deterministic
Optimal stochastic control in natural resource management: Framework and examples
Williams, B.K.
1982-01-01
A framework is presented for the application of optimal control methods to natural resource problems. An expression of the optimal control problem appropriate for renewable natural resources is given and its application to Markovian systems is presented in some detail. Three general approaches are outlined for determining optimal control of infinite time horizon systems and three examples from the natural resource literature are used for illustration.
First-Order Frameworks for Managing Models in Engineering Optimization
NASA Technical Reports Server (NTRS)
Alexandrov, Natlia M.; Lewis, Robert Michael
2000-01-01
Approximation/model management optimization (AMMO) is a rigorous methodology for attaining solutions of high-fidelity optimization problems with minimal expense in high- fidelity function and derivative evaluation. First-order AMMO frameworks allow for a wide variety of models and underlying optimization algorithms. Recent demonstrations with aerodynamic optimization achieved three-fold savings in terms of high- fidelity function and derivative evaluation in the case of variable-resolution models and five-fold savings in the case of variable-fidelity physics models. The savings are problem dependent but certain trends are beginning to emerge. We give an overview of the first-order frameworks, current computational results, and an idea of the scope of the first-order framework applicability.
Framework for computationally efficient optimal irrigation scheduling using ant colony optimization
Technology Transfer Automated Retrieval System (TEKTRAN)
A general optimization framework is introduced with the overall goal of reducing search space size and increasing the computational efficiency of evolutionary algorithm application for optimal irrigation scheduling. The framework achieves this goal by representing the problem in the form of a decisi...
Acoustic design by topology optimization
NASA Astrophysics Data System (ADS)
Dühring, Maria B.; Jensen, Jakob S.; Sigmund, Ole
2008-11-01
To bring down noise levels in human surroundings is an important issue and a method to reduce noise by means of topology optimization is presented here. The acoustic field is modeled by Helmholtz equation and the topology optimization method is based on continuous material interpolation functions in the density and bulk modulus. The objective function is the squared sound pressure amplitude. First, room acoustic problems are considered and it is shown that the sound level can be reduced in a certain part of the room by an optimized distribution of reflecting material in a design domain along the ceiling or by distribution of absorbing and reflecting material along the walls. We obtain well defined optimized designs for a single frequency or a frequency interval for both 2D and 3D problems when considering low frequencies. Second, it is shown that the method can be applied to design outdoor sound barriers in order to reduce the sound level in the shadow zone behind the barrier. A reduction of up to 10 dB for a single barrier and almost 30 dB when using two barriers are achieved compared to utilizing conventional sound barriers.
Optimized quadrature surface coil designs
Kumar, Ananda; Bottomley, Paul A.
2008-01-01
Background Quadrature surface MRI/MRS detectors comprised of circular loop and figure-8 or butterfly-shaped coils offer improved signal-to-noise-ratios (SNR) compared to single surface coils, and reduced power and specific absorption rates (SAR) when used for MRI excitation. While the radius of the optimum loop coil for performing MRI at depth d in a sample is known, the optimum geometry for figure-8 and butterfly coils is not. Materials and methods The geometries of figure-8 and square butterfly detector coils that deliver the optimum SNR are determined numerically by the electromagnetic method of moments. Figure-8 and loop detectors are then combined to create SNR-optimized quadrature detectors whose theoretical and experimental SNR performance are compared with a novel quadrature detector comprised of a strip and a loop, and with two overlapped loops optimized for the same depth at 3 T. The quadrature detection efficiency and local SAR during transmission for the three quadrature configurations are analyzed and compared. Results The SNR-optimized figure-8 detector has loop radius r8 ∼ 0.6d, so r8/r0 ∼ 1.3 in an optimized quadrature detector at 3 T. The optimized butterfly coil has side length ∼ d and crossover angle of ≥ 150° at the center. Conclusions These new design rules for figure-8 and butterfly coils optimize their performance as linear and quadrature detectors. PMID:18057975
Eslick, John C.; Ng, Brenda; Gao, Qianwen; Tong, Charles H.; Sahinidis, Nikolaos V.; Miller, David C.
2014-12-31
Under the auspices of the U.S. Department of Energy’s Carbon Capture Simulation Initiative (CCSI), a Framework for Optimization and Quantification of Uncertainty and Sensitivity (FOQUS) has been developed. This tool enables carbon capture systems to be rapidly synthesized and rigorously optimized, in an environment that accounts for and propagates uncertainties in parameters and models. FOQUS currently enables (1) the development of surrogate algebraic models utilizing the ALAMO algorithm, which can be used for superstructure optimization to identify optimal process configurations, (2) simulation-based optimization utilizing derivative free optimization (DFO) algorithms with detailed black-box process models, and (3) rigorous uncertainty quantification throughmore » PSUADE. FOQUS utilizes another CCSI technology, the Turbine Science Gateway, to manage the thousands of simulated runs necessary for optimization and UQ. Thus, this computational framework has been demonstrated for the design and analysis of a solid sorbent based carbon capture system.« less
Eslick, John C.; Ng, Brenda; Gao, Qianwen; Tong, Charles H.; Sahinidis, Nikolaos V.; Miller, David C.
2014-12-31
Under the auspices of the U.S. Department of Energy’s Carbon Capture Simulation Initiative (CCSI), a Framework for Optimization and Quantification of Uncertainty and Sensitivity (FOQUS) has been developed. This tool enables carbon capture systems to be rapidly synthesized and rigorously optimized, in an environment that accounts for and propagates uncertainties in parameters and models. FOQUS currently enables (1) the development of surrogate algebraic models utilizing the ALAMO algorithm, which can be used for superstructure optimization to identify optimal process configurations, (2) simulation-based optimization utilizing derivative free optimization (DFO) algorithms with detailed black-box process models, and (3) rigorous uncertainty quantification through PSUADE. FOQUS utilizes another CCSI technology, the Turbine Science Gateway, to manage the thousands of simulated runs necessary for optimization and UQ. Thus, this computational framework has been demonstrated for the design and analysis of a solid sorbent based carbon capture system.
A Framework for Optimizing the Placement of Tidal Turbines
NASA Astrophysics Data System (ADS)
Nelson, K. S.; Roberts, J.; Jones, C.; James, S. C.
2013-12-01
Power generation with marine hydrokinetic (MHK) current energy converters (CECs), often in the form of underwater turbines, is receiving growing global interest. Because of reasonable investment, maintenance, reliability, and environmental friendliness, this technology can contribute to national (and global) energy markets and is worthy of research investment. Furthermore, in remote areas, small-scale MHK energy from river, tidal, or ocean currents can provide a local power supply. However, little is known about the potential environmental effects of CEC operation in coastal embayments, estuaries, or rivers, or of the cumulative impacts of these devices on aquatic ecosystems over years or decades of operation. There is an urgent need for practical, accessible tools and peer-reviewed publications to help industry and regulators evaluate environmental impacts and mitigation measures, while establishing best sitting and design practices. Sandia National Laboratories (SNL) and Sea Engineering, Inc. (SEI) have investigated the potential environmental impacts and performance of individual tidal energy converters (TECs) in Cobscook Bay, ME; TECs are a subset of CECs that are specifically deployed in tidal channels. Cobscook Bay is the first deployment location of Ocean Renewable Power Company's (ORPC) TidGenTM unit. One unit is currently in place with four more to follow. Together, SNL and SEI built a coarse-grid, regional-scale model that included Cobscook Bay and all other landward embayments using the modeling platform SNL-EFDC. Within SNL-EFDC tidal turbines are represented using a unique set of momentum extraction, turbulence generation, and turbulence dissipation equations at TEC locations. The global model was then coupled to a local-scale model that was centered on the proposed TEC deployment locations. An optimization frame work was developed that used the refined model to determine optimal device placement locations that maximized array performance. Within the
Optimal design of solidification processes
NASA Technical Reports Server (NTRS)
Dantzig, Jonathan A.; Tortorelli, Daniel A.
1991-01-01
An optimal design algorithm is presented for the analysis of general solidification processes, and is demonstrated for the growth of GaAs crystals in a Bridgman furnace. The system is optimal in the sense that the prespecified temperature distribution in the solidifying materials is obtained to maximize product quality. The optimization uses traditional numerical programming techniques which require the evaluation of cost and constraint functions and their sensitivities. The finite element method is incorporated to analyze the crystal solidification problem, evaluate the cost and constraint functions, and compute the sensitivities. These techniques are demonstrated in the crystal growth application by determining an optimal furnace wall temperature distribution to obtain the desired temperature profile in the crystal, and hence to maximize the crystal's quality. Several numerical optimization algorithms are studied to determine the proper convergence criteria, effective 1-D search strategies, appropriate forms of the cost and constraint functions, etc. In particular, we incorporate the conjugate gradient and quasi-Newton methods for unconstrained problems. The efficiency and effectiveness of each algorithm is presented in the example problem.
Research on optimization-based design
NASA Astrophysics Data System (ADS)
Balling, R. J.; Parkinson, A. R.; Free, J. C.
1989-04-01
Research on optimization-based design is discussed. Illustrative examples are given for cases involving continuous optimization with discrete variables and optimization with tolerances. Approximation of computationally expensive and noisy functions, electromechanical actuator/control system design using decomposition and application of knowledge-based systems and optimization for the design of a valve anti-cavitation device are among the topics covered.
Optimizing Trial Designs for Targeted Therapies
Beckman, Robert A.; Burman, Carl-Fredrik; König, Franz; Stallard, Nigel; Posch, Martin
2016-01-01
An important objective in the development of targeted therapies is to identify the populations where the treatment under consideration has positive benefit risk balance. We consider pivotal clinical trials, where the efficacy of a treatment is tested in an overall population and/or in a pre-specified subpopulation. Based on a decision theoretic framework we derive optimized trial designs by maximizing utility functions. Features to be optimized include the sample size and the population in which the trial is performed (the full population or the targeted subgroup only) as well as the underlying multiple test procedure. The approach accounts for prior knowledge of the efficacy of the drug in the considered populations using a two dimensional prior distribution. The considered utility functions account for the costs of the clinical trial as well as the expected benefit when demonstrating efficacy in the different subpopulations. We model utility functions from a sponsor’s as well as from a public health perspective, reflecting actual civil interests. Examples of optimized trial designs obtained by numerical optimization are presented for both perspectives. PMID:27684573
Optimal design of airlift fermenters
Moresi, M.
1981-11-01
In this article a modeling of a draft-tube airlift fermenter (ALF) based on perfect back-mixing of liquid and plugflow for gas bubbles has been carried out to optimize the design and operation of fermentation units at different working capacities. With reference to a whey fermentation by yeasts the economic optimization has led to a slim ALF with an aspect ratio of about 15. As far as power expended per unit of oxygen transfer is concerned, the responses of the model are highly influenced by kLa. However, a safer use of the model has been suggested in order to assess the feasibility of the fermentation process under study. (Refs. 39).
When Playing Meets Learning: Methodological Framework for Designing Educational Games
NASA Astrophysics Data System (ADS)
Linek, Stephanie B.; Schwarz, Daniel; Bopp, Matthias; Albert, Dietrich
Game-based learning builds upon the idea of using the motivational potential of video games in the educational context. Thus, the design of educational games has to address optimizing enjoyment as well as optimizing learning. Within the EC-project ELEKTRA a methodological framework for the conceptual design of educational games was developed. Thereby state-of-the-art psycho-pedagogical approaches were combined with insights of media-psychology as well as with best-practice game design. This science-based interdisciplinary approach was enriched by enclosed empirical research to answer open questions on educational game-design. Additionally, several evaluation-cycles were implemented to achieve further improvements. The psycho-pedagogical core of the methodology can be summarized by the ELEKTRA's 4Ms: Macroadaptivity, Microadaptivity, Metacognition, and Motivation. The conceptual framework is structured in eight phases which have several interconnections and feedback-cycles that enable a close interdisciplinary collaboration between game design, pedagogy, cognitive science and media psychology.
Optimal branching designs in respiratory systems
NASA Astrophysics Data System (ADS)
Park, Keunhwan; Kim, Wonjung; Kim, Ho-Young
2015-11-01
In nature, the size of the flow channels systematically decreases with multiple generations of branching, and a mother branch is ultimately divided into numerous terminal daughters. One important feature of branching designs is an increase in the total cross-sectional area along with generation, which provide more time and area for mass transfer at the terminal branches. However, the expansion of the total cross-sectional area can be costly due to the maintenance of redundant branches or the additional viscous resistance. Accordingly, we expect to find optimal designs in natural branching systems. Here we present two examples of branching designs in respiratory systems: fish gills and human lung airways. Fish gills consist of filaments with well-ordered lamellar structures. By developing a mathematical model of oxygen transfer rate as a function of the dimensions of fish gills, we demonstrate that the interlamellar distance has been optimized to maximize the oxygen transfer rate. Using the same framework, we examine the diameter reduction ratio in human lung airways, which branch by dichotomy with a systematic reduction of their diameters. Our mathematical model for oxygen transport in the airways enables us to unveil the design principle of human lung airways.
A duality framework for stochastic optimal control of complex systems
Malikopoulos, Andreas A.
2016-01-01
In this study, we address the problem of minimizing the long-run expected average cost of a complex system consisting of interactive subsystems. We formulate a multiobjective optimization problem of the one-stage expected costs of the subsystems and provide a duality framework to prove that the control policy yielding the Pareto optimal solution minimizes the average cost criterion of the system. We provide the conditions of existence and a geometric interpretation of the solution. For practical situations having constraints consistent with those studied here, our results imply that the Pareto control policy may be of value when we seek to derive online the optimal control policy in complex systems.
A Framework for Optimal Control Allocation with Structural Load Constraints
NASA Technical Reports Server (NTRS)
Frost, Susan A.; Taylor, Brian R.; Jutte, Christine V.; Burken, John J.; Trinh, Khanh V.; Bodson, Marc
2010-01-01
Conventional aircraft generally employ mixing algorithms or lookup tables to determine control surface deflections needed to achieve moments commanded by the flight control system. Control allocation is the problem of converting desired moments into control effector commands. Next generation aircraft may have many multipurpose, redundant control surfaces, adding considerable complexity to the control allocation problem. These issues can be addressed with optimal control allocation. Most optimal control allocation algorithms have control surface position and rate constraints. However, these constraints are insufficient to ensure that the aircraft's structural load limits will not be exceeded by commanded surface deflections. In this paper, a framework is proposed to enable a flight control system with optimal control allocation to incorporate real-time structural load feedback and structural load constraints. A proof of concept simulation that demonstrates the framework in a simulation of a generic transport aircraft is presented.
Multilevel design optimization and the effect of epistemic uncertainty
NASA Astrophysics Data System (ADS)
Nesbit, Benjamin Edward
This work presents the state of the art in hierarchically decomposed multilevel optimization. This work is expanded with the inclusion of evidence theory with the multilevel framework for the quantification of epistemic uncertainty. The novel method, Evidence-Based Multilevel Design optimization, is then used to solve two analytical optimization problems. This method is also used to explore the effect of the belief structure on the final solution. A methodology is presented to reduce the costs of evidence-based optimization through manipulation of the belief structure. In addition, a transport aircraft wing is also solved with multilevel optimization without uncertainty. This complex, real world optimization problem shows the capability of decomposed multilevel framework to reduce costs of solving computationally expensive problems with black box analyses.
An optimal structural design algorithm using optimality criteria
NASA Technical Reports Server (NTRS)
Taylor, J. E.; Rossow, M. P.
1976-01-01
An algorithm for optimal design is given which incorporates several of the desirable features of both mathematical programming and optimality criteria, while avoiding some of the undesirable features. The algorithm proceeds by approaching the optimal solution through the solutions of an associated set of constrained optimal design problems. The solutions of the constrained problems are recognized at each stage through the application of optimality criteria based on energy concepts. Two examples are described in which the optimal member size and layout of a truss is predicted, given the joint locations and loads.
A Bayesian A-optimal and model robust design criterion.
Zhou, Xiaojie; Joseph, Lawrence; Wolfson, David B; Bélisle, Patrick
2003-12-01
Suppose that the true model underlying a set of data is one of a finite set of candidate models, and that parameter estimation for this model is of primary interest. With this goal, optimal design must depend on a loss function across all possible models. A common method that accounts for model uncertainty is to average the loss over all models; this is the basis of what is known as Läuter's criterion. We generalize Läuter's criterion and show that it can be placed in a Bayesian decision theoretic framework, by extending the definition of Bayesian A-optimality. We use this generalized A-optimality to find optimal design points in an environmental safety setting. In estimating the smallest detectable trace limit in a water contamination problem, we obtain optimal designs that are quite different from those suggested by standard A-optimality.
Optimizing SRF Gun Cavity Profiles in a Genetic Algorithm Framework
Alicia Hofler, Pavel Evtushenko, Frank Marhauser
2009-09-01
Automation of DC photoinjector designs using a genetic algorithm (GA) based optimization is an accepted practice in accelerator physics. Allowing the gun cavity field profile shape to be varied can extend the utility of this optimization methodology to superconducting and normal conducting radio frequency (SRF/RF) gun based injectors. Finding optimal field and cavity geometry configurations can provide guidance for cavity design choices and verify existing designs. We have considered two approaches for varying the electric field profile. The first is to determine the optimal field profile shape that should be used independent of the cavity geometry, and the other is to vary the geometry of the gun cavity structure to produce an optimal field profile. The first method can provide a theoretical optimal and can illuminate where possible gains can be made in field shaping. The second method can produce more realistically achievable designs that can be compared to existing designs. In this paper, we discuss the design and implementation for these two methods for generating field profiles for SRF/RF guns in a GA based injector optimization scheme and provide preliminary results.
A Framework for Parallel Nonlinear Optimization by Partitioning Localized Constraints
Xu, You; Chen, Yixin
2008-06-28
We present a novel parallel framework for solving large-scale continuous nonlinear optimization problems based on constraint partitioning. The framework distributes constraints and variables to parallel processors and uses an existing solver to handle the partitioned subproblems. In contrast to most previous decomposition methods that require either separability or convexity of constraints, our approach is based on a new constraint partitioning theory and can handle nonconvex problems with inseparable global constraints. We also propose a hypergraph partitioning method to recognize the problem structure. Experimental results show that the proposed parallel algorithm can efficiently solve some difficult test cases.
SoMIR framework for designing high-NDBP photonic crystal waveguides.
Mirjalili, Seyed Mohammad
2014-06-20
This work proposes a modularized framework for designing the structure of photonic crystal waveguides (PCWs) and reducing human involvement during the design process. The proposed framework consists of three main modules: parameters module, constraints module, and optimizer module. The first module is responsible for defining the structural parameters of a given PCW. The second module defines various limitations in order to achieve desirable optimum designs. The third module is the optimizer, in which a numerical optimization method is employed to perform optimization. As case studies, two new structures called Ellipse PCW (EPCW) and Hypoellipse PCW (HPCW) with different shape of holes in each row are proposed and optimized by the framework. The calculation results show that the proposed framework is able to successfully optimize the structures of the new EPCW and HPCW. In addition, the results demonstrate the applicability of the proposed framework for optimizing different PCWs. The results of the comparative study show that the optimized EPCW and HPCW provide 18% and 9% significant improvements in normalized delay-bandwidth product (NDBP), respectively, compared to the ring-shape-hole PCW, which has the highest NDBP in the literature. Finally, the simulations of pulse propagation confirm the manufacturing feasibility of both optimized structures.
ERIC Educational Resources Information Center
Triantafyllakos, George; Palaigeorgiou, George; Tsoukalas, Ioannis A.
2011-01-01
In this paper, we present a framework for the development of collaborative design games that can be employed in participatory design sessions with students for the design of educational applications. The framework is inspired by idea generation theory and the design games literature, and guides the development of board games which, through the use…
Optimal design of compact spur gear reductions
NASA Technical Reports Server (NTRS)
Savage, M.; Lattime, S. B.; Kimmel, J. A.; Coe, H. H.
1992-01-01
The optimal design of compact spur gear reductions includes the selection of bearing and shaft proportions in addition to gear mesh parameters. Designs for single mesh spur gear reductions are based on optimization of system life, system volume, and system weight including gears, support shafts, and the four bearings. The overall optimization allows component properties to interact, yielding the best composite design. A modified feasible directions search algorithm directs the optimization through a continuous design space. Interpolated polynomials expand the discrete bearing properties and proportions into continuous variables for optimization. After finding the continuous optimum, the designer can analyze near optimal designs for comparison and selection. Design examples show the influence of the bearings on the optimal configurations.
A Communication-Optimal Framework for Contracting Distributed Tensors
Rajbhandari, Samyam; NIkam, Akshay; Lai, Pai-Wei; Stock, Kevin; Krishnamoorthy, Sriram; Sadayappan, Ponnuswamy
2014-11-16
Tensor contractions are extremely compute intensive generalized matrix multiplication operations encountered in many computational science fields, such as quantum chemistry and nuclear physics. Unlike distributed matrix multiplication, which has been extensively studied, limited work has been done in understanding distributed tensor contractions. In this paper, we characterize distributed tensor contraction algorithms on torus networks. We develop a framework with three fundamental communication operators to generate communication-efficient contraction algorithms for arbitrary tensor contractions. We show that for a given amount of memory per processor, our framework is communication optimal for all tensor contractions. We demonstrate performance and scalability of our framework on up to 262,144 cores of BG/Q supercomputer using five tensor contraction examples.
Design optimization method for Francis turbine
NASA Astrophysics Data System (ADS)
Kawajiri, H.; Enomoto, Y.; Kurosawa, S.
2014-03-01
This paper presents a design optimization system coupled CFD. Optimization algorithm of the system employs particle swarm optimization (PSO). Blade shape design is carried out in one kind of NURBS curve defined by a series of control points. The system was applied for designing the stationary vanes and the runner of higher specific speed francis turbine. As the first step, single objective optimization was performed on stay vane profile, and second step was multi-objective optimization for runner in wide operating range. As a result, it was confirmed that the design system is useful for developing of hydro turbine.
Analysis and System Design Framework for Infrared Spatial Heterodyne Spectrometers
Cooke, B.J.; Smith, B.W.; Laubscher, B.E.; Villeneuve, P.V.; Briles, S.D.
1999-04-05
The authors present a preliminary analysis and design framework developed for the evaluation and optimization of infrared, Imaging Spatial Heterodyne Spectrometer (SHS) electro-optic systems. Commensurate with conventional interferometric spectrometers, SHS modeling requires an integrated analysis environment for rigorous evaluation of system error propagation due to detection process, detection noise, system motion, retrieval algorithm and calibration algorithm. The analysis tools provide for optimization of critical system parameters and components including : (1) optical aperture, f-number, and spectral transmission, (2) SHS interferometer grating and Littrow parameters, and (3) image plane requirements as well as cold shield, optical filtering, and focal-plane dimensions, pixel dimensions and quantum efficiency, (4) SHS spatial and temporal sampling parameters, and (5) retrieval and calibration algorithm issues.
Design sensitivity analysis and optimization tool (DSO) for sizing design applications
NASA Technical Reports Server (NTRS)
Chang, Kuang-Hua; Choi, Kyung K.; Perng, Jyh-Hwa
1992-01-01
The DSO tool, a structural design software system that provides the designer with a graphics-based menu-driven design environment to perform easy design optimization for general applications, is presented. Three design stages, preprocessing, design sensitivity analysis, and postprocessing, are implemented in the DSO to allow the designer to carry out the design process systematically. A framework, including data base, user interface, foundation class, and remote module, has been designed and implemented to facilitate software development for the DSO. A number of dedicated commercial software/packages have been integrated in the DSO to support the design procedures. Instead of parameterizing an FEM, design parameters are defined on a geometric model associated with physical quantities, and the continuum design sensitivity analysis theory is implemented to compute design sensitivity coefficients using postprocessing data from the analysis codes. A tracked vehicle road wheel is given as a sizing design application to demonstrate the DSO's easy and convenient design optimization process.
Multidisciplinary aircraft conceptual design optimization considering fidelity uncertainties
NASA Astrophysics Data System (ADS)
Neufeld, Daniel
Aircraft conceptual design traditionally utilizes simplified analysis methods and empirical equations to establish the basic layout of new aircraft. Applying optimization methods to aircraft conceptual design may yield solutions that are found to violate constraints when more sophisticated analysis methods are introduced. The designer's confidence that proposed conceptual designs will meet their performance targets is limited when conventional optimization approaches are utilized. Therefore, there is a need for an optimization approach that takes into account the uncertainties that arise when traditional analysis methods are used in aircraft conceptual design optimization. This research introduces a new aircraft conceptual design optimization approach that utilizes the concept of Reliability Based Design Optimization (RBDO). RyeMDO, a framework for multi-objective, multidisciplinary RBDO was developed for this purpose. The performance and effectiveness of the RBDO-MDO approaches implemented in RyeMDO were evaluated to identify the most promising approaches for aircraft conceptual design optimization. Additionally, an approach for quantifying the errors introduced by approximate analysis methods was developed. The approach leverages available historical data to quantify the uncertainties introduced by approximate analysis methods in two engineering case studies: the conceptual design optimization of an aircraft wing box structure and the conceptual design optimization of a commercial aircraft. The case studies were solved with several of the most promising RBDO-MDO integrated approaches. The proposed approach yields more conservative solutions and estimates the risk associated with each solution, enabling designers to reduce the likelihood that conceptual aircraft designs will fail to meet objectives later in the design process.
Integrated structure/control law design by multilevel optimization
NASA Technical Reports Server (NTRS)
Gilbert, Michael G.; Schmidt, David K.
1989-01-01
A new approach to integrated structure/control law design based on multilevel optimization is presented. This new approach is applicable to aircraft and spacecraft and allows for the independent design of the structure and control law. Integration of the designs is achieved through use of an upper level coordination problem formulation within the multilevel optimization framework. The method requires the use of structure and control law design sensitivity information. A general multilevel structure/control law design problem formulation is given, and the use of Linear Quadratic Gaussian (LQG) control law design and design sensitivity methods within the formulation is illustrated. Results of three simple integrated structure/control law design examples are presented. These results show the capability of structure and control law design tradeoffs to improve controlled system performance within the multilevel approach.
Design of crashworthy structures with controlled behavior in HCA framework
NASA Astrophysics Data System (ADS)
Bandi, Punit
The field of crashworthiness design is gaining more interest and attention from automakers around the world due to increasing competition and tighter safety norms. In the last two decades, topology and topometry optimization methods from structural optimization have been widely explored to improve existing designs or conceive new designs with better crashworthiness. Although many gradient-based and heuristic methods for topology- and topometry-based crashworthiness design are available these days, most of them result in stiff structures that are suitable only for a set of vehicle components in which maximizing the energy absorption or minimizing the intrusion is the main concern. However, there are some other components in a vehicle structure that should have characteristics of both stiffness and flexibility. Moreover, the load paths within the structure and potential buckle modes also play an important role in efficient functioning of such components. For example, the front bumper, side frame rails, steering column, and occupant protection devices like the knee bolster should all exhibit controlled deformation and collapse behavior. The primary objective of this research is to develop new methodologies to design crashworthy structures with controlled behavior. The well established Hybrid Cellular Automaton (HCA) method is used as the basic framework for the new methodologies, and compliant mechanism-type (sub)structures are the highlight of this research. The ability of compliant mechanisms to efficiently transfer force and/or motion from points of application of input loads to desired points within the structure is used to design solid and tubular components that exhibit controlled deformation and collapse behavior under crash loads. In addition, a new methodology for controlling the behavior of a structure under multiple crash load scenarios by adaptively changing the contributions from individual load cases is developed. Applied to practical design problems
A duality framework for stochastic optimal control of complex systems
Malikopoulos, Andreas A.
2016-01-01
In this study, we address the problem of minimizing the long-run expected average cost of a complex system consisting of interactive subsystems. We formulate a multiobjective optimization problem of the one-stage expected costs of the subsystems and provide a duality framework to prove that the control policy yielding the Pareto optimal solution minimizes the average cost criterion of the system. We provide the conditions of existence and a geometric interpretation of the solution. For practical situations having constraints consistent with those studied here, our results imply that the Pareto control policy may be of value when we seek to derivemore » online the optimal control policy in complex systems.« less
McCorkle, Douglas S.; Bryden, Kenneth M.
2011-01-01
Several recent reports and workshops have identified integrated computational engineering as an emerging technology with the potential to transform engineering design. The goal is to integrate geometric models, analyses, simulations, optimization and decision-making tools, and all other aspects of the engineering process into a shared, interactive computer-generated environment that facilitates multidisciplinary and collaborative engineering. While integrated computational engineering environments can be constructed from scratch with high-level programming languages, the complexity of these proposed environments makes this type of approach prohibitively slow and expensive. Rather, a high-level software framework is needed to provide the user with the capability to construct an application in an intuitive manner using existing models and engineering tools with minimal programming. In this paper, we present an exploratory open source software framework that can be used to integrate the geometric models, computational fluid dynamics (CFD), and optimization tools needed for shape optimization of complex systems. This framework is demonstrated using the multiphase flow analysis of a complete coal transport system for an 800 MW pulverized coal power station. The framework uses engineering objects and three-dimensional visualization to enable the user to interactively design and optimize the performance of the coal transport system.
Program Aids Analysis And Optimization Of Design
NASA Technical Reports Server (NTRS)
Rogers, James L., Jr.; Lamarsh, William J., II
1994-01-01
NETS/ PROSSS (NETS Coupled With Programming System for Structural Synthesis) computer program developed to provide system for combining NETS (MSC-21588), neural-network application program and CONMIN (Constrained Function Minimization, ARC-10836), optimization program. Enables user to reach nearly optimal design. Design then used as starting point in normal optimization process, possibly enabling user to converge to optimal solution in significantly fewer iterations. NEWT/PROSSS written in C language and FORTRAN 77.
Integrated multidisciplinary design optimization of rotorcraft
NASA Technical Reports Server (NTRS)
Adelman, Howard M.; Mantay, Wayne R.
1989-01-01
The NASA/Army research plan for developing the logic elements for helicopter rotor design optimization by integrating appropriate disciplines and accounting for important interactions among the disciplines is discussed. The paper describes the optimization formulation in terms of the objective function, design variables, and constraints. The analysis aspects are discussed, and an initial effort at defining the interdisciplinary coupling is summarized. Results are presented on the achievements made in the rotor aerodynamic performance optimization for minimum hover horsepower, rotor dynamic optimization for vibration reduction, rotor structural optimization for minimum weight, and integrated aerodynamic load/dynamics optimization for minimum vibration and weight.
Vehicle systems design optimization study
Gilmour, J. L.
1980-04-01
The optimization of an electric vehicle layout requires a weight distribution in the range of 53/47 to 62/38 in order to assure dynamic handling characteristics comparable to current production internal combustion engine vehicles. It is possible to achieve this goal and also provide passenger and cargo space comparable to a selected current production sub-compact car either in a unique new design or by utilizing the production vehicle as a base. Necessary modification of the base vehicle can be accomplished without major modification of the structure or running gear. As long as batteries are as heavy and require as much space as they currently do, they must be divided into two packages - one at front under the hood and a second at the rear under the cargo area - in order to achieve the desired weight distribution. The weight distribution criteria requires the placement of batteries at the front of the vehicle even when the central tunnel is used for the location of some batteries. The optimum layout has a front motor and front wheel drive. This configuration provides the optimum vehicle dynamic handling characteristics and the maximum passsenger and cargo space for a given size vehicle.
Flat-plate photovoltaic array design optimization
NASA Technical Reports Server (NTRS)
Ross, R. G., Jr.
1980-01-01
An analysis is presented which integrates the results of specific studies in the areas of photovoltaic structural design optimization, optimization of array series/parallel circuit design, thermal design optimization, and optimization of environmental protection features. The analysis is based on minimizing the total photovoltaic system life-cycle energy cost including repair and replacement of failed cells and modules. This approach is shown to be a useful technique for array optimization, particularly when time-dependent parameters such as array degradation and maintenance are involved.
Multi-Disciplinary Design Optimization Using WAVE
NASA Technical Reports Server (NTRS)
Irwin, Keith
2000-01-01
develop an associative control structure (framework) in the UG WAVE environment enabling multi-disciplinary design of turbine propulsion systems. The capabilities of WAVE were evaluated to assess its use as a rapid optimization and productivity tool. This project also identified future WAVE product enhancements that will make the tool still more beneficial for product development.
Design optimization of a torpedo shell structure
NASA Astrophysics Data System (ADS)
Yu, De-Hai; Song, Bao-Wei; Li, Jia-Wang; Yang, Shi-Xing
2008-03-01
An optimized methodology to design a more robust torpedo shell is proposed. The method has taken into account reliability requirements and controllable and uncontrollable factors such as geometry, load, material properties, manufacturing processes, installation, etc. as well as human and environmental factors. The result is a more realistic shell design. Our reliability optimization design model was developed based on sensitivity analysis. Details of the design model are given in this paper. An example of a torpedo shell design based on this model is given and demonstrates that the method produces designs that are more effective and reliable than traditional torpedo shell designs. This method can be used for other torpedo system designs.
Scalar and Multivariate Approaches for Optimal Network Design in Antarctica
NASA Astrophysics Data System (ADS)
Hryniw, Natalia
Observations are crucial for weather and climate, not only for daily forecasts and logistical purposes, for but maintaining representative records and for tuning atmospheric models. Here scalar theory for optimal network design is expanded in a multivariate framework, to allow for optimal station siting for full field optimization. Ensemble sensitivity theory is expanded to produce the covariance trace approach, which optimizes for the trace of the covariance matrix. Relative entropy is also used for multivariate optimization as an information theory approach for finding optimal locations. Antarctic surface temperature data is used as a testbed for these methods. Both methods produce different results which are tied to the fundamental physical parameters of the Antarctic temperature field.
Integrated multidisciplinary design optimization of rotorcraft
NASA Technical Reports Server (NTRS)
Adelman, Howard M.; Mantay, Wayne R.
1989-01-01
The NASA/Army research plan for developing the logic elements for helicopter rotor design optimization by integrating appropriate disciplines and accounting for important interactions among the disciplines is discussed. The optimization formulation is described in terms of the objective function, design variables, and constraints. The analysis aspects are discussed, and an initial effort at defining the interdisciplinary coupling is summarized. Results are presented on the achievements made in the rotor dynamic optimization for vibration reduction, rotor structural optimization for minimum weight, and integrated aerodynamic load/dynamics optimization for minimum vibration and weight.
ELDRED, MICHAEL S.; GIUNTA, ANTHONY A.; VAN BLOEMEN WAANDERS, BART G.; WOJTKIEWICZ JR., STEVEN F.; HART, WILLIAM E.; ALLEVA, MARIO
2002-04-01
The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, analytic reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a developers manual for the DAKOTA software and describes the DAKOTA class hierarchies and their interrelationships. It derives directly from annotation of the actual source code and provides detailed class documentation, including all member functions and attributes.
Best management practices (BMPs) are perceived as being effective in reducing nutrient loads transported from non-point sources (NPS) to receiving water bodies. The objective of this study was to develop a modeling-optimization framework that can be used by watershed management p...
An OER Architecture Framework: Needs and Design
ERIC Educational Resources Information Center
Khanna, Pankaj; Basak, P. C.
2013-01-01
This paper describes an open educational resources (OER) architecture framework that would bring significant improvements in a well-structured and systematic way to the educational practices of distance education institutions of India. The OER architecture framework is articulated with six dimensions: pedagogical, technological, managerial,…
An Optimal Framework for Smart Growth Decision-Making
NASA Astrophysics Data System (ADS)
Moglen, G. E.; Gabriel, S. A.; Faria, J. A.
2002-05-01
Increasing awareness about the problems brought on by urban sprawl have led to proactive measures to guide future development. Such efforts have largely been grouped under the term, "Smart Growth". Although not widely recognized as such, the "smart" in smart growth implies an optimization of some quantity or objective while undertaking new forms of urban development. This presentation develops formal definitions of optimal development from the perspectives of four different types of individuals: a government planner, a land developer, a hydrologist, and a conservationist. Four different objective functions are posed that are consistent with each of these individuals' perspectives. We illustrate the differences in consequences on future development given these different objective functions in Montgomery County, Maryland. Differences between the solutions to the various perspectives graphically illustrate that "smart growth" is in the eye of the beholder. The mapped solutions to "smart growth" from the individual perspectives vary considerably. Aside from the obvious differences between these solutions, this presentation will highlight some interesting, but more subtle results, such as the existence of parcels that are considered "optimal" for development across all perspectives. The robustness of such parcels to varied definitions of optimality suggests they reflect a more broadly defined "smart growth". Although couched in the context of an illustrative example, this presentation emphasizes the need to apply rigorous, quantitative tools in a meaningful framework to address smart growth. The result is a tool that a range of parties can use to plan future development in ways that are environmentally and fiscally responsible and economically viable. The details of the construction of this framework involve precise articulation of individual objectives and good communication between all parties with a stake in the future land development.
Optimality of a Fully Stressed Design
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Hopkins, Dale A.
1998-01-01
For a truss a fully stressed state is reached and when all its members are utilized to their full strength capacity. Historically, engineers considered such a design optimum. But recently this optimality has been questioned, especially since the weight of the structure is not explicitly used in fully stressed design calculations. This paper examines optimality of the full stressed design (FSD) with analytical and graphical illustrations. Solutions for a set of examples obtained by using the FSD method and optimization methods numerically confirm the optimality of the FSD. The FSD, which can be obtained with a small amount of calculation, can be extended to displacement constraints and to nontruss-type structures.
Montessori Education and Optimal Experience: A Framework for New Research.
ERIC Educational Resources Information Center
Rathunde, Kevin
2001-01-01
Discusses similarities between Montessori method and optimal experience theory. Considers three conceptual similarities between these educational theories: the child as focal point of human society; the role of deep concentration in learning and the evolution of human nature; and the understanding that social contexts can be designed to promote…
Yang, Guoxiang; Best, Elly P H
2015-09-15
Best management practices (BMPs) can be used effectively to reduce nutrient loads transported from non-point sources to receiving water bodies. However, methodologies of BMP selection and placement in a cost-effective way are needed to assist watershed management planners and stakeholders. We developed a novel modeling-optimization framework that can be used to find cost-effective solutions of BMP placement to attain nutrient load reduction targets. This was accomplished by integrating a GIS-based BMP siting method, a WQM-TMDL-N modeling approach to estimate total nitrogen (TN) loading, and a multi-objective optimization algorithm. Wetland restoration and buffer strip implementation were the two BMP categories used to explore the performance of this framework, both differing greatly in complexity of spatial analysis for site identification. Minimizing TN load and BMP cost were the two objective functions for the optimization process. The performance of this framework was demonstrated in the Tippecanoe River watershed, Indiana, USA. Optimized scenario-based load reduction indicated that the wetland subset selected by the minimum scenario had the greatest N removal efficiency. Buffer strips were more effective for load removal than wetlands. The optimized solutions provided a range of trade-offs between the two objective functions for both BMPs. This framework can be expanded conveniently to a regional scale because the NHDPlus catchment serves as its spatial computational unit. The present study demonstrated the potential of this framework to find cost-effective solutions to meet a water quality target, such as a 20% TN load reduction, under different conditions. PMID:26188990
Yang, Guoxiang; Best, Elly P H
2015-09-15
Best management practices (BMPs) can be used effectively to reduce nutrient loads transported from non-point sources to receiving water bodies. However, methodologies of BMP selection and placement in a cost-effective way are needed to assist watershed management planners and stakeholders. We developed a novel modeling-optimization framework that can be used to find cost-effective solutions of BMP placement to attain nutrient load reduction targets. This was accomplished by integrating a GIS-based BMP siting method, a WQM-TMDL-N modeling approach to estimate total nitrogen (TN) loading, and a multi-objective optimization algorithm. Wetland restoration and buffer strip implementation were the two BMP categories used to explore the performance of this framework, both differing greatly in complexity of spatial analysis for site identification. Minimizing TN load and BMP cost were the two objective functions for the optimization process. The performance of this framework was demonstrated in the Tippecanoe River watershed, Indiana, USA. Optimized scenario-based load reduction indicated that the wetland subset selected by the minimum scenario had the greatest N removal efficiency. Buffer strips were more effective for load removal than wetlands. The optimized solutions provided a range of trade-offs between the two objective functions for both BMPs. This framework can be expanded conveniently to a regional scale because the NHDPlus catchment serves as its spatial computational unit. The present study demonstrated the potential of this framework to find cost-effective solutions to meet a water quality target, such as a 20% TN load reduction, under different conditions.
Design optimization studies using COSMIC NASTRAN
NASA Technical Reports Server (NTRS)
Pitrof, Stephen M.; Bharatram, G.; Venkayya, Vipperla B.
1993-01-01
The purpose of this study is to create, test and document a procedure to integrate mathematical optimization algorithms with COSMIC NASTRAN. This procedure is very important to structural design engineers who wish to capitalize on optimization methods to ensure that their design is optimized for its intended application. The OPTNAST computer program was created to link NASTRAN and design optimization codes into one package. This implementation was tested using two truss structure models and optimizing their designs for minimum weight, subject to multiple loading conditions and displacement and stress constraints. However, the process is generalized so that an engineer could design other types of elements by adding to or modifying some parts of the code.
Optimization, an Important Stage of Engineering Design
ERIC Educational Resources Information Center
Kelley, Todd R.
2010-01-01
A number of leaders in technology education have indicated that a major difference between the technological design process and the engineering design process is analysis and optimization. The analysis stage of the engineering design process is when mathematical models and scientific principles are employed to help the designer predict design…
Multidisciplinary design optimization of optomechanical devices
NASA Astrophysics Data System (ADS)
Williams, Antonio St. Clair Lloyd
2000-11-01
The current process for designing optomechanical devices typically involves independent design optimization within each discipline. For instance, an optics engineer would optimize the optics of the device for image quality using ray-tracing software. The structural engineer would optimize the design to minimize deformation using the finite element method. Independently optimizing the optics and structures of optomechanical systems negates the possibility of exploiting the interdisciplinary interactions. This can lead to increased product development time and cost. Multidisciplinary Design Optimization (MDO) techniques have been in development over the last decade and have been applied primarily to aerospace problems. The goal of MDO is to take advantage of the interactions between disciplines as well as to improve the product development time. The application of MDO formulations to the design of Optomechanical systems has not been achieved thus far. The aim of this study is to evaluate and develop MDO formulations for optomechanical devices that may be used to reduce the product development time and cost. In addition, the feasibility of obtaining a more global optimum design using these multidisciplinary optimization techniques is investigated. Several MDO formulations were evaluated during this study and compared to the current design optimization process. The formulations evaluated were the Multidisciplinary Design Feasible (MDF), the Sequenced Individual Discipline Feasible (SDO-IDF), and the Sequenced Multidisciplinary Design Feasible (SDO-MDF). The current optimization process is called Independent Design Optimization (IDO). For the examples examined, the results showed that the IDO formulation optimizes each discipline but does not guarantee a multidisciplinary optimum for coupled problems. The SDO-MDF formulation was found to be the least efficient of the formulations examined, while the SDO-IDF showed the most promise in terms of efficiency.
Multidisciplinary Optimization Methods for Aircraft Preliminary Design
NASA Technical Reports Server (NTRS)
Kroo, Ilan; Altus, Steve; Braun, Robert; Gage, Peter; Sobieski, Ian
1994-01-01
This paper describes a research program aimed at improved methods for multidisciplinary design and optimization of large-scale aeronautical systems. The research involves new approaches to system decomposition, interdisciplinary communication, and methods of exploiting coarse-grained parallelism for analysis and optimization. A new architecture, that involves a tight coupling between optimization and analysis, is intended to improve efficiency while simplifying the structure of multidisciplinary, computation-intensive design problems involving many analysis disciplines and perhaps hundreds of design variables. Work in two areas is described here: system decomposition using compatibility constraints to simplify the analysis structure and take advantage of coarse-grained parallelism; and collaborative optimization, a decomposition of the optimization process to permit parallel design and to simplify interdisciplinary communication requirements.
Building a Framework for Engineering Design Experiences in High School
ERIC Educational Resources Information Center
Denson, Cameron D.; Lammi, Matthew
2014-01-01
In this article, Denson and Lammi put forth a conceptual framework that will help promote the successful infusion of engineering design experiences into high school settings. When considering a conceptual framework of engineering design in high school settings, it is important to consider the complex issue at hand. For the purposes of this…
A Design Framework for Online Teacher Professional Development Communities
ERIC Educational Resources Information Center
Liu, Katrina Yan
2012-01-01
This paper provides a design framework for building online teacher professional development communities for preservice and inservice teachers. The framework is based on a comprehensive literature review on the latest technology and epistemology of online community and teacher professional development, comprising four major design factors and three…
Magnetic design optimization using variable metrics
Davey, K.R.
1995-11-01
The optimal design of a magnet assembly for a magnetic levitated train is approached using a three step process. First, the key parameters within the objective performance index are computed for the variation range of the problem. Second, the performance index is fitted to a smooth polynomial involving products of the powers of all variables. Third, a constrained optimization algorithm is employed to predict the optimal choice of the variables. An assessment of the integrity of the optimization program is obtained by comparing the final optimized solution with that predicted by the field analysis in the final configuration. Additional field analysis is recommended around the final solution to fine tune the solution.
A multi-fidelity framework for physics based rotor blade simulation and optimization
NASA Astrophysics Data System (ADS)
Collins, Kyle Brian
with lower fidelity models. This thesis documents the development of automated low and high fidelity physics based rotor simulation frameworks. The low fidelity framework uses a comprehensive code with simplified aerodynamics. The high fidelity model uses a parallel processor capable CFD/CSD methodology. Both low and high fidelity frameworks include an aeroacoustic simulation for prediction of noise. A synergistic process is developed that uses both the low and high fidelity frameworks together to build approximate models of important high fidelity metrics as functions of certain design variables. To test the process, a 4-bladed hingeless rotor model is used as a baseline. The design variables investigated include tip geometry and spanwise twist distribution. Approximation models are built for metrics related to rotor efficiency and vibration using the results from 60+ high fidelity (CFD/CSD) experiments and 400+ low fidelity experiments. Optimization using the approximation models found the Pareto Frontier anchor points, or the design having maximum rotor efficiency and the design having minimum vibration. Various Pareto generation methods are used to find designs on the frontier between these two anchor designs. When tested in the high fidelity framework, the Pareto anchor designs are shown to be very good designs when compared with other designs from the high fidelity database. This provides evidence that the process proposed has merit. Ultimately, this process can be utilized by industry rotor designers with their existing tools to bring high fidelity analysis into the preliminary design stage of rotors. In conclusion, the methods developed and documented in this thesis have made several novel contributions. First, an automated high fidelity CFD based forward flight simulation framework has been built for use in preliminary design optimization. The framework was built around an integrated, parallel processor capable CFD/CSD/AA process. Second, a novel method of
NASA Astrophysics Data System (ADS)
Machnes, S.; Sander, U.; Glaser, S. J.; de Fouquières, P.; Gruslys, A.; Schirmer, S.; Schulte-Herbrüggen, T.
2011-08-01
For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. Moreover, we introduce a unifying algorithmic framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.
Machnes, S.; Sander, U.; Glaser, S. J.; Schulte-Herbrueggen, T.; Fouquieres, P. de; Gruslys, A.; Schirmer, S.
2011-08-15
For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. Moreover, we introduce a unifying algorithmic framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.
Optimization design of electromagnetic shielding composites
NASA Astrophysics Data System (ADS)
Qu, Zhaoming; Wang, Qingguo; Qin, Siliang; Hu, Xiaofeng
2013-03-01
The effective electromagnetic parameters physical model of composites and prediction formulas of composites' shielding effectiveness and reflectivity were derived based on micromechanics, variational principle and electromagnetic wave transmission theory. The multi-objective optimization design of multilayer composites was carried out using genetic algorithm. The optimized results indicate that material parameter proportioning of biggest absorption ability can be acquired under the condition of the minimum shielding effectiveness can be satisfied in certain frequency band. The validity of optimization design model was verified and the scheme has certain theoretical value and directive significance to the design of high efficiency shielding composites.
Design of quasi-phasematching gratings via convex optimization.
Phillips, C R; Gallmann, L; Fejer, M M
2013-04-22
We propose a new approach to quasi-phasematching (QPM) design based on convex optimization. We show that with this approach, globally optimum solutions to several important QPM design problems can be determined. The optimization framework is highly versatile, enabling the user to trade-off different objectives and constraints according to the particular application. The convex problems presented consist of simple objective and constraint functions involving a few thousand variables, and can therefore be solved quite straightforwardly. We consider three examples: (1) synthesis of a target pulse profile via difference frequency generation (DFG) from two ultrashort input pulses, (2) the design of a custom DFG transfer function, and (3) a new approach enabling the suppression of spectral gain narrowing in chirped-QPM-based optical parametric chirped pulse amplification (OPCPA). These examples illustrate the power and versatility of convex optimization in the context of QPM devices. PMID:23609719
Optimization methods applied to hybrid vehicle design
NASA Technical Reports Server (NTRS)
Donoghue, J. F.; Burghart, J. H.
1983-01-01
The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.
Optimality criteria solution strategies in multiple constraint design optimization
NASA Technical Reports Server (NTRS)
Levy, R.; Parzynski, W.
1981-01-01
Procedures and solution strategies are described to solve the conventional structural optimization problem using the Lagrange multiplier technique. The multipliers, obtained through solution of an auxiliary nonlinear optimization problem, lead to optimality criteria to determine the design variables. It is shown that this procedure is essentially equivalent to an alternative formulation using a dual method Lagrangian function objective. Although mathematical formulations are straight-forward, successful applications and computational efficiency depend upon execution procedure strategies. Strategies examined, with application examples, include selection of active constraints, move limits, line search procedures, and side constraint boundaries.
Interaction prediction optimization in multidisciplinary design optimization problems.
Meng, Debiao; Zhang, Xiaoling; Huang, Hong-Zhong; Wang, Zhonglai; Xu, Huanwei
2014-01-01
The distributed strategy of Collaborative Optimization (CO) is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In this paper, one large-scale systems control strategy, the interaction prediction method (IPM), is introduced to enhance CO. IPM is utilized for controlling subsystems and coordinating the produce process in large-scale systems originally. We combine the strategy of IPM with CO and propose the Interaction Prediction Optimization (IPO) method to solve MDO problems. As a hierarchical strategy, there are a system level and a subsystem level in IPO. The interaction design variables (including shared design variables and linking design variables) are operated at the system level and assigned to the subsystem level as design parameters. Each discipline objective is considered and optimized at the subsystem level simultaneously. The values of design variables are transported between system level and subsystem level. The compatibility constraints are replaced with the enhanced compatibility constraints to reduce the dimension of design variables in compatibility constraints. Two examples are presented to show the potential application of IPO for MDO.
Optimal design of thermally coupled distillation columns
Duennebier, G.; Pantelides, C.C.
1999-01-01
This paper considers the optimal design of thermally coupled distillation columns and dividing wall columns using detailed column models and mathematical optimization. The column model used is capable of describing both conventional and thermally coupled columns, which allows comparisons of different structural alternatives to be made. Possible savings in both operating and capital costs of up to 30% are illustrated using two case studies.
Turbomachinery Airfoil Design Optimization Using Differential Evolution
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.; Biegel, Bryan (Technical Monitor)
2002-01-01
An aerodynamic design optimization procedure that is based on a evolutionary algorithm known at Differential Evolution is described. Differential Evolution is a simple, fast, and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems, including highly nonlinear systems with discontinuities and multiple local optima. The method is combined with a Navier-Stokes solver that evaluates the various intermediate designs and provides inputs to the optimization procedure. An efficient constraint handling mechanism is also incorporated. Results are presented for the inverse design of a turbine airfoil from a modern jet engine and compared to earlier methods. The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated. Substantial reductions in the overall computing time requirements are achieved by using the algorithm in conjunction with neural networks.
Turbomachinery Airfoil Design Optimization Using Differential Evolution
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)
2002-01-01
An aerodynamic design optimization procedure that is based on a evolutionary algorithm known at Differential Evolution is described. Differential Evolution is a simple, fast, and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems, including highly nonlinear systems with discontinuities and multiple local optima. The method is combined with a Navier-Stokes solver that evaluates the various intermediate designs and provides inputs to the optimization procedure. An efficient constraint handling mechanism is also incorporated. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated. Substantial reductions in the overall computing time requirements are achieved by using the algorithm in conjunction with neural networks.
NASA Astrophysics Data System (ADS)
Pingen, Georg
The objective of this work is the development of a formal design approach for fluidic systems, providing conceptually novel design layouts with the provision of only boundary conditions and some basic parameters. The lattice Boltzmann method (LBM) is chosen as a flow model due to its simplicity, inherent use of immersed boundary methods, parallelizability, and general flexibility. Immersed Boundary Methods in the form of a Brinkmann penalization are used to continuously vary the flow from fluid to solid, leading to a material distribution based boundary representation. An analytical adjoint sensitivity analysis is derived for the lattice Boltzmann method, enabling the combination of the lattice Boltzmann method with optimization techniques. This results in the first application of design optimization with the lattice Boltzmann method. In particular, the first LBM topology optimization framework for 2D and 3D problems is developed and validated with numerical design optimization problems for drag and pressure drop minimization. To improve the parallel scalability of the LBM sensitivity analysis and permit the solution of large 2D and 3D problems, iterative solvers are studied and a parallel GMRES Schur Complement method is applied to the solution of the linear adjoint problem in the LBM sensitivity analysis. This leads to improved parallel scalability through reduced memory use and algorithmic speedup. The potential of the developed design approach for fluidic systems is illustrated with the optimization of a 3D dual-objective fixed-geometry valve. The use of a parametric level-set method coupled with the LBM material distribution based topology optimization framework is shown to provide further versatility for design applications. Finally, the use of a penalty formulation of the fluid volume constraint permits the topology optimization of flows at moderate Reynolds numbers for a steady-state pipe bend application. Concluding, this work has led to the development of
A Novel Modeling Framework for Heterogeneous Catalyst Design
NASA Astrophysics Data System (ADS)
Katare, Santhoji; Bhan, Aditya; Caruthers, James; Delgass, Nicholas; Lauterbach, Jochen; Venkatasubramanian, Venkat
2002-03-01
A systems-oriented, integrated knowledge architecture that enables the use of data from High Throughput Experiments (HTE) for catalyst design is being developed. Higher-level critical reasoning is required to extract information efficiently from the increasingly available HTE data and to develop predictive models that can be used for design purposes. Towards this objective, we have developed a framework that aids the catalyst designer in negotiating the data and model complexities. Traditional kinetic and statistical tools have been systematically implemented and novel artificial intelligence tools have been developed and integrated to speed up the process of modeling catalytic reactions. Multiple nonlinear models that describe CO oxidation on supported metals have been screened using qualitative and quantitative features based optimization ideas. Physical constraints of the system have been used to select the optimum model parameters from the multiple solutions to the parameter estimation problem. Preliminary results about the selection of catalyst descriptors that match a target performance and the use of HTE data for refining fundamentals based models will be discussed.
Interior Design Education within a Human Ecological Framework
ERIC Educational Resources Information Center
Kaup, Migette L.; Anderson, Barbara G.; Honey, Peggy
2007-01-01
An education based in human ecology can greatly benefit interior designers as they work to understand and improve the human condition. Design programs housed in colleges focusing on human ecology can improve the interior design profession by taking advantage of their home base and emphasizing the human ecological framework in the design curricula.…
An Integrated Framework for CBI Screen Design and Layout.
ERIC Educational Resources Information Center
Hannafin, Michael J.; Hooper, Simon
1989-01-01
Discusses the importance of screen design in computer-based instruction (CBI), and presents a framework for screen design decisions based on the ROPES (Retrieving, Orienting, Presenting, Encoding, and Sequencing) meta-model for instructional design. Psychological, instructional, and technological foundations of screen design are discussed, and…
Multidisciplinary design optimization using genetic algorithms
NASA Technical Reports Server (NTRS)
Unal, Resit
1994-01-01
Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared
Designing string-of-beads vaccines with optimal spacers.
Schubert, Benjamin; Kohlbacher, Oliver
2016-01-26
String-of-beads polypeptides allow convenient delivery of epitope-based vaccines. The success of a polypeptide relies on efficient processing: constituent epitopes need to be recovered while avoiding neo-epitopes from epitope junctions. Spacers between epitopes are employed to ensure this, but spacer selection is non-trivial.We present a framework to determine optimally the length and sequence of a spacer through multi-objective optimization for human leukocyte antigen class I restricted polypeptides. The method yields string-of-bead vaccines with flexible spacer lengths that increase the predicted epitope recovery rate fivefold while reducing the immunogenicity from neo-epitopes by 44% compared to designs without spacers.
Multiobjective optimization-based design of wearable electrocardiogram monitoring systems.
Martinez-Tabares, F J; Jaramillo-Garzón, J A; Castellanos-Dominguez, G
2014-01-01
Nowadays, the use of Wearable User Interfaces has been extensively growing in medical monitoring applications. However, production and manufacture of prototypes without automation tools may lead to non viable results since it is often common to find an optimization problem where several variables are in conflict with each other. Thus, it is necessary to design a strategy for balancing the variables and constraints, systematizing the design in order to reduce the risks that are present when it is exclusively guided by the intuition of the developer. This paper proposes a framework for designing wearable ECG monitoring systems using multi-objective optimization. The main contributions of this work are the model to automate the design process, including a mathematical expression relating the principal variables that make up the criteria of functionality and wearability. We also introduce a novel yardstick for deciding the location of electrodes, based on reducing interference from ECG by maximizing the electrode-skin contact.
Optimal Experimental Design for Model Discrimination
Myung, Jay I.; Pitt, Mark A.
2009-01-01
Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design variables (e.g., presentation schedule, stimulus structure) that will be most informative in differentiating them. Recent developments in sampling-based search methods in statistics make it possible to determine these values, and thereby identify an optimal experimental design. After describing the method, it is demonstrated in two content areas in cognitive psychology in which models are highly competitive: retention (i.e., forgetting) and categorization. The optimal design is compared with the quality of designs used in the literature. The findings demonstrate that design optimization has the potential to increase the informativeness of the experimental method. PMID:19618983
Optimal design of reverse osmosis module networks
Maskan, F.; Wiley, D.E.; Johnston, L.P.M.; Clements, D.J.
2000-05-01
The structure of individual reverse osmosis modules, the configuration of the module network, and the operating conditions were optimized for seawater and brackish water desalination. The system model included simple mathematical equations to predict the performance of the reverse osmosis modules. The optimization problem was formulated as a constrained multivariable nonlinear optimization. The objective function was the annual profit for the system, consisting of the profit obtained from the permeate, capital cost for the process units, and operating costs associated with energy consumption and maintenance. Optimization of several dual-stage reverse osmosis systems were investigated and compared. It was found that optimal network designs are the ones that produce the most permeate. It may be possible to achieve economic improvements by refining current membrane module designs and their operating pressures.
Vehicle systems design optimization study
NASA Technical Reports Server (NTRS)
Gilmour, J. L.
1980-01-01
The optimum vehicle configuration and component locations are determined for an electric drive vehicle based on using the basic structure of a current production subcompact vehicle. The optimization of an electric vehicle layout requires a weight distribution in the range of 53/47 to 62/38 in order to assure dynamic handling characteristics comparable to current internal combustion engine vehicles. Necessary modification of the base vehicle can be accomplished without major modification of the structure or running gear. As long as batteries are as heavy and require as much space as they currently do, they must be divided into two packages, one at front under the hood and a second at the rear under the cargo area, in order to achieve the desired weight distribution. The weight distribution criteria requires the placement of batteries at the front of the vehicle even when the central tunnel is used for the location of some batteries. The optimum layout has a front motor and front wheel drive. This configuration provides the optimum vehicle dynamic handling characteristics and the maximum passenger and cargo space for a given size vehicle.
Torsional ultrasonic transducer computational design optimization.
Melchor, J; Rus, G
2014-09-01
A torsional piezoelectric ultrasonic sensor design is proposed in this paper and computationally tested and optimized to measure shear stiffness properties of soft tissue. These are correlated with a number of pathologies like tumors, hepatic lesions and others. The reason is that, whereas compressibility is predominantly governed by the fluid phase of the tissue, the shear stiffness is dependent on the stroma micro-architecture, which is directly affected by those pathologies. However, diagnostic tools to quantify them are currently not well developed. The first contribution is a new typology of design adapted to quasifluids. A second contribution is the procedure for design optimization, for which an analytical estimate of the Robust Probability Of Detection, called RPOD, is presented for use as optimality criteria. The RPOD is formulated probabilistically to maximize the probability of detecting the least possible pathology while minimizing the effect of noise. The resulting optimal transducer has a resonance frequency of 28 kHz.
Rate distortion optimization for H.264 interframe coding: a general framework and algorithms.
Yang, En-Hui; Yu, Xiang
2007-07-01
Rate distortion (RD) optimization for H.264 interframe coding with complete baseline decoding compatibility is investigated on a frame basis. Using soft decision quantization (SDQ) rather than the standard hard decision quantization, we first establish a general framework in which motion estimation, quantization, and entropy coding (in H.264) for the current frame can be jointly designed to minimize a true RD cost given previously coded reference frames. We then propose three RD optimization algorithms--a graph-based algorithm for near optimal SDQ in H.264 baseline encoding given motion estimation and quantization step sizes, an algorithm for near optimal residual coding in H.264 baseline encoding given motion estimation, and an iterative overall algorithm to optimize H.264 baseline encoding for each individual frame given previously coded reference frames-with them embedded in the indicated order. The graph-based algorithm for near optimal SDQ is the core; given motion estimation and quantization step sizes, it is guaranteed to perform optimal SDQ if the weak adjacent block dependency utilized in the context adaptive variable length coding of H.264 is ignored for optimization. The proposed algorithms have been implemented based on the reference encoder JM82 of H.264 with complete compatibility to the baseline profile. Experiments show that for a set of typical video testing sequences, the graph-based algorithm for near optimal SDQ, the algorithm for near optimal residual coding, and the overall algorithm achieve on average, 6%, 8%, and 12%, respectively, rate reduction at the same PSNR (ranging from 30 to 38 dB) when compared with the RD optimization method implemented in the H.264 reference software.
Theoretical Foundation of Copernicus: A Unified System for Trajectory Design and Optimization
NASA Technical Reports Server (NTRS)
Ocampo, Cesar; Senent, Juan S.; Williams, Jacob
2010-01-01
The fundamental methods are described for the general spacecraft trajectory design and optimization software system called Copernicus. The methods rely on a unified framework that is used to model, design, and optimize spacecraft trajectories that may operate in complex gravitational force fields, use multiple propulsion systems, and involve multiple spacecraft. The trajectory model, with its associated equations of motion and maneuver models, are discussed.
A Design Framework for Syllabus Generator
ERIC Educational Resources Information Center
Abdous, M'hammed; He, Wu
2008-01-01
A well-designed syllabus provides students with a roadmap for an engaging and successful learning experience, whereas a poorly designed syllabus impedes communication between faculty and students, increases student anxiety and potential complaints, and reduces overall teaching effectiveness. In an effort to facilitate, streamline, and improve…
Stress constraints in optimality criteria design
NASA Technical Reports Server (NTRS)
Levy, R.
1982-01-01
Procedures described emphasize the processing of stress constraints within optimality criteria designs for low structural weight with stress and compliance constraints. Prescreening criteria are used to partition stress constraints into either potentially active primary sets or passive secondary sets that require minimal processing. Side constraint boundaries for passive constraints are derived by projections from design histories to modify conventional stress-ratio boundaries. Other procedures described apply partial structural modification reanalysis to design variable groups to correct stress constraint violations of unfeasible designs. Sample problem results show effective design convergence and, in particular, advantages for reanalysis in obtaining lower feasible design weights.
Integrating Hybrid Life Cycle Assessment with Multiobjective Optimization: A Modeling Framework.
Yue, Dajun; Pandya, Shyama; You, Fengqi
2016-02-01
By combining life cycle assessment (LCA) with multiobjective optimization (MOO), the life cycle optimization (LCO) framework holds the promise not only to evaluate the environmental impacts for a given product but also to compare different alternatives and identify both ecologically and economically better decisions. Despite the recent methodological developments in LCA, most LCO applications are developed upon process-based LCA, which results in system boundary truncation and underestimation of the true impact. In this study, we propose a comprehensive LCO framework that seamlessly integrates MOO with integrated hybrid LCA. It quantifies both direct and indirect environmental impacts and incorporates them into the decision making process in addition to the more traditional economic criteria. The proposed LCO framework is demonstrated through an application on sustainable design of a potential bioethanol supply chain in the UK. Results indicate that the proposed hybrid LCO framework identifies a considerable amount of indirect greenhouse gas emissions (up to 58.4%) that are essentially ignored in process-based LCO. Among the biomass feedstock options considered, using woody biomass for bioethanol production would be the most preferable choice from a climate perspective, while the mixed use of wheat and wheat straw as feedstocks would be the most cost-effective one. PMID:26752618
A Framework for Designing Cluster Randomized Trials with Binary Outcomes
ERIC Educational Resources Information Center
Spybrook, Jessaca; Martinez, Andres
2011-01-01
The purpose of this paper is to provide a frame work for approaching a power analysis for a CRT (cluster randomized trial) with a binary outcome. The authors suggest a framework in the context of a simple CRT and then extend it to a blocked design, or a multi-site cluster randomized trial (MSCRT). The framework is based on proportions, an…
Virtual Reality Hypermedia Design Frameworks for Science Instruction.
ERIC Educational Resources Information Center
Maule, R. William; Oh, Byron; Check, Rosa
This paper reports on a study that conceptualizes a research framework to aid software design and development for virtual reality (VR) computer applications for instruction in the sciences. The framework provides methodologies for the processing, collection, examination, classification, and presentation of multimedia information within hyperlinked…
A Computational Framework for Cable Layout Design in Complex Products
NASA Astrophysics Data System (ADS)
Shang, Wei; Liu, Jian-hua; Ning, Ru-xin; Liu, Jia-shun
The cable layout design in complex products has been challenging because of various strict constraints. In this paper, we present a computationalframework which provides a rich solution for the cable layout problems. The framework centers at the digital mockup of the product,and the digital model of the cable bundle in the productis introduced as an essential part. The design process in the framework is carried out in a virtual environment with a wide range of supporting techniques and tools integrated, including path planning techniques, physically-based model, assembly simulation techniques and more. The techniques and tools respectively emphasizeon different aspects in this problem domain. Besides, the designers play an important role in the framework. They drive the whole design process and make decisions with their knowledge on issues that current techniques cannot solve. A prototype system is developed and applied in practical product development process. The results show that the framework is practical and promising.
Toward a More Flexible Web-Based Framework for Multidisciplinary Design
NASA Technical Reports Server (NTRS)
Rogers, J. L.; Salas, A. O.
1999-01-01
In today's competitive environment, both industry and government agencies are under pressure to reduce the time and cost of multidisciplinary design projects. New tools have been introduced to assist in this process by facilitating the integration of and communication among diverse disciplinary codes. One such tool, a framework for multidisciplinary design, is defined as a hardware-software architecture that enables integration, execution, and communication among diverse disciplinary processes. An examination of current frameworks reveals weaknesses in various areas, such as sequencing, monitoring, controlling, and displaying the design process. The objective of this research is to explore how Web technology can improve these areas of weakness and lead toward a more flexible framework. This article describes a Web-based system that optimizes and controls the execution sequence of design processes in addition to monitoring the project status and displaying the design results.
Global Design Optimization for Fluid Machinery Applications
NASA Technical Reports Server (NTRS)
Shyy, Wei; Papila, Nilay; Tucker, Kevin; Vaidyanathan, Raj; Griffin, Lisa
2000-01-01
Recent experiences in utilizing the global optimization methodology, based on polynomial and neural network techniques for fluid machinery design are summarized. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. Another advantage is that these methods do not need to calculate the sensitivity of each design variable locally. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables and methods for predicting the model performance. Examples of applications selected from rocket propulsion components including a supersonic turbine and an injector element and a turbulent flow diffuser are used to illustrate the usefulness of the global optimization method.
A design framework for exploratory geovisualization in epidemiology
Robinson, Anthony C.
2009-01-01
This paper presents a design framework for geographic visualization based on iterative evaluations of a toolkit designed to support cancer epidemiology. The Exploratory Spatio-Temporal Analysis Toolkit (ESTAT), is intended to support visual exploration through multivariate health data. Its purpose is to provide epidemiologists with the ability to generate new hypotheses or further refine those they may already have. Through an iterative user-centered design process, ESTAT has been evaluated by epidemiologists at the National Cancer Institute (NCI). Results of these evaluations are discussed, and a design framework based on evaluation evidence is presented. The framework provides specific recommendations and considerations for the design and development of a geovisualization toolkit for epidemiology. Its basic structure provides a model for future design and evaluation efforts in information visualization. PMID:20390052
Response Surface Model Building and Multidisciplinary Optimization Using D-Optimal Designs
NASA Technical Reports Server (NTRS)
Unal, Resit; Lepsch, Roger A.; McMillin, Mark L.
1998-01-01
This paper discusses response surface methods for approximation model building and multidisciplinary design optimization. The response surface methods discussed are central composite designs, Bayesian methods and D-optimal designs. An over-determined D-optimal design is applied to a configuration design and optimization study of a wing-body, launch vehicle. Results suggest that over determined D-optimal designs may provide an efficient approach for approximation model building and for multidisciplinary design optimization.
Regression analysis as a design optimization tool
NASA Technical Reports Server (NTRS)
Perley, R.
1984-01-01
The optimization concepts are described in relation to an overall design process as opposed to a detailed, part-design process where the requirements are firmly stated, the optimization criteria are well established, and a design is known to be feasible. The overall design process starts with the stated requirements. Some of the design criteria are derived directly from the requirements, but others are affected by the design concept. It is these design criteria that define the performance index, or objective function, that is to be minimized within some constraints. In general, there will be multiple objectives, some mutually exclusive, with no clear statement of their relative importance. The optimization loop that is given adjusts the design variables and analyzes the resulting design, in an iterative fashion, until the objective function is minimized within the constraints. This provides a solution, but it is only the beginning. In effect, the problem definition evolves as information is derived from the results. It becomes a learning process as we determine what the physics of the system can deliver in relation to the desirable system characteristics. As with any learning process, an interactive capability is a real attriubute for investigating the many alternatives that will be suggested as learning progresses.
Optimizing Health Care Coalitions: Conceptual Frameworks and a Research Agenda.
Hupert, Nathaniel; Biala, Karen; Holland, Tara; Baehr, Avi; Hasan, Aisha; Harvey, Melissa
2015-12-01
The US health care system has maintained an objective of preparedness for natural or manmade catastrophic events as part of its larger charge to deliver health services for the American population. In 2002, support for hospital-based preparedness activities was bolstered by the creation of the National Bioterrorism Hospital Preparedness Program, now called the Hospital Preparedness Program, in the US Department of Health and Human Services. Since 2012, this program has promoted linking health care facilities into health care coalitions that build key preparedness and emergency response capabilities. Recognizing that well-functioning health care coalitions can have a positive impact on the health outcomes of the populations they serve, this article informs efforts to optimize health care coalition activity. We first review the landscape of health care coalitions in the United States. Then, using principles from supply chain management and high-reliability organization theory, we present 2 frameworks extending beyond the Office of the Assistant Secretary for Preparedness and Response's current guidance in a way that may help health care coalition leaders gain conceptual insight into how different enterprises achieve similar ends relevant to emergency response. We conclude with a proposed research agenda to advance understanding of how coalitions can contribute to the day-to-day functioning of health care systems and disaster preparedness. PMID:26545194
A Framework for the Design of Service Systems
NASA Astrophysics Data System (ADS)
Tan, Yao-Hua; Hofman, Wout; Gordijn, Jaap; Hulstijn, Joris
We propose a framework for the design and implementation of service systems, especially to design controls for long-term sustainable value co-creation. The framework is based on the software support tool e3-control. To illustrate the framework we use a large-scale case study, the Beer Living Lab, for simplification of customs procedures in international trade. The BeerLL shows how value co-creation can be achieved by reduction of administrative burden in international beer export due to electronic customs. Participants in the BeerLL are Heineken, IBM and Dutch Tax & Customs.
Achieving Equivalence: A Transnational Curriculum Design Framework
ERIC Educational Resources Information Center
Clarke, Angela; Johal, Terry; Sharp, Kristen; Quinn, Shayna
2016-01-01
Transnational education is now essential to university international development strategies. As a result, tertiary educators are expected to engage with the complexities of diverse cultural contexts, different delivery modes, and mixed student cohorts to design quality learning experiences for all. To support this transition we developed a…
Towards a Framework for Professional Curriculum Design
ERIC Educational Resources Information Center
Winch, Christopher
2015-01-01
Recent reviews of vocational qualifications in England have noted problems with their restricted nature. However, the underlying issue of how to conceptualise professional agency in curriculum design has not been properly addressed, either by the Richard or the Whitehead reviews. Drawing on comparative work in England and Europe it is argued that…
Living Design Memory: Framework, Implementation, Lessons Learned.
ERIC Educational Resources Information Center
Terveen, Loren G.; And Others
1995-01-01
Discusses large-scale software development and describes the development of the Designer Assistant to improve software development effectiveness. Highlights include the knowledge management problem; related work, including artificial intelligence and expert systems, software process modeling research, and other approaches to organizational memory;…
Design of optimized piezoelectric HDD-sliders
NASA Astrophysics Data System (ADS)
Nakasone, Paulo H.; Yoo, Jeonghoon; Silva, Emilio C. N.
2010-04-01
As storage data density in hard-disk drives (HDDs) increases for constant or miniaturizing sizes, precision positioning of HDD heads becomes a more relevant issue to ensure enormous amounts of data to be properly written and read. Since the traditional single-stage voice coil motor (VCM) cannot satisfy the positioning requirement of high-density tracks per inch (TPI) HDDs, dual-stage servo systems have been proposed to overcome this matter, by using VCMs to coarsely move the HDD head while piezoelectric actuators provides fine and fast positioning. Thus, the aim of this work is to apply topology optimization method (TOM) to design novel piezoelectric HDD heads, by finding optimal placement of base-plate and piezoelectric material to high precision positioning HDD heads. Topology optimization method is a structural optimization technique that combines the finite element method (FEM) with optimization algorithms. The laminated finite element employs the MITC (mixed interpolation of tensorial components) formulation to provide accurate and reliable results. The topology optimization uses a rational approximation of material properties to vary the material properties between 'void' and 'filled' portions. The design problem consists in generating optimal structures that provide maximal displacements, appropriate structural stiffness and resonance phenomena avoidance. The requirements are achieved by applying formulations to maximize displacements, minimize structural compliance and maximize resonance frequencies. This paper presents the implementation of the algorithms and show results to confirm the feasibility of this approach.
Design optimization for cost and quality: The robust design approach
NASA Technical Reports Server (NTRS)
Unal, Resit
1990-01-01
Designing reliable, low cost, and operable space systems has become the key to future space operations. Designing high quality space systems at low cost is an economic and technological challenge to the designer. A systematic and efficient way to meet this challenge is a new method of design optimization for performance, quality, and cost, called Robust Design. Robust Design is an approach for design optimization. It consists of: making system performance insensitive to material and subsystem variation, thus allowing the use of less costly materials and components; making designs less sensitive to the variations in the operating environment, thus improving reliability and reducing operating costs; and using a new structured development process so that engineering time is used most productively. The objective in Robust Design is to select the best combination of controllable design parameters so that the system is most robust to uncontrollable noise factors. The robust design methodology uses a mathematical tool called an orthogonal array, from design of experiments theory, to study a large number of decision variables with a significantly small number of experiments. Robust design also uses a statistical measure of performance, called a signal-to-noise ratio, from electrical control theory, to evaluate the level of performance and the effect of noise factors. The purpose is to investigate the Robust Design methodology for improving quality and cost, demonstrate its application by the use of an example, and suggest its use as an integral part of space system design process.
Using Approximations to Accelerate Engineering Design Optimization
NASA Technical Reports Server (NTRS)
Torczon, Virginia; Trosset, Michael W.
1998-01-01
Optimization problems that arise in engineering design are often characterized by several features that hinder the use of standard nonlinear optimization techniques. Foremost among these features is that the functions used to define the engineering optimization problem often are computationally intensive. Within a standard nonlinear optimization algorithm, the computational expense of evaluating the functions that define the problem would necessarily be incurred for each iteration of the optimization algorithm. Faced with such prohibitive computational costs, an attractive alternative is to make use of surrogates within an optimization context since surrogates can be chosen or constructed so that they are typically much less expensive to compute. For the purposes of this paper, we will focus on the use of algebraic approximations as surrogates for the objective. In this paper we introduce the use of so-called merit functions that explicitly recognize the desirability of improving the current approximation to the objective during the course of the optimization. We define and experiment with the use of merit functions chosen to simultaneously improve both the solution to the optimization problem (the objective) and the quality of the approximation. Our goal is to further improve the effectiveness of our general approach without sacrificing any of its rigor.
Optimization and surgical design for applications in pediatric cardiology
NASA Astrophysics Data System (ADS)
Marsden, Alison; Bernstein, Adam; Taylor, Charles; Feinstein, Jeffrey
2007-11-01
The coupling of shape optimization to cardiovascular blood flow simulations has potential to improve the design of current surgeries and to eventually allow for optimization of surgical designs for individual patients. This is particularly true in pediatric cardiology, where geometries vary dramatically between patients, and unusual geometries can lead to unfavorable hemodynamic conditions. Interfacing shape optimization to three-dimensional, time-dependent fluid mechanics problems is particularly challenging because of the large computational cost and the difficulty in computing objective function gradients. In this work a derivative-free optimization algorithm is coupled to a three-dimensional Navier-Stokes solver that has been tailored for cardiovascular applications. The optimization code employs mesh adaptive direct search in conjunction with a Kriging surrogate. This framework is successfully demonstrated on several geometries representative of cardiovascular surgical applications. We will discuss issues of cost function choice for surgical applications, including energy loss and wall shear stress distribution. In particular, we will discuss the creation of new designs for the Fontan procedure, a surgery done in pediatric cardiology to treat single ventricle heart defects.
Application of Optimal Designs to Item Calibration
Lu, Hung-Yi
2014-01-01
In computerized adaptive testing (CAT), examinees are presented with various sets of items chosen from a precalibrated item pool. Consequently, the attrition speed of the items is extremely fast, and replenishing the item pool is essential. Therefore, item calibration has become a crucial concern in maintaining item banks. In this study, a two-parameter logistic model is used. We applied optimal designs and adaptive sequential analysis to solve this item calibration problem. The results indicated that the proposed optimal designs are cost effective and time efficient. PMID:25188318
Mathematical Optimization for Engineering Design Problems
NASA Astrophysics Data System (ADS)
Dandurand, Brian C.
Applications in engineering design and the material sciences motivate the development of optimization theory in a manner that additionally draws from other branches of mathematics including the functional, complex, and numerical analyses. The first contribution, motivated by an automotive design application, extends multiobjective optimization theory under the assumption that the problem information is not available in its entirety to a single decision maker as traditionally assumed in the multiobjective optimization literature. Rather, the problem information and the design control are distributed among different decision makers. This requirement appears in the design of an automotive system whose subsystem components themselves correspond to highly involved design subproblems each of whose performance is measured by multiple criteria. This leads to a system/subsystem interaction requiring a coordination whose algorithmic foundation is developed and rigorously examined mathematically. The second contribution develops and analyzes a parameter estimation approach motivated from a time domain modeling problem in the material sciences. In addition to drawing from the theory of least-squares optimization and numerical analysis, the development of a mathematical foundation for comparing a baseline parameter estimation approach with an alternative parameter estimation approach relies on theory from both the functional and complex analyses. The application of the developed theory and algorithms associated with both contributions is also discussed.
Optimization methods for alternative energy system design
NASA Astrophysics Data System (ADS)
Reinhardt, Michael Henry
An electric vehicle heating system and a solar thermal coffee dryer are presented as case studies in alternative energy system design optimization. Design optimization tools are compared using these case studies, including linear programming, integer programming, and fuzzy integer programming. Although most decision variables in the designs of alternative energy systems are generally discrete (e.g., numbers of photovoltaic modules, thermal panels, layers of glazing in windows), the literature shows that the optimization methods used historically for design utilize continuous decision variables. Integer programming, used to find the optimal investment in conservation measures as a function of life cycle cost of an electric vehicle heating system, is compared to linear programming, demonstrating the importance of accounting for the discrete nature of design variables. The electric vehicle study shows that conservation methods similar to those used in building design, that reduce the overall UA of a 22 ft. electric shuttle bus from 488 to 202 (Btu/hr-F), can eliminate the need for fossil fuel heating systems when operating in the northeast United States. Fuzzy integer programming is presented as a means of accounting for imprecise design constraints such as being environmentally friendly in the optimization process. The solar thermal coffee dryer study focuses on a deep-bed design using unglazed thermal collectors (UTC). Experimental data from parchment coffee drying are gathered, including drying constants and equilibrium moisture. In this case, fuzzy linear programming is presented as a means of optimizing experimental procedures to produce the most information under imprecise constraints. Graphical optimization is used to show that for every 1 m2 deep-bed dryer, of 0.4 m depth, a UTC array consisting of 5, 1.1 m 2 panels, and a photovoltaic array consisting of 1, 0.25 m 2 panels produces the most dry coffee per dollar invested in the system. In general this study
Instrument design and optimization using genetic algorithms
Hoelzel, Robert; Bentley, Phillip M.; Fouquet, Peter
2006-10-15
This article describes the design of highly complex physical instruments by using a canonical genetic algorithm (GA). The procedure can be applied to all instrument designs where performance goals can be quantified. It is particularly suited to the optimization of instrument design where local optima in the performance figure of merit are prevalent. Here, a GA is used to evolve the design of the neutron spin-echo spectrometer WASP which is presently being constructed at the Institut Laue-Langevin, Grenoble, France. A comparison is made between this artificial intelligence approach and the traditional manual design methods. We demonstrate that the search of parameter space is more efficient when applying the genetic algorithm, and the GA produces a significantly better instrument design. Furthermore, it is found that the GA increases flexibility, by facilitating the reoptimization of the design after changes in boundary conditions during the design phase. The GA also allows the exploration of 'nonstandard' magnet coil geometries. We conclude that this technique constitutes a powerful complementary tool for the design and optimization of complex scientific apparatus, without replacing the careful thought processes employed in traditional design methods.
Branch target buffer design and optimization
NASA Technical Reports Server (NTRS)
Perleberg, Chris H.; Smith, Alan J.
1993-01-01
Consideration is given to two major issues in the design of branch target buffers (BTBs), with the goal of achieving maximum performance for a given number of bits allocated to the BTB design. The first issue is BTB management; the second is what information to keep in the BTB. A number of solutions to these problems are reviewed, and various optimizations in the design of BTBs are discussed. Design target miss ratios for BTBs are developed, making it possible to estimate the performance of BTBs for real workloads.
Design of a Model Execution Framework: Repetitive Object-Oriented Simulation Environment (ROSE)
NASA Technical Reports Server (NTRS)
Gray, Justin S.; Briggs, Jeffery L.
2008-01-01
The ROSE framework was designed to facilitate complex system analyses. It completely divorces the model execution process from the model itself. By doing so ROSE frees the modeler to develop a library of standard modeling processes such as Design of Experiments, optimizers, parameter studies, and sensitivity studies which can then be applied to any of their available models. The ROSE framework accomplishes this by means of a well defined API and object structure. Both the API and object structure are presented here with enough detail to implement ROSE in any object-oriented language or modeling tool.
Collective Framework and Performance Optimizations to Open MPI for Cray XT Platforms
Ladd, Joshua S; Gorentla Venkata, Manjunath; Shamis, Pavel; Graham, Richard L
2011-01-01
The performance and scalability of collective operations plays a key role in the performance and scalability of many scientific applications. Within the Open MPI code base we have developed a general purpose hierarchical collective operations framework called Cheetah, and applied it at large scale on the Oak Ridge Leadership Computing Facility's Jaguar (OLCF) platform, obtaining better performance and scalability than the native MPI implementation. This paper discuss Cheetah's design and implementation, and optimizations to the framework for Cray XT 5 platforms. Our results show that the Cheetah's Broadcast and Barrier perform better than the native MPI implementation. For medium data, the Cheetah's Broadcast outperforms the native MPI implementation by 93% for 49,152 processes problem size. For small and large data, it out performs the native MPI implementation by 10% and 9%, respectively, at 24,576 processes problem size. The Cheetah's Barrier performs 10% better than the native MPI implementation for 12,288 processes problem size.
Integrated structural-aerodynamic design optimization
NASA Technical Reports Server (NTRS)
Haftka, R. T.; Kao, P. J.; Grossman, B.; Polen, D.; Sobieszczanski-Sobieski, J.
1988-01-01
This paper focuses on the processes of simultaneous aerodynamic and structural wing design as a prototype for design integration, with emphasis on the major difficulty associated with multidisciplinary design optimization processes, their enormous computational costs. Methods are presented for reducing this computational burden through the development of efficient methods for cross-sensitivity calculations and the implementation of approximate optimization procedures. Utilizing a modular sensitivity analysis approach, it is shown that the sensitivities can be computed without the expensive calculation of the derivatives of the aerodynamic influence coefficient matrix, and the derivatives of the structural flexibility matrix. The same process is used to efficiently evaluate the sensitivities of the wing divergence constraint, which should be particularly useful, not only in problems of complete integrated aircraft design, but also in aeroelastic tailoring applications.
Global optimization of bilinear engineering design models
Grossmann, I.; Quesada, I.
1994-12-31
Recently Quesada and Grossmann have proposed a global optimization algorithm for solving NLP problems involving linear fractional and bilinear terms. This model has been motivated by a number of applications in process design. The proposed method relies on the derivation of a convex NLP underestimator problem that is used within a spatial branch and bound search. This paper explores the use of alternative bounding approximations for constructing the underestimator problem. These are applied in the global optimization of problems arising in different engineering areas and for which different relaxations are proposed depending on the mathematical structure of the models. These relaxations include linear and nonlinear underestimator problems. Reformulations that generate additional estimator functions are also employed. Examples from process design, structural design, portfolio investment and layout design are presented.
Multidisciplinary Concurrent Design Optimization via the Internet
NASA Technical Reports Server (NTRS)
Woodard, Stanley E.; Kelkar, Atul G.; Koganti, Gopichand
2001-01-01
A methodology is presented which uses commercial design and analysis software and the Internet to perform concurrent multidisciplinary optimization. The methodology provides a means to develop multidisciplinary designs without requiring that all software be accessible from the same local network. The procedures are amenable to design and development teams whose members, expertise and respective software are not geographically located together. This methodology facilitates multidisciplinary teams working concurrently on a design problem of common interest. Partition of design software to different machines allows each constituent software to be used on the machine that provides the most economy and efficiency. The methodology is demonstrated on the concurrent design of a spacecraft structure and attitude control system. Results are compared to those derived from performing the design with an autonomous FORTRAN program.
NASA Technical Reports Server (NTRS)
Mayer, Richard J.; Blinn, Thomas M.; Mayer, Paula S. D.; Ackley, Keith A.; Crump, Wes; Sanders, Les
1991-01-01
The design of the Framework Processor (FP) component of the Framework Programmable Software Development Platform (FFP) is described. The FFP is a project aimed at combining effective tool and data integration mechanisms with a model of the software development process in an intelligent integrated software development environment. Guided by the model, this Framework Processor will take advantage of an integrated operating environment to provide automated support for the management and control of the software development process so that costly mistakes during the development phase can be eliminated.
Optimized Uncertainty Quantification Algorithm Within a Dynamic Event Tree Framework
J. W. Nielsen; Akira Tokuhiro; Robert Hiromoto
2014-06-01
Methods for developing Phenomenological Identification and Ranking Tables (PIRT) for nuclear power plants have been a useful tool in providing insight into modelling aspects that are important to safety. These methods have involved expert knowledge with regards to reactor plant transients and thermal-hydraulic codes to identify are of highest importance. Quantified PIRT provides for rigorous method for quantifying the phenomena that can have the greatest impact. The transients that are evaluated and the timing of those events are typically developed in collaboration with the Probabilistic Risk Analysis. Though quite effective in evaluating risk, traditional PRA methods lack the capability to evaluate complex dynamic systems where end states may vary as a function of transition time from physical state to physical state . Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems. A limitation of DPRA is its potential for state or combinatorial explosion that grows as a function of the number of components; as well as, the sampling of transition times from state-to-state of the entire system. This paper presents a method for performing QPIRT within a dynamic event tree framework such that timing events which result in the highest probabilities of failure are captured and a QPIRT is performed simultaneously while performing a discrete dynamic event tree evaluation. The resulting simulation results in a formal QPIRT for each end state. The use of dynamic event trees results in state explosion as the number of possible component states increases. This paper utilizes a branch and bound algorithm to optimize the solution of the dynamic event trees. The paper summarizes the methods used to implement the branch-and-bound algorithm in solving the discrete dynamic event trees.
A concept ideation framework for medical device design.
Hagedorn, Thomas J; Grosse, Ian R; Krishnamurty, Sundar
2015-06-01
Medical device design is a challenging process, often requiring collaboration between medical and engineering domain experts. This collaboration can be best institutionalized through systematic knowledge transfer between the two domains coupled with effective knowledge management throughout the design innovation process. Toward this goal, we present the development of a semantic framework for medical device design that unifies a large medical ontology with detailed engineering functional models along with the repository of design innovation information contained in the US Patent Database. As part of our development, existing medical, engineering, and patent document ontologies were modified and interlinked to create a comprehensive medical device innovation and design tool with appropriate properties and semantic relations to facilitate knowledge capture, enrich existing knowledge, and enable effective knowledge reuse for different scenarios. The result is a Concept Ideation Framework for Medical Device Design (CIFMeDD). Key features of the resulting framework include function-based searching and automated inter-domain reasoning to uniquely enable identification of functionally similar procedures, tools, and inventions from multiple domains based on simple semantic searches. The significance and usefulness of the resulting framework for aiding in conceptual design and innovation in the medical realm are explored via two case studies examining medical device design problems. PMID:25956618
Learning Experience as Transaction: A Framework for Instructional Design
ERIC Educational Resources Information Center
Parrish, Patrick E.; Wilson, Brent G.; Dunlap, Joanna C.
2011-01-01
This article presents a framework for understanding learning experience as an object for instructional design--as an object for design as well as research and understanding. Compared to traditional behavioral objectives or discrete cognitive skills, the object of experience is more holistic, requiring simultaneous attention to cognition, behavior,…
A concept ideation framework for medical device design.
Hagedorn, Thomas J; Grosse, Ian R; Krishnamurty, Sundar
2015-06-01
Medical device design is a challenging process, often requiring collaboration between medical and engineering domain experts. This collaboration can be best institutionalized through systematic knowledge transfer between the two domains coupled with effective knowledge management throughout the design innovation process. Toward this goal, we present the development of a semantic framework for medical device design that unifies a large medical ontology with detailed engineering functional models along with the repository of design innovation information contained in the US Patent Database. As part of our development, existing medical, engineering, and patent document ontologies were modified and interlinked to create a comprehensive medical device innovation and design tool with appropriate properties and semantic relations to facilitate knowledge capture, enrich existing knowledge, and enable effective knowledge reuse for different scenarios. The result is a Concept Ideation Framework for Medical Device Design (CIFMeDD). Key features of the resulting framework include function-based searching and automated inter-domain reasoning to uniquely enable identification of functionally similar procedures, tools, and inventions from multiple domains based on simple semantic searches. The significance and usefulness of the resulting framework for aiding in conceptual design and innovation in the medical realm are explored via two case studies examining medical device design problems.
Optimization of confocal scanning laser ophthalmoscope design
Dhalla, Al-Hafeez; Kelly, Michael P.; Farsiu, Sina; Izatt, Joseph A.
2013-01-01
Abstract. Confocal scanning laser ophthalmoscopy (cSLO) enables high-resolution and high-contrast imaging of the retina by employing spatial filtering for scattered light rejection. However, to obtain optimized image quality, one must design the cSLO around scanner technology limitations and minimize the effects of ocular aberrations and imaging artifacts. We describe a cSLO design methodology resulting in a simple, relatively inexpensive, and compact lens-based cSLO design optimized to balance resolution and throughput for a 20-deg field of view (FOV) with minimal imaging artifacts. We tested the imaging capabilities of our cSLO design with an experimental setup from which we obtained fast and high signal-to-noise ratio (SNR) retinal images. At lower FOVs, we were able to visualize parafoveal cone photoreceptors and nerve fiber bundles even without the use of adaptive optics. Through an experiment comparing our optimized cSLO design to a commercial cSLO system, we show that our design demonstrates a significant improvement in both image quality and resolution. PMID:23864013
Design Optimization of Structural Health Monitoring Systems
Flynn, Eric B.
2014-03-06
Sensor networks drive decisions. Approach: Design networks to minimize the expected total cost (in a statistical sense, i.e. Bayes Risk) associated with making wrong decisions and with installing maintaining and running the sensor network itself. Search for optimal solutions using Monte-Carlo-Sampling-Adapted Genetic Algorithm. Applications include structural health monitoring and surveillance.
MDO can help resolve the designer's dilemma. [multidisciplinary design optimization
NASA Technical Reports Server (NTRS)
Sobieszczanski-Sobieski, Jaroslaw; Tulinius, Jan R.
1991-01-01
Multidisciplinary design optimization (MDO) is presented as a rapidly growing body of methods, algorithms, and techniques that will provide a quantum jump in the effectiveness and efficiency of the quantitative side of design, and will turn that side into an environment in which the qualitative side can thrive. MDO borrows from CAD/CAM for graphic visualization of geometrical and numerical data, data base technology, and in computer software and hardware. Expected benefits from this methodology are a rational, mathematically consistent approach to hypersonic aircraft designs, designs pushed closer to the optimum, and a design process either shortened or leaving time available for different concepts to be explored.
Design Oriented Structural Modeling for Airplane Conceptual Design Optimization
NASA Technical Reports Server (NTRS)
Livne, Eli
1999-01-01
The main goal for research conducted with the support of this grant was to develop design oriented structural optimization methods for the conceptual design of airplanes. Traditionally in conceptual design airframe weight is estimated based on statistical equations developed over years of fitting airplane weight data in data bases of similar existing air- planes. Utilization of such regression equations for the design of new airplanes can be justified only if the new air-planes use structural technology similar to the technology on the airplanes in those weight data bases. If any new structural technology is to be pursued or any new unconventional configurations designed the statistical weight equations cannot be used. In such cases any structural weight estimation must be based on rigorous "physics based" structural analysis and optimization of the airframes under consideration. Work under this grant progressed to explore airframe design-oriented structural optimization techniques along two lines of research: methods based on "fast" design oriented finite element technology and methods based on equivalent plate / equivalent shell models of airframes, in which the vehicle is modelled as an assembly of plate and shell components, each simulating a lifting surface or nacelle / fuselage pieces. Since response to changes in geometry are essential in conceptual design of airplanes, as well as the capability to optimize the shape itself, research supported by this grant sought to develop efficient techniques for parametrization of airplane shape and sensitivity analysis with respect to shape design variables. Towards the end of the grant period a prototype automated structural analysis code designed to work with the NASA Aircraft Synthesis conceptual design code ACS= was delivered to NASA Ames.
Multidisciplinary design optimization of low-noise transport aircraft
NASA Astrophysics Data System (ADS)
Leifsson, Leifur Thor
The objective of this research is to examine how to design low-noise transport aircraft using Multidisciplinary Design Optimization (MDO). The subject is approached by designing for low-noise both implicitly and explicitly. The explicit design approach involves optimizing an aircraft while explicitly constraining the noise level. An MDO framework capable of optimizing both a cantilever wing and a Strut-Braced-Wing (SBW) aircraft was developed. The objective is to design aircraft for low-airframe-noise at the approach conditions and quantify the change in weight and performance with respect to a traditionally designed aircraft. The results show that reducing airframe noise by reducing approach speed alone, will not provide significant noise reduction without a large performance and weight penalty. Therefore, more dramatic changes to the aircraft design are needed to achieve a significant airframe noise reduction. Another study showed that the trailing-edge flap can be eliminated, as well as all the noise associated with that device, without incurring a significant weight and performance penalty. Lastly, an airframe noise analysis showed that a SBW aircraft with short fuselage-mounted landing gear could have a similar or potentially a lower airframe noise level than a comparable cantilever wing aircraft. The implicit design approach involves selecting a configuration that supports a low-noise operation, and optimizing for performance. In this study a Blended-Wing-Body (BWB) transport aircraft, with a conventional and a distributed propulsion system, was optimized for minimum take-off gross weight. The effects of distributed propulsion were studied using an MDO framework previously developed at Virginia Tech. The results show that more than two thirds of the theoretical savings of distributed propulsion are required for the BWB designs with a distributed propulsion system to have comparable gross weight as those with a conventional propulsion system. Therefore
A Web Services Composition Design framework based on Agent Organization
NASA Astrophysics Data System (ADS)
Li, JiaJia; Li, Bin; Zhang, Xiaowei
Computing environments are becoming more open, distributed and pervasive. The web services compositions build for these dynamic environments will need to become more adaptable and adaptive to unexpected event. This paper defines a way for web services composition which based on agent organization. The functions of three layers, classification of agent, and agent model and agents design in this framework are introduced in details. It realizes a reliable and flexible web services composition using this framework.
Optimization and Inverse Design of Pump Impeller
NASA Astrophysics Data System (ADS)
Miyauchi, S.; Zhu, B.; Luo, X.; Piao, B.; Matsumoto, H.; Sano, M.; Kassai, N.
2012-11-01
As for pump impellers, the meridional flow channel and blade-to-blade flow channel, which are relatively independent of each other but greatly affect performance, are designed in parallel. And the optimization design is used for the former and the inverse design is used for the latter. To verify this new design method, a mixed-flow impeller was made. Next, we use Tani's inverse design method for the blade loading of inverse design. It is useful enough to change a deceleration rate freely and greatly. And it can integrally express the rear blade loading of various methods by NACA, Zangeneh and Stratford. We controlled the deceleration rate by shape parameter m, and its value became almost same with Tani's recommended value of the laminar airfoil.
Application of heuristic optimization in aircraft design
NASA Astrophysics Data System (ADS)
Hu, Zhenning
Genetic algorithms and the related heuristic optimization strategies are introduced and their applications in the aircraft design are developed. Generally speaking, genetic algorithms belong to non-deterministic direct search methods, which are most powerful in finding optimum or near-optimum solutions of a very complex system where a little priori knowledge is known. Therefore they have a wide application in aerospace systems. Two major aircraft optimal design projects are illustrated in this dissertation. The first is the application of material optimization of aligned fiber laminate composites in the presence of stress concentrations. After a large number of tests on laminates with different layers, genetic algorithms find an alignment pattern in a certain range for the Boeing Co. specified material. The second project is the application of piezoelectric actuator placement on a generic tail skins to reduce the 2nd mode vibration caused by buffet, which is part of a Boeing project to control the buffet effect on aircraft. In this project, genetic algorithms are closely involved with vibration analysis and finite element analysis. Actuator optimization strategies are first tested on the theoretical beam models to gain experience, and then the generic tail model is applied. Genetic algorithms achieve a great success in optimizing up to 888 actuator parameters on the tail skins.
NASA Astrophysics Data System (ADS)
Takeda, Norio
We verified the generalization ability of the response surfaces of artificial neural networks (NNs), and that the surfaces could be applied to an engineering-design problem. A Bayesian framework to regularize NNs, which was proposed by Gull and Skilling, can be used to generate NN response surfaces with excellent generalization ability, i.e., to determine the regularizing constants in an objective function minimized during NN learning. This well-generalized NN might be useful to find an optimal solution in the process of response surface methodology (RSM). We, therefore, describe three rules based on the Bayesian framework to update the regularizing constants, utilizing these rules to generate NN response surfaces with noisy teacher data drawn from a typical unimodal or multimodal function. Good generalization ability was achieved with regularized NN response surfaces, even though an update rule including trace evaluation failed to determine the regularizing constants regardless of the response function. We, next, selected the most appropriate update rule, which included eigenvalue evaluation, and then the NN response surface regularized using the update rule was applied to finding the optimal solution to an illustrative engineering-design problem. The NN response surface did not fit the noise in the teacher data, and consequently, it could effectively be used to achieve a satisfactory solution. This may increase the opportunities for using NN in the process of RSM.
High Speed Civil Transport Design Using Collaborative Optimization and Approximate Models
NASA Technical Reports Server (NTRS)
Manning, Valerie Michelle
1999-01-01
The design of supersonic aircraft requires complex analysis in multiple disciplines, posing, a challenge for optimization methods. In this thesis, collaborative optimization, a design architecture developed to solve large-scale multidisciplinary design problems, is applied to the design of supersonic transport concepts. Collaborative optimization takes advantage of natural disciplinary segmentation to facilitate parallel execution of design tasks. Discipline-specific design optimization proceeds while a coordinating mechanism ensures progress toward an optimum and compatibility between disciplinary designs. Two concepts for supersonic aircraft are investigated: a conventional delta-wing design and a natural laminar flow concept that achieves improved performance by exploiting properties of supersonic flow to delay boundary layer transition. The work involves the development of aerodynamics and structural analyses, and integration within a collaborative optimization framework. It represents the most extensive application of the method to date.
Design criteria for optimal photosynthetic energy conversion
NASA Astrophysics Data System (ADS)
Fingerhut, Benjamin P.; Zinth, Wolfgang; de Vivie-Riedle, Regina
2008-12-01
Photochemical solar energy conversion is considered as an alternative of clean energy. For future light converting nano-machines photosynthetic reaction centers are used as prototypes optimized during evolution. We introduce a reaction scheme for global optimization and simulate the ultrafast charge separation in photochemical energy conversion. Multiple molecular charge carriers are involved in this process and are linked by Marcus-type electron transfer. In combination with evolutionary algorithms, we unravel the biological strategies for high quantum efficiency in photosynthetic reaction centers and extend these concepts to the design of artificial photochemical devices for energy conversion.
Aircraft family design using enhanced collaborative optimization
NASA Astrophysics Data System (ADS)
Roth, Brian Douglas
Significant progress has been made toward the development of multidisciplinary design optimization (MDO) methods that are well-suited to practical large-scale design problems. However, opportunities exist for further progress. This thesis describes the development of enhanced collaborative optimization (ECO), a new decomposition-based MDO method. To support the development effort, the thesis offers a detailed comparison of two existing MDO methods: collaborative optimization (CO) and analytical target cascading (ATC). This aids in clarifying their function and capabilities, and it provides inspiration for the development of ECO. The ECO method offers several significant contributions. First, it enhances communication between disciplinary design teams while retaining the low-order coupling between them. Second, it provides disciplinary design teams with more authority over the design process. Third, it resolves several troubling computational inefficiencies that are associated with CO. As a result, ECO provides significant computational savings (relative to CO) for the test cases and practical design problems described in this thesis. New aircraft development projects seldom focus on a single set of mission requirements. Rather, a family of aircraft is designed, with each family member tailored to a different set of requirements. This thesis illustrates the application of decomposition-based MDO methods to aircraft family design. This represents a new application area, since MDO methods have traditionally been applied to multidisciplinary problems. ECO offers aircraft family design the same benefits that it affords to multidisciplinary design problems. Namely, it simplifies analysis integration, it provides a means to manage problem complexity, and it enables concurrent design of all family members. In support of aircraft family design, this thesis introduces a new wing structural model with sufficient fidelity to capture the tradeoffs associated with component
Robust Design Optimization via Failure Domain Bounding
NASA Technical Reports Server (NTRS)
Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.
2007-01-01
This paper extends and applies the strategies recently developed by the authors for handling constraints under uncertainty to robust design optimization. For the scope of this paper, robust optimization is a methodology aimed at problems for which some parameters are uncertain and are only known to belong to some uncertainty set. This set can be described by either a deterministic or a probabilistic model. In the methodology developed herein, optimization-based strategies are used to bound the constraint violation region using hyper-spheres and hyper-rectangles. By comparing the resulting bounding sets with any given uncertainty model, it can be determined whether the constraints are satisfied for all members of the uncertainty model (i.e., constraints are feasible) or not (i.e., constraints are infeasible). If constraints are infeasible and a probabilistic uncertainty model is available, upper bounds to the probability of constraint violation can be efficiently calculated. The tools developed enable approximating not only the set of designs that make the constraints feasible but also, when required, the set of designs for which the probability of constraint violation is below a prescribed admissible value. When constraint feasibility is possible, several design criteria can be used to shape the uncertainty model of performance metrics of interest. Worst-case, least-second-moment, and reliability-based design criteria are considered herein. Since the problem formulation is generic and the tools derived only require standard optimization algorithms for their implementation, these strategies are easily applicable to a broad range of engineering problems.
A hybrid-algorithm-based parallel computing framework for optimal reservoir operation
NASA Astrophysics Data System (ADS)
Li, X.; Wei, J.; Li, T.; Wang, G.
2012-12-01
Up to date, various optimization models have been developed to offer optimal operating policies for reservoirs. Each optimization model has its own merits and limitations, and no general algorithm exists even today. At times, some optimization models have to be combined to obtain desired results. In this paper, we present a parallel computing framework to combine various optimization models in a different way compared to traditional serial computing. This framework consists of three functional processor types, that is, master processor, slave processor and transfer processor. The master processor has a full computation scheme that allocates optimization models to slave processors; slave processors perform allocated optimization models; the transfer processor is in charge of the solution communication among all slave processors. Based on these, the proposed framework can perform various optimization models in parallel. Because of the solution communication, the framework can also integrate the merits of involved optimization models while in iteration and the performance of each optimization model can therefore be improved. And more, it can be concluded the framework can effectively improve the solution quality and increase the solution speed by making full use of computing power of parallel computers.
Generalized mathematical models in design optimization
NASA Technical Reports Server (NTRS)
Papalambros, Panos Y.; Rao, J. R. Jagannatha
1989-01-01
The theory of optimality conditions of extremal problems can be extended to problems continuously deformed by an input vector. The connection between the sensitivity, well-posedness, stability and approximation of optimization problems is steadily emerging. The authors believe that the important realization here is that the underlying basis of all such work is still the study of point-to-set maps and of small perturbations, yet what has been identified previously as being just related to solution procedures is now being extended to study modeling itself in its own right. Many important studies related to the theoretical issues of parametric programming and large deformation in nonlinear programming have been reported in the last few years, and the challenge now seems to be in devising effective computational tools for solving these generalized design optimization models.
Optimal design of a space power system
NASA Technical Reports Server (NTRS)
Chun, Young W.; Braun, James F.
1990-01-01
The aerospace industry, like many other industries, regularly applies optimization techniques to develop designs which reduce cost, maximize performance, and minimize weight. The desire to minimize weight is of particular importance in space-related products since the costs of launch are directly related to payload weight, and launch vehicle capabilities often limit the allowable weight of a component or system. With these concerns in mind, this paper presents the optimization of a space-based power generation system for minimum mass. The goal of this work is to demonstrate the use of optimization techniques on a realistic and practical engineering system. The power system described uses thermoelectric devices to convert heat into electricity. The heat source for the system is a nuclear reactor. Waste heat is rejected from the system to space by a radiator.
Design Methods and Optimization for Morphing Aircraft
NASA Technical Reports Server (NTRS)
Crossley, William A.
2005-01-01
This report provides a summary of accomplishments made during this research effort. The major accomplishments are in three areas. The first is the use of a multiobjective optimization strategy to help identify potential morphing features that uses an existing aircraft sizing code to predict the weight, size and performance of several fixed-geometry aircraft that are Pareto-optimal based upon on two competing aircraft performance objectives. The second area has been titled morphing as an independent variable and formulates the sizing of a morphing aircraft as an optimization problem in which the amount of geometric morphing for various aircraft parameters are included as design variables. This second effort consumed most of the overall effort on the project. The third area involved a more detailed sizing study of a commercial transport aircraft that would incorporate a morphing wing to possibly enable transatlantic point-to-point passenger service.
NASA Technical Reports Server (NTRS)
Depenbrock, Brett T.; Balint, Tibor S.; Sheehy, Jeffrey A.
2014-01-01
Research and development organizations that push the innovation edge of technology frequently encounter challenges when attempting to identify an investment strategy and to accurately forecast the cost and schedule performance of selected projects. Fast moving and complex environments require managers to quickly analyze and diagnose the value of returns on investment versus allocated resources. Our Project Assessment Framework through Design (PAFTD) tool facilitates decision making for NASA senior leadership to enable more strategic and consistent technology development investment analysis, beginning at implementation and continuing through the project life cycle. The framework takes an integrated approach by leveraging design principles of useability, feasibility, and viability and aligns them with methods employed by NASA's Independent Program Assessment Office for project performance assessment. The need exists to periodically revisit the justification and prioritization of technology development investments as changes occur over project life cycles. The framework informs management rapidly and comprehensively about diagnosed internal and external root causes of project performance.
Multidisciplinary Design Optimization of A Highly Flexible Aeroservoelastic Wing
NASA Astrophysics Data System (ADS)
Haghighat, Sohrab
A multidisciplinary design optimization framework is developed that integrates control system design with aerostructural design for a highly-deformable wing. The objective of this framework is to surpass the existing aircraft endurance limits through the use of an active load alleviation system designed concurrently with the rest of the aircraft. The novelty of this work is two fold. First, a unified dynamics framework is developed to represent the full six-degree-of-freedom rigid-body along with the structural dynamics. It allows for an integrated control design to account for both manoeuvrability (flying quality) and aeroelasticity criteria simultaneously. Secondly, by synthesizing the aircraft control system along with the structural sizing and aerodynamic shape design, the final design has the potential to exploit synergies among the three disciplines and yield higher performing aircraft. A co-rotational structural framework featuring Euler--Bernoulli beam elements is developed to capture the wing's nonlinear deformations under the effect of aerodynamic and inertial loadings. In this work, a three-dimensional aerodynamic panel code, capable of calculating both steady and unsteady loadings is used. Two different control methods, a model predictive controller (MPC) and a 2-DOF mixed-norm robust controller, are considered in this work to control a highly flexible aircraft. Both control techniques offer unique advantages that make them promising for controlling a highly flexible aircraft. The control system works towards executing time-dependent manoeuvres along with performing gust/manoeuvre load alleviation. The developed framework is investigated for demonstration in two design cases: one in which the control system simply worked towards achieving or maintaining a target altitude, and another where the control system is also performing load alleviation. The use of the active load alleviation system results in a significant improvement in the aircraft performance
Direct optimization method for reentry trajectory design
NASA Astrophysics Data System (ADS)
Jallade, S.; Huber, P.; Potti, J.; Dutruel-Lecohier, G.
The software package called `Reentry and Atmospheric Transfer Trajectory' (RATT) was developed under ESA contract for the design of atmospheric trajectories. It includes four software TOP (Trajectory OPtimization) programs, which optimize reentry and aeroassisted transfer trajectories. 6FD and 3FD (6 and 3 degrees of freedom Flight Dynamic) are devoted to the simulation of the trajectory. SCA (Sensitivity and Covariance Analysis) performs covariance analysis on a given trajectory with respect to different uncertainties and error sources. TOP provides the optimum guidance law of a three degree of freedom reentry of aeroassisted transfer (AAOT) trajectories. Deorbit and reorbit impulses (if necessary) can be taken into account in the optimization. A wide choice of cost function is available to the user such as the integrated heat flux, or the sum of the velocity impulses, or a linear combination of both of them for trajectory and vehicle design. The crossrange and the downrange can be maximized during reentry trajectory. Path constraints are available on the load factor, the heat flux and the dynamic pressure. Results on these proposed options are presented. TOPPHY is the part of the TOP software corresponding to the definition and the computation of the optimization problemphysics. TOPPHY can interface with several optimizes with dynamic solvers: TOPOP and TROPIC using direct collocation methods and PROMIS using direct multiple shooting method. TOPOP was developed in the frame of this contract, it uses Hermite polynomials for the collocation method and the NPSOL optimizer from the NAG library. Both TROPIC and PROMIS were developed by the DLR (Deutsche Forschungsanstalt fuer Luft und Raumfahrt) and use the SLSQP optimizer. For the dynamic equation resolution, TROPIC uses a collocation method with Splines and PROMIS uses a multiple shooting method with finite differences. The three different optimizers including dynamics were tested on the reentry trajectory of the
Relativistic framework for non-magnetic analysis and design
NASA Astrophysics Data System (ADS)
Laborde, Benjamin
2005-04-01
This paper describes a framework for relativistic analysis with effects identical to that of magnetism, but without using magnetism, and uses this framework to design a device which would be difficult or impossible under magnet analysis. With this framework it is possible to analyze electrical systems completely with relativistic electrodynamics, rather than magnetism and electrostatics, with no loss of accuracy, since the two systems are identical. The framework demonstrates the equivalence of magnetism and relativistic electric charge with a mathematical proof using the classical parallel wires experiment. The paper then proceeds to use this result to design an electric propulsion device through relativistic analysis, rather than magnetic analysis. The benefit of this approach is that it liberates us from the magnetic field, and ascribes the forces on a conducting wire to the current in another wire, some distance away, rather than to a magnetic field in the region of the first wire, as in classical analysis. With this new framework we are able to design devices previously unknown in the magnetic domain. The paper describes one such device, the Action Motor, for producing a one-way force, with potential applications in spacecraft propulsion.
NASA Astrophysics Data System (ADS)
Zhao, Hongxia; Icoz, Tunc; Jaluria, Yogesh; Knight, Doyle
2003-11-01
The Data Driven Design Optimization Methodology (DDDOM) incorporates experiment and simulation synergistically to achieve better designs in less time than conventional methods. It is developed on the basis of the advanced experimental technology (e.g., Rapid Prototyping) and computational technology (e.g., Parallel Processing, Grid Computing). The DDDOM is comprised of six elements: User Interface, Controller, Optimizer, Experiment, Surrogate Model and Simulation. The DDDOM Controller is the central element of DDDOM. It initiates experiment and simulation, monitors and coordinates their progress. The software system and its user interface are written in Perl/Tk. The DDDOM is applied to a cooling problem for electronic equipment. In this problem, two dimensional mixed convection heat transfer over two isothermal protruding heating elements (simulating electronic components) located at the bottom surface of a horizontal channel was considered. Air is the coolant fluid. The bottom plate is assumed to be insulated and the top plate is kept at the ambient temperature of the incoming air flow. The heat sources have fixed temperature. The flow conditions are defined by the mean Reynolds number (Re), Grashof number (Gr) and Prandtl number (Pr). The objective is to find the optimum heat source locations for fixed Re, Gr and Pr number such that the pressure drop will be minimized. The optimization loop is done by using a simulation code and an optimizer, CFSQP.
Optimized design of LED plant lamp
NASA Astrophysics Data System (ADS)
Chen, Jian-sheng; Cai, Ruhai; Zhao, Yunyun; Zhao, Fuli; Yang, Bowen
2014-12-01
In order to fabricate the optimized LED plant lamp we demonstrated an optical spectral exploration. According to the mechanism of higher plant photosynthesis process and the spectral analysis we demonstrate an optical design of the LED plant lamp. Furthermore we built two kins of prototypes of the LED plant lamps which are suitable for the photosynthesis of higher green vegetables. Based on the simulation of the lamp box of the different alignment of the plants we carried out the growing experiment of green vegetable and obtain the optimized light illumination as well as the spectral profile. The results show that only blue and red light are efficient for the green leave vegetables. Our work is undoubtedly helpful for the LED plant lamping design and manufacture.
A Multiobjective Optimization Framework for Stochastic Control of Complex Systems
Malikopoulos, Andreas; Maroulas, Vasileios; Xiong, Professor Jie
2015-01-01
This paper addresses the problem of minimizing the long-run expected average cost of a complex system consisting of subsystems that interact with each other and the environment. We treat the stochastic control problem as a multiobjective optimization problem of the one-stage expected costs of the subsystems, and we show that the control policy yielding the Pareto optimal solution is an optimal control policy that minimizes the average cost criterion for the entire system. For practical situations with constraints consistent to those we study here, our results imply that the Pareto control policy may be of value in deriving online an optimal control policy in complex systems.
Optimized design for an electrothermal microactuator
NASA Astrophysics Data System (ADS)
Cǎlimǎnescu, Ioan; Stan, Liviu-Constantin; Popa, Viorica
2015-02-01
In micromechanical structures, electrothermal actuators are known to be capable of providing larger force and reasonable tip deflection compared to electrostatic ones. Many studies have been devoted to the analysis of the flexure actuators. One of the most popular electrothermal actuators is called `U-shaped' actuator. The device is composed of two suspended beams with variable cross sections joined at the free end, which constrains the tip to move in an arcing motion while current is passed through the actuator. The goal of this research is to determine via FEA the best fitted geometry of the microactuator (optimization input parameters) in order to render some of the of the output parameters such as thermal strain or total deformations to their maximum values. The software to generate the CAD geometry was SolidWorks 2010 and all the FEA analysis was conducted with Ansys 13 TM. The optimized model has smaller geometric values of the input parameters that is a more compact geometry; The maximum temperature reached a smaller value for the optimized model; The calculated heat flux is with 13% bigger for the optimized model; the same for Joule Heat (26%), Total deformation (1.2%) and Thermal Strain (8%). By simple optimizing the design the dimensions and the performance of the micro actuator resulted more compact and more efficient.
Slot design of optimized electromagnetic pump
Leboucher, L. . Institut de Mecanique); Villani, D. )
1993-11-01
Electromagnetic pumps are used for the transportation of liquid metals such as the cooling sodium of fast breeder nuclear reactors. The design of this induction machine is close to that of a tubular linear induction motor. A non uniform slot distribution is used to optimize electromagnetic pumps. This geometry is tested with a finite element code. The performances are compared with the regular slot distribution of Industrial prototypes.
A framework for designing and analyzing binary decision-making strategies in cellular systems†
Porter, Joshua R.; Andrews, Burton W.; Iglesias, Pablo A.
2015-01-01
Cells make many binary (all-or-nothing) decisions based on noisy signals gathered from their environment and processed through noisy decision-making pathways. Reducing the effect of noise to improve the fidelity of decision-making comes at the expense of increased complexity, creating a tradeoff between performance and metabolic cost. We present a framework based on rate distortion theory, a branch of information theory, to quantify this tradeoff and design binary decision-making strategies that balance low cost and accuracy in optimal ways. With this framework, we show that several observed behaviors of binary decision-making systems, including random strategies, hysteresis, and irreversibility, are optimal in an information-theoretic sense for various situations. This framework can also be used to quantify the goals around which a decision-making system is optimized and to evaluate the optimality of cellular decision-making systems by a fundamental information-theoretic criterion. As proof of concept, we use the framework to quantify the goals of the externally triggered apoptosis pathway. PMID:22370552
Guan, Xiangmin; Zhang, Xuejun; Zhu, Yanbo; Sun, Dengfeng; Lei, Jiaxing
2015-01-01
Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology. PMID:26180840
Guan, Xiangmin; Zhang, Xuejun; Zhu, Yanbo; Sun, Dengfeng; Lei, Jiaxing
2015-01-01
Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology. PMID:26180840
Design Of Theoretically Optimal Thermoacoustic Cooling Device
NASA Astrophysics Data System (ADS)
Tisovský, Tomáš; Vít, Tomáš
2016-03-01
The aim of this article is to design theoretically optimal thermoacoustic cooling device. The opening chapter gives the reader brief introduction to thermoacoustic, specializing in the thermoacoustic principle in refrigerator regime. Subsequent part of the article aims to explain the principle on which thermoacoustic is simulated in DeltaEC. Numbers of executed numerical simulations are listed and the resulting thermoacoustic cooling device design is presented along with its main operation characteristics. In conclusion, recommendations for future experimental work are given and the results are discussed.
General purpose optimization software for engineering design
NASA Technical Reports Server (NTRS)
Vanderplaats, G. N.
1990-01-01
The author has developed several general purpose optimization programs over the past twenty years. The earlier programs were developed as research codes and served that purpose reasonably well. However, in taking the formal step from research to industrial application programs, several important lessons have been learned. Among these are the importance of clear documentation, immediate user support, and consistent maintenance. Most important has been the issue of providing software that gives a good, or at least acceptable, design at minimum computational cost. Here, the basic issues developing optimization software for industrial applications are outlined and issues of convergence rate, reliability, and relative minima are discussed. Considerable feedback has been received from users, and new software is being developed to respond to identified needs. The basic capabilities of this software are outlined. A major motivation for the development of commercial grade software is ease of use and flexibility, and these issues are discussed with reference to general multidisciplinary applications. It is concluded that design productivity can be significantly enhanced by the more widespread use of optimization as an everyday design tool.
Optimal design of a tidal turbine
NASA Astrophysics Data System (ADS)
Kueny, J. L.; Lalande, T.; Herou, J. J.; Terme, L.
2012-11-01
An optimal design procedure has been applied to improve the design of an open-center tidal turbine. A specific software developed in C++ enables to generate the geometry adapted to the specific constraints imposed to this machine. Automatic scripts based on the AUTOGRID, IGG, FINE/TURBO and CFView software of the NUMECA CFD suite are used to evaluate all the candidate geometries. This package is coupled with the optimization software EASY, which is based on an evolutionary strategy completed by an artificial neural network. A new technique is proposed to guarantee the robustness of the mesh in the whole range of the design parameters. An important improvement of the initial geometry has been obtained. To limit the whole CPU time necessary for this optimization process, the geometry of the tidal turbine has been considered as axisymmetric, with a uniform upstream velocity. A more complete model (12 M nodes) has been built in order to analyze the effects related to the sea bed boundary layer, the proximity of the sea surface, the presence of an important triangular basement supporting the turbine and a possible incidence of the upstream velocity.
Analysis and design optimization of flexible pavement
Mamlouk, M.S.; Zaniewski, J.P.; He, W.
2000-04-01
A project-level optimization approach was developed to minimize total pavement cost within an analysis period. Using this approach, the designer is able to select the optimum initial pavement thickness, overlay thickness, and overlay timing. The model in this approach is capable of predicting both pavement performance and condition in terms of roughness, fatigue cracking, and rutting. The developed model combines the American Association of State Highway and Transportation Officials (AASHTO) design procedure and the mechanistic multilayer elastic solution. The Optimization for Pavement Analysis (OPA) computer program was developed using the prescribed approach. The OPA program incorporates the AASHTO equations, the multilayer elastic system ELSYM5 model, and the nonlinear dynamic programming optimization technique. The program is PC-based and can run in either a Windows 3.1 or a Windows 95 environment. Using the OPA program, a typical pavement section was analyzed under different traffic volumes and material properties. The optimum design strategy that produces the minimum total pavement cost in each case was determined. The initial construction cost, overlay cost, highway user cost, and total pavement cost were also calculated. The methodology developed during this research should lead to more cost-effective pavements for agencies adopting the recommended analysis methods.
Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm
Svečko, Rajko
2014-01-01
This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749
Optimal robust motion controller design using multiobjective genetic algorithm.
Sarjaš, Andrej; Svečko, Rajko; Chowdhury, Amor
2014-01-01
This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm-differential evolution. PMID:24987749
A Review of Literacy Frameworks for Learning Environments Design
ERIC Educational Resources Information Center
Rebmann, Kristen Radsliff
2013-01-01
This article charts the development of three literacy research frameworks: multiliteracies, new literacies, and popular literacies. By reviewing the literature surrounding three current conceptions of literacy, an attempt is made to form an integrative grouping that captures the most relevant elements of each for learning environments design.…
A Proposed Conceptual Framework for Curriculum Design in Physical Fitness.
ERIC Educational Resources Information Center
Miller, Peter V.; Beauchamp, Larry S.
A physical fitness curriculum, designed to provide cumulative benefits in a sequential pattern, is based upon a framework of a conceptual structure. The curriculum's ultimate goal is the achievement of greater physiological efficiency through a holistic approach that would strengthen circulatory-respiratory, mechanical, and neuro-muscular…
Sustainable Supply Chain Design by the P-Graph Framework
The present work proposes a computer-aided methodology for designing sustainable supply chains in terms of sustainability metrics by resorting to the P-graph framework. The methodology is an outcome of the collaboration between the Office of Research and Development (ORD) of the ...
TARDIS: An Automation Framework for JPL Mission Design and Navigation
NASA Technical Reports Server (NTRS)
Roundhill, Ian M.; Kelly, Richard M.
2014-01-01
Mission Design and Navigation at the Jet Propulsion Laboratory has implemented an automation framework tool to assist in orbit determination and maneuver design analysis. This paper describes the lessons learned from previous automation tools and how they have been implemented in this tool. In addition this tool has revealed challenges in software implementation, testing, and user education. This paper describes some of these challenges and invites others to share their experiences.
Pareto Optimal Design for Synthetic Biology.
Patanè, Andrea; Santoro, Andrea; Costanza, Jole; Carapezza, Giovanni; Nicosia, Giuseppe
2015-08-01
Recent advances in synthetic biology call for robust, flexible and efficient in silico optimization methodologies. We present a Pareto design approach for the bi-level optimization problem associated to the overproduction of specific metabolites in Escherichia coli. Our method efficiently explores the high dimensional genetic manipulation space, finding a number of trade-offs between synthetic and biological objectives, hence furnishing a deeper biological insight to the addressed problem and important results for industrial purposes. We demonstrate the computational capabilities of our Pareto-oriented approach comparing it with state-of-the-art heuristics in the overproduction problems of i) 1,4-butanediol, ii) myristoyl-CoA, i ii) malonyl-CoA , iv) acetate and v) succinate. We show that our algorithms are able to gracefully adapt and scale to more complex models and more biologically-relevant simulations of the genetic manipulations allowed. The Results obtained for 1,4-butanediol overproduction significantly outperform results previously obtained, in terms of 1,4-butanediol to biomass formation ratio and knock-out costs. In particular overproduction percentage is of +662.7%, from 1.425 mmolh⁻¹gDW⁻¹ (wild type) to 10.869 mmolh⁻¹gDW⁻¹, with a knockout cost of 6. Whereas, Pareto-optimal designs we have found in fatty acid optimizations strictly dominate the ones obtained by the other methodologies, e.g., biomass and myristoyl-CoA exportation improvement of +21.43% (0.17 h⁻¹) and +5.19% (1.62 mmolh⁻¹gDW⁻¹), respectively. Furthermore CPU time required by our heuristic approach is more than halved. Finally we implement pathway oriented sensitivity analysis, epsilon-dominance analysis and robustness analysis to enhance our biological understanding of the problem and to improve the optimization algorithm capabilities.
Transonic rotor tip design using numerical optimization
NASA Technical Reports Server (NTRS)
Tauber, Michael E.; Langhi, Ronald G.
1985-01-01
The aerodynamic design procedure for a new blade tip suitable for operation at transonic speeds is illustrated. For the first time, 3 dimensional numerical optimization was applied to rotor tip design, using the recent derivative of the ROT22 code, program R22OPT. Program R22OPT utilized an efficient quasi-Newton optimization algorithm. Multiple design objectives were specified. The delocalization of the shock wave was to be eliminated in forward flight for an advance ratio of 0.41 and a tip Mach number of 0.92 at psi = 90 deg. Simultaneously, it was sought to reduce torque requirements while maintaining effective restoring pitching moments. Only the outer 10 percent of the blade span was modified; the blade area was not to be reduced by more than 3 percent. The goal was to combine the advantages of both sweptback and sweptforward blade tips. A planform that featured inboard sweepback was combined with a sweptforward tip and a taper ratio of 0.5. Initially, the ROT22 code was used to find by trial and error a planform geometry which met the design goals. This configuration had an inboard section with a leading edge sweep of 20 deg and a tip section swept forward at 25 deg; in addition, the airfoils were modified.
Machine Learning Techniques in Optimal Design
NASA Technical Reports Server (NTRS)
Cerbone, Giuseppe
1992-01-01
Many important applications can be formalized as constrained optimization tasks. For example, we are studying the engineering domain of two-dimensional (2-D) structural design. In this task, the goal is to design a structure of minimum weight that bears a set of loads. A solution to a design problem in which there is a single load (L) and two stationary support points (S1 and S2) consists of four members, E1, E2, E3, and E4 that connect the load to the support points is discussed. In principle, optimal solutions to problems of this kind can be found by numerical optimization techniques. However, in practice [Vanderplaats, 1984] these methods are slow and they can produce different local solutions whose quality (ratio to the global optimum) varies with the choice of starting points. Hence, their applicability to real-world problems is severely restricted. To overcome these limitations, we propose to augment numerical optimization by first performing a symbolic compilation stage to produce: (a) objective functions that are faster to evaluate and that depend less on the choice of the starting point and (b) selection rules that associate problem instances to a set of recommended solutions. These goals are accomplished by successive specializations of the problem class and of the associated objective functions. In the end, this process reduces the problem to a collection of independent functions that are fast to evaluate, that can be differentiated symbolically, and that represent smaller regions of the overall search space. However, the specialization process can produce a large number of sub-problems. This is overcome by deriving inductively selection rules which associate problems to small sets of specialized independent sub-problems. Each set of candidate solutions is chosen to minimize a cost function which expresses the tradeoff between the quality of the solution that can be obtained from the sub-problem and the time it takes to produce it. The overall solution
An optimal stratified Simon two-stage design.
Parashar, Deepak; Bowden, Jack; Starr, Colin; Wernisch, Lorenz; Mander, Adrian
2016-07-01
In Phase II oncology trials, therapies are increasingly being evaluated for their effectiveness in specific populations of interest. Such targeted trials require designs that allow for stratification based on the participants' molecular characterisation. A targeted design proposed by Jones and Holmgren (JH) Jones CL, Holmgren E: 'An adaptive Simon two-stage design for phase 2 studies of targeted therapies', Contemporary Clinical Trials 28 (2007) 654-661.determines whether a drug only has activity in a disease sub-population or in the wider disease population. Their adaptive design uses results from a single interim analysis to decide whether to enrich the study population with a subgroup or not; it is based on two parallel Simon two-stage designs. We study the JH design in detail and extend it by providing a few alternative ways to control the familywise error rate, in the weak sense as well as the strong sense. We also introduce a novel optimal design by minimising the expected sample size. Our extended design contributes to the much needed framework for conducting Phase II trials in stratified medicine. © 2016 The Authors Pharmaceutical Statistics Published by John Wiley & Sons Ltd.
Thermodynamic framework for discrete optimal control in multiphase flow systems
NASA Astrophysics Data System (ADS)
Sieniutycz, Stanislaw
1999-08-01
Bellman's method of dynamic programming is used to synthesize diverse optimization approaches to active (work producing) and inactive (entropy generating) multiphase flow systems. Thermal machines, optimally controlled unit operations, nonlinear heat conduction, spontaneous relaxation processes, and self-propagating wave fronts are all shown to satisfy a discrete Hamilton-Jacobi-Bellman equation and a corresponding discrete optimization algorithm of Pontryagin's type, with the maximum principle for a Hamiltonian. The extremal structures are always canonical. A common unifying criterion is set for all considered systems, which is the criterion of a minimum generated entropy. It is shown that constraints can modify the entropy functionals in a different way for each group of the processes considered; thus the resulting structures of these functionals may differ significantly. Practical conclusions are formulated regarding the energy savings and energy policy in optimally controlled systems.
Design search and optimization in aerospace engineering.
Keane, A J; Scanlan, J P
2007-10-15
In this paper, we take a design-led perspective on the use of computational tools in the aerospace sector. We briefly review the current state-of-the-art in design search and optimization (DSO) as applied to problems from aerospace engineering, focusing on those problems that make heavy use of computational fluid dynamics (CFD). This ranges over issues of representation, optimization problem formulation and computational modelling. We then follow this with a multi-objective, multi-disciplinary example of DSO applied to civil aircraft wing design, an area where this kind of approach is becoming essential for companies to maintain their competitive edge. Our example considers the structure and weight of a transonic civil transport wing, its aerodynamic performance at cruise speed and its manufacturing costs. The goals are low drag and cost while holding weight and structural performance at acceptable levels. The constraints and performance metrics are modelled by a linked series of analysis codes, the most expensive of which is a CFD analysis of the aerodynamics using an Euler code with coupled boundary layer model. Structural strength and weight are assessed using semi-empirical schemes based on typical airframe company practice. Costing is carried out using a newly developed generative approach based on a hierarchical decomposition of the key structural elements of a typical machined and bolted wing-box assembly. To carry out the DSO process in the face of multiple competing goals, a recently developed multi-objective probability of improvement formulation is invoked along with stochastic process response surface models (Krigs). This approach both mitigates the significant run times involved in CFD computation and also provides an elegant way of balancing competing goals while still allowing the deployment of the whole range of single objective optimizers commonly available to design teams. PMID:17519198
Optimizing Advanced Power System Designs Under Uncertainty
Rubin, E.S.; Diwekar; Frey, H.C.
1996-12-31
This paper describes recent developments in ongoing research to develop and demonstrate advanced computer-based methods for dealing with uncertainties that are critical to the design of advanced coal-based power systems. Recent developments include new deterministic and stochastic methods for simulation, optimization, and synthesis of advanced process designs. Results are presented illustrating the use of these new modeling tools for the design and analysis of several advanced systems of current interest to the U.S. Department of Energy, including the technologies of integrated gasification combined cycle (IGCC), advanced pressurized fluid combustion (PFBC), and the externally fired combined cycle (EFCC) process. The new methods developed in this research can be applied generally to any chemical or energy conversion process to reduce the technological risks associated with uncertainties in process performance and cost.
A Tutorial on Adaptive Design Optimization
Myung, Jay I.; Cavagnaro, Daniel R.; Pitt, Mark A.
2013-01-01
Experimentation is ubiquitous in the field of psychology and fundamental to the advancement of its science, and one of the biggest challenges for researchers is designing experiments that can conclusively discriminate the theoretical hypotheses or models under investigation. The recognition of this challenge has led to the development of sophisticated statistical methods that aid in the design of experiments and that are within the reach of everyday experimental scientists. This tutorial paper introduces the reader to an implementable experimentation methodology, dubbed Adaptive Design Optimization, that can help scientists to conduct “smart” experiments that are maximally informative and highly efficient, which in turn should accelerate scientific discovery in psychology and beyond. PMID:23997275
An Optimization Framework for Dynamic, Distributed Real-Time Systems
NASA Technical Reports Server (NTRS)
Eckert, Klaus; Juedes, David; Welch, Lonnie; Chelberg, David; Bruggerman, Carl; Drews, Frank; Fleeman, David; Parrott, David; Pfarr, Barbara
2003-01-01
Abstract. This paper presents a model that is useful for developing resource allocation algorithms for distributed real-time systems .that operate in dynamic environments. Interesting aspects of the model include dynamic environments, utility and service levels, which provide a means for graceful degradation in resource-constrained situations and support optimization of the allocation of resources. The paper also provides an allocation algorithm that illustrates how to use the model for producing feasible, optimal resource allocations.
A framework for the design of ambulance sirens.
Catchpole, K; McKeown, D
2007-08-01
Ambulance sirens are essential for assisting the safe and rapid arrival of an ambulance at the scene of an emergency. In this study, the parameters upon which sirens may be designed were examined and a framework for emergency vehicle siren design was proposed. Validity for the framework was supported through acoustic measurements and the evaluation of ambulance transit times over 240 emergency runs using two different siren systems. Modifying existing siren sounds to add high frequency content would improve vehicle penetration, detectability and sound localization cues, and mounting the siren behind the radiator grill, rather than on the light bar or under the wheel arch, would provide less unwanted noise while maintaining or improving the effective distance in front of the vehicle. Ultimately, these considerations will benefit any new attempt to design auditory warnings for the emergency services. PMID:17558670
Harmonizing and Optimizing Fish Testing Methods: The OECD Framework Project
The Organisation for Economic Cooperation and Development (OECD) serves a key role in the international harmonization of testing of a wide variety of chemicals. An integrated fish testing framework project was initiated in mid-2009 through the OECD with the US as the lead country...
Multidisciplinary design optimization for sonic boom mitigation
NASA Astrophysics Data System (ADS)
Ozcer, Isik A.
product design. The simulation tools are used to optimize three geometries for sonic boom mitigation. The first is a simple axisymmetric shape to be used as a generic nose component, the second is a delta wing with lift, and the third is a real aircraft with nose and wing optimization. The objectives are to minimize the pressure impulse or the peak pressure in the sonic boom signal, while keeping the drag penalty under feasible limits. The design parameters for the meridian profile of the nose shape are the lengths and the half-cone angles of the linear segments that make up the profile. The design parameters for the lifting wing are the dihedral angle, angle of attack, non-linear span-wise twist and camber distribution. The test-bed aircraft is the modified F-5E aircraft built by Northrop Grumman, designated the Shaped Sonic Boom Demonstrator. This aircraft is fitted with an optimized axisymmetric nose, and the wings are optimized to demonstrate optimization for sonic boom mitigation for a real aircraft. The final results predict 42% reduction in bow shock strength, 17% reduction in peak Deltap, 22% reduction in pressure impulse, 10% reduction in foot print size, 24% reduction in inviscid drag, and no loss in lift for the optimized aircraft. Optimization is carried out using response surface methodology, and the design matrices are determined using standard DoE techniques for quadratic response modeling.
Designing optimal greenhouse gas monitoring networks for Australia
NASA Astrophysics Data System (ADS)
Ziehn, T.; Law, R. M.; Rayner, P. J.; Roff, G.
2016-01-01
Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground-based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimise the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to five new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that, while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.
ODIN: Optimal design integration system. [reusable launch vehicle design
NASA Technical Reports Server (NTRS)
Glatt, C. R.; Hague, D. S.
1975-01-01
The report provides a summary of the Optimal Design Integration (ODIN) System as it exists at Langley Research Center. A discussion of the ODIN System, the executive program and the data base concepts are presented. Two examples illustrate the capabilities of the system which have been exploited. Appended to the report are a summary of abstracts for the ODIN library programs and a description of the use of the executive program in linking the library programs.
A Framework for Preliminary Design of Aircraft Structures Based on Process Information. Part 1
NASA Technical Reports Server (NTRS)
Rais-Rohani, Masoud
1998-01-01
This report discusses the general framework and development of a computational tool for preliminary design of aircraft structures based on process information. The described methodology is suitable for multidisciplinary design optimization (MDO) activities associated with integrated product and process development (IPPD). The framework consists of three parts: (1) product and process definitions; (2) engineering synthesis, and (3) optimization. The product and process definitions are part of input information provided by the design team. The backbone of the system is its ability to analyze a given structural design for performance as well as manufacturability and cost assessment. The system uses a database on material systems and manufacturing processes. Based on the identified set of design variables and an objective function, the system is capable of performing optimization subject to manufacturability, cost, and performance constraints. The accuracy of the manufacturability measures and cost models discussed here depend largely on the available data on specific methods of manufacture and assembly and associated labor requirements. As such, our focus in this research has been on the methodology itself and not so much on its accurate implementation in an industrial setting. A three-tier approach is presented for an IPPD-MDO based design of aircraft structures. The variable-complexity cost estimation methodology and an approach for integrating manufacturing cost assessment into design process are also discussed. This report is presented in two parts. In the first part, the design methodology is presented, and the computational design tool is described. In the second part, a prototype model of the preliminary design Tool for Aircraft Structures based on Process Information (TASPI) is described. Part two also contains an example problem that applies the methodology described here for evaluation of six different design concepts for a wing spar.
Network inference via adaptive optimal design
2012-01-01
Background Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the most recent data that become available. The presented approach allows a reliable reconstruction of the network and addresses an important issue, i.e., the analysis and the propagation of uncertainties as they exist in both the data and in our own knowledge. These two types of uncertainties have their immediate ramifications for the uncertainties in the parameter estimates and, hence, are taken into account from the very beginning of our experimental design. Findings The method is demonstrated for two small networks that include a genetic network for mRNA synthesis and degradation and an oscillatory network describing a molecular network underlying adenosine 3’-5’ cyclic monophosphate (cAMP) as observed in populations of Dyctyostelium cells. In both cases a substantial reduction in parameter uncertainty was observed. Extension to larger scale networks is possible but needs a more rigorous parameter estimation algorithm that includes sparsity as a constraint in the optimization procedure. Conclusion We conclude that a careful experiment design very often (but not always) pays off in terms of reliability in the inferred network topology. For large scale networks a better parameter estimation algorithm is required that includes sparsity as an additional constraint. These algorithms are available in the literature and can also be used in an adaptive optimal design setting as demonstrated in this paper. PMID:22999252
Design, optimization, and control of tensegrity structures
NASA Astrophysics Data System (ADS)
Masic, Milenko
The contributions of this dissertation may be divided into four categories. The first category involves developing a systematic form-finding method for general and symmetric tensegrity structures. As an extension of the available results, different shape constraints are incorporated in the problem. Methods for treatment of these constraints are considered and proposed. A systematic formulation of the form-finding problem for symmetric tensegrity structures is introduced, and it uses the symmetry to reduce both the number of equations and the number of variables in the problem. The equilibrium analysis of modular tensegrities exploits their peculiar symmetry. The tensegrity similarity transformation completes the contributions in the area of enabling tools for tensegrity form-finding. The second group of contributions develops the methods for optimal mass-to-stiffness-ratio design of tensegrity structures. This technique represents the state-of-the-art for the static design of tensegrity structures. It is an extension of the results available for the topology optimization of truss structures. Besides guaranteeing that the final design satisfies the tensegrity paradigm, the problem constrains the structure from different modes of failure, which makes it very general. The open-loop control of the shape of modular tensegrities is the third contribution of the dissertation. This analytical result offers a closed form solution for the control of the reconfiguration of modular structures. Applications range from the deployment and stowing of large-scale space structures to the locomotion-inducing control for biologically inspired structures. The control algorithm is applicable regardless of the size of the structures, and it represents a very general result for a large class of tensegrities. Controlled deployments of large-scale tensegrity plates and tensegrity towers are shown as examples that demonstrate the full potential of this reconfiguration strategy. The last
Final Project Report: A Polyhedral Transformation Framework for Compiler Optimization
Sadayappan, Ponnuswamy; Rountev, Atanas
2015-06-15
The project developed the polyhedral compiler transformation module PolyOpt/Fortran in the ROSE compiler framework. PolyOpt/Fortran performs automated transformation of affine loop nests within FORTRAN programs for enhanced data locality and parallel execution. A FORTAN version of the Polybench library was also developed by the project. A third development was a dynamic analysis approach to gauge vectorization potential within loops of programs; software (DDVec) for automated instrumentation and dynamic analysis of programs was developed.
Probabilistic Finite Element Analysis & Design Optimization for Structural Designs
NASA Astrophysics Data System (ADS)
Deivanayagam, Arumugam
This study focuses on implementing probabilistic nature of material properties (Kevlar® 49) to the existing deterministic finite element analysis (FEA) of fabric based engine containment system through Monte Carlo simulations (MCS) and implementation of probabilistic analysis in engineering designs through Reliability Based Design Optimization (RBDO). First, the emphasis is on experimental data analysis focusing on probabilistic distribution models which characterize the randomness associated with the experimental data. The material properties of Kevlar® 49 are modeled using experimental data analysis and implemented along with an existing spiral modeling scheme (SMS) and user defined constitutive model (UMAT) for fabric based engine containment simulations in LS-DYNA. MCS of the model are performed to observe the failure pattern and exit velocities of the models. Then the solutions are compared with NASA experimental tests and deterministic results. MCS with probabilistic material data give a good prospective on results rather than a single deterministic simulation results. The next part of research is to implement the probabilistic material properties in engineering designs. The main aim of structural design is to obtain optimal solutions. In any case, in a deterministic optimization problem even though the structures are cost effective, it becomes highly unreliable if the uncertainty that may be associated with the system (material properties, loading etc.) is not represented or considered in the solution process. Reliable and optimal solution can be obtained by performing reliability optimization along with the deterministic optimization, which is RBDO. In RBDO problem formulation, in addition to structural performance constraints, reliability constraints are also considered. This part of research starts with introduction to reliability analysis such as first order reliability analysis, second order reliability analysis followed by simulation technique that
Inter occasion variability in individual optimal design.
Kristoffersson, Anders N; Friberg, Lena E; Nyberg, Joakim
2015-12-01
Inter occasion variability (IOV) is of importance to consider in the development of a design where individual pharmacokinetic or pharmacodynamic parameters are of interest. IOV may adversely affect the precision of maximum a posteriori (MAP) estimated individual parameters, yet the influence of inclusion of IOV in optimal design for estimation of individual parameters has not been investigated. In this work two methods of including IOV in the maximum a posteriori Fisher information matrix (FIMMAP) are evaluated: (i) MAP occ-the IOV is included as a fixed effect deviation per occasion and individual, and (ii) POP occ-the IOV is included as an occasion random effect. Sparse sampling schedules were designed for two test models and compared to a scenario where IOV is ignored, either by omitting known IOV (Omit) or by mimicking a situation where unknown IOV has inflated the IIV (Inflate). Accounting for IOV in the FIMMAP markedly affected the designs compared to ignoring IOV and, as evaluated by stochastic simulation and estimation, resulted in superior precision in the individual parameters. In addition MAPocc and POP occ accurately predicted precision and shrinkage. For the investigated designs, the MAP occ method was on average slightly superior to POP occ and was less computationally intensive.
Inter occasion variability in individual optimal design.
Kristoffersson, Anders N; Friberg, Lena E; Nyberg, Joakim
2015-12-01
Inter occasion variability (IOV) is of importance to consider in the development of a design where individual pharmacokinetic or pharmacodynamic parameters are of interest. IOV may adversely affect the precision of maximum a posteriori (MAP) estimated individual parameters, yet the influence of inclusion of IOV in optimal design for estimation of individual parameters has not been investigated. In this work two methods of including IOV in the maximum a posteriori Fisher information matrix (FIMMAP) are evaluated: (i) MAP occ-the IOV is included as a fixed effect deviation per occasion and individual, and (ii) POP occ-the IOV is included as an occasion random effect. Sparse sampling schedules were designed for two test models and compared to a scenario where IOV is ignored, either by omitting known IOV (Omit) or by mimicking a situation where unknown IOV has inflated the IIV (Inflate). Accounting for IOV in the FIMMAP markedly affected the designs compared to ignoring IOV and, as evaluated by stochastic simulation and estimation, resulted in superior precision in the individual parameters. In addition MAPocc and POP occ accurately predicted precision and shrinkage. For the investigated designs, the MAP occ method was on average slightly superior to POP occ and was less computationally intensive. PMID:26452548
Automated design of multiphase space missions using hybrid optimal control
NASA Astrophysics Data System (ADS)
Chilan, Christian Miguel
A modern space mission is assembled from multiple phases or events such as impulsive maneuvers, coast arcs, thrust arcs and planetary flybys. Traditionally, a mission planner would resort to intuition and experience to develop a sequence of events for the multiphase mission and to find the space trajectory that minimizes propellant use by solving the associated continuous optimal control problem. This strategy, however, will most likely yield a sub-optimal solution, as the problem is sophisticated for several reasons. For example, the number of events in the optimal mission structure is not known a priori and the system equations of motion change depending on what event is current. In this work a framework for the automated design of multiphase space missions is presented using hybrid optimal control (HOC). The method developed uses two nested loops: an outer-loop that handles the discrete dynamics and finds the optimal mission structure in terms of the categorical variables, and an inner-loop that performs the optimization of the corresponding continuous-time dynamical system and obtains the required control history. Genetic algorithms (GA) and direct transcription with nonlinear programming (NLP) are introduced as methods of solution for the outer-loop and inner-loop problems, respectively. Automation of the inner-loop, continuous optimal control problem solver, required two new technologies. The first is a method for the automated construction of the NLP problems resulting from the use of a direct solver for systems with different structures, including different numbers of categorical events. The method assembles modules, consisting of parameters and constraints appropriate to each event, sequentially according to the given mission structure. The other new technology is for a robust initial guess generator required by the inner-loop NLP problem solver. Two new methods were developed for cases including low-thrust trajectories. The first method, based on GA
Computational design optimization for microfluidic magnetophoresis
Plouffe, Brian D.; Lewis, Laura H.; Murthy, Shashi K.
2011-01-01
Current macro- and microfluidic approaches for the isolation of mammalian cells are limited in both efficiency and purity. In order to design a robust platform for the enumeration of a target cell population, high collection efficiencies are required. Additionally, the ability to isolate pure populations with minimal biological perturbation and efficient off-chip recovery will enable subcellular analyses of these cells for applications in personalized medicine. Here, a rational design approach for a simple and efficient device that isolates target cell populations via magnetic tagging is presented. In this work, two magnetophoretic microfluidic device designs are described, with optimized dimensions and operating conditions determined from a force balance equation that considers two dominant and opposing driving forces exerted on a magnetic-particle-tagged cell, namely, magnetic and viscous drag. Quantitative design criteria for an electromagnetic field displacement-based approach are presented, wherein target cells labeled with commercial magnetic microparticles flowing in a central sample stream are shifted laterally into a collection stream. Furthermore, the final device design is constrained to fit on standard rectangular glass coverslip (60 (L)×24 (W)×0.15 (H) mm3) to accommodate small sample volume and point-of-care design considerations. The anticipated performance of the device is examined via a parametric analysis of several key variables within the model. It is observed that minimal currents (<500 mA) are required to generate magnetic fields sufficient to separate cells from the sample streams flowing at rate as high as 7 ml∕h, comparable to the performance of current state-of-the-art magnet-activated cell sorting systems currently used in clinical settings. Experimental validation of the presented model illustrates that a device designed according to the derived rational optimization can effectively isolate (∼100%) a magnetic-particle-tagged cell
A Framework for Robust Multivariable Optimization of Integrated Circuits in Space Applications
NASA Technical Reports Server (NTRS)
DuMonthier, Jeffrey; Suarez, George
2013-01-01
Application Specific Integrated Circuit (ASIC) design for space applications involves multiple challenges of maximizing performance, minimizing power and ensuring reliable operation in extreme environments. This is a complex multidimensional optimization problem which must be solved early in the development cycle of a system due to the time required for testing and qualification severely limiting opportunities to modify and iterate. Manual design techniques which generally involve simulation at one or a small number of corners with a very limited set of simultaneously variable parameters in order to make the problem tractable are inefficient and not guaranteed to achieve the best possible results within the performance envelope defined by the process and environmental requirements. What is required is a means to automate design parameter variation, allow the designer to specify operational constraints and performance goals, and to analyze the results in a way which facilitates identifying the tradeoffs defining the performance envelope over the full set of process and environmental corner cases. The system developed by the Mixed Signal ASIC Group (MSAG) at the Goddard Space Flight Center is implemented as framework of software modules, templates and function libraries. It integrates CAD tools and a mathematical computing environment, and can be customized for new circuit designs with only a modest amount of effort as most common tasks are already encapsulated. Customization is required for simulation test benches to determine performance metrics and for cost function computation. Templates provide a starting point for both while toolbox functions minimize the code required. Once a test bench has been coded to optimize a particular circuit, it is also used to verify the final design. The combination of test bench and cost function can then serve as a template for similar circuits or be re-used to migrate the design to different processes by re-running it with the
Distribution Matching with the Bhattacharyya Similarity: A Bound Optimization Framework.
Ben Ayed, Ismail; Punithakumar, Kumaradevan; Shuo Li
2015-09-01
We present efficient graph cut algorithms for three problems: (1) finding a region in an image, so that the histogram (or distribution) of an image feature within the region most closely matches a given model; (2) co-segmentation of image pairs and (3) interactive image segmentation with a user-provided bounding box. Each algorithm seeks the optimum of a global cost function based on the Bhattacharyya measure, a convenient alternative to other matching measures such as the Kullback-Leibler divergence. Our functionals are not directly amenable to graph cut optimization as they contain non-linear functions of fractional terms, which make the ensuing optimization problems challenging. We first derive a family of parametric bounds of the Bhattacharyya measure by introducing an auxiliary labeling. Then, we show that these bounds are auxiliary functions of the Bhattacharyya measure, a result which allows us to solve each problem efficiently via graph cuts. We show that the proposed optimization procedures converge within very few graph cut iterations. Comprehensive and various experiments, including quantitative and comparative evaluations over two databases, demonstrate the advantages of the proposed algorithms over related works in regard to optimality, computational load, accuracy and flexibility.
SysSon - A Framework for Systematic Sonification Design
NASA Astrophysics Data System (ADS)
Vogt, Katharina; Goudarzi, Visda; Holger Rutz, Hanns
2015-04-01
SysSon is a research approach on introducing sonification systematically to a scientific community where it is not yet commonly used - e.g., in climate science. Thereby, both technical and socio-cultural barriers have to be met. The approach was further developed with climate scientists, who participated in contextual inquiries, usability tests and a workshop of collaborative design. Following from these extensive user tests resulted our final software framework. As frontend, a graphical user interface allows climate scientists to parametrize standard sonifications with their own data sets. Additionally, an interactive shell allows to code new sonifications for users competent in sound design. The framework is a standalone desktop application, available as open source (for details see http://sysson.kug.ac.at/) and works with data in NetCDF format.
Optimal Designs of Staggered Dean Vortex Micromixers
Chen, Jyh Jian; Chen, Chun Huei; Shie, Shian Ruei
2011-01-01
A novel parallel laminar micromixer with a two-dimensional staggered Dean Vortex micromixer is optimized and fabricated in our study. Dean vortices induced by centrifugal forces in curved rectangular channels cause fluids to produce secondary flows. The split-and-recombination (SAR) structures of the flow channels and the impinging effects result in the reduction of the diffusion distance of two fluids. Three different designs of a curved channel micromixer are introduced to evaluate the mixing performance of the designed micromixer. Mixing performances are demonstrated by means of a pH indicator using an optical microscope and fluorescent particles via a confocal microscope at different flow rates corresponding to Reynolds numbers (Re) ranging from 0.5 to 50. The comparison between the experimental data and numerical results shows a very reasonable agreement. At a Re of 50, the mixing length at the sixth segment, corresponding to the downstream distance of 21.0 mm, can be achieved in a distance 4 times shorter than when the Re equals 1. An optimization of this micromixer is performed with two geometric parameters. These are the angle between the lines from the center to two intersections of two consecutive curved channels, θ, and the angle between two lines of the centers of three consecutive curved channels, ϕ. It can be found that the maximal mixing index is related to the maximal value of the sum of θ and ϕ, which is equal to 139.82°. PMID:21747691
Design optimization of functionally graded dental implant.
Hedia, H S; Mahmoud, Nemat-Alla
2004-01-01
The continuous increase of man's life span, and the growing confidence in using artificial materials inside the human body necessities introducing more effective prosthesis and implant materials. However, no artificial implant has biomechanical properties equivalent to the original tissue. Recently, titanium and bioceramic materials, such as hydroxyapatite are extensively used as fabrication materials for dental implant due to their high compatibility with hard tissue and living bone. Titanium has reasonable stiffness and strength while hydroxyapatite has low stiffness, low strength and high ability to reach full integration with living bone. In order to obtain good dental implantation of the biomaterial; full integration of the implant with living bone should be satisfied. Minimum stresses in the implant and the bone must be achieved to increase the life of the implant and prevent bone resorption. Therefore, the aim of the current investigation is to design an implant made from functionally graded material (FGM) to achieve the above advantages. The finite element method and optimization technique are used to reach the required implant design. The optimal materials of the FGM dental implant are found to be hydroxyapatite/titanium. The investigations have shown that the maximum stress in the bone for the hydroxyapatite/titanium FGM implant has been reduced by about 22% and 28% compared to currently used titanium and stainless steel dental implants, respectively.
Design and architecture of the Mars relay network planning and analysis framework
NASA Technical Reports Server (NTRS)
Cheung, K. M.; Lee, C. H.
2002-01-01
In this paper we describe the design and architecture of the Mars Network planning and analysis framework that supports generation and validation of efficient planning and scheduling strategy. The goals are to minimize the transmitting time, minimize the delaying time, and/or maximize the network throughputs. The proposed framework would require (1) a client-server architecture to support interactive, batch, WEB, and distributed analysis and planning applications for the relay network analysis scheme, (2) a high-fidelity modeling and simulation environment that expresses link capabilities between spacecraft to spacecraft and spacecraft to Earth stations as time-varying resources, and spacecraft activities, link priority, Solar System dynamic events, the laws of orbital mechanics, and other limiting factors as spacecraft power and thermal constraints, (3) an optimization methodology that casts the resource and constraint models into a standard linear and nonlinear constrained optimization problem that lends itself to commercial off-the-shelf (COTS)planning and scheduling algorithms.
Design and applications of a multimodality image data warehouse framework.
Wong, Stephen T C; Hoo, Kent Soo; Knowlton, Robert C; Laxer, Kenneth D; Cao, Xinhau; Hawkins, Randall A; Dillon, William P; Arenson, Ronald L
2002-01-01
A comprehensive data warehouse framework is needed, which encompasses imaging and non-imaging information in supporting disease management and research. The authors propose such a framework, describe general design principles and system architecture, and illustrate a multimodality neuroimaging data warehouse system implemented for clinical epilepsy research. The data warehouse system is built on top of a picture archiving and communication system (PACS) environment and applies an iterative object-oriented analysis and design (OOAD) approach and recognized data interface and design standards. The implementation is based on a Java CORBA (Common Object Request Broker Architecture) and Web-based architecture that separates the graphical user interface presentation, data warehouse business services, data staging area, and backend source systems into distinct software layers. To illustrate the practicality of the data warehouse system, the authors describe two distinct biomedical applications--namely, clinical diagnostic workup of multimodality neuroimaging cases and research data analysis and decision threshold on seizure foci lateralization. The image data warehouse framework can be modified and generalized for new application domains.
Optimality criteria: A basis for multidisciplinary design optimization
NASA Astrophysics Data System (ADS)
Venkayya, V. B.
1989-01-01
This paper presents a generalization of what is frequently referred to in the literature as the optimality criteria approach in structural optimization. This generalization includes a unified presentation of the optimality conditions, the Lagrangian multipliers, and the resizing and scaling algorithms in terms of the sensitivity derivatives of the constraint and objective functions. The by-product of this generalization is the derivation of a set of simple nondimensional parameters which provides significant insight into the behavior of the structure as well as the optimization algorithm. A number of important issues, such as, active and passive variables, constraints and three types of linking are discussed in the context of the present derivation of the optimality criteria approach. The formulation as presented in this paper brings multidisciplinary optimization within the purview of this extremely efficient optimality criteria approach.
Fourier transform spectrometer optimal design considerations
NASA Astrophysics Data System (ADS)
Macoy, Norman H.
1999-10-01
The systems engineering aspects of evolving and developing the optimal design for Fourier transform interferometers are presented in this paper. A Fourier transform spectrometer (FTS) is a versatile electro-optical sensor for remote sensing, hyperspectral imaging, and laboratory chemical kinetics. Principal features include broad spectral coverage and high spectral resolution (Fellgate advantage) and high throughput (Jacquinot advantage). Due to its versatility, across various requirements, e.g. (resolution, bandwidth and aperture) sensor architecture contains an N-dimensional parametric trade matrix that needs to be readily assessed. Specifically considered are the logical steps utilized to flow down primary (customer) requirements and specifications to secondary (derived) requirements. Configurational aspects, generic trades, and parametric selections are emphasized for non-imagers as well as for imaging FTS. With an appropriately designed robust sensor, the noise equivalent spectral radiance or NE(Delta) N performance will be largely dictated by the scene and the instrument background flux. The performance will not be dictated by noise terms associated with interferogram encoding and signal handling. The mathematical formalism of interferometric error source types and photon limited design expressions are presented. The composition of these expressions are examined from the points of view of optical band limiting and some useful trade rules parametrically relating scan time and S/N to spectral resolution. For a well designed and executed interferometer, typical performance data are presented in terms of modulation index, calibrated radiometric atmospheric spectral signatures, and atmospheric spectral signatures for two spectral resolutions.
NASA Astrophysics Data System (ADS)
Tran, T.
With the onset of the SmallSat era, the RSO catalog is expected to see continuing growth in the near future. This presents a significant challenge to the current sensor tasking of the SSN. The Air Force is in need of a sensor tasking system that is robust, efficient, scalable, and able to respond in real-time to interruptive events that can change the tracking requirements of the RSOs. Furthermore, the system must be capable of using processed data from heterogeneous sensors to improve tasking efficiency. The SSN sensor tasking can be regarded as an economic problem of supply and demand: the amount of tracking data needed by each RSO represents the demand side while the SSN sensor tasking represents the supply side. As the number of RSOs to be tracked grows, demand exceeds supply. The decision-maker is faced with the problem of how to allocate resources in the most efficient manner. Braxton recently developed a framework called Multi-Objective Resource Optimization using Genetic Algorithm (MOROUGA) as one of its modern COTS software products. This optimization framework took advantage of the maturing technology of evolutionary computation in the last 15 years. This framework was applied successfully to address the resource allocation of an AFSCN-like problem. In any resource allocation problem, there are five key elements: (1) the resource pool, (2) the tasks using the resources, (3) a set of constraints on the tasks and the resources, (4) the objective functions to be optimized, and (5) the demand levied on the resources. In this paper we explain in detail how the design features of this optimization framework are directly applicable to address the SSN sensor tasking domain. We also discuss our validation effort as well as present the result of the AFSCN resource allocation domain using a prototype based on this optimization framework.
Incorporating User Preferences Within an Optimal Traffic Flow Management Framework
NASA Technical Reports Server (NTRS)
Rios, Joseph Lucio; Sheth, Kapil S.; Guiterrez-Nolasco, Sebastian Armardo
2010-01-01
The effectiveness of future decision support tools for Traffic Flow Management in the National Airspace System will depend on two major factors: computational burden and collaboration. Previous research has focused separately on these two aspects without consideration of their interaction. In this paper, their explicit combination is examined. It is shown that when user preferences are incorporated with an optimal approach to scheduling, runtime is not adversely affected. A benefit-cost ratio is used to measure the influence of user preferences on an optimal solution. This metric shows user preferences can be accommodated without inordinately, negatively affecting the overall system delay. Specifically, incorporating user preferences will increase delays proportionally to increased user satisfaction.
CFD based draft tube hydraulic design optimization
NASA Astrophysics Data System (ADS)
McNabb, J.; Devals, C.; Kyriacou, S. A.; Murry, N.; Mullins, B. F.
2014-03-01
The draft tube design of a hydraulic turbine, particularly in low to medium head applications, plays an important role in determining the efficiency and power characteristics of the overall machine, since an important proportion of the available energy, being in kinetic form leaving the runner, needs to be recovered by the draft tube into static head. For large units, these efficiency and power characteristics can equate to large sums of money when considering the anticipated selling price of the energy produced over the machine's life-cycle. This same draft tube design is also a key factor in determining the overall civil costs of the powerhouse, primarily in excavation and concreting, which can amount to similar orders of magnitude as the price of the energy produced. Therefore, there is a need to find the optimum compromise between these two conflicting requirements. In this paper, an elaborate approach is described for dealing with this optimization problem. First, the draft tube's detailed geometry is defined as a function of a comprehensive set of design parameters (about 20 of which a subset is allowed to vary during the optimization process) and are then used in a non-uniform rational B-spline based geometric modeller to fully define the wetted surfaces geometry. Since the performance of the draft tube is largely governed by 3D viscous effects, such as boundary layer separation from the walls and swirling flow characteristics, which in turn governs the portion of the available kinetic energy which will be converted into pressure, a full 3D meshing and Navier-Stokes analysis is performed for each design. What makes this even more challenging is the fact that the inlet velocity distribution to the draft tube is governed by the runner at each of the various operating conditions that are of interest for the exploitation of the powerhouse. In order to determine these inlet conditions, a combined steady-state runner and an initial draft tube analysis, using a
A Human Factors Framework for Payload Display Design
NASA Technical Reports Server (NTRS)
Dunn, Mariea C.; Hutchinson, Sonya L.
1998-01-01
During missions to space, one charge of the astronaut crew is to conduct research experiments. These experiments, referred to as payloads, typically are controlled by computers. Crewmembers interact with payload computers by using visual interfaces or displays. To enhance the safety, productivity, and efficiency of crewmember interaction with payload displays, particular attention must be paid to the usability of these displays. Enhancing display usability requires adoption of a design process that incorporates human factors engineering principles at each stage. This paper presents a proposed framework for incorporating human factors engineering principles into the payload display design process.
Optimizing Monitoring Designs under Alternative Objectives
Gastelum, Jason A.; USA, Richland Washington; Porter, Ellen A.; USA, Richland Washington
2014-12-31
This paper describes an approach to identify monitoring designs that optimize detection of CO2 leakage from a carbon capture and sequestration (CCS) reservoir and compares the results generated under two alternative objective functions. The first objective function minimizes the expected time to first detection of CO2 leakage, the second more conservative objective function minimizes the maximum time to leakage detection across the set of realizations. The approach applies a simulated annealing algorithm that searches the solution space by iteratively mutating the incumbent monitoring design. The approach takes into account uncertainty by evaluating the performance of potential monitoring designs across amore » set of simulated leakage realizations. The approach relies on a flexible two-tiered signature to infer that CO2 leakage has occurred. This research is part of the National Risk Assessment Partnership, a U.S. Department of Energy (DOE) project tasked with conducting risk and uncertainty analysis in the areas of reservoir performance, natural leakage pathways, wellbore integrity, groundwater protection, monitoring, and systems level modeling.« less
Optimizing Monitoring Designs under Alternative Objectives
Gastelum, Jason A.; USA, Richland Washington; Porter, Ellen A.; USA, Richland Washington
2014-12-31
This paper describes an approach to identify monitoring designs that optimize detection of CO2 leakage from a carbon capture and sequestration (CCS) reservoir and compares the results generated under two alternative objective functions. The first objective function minimizes the expected time to first detection of CO2 leakage, the second more conservative objective function minimizes the maximum time to leakage detection across the set of realizations. The approach applies a simulated annealing algorithm that searches the solution space by iteratively mutating the incumbent monitoring design. The approach takes into account uncertainty by evaluating the performance of potential monitoring designs across a set of simulated leakage realizations. The approach relies on a flexible two-tiered signature to infer that CO2 leakage has occurred. This research is part of the National Risk Assessment Partnership, a U.S. Department of Energy (DOE) project tasked with conducting risk and uncertainty analysis in the areas of reservoir performance, natural leakage pathways, wellbore integrity, groundwater protection, monitoring, and systems level modeling.
Optimal Ground Source Heat Pump System Design
Ozbek, Metin; Yavuzturk, Cy; Pinder, George
2015-04-15
Despite the facts that GSHPs first gained popularity as early as the 1940’s and they can achieve 30 to 60 percent in energy savings and carbon emission reductions relative to conventional HVAC systems, the use of geothermal energy in the U.S. has been less than 1 percent of the total energy consumption. The key barriers preventing this technically-mature technology from reaching its full commercial potential have been its high installation cost and limited consumer knowledge and trust in GSHP systems to deliver the technology in a cost-effective manner in the market place. Led by ENVIRON, with support from University Hartford and University of Vermont, the team developed and tested a software-based a decision making tool (‘OptGSHP’) for the least-cost design of ground-source heat pump (‘GSHP’) systems. OptGSHP combines state of the art optimization algorithms with GSHP-specific HVAC and groundwater flow and heat transport simulation. The particular strength of OptGSHP is in integrating heat transport due to groundwater flow into the design, which most of the GSHP designs do not get credit for and therefore are overdesigned.
An enhanced BSIM modeling framework for selfheating aware circuit design
NASA Astrophysics Data System (ADS)
Schleyer, M.; Leuschner, S.; Baumgartner, P.; Mueller, J.-E.; Klar, H.
2014-11-01
This work proposes a modeling framework to enhance the industry-standard BSIM4 MOSFET models with capabilities for coupled electro-thermal simulations. An automated simulation environment extracts thermal information from model data as provided by the semiconductor foundry. The standard BSIM4 model is enhanced with a Verilog-A based wrapper module, adding thermal nodes which can be connected to a thermal-equivalent RC network. The proposed framework allows a fully automated extraction process based on the netlist of the top-level design and the model library. A numerical analysis tool is used to control the extraction flow and to obtain all required parameters. The framework is used to model self-heating effects on a fully integrated class A/AB power amplifier (PA) designed in a standard 65 nm CMOS process. The PA is driven with +30 dBm output power, leading to an average temperature rise of approximately 40 °C over ambient temperature.
Robust Multivariable Optimization and Performance Simulation for ASIC Design
NASA Technical Reports Server (NTRS)
DuMonthier, Jeffrey; Suarez, George
2013-01-01
Application-specific-integrated-circuit (ASIC) design for space applications involves multiple challenges of maximizing performance, minimizing power, and ensuring reliable operation in extreme environments. This is a complex multidimensional optimization problem, which must be solved early in the development cycle of a system due to the time required for testing and qualification severely limiting opportunities to modify and iterate. Manual design techniques, which generally involve simulation at one or a small number of corners with a very limited set of simultaneously variable parameters in order to make the problem tractable, are inefficient and not guaranteed to achieve the best possible results within the performance envelope defined by the process and environmental requirements. What is required is a means to automate design parameter variation, allow the designer to specify operational constraints and performance goals, and to analyze the results in a way that facilitates identifying the tradeoffs defining the performance envelope over the full set of process and environmental corner cases. The system developed by the Mixed Signal ASIC Group (MSAG) at the Goddard Space Flight Center is implemented as a framework of software modules, templates, and function libraries. It integrates CAD tools and a mathematical computing environment, and can be customized for new circuit designs with only a modest amount of effort as most common tasks are already encapsulated. Customization is required for simulation test benches to determine performance metrics and for cost function computation.
Sampling design optimization for spatial functions
Olea, R.A.
1984-01-01
A new procedure is presented for minimizing the sampling requirements necessary to estimate a mappable spatial function at a specified level of accuracy. The technique is based on universal kriging, an estimation method within the theory of regionalized variables. Neither actual implementation of the sampling nor universal kriging estimations are necessary to make an optimal design. The average standard error and maximum standard error of estimation over the sampling domain are used as global indices of sampling efficiency. The procedure optimally selects those parameters controlling the magnitude of the indices, including the density and spatial pattern of the sample elements and the number of nearest sample elements used in the estimation. As an illustration, the network of observation wells used to monitor the water table in the Equus Beds of Kansas is analyzed and an improved sampling pattern suggested. This example demonstrates the practical utility of the procedure, which can be applied equally well to other spatial sampling problems, as the procedure is not limited by the nature of the spatial function. ?? 1984 Plenum Publishing Corporation.
Space tourism optimized reusable spaceplane design
NASA Astrophysics Data System (ADS)
Penn, Jay P.; Lindley, Charles A.
1997-01-01
Market surveys suggest that a viable space tourism industry will require flight rates about two orders of magnitude higher than those required for conventional spacelift. Although enabling round-trip cost goals for a viable space tourism business are about $240 per pound ($529/kg), or $72,000 per passenger round-trip, goals should be about $50 per pound ($110/kg) or approximately $15,000 for a typical passenger and baggage. The lower price will probably open space tourism to the general population. Vehicle reliabilities must approach those of commercial aircraft as closely as possible. This paper addresses the development of spaceplanes optimized for the ultra-high flight rate and high reliability demands of the space tourism mission. It addresses the fundamental operability, reliability, and cost drivers needed to satisfy this mission need. Figures of merit similar to those used to evaluate the economic viability of conventional commercial aircraft are developed, including items such as payload/vehicle dry weight, turnaround time, propellant cost per passenger, and insurance and depreciation costs, which show that infrastructure can be developed for a viable space tourism industry. A reference spaceplane design optimized for space tourism is described. Subsystem allocations for reliability, operability, and costs are made and a route to developing such a capability is discussed. The vehicle's ability to also satisfy the traditional spacelift market is shown.
Three Program Architecture for Design Optimization
NASA Technical Reports Server (NTRS)
Miura, Hirokazu; Olson, Lawrence E. (Technical Monitor)
1998-01-01
In this presentation, I would like to review historical perspective on the program architecture used to build design optimization capabilities based on mathematical programming and other numerical search techniques. It is rather straightforward to classify the program architecture in three categories as shown above. However, the relative importance of each of the three approaches has not been static, instead dynamically changing as the capabilities of available computational resource increases. For example, we considered that the direct coupling architecture would never be used for practical problems, but availability of such computer systems as multi-processor. In this presentation, I would like to review the roles of three architecture from historical as well as current and future perspective. There may also be some possibility for emergence of hybrid architecture. I hope to provide some seeds for active discussion where we are heading to in the very dynamic environment for high speed computing and communication.
Optimal design of robot accuracy compensators
Zhuang, H.; Roth, Z.S. . Robotics Center and Electrical Engineering Dept.); Hamano, Fumio . Dept. of Electrical Engineering)
1993-12-01
The problem of optimal design of robot accuracy compensators is addressed. Robot accuracy compensation requires that actual kinematic parameters of a robot be previously identified. Additive corrections of joint commands, including those at singular configurations, can be computed without solving the inverse kinematics problem for the actual robot. This is done by either the damped least-squares (DLS) algorithm or the linear quadratic regulator (LQR) algorithm, which is a recursive version of the DLS algorithm. The weight matrix in the performance index can be selected to achieve specific objectives, such as emphasizing end-effector's positioning accuracy over orientation accuracy or vice versa, or taking into account proximity to robot joint travel limits and singularity zones. The paper also compares the LQR and the DLS algorithms in terms of computational complexity, storage requirement, and programming convenience. Simulation results are provided to show the effectiveness of the algorithms.
Three-dimensional hemodynamic design optimization of stents for cerebral aneurysms.
Lee, Chang-Joon; Srinivas, Karkenahalli; Qian, Yi
2014-03-01
Flow-diverting stents occlude aneurysms by diverting the blood flow from entering the aneurysm sac. Their effectiveness is determined by the thrombus formation rate, which depends greatly on stent design. The aim of this study was to provide a general framework for efficient stent design using design optimization methods, with a focus on stent hemodynamics as the starting point. Kriging method was used for completing design optimization. Three different cases of idealized stents were considered, and 40-60 samples from each case were evaluated using computational fluid dynamics. Using maximum velocity and vorticity reduction as objective functions, the optimized designs were identified from the samples. A number of optimized stent designs have been found from optimization, which revealed that a combination of high pore density and thin struts is desired. Additionally, distributing struts near the proximal end of aneurysm neck was found to be effective. The success of the methods and framework devised in this study offers a future possibility of incorporating other disciplines to carry out multidisciplinary design optimization.
Ecohydrology frameworks for green infrastructure design and ecosystem service provision
NASA Astrophysics Data System (ADS)
Pavao-Zuckerman, M.; Knerl, A.; Barron-Gafford, G.
2014-12-01
Urbanization is a dominant form of landscape change that affects the structure and function of ecosystems and alters control points in biogeochemical and hydrologic cycles. Green infrastructure (GI) has been proposed as a solution to many urban environmental challenges and may be a way to manage biogeochemical control points. Despite this promise, there has been relatively limited empirical focus to evaluate the efficacy of GI, relationships between design and function, and the ability of GI to provide ecosystem services in cities. This work has been driven by goals of adapting GI approaches to dryland cities and to harvest rain and storm water for providing ecosystem services related to storm water management and urban heat island mitigation, as well as other co-benefits. We will present a modification of ecohydrologic theory for guiding the design and function of green infrastructure for dryland systems that highlights how GI functions in context of Trigger - Transfer - Reserve - Pulse (TTRP) dynamic framework. Here we also apply this TTRP framework to observations of established street-scape green infrastructure in Tucson, AZ, and an experimental installation of green infrastructure basins on the campus of Biosphere 2 (Oracle, AZ) where we have been measuring plant performance and soil biogeochemical functions. We found variable sensitivity of microbial activity, soil respiration, N-mineralization, photosynthesis and respiration that was mediated both by elements of basin design (soil texture and composition, choice of surface mulches) and antecedent precipitation inputs and soil moisture conditions. The adapted TTRP framework and field studies suggest that there are strong connections between design and function that have implications for stormwater management and ecosystem service provision in dryland cities.
A Framework for the Optimization of Discrete-Event Simulation Models
NASA Technical Reports Server (NTRS)
Joshi, B. D.; Unal, R.; White, N. H.; Morris, W. D.
1996-01-01
With the growing use of computer modeling and simulation, in all aspects of engineering, the scope of traditional optimization has to be extended to include simulation models. Some unique aspects have to be addressed while optimizing via stochastic simulation models. The optimization procedure has to explicitly account for the randomness inherent in the stochastic measures predicted by the model. This paper outlines a general purpose framework for optimization of terminating discrete-event simulation models. The methodology combines a chance constraint approach for problem formulation, together with standard statistical estimation and analyses techniques. The applicability of the optimization framework is illustrated by minimizing the operation and support resources of a launch vehicle, through a simulation model.
Constrained Aeroacoustic Shape Optimization Using the Surrogate Management Framework
NASA Technical Reports Server (NTRS)
Marsden, Alison L.; Wang, Meng; Dennis, John E., Jr.
2003-01-01
Reduction of noise generated by turbulent flow past the trailing-edge of a lifting surface is a challenge in many aeronautical and naval applications. Numerical predictions of trailing-edge noise necessitate the use of advanced simulation techniques such as large-eddy simulation (LES) in order to capture a wide range of turbulence scales which are the source of broadband noise. Aeroacoustic calculations of the flow over a model airfoil trailing edge using LES and aeroacoustic theory have been presented in Wang and Moin and were shown to agree favorably with experiments. The goal of the present work is to apply shape optimization to the trailing edge flow previously studied, in order to control aerodynamic noise.
Optimal screening designs for biomedical technology
Torney, D.C.; Bruno, W.J.; Knill, E.
1997-10-01
This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at Los Alamos National Laboratory (LANL). Screening a large number of different types of molecules to isolate a few with desirable properties is essential in biomedical technology. For example, trying to find a particular gene in the Human genome could be akin to looking for a needle in a haystack. Fortunately, testing of mixtures, or pools, of molecules allows the desirable ones to be identified, using a number of experiments proportional only to the logarithm of the total number of experiments proportional only to the logarithm of the total number of types of molecules. We show how to capitalize upon this potential by using optimize pooling schemes, or designs. We propose efficient non-adaptive pooling designs, such as {open_quotes}random sets{close_quotes} designs and modified {open_quotes}row and column{close_quotes} designs. Our results have been applied in the pooling and unique-sequence screening of clone libraries used in the Human Genome Project and in the mapping of Human chromosome 16. This required the use of liquid-transferring robots and manifolds--for the largest clone libraries. Finally, we developed an efficient technique for finding the posterior probability each molecule has the desirable property, given the pool assay results. This technique works well, in practice, even if there are substantial rates of errors in the pool assay data. Both our methods and our results are relevant to a broad spectrum of research in modern biology.
Seed Design Framework for Mapping SOLiD Reads
NASA Astrophysics Data System (ADS)
Noé, Laurent; Gîrdea, Marta; Kucherov, Gregory
The advent of high-throughput sequencing technologies constituted a major advance in genomic studies, offering new prospects in a wide range of applications. We propose a rigorous and flexible algorithmic solution to mapping SOLiD color-space reads to a reference genome. The solution relies on an advanced method of seed design that uses a faithful probabilistic model of read matches and, on the other hand, a novel seeding principle especially adapted to read mapping. Our method can handle both lossy and lossless frameworks and is able to distinguish, at the level of seed design, between SNPs and reading errors. We illustrate our approach by several seed designs and demonstrate their efficiency.
A Framework for Designing Scaffolds That Improve Motivation and Cognition
Belland, Brian R.; Kim, ChanMin; Hannafin, Michael J.
2013-01-01
A problematic, yet common, assumption among educational researchers is that when teachers provide authentic, problem-based experiences, students will automatically be engaged. Evidence indicates that this is often not the case. In this article, we discuss (a) problems with ignoring motivation in the design of learning environments, (b) problem-based learning and scaffolding as one way to help, (c) how scaffolding has strayed from what was originally equal parts motivational and cognitive support, and (d) a conceptual framework for the design of scaffolds that can enhance motivation as well as cognitive outcomes. We propose guidelines for the design of computer-based scaffolds to promote motivation and engagement while students are solving authentic problems. Remaining questions and suggestions for future research are then discussed. PMID:24273351
Microsystem design framework based on tool adaptations and library developments
NASA Astrophysics Data System (ADS)
Karam, Jean Michel; Courtois, Bernard; Rencz, Marta; Poppe, Andras; Szekely, Vladimir
1996-09-01
Besides foundry facilities, Computer-Aided Design (CAD) tools are also required to move microsystems from research prototypes to an industrial market. This paper describes a Computer-Aided-Design Framework for microsystems, based on selected existing software packages adapted and extended for microsystem technology, assembled with libraries where models are available in the form of standard cells described at different levels (symbolic, system/behavioral, layout). In microelectronics, CAD has already attained a highly sophisticated and professional level, where complete fabrication sequences are simulated and the device and system operation is completely tested before manufacturing. In comparison, the art of microsystem design and modelling is still in its infancy. However, at least for the numerical simulation of the operation of single microsystem components, such as mechanical resonators, thermo-elements, elastic diaphragms, reliable simulation tools are available. For the different engineering disciplines (like electronics, mechanics, optics, etc) a lot of CAD-tools for the design, simulation and verification of specific devices are available, but there is no CAD-environment within which we could perform a (micro-)system simulation due to the different nature of the devices. In general there are two different approaches to overcome this limitation: the first possibility would be to develop a new framework tailored for microsystem-engineering. The second approach, much more realistic, would be to use the existing CAD-tools which contain the most promising features, and to extend these tools so that they can be used for the simulation and verification of microsystems and of the devices involved. These tools are assembled with libraries in a microsystem design environment allowing a continuous design flow. The approach is driven by the wish to make microsystems accessible to a large community of people, including SMEs and non-specialized academic institutions.
Optimization of the cyclotide framework to improve cell penetration properties
Huang, Yen-Hua; Chaousis, Stephanie; Cheneval, Olivier; Craik, David J.; Henriques, Sónia T.
2015-01-01
Cell penetrating peptides have been regarded as promising vectors to deliver hydrophilic molecules inside cells. Although they are great tools for research and have high potential as drug delivery systems, their application as drugs is impaired by their low stability in serum. Cyclotides, cyclic disulfide-rich peptides from plants, are ultra-stable molecules that have inspired applications in drug design as they can be used as scaffolds to stabilize linear bioactive sequences. Recently, they have also been shown to possess cell-penetrating properties. The combination of their remarkable stability and cell-penetrating properties opens new avenues for the application of peptides to bind to and inhibit intracellular proteins. Nevertheless, for a broader application of these molecules as vectors is of utmost importance to improve their cellular internalization efficiency. In this study we successfully modified MCoTI-II, one of the most widely studied cyclotide scaffolds in drug design, and improved its internalization properties. The internalization of the newly designed MCoTI-II is as efficient as the gold standard cell-penetrating peptide (CPP) TAT and maintains all the required features as a template to graft desired bioactivities. PMID:25709580
Optimization of the cyclotide framework to improve cell penetration properties.
Huang, Yen-Hua; Chaousis, Stephanie; Cheneval, Olivier; Craik, David J; Henriques, Sónia T
2015-01-01
Cell penetrating peptides have been regarded as promising vectors to deliver hydrophilic molecules inside cells. Although they are great tools for research and have high potential as drug delivery systems, their application as drugs is impaired by their low stability in serum. Cyclotides, cyclic disulfide-rich peptides from plants, are ultra-stable molecules that have inspired applications in drug design as they can be used as scaffolds to stabilize linear bioactive sequences. Recently, they have also been shown to possess cell-penetrating properties. The combination of their remarkable stability and cell-penetrating properties opens new avenues for the application of peptides to bind to and inhibit intracellular proteins. Nevertheless, for a broader application of these molecules as vectors is of utmost importance to improve their cellular internalization efficiency. In this study we successfully modified MCoTI-II, one of the most widely studied cyclotide scaffolds in drug design, and improved its internalization properties. The internalization of the newly designed MCoTI-II is as efficient as the gold standard cell-penetrating peptide (CPP) TAT and maintains all the required features as a template to graft desired bioactivities. PMID:25709580
Chip Design Process Optimization Based on Design Quality Assessment
NASA Astrophysics Data System (ADS)
Häusler, Stefan; Blaschke, Jana; Sebeke, Christian; Rosenstiel, Wolfgang; Hahn, Axel
2010-06-01
Nowadays, the managing of product development projects is increasingly challenging. Especially the IC design of ASICs with both analog and digital components (mixed-signal design) is becoming more and more complex, while the time-to-market window narrows at the same time. Still, high quality standards must be fulfilled. Projects and their status are becoming less transparent due to this complexity. This makes the planning and execution of projects rather difficult. Therefore, there is a need for efficient project control. A main challenge is the objective evaluation of the current development status. Are all requirements successfully verified? Are all intermediate goals achieved? Companies often develop special solutions that are not reusable in other projects. This makes the quality measurement process itself less efficient and produces too much overhead. The method proposed in this paper is a contribution to solve these issues. It is applied at a German design house for analog mixed-signal IC design. This paper presents the results of a case study and introduces an optimized project scheduling on the basis of quality assessment results.
Optimal design of artificial reefs for sturgeon
NASA Astrophysics Data System (ADS)
Yarbrough, Cody; Cotel, Aline; Kleinheksel, Abby
2015-11-01
The Detroit River, part of a busy corridor between Lakes Huron and Erie, was extensively modified to create deep shipping channels, resulting in a loss of spawning habitat for lake sturgeon and other native fish (Caswell et al. 2004, Bennion and Manny 2011). Under the U.S.- Canada Great Lakes Water Quality Agreement, there are remediation plans to construct fish spawning reefs to help with historic habitat losses and degraded fish populations, specifically sturgeon. To determine optimal reef design, experimental work has been undertaken. Different sizes and shapes of reefs are tested for a given set of physical conditions, such as flow depth and flow velocity, matching the relevant dimensionless parameters dominating the flow physics. The physical conditions are matched with the natural conditions encountered in the Detroit River. Using Particle Image Velocimetry, Acoustic Doppler Velocimetry and dye studies, flow structures, vorticity and velocity gradients at selected locations have been identified and quantified to allow comparison with field observations and numerical model results. Preliminary results are helping identify the design features to be implemented in the next phase of reef construction. Sponsored by NOAA.
A Robust Kalman Framework with Resampling and Optimal Smoothing
Kautz, Thomas; Eskofier, Bjoern M.
2015-01-01
The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data. The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies. PMID:25734647
A robust Kalman framework with resampling and optimal smoothing.
Kautz, Thomas; Eskofier, Bjoern M
2015-02-27
The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data. The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies.
Application of numerical optimization to rotor aerodynamic design
NASA Technical Reports Server (NTRS)
Pleasants, W. A., III; Wiggins, T. J.
1984-01-01
Based on initial results obtained from the performance optimization code, a number of observations can be made regarding the utility of optimization codes in supporting design of rotors for improved performance. (1) The primary objective of improving the productivity and responsiveness of current design methods can be met. (2) The use of optimization allows the designer to consider a wider range of design variables in a greatly compressed time period. (3) Optimization requires the user to carefully define his problem to avoid unproductive use of computer resources. (4) Optimization will increase the burden on the analyst to validate designs and to improve the accuracy of analysis methods. (5) Direct calculation of finite difference derivatives by the optimizer was not prohibitive for this application but was expensive. Approximate analysis in some form would be considered to improve program response time. (6) Program developement is not complete and will continue to evolve to integrate new analysis methods, design problems, and alternate optimizer options.
A Robust Control Design Framework for Substructure Models
NASA Technical Reports Server (NTRS)
Lim, Kyong B.
1994-01-01
A framework for designing control systems directly from substructure models and uncertainties is proposed. The technique is based on combining a set of substructure robust control problems by an interface stiffness matrix which appears as a constant gain feedback. Variations of uncertainties in the interface stiffness are treated as a parametric uncertainty. It is shown that multivariable robust control can be applied to generate centralized or decentralized controllers that guarantee performance with respect to uncertainties in the interface stiffness, reduced component modes and external disturbances. The technique is particularly suited for large, complex, and weakly coupled flexible structures.
Research and design of web application framework based on AJAX
NASA Astrophysics Data System (ADS)
Zhang, Yan-feng; Liu, San-jun
2013-03-01
AJAX is an emerging presentation layer technology of Web, which allows dynamic, fast, and flexible Web application procedures to be built. AJAX can eliminate the dependence on the form in the tradition HTTP communication mode, which can achieve a fast and lightweight asynchronous communication. This paper firstly introduces the work principle of the AJAX technology, and combines the AJAX technology with the Web services technology to design a new Web application framework based on AJAX, to achieve an asynchronous communication of the browser directly with the back-end services.
Hard and Soft Constraints in Reliability-Based Design Optimization
NASA Technical Reports Server (NTRS)
Crespo, L.uis G.; Giesy, Daniel P.; Kenny, Sean P.
2006-01-01
This paper proposes a framework for the analysis and design optimization of models subject to parametric uncertainty where design requirements in the form of inequality constraints are present. Emphasis is given to uncertainty models prescribed by norm bounded perturbations from a nominal parameter value and by sets of componentwise bounded uncertain variables. These models, which often arise in engineering problems, allow for a sharp mathematical manipulation. Constraints can be implemented in the hard sense, i.e., constraints must be satisfied for all parameter realizations in the uncertainty model, and in the soft sense, i.e., constraints can be violated by some realizations of the uncertain parameter. In regard to hard constraints, this methodology allows (i) to determine if a hard constraint can be satisfied for a given uncertainty model and constraint structure, (ii) to generate conclusive, formally verifiable reliability assessments that allow for unprejudiced comparisons of competing design alternatives and (iii) to identify the critical combination of uncertain parameters leading to constraint violations. In regard to soft constraints, the methodology allows the designer (i) to use probabilistic uncertainty models, (ii) to calculate upper bounds to the probability of constraint violation, and (iii) to efficiently estimate failure probabilities via a hybrid method. This method integrates the upper bounds, for which closed form expressions are derived, along with conditional sampling. In addition, an l(sub infinity) formulation for the efficient manipulation of hyper-rectangular sets is also proposed.
ERIC Educational Resources Information Center
Lee, Sung Heum; Boling, Elizabeth
1999-01-01
Identifies guidelines from the literature relating to screen design and design of interactive instructional materials. Describes two types of guidelines--those aimed at enhancing motivation and those aimed at preventing loss of motivation--for typography, graphics, color, and animation and audio. Proposes a framework for considering motivation in…
NASA Astrophysics Data System (ADS)
Zsáki, Attila M.; Curran, John H.
2005-11-01
Many field problems, from stress analysis, heat transfer to contaminant transport, deal with disturbances in a continuum caused by a source (defined by its discrete geometry) and a region of interest (where a solution is sought). Depending on the location of regions of interest in relation to the sources, the level of geometric detail necessary to represent the sources in a model can vary considerably. A practical application of stress analysis in mining is the evaluation of the effects of continuous excavation on the states of stress around mine openings. Labour intensive model preparation and lengthy computation coupled with the interpretation of analysis results can have considerable impact on the successful operation of an underground mine, where stope failures can cost tens of millions of dollars and possibly lead to closure of the mine.A framework is proposed based on continuum mechanics principles to automatically optimize the level of geometric detail required for an analysis by simplifying the model geometry using expanded and modified algorithms that originated in computer graphics. This reduction in model size directly translates to savings in computational time. The results obtained from an optimized model have accuracy comparable to the uncertainty in input data (e.g. rock mass properties, geology, etc.). This first paper defines the optimization framework, while a companion paper investigates its efficiency and application to practical mining and excavation-related problems. Copyright
Eldred, M.S.; Hart, W.E.; Bohnhoff, W.J.; Romero, V.J.; Hutchinson, S.A.; Salinger, A.G.
1996-08-01
the benefits of applying optimization to computational models are well known, but their range of widespread application to date has been limited. This effort attempts to extend the disciplinary areas to which optimization algorithms may be readily applied through the development and application of advanced optimization strategies capable of handling the computational difficulties associated with complex simulation codes. Towards this goal, a flexible software framework is under continued development for the application of optimization techniques to broad classes of engineering applications, including those with high computational expense and nonsmooth, nonconvex design space features. Object-oriented software design with C++ has been employed as a tool in providing a flexible, extensible, and robust multidisciplinary toolkit with computationally intensive simulations. In this paper, demonstrations of advanced optimization strategies using the software are presented in the hybridization and parallel processing research areas. Performance of the advanced strategies is compared with a benchmark nonlinear programming optimization.
Antimicrobial Peptides Design by Evolutionary Multiobjective Optimization
Maccari, Giuseppe; Di Luca, Mariagrazia; Nifosí, Riccardo; Cardarelli, Francesco; Signore, Giovanni; Boccardi, Claudia; Bifone, Angelo
2013-01-01
Antimicrobial peptides (AMPs) are an abundant and wide class of molecules produced by many tissues and cell types in a variety of mammals, plant and animal species. Linear alpha-helical antimicrobial peptides are among the most widespread membrane-disruptive AMPs in nature, representing a particularly successful structural arrangement in innate defense. Recently, AMPs have received increasing attention as potential therapeutic agents, owing to their broad activity spectrum and their reduced tendency to induce resistance. The introduction of non-natural amino acids will be a key requisite in order to contrast host resistance and increase compound's life. In this work, the possibility to design novel AMP sequences with non-natural amino acids was achieved through a flexible computational approach, based on chemophysical profiles of peptide sequences. Quantitative structure-activity relationship (QSAR) descriptors were employed to code each peptide and train two statistical models in order to account for structural and functional properties of alpha-helical amphipathic AMPs. These models were then used as fitness functions for a multi-objective evolutional algorithm, together with a set of constraints for the design of a series of candidate AMPs. Two ab-initio natural peptides were synthesized and experimentally validated for antimicrobial activity, together with a series of control peptides. Furthermore, a well-known Cecropin-Mellitin alpha helical antimicrobial hybrid (CM18) was optimized by shortening its amino acid sequence while maintaining its activity and a peptide with non-natural amino acids was designed and tested, demonstrating the higher activity achievable with artificial residues. PMID:24039565
Design and optimization of a brachytherapy robot
NASA Astrophysics Data System (ADS)
Meltsner, Michael A.
Trans-rectal ultrasound guided (TRUS) low dose rate (LDR) interstitial brachytherapy has become a popular procedure for the treatment of prostate cancer, the most common type of non-skin cancer among men. The current TRUS technique of LDR implantation may result in less than ideal coverage of the tumor with increased risk of negative response such as rectal toxicity and urinary retention. This technique is limited by the skill of the physician performing the implant, the accuracy of needle localization, and the inherent weaknesses of the procedure itself. The treatment may require 100 or more sources and 25 needles, compounding the inaccuracy of the needle localization procedure. A robot designed for prostate brachytherapy may increase the accuracy of needle placement while minimizing the effect of physician technique in the TRUS procedure. Furthermore, a robot may improve associated toxicities by utilizing angled insertions and freeing implantations from constraints applied by the 0.5 cm-spaced template used in the TRUS method. Within our group, Lin et al. have designed a new type of LDR source. The "directional" source is a seed designed to be partially shielded. Thus, a directional, or anisotropic, source does not emit radiation in all directions. The source can be oriented to irradiate cancerous tissues while sparing normal ones. This type of source necessitates a new, highly accurate method for localization in 6 degrees of freedom. A robot is the best way to accomplish this task accurately. The following presentation of work describes the invention and optimization of a new prostate brachytherapy robot that fulfills these goals. Furthermore, some research has been dedicated to the use of the robot to perform needle insertion tasks (brachytherapy, biopsy, RF ablation, etc.) in nearly any other soft tissue in the body. This can be accomplished with the robot combined with automatic, magnetic tracking.
Design of Functional Materials with Hydrogen-Bonded Host Frameworks
NASA Astrophysics Data System (ADS)
Soegiarto, Airon Cosanova
The properties of molecular crystals are governed by the attributes of their molecular constituents and their solid-state arrangements, making control of crystal packing paramount when designing new materials with targeted functions. One effective strategy involves the use of robust host frameworks that encapsulate functional guests in molecular-scale cavities with tailored shapes, sizes, and chemical environments that enable systematic regulation of solid state properties. This approach promises to simplify the synthesis of molecular materials by decoupling the design of structure, provided by the host framework, from function, introduced by the guests. This thesis has reported a series of crystalline, structurally robust hosts based on guanidinium cations (G = (C(NH2) 3 +) and the sulfonate moieties of organodisulfonate anions (DS; S = -O3S-R-SO3 -). The host framework is based on layers of 2-D GS sheet, which are interconnected by the organic residues (pillars) of the disulfonates, thereby producing a lamellar architecture with inclusion cavities, occupied by guest molecules, between the sheets. Notably, the GDS inclusion compounds exhibit numerous architectures such as bilayer, simple brick, and zigzag brick -- each endowed with uniquely sized and shaped cavities, suggesting that the aggregation motifs of the included guests can be controlled within the host lattice. Furthermore, the selectivity toward different architectures is governed by the relative size of the pillars and guests, allowing the construction of a "structural phase diagram" which can be used to predict the solid-state architecture of untested host-guest combination. Consequently, a variety of functional molecules have been included in order to exploit these features. Chapter 3 reports the inclusion of polyconjugated molecules within the GDS hosts, generating various guest aggregation motifs -- edge-to-edge to face-to-edge to end-to-end. The effects of the various host and/or guest aggregation
Space tourism optimized reusable spaceplane design
Penn, J.P.; Lindley, C.A.
1997-01-01
Market surveys suggest that a viable space tourism industry will require flight rates about two orders of magnitude higher than those required for conventional spacelift. Although enabling round-trip cost goals for a viable space tourism business are about {dollar_sign}240 per pound ({dollar_sign}529/kg), or {dollar_sign}72,000 per passenger round-trip, goals should be about {dollar_sign}50 per pound ({dollar_sign}110/kg) or approximately {dollar_sign}15,000 for a typical passenger and baggage. The lower price will probably open space tourism to the general population. Vehicle reliabilities must approach those of commercial aircraft as closely as possible. This paper addresses the development of spaceplanes optimized for the ultra-high flight rate and high reliability demands of the space tourism mission. It addresses the fundamental operability, reliability, and cost drivers needed to satisfy this mission need. Figures of merit similar to those used to evaluate the economic viability of conventional commercial aircraft are developed, including items such as payload/vehicle dry weight, turnaround time, propellant cost per passenger, and insurance and depreciation costs, which show that infrastructure can be developed for a viable space tourism industry. A reference spaceplane design optimized for space tourism is described. Subsystem allocations for reliability, operability, and costs are made and a route to developing such a capability is discussed. The vehicle{close_quote}s ability to also satisfy the traditional spacelift market is shown. {copyright} {ital 1997 American Institute of Physics.}
Design time optimization for hardware watermarking protection of HDL designs.
Castillo, E; Morales, D P; García, A; Parrilla, L; Todorovich, E; Meyer-Baese, U
2015-01-01
HDL-level design offers important advantages for the application of watermarking to IP cores, but its complexity also requires tools automating these watermarking algorithms. A new tool for signature distribution through combinational logic is proposed in this work. IPP@HDL, a previously proposed high-level watermarking technique, has been employed for evaluating the tool. IPP@HDL relies on spreading the bits of a digital signature at the HDL design level using combinational logic included within the original system. The development of this new tool for the signature distribution has not only extended and eased the applicability of this IPP technique, but it has also improved the signature hosting process itself. Three algorithms were studied in order to develop this automated tool. The selection of a cost function determines the best hosting solutions in terms of area and performance penalties on the IP core to protect. An 1D-DWT core and MD5 and SHA1 digital signatures were used in order to illustrate the benefits of the new tool and its optimization related to the extraction logic resources. Among the proposed algorithms, the alternative based on simulated annealing reduces the additional resources while maintaining an acceptable computation time and also saving designer effort and time. PMID:25861681
Design time optimization for hardware watermarking protection of HDL designs.
Castillo, E; Morales, D P; García, A; Parrilla, L; Todorovich, E; Meyer-Baese, U
2015-01-01
HDL-level design offers important advantages for the application of watermarking to IP cores, but its complexity also requires tools automating these watermarking algorithms. A new tool for signature distribution through combinational logic is proposed in this work. IPP@HDL, a previously proposed high-level watermarking technique, has been employed for evaluating the tool. IPP@HDL relies on spreading the bits of a digital signature at the HDL design level using combinational logic included within the original system. The development of this new tool for the signature distribution has not only extended and eased the applicability of this IPP technique, but it has also improved the signature hosting process itself. Three algorithms were studied in order to develop this automated tool. The selection of a cost function determines the best hosting solutions in terms of area and performance penalties on the IP core to protect. An 1D-DWT core and MD5 and SHA1 digital signatures were used in order to illustrate the benefits of the new tool and its optimization related to the extraction logic resources. Among the proposed algorithms, the alternative based on simulated annealing reduces the additional resources while maintaining an acceptable computation time and also saving designer effort and time.
Design Time Optimization for Hardware Watermarking Protection of HDL Designs
Castillo, E.; Morales, D. P.; García, A.; Parrilla, L.; Todorovich, E.; Meyer-Baese, U.
2015-01-01
HDL-level design offers important advantages for the application of watermarking to IP cores, but its complexity also requires tools automating these watermarking algorithms. A new tool for signature distribution through combinational logic is proposed in this work. IPP@HDL, a previously proposed high-level watermarking technique, has been employed for evaluating the tool. IPP@HDL relies on spreading the bits of a digital signature at the HDL design level using combinational logic included within the original system. The development of this new tool for the signature distribution has not only extended and eased the applicability of this IPP technique, but it has also improved the signature hosting process itself. Three algorithms were studied in order to develop this automated tool. The selection of a cost function determines the best hosting solutions in terms of area and performance penalties on the IP core to protect. An 1D-DWT core and MD5 and SHA1 digital signatures were used in order to illustrate the benefits of the new tool and its optimization related to the extraction logic resources. Among the proposed algorithms, the alternative based on simulated annealing reduces the additional resources while maintaining an acceptable computation time and also saving designer effort and time. PMID:25861681
Microgravity isolation system design: A modern control synthesis framework
NASA Technical Reports Server (NTRS)
Hampton, R. D.; Knospe, C. R.; Allaire, P. E.; Grodsinsky, C. M.
1994-01-01
Manned orbiters will require active vibration isolation for acceleration-sensitive microgravity science experiments. Since umbilicals are highly desirable or even indispensable for many experiments, and since their presence greatly affects the complexity of the isolation problem, they should be considered in control synthesis. A general framework is presented for applying extended H2 synthesis methods to the three-dimensional microgravity isolation problem. The methodology integrates control and state frequency weighting and input and output disturbance accommodation techniques into the basic H2 synthesis approach. The various system models needed for design and analysis are also presented. The paper concludes with a discussion of a general design philosophy for the microgravity vibration isolation problem.
Microgravity isolation system design: A modern control synthesis framework
NASA Technical Reports Server (NTRS)
Hampton, R. D.; Knospe, C. R.; Allaire, P. E.; Grodsinsky, C. M.
1994-01-01
Manned orbiters will require active vibration isolation for acceleration-sensitive microgravity science experiments. Since umbilicals are highly desirable or even indispensable for many experiments, and since their presence greatly affects the complexity of the isolation problem, they should be considered in control synthesis. In this paper a general framework is presented for applying extended H2 synthesis methods to the three-dimensional microgravity isolation problem. The methodology integrates control and state frequency weighting and input and output disturbance accommodation techniques into the basic H2 synthesis approach. The various system models needed for design and analysis are also presented. The paper concludes with a discussion of a general design philosophy for the microgravity vibration isolation problem.
Microgravity isolation system design: A modern control analysis framework
NASA Technical Reports Server (NTRS)
Hampton, R. D.; Knospe, C. R.; Allaire, P. E.; Grodsinsky, C. M.
1994-01-01
Many acceleration-sensitive, microgravity science experiments will require active vibration isolation from the manned orbiters on which they will be mounted. The isolation problem, especially in the case of a tethered payload, is a complex three-dimensional one that is best suited to modern-control design methods. These methods, although more powerful than their classical counterparts, can nonetheless go only so far in meeting the design requirements for practical systems. Once a tentative controller design is available, it must still be evaluated to determine whether or not it is fully acceptable, and to compare it with other possible design candidates. Realistically, such evaluation will be an inherent part of a necessary iterative design process. In this paper, an approach is presented for applying complex mu-analysis methods to a closed-loop vibration isolation system (experiment plus controller). An analysis framework is presented for evaluating nominal stability, nominal performance, robust stability, and robust performance of active microgravity isolation systems, with emphasis on the effective use of mu-analysis methods.
An Integrated Framework Advancing Membrane Protein Modeling and Design
Weitzner, Brian D.; Duran, Amanda M.; Tilley, Drew C.; Elazar, Assaf; Gray, Jeffrey J.
2015-01-01
Membrane proteins are critical functional molecules in the human body, constituting more than 30% of open reading frames in the human genome. Unfortunately, a myriad of difficulties in overexpression and reconstitution into membrane mimetics severely limit our ability to determine their structures. Computational tools are therefore instrumental to membrane protein structure prediction, consequently increasing our understanding of membrane protein function and their role in disease. Here, we describe a general framework facilitating membrane protein modeling and design that combines the scientific principles for membrane protein modeling with the flexible software architecture of Rosetta3. This new framework, called RosettaMP, provides a general membrane representation that interfaces with scoring, conformational sampling, and mutation routines that can be easily combined to create new protocols. To demonstrate the capabilities of this implementation, we developed four proof-of-concept applications for (1) prediction of free energy changes upon mutation; (2) high-resolution structural refinement; (3) protein-protein docking; and (4) assembly of symmetric protein complexes, all in the membrane environment. Preliminary data show that these algorithms can produce meaningful scores and structures. The data also suggest needed improvements to both sampling routines and score functions. Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design. PMID:26325167
An Integrated Framework Advancing Membrane Protein Modeling and Design.
Alford, Rebecca F; Koehler Leman, Julia; Weitzner, Brian D; Duran, Amanda M; Tilley, Drew C; Elazar, Assaf; Gray, Jeffrey J
2015-09-01
Membrane proteins are critical functional molecules in the human body, constituting more than 30% of open reading frames in the human genome. Unfortunately, a myriad of difficulties in overexpression and reconstitution into membrane mimetics severely limit our ability to determine their structures. Computational tools are therefore instrumental to membrane protein structure prediction, consequently increasing our understanding of membrane protein function and their role in disease. Here, we describe a general framework facilitating membrane protein modeling and design that combines the scientific principles for membrane protein modeling with the flexible software architecture of Rosetta3. This new framework, called RosettaMP, provides a general membrane representation that interfaces with scoring, conformational sampling, and mutation routines that can be easily combined to create new protocols. To demonstrate the capabilities of this implementation, we developed four proof-of-concept applications for (1) prediction of free energy changes upon mutation; (2) high-resolution structural refinement; (3) protein-protein docking; and (4) assembly of symmetric protein complexes, all in the membrane environment. Preliminary data show that these algorithms can produce meaningful scores and structures. The data also suggest needed improvements to both sampling routines and score functions. Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design. PMID:26325167
Assay optimization: a statistical design of experiments approach.
Altekar, Maneesha; Homon, Carol A; Kashem, Mohammed A; Mason, Steven W; Nelson, Richard M; Patnaude, Lori A; Yingling, Jeffrey; Taylor, Paul B
2007-03-01
With the transition from manual to robotic HTS in the last several years, assay optimization has become a significant bottleneck. Recent advances in robotic liquid handling have made it feasible to reduce assay optimization timelines with the application of statistically designed experiments. When implemented, they can efficiently optimize assays by rapidly identifying significant factors, complex interactions, and nonlinear responses. This article focuses on the use of statistically designed experiments in assay optimization.
Aerostructural analysis and design optimization of composite aircraft
NASA Astrophysics Data System (ADS)
Kennedy, Graeme James
High-performance composite materials exhibit both anisotropic strength and stiffness properties. These anisotropic properties can be used to produce highly-tailored aircraft structures that meet stringent performance requirements, but these properties also present unique challenges for analysis and design. New tools and techniques are developed to address some of these important challenges. A homogenization-based theory for beams is developed to accurately predict the through-thickness stress and strain distribution in thick composite beams. Numerical comparisons demonstrate that the proposed beam theory can be used to obtain highly accurate results in up to three orders of magnitude less computational time than three-dimensional calculations. Due to the large finite-element model requirements for thin composite structures used in aerospace applications, parallel solution methods are explored. A parallel direct Schur factorization method is developed. The parallel scalability of the direct Schur approach is demonstrated for a large finite-element problem with over 5 million unknowns. In order to address manufacturing design requirements, a novel laminate parametrization technique is presented that takes into account the discrete nature of the ply-angle variables, and ply-contiguity constraints. This parametrization technique is demonstrated on a series of structural optimization problems including compliance minimization of a plate, buckling design of a stiffened panel and layup design of a full aircraft wing. The design and analysis of composite structures for aircraft is not a stand-alone problem and cannot be performed without multidisciplinary considerations. A gradient-based aerostructural design optimization framework is presented that partitions the disciplines into distinct process groups. An approximate Newton-Krylov method is shown to be an efficient aerostructural solution algorithm and excellent parallel scalability of the algorithm is demonstrated. An
Integrated topology and shape optimization in structural design
NASA Technical Reports Server (NTRS)
Bremicker, M.; Chirehdast, M.; Kikuchi, N.; Papalambros, P. Y.
1990-01-01
Structural optimization procedures usually start from a given design topology and vary its proportions or boundary shapes to achieve optimality under various constraints. Two different categories of structural optimization are distinguished in the literature, namely sizing and shape optimization. A major restriction in both cases is that the design topology is considered fixed and given. Questions concerning the general layout of a design (such as whether a truss or a solid structure should be used) as well as more detailed topology features (e.g., the number and connectivities of bars in a truss or the number of holes in a solid) have to be resolved by design experience before formulating the structural optimization model. Design quality of an optimized structure still depends strongly on engineering intuition. This article presents a novel approach for initiating formal structural optimization at an earlier stage, where the design topology is rigorously generated in addition to selecting shape and size dimensions. A three-phase design process is discussed: an optimal initial topology is created by a homogenization method as a gray level image, which is then transformed to a realizable design using computer vision techniques; this design is then parameterized and treated in detail by sizing and shape optimization. A fully automated process is described for trusses. Optimization of two dimensional solid structures is also discussed. Several application-oriented examples illustrate the usefulness of the proposed methodology.
Communication Optimizations for a Wireless Distributed Prognostic Framework
NASA Technical Reports Server (NTRS)
Saha, Sankalita; Saha, Bhaskar; Goebel, Kai
2009-01-01
Distributed architecture for prognostics is an essential step in prognostic research in order to enable feasible real-time system health management. Communication overhead is an important design problem for such systems. In this paper we focus on communication issues faced in the distributed implementation of an important class of algorithms for prognostics - particle filters. In spite of being computation and memory intensive, particle filters lend well to distributed implementation except for one significant step - resampling. We propose new resampling scheme called parameterized resampling that attempts to reduce communication between collaborating nodes in a distributed wireless sensor network. Analysis and comparison with relevant resampling schemes is also presented. A battery health management system is used as a target application. A new resampling scheme for distributed implementation of particle filters has been discussed in this paper. Analysis and comparison of this new scheme with existing resampling schemes in the context for minimizing communication overhead have also been discussed. Our proposed new resampling scheme performs significantly better compared to other schemes by attempting to reduce both the communication message length as well as number total communication messages exchanged while not compromising prediction accuracy and precision. Future work will explore the effects of the new resampling scheme in the overall computational performance of the whole system as well as full implementation of the new schemes on the Sun SPOT devices. Exploring different network architectures for efficient communication is an importance future research direction as well.
Computer aided optimal design of space reflectors and radiation concentrators
NASA Astrophysics Data System (ADS)
Saprykin, Oleg A.; Spirochkin, Yuriy K.; Kinelev, Vladimir G.; Sulimov, Valeriy D.
1998-06-01
The goal of space radiation receiver design is achievement of its maximal reflecting properties under some technological and financial restrictions. Optimal design problems of this type are characterized by nonconvex nondifferentiable objective functions. A numerical technique for optimal design of the structures and an applied software REFLEX under development are proposed.
NASA Astrophysics Data System (ADS)
Wang, Bo; Tian, Kuo; Zhao, Haixin; Hao, Peng; Zhu, Tianyu; Zhang, Ke; Ma, Yunlong
2016-09-01
In order to improve the post-buckling optimization efficiency of hierarchical stiffened shells, a multilevel optimization framework accelerated by adaptive equivalent strategy is presented in this paper. Firstly, the Numerical-based Smeared Stiffener Method (NSSM) for hierarchical stiffened shells is derived by means of the numerical implementation of asymptotic homogenization (NIAH) method. Based on the NSSM, a reasonable adaptive equivalent strategy for hierarchical stiffened shells is developed from the concept of hierarchy reduction. Its core idea is to self-adaptively decide which hierarchy of the structure should be equivalent according to the critical buckling mode rapidly predicted by NSSM. Compared with the detailed model, the high prediction accuracy and efficiency of the proposed model is highlighted. On the basis of this adaptive equivalent model, a multilevel optimization framework is then established by decomposing the complex entire optimization process into major-stiffener-level and minor-stiffener-level sub-optimizations, during which Fixed Point Iteration (FPI) is employed to accelerate convergence. Finally, the illustrative examples of the multilevel framework is carried out to demonstrate its efficiency and effectiveness to search for the global optimum result by contrast with the single-level optimization method. Remarkably, the high efficiency and flexibility of the adaptive equivalent strategy is indicated by compared with the single equivalent strategy.
Optimizing Adhesive Design by Understanding Compliance.
King, Daniel R; Crosby, Alfred J
2015-12-23
Adhesives have long been designed around a trade-off between adhesive strength and releasability. Geckos are of interest because they are the largest organisms which are able to climb utilizing adhesive toepads, yet can controllably release from surfaces and perform this action over and over again. Attempting to replicate the hierarchical, nanoscopic features which cover their toepads has been the primary focus of the adhesives field until recently. A new approach based on a scaling relation which states that reversible adhesive force capacity scales with (A/C)(1/2), where A is the area of contact and C is the compliance of the adhesive, has enabled the creation of high strength, reversible adhesives without requiring high aspect ratio, fibrillar features. Here we introduce an equation to calculate the compliance of adhesives, and utilize this equation to predict the shear adhesive force capacity of the adhesive based on the material components and geometric properties. Using this equation, we have investigated important geometric parameters which control force capacity and have shown that by controlling adhesive shape, adhesive force capacity can be increased by over 50% without varying pad size. Furthermore, we have demonstrated that compliance of the adhesive far from the interface still influences shear adhesive force capacity. Utilizing this equation will allow for the production of adhesives which are optimized for specific applications in commercial and industrial settings. PMID:26618537
Optimizing Adhesive Design by Understanding Compliance.
King, Daniel R; Crosby, Alfred J
2015-12-23
Adhesives have long been designed around a trade-off between adhesive strength and releasability. Geckos are of interest because they are the largest organisms which are able to climb utilizing adhesive toepads, yet can controllably release from surfaces and perform this action over and over again. Attempting to replicate the hierarchical, nanoscopic features which cover their toepads has been the primary focus of the adhesives field until recently. A new approach based on a scaling relation which states that reversible adhesive force capacity scales with (A/C)(1/2), where A is the area of contact and C is the compliance of the adhesive, has enabled the creation of high strength, reversible adhesives without requiring high aspect ratio, fibrillar features. Here we introduce an equation to calculate the compliance of adhesives, and utilize this equation to predict the shear adhesive force capacity of the adhesive based on the material components and geometric properties. Using this equation, we have investigated important geometric parameters which control force capacity and have shown that by controlling adhesive shape, adhesive force capacity can be increased by over 50% without varying pad size. Furthermore, we have demonstrated that compliance of the adhesive far from the interface still influences shear adhesive force capacity. Utilizing this equation will allow for the production of adhesives which are optimized for specific applications in commercial and industrial settings.
A robust optimization methodology for preliminary aircraft design
NASA Astrophysics Data System (ADS)
Prigent, S.; Maréchal, P.; Rondepierre, A.; Druot, T.; Belleville, M.
2016-05-01
This article focuses on a robust optimization of an aircraft preliminary design under operational constraints. According to engineers' know-how, the aircraft preliminary design problem can be modelled as an uncertain optimization problem whose objective (the cost or the fuel consumption) is almost affine, and whose constraints are convex. It is shown that this uncertain optimization problem can be approximated in a conservative manner by an uncertain linear optimization program, which enables the use of the techniques of robust linear programming of Ben-Tal, El Ghaoui, and Nemirovski [Robust Optimization, Princeton University Press, 2009]. This methodology is then applied to two real cases of aircraft design and numerical results are presented.
Simultaneous optimal experimental design for in vitro binding parameter estimation.
Ernest, C Steven; Karlsson, Mats O; Hooker, Andrew C
2013-10-01
Simultaneous optimization of in vitro ligand binding studies using an optimal design software package that can incorporate multiple design variables through non-linear mixed effect models and provide a general optimized design regardless of the binding site capacity and relative binding rates for a two binding system. Experimental design optimization was employed with D- and ED-optimality using PopED 2.8 including commonly encountered factors during experimentation (residual error, between experiment variability and non-specific binding) for in vitro ligand binding experiments: association, dissociation, equilibrium and non-specific binding experiments. Moreover, a method for optimizing several design parameters (ligand concentrations, measurement times and total number of samples) was examined. With changes in relative binding site density and relative binding rates, different measurement times and ligand concentrations were needed to provide precise estimation of binding parameters. However, using optimized design variables, significant reductions in number of samples provided as good or better precision of the parameter estimates compared to the original extensive sampling design. Employing ED-optimality led to a general experimental design regardless of the relative binding site density and relative binding rates. Precision of the parameter estimates were as good as the extensive sampling design for most parameters and better for the poorly estimated parameters. Optimized designs for in vitro ligand binding studies provided robust parameter estimation while allowing more efficient and cost effective experimentation by reducing the measurement times and separate ligand concentrations required and in some cases, the total number of samples. PMID:23943088
Integrated Layout and Support Structure Optimization for Offshore Wind Farm Design
NASA Astrophysics Data System (ADS)
Ashuri, T.; Ponnurangam, C.; Zhang, J.; Rotea, M.
2016-09-01
This paper develops a multidisciplinary design optimization framework for integrated design optimization of offshore wind farm layout and support structure. A computational model is developed to characterize the physics of the wind farm wake, aerodynamic and hydrodynamic loads, response of the support structure to these loads, soil- structure interaction, as well as different cost elements. Levelized cost of energy is introduced as the objective function. The design constraints are the farm external boundary, and support structure buckling, first modal-frequency, fatigue damage and ultimate stresses. To evaluate the effectiveness of the proposed approach, four optimization scenarios are considered: a feasible baseline design, optimization of layout only, optimization of support structure only, and integrated design of the layout and support structure. Compared to the baseline design, the optimization results show that the isolated support structure design reduces the levelized cost of energy by 0.6%, the isolated layout design reduces the levelized cost of energy by 2.0%, and the integrated layout and support structure design reduces the levelized cost of energy by 2.6%.
NASA Astrophysics Data System (ADS)
Potters, M. G.; Bombois, X.; Mansoori, M.; Van den Hof, Paul M. J.
2016-08-01
Estimation of physical parameters in dynamical systems driven by linear partial differential equations is an important problem. In this paper, we introduce the least costly experiment design framework for these systems. It enables parameter estimation with an accuracy that is specified by the experimenter prior to the identification experiment, while at the same time minimising the cost of the experiment. We show how to adapt the classical framework for these systems and take into account scaling and stability issues. We also introduce a progressive subdivision algorithm that further generalises the experiment design framework in the sense that it returns the lowest cost by finding the optimal input signal, and optimal sensor and actuator locations. Our methodology is then applied to a relevant problem in heat transfer studies: estimation of conductivity and diffusivity parameters in front-face experiments. We find good correspondence between numerical and theoretical results.
A new interval optimization method considering tolerance design
NASA Astrophysics Data System (ADS)
Jiang, C.; Xie, H. C.; Zhang, Z. G.; Han, X.
2015-12-01
This study considers the design variable uncertainty in the actual manufacturing process for a product or structure and proposes a new interval optimization method based on tolerance design, which can provide not only an optimal design but also the allowable maximal manufacturing errors that the design can bear. The design variables' manufacturing errors are depicted using the interval method, and an interval optimization model for the structure is constructed. A dimensionless design tolerance index is defined to describe the overall uncertainty of all design variables, and by combining the nominal objective function, a deterministic two-objective optimization model is built. The possibility degree of interval is used to represent the reliability of the constraints under uncertainty, through which the model is transformed to a deterministic optimization problem. Three numerical examples are investigated to verify the effectiveness of the present method.
Mugdh, Mrinal; Pilla, Satya
2012-01-01
Health care providers in the United States are constantly faced with the enormous challenge of optimizing their revenue cycle to improve their overall financial performance. These challenges keep evolving in both scope and complexity owing to a host of internal and external factors. Furthermore, given the lack of control that health care providers have over external factors, any attempt to successfully optimize the revenue cycle hinges on several critical improvements aimed at realigning the internal factors. This study provides an integrated change management model that aims to reengineer and realign the people-process-technology framework by using the principles of lean and Six Sigma for revenue cycle optimization.
Optimal design and uncertainty quantification in blood flow simulations for congenital heart disease
NASA Astrophysics Data System (ADS)
Marsden, Alison
2009-11-01
Recent work has demonstrated substantial progress in capabilities for patient-specific cardiovascular flow simulations. Recent advances include increasingly complex geometries, physiological flow conditions, and fluid structure interaction. However inputs to these simulations, including medical image data, catheter-derived pressures and material properties, can have significant uncertainties associated with them. For simulations to predict clinically useful and reliable output information, it is necessary to quantify the effects of input uncertainties on outputs of interest. In addition, blood flow simulation tools can now be efficiently coupled to shape optimization algorithms for surgery design applications, and these tools should incorporate uncertainty information. We present a unified framework to systematically and efficient account for uncertainties in simulations using adaptive stochastic collocation. In addition, we present a framework for derivative-free optimization of cardiovascular geometries, and layer these tools to perform optimization under uncertainty. These methods are demonstrated using simulations and surgery optimization to improve hemodynamics in pediatric cardiology applications.
Neural network optimization, components, and design selection
NASA Astrophysics Data System (ADS)
Weller, Scott W.
1990-07-01
Neural Networks are part of a revived technology which has received a lot of hype in recent years. As is apt to happen in any hyped technology, jargon and predictions make its assimilation and application difficult. Nevertheless, Neural Networks have found use in a number of areas, working on non-trivial and noncontrived problems. For example, one net has been trained to "read", translating English text into phoneme sequences. Other applications of Neural Networks include data base manipulation and the solving of muting and classification types of optimization problems. Neural Networks are constructed from neurons, which in electronics or software attempt to model but are not constrained by the real thing, i.e., neurons in our gray matter. Neurons are simple processing units connected to many other neurons over pathways which modify the incoming signals. A single synthetic neuron typically sums its weighted inputs, runs this sum through a non-linear function, and produces an output. In the brain, neurons are connected in a complex topology: in hardware/software the topology is typically much simpler, with neurons lying side by side, forming layers of neurons which connect to the layer of neurons which receive their outputs. This simplistic model is much easier to construct than the real thing, and yet can solve real problems. The information in a network, or its "memory", is completely contained in the weights on the connections from one neuron to another. Establishing these weights is called "training" the network. Some networks are trained by design -- once constructed no further learning takes place. Other types of networks require iterative training once wired up, but are not trainable once taught Still other types of networks can continue to learn after initial construction. The main benefit to using Neural Networks is their ability to work with conflicting or incomplete ("fuzzy") data sets. This ability and its usefulness will become evident in the following
INSTITUTIONALIZING SAFEGUARDS-BY-DESIGN: HIGH-LEVEL FRAMEWORK
Trond Bjornard PhD; Joseph Alexander; Robert Bean; Brian Castle; Scott DeMuth, Ph.D.; Phillip Durst; Michael Ehinger; Prof. Michael Golay, Ph.D.; Kevin Hase, Ph.D.; David J. Hebditch, DPhil; John Hockert, Ph.D.; Bruce Meppen; James Morgan; Jerry Phillips, Ph.D., PE
2009-02-01
participation in facility design options analysis in the conceptual design phase to enhance intrinsic features, among others. The SBD process is unlikely to be broadly applied in the absence of formal requirements to do so, or compelling evidence of its value. Neither exists today. A formal instrument to require the application of SBD is needed and would vary according to both the national and regulatory environment. Several possible approaches to implementation of the requirements within the DOE framework are explored in this report. Finally, there are numerous barriers to the implementation of SBD, including the lack of a strong safeguards culture, intellectual property concerns, the sensitive nature of safeguards information, and the potentially divergent or conflicting interests of participants in the process. In terms of SBD implementation in the United States, there are no commercial nuclear facilities that are under IAEA safeguards. Efforts to institutionalize SBD must address these issues. Specific work in FY09 could focus on the following: finalizing the proposed SBD process for use by DOE and performing a pilot application on a DOE project in the planning phase; developing regulatory options for mandating SBD; further development of safeguards-related design guidance, principles and requirements; development of a specific SBD process tailored to the NRC environment; and development of an engagement strategy for the IAEA and other international partners.
Optimal Management and Design of Energy Systems under Atmospheric Uncertainty
NASA Astrophysics Data System (ADS)
Anitescu, M.; Constantinescu, E. M.; Zavala, V.
2010-12-01
optimal management and design of energy systems, such as the power grid or building systems, under atmospheric conditions uncertainty. The framework is defined in terms of a mathematical paradigm called stochastic programming: minimization of the expected value of the decision-makers objective function subject to physical and operational constraints, such as low blackout porbability, that are enforced on each scenario. We report results on testing the framework on the optimal management of power grid systems under high wind penetration scenarios, a problem whose time horizon is in the order of days. We discuss the computational effort of scenario generation which involves running WRF at high spatio-temporal resolution dictated by the operational constraints as well as solving the optimal dispatch problem. We demonstrate that accounting for uncertainty in atmospheric conditions results in blackout prevention, whereas decisions using only mean forecast does not. We discuss issues in using the framework for planning problems, whose time horizon is of several decades and what requirements this problem would entail from climate simulation systems.
NASA Astrophysics Data System (ADS)
Irfan, Muhammad; Bilal Khurshid, Muhammad; Bai, Qiang; Labi, Samuel; Morin, Thomas L.
2012-05-01
This article presents a framework and an illustrative example for identifying the optimal pavement maintenance and rehabilitation (M&R) strategy using a mixed-integer nonlinear programming model. The objective function is to maximize the cost-effectiveness expressed as the ratio of the effectiveness to the cost. The constraints for the optimization problem are related to performance, budget, and choice. Two different formulations of effectiveness are derived using treatment-specific performance models for each constituent treatment of the strategy; and cost is expressed in terms of the agency and user costs over the life cycle. The proposed methodology is demonstrated using a case study. Probability distributions are established for the optimization input variables and Monte Carlo simulations are carried out to yield optimal solutions. Using the results of these simulations, M&R strategy contours are developed as a novel tool that can help pavement managers quickly identify the optimal M&R strategy for a given pavement section.
Designing Metal-Organic Frameworks for Catalytic Applications
NASA Astrophysics Data System (ADS)
Ma, Liqing; Lin, Wenbin
Metal-organic frameworks (MOFs) are constructed by linking organic bridging ligands with metal-connecting points to form infinite network structures. Fine tuning the porosities of and functionalities within MOFs through judicious choices of bridging ligands and metal centers has allowed their use as efficient heterogeneous catalysts. This chapter reviews recent developments in designing porous MOFs for a variety of catalytic reactions. Following a brief introduction to MOFs and a comparison between porous MOFs and zeolites, we categorize catalytically active achiral MOFs based on the types of catalytic sites and organic transformations. The unsaturated metal-connecting points in MOFs can act as catalytic sites, so can the functional groups that are built into the framework of a porous MOF. Noble metal nanoparticles can also be entrapped inside porous MOFs for catalytic reactions. Furthermore, the channels of porous MOFs have been used as reaction hosts for photochemical and polymerization reactions. We also summarize the latest results of heterogeneous asymmetric catalysis using homochiral MOFs. Three distinct strategies have been utilized to develop homochiral MOFs for catalyzing enantioselective reactions, namely the synthesis of homochiral MOFs from achiral building blocks by seeding or by statistically manipulating the crystal growth, directing achiral ligands to form homochiral MOFs in chiral environments, and incorporating chiral linker ligands with functionalized groups. The applications of homochiral MOFs in several heterogeneous asymmetric catalytic reactions are also discussed. The ability to synthesize value-added chiral molecules using homochiral MOF catalysts differentiates them from traditional zeolite catalysis, and we believe that in the future many more homochiral MOFs will be designed for catalyzing numerous asymmetric organic transformations.
INNOVATIVE METHODS FOR THE OPTIMIZATION OF GRAVITY STORM SEWER DESIGN
The purpose of this paper is to describe a new method for optimizing the design of urban storm sewer systems. Previous efforts to optimize gravity sewers have met with limited success because classical optimization methods require that the problem be well behaved, e.g. describ...
A multiple objective optimization approach to aircraft control systems design
NASA Technical Reports Server (NTRS)
Tabak, D.; Schy, A. A.; Johnson, K. G.; Giesy, D. P.
1979-01-01
The design of an aircraft lateral control system, subject to several performance criteria and constraints, is considered. While in the previous studies of the same model a single criterion optimization, with other performance requirements expressed as constraints, has been pursued, the current approach involves a multiple criteria optimization. In particular, a Pareto optimal solution is sought.
Post-Optimality Analysis In Aerospace Vehicle Design
NASA Technical Reports Server (NTRS)
Braun, Robert D.; Kroo, Ilan M.; Gage, Peter J.
1993-01-01
This analysis pertains to the applicability of optimal sensitivity information to aerospace vehicle design. An optimal sensitivity (or post-optimality) analysis refers to computations performed once the initial optimization problem is solved. These computations may be used to characterize the design space about the present solution and infer changes in this solution as a result of constraint or parameter variations, without reoptimizing the entire system. The present analysis demonstrates that post-optimality information generated through first-order computations can be used to accurately predict the effect of constraint and parameter perturbations on the optimal solution. This assessment is based on the solution of an aircraft design problem in which the post-optimality estimates are shown to be within a few percent of the true solution over the practical range of constraint and parameter variations. Through solution of a reusable, single-stage-to-orbit, launch vehicle design problem, this optimal sensitivity information is also shown to improve the efficiency of the design process, For a hierarchically decomposed problem, this computational efficiency is realized by estimating the main-problem objective gradient through optimal sep&ivity calculations, By reducing the need for finite differentiation of a re-optimized subproblem, a significant decrease in the number of objective function evaluations required to reach the optimal solution is obtained.
Optimization of LiMn2O4 electrode properties in a gradient- and surrogate-based framework
NASA Astrophysics Data System (ADS)
Du, Wenbo; Xue, Nansi; Gupta, Amit; Sastry, Ann M.; Martins, Joaquim R. R. A.; Shyy, Wei
2013-06-01
In this study, the effects of discharge rate and LiMn2O4 cathode properties (thickness, porosity, particle size, and solid-state diffusivity and conductivity) on the gravimetric energy and power density of a lithium-ion battery cell are analyzed simultaneously using a cell-level model. Surrogate-based analysis tools are applied to simulation data to construct educed-order models, which are in turn used to perform global sensitivity analysis to compare the relative importance of cathode properties. Based on these results, the cell is then optimized for several distinct physical scenarios using gradient-based methods. The complementary nature of the gradient- and surrogate-based tools is demonstrated by establishing proper bounds and constraints with the surrogate model, and then obtaining accurate optimized solutions with the gradient-based optimizer. These optimal solutions enable the quantification of the tradeoffs between energy and power density, and the effect of optimizing the electrode thickness and porosity. In conjunction with known guidelines, the numerical optimization framework developed herein can be applied directly to cell and pack design.
A design optimization process for Space Station Freedom
NASA Technical Reports Server (NTRS)
Chamberlain, Robert G.; Fox, George; Duquette, William H.
1990-01-01
The Space Station Freedom Program is used to develop and implement a process for design optimization. Because the relative worth of arbitrary design concepts cannot be assessed directly, comparisons must be based on designs that provide the same performance from the point of view of station users; such designs can be compared in terms of life cycle cost. Since the technology required to produce a space station is widely dispersed, a decentralized optimization process is essential. A formulation of the optimization process is provided and the mathematical models designed to facilitate its implementation are described.
NASA Astrophysics Data System (ADS)
Gavrishchaka, Valeriy V.; Kovbasinskaya, Maria; Monina, Maria
2008-11-01
Novelty detection is a very desirable additional feature of any practical classification or forecasting system. Novelty and rare patterns detection is the main objective in such applications as fault/abnormality discovery in complex technical and biological systems, fraud detection and risk management in financial and insurance industry. Although many interdisciplinary approaches for rare event modeling and novelty detection have been proposed, significant data incompleteness due to the nature of the problem makes it difficult to find a universal solution. Even more challenging and much less formalized problem is novelty detection in complex strategies and models where practical performance criteria are usually multi-objective and the best state-of-the-art solution is often not known due to the complexity of the task and/or proprietary nature of the application area. For example, it is much more difficult to detect a series of small insider trading or other illegal transactions mixed with valid operations and distributed over long time period according to a well-designed strategy than a single, large fraudulent transaction. Recently proposed boosting-based optimization was shown to be an effective generic tool for the discovery of stable multi-component strategies/models from the existing parsimonious base strategies/models in financial and other applications. Here we outline how the same framework can be used for novelty and fraud detection in complex strategies and models.
Framework for Implementing Engineering Senior Design Capstone Courses and Design Clinics
ERIC Educational Resources Information Center
Franchetti, Matthew; Hefzy, Mohamed Samir; Pourazady, Mehdi; Smallman, Christine
2012-01-01
Senior design capstone projects for engineering students are essential components of an undergraduate program that enhances communication, teamwork, and problem solving skills. Capstone projects with industry are well established in management, but not as heavily utilized in engineering. This paper outlines a general framework that can be used by…
Architectural Design and the Learning Environment: A Framework for School Design Research
ERIC Educational Resources Information Center
Gislason, Neil
2010-01-01
This article develops a theoretical framework for studying how instructional space, teaching and learning are related in practice. It is argued that a school's physical design can contribute to the quality of the learning environment, but several non-architectural factors also determine how well a given facility serves as a setting for teaching…
A Matrix-Free Algorithm for Multidisciplinary Design Optimization
NASA Astrophysics Data System (ADS)
Lambe, Andrew Borean
Multidisciplinary design optimization (MDO) is an approach to engineering design that exploits the coupling between components or knowledge disciplines in a complex system to improve the final product. In aircraft design, MDO methods can be used to simultaneously design the outer shape of the aircraft and the internal structure, taking into account the complex interaction between the aerodynamic forces and the structural flexibility. Efficient strategies are needed to solve such design optimization problems and guarantee convergence to an optimal design. This work begins with a comprehensive review of MDO problem formulations and solution algorithms. First, a fundamental MDO problem formulation is defined from which other formulations may be obtained through simple transformations. Using these fundamental problem formulations, decomposition methods from the literature are reviewed and classified. All MDO methods are presented in a unified mathematical notation to facilitate greater understanding. In addition, a novel set of diagrams, called extended design structure matrices, are used to simultaneously visualize both data communication and process flow between the many software components of each method. For aerostructural design optimization, modern decomposition-based MDO methods cannot efficiently handle the tight coupling between the aerodynamic and structural states. This fact motivates the exploration of methods that can reduce the computational cost. A particular structure in the direct and adjoint methods for gradient computation motivates the idea of a matrix-free optimization method. A simple matrix-free optimizer is developed based on the augmented Lagrangian algorithm. This new matrix-free optimizer is tested on two structural optimization problems and one aerostructural optimization problem. The results indicate that the matrix-free optimizer is able to efficiently solve structural and multidisciplinary design problems with thousands of variables and
A Matrix-Free Algorithm for Multidisciplinary Design Optimization
NASA Astrophysics Data System (ADS)
Lambe, Andrew Borean
Multidisciplinary design optimization (MDO) is an approach to engineering design that exploits the coupling between components or knowledge disciplines in a complex system to improve the final product. In aircraft design, MDO methods can be used to simultaneously design the outer shape of the aircraft and the internal structure, taking into account the complex interaction between the aerodynamic forces and the structural flexibility. Efficient strategies are needed to solve such design optimization problems and guarantee convergence to an optimal design. This work begins with a comprehensive review of MDO problem formulations and solution algorithms. First, a fundamental MDO problem formulation is defined from which other formulations may be obtained through simple transformations. Using these fundamental problem formulations, decomposition methods from the literature are reviewed and classified. All MDO methods are presented in a unified mathematical notation to facilitate greater understanding. In addition, a novel set of diagrams, called extended design structure matrices, are used to simultaneously visualize both data communication and process flow between the many software components of each method. For aerostructural design optimization, modern decomposition-based MDO methods cannot efficiently handle the tight coupling between the aerodynamic and structural states. This fact motivates the exploration of methods that can reduce the computational cost. A particular structure in the direct and adjoint methods for gradient computation. motivates the idea of a matrix-free optimization method. A simple matrix-free optimizer is developed based on the augmented Lagrangian algorithm. This new matrix-free optimizer is tested on two structural optimization problems and one aerostructural optimization problem. The results indicate that the matrix-free optimizer is able to efficiently solve structural and multidisciplinary design problems with thousands of variables and
Malikopoulos, Andreas
2015-01-01
The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and we show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion. Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.
Malikopoulos, Andreas
2015-01-01
The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and we show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.more » Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.« less
New Approaches to HSCT Multidisciplinary Design and Optimization
NASA Technical Reports Server (NTRS)
Schrage, Daniel P.; Craig, James I.; Fulton, Robert E.; Mistree, Farrokh
1999-01-01
New approaches to MDO have been developed and demonstrated during this project on a particularly challenging aeronautics problem- HSCT Aeroelastic Wing Design. To tackle this problem required the integration of resources and collaboration from three Georgia Tech laboratories: ASDL, SDL, and PPRL, along with close coordination and participation from industry. Its success can also be contributed to the close interaction and involvement of fellows from the NASA Multidisciplinary Analysis and Optimization (MAO) program, which was going on in parallel, and provided additional resources to work the very complex, multidisciplinary problem, along with the methods being developed. The development of the Integrated Design Engineering Simulator (IDES) and its initial demonstration is a necessary first step in transitioning the methods and tools developed to larger industrial sized problems of interest. It also provides a framework for the implementation and demonstration of the methodology. Attachment: Appendix A - List of publications. Appendix B - Year 1 report. Appendix C - Year 2 report. Appendix D - Year 3 report. Appendix E - accompanying CDROM.
Progress in multidisciplinary design optimization at NASA Langley
NASA Technical Reports Server (NTRS)
Padula, Sharon L.
1993-01-01
Multidisciplinary Design Optimization refers to some combination of disciplinary analyses, sensitivity analysis, and optimization techniques used to design complex engineering systems. The ultimate objective of this research at NASA Langley Research Center is to help the US industry reduce the costs associated with development, manufacturing, and maintenance of aerospace vehicles while improving system performance. This report reviews progress towards this objective and highlights topics for future research. Aerospace design problems selected from the author's research illustrate strengths and weaknesses in existing multidisciplinary optimization techniques. The techniques discussed include multiobjective optimization, global sensitivity equations and sequential linear programming.
Optimal flexible sample size design with robust power.
Zhang, Lanju; Cui, Lu; Yang, Bo
2016-08-30
It is well recognized that sample size determination is challenging because of the uncertainty on the treatment effect size. Several remedies are available in the literature. Group sequential designs start with a sample size based on a conservative (smaller) effect size and allow early stop at interim looks. Sample size re-estimation designs start with a sample size based on an optimistic (larger) effect size and allow sample size increase if the observed effect size is smaller than planned. Different opinions favoring one type over the other exist. We propose an optimal approach using an appropriate optimality criterion to select the best design among all the candidate designs. Our results show that (1) for the same type of designs, for example, group sequential designs, there is room for significant improvement through our optimization approach; (2) optimal promising zone designs appear to have no advantages over optimal group sequential designs; and (3) optimal designs with sample size re-estimation deliver the best adaptive performance. We conclude that to deal with the challenge of sample size determination due to effect size uncertainty, an optimal approach can help to select the best design that provides most robust power across the effect size range of interest. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26999385
A nested partitions framework for beam angle optimization in intensity-modulated radiation therapy.
D'Souza, Warren D; Zhang, Hao H; Nazareth, Daryl P; Shi, Leyuan; Meyer, Robert R
2008-06-21
Coupling beam angle optimization with dose optimization in intensity-modulated radiation therapy (IMRT) increases the size and complexity of an already large-scale combinatorial optimization problem. We have developed a novel algorithm, nested partitions (NP), that is capable of finding suitable beam angle sets by guiding the dose optimization process. NP is a metaheuristic that is flexible enough to guide the search of a heuristic or deterministic dose optimization algorithm. The NP method adaptively samples from the entire feasible region, or search space, and coordinates the sampling effort with a systematic partitioning of the feasible region at successive iterations, concentrating the search in promising subsets. We used a 'warm-start' approach by initiating NP with beam angle samples derived from an integer programming (IP) model. In this study, we describe our implementation of the NP framework with a commercial optimization algorithm. We compared the NP framework with equi-spaced beam angle selection, the IP method, greedy heuristic and random sampling heuristic methods. The results of the NP approach were evaluated using two clinical cases (head and neck and whole pelvis) involving the primary tumor and nodal volumes. Our results show that NP produces better quality solutions than the alternative considered methods. PMID:18523351
A nested partitions framework for beam angle optimization in intensity-modulated radiation therapy
NASA Astrophysics Data System (ADS)
D'Souza, Warren D.; Zhang, Hao H.; Nazareth, Daryl P.; Shi, Leyuan; Meyer, Robert R.
2008-06-01
Coupling beam angle optimization with dose optimization in intensity-modulated radiation therapy (IMRT) increases the size and complexity of an already large-scale combinatorial optimization problem. We have developed a novel algorithm, nested partitions (NP), that is capable of finding suitable beam angle sets by guiding the dose optimization process. NP is a metaheuristic that is flexible enough to guide the search of a heuristic or deterministic dose optimization algorithm. The NP method adaptively samples from the entire feasible region, or search space, and coordinates the sampling effort with a systematic partitioning of the feasible region at successive iterations, concentrating the search in promising subsets. We used a 'warm-start' approach by initiating NP with beam angle samples derived from an integer programming (IP) model. In this study, we describe our implementation of the NP framework with a commercial optimization algorithm. We compared the NP framework with equi-spaced beam angle selection, the IP method, greedy heuristic and random sampling heuristic methods. The results of the NP approach were evaluated using two clinical cases (head and neck and whole pelvis) involving the primary tumor and nodal volumes. Our results show that NP produces better quality solutions than the alternative considered methods.
Topology and boundary shape optimization as an integrated design tool
NASA Technical Reports Server (NTRS)
Bendsoe, Martin Philip; Rodrigues, Helder Carrico
1990-01-01
The optimal topology of a two dimensional linear elastic body can be computed by regarding the body as a domain of the plane with a high density of material. Such an optimal topology can then be used as the basis for a shape optimization method that computes the optimal form of the boundary curves of the body. This results in an efficient and reliable design tool, which can be implemented via common FEM mesh generator and CAD type input-output facilities.
A study of commuter airplane design optimization
NASA Technical Reports Server (NTRS)
Roskam, J.; Wyatt, R. D.; Griswold, D. A.; Hammer, J. L.
1977-01-01
Problems of commuter airplane configuration design were studied to affect a minimization of direct operating costs. Factors considered were the minimization of fuselage drag, methods of wing design, and the estimated drag of an airplane submerged in a propellor slipstream; all design criteria were studied under a set of fixed performance, mission, and stability constraints. Configuration design data were assembled for application by a computerized design methodology program similar to the NASA-Ames General Aviation Synthesis Program.
Control structure interaction/optimized design
NASA Technical Reports Server (NTRS)
Mclaren, Mark; Purvis, Chris
1994-01-01
The objective of this study is to apply the integrated design methodology to the mature GOES-1 spacecraft design, and to assess the possible advantages to be gained using this approach over the conventional sequential design approach used for the current design. In the process, the development of this technology into a tool that can be utilized for future near-term spacecraft designs is emphasized.
Design and Optimization of Composite Gyroscope Momentum Wheel Rings
NASA Technical Reports Server (NTRS)
Bednarcyk, Brett A.; Arnold, Steven M.
2007-01-01
Stress analysis and preliminary design/optimization procedures are presented for gyroscope momentum wheel rings composed of metallic, metal matrix composite, and polymer matrix composite materials. The design of these components involves simultaneously minimizing both true part volume and mass, while maximizing angular momentum. The stress analysis results are combined with an anisotropic failure criterion to formulate a new sizing procedure that provides considerable insight into the design of gyroscope momentum wheel ring components. Results compare the performance of two optimized metallic designs, an optimized SiC/Ti composite design, and an optimized graphite/epoxy composite design. The graphite/epoxy design appears to be far superior to the competitors considered unless a much greater premium is placed on volume efficiency compared to mass efficiency.
An uncertain multidisciplinary design optimization method using interval convex models
NASA Astrophysics Data System (ADS)
Li, Fangyi; Luo, Zhen; Sun, Guangyong; Zhang, Nong
2013-06-01
This article proposes an uncertain multi-objective multidisciplinary design optimization methodology, which employs the interval model to represent the uncertainties of uncertain-but-bounded parameters. The interval number programming method is applied to transform each uncertain objective function into two deterministic objective functions, and a satisfaction degree of intervals is used to convert both the uncertain inequality and equality constraints to deterministic inequality constraints. In doing so, an unconstrained deterministic optimization problem will be constructed in association with the penalty function method. The design will be finally formulated as a nested three-loop optimization, a class of highly challenging problems in the area of engineering design optimization. An advanced hierarchical optimization scheme is developed to solve the proposed optimization problem based on the multidisciplinary feasible strategy, which is a well-studied method able to reduce the dimensions of multidisciplinary design optimization problems by using the design variables as independent optimization variables. In the hierarchical optimization system, the non-dominated sorting genetic algorithm II, sequential quadratic programming method and Gauss-Seidel iterative approach are applied to the outer, middle and inner loops of the optimization problem, respectively. Typical numerical examples are used to demonstrate the effectiveness of the proposed methodology.
NASA Technical Reports Server (NTRS)
Braun, R. D.; Kroo, I. M.
1995-01-01
Collaborative optimization is a design architecture applicable in any multidisciplinary analysis environment but specifically intended for large-scale distributed analysis applications. In this approach, a complex problem is hierarchically de- composed along disciplinary boundaries into a number of subproblems which are brought into multidisciplinary agreement by a system-level coordination process. When applied to problems in a multidisciplinary design environment, this scheme has several advantages over traditional solution strategies. These advantageous features include reducing the amount of information transferred between disciplines, the removal of large iteration-loops, allowing the use of different subspace optimizers among the various analysis groups, an analysis framework which is easily parallelized and can operate on heterogenous equipment, and a structural framework that is well-suited for conventional disciplinary organizations. In this article, the collaborative architecture is developed and its mathematical foundation is presented. An example application is also presented which highlights the potential of this method for use in large-scale design applications.
Design optimization of a magnetorheological brake in powered knee orthosis
NASA Astrophysics Data System (ADS)
Ma, Hao; Liao, Wei-Hsin
2015-04-01
Magneto-rheological (MR) fluids have been utilized in devices like orthoses and prostheses to generate controllable braking torque. In this paper, a flat shape rotary MR brake is designed for powered knee orthosis to provide adjustable resistance. Multiple disk structure with interior inner coil is adopted in the MR brake configuration. In order to increase the maximal magnetic flux, a novel internal structure design with smooth transition surface is proposed. Based on this design, a parameterized model of the MR brake is built for geometrical optimization. Multiple factors are considered in the optimization objective: braking torque, weight, and, particularly, average power consumption. The optimization is then performed with Finite Element Analysis (FEA), and the optimal design is obtained among the Pareto-optimal set considering the trade-offs in design objectives.
Optimal shielding design for minimum materials cost or mass
Woolley, Robert D.
2015-12-02
The mathematical underpinnings of cost optimal radiation shielding designs based on an extension of optimal control theory are presented, a heuristic algorithm to iteratively solve the resulting optimal design equations is suggested, and computational results for a simple test case are discussed. A typical radiation shielding design problem can have infinitely many solutions, all satisfying the problem's specified set of radiation attenuation requirements. Each such design has its own total materials cost. For a design to be optimal, no admissible change in its deployment of shielding materials can result in a lower cost. This applies in particular to very smallmore » changes, which can be restated using the calculus of variations as the Euler-Lagrange equations. Furthermore, the associated Hamiltonian function and application of Pontryagin's theorem lead to conditions for a shield to be optimal.« less
Gearbox design for uncertain load requirements using active robust optimization
NASA Astrophysics Data System (ADS)
Salomon, Shaul; Avigad, Gideon; Purshouse, Robin C.; Fleming, Peter J.
2016-04-01
Design and optimization of gear transmissions have been intensively studied, but surprisingly the robustness of the resulting optimal design to uncertain loads has never been considered. Active Robust (AR) optimization is a methodology to design products that attain robustness to uncertain or changing environmental conditions through adaptation. In this study the AR methodology is utilized to optimize the number of transmissions, as well as their gearing ratios, for an uncertain load demand. The problem is formulated as a bi-objective optimization problem where the objectives are to satisfy the load demand in the most energy efficient manner and to minimize production cost. The results show that this approach can find a set of robust designs, revealing a trade-off between energy efficiency and production cost. This can serve as a useful decision-making tool for the gearbox design process, as well as for other applications.
Optimal shielding design for minimum materials cost or mass
Woolley, Robert D.
2015-12-02
The mathematical underpinnings of cost optimal radiation shielding designs based on an extension of optimal control theory are presented, a heuristic algorithm to iteratively solve the resulting optimal design equations is suggested, and computational results for a simple test case are discussed. A typical radiation shielding design problem can have infinitely many solutions, all satisfying the problem's specified set of radiation attenuation requirements. Each such design has its own total materials cost. For a design to be optimal, no admissible change in its deployment of shielding materials can result in a lower cost. This applies in particular to very small changes, which can be restated using the calculus of variations as the Euler-Lagrange equations. Furthermore, the associated Hamiltonian function and application of Pontryagin's theorem lead to conditions for a shield to be optimal.
Optimal input design for aircraft instrumentation systematic error estimation
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.
1991-01-01
A new technique for designing optimal flight test inputs for accurate estimation of instrumentation systematic errors was developed and demonstrated. A simulation model of the F-18 High Angle of Attack Research Vehicle (HARV) aircraft was used to evaluate the effectiveness of the optimal input compared to input recorded during flight test. Instrumentation systematic error parameter estimates and their standard errors were compared. It was found that the optimal input design improved error parameter estimates and their accuracies for a fixed time input design. Pilot acceptability of the optimal input design was demonstrated using a six degree-of-freedom fixed base piloted simulation of the F-18 HARV. The technique described in this work provides a practical, optimal procedure for designing inputs for data compatibility experiments.
NASA Astrophysics Data System (ADS)
Li, Shuang; Zhu, Yongsheng; Wang, Yukai
2014-02-01
Asteroid deflection techniques are essential in order to protect the Earth from catastrophic impacts by hazardous asteroids. Rapid design and optimization of low-thrust rendezvous/interception trajectories is considered as one of the key technologies to successfully deflect potentially hazardous asteroids. In this paper, we address a general framework for the rapid design and optimization of low-thrust rendezvous/interception trajectories for future asteroid deflection missions. The design and optimization process includes three closely associated steps. Firstly, shape-based approaches and genetic algorithm (GA) are adopted to perform preliminary design, which provides a reasonable initial guess for subsequent accurate optimization. Secondly, Radau pseudospectral method is utilized to transcribe the low-thrust trajectory optimization problem into a discrete nonlinear programming (NLP) problem. Finally, sequential quadratic programming (SQP) is used to efficiently solve the nonlinear programming problem and obtain the optimal low-thrust rendezvous/interception trajectories. The rapid design and optimization algorithms developed in this paper are validated by three simulation cases with different performance indexes and boundary constraints.
Optimal Design of Calibration Signals in Space Borne Gravitational Wave Detectors
NASA Technical Reports Server (NTRS)
Nofrarias, Miquel; Karnesis, Nikolaos; Gibert, Ferran; Armano, Michele; Audley, Heather; Danzmann, Karsten; Diepholz, Ingo; Dolesi, Rita; Ferraioli, Luigi; Thorpe, James I.
2014-01-01
Future space borne gravitational wave detectors will require a precise definition of calibration signals to ensure the achievement of their design sensitivity. The careful design of the test signals plays a key role in the correct understanding and characterization of these instruments. In that sense, methods achieving optimal experiment designs must be considered as complementary to the parameter estimation methods being used to determine the parameters describing the system. The relevance of experiment design is particularly significant for the LISA Pathfinder mission, which will spend most of its operation time performing experiments to characterize key technologies for future space borne gravitational wave observatories. Here we propose a framework to derive the optimal signals in terms of minimum parameter uncertainty to be injected to these instruments during its calibration phase. We compare our results with an alternative numerical algorithm which achieves an optimal input signal by iteratively improving an initial guess. We show agreement of both approaches when applied to the LISA Pathfinder case.
Optimizing spacecraft design - optimization engine development : progress and plans
NASA Technical Reports Server (NTRS)
Cornford, Steven L.; Feather, Martin S.; Dunphy, Julia R; Salcedo, Jose; Menzies, Tim
2003-01-01
At JPL and NASA, a process has been developed to perform life cycle risk management. This process requires users to identify: goals and objectives to be achieved (and their relative priorities), the various risks to achieving those goals and objectives, and options for risk mitigation (prevention, detection ahead of time, and alleviation). Risks are broadly defined to include the risk of failing to design a system with adequate performance, compatibility and robustness in addition to more traditional implementation and operational risks. The options for mitigating these different kinds of risks can include architectural and design choices, technology plans and technology back-up options, test-bed and simulation options, engineering models and hardware/software development techniques and other more traditional risk reduction techniques.
Zarepisheh, Masoud; Uribe-Sanchez, Andres F.; Li, Nan; Jia, Xun; Jiang, Steve B.
2014-04-15
Purpose: To establish a new mathematical framework for radiotherapy treatment optimization with voxel-dependent optimization parameters. Methods: In the treatment plan optimization problem for radiotherapy, a clinically acceptable plan is usually generated by an optimization process with weighting factors or reference doses adjusted for a set of the objective functions associated to the organs. Recent discoveries indicate that adjusting parameters associated with each voxel may lead to better plan quality. However, it is still unclear regarding the mathematical reasons behind it. Furthermore, questions about the objective function selection and parameter adjustment to assure Pareto optimality as well as the relationship between the optimal solutions obtained from the organ-based and voxel-based models remain unanswered. To answer these questions, the authors establish in this work a new mathematical framework equipped with two theorems. Results: The new framework clarifies the different consequences of adjusting organ-dependent and voxel-dependent parameters for the treatment plan optimization of radiation therapy, as well as the impact of using different objective functions on plan qualities and Pareto surfaces. The main discoveries are threefold: (1) While in the organ-based model the selection of the objective function has an impact on the quality of the optimized plans, this is no longer an issue for the voxel-based model since the Pareto surface is independent of the objective function selection and the entire Pareto surface could be generated as long as the objective function satisfies certain mathematical conditions; (2) All Pareto solutions generated by the organ-based model with different objective functions are parts of a unique Pareto surface generated by the voxel-based model with any appropriate objective function; (3) A much larger Pareto surface is explored by adjusting voxel-dependent parameters than by adjusting organ-dependent parameters, possibly
Reusable rocket engine intelligent control system framework design, phase 2
NASA Technical Reports Server (NTRS)
Nemeth, ED; Anderson, Ron; Ols, Joe; Olsasky, Mark
1991-01-01
Elements of an advanced functional framework for reusable rocket engine propulsion system control are presented for the Space Shuttle Main Engine (SSME) demonstration case. Functional elements of the baseline functional framework are defined in detail. The SSME failure modes are evaluated and specific failure modes identified for inclusion in the advanced functional framework diagnostic system. Active control of the SSME start transient is investigated, leading to the identification of a promising approach to mitigating start transient excursions. Key elements of the functional framework are simulated and demonstration cases are provided. Finally, the advanced function framework for control of reusable rocket engines is presented.
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
Formulation of a parametric systems design framework for disaster response planning
NASA Astrophysics Data System (ADS)
Mma, Stephanie Weiya
The occurrence of devastating natural disasters in the past several years have prompted communities, responding organizations, and governments to seek ways to improve disaster preparedness capabilities locally, regionally, nationally, and internationally. A holistic approach to design used in the aerospace and industrial engineering fields enables efficient allocation of resources through applied parametric changes within a particular design to improve performance metrics to selected standards. In this research, this methodology is applied to disaster preparedness, using a community's time to restoration after a disaster as the response metric. A review of the responses from Hurricane Katrina and the 2010 Haiti earthquake, among other prominent disasters, provides observations leading to some current capability benchmarking. A need for holistic assessment and planning exists for communities but the current response planning infrastructure lacks a standardized framework and standardized assessment metrics. Within the humanitarian logistics community, several different metrics exist, enabling quantification and measurement of a particular area's vulnerability. These metrics, combined with design and planning methodologies from related fields, such as engineering product design, military response planning, and business process redesign, provide insight and a framework from which to begin developing a methodology to enable holistic disaster response planning. The developed methodology was applied to the communities of Shelby County, TN and pre-Hurricane-Katrina Orleans Parish, LA. Available literature and reliable media sources provide information about the different values of system parameters within the decomposition of the community aspects and also about relationships among the parameters. The community was modeled as a system dynamics model and was tested in the implementation of two, five, and ten year improvement plans for Preparedness, Response, and Development
Optimizing Experimental Designs: Finding Hidden Treasure.
Technology Transfer Automated Retrieval System (TEKTRAN)
Classical experimental design theory, the predominant treatment in most textbooks, promotes the use of blocking designs for control of spatial variability in field studies and other situations in which there is significant variation among heterogeneity among experimental units. Many blocking design...
Kreitler, Jason; Stoms, David M; Davis, Frank W
2014-01-01
Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management.
Stoms, David M.; Davis, Frank W.
2014-01-01
Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management. PMID:25538868
Kreitler, Jason R.; Stoms, David M.; Davis, Frank W.
2014-01-01
Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management.
Synthetic Gene Design Using Codon Optimization On-Line (COOL).
Yu, Kai; Ang, Kok Siong; Lee, Dong-Yup
2017-01-01
Codon optimization has been widely used for designing native or synthetic genes to enhance their expression in heterologous host organisms. We recently developed Codon Optimization On-Line (COOL) which is a web-based tool to provide multi-objective codon optimization functionality for synthetic gene design. COOL provides a simple and flexible interface for customizing codon optimization based on several design parameters such as individual codon usage, codon pairing, and codon adaptation index. User-defined sequences can also be compared against the COOL optimized ones to show the extent by which the user's sequences can be evaluated and further improved. The utility of COOL is demonstrated via a case study where the codon optimized sequence of an invertase enzyme is generated for the enhanced expression in E. coli. PMID:27671929
Framework GRASP: routine library for optimize processing of aerosol remote sensing observation
NASA Astrophysics Data System (ADS)
Fuertes, David; Torres, Benjamin; Dubovik, Oleg; Litvinov, Pavel; Lapyonok, Tatyana; Ducos, Fabrice; Aspetsberger, Michael; Federspiel, Christian
The present the development of a Framework for the Generalized Retrieval of Aerosol and Surface Properties (GRASP) developed by Dubovik et al., (2011). The framework is a source code project that attempts to strengthen the value of the GRASP inversion algorithm by transforming it into a library that will be used later for a group of customized application modules. The functions of the independent modules include the managing of the configuration of the code execution, as well as preparation of the input and output. The framework provides a number of advantages in utilization of the code. First, it implements loading data to the core of the scientific code directly from memory without passing through intermediary files on disk. Second, the framework allows consecutive use of the inversion code without the re-initiation of the core routine when new input is received. These features are essential for optimizing performance of the data production in processing of large observation sets, such as satellite images by the GRASP. Furthermore, the framework is a very convenient tool for further development, because this open-source platform is easily extended for implementing new features. For example, it could accommodate loading of raw data directly onto the inversion code from a specific instrument not included in default settings of the software. Finally, it will be demonstrated that from the user point of view, the framework provides a flexible, powerful and informative configuration system.
Adaptive change in electrically stimulated muscle: a framework for the design of clinical protocols.
Salmons, Stanley
2009-12-01
Adult mammalian skeletal muscles have a remarkable capacity for adapting to increased use. Although this behavior is familiar from the changes brought about by endurance exercise, it is seen to a much greater extent in the response to long-term neuromuscular stimulation. The associated phenomena include a markedly increased resistance to fatigue, and this is the key to several clinical applications. However, a more rational basis is needed for designing regimes of stimulation that are conducive to an optimal outcome. In this review I examine relevant factors, such as the amount, frequency, and duty cycle of stimulation, the influence of force generation, and the animal model. From these considerations a framework emerges for the design of protocols that yield an overall functional profile appropriate to the application. Three contrasting examples illustrate the issues that need to be addressed clinically. PMID:19902542
Execution of Multidisciplinary Design Optimization Approaches on Common Test Problems
NASA Technical Reports Server (NTRS)
Balling, R. J.; Wilkinson, C. A.
1997-01-01
A class of synthetic problems for testing multidisciplinary design optimization (MDO) approaches is presented. These test problems are easy to reproduce because all functions are given as closed-form mathematical expressions. They are constructed in such a way that the optimal value of all variables and the objective is unity. The test problems involve three disciplines and allow the user to specify the number of design variables, state variables, coupling functions, design constraints, controlling design constraints, and the strength of coupling. Several MDO approaches were executed on two sample synthetic test problems. These approaches included single-level optimization approaches, collaborative optimization approaches, and concurrent subspace optimization approaches. Execution results are presented, and the robustness and efficiency of these approaches an evaluated for these sample problems.
Multidisciplinary design optimization of mechatronic vehicles with active suspensions
NASA Astrophysics Data System (ADS)
He, Yuping; McPhee, John
2005-05-01
A multidisciplinary optimization method is applied to the design of mechatronic vehicles with active suspensions. The method is implemented in a GA-A'GEM-MATLAB simulation environment in such a way that the linear mechanical vehicle model is designed in a multibody dynamics software package, i.e. A'GEM, the controllers and estimators are constructed using linear quadratic Gaussian (LQG) method, and Kalman filter algorithm in Matlab, then the combined mechanical and control model is optimized simultaneously using a genetic algorithm (GA). The design variables include passive parameters and control parameters. In the numerical optimizations, both random and deterministic road inputs and both perfect measurement of full state variables and estimated limited state variables are considered. Optimization results show that the active suspension systems based on the multidisciplinary optimization method have better overall performance than those derived using conventional design methods with the LQG algorithm.
NASA Astrophysics Data System (ADS)
Richardson, J. N.; Filomeno Coelho, R.; Adriaenssens, S.
2016-02-01
In this article, a unified framework is introduced for robust structural topology optimization for 2D and 3D continuum and truss problems. The uncertain material parameters are modelled using a spatially correlated random field which is discretized using the Karhunen-Loève expansion. The spectral stochastic finite element method is used, with a polynomial chaos expansion to propagate uncertainties in the material characteristics to the response quantities. In continuum structures, either 2D or 3D random fields are modelled across the structural domain, while representation of the material uncertainties in linear truss elements is achieved by expanding 1D random fields along the length of the elements. Several examples demonstrate the method on both 2D and 3D continuum and truss structures, showing that this common framework provides an interesting insight into robustness versus optimality for the test problems considered.
A Markovian state-space framework for integrating flexibility into space system design decisions
NASA Astrophysics Data System (ADS)
Lafleur, Jarret M.
The past decades have seen the state of the art in aerospace system design progress from a scope of simple optimization to one including robustness, with the objective of permitting a single system to perform well even in off-nominal future environments. Integrating flexibility, or the capability to easily modify a system after it has been fielded in response to changing environments, into system design represents a further step forward. One challenge in accomplishing this rests in that the decision-maker must consider not only the present system design decision, but also sequential future design and operation decisions. Despite extensive interest in the topic, the state of the art in designing flexibility into aerospace systems, and particularly space systems, tends to be limited to analyses that are qualitative, deterministic, single-objective, and/or limited to consider a single future time period. To address these gaps, this thesis develops a stochastic, multi-objective, and multi-period framework for integrating flexibility into space system design decisions. Central to the framework are five steps. First, system configuration options are identified and costs of switching from one configuration to another are compiled into a cost transition matrix. Second, probabilities that demand on the system will transition from one mission to another are compiled into a mission demand Markov chain. Third, one performance matrix for each design objective is populated to describe how well the identified system configurations perform in each of the identified mission demand environments. The fourth step employs multi-period decision analysis techniques, including Markov decision processes from the field of operations research, to find efficient paths and policies a decision-maker may follow. The final step examines the implications of these paths and policies for the primary goal of informing initial system selection. Overall, this thesis unifies state-centric concepts of
Optimal Design of Aortic Leaflet Prosthesis
NASA Technical Reports Server (NTRS)
Ghista, Dhanjoo N.; Reul, Helmut; Ray, Gautam; Chandran, K. B.
1978-01-01
The design criteria for an optimum prosthetic-aortic leaflet valve are a smooth washout in the valve cusps, minimal leaflet stress, minimal transmembrane pressure for the valve to open, an adequate lifetime (for a given blood-compatible leaflet material's fatigue data). A rigorous design analysis is presented to obtain the prosthetic tri-leaflet aortic valve leaflet's optimum design parameters. Four alternative optimum leaflet geometries are obtained to satisfy the criteria of a smooth washout and minimal leaflet stress. The leaflet thicknesses of these four optimum designs are determined by satisfying the two remaining design criteria for minimal transmembrane opening pressure and adequate fatigue lifetime, which are formulated in terms of the elastic and fatigue properties of the selected leaflet material - Avcothane-51 (of the Avco-Everett Co. of Massachusetts). Prosthetic valves are fabricated on the basis of the optimum analysis and the resulting detailed engineering drawings of the designs are also presented in the paper.
Lessons Learned During Solutions of Multidisciplinary Design Optimization Problems
NASA Technical Reports Server (NTRS)
Patnaik, Suna N.; Coroneos, Rula M.; Hopkins, Dale A.; Lavelle, Thomas M.
2000-01-01
Optimization research at NASA Glenn Research Center has addressed the design of structures, aircraft and airbreathing propulsion engines. During solution of the multidisciplinary problems several issues were encountered. This paper lists four issues and discusses the strategies adapted for their resolution: (1) The optimization process can lead to an inefficient local solution. This deficiency was encountered during design of an engine component. The limitation was overcome through an augmentation of animation into optimization. (2) Optimum solutions obtained were infeasible for aircraft and air-breathing propulsion engine problems. Alleviation of this deficiency required a cascading of multiple algorithms. (3) Profile optimization of a beam produced an irregular shape. Engineering intuition restored the regular shape for the beam. (4) The solution obtained for a cylindrical shell by a subproblem strategy converged to a design that can be difficult to manufacture. Resolution of this issue remains a challenge. The issues and resolutions are illustrated through six problems: (1) design of an engine component, (2) synthesis of a subsonic aircraft, (3) operation optimization of a supersonic engine, (4) design of a wave-rotor-topping device, (5) profile optimization of a cantilever beam, and (6) design of a cvlindrical shell. The combined effort of designers and researchers can bring the optimization method from academia to industry.
Origami Optimization: Role of Symmetry in Accelerating Design
NASA Astrophysics Data System (ADS)
Buskohl, Philip; Fuchi, Kazuko; Bazzan, Giorgio; Durstock, Michael; Reich, Gregory; Joo, James; Vaia, Richard
Origami structures morph between 2D and 3D conformations along predetermined fold lines that efficiently program the form, function and mobility of the structure. Design optimization tools have recently been developed to predict optimal fold patterns with mechanics-based metrics, such as the maximal energy storage, auxetic response and actuation. Origami actuator design problems possess inherent symmetries associated with the grid, mechanical boundary conditions and the objective function, which are often exploited to reduce the design space and computational cost of optimization. However, enforcing symmetry eliminates the prediction of potentially better performing asymmetric designs, which are more likely to exist given the discrete nature of fold line optimization. To better understand this effect, actuator design problems with different combinations of rotation and reflection symmetries were optimized while varying the number of folds allowed in the final design. In each case, the optimal origami patterns transitioned between symmetric and asymmetric solutions depended on the number of folds available for the design, with fewer symmetries present with more fold lines allowed. This study investigates the interplay of symmetry and discrete vs continuous optimization in origami actuators and provides insight into how the symmetries of the reference grid regulate the performance landscape. This work was supported by the Air Force Office of Scientific Research.
Design optimization of a portable, micro-hydrokinetic turbine
NASA Astrophysics Data System (ADS)
Schleicher, W. Chris
Marine and hydrokinetic (MHK) technology is a growing field that encompasses many different types of turbomachinery that operate on the kinetic energy of water. Micro hydrokinetics are a subset of MHK technology comprised of units designed to produce less than 100 kW of power. A propeller-type hydrokinetic turbine is investigated as a solution for a portable micro-hydrokinetic turbine with the needs of the United States Marine Corps in mind, as well as future commercial applications. This dissertation investigates using a response surface optimization methodology to create optimal turbine blade designs under many operating conditions. The field of hydrokinetics is introduced. The finite volume method is used to solve the Reynolds-Averaged Navier-Stokes equations with the k ω Shear Stress Transport model, for different propeller-type hydrokinetic turbines. The adaptive response surface optimization methodology is introduced as related to hydrokinetic turbines, and is benchmarked with complex algebraic functions. The optimization method is further studied to characterize the size of the experimental design on its ability to find optimum conditions. It was found that a large deviation between experimental design points was preferential. Different propeller hydrokinetic turbines were designed and compared with other forms of turbomachinery. It was found that the rapid simulations usually under predict performance compare to the refined simulations, and for some other designs it drastically over predicted performance. The optimization method was used to optimize a modular pump-turbine, verifying that the optimization work for other hydro turbine designs.
Wang, Wei; Slepčev, Dejan; Basu, Saurav; Ozolek, John A.
2012-01-01
Transportation-based metrics for comparing images have long been applied to analyze images, especially where one can interpret the pixel intensities (or derived quantities) as a distribution of ‘mass’ that can be transported without strict geometric constraints. Here we describe a new transportation-based framework for analyzing sets of images. More specifically, we describe a new transportation-related distance between pairs of images, which we denote as linear optimal transportation (LOT). The LOT can be used directly on pixel intensities, and is based on a linearized version of the Kantorovich-Wasserstein metric (an optimal transportation distance, as is the earth mover’s distance). The new framework is especially well suited for computing all pairwise distances for a large database of images efficiently, and thus it can be used for pattern recognition in sets of images. In addition, the new LOT framework also allows for an isometric linear embedding, greatly facilitating the ability to visualize discriminant information in different classes of images. We demonstrate the application of the framework to several tasks such as discriminating nuclear chromatin patterns in cancer cells, decoding differences in facial expressions, galaxy morphologies, as well as sub cellular protein distributions. PMID:23729991
Optimal design of spatial distribution networks
NASA Astrophysics Data System (ADS)
Gastner, Michael T.; Newman, M. E. J.
2006-07-01
We consider the problem of constructing facilities such as hospitals, airports, or malls in a country with a nonuniform population density, such that the average distance from a person’s home to the nearest facility is minimized. We review some previous approximate treatments of this problem that indicate that the optimal distribution of facilities should have a density that increases with population density, but does so slower than linearly, as the two-thirds power. We confirm this result numerically for the particular case of the United States with recent population data using two independent methods, one a straightforward regression analysis, the other based on density-dependent map projections. We also consider strategies for linking the facilities to form a spatial network, such as a network of flights between airports, so that the combined cost of maintenance of and travel on the network is minimized. We show specific examples of such optimal networks for the case of the United States.
Vasquez Osorio, Eliana M.; Hoogeman, Mischa S.; Bondar, Luiza; Levendag, Peter C.; Heijmen, Ben J. M.
2009-07-15
Technical improvements in planning and dose delivery and in verification of patient positioning have substantially widened the therapeutic window for radiation treatment of cancer. However, changes in patient anatomy during the treatment limit the exploitation of these new techniques. To further improve radiation treatments, anatomical changes need to be modeled and accounted for. Nonrigid registration can be used for this purpose. This article describes the design, the implementation, and the validation of a new framework for nonrigid registration for radiotherapy applications. The core of this framework is an improved version of the thin plate spline robust point matching (TPS-RPM) algorithm. The TPS-RPM algorithm estimates a global correspondence and a transformation between the points that represent organs of interest belonging to two image sets. However, the algorithm does not allow for the inclusion of prior knowledge on the correspondence of subset of points, and therefore, it can lead to inconsistent anatomical solutions. In this article TPS-RPM was improved by employing a novel correspondence filter that supports simultaneous registration of multiple structures. The improved method allows for coherent organ registration and for the inclusion of user-defined landmarks, lines, and surfaces inside and outside of structures of interest. A procedure to generate control points from segmented organs is described. The framework parameters r and {lambda}, which control the number of points and the nonrigidness of the transformation, respectively, were optimized for three sites with different degrees of deformation (head and neck, prostate, and cervix) using two cases per site. For the head and neck cases, the salivary glands were manually contoured on CT scans, for the prostate cases the prostate and the vesicles, and for the cervix cases the cervix uterus, the bladder, and the rectum. The transformation error obtained using the best set of parameters was below 1
Vásquez Osorio, Eliana M; Hoogeman, Mischa S; Bondar, Luiza; Levendag, Peter C; Heijmen, Ben J M
2009-07-01
Technical improvements in planning and dose delivery and in verification of patient positioning have substantially widened the therapeutic window for radiation treatment of cancer. However, changes in patient anatomy during the treatment limit the exploitation of these new techniques. To further improve radiation treatments, anatomical changes need to be modeled and accounted for Nonrigid registration can be used for this purpose. This article describes the design, the implementation, and the validation of a new framework for nonrigid registration for radiotherapy applications. The core of this framework is an improved version of the thin plate spline robust point matching (TPS-RPM) algorithm. The TPS-RPM algorithm estimates a global correspondence and a transformation between the points that represent organs of interest belonging to two image sets. However, the algorithm does not allow for the inclusion of prior knowledge on the correspondence of subset of points, and therefore, it can lead to inconsistent anatomical solutions. In this article TPS-RPM was improved by employing a novel correspondence filter that supports simultaneous registration of multiple structures. The improved method allows for coherent organ registration and for the inclusion of user-defined landmarks, lines, and surfaces inside and outside of structures of interest. A procedure to generate control points from segmented organs is described. The framework parameters r and lambda, which control the number of points and the nonrigidness of the transformation, respectively, were optimized for three sites with different degrees of deformation (head and neck, prostate, and cervix) using two cases per site. For the head and neck cases, the salivary glands were manually contoured on CT scans, for the prostate cases the prostate and the vesicles, and for the cervix cases the cervix uterus, the bladder, and the rectum. The transformation error obtained using the best set of parameters was below 1 mm
Optimal design of geodesically stiffened composite cylindrical shells
NASA Technical Reports Server (NTRS)
Gendron, G.; Guerdal, Z.
1992-01-01
An optimization system based on the finite element code Computations Structural Mechanics (CSM) Testbed and the optimization program, Automated Design Synthesis (ADS), is described. The optimization system can be used to obtain minimum-weight designs of composite stiffened structures. Ply thickness, ply orientations, and stiffener heights can be used as design variables. Buckling, displacement, and material failure constraints can be imposed on the design. The system is used to conduct a design study of geodesically stiffened shells. For comparison purposes, optimal designs of unstiffened shells and shells stiffened by rings and stingers are also obtained. Trends in the design of geodesically stiffened shells are identified. An approach to include local stress concentrations during the design optimization process is then presented. The method is based on a global/local analysis technique. It employs spline interpolation functions to determine displacements and rotations from a global model which are used as 'boundary conditions' for the local model. The organization of the strategy in the context of an optimization process is described. The method is validated with an example.
Robustness Analysis and Optimally Robust Control Design via Sum-of-Squares
NASA Technical Reports Server (NTRS)
Dorobantu, Andrei; Crespo, Luis G.; Seiler, Peter J.
2012-01-01
A control analysis and design framework is proposed for systems subject to parametric uncertainty. The underlying strategies are based on sum-of-squares (SOS) polynomial analysis and nonlinear optimization to design an optimally robust controller. The approach determines a maximum uncertainty range for which the closed-loop system satisfies a set of stability and performance requirements. These requirements, de ned as inequality constraints on several metrics, are restricted to polynomial functions of the uncertainty. To quantify robustness, SOS analysis is used to prove that the closed-loop system complies with the requirements for a given uncertainty range. The maximum uncertainty range, calculated by assessing a sequence of increasingly larger ranges, serves as a robustness metric for the closed-loop system. To optimize the control design, nonlinear optimization is used to enlarge the maximum uncertainty range by tuning the controller gains. Hence, the resulting controller is optimally robust to parametric uncertainty. This approach balances the robustness margins corresponding to each requirement in order to maximize the aggregate system robustness. The proposed framework is applied to a simple linear short-period aircraft model with uncertain aerodynamic coefficients.
New approaches to the design optimization of hydrofoils
NASA Astrophysics Data System (ADS)
Beyhaghi, Pooriya; Meneghello, Gianluca; Bewley, Thomas
2015-11-01
Two simulation-based approaches are developed to optimize the design of hydrofoils for foiling catamarans, with the objective of maximizing efficiency (lift/drag). In the first, a simple hydrofoil model based on the vortex-lattice method is coupled with a hybrid global and local optimization algorithm that combines our Delaunay-based optimization algorithm with a Generalized Pattern Search. This optimization procedure is compared with the classical Newton-based optimization method. The accuracy of the vortex-lattice simulation of the optimized design is compared with a more accurate and computationally expensive LES-based simulation. In the second approach, the (expensive) LES model of the flow is used directly during the optimization. A modified Delaunay-based optimization algorithm is used to maximize the efficiency of the optimization, which measures a finite-time averaged approximation of the infinite-time averaged value of an ergodic and stationary process. Since the optimization algorithm takes into account the uncertainty of the finite-time averaged approximation of the infinite-time averaged statistic of interest, the total computational time of the optimization algorithm is significantly reduced. Results from the two different approaches are compared.
Optimization applications in aircraft engine design and test
NASA Technical Reports Server (NTRS)
Pratt, T. K.
1984-01-01
Starting with the NASA-sponsored STAEBL program, optimization methods based primarily upon the versatile program COPES/CONMIN were introduced over the past few years to a broad spectrum of engineering problems in structural optimization, engine design, engine test, and more recently, manufacturing processes. By automating design and testing processes, many repetitive and costly trade-off studies have been replaced by optimization procedures. Rather than taking engineers and designers out of the loop, optimization has, in fact, put them more in control by providing sophisticated search techniques. The ultimate decision whether to accept or reject an optimal feasible design still rests with the analyst. Feedback obtained from this decision process has been invaluable since it can be incorporated into the optimization procedure to make it more intelligent. On several occasions, optimization procedures have produced novel designs, such as the nonsymmetric placement of rotor case stiffener rings, not anticipated by engineering designers. In another case, a particularly difficult resonance contraint could not be satisfied using hand iterations for a compressor blade, when the STAEBL program was applied to the problem, a feasible solution was obtained in just two iterations.
A study of commuter airplane design optimization
NASA Technical Reports Server (NTRS)
Keppel, B. V.; Eysink, H.; Hammer, J.; Hawley, K.; Meredith, P.; Roskam, J.
1978-01-01
The usability of the general aviation synthesis program (GASP) was enhanced by the development of separate computer subroutines which can be added as a package to this assembly of computerized design methods or used as a separate subroutine program to compute the dynamic longitudinal, lateral-directional stability characteristics for a given airplane. Currently available analysis methods were evaluated to ascertain those most appropriate for the design functions which the GASP computerized design program performs. Methods for providing proper constraint and/or analysis functions for GASP were developed as well as the appropriate subroutines.
Rethinking modeling framework design: object modeling system 3.0
Technology Transfer Automated Retrieval System (TEKTRAN)
The Object Modeling System (OMS) is a framework for environmental model development, data provisioning, testing, validation, and deployment. It provides a bridge for transferring technology from the research organization to the program delivery agency. The framework provides a consistent and efficie...
Optimizing Balanced Incomplete Block Designs for Educational Assessments
ERIC Educational Resources Information Center
van der Linden, Wim J.; Veldkamp, Bernard P.; Carlson, James E.
2004-01-01
A popular design in large-scale educational assessments as well as any other type of survey is the balanced incomplete block design. The design is based on an item pool split into a set of blocks of items that are assigned to sets of "assessment booklets." This article shows how the problem of calculating an optimal balanced incomplete block…
Kafetzoglou, Stella; Aristomenopoulos, Giorgos; Papavassiliou, Symeon
2015-01-01
Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting-both in terms of data and energy-data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios. PMID:26270664
Rajavel, Rajkumar; Thangarathinam, Mala
2015-01-01
Optimization of negotiation conflict in the cloud service negotiation framework is identified as one of the major challenging issues. This negotiation conflict occurs during the bilateral negotiation process between the participants due to the misperception, aggressive behavior, and uncertain preferences and goals about their opponents. Existing research work focuses on the prerequest context of negotiation conflict optimization by grouping similar negotiation pairs using distance, binary, context-dependent, and fuzzy similarity approaches. For some extent, these approaches can maximize the success rate and minimize the communication overhead among the participants. To further optimize the success rate and communication overhead, the proposed research work introduces a novel probabilistic decision making model for optimizing the negotiation conflict in the long-term negotiation context. This decision model formulates the problem of managing different types of negotiation conflict that occurs during negotiation process as a multistage Markov decision problem. At each stage of negotiation process, the proposed decision model generates the heuristic decision based on the past negotiation state information without causing any break-off among the participants. In addition, this heuristic decision using the stochastic decision tree scenario can maximize the revenue among the participants available in the cloud service negotiation framework.
Rajavel, Rajkumar; Thangarathinam, Mala
2015-01-01
Optimization of negotiation conflict in the cloud service negotiation framework is identified as one of the major challenging issues. This negotiation conflict occurs during the bilateral negotiation process between the participants due to the misperception, aggressive behavior, and uncertain preferences and goals about their opponents. Existing research work focuses on the prerequest context of negotiation conflict optimization by grouping similar negotiation pairs using distance, binary, context-dependent, and fuzzy similarity approaches. For some extent, these approaches can maximize the success rate and minimize the communication overhead among the participants. To further optimize the success rate and communication overhead, the proposed research work introduces a novel probabilistic decision making model for optimizing the negotiation conflict in the long-term negotiation context. This decision model formulates the problem of managing different types of negotiation conflict that occurs during negotiation process as a multistage Markov decision problem. At each stage of negotiation process, the proposed decision model generates the heuristic decision based on the past negotiation state information without causing any break-off among the participants. In addition, this heuristic decision using the stochastic decision tree scenario can maximize the revenue among the participants available in the cloud service negotiation framework. PMID:26543899
Kafetzoglou, Stella; Aristomenopoulos, Giorgos; Papavassiliou, Symeon
2015-01-01
Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting—both in terms of data and energy—data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios. PMID:26270664
Kafetzoglou, Stella; Aristomenopoulos, Giorgos; Papavassiliou, Symeon
2015-08-11
Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting-both in terms of data and energy-data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios.
Rajavel, Rajkumar; Thangarathinam, Mala
2015-01-01
Optimization of negotiation conflict in the cloud service negotiation framework is identified as one of the major challenging issues. This negotiation conflict occurs during the bilateral negotiation process between the participants due to the misperception, aggressive behavior, and uncertain preferences and goals about their opponents. Existing research work focuses on the prerequest context of negotiation conflict optimization by grouping similar negotiation pairs using distance, binary, context-dependent, and fuzzy similarity approaches. For some extent, these approaches can maximize the success rate and minimize the communication overhead among the participants. To further optimize the success rate and communication overhead, the proposed research work introduces a novel probabilistic decision making model for optimizing the negotiation conflict in the long-term negotiation context. This decision model formulates the problem of managing different types of negotiation conflict that occurs during negotiation process as a multistage Markov decision problem. At each stage of negotiation process, the proposed decision model generates the heuristic decision based on the past negotiation state information without causing any break-off among the participants. In addition, this heuristic decision using the stochastic decision tree scenario can maximize the revenue among the participants available in the cloud service negotiation framework. PMID:26543899
NASA Astrophysics Data System (ADS)
Ibrahim, Ireen Munira; Liong, Choong-Yeun; Bakar, Sakhinah Abu; Ahmad, Norazura; Najmuddin, Ahmad Farid
2015-12-01
The Emergency Department (ED) is a very complex system with limited resources to support increase in demand. ED services are considered as good quality if they can meet the patient's expectation. Long waiting times and length of stay is always the main problem faced by the management. The management of ED should give greater emphasis on their capacity of resources in order to increase the quality of services, which conforms to patient satisfaction. This paper is a review of work in progress of a study being conducted in a government hospital in Selangor, Malaysia. This paper proposed a simulation optimization model framework which is used to study ED operations and problems as well as to find an optimal solution to the problems. The integration of simulation and optimization is hoped can assist management in decision making process regarding their resource capacity planning in order to improve current and future ED operations.
SEMICONDUCTOR DEVICES: Optimization of grid design for solar cells
NASA Astrophysics Data System (ADS)
Wen, Liu; Yueqiang, Li; Jianjun, Chen; Yanling, Chen; Xiaodong, Wang; Fuhua, Yang
2010-01-01
By theoretical simulation of two grid patterns that are often used in concentrator solar cells, we give a detailed and comprehensive analysis of the influence of the metal grid dimension and various losses directly associated with it during optimization of grid design. Furthermore, we also perform the simulation under different concentrator factors, making the optimization of the front contact grid for solar cells complete.
Starting designs for the computer optimization of optical coatings
NASA Astrophysics Data System (ADS)
Baumeister, Philip
1995-08-01
Several generic starting designs are used for the computer optimization of multilayer optical coatings. The first is a stack of many thin layers. Another, which is applicable to the needle-layer optimization method, is at least one thick layer. Examples include the following metallic reflector, dark mirror, and total internal reflection with prescribed differential phase shift.
Post-optimality analysis in aerospace vehicle design
NASA Technical Reports Server (NTRS)
Braun, Robert D.; Kroo, Ilan M.; Gage, Peter J.
1993-01-01
This analysis pertains to the applicability of optimal sensitivity information to aerospace vehicle design. The present analysis demonstrates that post-optimality information generated through first-order computations can be used to accurately predict file effect of constraint and parameter perturbations on the optimal solution. This assessment is based on the solution of an aircraft design problem in which the post-optimality estimates are shown to be within a few percent of the true solution over the practical range of constraint and parameter variations. Through solution of a reusable, single-stage-to-orbit, launch vehicle design problem, this optimal sensitivity information is also shown to improve the efficiency of the design process. For a hierarchically decomposed problem, this computational efficiency is realizable by estimating the main-problem objective gradient through optimal sensitivity calculations. By reducing the need for finite differentiation of a re-optimized subproblem, a significant decrease in the number of objective function evaluations required to reach the optimal solution is obtained.
Precision of Sensitivity in the Design Optimization of Indeterminate Structures
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Pai, Shantaram S.; Hopkins, Dale A.
2006-01-01
Design sensitivity is central to most optimization methods. The analytical sensitivity expression for an indeterminate structural design optimization problem can be factored into a simple determinate term and a complicated indeterminate component. Sensitivity can be approximated by retaining only the determinate term and setting the indeterminate factor to zero. The optimum solution is reached with the approximate sensitivity. The central processing unit (CPU) time to solution is substantially reduced. The benefit that accrues from using the approximate sensitivity is quantified by solving a set of problems in a controlled environment. Each problem is solved twice: first using the closed-form sensitivity expression, then using the approximation. The problem solutions use the CometBoards testbed as the optimization tool with the integrated force method as the analyzer. The modification that may be required, to use the stiffener method as the analysis tool in optimization, is discussed. The design optimization problem of an indeterminate structure contains many dependent constraints because of the implicit relationship between stresses, as well as the relationship between the stresses and displacements. The design optimization process can become problematic because the implicit relationship reduces the rank of the sensitivity matrix. The proposed approximation restores the full rank and enhances the robustness of the design optimization method.
Optimality criteria design and stress constraint processing
NASA Technical Reports Server (NTRS)
Levy, R.
1982-01-01
Methods for pre-screening stress constraints into either primary or side-constraint categories are reviewed; a projection method, which is developed from prior cycle stress resultant history, is introduced as an additional screening parameter. Stress resultant projections are also employed to modify the traditional stress-ratio, side-constraint boundary. A special application of structural modification reanalysis is applied to the critical stress constraints to provide feasible designs that are preferable to those obtained by conventional scaling. Sample problem executions show relatively short run times and fewer design cycle iterations to achieve low structural weights; those attained are comparable to the minimum values developed elsewhere.
Minimum weight design of structures via optimality criteria
NASA Technical Reports Server (NTRS)
Kiusalaas, J.
1972-01-01
The state of the art of automated structural design through the use of optimality criteria, with emphasis on aerospace applications is reviewed. Constraints on stresses, displacements, and buckling strengths under static loading, as well as lower bound limits on natural frequencies and flutter speeds are presented. It is presumed that the reader is experienced in finite element methods of analysis, but is not familiar with optimal design techniques.
Optimal design of a touch trigger probe
NASA Astrophysics Data System (ADS)
Li, Rui-Jun; Xiang, Meng; Fan, Kuang-Chao; Zhou, Hao; Feng, Jian
2015-02-01
A tungsten stylus with a ruby ball tip was screwed into a floating plate, which was supported by four leaf springs. The displacement of the tip caused by the contact force in 3D could be transferred into the tilt or vertical displacement of a plane mirror mounted on the floating plate. A quadrant photo detector (QPD) based two dimensional angle sensor was used to detect the tilt or the vertical displacement of the plane mirror. The structural parameters of the probe are optimized for equal sensitivity and equal stiffness in a displacement range of +/-5 μm, and a restricted horizontal size of less than 40 mm. Simulation results indicated that the stiffness was less than 0.6 mN/μm and equal in 3D. Experimental results indicated that the probe could be used to achieve a resolution of 1 nm.
Ceramic processing: Experimental design and optimization
NASA Technical Reports Server (NTRS)
Weiser, Martin W.; Lauben, David N.; Madrid, Philip
1992-01-01
The objectives of this paper are to: (1) gain insight into the processing of ceramics and how green processing can affect the properties of ceramics; (2) investigate the technique of slip casting; (3) learn how heat treatment and temperature contribute to density, strength, and effects of under and over firing to ceramic properties; (4) experience some of the problems inherent in testing brittle materials and learn about the statistical nature of the strength of ceramics; (5) investigate orthogonal arrays as tools to examine the effect of many experimental parameters using a minimum number of experiments; (6) recognize appropriate uses for clay based ceramics; and (7) measure several different properties important to ceramic use and optimize them for a given application.
Optimizing Organization Design for the Future.
ERIC Educational Resources Information Center
Creth, Sheila
2000-01-01
Discussion of planning organization design within the higher education environment stresses the goal of integrating structure and process to maintain stability while increasing organizational flexibility. Considers organization culture, organization structure and processes, networked organizations, a networked organization in action, and personal…
Optimal Experimental Design for Model Discrimination
ERIC Educational Resources Information Center
Myung, Jay I.; Pitt, Mark A.
2009-01-01
Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design variables (e.g., presentation schedule, stimulus structure) that will be most informative in differentiating them. Recent developments in sampling-based search methods in statistics make it…
Integrated design optimization research and development in an industrial environment
NASA Technical Reports Server (NTRS)
Kumar, V.; German, Marjorie D.; Lee, S.-J.
1989-01-01
An overview is given of a design optimization project that is in progress at the GE Research and Development Center for the past few years. The objective of this project is to develop a methodology and a software system for design automation and optimization of structural/mechanical components and systems. The effort focuses on research and development issues and also on optimization applications that can be related to real-life industrial design problems. The overall technical approach is based on integration of numerical optimization techniques, finite element methods, CAE and software engineering, and artificial intelligence/expert systems (AI/ES) concepts. The role of each of these engineering technologies in the development of a unified design methodology is illustrated. A software system DESIGN-OPT has been developed for both size and shape optimization of structural components subjected to static as well as dynamic loadings. By integrating this software with an automatic mesh generator, a geometric modeler and an attribute specification computer code, a software module SHAPE-OPT has been developed for shape optimization. Details of these software packages together with their applications to some 2- and 3-dimensional design problems are described.
Application of optimization techniques to vehicle design: A review
NASA Technical Reports Server (NTRS)
Prasad, B.; Magee, C. L.
1984-01-01
The work that has been done in the last decade or so in the application of optimization techniques to vehicle design is discussed. Much of the work reviewed deals with the design of body or suspension (chassis) components for reduced weight. Also reviewed are studies dealing with system optimization problems for improved functional performance, such as ride or handling. In reviewing the work on the use of optimization techniques, one notes the transition from the rare mention of the methods in the 70's to an increased effort in the early 80's. Efficient and convenient optimization and analysis tools still need to be developed so that they can be regularly applied in the early design stage of the vehicle development cycle to be most effective. Based on the reported applications, an attempt is made to assess the potential for automotive application of optimization techniques. The major issue involved remains the creation of quantifiable means of analysis to be used in vehicle design. The conventional process of vehicle design still contains much experience-based input because it has not yet proven possible to quantify all important constraints. This restraint on the part of the analysis will continue to be a major limiting factor in application of optimization to vehicle design.
Rapid Modeling, Assembly and Simulation in Design Optimization
NASA Technical Reports Server (NTRS)
Housner, Jerry
1997-01-01
A new capability for design is reviewed. This capability provides for rapid assembly of detail finite element models early in the design process where costs are most effectively impacted. This creates an engineering environment which enables comprehensive analysis and design optimization early in the design process. Graphical interactive computing makes it possible for the engineer to interact with the design while performing comprehensive design studies. This rapid assembly capability is enabled by the use of Interface Technology, to couple independently created models which can be archived and made accessible to the designer. Results are presented to demonstrate the capability.
Design of ophthalmic lens by using optimized aspheric surface coefficients
NASA Astrophysics Data System (ADS)
Chang, Ming-Wen; Sun, Wen-Shing; Tien, Chuen-Lin
1998-09-01
Coddington's equations can be used to eliminate the oblique astigmatic error in the design of ophthalmic lens of spherical or other conicoidal surfaces. But it is difficult to get satisfactory result in the designing of the nonconic aspheric ophthalmic lens. In this paper we present an efficient approach based on optimization of aspheric coefficients, which enables the design program to obtain the minimum aberrations. Many higher order coefficients of aspheric surfaces can easily result in inflection point, which increases the difficulty in manufacturing. We solved the problem by taking it as one of the optimization constraints. The design of nonconic aspheric ophthalmic lens could also make the spectacle lenses well thinner in thickness and well flatter in shape than the design of spherical ophthalmic lens and other conicoidal ophthalmic lens. Damped least square methods are used in our design. Aspherical myopia ophthalmic lenses, aspherical hypermetropic lenses and cataract lenses were designed. Comparisons of design examples' results are given.
Optimization of hydraulic machinery by exploiting previous successful designs
NASA Astrophysics Data System (ADS)
Kyriacou, S. A.; Weissenberger, S.; Grafenberger, P.; Giannakoglou, K. C.
2010-08-01
A design-optimization method for hydraulic machinery is proposed. Optimal designs are obtained using the appropriate CFD evaluation software driven by an evolutionary algorithm which is also assisted by artificial neural networks used as surrogate evaluation models or metamodels. As shown in a previous IAHR paper by the same authors, such an optimization method substantially reduces the CPU cost, since the metamodels can discard numerous non-promising candidate solutions generated during the evolution, at almost negligible CPU cost, without evaluating them by means of the costly CFD tool. The present paper extends the optimization method of the previous paper by making it capable to accommodate and exploit pieces of useful information archived during previous relevant successful designs. So, instead of parameterizing the geometry of the hydraulic machine components, which inevitably leads to many design variables, enough to slow down the design procedure, in the proposed method all new designs are expressed as weighted combinations of the archived ones. The archived designs act as the design space bases. The role of the optimization algorithms is to find the set (or sets, for more than one objectives, where the Pareto front of non-dominated solutions is sought) of weight values, corresponding to the hydraulic machine configuration(s) with optimal performance. Since the number of weights is much less that the number of design variables of the conventional shape parameterization, the design space dimension reduces and the CPU cost of the metamodel-assisted evolutionary algorithm is much lower. The design of a Francis runner is used to demonstrate the capabilities of the proposed method.
Optimized design of fiber architecture for pultruded beams
Lopez-Anido, R.; GangaRao, H.V.S.; Bendidi, R.; Al-Megdad, M.
1996-11-01
The goal of this paper is to present the design optimization of a reinforced plastic (RP) wide flange (WF) pultruded beam. A commercially available open section, WF 12x12x1/2, was selected to optimize the stiffness to weight ratio and to manufacture through pultrusion process. Two WF beams with optimized fiber architectures that include bi-directional fabrics were pultruded and tested in bending. A simple analytical model that predicts stiffness properties of pultruded sections based on properties of constituents is introduced. Analytical-experimental correlations are presented. The optimized WF sections increased in bending stiffness by about 40% over the existing unidirectional WF sections.
[Design and optimization of a centrifugal pump for CPCR].
Pei, J; Tan, X; Chen, K; Li, X
2000-06-01
Requirements for an optimal centrifugal pump, the vital component in the equipment for cardiopulmonary cerebral resuscitation(CPCR), have been presented. The performance of the Sarns centrifugal pump (Sarns, Inc./3M, Ann arbor, MI, U.S.A) was tested. The preliminarily optimized model for CPCR was designed according to the requirements of CPCR and to the comparison and analysis of several clinically available centrifugal pumps. The preliminary tests using the centrifugal pump made in our laboratory(Type CPCR-I) have confirmed the design and the optimization.
Optimal brushless DC motor design using genetic algorithms
NASA Astrophysics Data System (ADS)
Rahideh, A.; Korakianitis, T.; Ruiz, P.; Keeble, T.; Rothman, M. T.
2010-11-01
This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.
Towards Robust Designs Via Multiple-Objective Optimization Methods
NASA Technical Reports Server (NTRS)
Man Mohan, Rai
2006-01-01
Fabricating and operating complex systems involves dealing with uncertainty in the relevant variables. In the case of aircraft, flow conditions are subject to change during operation. Efficiency and engine noise may be different from the expected values because of manufacturing tolerances and normal wear and tear. Engine components may have a shorter life than expected because of manufacturing tolerances. In spite of the important effect of operating- and manufacturing-uncertainty on the performance and expected life of the component or system, traditional aerodynamic shape optimization has focused on obtaining the best design given a set of deterministic flow conditions. Clearly it is important to both maintain near-optimal performance levels at off-design operating conditions, and, ensure that performance does not degrade appreciably when the component shape differs from the optimal shape due to manufacturing tolerances and normal wear and tear. These requirements naturally lead to the idea of robust optimal design wherein the concept of robustness to various perturbations is built into the design optimization procedure. The basic ideas involved in robust optimal design will be included in this lecture. The imposition of the additional requirement of robustness results in a multiple-objective optimization problem requiring appropriate solution procedures. Typically the costs associated with multiple-objective optimization are substantial. Therefore efficient multiple-objective optimization procedures are crucial to the rapid deployment of the principles of robust design in industry. Hence the companion set of lecture notes (Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks ) deals with methodology for solving multiple-objective Optimization problems efficiently, reliably and with little user intervention. Applications of the methodologies presented in the companion lecture to robust design will be included here. The
Optimal design of Purcell's three-link swimmer.
Giraldi, Laetitia; Martinon, Pierre; Zoppello, Marta
2015-02-01
In this paper we address the question of the optimal design for the Purcell three-link swimmer. More precisely, we investigate the best link length ratio which maximizes its displacement. The dynamics of the swimmer is expressed as an ordinary differential equation, using the resistive force theory. Among a set of optimal strategies of deformation (strokes), we provide an asymptotic estimate of the displacement for small deformations, from which we derive the optimal link ratio. Numerical simulations are in good agreement with this theoretical estimate and also cover larger amplitudes of deformation. Compared with the classical design of the Purcell swimmer, we observe a gain in displacement of roughly 60%. PMID:25768602
Stress-strain analysis and optimal design of aircraft structures
NASA Astrophysics Data System (ADS)
Liakhovenko, I. A.
The papers contained in this volume present results of theoretical and experimental research related to the stress-strain analysis and optimal design of aircraft structures. Topics discussed include a study of the origin of residual stresses and strains in the transparencies of supersonic aircraft, methodology for studying the fracture of aircraft structures in static tests, and the stability of a multispan panel under combined loading. The discussion also covers optimization of the stiffness and mass characteristics of lifting surface structures modeled by an elastic beam, a study of the strength of a closed system of wings, and a method for the optimal design of a large-aspect-ratio wing.
Application of clustering global optimization to thin film design problems.
Lemarchand, Fabien
2014-03-10
Refinement techniques usually calculate an optimized local solution, which is strongly dependent on the initial formula used for the thin film design. In the present study, a clustering global optimization method is used which can iteratively change this initial formula, thereby progressing further than in the case of local optimization techniques. A wide panel of local solutions is found using this procedure, resulting in a large range of optical thicknesses. The efficiency of this technique is illustrated by two thin film design problems, in particular an infrared antireflection coating, and a solar-selective absorber coating. PMID:24663856
Application of clustering global optimization to thin film design problems.
Lemarchand, Fabien
2014-03-10
Refinement techniques usually calculate an optimized local solution, which is strongly dependent on the initial formula used for the thin film design. In the present study, a clustering global optimization method is used which can iteratively change this initial formula, thereby progressing further than in the case of local optimization techniques. A wide panel of local solutions is found using this procedure, resulting in a large range of optical thicknesses. The efficiency of this technique is illustrated by two thin film design problems, in particular an infrared antireflection coating, and a solar-selective absorber coating.
Multi-objective optimal design of lithium-ion battery packs based on evolutionary algorithms
NASA Astrophysics Data System (ADS)
Severino, Bernardo; Gana, Felipe; Palma-Behnke, Rodrigo; Estévez, Pablo A.; Calderón-Muñoz, Williams R.; Orchard, Marcos E.; Reyes, Jorge; Cortés, Marcelo
2014-12-01
Lithium-battery energy storage systems (LiBESS) are increasingly being used on electric mobility and stationary applications. Despite its increasing use and improvements of the technology there are still challenges associated with cost reduction, increasing lifetime and capacity, and higher safety. A correct battery thermal management system (BTMS) design is critical to achieve these goals. In this paper, a general framework for obtaining optimal BTMS designs is proposed. Due to the trade-off between the BTMS's design goals and the complex modeling of thermal response inside the battery pack, this paper proposes to solve this problem using a novel Multi-Objective Particle Swarm Optimization (MOPSO) approach. A theoretical case of a module with 6 cells and a real case of a pack used in a Solar Race Car are presented. The results show the capabilities of the proposal methodology, in which improved designs for battery packs are obtained.
Cai, Jiandong; Zhang, Li; Wang, Michael Yu
2015-12-15
In multifrequency atomic force microscopy (AFM), probe’s characteristic of assigning resonance frequencies to integer harmonics results in a remarkable improvement of detection sensitivity at specific harmonic components. The selection criterion of harmonic order is based on its amplitude’s sensitivity on material properties, e.g., elasticity. Previous studies on designing harmonic probe are unable to provide a large design capability along with maintaining the structural integrity. Herein, we propose a harmonic probe with step cross section, in which it has variable width in top and bottom steps, while the middle step in cross section is kept constant. Higher order resonance frequencies are tailored to be integer times of fundamental resonance frequency. The probe design is implemented within a structural optimization framework. The optimally designed probe is micromachined using focused ion beam milling technique, and then measured with an AFM. The measurement results agree well with our resonance frequency assignment requirement.
NASA Astrophysics Data System (ADS)
Cai, Jiandong; Wang, Michael Yu; Zhang, Li
2015-12-01
In multifrequency atomic force microscopy (AFM), probe's characteristic of assigning resonance frequencies to integer harmonics results in a remarkable improvement of detection sensitivity at specific harmonic components. The selection criterion of harmonic order is based on its amplitude's sensitivity on material properties, e.g., elasticity. Previous studies on designing harmonic probe are unable to provide a large design capability along with maintaining the structural integrity. Herein, we propose a harmonic probe with step cross section, in which it has variable width in top and bottom steps, while the middle step in cross section is kept constant. Higher order resonance frequencies are tailored to be integer times of fundamental resonance frequency. The probe design is implemented within a structural optimization framework. The optimally designed probe is micromachined using focused ion beam milling technique, and then measured with an AFM. The measurement results agree well with our resonance frequency assignment requirement.
NASA Technical Reports Server (NTRS)
Crespo, Luis G.; Bushnell, Dennis M. (Technical Monitor)
2002-01-01
This paper presents a study on the optimization of systems with structured uncertainties, whose inputs and outputs can be exhaustively described in the probabilistic sense. By propagating the uncertainty from the input to the output in the space of the probability density functions and the moments, optimization problems that pursue performance, robustness and reliability based designs are studied. Be specifying the desired outputs in terms of desired probability density functions and then in terms of meaningful probabilistic indices, we settle a computationally viable framework for solving practical optimization problems. Applications to static optimization and stability control are used to illustrate the relevance of incorporating uncertainty in the early stages of the design. Several examples that admit a full probabilistic description of the output in terms of the design variables and the uncertain inputs are used to elucidate the main features of the generic problem and its solution. Extensions to problems that do not admit closed form solutions are also evaluated. Concrete evidence of the importance of using a consistent probabilistic formulation of the optimization problem and a meaningful probabilistic description of its solution is provided in the examples. In the stability control problem the analysis shows that standard deterministic approaches lead to designs with high probability of running into instability. The implementation of such designs can indeed have catastrophic consequences.
Optimal design of a pilot OTEC power plant in Taiwan
Tseng, C.H.; Kao, K.Y. ); Yang, J.C. )
1991-12-01
In this paper, an optimal design concept has been utilized to find the best designs for a complex and large-scale ocean thermal energy conversion (OTEC) plant. THe OTEC power plant under this study is divided into three major subsystems consisting of power subsystem, seawater pipe subsystem, and containment subsystem. The design optimization model for the entire OTEC plant is integrated from these sub-systems under the considerations of their own various design criteria and constraints. The mathematical formulations of this optimization model for the entire OTEC plant are described. The design variables, objective function, and constraints for a pilot plant under the constraints of the feasible technologies at this stage in Taiwan have been carefully examined and selected.
Formulation for Simultaneous Aerodynamic Analysis and Design Optimization
NASA Technical Reports Server (NTRS)
Hou, G. W.; Taylor, A. C., III; Mani, S. V.; Newman, P. A.
1993-01-01
An efficient approach for simultaneous aerodynamic analysis and design optimization is presented. This approach does not require the performance of many flow analyses at each design optimization step, which can be an expensive procedure. Thus, this approach brings us one step closer to meeting the challenge of incorporating computational fluid dynamic codes into gradient-based optimization techniques for aerodynamic design. An adjoint-variable method is introduced to nullify the effect of the increased number of design variables in the problem formulation. The method has been successfully tested on one-dimensional nozzle flow problems, including a sample problem with a normal shock. Implementations of the above algorithm are also presented that incorporate Newton iterations to secure a high-quality flow solution at the end of the design process. Implementations with iterative flow solvers are possible and will be required for large, multidimensional flow problems.
Finite element based electric motor design optimization
NASA Technical Reports Server (NTRS)
Campbell, C. Warren
1993-01-01
The purpose of this effort was to develop a finite element code for the analysis and design of permanent magnet electric motors. These motors would drive electromechanical actuators in advanced rocket engines. The actuators would control fuel valves and thrust vector control systems. Refurbishing the hydraulic systems of the Space Shuttle after each flight is costly and time consuming. Electromechanical actuators could replace hydraulics, improve system reliability, and reduce down time.
Finite element based electric motor design optimization
NASA Astrophysics Data System (ADS)
Campbell, C. Warren
1993-11-01
The purpose of this effort was to develop a finite element code for the analysis and design of permanent magnet electric motors. These motors would drive electromechanical actuators in advanced rocket engines. The actuators would control fuel valves and thrust vector control systems. Refurbishing the hydraulic systems of the Space Shuttle after each flight is costly and time consuming. Electromechanical actuators could replace hydraulics, improve system reliability, and reduce down time.
New approaches to optimization in aerospace conceptual design
NASA Technical Reports Server (NTRS)
Gage, Peter J.
1995-01-01
Aerospace design can be viewed as an optimization process, but conceptual studies are rarely performed using formal search algorithms. Three issues that restrict the success of automatic search are identified in this work. New approaches are introduced to address the integration of analyses and optimizers, to avoid the need for accurate gradient information and a smooth search space (required for calculus-based optimization), and to remove the restrictions imposed by fixed complexity problem formulations. (1) Optimization should be performed in a flexible environment. A quasi-procedural architecture is used to conveniently link analysis modules and automatically coordinate their execution. It efficiently controls a large-scale design tasks. (2) Genetic algorithms provide a search method for discontinuous or noisy domains. The utility of genetic optimization is demonstrated here, but parameter encodings and constraint-handling schemes must be carefully chosen to avoid premature convergence to suboptimal designs. The relationship between genetic and calculus-based methods is explored. (3) A variable-complexity genetic algorithm is created to permit flexible parameterization, so that the level of description can change during optimization. This new optimizer automatically discovers novel designs in structural and aerodynamic tasks.
Identification of vehicle suspension parameters by design optimization
NASA Astrophysics Data System (ADS)
Tey, J. Y.; Ramli, R.; Kheng, C. W.; Chong, S. Y.; Abidin, M. A. Z.
2014-05-01
The design of a vehicle suspension system through simulation requires accurate representation of the design parameters. These parameters are usually difficult to measure or sometimes unavailable. This article proposes an efficient approach to identify the unknown parameters through optimization based on experimental results, where the covariance matrix adaptation-evolutionary strategy (CMA-es) is utilized to improve the simulation and experimental results against the kinematic and compliance tests. This speeds up the design and development cycle by recovering all the unknown data with respect to a set of kinematic measurements through a single optimization process. A case study employing a McPherson strut suspension system is modelled in a multi-body dynamic system. Three kinematic and compliance tests are examined, namely, vertical parallel wheel travel, opposite wheel travel and single wheel travel. The problem is formulated as a multi-objective optimization problem with 40 objectives and 49 design parameters. A hierarchical clustering method based on global sensitivity analysis is used to reduce the number of objectives to 30 by grouping correlated objectives together. Then, a dynamic summation of rank value is used as pseudo-objective functions to reformulate the multi-objective optimization to a single-objective optimization problem. The optimized results show a significant improvement in the correlation between the simulated model and the experimental model. Once accurate representation of the vehicle suspension model is achieved, further analysis, such as ride and handling performances, can be implemented for further optimization.
Optimization design of the precision optoelectronic tracking turntable frame
NASA Astrophysics Data System (ADS)
Li, Jie
2010-10-01
Opto-electric scouting & tracking device is used to scouting the object of hemisphere airspace and tracing of movement tail of object in real time. The precision turntable was important parts of scouting device and it was crucial to the scouting device's technology guideline, such as tracking precision, scouting range, volume and quality etc. To achieving the purpose which scouting & tracking device's volume smallness, quality light, rigid bigness and precision highness characteristics, the mechanical structure of turntable was designed in this paper. Then, the static and dynamic analysis of the precision turntable frame was done using the finite element method. The static analysis results show the intensity and rigid requirement of tracking turntable frame was satisfied, and it had big space to reducing. So the structure optimization design can be done to reduce the frame's volume and moment of inertia. The optimization design of turntable frame was done based on the establishing the optimizing mathematics model. The objective function of optimization was minimizing frame volume. The optimizing result indicated the optimizing effect was distinct. The volume of precision opto-electronic tracking turntable frame reduced 15%. The intensity and rigid of precision opto-electronic tracking turntable frame were verified after optimization, the results was satisfied to the design requirement. It provided important reference to improving the Opto-electronic scouting and tracking device.
Kornelakis, Aris
2010-12-15
Particle Swarm Optimization (PSO) is a highly efficient evolutionary optimization algorithm. In this paper a multiobjective optimization algorithm based on PSO applied to the optimal design of photovoltaic grid-connected systems (PVGCSs) is presented. The proposed methodology intends to suggest the optimal number of system devices and the optimal PV module installation details, such that the economic and environmental benefits achieved during the system's operational lifetime period are both maximized. The objective function describing the economic benefit of the proposed optimization process is the lifetime system's total net profit which is calculated according to the method of the Net Present Value (NPV). The second objective function, which corresponds to the environmental benefit, equals to the pollutant gas emissions avoided due to the use of the PVGCS. The optimization's decision variables are the optimal number of the PV modules, the PV modules optimal tilt angle, the optimal placement of the PV modules within the available installation area and the optimal distribution of the PV modules among the DC/AC converters. (author)
Churchill, Nathan W; Spring, Robyn; Afshin-Pour, Babak; Dong, Fan; Strother, Stephen C
2015-01-01
BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the "pipeline") significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard "fixed" preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets.
An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI
Churchill, Nathan W.; Spring, Robyn; Afshin-Pour, Babak; Dong, Fan; Strother, Stephen C.
2015-01-01
BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the “pipeline”) significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard “fixed” preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets. PMID:26161667
a Novel Framework for Incorporating Sustainability Into Biomass Feedstock Design
NASA Astrophysics Data System (ADS)
Gopalakrishnan, G.; Negri, C.
2012-12-01
There is a strong society need to evaluate and understand the sustainability of biofuels, especially due to the significant increases in production mandated by many countries, including the United States. Biomass feedstock production is an important contributor to environmental, social and economic impacts from biofuels. We present a systems approach where the agricultural, urban, energy and environmental sectors are considered as components of a single system and environmental liabilities are used as recoverable resources for biomass feedstock production. A geospatial analysis evaluating marginal land and degraded water resources to improve feedstock productivity with concomitant environmental restoration was conducted for the major corn producing states in the US. The extent and availability of these resources was assessed and geospatial techniques used to identify promising opportunities to implement this approach. Utilizing different sources of marginal land (roadway buffers, contaminated land) could result in a 7-fold increase in land availability for feedstock production and provide ecosystem services such as water quality improvement and carbon sequestration. Spatial overlap between degraded water and marginal land resources was found to be as high as 98% and could maintain sustainable feedstock production on marginal lands through the supply of water and nutrients. Multi-objective optimization was used to quantify the tradeoffs between net revenue, improvements in water quality and carbon sequestration at the farm scale using this design. Results indicated that there is an initial opportunity where land that is marginally productive for row crops and of marginal value for conservation purposes could be used to grow bioenergy crops such that that water quality and carbon sequestration benefits are obtained.
A predictive machine learning approach for microstructure optimization and materials design
NASA Astrophysics Data System (ADS)
Liu, Ruoqian; Kumar, Abhishek; Chen, Zhengzhang; Agrawal, Ankit; Sundararaghavan, Veera; Choudhary, Alok
2015-06-01
This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. Experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.
A predictive machine learning approach for microstructure optimization and materials design
Liu, Ruoqian; Kumar, Abhishek; Chen, Zhengzhang; Agrawal, Ankit; Sundararaghavan, Veera; Choudhary, Alok
2015-06-23
This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniquenessmore » of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. In conclusion, experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.« less
A predictive machine learning approach for microstructure optimization and materials design
Liu, Ruoqian; Kumar, Abhishek; Chen, Zhengzhang; Agrawal, Ankit; Sundararaghavan, Veera; Choudhary, Alok
2015-01-01
This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. Experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise. PMID:26100717
A predictive machine learning approach for microstructure optimization and materials design
Liu, Ruoqian; Kumar, Abhishek; Chen, Zhengzhang; Agrawal, Ankit; Sundararaghavan, Veera; Choudhary, Alok
2015-06-23
This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. In conclusion, experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.
A predictive machine learning approach for microstructure optimization and materials design.
Liu, Ruoqian; Kumar, Abhishek; Chen, Zhengzhang; Agrawal, Ankit; Sundararaghavan, Veera; Choudhary, Alok
2015-06-23
This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. Experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.
Multi-stage optimal design for groundwater remediation: a hybrid bi-level programming approach.
Zou, Yun; Huang, Guo H; He, Li; Li, Hengliang
2009-08-11
This paper presents the development of a hybrid bi-level programming approach for supporting multi-stage groundwater remediation design. To investigate remediation performances, a subsurface model was employed to simulate contaminant transport. A mixed-integer nonlinear optimization model was formulated in order to evaluate different remediation strategies. Multivariate relationships based on a filtered stepwise clustering analysis were developed to facilitate the incorporation of a simulation model within a nonlinear optimization framework. By using the developed statistical relationships, predictions needed for calculating the objective function value can be quickly obtained during the search process. The main advantage of the developed approach is that the remediation strategy can be adjusted from stage to stage, which makes the optimization more realistic. The proposed approach was examined through its application to a real-world aquifer remediation case in western Canada. The optimization results based on this application can help the decision makers to comprehensively evaluate remediation performance.
Fuel Injector Design Optimization for an Annular Scramjet Geometry
NASA Astrophysics Data System (ADS)
Steffen, Christopher J., Jr.
2003-01-01
A four-parameter, three-level, central composite experiment design has been used to optimize the configuration of an annular scramjet injector geometry using computational fluid dynamics. The computational fluid dynamic solutions played the role of computer experiments, and response surface methodology was used to capture the simulation results for mixing efficiency and total pressure recovery within the scramjet flowpath. An optimization procedure, based upon the response surface results of mixing efficiency, was used to compare the optimal design configuration against the target efficiency value of 92.5%. The results of three different optimization procedures are presented and all point to the need to look outside the current design space for different injector geometries that can meet or exceed the stated mixing efficiency target.
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.
Improved method for transonic airfoil design-by-optimization
NASA Technical Reports Server (NTRS)
Kennelly, R. A., Jr.
1983-01-01
An improved method for use of optimization techniques in transonic airfoil design is demonstrated. FLO6QNM incorporates a modified quasi-Newton optimization package, and is shown to be more reliable and efficient than the method developed previously at NASA-Ames, which used the COPES/CONMIN optimization program. The design codes are compared on a series of test cases with known solutions, and the effects of problem scaling, proximity of initial point to solution, and objective function precision are studied. In contrast to the older method, well-converged solutions are shown to be attainable in the context of engineering design using computational fluid dynamics tools, a new result. The improvements are due to better performance by the optimization routine and to the use of problem-adaptive finite difference step sizes for gradient evaluation.
On Optimal Input Design and Model Selection for Communication Channels
Li, Yanyan; Djouadi, Seddik M; Olama, Mohammed M
2013-01-01
In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.
Geometry Modeling and Grid Generation for Design and Optimization
NASA Technical Reports Server (NTRS)
Samareh, Jamshid A.
1998-01-01
Geometry modeling and grid generation (GMGG) have played and will continue to play an important role in computational aerosciences. During the past two decades, tremendous progress has occurred in GMGG; however, GMGG is still the biggest bottleneck to routine applications for complicated Computational Fluid Dynamics (CFD) and Computational Structures Mechanics (CSM) models for analysis, design, and optimization. We are still far from incorporating GMGG tools in a design and optimization environment for complicated configurations. It is still a challenging task to parameterize an existing model in today's Computer-Aided Design (CAD) systems, and the models created are not always good enough for automatic grid generation tools. Designers may believe their models are complete and accurate, but unseen imperfections (e.g., gaps, unwanted wiggles, free edges, slivers, and transition cracks) often cause problems in gridding for CSM and CFD. Despite many advances in grid generation, the process is still the most labor-intensive and time-consuming part of the computational aerosciences for analysis, design, and optimization. In an ideal design environment, a design engineer would use a parametric model to evaluate alternative designs effortlessly and optimize an existing design for a new set of design objectives and constraints. For this ideal environment to be realized, the GMGG tools must have the following characteristics: (1) be automated, (2) provide consistent geometry across all disciplines, (3) be parametric, and (4) provide sensitivity derivatives. This paper will review the status of GMGG for analysis, design, and optimization processes, and it will focus on some emerging ideas that will advance the GMGG toward the ideal design environment.
Role of Design Standards in Wind Plant Optimization (Presentation)
Veers, P.; Churchfield, M.; Lee, S.; Moon, J.; Larsen, G.
2013-10-01
When a turbine is optimized, it is done within the design constraints established by the objective criteria in the international design standards used to certify a design. Since these criteria are multifaceted, it is a challenging task to conduct the optimization, but it can be done. The optimization is facilitated by the fact that a standard turbine model is subjected to standard inflow conditions that are well characterized in the standard. Examples of applying these conditions to rotor optimization are examined. In other cases, an innovation may provide substantial improvement in one area, but be challenged to impact all of the myriad design load cases. When a turbine is placed in a wind plant, the challenge is magnified. Typical design practice optimizes the turbine for stand-alone operation, and then runs a check on the actual site conditions, including wakes from all nearby turbines. Thus, each turbine in a plant has unique inflow conditions. The possibility of creating objective and consistent inflow conditions for turbines within a plant, for used in optimization of the turbine and the plant, are examined with examples taken from LES simulation.
A multi-objective optimization framework to model 3D river and landscape evolution processes
NASA Astrophysics Data System (ADS)
Bizzi, Simone; Castelletti, Andrea; Cominola, Andrea; Mason, Emanuele; Paik, Kyungrock
2013-04-01
Water and sediment interactions shape hillslopes, regulate soil erosion and sedimentation, and organize river networks. Landscape evolution and river organization occur at various spatial and temporal scale and the understanding and modelling of them is highly complex. The idea of a least action principle governing river networks evolution has been proposed many times as a simpler approach among the ones existing in the literature. These theories assume that river networks, as observed in nature, self-organize and act on soil transportation in order to satisfy a particular "optimality" criterion. Accordingly, river and landscape weathering can be simulated by solving an optimization problem, where the choice of the criterion to be optimized becomes the initial assumption. The comparison between natural river networks and optimized ones verifies the correctness of this initial assumption. Yet, various criteria have been proposed in literature and there is no consensus on which is better able to explain river network features observed in nature like network branching and river bed profile: each one is able to reproduce some river features through simplified modelling of the natural processes, but it fails to characterize the whole complexity (3D and its dynamic) of the natural processes. Some of the criteria formulated in the literature partly conflict: the reason is that their formulation rely on mathematical and theoretical simplifications of the natural system that are suitable for specific spatial and temporal scale but fails to represent the whole processes characterizing landscape evolution. In an attempt to address some of these scientific questions, we tested the suitability of using a multi-objective optimization framework to describe river and landscape evolution in a 3D spatial domain. A synthetic landscape is used to this purpose. Multiple, alternative river network evolutions, corresponding to as many tradeoffs between the different and partly
3D transrectal ultrasound (TRUS) prostate segmentation based on optimal feature learning framework
NASA Astrophysics Data System (ADS)
Yang, Xiaofeng; Rossi, Peter J.; Jani, Ashesh B.; Mao, Hui; Curran, Walter J.; Liu, Tian
2016-03-01
We propose a 3D prostate segmentation method for transrectal ultrasound (TRUS) images, which is based on patch-based feature learning framework. Patient-specific anatomical features are extracted from aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified by the feature selection process to train the kernel support vector machine (KSVM). The well-trained SVM was used to localize the prostate of the new patient. Our segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentations (gold standard). The mean volume Dice overlap coefficient was 89.7%. In this study, we have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentations.
An optimal trajectory design for debris deorbiting
NASA Astrophysics Data System (ADS)
Ouyang, Gaoxiang; Dong, Xin; Li, Xin; Zhang, Yang
2016-01-01
The problem of deorbiting debris is studied in this paper. As a feasible measure, a disposable satellite would be launched, attach to debris, and deorbit the space debris using a technology named electrodynamic tether (EDT). In order to deorbit multiple debris as many as possible, a suboptimal but feasible and efficient trajectory set has been designed to allow a deorbiter satellite tour the LEO small bodies per one mission. Finally a simulation given by this paper showed that a 600 kg satellite is capable of deorbiting 6 debris objects in about 230 days.
Multiobjective hyper heuristic scheme for system design and optimization
NASA Astrophysics Data System (ADS)
Rafique, Amer Farhan
2012-11-01
As system design is becoming more and more multifaceted, integrated, and complex, the traditional single objective optimization trends of optimal design are becoming less and less efficient and effective. Single objective optimization methods present a unique optimal solution whereas multiobjective methods present pareto front. The foremost intent is to predict a reasonable distributed pareto-optimal solution set independent of the problem instance through multiobjective scheme. Other objective of application of intended approach is to improve the worthiness of outputs of the complex engineering system design process at the conceptual design phase. The process is automated in order to provide the system designer with the leverage of the possibility of studying and analyzing a large multiple of possible solutions in a short time. This article presents Multiobjective Hyper Heuristic Optimization Scheme based on low level meta-heuristics developed for the application in engineering system design. Herein, we present a stochastic function to manage meta-heuristics (low-level) to augment surety of global optimum solution. Generic Algorithm, Simulated Annealing and Swarm Intelligence are used as low-level meta-heuristics in this study. Performance of the proposed scheme is investigated through a comprehensive empirical analysis yielding acceptable results. One of the primary motives for performing multiobjective optimization is that the current engineering systems require simultaneous optimization of conflicting and multiple. Random decision making makes the implementation of this scheme attractive and easy. Injecting feasible solutions significantly alters the search direction and also adds diversity of population resulting in accomplishment of pre-defined goals set in the proposed scheme.
Application of Frameworks in the Analysis and (Re)design of Interactive Visual Learning Tools
ERIC Educational Resources Information Center
Liang, Hai-Ning; Sedig, Kamran
2009-01-01
Interactive visual learning tools (IVLTs) are software environments that encode and display information visually and allow learners to interact with the visual information. This article examines the application and utility of frameworks in the analysis and design of IVLTs at the micro level. Frameworks play an important role in any design. They…
Professional Development of Instructional Designers: A Proposed Framework Based on a Singapore Study
ERIC Educational Resources Information Center
Cheong, Eleen; Wettasinghe, Marissa C.; Murphy, James
2006-01-01
This article presents a professional development action plan or framework for instructional designers (IDs) working as external consultants for corporate companies. It also describes justifications why such an action plan is necessary for these professionals. The framework aims to help practising instructional designers to continuously and…
ERIC Educational Resources Information Center
Bozkurt, Ipek; Helm, James
2013-01-01
This paper develops a systems engineering-based framework to assist in the design of an online engineering course. Specifically, the purpose of the framework is to provide a structured methodology for the design, development and delivery of a fully online course, either brand new or modified from an existing face-to-face course. The main strength…
Miaou, S.-P.; Pillai, R.S.; Summers, M.S.; Rathi, A.K.; Lieu, H.C.
1997-01-01
The success of Advanced Traveler Information 5ystems (ATIS) and Advanced Traffic Management Systems (ATMS) depends on the availability and dissemination of timely and accurate estimates of current and emerging traffic network conditions. Real-time Dynamic Traffic Assignment (DTA) systems are being developed to provide the required timely information. The DTA systems will provide faithful and coherent real-time, pre-trip, and en-route guidance/information which includes routing, mode, and departure time suggestions for use by travelers, ATIS, and ATMS. To ensure the credibility and deployment potential of such DTA systems, an evaluation system supporting all phases of DTA system development has been designed and presented in this paper. This evaluation system is called the DTA System Laboratory (DSL). A major component of the DSL is a ground- truth simulator, the DTA Evaluation System (DES). The DES is envisioned to be a virtual representation of a transportation system in which ATMS and ATIS technologies are deployed. It simulates the driving and decision-making behavior of travelers in response to ATIS and ATMS guidance, information, and control. This paper presents the major evaluation requirements for a DTA Systems, a modular modeling framework for the DES, and a distributed DES design. The modeling framework for the DES is modular, meets the requirements, can be assembled using both legacy and independently developed modules, and can be implemented as a either a single process or a distributed system. The distributed design is extendible, provides for the optimization of distributed performance, and object-oriented design within each distributed component. A status report on the development of the DES and other research applications is also provided.
Unified Simulation and Analysis Framework for Deep Space Navigation Design
NASA Technical Reports Server (NTRS)
Anzalone, Evan; Chuang, Jason; Olsen, Carrie
2013-01-01
As the technology that enables advanced deep space autonomous navigation continues to develop and the requirements for such capability continues to grow, there is a clear need for a modular expandable simulation framework. This tool's purpose is to address multiple measurement and information sources in order to capture system capability. This is needed to analyze the capability of competing navigation systems as well as to develop system requirements, in order to determine its effect on the sizing of the integrated vehicle. The development for such a framework is built upon Model-Based Systems Engineering techniques to capture the architecture of the navigation system and possible state measurements and observations to feed into the simulation implementation structure. These models also allow a common environment for the capture of an increasingly complex operational architecture, involving multiple spacecraft, ground stations, and communication networks. In order to address these architectural developments, a framework of agent-based modules is implemented to capture the independent operations of individual spacecraft as well as the network interactions amongst spacecraft. This paper describes the development of this framework, and the modeling processes used to capture a deep space navigation system. Additionally, a sample implementation describing a concept of network-based navigation utilizing digitally transmitted data packets is described in detail. This developed package shows the capability of the modeling framework, including its modularity, analysis capabilities, and its unification back to the overall system requirements and definition.
Superconducting Fault Current Limiter optimized design
NASA Astrophysics Data System (ADS)
Tixador, Pascal; Badel, Arnaud
2015-11-01
The SuperConducting Fault Current Limiter (SCFCL) appears as one of the most promising SC applications for the electrical grids. Despite its advantages and many successful field experiences the market of SCFCL has difficulties to take off even if the first orders for permanent operation in grids are taken. The analytical design of resistive SCFCL will be discussed with the objective to reduce the quantity of SC conductor (length and section) to be more cost-effective. For that the SC conductor must have a high resistivity in normal state. It can be achieved by using high resistivity alloy for shunt, such as Hastelloy®. One of the most severe constraint is that the SCFCL should operate safely for any faults, especially those with low prospective short-circuit currents. This constraint requires to properly design the thickness of the SC tape in order to limit the hot spot temperature. An operation at 65 K appears as very interesting since it decreases the SC cost at least by a factor 2 with a simple LN2 cryogenics. Taking into account the cost reduction in a near future, the SC conductor cost could be rather low, half a dollar per kV A.
Performance Trend of Different Algorithms for Structural Design Optimization
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.
1996-01-01
Nonlinear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Center, a project was initiated to assess performance of different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with the sequential unconstrained minimizations technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.
Comparative Evaluation of Different Optimization Algorithms for Structural Design Applications
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.
1996-01-01
Non-linear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Centre, a project was initiated to assess the performance of eight different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using the eight different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems, however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with Sequential Unconstrained Minimizations Technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.
Loizzo, Joseph
2009-08-01
This overview surveys the new optimism about the aging mind/brain, focusing on the potential for self-regulation practices to advance research in stress-protection and optimal health. It reviews recent findings and offers a research framework. The review links the age-related biology of stress and regeneration to the variability of mind/brain function found under a range of conditions from trauma to enrichment. The framework maps this variation along a biphasic continuum from atrophic dysfunction to peak performance. It adopts the concept of allostatic load as a measure of the wear-and-tear caused by stress, and environmental enrichment as a measure of the use-dependent enhancement caused by positive reinforcement. It frames the dissociation, aversive affect and stereotyped reactions linked with stress as cognitive, affective and behavioral forms of allostatic drag; and the association, positive affect, and creative responses in enrichment as forms of allostatic lift. It views the human mind/brain as a heterarchy of higher intelligence systems that shift between a conservative, egocentric mode heightening self-preservation and memory and a generative, altruistic mode heightening self-correction and learning. Cultural practices like meditation and psychotherapy work by teaching the self-regulation of shifts from the conservative to the generative mode. This involves a systems shift from allostatic drag to allostatic lift, minimizing wear-and-tear and optimizing plasticity and learning. For cultural practices to speed research and application, a universal typology is needed. This framework includes a typology aligning current brain models of stress and learning with traditional Indo-Tibetan models of meditative stress-cessation and learning enrichment.
Global Design Optimization for Aerodynamics and Rocket Propulsion Components
NASA Technical Reports Server (NTRS)
Shyy, Wei; Papila, Nilay; Vaidyanathan, Rajkumar; Tucker, Kevin; Turner, James E. (Technical Monitor)
2000-01-01
Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial insight into engineering processes involving fluid flows, and can be fruitfully utilized to help improve the design of practical devices. In particular, rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performance, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. In this article, we review recent progress made in establishing suitable global optimization techniques employing neural network and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level techniques, and assessment of relative performance between polynomials and neural networks. Examples drawn from wing aerodynamics, turbulent diffuser flows, gas-gas injectors, and supersonic turbines are employed to help demonstrate the issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design
Enhanced Multiobjective Optimization Technique for Comprehensive Aerospace Design. Part A
NASA Technical Reports Server (NTRS)
Chattopadhyay, Aditi; Rajadas, John N.
1997-01-01
A multidisciplinary design optimization procedure which couples formal multiobjectives based techniques and complex analysis procedures (such as computational fluid dynamics (CFD) codes) developed. The procedure has been demonstrated on a specific high speed flow application involving aerodynamics and acoustics (sonic boom minimization). In order to account for multiple design objectives arising from complex performance requirements, multiobjective formulation techniques are used to formulate the optimization problem. Techniques to enhance the existing Kreisselmeier-Steinhauser (K-S) function multiobjective formulation approach have been developed. The K-S function procedure used in the proposed work transforms a constrained multiple objective functions problem into an unconstrained problem which then is solved using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Weight factors are introduced during the transformation process to each objective function. This enhanced procedure will provide the designer the capability to emphasize specific design objectives during the optimization process. The demonstration of the procedure utilizes a computational Fluid dynamics (CFD) code which solves the three-dimensional parabolized Navier-Stokes (PNS) equations for the flow field along with an appropriate sonic boom evaluation procedure thus introducing both aerodynamic performance as well as sonic boom as the design objectives to be optimized simultaneously. Sensitivity analysis is performed using a discrete differentiation approach. An approximation technique has been used within the optimizer to improve the overall computational efficiency of the procedure in order to make it suitable for design applications in an industrial setting.
Parallel optimization algorithms and their implementation in VLSI design
NASA Technical Reports Server (NTRS)
Lee, G.; Feeley, J. J.
1991-01-01
Two new parallel optimization algorithms based on the simplex method are described. They may be executed by a SIMD parallel processor architecture and be implemented in VLSI design. Several VLSI design implementations are introduced. An application example is reported to demonstrate that the algorithms are effective.
Teaching Optimal Design of Experiments Using a Spreadsheet
ERIC Educational Resources Information Center
Goos, Peter; Leemans, Herlinde
2004-01-01
In this paper, we present an interactive teaching approach to introduce the concept of optimal design of experiments to students. Our approach is based on the use of spreadsheets. One advantage of this approach is that no complex mathematical theory is needed nor that any design construction algorithm has to be discussed at the introductory stage.…
[COSMOS motion design optimization in the CT table].
Shang, Hong; Huang, Jian; Ren, Chao
2013-03-01
Through the CT Table dynamic simulation by COSMOS Motion, analysis the hinge of table and the motor force, then optimize the position of the hinge of table, provide the evidence of selecting bearing and motor, meanwhile enhance the design quality of the CT table and reduce the product design cost.
Optimal Test Design with Rule-Based Item Generation
ERIC Educational Resources Information Center
Geerlings, Hanneke; van der Linden, Wim J.; Glas, Cees A. W.
2013-01-01
Optimal test-design methods are applied to rule-based item generation. Three different cases of automated test design are presented: (a) test assembly from a pool of pregenerated, calibrated items; (b) test generation on the fly from a pool of calibrated item families; and (c) test generation on the fly directly from calibrated features defining…
A PDE Sensitivity Equation Method for Optimal Aerodynamic Design
NASA Technical Reports Server (NTRS)
Borggaard, Jeff; Burns, John
1996-01-01
The use of gradient based optimization algorithms in inverse design is well established as a practical approach to aerodynamic design. A typical procedure uses a simulation scheme to evaluate the objective function (from the approximate states) and its gradient, then passes this information to an optimization algorithm. Once the simulation scheme (CFD flow solver) has been selected and used to provide approximate function evaluations, there are several possible approaches to the problem of computing gradients. One popular method is to differentiate the simulation scheme and compute design sensitivities that are then used to obtain gradients. Although this black-box approach has many advantages in shape optimization problems, one must compute mesh sensitivities in order to compute the design sensitivity. In this paper, we present an alternative approach using the PDE sensitivity equation to develop algorithms for computing gradients. This approach has the advantage that mesh sensitivities need not be computed. Moreover, when it is possible to use the CFD scheme for both the forward problem and the sensitivity equation, then there are computational advantages. An apparent disadvantage of this approach is that it does not always produce consistent derivatives. However, for a proper combination of discretization schemes, one can show asymptotic consistency under mesh refinement, which is often sufficient to guarantee convergence of the optimal design algorithm. In particular, we show that when asymptotically consistent schemes are combined with a trust-region optimization algorithm, the resulting optimal design method converges. We denote this approach as the sensitivity equation method. The sensitivity equation method is presented, convergence results are given and the approach is illustrated on two optimal design problems involving shocks.
Achieving optimal SERS through enhanced experimental design
Fisk, Heidi; Westley, Chloe; Turner, Nicholas J.
2016-01-01
One of the current limitations surrounding surface‐enhanced Raman scattering (SERS) is the perceived lack of reproducibility. SERS is indeed challenging, and for analyte detection, it is vital that the analyte interacts with the metal surface. However, as this is analyte dependent, there is not a single set of SERS conditions that are universal. This means that experimental optimisation for optimum SERS response is vital. Most researchers optimise one factor at a time, where a single parameter is altered first before going onto optimise the next. This is a very inefficient way of searching the experimental landscape. In this review, we explore the use of more powerful multivariate approaches to SERS experimental optimisation based on design of experiments and evolutionary computational methods. We particularly focus on colloidal‐based SERS rather than thin film preparations as a result of their popularity. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley & Sons, Ltd. PMID:27587905
Optimization of Designs for Nanotube-based Scanning Probes
NASA Technical Reports Server (NTRS)
Harik, V. M.; Gates, T. S.; Bushnell, Dennis M. (Technical Monitor)
2002-01-01
Optimization of designs for nanotube-based scanning probes, which may be used for high-resolution characterization of nanostructured materials, is examined. Continuum models to analyze the nanotube deformations are proposed to help guide selection of the optimum probe. The limitations on the use of these models that must be accounted for before applying to any design problem are presented. These limitations stem from the underlying assumptions and the expected range of nanotube loading, end conditions, and geometry. Once the limitations are accounted for, the key model parameters along with the appropriate classification of nanotube structures may serve as a basis for the design optimization of nanotube-based probe tips.
Concepts for designing and fabricating metal implant frameworks for hybrid implant prostheses.
Drago, Carl; Howell, Kent
2012-07-01
Edentulous patients have reported difficulties in managing complete dentures; they have also reported functional concerns and higher expectations regarding complete dentures than the dentists who have treated them. Some of the objectives of definitive fixed implant prosthodontic care include predictable, long-term prostheses, improved function, and maintenance of alveolar bone. One of the keys to long-term clinical success is the design and fabrication of metal frameworks that support implant prostheses. Multiple, diverse methods have been reported regarding framework design in implant prosthodontics. Original designs were developed empirically, without the benefit of laboratory testing. Prosthetic complications reported after occlusal loading included screw loosening, screw fracture, prosthesis fracture, crestal bone loss around implants, and implant loss. Numerous authors promoted accurately fitting frameworks; however, it has been noted that metal frameworks do not fit accurately. Passively fitting metal implant frameworks and implants have not been realized. Biologic consequences of ill-fitting frameworks were not well understood. Basic engineering principles were then incorporated into implant framework designs; however, laboratory testing was lacking. It has been reported that I- and L-beam designs were the best clinical option. With the advent of CAD/CAM protocols, milled titanium frameworks became quite popular in implant prosthodontics. The purpose of this article is to discuss current and past literature regarding implant-retained frameworks for full-arch, hybrid restorations. Benefits, limitations, and complications associated with this type of prosthesis will be reviewed. This discussion will include the relative inaccuracy of casting/implant fit and improved accuracy noted with CAD/CAM framework/implant fit; cantilever extensions relative to the A/P implant spread; and mechanical properties associated with implant frameworks including I- and L
Optimal Bayesian Adaptive Design for Test-Item Calibration.
van der Linden, Wim J; Ren, Hao
2015-06-01
An optimal adaptive design for test-item calibration based on Bayesian optimality criteria is presented. The design adapts the choice of field-test items to the examinees taking an operational adaptive test using both the information in the posterior distributions of their ability parameters and the current posterior distributions of the field-test parameters. Different criteria of optimality based on the two types of posterior distributions are possible. The design can be implemented using an MCMC scheme with alternating stages of sampling from the posterior distributions of the test takers' ability parameters and the parameters of the field-test items while reusing samples from earlier posterior distributions of the other parameters. Results from a simulation study demonstrated the feasibility of the proposed MCMC implementation for operational item calibration. A comparison of performances for different optimality criteria showed faster calibration of substantial numbers of items for the criterion of D-optimality relative to A-optimality, a special case of c-optimality, and random assignment of items to the test takers.
Optimal Design of Pipeline Based on the Shortest Path
NASA Astrophysics Data System (ADS)
Chu, Fei-xue; Chen, Shi-yi
Design and operation of long-distance pipeline are complex engineering tasks. Even small improvement in the design of a pipeline system can lead to substantial savings in capital. In this paper, graph theory was used to analyze the problem of pipeline optimal design. The candidate pump station locations were taken as the vertexes and the total cost of the pipeline system between the two vertexes corresponded to the edge weight. An algorithm recursively calling the Dijkstra algorithm was designed and analyzed to obtain N shortest paths. The optimal process program and the quasi-optimal process programs were obtained at the same time, which could be used in decision-making. The algorithm was tested by a real example. The result showed that it could meet the need of real application.
NASA Astrophysics Data System (ADS)
Schoettle, U. M.; Hillesheimer, M.
1991-08-01
An iterative multistep procedure for performance optimization of launch vehicles is described, which is being developed to support trade-off and sensitivity studies. Two major steps involved in the automated technique are the optimum trajectory shaping employing approximate control models and the vehicle design. Both aspects are discussed in this paper. Simulation examples are presented, first to demonstrate the approach taken for flight path optimization; second, to verify the coupled trajectory and design optimization procedure; and finally, to assess the impact of different mission requirements on an airbreathing Saenger-type vehicle.
Sutradhar, Alok; Park, Jaejong; Carrau, Diana; Nguyen, Tam H; Miller, Michael J; Paulino, Glaucio H
2016-07-01
Large craniofacial defects require efficient bone replacements which should not only provide good aesthetics but also possess stable structural function. The proposed work uses a novel multiresolution topology optimization method to achieve the task. Using a compliance minimization objective, patient-specific bone replacement shapes can be designed for different clinical cases that ensure revival of efficient load transfer mechanisms in the mid-face. In this work, four clinical cases are introduced and their respective patient-specific designs are obtained using the proposed method. The optimized designs are then virtually inserted into the defect to visually inspect the viability of the design . Further, once the design is verified by the reconstructive surgeon, prototypes are fabricated using a 3D printer for validation. The robustness of the designs are mechanically tested by subjecting them to a physiological loading condition which mimics the masticatory activity. The full-field strain result through 3D image correlation and the finite element analysis implies that the solution can survive the maximum mastication of 120 lb. Also, the designs have the potential to restore the buttress system and provide the structural integrity. Using the topology optimization framework in designing the bone replacement shapes would deliver surgeons new alternatives for rather complicated mid-face reconstruction. PMID:26660897
Sutradhar, Alok; Park, Jaejong; Carrau, Diana; Nguyen, Tam H; Miller, Michael J; Paulino, Glaucio H
2016-07-01
Large craniofacial defects require efficient bone replacements which should not only provide good aesthetics but also possess stable structural function. The proposed work uses a novel multiresolution topology optimization method to achieve the task. Using a compliance minimization objective, patient-specific bone replacement shapes can be designed for different clinical cases that ensure revival of efficient load transfer mechanisms in the mid-face. In this work, four clinical cases are introduced and their respective patient-specific designs are obtained using the proposed method. The optimized designs are then virtually inserted into the defect to visually inspect the viability of the design . Further, once the design is verified by the reconstructive surgeon, prototypes are fabricated using a 3D printer for validation. The robustness of the designs are mechanically tested by subjecting them to a physiological loading condition which mimics the masticatory activity. The full-field strain result through 3D image correlation and the finite element analysis implies that the solution can survive the maximum mastication of 120 lb. Also, the designs have the potential to restore the buttress system and provide the structural integrity. Using the topology optimization framework in designing the bone replacement shapes would deliver surgeons new alternatives for rather complicated mid-face reconstruction.
NASA Astrophysics Data System (ADS)
Yamaguchi, Hideshi; Soeda, Takeshi
2015-03-01
A practical framework for an electron beam induced current (EBIC) technique has been established for conductive materials based on a numerical optimization approach. Although the conventional EBIC technique is useful for evaluating the distributions of dopants or crystal defects in semiconductor transistors, issues related to the reproducibility and quantitative capability of measurements using this technique persist. For instance, it is difficult to acquire high-quality EBIC images throughout continuous tests due to variation in operator skill or test environment. Recently, due to the evaluation of EBIC equipment performance and the numerical optimization of equipment items, the constant acquisition of high contrast images has become possible, improving the reproducibility as well as yield regardless of operator skill or test environment. The technique proposed herein is even more sensitive and quantitative than scanning probe microscopy, an imaging technique that can possibly damage the sample. The new technique is expected to benefit the electrical evaluation of fragile or soft materials along with LSI materials.
Optimal design of composite hip implants using NASA technology
NASA Technical Reports Server (NTRS)
Blake, T. A.; Saravanos, D. A.; Davy, D. T.; Waters, S. A.; Hopkins, D. A.
1993-01-01
Using an adaptation of NASA software, we have investigated the use of numerical optimization techniques for the shape and material optimization of fiber composite hip implants. The original NASA inhouse codes, were originally developed for the optimization of aerospace structures. The adapted code, which was called OPORIM, couples numerical optimization algorithms with finite element analysis and composite laminate theory to perform design optimization using both shape and material design variables. The external and internal geometry of the implant and the surrounding bone is described with quintic spline curves. This geometric representation is then used to create an equivalent 2-D finite element model of the structure. Using laminate theory and the 3-D geometric information, equivalent stiffnesses are generated for each element of the 2-D finite element model, so that the 3-D stiffness of the structure can be approximated. The geometric information to construct the model of the femur was obtained from a CT scan. A variety of test cases were examined, incorporating several implant constructions and design variable sets. Typically the code was able to produce optimized shape and/or material parameters which substantially reduced stress concentrations in the bone adjacent of the implant. The results indicate that this technology can provide meaningful insight into the design of fiber composite hip implants.
ERIC Educational Resources Information Center
Gynther, Karsten
2016-01-01
The research project has developed a design framework for an adaptive MOOC that complements the MOOC format with blended learning. The design framework consists of a design model and a series of learning design principles which can be used to design in-service courses for teacher professional development. The framework has been evaluated by…
Aerodynamic design optimization by using a continuous adjoint method
NASA Astrophysics Data System (ADS)
Luo, JiaQi; Xiong, JunTao; Liu, Feng
2014-07-01
This paper presents the fundamentals of a continuous adjoint method and the applications of this method to the aerodynamic design optimization of both external and internal flows. General formulation of the continuous adjoint equations and the corresponding boundary conditions are derived. With the adjoint method, the complete gradient information needed in the design optimization can be obtained by solving the governing flow equations and the corresponding adjoint equations only once for each cost function, regardless of the number of design parameters. An inverse design of airfoil is firstly performed to study the accuracy of the adjoint gradient and the effectiveness of the adjoint method as an inverse design method. Then the method is used to perform a series of single and multiple point design optimization problems involving the drag reduction of airfoil, wing, and wing-body configuration, and the aerodynamic performance improvement of turbine and compressor blade rows. The results demonstrate that the continuous adjoint method can efficiently and significantly improve the aerodynamic performance of the design in a shape optimization problem.
A modeling framework for optimal long-term care insurance purchase decisions in retirement planning.
Gupta, Aparna; Li, Lepeng
2004-05-01
The level of need and costs of obtaining long-term care (LTC) during retired life require that planning for it is an integral part of retirement planning. In this paper, we divide retirement planning into two phases, pre-retirement and post-retirement. On the basis of four interrelated models for health evolution, wealth evolution, LTC insurance premium and coverage, and LTC cost structure, a framework for optimal LTC insurance purchase decisions in the pre-retirement phase is developed. Optimal decisions are obtained by developing a trade-off between post-retirement LTC costs and LTC insurance premiums and coverage. Two-way branching models are used to model stochastic health events and asset returns. The resulting optimization problem is formulated as a dynamic programming problem. We compare the optimal decision under two insurance purchase scenarios: one assumes that insurance is purchased for good and other assumes it may be purchased, relinquished and re-purchased. Sensitivity analysis is performed for the retirement age.
NASA Astrophysics Data System (ADS)
Wang, Dafang; Kirby, Robert M.; MacLeod, Rob S.; Johnson, Chris R.
2013-10-01
With the goal of non-invasively localizing cardiac ischemic disease using body-surface potential recordings, we attempted to reconstruct the transmembrane potential (TMP) throughout the myocardium with the bidomain heart model. The task is an inverse source problem governed by partial differential equations (PDE). Our main contribution is solving the inverse problem within a PDE-constrained optimization framework that enables various physically-based constraints in both equality and inequality forms. We formulated the optimality conditions rigorously in the continuum before deriving finite element discretization, thereby making the optimization independent of discretization choice. Such a formulation was derived for the L2-norm Tikhonov regularization and the total variation minimization. The subsequent numerical optimization was fulfilled by a primal-dual interior-point method tailored to our problem’s specific structure. Our simulations used realistic, fiber-included heart models consisting of up to 18,000 nodes, much finer than any inverse models previously reported. With synthetic ischemia data we localized ischemic regions with roughly a 10% false-negative rate or a 20% false-positive rate under conditions up to 5% input noise. With ischemia data measured from animal experiments, we reconstructed TMPs with roughly 0.9 correlation with the ground truth. While precisely estimating the TMP in general cases remains an open problem, our study shows the feasibility of reconstructing TMP during the ST interval as a means of ischemia localization.
Optimization for performance-based design under seismic demands, including social costs
NASA Astrophysics Data System (ADS)
Möller, Oscar; Foschi, Ricardo O.; Ascheri, Juan P.; Rubinstein, Marcelo; Grossman, Sergio
2015-06-01
Performance-based design in earthquake engineering is a structural optimization problem that has, as the objective, the determination of design parameters for the minimization of total costs, while at the same time satisfying minimum reliability levels for the specified performance criteria. Total costs include those for construction and structural damage repairs, those associated with non-structural components and the social costs of economic losses, injuries and fatalities. This paper presents a general framework to approach this problem, using a numerical optimization strategy and incorporating the use of neural networks for the evaluation of dynamic responses and the reliability levels achieved for a given set of design parameters. The strategy is applied to an example of a three-story office building. The results show the importance of considering the social costs, and the optimum failure probabilities when minimum reliability constraints are not taken into account.
A superlinear interior points algorithm for engineering design optimization
NASA Technical Reports Server (NTRS)
Herskovits, J.; Asquier, J.
1990-01-01
We present a quasi-Newton interior points algorithm for nonlinear constrained optimization. It is based on a general approach consisting of the iterative solution in the primal and dual spaces of the equalities in Karush-Kuhn-Tucker optimality conditions. This is done in such a way to have primal and dual feasibility at each iteration, which ensures satisfaction of those optimality conditions at the limit points. This approach is very strong and efficient, since at each iteration it only requires the solution of two linear systems with the same matrix, instead of quadratic programming subproblems. It is also particularly appropriate for engineering design optimization inasmuch at each iteration a feasible design is obtained. The present algorithm uses a quasi-Newton approximation of the second derivative of the Lagrangian function in order to have superlinear asymptotic convergence. We discuss theoretical aspects of the algorithm and its computer implementation.
Airfoil Design and Optimization by the One-Shot Method
NASA Technical Reports Server (NTRS)
Kuruvila, G.; Taasan, Shlomo; Salas, M. D.
1995-01-01
An efficient numerical approach for the design of optimal aerodynamic shapes is presented in this paper. The objective of any optimization problem is to find the optimum of a cost function subject to a certain state equation (governing equation of the flow field) and certain side constraints. As in classical optimal control methods, the present approach introduces a costate variable (Lagrange multiplier) to evaluate the gradient of the cost function. High efficiency in reaching the optimum solution is achieved by using a multigrid technique and updating the shape in a hierarchical manner such that smooth (low-frequency) changes are done separately from high-frequency changes. Thus, the design variables are changed on a grid where their changes produce nonsmooth (high-frequency) perturbations that can be damped efficiently by the multigrid. The cost of solving the optimization problem is approximately two to three times the cost of the equivalent analysis problem.
Reliability-Based Design Optimization of a Composite Airframe Component
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Pai, Shantaram S.; Coroneos, Rula M.
2009-01-01
A stochastic design optimization methodology (SDO) has been developed to design components of an airframe structure that can be made of metallic and composite materials. The design is obtained as a function of the risk level, or reliability, p. The design method treats uncertainties in load, strength, and material properties as distribution functions, which are defined with mean values and standard deviations. A design constraint or a failure mode is specified as a function of reliability p. Solution to stochastic optimization yields the weight of a structure as a function of reliability p. Optimum weight versus reliability p traced out an inverted-S-shaped graph. The center of the inverted-S graph corresponded to 50 percent (p = 0.5) probability of success. A heavy design with weight approaching infinity could be produced for a near-zero rate of failure that corresponds to unity for reliability p (or p = 1). Weight can be reduced to a small value for the most failure-prone design with a reliability that approaches zero (p = 0). Reliability can be changed for different components of an airframe structure. For example, the landing gear can be designed for a very high reliability, whereas it can be reduced to a small extent for a raked wingtip. The SDO capability is obtained by combining three codes: (1) The MSC/Nastran code was the deterministic analysis tool, (2) The fast probabilistic integrator, or the FPI module of the NESSUS software, was the probabilistic calculator, and (3) NASA Glenn Research Center s optimization testbed CometBoards became the optimizer. The SDO capability requires a finite element structural model, a material model, a load model, and a design model. The stochastic optimization concept is illustrated considering an academic example and a real-life raked wingtip structure of the Boeing 767-400 extended range airliner made of metallic and composite materials.
Lacroix, Frédéric; Beddar, A. Sam; Guillot, Mathieu; Beaulieu, Luc; Gingras, Luc
2009-01-01
Purpose: The design of novel plastic scintillation detectors (PSDs) is impeded by the lack of a suitable framework to simulate and predict their performance. The authors propose to use the signal-to-noise ratio (SNR) to model the performance of PSDs that use charge-coupled devices (CCDs) as photodetectors. Methods: In PSDs using CCDs, the SNR is inversely related to the normalized standard deviation of the dose measurement. Thus, optimizing the SNR directly optimizes the system’s precision. In this work, a model of SNR as a function of the system parameters is derived for optical fiber-based PSD systems. Furthermore, this proposed model is validated using experimental results. A formula for the efficiency of fiber coupling to CCDs is derived and used to simulate the performance of a PSD under varying magnifications. Results: The proposed model is shown to simulate the experimental performance of an actual PSD to a suitable degree of accuracy under various conditions. Conclusions: The SNR constitutes a useful tool to simulate the dosimetric precision of PSDs. Using the SNR model, recommendations for the design and optimization of PSDs are provided. Using the same framework, recommendations for non-fiber-based PSDs are also provided. PMID:19994531
Evaluation of Methods for Multidisciplinary Design Optimization (MDO). Part 2
NASA Technical Reports Server (NTRS)
Kodiyalam, Srinivas; Yuan, Charles; Sobieski, Jaroslaw (Technical Monitor)
2000-01-01
A new MDO method, BLISS, and two different variants of the method, BLISS/RS and BLISS/S, have been implemented using iSIGHT's scripting language and evaluated in this report on multidisciplinary problems. All of these methods are based on decomposing a modular system optimization system into several subtasks optimization, that may be executed concurrently, and the system optimization that coordinates the subtasks optimization. The BLISS method and its variants are well suited for exploiting the concurrent processing capabilities in a multiprocessor machine. Several steps, including the local sensitivity analysis, local optimization, response surfaces construction and updates are all ideally suited for concurrent processing. Needless to mention, such algorithms that can effectively exploit the concurrent processing capabilities of the compute servers will be a key requirement for solving large-scale industrial design problems, such as the automotive vehicle problem detailed in Section 3.4.
The effect of code expanding optimizations on instruction cache design
NASA Technical Reports Server (NTRS)
Chen, William Y.; Chang, Pohua P.; Conte, Thomas M.; Hwu, Wen-Mei W.
1991-01-01
It is shown that code expanding optimizations have strong and non-intuitive implications on instruction cache design. Three types of code expanding optimizations are studied: instruction placement, function inline expansion, and superscalar optimizations. Overall, instruction placement reduces the miss ratio of small caches. Function inline expansion improves the performance for small cache sizes, but degrades the performance of medium caches. Superscalar optimizations increases the cache size required for a given miss ratio. On the other hand, they also increase the sequentiality of instruction access so that a simple load-forward scheme effectively cancels the negative effects. Overall, it is shown that with load forwarding, the three types of code expanding optimizations jointly improve the performance of small caches and have little effect on large caches.
Commercial Building Design Pathways Using Optimization Analysis: Preprint
Long, N.; Hirsch, A.; Lobato, C.; Macumber, D.
2010-08-01
Whole-building simulation and analysis has demonstrated a significant energy savings potential in a wide variety of design projects. Commercial building design, however, traditionally integrates simulation and modeling analyses too late in the design process to make a substantial impact on energy use. The National Renewable Energy Laboratory (NREL) commercial building group created an optimization platform called Opt-E-Plus that uses multivariate and multi-objective optimization theory to navigate a large parameter space and find economically valid, energy-saving solutions. The analysis results provide designers and engineers valuable information that influences the design. The pathways are not full 'construction ready' design alternatives; rather, they offer guidance about performance and cost criteria to reach a range of energy and economic goals. Having this knowledge early in the design phase helps designers establish project goals and direct the design pathway before they make important decisions. Opt-E-Plus has been deployed on several projects, including a retrofit mixed-use building, a new NREL office building, and several nationwide design guides. Each of these projects had different design criteria, goals, and audiences. In each case the analysis results provided pathways that helped inform the design process.
From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation
Blazewicz, Marek; Hinder, Ian; Koppelman, David M.; Brandt, Steven R.; Ciznicki, Milosz; Kierzynka, Michal; Löffler, Frank; Schnetter, Erik; Tao, Jian
2013-01-01
Starting from a high-level problem description in terms of partial differential equations using abstract tensor notation, the Chemora framework discretizes, optimizes, and generates complete high performance codes for a wide range of compute architectures. Chemora extends the capabilities of Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient manner for complex applications, without low-level code tuning. Chemora achieves parallelism through MPI and multi-threading, combining OpenMP and CUDA. Optimizations include high-level code transformations, efficient loop traversal strategies, dynamically selected data and instruction cache usage strategies, and JIT compilation of GPU code tailored to the problem characteristics. The discretizationmore » is based on higher-order finite differences on multi-block domains. Chemora's capabilities are demonstrated by simulations of black hole collisions. This problem provides an acid test of the framework, as the Einstein equations contain hundreds of variables and thousands of terms.« less
Sankaran, Sethuraman; Humphrey, Jay D.; Marsden, Alison L.
2013-01-01
Computational models for vascular growth and remodeling (G&R) are used to predict the long-term response of vessels to changes in pressure, flow, and other mechanical loading conditions. Accurate predictions of these responses are essential for understanding numerous disease processes. Such models require reliable inputs of numerous parameters, including material properties and growth rates, which are often experimentally derived, and inherently uncertain. While earlier methods have used a brute force approach, systematic uncertainty quantification in G&R models promises to provide much better information. In this work, we introduce an efficient framework for uncertainty quantification and optimal parameter selection, and illustrate it via several examples. First, an adaptive sparse grid stochastic collocation scheme is implemented in an established G&R solver to quantify parameter sensitivities, and near-linear scaling with the number of parameters is demonstrated. This non-intrusive and parallelizable algorithm is compared with standard sampling algorithms such as Monte-Carlo. Second, we determine optimal arterial wall material properties by applying robust optimization. We couple the G&R simulator with an adaptive sparse grid collocation approach and a derivative-free optimization algorithm. We show that an artery can achieve optimal homeostatic conditions over a range of alterations in pressure and flow; robustness of the solution is enforced by including uncertainty in loading conditions in the objective function. We then show that homeostatic intramural and wall shear stress is maintained for a wide range of material properties, though the time it takes to achieve this state varies. We also show that the intramural stress is robust and lies within 5% of its mean value for realistic variability of the material parameters. We observe that prestretch of elastin and collagen are most critical to maintaining homeostasis, while values of the material properties are
Genetic-evolution-based optimization methods for engineering design
NASA Technical Reports Server (NTRS)
Rao, S. S.; Pan, T. S.; Dhingra, A. K.; Venkayya, V. B.; Kumar, V.
1990-01-01
This paper presents the applicability of a biological model, based on genetic evolution, for engineering design optimization. Algorithms embodying the ideas of reproduction, crossover, and mutation are developed and applied to solve different types of structural optimization problems. Both continuous and discrete variable optimization problems are solved. A two-bay truss for maximum fundamental frequency is considered to demonstrate the continuous variable case. The selection of locations of actuators in an actively controlled structure, for minimum energy dissipation, is considered to illustrate the discrete variable case.
Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
2005-01-01
This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.
Optimized bio-inspired stiffening design for an engine nacelle.
Lazo, Neil; Vodenitcharova, Tania; Hoffman, Mark
2015-12-01
Structural efficiency is a common engineering goal in which an ideal solution provides a structure with optimized performance at minimized weight, with consideration of material mechanical properties, structural geometry, and manufacturability. This study aims to address this goal in developing high performance lightweight, stiff mechanical components by creating an optimized design from a biologically-inspired template. The approach is implemented on the optimization of rib stiffeners along an aircraft engine nacelle. The helical and angled arrangements of cellulose fibres in plants were chosen as the bio-inspired template. Optimization of total displacement and weight was carried out using a genetic algorithm (GA) coupled with finite element analysis. Iterations showed a gradual convergence in normalized fitness. Displacement was given higher emphasis in optimization, thus the GA optimization tended towards individual designs with weights near the mass constraint. Dominant features of the resulting designs were helical ribs with rectangular cross-sections having large height-to-width ratio. Displacement reduction was at 73% as compared to an unreinforced nacelle, and is attributed to the geometric features and layout of the stiffeners, while mass is maintained within the constraint. PMID:26531222
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Pai, Shantaram S.; Coroneos, Rula M.
2010-01-01
Structural design generated by traditional method, optimization method and the stochastic design concept are compared. In the traditional method, the constraints are manipulated to obtain the design and weight is back calculated. In design optimization, the weight of a structure becomes the merit function with constraints imposed on failure modes and an optimization algorithm is used to generate the solution. Stochastic design concept accounts for uncertainties in loads, material properties, and other parameters and solution is obtained by solving a design optimization problem for a specified reliability. Acceptable solutions were produced by all the three methods. The variation in the weight calculated by the methods was modest. Some variation was noticed in designs calculated by the methods. The variation may be attributed to structural indeterminacy. It is prudent to develop design by all three methods prior to its fabrication. The traditional design method can be improved when the simplified sensitivities of the behavior constraint is used. Such sensitivity can reduce design calculations and may have a potential to unify the traditional and optimization methods. Weight versus reliabilitytraced out an inverted-S-shaped graph. The center of the graph corresponded to mean valued design. A heavy design with weight approaching infinity could be produced for a near-zero rate of failure. Weight can be reduced to a small value for a most failure-prone design. Probabilistic modeling of load and material properties remained a challenge.
An evolutionary based Bayesian design optimization approach under incomplete information
NASA Astrophysics Data System (ADS)
Srivastava, Rupesh; Deb, Kalyanmoy
2013-02-01
Design optimization in the absence of complete information about uncertain quantities has been recently gaining consideration, as expensive repetitive computation tasks are becoming tractable due to the invention of faster and parallel computers. This work uses Bayesian inference to quantify design reliability when only sample measurements of the uncertain quantities are available. A generalized Bayesian reliability based design optimization algorithm has been proposed and implemented for numerical as well as engineering design problems. The approach uses an evolutionary algorithm (EA) to obtain a trade-off front between design objectives and reliability. The Bayesian approach provides a well-defined link between the amount of available information and the reliability through a confidence measure, and the EA acts as an efficient optimizer for a discrete and multi-dimensional objective space. Additionally, a GPU-based parallelization study shows computational speed-up of close to 100 times in a simulated scenario wherein the constraint qualification checks may be time consuming and could render a sequential implementation that can be impractical for large sample sets. These results show promise for the use of a parallel implementation of EAs in handling design optimization problems under uncertainties.
Use of the Collaborative Optimization Architecture for Launch Vehicle Design
NASA Technical Reports Server (NTRS)
Braun, R. D.; Moore, A. A.; Kroo, I. M.
1996-01-01
Collaborative optimization is a new design architecture specifically created for large-scale distributed-analysis applications. In this approach, problem is decomposed into a user-defined number of subspace optimization problems that are driven towards interdisciplinary compatibility and the appropriate solution by a system-level coordination process. This decentralized design strategy allows domain-specific issues to be accommodated by disciplinary analysts, while requiring interdisciplinary decisions to be reached by consensus. The present investigation focuses on application of the collaborative optimization architecture to the multidisciplinary design of a single-stage-to-orbit launch vehicle. Vehicle design, trajectory, and cost issues are directly modeled. Posed to suit the collaborative architecture, the design problem is characterized by 5 design variables and 16 constraints. Numerous collaborative solutions are obtained. Comparison of these solutions demonstrates the influence which an priori ascent-abort criterion has on development cost. Similarly, objective-function selection is discussed, demonstrating the difference between minimum weight and minimum cost concepts. The operational advantages of the collaborative optimization
Optimal Input Signal Design for Data-Centric Estimation Methods
Deshpande, Sunil; Rivera, Daniel E.
2013-01-01
Data-centric estimation methods such as Model-on-Demand and Direct Weight Optimization form attractive techniques for estimating unknown functions from noisy data. These methods rely on generating a local function approximation from a database of regressors at the current operating point with the process repeated at each new operating point. This paper examines the design of optimal input signals formulated to produce informative data to be used by local modeling procedures. The proposed method specifically addresses the distribution of the regressor vectors. The design is examined for a linear time-invariant system under amplitude constraints on the input. The resulting optimization problem is solved using semidefinite relaxation methods. Numerical examples show the benefits in comparison to a classical PRBS input design. PMID:24317042
Fatigue reliability based optimal design of planar compliant micropositioning stages.
Wang, Qiliang; Zhang, Xianmin
2015-10-01
Conventional compliant micropositioning stages are usually developed based on static strength and deterministic methods, which may lead to either unsafe or excessive designs. This paper presents a fatigue reliability analysis and optimal design of a three-degree-of-freedom (3 DOF) flexure-based micropositioning stage. Kinematic, modal, static, and fatigue stress modelling of the stage were conducted using the finite element method. The maximum equivalent fatigue stress in the hinges was derived using sequential quadratic programming. The fatigue strength of the hinges was obtained by considering various influencing factors. On this basis, the fatigue reliability of the hinges was analysed using the stress-strength interference method. Fatigue-reliability-based optimal design of the stage was then conducted using the genetic algorithm and MATLAB. To make fatigue life testing easier, a 1 DOF stage was then optimized and manufactured. Experimental results demonstrate the validity of the approach.
Fatigue reliability based optimal design of planar compliant micropositioning stages
NASA Astrophysics Data System (ADS)
Wang, Qiliang; Zhang, Xianmin
2015-10-01
Conventional compliant micropositioning stages are usually developed based on static strength and deterministic methods, which may lead to either unsafe or excessive designs. This paper presents a fatigue reliability analysis and optimal design of a three-degree-of-freedom (3 DOF) flexure-based micropositioning stage. Kinematic, modal, static, and fatigue stress modelling of the stage were conducted using the finite element method. The maximum equivalent fatigue stress in the hinges was derived using sequential quadratic programming. The fatigue strength of the hinges was obtained by considering various influencing factors. On this basis, the fatigue reliability of the hinges was analysed using the stress-strength interference method. Fatigue-reliability-based optimal design of the stage was then conducted using the genetic algorithm and MATLAB. To make fatigue life testing easier, a 1 DOF stage was then optimized and manufactured. Experimental results demonstrate the validity of the approach.
Fatigue reliability based optimal design of planar compliant micropositioning stages.
Wang, Qiliang; Zhang, Xianmin
2015-10-01
Conventional compliant micropositioning stages are usually developed based on static strength and deterministic methods, which may lead to either unsafe or excessive designs. This paper presents a fatigue reliability analysis and optimal design of a three-degree-of-freedom (3 DOF) flexure-based micropositioning stage. Kinematic, modal, static, and fatigue stress modelling of the stage were conducted using the finite element method. The maximum equivalent fatigue stress in the hinges was derived using sequential quadratic programming. The fatigue strength of the hinges was obtained by considering various influencing factors. On this basis, the fatigue reliability of the hinges was analysed using the stress-strength interference method. Fatigue-reliability-based optimal design of the stage was then conducted using the genetic algorithm and MATLAB. To make fatigue life testing easier, a 1 DOF stage was then optimized and manufactured. Experimental results demonstrate the validity of the approach. PMID:26520994
Reliability-based design optimization under stationary stochastic process loads
NASA Astrophysics Data System (ADS)
Hu, Zhen; Du, Xiaoping
2016-08-01
Time-dependent reliability-based design ensures the satisfaction of reliability requirements for a given period of time, but with a high computational cost. This work improves the computational efficiency by extending the sequential optimization and reliability analysis (SORA) method to time-dependent problems with both stationary stochastic process loads and random variables. The challenge of the extension is the identification of the most probable point (MPP) associated with time-dependent reliability targets. Since a direct relationship between the MPP and reliability target does not exist, this work defines the concept of equivalent MPP, which is identified by the extreme value analysis and the inverse saddlepoint approximation. With the equivalent MPP, the time-dependent reliability-based design optimization is decomposed into two decoupled loops: deterministic design optimization and reliability analysis, and both are performed sequentially. Two numerical examples are used to show the efficiency of the proposed method.
Reliable numerical computation in an optimal output-feedback design
NASA Technical Reports Server (NTRS)
Vansteenwyk, Brett; Ly, Uy-Loi
1991-01-01
A reliable algorithm is presented for the evaluation of a quadratic performance index and its gradients with respect to the controller design parameters. The algorithm is a part of a design algorithm for optimal linear dynamic output-feedback controller that minimizes a finite-time quadratic performance index. The numerical scheme is particularly robust when it is applied to the control-law synthesis for systems with densely packed modes and where there is a high likelihood of encountering degeneracies in the closed-loop eigensystem. This approach through the use of an accurate Pade series approximation does not require the closed-loop system matrix to be diagonalizable. The algorithm was included in a control design package for optimal robust low-order controllers. Usefulness of the proposed numerical algorithm was demonstrated using numerous practical design cases where degeneracies occur frequently in the closed-loop system under an arbitrary controller design initialization and during the numerical search.