Showing papers on "Optimal design published in 2015"

Multiobjective Optimization of Switched Reluctance Motors Based on Design of Experiments and Particle Swarm Optimization

Abstract: This paper proposes a comprehensive framework for multiobjective design optimization of switched reluctance motors (SRMs) based on a combination of the design of experiments and particle swarm optimization (PSO) approaches. First, the definitive screening design was employed to perform sensitivity analyses to identify significant design variables without bias of interaction effects between design variables. Next, optimal third-order response surface (RS) models were constructed based on the Audze–Eglais Latin hypercube design using the selected significant design variables. The constructed optimal RS models consist of only significant regression terms, which were selected by using PSO. Then, a PSO-based multiobjective optimization coupled with the constructed RS models, instead of the finite-element analysis, was performed to generate the Pareto front with a significantly reduced computational cost. A sample SRM design with multiple optimization objectives, i.e., maximizing torque per active mass, maximizing efficiency, and minimizing torque ripple, was conducted to verify the effectiveness of the proposed optimal design framework.

Handbook of Design and Analysis of Experiments

Abstract: General Principles History and Overview of Design and Analysis of Experiments Klaus Hinkelmann Introduction to Linear Models Linda M. Haines Designs for Linear Models Blocking with Independent Responses John P. Morgan Crossover Designs Mausumi Bose and Aloke Dey Response Surface Experiments and Designs Andre I. Khuri and Siuli Mukhopadhyay Design for Linear Regression Models with Correlated Errors Holger Dette, Andrey Pepelyshev, and Anatoly Zhigljavsky Designs Accommodating Multiple Factor Regular Fractional Factorial Designs Robert Mee and Angela Dean Multistratum Fractional Factorial Designs Derek Bingham Nonregular Factorial and Supersaturated Designs Hongquan Xu Structures Defined by Factors R.A. Bailey Algebraic Method in Experimental Design Hugo Maruri-Aguilar and Henry P. Wynn Optimal Design for Nonlinear and Spatial Models Optimal Design for Nonlinear and Spatial Models: Introduction and Historical Overview Douglas P. Wiens Designs for Generalized Linear Models Anthony C. Atkinson and David C. Woods Designs for Selected Nonlinear Models Stefanie Biedermann and Min Yang Optimal Design for Spatial Models Zhengyuan Zhu and Evangelos Evangelou Computer Experiments Design of Computer Experiments: Introduction and Background Max Morris and Leslie Moore Latin Hypercubes and Space-Filling Designs C. Devon Lin and Boxin Tang Design for Sensitivity Analysis William Becker and Andrea Saltelli Expected Improvement Designs William I. Notz Cross-Cutting Issues Robustness of Design Douglas P. Wiens Algorithmic Searches for Optimal Designs Abhyuday Mandal, Weng Kee Wong, and Yaming Yu Design for Contemporary Applications Design for Discrete Choice Experiments Heiko Grossmann and Rainer Schwabe Plate Designs in High-Throughput Screening Experiments for Drug Discovery Xianggui Qu (Harvey) and Stanley Young Up-and-Down Designs for Dose-Finding Nancy Flournoy and Assaf P. Oron Optimal Design for Event-Related fMRI Studies Jason Ming-Hung Kao and John Stufken Index

Framework for the Buckling Optimization of Variable-Angle Tow Composite Plates

Abstract: Variable-angle tow describes fibers in a composite lamina that have been steered curvilinearly. In doing so, substantially enlarged freedom for stiffness tailoring of composite laminates is enabled. Variable-angle tow composite structures have been shown to have improved buckling and postbuckling load-carrying capability when compared to straight fiber composites. However, their structural analysis and optimal design is more computationally expensive due to the exponential increase in number of variables associated with spatially varying planar fiber orientations in addition to stacking sequence considerations. In this work, an efficient two-level optimization framework using lamination parameters as design variables has been enhanced and generalized to the design of variable-angle tow plates. New explicit stiffness matrices are found in terms of component material invariants and lamination parameters. The convex hull property of B-splines is exploited to ensure pointwise feasibility of lamination parameters. In addition, a set of new explicit closed-form expressions defines the feasible region of two in-plane and two out-of-plane lamination parameters, which are used for the design of orthotropic laminates. Finally, numerical examples of plates under compression loading with different boundary conditions and aspect ratios are investigated. Reliable optimal solutions demonstrate the robustness and computational efficiency of the proposed optimization methodology.

Bayesian optimization for materials design

Abstract: We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian process regression, which allows predicting the performance of a new design based on previously tested designs. After providing a detailed introduction to Gaussian process regression, we introduce two Bayesian optimization methods: expected improvement, for design problems with noise-free evaluations; and the knowledge-gradient method, which generalizes expected improvement and may be used in design problems with noisy evaluations. Both methods are derived using a value-of-information analysis, and enjoy one-step Bayes-optimality.

Design of Near Optimal Decision Rules in Multistage Adaptive Mixed-Integer Optimization

Abstract: In recent years, decision rules have been established as the preferred solution method for addressing computationally demanding, multistage adaptive optimization problems. Despite their success, existing decision rules (a) are typically constrained by their a priori design and (b) do not incorporate in their modeling adaptive binary decisions. To address these problems, we first derive the structure for optimal decision rules involving continuous and binary variables as piecewise linear and piecewise constant functions, respectively. We then propose a methodology for the optimal design of such decision rules that have a finite number of pieces and solve the problem robustly using mixed-integer optimization. We demonstrate the effectiveness of the proposed methods in the context of two multistage inventory control problems. We provide global lower bounds and show that our approach is (i) practically tractable and (ii) provides high quality solutions that outperform alternative methods.

Performance assessment and optimization of fluid viscous dampers through life-cycle cost criteria and comparison to alternative design approaches

Abstract: The performance assessment and optimal design of fluid viscous dampers through life-cycle cost criteria is discussed in this paper. A probabilistic, simulation-based framework is described for estimating the life-cycle cost and a stochastic search approach is developed to support an efficient optimization under different design scenarios (corresponding to different seismicity characteristics). Earthquake losses are estimated using an assembly-based vulnerability approach utilizing the nonlinear dynamic response of the structure whereas a point source stochastic ground motion model, extended here to address near-fault pulse effects, is adopted to describe the seismic hazard. Stochastic simulation is utilized for estimation of all the necessary probabilistic quantities, and for reducing the computational burden a surrogate modeling methodology is integrated within the framework. Two simplified design approaches are also examined, the first considering the optimization of the stationary response, utilizing statistical linearization to address nonlinear damper characteristics, and the second adopting an equivalent lateral force procedure that defines a targeted damping ratio for the structure. These designs are compared against the optimal life-cycle cost one, whereas a compatible comparison is facilitated by establishing an appropriate connection between the seismic input required for the simplified designs and the probabilistic earthquake hazard model. As an illustrative example, the retrofitting of a three-story reinforced concrete office building with nonlinear dampers is considered.

Efficient Bayesian experimental design for contaminant source identification

Abstract: In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameters identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from concentration measurements in identifying unknown parameters. In this approach, the sampling locations that give the maximum expected relative entropy are selected as the optimal design. After the sampling locations are determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport equation. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. It is shown that the methods can be used to assist in both single sampling location and monitoring network design for contaminant source identifications in groundwater.

A Generalized Optimality Criteria Method for Optimization of Additively Manufactured Multimaterial Lattice Structures

Abstract: Recent progress in Additive Manufacturing (AM) allows for printing customized products with multiple materials and complex geometries that could form the basis of multi-material designs with high performance and novel functions. Effectively designing such complex products for optimal performance within the confines of additive manufacturing constraints is challenging due to the need to consider fabrication constraints while searching for optimal designs with a large number of variables, which stem from new AM capabilities. In this study, fabrication constraints are addressed through empirically characterizing multiple printed materials’ Young’s modulus and density using a multi-material inkjet-based 3D-printer. Data curves are modeled for the empirical data describing two base printing materials and twelve mixtures of them as inputs for a computational optimization process. An optimality criteria method is developed to search for solutions of multi-material lattices with fixed topology and truss cross-section sizes. Two representative optimization studies are presented and demonstrate higher performance with multi-material approaches in comparison to using a single material. These include the optimization of a cubic lattice structure that must adhere to a fixed displacement constraint and a compliant beam lattice structure that must meet multiple fixed displacement constraints. Results demonstrate the feasibility of the approach as a general synthesis and optimization method for multi-material, lightweight lattice structures that are large-scale and manufacturable on a commercial AM printer directly from the design optimization results.

A Near-Optimal Power Management Strategy for Rapid Component Sizing of Multimode Power Split Hybrid Vehicles

Abstract: In the design of hybrid vehicles, it is important to identify proper component sizes along with the optimal control. When the design search space is large, exhaustive optimal control strategies, such as dynamic programming (DP) is too time consuming to be used. Instead, a near-optimal method that is orders of magnitude faster than DP is needed. One such near-optimal method is developed and presented in this paper. This method is applied to identify the optimal powertrain parameters of all power-split hybrid configurations utilizing a single planetary gear. There are 12 possible configurations, six input and output splits, and each configuration has up to four modes. Based on the analysis of the efficiency of powertrain components of the four modes, and the power-weighted efficiency concept, we show that the computation time can be reduced by a factor of 10 000 without consequential performance compromise, compared with the DP approach. The optimal design of each configuration is analyzed and presented.

Bayesian Optimization for Synthetic Gene Design

Abstract: We address the problem of synthetic gene design using Bayesian optimization. The main issue when designing a gene is that the design space is defined in terms of long strings of characters of different lengths, which renders the optimization intractable. We propose a three-step approach to deal with this issue. First, we use a Gaussian process model to emulate the behavior of the cell. As inputs of the model, we use a set of biologically meaningful gene features, which allows us to define optimal gene designs rules. Based on the model outputs we define a multi-task acquisition function to optimize simultaneously severals aspects of interest. Finally, we define an evaluation function, which allow us to rank sets of candidate gene sequences that are coherent with the optimal design strategy. We illustrate the performance of this approach in a real gene design experiment with mammalian cells.

Minimax optimal designs via particle swarm optimization methods

Abstract: Particle swarm optimization (PSO) techniques are widely used in applied fields to solve challenging optimization problems but they do not seem to have made an impact in mainstream statistical applications hitherto. PSO methods are popular because they are easy to implement and use, and seem increasingly capable of solving complicated problems without requiring any assumption on the objective function to be optimized. We modify PSO techniques to find minimax optimal designs, which have been notoriously challenging to find to date even for linear models, and show that the PSO methods can readily generate a variety of minimax optimal designs in a novel and interesting way, including adapting the algorithm to generate standardized maximin optimal designs.

Establishing the Relative Merits of Interior and Spoke-Type Permanent-Magnet Machines With Ferrite or NdFeB Through Systematic Design Optimization

Abstract: In this paper, a multiobjective design optimization method combining design-of-experiments techniques and differential-evolution algorithms is presented. The method was implemented and utilized in order to provide practical engineering insights for the optimal design of interior and spoke-type permanent-magnet machines. Two combinations with 12 slots and 8 poles and 12 slots and 10 poles, respectively, have been studied in conjunction with rare-earth neodymium-iron-boron (NdFeB) and ferrites. As part of the optimization process, a computationally efficient finite-element electromagnetic analysis was employed for estimating the performance of thousands of candidate designs. Three optimization objectives were concurrently considered for minimum total material cost, power losses, and torque ripple, respectively. Independent variables were considered for both the stator and rotor geometries. A discussion based on a systematic comparison is included, showing, among other things and despite common misconception, that comparable cost versus loss Paretos can be achieved with any of the rotor topologies studied.

Electric Vehicle Design Optimization: Integration of a High-Fidelity Interior-Permanent-Magnet Motor Model

Abstract: The simulation-based design optimization of an electric-vehicle (EV) propulsion system requires integration of a system model with detailed models of the components. In particular, a high-fidelity interior-permanent-magnet (IPM) motor model is necessary to capture important physical effects, such as magnetic saturation. The system optimization challenge is to maintain adequate model fidelity with acceptable computational cost. This paper proposes a design method that incorporates a high-fidelity motor, high-voltage power electronics, and vehicle propulsion simulation models in a system design optimization formulation that maximizes energy efficiency of a compact EV on a given drive cycle. The resulting optimal design and associated energy efficiency for a variety of drive cycles and performance requirements are presented and discussed.

Optimal Design of an Interior Permanent Magnet Synchronous Motor by Using a New Surrogate-Assisted Multi-Objective Optimization

Abstract: To optimize an interior permanent magnet synchronous motor (IPMSM) design for a fuel cell electric vehicle, a new surrogate-assisted multi-objective optimization (MOO) algorithm is proposed in this paper. The proposed algorithm is a multi-objective algorithm (MOO) that can account for three kinds of objectives such as the torque amplitude, torque ripple, and magnet usage simultaneously to improve the power transmission and to reduce the noise, vibration, and cost for various design variables. While the conventional MOO algorithms have a series that requires many function evaluations, especially considering many objectives and design variables, the proposed algorithm can create an accurate and well-distributed Pareto front set with few function evaluations. In comparison with the conventional MOO algorithms, the outstanding performance of the proposed algorithm is verified. Finally, the proposed algorithm is applied to an optimal design process of an IPMSM.

Incorporating fabrication cost into topology optimization of discrete structures and lattices

Abstract: In this article, we propose a method to incorporate fabrication cost in the topology optimization of light and stiff truss structures and periodic lattices. The fabrication cost of a design is estimated by assigning a unit cost to each truss element, meant to approximate the cost of element placement and associated connections. A regularized Heaviside step function is utilized to estimate the number of elements existing in the design domain. This makes the cost function smooth and differentiable, thus enabling the application of gradient-based optimization schemes. We demonstrate the proposed method with classic examples in structural engineering and in the design of a material lattice, illustrating the effect of the fabrication unit cost on the optimal topologies. We also show that the proposed method can be efficiently used to impose an upper bound on the allowed number of elements in the optimal design of a truss system. Importantly, compared to traditional approaches in structural topology optimization, the proposed algorithm reduces the computational time and reduces the dependency on the threshold used for element removal.

An improved magnetic charged system search for optimization of truss structures with continuous and discrete variables

Abstract: An improved magnetic charged system search (IMCSS) is presented for optimization of truss structures.In IMCSS some of the most effective parameters in the convergence rate of HS scheme have been improved to achieve the best convergence.The IMCSS algorithm is applied for optimal design problem with both continuous and discrete variables. In this study, an improved magnetic charged system search (IMCSS) is presented for optimization of truss structures. The algorithm is based on magnetic charged system search (MCSS) and improved scheme of harmony search algorithm (IHS). In IMCSS some of the most effective parameters in the convergence rate of the HS scheme have been improved to achieve a better convergence, especially in the final iterations and explore better results than previous studies. The IMCSS algorithm is applied for optimal design problem with both continuous and discrete variables. In comparison to the results of the previous studies, the efficiency and robustness of the proposed algorithm in fast convergence and achieving the optimal values for weight of structures, is demonstrated.

A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models

Abstract: Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1].

Optimal design of auxetic hexachiral metamaterials with local resonators

Abstract: A parametric beam lattice model is formulated to analyse the propagation properties of elastic in-plane waves in an auxetic material based on a hexachiral topology of the periodic cell, equipped with inertial local resonators. The Floquet-Bloch boundary conditions are imposed on a reduced order linear model in the only dynamically active degrees-offreedom. Since the resonators can be designed to open and shift band gaps, an optimal design, focused on the largest possible gap in the low-frequency range, is achieved by solving a maximization problem in the bounded space of the significant geometrical and mechanical parameters. A local optimized solution, for a the lowest pair of consecutive dispersion curves, is found by employing the globally convergent version of the Method of Moving asymptotes, combined with Monte Carlo and quasi-Monte Carlo multi-start techniques.