Multiple Design Objectives Research Articles

Support Tools for Simulation-Based Optimal Design of Power Networks With Embedded Power Electronics

This paper presents a gradient-based nonlinear optimization wrapper for an electromagnetic transient simulation program to assist in the design of hardware and control parameters of flexible AC transmission system (FACTS) apparatus. A new second-order sensitivity analysis method is introduced that quantifies the sensitivity of the optimal solution to parameter uncertainties. Using this approach, the sensitivity of the harmonic elimination capability of a pulsewidth-modulated voltage-source converter to variations in switching angles is investigated. This paper also adapts the method of Pareto optimization to the simulation-based design of FACTS apparatus when multiple design objectives are to be satisfied. The developed Pareto optimization tool generates a Pareto frontier plot to visualize the design tradeoffs between the multiple objectives. The effectiveness of the tool is demonstrated through a design example that selects optimal values for the DC-bus capacitor and the control settings of a STATCOM.

Multidisciplinary heat generating logic block placement optimization using genetic algorithm

A multidisciplinary optimization methodology for placement of heat generating semiconductor logic blocks on integrated circuit chips is presented. The methodology includes thermal and wiring length criteria, which are optimized simultaneously using a genetic algorithm. An effective thermal performance prediction methodology based on a superposition method is used to determine the temperature distribution on a silicon chip due to multiple heat generating logic blocks. Using the superposition method, the predicted temperature distribution in the silicon chip is obtained in much shorter time than with a detailed finite element model and with comparable accuracy. The main advantage of the present multidisciplinary design and optimization methodology is its ability to handle multiple design objectives simultaneously for optimized placement of heat generating logic blocks. Capabilities of the present methodology are demonstrated by applying it to several standard benchmarks. The multidisciplinary logic block placement optimization results indicate that the maximum temperature on a silicon chip can be reduced by up to 7.5°C, compared to the case in which only the wiring length is minimized.

The vibration control of speed-dependent flexible rotor/magnetic bearing systems using linear matrix inequality gain-scheduled H∞ design

The dynamics of rotor/magnetic bearing systems are speed dependent due to gyroscopic effects and other aerodynamic influences. This paper introduces a linear matrix inequality (LMI)-based design method that enables the speed dependence to be embedded within robust gain-scheduled controllers. By utilizing an augmented plant that closely resembles the physical system, it allows for the formulation of multiple design objectives in a unified framework. In particular, a control design for minimized rotor displacements or bearing transmitted forces is considered and the importance of achieving performance over the whole speed range is addressed. If speed dependence is ignored in the controller design, system instability may occur at particular rotational speeds. However, the LMI-based gain-scheduling approach is shown to maintain closed-loop stability over the full speed range. The results are demonstrated using a system model and through measurements taken from a flexible rotor/magnetic bearing facility.

Design of a motorcycle frame using neuroacceleration strategies in MOEAs

Designing a low-budget lightweight motorcycle frame with superior dynamic and mechanical properties is a complex engineering problem. This complexity is due in part to the presence of multiple design objectives--mass, structural stress and rigidity--, the high computational cost of the finite element (FE) simulations used to evaluate the objectives, and the nature of the design variables in the frame's geometry (discrete and continuous). Therefore, this paper presents a neuroacceleration strategy for multiobjective evolutionary algorithms (MOEAs) based on the combined use of real (FE simulations) and approximate fitness function evaluations. The proposed approach accelerates convergence to the Pareto optimal front (POF) comprised of nondominated frame designs. The proposed MOEA uses a mixed genotype to encode discrete and continuous design variables, and a set of genetic operators applied according to the type of variable. The results show that the proposed neuro-accelerated MOEAs, NN-NSGA II and NN-MicroGA, improve upon the performance of their original counterparts, NSGA II and MicroGA. Thus, this neuroacceleration strategy is shown to be effective and probably applicable to other FE-based engineering design problems.

A methodological concept for material selection of highly sensitive components based on multiple criteria decision analysis

Material selection of highly sensitive components is one of the most challenging issues in the design and development of structural elements in aerospace and nuclear industry. This work compares some of the most widely potential multi-criteria decision making models for addressing all the stages in solving a material selection problem of highly sensitive components involving conflicting as well as multiple design objectives. For the first step, the compensatory models are discussed and employed to solve a multi-criteria material selection for a thermal loaded conductor in the presence of its required multi-functional characteristics. For the next step, using different versions of the non-compensatory methods examine the outranking approach to solve the same problem. The results are compared to each other to verify the effect of compensations and non-compensations in the methods and their sensitivity to ranking stability. It is of particular interest to see how different approaches of the Multiple Attribute Decision Making (MADM) models differ from each other when criterion of cost is a critical factor in the problem. The effect of individual attributes of cost criterion has been studied to ensure the reliability of the chosen candidate material by MADM models.

Multidisciplinary collaborative optimization using fuzzy satisfaction degree and fuzzy sufficiency degree model

Collaborative optimization (CO) is a bi-level multidisciplinary design optimization (MDO) method for large-scale and distributed-analysis engineering design problems. Its architecture consists of optimization at both the system-level and autonomous discipline levels. The system-level optimization maintains the compatibility among coupled subsystems. In many engineering design applications, there are uncertainties associated with optimization models. These will cause the design objective and constraints, such as weight, price and volume, and their boundaries, to be fuzzy sets. In addition the multiple design objectives are generally not independent of each other, that makes the decision-making become complicated in the presence of conflicting objectives. The above factors considerably increase the modeling and computational difficulties in CO. To relieve the aforementioned difficulties, this paper proposes a new method that uses a fuzzy satisfaction degree model and a fuzzy sufficiency degree model in optimization at both the system level and the discipline level. In addition, two fuzzy multi-objective collaborative optimization strategies (Max–Min and α-cut method) are introduced. The former constructs the sufficiency degree for constraints and the satisfaction degree for design objectives in each discipline respectively, and adopts the Weighted Max–Min method to maximize an aggregation of them. The acceptable level is set up as the shared design variable between disciplines, and is maximized at the system level. In the second strategy, the decision-making space of the constraints is distributed in each discipline independently through the allocation of the levels of α. At the system level, the overall satisfaction degree for all disciplines is finally maximized. The illustrative mathematical example and engineering design problem are provided to demonstrate the feasibility of the proposed methods.

Magnetic Design for the PediaFlow Ventricular Assist Device

This article describes a design process for a new pediatric ventricular assist device, the PediaFlow. The pump is embodied in a magnetically levitated turbodynamic design that was developed explicitly based on the requirements for chronic support of infants and small children. The procedure entailed the consideration of multiple pump topologies, from which an axial mixed-flow configuration was chosen for further development. The magnetic design includes permanent-magnet (PM) passive bearings for radial support of the rotor, an actively controlled thrust actuator for axial support, and a brushless direct current (DC) motor for rotation. These components are closely coupled both geometrically and magnetically, and were therefore optimized in parallel, using electromagnetic, rotordynamic models and fluid models, and in consideration of hydrodynamic requirements. Multiple design objectives were considered, including efficiency, size, and margin between critical speeds to operating speed. The former depends upon the radial and yaw stiffnesses of the PM bearings. Analytical expressions for the stiffnesses were derived and verified through finite element analysis (FEA). A toroidally wound motor was designed for high efficiency and minimal additional negative radial stiffness. The design process relies heavily on optimization at the component level and system level. The results of this preliminary design optimization yielded a pump design with an overall stability margin of 15%, based on a pressure rise of 100 mm Hg at 0.5 lpm running at 16,000 rpm.

Designing Platforms for Customizable Products and Processes in Markets of Non-Uniform Demand

The foremost difficulty in making the transition to mass customization is how to offer product variety affordably. The answer to this quandary lies in the successful management of modularity and commonality in the development of products and their production processes. While several platform design techniques have emerged as a means to offer modularity and commonality, they are limited by an inability to handle multiple modes of offering variety for multiple design specifications. The Product Platform Constructal Theory Method (PPCTM) is a technique that enables a designer to develop platforms for customizable products while handling issues of multiple levels of commonality, multiple product specifications, and the inherent tradeoffs between platform extent and performance. The method is limited, however, by its inability to handle multiple design objectives and its reliance on the assumption that demand in the market is uniform for each product variant. The authors address these limitations in this study by infusing the utility-based compromise decision support problem and demand modeling techniques. The authors further augment the PPCTM by extending its use to a new domain: the design of process parameter platforms. The augmented approach is illustrated through a tutorial example: the design of a product and a process parameter platform for the realization of a line of customizable cantilever beams.

Piezoelectric transducer design via multiobjective optimization

The design of piezoelectric transducers is usually based on single-objective optimization only. In most practical applications of piezoelectric transducers, however, there exist multiple design objectives that often are contradictory to each other by their very nature. It is impossible to find a solution at which each objective function gets its optimal value simultaneously. Our design approach is to first find a set of Pareto-optimal solutions, which can be considered to be best compromises among multiple design objectives. Among these Pareto-optimal solutions, the designer can then select the one solution which he considers to be the best one. In this paper we investigate the optimal design of a Langevin transducer. The design problem is formulated mathematically as a constrained multiobjective optimization problem. The maximum vibration amplitude and the minimum electrical input power are considered as optimization objectives. Design variables involve continuous variables (dimensions of the transducer) and discrete variables (the number of piezoelectric rings and material types). In order to formulate the optimization problem, the behavior of piezoelectric transducers is modeled using the transfer matrix method based on analytical models. Multiobjective evolutionary algorithms are applied in the optimization process and a set of Pareto-optimal designs is calculated. The optimized results are analyzed and the preferred design is determined.

Fast Multidisciplinary Design Optimization via Taguchi Methods and Soft Computing

It is difficult to perform multidisciplinary design optimization using traditional searchbased optimization techniques due to possible conflicts among objectives from different disciplines,thetimeconsumingsearch,andthepossibilityofdivergence.Toovercomethedifficulties, this paper presents simulation-based design optimization techniques using Taguchi methods and soft computing (i.e. fuzzy logic and neural networks). An aircraft engine cycle design optimization with four conflicting design objectives is used to validate the presented approach. The result shows significant performance improvement in optimizing single and multiple design objectives. HE emerging field of Multidisciplinary Design Optimization (MDO) seeks to improve design methodology to rapidly and efficiently explore multiple-dimension design spaces with the goal of increasing system performance significantly, thereby reducing end-product cost substantially. Search-based and simulation-based are the two major system design approaches. The former is traditional and mathematical, and has existed for a long time. The optimum solution has to do with the selected starting point, and the optimization method used. A possibility of divergence in solution seeking is a major drawback in this approach. In contrast, the simulation-based approach uses the analysis and evaluation of a candidate solution, and the assessment of the degree to which the candidate satisfies the requirement. This optimum design tool uses the simulation-based approach. 1 With this new approach, the optimum solution can be obtained in real time. The traditional search-based optimization is a typical example of hard computing. In hard computing, the prime desiderata are precision, certainty and vigor. In contrast, in soft computing the principal notion is that precision and certainty carry a cost; and that computation, reasoning, and decision-making should exploit (whenever possible) the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth for obtaining low cost solutions. Fuzzy logic and neural networks, the two major soft computing techniques, have very contrasting application requirements. Fuzzy systems are appropriate if sufficient expert knowledge about the process is available, while neural systems are useful if sufficient data are available or measurable. Furthermore, neural networks possess the ability to learn the input-output relationship. A trained neural network provides instantly input-to-output mapping with reasonably good accuracy, but without knowledge representation. Fuzzy logic, on the other hand, possesses the ability for knowledge representation and inference, but has no capabilities for automated learning. Thus, fuzzy logic and neural networks compensate each other in terms of information processing.

An intelligent moving object optimization algorithm for design problems with mixed variables, mixed constraints and multiple objectives

This paper presents an optimization algorithm for engineering design problems having a mix of continuous, discrete and integer variables; a mix of linear, non-linear, differentiable, non-differential, equality, inequality and even discontinuous design constraints; and conflicting multiple design objectives. The intelligent movement of objects (vertices and compounds) is simulated in the algorithm based on a Nelder–Mead simplex with added features to handle variable types, bound and design constraints, local optima, search initiation from an infeasible region and numerical instability, which are the common requirements for large-scale, complex optimization problems in various engineering and business disciplines. The algorithm is called an INTElligent Moving Object algorithm and tested for a wide range of benchmark problems. Validation results for several examples, which are manageable within the scope of this paper, are presented herein. Satisfactory results have been obtained for all the test problems, hence, highlighting the benefits of the proposed method.

Topology optimization for femto suspension design

The ongoing increase of track density requirements of hard disk drives (HDDs) and decrease of flying height of sliders have brought along formidable challenges to suspension design. Conventional design processes are quite tedious and inefficient. This paper presents a HDD suspension design process by using topology optimization. An efficient structural topology optimization method, based on the second derivatives information, is proposed to generate structures which satisfy multiple design objectives including both compliance and eigenfrequencies. This topology optimization approach is successfully applied in the HDD suspension design. The design begins with a very simple initial draft, and the design objectives are defined to minimize the spring rate and maximize the resonant frequencies of first bending, first torsion and sway modes of a suspension. An optimal design concept can be generated from the topology optimization. Then the design is further tuned by using the shape optimization. Finally, an optimal suspension design for femto sliders with much better dynamic characteristics is presented.

Multiobjective optimization of three-stage spur gear reduction units using interactive physical programming

The preliminary design optimization of multi-stage spur gear reduction units has been a subject of considerable interest, since many high-performance power transmission applications (e.g., automotive and aerospace) require high-performance gear reduction units. There are multiple objectives in the optimal design of multi-stage spur gear reduction unit, such as minimizing the volume and maximizing the surface fatigue life. It is reasonable to formulate the design of spur gear reduction unit as a multi-objective optimization problem, and find an appropriate approach to solve it. In this paper an interactive physical programming approach is developed to place physical programming into an interactive framework in a natural way. Class functions, which are used to represent the designer’s preferences on design objectives, are fixed during the interactive physical programming procedure. After a Pareto solution is generated, a preference offset is added into the class function of each objective based on whether the designer would like to improve this objective or sacrifice the objective so as to improve other objectives. The preference offsets are adjusted during the interactive physical programming procedure, and an optimal solution that satisfies the designer’s preferences is supposed to be obtained by the end of the procedure. An optimization problem of three-stage spur gear reduction unit is given to illustrate the effectiveness of the proposed approach.

Ecologically and economically conscious design of the injected pultrusion process via multi-objective optimization

Injected pultrusion (IP) is an environmentally benign continuous process for low-cost manufacture of prismatic polymer composites. IP has been of recent regulatory interest as an option to achieve significant vapour emissions reduction. This work describes the design of the IP process with multiple design objectives. In our previous work (Srinivasagupta D et al 2003 J. Compos. Mater. at press), an algorithm for economic design using a validated three-dimensional physical model of the IP process was developed, subject to controllability considerations. In this work, this algorithm was used in a multi-objective optimization approach to simultaneously meet economic, quality related, and environmental objectives. The retrofit design of a bench-scale set-up was considered, and the concept of exergy loss in the process, as well as in vapour emission, was introduced.The multi-objective approach was able to determine the optimal values of the processing parameters such as heating zone temperatures and resin injection pressure, as well as the equipment specifications (die dimensions, heater, puller and pump ratings) that satisfy the various objectives in a weighted sense, and result in enhanced throughput rates. The economic objective did not coincide with the environmental objective, and a compromise became necessary. It was seen that most of the exergy loss is in the conversion of electric power into process heating. Vapour exergy loss was observed to be negligible for the most part.

Design for Operability: A Singular-Value Optimization Approach within a Multiple-Objective Framework

Design-for-operability is a field of active research in process systems engineering because of the high economic impact of design on operability. Dynamic operability has been widely studied by means of the so-called (open-loop) controllability and resiliency indices, which are mostly based on linearized (Laplace/frequency domains) models of the dynamic systems. In particular, the minimum singular value of the transfer function matrix is a fair measure of resilience to disturbances. Among the plethora of approaches to design for dynamic operability, a multiple-objective formulation between cost and controllability naturally arises because of the conflicting characters of the two objectives. In this contribution the cost/minimum-singular-value multiple-objective design problem is solved for the meaningful reactor-separator-recycle system and two different control strategies. The generation of the noninferior solution set is performed here within the framework of an eigenvalue optimization approach.

Modeling and Optimization of Passively Damped Adaptive Composite Structures

A smart structural model is used to optimally determine both the placement of piezoelectric actuators and parameters describing associated electrical components in a passively damped structure. The technique utilizes a recently developed coupled piezoelectric-mechanical theory to analytically determine the response of arbitrary structures with piezoelectric materials and attached electrical circuitry. The theory simultaneously models both the structural and the electrical components, and the complex state of strain that may exist in the piezoelectric patches, thereby providing accurate mechanical and electrical response. A robust multiobjective optimization procedure is developed to design the passive system for simultaneous damping of several critical modes of interest. The influence of stacking sequence in augmenting passive damping can also be examined by including ply orientations as design variables. Since the optimization problem now involves both continuous and discrete design variables, a hybrid optimization technique is used that allows the inclusion of both types of design variables. Also, since multiple design objectives are introduced, the Kreisselmeier-Steinhauser function approach is used allowing the multiple and conflicting design objectives and constraints to be combined into a single unconstrained function. Results demonstrate the ability of this technique to determine the optimal tuning parameters for damping of multiple modes of vibration using a limited number of piezoelectric actuators.

A direct displacement‐based seismic design procedure to fulfill multiple performance objectives

Damage evidence after the Chi‐Chi earthquake highlighted the im‐portance of performance‐based earthquake engineering (PBEE), which stresses the inelastic behavior of structural systems under severe earthquake ground motions. In this paper, a direct displacement‐based seismic design procedure is implemented and demonstrated through an example to fulfill multiple performance objectives in practical design.

Normative design of organizations. II. Organizational structure

For pt.I. see ibid., p. 346-59. This paper presents a multiobjective structural optimization process of designing an organization to execute a specific mission. We provide mathematical formulations for optimization problems arising in Phases II and III of our organizational design process and polynomial algorithms to solve the corresponding problems. Our organizational design methodology applies specific optimization techniques at different phases of the design, efficiently matching the structure of a mission (in particular, the one defined by the courses of action obtained from mission planning) to that of an organization. It allows an analyst to obtain an acceptable tradeoff among multiple mission and design objectives, as well as between computational complexity and solution efficiency (desired degree of suboptimality).

QFD-based optimisation using response surface method

This paper suggests a customer-oriented optimisation method, called Quality Function Deployment (QFD) based optimisation method, to determine target levels of multiple design objectives that attain the maximum customer satisfaction level, and also satisfy a Pareto optimality condition. The customer satisfaction level is defined by QFD, and the numerical association is identified through the use of the Response Surface Method (RSM) that constructs an approximate model for Pareto set. This decision is made by solving an optimisation problem of which objective function is the customer satisfaction level and constraint of the non-inferior solution that follows customers' preferences most. This regression model enables to not only easily investigate both the feasibility and the Pareto-optimality for a set of design objective values, but also support trade-off among various perspectives by having a clear understanding of the association. It also allows the exploration of the effects of ambiguity included in QFD-based optimisation. Since there always exists ambiguity that originates from the process of the value judgements in QFD, it is necessary to investigate what effects the ambiguity has on the determination of the target levels. To deal with such ambiguity systematically, this paper suggests an exploratory approach, and introduces orthogonal array and Analysis of Variance (ANOVA) table developed by Taguchi.

Topology Design of Truss Structures in a Multicriteria Environment

As an analogy of the general design process, this article presents a novel design approach that could generate structural design alternatives having different topologies and then select the optimal structures from them together with simultaneously determining the optimal design variables related to geometry and member size subjected to a multiple objective design environment. For this purpose, a specialized genetic algorithm, called StrGA_DeAl+MOGA, that can handle the design alternatives and multicriteria problems very effectively is developed for the optimal structural design. To validate the developed method, plain‐truss design problems are considered as illustrative examples. To begin with, the promising topologies are generated under the name of “design alternatives” with consideration of the given multiobjective environment. Based on the selected topology of truss structures, the sizing or geometric optimization process starts to determine the optimal design parameters. Three‐bar and ten‐bar truss problems are treated in the article to test the concept and methodology.