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Review of hybrid electric powered aircraft,its conceptual design and energy management methodologies

Chinese Journal of Aeronautics 2021年第4期34卷 432-450页

作者： Ye XIE Al SAVVARISAL Antonios TSOURDOS Dan ZHANG Jason GU Intelligent Robot Research Centre Zhejiang LabHangzhou311100China School of Aerospace Transport and ManufacturingCranfield UniversityBedford MK430ALUnited Kingdom York University North YorkToronto M3J 1P3Canada Dalhousie University HalifaxNova Scotia B3H 4R2Canada

The paper overviews the state-of-art of aircraft powered by hybrid electric propulsion *** research status of the design and energy management of hybrid aircraft and hybrid propulsion systems are further *** first contribution of the review is to demonstrate that,in the context of relatively underdeveloped electrical storage technologies,the study of mid-scale hybrid aircraft can contribute the most to both theoretical and practical ***,the profits and potential drawbacks of applying hybrid propulsion to mid-scale hybrid airplanes have not been thoroughly ***,as summed in the overview of design methodologies,the multi-objective optimization transcends the single-objective *** potential of the hybrid propulsion system can be thoroughly evaluated in only one optimization run,if several objectives optimized *** there are few researches covering the conceptual design of hybrid aircraft using multi-objective *** review of the most popular energy management strategies discloses the third research gap—current methodologies favoured in hybrid ground vehicles do not consider the aircraft ***,both non-causal and causal energy management are needed for performing a complicated flight mission with several sub-tasks.

关键词： Aircraft Convex optimization Energy management Fuzzy logic control Hybrid electric propulsion system non-dominated sorting genetic algorithm Sizing

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Multiobjective optimization scheme for industrial synthesis gas sweetening plant in GTL process

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Journal of Natural Gas Chemistry 2011年第1期20卷 99-109页

作者： Alireza Behroozsarand Akbar Zamaniyan Department of Chemical Engineering Islamic Azad University Department of Natural Gas Conversion Gas Research Division Research Institute of Petroleum Industry (RIPI)

In industrial amine plants the optimized operating conditions are obtained from the conclusion of occurred events and challenges that are normal in the working units. For the sake of reducing the costs, time consuming, and preventing unsuitable accidents, the optimization could be performed by a computer program. In this paper, simulation and parameter analysis of amine plant is performed at first. The optimization of this unit is studied using non-dominated sorting genetic algorithm-II in order to produce sweet gas with CO 2 mole percentage less than 2.0% and H 2 S concentration less than 10 ppm for application in Fischer-Tropsch synthesis. The simulation of the plant in HYSYS v.3.1 software has been linked with MATLAB code for real-parameter NSGA-II to simulate and optimize the amine process. Three scenarios are selected to cover the effect of （DEA/MDEA） mass composition percent ratio at amine solution on objective functions. Results show that sour gas temperature and pressure of 33.98 ？ C and 14.96 bar, DEA/CO 2 molar flow ratio of 12.58, regeneration gas temperature and pressure of 94.92 ？ C and 3.0 bar, regenerator pressure of 1.53 bar, and ratio of DEA/MDEA = 20%/10% are the best values for minimizing plant energy consumption, amine circulation rate, and carbon dioxide recovery.

关键词： amine plant multiobjective optimization non-dominated sorting genetic algorithm amine circulation rate

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Cutting parameter optimization for machining operations considering carbon emissions

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JOURNAL OF CLEANER PRODUCTION 2019年 208卷 937-950页

作者： Zhou, Guanghui Lu, Qi Xiao, Zhongdong Zhou, Ce Tian, Changle Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710054 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China

With the aggravation of global warming and implementation of carbon policies such as carbon tax and carbon labeling, manufacturing enterprises have to cope with the stress from eco-environment and production cost. Cutting parameters in the part machining process have a huge impact on energy and material consumption, cutting time and economy of manufacturing system. The traditional cutting parameters are always selected based on human experience and handbooks, which lacks the attention on energy and material saving. In this paper, a cutting parameter optimization method for machining operations considering carbon emissions is proposed to balance cutting indexes, i.e., carbon emissions, cutting time and cutting cost, in the part machining process. Firstly, the numerical relationships between cutting parameters and cutting indexes are analyzed. Particularly, considering the processing capacity of a workshop and machining features, formalized tables named c-PBOM-W (Carbon emissions integrated Process Bill of Material for Workshop) and c-PBOM-P (Carbon emissions integrated Process Bill of Material for Part) are designed to evaluate carbon emissions easily. Then, a multi-objective parameter optimization model is proposed. By introducing non-cooperative game theory, an improved algorithm, non-cooperative Game theory Integrated NSGA-II (NG-NSGA-II), is developed to solve the model. Finally, a cylindrical turning is taken as a case to illustrate the rationality of the proposed cutting parameter optimization method. The simulation results show that the NG-NSGA-II has a better performance than NSGA-II. Thus, the proposed method could provide the optimal cutting parameters in the part machining process to decrease carbon emissions, cutting time, and cutting cost. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Carbon emissions Cutting parameters Multi-objective optimization non-cooperative game theory non-dominated sorting genetic algorithm

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A Novel Two-Level Optimization Strategy for Multi-Debris Active Removal Mission in LEO

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Computer Modeling in Engineering & Sciences 2020年第1期122卷 149-174页

作者： Junfeng Zhao Weiming Feng Jianping Yuan Shandong University Jinan250031China Northwestern Polytechnical University Xi’an710072China National Key Laboratory of Aerospace Flight Dynamics Xi’an710072China

Recent studies of the space debris environment in Low Earth Orbit(LEO)have shown that the critical density of space debris has been reached in certain *** Active Debris Removal(ADR)mission,to mitigate the space debris density and stabilize the space debris environment,has been considered as a most effective *** this paper,a novel two-level optimization strategy for multi-debris removal mission in LEO is proposed,which includes the low-level and high-level optimization *** improve the overall performance of the multi-debris active removal mission and obtain multiple Pareto-optimal solutions,the ADR mission is seen as a Time-Dependant Traveling Salesman Problem(TDTSP)with two objective functions to minimize the total mission duration and the total propellant *** problem includes the sequence optimization to determine the sequence of removal of space debris and the transferring optimization to define the orbital *** optimization models for the two-level optimization strategy are built in solving the multi-debris removal mission,and the optimal Pareto solution is successfully obtained by using the non-dominated sorting genetic algorithm II(NSGA-II).Two test cases are presented,which show that the low level optimization strategy can successfully obtain the optimal sequences and the initial solution of the ADR mission and the high level optimization strategy can efficiently and robustly find the feasible optimal solution for long duration perturbed rendezvous problem.

关键词： Two-level optimization strategy active debris removal non-dominated sorting genetic algorithm bi-objective optimization LEO

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Multi-objective optimization in electrochemical micro-drilling of Ti6Al4V using nature-inspired techniques

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MATERIALS AND MANUFACTURING PROCESSES 2023年第15期38卷 1942-1954页

作者： Acharya, Biswesh Ranjan Sethi, Abhijeet Das, Amit Kumar Saha, Partha Pratihar, Dilip Kumar Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur 721302 India Natl Inst Ind Engn NITIE Mumbai India

Current study investigates the efficacy of electrochemical micro-drilling (ECMD) process during micro-hole fabrication on a Ti6Al4V alloy sheet of 1.2 mm thickness using a cylindrical-shaped micro-tool. Ethylene glycol-based electrolyte was selected to overcome excessive passivation. Experiments were conducted by altering voltage, feed rate (FR), and electrolyte concentration (EC) using a central-composite-design to observe their effects on the output parameters. The desired response parameters, such as radial overcut (ROC), and taper were examined while fabricating dimensionally accurate micro-holes including the volume removal rate (VRR) that occurred during the process. ANOVA results established voltage as the major significant input parameter that affects all the responses, followed by FR. In addition, different nature-inspired optimization techniques, namely Multi-Objective Grey-Wolf Optimizer (MOGWO), Multi-Objective Bonobo Optimizer (MOBO), and Multi-Objective Particle Swarm Optimization (MOPSO), were employed. MOBO reported the best Pareto-surface in terms of uniformity and spread measures and suggested a FR of 0.3 mu m/s for optimal micro-drilling operations.

关键词： Ti6Al4V Electrochemical micro-drilling Multi-objective bonobo optimizer Multi-objective particle swarm optimization Multi-objective grey wolf optimizer non-dominated sorting genetic algorithm

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A novel hybrid feature selection and modified KNN prediction model for coal and gas outbursts

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第5期39卷 7671-7691页

作者： Liu, Xuning Zhang, Guoying Zhang, Zixian China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing Peoples R China Shijiazhuang Univ Dept Comp Engn Shijiazhuang Hebei Peoples R China Liaocheng Univ Sch Foreign Univ Liaocheng Shandong Peoples R China

The feature selection of influencing factors of coal and gas outbursts is of great significance for presenting the most discriminative features and improving prediction performance of a classifier, the paper presents an effective hybrid feature selection and modified outbursts classifier framework which aims at solving exiting coal and gas outbursts prediction problems. First, a measurement standard based on maximum information coefficient(MIC) is employed to identify the wide correlations between two variables;Second, based on a ranking procedure using non-dominated sorting genetic algorithm(NSGAII), maximum relevance minimum redundancy(MRMR) algorithm is subsequently performed to find out candidate feature set highly related to the class label and uncorrelated with each other;Third, random forest(RF) is employed to search the optimal feature subset from the candidate feature set, then the optimal feature subset that influences the classification performance of coal and gas outbursts is obtained;Finally, an improved classifier model has been proposed that combines gradient boosting decision tree(GBDT) and k-nearest neighbor(KNN) for outbursts prediction. In the modified classifier model, the GBDT is utilized to assign different weights to features, then the weighted features are input into the KNN to verify the effectiveness of proposed method on coal and gas outbursts dataset. The experimental results conclude that our proposed scheme is effective in the number of feature and prediction accuracy when compared with other related state-of-the-art prediction models based on feature selection for coal and gas outbursts.

关键词： Coal and gas outbursts Maximum information coefficient non-dominated sorting genetic algorithm Maximum relevance minimum redundancy Random forest Gradient boosting decision tree K-nearest neighbor

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Multiobjective optimization for force and moment balance of a four-bar linkage using evolutionary algorithms

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2011年第12期25卷 2971-2977页

作者： Farmani, Mohammad Reza Jaamialahmadi, Abdolrahman Babaie, Meisam KN Toosi Univ Technol Dept Aerosp Engn Tehran Iran Ferdowsi Univ Mashhad Fac Engn Dept Mech Engn Mashhad Iran Queensland Univ Technol Dept Build Environm & Engn Brisbane Qld 4001 Australia

In this study, force and moment balance of a planar four-bar linkage is implemented using evolutionary algorithms. In the current problem, the concepts of inertia counterweights and physical pendulum are utilized to complete balance of all mass effects, independent of input angular velocity. A proposed multiobjective particle swarm optimization, and non-dominated soiling genetic algorithm II are applied to minimize two objective functions subject to some design constraints. The applied algorithms produced a set of feasible solutions called pareto optimal solutions for the design problem. Finally, a fuzzy decision maker is utilized to select the best solution among the obtained pareto solutions. The results show that optimal solutions minimize the weights of applied counterweights and eliminate both shaking forces and moments transmitted to the ground, simultaneously.

关键词： Force and moment balance Four-bar linkage Multiobjective particle swarm optimization non-dominated sorting genetic algorithm Fuzzy decision maker

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Constructability optimal design of reinforced concrete retaining walls using a multi-objective genetic algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2013年第2期47卷 227-245页

作者： Kaveh, A. Kalateh-Ahani, M. Fahimi-Farzam, M. Iran Univ Sci & Technol Sch Civil Engn Ctr Excellence Fundamental Studies Struct Engn Tehran 16 Iran

The term "constructability" in regard to cast-in-place concrete construction refers mainly to the ease of reinforcing steel placement. Bar congestion complicates steel placement, hinders concrete placement and as a result leads to improper consolidation of concrete around bars affecting the integrity of the structure. In this paper, a multi-objective approach, based on the non-dominated sorting genetic algorithm (NSGA-II) is developed for optimal design of reinforced concrete cantilever retaining walls, considering minimization of the economic cost and reinforcing bar congestion as the objective functions. The structural model to be optimized involves 35 design variables, which define the geometry, the type of concrete grades, and the reinforcement used. The seismic response of the retaining walls is investigated using the well-known Mononobe-Okabe analysis method to define the dynamic lateral earth pressure. The results obtained from numerical application of the proposed framework demonstrate its capabilities in solving the present multiobjective optimization problem.

关键词： reinforced concrete cantilever retaining wall constructability reinforcing bar congestion multi-objective optimization non-dominated sorting genetic algorithm

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Multi-objective modelling and optimal parameter selection of a multi-pass milling process considering uncertain milling stability constraint

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第9-10期120卷 6225-6240页

作者： Deng, Congying Shu, Jie Ma, Ying Lu, Sheng Zhao, Yang Miao, Jianguo Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing 400065 Peoples R China Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610065 Peoples R China Chongqing Univ Posts & Telecommun Inst Adv Study Chongqing 400065 Peoples R China

Optimizing machining parameters and pass numbers under machining stability constraint is important for manufacture engineers planning a multi-pass milling operation. Considering that milling stability varies with the machine tool dynamics when the tool and workpiece position changes, a generalized regression neural network (GRNN) is adopted to obtain the position-dependent stability constraint. Then a multi-objective optimization model of a multi-pass milling is established, where the objectives are the total cutting time (T-c) and surface roughness (R-a) and the variables are the machining position and cutting parameters for each pass. A non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the multiobjective optimization model for providing an optimal Pareto front, from which one satisfactory solution well balancing the T-c and R-a is selected by combining the entropy-weighted algorithm (EWA) and technique for ordering preferences by similarity to ideal solution (TOPSIS). A case study was carried out to establish a multi-objective optimization model after the milling stability constraint and surface roughness were predicted with the aid of GRNN and back-propagation neural network (BPNN), respectively. An ideal solution containing optimal machining position and cutting parameters was solved using NSGA-II, EWA, and TOPSIS, which was compared with solutions of two mono-objective models for optimizing T-c and R-a, respectively, to validate its feasibility. The observed stability and acceptable R-a value of the milling test under the ideal solution also indicated that the proposed optimization method can realize a trade-off between two conflict objectives and fascinate the process planning of a multi-pass milling.

关键词： Multi-objective optimization Multi-pass milling Position-dependent milling stability Artificial neural network non-dominated sorting genetic algorithm

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Improved genetic algorithm based on particle swarm optimization-inspired reference point placement

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ENGINEERING OPTIMIZATION 2019年第7期51卷 1097-1114页

作者： Essiet, Ima O. Sun, Yanxia Wang, Zenghui Univ Johannesburg Dept Elect & Elect Engn Sci Johannesburg South Africa Univ South Africa Dept Elect & Min Engn Pretoria South Africa

This article investigates the use of optimal reference point placement to improve performance of non-dominated sorting genetic algorithm (NSGA). Placement of reference points for many-objective optimization is inspired by wheel and Von Neumann topologies of Particle Swarm Optimization (PSO). Results obtained show that the pattern of reference point placement determines performance efficiency of NSGA. The better-performing wheel topology (called wheel reference point genetic algorithm (wRPGA), is compared to three other many-objective evolutionary algorithms: knee-driven evolutionary algorithm (KnEA), non-dominated sorting genetic algorithm III (NSGAIII) and multi-objective evolutionary algorithm based on dominance and decomposition (MOEAD/D). The selected many-objective benchmark problems are Walking Fish Group 2 (WFG2) and Deb-Thiele-Laumanns-Zitzler 2 (DTLZ2). It is also tested on a 3-objective cost function for a hypothetical model of a stand-alone microgrid. Through the simulations, the wheel configuration performed 88.9% better than the Von Neumann configuration. The wheel topology also achieved better performance with respect to inverted generational distance (IGD) compared to KnEA, NSGAIII and MOEAD/D for 7 out of 15 IEEE Congress on Evolutionary Computation (CEC) 2017 benchmark problems. wRPGA gave a good approximation of the Pareto front for the 3-objective model representing the hypothetical microgrid.

关键词： non-dominated sorting genetic algorithm reference points optimization inverted generational distance hyper-plane

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