Showing papers on "Optimal design published in 2019"
Posted Content•
TL;DR: It is shown theoretically and with simulations that the estimation variance is one order smaller compared to existing methods with on/off IRS activation patterns as proposed in the literature.
Abstract: In a wireless system with Intelligent Reflective Surfaces (IRS) containing many passive elements, we consider the problem of channel estimation. All the links from the transmitter to the receiver via each IRS elements (or groups) are estimated. As the estimation performance are dependent on the setting of the IRS, we design an optimal channel estimation scheme where the IRS elements follow an optimal series of activation patterns. The optimal design is guided by results for the minimum variance unbiased estimation. The IRS setting during the channel estimation period mimics a series of discrete Fourier transforms. We show theoretically and with simulations that the estimation variance is one order smaller compared to existing on/off methods proposed in the literature.
148 citations
TL;DR: This study showed how to find the optimal balance between the reliability and output filter size in the system with respect to several design constraints.
Abstract: This paper proposes a new methodology for automated design of power electronic systems realized through the use of artificial intelligence. Existing approaches do not consider the system's reliability as a performance metric or are limited to reliability evaluation for a certain fixed set of design parameters. The method proposed in this paper establishes a functional relationship between design parameters and reliability metrics, and uses them as the basis for optimal design. The first step in this new framework is to create a nonparametric surrogate model of the power converter that can quickly map the variables characterizing the operating conditions (e.g., ambient temperature and irradiation) and design parameters (e.g., switching frequency and dc link voltage) into variables characterizing the thermal stress of a converter (e.g., mean temperature and temperature variation of its devices). This step can be carried out by training a dedicated artificial neural network (ANN) either on experimental or simulation data. The resulting network is named as $\text{ANN}_{1}$ and can be deployed as an accurate surrogate converter model. This model can then be used to quickly map the yearly mission profile into a thermal stress profile of any selected device for a large set of design parameter values. The resulting data is then used to train $\text{ANN}_{2}$ , which becomes an overall system representation that explicitly maps the design parameters into a yearly lifetime consumption. To verify the proposed methodology, $\text{ANN}_{2}$ is deployed in conjunction with the standard converter design tools on an exemplary grid-connected PV converter case study. This study showed how to find the optimal balance between the reliability and output filter size in the system with respect to several design constraints. This paper is also accompanied by a comprehensive dataset that was used for training the ANNs.
122 citations
TL;DR: A framework for the robust design of multi-energy systems when limited information on the input data is available is proposed, showing that the proposed robust scenario allows obtaining a robust and optimal system design through a deterministic optimization problem, hence maintaining the computation complexity at a minimum.
96 citations
TL;DR: In this paper, the authors proposed an optimal shape of the concave and convex sides of a hydraulic Savonius turbine to maximize the output power of the turbine by modifying the blade profile.
84 citations
TL;DR: The proposed energy-based design philosophy is found to be better able to control the overall seismic response of the structure than alternative procedures that are not based on energy concepts and that minimize other performance indices.
75 citations
TL;DR: A multi-objective optimization problem is developed in order to manage the tradeoff between the minimization of fluid power dissipation and the maximization of heat exchange and the results show that the optimized cooling channel can achieve a lower thermal resistance and a higher Nusselts number in comparison to the conventional parallel channel.
72 citations
TL;DR: This paper leverages recent results that provide an exact characterization of the performance of the spectral method in the high-dimensional limit to map the task of optimal design to a constrained optimization problem in a weighted L^2 function space.
Abstract: We present the optimal design of a spectral method widely used to initialize nonconvex optimization algorithms for solving phase retrieval and other signal recovery problems. This paper leverages recent results that provide an exact characterization of the performance of the spectral method in the high-dimensional limit. This characterization allows us to map the task of optimal design to a constrained optimization problem in a weighted $L^2$ function space. The latter has a closed-form solution. Interestingly, under a mild technical condition, our results show that there exists a fixed design that is uniformly optimal over all sampling ratios. Numerical simulations demonstrate the performance improvement brought by the proposed optimal design over existing constructions in the literature. In a recent work, Mondelli and Montanari have shown the existence of a weak recovery threshold below which the spectral method cannot provide useful estimates. Our results serve to complement that work by deriving the fundamental limit of the spectral method beyond the aforementioned threshold.
66 citations
TL;DR: It is concluded that the TMDI, properly optimized with the proposed procedure, can effectively reduce the response of base isolated structures even under strong earthquakes.
64 citations
TL;DR: A multiscale level set topology optimization method for designing shell-infill structures and the shell-coated macro structure and the micro infill are optimized concurrently to achieve the optimal shell- infill design with prescribed volume fractions.
63 citations
TL;DR: The deep learning method used has produced superior optimal designs compared to the numerical methods and is of profound interest to researcher involved further in the optimization and design of flexoelectric structures.
56 citations
TL;DR: This paper proposes an optimal design algorithm for distributed secondary voltage control in islanded microgrids (MGs), including communication topology and controller gains, which customizes the optimal design framework of the topological and controller, which have been largely ignored in the existing literatures.
Abstract: This paper proposes an optimal design algorithm for distributed secondary voltage control in islanded microgrids (MGs), including communication topology and controller gains First, upon the consensus-based secondary voltage control, the sufficient condition for network connectivity of communication topology is revealed by the reachability matrix A multi-objective optimization criterion is first proposed for the network design, taking the convergence performance, network-relevant time delays, and communication costs into consideration After obtaining the Pareto frontier of this multi-objective model, an optimal network is selected to meet the practical requirements Based on static output feedback, a small-signal dynamic model of an MG installed with a secondary voltage controller is established, where the distributed secondary voltage controller can be converted into an equivalent decentralized controller Thereby, a linear quadratic regulator is formulated for the near-optimal design of controller parameters Our approach customizes the optimal design framework of the topology and controller, which have been largely ignored in the existing literatures Therefore, it promises to improve the performance of distributed secondary control The effectiveness of the proposed methodology is verified by a simulation study
TL;DR: Based on parametric studies, optimal thermoelectric module height to achieve maximum output power is found to be 1.1 mm at the given thermal condition, slightly lower compared with the value used for most commercial products, which are around 1.5 mm.
TL;DR: To minimize the Fmax and maximum SEA under the constraint of Favg, a multi-objective robust optimization methodology is adopted and the robust optimization optimal design is more acceptable considering the robustness, which means the robust optimize is more attractive than deterministic optimization in practical engineering application.
Abstract: Based on the Simplified Super Folding Element (SSFE) theory, the theoretical prediction of average crushing force (Favg) for multi-cell thin-walled structures is inferred and a combined five-cell thin-walled structure used in high speed train is proposed and investigated in this paper. The finite element model of the proposed structure and the theoretical prediction are validated by a full scaled impact experiment. Then, parametric studies are performed to evaluate the effects of design variables, including the thickness (t) and the side length (a) of the orthohexagonal cell, on collision responses based on the validated FE model and theoretical prediction. It is found that both specific energy absorption (SEA) and the maximum initial force (Fmax) are obviously affected by the design parameters. Particularly, the effect of parameter t on crushing performance is greater than that of parameter a. In further, to minimize the Fmax and maximum SEA under the constraint of Favg, a multi-objective robust optimization methodology is adopted. The Optimal Latin Hypercube Design (OLHD) and orthogonal design are combined to perform Design of Experiment (DoE) and dual response surface models (DRSM) are constructed for the optimization. The optimal results of deterministic optimization indicate that the Fmax decreases by 11.07% compared with the original design while the robust optimization optimal result of Fmax decreases by 10.01%. However, the robust optimization optimal design is more acceptable considering the robustness, which means the robust optimization is more attractive than deterministic optimization in practical engineering application.
TL;DR: A coordinated optimal design method is proposed as a computation cost-effective method for stand-alone and grid-connected zero/low energy buildings and their energy systems on the basis of multi-stage design optimization methods to effectively consider the interactions between building envelope and energy system design optimizations.
Book•
07 Sep 2019
TL;DR: In this article, the authors present a method for estimating the minimum size of an experiment for given precision sample size in completely randomised designs, based on confidence intervals and hypothesis testing.
Abstract: Introduction Experimentation and empirical research Designing experiments Some basic definitions Block designs About the R-programs Determining the Minimal Size of an Experiment for Given Precision Sample Size Determination in Completely Randomised Designs Introduction Confidence estimation Selection procedures Testing hypotheses Summary of sample size formulae Size of Experiments in Analysis of Variance Models Introduction One-way layout Two-way layout Three-way layout Sample Size Determination in Model II of Regression Analysis Introduction Confidence intervals Hypothesis testing Selection procedures Sequential Designs Introduction Wald's sequential likelihood ratio test (SLRT) for one-parametric exponential families Test about means for unknown variances Triangular designs A sequential selection procedure Construction of Optimal Designs Constructing Balanced Incomplete Block Designs Introduction Basic definitions Construction of BIBD Constructing Fractional Factorial Designs Introduction and basic notations Factorial designs|basic definitions Fractional factorials design with two levels of each factor (2p-k designs) Fractional factorial designs with three levels of each factor (3p-k-designs) Exact Optimal Designs and Sample Sizes in Model I of Regression Analysis Introduction Exact PHI-optimal designs Determining the size of an experiment Special Designs Second Order Designs Central composite designs Doehlert designs D-optimum and G-optimum second order designs Comparing the determinant criterion for some examples Mixture Designs Introduction The simplex lattice designs Simplex centroid designs Extreme vertice designs Augmented designs Constructing optimal mixture designs with R An example Theoretical Background Non-central distributions Groups, fields and finite geometries Difference sets Hadamard matrices Existence and non-existence of non-trivial BIBD Conference matrices Index
TL;DR: A comparison of the two approaches for the optimization results and process is performed and it is demonstrated that the weight of the designed structure by the reliability-based optimization is not heavier than that by the safety factor design method under the same reliability requirements.
TL;DR: This study proposes a novel black-box modeling method for the PV modules using a new modified one-dimensional deep residual network (1-D ResNet) and measured I-V characteristic curves, which can predict a whole I-v curve at a time for arbitrary operating conditions.
TL;DR: A methodology for the multi-objective optimization of the passive suspension system of a full-car model travelling in a random road profile provides a Pareto-optimal front, which consists of a set of non-dominated solutions that minimize the three objective functions.
Abstract: The development of a methodology that enables the optimization of passive suspension system parameters, providing a group of optimal solutions, could be an excellent approach to obtain a fast improvement tool during the design of suspension systems. Thus, this paper proposes a methodology for the multi-objective optimization of the passive suspension system of a full-car model travelling in a random road profile. For this purpose, a numerical-computational routine is developed, which integrates the NSGA-II with the vertical dynamic analysis, in the time domain, of an eight degrees-of-freedom vehicle model with a seat. Three objective functions, which take into account passenger comfort and safety, are considered. The proposed methodology provides a Pareto-optimal front, which consists of a set of non-dominated solutions that minimize the three objective functions. Comparing the results of the dynamic analyses of the vehicle model with optimized and non-optimized suspension systems, it was verified that the optimization allowed a reduction of up to 21.14% of the weighted RMS value of the driver seat vertical acceleration, a parameter directly related to comfort, while maintaining or improving the trade-off with safety. The Pareto-optimal front has proven to be an excellent support tool to aid the designer in the determination of the parameters that best fit the suspension system to produce the desired dynamic behavior in vehicles. Thus, the proposed optimization methodology can be recommended as an effective tool for the optimal design of passive suspension system parameters. Finally, this work shows that the design of suspension parameters can be done taking into account passenger comfort and safety at same time.
TL;DR: The PPFO inherits the features of predator-prey concept and firefly optimization and helps to get away from the suboptimal traps, thereby ensuring global optimal design.
Abstract: The nonlinear behavior of present day power electronic based equipments generates harmonics that lead to overheating, efficiency reduction and malfunctioning of the utility equipments. Shunt active power filters (SAPF), comprising of a three phase voltage source inverter (VSI) and a dc capacitor, are commonly used to eliminate the harmonics at source side. The design of these filters is very important in obtaining satisfactory performances. The classical design methods and the tuning of controller gain parameters may not yield optimal performances. This paper formulates the design of SAPF as an optimization problem with an objective of minimizing the total harmonic distortion (THD) and solves it using predator-prey based firefly optimization (PPFO). The PPFO inherits the features of predator-prey concept and firefly optimization (FO) and helps to get away from the suboptimal traps, thereby ensuring global optimal design. The paper also includes the simulation results of a few test cases representing different types of harmonic loads and illustrates the superior performances of the proposed design philosophy.
TL;DR: Simulation and statistical results reveal that the superiority of the proposed approach is statistically significant and demonstrate that the proposed digital differentiators and integrators significantly outperform all state-of-the-art designs in terms of the magnitude response.
Abstract: This paper proposes an approach based on a weighted L 1 -norm optimization criterion and employs it in conjunction with salp swarm algorithm (SSA) to design 2nd- to 4th-order wideband infinite impulse response (IIR) digital differentiators (DDs). Integration of the proposed fitness function and SSA allows both the magnitude and phase responses to be improved. An extensive simulation is carried out to explore the performance of the proposed approach. First, a comparative study with widely used methods, namely, real-coded genetic algorithm (RCGA) and particle swarm optimization (PSO), is performed to examine accuracy, robustness, consistency and efficiency. Afterwards, the magnitude and phase responses of the proposed digital differentiators are compared with those of the existing designs in the literature. New wideband IIR digital integrators (DIs) are derived by inverting transfer functions of their respective digital differentiators and then compared with the existing designs in the literature in terms of the magnitude and phase responses. Simulation and statistical results reveal that the superiority of the proposed approach is statistically significant and demonstrate that the proposed digital differentiators and integrators significantly outperform all state-of-the-art designs in terms of the magnitude response as measured by the absolute relative error ( A R E ) with almost linear phase responses in wideband frequency regions.
TL;DR: An optimization-oriented method for modeling power converters and their components as posynomial functions is presented, allowing multi-objective optimization of converters to be formulated as a geometric program, a type of convex optimization problem, which allows the use of fast, powerful solvers that guarantee global optimality of solutions.
Abstract: Multi-objective optimization of power converters is a time-consuming task, especially when multiple operating points and multiple converter topologies must be considered. As a result, various steps are often taken to simplify the design problem and restrict the size of the design space prior to going through an optimization procedure. While this saves time, it produces potentially sub-optimal designs, and existing approaches must tradeoff between running time and design optimality. This paper presents an optimization-oriented method for modeling power converters and their components as posynomial functions, allowing multi-objective optimization of converters to be formulated as a geometric program, a type of convex optimization problem. This allows the use of fast, powerful solvers that guarantee global optimality of solutions. The method is demonstrated using the example of low-power multi-level flying capacitor step-down converters. Results show that, using geometric programming, sets of globally Pareto-optimal designs of two-, three-, and four-level converters with respect to efficiency and power density, for one design space and one operating point, can be generated in as little as 25 s, on a mid- to upper range laptop computer. Thus, optimal designs for three different converter topologies for hundreds of different operating points and/or design spaces can be generated in several hours—less than the time required to globally optimize one converter topology at one operating point for one design space using currently prevalent methods. This paper also demonstrates how geometric programming can be used to quickly perform sensitivity and tradeoff analysis of optimal converter designs.
21 Jan 2019
TL;DR: A grid on/off search method for the rotor profile is proposed to mitigate torque pulsation and an optimal design was found that has the lowest torque ripple with a higher average torque compared to the original design.
Abstract: Interior permanent-magnet synchronous motors (IPMSMs) have been widely used due to their high-efficiency and high-power densities. Minimization of torque pulsation resulting in vibration and acoustic noise is one of the important design considerations for IPMSMs. In this paper, a grid on/off search method for the rotor profile is proposed to mitigate torque pulsation. Selection of the rotor profile is due to the fact that air gap is the most sensitive parameter in electric machines wherein changes in flux densities can cause substantial differences in the distribution of forces. A layer comprised 20 partitions with a 0.1 mm thickness and 3° wide grids have been introduced to the rotor surface for each pole, and the possible geometries have been analyzed using the finite-element method in ANSYS Maxwell. An optimal design was found that has the lowest torque ripple with a higher average torque compared to the original design. Genetic algorithm has also been applied to the method to automate the coupling between Maxwell and MATLAB, thereby saving the simulation time. Complete structural analysis has been done for both of the original and optimal designs to verify the superiority and feasibility of the proposed design.
TL;DR: The use of physical models can represent a limitation in various cases: a) when extended networks are considered (several thousands of nodes); b) when multiple simulations are required in real-time; c) when multi-energy networks are optimized.
TL;DR: Flow field analysis illustrates that the extended radial space, strengthened centrifugal field and inlet pre-classification effect of the optimal design improve the classification sharpness, however, the narrower inlet cross area leads to a higher pressure drop.
TL;DR: An integrated optimal design method for lightweight serial robots by the use of part-level topology optimization and parametric system optimization and the optimal design of a serial painting robot is implemented to demonstrate the effectiveness of the proposed method.
TL;DR: In this article, a multi-objective optimization of corrugated tube inserted with multi-channel twisted tape (CMCT) is conducted to obtain the optimal performance, using Response Surface Methodology (RSM) and Non-dominated Sorting Genetic Algorithm (NSGA-II).
TL;DR: It is demonstrated that the proposed topology optimization method is capable of effectively determining the optimal design of jacket platform structures.
TL;DR: This work optimize frequentist single- and adaptive two-stage trial designs for the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated and shows that partial enrichment designs can substantially improve the expected utilities.
Abstract: Based on a Bayesian decision theoretic approach, we optimize frequentist single- and adaptive two-stage trial designs for the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated. In such settings, the losses and gains of decisions can be quantified by utility functions that account for the preferences of different stakeholders. In particular, we optimize expected utilities from the perspectives both of a commercial sponsor, maximizing the net present value, and also of the society, maximizing cost-adjusted expected health benefits of a new treatment for a specific population. We consider single-stage and adaptive two-stage designs with partial enrichment, where the proportion of patients recruited from the subgroup is a design parameter. For the adaptive designs, we use a dynamic programming approach to derive optimal adaptation rules. The proposed designs are compared to trials which are non-enriched (i.e. the proportion of patients in the subgroup corresponds to the prevalence in the underlying population). We show that partial enrichment designs can substantially improve the expected utilities. Furthermore, adaptive partial enrichment designs are more robust than single-stage designs and retain high expected utilities even if the expected utilities are evaluated under a different prior than the one used in the optimization. In addition, we find that trials optimized for the sponsor utility function have smaller sample sizes compared to trials optimized under the societal view and may include the overall population (with patients from the complement of the subgroup) even if there is substantial evidence that the therapy is only effective in the subgroup.
TL;DR: In this paper, the design of hybrid composite laminates made of high-stiffness skin and low-stinkness core layers is presented, where the objective is the simultaneous maximization of fundamental frequency (or the gap between two consecutive frequencies) and minimization of cost by seeking the optimal stacking sequences of both skin and core layers.
TL;DR: The results demonstrate that the topologically optimal SCBFs not only have the least structural weight, but also they are of considerable collapse safety in comparison to optimalSCBFs with fixed topology of braces.