Prediction of gas–solid erosion wear of bionic surfaces based on machine learning and unimodal intelligent optimization algorithm
•A new smooth and bionic surface erosion test dataset is established.•Visualization and analysis of the optimal use environment for each bionic surface.•A new gas–solid erosion wear prediction model (IHHO-SVR) was established. Solid particle erosion wear is an inevitable phenomenon in industrial pro...
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| Veröffentlicht in: | Engineering failure analysis Jg. 163; S. 108453 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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01.09.2024
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| ISSN: | 1350-6307 |
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| Abstract | •A new smooth and bionic surface erosion test dataset is established.•Visualization and analysis of the optimal use environment for each bionic surface.•A new gas–solid erosion wear prediction model (IHHO-SVR) was established.
Solid particle erosion wear is an inevitable phenomenon in industrial production, with erosion removal mass serving as a crucial metric for assessing the wear rate per unit area on the impacted surface. Developing predictive models to estimate the degree of mass removal is crucial for effectively controlling, evaluating, and preventing severe damage resulting from solid particle erosion wear. In this study, we constructed a comprehensive dataset comprising smooth and bionic surfaces, encompassing inner, outer, and planar surfaces. The dataset was used in a multifactorial erosion experiment, considering adjustable erosion angles, solid particle incident gas velocities, and solid particle diameter sizes. Through visualization and analysis of the obtained dataset, we identified optimal scenarios for bionic surface erosion resistance, offering insights for structural design against bionic erosion resistance. Furthermore, we compared machine learning algorithms to address the prediction problem, resulting in the selection of the best-performing regression algorithm, SVR (Support Vector Regression). Additionally, we compared the performance of other advanced intelligent optimization algorithms using unimodal benchmark functions, finding that HHO (Harris Hawks Optimization) emerged as the optimal choice for unimodal optimization. Building on HHO, we developed the IHHO (Improved Harris Hawks Optimization)-SVR model, using experimental data from erosion tests as the training dataset. This model can predict gas–solid two-phase flow erosion patterns, encompassing various wall types, solid particle sizes, solid incident gas velocities, and impact angles. Due to its robustness and rapid prediction capabilities, the model is expected to serve as a cost-effective tool for predicting erosion removal mass in gas–solid two-phase flow scenarios. |
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| AbstractList | •A new smooth and bionic surface erosion test dataset is established.•Visualization and analysis of the optimal use environment for each bionic surface.•A new gas–solid erosion wear prediction model (IHHO-SVR) was established.
Solid particle erosion wear is an inevitable phenomenon in industrial production, with erosion removal mass serving as a crucial metric for assessing the wear rate per unit area on the impacted surface. Developing predictive models to estimate the degree of mass removal is crucial for effectively controlling, evaluating, and preventing severe damage resulting from solid particle erosion wear. In this study, we constructed a comprehensive dataset comprising smooth and bionic surfaces, encompassing inner, outer, and planar surfaces. The dataset was used in a multifactorial erosion experiment, considering adjustable erosion angles, solid particle incident gas velocities, and solid particle diameter sizes. Through visualization and analysis of the obtained dataset, we identified optimal scenarios for bionic surface erosion resistance, offering insights for structural design against bionic erosion resistance. Furthermore, we compared machine learning algorithms to address the prediction problem, resulting in the selection of the best-performing regression algorithm, SVR (Support Vector Regression). Additionally, we compared the performance of other advanced intelligent optimization algorithms using unimodal benchmark functions, finding that HHO (Harris Hawks Optimization) emerged as the optimal choice for unimodal optimization. Building on HHO, we developed the IHHO (Improved Harris Hawks Optimization)-SVR model, using experimental data from erosion tests as the training dataset. This model can predict gas–solid two-phase flow erosion patterns, encompassing various wall types, solid particle sizes, solid incident gas velocities, and impact angles. Due to its robustness and rapid prediction capabilities, the model is expected to serve as a cost-effective tool for predicting erosion removal mass in gas–solid two-phase flow scenarios. |
| ArticleNumber | 108453 |
| Author | Han, Zhiwu Zhang, Shuaijun Zhang, Junqiu Yu, Haiyue Liu, Haonan |
| Author_xml | – sequence: 1 givenname: Haiyue orcidid: 0000-0002-3543-5886 surname: Yu fullname: Yu, Haiyue email: y293541687@126.com organization: School of Mechanical Engineering, Liaoning Technical University, Fuxin 123000, China – sequence: 2 givenname: Haonan surname: Liu fullname: Liu, Haonan organization: School of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, China – sequence: 3 givenname: Shuaijun surname: Zhang fullname: Zhang, Shuaijun organization: State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China – sequence: 4 givenname: Junqiu surname: Zhang fullname: Zhang, Junqiu email: junqiuzhang@jlu.edu.cn organization: Key Laboratory of Bionic Engineering of Ministry of Education, Jilin University, Changchun 130022, China – sequence: 5 givenname: Zhiwu surname: Han fullname: Han, Zhiwu organization: Key Laboratory of Bionic Engineering of Ministry of Education, Jilin University, Changchun 130022, China |
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| Keywords | Bionic surface Data visual analysis Support vector regression Erosion wear Improved Harris Hawks optimization |
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Eng. doi: 10.1016/j.petrol.2022.111042 – start-page: 41 year: 2015 ident: 10.1016/j.engfailanal.2024.108453_b0060 article-title: Sliding wear and solid-particle erosion resistance of a novel high-tungsten Stellite alloy publication-title: Wear doi: 10.1016/j.wear.2014.10.012 – volume: 376 start-page: 95 year: 2020 ident: 10.1016/j.engfailanal.2024.108453_b0115 article-title: Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.09.074 – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.engfailanal.2024.108453_b0130 article-title: The whale optimization algorithm publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2016.01.008 – volume: 364 start-page: 785 year: 2020 ident: 10.1016/j.engfailanal.2024.108453_b0030 article-title: Effect of superficial air and water velocities on the erosion of horizontal elbow in slug flow publication-title: Powder Technol. doi: 10.1016/j.powtec.2020.01.067 – start-page: 983 year: 2018 ident: 10.1016/j.engfailanal.2024.108453_b0080 article-title: Random forest regression prediction of solid particle Erosion in elbows publication-title: Powder Technol. doi: 10.1016/j.powtec.2018.07.055 – start-page: 850 year: 2014 ident: 10.1016/j.engfailanal.2024.108453_b0010 article-title: A comprehensive review of solid particle erosion modeling for oil and gas wells and pipelines applications publication-title: J. Nat. Gas Sci. Eng. doi: 10.1016/j.jngse.2014.10.001 – start-page: 87 year: 2014 ident: 10.1016/j.engfailanal.2024.108453_b0015 article-title: Effect of impact angle and testing time on erosion of stainless steel at higher velocities publication-title: Wear doi: 10.1016/j.wear.2014.10.010 – volume: 43 start-page: 257 issue: 07 year: 2022 ident: 10.1016/j.engfailanal.2024.108453_b0025 article-title: Numerical research on erosion and wear of wind turbine blades in sand-carrying wind publication-title: Acta Energiae Solaris Sinica – volume: 8 issue: 1 year: 2020 ident: 10.1016/j.engfailanal.2024.108453_b0140 article-title: A novel swarm intelligence optimization approach: sparrow search algorithm publication-title: Syst. Sci. Control Eng. – volume: 21 start-page: 307 issue: 2 year: 2003 ident: 10.1016/j.engfailanal.2024.108453_b0035 article-title: Developments of reserch on the solid particle erosion of materials publication-title: J. Mater. Sci. Eng |
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| Title | Prediction of gas–solid erosion wear of bionic surfaces based on machine learning and unimodal intelligent optimization algorithm |
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