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Optimization of Smooth Straight-Through Labyrinth Seal Based on XGBoost and improved genetic algorithm

JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME 2025年第3期147卷 031007页

作者： Liu, Hao Li, Guoqing Kang, Chenyang Ruan, Yunhong Wang, Ruofan Lu, Xingen Chinese Acad Sci Inst Engn Thermophys Key Lab Light Duty Gas Turbine Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Aeronaut & Astronaut Beijing 100049 Peoples R China Natl Key Lab Sci & Technol Adv Light Duty Gas Turb Beijing 100190 Peoples R China

Improving the sealing performance of labyrinth seal is beneficial in reducing the loss of downstream components as well as improving the aero-engines' efficiency. Meanwhile, the optimization of labyrinth seal is more cost-effective than optimizing other components of aero-engines. Based on extreme gradient boosting (XGBoost) and improved genetic algorithm (GA), an automatic optimizer for smooth straight-through labyrinth seal is proposed. According to the ordering of feature importance, the ten selected geometric parameters of smooth straight-through labyrinth seals with two gaps (0.1 mm, 0.25 mm) are optimized when the axial length of labyrinth seal is limited. The optimization results show that the leakage rate of the optimized labyrinth seal with two gaps is 19.67% and 23.80% lower than the reference labyrinth seal, respectively. It is found that the decrease of fin height, fin angle, fillet radius, and fin width, and the increase of fin pitch are beneficial to improve the sealing performance of smooth straight-through labyrinth seal. The analysis of flow field reveals the reasons for the improvement of sealing performance due to the change of geometric parameters. However, it must be noted that the optimization effect decreases as the pressure ratio increases, primarily as the result of the increase in carry-over coefficient. This work provides a new approach to optimize smooth straight-through labyrinth seal.

关键词： smooth straight-through labyrinth seal optimization XGBoost improve genetic algorithm leakage

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Research on Complete Coverage Path Planning of Agricultural Robots Based on Markov Chain improved genetic algorithm

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APPLIED SCIENCES-BASEL 2024年第21期14卷 9868页

作者： Han, Jiangyi Li, Weihao Xia, Weimin Wang, Fan Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China

Featured Application This study can be used for navigation operations of agricultural equipment or vehicles in farm *** Due to the limitations of low coverage, high repetition rate, and slow convergence speed of the basic genetic algorithm (GA) in robot complete coverage path planning, the state transition matrix of the Markov chain is introduced to guide individual mutation based on the genetic mutation path planning algorithm, which can improve the quality of population individuals, enhancing the search ability and convergence speed of the genetic algorithm. The proposed improved genetic algorithm is used for complete coverage path planning simulation analysis in different work areas. The analysis results show that compared to traditional genetic algorithms, the improved genetic algorithm proposed in this paper reduces the average path length by 21.8%, the average number of turns by 6 times, the repetition rate by 83.8%, and the coverage rate by 7.76% in 6 different work areas. The results prove that the proposed improved genetic algorithm is applicable in complete coverage path planning. To verify whether the Markov chain genetic algorithm (MCGA) proposed is suitable for agricultural robot path tracking and operation, it was used to plan the path of an actual land parcel. An automatic navigation robot can track the planned path, which can verify the feasibility of the MCGA proposed.

关键词： agricultural robots complete coverage path planning Markov chain improve genetic algorithm

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OPTIMIZATION OF SMOOTH STRAIGHT-THROUGH LABYRINTH SEAL BASED ON XGBOOST AND improveD GA 69

OPTIMIZATION OF SMOOTH STRAIGHT-THROUGH LABYRINTH SEAL BASED...

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69th ASME Turbomachinery Technical Conference and Exposition (ASME Turbo Expo) (GT)

作者： Liu, Hao Li, Guoqing Chinese Acad Sci Inst Engn Thermophys Key Lab Light Duty Gas Turbine Beijing Peoples R China Univ Chinese Acad Sci Sch Aeronaut & Astronaut Beijing Peoples R China Natl Key Lab Sci & Technol Adv Light Duty Gas Tur Beijing Peoples R China

ISBN: (纸本)9780791888001

Improving the sealing performance of labyrinth seal is beneficial in reducing the loss of downstream components as well as improving the aero-engines' efficiency. Meanwhile, the optimization of labyrinth seal is more cost-effective than optimizing other components of aero-engines. Based on extreme gradient boosting (XGBoost) and improved genetic algorithm (GA), an automatic optimizer for smooth straight-through labyrinth seal is proposed. According to the ordering of feature importance, the 10 selected geometric parameters of smooth straight-through labyrinth seals with two gaps (0.1 mm, 0.25 mm) are optimized when the axial length of labyrinth seal is limited. The optimization results show that the leakage rate of the optimized labyrinth seal with two gaps is 19.67% and 23.80% lower than the reference labyrinth seal, respectively. It is found that the decrease of fin height, fin angle, fillet radius, and fin width, and the increase of fin pitch are beneficial to improve the sealing performance of smooth straight-through labyrinth seal. The analysis of flow field reveals the reasons for the improvement of sealing performance due to the change of geometric parameters. However, it must be noted that the optimization effect decreases as the pressure ratio increases, primarily as the result of the increase in carry-over coefficient. This work provides a new approach to optimize smooth straight-through labyrinth seal.

关键词： Smooth straight-through labyrinth seal optimization XGBoost improve genetic algorithm leakage

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Ontology-Based Optimization Method for Multi-Source Heterogeneous Data Organization in Large Power Grid Planning and Operation Platforms 3

Ontology-Based Optimization Method for Multi-Source Heteroge...

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3rd International Conference on Energy and Electrical Power Systems (ICEEPS)

作者： Tang, Yingqi Zhao, Ligang Su, Yinsheng Zhou, Baorong Xu, Yuan Huang, Guanbiao Hu, Jiaming Li, Kaihang CSG Elect Power Res Inst State Key Lab HVDC Technol Guangzhou Peoples R China CSG Power Dispatching & Control Ctr Guangzhou Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

ISBN: (纸本)9798350375145;9798350375138

The construction of a large power grid planning and operation platform requires the integration and organization of multi-source heterogeneous data. However, the various types of power grid business have differing levels of importance and high demands for timely response, which current data organization methods struggle to meet. This paper proposes an ontology-based optimization method for multi-source heterogeneous data organization in large power grid planning and operation platforms. A mathematical optimization model is constructed, taking into account both business retrieval efficiency and data redundancy. Additionally, the genetic algorithm is improved based on the specific characteristics of this optimization problem. The method proposed in this paper is then applied to integrate data from multiple power software applications. Experimental results show that this method effectively balances data retrieval efficiency and storage space utilization, achieving automated organization optimization of multi-source heterogeneous data for power grid planning and operation.

关键词： multi-source heterogeneous data organization power grid planning and operation optimization ontology improve genetic algorithm

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Application of Engineering Safety System Based on improved genetic algorithm in Water Resources

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Procedia Computer Science 2025年 259卷 1049-1055页

作者： Qiang Xiao Dongdong Chu Dan Zou Zhifang Peng Jiangsu Hydraulic Engineering Construction Co. Ltd Yangzhou 225000 Jiangsu China Jiangsu Water Conservancy Science Research Institute Nanjing 210017 Jiangsu China Yangzhou Urban River Management Office Yangzhou 225000 Jiangsu China

The hydraulic engineering safety is extremely important in protecting the safety of people’s lives and property. To gain improvement on efficiency and reliability of safety system of water conservancy engineering, many people are committed to start exploring a new application of genetic algorithms in the safety system of water conservancy engineering. This paper introduces an improved genetic algorithm to design optimization algorithm according to the characteristic and safety principle of water conservancy engineering safetysystem. The algorithm has taken into account the safety index, constraints and optimization objectives of the system, and findste optimal solution through the genetic algorithm evolution and optimization process. According to the experiment result, the engineering safety system based on improved genetic algorithm has significant application effect in water conservancyengineering. The system is designed to find the optimal solution to different water conservancy engineering safety problems, which can promote the safety and efficiency of the system.

关键词： engineering safety system water conservancy engineering improve genetic algorithm flood exceedance rate

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A Traditional Cultural Route Planning and Design System Based on improved genetic algorithm

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Procedia Computer Science 2024年 247卷 211-217页

作者： Tao Zhang Fengzhen Gao Shandong Vocational and Technical College of Commerce Jinan 250103 Shandong China

The planning and design of traditional cultural routes are of great significance in the field of cultural tourism, but they face challenges such as a large search space and long system planning time. This study is based on an improved genetic algorithm, introducing new genetic operators, optimization strategies, and heuristic strategies to improve the coverage of cultural attractions and the effectiveness of system planning time. Meanwhile, this paper adopts parallel computing technology to accelerate algorithm operations and considers multi-objective optimization problems, comprehensively considering multiple objectives such as cultural attraction coverage, travel time, and transportation routes. The experimental results demonstrate that the traditional cultural route planning and design system based on the improved genetic algorithm has achieved a significant improvement of the coverage of cultural attractions. The optimization algorithm has improvement in efficiency, while shortening the search space, resulting in the creation of more comprehensive, diverse, and efficient route planning schemes, thereby enhancing the coverage of cultural attractions.

关键词： Line Planning and Design Traditional Culture improve genetic algorithm Coverage Rate Of Cultural Attractions

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Model predictive control method of a parallel electromagnetic isolation system based on the improved genetic algorithm

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JOURNAL OF VIBRATION AND CONTROL 2020年第21-22期26卷 2001-2012页

作者： Zhang, Lei Zhuan, Xiangtao Wuhan Univ Sch Elect Engn & Automat Dept Artificial Intelligence & Automat Wuhan Peoples R China Wuhan Univ Shenzhen Res Inst Shenzhen Peoples R China

To improve the vibration isolation performance for a parallel electromagnetic isolation system, an improved genetic algorithm to optimize the Q and R matrices in the control objective function for a model predictive control approach is proposed. In this study, a parallel electromagnetic isolation system with two electromagnetic isolation units is designed to expand the vibration isolation range to isolate the large object. The dynamical equation and state equation of the parallel electromagnetic isolation system are built. The nonlinear relationship among electromagnetic force, coil current, and gap is calculated by COMSOL Multiphysics to design the model predictive control controller. Meanwhile, an improved genetic algorithm by the variable chromosome length coevolutionary method is presented to tackle two issues. The first issue is that the parameters of Q and R matrices in the control objective function are mainly selected by trial and error. The other issue is that the model predictive control approach needs to determine prediction steps which may lead to the model predictive control approach suffering from heavy computation or an inaccurate prediction model. Simulation and experimental results demonstrate that the parallel electromagnetic isolation system with model predictive control method based on the improved genetic algorithm can achieve better vibration isolation performance in comparison with the passive isolation system.

关键词： Model predictive control method improve genetic algorithm active control vibration isolation parameter optimization

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Application Research of a Novel genetic algorithm-Based BP Neural Network

Application Research of a Novel Genetic Algorithm-Based BP N...

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2011 4th IEEE International Conference on Computer Science and Information Technology(ICCSIT 2011)

作者： WU Hui-qin CHEN Jun LING Xu-dong Zhenjiang Watercraft College of PLA

ISBN: (纸本)9781612848334

It has proposed a kind of hybrid method based on genetic algorithm (GA) and BP neural network (NN) and applied it to the systematic safety evaluation of the weapon The traditional neural network is unavoidable to present local extreme value question, may result in failing training. It utilizes improved genetic algorithm to optimize the weight of neural network and get the final assessment value through twice training of neural network. The simulation result implies that the convergence speed of hybrid algorithm is quick and it can avoid local extreme value question effectively.

关键词： improve genetic algorithm BP neural network Weight

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