{"title":"联合收割机v带动态能耗建模","authors":"Baoyan Xu, Yehong Liu, Yining Fu, Xin Wang","doi":"10.1016/j.inpa.2026.01.016","DOIUrl":null,"url":null,"abstract":"<div><div>V-belts in combine harvesters are prone to burnout failures under high-load continuous operations, compromising transmission reliability. However, the underlying thermomechanical coupling mechanisms remain poorly understood. To address this limitation, this study presents a dynamic energy dissipation model that decouples multiple heat generation mechanisms—bending hysteresis, compressive deformation, tensile strain, and external sliding friction—in V-belt drives. Finite element analysis is used to characterize the spatiotemporal loading behavior of internal friction sources, and energy dissipation quantification methods are developed based on viscoelastic theory and Coulomb friction law. A steady-state heat transfer framework is established, and critical thermal boundary parameters are identified through inverse modeling. The integrated thermomechanical model is validated by comparing simulated surface temperature fields with experimental data obtained from a custom-built test rig. The results show that predicted steady-state temperatures are marginally overestimated, with relative errors below 5%, exhibiting clear dependence on driving pulley speed and driven pulley load. The analysis reveals that high-speed operation intensifies internal hysteresis heating, whereas high-load conditions amplify external friction losses—synergistically leading to elevated thermal stress and increased burnout risk. Thus, the study elucidates the failure mechanisms under combined dynamic loads, enabling physics-driven design optimization and predictive maintenance for agricultural machinery powertrains.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 3","pages":"Pages 433-450"},"PeriodicalIF":8.9000,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic energy consumption modeling of V-belt in combine harvesters\",\"authors\":\"Baoyan Xu, Yehong Liu, Yining Fu, Xin Wang\",\"doi\":\"10.1016/j.inpa.2026.01.016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>V-belts in combine harvesters are prone to burnout failures under high-load continuous operations, compromising transmission reliability. However, the underlying thermomechanical coupling mechanisms remain poorly understood. To address this limitation, this study presents a dynamic energy dissipation model that decouples multiple heat generation mechanisms—bending hysteresis, compressive deformation, tensile strain, and external sliding friction—in V-belt drives. Finite element analysis is used to characterize the spatiotemporal loading behavior of internal friction sources, and energy dissipation quantification methods are developed based on viscoelastic theory and Coulomb friction law. A steady-state heat transfer framework is established, and critical thermal boundary parameters are identified through inverse modeling. The integrated thermomechanical model is validated by comparing simulated surface temperature fields with experimental data obtained from a custom-built test rig. The results show that predicted steady-state temperatures are marginally overestimated, with relative errors below 5%, exhibiting clear dependence on driving pulley speed and driven pulley load. The analysis reveals that high-speed operation intensifies internal hysteresis heating, whereas high-load conditions amplify external friction losses—synergistically leading to elevated thermal stress and increased burnout risk. Thus, the study elucidates the failure mechanisms under combined dynamic loads, enabling physics-driven design optimization and predictive maintenance for agricultural machinery powertrains.</div></div>\",\"PeriodicalId\":53443,\"journal\":{\"name\":\"Information Processing in Agriculture\",\"volume\":\"13 3\",\"pages\":\"Pages 433-450\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2026-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Processing in Agriculture\",\"FirstCategoryId\":\"1091\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214317326000168\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2026/1/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317326000168","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/1/30 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
Dynamic energy consumption modeling of V-belt in combine harvesters
V-belts in combine harvesters are prone to burnout failures under high-load continuous operations, compromising transmission reliability. However, the underlying thermomechanical coupling mechanisms remain poorly understood. To address this limitation, this study presents a dynamic energy dissipation model that decouples multiple heat generation mechanisms—bending hysteresis, compressive deformation, tensile strain, and external sliding friction—in V-belt drives. Finite element analysis is used to characterize the spatiotemporal loading behavior of internal friction sources, and energy dissipation quantification methods are developed based on viscoelastic theory and Coulomb friction law. A steady-state heat transfer framework is established, and critical thermal boundary parameters are identified through inverse modeling. The integrated thermomechanical model is validated by comparing simulated surface temperature fields with experimental data obtained from a custom-built test rig. The results show that predicted steady-state temperatures are marginally overestimated, with relative errors below 5%, exhibiting clear dependence on driving pulley speed and driven pulley load. The analysis reveals that high-speed operation intensifies internal hysteresis heating, whereas high-load conditions amplify external friction losses—synergistically leading to elevated thermal stress and increased burnout risk. Thus, the study elucidates the failure mechanisms under combined dynamic loads, enabling physics-driven design optimization and predictive maintenance for agricultural machinery powertrains.
期刊介绍:
Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining