Showing papers on "Optimal design published in 2020"
09 Apr 2020
TL;DR: In this article, the authors consider the problem of channel estimation in a wireless system with IRs and design an optimal channel estimation scheme where the IRS elements follow an optimal series of activation patterns.
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. We show that the estimation performance are dependent on the setting of the IRS, and 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 the discrete Fourier transforms. We show 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.
225 citations
TL;DR: In this paper, optimal designs of lattice structure filled square thin-walled tubes are investigated under axial impact loading by using a compromise programming based multi-objective crashworthiness optimization procedure.
Abstract: Thin-walled tubes have been mostly used in passive vehicle safety systems as crash energy absorber. With the use of additive manufacturing technology, it is possible to produce novel filler materials to further enhance the crashworthiness performance of thin-walled tubes. In this study, optimal designs of novel lattice structure filled square thin-walled tubes are investigated under axial impact loading by using a compromise programming based multi-objective crashworthiness optimization procedure. Types of filler lattice structures (i.e., body-centered cubic, BCC and body-centered cubic with vertical strut, BCC-Z), diameter of lattice member, number of lattice unit cells and tube thickness are considered as design parameters, and the optimum values of these design parameters are sought for minimizing the peak crash force (PCF) and maximizing the specific energy absorption (SEA) values. The validated finite element models are utilized in order to construct the sample design space and carrying out results verification; an artificial neural network is employed for predicting values of the objective functions; the weighted superposition attraction algorithm is used to generate design alternatives and searching for their optimal combination. The compromise programming approach is used to combine multi-objectives and to produce various optimal design alternatives. The optimization results showed that the proposed approach is able to provide good solutions with high accuracy and proper selection of design parameters can effectively enhance the crashworthiness performance of the lattice structure filled thin-walled tubes. The optimum results revealed that BCC hybrid designs have generally superior crashworthiness performance compared to that of their BCC-Z counterparts for the same compromise solutions. In particular, the PCF value of the optimized BCC-Z hybrid structures is up to 44% higher than that of BCC hybrid structures while these structures have similar energy absorption performances. The compromise solutions also show that the SEA of BCC and BCC-Z hybrid structures increases respectively by 29% and 51% depending on the selected weight factors for the design objectives.
68 citations
TL;DR: A combined multi-objective optimization and robustness analysis framework for optimal design of building integrated energy system and an acceptable robustness value is identified, considering both the selected objectives and the operation constraints’ probability of failure.
67 citations
TL;DR: In this article, a multi-disciplinary optimization design method based on an approximation model was adopted to improve the comprehensive performance of axial-flow pump impellers and fully consider the interaction and mutual influences of the hydraulic and structural designs.
Abstract: This study adopts a multi-disciplinary optimization design method based on an approximation model to improve the comprehensive performance of axial-flow pump impellers and fully consider the interaction and mutual influences of the hydraulic and structural designs. The lightweight research on axial-flow pump impellers takes the blade mass and efficiency of the design condition as the objective functions and the head, efficiency, maximum stress value, and maximum deformation value under small flow condition as constraints. In the optimization process, the head of the design condition remains unchanged or varies in a small range. Results show that the mass of a single blade was reduced from 0.947 to 0.848 kg, reaching a decrease of 10.47%, and the efficiency of the design condition increased from 93.91% to 94.49%, with an increase rate of 0.61%. Accordingly, the optimization effect was evident. In addition, the error between the approximate model results and calculation results of each response was within 0.5%, except for the maximum stress value. This outcome shows that the accuracy of the approximate model was high, and the analysis result is reliable. The results provide guidance for the optimal design of axial-flow pump impellers.
63 citations
TL;DR: Based on results from response history analysis under both artificial and natural ground motion records, the design method proves to be effective to meet the desired demand of sloshing height response, while simultaneously reducing base shear force and isolation displacement.
61 citations
TL;DR: An optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA), which improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum.
Abstract: The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition‐based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters.
60 citations
TL;DR: The results show that integrating multimedia simulation into optimization design teaching could be a good way to improve the practice of teaching and to promote knowledge learning and optimal design capabilities.
Abstract: In recent years, with application of optimization technology and computer technology in the field of optimal design, it has become an essential course for mechanical students How to effectively apply multimedia optimal methods to the optimal design course is the problem that we need to solve The teaching mode of optimal design course is explored by multimedia software simulation analysis The existing problems in the teaching of optimal design are introduced in this dissertation And the case of the optimal design teaching according to the teaching method of multimedia software is realized The finite element analysis of reducer and the application of genetic algorithm in it, for example, emphasize how multimedia simulation can be effectively applied to optimal design We take the gear reducer as a case to carry out the modal analysis and gear meshing analysis and optimize its structure by using genetic algorithm Making full use of multimedia software, combined with the course knowledge points and simulation software to cultivate the actual design and application capabilities, paying attention to the acquisition of optimization design methods, could improve the quality of optimization design personnel training The results show that integrating multimedia simulation into optimization design teaching could be a good way to improve the practice of teaching and to promote knowledge learning and optimal design capabilities It has a great significance for the development and application of optimal design based on multimedia
60 citations
TL;DR: The energy regulation response mechanism of the hydrolic-transmission system in the wave energy power-generation system is proved and the energy output curve under different parameter regulations is drawn.
Abstract: This paper develops the dynamic response of a hydrolic-transmission system of wave-power devices under random wave conditions. Through theoretical calculation and experiment analysis, the mathematical model of the hydrolic-transmission system was built to make clear which parameters are related to electric-energy output. The working characteristics of the main parameters are developed through the designed experimental platform. The charging pressure of the accumulator, which affects the rigidity of the hydrolic-transmission system, is analyzed. The throttle valve opening and symmetrical electric loads, which affect the stability and efficiency of the electric energy output, are analyzed. Thus, the energy output curve under different parameter regulations is drawn. Through the orthogonal experimental method, the law curve is further modified, the design principle of hydraulic system parameters under the sea level condition is established, and the optimal design scheme and regulation strategy to the hydraulic conversion system of the power generation device is obtained, to solve the problem that the multiparameter coupling cannot be adjusted quickly and effectively. The energy regulation response mechanism of the hydrolic-transmission system in the wave energy power-generation system is proved.
57 citations
TL;DR: In this article, the optimal design of a TMDI equipped on a multi-degree of freedom (MDOF) structure through a generalized model is presented, where the primary oscillator, representing the original MDOF structure and the secondary oscillator representing the control system, are considered in both configurations grounded and ungrounded.
55 citations
TL;DR: Although the results of proposed model turn to be more conservative which means low efficiency in economy, the planning scheme is able to adapt to different uncertain scenarios and maintain the reliability of operation, which means that the robustness of the result were enhanced.
54 citations
TL;DR: The results show that the ANN model based on the proposed method achieves better modeling performance and yields better optimal design than the ANN models based on conventional sampling methods.
Abstract: In this letter, we propose an efficient hybrid sampling method for microwave component modeling and optimization. The sampling method adaptively chooses samples from global and local samples to form a data set. The local samples are obtained using a greedy-like sampling method to exploit potential optimal solutions. The global samples are chosen using random sampling with minimum distance rejection to ensure the uniformity of the samples in the design space. The obtained data set is used to establish a surrogate model using the artificial neural networks (ANNs), and the optimal design parameters are obtained by optimizing the ANN model. A bandstop microstrip filter is taken as an example to verify the performance of the sampling method. The results show that the ANN model based on the proposed method achieves better modeling performance and yields better optimal design than the ANN model based on conventional sampling methods.
TL;DR: A novel optimization method integrating a genetic algorithm, an artificial neural network, an ANN, multivariate regression analysis, and a fuzzy logic controller was proposed in this paper to optimize the indoor environment and energy consumption based on simulation results.
TL;DR: In this paper, an optimization scheme for the resonator distribution in rainbow metamaterials was proposed, aiming at minimizing the maximum and average receptance values respectively, with the objective function for both single and multiple frequency ranges optimization set up with the frequency response functions predicted by an analytical model.
TL;DR: In this article, a novel liquid cooling system with symmetrical double-layer reverting bifurcation channel was performed by combining experimental, numerical simulation and multi-objective optimization techniques.
TL;DR: In this article, an optimization algorithm has been also implemented for simultaneously maximizing the output power and total efficiency of the SDSS using the Multi-Objective Particle Swarm Optimization (MOPSO).
TL;DR: In this article, the authors developed convolutional neural network models to predict optimized designs for a given set of boundary conditions, loads, and optimization constraints for linear elastic structures with and without stress constraint.
TL;DR: State-of-the-art nonlinear time-domain analyses show that the linearized model is conservative in general, but reasonably accurate in capturing trends, suggesting that the presented methodology is suitable for preliminary integrated design calculations.
TL;DR: Three stochastic approaches to generating diverse and competitive designs by penalizing elemental sensitivities, changing initial designs, and integrating the genetic algorithm into the bi-directional evolutionary structural optimization (BESO) technique are proposed.
Proceedings Article•
12 Jul 2020TL;DR: It is shown that training a neural network to maximise a lower bound on MI allows us to jointly determine the optimal design and the posterior and gracefully extends Bayesian experimental design for implicit models to higher design dimensions.
Abstract: Implicit stochastic models, where the data-generation distribution is intractable but sampling is possible, are ubiquitous in the natural sciences. The models typically have free parameters that need to be inferred from data collected in scientific experiments. A fundamental question is how to design the experiments so that the collected data are most useful. The field of Bayesian experimental design advocates that, ideally, we should choose designs that maximise the mutual information (MI) between the data and the parameters. For implicit models, however, this approach is severely hampered by the high computational cost of computing posteriors and maximising MI, in particular when we have more than a handful of design variables to optimise. In this paper, we propose a new approach to Bayesian experimental design for implicit models that leverages recent advances in neural MI estimation to deal with these issues. We show that training a neural network to maximise a lower bound on MI allows us to jointly determine the optimal design and the posterior. Simulation studies illustrate that this gracefully extends Bayesian experimental design for implicit models to higher design dimensions.
TL;DR: The model results suggest that 32-fiber SDM systems are a practical spot for near-future systems close to cost-per-unit-capacity limit of the wet-plant, and a detailed model that combines physical and economic aspects of the submarine links is described.
Abstract: We consider both physical and economic aspects of submarine transmission to identify optimal designs of submarine space division multiplexed (SDM) systems. We focus on single mode-based SDM systems as the first practical step for implementation of high capacity, low cost-per-unit-capacity systems based on SDM principles. We review power efficiency models in EDFA-based long-haul systems and show existence of optimum SNR. We demonstrate relationship between the power efficiency and cost-per-unit-capacity optimizations for submarine links. We also describe a detailed model that combines physical and economic aspects of the submarine links. The model uses “least amount of technological change” approach based on the existing components and simple scaling rules for repeater and cable designs. The model results suggest that 32-fiber SDM systems are a practical spot for near-future systems close to cost-per-unit-capacity limit of the wet-plant. Further increase of the number of fibers in a cable has diminishing returns for current technologies and cost structure. We also discuss requirements for transponders in terms of spectral efficiency and nonlinearity compensation.
TL;DR: This work embeds artificial neural networks (ANNs) as surrogate models in the deterministic global optimization of membrane processes with accurate transport models – avoiding the utilization of inaccurate approximations through heuristics or short-cut models.
TL;DR: Stochastic optimization of multiple NESs configured in parallel and series configurations attached to a simply supported pipe conveying fluid is investigated, demonstrating that the stochastic optima are much more robust than the deterministic one, and is able to deal with various sources of uncertainties particularly in initial excitation and flow velocity.
TL;DR: This paper provides a procedure of determining optimal designs of simple step-stress accelerated life tests for one-shot devices by minimizing the asymptotic variance of the maximum likelihood estimate of reliability at normal operating conditions under Weibull distributions, with respect to inspection times and sample allocations.
TL;DR: This work investigates the design efficiency of the two- phase study, as measured by the semiparametric efficiency bound for estimating the regression coefficients of expensive covariates, and develops optimal or approximately optimal two-phase designs, which can be substantially more efficient than the existing designs.
Abstract: The two-phase design is a cost-effective sampling strategy to evaluate the effects of covariates on an outcome when certain covariates are too expensive to be measured on all study subjects. Under ...
01 Jan 2020
TL;DR: Stochastic and heuristic optimization algorithms are presented, such as genetic, simulated annealing, particle swarm optimization algorithms, and knowledge-based expert system for rating of a given network.
Abstract: For rating of a given network, a mathematical model with its explicit analytical solution is presented for the calculation of target temperatures, hot and cold utilities, and total annual cost (TAC). One example is calculated; the program is attached. For sizing problems, two methods are described, the matrix formulation and the nonlinear programming. For synthesis, first, the Pinch technology using composite curves is explained and demonstrated with two examples. Then, different mathematical programming methods are described. Finally, stochastic and heuristic optimization algorithms are presented, such as genetic, simulated annealing, particle swarm optimization algorithms, and knowledge-based expert system. Twenty-seven synthesis examples with TAC optimization from the literature using different optimization methods are summarized and verified.
TL;DR: A novel reliability design and optimization method of the planetary gear, using the genetic algorithm, based on Kriging model is proposed, which can significantly improve the calculation efficiency.
TL;DR: The results show the ability of the MFO to optimize automobile components in the industry.
Abstract: In order to present an integrated approach to optimal automobile component design, this research is focused on a shape optimization problem of a bracket using moth-flame optimization algor...
TL;DR: Connections between design for integration (quadrature design), construction of the (continuous) BLUE for the location model, space-filling design, and minimization of energy (kernel discrepancy) for signed measures are investigated.
Abstract: A standard objective in computer experiments is to approximate the behavior of an unknown function on a compact domain from a few evaluations inside the domain. When little is known about the function, space-filling design is advisable: typically, points of evaluation spread out across the available space are obtained by minimizing a geometrical (for instance, covering radius) or a discrepancy criterion measuring distance to uniformity.
The paper investigates connections between design for integration (quadrature design), construction of the (continuous) BLUE for the location model, space-filling design, and minimization of energy (kernel discrepancy) for signed measures. Integrally strictly positive definite kernels define strictly convex energy functionals, with an equivalence between the notions of potential and directional derivative, showing the strong relation between discrepancy minimization and more traditional design of optimal experiments. In particular, kernel herding algorithms, which are special instances of vertex-direction methods used in optimal design, can be applied to the construction of point sequences with suitable space-filling properties.
TL;DR: This study presents an optimal design procedure, including topology and geometry optimization methods to design a compliant constant-force mechanism, which can generate a nearly constant output force over a range of input displacements and test results show the constant- force gripper can be used in handling of size-varied fragile objects.
Abstract: This study presents an optimal design procedure, including topology and geometry optimization methods to design a compliant constant-force mechanism, which can generate a nearly constant output for...
TL;DR: The proposed method improves the efficiency performance of the electric compressor by improving the torque characteristics of the initial PMSM model by using the optimal Latin hypercube design to optimally distribute the experimental points evenly and improve the space filling characteristics.
Abstract: In this study, a shape design optimization method is proposed to improve the efficiency of a 3 kW permanent magnet synchronous motor (PMSM) used in an electric compressor intended for use in an electric vehicle. The proposed method improves the efficiency performance of the electric compressor by improving the torque characteristics of the initial PMSM model. The dimensions of the rotor were set as the design variables and were chosen to maximize efficiency and reduce cogging torque. During the determination of the design points with conventional Latin hypercube design, the experimental points may be closely related to each other. Therefore, the optimal Latin hypercube design was used to optimally distribute the experimental points evenly and improve the space filling characteristics. The Kriging model was used as an interpolation model to predict the optimal values of the design variables. This allowed the formulation of more accurate prediction models with multiple design variables, complex reactions, or nonlinearities. A genetic algorithm was used to identify the optimal solution for the design variables. It was used to satisfy the objective function and to determine the optimal design variables based on established constraints. The optimal design results obtained based on the proposed shape optimization method were confirmed by finite element analyses. For practical verification, the optimal model of the prototype PMSM of an electric compressor was manufactured, and a 1.5% improvement in its efficiency performance was confirmed based on an experimental dynamometer test.