Ahmed Zeeshan, Dilawar Hussain, Zaheer Asghar, Muhammad Mubashir Bhatti, Faisal Z. Duraihem
{"title":"Thermal optimization of MHD nanofluid over a wedge by using response surface methodology: Sensitivity analysis","authors":"Ahmed Zeeshan, Dilawar Hussain, Zaheer Asghar, Muhammad Mubashir Bhatti, Faisal Z. Duraihem","doi":"10.1016/j.jppr.2023.10.003","DOIUrl":null,"url":null,"abstract":"<p>It is well documented that heat transfer is enhanced with addition of nanosized particles in fluid. But, in a mechanical system there are variety of factors influences the heat transfer. Some factors are significant while others are not. In this paper, authors will discuss sensitivity of different input parameters such as <em>Le</em>, <em>Nt</em> and <em>Nb</em> on output responses <span><math><mrow is=\"true\"><msub is=\"true\"><mrow is=\"true\"><mi is=\"true\">N</mi><mi is=\"true\">u</mi></mrow><mi is=\"true\">x</mi></msub></mrow></math></span> and <span><math><mrow is=\"true\"><msub is=\"true\"><mrow is=\"true\"><mi is=\"true\">S</mi><mi is=\"true\">h</mi></mrow><mi is=\"true\">x</mi></msub></mrow></math></span>. To achieve this goal, the problem is modeled using basic conservation laws. The formulated model is a set of PDEs, which are converted to set of non-linear ODEs by using similarity transformation. Then these ODEs are solved numerically by using MATLAB built in package bvp4c and compared the numerical results with existing work and found good results. Sensitivity analysis is performed by employing RSM to determine the relationship between the input parameters such that <span><math><mrow is=\"true\"><mn is=\"true\">0.1</mn><mrow is=\"true\"><mo is=\"true\">≤</mo><mi is=\"true\">L</mi><mi is=\"true\">e</mi><mo is=\"true\" linebreak=\"goodbreak\" linebreakstyle=\"after\">≤</mo></mrow><mn is=\"true\">1</mn></mrow></math></span>, <span><math><mrow is=\"true\"><mn is=\"true\">0.1</mn><mrow is=\"true\"><mo is=\"true\">≤</mo><mi is=\"true\">N</mi><mi is=\"true\">t</mi><mo is=\"true\" linebreak=\"goodbreak\" linebreakstyle=\"after\">≤</mo></mrow><mn is=\"true\">1</mn></mrow></math></span> and <span><math><mrow is=\"true\"><mn is=\"true\">0.1</mn><mrow is=\"true\"><mo is=\"true\">≤</mo><mi is=\"true\">N</mi><mi is=\"true\">b</mi><mo is=\"true\" linebreak=\"goodbreak\" linebreakstyle=\"after\">≤</mo></mrow><mn is=\"true\">1</mn></mrow></math></span> and the output responses (<span><math><mrow is=\"true\"><msub is=\"true\"><mrow is=\"true\"><mi is=\"true\">N</mi><mi is=\"true\">u</mi></mrow><mi is=\"true\">x</mi></msub></mrow></math></span> and <span><math><mrow is=\"true\"><msub is=\"true\"><mrow is=\"true\"><mi is=\"true\">S</mi><mi is=\"true\">h</mi></mrow><mi is=\"true\">x</mi></msub></mrow></math></span>). ANOVA tables are generated by using RSM. By using the ANOVA tables the correlations between input parameters and output response are developed. To check the validity of correlated equations, the residuals are plotted graphically and show best correlations between input parameters and output responses. The high values of <span><math><mrow is=\"true\"><msup is=\"true\"><mi is=\"true\">R</mi><mn is=\"true\">2</mn></msup><mrow is=\"true\"><mo is=\"true\">=</mo><mn is=\"true\">98.65</mn></mrow></mrow></math></span> and <span><math><mrow is=\"true\"><mtext is=\"true\">Adj</mtext><msup is=\"true\"><mi is=\"true\">R</mi><mn is=\"true\">2</mn></msup><mrow is=\"true\"><mo is=\"true\">=</mo><mn is=\"true\">97.43</mn></mrow></mrow></math></span> for <span><math><mrow is=\"true\"><msub is=\"true\"><mrow is=\"true\"><mi is=\"true\">N</mi><mi is=\"true\">u</mi></mrow><mi is=\"true\">x</mi></msub></mrow></math></span> and <span><math><mrow is=\"true\"><msup is=\"true\"><mi is=\"true\">R</mi><mn is=\"true\">2</mn></msup><mrow is=\"true\"><mo is=\"true\">=</mo><mn is=\"true\">97.83</mn></mrow></mrow></math></span> and <span><math><mrow is=\"true\"><mtext is=\"true\">Adj</mtext><msup is=\"true\"><mi is=\"true\">R</mi><mn is=\"true\">2</mn></msup><mrow is=\"true\"><mo is=\"true\">=</mo><mn is=\"true\">95.88</mn></mrow></mrow></math></span> for <span><math><mrow is=\"true\"><msub is=\"true\"><mrow is=\"true\"><mi is=\"true\">S</mi><mi is=\"true\">h</mi></mrow><mi is=\"true\">x</mi></msub></mrow></math></span> demonstrates the high validity of ANOVA results to perform sensitivity analysis. Finally, we have conducted a sensitivity analysis of the responses and came to the important results that <em>Nt</em> and <em>Nb</em> is most sensitive to Nusselt number and Sherwood number respectively.</p>","PeriodicalId":51341,"journal":{"name":"Propulsion and Power Research","volume":"1 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Propulsion and Power Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.jppr.2023.10.003","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
引用次数: 0
Abstract
It is well documented that heat transfer is enhanced with addition of nanosized particles in fluid. But, in a mechanical system there are variety of factors influences the heat transfer. Some factors are significant while others are not. In this paper, authors will discuss sensitivity of different input parameters such as Le, Nt and Nb on output responses and . To achieve this goal, the problem is modeled using basic conservation laws. The formulated model is a set of PDEs, which are converted to set of non-linear ODEs by using similarity transformation. Then these ODEs are solved numerically by using MATLAB built in package bvp4c and compared the numerical results with existing work and found good results. Sensitivity analysis is performed by employing RSM to determine the relationship between the input parameters such that , and and the output responses ( and ). ANOVA tables are generated by using RSM. By using the ANOVA tables the correlations between input parameters and output response are developed. To check the validity of correlated equations, the residuals are plotted graphically and show best correlations between input parameters and output responses. The high values of and for and and for demonstrates the high validity of ANOVA results to perform sensitivity analysis. Finally, we have conducted a sensitivity analysis of the responses and came to the important results that Nt and Nb is most sensitive to Nusselt number and Sherwood number respectively.
期刊介绍:
Propulsion and Power Research is a peer reviewed scientific journal in English established in 2012. The Journals publishes high quality original research articles and general reviews in fundamental research aspects of aeronautics/astronautics propulsion and power engineering, including, but not limited to, system, fluid mechanics, heat transfer, combustion, vibration and acoustics, solid mechanics and dynamics, control and so on. The journal serves as a platform for academic exchange by experts, scholars and researchers in these fields.