Showing papers on "Optimal design published in 2021"
09 Feb 2021
TL;DR: In this article, the authors present a survey of the state-of-the-art techniques for the planning and implementation of experiments, including replication, randomization, and blocking.
Abstract: Preface to the Second Edition. Preface to the First Edition. Suggestions of Topics for Instructors. List of Experiments and Data Sets. 1 Basic Concepts for Experimental Design and Introductory Regression Analysis. 1.1 Introduction and Historical Perspective. 1.2 A Systematic Approach to the Planning and Implementation of Experiments. 1.3 Fundamental Principles: Replication, Randomization, and Blocking. 1.4 Simple Linear Regression. 1.5 Testing of Hypothesis and Interval Estimation. 1.6 Multiple Linear Regression. 1.7 Variable Selection in Regression Analysis. 1.8 Analysis of Air Pollution Data. 1.9 Practical Summary. 2 Experiments with a Single Factor. 2.1 One-Way Layout. 2.2 Multiple Comparisons. 2.3 Quantitative Factors and Orthogonal Polynomials. 2.4 Expected Mean Squares and Sample Size Determination. 2.5 One-Way Random Effects Model. 2.6 Residual Analysis: Assessment of Model Assumptions. 2.7 Practical Summary. 3 Experiments with More Than One Factor. 3.1 Paired Comparison Designs. 3.2 Randomized Block Designs. 3.3 Two-Way Layout: Factors With Fixed Levels. 3.4 Two-Way Layout: Factors With Random Levels. 3.5 Multi-Way Layouts. 3.6 Latin Square Designs: Two Blocking Variables. 3.7 Graeco-Latin Square Designs. 3.8 Balanced Incomplete Block Designs. 3.9 Split-Plot Designs. 3.10 Analysis of Covariance: Incorporating Auxiliary Information. 3.11 Transformation of the Response. 3.12 Practical Summary. 4 Full Factorial Experiments at Two Levels. 4.1 An Epitaxial Layer Growth Experiment. 4.2 Full Factorial Designs at Two Levels: A General Discussion. 4.3 Factorial Effects and Plots. 4.4 Using Regression to Compute Factorial Effects. 4.5 ANOVA Treatment of Factorial Effects. 4.6 Fundamental Principles for Factorial Effects: Effect Hierarchy, Effect Sparsity, and Effect Heredity. 4.7 Comparisons with the "One-Factor-at-a-Time" Approach. 4.8 Normal and Half-Normal Plots for Judging Effect Significance. 4.9 Lenth's Method: Testing Effect Significance for Experiments Without Variance Estimates. 4.10 Nominal-the-Best Problem and Quadratic Loss Function. 4.11 Use of Log Sample Variance for Dispersion Analysis. 4.12 Analysis of Location and Dispersion: Revisiting the Epitaxial Layer Growth Experiment. 4.13 Test of Variance Homogeneity and Pooled Estimate of Variance. 4.14 Studentized Maximum Modulus Test: Testing Effect Significance for Experiments with Variance Estimates. 4.15 Blocking and Optimal Arrangement of 2 k Factorial Designs in 2 q Blocks. 4.16 Practical Summary. 5 Fractional Factorial Experiments at Two Levels. 5.1 A Leaf Spring Experiment. 5.2 Fractional Factorial Designs: Effect Aliasing and the Criteria Of Resolution and Minimum Aberration. 5.3 Analysis of Fractional Factorial Experiments. 5.4 Techniques for Resolving the Ambiguities in Aliased Effects. 5.5 Selection of 2 k-p Designs Using Minimum Aberration and Related Criteria. 5.6 Blocking in Fractional Factorial Designs. 5.7 Practical Summary. 6 Full Factorial and Fractional Factorial Experiments at Three Levels. 6.1 A Seat-Belt Experiment. 6.2 Larger-the-Better and Smaller-the-Better Problems. 6.3 3 k Full Factorial Designs. 6.4 3 k-p Fractional Factorial Designs. 6.5 Simple Analysis Methods: Plots and Analysis of Variance. 6.6 An Alternative Analysis Method. 6.7 Analysis Strategies for Multiple Responses I: Out-of-Spec Probabilities. 6.8 Blocking in 3 k and 3 k-p Designs. 6.9 Practical Summary. 7 Other Design and Analysis Techniques for Experiments at More Than Two Levels. 7.1 A Router Bit Experiment Based on a Mixed Two-Level and Four-Level Design. 7.2 Method of Replacement and Construction of 2 m 4 n Designs. 7.3 Minimum Aberration 2 m 4 n Designs with n = 1, 2. 7.4 An Analysis Strategy for 2 m 4 n Experiments. 7.5 Analysis of the Router Bit Experiment. 7.6 A Paint Experiment Based on a Mixed Two-Level and Three-Level Design. 7.7 Design and Analysis of 36-Run Experiments at Two And Three Levels. 7.8 r k-p Fractional Factorial Designs for any Prime Number r . 7.9 Related Factors: Method of Sliding Levels, Nested Effects Analysis, and Response Surface Modeling. 7.10 Practical Summary. 8 Nonregular Designs: Construction and Properties. 8.1 Two Experiments: Weld-Repaired Castings and Blood Glucose Testing. 8.2 Some Advantages of Nonregular Designs Over the 2 k-p and 3 k-p Series of Designs. 8.3 A Lemma on Orthogonal Arrays. 8.4 Plackett-Burman Designs and Hall's Designs. 8.5 A Collection of Useful Mixed-Level Orthogonal Arrays. 8.6 Construction of Mixed-Level Orthogonal Arrays Based on Difference Matrices. 8.7 Construction of Mixed-Level Orthogonal Arrays Through the Method of Replacement. 8.8 Orthogonal Main-Effect Plans Through Collapsing Factors. 8.9 Practical Summary. 9 Experiments with Complex Aliasing. 9.1 Partial Aliasing of Effects and the Alias Matrix. 9.2 Traditional Analysis Strategy: Screening Design and Main Effect Analysis. 9.3 Simplification of Complex Aliasing via Effect Sparsity. 9.4 An Analysis Strategy for Designs with Complex Aliasing. 9.5 A Bayesian Variable Selection Strategy for Designs with Complex Aliasing. 9.6 Supersaturated Designs: Design Construction and Analysis. 9.7 Practical Summary. 10 Response Surface Methodology. 10.1 A Ranitidine Separation Experiment. 10.2 Sequential Nature of Response Surface Methodology. 10.3 From First-Order Experiments to Second-Order Experiments: Steepest Ascent Search and Rectangular Grid Search. 10.4 Analysis of Second-Order Response Surfaces. 10.5 Analysis of the Ranitidine Experiment. 10.6 Analysis Strategies for Multiple Responses II: Contour Plots and the Use of Desirability Functions. 10.7 Central Composite Designs. 10.8 Box-Behnken Designs and Uniform Shell Designs. 10.9 Practical Summary. 11 Introduction to Robust Parameter Design. 11.1 A Robust Parameter Design Perspective of the Layer Growth and Leaf Spring Experiments. 11.2 Strategies for Reducing Variation. 11.3 Noise (Hard-to-Control) Factors. 11.4 Variation Reduction Through Robust Parameter Design. 11.5 Experimentation and Modeling Strategies I: Cross Array. 11.6 Experimentation and Modeling Strategies II: Single Array and Response Modeling. 11.7 Cross Arrays: Estimation Capacity and Optimal Selection. 11.8 Choosing Between Cross Arrays and Single Arrays. 11.9 Signal-to-Noise Ratio and Its Limitations for Parameter Design Optimization. 11.10 Further Topics. 11.11 Practical Summary. 12 Robust Parameter Design for Signal-Response Systems. 12.1 An Injection Molding Experiment. 12.2 Signal-Response Systems and their Classification. 12.3 Performance Measures for Parameter Design Optimization. 12.4 Modeling and Analysis Strategies. 12.5 Analysis of the Injection Molding Experiment. 12.6 Choice of Experimental Plans. 12.7 Practical Summary. 13 Experiments for Improving Reliability. 13.1 Experiments with Failure Time Data. 13.2 Regression Model for Failure Time Data. 13.3 A Likelihood Approach for Handling Failure Time Data with Censoring. 13.4 Design-Dependent Model Selection Strategies. 13.5 A Bayesian Approach to Estimation and Model Selection for Failure Time Data. 13.6 Analysis of Reliability Experiments with Failure Time Data. 13.7 Other Types of Reliability Data. 13.8 Practical Summary. 14 Analysis of Experiments with Nonnormal Data. 14.1 A Wave Soldering Experiment with Count Data. 14.2 Generalized Linear Models. 14.3 Likelihood-Based Analysis of Generalized Linear Models. 14.4 Likelihood-Based Analysis of the Wave Soldering Experiment. 14.5 Bayesian Analysis of Generalized Linear Models. 14.6 Bayesian Analysis of the Wave Soldering Experiment. 14.7 Other Uses and Extensions of Generalized Linear Models and Regression Models for Nonnormal Data. 14.8 Modeling and Analysis for Ordinal Data. 14.9 Analysis of Foam Molding Experiment. 14.10 Scoring: A Simple Method for Analyzing Ordinal Data. 14.11 Practical Summary. Appendix A Upper Tail Probabilities of the Standard Normal Distribution. Appendix B Upper Percentiles of the t Distribution. Appendix C Upper Percentiles of the chi 2 Distribution. Appendix D Upper Percentiles of the F Distribution. Appendix E Upper Percentiles of the Studentized Range Distribution. Appendix F Upper Percentiles of the Studentized Maximum Modulus Distribution. Appendix G Coefficients of Orthogonal Contrast Vectors. Appendix H Critical Values for Lenth's Method. Author Index. Subject Index.
588 citations
TL;DR: It is found that the proposed method can provide optimal design schemes with a better performance, such as smaller torque ripple and lower power loss for the investigated IPMSM, while the needed computation cost is reduced significantly.
Abstract: The multiobjective optimization design of interior permanent magnet synchronous motors (IPMSMs) is a challenge due to the high dimension and huge computation cost of finite element analysis. This article presents a new multilevel optimization strategy for efficient multiobjective optimization of an IPMSM. To determine the multilevel optimization strategy, Pearson correlation coefficient analysis and cross-factor variance analysis techniques are employed to evaluate the correlations of design parameters and optimization objectives. A three-level optimization structure is obtained for the investigated IPMSM based on the analysis results, and different optimization parameters and objectives are assigned to different levels. To improve the optimization efficiency, the Kriging model is employed to approximate the finite element analysis for the multiobjective optimization in each level. It is found that the proposed method can provide optimal design schemes with a better performance, such as smaller torque ripple and lower power loss for the investigated IPMSM, while the needed computation cost is reduced significantly. Finally, experimental results based on a prototype are provided to validate the effectiveness of the proposed optimization method. The proposed method can be applied for the efficient multiobjective optimization of other electrical machines with high dimensions.
172 citations
TL;DR: In this article, a B-spline-based generative adversarial network (GAN) is used to filter out unrealistic airfoils for a reduced design space that contains all relevant airfoil shapes.
60 citations
TL;DR: A complete step-by-step optimal design methodology based on time-domain analysis for an LLC resonant converter has been proposed and has the advantages of high accuracy and small computation requirement, which makes it application in industry possible.
Abstract: LLC resonant converters have been widely used in many different industrial applications. Analysis and design methodologies have great effect on the converter performance. Accordingly, a complete step-by-step optimal design methodology based on time-domain analysis has been proposed for an LLC resonant converter in this article. The proposed design methodology is implemented under the worst operation condition, and the following considerations are included to obtain the suitable design area: operation mode; voltage stress for resonant capacitor; zero voltage switching operation for primary switches; and resonant tank root-mean-square current. Then, by finding all possible design candidates and comparing them based on the power loss model, the optimized design candidate can be found. Compared with the existing design methodologies, the proposed one has the advantages of high accuracy and small computation requirement, which makes it application in industry possible. Finally, a 192-W experimental prototype was built to validate the effectiveness of the proposed design methodology. In addition, a MATLAB graphical user interference program was built based on the proposed design methodology to visualize and facilitate the design process for engineers.
57 citations
TL;DR: The proposed multimode optimization method can improve the foremost performance of the SSRM under all driving modes and is shown to improve the stability and reduce the fuel consumption of the vehicles.
Abstract: The belt-driven starter/generator (BSG), as a cost-effective solution, has been widely employed in hybrid electric vehicles (HEVs) to improve the stability and reduce the fuel consumption of the vehicles. It can provide more than 10% reduction in CO2. Electrical machine is the heart of the BSG system, which is functioned both as motor and generator. In order to optimize both aspects of motor and generator simultaneously, this paper presents a new multimode optimization method for the switched reluctance machines. First, the general multimode concept and optimization method are presented. The switched reluctance motor and the switched reluctance generator are the two operation modes. The optimization models are established based on motor and generator functions. Sensitivity analysis, surrogate models and genetic algorithms are employed to improve the efficiency of the multimode optimization. Then, a design example of a segmented-rotor switched reluctance machine (SSRM) is investigated. Seven design variables and four driving modes are considered in the multiobjective optimization model. The Kriging model is employed to approximate the finite element model (FEM) in the optimization. Finally, the optimization results are depicted, and an optimal solution is selected. The comparison between the initial and optimal designs shows that the proposed method can improve the foremost performance of the SSRM under all driving modes.
42 citations
TL;DR: This work develops a computational approach based on a Taylor approximation and an approximate Newton method for optimization that is scalable with respect to the dimension of both the design variables and uncertain parameters, and applies the method to a deterministic large-scale optimal cloaking problem with complex geometry.
37 citations
02 Jan 2021-Microsystem Technologies-micro-and Nanosystems-information Storage and Processing Systems
TL;DR: In this paper, the authors proposed a bi-algorithm approach for multi-objective optimization design of a compliant gripper mechanism as a robot arm through an effective hybrid algorithm of fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS).
Abstract: This investigation confronts the note-worthy improvement configuration gap in which such a design method could be better focused on the multi-objective optimization design of a compliant gripper mechanism as a robot arm through an effective hybrid algorithm of fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS). We found that the proposed bi-algorithm approach is more compelling than theoretical ideas like auxiliary shape changes, materials, and directors of mechanisms when designing the compliant gripper mechanism with a set of novel multi-objective optimization design recently. In particular, it explores whether the compliant gripper mechanism shapes affect picking things up. In this unique study, we considered displacement values and the frequency values as response parameters during the simulation and the optimization design process. To test the effectiveness of the optimal design method, we proposed an initial compliant gripper mechanism carried out through the numerically experimental matrix—the Box–Behnken design. After that, we simulated the numerical model by utilizing the finite element method incorporating the approaches of desirability function, fuzzy logic system, and ANFIS. The results turn larger than those of the previous approaches. Moreover, numerical results reveal that the suggested hybrid method has a computational exactness more conspicuous than that of the Taguchi method. In short, the principle accomplishments with variables to the compliant gripper mechanism optimization design can be summarized up as follows: (i) the promising and potential proposed approach could meet the clients’ prerequisite, (ii) the idea of multi-objective optimization design ought to be re-considered when designing compliant gripper mechanism as well as applying related designing fields at the diminished expenses and the shortage time.
36 citations
TL;DR: This study presents a new approach based on deep residual learning to make the search for optimal design solutions more efficient, applied to the problem of system design optimization for an Italian multi-family building case-study equipped with a solar cooling system.
34 citations
TL;DR: The results show that the proposed AVR design provides the most optimal dynamic response and enhanced stability among the considered AVR designs, thus proves its efficacy and essence.
Abstract: Considering the superior control characteristics and increased tuning flexibility of the Fractional-Order Proportional Integral Derivative (FOPID) controller than the conventional PID regulator, this article attempts to explore its application in the optimal design of the Automatic Voltage Regulator (AVR). Since FOPID has two additional tuning parameters (µ and ʎ) than its mentioned conventional counterpart, its tuning process is comparatively difficult. To overcome the stated issue, a self-regulated off-line optimal tuning method based on the Gradient-Based Optimization (GBO) algorithm is adopted in the current study. The optimal FOPID gains are obtained by minimizing the selected Fitness Function (FF) that is chosen as Integral Time Absolute Error (ITAE) in the current study. The simulations are performed using MATLAB/SIMULINK 2018a to test and compare the performance of the proposed GBO-based optimal AVR design based on the dynamic response, stability, and robustness evaluation metrics with some of the recently published metaheuristic optimization algorithm based optimal AVR designs in the literature. The results show that the proposed AVR design provides the most optimal dynamic response and enhanced stability among the considered AVR designs, thus proves its efficacy and essence.
32 citations
TL;DR: It is shown that the proposed methodology can be adopted to obtain the optimal designs of the SMA-restrained sliding bearings for highway bridges, which can minimize the seismic risk of the whole bridge system under near-fault ground motions.
31 citations
15 Jun 2021
TL;DR: A combined thermal and mechanical finite element analysis and evolutionary optimization-based novel approach for estimating the optimal design parameters of the ventilated brake disc gives 12.74% higher fatigue life compared to parametric analysis.
Abstract: The brake discs are subjected to thermal load due to sliding by the brake pad and fluctuating loads because of the braking load. This combined loading problem requires simulation using coupled thermo-mechanical analysis for design evaluation. This work presents a combined thermal and mechanical finite element analysis and evolutionary optimization-based novel approach for estimating the optimal design parameters of the ventilated brake disc. Five parameters controlling the design: Inboard plate thickness, outboard plate thickness, vane height, effective offset, and center hole radius were considered, and simulation runs were planned. 27 brake disc designs with design parameters as recommended by the Taguchi method (L27) were modeled using SOLIDWORKS, and the FEA simulation runs were carried out using ANSYS thermal & structural analysis tool. The fatigue life results were analyzed using a 3D surface plot for the effect of the design parameters on the response, contour plots for the determination of maximum response, and statistical regression analysis for model interpretation and predictive modeling. Finally, the two most accurate and widely used evolutionary optimization algorithms: genetic algorithm (GA) and particle swarm optimization (PSO) were applied to determine the optimal design parameters for the ventilated brake disc. The brake disc of design parameters predicted by GA and (PSO), gives 12.74% higher fatigue life compared to parametric analysis. These results have shown that the developed approach can be utilized effectively and reliably for solving, design ventilated brake disc problem in the industry.
TL;DR: In this paper, the authors proposed a sensor selection method based on the proximal splitting algorithm and the A-optimal design of experiment using the alternating direction method of multipliers (ADMM) algorithm.
Abstract: The present study proposes a sensor selection method based on the proximal splitting algorithm and the A-optimal design of experiment using the alternating direction method of multipliers (ADMM) algorithm. The performance of the proposed method was evaluated with a random sensor problem and compared with previously proposed methods, such as the greedy and convex relaxation methods. The performance of the proposed method is better than the existing greedy and convex relaxation methods in terms of the A-optimality criterion. Although, the proposed method requires a longer computational time than the greedy method, it is quite shorter than that of convex relaxation method in large-scale problems. Then the proposed method was applied to the data-driven sparse-sensor-selection problem. The dataset adopted was the National Oceanic and Atmospheric Administration optimum interpolation sea surface temperature dataset. At a number of sensors larger than that of the latent variables, the proposed method showed similar and better performance compared with previously proposed methods in terms of the A-optimality criterion and reconstruction error.
TL;DR: In this article, the authors designed an optimal bridge-type compliant mechanism flexure hinge with a high magnification ratio, low stress by using a flexure joint, and especially no friction and no bending.
Abstract: Compliant mechanisms' design aims to create a larger workspace and simple structural shapes because these mechanical systems usually have small dimensions, reduced friction, and less bending. From that request, we designed optimal bridge-type compliant mechanism flexure hinges with a high magnification ratio, low stress by using a flexure joint, and especially no friction and no bending. This joint was designed with optimal dimensions for the studied mechanism by using the method of grey relational analysis (GRA), which is based on the Taguchi method (TM), and finite element analysis (FEA). Grey relational grade (GRG) has been estimated by an artificial neural network (ANN). The optimal values were in good agreement with the predicted value of the Taguchi method and regression analysis. The finite element analysis, signal-to-noise analysis, surface plot, and analysis of variance demonstrated that the design dimensions significantly affected the equivalent stress and displacement. The optimal values of displacement were also verified by the experiment. The outcomes were in good agreement with a deviation lower than 6%. Specifically, the displacement amplification ratio was obtained as 65.36 times compared with initial design.
TL;DR: A reliability-based design framework for optimal design of composite stacking sequence is presented, for the first time to consider both delamination and material property uncertainties from manufacturing process, and provides a valuable tool in composite structure design.
TL;DR: A simulation model and a design methodology for solar thermoelectric generator are proposed and it is found that the optimal design performs can potentially achieve 5.47 W output power compared with the output power of 1.95 W from the original design in most operating conditions.
01 Mar 2021
TL;DR: The variation of the resolving coefficient shows that the application of the hybridized model to design concept selection is viable in terms of stability and uniformity over a wide range of resolving coefficient.
Abstract: The importance of concept selection in the engineering design process cannot be overemphasized. It is a major activity that can assist manufacturers to identify optimal conceptual design before prototyping can commence. This article presents the identification of optimal conceptual design by hybridizing two Multi-Criteria Decision-Making (MCDM) models which are; Fuzzy Analytic Hierarchy Process (F-AHP) and Fuzzy Grey Relational Analysis (F-GRA). The selection of F-AHP and F-GRA is based on the ability of the F-AHP to determine weights of design features and sub-features without prejudice and the ability of the F-GRA to create comparability elements for the design concepts considering their distances from an ideal design. An extensive analysis on the formulation of mathematical model for the two MCDM models is presented which is followed by the application of the hybridized model to the appraisal of conceptual designs of pipe bending machine. The hybridized model provided satisfactory results by its ability to identify one of the designs as an optimal design considering the values obtained from the fuzzified grey relational grades. The performance of the model was validated by a sensitivity analysis using two defuzzification methods and various values of the distinguishing or resolving coefficient. The variation of the resolving coefficient shows that the application of the hybridized model to design concept selection is viable in terms of stability and uniformity over a wide range of resolving coefficient.
TL;DR: In this paper, a marine condenser with exhausted steam as the working fluid is researched, and constructal designs of the condenser are numerically conducted based on single and multi-objective optimizations, respectively.
Abstract: A marine condenser with exhausted steam as the working fluid is researched in this paper. Constructal designs of the condenser are numerically conducted based on single and multi-objective optimizations, respectively. In the single objective optimization, there is an optimal dimensionless tube diameter leading to the minimum total pumping power required by the condenser. After constructal optimization, the total pumping power is decreased by 42.3%. In addition, with the increase in mass flow rate of the steam and heat transfer area and the decrease in total heat transfer rate, the minimum total pumping power required by the condenser decreases. In the multi-objective optimization, the Pareto optimal set of the entropy generation rate and total pumping power is gained. The optimal results gained by three decision methods in the Pareto optimal set and single objective optimizations are compared by the deviation index. The optimal construct gained by the TOPSIS decision method corresponding to the smallest deviation index is recommended in the optimal design of the condenser. These research ideas can also be used to design other heat transfer devices.
TL;DR: In this article, a data-driven multi-objective optimization method is proposed to investigate the design problems for minimizing the structural weight and maximizing the sound transmission loss of a periodic beam with multiple acoustic black holes.
TL;DR: The purpose of this article is to persuade experimenters to choose A-optimal designs rather than D-optical designs for screening experiments.
Abstract: The purpose of this article is to persuade experimenters to choose A-optimal designs rather than D-optimal designs for screening experiments. The primary reason for this advice is that the A-optima...
TL;DR: A machine learning methodology is illustrated to attack the inverse design problem concerning the optimization of the dispersion properties characterizing a novel layered mechanical metamaterial, conceived starting from the bi-tetrachiral periodic topology.
TL;DR: A way to optimally select the design variables through analytical solution of a constrained optimization problem is presented and the choice of the design parameters results in minimization of the worst case inductor rms current over the entire operating range of the converter which leads to both efficiency and size optimization.
Abstract: This article presents a systematic design procedure for a dual-active-bridge (DAB) dc–dc converter. Design of a DAB converter involves determination of two key parameters, i.e., transformer turns ratio and the series inductance value. Existing literature addresses this problem through numerical optimization, which is computation intensive and does not provide much insight. In general, loss is minimized by applying equal weightage to all operating conditions, which may not be practical. Given an operating power range, terminal voltage range, and switching frequency, this article presents a way to optimally select the design variables through analytical solution of a constrained optimization problem. Analysis is carried out in the time domain, and an optimal triple-phase-shift modulation strategy is considered that ensures minimum inductor rms current and soft switching. The choice of the design parameters results in minimization of the worst-case inductor rms current over the entire operating range of the converter, which leads to both efficiency and size optimization. A procedure for selection of devices and filter capacitors and design of magnetics is given. A 2.6-kW experimental prototype is designed to validate the theoretical analysis.
TL;DR: In this paper, a deep neuron network and a genetic programming (GPC) model were used to predict the failure load of a single lap adhesive joint by considering a mix of continuous and discrete design variables.
Abstract: The aerospace, automotive and marine industries have witnessed a rapid increase of using adhesive bonded joints due to their advantages in joining dissimilar and/or new engineering materials. Joint strength is the key property in evaluating the capability of the adhesive joint. In this paper, developments of black-box and grey-box machine learning (ML) models are presented to allow accurate predictions of the failure load of single lap joints by considering a mix of continuous and discrete design (geometry and material) variables. Firstly, the failure loads of 300 single lap joint samples with different geometry/material parameters are calculated by FE models to generate a data set of which accuracy is validated by experimental results. Then, a deep neuron network (black-box) and a genetic programming (grey-box) model are developed for accurately predicting the failure load of the joint. Based on both ML models, a case study is conducted to explore the relationships between specific design variables and overall mechanical performances of the single lap adhesive joint, and optimal designs of structure and material can be obtained.
TL;DR: The simulation results confirm that the torque and suspension force capacity of optimal motor are improved significantly in comparison with initial design, whereas the value of power factor reaches 0.81.
Abstract: In order to improve the performance (low torque ripple, low suspension ripple, and high power factor) of the permanent magnet assisted bearingless synchronous reluctance motor (PMa-BSynRM), the multiobjective optimal design based on fast nondominated sorting genetic algorithm (NSGA-Ⅱ) of PMa-BSynRM rotor topology is investigated in this article. First, the structure and operation principle of the PMa-BSynRM are introduced. Second, the initial rotor design that gives optimizer the ability to produce a variety of barrier shapes and the comprehensive sensitive analysis that evaluates the influence of each design variable on optimization objectives are presented. Third, the optimal design is selected from the Pareto front, which is generated by NSGA-Ⅱ, and validated by finite-element analysis. The simulation results confirm that the torque and suspension force capacity of optimal motor are improved significantly in comparison with initial design, whereas the value of power factor reaches 0.81. Finally, the optimal prototype motor is manufactured, and experimental results confirm the validity and superiority of the optimized design.
TL;DR: In this article, the authors presented the optimal design for novel negative-stiffness non-traditional inerter-based dynamic vibration absorbers called NS-NIDVAs.
TL;DR: Two different optimization models based on mixed-integer linear programming with objectives to minimize the total energy costs and carbon dioxide emissions are developed and show larger capacities of technologies than non-piecewise affine fixed cost function based models.
TL;DR: A machine-learning (ML)-aided optimal S ST design framework is proposed, which involves the objectives of maximizing efficiency and power density and assists in the genesis of optimal SST design limits for several combinations of semiconductor devices and switching frequencies.
Abstract: Due to the lack of a comprehensive multi-objective Solid-State-Transformer (SST) design framework, SST designs are mostly obtained through trial/experience. In this paper, a machine learning (ML) aided optimal SST design framework is proposed, which involves the objectives of maximizing efficiency (η) and power density (ρ). The challenges of computationally expensive magnetics design, coupled with the correlation between magnetics design and performance of semiconductor devices, are tackled by developing a hybrid local optimization algorithm. This local optimization is subsequently learnt through ML techniques, using a limited number of optimal design datasets, and thus, assists in genesis of optimal SST design limits for several combinations of semiconductor devices and switching frequencies. The proposed framework is implemented for a Cascaded Matrix-based Dual-Active-Bridge (CMB-DAB) SST comprising of SiC MOSFETs to demonstrate the optimization routine. The optimization results exhibit low-error fits of the selected ML models and the η-ρ limits in different categories of optimal SST designs. The SiC based SST designs are also observed to offer better η-ρ optimal designs compared to Si based SSTs. A laboratory scale CMB-DAB prototype with experimental measurements is also presented to validate the proposed design optimization framework at a scaled-down level.
TL;DR: In this paper, seven state-of-the-art genetic algorithms were employed and benchmarked to optimise two conflicting objectives, maximising the compressive stiffness while minimising the weight.
TL;DR: Numerical results indicate that the optimal dimensions of the device can be found efficiently and accurately, and that there appears to be no obvious benefit in the use of three barges, over a two-barge system.
TL;DR: A systematic approach to design a set of spatially distributed TMDs aiming at minimizing the system response when upper bounds for damping ratios should be taken into account is proposed and a straightforward way to analyze the level of robustness of the optimum design under different levels of model uncertainties is proposed.
TL;DR: A systematic analysis procedure of a grid-following converter under weak-grid conditions and large-signal disturbances including the outer dc-link and ac-side voltage control loops is proposed and a recommendation and a design guideline for converter constraints and outer-loop controller parameters are given.
Abstract: Modeling and design-oriented control of transient stability of grid-following converters have attained an increasing interest in recent years. Despite novel nonlinear models enabling a design-oriented enhanced transient stability controller, the focus has so far been limited to study only the synchronization dynamics of the phase-locked loop. To expand upon the knowledge of the large-signal performance and stability, this article proposes a systematic analysis procedure of a grid-following converter under weak-grid conditions and large-signal disturbances including the outer dc-link and ac-side voltage control loops. A reduced-order large-signal model is used to analyze the large-signal nonlinear behavior of the system using the area of the basin of attraction as a measure for large-signal robustness. Here, stabilizing and destabilizing trends for outer-loop controller parameters are given. Through a surrogate-model expensive black-box optimization algorithm, a computational-efficient optimal design of the outer-loop controller parameters is proposed to maximize the large-signal robustness. Finally, a recommendation and a design guideline for converter constraints and outer-loop controller parameters are given. This can be used to identify the influencing parameters for grid-following converters under large-signal disturbances, and as a tool for fast controller optimization toward large-signal robustness.