Evaluating Multi-criteria Evolutionary Algorithms for Airfoil Optimisation

The low order Taylor’s series expansion was employed in this study to estimate the reliability indices of the failure criteria for reliability-based design optimization of a linear static structure subjected to random loads and boundary conditions. By taking the advantage of the linear superposition principle, only a few analyses of the structure subjected to unit-loads are needed through the entire optimization process to produce acceptable results. Two structural examples are presented in this study to illustrate the effectiveness of the proposed approach for reliability-based design optimization: one deals with a truss structure subjected to random multiple point constraints, and the other conducts shape design optimization of a plane stress problem subjected to random point loads. Both examples were formulated and solved by the finite element method. The first example used the penalty method to reformulate the multiple point constraints as external loads, while the second example introduced an approach to propagate the uncertainty linearly from the nodal displacement vector to the nodal von Mises stress vector. The final designs obtained from the reliability-based design optimization were validated through Monte Carlo simulation. This validation process was completed with only four unit-load analyses for the first example and two for the second example.

This paper is concerned with designing an optimum flexible wing structure to enhance roll maneuver capability at high dynamic pressures using embedded actuating system. A wing optimized to minimize the weight, with constraints on strength for symmetric pull up maneuver and constraints on the frequency distribution was used for this study. Elastic twist and camber is achieved by providing a system of actuating elements distributed within the internal substructure of the wing to provide control forces. The equilibrium equations were developed for the steady roll maneuver of a wing subjected to aerodynamic loads and actuating forces. Optimal control design approach was used to determine the distribution of actuating forces. The total straing energy was calculated as a measure of power requirement to produce twist and camber, to achieve specified flexible roll rate at different Mach numbers.

An efficient approach for reliability-based design optimization (RBDO) of series systems is presented. A modified formulation of the RBDO problem is employed in which the reliabilities of the failure modes of a system are included in the set of the design variables. This allows for an optimal apportionment of the reliability of a system among its failure modes. A sequential optimization and reliability assessment method is used to efficiently determine the optimum design. Here, the constraints on the reliabilities of the failure modes of the RBDO problem are replaced with approximate deterministic constraints. The proposed approach is demonstrated on two example problems that have been solved in previous studies without optimizing the required reliability levels of the failure modes. The first example performs RBDO to a cantilever beam with a rectangular cross section under lateral and vertical loads. The constraints are on the strength and the maximum allowable displacement. The second example performs RBDO to a cable-stayed box girder with five failure modes. Compared to the designs found by previous studies, the new approach finds designs with lower mass but without reducing the system reliability.

Fabless design approach for lateral optimization of low voltage GaN power HEMTs

In Evolutionary Algorithms (EA), the selection scheme is a pivotal component, where it relies on the fitness value of individuals to apply the Darwinian principle of survival of the fittest. In Particle Swarm Optimization (PSO) there is only one place employed the idea of selection scheme in global best operator in which the components of best solution have been selected in the process of deriving the search and used them in generation the upcoming solutions. However, this selection process might be affecting the diversity aspect of PSO since the search infer into the best solution rather than the whole search. In this paper, new selection schemes which replace the global best selection schemes are investigated, comprising fitness-proportional, tournament, linear rank and exponential rank. The proposed selection schemes are individually altered and incorporated in the process of PSO and each adoption is realized as a new PSO variation. The performance of the proposed PSO variations is evaluated. The experimental results using benchmark functions show that the selection schemes directly affect the performance of PSO algorithm. Finally, a parameter sensitivity analysis of the new PSO variations is analyzed.

The application of Dr. Genichi Taguchi's approach for design optimization, called Robust Design, to the design of human-computer interface software is investigated. The Taguchi Method is used to select a near optimum set of interface design alternatives to improve user acceptance of the resulting interface software product with minimum sensitivity to uncontrollable noise caused by human behavioral characteristics. Design alternatives for interaction with personal micro-computers are identified. Several important and representative alternatives are chosen as design parameters for the Taguchi matrix experiment. A noise field with three human behavioral characteristics as noise factors were chosen as a representative noise array. Task accomplishment scenarios were developed for demonstration of the design parameters on an interactive human-computer interface. Experimentation was conducted using selected human subjects to study the effect of the various settings of the design parameters on user acceptance of the interface. Using the results of the matrix experiment, a near optimum set of design parameter values was selected. A verification experiment was developed and performed using the predicted near optimum design parameter values. Analysis of the follow-up experiment indicated improved levels of user acceptance with the near optimum values. This study suggests that the Taguchi Method of design optimization is applicable to human-machine engineering in general, and to the design of human-computer interface software in particular, as a means of selecting a near optimum set of design alternatives. This methodology is useful in reducing the number of total experiments required for optimization where several design alternatives exist in a richly interdependent context.

A compound parabolic concentrator (CPC) is a non-imaging device generally used in PV, thermal, or PV/thermal hybrid systems for the concentration of solar radiation on the target surface. This paper presents the geometric design, statistical modeling, parametric analysis, and geometric optimization of a two-dimensional low concentration symmetric compound parabolic concentrator for potential use in building-integrated and rooftop photovoltaic applications. The CPC was initially designed for a concentration ratio of “2×” and an acceptance half-angle of 30°. A MATLAB code was developed in-house to provoke the CPC reflector’s profile. The height, aperture width, and concentration ratios were computed for different acceptance half-angles and receiver widths. The interdependence of optical concentration ratio and acceptance half-angle was demonstrated for a wide span of acceptance half-angles. The impact of the truncation ratio on the geometric parameters was investigated to identify the optimum truncation position. The profile of truncated CPC for different truncation positions was compared with full CPC. A detailed statistical analysis was performed to analyze the synergistic effects of independent design parameters on the responses using the response surface modeling approach. A set of optimized design parameters was obtained by establishing specified optimization criteria. A 50% truncated CPC with an acceptance half-angle of 21.58° and receiver width of 193.98 mm resulted in optimum geometric dimensions.

Optimal Design of a Reconfigurable Machine Tool Considering Machine Configurations and Configuration Changes

The first attempt to improve battery performance through model-based design optimization was made by W. Tiedemann and J. Newman in 19751. They varied the electrode thickness and porosity and then observed the effects on the effective capacity. Newman later applied the reaction-zone model to maximize the specific energy of the system using the same varying and observing method2. V. Ramadesigan et al.3developed optimal spatially varying porosity for porous electrodes by considering ohmic resistance model in the electrode. They minimized the ohmic resistance in the positive electrode by varying porosity using control vector parameterization (CVP) method. They also introduced the idea of using graded electrode to further improve the performace. Since the introduction of the pseudo-2D (P2D) model by the Newman group, many efforts have been made in utilizing the P2D model in battery design. W. Du et al. applied an optimization framework based on surrogate method4. S. Golmon et al. performed the multi-objective and multi-design-parameter optimization problem with adjoint sensitivity analysis5 incorporating mechanical stress-strain relationship. S. De and his coworkers6 used a reformulated model, to perform simultaneous optimization of multiple design parameters. N. Xue et al. 7 applied the gradient-based algorithm framework to optimize the cell design. More recently, Y. Dai and V. Srinivasan8used a gradient-free direct search method to maximize the specific energy. Z. Du et al. studies the effects of various design parameters on thick electrode performance9. Most of the aforementioned optimization research, used the sequential optimization approach, also known as CVP. CVP takes the differential algebraic equation (DAE) system describing the battery physics, applies a certain nonlinear programming (NLP) solver to them, discretizes only the control variables, and solves the model. The only exception being the effort by Golmon et al which used an indirect approach. Note that as of today, global optimization cannot be guaranteed when CVP type approaches are used with P2D type models. In general optimization approaches can be classified as indirect and direct approach. In the direct approach there are two main categories – sequential and simultaneous approach. The simultaneous approach10, on the other hand, discretizes both the control variables and the design variables before solving the problem. When used for optimization, the DAE system will only be solved once at the optimal point, compared to the repeated numerical integration needed for sequential optimization. By using high order discretization scheme on both the state and the design variables, simultaneous optimization approach results in faster determination of the optimum with fewer time steps. Furthermore, this approach offers more flexibility over constraints on the state variables. Since the state variables are also discretized, it is possible to apply equality/inequality constraints on their internal values. In this work, we will first apply the simultaneous optimization approach to a resistance model similar to the one mentioned earlier3, and compare its performance with the sequential approach. We will also explore the application of the same optimization approach to the P2D model, and identify the challenges. It is viewed as a controversial topic in the literature wherein different authors disagree on the relevance of graded porosity for improved performance8. By using a simultaneous optimization approach and guaranteeing global optimization for convex problem statements, we hope to provide additional insights. Acknowledgements The authors are thankful for the financial support by the Clean Energy Institute at the University of Washington and the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Vehicle Technologies of the U.S. Department of Energy through the Advanced Battery Materials Research (BMR) Program (Battery500 Consortium).

Since its introduction, the NASA Common Research Model has proved a useful aerodynamic benchmark for predicting computational-fluid-dynamics-based drag and aerodynamic design optimization. The model was originally conceived as a purely aerodynamic benchmark, and as such the wing geometry corresponds to the deflected shape at its nominal flight condition. However, interest has been growing to extend this model to aeroelastic studies. Unfortunately, because of its predefined deflection, the model is not suitable for aeroelastic analysis and design. To address this issue, an undeflected Common Research Model is defined through an inverse design procedure that includes the outer mold line geometry of the undeflected wing and the corresponding internal wingbox structure. Additionally, because modern transport aircraft are trending toward higher-aspect-ratio wing designs, a higher-aspect-ratio variant of the Common Research Model wing is developed to assess next-generation wing designs. This variant has an aspect ratio of 13.5 and is designed by using buffet-constrained multipoint aerostructural optimization. The purpose of these models is to provide publicly available benchmarks for aeroelastic wing analysis and design optimization.

In this study, high efficient design tool is developed with several informatics approaches for Multidisciplinary Design Optimization (MDO) and knowledge discovery of supersonic wing design. In this design, Multi-Objective Genetic Algorithm (MOGA) is applied as an optimizer, while Kriging model is also used to reduce computational cost. To obtain the information of the design space, functional ANalysis Of VAriance (ANOVA) and Parallel Coordinate Plot (PCP) is applied. For Kriging model construction, 107 sample points are evaluated. This tool is applied to the multidisciplinary design problem of supersonic wing. The objective functions are to maximize lift to drag ratio and to minimize sonic boom intensity at supersonic cruise, and to minimize wing weight. According to the results, there are trade-off relationships among three objective functions. The ANOVA results indicate that the cambers of the wing section at the root and the kink have an influence on the lift to drag ratio, the inner wing sweep back angle affects the sonic boom intensity, and the camber of wing section at the kink and aspect ratio affect the wing weight. The design space information could be visualized quantitatively from the sampling results with PCP technique. Since the design space exploration using MOGA is carried out based on Kriging surrogate models, the proposed MDO process is effective in terms of computational cost.

In this investigation, a decomposition approach is employed for reliability-based design optimization of multidisciplinary systems. In the last few years, a variety of different approaches for reliability-based design optimization have been proposed in the literature. Traditionally, reliability-based design optimization is formulated as a nested optimization problem, where the inner loop, generally involves the solution to an optimization problem for computing the probability of failure corresponding to the failure modes. Such a formulation is by nature computationally intensive, requiring a large numbers of function and constraint evaluations. To alleviate this problem, researchers have developed single level reliability-based optimization formulations. The single level approach is expensive for multidisciplinary systems because of the fact that the complex coupled nature of multidisciplinary systems are likewise computationally intensive requiring iterative solvers. To reduce the computational effort of performing reliability-based design optimization in application to multidisciplinary systems, a decomposition approach for optimization is employed. The method of Simultaneous ANalysis and Design (SAND) is employed as an optimization driver within a single level reliability-based design optimization strategy. The methodology is illustrated in application to multidisciplinary test problems.

This paper presents an optimum design approach to a two-phase switched reluctance motor (SRM) drive. The proposed drive is designed for use as a compressor drive in a small refrigerator as an alternative to the existing brushless dc motor with rare-earth magnets. In the proposed approach, three genetic algorithm loops work to optimize the lamination design and to meet the requirements for the target application while simultaneously fine-tuning the control parameters. To achieve design optimization within a realistic timescale, the repeated calculation required to obtain fitness evaluation does not use finite-element analysis but instead consists of a dynamic simulator based on an analytical expression of magnetizing curves and a geometric flux-tube-based nonlinear magnetic analysis developed specifically for this class of motor. Design results show that the proposed approach can autonomously find a feasible design solution of an SRM drive for the target application from a huge search space. The experimental studies using a two-phase 8/6 prototype manufactured in accordance with the optimized design parameters show the validity of the proposed design approach.

An optimal concurrent product design and service planning approach through simulation-based evaluation considering the whole product life-cycle span

An optimal design approach is developed for permanent magnet motors. First, parameterized design model is built. Second the significant shape parameters are identified using the Fisher test of ANOVA. Finally, an optimal design loop, delivering as final output optimized geometries of Permanent Magnet motors, is implemented.