Showing papers on "Optimal design published in 2009"

Bi-directional evolutionary topology optimization of continuum structures with one or multiple materials

Abstract: There are several well-established techniques for the generation of solid-void optimal topologies such as solid isotropic material with penalization (SIMP) method and evolutionary structural optimization (ESO) and its later version bi-directional ESO (BESO) methods. Utilizing the material interpolation scheme, a new BESO method with a penalization parameter is developed in this paper. A number of examples are presented to demonstrate the capabilities of the proposed method for achieving convergent optimal solutions for structures with one or multiple materials. The results show that the optimal designs from the present BESO method are independent on the degree of penalization. The resulted optimal topologies and values of the objective function compare well with those of SIMP method.

Split-Plot Designs - What, Why, and How

Abstract: The past decade has seen rapid advances in the development of new methods for the design and analysis of split-plot experiments. Unfortunately, the value of these designs for industrial experimentation has not been fully appreciated. In this paper, we review recent developments and provide guidelines for the use of split-plot designs in industrial applications.

Sequential optimal design of neurophysiology experiments

Abstract: Adaptively optimizing experiments has the potential to significantly reduce the number of trials needed to build parametric statistical models of neural systems. However, application of adaptive methods to neurophysiology has been limited by severe computational challenges. Since most neurons are high-dimensional systems, optimizing neurophysiology experiments requires computing high-dimensional integrations and optimizations in real time. Here we present a fast algorithm for choosing the most informative stimulus by maximizing the mutual information between the data and the unknown parameters of a generalized linear model (GLM) that we want to fit to the neuron's activity. We rely on important log concavity and asymptotic normality properties of the posterior to facilitate the required computations. Our algorithm requires only low-rank matrix manipulations and a two-dimensional search to choose the optimal stimulus. The average running time of these operations scales quadratically with the dimensionality of the GLM, making real-time adaptive experimental design feasible even for high-dimensional stimulus and parameter spaces. For example, we require roughly 10 milliseconds on a desktop computer to optimize a 100-dimensional stimulus. Despite using some approximations to make the algorithm efficient, our algorithm asymptotically decreases the uncertainty about the model parameters at a rate equal to the maximum rate predicted by an asymptotic analysis. Simulation results show that picking stimuli by maximizing the mutual information can speed up convergence to the optimal values of the parameters by an order of magnitude compared to using random (nonadaptive) stimuli. Finally, applying our design procedure to real neurophysiology experiments requires addressing the nonstationarities that we would expect to see in neural responses; our algorithm can efficiently handle both fast adaptation due to spike history effects and slow, nonsystematic drifts in a neuron's activity.

Optimal experimental design for model discrimination.

Abstract: Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design variables (e.g., presentation schedule, stimulus structure) that will be most informative in differentiating them. Recent developments in sampling-based search methods in statistics make it possible to determine these values and thereby identify an optimal experimental design. After describing the method, it is demonstrated in 2 content areas in cognitive psychology in which models are highly competitive: retention (i.e., forgetting) and categorization. The optimal design is compared with the quality of designs used in the literature. The findings demonstrate that design optimization has the potential to increase the informativeness of the experimental method.

A Framework for Optimal Planning in Large Distribution Networks

Abstract: Large scale distribution system planning is a relatively complex and reasonably difficult problem. This paper proposes the application of improved genetic algorithm (GA) for the optimal design of large scale distribution systems in order to provide optimal sizing and locating of the high and medium voltage (HV and MV) substations, as well as medium voltage (MV) feeders routing, using their corresponding fixed and variable costs associated with operational and optimization constraints. The novel approach presented in the paper solves hard satisfactory optimization problems with different constraints in large scale distribution networks. This paper presents a new concept based on loss characteristic matrix introduced for optimal locating of MV substations, followed by new methodology based on graph theory and GA for optimal locating of the HV substations and MV feeders routing in a real size distribution network. Minimum spanning tree algorithm is employed to generate set of feasible initial population. In the present article to reduce computational burden and avoid huge search space leading to infeasible solutions, special coding methods are generated for GA operators to solve optimal feeders routing. The proposed coding methods guarantee the validity of the solution during the progress of the genetic algorithm toward the global optimal solution. The developed GA-based software is tested in a real size large scale distribution system and the well satisfactory results are presented.

An Introduction to Optimal Designs for Social and Biomedical Research

Abstract: Preface . Acknowledgements. 1 Introduction to designs . 1.1 Introduction. 1.2 Stages of the research process. 1.3 Research design. 1.4 Types of research designs. 1.5 Requirements for a 'good' design. 1.6 Ethical aspects of design choice. 1.7 Exact versus approximate designs. 1.8 Examples. 1.9 Summary. 2 Designs for simple linear regression . 2.1 Design problem for a linear model. 2.2 Designs for radiation-dosage example. 2.3 Relative efficiency and sample size. 2.4 Simultaneous inference. 2.5 Optimality criteria. 2.6 Relative efficiency. 2.7 Matrix formulation of designs for linear regression. 2.8 Summary. 3 Designs for multiple linear regression analysis . 3.1 Design problem for multiple linear regression. 3.2 Designs for vocabulary-growth study. 3.3 Relative efficiency and sample size. 3.4 Simultaneous inference. 3.5 Optimality criteria for a subset of parameters. 3.6 Relative efficiency. 3.7 Designs for polynomial regression model. 3.8 The Poggendorff and Ponzo illusion study. 3.9 Uncertainty about best fitting regression models. 3.10 Matrix notation of designs for multiple regression models. 3.11 Summary. 4 Designs for analysis of variance models . 4.1 A typical design problem for an analysis of variance model. 4.2 Estimation of parameters and efficiency. 4.3 Simultaneous inference and optimality criteria. 4.4 Designs for groups under stress study. 4.5 Specific hypotheses and contrasts. 4.6 Designs for the composite faces study. 4.7 Balanced designs versus unbalanced designs. 4.8 Matrix notation for Groups under Stress study. 4.9 Summary. 5 Designs for logistic regression models . 5.1 Design problem for logistic regression. 5.2 The design. 5.3 The logistic regression model. 5.4 Approaches to deal with local optimality. 5.5 Designs for calibration of item parameters in item response theory models. 5.6 Matrix formulation of designs for logistic regression. 5.7 Summary. 6 Designs for multilevel models . 6.1 Design problem for multilevel models. 6.2 The multilevel regression model. 6.3 Cluster versus subject randomization. 6.4 Cost function. 6.5 Example: Nursing home study. 6.6 Optimal design and power. 6.7 Design effect in multilevel surveys. 6.8 Matrix formulation of the multilevel model . 6.9 Summary. 7 Longitudinal designs for repeated measurement models . 7.1 Design problem for repeated measurements. 7.2 The design. 7.3 Analysis techniques for repeated measures. 7.4 The linear mixed effects model for repeated measurement data. 7.5 Variance-covariance structures. 7.6 Estimation of parameters and efficiency. 7.7 Bone mineral density example. 7.8 Cost function. 7.9 D-optimal designs for linear mixed effects models with autocorrelated errors. 7.10 Miscellanea. 7. 11 Matrix formulation of the linear mixed effects model. 7. 12 Summary. 8 Two-treatment crossover designs . 8.1 Design problem for crossover studies. 8.2 The design. 8.3 Confounding treatment effects with nuisance effects. 8.4 The linear model for crossover designs. 8.5 Estimation of parameters and efficiency. 8.6 Cost and efficiency of the crossover design. 8.7 Optimal crossover designs for two treatments. 8.8 Matrix formulation of the mixed model for crossover designs. 8.9 Summary. 9 Alternative optimal designs for linear models . 9.1 Introduction. 9.2 Information matrix. 9.3 D A - or Ds-optimal designs. 9.4 Extrapolation optimal design. 9.5 L-optimal designs. 9.6 Bayesian optimal designs. 9.7 Minimax optimal design. 9.8 Multiple-objective optimal designs. 9.9 Summary. 10 Optimal designs for nonlinear models . 10.1 Introduction. 10.2 Linear models versus nonlinear models. 10.3 Design issues for nonlinear models. 10.4 Alternative optimal designs with examples. 10.5 Bayesian optimal designs. 10.6 Minimax optimal design. 10.7 Multiple-objective optimal designs. 10.8 Optimal design for model discrimination. 10.9 Summary. 11 Resources for the construction of optimal designs . 11.1 Introduction. 11.2 Sequential construction of optimal designs. 11.3 Exchange of design points. 11.4 Other algorithms. 11.5 Optimal design software. 11.6 A web site for finding optimal designs. 11.7 Summary. References . Author Index. Subject Index.

Integrated layout design of multi‐component system

Abstract: A new integrated layout optimization method is proposed here for the design of multi-component systems. By introducing movable components into the design domain, the components layout and the supporting structural topology are optimized simultaneously. The developed design procedure mainly consists of three parts: (i) Introduction of non-overlap constraints between components. The finite circle method (FCM) is used to avoid the components overlaps and also overlaps between components and the design domain boundaries. (ii) Layout optimization of the components and supporting structure. Locations and orientations of the components are assumed as geometrical design variables for the optimal placement while topology design variables of the supporting structure are defined by the density points. Meanwhile, embedded meshing techniques are developed to take into account the finite element mesh change caused by the component movements. (iii) Consistent material interpolation scheme between element stiffness and inertial load. The commonly used solid isotropic material with penalization model is improved to avoid the singularity of localized deformation in the presence of design dependent loading when the element stiffness and the involved inertial load are weakened by the element material removal. Finally, to validate the proposed design procedure, a variety of multi-component system layout design problems are tested and solved on account of inertia loads and gravity center position constraint. Solutions are compared with traditional topology designs without component. Copyright © 2008 John Wiley & Sons, Ltd.

Fast Computation of Designs Robust to Parameter Uncertainty for Nonlinear Settings

Abstract: Experimental design in nonlinear settings is complicated by the fact that the efficiency of a design depends on the unknown parameter values Thus good designs need to be efficient over a range of likely parameter values Bayesian design criteria provide a natural framework for achieving such robustness, by averaging local design criteria over a prior distribution on the parameters A major drawback to the use of such criteria is the heavy computational burden that they impose We present a clever quadrature scheme that greatly improves the feasibility of using Bayesian design criteria We illustrate the method on some designed experiments

Multiobjective Optimal Design for Interior Permanent Magnet Synchronous Motor

Abstract: This paper presents a method on a multiobjective optimal design of interior permanent magnet synchronous motor (IPMSM) with V-shaped permanent magnet rotor for high performance. In general, a design method adopting equivalent magnetic circuit is used for basic design of IPMSM. However, its use may give us wrong design result because of air-gap flux calculation method by using lumped reluctance parameters. Moreover, it has difficulty in considering arrangement of permanent magnets and barriers, but on the other hand, there exists high degree of freedom of permanent magnet rotor design of IPMSM. Therefore, finite element method (FEM) is needed for the precise design considering arrangement of permanent magnets and barriers. In order for effective V-shaped permanent magnet rotor design, Taguchi method by adopting five multiobjective functions is proposed as an optimal design method. The different optimal design results are suggested by adjusting weighting value of each objective function. Finally, the characteristics of optimal design model with V-shaped permanent magnet rotor are verified by experiment.

Methodology for the design optimisation and the economic analysis of grid-connected photovoltaic systems

Abstract: In this study, a methodology for the design optimisation and the economic analysis of photovoltaic grid-connected systems (PVGCSs) is presented. The purpose of the proposed methodology is to suggest, among a list of commercially available system devices, the optimal number and type of system devices and the optimal values of the photovoltaic (PV) module installation details, such that the total net economic benefit achieved during the system operational lifetime period is maximised. The decision variables included in the optimisation process are the optimal number and type of the PV modules and the DC/AC converters, the PV modules optimal tilt angle, the optimal arrangement of the PV modules within the available installation area and the optimal distribution of the PV modules among the DC/AC converters. The economic viability of the resulting PVGCS configuration is explored according to the net present value, the discounted payback period and the internal rate of return methods. The proposed method has been applied for the optimal design of a PVGCS interconnected to the electric network of an island with significant solar irradiation potential and the corresponding optimal sizing and economic analysis results are presented.

The Application of Particle Swarm Optimization to Passive and Hybrid Active Power Filter Design

Abstract: This paper proposes an optimal design method for passive power filters (PPFs) and hybrid active power filters (HAPFs) set at high voltage levels to satisfy the requirements of harmonic filtering and reactive power compensation. Multiobjective optimization models for PPF and HAPF were constructed. Detuning effects and faults were also considered by constructing constraints during the optimal process, which improved the reliability and practicability of the designed filters. An effective strategy was adopted to solve the multiobjective optimization problems for the designs of PPF and HAPF. Furthermore, the particle swarm optimization algorithm was developed for searching an optimal solution of planning of filters. An application of the method to an industrial case involving harmonic and reactive power problems indicated the superiority and practicality of the proposed design methods.

Monotonic convergence of a general algorithm for computing optimal designs

Abstract: Monotonic convergence is established for a general class of multiplicative algorithms introduced by Silvey, Titterington and Torsney [Comm. Statist. Theory Methods 14 (1978) 1379--1389] for computing optimal designs. A conjecture of Titterington [Appl. Stat. 27 (1978) 227--234] is confirmed as a consequence. Optimal designs for logistic regression are used as an illustration.

Accelerated Microwave Design Optimization With Tuning Space Mapping

Abstract: We introduce a tuning space-mapping technology for microwave design optimization. The general tuning space-mapping algorithm is formulated, which is based on a so-called tuning model, as well as on a calibration process that translates the adjustment of the tuning model parameters into relevant updates of the design variables. The tuning model is developed in a fast circuit-theory based simulator and typically includes the fine model data at the current design in the form of the properly formatted scattering parameter values. It also contains a set of tuning parameters, which are used to optimize the model so that it satisfies the design specification. The calibration process may involve analytical formulas that establish the dependence of the design variables on the tuning parameters. If the formulas are not known, the calibration process can be performed using an auxiliary space-mapping surrogate model. Although the tuning space mapping can be considered to be a specialized case of the standard space-mapping approach, it can offer even better performance because it enables engineers to exploit their experience within the context of efficient space mapping. Our approach is demonstrated using several microwave design optimization problems.

Robust Aerodynamic Design Optimization Using Polynomial Chaos

Abstract: This paper investigates the potential of polynomial chaos methods, when used in conjunction with computational fluid dynamics, to quantify the effects of uncertainty in the computational aerodynamic design process. The technique is shown to be an efficient and accurate means of simulating the inherent uncertainty and variability in manufacturing and flow conditions and thus can provide the basis for computationally feasible robust optimization with computational fluid dynamics. This paper presents polynomial chaos theory and the nonintrusive spectral projection implementation, using this to demonstrate polynomial chaos as a basis for robust optimization, focusing on the problem of maximizing the lift-to-drag ratio of a two-dimensional airfoil while minimizing its sensitivity to uncertainty in the leading-edge thickness. The results demonstrate that the robustly optimized designs are significantly less sensitive to input variation, compared with nonrobustly optimized airfoils. The results also indicate that the inherent geometric uncertainty could degrade the on-design as well as the offdesign performance of the nonrobust airfoil. This leads to the further conclusion that the global optimum for some design problems is unreachable without accounting for uncertainty.

Multi-step blended stacking sequence design of panel assemblies with buckling constraints

Abstract: In this paper, we propose a multi-step framework for design of composite panel assemblies and subsequent blending of the designs to ensure laminate continuity across multi-panel configurations. Multilevel optimisation is frequently used for solving complex optimisation problems. In composite design this approach leads to stacking sequence mismatch among adjacent structural components which is generally referred to as blending problem. To overcome stacking sequence mismatch, a guide-based genetic algorithm (GA) is used which in essence forces the design to be completely blended at any step in the design process. A serious drawback of guide based approach is that it necessitates repeated analysis of the entire structure within the GA iterations. A multi-step framework is proposed where the structure is first optimised using panel thickness and lamination parameters as continuous design variables. The continuous optimisation is performed using a successive convex approximation scheme. In the second step, discrete blended stacking sequences are obtained using a guide-based genetic algorithm. The fitness function in the guide-based GA is evaluated using convex approximations of the response. In this fashion, the cost of evaluating structural response within the GA optimisation is eliminated. The proposed framework is demonstrated via design of an eighteen panel horseshoe configuration, where each panel is optimised individually subject to a local buckling constraint. Numerical results indicate that the present algorithm is capable of producing near-optimal fully blended designs at a small fraction of the computational cost of traditional blending algorithms.

Support points of locally optimal designs for nonlinear models with two parameters

Abstract: We propose a new approach for identifying the support points of a locally optimal design when the model is a nonlinear model. In contrast to the commonly used geometric approach, we use an approach based on algebraic tools. Considerations are restricted to models with two parameters, and the general results are applied to often used special cases, including logistic, probit, double exponential and double reciprocal models for binary data, a loglinear Poisson regression model for count data, and the Michaelis-Menten model. The approach, which is also of value for multi-stage experiments, works both with constrained and unconstrained design regions and is relatively easy to implement.

Aeroelastic topology optimization of membrane structures for micro air vehicles

Abstract: This work considers the aeroelastic optimization of a membrane micro air vehicle wing through topology optimization. The low aspect ratio wing is discretized into panels: a two material formulation on the wetted surface is used, where each panel can be membrane (wing skin) or carbon fiber (laminate reinforcement). An analytical sensitivity analysis of the aeroelastic system is used for the gradient-based optimization of aerodynamic objective functions. An explicit penalty is added, as needed, to force the structure to a 0–1 distribution. The dependence of the solution upon initial design, angle of attack, mesh density, and objective function are presented. Deformation and pressure distributions along the wing are studied for various load-augmenting and load-alleviating designs (both baseline and optimized), in order to establish a link between stiffness distribution and aerodynamic performance of membrane micro air vehicle wings. The work concludes with an experimental validation of the superiority of select optimal designs.