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Study on Optimization of Interior Ballistic Performance of Cased Telescoped Ammunition Based on improved firefly algorithm

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PROPELLANTS EXPLOSIVES PYROTECHNICS 2023年第2期48卷 e202200254-e202200254页

作者： He, Xin-Jia Li, Chao Nanjing Univ Sci & Technol Sch Energy & Power Engn Nanjing 210094 Peoples R China

Considering that the evolution operators of common optimization algorithms such as genetic algorithm has a poor convergence speed and solution accuracy in the interior ballistic performance optimization of artillery, an improved firefly algorithm is selected for the interior ballistic performance optimization of cased telescoped ammunition (CTA). It is applied to the interior ballistic mathematical model of CTA to carry out single-objective optimization and multi-objective optimization respectively. The results show that in the single-objective optimization, the muzzle velocity obtained by using the improved firefly algorithm is larger than other commonly used optimization algorithms. Under the constraints of maximum chamber pressure and relative burnout point, the muzzle velocity can be increased by about 30 m/s. In the multi-objective optimization, three sub-objectives of muzzle velocity, ballistic efficiency and muzzle pressure are considered respectively, and the multi-objective firefly algorithm is applied to comprehensively optimize the interior ballistic performance of CTA. Then, the results are compared with the commonly used multi-objective optimization algorithms, such as non-dominated sorting genetic algorithm version II (NSGA-II) and non-dominated sorting genetic algorithm version III (NSGA-III), the solution results are a series of Pareto optimal solutions. The obtained optimization results improved the interior ballistic performance and launch safety, providing reference for the reasonable matching of charge structure parameters and the selection of optimization algorithm upon the interior ballistic performance optimization of CTA to some extent.

关键词： Cased telescoped ammunition improved firefly algorithm Multi-objective optimization problem Interior Ballistics Charge structure parameters

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improved firefly algorithm for Optimization of Aero-engine Controller Based on PIDNN

Improved Firefly Algorithm for Optimization of Aero-engine C...

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第40届中国控制会议

作者： Zongting Jiang Lingfeng Gou Chujia Sun Meng Zhang School of Power and Energy Northwestern Polytechnical University

In this paper, a two-control variable controller based on the Proportional-Integral-Derivative Neural Network（PIDNN）is designed to control a certain type of mixed exhaust turbofan engine. According to the working principle of the aero-engine, a two-variable small deviation state model of the aero-engine is firstly established. Then PIDNN including an input layer, a hidden layer and an output layer is used to design the controller of aero-engine states model. There are 4 nods in input layer, 6 in hide layer and 2 in output layer. To solve the problems of large steady-state error and long adjustment time of the PIDNN controller,this paper uses the improved firefly algorithm to dynamically adjust the initial connection weights of the PIDNN. The results show that the established aero-engine PIDNN controller based on the improved firefly algorithm has the characteristics of short adjustment time and high accuracy, which meets the requirements of aero-engine controller design.

关键词： Aero-engine PIDNN improved firefly algorithm

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A SAMPLING METHOD BASED ON improved firefly algorithm FOR PROFILE MEASUREMENT OF AVIATION ENGINE BLADE

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METROLOGY AND MEASUREMENT SYSTEMS 2019年第4期26卷 757-771页

作者： Huang, Zhi Zhao, Liao Li, Kai Wang, Hongyan Zhou, Tao Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Sichuan Peoples R China

Coordinate Measurement Machines (CMMs) have been extensively used in inspecting mechanical parts with higher accuracy. In order to enhance the efficiency and precision of the measurement of aviation engine blades, a sampling method of profile measurement of aviation engine blade based on the firefly algorithm is researched. Then, by comparing with the equal arc-length sampling algorithm (EAS) and the equi-parametric sampling algorithm (EPS) in one simulation, the proposed sampling algorithm shows its better sampling quality than the other two algorithms. Finally, the effectiveness of the algorithm is verified by an experimental example of blade profile. Both simulated and experimental results show that the method proposed in this paper can ensure the measurement accuracy by measuring a smaller number of points.

关键词： aviation engine blade coordinate measurement machine profile measurement improved firefly algorithm

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QCs scheduling scheme of genetic algorithm (GA) and improved firefly algorithm (FA)

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S4331-S4348页

作者： Dong, Liangcai Yang, Yang Sun, Siyun Shanghai Maritime Univ Sch Logist Engn Shanghai 201306 Peoples R China Shanghai Maritime Univ Engn Res Ctr Container Supply Chain Technol Minist Educ Shanghai 201306 Peoples R China

As an important part of container logistics, quay cranes (QCs) are crucial equipment in multimodal container transportation. The scheduling and allocation of QCs determine the operational efficiency of container terminals. By analyzing the way quay cranes are operated, this paper establishes a mixed-integer dynamic rolling-horizon programming model for the scheduling and allocation of QCs and proposes use of a genetic algorithm and two improved firefly algorithms based on segment encoding technology to formulate an optimum QC scheduling scheme. In doing so, the improved approach has made QC control more efficient and balanced.

关键词： Container terminal Entire quay length Quay cranes scheduling Genetic algorithm improved firefly algorithm

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Minimizing Electricity Fuel Cost of Thermal Generating Units by Using improved firefly algorithm

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JOURNAL OF ENGINEERING AND TECHNOLOGICAL SCIENCES 2019年第1期51卷 133-147页

作者： Thang Trung Nguyen Bao Quoc Nguyen Phuong Duy Nguyen Chiem Trong Hien Ton Duc Thang Univ Power Syst Optimizat Res Grp Fac Elect & Elect Engn 19 Nguyen Huu Tho St Ho Chi Minh City 700000 Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn 19 Nguyen Huu Tho St Ho Chi Minh City 700000 Vietnam Saigon Univ Fac Elect Telecommun 273 An Duong Vuong St Ho Chi Minh City 700000 Vietnam Ho Chi Minh City Univ Food Ind Fac Elect & Elect Technol 140 Le Trong Tan St Ho Chi Minh City 700000 Vietnam

This paper presents the application of an improved firefly algorithm (IFA) for minimizing total electricity generation fuel cost while all loads are supplied by thermal generating units. The proposed IFA was developed by combining two proposed improvements of the firefly algorithm (FA), i.e. improvement of the distance between two considered solutions and improvement of the new-solution production technique. The effect of each proposed improvement on the conventional firefly algorithm (FA) and the performance of IFA were investigated in two study cases, i.e. single- and multi-fuel option based thermal generating units. In the first case, three different systems with three, six and twenty units were employed, while a ten-unit system with four different loads was tested in the second case. The comparison results between IFA and existing methods, including three other FA variants, revealed that the two proposed improvements of FA are very efficient and make IFA a very promising meta-heuristic algorithm for minimizing fuel cost of thermal generating units.

关键词： improved firefly algorithm multi-fuel single-fuel thermal generating units total fuel

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Reactive Power and Voltage Optimization for Regional Power Grids with New Energy Stations under the Goal of "Double Carbon" 9

Reactive Power and Voltage Optimization for Regional Power G...

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9th International Conference on Power and Renewable Energy

作者： Yang, Xiankui Li, Hui Qi, Kun Wei, Ling Beijing Informat Sci & Technol Univ Sch Automat Beijing Peoples R China Elect Power Dev Res Inst China Elect Council Beijing Peoples R China

ISBN: (纸本)9798350377477;9798350377460

In view of the problems caused by the high proportion of new energy grid connection, such as large active network loss and voltage fluctuation, high reactive power cost and environmental protection cost, a reactive voltage optimization model based on the time-series segmentation strategy of new energy output is proposed. The model uses the Top-Down algorithm to segment the process according to the maximum dispersion of the average value of new energy output between each period and the minimum dispersion within each period. It adopts a reactive pricing strategy to allocate the output of reactive power sources reasonably, and takes the minimum sum of standardized active network loss, node voltage deviation, reactive power source cost, and environmental protection cost of thermal power units as the optimization objective. The improved firefly algorithm is used to solve the optimization model. Finally, the effectiveness of the proposed method is verified using the IEEE-30 bus system.

关键词： reactive power and voltage optimization new energy power segmentation strategy reactive power pricing strategy improved firefly algorithm

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Research on intelligent prediction of hydrogen pipeline leakage fire based on Finite Ridgelet neural network

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2022年第55期47卷 23316-23323页

作者： Zhao, Bin Li, Shasha Gao, Diankui Xu, Lizhi Zhang, Yuanyuan Liaoning Petrochem Univ Sch Mech Engn Fushun 113001 Liaoning Peoples R China

In order to effectively predict fire risk of hydrogen pipeline leakage and improve the safe level of hydrogen pipeline, the finite Ridgelet neural network optimized by improved firefly algorithm is constructed. Related researches on pipeline leakage fire risk and corresponding prediction models are summarized. The finite Ridgelet neural network is constructed by using finite Ridgelet function as excitation function of node in hidden layer, and the structure of finite Ridgelet neural network is established. The improved firefly algorithm based on location update mechanism is designed to optimize the parameters of finite Ridgelet neural network. And then the fire risk level prediction procedure of hydrogen pipeline is designed. Finally, forty training samples and ten testing samples are selected to carry out fire risk level prediction analysis, results show that the proposed finite Ridgelet neural network optimized by improved firefly algorithm has advantages in prediction precision and accuracy, which can effectively predict the safety status of hydrogen pipeline. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Finite ridgelet neural network improved firefly algorithm Hydrogen pipeline Fire risk

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Research on enhancing power plant net power by integrating modeling heat transfer and operation optimization of once-through cooling water system

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CASE STUDIES IN THERMAL ENGINEERING 2024年 61卷

作者： Wang, Huijie Qiu, Baoyun Zhao, Fangling Yan, Tianxu Li, Chen Yangzhou Univ Coll Hydraul Sci & Engn Yangzhou 225000 Peoples R China Yangzhou Univ Coll Elect Energy & Power Engn Yangzhou 225000 Peoples R China Jiangsu Univ Natl Res Ctr Pumps Zhenjiang 212000 Peoples R China

For low power generation efficiency and water source thermal pollution caused by irrational operation of once-through cooling water systems (OCWSs) in condensing power plants, this paper advances a method to maximize the power plant net power by OCWS operation optimization and a 2 x 330 MW power plant is taken as a study case. First, the power plant thermodynamic system is simulated by Ebsilon to obtain exhausted steam parameters. Then, a heat transfer analysis method for the whole OCWS is investigated to calculate the system water temperature. Next, a maximum net power optimization model is deduced and established. Particularly, the model sets the temperature difference between OCWS outlet and inlet as a constraint. Finally, an improved firefly algorithm is employed to solve the model to get the numbers of constant-speed pumps and variable-speed pumps in operation and the frequency conversion ratio of the latter. This paper analyses the optimization under various scenarios and the sensitivity of optimization results. After optimization, the net power of the case power plant is increased up to 2080.5 kW, approximately 0.524 %. The OCWS temperature difference is decreased under most conditions. The method effectively enhances power plant net power and reduces drainage impact on the environment.

关键词： Condensing power plant Once-through cooling water system Net generated power Heat transfer modeling Operation optimization improved firefly algorithm

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Autonomous resource allocation of smart workshop for cloud machining orders

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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING 2021年第2期35卷 226-239页

作者： Hui, Jizhuang Lei, Jingyuan Ding, Kai Zhang, Fuqiang Lv, Jingxiang Changan Univ Inst Smart Mfg Syst Middle Sect Naner Huan Rd Xian Xian 710064 Shaanxi Peoples R China Changan Univ Key Lab Rd Construct Technol & Equipment MOE Xian 710064 Shaanxi Peoples R China

In order to realize the online allocation of collaborative processing resource of smart workshop in the context of cloud manufacturing, a multi-objective optimization model of workshop collaborative resources (MOM-WCR) was proposed. Considering the optimization objectives of processing time, processing cost, product qualification rate, and resource utilization, MOM-WCR was constructed. Based on the time sequence of workshop processing tasks, the workshop collaborative manufacturing resource was integrated in MOM-WCR. Fuzzy analytic hierarchy process (FAHP) was adopted to simplified the multi-objective problem into the single-objective problem. Then, the improved firefly algorithm which integrated the particle swarm algorithm (IFA-PSA) was used to solve MOM-WCR. Finally, a group of connecting rod processing experiments were used to verify the model proposed in this paper. The results show that the model is feasible in the application of workshop-level resource allocation in the context of cloud manufacturing, and the improved firefly algorithm shows good performance in solving the multi-objective resource allocation problem.

关键词： Cloud machining orders fuzzy analysis improved firefly algorithm particle swarm optimization resource allocation

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Optimal Fractional-Order Proportion Integration Differentiation Control of Proton Exchange Membrane Electrolyzer for Offshore Wind Power Hydrogen Production System

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ENERGY TECHNOLOGY 2023年第9期11卷 2300100-2300100页

作者： Tong, Fan Wei, Wuqing Jin, Mingyue Sun, Xi Wang, Qiuyuan Luo, Qin Shanghai Invest Design & Res Inst Co Ltd Dept Power Planning & Design Shanghai 200335 Peoples R China North China Elect Power Univ Dept Automat Baoding 071003 Peoples R China

Offshore wind power is mostly of strong randomness and unpredictability, which brings a great challenge to proton exchange membrane (PEM) water electrolysis-based hydrogen production. Besides, volatile temperature and pressure tend to impose prominent impacts on electrolyzer parameters as well. By analyzing the electrochemical characteristics of PEM electrolyzers, the electrochemical model of PEM electrolyzer in offshore wind power generation hydrogen production system is established. To ensure the control performance of PEM electrolyzers, the fractional-order proportion integration differentiation (FOPID) control strategy and the improved firefly algorithm (IFA) are introduced, and the relevant parameters are optimized according to the overshoot and stability time as evaluation indicators. In order to adapt to the FOPID control requirements, a step-type inertia weighting factor is employed to improve the classical firefly algorithm to avoid local optimum, and a mutation mechanism is used to expand the search range. Verification results show that the proposed IFAFOPID controller is superior to the traditional PID controller with a smaller overshoot and a shorter transition time subject to different disturbances, and thus is more beneficial to achieve precise voltage control and obtain stable hydrogen output.

关键词： fractional-order proportion integration differentiation (PID) regulation improved firefly algorithm parameter optimization proton exchange membrane electrolyzer

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