Optimal design procedures for two-level fractional factorial experiments given partial prior information about parameters
Design optimization for fractional factorial experiments by finite decision problem based on prior information
Holms, A. G.↗
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Results for “design optimization”
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Design Optimization and Analysis of a Composite Honeycomb Intertank
Intertanks, the structure between tanks of launch vehicles, are prime candidates for weight reduction of rockets. This paper discusses the optimization and detailed analysis of a 96 in (2.44 m) diameter, 77 in (1.85 m) tall intertank. The structure has composite face sheets and an aluminum honeycomb core. The ends taper to a thick built up laminate for a double lap bolted shear joint. It is made in 8 full length panels joined with bonded double lap joints. The nominal load is 4000 lb/in (7 x 10(exp 5) N/m). Optimization is by Genetic Algorithm and minimizes weight by varying C, core thickness, number and orientation of acreage and buildup plies, and the size, number and spacing of bolts. A variety of cases were run with populations up to 2000 and chromosomes as long as 150 bits. Constraints were buckling, face stresses (normal, shear, wrinkling and dimpling, bolt stress, and bolt hole stresses (bearing, net tension, wedge splitting, shear out and tension/shear out). Analysis is by a combination of theoretical solutions and empirical data. After optimization, a series of coupon tests were performed in conjunction with a rigorous analysis involving a variety of finite element models. The analysis and test resulted in several small changes to the optimized design. The intertank has undergone a 250,000 lb (1.1 x 10(exp 6) N) limit load test and been mated with a composite liquid hydrogen tank. The tank/intertank unit is being installed in a test stand where it will see 200 thermal/load cycles. Afterwards the intertank will be demated and loaded in compression to failure.
Finckenor, Jeffrey↗
DSN 70-meter antenna microwave optics design and performance improvements. Part 1: Design optimization
The design optimizations associated with the microwave and structural upgrade of the DSN 64-m antennas are discussed. Expected area efficiency/gain performances at S- and X-band are given for both the original 64-m systems and the upgraded 70-m systems, and error estimates are developed. The DSN 70-m Upgrade Project specifications, based on predesign estimates, were 1.4-dB gain at S-band and 1.9-dB at X-band, with no degradation to critical receiving system noise temperatures. The measurements show an S-band gain increase of 1.9 dB and an average increase of 2.1 dB at X-band. The Project also delivered small receiving system noise decreases at both frequency bands. The three DSN 70-m antennas, in the initial state of mechanical adjustment as of the end of calendar year 1988, are performing with very high peak microwave area efficiencies at very nearly the engineering design expectations of 76 percent at S-band and 71 percent at X-band.
Bathker, D. A.↗
LCOE Design Optimization Using Genetic Algorithm with Improved Component Models for Medium-Voltage Transformerless PV Inverters
For real-world installations of photovoltaic and other renewable energy resources, the critical design metric is the levelized cost of energy (LCOE); however, many power electronics design optimizations are performed with efficiency and power density as the primary design goals. Recent work has shown that a new LCOE-focused optimization approach can yield improved system designs balancing cost and energy generation. This paper expands the LCOE optimization approach by considering comprehensive optimization parameters, adding new modeling of inductor cost, extending the semiconductor model to include effects of losses on housing cost, and implementing a genetic algorithm to improve computation efficiency.
41 EE - Solar Energy Technologies Office (EE-4S)↗
Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization
Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.
Renganathan, S. Ashwin↗
Shape design sensitivity analysis and optimal design of structural systems
The material derivative concept of continuum mechanics and an adjoint variable method of design sensitivity analysis are used to relate variations in structural shape to measures of structural performance. A domain method of shape design sensitivity analysis is used to best utilize the basic character of the finite element method that gives accurate information not on the boundary but in the domain. Implementation of shape design sensitivty analysis using finite element computer codes is discussed. Recent numerical results are used to demonstrate the accuracy obtainable using the method. Result of design sensitivity analysis is used to carry out design optimization of a built-up structure.
Choi, Kyung K.↗
Multidisciplinary Analysis and Optimal Design: As Easy as it Sounds?
The viewgraph presentation examines optimal design for precision, large aperture structures. Discussion focuses on aspects of design optimization, code architecture and current capabilities, and planned activities and collaborative area suggestions. The discussion of design optimization examines design sensitivity analysis; practical considerations; and new analytical environments including finite element-based capability for high-fidelity multidisciplinary analysis, design sensitivity, and optimization. The discussion of code architecture and current capabilities includes basic thermal and structural elements, nonlinear heat transfer solutions and process, and optical modes generation.
design optimization↗
Design Optimization of GaN Vertical Power Diodes and Comparison to Si and SiC.
Abstract not provided.
Flicker, Jack David↗
A Stochastic Programming Approach to the Design Optimization of Layered Physical Protection Systems.
Abstract not provided.
Brown, Nathanael J. K.↗
The Impact of False and Nuisance Alarms on the Design Optimization of Physical Security Systems.
Abstract not provided.
Bandlow, Alisa↗
Coupling multi-fidelity xRAGE with machine learning for graded inner shell design optimization in double shell capsules
Bayesian optimization has shown promise for the design optimization of inertial confinement fusion targets. Specifically, in Vazirani et al. [Phys. Plasmas 28 , 122709 (2021)], optimal designs for double shell capsules with graded inner shells were identified using one-dimensional xRAGE simulation yield calculations. While the machine learning models were able to accurately learn and predict one-dimensional simulation target performance, using simulations with higher fidelity would improve design optimization and better match with the expected experimental performance. However, higher fidelity physics modeling, i.e., two-dimensional xRAGE simulations, requires significantly larger computational time/cost, usually at least an order of magnitude, in comparison with one-dimensional simulations. This study presents a multi-fidelity Bayesian optimization, in which the machine learning model leverages low-fidelity (one-dimensional xRAGE) and high-fidelity (two-dimensional xRAGE) simulations to more accurately predict “pre-shot” target performance with respect to the expected experimental performance. By building a multi-fidelity Bayesian optimization framework coupled with xRAGE, the low-fidelity and high-fidelity simulations are able to inform one another, such that we have: (1) improved physics modeling in comparison with using low-fidelity simulations alone, (2) reduced computational time/cost in comparison with using high-fidelity simulations alone, and (3) more confidence in the expected performance of optimized targets during real-world experiments. In the future, we plan to use this robust multi-fidelity Bayesian optimization methodology to expedite the design of graded inner shells further and eventually full capsules as a part of the current double shell campaign at the National Ignition Facility.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Cost- and Risk-Based Seismic Design Optimization of Nuclear Power Plant Safety Systems
Seismic analysis, design, and qualification of systems, structures, and components (SSCs) is a significant contributor to the capital cost of a nuclear power plant. To reduce capital costs of advanced nuclear power plants and make commercial nuclear energy more competitive, innovations are needed in their structural design and construction, and not just in the reactor core and associated systems. Seismic isolation has been identified as an important cost-cutting technology that enables standardization of equipment across various sites. This paper develops and demonstrates a cost- and risk-based seismic design optimization of a representative safety system in a nuclear power plant with the dual goals of minimizing overnight capital cost and meeting safety goals. The design optimization can also include component seismic isolation, in which case, the optimized design includes a set of equipment that needs to be seismically isolated to minimize capital cost. The open-source codes MASTODON and Dakota are used for seismic probabilistic risk assessment and design optimization, respectively. A generic nuclear facility with a safety system comprising SSCs that are common to nuclear power plants is considered for the demonstration of the design optimization and is assumed to be located at the Idaho National Laboratory site. Generic costs and seismic design cost functions are assumed for the SSCs of the safety system. The sum of the costs of the SSCs is minimized in the optimization process, while the risk of failure of the safety system is provided as a constraint. Furthermore, results show that the optimization process reduces capital costs significantly while automatically prioritizing the safety of SSCs that contribute most to the risk of the safety system.
42 ENGINEERING↗
Optimal Design of Integrated Systems Health Management (ISHM) Systems for improving safety in NASA's Exploration Vehicles: A Two-Level Multidisciplinary Design Approach
Integrated Vehicle Health Management (ISHM) systems are used to detect, assess, and isolate functional failures in order to improve safety of space systems such as Orbital Space Planes (OSPs). An ISHM system, as a whole, consists of several subsystems that monitor different components of an OSP including: Spacecraft, Launch Vehicle, Ground Control, and the International Space Station. In this research, therefore, we propose a new methodology to design and optimize ISHM as a distributed system with multiple disciplines (that correspond to different subsystems of OSP safety). A paramount amount of interest has been given in the literature to the multidisciplinary design optimization of problems with such architecture (as will be reviewed in the full paper).
Mehr, Ali Farhang↗
Design optimization for cost and quality: The robust design approach
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.
Unal, Resit↗
Improving Discrete-Sensitivity-Based Approach for Practical Design Optimization
In developing the automated methodologies for simulation-based optimal shape designs, their accuracy, efficiency and practicality are the defining factors to their success. To that end, four recent improvements to the building blocks of such a methodology, intended for more practical design optimization, have been reported. First, in addition to a polynomial-based parameterization, a partial differential equation (PDE) based parameterization was shown to be a practical tool for a number of reasons. Second, an alternative has been incorporated to one of the tedious phases of developing such a methodology, namely, the automatic differentiation of the computer code for the flow analysis in order to generate the sensitivities. Third, by extending the methodology for the thin-layer Navier-Stokes (TLNS) based flow simulations, the more accurate flow physics was made available. However, the computer storage requirement for a shape optimization of a practical configuration with the -fidelity simulations (TLNS and dense-grid based simulations), required substantial computational resources. Therefore, the final improvement reported herein responded to this point by including the alternating-direct-implicit (ADI) based system solver as an alternative to the preconditioned biconjugate (PbCG) and other direct solvers.
Baysal, Oktay↗