Showing papers on "Optimal design published in 2017"

Topology optimization for heat conduction using generative design algorithms

Abstract: In this article we present a new approach to topological design for steady-state heat conduction. The method capitalizes on the use of a generative algorithm to represent topology, resulting in a decrease in the number of variables in the design description. Using a generative algorithm as a design abstraction, the optimization technique is targeted to dendritic topologies that are known to perform well for heat conduction. Specifically, a traditional topology optimization technique (SIMP) is confirmed to produce branching characteristics in optimal designs. The Space Colonization Algorithm, which can generate similar topological patterns, is selected for in-depth investigation. A genetic algorithm drives generation of design candidates, providing a highly diversified search of the target design space. Finally, several synthesized optimal designs for steady-state heat conduction, derived using the described algorithms, are compared using commercial finite element software.

Process design and control optimization: A simultaneous approach by multi-parametric programming

Abstract: We present a framework for the application of design and control optimization via multiparametric programming through four case studies. We develop design dependent multi-parametric model predictive controllers that are able to provide the optimal control actions as functions of the system state and the design of the process at hand, via our recently introduced PAROC framework1. The process and the design dependent explicit controllers undergo a Mixed Integer Dynamic Optimization (MIDO) step for the determination of the optimal design. The result of the MIDO is the optimal design of the process under optimal operation. We demonstrate the framework through case studies of a tank, a continuously stirred tank reactor, a binary distillation column and a residential cogeneration unit. This article is protected by copyright. All rights reserved.

Multiobjective Optimization of Medium-Frequency Transformers for Isolated Soft-Switching Converters Using a Genetic Algorithm

Abstract: The main challenge of medium-frequency transformers is the number of design parameters, constraints and objectives, and the difficulty of handling them on a particular design. This paper presents a novel computer-aided optimal design for MF transformers using a multiobjective genetic algorithm, in particular the nondominated sorting genetic algorithm II. The proposed methodology has the aim of reaching the best MF transformer for a given power converter topology, by optimizing transformer efficiency, weight, and also, transformer leakage and magnetizing inductances at the same time. The proposed methodology and the optimal solutions are validated with the design and the development of two 10-kVA/500-V transformers considering two different topologies. Finally, some experimental measurements are presented so as to demonstrate the proposed models and the performance of built transformers.

Connecting e-hailing to mass transit platform: Analysis of relative spatial position

Abstract: This paper analyzes and compares two different relative spatial position (RSP) designs in an integrated e-hailing/fixed-route transit system: a zone-based design that operates e-hailing vehicles within a zone, and a line-based design that operates e-hailing vehicles along a fixed-route transit line and with a stable headway. To conduct a meaningful comparison, the optimal design problems for both systems are formulated using a same analytical framework based on the continuous approximation approach. A comprehensive numerical experiment is performed to compare various cost components corresponding to the optimal designs, and a discrete-event simulation model is developed to validate the analysis. The analytical and simulation results agree with each other well, with a discrepancy in the total system cost less than 5% in most test scenarios. These results also suggest that the line-based system consistently outperforms the zone-based system in terms of both agency and user costs, for all scenarios tested. Compared to the zone-based design, the line-based design features a sparser fixed-route network (resulting in larger stop spacing) but a higher dispatching frequency. It is concluded that the higher efficiency of the line-based design is likely derived from the strategy of operating e-hailing vehicles with a more regular route/headway structure and allowing ride-sharing.

Design and Optimization of a New Magnetic-Geared Pole-Changing Hybrid Excitation Machine

Abstract: This paper presents a new magnetic-gear pole-changing hybrid excitation machine (MG-PCHEM), which can provide high torque density and improved flux-weakening ability. The key is to flexibly change the pole-pair number of inner excitation sources by injecting variable dc field currents, and, hence, regulate the dominant pole-pair flux components to realize an effective magnetic field adjustment. In the following paper, the machine configuration and its operation principle of flux control are introduced first. Then, the influences of some leading design parameters are analyzed including the slot-pole combination and other dimension parameters, and after which a comparative study is carried out with traditional single-stator topology. Based on the optimal design, electromagnetic performance of the MG-PCHEM is further evaluated by using the finite-element method. Finally, a prototype is designed, manufactured, and fully tested. Relevant experimental results verify the feasibility of this new hybrid solution as well as the finite-element predictions.

Optimal design of permanent magnet linear synchronous motors based on Taguchi method

Abstract: This study focuses on optimisation design of permanent magnet linear synchronous motors that are applied in laser engraving machines with no cutting force. Traditional analytical optimisation method based on magnetic field with particle swarm optimisation algorithm was introduced to obtain the best combination of motor structure parameters. By contrast, the novel optimisation design method - Taguchi method based on orthogonal array was proposed to optimise the thrust and thrust ripple. After the design of experiments using finite-element analysis, the relative importance of each design parameter was estimated in detail. Experimental results of prototype can certify the superiority and validity of Taguchi optimisation method.

Replication or exploration? Sequential design for stochastic simulation experiments

Abstract: We investigate the merits of replication, and provide methods for optimal design (including replicates), with the goal of obtaining globally accurate emulation of noisy computer simulation experiments. We first show that replication can be beneficial from both design and computational perspectives, in the context of Gaussian process surrogate modeling. We then develop a lookahead based sequential design scheme that can determine if a new run should be at an existing input location (i.e., replicate) or at a new one (explore). When paired with a newly developed heteroskedastic Gaussian process model, our dynamic design scheme facilitates learning of signal and noise relationships which can vary throughout the input space. We show that it does so efficiently, on both computational and statistical grounds. In addition to illustrative synthetic examples, we demonstrate performance on two challenging real-data simulation experiments, from inventory management and epidemiology.

Simulation and optimization of magnetic negative stiffness dampers

Abstract: This paper presents the detailed modelling, parametric studies, and optimizations for two recently proposed magnetic negative stiffness dampers (MNSDs). Both dampers are composed of several coaxially arranged permanent magnets and a conductive pipe. The novel MNSDs can efficiently integrate negative stiffness and eddy-current damping in compact and simple configurations. However, the optimal design of MNSDs has never been investigated. Therefore, this paper establishes numerical models for MNSDs, and the accuracy of the model is validated through a comparison with the experimental results. The effects of magnet arrangement and dimensions on the negative stiffness and eddy-current damping characteristics are systematically investigated through parametric studies. The MNSDs are also individually optimized to maximize the negative stiffness and eddy-current damping coefficients. Based on the optimization results, some optimal design formulas are obtained to facilitate the quick design of MNSDs for different vibration suppression applications in the future.

Model predictive controller-based multi-model control system for longitudinal stability of distributed drive electric vehicle.

Abstract: Distributed drive electric vehicle(DDEV) has been widely researched recently, its longitudinal stability is a very important research topic. Conventional wheel slip ratio control strategies are usually designed for one special operating mode and the optimal performance cannot be obtained as DDEV works under various operating modes. In this paper, a novel model predictive controller-based multi-model control system (MPC-MMCS) is proposed to solve the longitudinal stability problem of DDEV. Firstly, the operation state of DDEV is summarized as three kinds of typical operating modes. A submodel set is established to accurately represent the state value of the corresponding operating mode. Secondly, the matching degree between the state of actual DDEV and each submodel is analyzed. The matching degree is expressed as the weight coefficient and calculated by a modified recursive Bayes theorem. Thirdly, a nonlinear MPC is designed to achieve the optimal wheel slip ratio for each submodel. The optimal design of MPC is realized by parallel chaos optimization algorithm(PCOA)with computational accuracy and efficiency. Finally, the control output of MPC-MMCS is computed by the weighted output of each MPC to achieve smooth switching between operating modes. The proposed MPC-MMCS is evaluated on eight degrees of freedom(8DOF)DDEV model simulation platform and simulation results of different condition show the benefits of the proposed control system.

A Novel Parametric Modeling Method and Optimal Design for Savonius Wind Turbines

Abstract: Under the inspiration of polar coordinates, a novel parametric modeling and optimization method for Savonius wind turbines was proposed to obtain the highest power output, in which a quadratic polynomial curve was bent to describe a blade. Only two design parameters are needed for the shape-complicated blade. Therefore, this novel method reduces sampling scale. A series of transient simulations was run to get the optimal performance coefficient (power coefficient C p) for different modified turbines based on computational fluid dynamics (CFD) method. Then, a global response surface model and a more precise local response surface model were created according to Kriging Method. These models defined the relationship between optimization objective Cp and design parameters. Particle swarm optimization (PSO) algorithm was applied to find the optimal design based on these response surface models. Finally, the optimal Savonius blade shaped like a “hook” was obtained. Cm (torque coefficient), Cp and flow structure were compared for the optimal design and the classical design. The results demonstrate that the optimal Savonius turbine has excellent comprehensive performance. The power coefficient Cp is significantly increased from 0.247 to 0.262 (6% higher). The weight of the optimal blade is reduced by 17.9%.

Minimax and maximin space-filling designs: some properties and methods for construction

Abstract: A few properties of minimax and maximin optimal designs in a compact subset of Rd are presented, and connections with other space-filling constructions are indicated. Several methods are given for the evaluation of the minimax-distance (or dispersion) criterion for a given n-point design. Various optimisation methods are proposed and their limitations, in particular in terms of dimension d, are indicated. A large majority of the results presented are not new, but their collection in a single document containing a respectable bibliography will hopefully be useful to the reader.

Reliability‐based performance optimization of TMD for vibration control of structures with uncertainty in parameters and excitation

Abstract: Summary Recent development of system identification using Bayesian models or stochastic filtering provides probabilistic descriptions (i.e., probability density function or statistical parameters like mean and variance) of the identified model parameters (e.g., mass, stiffness, and damping). Optimal design of passive controllers for these systems whose parameters are uncertain has remained an open problem. With this in view, the present study aims to develop numerical solution scheme for the optimal design of tuned mass damper (TMD) operating in uncertain environment. Deterministic design of TMD in these cases suffers detuning as the system parameters are random. Thus, a reliability-based design optimization (RBDO) scheme is presented in this paper for better performance of the TMD when exposed to uncertainties. To solve the RBDO problem, response surface methodology is used along with the moving least squares technique. Dual response surfaces are used for separate handling of optimization and reliability analysis. First response surface performs optimization of the design variables of TMD, while the second response surfaces are used for the estimation of the statistical properties like mean and variance to satisfy the constrained conditions. Numerical analysis is presented to show the effectiveness of the proposed algorithm for RBDO of single degree of freedom-TMD system as a proof of concept. The proposed meta-model-based algorithm can be applied for the optimal design of controller for large structures where conventional technique may face difficulty to handle both optimization and uncertainty quantification simultaneously. Copyright © 2016 John Wiley & Sons, Ltd.

Design Procedure and Optimal Guidelines for Overall Enhancement of Steady-State and Transient Performances of Line Start Permanent Magnet Motors

Abstract: This paper proposes a design procedure in addition to some optimal design guidelines for line start permanent magnet (LSPM) motors, independent from their ratings. Major transient and steady-state characteristics of LSPM motors, including magnet braking torque, synchronizing torque, efficiency, and power factor, are analyzed with respect to the main motor design parameters, i.e., motor back electromotive force, reactances, and saliency ratio. The contradicting behaviors of the motor characteristics with respect to the motor parameters are fully investigated. Design guidelines are developed to wisely compromise the performance characteristics by selecting optimal parameters. A design procedure considering the guidelines is proposed. By following the procedure and guidelines, an LSPM motor is designed and optimal values of the motor parameters are determined. Accordingly, a prototype LSPM motor is built and tested. The finite element method (FEM)-based simulation results in close agreement with the experimental results confirm superior steady-state and transient performance characteristics of the motor, thus validating the developed guidelines and the proposed design procedure.

Integrated Optimal Design of a Smart Microgrid With Storage

Abstract: In this paper, we investigate a design approach aiming at simultaneously integrating the energy management and the sizing of a small microgrid with storage. We particularly underline the complexity of the resulting optimization problem and how it can be solved using suitable optimization methods in compliance with relevant models of the microgrid. We specifically show the reduction of the computational time allowing the microgrid simulation over long time durations in the optimization process in order to take seasonal variations into account. The developed approach allows performing many optimal designs in order to find the appropriate price context that could favor the installation of storage devices.

Optimal sizing of CMOS analog circuits using gravitational search algorithm with particle swarm optimization

Abstract: In this paper, a hybrid population based meta-heuristic search algorithm named as gravitational search algorithm (GSA) combined with particle swarm optimization (PSO) (GSA–PSO) is proposed for the optimal designs of two commonly used analog circuits, namely, complementary metal oxide semiconductor (CMOS) differential amplifier circuit with current mirror load and CMOS two-stage operational amplifier circuit. PSO and GSA are simple, population based robust evolutionary algorithms but have the problem of suboptimality, individually. The proposed GSA–PSO based approach has overcome this disadvantage faced by both the PSO and the GSA algorithms and is employed in this paper for the optimal designs of two amplifier circuits. The transistors’ sizes are optimized using GSA–PSO in order to minimize the areas occupied by the circuits and to improve the design/performance parameters of the circuits. Various design specifications/performance parameters are optimized to optimize the transistor’s sizes and some other design parameters using GSA–PSO. By using the optimal transistor sizes, Simulation Program with Integrated Circuit Emphasis simulation has been carried out in order to show the performance parameters. The simulation results justify the superiority of GSA–PSO over differential evolution, harmony search, artificial bee colony and PSO in terms of convergence speed, design specifications and performance parameters of the optimal design of the analog CMOS amplifier circuits. It is shown that GSA–PSO based design technique for each amplifier circuit yields the least MOS area, and each designed circuit is shown to have the best performance parameters like gain, power dissipation etc., as compared with those of other recently reported literature. Still the difficulties and challenges faced in this work are proper tuning of control parameters of the algorithms GSA and PSO, some conflicting design/performance parameters and design specifications, which have been partially overcome by repeated manual tuning. Multi-objective optimization may be the proper alternative way to overcome the above difficulties.

Optimal design methodology for dual active bridge converter under wide voltage variation

Abstract: Dual active bridge (DAB) converter is a popular topology for bidirectional on-board electric vehicle (EV) charger systems and other energy storage applications. In such systems, wide variation in the battery voltage makes design of the DAB converter non-trivial. The focus of this paper is on the optimal selection of DAB parameters when there is a wide variation in the output voltage. Different optimal strategies based on a minimal Euclidean norm approach, namely ‘minimal rms current design’, ‘minimal peak current design’ and ‘minimal power loss design’ are compared to illustrate the effect on system performance with output voltage variation. The proposed optimal design approaches are shown to be superior to a conventional design approach. A loss model for the DAB converter that can be used in the optimal design process is presented. The proposed optimal strategies are validated with experimental results using a laboratory prototype.

Optimal design of large-scale space steel frames using cascade enhanced colliding body optimization

Abstract: In structural size optimization usually a relatively small number of design variables is used. However, for large-scale space steel frames a large number of design variables should be utilized. This problem produces difficulty for the optimizer. In addition, the problems are highly non-linear and the structural analysis takes a lot of computational time. The idea of cascade optimization method which allows a single optimization problem to be tackled in a number of successive autonomous optimization stages, can be employed to overcome the difficulty. In each stage of cascade procedure, a design variable configuration is defined for the problem in a manner that at early stages, the optimizer deals with small number of design variables and at subsequent stages gradually faces with the main problem consisting of a large number of design variables. In order to investigate the efficiency of this method, in all stages of cascade procedure the utilized optimization algorithm is the enhanced colliding bodies optimization which is a powerful metaheuritic. Three large-scale space steel frames with 1860, 3590 and 3328 members are investigated for testing the algorithm. Numerical results show that the utilized method is an efficient tool for optimal design of large-scale space steel frames.