{"title":"Numerical simulation for heat optimization via nanofluid in the presence of activation energy: A case of dust particles","authors":"Bilal Ahmad , Bagh Ali , Muhammad Ozair Ahmed","doi":"10.1016/j.ijft.2025.101248","DOIUrl":null,"url":null,"abstract":"<div><div>Researchers and manufacturers’ primary focus is on the dissipation of energy throughout the heat transfer process. The use of traditional fluids, which have poor heat transfer qualities, was the primary cause of the inefficiency of heat exchange devices during transportation. Conversely, when we replaced the fluids with nanofluids that possessed favorable thermal conductivity qualities, thermal devices performed better. We utilized a variety of nanoparticles due to their high heat conductivity. This study examines the importance of using nanofluid in flow of heat transfer. The model of flow consisted of partial differential equations (PDEs) representing equations for concentration, momentum, energy transmission, and continuity. We transformed the generated model into ordinary differential equations (ODEs) using feasible analogies. The MATLAB environment was used to perform numerical simulations that established the profiles of concentration, velocity, and thermal transfer. We also evaluated the effects of a wide range of factors, including Deborah, Hartman, buoyancy, the size of an external heat source, and other chemical reactions. Nanoparticles increase thermal conductivity. We also juxtapose the results with those from previously published studies. Furthermore, as the Nusselt number and skin friction increase, they exhibit a positive correlation with the variables linked to the Hartman number and buoyancy parameter. The heat transfer rates are 29.26%. 37.12 In the order mentioned, as a result, heat transmission rates increased by 14.23%. There is no text provided. At higher levels of the MHD fluid parameter, the temperature profiles dropped and the velocity profiles rose. The temperature profile rises as the external heat source gets stronger. On the contrary, the buoyancy parameters rise as it goes down. This topic is relevant in various domains, including heat exchangers, electronic device cooling, and automotive cooling systems.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"28 ","pages":"Article 101248"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Thermofluids","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666202725001958","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Chemical Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
Researchers and manufacturers’ primary focus is on the dissipation of energy throughout the heat transfer process. The use of traditional fluids, which have poor heat transfer qualities, was the primary cause of the inefficiency of heat exchange devices during transportation. Conversely, when we replaced the fluids with nanofluids that possessed favorable thermal conductivity qualities, thermal devices performed better. We utilized a variety of nanoparticles due to their high heat conductivity. This study examines the importance of using nanofluid in flow of heat transfer. The model of flow consisted of partial differential equations (PDEs) representing equations for concentration, momentum, energy transmission, and continuity. We transformed the generated model into ordinary differential equations (ODEs) using feasible analogies. The MATLAB environment was used to perform numerical simulations that established the profiles of concentration, velocity, and thermal transfer. We also evaluated the effects of a wide range of factors, including Deborah, Hartman, buoyancy, the size of an external heat source, and other chemical reactions. Nanoparticles increase thermal conductivity. We also juxtapose the results with those from previously published studies. Furthermore, as the Nusselt number and skin friction increase, they exhibit a positive correlation with the variables linked to the Hartman number and buoyancy parameter. The heat transfer rates are 29.26%. 37.12 In the order mentioned, as a result, heat transmission rates increased by 14.23%. There is no text provided. At higher levels of the MHD fluid parameter, the temperature profiles dropped and the velocity profiles rose. The temperature profile rises as the external heat source gets stronger. On the contrary, the buoyancy parameters rise as it goes down. This topic is relevant in various domains, including heat exchangers, electronic device cooling, and automotive cooling systems.