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A Simply Structured Thin Carbon-Fiber-Reinforced Material for Highly Efficient Microwave Absorption

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2020年第1期68卷 341-350页

作者： Li, Changyou Zong, Yali Zhang, Ruonan Northwestern Polytech Univ Dept Elect Engn Xian 710129 Peoples R China Northwestern Polytech Univ Dept Commun Engn Xian 710129 Peoples R China

A thin laminated composite is proposed in this article for highly efficient microwave absorption at both high frequency and low frequency. It is composed of two layers of carbon-fiber-reinforced planar structure. In each layer, periodically arranged fiber array with the axes of the fibers parallel to each other is embedded into a ceramic matrix which is used to bind the fibers together and to provide necessary mechanical or chemical properties. Stacking up two layers of the planar structure with the fibers in different layers orientated into different directions produces a lamina which can be further parallely stacked up to provide the whole laminated material. The influence of the structural and electromagnetic parameters on the microwave absorption efficiency is first studied in detail with scattering matrix and boundary-mode matching method. Then the genetic algorithm and the sequential quadratic programming methods are used to form a two-step strategy based on the mode-matching method to optimize the structural and electromagnetic parameters for a highly efficient microwave absorption in both high-frequency and low-frequency bands. Numerical examples are also given to demonstrate the high absorption efficiency of the designed laminated material.

关键词： Laminates Absorption Electromagnetics Microwave theory and techniques Ceramics Electromagnetic scattering High frequency Boundary matching method carbon-fiber-reinforced laminate genetic algorithm microwave absorbing material sequential quadratic programming

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Receding horizon optimal control of HEVs with on-board prediction of driver's power demand

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第12期14卷 1534-1545页

作者： Zhang, Bo Xu, Fuguo Shen, Tielong Sophia Univ Dept Engn & Appl Sci Tokyo 1028554 Japan

To improve a parallel hybrid electric vehicle's (HEV's) fuel economy, this study develops a real-time optimisation strategy with a learning-based method that predicts the driver's power demand under the connected environment. This demand is strongly constrained by the total power generated by the energy sources. Therefore, a key issue of solving the energy management problem in real time by model-based predictive optimisation is to predict the power demand of each receding horizon. The proposed optimisation strategy consists of two layers. The upper layer provides the prediction of the driver's torque demand. Gaussian process regression (GPR) is used to predict the driver's demand with the uncertain and stochastic estimation between the traffic environment and torque demand. Vehicle-to-vehicle and vehicle-to-infrastructure data are used as the inputs of the GPR model. The lower layer performs finite-horizon optimisation based on the cost function of energy consumption. A receding horizon control (RHC) problem is formulated, and optimisation is achieved by a sequential quadratic programming algorithm. To validate the proposed optimisation strategy, a powertrain control co-simulation platform with a traffic-in-the-loop environment is constructed, and results validation with the platform is demonstrated. The comparisons with the dynamic programming and no-prediction RHC results show that the proposed strategy can improve fuel economy.

关键词： optimal control energy management systems regression analysis hybrid electric vehicles quadratic programming fuel economy predictive control optimisation Gaussian processes learning (artificial intelligence) power system simulation control engineering computing power engineering computing power system control parallel hybrid electric vehicle real-time optimisation strategy learning-based method driver power demand energy management problem model-based predictive optimisation vehicle-to-vehicle data vehicle-to-infrastructure data receding horizon control traffic-in-the-loop environment on-board prediction receding horizon optimal control parallel HEV RHC finite-horizon optimisation powertrain control cosimulation Gaussian process regression sequential quadratic programming

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Thermo-mechanical analysis and optimization of functionally graded rotating disks

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JOURNAL OF STRAIN ANALYSIS FOR ENGINEERING DESIGN 2020年第5-6期55卷 159-171页

作者： Abdalla, Hassan Mohamed Abdelalim Casagrande, Daniele Moro, Luciano Univ Udine Polytech Dept Engn & Architecture Via Sci 206 I-33100 Udine Italy

The behavior of thermo-mechanical stresses in functionally graded axisymmetric rotating hollow disks with variable thickness is analyzed. The material is assumed to be functionally graded in the radial direction. First, a two-dimensional axisymmetric model of the functionally graded rotating disk is developed using the finite element method. Exact solutions for stresses are then obtained assuming that the plane theory of elasticity holds. These solutions are in accordance with finite element ones, thus showing the validity of the assumption. Finally, in order to reduce the maximum equivalent stress along the radius, the optimization of the material distribution is addressed. To avoid subsequent finite element simulations in the optimization process, which can be computationally demanding, a nonlinear constrained optimization problem is proposed, for which the solution is obtained numerically by the sequential quadratic programming method, showing prominent results in terms of equivalent stress uniformity.

关键词： Functionally graded material rotating disk optimization thermoelasticity sequential quadratic programming variable thickness

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Optimization of a piezoelectric wind energy harvester with a stepped beam

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2020年第11期34卷 4357-4366页

作者： Zhang, Jiantao Qu, Dong Fang, Zhou Shu, Chang Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China

A galloping-based piezoelectric energy harvester using the stepped cantilever beam is proposed and investigated. Transverse galloping is induced with the square cross sectioned bluff body. When the wind speed exceeds the critical wind speed, the self-excited oscillation of the harvester occurs and more output power is generated. To obtain the optimal design of the energy harvester, the sequential quadratic programming (SQP) and the evolution strategy (ES) are employed to determine the optimal solution. The finite element method is used to calculate the output voltage of the harvester. After optimization, the output voltage of the optimal harvester is significantly improved in comparison with that of the initial one. Two prototype harvesters based on the initial and optimal dimensions were fabricated and measured experimentally. An open-circuit rms voltage of 36 V and an output power of 0.52 mW were obtained at the wind speed of 14 m/s for the optimal harvester. They are about 8.3 times and 4.73 times of that of the initial harvester. The validity of the optimal design is verified with the experimental results.

关键词： Evolution strategy Finite element method Optimization design Piezoelectric wind energy harvester sequential quadratic programming

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Design optimization of composite wind turbine blades considering tortuous lightning strike and non-proportional multi-axial fatigue damage

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ENGINEERING OPTIMIZATION 2020年第11期52卷 1868-1886页

作者： Hu, Weifei Zhao, Wentao Wang, Yeqing Liu, Zhenyu Cheng, Jin Tan, Jianrong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou Zhejiang Peoples R China Zhejiang Univ Sch Mech Engn Hangzhou Zhejiang Peoples R China Mississippi State Univ Dept Aerosp Engn Starkville MS USA

This article presents a design optimization framework which integrates realistic lightning strike electrostatic and fatigue analyses for designing reliable and economical composite wind turbine blades. The novel aspects of this work include: a parametric tortuous lightning stepped leader model that reflects one of the true natural characteristics of the lightning phenomenon;and characterization of both the lightning strike dielectric breakdown failure and multi-axial fatigue failure mechanisms for structural design of composite wind turbine blades. A case study of the structural design optimization of a 5 MW composite wind turbine blade is tested using the framework with two optimization solvers: sequential quadratic programming (SQP) and Bayesian optimization (BO). SQP produces a superior optimal design to BO. In the optimum blade design based on the SQP algorithm, the lightning safety ratio increased by 32% and the expected fatigue life increased more than 15 times compared with the initial blade design.

关键词： Composite material lightning strike wind turbine fatigue sequential quadratic programming Bayesian optimization

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Fast Multi-Objective Aerodynamic Optimization Using sequential Domain Patching and Multifidelity Models

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JOURNAL OF AIRCRAFT 2020年第3期57卷 388-398页

作者： Amrit, Anand Leifsson, Leifur Koziel, Slawomir Iowa State Univ Dept Aerosp Engn 537 Bissel Rd Ames IA 50011 USA Reykjavik Univ Engn Optimizat & Modeling Ctr Sch Sci & Engn Menntavegur 1 IS-102 Reykjavik Iceland

Exploration of design tradeoffs for aerodynamic surfaces requires solving of multi-objective optimization (MOO) problems. The major bottleneck here is the time-consuming evaluations of the computational fluid dynamics (CFD) model used to capture the nonlinear physics involved in designing aerodynamic surfaces. This, in conjunction with a large number of simulations necessary to yield a set of designs representing the best possible tradeoffs between conflicting objectives (referred to as a Pareto front), makes CFD-driven MOO very challenging. This paper presents a computationally efficient methodology aimed at expediting the MOO process for aerodynamic design problems. The extreme points of the Pareto front are obtained quickly using single-objective optimizations. Starting from these extreme points, identification of an initial set of Pareto-optimal designs is carried out using a sequential domain patching algorithm. Refinement of the Pareto front, originally obtained at the level of the low-fidelity CFD model, is carried out using local response surface approximations and adaptive corrections. The proposed algorithm is validated using a few multi-objective analytical problems and an aerodynamic problem involving MOO of two-dimensional transonic airfoil shapes where the figures of interest are the drag and pitching moment coefficients. A multifidelity model is constructed using CFD model and control points parameterizing the shape of the airfoil. The results demonstrate that an entire or a part of the Pareto front can be obtained at a low cost when considering up to eight design variables.

关键词： Aerodynamic Shape Optimization Morphing Airfoil CFD Simulation Surrogate Model Reynolds Averaged Navier Stokes Multi Objective Evolutionary Algorithms High Performance Computing Multi Objective Genetic Algorithm sequential quadratic programming Lift Coefficient

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Hopfield learning-based and non-linear programming methods for resource allocation in OCDMA networks

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IET COMMUNICATIONS 2020年第12期14卷 1925-1936页

作者： Pendeza Martinez, Cristiane A. Durand, Fabio Renan Abrao, Taufik Goedtel, Alessandro Univ Tecnol Fed Parana Campus Cornelio Procopio Ave Alberto Carazzai 1640 BR-86300000 Cornelio Procopio PR Brazil Univ Estadual Londrina Dept Elect Engn Rod Celso Garcia Cid PR445POB 10-011 BR-86057970 Londrina PR Brazil

This study proposes the deployment of the Hopfield neural network (H-NN) approach to optimally assign power in optical code division multiple access (OCDMA) systems. Figures of merit such as the feasibility of solutions and complexity are compared with the classical power allocation methods found in the literature, such as sequential quadratic programming (SQP) and augmented Lagrangian method. The analysed methods are used to solve constrained non-linear optimisation problems in the context of resource allocation for optical networks, especially to deal with energy efficiency in OCDMA networks. The promising performance-complexity trade-off of the modified H-NN is demonstrated through numerical results performed in comparison with classic methods for general problems in non-linear programming. The evaluation is carried out considering challenging OCDMA networks in which different levels of service quality required were considered for large numbers of optical users. The numerical results demonstrated that the three power allocation methods attain suitable convergence for different network sizes, while both the mH-NN and SQP methods achieve suitable equilibrium with less complexity.

关键词： code division multiple access nonlinear programming resource allocation Hopfield neural nets telecommunication computing optical fibre networks quality of service general problems OCDMA networks optical users different network sizes mH-NN SQP methods Hopfield learning-based nonlinear programming methods resource allocation Hopfield neural network approach optical code division multiple access systems classical power allocation methods sequential quadratic programming Lagrangian method analysed methods nonlinear optimisation problems optical networks performance-complexity trade-off modified H-NN

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Intelligence computing approach for solving second order system of Emden-Fowler model

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第6期38卷 7391-7406页

作者： Sabir, Zulqurnain Amin, Fazli Pohl, Daniel Guirao, Juan L. G. Hazara Univ Dept Math & Stat Mansehra Pakistan Kinectrics Inc Design Engn Toronto ON Canada Tech Univ Cartagena Hosp Marina Dept Appl Math & Stat Cartagena 30203 Spain

In this research study, an advance computational intelligence paradigm is used for solving second order Emden -Fowler system (EFS) based on artificial neural network, genetic algorithm (GA) which is a famous global search method, sequential quadratic programming (SQP) known as rapid local refinement and the hybrid of GA-SQP. The proficiency of the designed scheme is inspected by solving the three examples of EFS to check the efficiency, consistency, precision and exactness of the technique. The numerical outcomes of the purposed scheme are compared with the exact solution that shows the significance of the scheme based on accuracy, correctness and convergence. Moreover, statistical explorations have been executed to verify the precision and accuracy of the outcomes based on performance measures of mean absolute deviation, root mean squared error and variance account for.

关键词： Emden-Fowler system artificial neural network genetic algorithm sequential quadratic programming statistical analysis

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SOLVING CHANCE-CONSTRAINED PROBLEMS VIA A SMOOTH SAMPLE-BASED NONLINEAR APPROXIMATION

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SIAM JOURNAL ON OPTIMIZATION 2020年第3期30卷 2221-2250页

作者： Pena-Ordieres, Alejandra Luedtke, James R. Wachter, Andreas Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA Univ Wisconsin Dept Ind & Syst Engn Madison WI 53706 USA

We introduce a new method for solving nonlinear continuous optimization problems with chance constraints. Our method is based on a reformulation of the probabilistic constraint as a quantile function. The quantile function is approximated via a differentiable sample average approximation. We provide theoretical statistical guarantees of the approximation and illustrate empirically that the reformulation can be directly used by standard nonlinear optimization solvers in the case of single chance constraints. Furthermore, we propose an SeiQP-type trust -region method to solve instances with joint chance constraints. We demonstrate the performance of the method on several problems and show that it scales well with the sample size and that the smoothing can be used to counteract the bias in the chance constraint approximation induced by the sample approximation.

关键词： chance constraints nonlinear optimization quantile function sample average approximation smoothing sequential quadratic programming trust region

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Integrated intelligent computing for heat transfer and thermal radiation-based two-phase MHD nanofluid flow model

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NEURAL COMPUTING & APPLICATIONS 2020年第7期32卷 2845-2877页

作者： Raja, Muhammad Asif Zahoor Mehmood, Ammara Khan, Adeel Ahmad Zameer, Aneela COMSATS Inst Informat Technol Dept Elect Engn Attock 43600 Pakistan Pakistan Inst Engn & Appl Sci Dept Elect Engn Islamabad Pakistan Allama Iqbal Open Univ Dept Phys Islamabad Pakistan PIEAS Dept Comp & Informat Sci Islamabad 45650 Pakistan

In this work, novel application of integrated computational heuristics is presented for computational fluid mechanics problem arising in the study of heat transfer and thermal radiation in two-phase magnetohydrodynamic (MHD) fluid flow model involving nanoparticles using the accurate approximation ability of neural networks hybrid with global exploration of genetic algorithm aided with local search exploitation of sequential quadratic programming. The networks are designed and arbitrarily combined to formulate mean squared error-based objective function for solving and governing nonlinear nanofluidic system. The designed methodology is evaluated to study the dynamics of the system by means of velocities, temperature and concentration profiles for prevailing factors based on variation in Reynolds and Schmidt numbers, as well as, rotation, radiation, magnetic, thermophoretic and Brownian parameters. The pragmatic worth of the scheme is established through statistical inferences in terms of accuracy, convergence and complexity metrics.

关键词： Neural networks Genetic algorithms sequential quadratic programming Fluid mechanics Nanofluids Magnetohydrodynamic

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