Multilevel Methods for Optimal Design
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nonlinear optimization↗
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A strategy for reducing turnaround time in design optimization using a distributed computer system
There is a need to explore methods for reducing lengthly computer turnaround or clock time associated with engineering design problems. Different strategies can be employed to reduce this turnaround time. One strategy is to run validated analysis software on a network of existing smaller computers so that portions of the computation can be done in parallel. This paper focuses on the implementation of this method using two types of problems. The first type is a traditional structural design optimization problem, which is characterized by a simple data flow and a complicated analysis. The second type of problem uses an existing computer program designed to study multilevel optimization techniques. This problem is characterized by complicated data flow and a simple analysis. The paper shows that distributed computing can be a viable means for reducing computational turnaround time for engineering design problems that lend themselves to decomposition. Parallel computing can be accomplished with a minimal cost in terms of hardware and software.
Young, Katherine C.↗
Preliminary analysis and design optimization of the short spacer truss of Space Station Freedom
The analysis, dynamic simulation, and design optimization of the short spacer truss of the Space Station Freedom are presented in this report. The short spacer truss will be positioned between the integrated equipment assembly (IEA) and another truss, called the long spacer truss, in the Space Station Freedom. During its launch in the Space Shuttle, the truss will be subjected to considerable in-span distributed inertia loads due to shuttle accelerations. The short spacer truss, therefore, has been modeled as a space frame to account for flexural response. Several parameters have been assumed, since the design specifications are in the process of development; hence the results presented should be considered preliminary. However, the automated analysis and design capabilities that have been developed can readily be used to generate an optimum design of the short spacer truss once the actual specifications have been determined. This report includes static and dynamic analyses of the short spacer truss, which have been obtained with the linear elastic code LE-HOST (in these analyses, LE-HOST data files have been automated to facilitate their future use for different design specifications of the short spacer truss); the dynamic animation of the short spacer truss, which has been carried out by using the results of the dynamic analysis and a post-processing feature of the modeling code PATRAN; and the optimum-weight design of the spacer truss, which was obtained under prescribed stress, displacement, and frequency constraints by using the design code COMETBOARDS. Examination of the analysis and design results revealed that the design could be improved if the configuration of the short spacer truss were modified to a certain extent. A modified configuration, which may simplify fabrication, has been suggested. The performance of this configuration has been evaluated and was found to be satisfactory under both static and dynamic conditions.
Gendy, A. S.↗
Global Design Optimization for Aerodynamics and Rocket Propulsion Components
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 practices and the need for future research are identified.
Shyy, Wei↗
Optimal design procedures for two-level fractional factorial experiments given partial prior information about parameters
Optimal design procedure as experimental design finite decision problem, using Bayes and minimax techniques
Holms, A. G.↗
Discrete Adjoint-Based Design Optimization of Unsteady Turbulent Flows on Dynamic Unstructured Grids
An adjoint-based methodology for design optimization of unsteady turbulent flows on dynamic unstructured grids is described. The implementation relies on an existing unsteady three-dimensional unstructured grid solver capable of dynamic mesh simulations and discrete adjoint capabilities previously developed for steady flows. The discrete equations for the primal and adjoint systems are presented for the backward-difference family of time-integration schemes on both static and dynamic grids. The consistency of sensitivity derivatives is established via comparisons with complex-variable computations. The current work is believed to be the first verified implementation of an adjoint-based optimization methodology for the true time-dependent formulation of the Navier-Stokes equations in a practical computational code. Large-scale shape optimizations are demonstrated for turbulent flows over a tiltrotor geometry and a simulated aeroelastic motion of a fighter jet.
Nielsen, Eric J.↗
Deterministic Design Optimization of Structures in OpenMDAO Framework
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.
Coroneos, Rula M.↗
Deterministic Design Optimization of Structures in OpenMDAO Framework
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.
Coroneos, Rula M.↗
Transfer learning driven design optimization for inertial confinement fusion
Transfer learning is a promising approach to create predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations and then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previously, this technique has been used to create predictive models of Omega [Humbird et al., IEEE Trans. Plasma Sci. 48, 61–70 (2019)] and NIF [Humbird et al., Phys. Plasmas 28, 042709 (2021); Kustowski et al., Mach. Learn. 3, 015035 (2022)] inertial confinement fusion (ICF) experiments that are more accurate than simulations alone. Here, in this work, we conduct a transfer learning driven hypothetical ICF campaign in which the goal is to maximize experimental neutron yield via Bayesian optimization. The transfer learning model achieves yields within 5% of the maximum achievable yield in a modest-sized design space in fewer than 20 experiments. Furthermore, we demonstrate that this method is more efficient at optimizing designs than traditional model calibration techniques commonly employed in ICF design. Such an approach to ICF design could enable robust optimization of experimental performance under uncertainty.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Lubrication of optimized-design tapered-roller bearings to 2.4 million DN
The performance of 120.65 mm (4.75 in.) bore high speed design, tapered roller bearings was investigated at shaft speeds to 20,000 rpm (2.4 million DN) under combined thrust and radial load. The test bearing design was computer optimized for high speed operation. Temperature distribution bearing heat generation were determined as a function of shaft speed, radial and thrust loads, lubricant flow rates, and lubricant inlet temperature. The high speed design, tapered roller bearing operated successfully at shaft speeds up to 20,000 rpm under heavy thrust and radial loads. Bearing temperatures and heat generation with the high speed design bearing were significantly less than those of a modified standard bearing tested previously. Cup cooling was effective in decreasing the high cup temperatures to levels equal to the cone temperature.
Parker, R. J.↗
Design, optimization and critical analysis of graphene based surface plasmon resonance sensor for DNA hybridization
In this paper, the design, optimization and critical analysis of Surface Plasmon Resonance sensor is presented, which works on angular interrogation method. It comprises of five layers which are “Prism–Copper–Germanium–Graphene–Sensing medium”. The influence of the chemical potential (µ{sub c}) of graphene on the sensor performance has been studied in detail. Thickness of all the layers has been optimized. Remarkable results are achieved for the optimal thickness of copper and germanium layers as 50 nm and 6 nm respectively, while two layers of graphene having µ{sub c} of 0.5 eV are introduced to boost-up the performance. Best results in terms of detection accuracy, sensitivity and quality factor have been achieved that fulfill the necessary requirements for good sensing applications.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Formulation of a methodology for power circuit design optimization
A methodology for optimizing power-processor designs is described which achieves optimization with respect to some power-processor characteristic deemed particularly desirable by the designer, such as weight or efficiency. Optimization theory based on Lagrange multipliers is reviewed together with nonlinear programming techniques employing penalty functions. The methodology, the task of which is to minimize an objective function subject to design constraints, is demonstrated with the aid of four examples: optimum-weight core selection for an inductor with a predetermined winding size, optimum-weight inductor design with a given loss constraint, optimum-loss inductor design with a given weight constraint, and a comparison of optimum-weight single- and two-stage input-filter designs with identical loss and other requirement constraints. Closed-form solutions for the first three examples are obtained by applying the Lagrange-multiplier method, but solutions for the last example are found numerically through the use of the sequential unconstrained minimization technique.
Yu, Y.↗
An overview of the Douglas Aircraft Company Aeroelastic Design Optimization Program (ADOP)
From a program manager's viewpoint, the history, scope and architecture of a major structural design program at Douglas Aircraft Company called Aeroelastic Design Optimization Program (ADOP) are described. ADOP was originally intended for the rapid, accurate, cost-effective evaluation of relatively small structural models at the advanced design level, resulting in improved proposal competitiveness and avoiding many costly changes later in the design cycle. Before release of the initial version in November 1987, however, the program was expanded to handle very large production-type analyses.
Dodd, Alan J.↗
Subsystem weight trade-off studies for the design optimization of an out-of-orbit Mars soft lander
Subsystem weight trade-offs for design optimization of out-of-orbit Mars soft lander, using atmosphere and surface environmental Monte Carlo model
Mukhopadhyay, A. K.↗
Optimal design procedure for two-level fractional factorial experiments given prior information about parameters
Development of optimal design procedures by posing experimental design problem as finite decision problem
Sidik, S. M.↗
Accounting for Proof Test Data in a Reliability Based Design Optimization Framework
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.
Ventor, Gerharad↗
Fine guidance sensor design optimization for Space Infrared Telescope Facility (SIRTF)
The present paper is concerned with the general issues of design optimization of a staring mosaic sensor for guide-star tracking, taking into account the choice of the proper interpolation algorithm. It was found that the final choice of algorithm depends on the severity of the positional accuracy requirement and the expected variation in the blur spot shape. The considered track algorithms estimate the position either of the peak or of the centroid of the image of a point source. Attention is given to aspects of problem definition, the total effect of error sources on algorithm accuracy, a parametric analysis of performance behavior, Monte Carlo results, and the design of the SIRTF Fine Guidance Sensor (FGS). The algorithm error, the signal-to-noise error, and the nonuniformity error are examined.
Grossman, S. B.↗
Selecting a near-optimal design for multiple criteria with improved robustness to different user priorities
In a decision-making process, relying on only one objective can often lead to oversimplified decisions that ignore important considerations. Incorporating multiple, and likely competing, objectives is critical for balancing trade-offs on different aspects of performance. When multiple objectives are considered, it is often hard to make a precise decision on how to weight the different objectives when combining their performance for ranking and selecting designs. We show that there are situations when selecting a design with near-optimality for a broad range of weight combinations of the criteria is a better test selection strategy compared with choosing a design that is strictly optimal under very restricted conditions. Here, we propose a new design selection strategy that identifies several top-ranked solutions across broad weight combinations using layered Pareto fronts and then selects the final design that offers the best robustness to different user priorities. This method involves identifying multiple leading solutions based on the primary objectives and comparing the alternatives using secondary objectives to make the final decision. We focus on the selection of screening designs because they are widely used both in industrial research, development, and operational testing. The method is illustrated with an example of selecting a single design from a catalog of designs of a fixed size. However, the method can be adapted to more general designed experiment selection problems that involve searching through a large design space.
97 MATHEMATICS AND COMPUTING↗