Journal of Thermal Analysis and Calorimetry最新文献

{"title":"Thermodynamics of Cattaneo–Christov heat flux theory on hybrid nanofluid flow with variable viscosity, convective boundary, and velocity slip","authors":"Nahid Fatima,&nbsp;Refka Ghodhbani,&nbsp;Aaqib Majeed,&nbsp;Nouman Ijaz,&nbsp;Najma Saleem","doi":"10.1007/s10973-024-13833-x","DOIUrl":"10.1007/s10973-024-13833-x","url":null,"abstract":"<div><p>This work aims to analyze the behavior of the Cattaneo–Christov heat flux theory on hybrid nanofluid flow, and the heat transportation that occurs across a stretchable porous space. In this study, energy equation incorporates the combined effects of thermal radiation and Cattaneo–Christov heat flux. Because of their potential uses in various domains, hybrid nanofluids—a more sophisticated type of nanofluids recognized for their improved thermal properties—are being studied. A two-dimensional hybrid nanofluid system with copper (Cu) and alumina oxide (AlO₂) nanoparticles distributed throughout a base fluid of water (H₂O) is described by the mathematical model created here. The study includes other elements like viscous dissipation and changing viscosity. Similarity transformations are used to turn the governing partial differential equations (PDEs) into ordinary differential equations, which are then numerically solved using a shooting approach and the MATLAB Bvp4c solver. The impact of crucial parameters on velocity and temperature profiles is carefully investigated in this study. These parameters include the Weissenberg number, magnetic parameter relaxation time, Prandtl number, thermal radiation parameter, velocity slip parameter, Biot number, convection parameter, suction parameter, heat source parameter, and Eckert number. In comparison to conventional nanofluids, the hybrid nanofluid's temperature profile shows a notable rise, according to the data. In certain circumstances, the results also closely match existing solutions. By outperforming traditional nanofluids, this discovery holds promise for improving the performance of industrial heat exchangers, automobile radiators, and electrical gadgets.</p></div>","PeriodicalId":678,"journal":{"name":"Journal of Thermal Analysis and Calorimetry","volume":"150 1","pages":"759 - 769"},"PeriodicalIF":3.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D thermally laminated MHD non-Newtonian nanofluids across a stretched sheet: intelligent computing paradigm","authors":"Hafiz Muhammad Shahbaz,&nbsp;Iftikhar Ahmad,&nbsp;Muhammad Asif Zahoor Raja,&nbsp;Hira Ilyas,&nbsp;Kottakkaran Sooppy Nisar,&nbsp;Muhammad Shoaib","doi":"10.1007/s10973-024-13747-8","DOIUrl":"10.1007/s10973-024-13747-8","url":null,"abstract":"<div><p>The primary subject of this article is the study of the viscous flow of nanofluids consisting of copper-methanol and water in the presence of a three-dimensional stretched sheet, which is subjected to magnetohydrodynamic effects (3D-MHD-NF) by employing artificial recurrent neural networks that are optimized using a Bayesian regularization technique (ARNN-BR). The viscosity effect is recognized to be dependent on temperature, with methanol and water being used as the base fluid. The presented model is employed in the manipulation and creation of surfaces within the field of nanotechnology. Its applications include stretching, shrinking, wrapping, and painting devices. The Adams method was employed to generate a dataset for the 3D-MHD-NF model for four scenarios by varying the Hartmann number (<i>H</i>), volume fraction of nanoparticle (<span>(varphi)</span>), and viscosity parameter (<i>α</i>). The ARNN-BR technique employed a random selection of data 70% for training, 20% for testing, and 10% for validity. It has been found that boundary layer becomes thinner as the volume percentage of nanoparticle increases. Additionally, it is observed that augmentation in the viscosity parameter results in a proportional rise in temperature. Moreover, it is observed that increment in the variables <i>H</i>, <i>φ</i>, and <i>α</i> have an impact on the velocity boundary thickness in both the <i>x-</i> and <i>y</i>-directions. The newly introduced ARNN-BR technique's dependability, stability, and convergence were assessed using a fitness measure based on mean squares errors, histogram drawings, regression, input-error cross-correlation, and autocorrelation analysis for each scenario of the 3D-MHD-NF model.</p></div>","PeriodicalId":678,"journal":{"name":"Journal of Thermal Analysis and Calorimetry","volume":"150 1","pages":"479 - 504"},"PeriodicalIF":3.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental studies and machine learning approaches for thermal parameters prediction and data analysis in closed-loop pulsating heat pipes with Al2O3-DI water nanofluid","authors":"Kamlesh Parmar,&nbsp;Nirmal Parmar,&nbsp;Ajit Kumar Parwani,&nbsp;Sumit Tripathi","doi":"10.1007/s10973-024-13859-1","DOIUrl":"10.1007/s10973-024-13859-1","url":null,"abstract":"<div><p>A closed-loop pulsating heat pipe (CLPHP) can provide effective and adaptable thermal solutions for various applications. This work presents extensive experimental studies on CLPHP to enhance thermal performance using nanofluid. The experimental studies are conducted using two different heat transfer fluids: deionized (DI) water and a nanofluid (Al₂O₃-DI water with 0.1 mass/% nanoparticles). Parametric studies are performed with different combinations of filling ratios (FR) and heat input values. To analyze the experimental data, an in-house Python library named PyPulseHeatPipe is developed, which facilitates statistical analysis, data visualization, and process data for machine learning from raw experimental data. Furthermore, the experimental datasets are used to train various machine learning (ML) models, including random forest regressor (RFR), extreme gradient boosting regressor, gradient boosting regressor, support vector machine, and K-nearest neighbors (KNN) to determine the thermal parameters for a given CLPHP. These models precisely predict the thermal performance of CLPHP using two novel approaches. The first approach predicts thermal resistance under given thermal properties such as evaporator temperature, pressure, FR, heat input, and heat transfer fluid, while the second approach predicts thermal parameters such as evaporator temperature, pressure, and heat input to achieve the desired thermal resistance. For the first approach, the RFR model performs the best among the trained ML models, with the lowest root mean square error (RMSE) of 0.0175 and the highest goodness of fit, with <i>R</i>² score and <i>R</i>²-adjusted (<i>R</i>²-adj.) of 0.9873 and 0.9872, respectively. For the second approach, the KNN model achieves the highest goodness of fit (<i>R</i>²-adj.) for evaporator temperature, pressure, and heat input values of around 0.9889, 0.9524, and 0.8149, respectively. This study establishes a foundation for the more efficient thermal design of CLPHP in various engineering systems by integrating experimental research with data-driven solutions through ML.</p></div>","PeriodicalId":678,"journal":{"name":"Journal of Thermal Analysis and Calorimetry","volume":"150 1","pages":"591 - 606"},"PeriodicalIF":3.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of exergy in a dimple-roughened solar thermal collector using MATLAB simulation","authors":"Raj Kumar,&nbsp;Muneesh Sethi,&nbsp;Abhishek Thakur,&nbsp;Adit Rana,&nbsp;Varun Goel,&nbsp;Daeho Lee,&nbsp;Tej Singh","doi":"10.1007/s10973-024-13707-2","DOIUrl":"10.1007/s10973-024-13707-2","url":null,"abstract":"<div><p>In the current study, the performance of a dimple-roughened solar thermal collector (DRSTC) is investigated within a (<span>({text{Re}}_{text{xx}})</span>) range that spans from 3000 to 48,000. Under constant solar intensity (<span>({I}_{text{sr}})</span>=1000 <span>({text{Wm}}^{-2})</span>), relative roughness height (<span>({e}_{text{d}}/{D}_{text{h}})</span>) varied from 0.021 to 0.036, relative roughness pitch (<span>(p/{e}_{text{d}})</span>) from 10 to 20, arc angle (<span>({alpha }_{text{a}})</span>) from 45 to 60°, and temperature rise parameter from 0.003 to 0.02, and the proposed model predicts exergy efficiency of the SAH, and the results obtained can be used as reference for the design of new solar thermal systems. The assessment makes use of advanced MATLAB simulations in order to evaluate the exergetic efficiency <span>({(}eta_{{{text{ex}}}} ))</span> of a DRSTC. At lower <span>({text{Re}}_{text{xx}})</span> values, <span>({(}eta_{{{text{ex}}}} ))</span> increases uniformly; however, stabilization and decline occur at higher <span>({text{Re}}_{text{xx}})</span> values. The highest <span>({(}eta_{{{text{ex}}}} ))</span> for the DRSTC is 1.47% under a temperature rise parameter <span>((Delta T/I_{{{text{sr}}}} ))</span> of 0.0071 <span>({text{Km}}^{2}{text{W}}^{-1})</span> for obtaining optimum values of <span>({e}_{text{d}}/{D}_{text{h}})</span> = 0.036, <span>(p/{e}_{text{d}})</span> = 10, and <span>(alpha_{a})</span> = 60°. This research illustrates the usefulness of MATLAB for solar energy system analysis and optimization by integrating simulation and experimental data. This investigation further supports the feasibility of the proposed collector design.</p></div>","PeriodicalId":678,"journal":{"name":"Journal of Thermal Analysis and Calorimetry","volume":"150 1","pages":"433 - 449"},"PeriodicalIF":3.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10973-024-13707-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scrutinization of local thermal non-equilibrium effects on stagnation point flow of hybrid nanofluid containing gyrotactic microorganisms: a bio-fuel cells and bio-microsystem technology application","authors":"Mostafa Mohamed Okasha,&nbsp;Munawar Abbas,&nbsp;Shoira Formanova,&nbsp;Zeshan Faiz,&nbsp;Ali Hasan Ali,&nbsp;Ali Akgül,&nbsp;Ibrahim Mahariq,&nbsp;Ahmed M. Galal","doi":"10.1007/s10973-024-13828-8","DOIUrl":"10.1007/s10973-024-13828-8","url":null,"abstract":"<div><p>The impact of Stefan blowing on the stagnation point flow of HNF (hybrid nanofluid) across a sheet containing gyrotactic microorganisms under local thermal non-equilibrium conditions (LTNECs) is briefly discussed in this paper. The present work uses a simplified mathematical model to inspect the characteristics of heat transfer in the absence of LTNECs (local thermal equilibrium conditions). LTNECs, traditionally provide two distinct fundamental temperature gradients for the liquid and solid phases simultaneously. A hybrid nanofluid is a mixture of water as the base fluid and single-walled carbon nanotubes and multi-walled carbon nanotubes . Gyrotactic microorganisms are included into nanoparticles to increase their thermal efficiency in a variety of systems, including microbial fuel cells, enzyme biosensors, bacteria powered micromixers, chip-shaped microdevices like bio-microsystems, and micro-volumes like microfluidic devices. This model can also help environmental engineering by enhancing wastewater treatment procedures by allowing microorganisms to break down pollutants more effectively. It advances the development of more productive photo bioreactors, increasing the output of biofuels in the field of renewable energy. Material scientists can utilize this concept to develop controlled nanostructured materials with consistent composition and thermal properties. The considerable similarity transformation is used to build ordinary differential equations for the nonlinear dimensionless system. This problem is solved numerically by using the Bvp4c method. The results determine that when the Stefan blowing parameter increases, fluid flow increases but temperature, mass transfer rate, and heat transfer are decreased.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":678,"journal":{"name":"Journal of Thermal Analysis and Calorimetry","volume":"150 1","pages":"797 - 811"},"PeriodicalIF":3.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational study of the thermophysical properties of graphene oxide/vacuum residue nanofluids for enhanced oil recovery","authors":"Abdulhakeem Yusuf,&nbsp;M. M. Bhatti,&nbsp;C. M. Khalique","doi":"10.1007/s10973-024-13921-y","DOIUrl":"10.1007/s10973-024-13921-y","url":null,"abstract":"<div><p>Prior research suggests that the use of nanotechnology may greatly improve the efficiency of enhanced oil recovery methods, especially hot fluid injection. The thermophysical characteristics of the nanofluid may have an enormous effect on how well the injection process works. However, it takes both time and resources to conduct laboratory analyses of the effects of thermophysical characteristics on the effectiveness of nanofluid-based improved oil recovery methods. Computational models can effectively forecast the thermophysical characteristics of nanofluids and how they affect oil recovery efficiency, which helps overcome this difficulty. The current study investigates the flow of vacuum residue (VR) fluid, which generates entropy when suspended graphene oxide (GO) nanoparticles. When mixed convection and variable thermal conductivity are present, a static/moving wedge allows the nanofluid to propagate. The continuity, energy, entropy, and momentum equations form the foundation of the governing model. We use certain similarity variables to simplify the suggested mathematical formulations into forms for nonlinear differential equations (DEs). We show the results of the reduced equations using the Chebyshev collocation method. We present the graphical and numerical results for all the emerging parameters. For enhanced oil recovery applications, the current results are beneficial.</p></div>","PeriodicalId":678,"journal":{"name":"Journal of Thermal Analysis and Calorimetry","volume":"150 1","pages":"771 - 783"},"PeriodicalIF":3.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10973-024-13921-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling and optimization of thermal conductivity for MWCNT-SiO2(20:80)/hydraulic oil-based hybrid nanolubricants using ANN and RSM","authors":"Abhisek Haldar,&nbsp;Sankhadeep Chatterjee,&nbsp;Ankit Kotia,&nbsp;Niranjan Kumar,&nbsp;Subrata Kumar Ghosh","doi":"10.1007/s10973-024-13888-w","DOIUrl":"10.1007/s10973-024-13888-w","url":null,"abstract":"<div><p>This research article presents the experimental evaluation of thermal conductivity for hydraulic oil-based hybrid nanolubricants with an aim to enhance the heat transfer potential in engineering applications. The nanolubricant samples were formulated at concentrations ranging from 0.3 to 1.8%. Using transient hot wire method, the thermal conductivity of nanolubricants were evaluated for all the samples from 30 to 80 °C. The maximum enhancement in thermal conductivity was 62.93% for the highest concentration. In this paper, response surface methodology (RSM) and artificial neural network (ANN) have been employed for prediction of the thermal conductivity of nanolubricants. In RSM, analysis of variance (ANOVA) and 3D surface plot techniques were used to determine the significance of the interaction parameters on the output. A new correlation has been proposed to predict the thermal conductivity of the nanolubricants with a <i>R</i>^<i>2</i> value of 0.9992. A combination of concentration and temperature (1.5783 vol% and 72.5695 °C) yielded to the maximum optimal thermal conductivity of 0.204526 Wm⁻¹ K⁻¹. In addition, multilayer perceptron, a type of neural network model, has been trained and tested to predict the thermal conductivity of the nanolubricants. Experiments have revealed that the ANN model consisting of only 10 hidden neurons has been able to achieve an average <i>R</i>^<i>2</i> of 0.98567 and RMSE of 0.02463 thereby establishing its ingenuity. Comparatively, it turned out that the RSM model was slightly more accurate in predicting thermal conductivity than the ANN model.</p></div>","PeriodicalId":678,"journal":{"name":"Journal of Thermal Analysis and Calorimetry","volume":"150 1","pages":"607 - 626"},"PeriodicalIF":3.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stage development characteristics of oxygen-lean combustion of coal in fire zone. Part I: The evolution law of pyrolysis and combustion stage characteristics","authors":"Guangyu Bai,&nbsp;Haihui Xin,&nbsp;Yi Yang,&nbsp;Junzhe Li,&nbsp;Xuyao Qi,&nbsp;Pengcheng Zhang,&nbsp;Jiakun Wang,&nbsp;Jinhu Liu,&nbsp;Liyang Ma","doi":"10.1007/s10973-024-13865-3","DOIUrl":"10.1007/s10973-024-13865-3","url":null,"abstract":"<div><p>Coal fire oxygen-lean combustion is a global catastrophe, well known and difficult to describe. To deepen the understanding of the stage characteristics under the competition between pyrolysis and oxidation in coal oxygen-lean combustion. In this study, a TA-Q600 simultaneous thermal analyzer was used to investigate the macroscopic mass characteristics of three typical low-rank coals during oxygen-lean combustion processes under different time-scale effects. Through a coupled competitive comparison of pyrolysis and combustion characteristic temperature points, the stage characteristics and evolution laws of coal in the oxygen-lean combustion process were comprehensively analyzed. The results showed that the stage development of coal pyrolysis can be divided into four stages; under different time-scale effect, the higher the coal rank, the better the separation between the thermal decomposition and the thermal polycondensation processes. The stage development patterns of coal structure conversion combustion were divided into three categories, and the stage development types were divided into six categories. The difference in the burnout state caused by the decrease in oxygen concentration includes to 4–7 different combustion progressions. When the oxygen concentration falls within the range of 5–1%, the coal combustion stage transitions and delays from semi-coke burnout to coal coke burnout. The evolution of the burnout state, induced by the oxygen concentration, remained unaffected by the coal rank but was relatively less influenced by the time-scale effect. With an increase in the coal rank under a 1% oxygen concentration, the stage progression total gradually diminishes. This characteristic remains unaffected by the time-scale effect. As the coal rank increased, the influence of the time-scale effect on the oxygen concentration of the stage development pattern evolution became increasingly evident. The results of the study guided the identification of the development progression in different areas of the fire zone and provided safer temperature and oxygen concentration indicators for fire suppression work and unsealing of the fire zone.</p></div>","PeriodicalId":678,"journal":{"name":"Journal of Thermal Analysis and Calorimetry","volume":"150 1","pages":"327 - 343"},"PeriodicalIF":3.0,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and optimization of thermal conductivity of synthesized MWCNT/water nanofluids using response surface methodology for heat transfer applications","authors":"Faisal Masood,&nbsp;Mohammad Azad Alam,&nbsp;Nursyarizal Bin Mohd Nor,&nbsp;Kashif Irshad,&nbsp;Irraivan Elamvazuthi,&nbsp;Shafiqur Rehman,&nbsp;Javed Akhter,&nbsp;Mohamed E. Zayed","doi":"10.1007/s10973-024-13847-5","DOIUrl":"10.1007/s10973-024-13847-5","url":null,"abstract":"<div><p>This paper reports on the experimental examination and optimization of a response surface methodology (RSM)-based predictive model for the thermal conductivity of aqueous multi-walled carbon nanotube (MWCNT)-based nanofluids for heat transfer applications. The design matrix was created with nanofluid temperature (°C) and nanoparticle concentration (mass/%) as independent variables, while thermal conductivity was considered as a response variable. Magnetic stirring and ultrasonication were used to produce nanofluid samples. The thermal conductivity of the prepared samples was measured, and quadratic models were selected through regression analysis. ANOVA was performed to validate the models. The maximum thermal conductivity value, i.e., 0.988 W m⁻¹ K⁻¹, was achieved at MWCNT particle content 0.5 mass/% and 60 °C temperature. A comprehensive optimization study was also performed for maximizing thermal conductivity. The optimal values for the thermal conductivity of nanofluids were found to be 0.8845 W m⁻¹ K⁻¹, whereas the optimal values for the control factors, i.e., nanofluid temperature and nanoparticles' concentration, were estimated to be 60 °C and 0.5 mass/%, respectively. The coefficient of determination <i>R</i>² for the thermal conductivity of the developed model was found to be 0.9866, which confirmed the suitability of the developed models. The optimized MWCNT/water nanofluid shows potential as an effective heat transfer fluid, particularly for solar thermal and hybrid photovoltaic/thermal applications.</p></div>","PeriodicalId":678,"journal":{"name":"Journal of Thermal Analysis and Calorimetry","volume":"150 1","pages":"573 - 584"},"PeriodicalIF":3.0,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical investigation of chemical reactive MHD fluid dynamics over a porous surface with Cattaneo–Christov heat flux","authors":"Saleem Nasir,&nbsp;Abdallah S. Berrouk","doi":"10.1007/s10973-024-13815-z","DOIUrl":"10.1007/s10973-024-13815-z","url":null,"abstract":"<div><p>A theoretical framework to investigate three-dimensional Williamson fluid flow over a bidirectional extended flat horizontal surface is proposed in this dissertation. Artificial intelligence and machine learning fields have seen tremendous growth in prominence along with the rapid advancement of related technology. This work trains a machine learning model based on artificial neural networks to handle the mathematical formulation incorporating heat source and Hall effects using the Levenberg–Marquardt approach. Additionally, the impact of activation energy on fluid concentration is incorporated into the analysis. Cattaneo-Christov double diffusion models are used to model heat transfer combined with the effects of thermal radiation. The solutions, serving as reference datasets for various scenarios, have been generated numerically using the BVP4C approach. Artificial neural networks are utilized for training, testing, and validating these numerical computations using a 70:15:15 ratio. The predictive model accuracy is evaluated using various statistical metrics, including linear regression, histograms, fitting analysis, and mean squared error evaluations, with the least error ranging between 10⁻³ and 10⁻⁴, based on individual error analysis of four parameters. The findings show that temperature rises with the <i>M</i> parameter, whereas velocity declines by increasing the <i>M</i> parameter. Concentration rises with increasing activation energy parameter and falls with decreasing <i>Sc</i>. The results show that artificial neural networks can provide a successful replacement for forecasts for the future, and the fluid flow structure simulated here may result in better industrial designs.</p></div>","PeriodicalId":678,"journal":{"name":"Journal of Thermal Analysis and Calorimetry","volume":"149 24","pages":"14877 - 14900"},"PeriodicalIF":3.0,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10973-024-13815-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}