Results for “optimization analysis” — Page 39

NASA NTRS2001

Reliability Analysis and Reliability-Based Design Optimization of Circular Composite Cylinders Under Axial Compression

This report describes the preliminary results of an investigation on component reliability analysis and reliability-based design optimization of thin-walled circular composite cylinders with average diameter and average length of 15 inches. Structural reliability is based on axial buckling strength of the cylinder. Both Monte Carlo simulation and First Order Reliability Method are considered for reliability analysis with the latter incorporated into the reliability-based structural optimization problem. To improve the efficiency of reliability sensitivity analysis and design optimization solution, the buckling strength of the cylinder is estimated using a second-order response surface model. The sensitivity of the reliability index with respect to the mean and standard deviation of each random variable is calculated and compared. The reliability index is found to be extremely sensitive to the applied load and elastic modulus of the material in the fiber direction. The cylinder diameter was found to have the third highest impact on the reliability index. Also the uncertainty in the applied load, captured by examining different values for its coefficient of variation, is found to have a large influence on cylinder reliability. The optimization problem for minimum weight is solved subject to a design constraint on element reliability index. The methodology, solution procedure and optimization results are included in this report.

Rais-Rohani, Masoud↗

NASA NTRSDate not supplied

Cloud Optimized Data Formats

Cloud computing offers the promise of being able to analyze Big Data earth Observations at scale, by allowing scientists to deploy many nodes at once to analyze the data. However, in order to take full advantage of cloud scalability, it is often necessary to reorganize and reformat the data to enable fine-grained, parallel access to the data in Web Object Storage. NASA recently conducted a study of several formats that are optimized for analysis in the cloud: Parquet, zarr, HDF (Hierarchical Data Format) in the Cloud, and Cloud-Optimized GeoTIFF (Tagged Image File Format). They were compared against non-cloud-optimized formats, netCDF (network Common Data Form) and GeoTIFF, with criteria based both on stewardship and analysis performance.

Christopher Lynnes↗

NASA NTRS1991

Design of a blade-stiffened composite panel with a hole

A procedure is developed analytically to design and produce an optimized stiffened panel with a centrally located hole. The PASCO panel-buckling analysis and sizing code is employed with the Engineering Analysis Language (EAL) code for finite-element analysis to optimize the design variables. Important design considerations are stiffener height and ply thicknesses in the skin and the stiffener laminates. The EAL code calculates the maximum strains around the hole boundary, and the resulting constraints are combined with the buckling constraint developed by the PASCO code. Cooptimization of the two codes is effected until convergence on the optimal solution is achieved. Results of the analytical procedure are experimentally verified by applying a compressive end-load to graphite-fiber laminated plates with center holes. The experimental results support the analytical procedure, and it is found that for the same weight, the soft-skin designs tested are found to have the best tolerance characteristics and resistance to failure.

Nagendra, S.↗

NASA NTRS1992

A study of equation solvers for linear and non-linear finite element analysis on parallel processing computers

Concurrent computing environments provide the means to achieve very high performance for finite element analysis of systems, provided the algorithms take advantage of multiple processors. The authors have examined several algorithms for both linear and nonlinear finite element analysis. The performance of these algorithms on an Alliant FX/80 parallel supercomputer has been studied. For single load case linear analysis, the optimal solution algorithm is strongly problem dependent. For multiple load cases or nonlinear analysis through a modified Newton-Raphson method, decomposition algorithms are shown to have a decided advantage over element-by-element preconditioned conjugate gradient algorithms.

Watson, Brian C.↗

NASA NTRS1999

Framework Requirements for MDO Application Development

Frameworks or problem solving environments that support application development form an active area of research. The Multidisciplinary Optimization Branch at NASA Langley Research Center is investigating frameworks for supporting multidisciplinary analysis and optimization research. The Branch has generated a list of framework requirements, based on the experience gained from the Framework for Interdisciplinary Design Optimization project and the information acquired during a framework evaluation process. In this study, four existing frameworks are examined against these requirements. The results of this examination suggest several topics for further framework research.

Salas, A. O.↗

NASA NTRS2005

Analysis of Chemical, REP, and SEP missions to the Trojan asteroids

Recent studies suggest significant benefits from using 1st and 2nd generation Radioisotope Power Systems (RPS) as a power source for electric propulsion (EP) missions to the outer planets. This study focuses on trajectories to the Trojan asteroids. A high level analysis is performed with chemical trajectories to determine potential canidates for REP trajectory optimization. Extensive analysis of direct trajectories using REP is performed on these candidates. Solar Electric Propulsion (SEP) trajectories are also considered for comparison against REP trajectories.

Trojan asteroids↗

DOE OSTI2017

Estimating Jupiter’s Gravity Field Using Juno Measurements, Trajectory Estimation Analysis, and a Flow Model Optimization

The upcoming Juno spacecraft measurements have the potential of improving our knowledge of Jupiter’s gravity field. The analysis of the Juno Doppler data will provide a very accurate reconstruction of spatial gravity variations, but these measurements will be very accurate only over a limited latitudinal range. In order to deduce the full gravity field of Jupiter, additional information needs to be incorporated into the analysis, especially regarding the Jovian flow structure and its depth, which can influence the measured gravity field. In this study we propose a new iterative method for the estimation of the Jupiter gravity field, using a simulated Juno trajectory, a trajectory estimation model, and an adjoint-based inverse model for the flow dynamics. We test this method both for zonal harmonics only and with a full gravity field including tesseral harmonics. The results show that this method can fit some of the gravitational harmonics better to the “measured” harmonics, mainly because of the added information from the dynamical model, which includes the flow structure. Thus, it is suggested that the method presented here has the potential of improving the accuracy of the expected gravity harmonics estimated from the Juno and Cassini radio science experiments.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1984

A new implementation of the programming system for structural synthesis (PROSSS-2)

This new implementation of the PROgramming System for Structural Synthesis (PROSSS-2) combines a general-purpose finite element computer program for structural analysis, a state-of-the-art optimization program, and several user-supplied, problem-dependent computer programs. The results are flexibility of the optimization procedure, organization, and versatility of the formulation of constraints and design variables. The analysis-optimization process results in a minimized objective function, typically the mass. The analysis and optimization programs are executed repeatedly by looping through the system until the process is stopped by a user-defined termination criterion. However, some of the analysis, such as model definition, need only be one time and the results are saved for future use. The user must write some small, simple FORTRAN programs to interface between the analysis and optimization programs. One of these programs, the front processor, converts the design variables output from the optimizer into the suitable format for input into the analyzer. Another, the end processor, retrieves the behavior variables and, optionally, their gradients from the analysis program and evaluates the objective function and constraints and optionally their gradients. These quantities are output in a format suitable for input into the optimizer. These user-supplied programs are problem-dependent because they depend primarily upon which finite elements are being used in the model. PROSSS-2 differs from the original PROSSS in that the optimizer and front and end processors have been integrated into the finite element computer program. This was done to reduce the complexity and increase portability of the system, and to take advantage of the data handling features found in the finite element program.

Rogers, James L., Jr.↗

DOE OSTI2023

Development of a Low-Cost, Sensitivity-Optimized Fluorescence Sensor for Visible Spectrum Analysis

This paper presents an economically viable, sensitivity-optimized fluorescence sensor for detecting low concentrations of fluorophores in the visible spectrum range. The sensor employs a deep ultraviolet (DUV) 275-nm LED and a dichroic mirror to establish a co-axial optical path for excitation and emission. Instead of utilizing a general-purposed spectrometer optimized for high spectral resolution, the sensor strikes a balance between resolution and sensitivity by using an appropriate aperture size and optimized optical design tailored for the spectral characteristics of fluorophores. Here, the manuscript demonstrates measurements of ppb-level rare earth element (REE) samples containing terbium (Tb), europium (Eu), dysprosium (Dy), and samarium (Sm) in aqueous solutions. Through balanced design that optimizes collection of fluorescence emission without losing spectral information, this work shows the feasibility of developing a low-cost, compact, and highly sensitive fluorescence sensors with improved detection sensitivity when compared to bench-top commercial spectrofluorometers at far higher costs.

47 OTHER INSTRUMENTATION↗

NASA NTRS2014

Aeroelastic Optimization Study Based on X-56A Model

A design process which incorporates the object-oriented multidisciplinary design, analysis, and optimization (MDAO) tool and the aeroelastic effects of high fidelity finite element models to characterize the design space was successfully developed and established. Two multidisciplinary design optimization studies using an object-oriented MDAO tool developed at NASA Armstrong Flight Research Center were presented. The first study demonstrates the use of aeroelastic tailoring concepts to minimize the structural weight while meeting the design requirements including strength, buckling, and flutter. A hybrid and discretization optimization approach was implemented to improve accuracy and computational efficiency of a global optimization algorithm. The second study presents a flutter mass balancing optimization study. The results provide guidance to modify the fabricated flexible wing design and move the design flutter speeds back into the flight envelope so that the original objective of X-56A flight test can be accomplished.

flutter constraints↗

DOE OSTI2020

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 Theory Manual

The Dakota 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.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (Theory Manual)

The Dakota 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.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 Theory Manual

The Dakota 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.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Theory Manual (V.6.15)

The Dakota 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.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.16 Theory Manual

The Dakota 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.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Applicability study of Bayesian optimization in core neutronic design using a toy model

At the Japan Atomic Energy Agency (JAEA), an innovative design approach named ARKADIA (Advanced Reactor Knowledge- and AI-aided Design Integration Approach through the whole plant life cycle) for advanced nuclear reactors is currently under development. One task in ARKADIA is to build a system that automatically optimizes core and fuel designs by conducting core neutronic and thermal-hydraulic calculations, fuel integrity evaluations, and plant dynamic analyses. This system will be implemented to automatically find an optimal design that minimizes (or maximizes) objective function defined by core performance while varying the core and fuel design parameters such as fuel pin diameter, core height and diameter. In this study, as the first step of system development, we focused only on core neutronic design and conducted a study of automatic optimization. As the optimization algorithm, Bayesian optimization (BO), an effective method for optimization problems with expensive computational cost of objective function, was utilized. The applicability of BO was studied based on single- and two-objective optimization examples of core neutronic design in a toy model. As a result, in the former, it was shown that BO can give the optimal solution, which matches the reference solution calculated by a brute force calculation well, with a small number of required calculations. Usability on core neutronic designs, where the computational cost per case is high, was confirmed. In the latter, it was found that BO can give a Pareto solutions-set that shows good agreement with the reference solution. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Optimized Performance and Cost Potential for Exemplar Indirect SCO 2 Coal Plants

This NETL report presents the techno-economic analysis (TEA) optimization results of coal-fired utility-scale power plants based on the indirect supercritical carbon dioxide (sCO 2 ) power cycles both with and without carbon capture and storage (CCS). For the plants without CCS, the heat source is an air-fired circulating fluidized bed (CFB). For plants with CCS, the heat source is an oxy-fired CFB. Four power cycle configurations were examined for this study: recompression cycle (RC) without and with reheat turbine (“RC without reheat” and “RC with reheat”), and partial cooling cycle (PCC) without and with reheat turbine (“PCC without reheat” and “PCC with reheat”).

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS1994

Weight minimization of structural components for launch in space shuttle

Minimizing the weight of structural components of the space station launched into orbit in a space shuttle can save cost, reduce the number of space shuttle missions, and facilitate on-orbit fabrication. Traditional manual design of such components, although feasible, cannot represent a minimum weight condition. At NASA Lewis Research Center, a design capability called CometBoards (Comparative Evaluation Test Bed of Optimization and Analysis Routines for the Design of Structures) has been developed especially for the design optimization of such flight components. Two components of the space station - a spacer structure and a support system - illustrate the capability of CometBoards. These components are designed for loads and behavior constraints that arise from a variety of flight accelerations and maneuvers. The optimization process using CometBoards reduced the weights of the components by one third from those obtained with traditional manual design. This paper presents a brief overview of the design code CometBoards and a description of the space station components, their design environments, behavior limitations, and attributes of their optimum designs.

Patnaik, Surya N.↗