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Chamber shape optimization for ultra-high-pressure water-jet nozzle based on computational fluid dynamics method and a data-driven surrogate model

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2025年第1期19卷

作者： Zhao, Wen-Tao Chen, Zheng-Shou Chen, Yuan-Jie Li, Jiang-Long Zhejiang Ocean Univ Dept Naval Architecture & Ocean Engn Zhoushan 316022 Peoples R China Zhejiang Univ Inst Ocean Engn & Technol Ocean Coll Zhoushan Peoples R China

The ultra-high-pressure (UHP) water-jet nozzle acts as one of the key units for the whole rotary sprayers, and its chamber shape plays a decisive role in improving the hydrodynamic performance of water jetting. Unfortunately, comprehensive and effective methods to optimize the nozzles' chamber shape are still lacking. By coupling a data-driven surrogate model with metaheuristic optimizer, a computational fluid dynamics (CFD)-based optimization scheme aiming at fully enhancing the hydrodynamic performance of UHP nozzles was proposed in this paper. Firstly, to ensure optimization accuracy, an improved whale optimization algorithm (IWOA) was developed. It combines the chaotic opposition-based learning (COBL) and the Cauchy-Gaussian mutation simulated annealing algorithms, incorporating a nonlinear convergence and adaptive inertia weight mechanism. Then, to reduce CFD computational costs and accelerate calculation speed, a data-driven surrogate model based on IWOA-support vector machine (SVM) was proposed. Herein, the parameter gamma and the penalty coefficient in SVM model, are trained using IWOA. The IWOA-SVM model was constructed using optimal Latin hypercube design (Opt. LHD) to sample nozzle structures, with peak wall shear stress (as objective function) calculated via CFD method. Finally, this optimization scheme was applied to optimize the chamber shape of the original nozzle commonly used in ship rust removal. The results reveal that the chamber shape of the optimized nozzle can greatly enhance the water-jet hydrodynamic performance by raising the peak wall shear stress by 13.88%. Additionally, the IWOA-SVM model demonstrates the capability to evaluate hydrodynamic performance with a maximum error of 2.853%. Therefore, the proposed optimization scheme provides novel insights for the overall design and optimization of the UHP water-jet nozzle.

关键词： Nozzle chamber optimization ultra-high-pressure water jetting data-driven surrogate model peak wall shear stress support vector machine (SVM)

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Optimization of dual-layer flow field in a water electrolyzer using a data-driven surrogate model

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Energy and AI 2024年第4期18卷 27-36页

作者： Lizhen Wu Zhefei Pan Shu Yuan Xiaoyu Huo Qiang Zheng Xiaohui Yan Liang An Department of Mechanical Engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Research Institute for Sports Science and Technology The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina Key Laboratory of Low-grade Energy Utilization Technologies and Systems(Chongqing University) Ministry of EducationChongqing400030China Institute of Engineering Thermophysics Chongqing UniversityChongqing 400030China Institute of Fuel Cells School of Mechanical EngineeringShanghai Jiao Tong University800 Dongchuan RoadShanghai 200240China Eastern Institute for Advanced Study Eastern Institute of TechnologyNingboZhejiang 315200China

Serious bubble clogging in flow-field channels will hinder the water supply to the electrode of proton exchange membrane water electrolyzer(PEMWE),deteriorating the cell *** order to address this issue,the dual-layer flow field design has been proposed in our previous *** this study,the VOF(volume of fluid)method is utilized to investigate the effects of different degassing layer and base heights on the bubble behavior in channel and determine the time for the bubbles to detach from the electrode ***,it is very timeconsuming to get the optimal combination of base layer and degassing layer heights due to the large number of potential cases,which needs to be calculated through computation-intensive physical ***,machine learning methods are adopted to accelerate the optimization.A data-driven surrogate model based on deep neural network(DNN)is developed and successfully trained using data obtained by the physical VOF *** on the highly efficient surrogate,genetic algorithm(GA)is further utilized to determine the optimal heights of base layer and degassing ***,the reliability of the optimization was validated by bubble visualization in channel and electrochemical characterization in PEMWE through experiments.

关键词： PEMWE Dual-layer flow field data-driven surrogate model Machine learning

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-30°C cold start optimization of PEMFC based on a data-driven surrogate model and multi-objective optimization algorithm

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DIGITAL CHEMICAL ENGINEERING 2024年 10卷

作者： Zhang, Fan Zhang, Xiyuan Wang, Bowen Zhai, Haipeng Wu, Kangcheng Wang, Zixuan Bao, Zhiming Tian, Wanli Duan, Weikang Zu, Bingfeng Gong, Zhengwei Jiao, Kui Tianjin Univ State key Lab Engines 135 Yaguan Rd Tianjin 300350 Peoples R China Tianjin Univ Natl Ind Educ Platform Energy Storage 135 Yaguan Rd Tianjin 300350 Peoples R China Hainan Univ 58 Renmin Ave Haikou 570228 Peoples R China FTXT Energy Technol Co Ltd Fuel Cell Stack R&D Ctr 6655 Jiasong North Rd Shanghai 201800 Peoples R China Dongfeng Motor Coperat Technol Ctr Wuhan 430056 Peoples R China Tianjin Univ Internal Combust Engine Res Inst 92 Weijin Rd Tianjin 300072 Peoples R China

Cold start is a critical operating scenario for the proton exchange membrane fuel cell (PEMFC), particularly in the field of transportation. Under sub -freezing temperatures, the water inside the cell will freeze and obstruct gas flow paths as well as cover catalyst reaction sites, resulting in a failed startup. This study proposes an optimization method for the -30 degrees C cold start of PEMFC based on a data -driven surrogate model to improve cold start performance and reduce irreversible damage to the cell. A validated PEMFC cold start mechanism model is utilized as the basis for developing an extreme learning machine (ELM) based data -driven surrogate model, which is trained using data collected from the mechanism model and has higher computational efficiency compared with the original model. In addition, the NSGA-II multi -objective optimization algorithm is employed to optimize the current loading strategies and operating parameters using the surrogate model as fitness function. The objectives are to enhance the minimum voltage and reduce startup duration time. Moreover, experimental validation confirms the effectiveness of the proposed method. The test results demonstrate that a cold start from -30 degrees C is achieved within 97 s, with the minimum voltage reaching 0.44 V. Notably, there is a reduction in startup time by 26 s and an increase in the minimum voltage by 0.06 V compared to the base case. This study establishes a foundation for researchers to adjust operating settings during cold start based on diverse applications and requirements.

关键词： PEMFC Cold start Multi-objective optimization data-driven surrogate model

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Prediction and optimization of gas distribution quality for high-temperature PEMFC based on data-driven surrogate model

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APPLIED ENERGY 2022年 327卷

作者： Deng, Shutong Zhang, Jun Zhang, Caizhi Luo, Mengzhu Ni, Meng Li, Yu Zeng, Tao Chongqing Univ Chongqing Automot Collaborat Innovat Ctr Sch Automot Engn State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Hong Kong Polytech Univ Res Inst Sustainable Urban Dev RISUD Res Inst Smart Energy RISE Dept Bldg & Real EstateKowloon Hong Kong Peoples R China Chongqing Univ Sch Elect Engn Chongqing 400044 Peoples R China Chongqing Changan New Energy Vehicle Technol Co Lt Chongqing 400000 Peoples R China

Suitable operating conditions can improve the internal gas distribution of high-temperature proton exchange membrane fuel cell (HT-PEMFC), reduce the occurrence of local gas starvation, and thus prolong the lifespan. Therefore, it is important to investigate the gas distribution quality of HT-PEMFC based on quantitative in-dicators. In this paper, data-driven surrogate models are established based on the simulation results of a three-dimensional validated numerical model to study the gas distribution quality of HT-PEMFC via two qualitative evaluation indexes, the value of mean and standard deviation of the reactant gas concentration in catalyst layer. In order to obtain the surrogate model more efficiently and accurately, genetic algorithm optimized deep belief network (GA-DBN) and Autogluon (an automated machine learning method) are applied. Based on the surrogate models, the influence of the operating condition parameters on each evaluation index is revealed, which provides theoretical basis for the life-extension design of HT-PEMFC. Then, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), a Multi-Criteria Decision Aid (MCDA) method, is used to select the optimal operating conditions according to the two evaluation indexes of gas distribution quality from the extensive computational results of the surrogate models, which provides specific guidance for the life duration improve-ment of HT-PEMFC.

关键词： HT-PEMFC Gas distribution quality data-driven surrogate model TOPSIS

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Optimization of maximum power density output for proton exchange membrane fuel cell based on a data-driven surrogate model

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APPLIED ENERGY 2022年 317卷

作者： Feng, ShengSen Huang, WenTao Huang, Zhe Jian, Qifei South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Guangdong Peoples R China

Operating conditions are of great significance for proton exchange membrane fuel cells (PEMFCs) and directly determine the output performance of PEMFC. In this paper, an optimization framework is proposed, which is combining a neural network data-driven surrogate model and a stochastic optimization algorithm to achieve multi-variable global optimization, and optimizes the operating conditions for enhancing the power density of the PEMFC. A numerical model of the PEMFC is constructed as the source of the database, and the database is used to train a data-driven surrogate model based on radial basis functions (RBF), which is a typical feed-forward neural network. Then the surrogate model is fed into a particle swarm optimization (PSO) algorithm to obtain the optimal solution for the best combination of operating conditions. Results show that the surrogate model can accurately predict the output voltage of the PEMFC model, where the squared correction factor (R-square) and the mean percentage error of the test set are 0.99638 and 4.4686% respectively. And the optimization framework using a combination of data-driven surrogate model and stochastic optimization algorithm can obtain the maximum power density of 0.6097 W cm-2, with a relative error of only 1.5321% to the PEMFC model results. The result shows that the framework can effectively handle the multivariate optimization of complex systems.

关键词： PEMFC Operating conditions data-driven surrogate model Stochastic optimization algorithm

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Multi-physics-resolved digital twin of proton exchange membrane fuel cells with a data-driven surrogate model

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Energy and AI 2020年第1期1卷 22-34页

作者： Bowen Wang Guobin Zhang Huizhi Wang Jin Xuan Kui Jiao State Key Laboratory of Engines Tianjin University135 Yaguan RoadTianjin 300350China Department of Chemical Engineering Loughborough UniversityLoughboroughUK Department of Mechanical Engineering Imperial College LondonLondonUK

The development of multi-physics-resolved digital twins of proton exchange membrane fuel cells(PEMFCs)is sig-nificant for the advancement of this ***,to solve this scientific issue,a surrogate modelling method that combines a state-of-the-art three-dimensional PEMFC physical model and data-driven model is *** surrogate modelling prediction results demonstrate that the test-set relative root mean square errors(rRMSEs)of the multi-physics fields range from 3.88%to 24.80%and can mirror the multi-physics field distribution charac-teristics *** summary,for multi-physics field prediction,the data-driven surrogate model has a comparable accuracy to the comprehensive 3D physical model;however,it considerably reduces the cost of computation and time and achieves the efficient multi-physics-resolved *** model-based designs based on the as-developed digital twin framework,*** PEMFC healthy operation envelope and the PEMFC state map,are *** study highlights the potential of combining data-driven approaches and comprehensive physical models to develop the digital twin of complex systems,such as PEMFCs.

关键词： Proton exchange membrane fuel cell Digital twin data-driven surrogate model Three-dimensional physical model model-based design

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Optimization of porous media flow field for proton exchange membrane fuel cell using a data-driven surrogate model

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ENERGY CONVERSION AND MANAGEMENT 2020年 226卷 113513-113513页

作者： Zhang, Guobin Wu, Lizhen Jiao, Kui Tian, Pengjie Wang, Bowen Wang, Yun Liu, Zhi Tianjin Univ State Key Lab Engines 135 Yaguan Rd Tianjin 300350 Peoples R China Univ Calif Irvine Dept Mech & Aerosp Engn Renewable Energy Resources Lab Irvine CA 92697 USA

Flow field design plays a key role in proton exchange membrane (PEM) fuel cells due to its decisive influence on reactant gas transfer, liquid water discharge, electron conductance, and heat transfer. Porous media flow field (e. g. metal foam) shows promise to improve cell performance in the concentration polarization regime and to benefit uniform distributions of oxygen, liquid, current density, and temperature, etc. In this study, a data-driven surrogate model based on support vector machine (SVM) is applied to optimize porous media flow field geometry. The training data is obtained from a validated three-dimensional (3D), multi-phase non-isothermal model. For better characterization, the complex structure is simplified as a 3D structured mesh consisting of a set of fibers with each fiber perpendicular to each other at the joint. The results show that the data-driven surrogate model based on SVM predicts similar results as the 3D physical model. The genetic algorithm (GA) is further used for optimization, in which the data-driven surrogate model is selected as a fitness evaluation function. The optimal values obtained by the surrogate model are verified by the 3D physical model, indicating that the proposed data-driven surrogate model is effective in the design and optimization of porous media flow field.

关键词： PEM fuel cell Porous media flow field data-driven surrogate model Machine learning

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Predicting weld pool metrics in laser welding of aluminum alloys using data-driven surrogate modeling: A FEA-DoE-GPRN hybrid approach

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2024年

作者： Duggirala, Aparna Acherjee, Bappa Mitra, Souren Jadavpur Univ Sch Laser Sci & Engn Kolkata India Birla Inst Technol Dept Prod & Ind Engn Ranchi 835215 Jharkhand India Jadavpur Univ Dept Prod Engn Kolkata India

Multi-physics computational models based on finite element analysis, offer detailed insights into the dynamics and metrics in the weld pool formed by laser welding. Conversely, data-driven surrogate models provide a cost-effective means to predict desired responses. These models establish statistical or mathematical correlations with input-output data, eliminating the need for additional simulations during design optimization. This study proposes a data-driven surrogate model, employing the Gaussian process regression network (GPRN), to predict weld pool metrics, such as weld width and depth of penetration in laser welding of aluminum alloy. A 3D computational fluid dynamics-based numerical model is initially constructed and experimentally validated to predict weld pool metrics. Subsequent experimental runs, guided by the design of experiments, include various configurations of process parameter settings. The developed numerical model computes weld pool metrics for each experimental run, forming a dataset for training and testing the GPRN model. The GPRN model is evaluated against simulated data, showing adequacy with a mean square error of 1.7 mu m and mean absolute percentage error of 10-7, with experimental validation further confirming its accuracy, revealing a minimum error of 1.7%, a maximum error of 8%, and an average error of 3%. The key contribution and novelty of this study lie in the development of the hybrid data-driven model, which accurately predicts weld pool metrics while minimizing experimental and computational efforts.

关键词： Gaussian process regression numerical simulation laser welding aluminum alloy data-driven surrogate model

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Hybrid control of hydraulic directional valves: Integrating physics-based and data-driven models for enhanced accuracy and efficiency

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ISA TRANSACTIONS 2025年 157卷 280-292页

作者： Glueck, Tobias Lobe, Amadeus Trachte, Adrian Bitzer, Matthias Kemmetmueller, Wolfgang AIT Austrian Inst Technol GmbH Ctr Vis Automat & Control Giefinggasse 4 A-1210 Vienna Austria Robert Bosch GmbH Robert Bosch Campus 1 D-71272 Renningen Germany Tech Univ Wien Automat & Control Inst Gusshausstr 27-29 A-1040 Vienna Austria

In this paper, we tackle the challenge of accurately controlling the position of the valve spool in hydraulic 4/3 two-stage directional control valves utilized in mobile applications. The pilot valve's overlapping design often leads to a significant dead zone, negatively impacting positioning accuracy and necessitating a sophisticated controller design. To overcome these challenges, we introduce a control strategy founded on a control-oriented model. This model enables systematic compensation for the dead zone, pressure-induced flow fluctuations, and the solenoid's nonlinearities, optimizing the valve's operation for enhanced tracking performance, as verified by test bench measurements. Addressing the limitations inherent in traditional physics-based design methodologies, we suggest approximating the system's primary nonlinearities with a data-driven surrogate model. We propose a solution tailored for systems that rely on minimal sensor information. By merging the advantages of both physics-based and data-driven models, we formulate a hybrid control strategy. This comprehensive approach not only ensures high tracking performance but also has the potential to expedite the commissioning process for new valve variants.

关键词： 4/3 two-stage directional control valve Hybrid control Dead zone compensation data-driven surrogate model

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Bottom-up approaches for rapid on-demand design of modular metaporous structures with tailored absorption

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2024年 263卷

作者： Pan, Baorui Song, Xiang Xu, Jingjian Zhou, Jie Sui, Dan Shui, Yunfei Zhang, Zhisheng Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China China North Ind Grp Xian Modern Control Technol Res Inst Xian 710065 Peoples R China

As a new trend in the studies on sound-absorbing structures design, customizable metaporous structures (MPSs) have drawn a great deal of interest due to their distinct advantages, which include designable broadband absorption, light weight, and low thickness. Conventional finite element-based design methods, such as trial-and-error and iterative optimization, are inefficient and expensive in terms of computational resources. Inspired by the widely used bottom-up method, in this study, we develop a data-driven metaheuristic optimization framework to accomplish the rapid on-demand design for a novel modular metaporous structure (MMPS) of only 30 mm thickness from the local to the global level. Two cascade variants of the genetic algorithm (GA) are employed for local optimization and global customized design, accordingly. The local design level involves the development of a surrogate-assisted genetic algorithm (SAGA) for design space optimization. On the basis of the structural dataset, a well-tuned convolutional neural network (CNN) model is developed for expediting the forward prediction of the acoustic properties of the two-dimensional (2D) MPS. In the subsequent phase, the validated CNN model is combined with SAGA to expedite the fitness evaluation for optimizing the structural units. At the global design stage, the developed SAGA is coupled with the population initialization process of an elitist genetic algorithm (EGA) to achieve inverse design for the MMPS with customizable broadband absorption based on the optimized design space. Furthermore, an acoustic measurement is performed on the fabricated MMPS samples to validate the finite element method (FEM) simulation and impedance design method. The utilization of this optimization method opens up fresh possibilities not solely for the customized design of sound-absorbing structures, but also for optimization processes in various other domains. Meanwhile, the proposed MMPS with customizable broadband absorption in

关键词： Modular metaporous structure Bottom-up method On-demand design data-driven surrogate model Metaheuristic algorithm

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