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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Nathan Palar, Pramudita Satria Zuhal, Lavi Rizki Fac Mech & Aerosp Engn Inst Teknol Bandung Bandung 40132 West Java Indonesia

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

This paper presents a robust multi-objective approach for structural topology optimization. The performance and the stability are two conflicting objectives, hence, it is sometimes preferable to treat them as multiple objectives to gain important insight regarding various optimal topologies. The optimization problem is formulated as a simultaneous minimization of expectation and standard deviation of compliance in the presence of uncertainties in loading magnitude, boundary conditions, or material properties. It is imperative to obtain a good representation of the Pareto front with as few finite element simulations as possible. This paper proposes the combination of topology optimization with Polynomial Chaos Expansion (PCE) and Pareto Optimal Tracing (POT) to seek diverse Pareto optimal solutions efficiently. A benchmark test case with two and three random loads is used to test efficacy of the proposed method and the results are compared to the deterministic optimization. We studied the impact of the PCE to reduce the number of realizations and POT to reduce the number of iterations on the estimation of the Pareto front with various combinations of weights assigned to the expectation and the standard deviation. Results show that robust topology optimization with POT and PCE yielded better and diverse topologies than the conventional approach, which restarts the search from the uniform distributed density as the baseline design.

关键词： Topology optimization Standard Deviation Material Properties Poisson's Ratio Simply Supported Beams Structural optimization optimization algorithm Monte Carlo Simulation Stiffness Matrices Collocation Method

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A new fractional Abel polynomials neural network method for nonlinear models of heat transfer

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2025年 162卷

作者： Hassani, Hossein Avazzadeh, Zakieh Turan-Dincel, Arzu Katani, Roghayeh Anand Int Coll Engn Dept Math Jaipur 303012 Rajasthan India Anhui Univ Stony Brook Inst Hefei 230601 Anhui Peoples R China Yildiz Tech Univ Dept Math Engn TR-34220 Istanbul Turkiye Univ Yasuj Fac Sci Yasuj Iran Univ South Africa Dept Math Sci Florida South Africa

Nowadays, the study and advancements in neural networks and optimization techniques are the most interesting research topics. This progress has empowered researchers to address more intricate and realistic challenges associated with nonlinear models. This research presents a new approach called the fractional Abel polynomials neural network (FAPNN) for solving nonlinear heat transfer models. The mentioned NN consists of three layers (input layer, hidden layer, output layer). Fractional Abel and arcsinh(t) functions have been used as activation functions in the hidden and output layers, respectively, to construct the FAPNN. To mitigate storage and computational costs, new operational matrices (OMs) are devised. Based on FAPNN and OMs for fractional Abel polynomials, a series solution is achieved for the nonlinear heat transfer models. The Lagrange multipliers method is adopted so that the nonlinear heat transfer models can be transformed into a class of nonlinear algebraic system of equations and we solve these equations using Matlab and Maple software. Ultimately, four sets of numerical examples are provided along with comparisons from the literature such as the variational iteration, homotopy perturbation, differential transformation, and Chebyshev wavelets methods, to confirm the effectiveness of the suggested FAPNN approach. Experimental results demonstrate the effectiveness of our proposed method.

关键词： Fractional Abel polynomials neural network Optimal solutions Nonlinear models of heat transfer optimization algorithm

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Abatement of Sewer Overflow Pollution Based on Distributed Optimal Control Approach

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ACS ES&T WATER 2022年第3期4卷 859-868页

作者： Zhao, Zhichao Zhang, Huijin Yu, Ziwen Yin, Hailong Xu, Zuxin Changjiang Inst Survey Planning Design & Res Wuhan 430010 Hubei Peoples R China Tongji Univ Key Lab Yangtze River Water Environm Minist Educ Shanghai 200092 Peoples R China Tongji Univ Shanghai Inst Pollut Control & Ecol Safety Shanghai 200092 Peoples R China Univ Florida Gainesville FL 32603 USA

Optimal control is promising to mitigate the pollution from combined sewer overflows during wet-weather days. In this study, a distributed optimal control approach to regulating multipoint controlling units of the urban sewer system was developed. By introducing a differential evolution algorithm with ranking-based mutation operators, good simulation results with lower overflow mass loadings can obtain higher evolutionary probabilities. Thus, the optimal solutions to minimize the overflow mass from a large sewer network can be efficiently obtained. The developed approach was demonstrated in a catchment (total coverage area of 22.4 km2) served by one wastewater treatment plant in Chaohu City, Anhui Province of China, with five pumping stations and 18 associated sewage or stormwater pumps as the distributed controllers. After a global optimization among various controlling units, performance of the sewer network in reducing its overflow was improved obviously through fully utilizing in-sewer storage and adjusting the sewer flow transportation harmoniously (i.e., an alternative mode of full storing and fast transportation in sequence). Accordingly, the overflow volume and the overflow mass loading of total nitrogen was reduced by 32.3% and 37.6%, respectively, without retrofitting the current sewer network. On the basis of the presented approach, decision-makers can develop a database for optimal regulation of sewer network under various rainfall scenarios and respond to sewer overflow control flexibly and cost-effectiveness.

关键词： urban sewers optimization algorithm sewer overflow integrated regulation mathematical model

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Fast Path Planning for Unmanned Aerial Vehicles by Self-Correction Based on Q-Learning

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2021年第4期18卷 203-211页

作者： Wang, Ziyang Yang, Hongbing Wu, Qingsong Zheng, Jiafei Soochow Univ Sch Mech & Elect Engn Suzhou 215006 Jiangsu Peoples R China

This paper addresses a path planning problem for unmanned aerial vehicles with correcting position errors through correction-point navigation, which requires a rapid response when determining the flight path. A two-layer nested iterative hybrid algorithm based on Q learning is proposed to achieve multiobjective optimization by minimizing path lengths and correction times while reducing the complexity of the algorithm and improving the efficiency of path planning. The lower-layer algorithm uses a Q-learning framework based on the experience playback mechanism and exploration/exploitation mechanism. The rewards and punishments for the Q-table values in the lower-layer algorithm are innovatively managed with the unit "path." The upper-layer algorithm is based on the Pareto multiobjective algorithm. The Pareto frontier is continuously updated with the solutions from the lower-layer algorithm, which timely provides the lower-layer algorithm feedback. Finally, simulation experiments are conducted to evaluate the effectiveness of the algorithm, and the results show that the proposed algorithm outperforms particle swarm optimization.

关键词： Path Planning UAV Particle Swarm optimization Pareto Frontier Reinforcement Learning Stochastic Dynamic Programming optimization algorithm Computing Performance Mathematical Models Genetic algorithm

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Golden jackal optimization: A novel nature-inspired optimizer for engineering applications

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期198卷 1页

作者： Chopra, Nitish Ansari, Muhammad Mohsin IKGPTU Dept Elect Engn St Soldier Grp Inst Jalandhar India Sind Inst Management & Technol Head Dept Elect Engn Karachi Pakistan St Soldier Grp Inst Dept Elect Engn Jalandhar India

A new nature-inspired optimization method, named the Golden Jackal optimization (GJO) algorithm is proposed, which aims to provide an alternative optimization method for solving real-world engineering problems. GJO is inspired by the collaborative hunting behaviour of the golden jackals (Canis aureus). The three elementary steps of algorithm are prey searching, enclosing, and pouncing, which are mathematically modelled and applied. The ability of proposed algorithm is assessed, by comparing with different state of the art meta heuristics, on benchmark functions. The proposed algorithm is further tested for solving seven different engineering design problems and introduces a real implementation of the proposed method in the field of electrical engineering. The results of the classical engineering design problems and real implementation verify that the proposed algorithm is appropriate for tackling challenging problems with unidentified search spaces.

关键词： Nature-inspired algorithm Golden Jackal optimization Metaheuristic Constrained problems optimization algorithm GJO

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Dynamic capacitated facility location problem in mobile renewable energy charging stations under sustainability consideration

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2024年 41卷

作者： Ala, Ali Deveci, Muhammet Bani, Erfan Amani Sadeghi, Amir Hossein Shanghai Jiao Tong Univ Dept Ind Engn & Management Sch Mech Engn Shanghai 200240 Peoples R China Natl Def Univ Turkish Naval Acad Dept Ind Engn TR-34940 Istanbul Turkiye Lebanese Amer Univ Dept Elect & Comp Engn Byblos Lebanon Imperial Coll London Royal Sch Mines South Kensington Campus London SW7 2AZ England Sharif Univ Technol Dept Ind Engn Tehran Iran North Carolina State Univ Dept Ind & Syst Engn Raleigh NC USA

The deployment of mobile renewable energy charging stations plays a crucial role in facilitating the overall adoption of electric vehicles and reducing reliance on fossil fuels. This study addresses the dynamic capacitated facility location problem in mobile charging stations from a sustainability perspective. This paper proposes Twostage stochastic programming with recourse that performs well for this application, and the location of the mobile renewable energy charging station (MRECS) management addresses the complex dynamics of reusable items. To solve this problem, we suggested dealing with differential evolutionary (DE) and DE Q-learning (DEQL) algorithms, as two novel optimization and reinforcement learning approaches, are presented as solution approaches to validate their performance. Evaluation of the outcomes reveals a considerable disparity between the algorithms, and DEQL performs better in solving the presented problem. In addition, DEQL could minimize the total operation cost and carbon emission by 7% and 20%, respectively. In contrast, the DE could decrease carbon emissions and total operation costs by 5% and 2.5%, respectively.

关键词： Renewable energy Stochastic programming optimization algorithm Reinforcement learning Facility location Sustainability

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Harnessing artificial neural networks for inverse design and analysis of MEMS-based Fabry-Pérot filters

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SENSORS AND ACTUATORS A-PHYSICAL 2024年 373卷

作者： Liu, Zhenya Wang, Junchao Wu, Yifan Zhang, Yidan Zhang, Meng Cao, Jing Liu, Jun Hangzhou Dianzi Univ Sch Integrated Circuit Sci & Engn Hangzhou 310018 Peoples R China China Elect Technol Grp Cooperat Informat Sci Acad Beijing 100041 Peoples R China

MEMS-based Fabry-P & eacute;rot (MEMS-FP) filters have become increasingly important in optical applications such as frequency selection spectroscopy and spectral analysis. Prior to the manufacturing of MEMS-FP devices, conducting a thorough theoretical analysis of their properties is crucial. Currently, Finite Element Analysis (FEA) is the primary method for investigating the theoretical performance of MEMS-FP during the design process. However, the complexity of modeling and prolonged simulation times associated with FEA software pose significant challenges, impeding the development timeline for MEMS-FP devices. To address these challenges, we proposed a machine learning -based method to computer -aided design of the MEMS-FP devices. Our approach involves using an Artificial Neural Network (ANN) to predict their physical properties and an inverse design method, utilizing optimization algorithms such as PSO, NSGA-II, and SA, to derive the optimal design scheme for MEMS-FP devices. Our method accelerates the prediction of physical properties of MEMSFP devices and enables the design automation of optimal schemes in just minutes within a limited design space. This approach not only reduces the research time and complexity associated with MEMS-FP devices but also shortens their development cycle. Furthermore, one specific MEMS-FP device is fabricated and tested for proof -of -concept of our proposed method.

关键词： MEMS-FP Machine learning optimization algorithm Design automation

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A modified modeling approach of virtual power plant via improved federated learning

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 158卷

作者： Taheri, Seyed Iman Davoodi, Mohammadreza Ali, Mohd. Hasan Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

The Virtual Power Plant (VPP) is a promising solution to power systems' challenges. However, its energy management system (EMS) faces challenges due to centralized Big-Data analysis, i.e., complexity and high computational cost. Decentralized strategies, e.g., federated learning (FL), have been proposed to mitigate these challenges. Although conventional FL can be implemented in VPPs, its effectiveness is limited by its typical structure, which relies on single-pattern modeling. Indeed, this particular structure leads to delays and inaccuracies in optimizing the heterogeneous generator dispatch patterns, particularly renewables. Hence, addressing this critical hindrance is essential for FL's efficient and effective deployment in VPPs. To overcome this drawback, this paper proposes novel VPP modeling utilizing multi-task FL modernized by multi-pattern modeling to decentralize the EMS processing. The proposed FL defines electricity generators' agents as clients collaborating to train the decentralized local data coordinated by a central server. The innovative idea is to create a novel multi-pattern-multi-task FL for electricity generation prediction so that the generator's agents learn to provide an optimized dispatch. Thus, the conventional multi-task FL is improved by proposing an optimized neural-network clustering technique. Simulation results demonstrate that multi-pattern-multi-task FL is 39%41% faster than the best-reported methods, i.e., teaching-learning-based and honey-bee-mating optimization algorithms. (c) 2017 Elsevier Inc. All rights reserved.

关键词： Virtual power plant Federated learning optimization algorithm Energy management system Distributed generation

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Optimizing of working conditions of vanadium redox flow battery based on artificial neural network and genetic algorithms

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JOURNAL OF ENERGY STORAGE 2024年 100卷

作者： Wang, Qiong Yan, Ruijie Ren, Longhui Qu, Zhiguo Jiang, Zhiyuan Wang, Zhengdong Zhang, Cheng Wang, Juan Xian Univ Architecture & Technol Sch Mech & Elect Engn 13 Yanta Rd Xian 710055 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Chem Engn & Technol 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Coventry Univ Inst Clean Growth & Future Mobil CALPS Cheetah Rd Coventry CV12TL England Xian Univ Architecture & Technol Sch Mech & Elect Engn Shanxi Key Lab Nanomat & Nanotechnol 13 Yanta Rd Xian 710055 Shaanxi Peoples R China

The integration of electrode compression in a vanadium redox flow battery (VRFB) with optimized operating conditions is essential for achieving the maximum net discharge power. This study presents the development of a 3D steady-state model for VRFB aimed at maximizing net discharge power (Pnet) by optimizing key working conditions, including electrode compression ratio (CR), applied current density (Iapp), and electrolyte inflow rate (Qin). The comprehensive impacts of CR, Iapp, and Qin on VRFB charging and discharging curves, energy efficiency (EE), and system efficiency (SE), and Pnet were investigated. Subsequently, the optimization of CR and Qin under a fixed applied current density range of 20-400 mA/cm2 was carried out employing a hybrid methodology integrating artificial neural network (ANN) and genetic algorithms (GA). Results reveals that increasing CR does not linearly improve system efficiency and net discharge power, with an optimal CR identified to maximize net discharge power. The optimal CR varies with operating conditions and is influenced by factors such as Qin and Iapp. Notably, both CR and Qin negatively impact net discharge power due to increased pump losses, while Iapp and state of charge (SOC) positively affect net discharge power through enhanced electron transfer and higher active substance concentration, respectively. The optimal CR is identified around 65 % for Iapp exceeding 60 mA/ cm2. Furthermore, the optimal Qin correlates directly with increasing Iapp, as higher Iapp rates necessitate greater Qin to sustain active species supply. This study offers an optimization approach for addressing coupled operating conditions and provides insights for achieving high net discharge power in VRFB operation.

关键词： Vanadium redox flow battery Net discharge power Compression ratio Flow rate Current density optimization algorithm

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Frigatebird optimizer: a novel metaheuristic algorithm

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PHYSICA SCRIPTA 2024年第12期99卷 125233-125233页

作者： Wang, Xiaowei Huangshan Univ Sch Tourism Huangshan Anhui Peoples R China

This paper proposes a new swarm intelligence optimization algorithm- the Frigate Bird Optimizer ( FBO ) . The algorithm is inspired by the unique fl ight and foraging behaviors of frigatebirds. Its optimization process is divided into two stages: The fi rst stage simulates frigatebirds harassing other seabirds to snatch food, and the search direction and radius of individuals have randomness and uncertainty, which is conducive to global exploration;The second stage simulates frigatebirds observing large fi sh driving small fi sh to leap out of the water and preying on them, and individuals tend to gather in the optimal search direction. By simulating the behaviors of frigatebirds under different survival strategies, the algorithm achieves extensive global search in the fi rst stage and fi ne- tuned local optimization by learning information in the second stage. To evaluate its performance, 46 functions in the CEC2014 and CEC2017 benchmark test sets are selected as objective functions and compared with 9 state-of-the-art meta-heuristic algorithms. The results show that the FBO algorithm has higher performance, excellent iterative optimization ability and strong robustness, and can be applied to different optimization domains.

关键词： swarm intelligence frigatebird metaheuristic optimization exploration and exploitation optimization algorithm engineering

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