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Results for “global optimization”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Finite element/finite volume approaches with adaptive time stepping strategies for transient thermal problems

An adaptive time stepping strategy for transient thermal analysis of engineering systems is described which computes the time step based on the local truncation error with a good global error control and obtains optimal time steps to be used during the analysis. Combined mesh partitionings involving FEM/FVM meshes based on physical situations to obtain numerically improved physical representations are also proposed. Numerical test cases are described and comparative pros and cons are identified for practical situations.

Mohan, Ram V.↗
NASA NTRS1993

Function approximations

A development status evaluation is presented for applications of main approximation concepts in nonlinear programming for structural optimization requiring local, midrange, and global approximations. It is noted that there is not yet a sufficient basis for a comprehensive evaluation of relative effectiveness among approximation concepts. Attention is presently given to the careful selection of intermediate-variable and response quantity, and improvements in the traditional Taylor-series expansion by inclusion of either some higher-order terms or information at various points in the design space.

Barthelemy, J.-F. M.↗
NASA NTRS2011

Communal Sensor Network for Adaptive Noise Reduction in Aircraft Engine Nacelles

Emergent behavior, a subject of much research in biology, sociology, and economics, is a foundational element of Complex Systems Science and is apropos in the design of sensor network systems. To demonstrate engineering for emergent behavior, a novel approach in the design of a sensor/actuator network is presented maintaining optimal noise attenuation as an adaptation to changing acoustic conditions. Rather than use the conventional approach where sensors are managed by a central controller, this new paradigm uses a biomimetic model where sensor/actuators cooperate as a community of autonomous organisms, sharing with neighbors to control impedance based on local information. From the combination of all individual actions, an optimal attenuation emerges for the global system.

Jones, Kennie H.↗
NASA NTRS1997

Optimization of a Parallel Ocean General Circulation Model

Global climate modeling is one of the grand chalenges of computational science, and ocean modeling plays an important role in both understanding the current climatic conditions and predicting the future climate change.

Optimization Ocean Modeling Parallel Computations↗
NASA NTRS1987

Computable optimal value bounds for generalized convex programs

It has been shown by Fiacco that convexity or concavity of the optimal value of a parametric nonlinear programming problem can readily be exploited to calculate global parametric upper and lower bounds on the optimal value function. The approach is attractive because it involves manipulation of information normally required to characterize solution optimality. A procedure is briefly described for calculating and improving the bounds as well as its extensions to generalized convex and concave functions. Several areas of applications are also indicated.

Fiacco, Anthony V.↗
DOE OSTI2016

Global Sensitivity Analysis with Small Sample Sizes: Ordinary Least Squares Approach

A new version of global sensitivity analysis is developed in this paper. This new version coupled with tools from statistics, machine learning, and optimization can devise small sample sizes that allow for the accurate ordering of sensitivity coefficients for the first 10-30 most sensitive chemical reactions in complex chemical-kinetic mechanisms, and is particularly useful for studying the chemistry in realistic devices. A key part of the paper is calibration of these small samples. Because these small sample sizes are developed for use in realistic combustion devices, the calibration is done over the ranges of conditions in such devices, with a test case being the operating conditions of a compression ignition engine studied earlier. Compression ignition engines operate under low-temperature combustion conditions with quite complicated chemistry making this calibration difficult, leading to the possibility of false positives and false negatives in the ordering of the reactions. So an important aspect of the paper is showing how to handle the trade-off between false positives and false negatives using ideas from the multiobjective optimization literature. The combination of the new global sensitivity method and the calibration are sample sizes a factor of approximately 10 times smaller than were available with our previous algorithm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2009

Numerical Optimization of the Thermal Field in Bridgman Detached Growth

The global modeling of the thermal field in two vertical Bridgman-like crystal growth configurations, has been performed to get optimal thermal conditions for a successful detached growth of Ge and CdTe crystals. These computations are performed using the CrysMAS code and expand upon our previous analysis [1] that propose a new mechanism involving the thermal field and meniscus position to explain stable conditions for dewetted Bridgman growth. The analysis of the vertical Bridgman configuration with two heaters, used by Palosz et al. for the detached growth of Ge, shows, consistent with their results, that the large wetting angle of germanium on boron nitride surfaces was an important factor to promote a successful detached growth. Our computations predict that by initiating growth much higher into the hot zone of the furnace, the thermal conditions will be favorable for continued detachment even for systems that did not exhibit high contact angles. The computations performed for a vertical gradient freeze configuration with three heaters representative of that used for the detached growth of CdTe, show favorable thermal conditions for dewetting during the entirely growth run described. Improved thermal conditions are also predicted for coated silica crucibles when the solid-liquid interface advances higher into the hot zone during the solidification process. The second set of experiments on CdTe growth described elsewhere has shown the reattachment of the crystal to the crucible after few centimeters of dewetted growth. The thermal modeling of this configuration shows a second solidification front appearing at the top of the sample and approaching the middle line across the third heater. In these conditions, the crystal grows detached from the bottom, but will be attached to the crucible in the upper part because of the solidification without gap in this region. The solidification with two interfaces can be avoided when the top of the sample is positioned below the middle position of the third furnace.

Stelian, C.↗
NASA NTRS1994

Optimal Preprocessing Of GPS Data

Improved technique for preprocessing data from Global Positioning System (GPS) receiver reduces processing time and number of data to be stored. Technique optimal in sense it maintains strength of data. Also sometimes increases ability to resolve ambiguities in numbers of cycles of received GPS carrier signals.

Wu, Sien-Chong↗
DOE OSTI2021

Asynchronous Iterative Solvers for Extreme-Scale Computing

The Asynchronous Iterative Solvers for Extreme-Scale Computing (AsyncIS) project aims to explore more efficient numerical algorithms by decreasing their overhead. AsyncIS does this by replacing the outer Krylov subspace solver with an asynchronous optimized Schwarz method, thereby removing the global synchronization and bulk synchronous operations typically used in numerical codes. AsyncIS—a U.S. Department of Energy (DOE)-funded collaboration between Georgia Tech, the University of Tennessee, Knoxville, Temple University, and Sandia National Laboratories—also focuses on the development and optimization of asynchronous preconditioners (i.e., preconditioners that are generated and/or applied in an asynchronous fashion). The novel preconditioning algorithms that provide fine-grained parallelism enable preconditioned Krylov solvers to run efficiently on large-scale distributed systems and manycore accelerators like GPUs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Non-linear Multidimensional Optimization for use in Wire Scanner Fitting

To ensure experiment efficiency and quality from the Continuous Electron Beam Accelerator at Jefferson Lab, beam energy, size, and position must be measured. Wire scanners are devices inserted into the beamline to produce measurements which are used to obtain beam properties. Extracting physical information from the wire scanner measurements begins by fitting Gaussian curves to the data. This study focuses on optimizing and automating this curve-fitting procedure. We use a hybrid approach combining the efficiency of Newton Conjugate Gradient (NCG) method with the global convergence of three nature-inspired (NI) optimization approaches: genetic algorithm, differential evolution, and particle-swarm. In this Python-implemented approach, augmenting the locally-convergent NCG with one of the globally-convergent methods ensures the quality, robustness, and automation of curve-fitting. After comparing the methods, we establish that given an initial data-derived guess, each finds a solution with the same chi-square- a measurement of the agreement of the fit to the data. NCG is the fastest method, so it is the first to attempt data-fitting. The curve-fitting procedure escalates to one of the globally-convergent NI methods only if NCG fails, thereby ensuring a successful fit. This method allows for the most optimal signal fit and can be easily applied to similar problems.

Terzic, Balsa↗
DOE OSTI2019

Efficient Distributed Optimization of Wind Farms Using Proximal Primal-Dual Algorithms: Preprint

This paper presents a distributed approach to perform real-time optimization of large wind farms. Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. This paper optimizes the overall power produced by a wind farm by formulating and solving a nonconvex optimization problem where the yaw angles are optimized to allow some turbines to operate in misaligned conditions and shape the aerodynamic interactions in a favorable way. The solution of the nonconvex smooth problem is tackled using a proximal primal-dual gradient method, which provably identifies a first-order stationary solution in a global sublinear manner. By adding auxiliary optimization variables for every pair of turbines that are coupled aerodynamically and properly adding consensus constraints into the underlying problem, a distributed algorithm with turbine-to-turbine message passing is obtained; this allows for turbines to be optimized in parallel using local information rather than information from the whole wind farm. This algorithm is computationally light, as it involves closed-form updates. This approach is demonstrated on a large wind farm with 60 turbines. The results indicate that similar performance can be achieved as with finite-difference gradient-based optimization at a fraction of the computational time and thus approach real-time control/optimization.

17 WIND ENERGY↗
DOE OSTI2019

Efficient Distributed Optimization of Wind Farms Using Proximal Primal-Dual Algorithms

This paper presents a distributed approach to performing real-time optimization of large wind farms. Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. This paper optimizes the overall power produced by a wind farm by formulating and solving a nonconvex optimization problem where the yaw angles are optimized to allow some turbines to operate in misaligned conditions and shape the aerodynamic interactions in a favorable way. The solution of the nonconvex smooth problem is tackled using a proximal primal-dual gradient method, which provably identifies a first-order stationary solution in a global sublinear manner. By adding auxiliary optimization variables for every pair of turbines that are coupled aerodynamically, and properly adding consensus constraints into the underlying problem, a distributed algorithm with turbine-to-turbine message passing is obtained; this allows for turbines to be optimized in parallel using local information rather than information from the whole wind farm. This algorithm is computationally light, as it involves closed-form updates. This approach is demonstrated on a large wind farm with 60 turbines. The results indicate that similar performance can be achieved as with finite-difference gradient-based optimization at a fraction of the computational time and thus approaching real-time control/optimization.

17 WIND ENERGY↗
DOE OSTI2020

Online accelerator optimization with a machine learning-based stochastic algorithm

Abstract Online optimization is critical for realizing the design performance of accelerators. Highly efficient stochastic optimization algorithms are needed for many online accelerator optimization problems in order to find the global optimum in the non-linear, coupled parameter space. In this study, we propose to use the multi-generation Gaussian process optimizer for online accelerator optimization and demonstrate that the algorithm is significantly more efficient than other stochastic algorithms that are commonly used in the accelerator community.

Zhang, Zhe (ORCID:0000000281430381)↗
DOE OSTI2014

Selecting Essential Information for Biosurveillance—A Multi-Criteria Decision Analysis

The National Strategy for Biosurveillancedefines biosurveillance as ‘‘the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels.’’ However, the strategy does not specify how ‘‘essential information’’ is to be identified and integrated into the current biosurveillance enterprise, or what the metrics qualify information as being ‘‘essential’’. Thequestion of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of ‘‘essential information’’ for use in biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

An active-set algorithm and a trust-region approach in constrained minimax problem

In this paper, we propose an algorithm to solve a finite minimax problem with side constraints. An active-set strategy is used in the algorithm to transform inequality constraints to equality constraints. This allows the use of the well-developed techniques for solving the equality constrained optimization problems. A trust-region globalization strategy is added to the proposed algorithm to ensure global convergence. A projected Hessian technique is used in the algorithm to overcome the difficulty of having an infeasible trust-region subproblem.A global convergence theory for the proposed algorithm is presented under standard assumptions. Finally, numerical experiments are reported to indicate that the new algorithm performs efficiently in practice.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

Multiobjective Optimization Using a Pareto Differential Evolution Approach

Differential Evolution is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. In this paper, the Differential Evolution algorithm is extended to multiobjective optimization problems by using a Pareto-based approach. The algorithm performs well when applied to several test optimization problems from the literature.

Madavan, Nateri K.↗
NASA NTRS1985

Influence of coherent mesoscale structures on satellite-based Doppler lidar wind measurements

Efforts to develop display routines for overlaying gridded and nongridded data sets are discussed. The primary objective is to have the capability to review global patterns of winds and lidar samples; to zoom in on particular wind features or global areas; and to display contours of wind components and derived fields (e.g., divergence, vorticity, deformation, etc.). Current considerations in support of a polar orbiting shuttle lidar mission are discussed. Ground truth for a shuttle lidar experiment may be limited to fortuitous alignment of lidar wind profiles and scheduled rawinsonde profiles. Any improvement on this would require special rawinsonde launches and/or optimization of the shuttle orbit with global wind measurement networks.

Emmitt, G. D.↗
NASA NTRS2016

Global Simulation of Aviation Operations

The simulation and analysis of global air traffic is limited due to a lack of simulation tools and the difficulty in accessing data sources. This paper provides a global simulation of aviation operations combining flight plans and real air traffic data with historical commercial city-pair aircraft type and schedule data and global atmospheric data. The resulting capability extends the simulation and optimization functions of NASA's Future Air Traffic Management Concept Evaluation Tool (FACET) to global scale. This new capability is used to present results on the evolution of global air traffic patterns from a concentration of traffic inside US, Europe and across the Atlantic Ocean to a more diverse traffic pattern across the globe with accelerated growth in Asia, Australia, Africa and South America. The simulation analyzes seasonal variation in the long-haul wind-optimal traffic patterns in six major regions of the world and provides potential time-savings of wind-optimal routes compared with either great circle routes or current flight-plans if available.

simulation↗