The aim of this paper is to summarize the results obtained experimentally by determining the characteristic drying curve based on the tests performed. The method adopted is to study the variation of the standardized drying rate f as a function of the reduced water content W. This leads to the convergence of the different values obtained around one average curve, which is the characteristic drying curve. The equation expressing the drying kinetics of the product is written as follows: f*=f(W). The dimensionless water content (-dW/dt) represents the continuity of relative humidity fluctuations during drying.
{"title":"Results of experimental research on drying Occimum basilicum","authors":"Sh.A. Sultanova , J.E. Safarov , A.A. Mambetsheripova , M.M. Pulatov , A.B. Usenov , B.M. Jumaev , Gunel Imanova","doi":"10.1016/j.ijft.2025.101536","DOIUrl":"10.1016/j.ijft.2025.101536","url":null,"abstract":"<div><div>The aim of this paper is to summarize the results obtained experimentally by determining the characteristic drying curve based on the tests performed. The method adopted is to study the variation of the standardized drying rate f as a function of the reduced water content W. This leads to the convergence of the different values obtained around one average curve, which is the characteristic drying curve. The equation expressing the drying kinetics of the product is written as follows: <em>f*=f(W)</em>. The dimensionless water content <em>(-dW/dt)</em> represents the continuity of relative humidity fluctuations during drying.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101536"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-07DOI: 10.1016/j.ijft.2025.101516
Ali Rehman , Abdullah Aziz Saad , Mustafa Inc , Siti Sabariah Binti Abas , Edrisa Jawo , K. Sudarmozhi
A base fluid containing a three-component mixture of distinct nanoparticles called a ternary hybrid nanofluid (THNF). In single- or binary-hybrid nanofluids (HNF), these ternary systems exhibit synergistic thermal effects that enhance heat transfer more efficiently. The purpose of this research is to present a semi-numerical simulation and model for analysing the thermal performance of unsteady squeezing flow of a non-Newtonian magneto-hydrodynamic couple-stress THNF confined between 2 parallel surfaces, with the influence of viscous dissipation and heat generation. The THNF, synthesised by dispersing in a non-Newtonian base fluid, was investigated to investigate its superior energy transfer capabilities under complex flow regimes. The key nonlinear PDEs, accounting for squeezing motion, coupling stress effects, magnetic field (MF) interaction, and nanoparticle suspension, are converted into dimensionless nonlinear ODEs via suitable similarity transformations. A semi-numerical approach, the Homotopy analysis method (HAM), combining analytical and numerical schemes, is employed to achieve high-accuracy solutions for velocity and temperature fields. The influence of important parameters, such as the unsteady parameter, the couple stress parameter, the magnetic parameter, the nanoparticle volume fraction, the heat generation parameter, the rotation parameter, and the Eckert number, on the velocity and temperature profiles is observed. The results show that adding ternary hybrid nanoparticles greatly increases thermal conductivity, while the coupling stress and MHD parameters control energy dissipation and flow resistance. For engineering applications such as lubrication systems, extrusion processes, microfluidics, and biomedical devices, the analysis shows that squeezing dynamics and unsteady effects significantly influence energy transfer improvements.
{"title":"Semi-numerical simulation for the thermal performance of unsteady squeezing non-Newtonian MHD couple stress ternary hybrid nanofluid flow between parallel surfaces","authors":"Ali Rehman , Abdullah Aziz Saad , Mustafa Inc , Siti Sabariah Binti Abas , Edrisa Jawo , K. Sudarmozhi","doi":"10.1016/j.ijft.2025.101516","DOIUrl":"10.1016/j.ijft.2025.101516","url":null,"abstract":"<div><div>A base fluid containing a three-component mixture of distinct nanoparticles called a ternary hybrid nanofluid (THNF). In single- or binary-hybrid nanofluids (HNF), these ternary systems exhibit synergistic thermal effects that enhance heat transfer more efficiently. The purpose of this research is to present a semi-numerical simulation and model for analysing the thermal performance of unsteady squeezing flow of a non-Newtonian magneto-hydrodynamic couple-stress THNF confined between 2 parallel surfaces, with the influence of viscous dissipation and heat generation. The THNF, synthesised by dispersing <span><math><mrow><mi>M</mi><mi>W</mi><mi>C</mi><mi>N</mi><mi>T</mi><mo>,</mo><mi>S</mi><mi>W</mi><mi>C</mi><mi>N</mi><mi>T</mi><mo>,</mo><mi>A</mi><mi>g</mi></mrow></math></span>in a non-Newtonian base fluid, was investigated to investigate its superior energy transfer capabilities under complex flow regimes. The key nonlinear PDEs, accounting for squeezing motion, coupling stress effects, magnetic field (MF) interaction, and nanoparticle suspension, are converted into dimensionless nonlinear ODEs via suitable similarity transformations. A semi-numerical approach, the Homotopy analysis method (HAM), combining analytical and numerical schemes, is employed to achieve high-accuracy solutions for velocity and temperature fields. The influence of important parameters, such as the unsteady parameter, the couple stress parameter, the magnetic parameter, the nanoparticle volume fraction, the heat generation parameter, the rotation parameter, and the Eckert number, on the velocity and temperature profiles is observed. The results show that adding ternary hybrid nanoparticles greatly increases thermal conductivity, while the coupling stress and MHD parameters control energy dissipation and flow resistance. For engineering applications such as lubrication systems, extrusion processes, microfluidics, and biomedical devices, the analysis shows that squeezing dynamics and unsteady effects significantly influence energy transfer improvements.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101516"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-31DOI: 10.1016/j.ijft.2025.101541
Mahmmoud M. Syam , Muhammed I. Syam , Kenan Yildirim
This study investigates the unsteady squeezing flow and heat transfer characteristics of a graphene-oxide/water nanofluid confined between two parallel plates undergoing time-dependent motion. A similarity transformation is used to convert the governing nonlinear partial differential equations into a set of coupled boundary-value problems, which are then solved using a modified operational matrix method (OMM). The proposed formulation avoids the stiffness commonly encountered in traditional OMM by introducing a forward-based coefficient computation strategy, reducing computational effort while maintaining high accuracy. The numerical results are validated through truncation error, boundary-condition deviation analysis, and comparison of the local Nusselt number against reference solutions, showing an error on the order of . A detailed parametric investigation is conducted to examine the influence of Brownian motion (), thermophoresis (), squeeze number (S), Eckert number (Ec), and Lewis number (Le) on velocity, temperature, and concentration distributions. The results show that increasing by 0.1 leads to approximately a 6%–12% rise in peak temperature gradients, while higher enhances thermal diffusion and reduces concentration gradients by nearly 8%–15% depending on . The squeeze parameter accelerates the flow and increases the wall shear stress by about 10%, whereas Ec significantly boosts the thermal boundary layer due to viscous dissipation effects. Source terms associated with nanoparticle diffusion, viscous heating, and unsteady squeezing motion play a key role in shaping the overall transport behavior. Overall, the modified OMM offers a fast, stable, and highly accurate alternative for solving nonlinear nanofluid boundary-value problems, and the presented results provide deeper insight into the thermal and mass transport mechanisms of graphene-oxide nanofluids under unsteady squeezing motion.
{"title":"Modeling and simulation of radiative MHD nanofluid flow with Joule heating over a variable-thickness sheet","authors":"Mahmmoud M. Syam , Muhammed I. Syam , Kenan Yildirim","doi":"10.1016/j.ijft.2025.101541","DOIUrl":"10.1016/j.ijft.2025.101541","url":null,"abstract":"<div><div>This study investigates the unsteady squeezing flow and heat transfer characteristics of a graphene-oxide/water nanofluid confined between two parallel plates undergoing time-dependent motion. A similarity transformation is used to convert the governing nonlinear partial differential equations into a set of coupled boundary-value problems, which are then solved using a modified operational matrix method (OMM). The proposed formulation avoids the stiffness commonly encountered in traditional OMM by introducing a forward-based coefficient computation strategy, reducing computational effort while maintaining high accuracy. The numerical results are validated through <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> truncation error, boundary-condition deviation analysis, and comparison of the local Nusselt number against reference solutions, showing an error on the order of <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>14</mn></mrow></msup></mrow></math></span>. A detailed parametric investigation is conducted to examine the influence of Brownian motion (<span><math><mrow><mi>N</mi><mi>b</mi></mrow></math></span>), thermophoresis (<span><math><mrow><mi>N</mi><mi>t</mi></mrow></math></span>), squeeze number (S), Eckert number (Ec), and Lewis number (Le) on velocity, temperature, and concentration distributions. The results show that increasing <span><math><mrow><mi>N</mi><mi>b</mi></mrow></math></span> by 0.1 leads to approximately a 6%–12% rise in peak temperature gradients, while higher <span><math><mrow><mi>N</mi><mi>t</mi></mrow></math></span> enhances thermal diffusion and reduces concentration gradients by nearly 8%–15% depending on <span><math><mi>ζ</mi></math></span>. The squeeze parameter accelerates the flow and increases the wall shear stress by about 10%, whereas Ec significantly boosts the thermal boundary layer due to viscous dissipation effects. Source terms associated with nanoparticle diffusion, viscous heating, and unsteady squeezing motion play a key role in shaping the overall transport behavior. Overall, the modified OMM offers a fast, stable, and highly accurate alternative for solving nonlinear nanofluid boundary-value problems, and the presented results provide deeper insight into the thermal and mass transport mechanisms of graphene-oxide nanofluids under unsteady squeezing motion.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101541"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-11-17DOI: 10.1016/j.ijft.2025.101496
Abbas Dorri, Masoud Darbandi
The dynamic stall phenomenon has been extensively studied in literature. Despite various innovative interventions focused on understanding its behavior, there are few efforts to study the influence of heat transfer on the dynamic stall performance of pitching airfoils, particularly in turbulent flows. This work investigates the effects of surface temperature variations on the dynamic stall of a NACA 0012 pitching airfoil at Re = 135,000. The temperature difference between the airfoil surface and the freestream temperatures was ΔT= 50, 100, and 150 K. The flow field around the airfoil was simulated using the computational fluid dynamics, and solving the Navier-Stokes equations incorporated with the k-ω/SST turbulence model. After validating the thermo-fluid solver, the aerodynamic response of the pitching airfoil was analyzed under upper surface cooling (USC) and upper surface heating (USH). The results showed that despite changes in the surface temperature, the drag coefficient remained nearly unchanged in both cases. However, the lift coefficient increased in USC and decreased in USH. In USC, the aerodynamic performance improved as much as 6.2 % at ΔT = 150 K. However, it was not affected that much in USH by varying ΔT. The USC tended to keep the flow attached to the surface, increasing the skin friction drag and lowering the pressure drag. The local Reynolds number increased since the USC raised the airflow velocity over the airfoil. Conversely, USH led to opposite effects on the flow characteristics. Overall, unlike USH, USC improved the dynamic stall performance of the pitching airfoil in the turbulent flow. The findings indicate that the airfoil’s surface heat transfer can effectively manipulate the dynamic stall behavior, offering a promising strategy for dynamic stall control in aeronautical applications.
{"title":"Effects of surface heat transfer on the dynamic stall performance of a pitching airfoil in turbulent flow","authors":"Abbas Dorri, Masoud Darbandi","doi":"10.1016/j.ijft.2025.101496","DOIUrl":"10.1016/j.ijft.2025.101496","url":null,"abstract":"<div><div>The dynamic stall phenomenon has been extensively studied in literature. Despite various innovative interventions focused on understanding its behavior, there are few efforts to study the influence of heat transfer on the dynamic stall performance of pitching airfoils, particularly in turbulent flows. This work investigates the effects of surface temperature variations on the dynamic stall of a NACA 0012 pitching airfoil at Re = 135,000. The temperature difference between the airfoil surface and the freestream temperatures was ΔT= 50, 100, and 150 K. The flow field around the airfoil was simulated using the computational fluid dynamics, and solving the Navier-Stokes equations incorporated with the k-ω/SST turbulence model. After validating the thermo-fluid solver, the aerodynamic response of the pitching airfoil was analyzed under upper surface cooling (USC) and upper surface heating (USH). The results showed that despite changes in the surface temperature, the drag coefficient remained nearly unchanged in both cases. However, the lift coefficient increased in USC and decreased in USH. In USC, the aerodynamic performance improved as much as 6.2 % at ΔT = 150 K. However, it was not affected that much in USH by varying ΔT. The USC tended to keep the flow attached to the surface, increasing the skin friction drag and lowering the pressure drag. The local Reynolds number increased since the USC raised the airflow velocity over the airfoil. Conversely, USH led to opposite effects on the flow characteristics. Overall, unlike USH, USC improved the dynamic stall performance of the pitching airfoil in the turbulent flow. The findings indicate that the airfoil’s surface heat transfer can effectively manipulate the dynamic stall behavior, offering a promising strategy for dynamic stall control in aeronautical applications.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101496"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-31DOI: 10.1016/j.ijft.2025.101542
Torikul Islam , B.M.Jewel Rana , Md.Yousuf Ali , Khan Enaet Hossain , Arnab Mukherjee , Saiful Islam , Mohammad Afikuzzaman
In the evolving field of fluid power and thermal systems, artificial neural networks (ANNs) are increasingly recognized for their robust ability to address nonlinear, coupled, and high-dimensional fluid dynamics problems. This study presents a neural network-assisted investigation of magneto-hydrodynamic Sisko nanofluid flow modelled as a blood-based magnetic suspension over an inclined stretching surface influenced by non-uniform heat generation and thermophoretic effects. The governing partial differential equations derived from mass, momentum, and energy conservation laws with complex boundary conditions are reduced to nonlinear ordinary differential equations through similarity transformations. The resulting system is first solved using MATLAB’s bvp4c solver, and the generated data is then used to train, validate, and test an ANN framework based on the Levenberg Marquardt backpropagation algorithm (BPLMA). The ANN model exhibits high predictive accuracy, with relative absolute errors ranging from 10⁻³ to 10⁻⁷ compared to the reference solution. The thermo-fluidic behaviour of shear-thinning and shear-thickening regimes is analysed under different concentrations of magnetic nanoparticles such as iron oxide and cobalt ferrite. For a 10 percent volume fraction increase, enhancements in heat transfer and reductions in mass transfer are observed, reaching up to 10 percent and 18.9 percent for iron oxide and 9.8 percent and 12 percent for cobalt ferrite, respectively, depending on the fluid rheology. Visualizations of streamlines, temperature fields, and concentration contours reveal intricate flow structures and nanoparticle distributions, offering valuable physical insights. Statistical evaluations including regression analysis, error histograms, and model fitness further support the reliability of the ANN approach. This work introduces a powerful hybrid computational methodology that integrates numerical simulation with machine learning to analyse non-Newtonian nanofluid behaviour and contributes to advancements in biomedical engineering, heat exchanger design, smart cooling systems, and microfluidic devices in fluid power applications. This work presents a novel computational framework that combines traditional numerical simulation with artificial intelligence to analyse complex non-Newtonian nanofluid behaviour. Unlike traditional methods that are often computationally intensive, the ANN model offers fast, accurate predictions and strong generalization across varying conditions. The novelty of this hybrid approach lies in its ability to enhance traditional techniques with AI driven efficiency, making it well suited for applications in biomedical engineering, heat exchanger design, smart cooling systems, and microfluidic devices.
{"title":"Artificial neural network modeling of magnetic nanoparticle-enhanced Sisko blood nanofluid flow over an inclined stretching surface with non-uniform heating and thermophoretic effects","authors":"Torikul Islam , B.M.Jewel Rana , Md.Yousuf Ali , Khan Enaet Hossain , Arnab Mukherjee , Saiful Islam , Mohammad Afikuzzaman","doi":"10.1016/j.ijft.2025.101542","DOIUrl":"10.1016/j.ijft.2025.101542","url":null,"abstract":"<div><div>In the evolving field of fluid power and thermal systems, artificial neural networks (ANNs) are increasingly recognized for their robust ability to address nonlinear, coupled, and high-dimensional fluid dynamics problems. This study presents a neural network-assisted investigation of magneto-hydrodynamic Sisko nanofluid flow modelled as a blood-based magnetic suspension over an inclined stretching surface influenced by non-uniform heat generation and thermophoretic effects. The governing partial differential equations derived from mass, momentum, and energy conservation laws with complex boundary conditions are reduced to nonlinear ordinary differential equations through similarity transformations. The resulting system is first solved using MATLAB’s bvp4c solver, and the generated data is then used to train, validate, and test an ANN framework based on the Levenberg Marquardt backpropagation algorithm (BPLMA). The ANN model exhibits high predictive accuracy, with relative absolute errors ranging from 10⁻³ to 10⁻⁷ compared to the reference solution. The thermo-fluidic behaviour of shear-thinning and shear-thickening regimes is analysed under different concentrations of magnetic nanoparticles such as iron oxide and cobalt ferrite. For a 10 percent volume fraction increase, enhancements in heat transfer and reductions in mass transfer are observed, reaching up to 10 percent and 18.9 percent for iron oxide and 9.8 percent and 12 percent for cobalt ferrite, respectively, depending on the fluid rheology. Visualizations of streamlines, temperature fields, and concentration contours reveal intricate flow structures and nanoparticle distributions, offering valuable physical insights. Statistical evaluations including regression analysis, error histograms, and model fitness further support the reliability of the ANN approach. This work introduces a powerful hybrid computational methodology that integrates numerical simulation with machine learning to analyse non-Newtonian nanofluid behaviour and contributes to advancements in biomedical engineering, heat exchanger design, smart cooling systems, and microfluidic devices in fluid power applications. This work presents a novel computational framework that combines traditional numerical simulation with artificial intelligence to analyse complex non-Newtonian nanofluid behaviour. Unlike traditional methods that are often computationally intensive, the ANN model offers fast, accurate predictions and strong generalization across varying conditions. The novelty of this hybrid approach lies in its ability to enhance traditional techniques with AI driven efficiency, making it well suited for applications in biomedical engineering, heat exchanger design, smart cooling systems, and microfluidic devices.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101542"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-20DOI: 10.1016/j.ijft.2025.101535
Md. Anonno Habib Akash , Md. Sohag Hossain
To prevent fuel damage and reactor instability, precise detection of boiling and burnout heat flux conditions is essential for nuclear power plant thermal safety. Using high-dimensional acoustic spectrum data acquired from controlled tests at high pressure thermo-physical bench, this paper investigates the use of supervised ML algorithms for the classification of thermal states, including normal boiling and burnout. Each of the 173 samples in the dataset is defined by 200 frequency-domain characteristics. A stratified 5-fold cross-validation pipeline was used to train seven ML models: Multilayer Perceptron, Logistic Regression, Support Vector Machine (RBF kernel), k-Nearest Neighbors, Random Forest, LightGBM, and CatBoost. Hyperparameters were adjusted using RandomizedSearchCV. Model interpretability was assessed with the use of SHAP values, permutation importance, and Gini scores, while feature selection was carried out using ANOVA F-statistics and Recursive Feature Elimination. Random Forest outperformed the other models in terms of test accuracy (88.57 %), recall consistency, and overall performance. Although they were not quite as stable in terms of interpretability, SVM and CatBoost also showed strong classification capabilities with high AUC values (≥ 0.82). The results show that ensemble-based classifiers work well in reactor settings with limited data and running in real-time. In order to provide insights into the performance of the models and their interpretability for safety-critical applications, this study builds a methodology for acoustic-based thermal diagnostics in nuclear systems.
{"title":"Machine learning based classification of boiling and burnout heat flux using acoustic signals in nuclear thermal systems","authors":"Md. Anonno Habib Akash , Md. Sohag Hossain","doi":"10.1016/j.ijft.2025.101535","DOIUrl":"10.1016/j.ijft.2025.101535","url":null,"abstract":"<div><div>To prevent fuel damage and reactor instability, precise detection of boiling and burnout heat flux conditions is essential for nuclear power plant thermal safety. Using high-dimensional acoustic spectrum data acquired from controlled tests at high pressure thermo-physical bench, this paper investigates the use of supervised ML algorithms for the classification of thermal states, including normal boiling and burnout. Each of the 173 samples in the dataset is defined by 200 frequency-domain characteristics. A stratified 5-fold cross-validation pipeline was used to train seven ML models: Multilayer Perceptron, Logistic Regression, Support Vector Machine (RBF kernel), k-Nearest Neighbors, Random Forest, LightGBM, and CatBoost. Hyperparameters were adjusted using RandomizedSearchCV. Model interpretability was assessed with the use of SHAP values, permutation importance, and Gini scores, while feature selection was carried out using ANOVA F-statistics and Recursive Feature Elimination. Random Forest outperformed the other models in terms of test accuracy (88.57 %), recall consistency, and overall performance. Although they were not quite as stable in terms of interpretability, SVM and CatBoost also showed strong classification capabilities with high AUC values (≥ 0.82). The results show that ensemble-based classifiers work well in reactor settings with limited data and running in real-time. In order to provide insights into the performance of the models and their interpretability for safety-critical applications, this study builds a methodology for acoustic-based thermal diagnostics in nuclear systems.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101535"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-01-05DOI: 10.1016/j.ijft.2026.101549
Kavana Nagarkar , Shamitha Shetty , Sher Afghan Khan , Abdul Aabid , Muneer Baig
The present numerical study examines hypersonic flow (Mach 5.9) over a blunt body, comparing configurations with and without a forward-facing cavity (FFC). Operating at 1200 Pa and 143 K free-stream conditions, the research focuses on critical parameters, including the drag coefficient, pressure fluctuations, and shock stand-off distance, using unsteady-state RANS simulations. The findings indicate that a forward-facing cavity reduces drag by up to 18% at an L/D ratio of 3. This improvement is attributed to an increased shock stand-off distance, which alters the flow dynamics around the body. The s-a turbulence model with three coefficient equations has satisfied the Navier-Stokes equations to simulate hypervelocity flow over a blunt body. The current time-dependent simulation has provided almost steady results after reaching 11 milliseconds. A comparative analysis of blunt bodies with and without cavities and with varying L/D ratios further demonstrates that deeper cavities enhance performance in hypervelocity conditions.
{"title":"Effects of forward-facing cavity on drag in hypervelocity projectiles: A computational approach","authors":"Kavana Nagarkar , Shamitha Shetty , Sher Afghan Khan , Abdul Aabid , Muneer Baig","doi":"10.1016/j.ijft.2026.101549","DOIUrl":"10.1016/j.ijft.2026.101549","url":null,"abstract":"<div><div>The present numerical study examines hypersonic flow (Mach 5.9) over a blunt body, comparing configurations with and without a forward-facing cavity (FFC). Operating at 1200 Pa and 143 K free-stream conditions, the research focuses on critical parameters, including the drag coefficient, pressure fluctuations, and shock stand-off distance, using unsteady-state RANS simulations. The findings indicate that a forward-facing cavity reduces drag by up to 18% at an L/D ratio of 3. This improvement is attributed to an increased shock stand-off distance, which alters the flow dynamics around the body. The s-a turbulence model with three coefficient equations has satisfied the Navier-Stokes equations to simulate hypervelocity flow over a blunt body. The current time-dependent simulation has provided almost steady results after reaching 11 milliseconds. A comparative analysis of blunt bodies with and without cavities and with varying L/D ratios further demonstrates that deeper cavities enhance performance in hypervelocity conditions.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101549"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents a numerical investigation of the three-dimensional rotating flow of a magnetohydrodynamic (MHD) Jeffrey nanofluid over a permeable stretching surface. The model comprehensively incorporates the effects of Hall and ion slip currents, Coriolis force, nonlinear thermal radiation, viscous dissipation, Joule heating, internal heat generation/absorption, and a first-order chemical reaction. The Buongiorno model is employed to account for Brownian motion and thermophoresis mechanisms in nanoparticle transport. The governing nonlinear partial differential equations are transformed into a system of coupled ordinary differential equations using similarity variables and solved numerically using a high-precision sixth-order Runge–Kutta (RK6) method with a shooting technique, implemented in Python programming. The numerical code is rigorously validated against established benchmark studies, showing excellent agreement. Simulation results, presented graphically and in tables, demonstrate that streamwise velocity increases with Hall and ion slip parameters but decreases with the relaxation parameter. The Nusselt number, quantifying heat transfer, is enhanced by Hall currents and the Prandtl number but suppressed by nonlinear thermal radiation. Conversely, the Sherwood number, representing nanoparticle mass transfer, increases with both the chemical reaction rate and nonlinear thermal radiation. These insights are vital for optimizing the performance of advanced engineering systems, including MHD power generators, nanofluid-based cooling technologies, and materials processing operations.
{"title":"Python-Based Simulation of Rotating MHD Jeffrey Nanofluid Flow over a Permeable Stretching Surface Subject to Hall and Ion Slip Effects","authors":"Wubale Demis Alamirew , Gurju Awgichew , Eshetu Haile","doi":"10.1016/j.ijft.2025.101517","DOIUrl":"10.1016/j.ijft.2025.101517","url":null,"abstract":"<div><div>This study presents a numerical investigation of the three-dimensional rotating flow of a magnetohydrodynamic (MHD) Jeffrey nanofluid over a permeable stretching surface. The model comprehensively incorporates the effects of Hall and ion slip currents, Coriolis force, nonlinear thermal radiation, viscous dissipation, Joule heating, internal heat generation/absorption, and a first-order chemical reaction. The Buongiorno model is employed to account for Brownian motion and thermophoresis mechanisms in nanoparticle transport. The governing nonlinear partial differential equations are transformed into a system of coupled ordinary differential equations using similarity variables and solved numerically using a high-precision sixth-order Runge–Kutta (RK6) method with a shooting technique, implemented in Python programming. The numerical code is rigorously validated against established benchmark studies, showing excellent agreement. Simulation results, presented graphically and in tables, demonstrate that streamwise velocity increases with Hall and ion slip parameters but decreases with the relaxation parameter. The Nusselt number, quantifying heat transfer, is enhanced by Hall currents and the Prandtl number but suppressed by nonlinear thermal radiation. Conversely, the Sherwood number, representing nanoparticle mass transfer, increases with both the chemical reaction rate and nonlinear thermal radiation. These insights are vital for optimizing the performance of advanced engineering systems, including MHD power generators, nanofluid-based cooling technologies, and materials processing operations.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101517"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01Epub Date: 2025-09-24DOI: 10.1016/j.ijft.2025.101426
M․ S․ Alam , M. Masud Parveg Nayon , T. Islam , M. Sajjad Hossain , M․ M․ Rahman
Superior thermal characteristics, including increased thermal conductivity, enhanced convective performance, and improved thermal stability, make nanofluids attractive substitutes for enhancing the effectiveness of heat transfer. It is therefore possible to circumvent the thermo-physical constraints of regular fluids by scattering appropriate nanoparticles. This study predicts and optimizes the thermal conductivity ratio of water-aluminum oxide nanofluids using statistical response surface methodology (RSM) and artificial neural networks (ANN). A Box-Behnken design (BBD) within the RSM framework was employed to explore the relationship between independent variables, such as nanoparticle concentration (1–4 %), temperature (293-323 K), and surfactant weight (776-3104 mg), and the response function thermal conductivity ratio. Canonical analysis was also conducted to identify significant interactions among variables. For ANN, the Levenberg-Marquardt (LM) algorithm is employed to optimize the network's performance with six neurons in the hidden layer. To create second-order polynomial equations for predictive modeling, a total of 17 experiments were conducted. The accuracy of the predictive performance of RSM and ANN was evaluated using the margin of deviation (MOD), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R²). The optimal ANN configuration exhibited a high R2 (0.9945) and a low MSE error (0.0030) as compared to the RSM model. Moreover, the average error for the ANN was 1.8192 %, which is significantly less than the 3.9773 % error of RSM. Both methods were successful in forecasting the thermal conductivity ratio of aluminum oxide–water nanofluids, although the ANN method was more accurate. According to these results, ANN is a practical and effective tool for evaluating and improving heat transfer systems based on nanofluids in industrial applications.
{"title":"Modeling and optimization of thermal conductivity ratio of Al2O3–water nanofluid using artificial neural network and Box-Behnken design based response surface methodology with canonical analysis","authors":"M․ S․ Alam , M. Masud Parveg Nayon , T. Islam , M. Sajjad Hossain , M․ M․ Rahman","doi":"10.1016/j.ijft.2025.101426","DOIUrl":"10.1016/j.ijft.2025.101426","url":null,"abstract":"<div><div>Superior thermal characteristics, including increased thermal conductivity, enhanced convective performance, and improved thermal stability, make nanofluids attractive substitutes for enhancing the effectiveness of heat transfer. It is therefore possible to circumvent the thermo-physical constraints of regular fluids by scattering appropriate nanoparticles. This study predicts and optimizes the thermal conductivity ratio of water-aluminum oxide nanofluids using statistical response surface methodology (RSM) and artificial neural networks (ANN). A Box-Behnken design (BBD) within the RSM framework was employed to explore the relationship between independent variables, such as nanoparticle concentration (1–4 %), temperature (293-323 K), and surfactant weight (776-3104 mg), and the response function thermal conductivity ratio. Canonical analysis was also conducted to identify significant interactions among variables. For ANN, the Levenberg-Marquardt (LM) algorithm is employed to optimize the network's performance with six neurons in the hidden layer. To create second-order polynomial equations for predictive modeling, a total of 17 experiments were conducted. The accuracy of the predictive performance of RSM and ANN was evaluated using the margin of deviation (MOD), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (<em>R²</em>). The optimal ANN configuration exhibited a high <em>R</em><sup>2</sup> (0.9945) and a low MSE error (0.0030) as compared to the RSM model. Moreover, the average error for the ANN was 1.8192 %, which is significantly less than the 3.9773 % error of RSM. Both methods were successful in forecasting the thermal conductivity ratio of aluminum oxide–water nanofluids, although the ANN method was more accurate. According to these results, ANN is a practical and effective tool for evaluating and improving heat transfer systems based on nanofluids in industrial applications.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"30 ","pages":"Article 101426"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Phase change materials (PCMs) are gaining significant attention for their efficiency in thermal energy storage. Recent research shows that PCMs can enhance heat storage systems' effectiveness when used in photovoltaic (PV) panels. By adding nanoparticles, thermal conductivity and heat transmission are improved. This study aimed to review the recent advancements and future challenges of PCMs based on metallic, carbonic, ceramic, and hybrid nanomaterials. The up-to-date references were taken from the Google search engine. Results indicated that metallic nanoparticles like copper (20 nm) can increase thermal conductivity by up to 46.3 % and diffusivity by 44.9 % with minor changes in phase transition temperatures. While carbonic materials like expanded graphite (EG) show latent heat retention trade-offs, they are 40 times more conductive than pure paraffin. Ceramic nanoparticles, such as Al2O₃ and Fe3O₄, enhance structural stability and reduce super-cooling, with Fe3O₄ composites showing a 60 % conductivity increase. Hybrid systems validated by predictive machine learning techniques integrate conductivity, nucleation, and thermal stability, using materials like graphene-WO₃ nano-fluids and SiO₂-CeO₂-paraffin. These developments highlight nanomaterials' potential to improve paraffin's low conductivity while balancing nanoparticle integration to maintain energy density. Challenges remain in addressing trade-offs like restricted natural convection and decreased latent heat (up to 35 % at high filler loadings). Structural modifications, such as radial fins combined with Al2O₃ nanoparticles, result in a 28.3 % faster melting rate, compensating for convection losses. Real-world applications demonstrate scalability, with Cu-paraffin composites achieving a 1.7 % efficiency gain and Gr-Ag hybrids extending operation by three hours. Environmentally friendly methods, such as plant-derived iron oxide nanoparticles, prioritize sustainability without compromising functionality. Future research should focus on scalable synthesis, optimal filler interactions, and durability testing to meet global demands for effective, sustainable thermal energy storage solutions.
{"title":"Nano enhanced phase change materials for thermal energy storage system applications: A comprehensive review of recent advancements and future challenges","authors":"Bayew Adera , Venkata Ramayya Ancha , Tassew Tadiwose , Eshetu Getahun","doi":"10.1016/j.ijft.2025.101418","DOIUrl":"10.1016/j.ijft.2025.101418","url":null,"abstract":"<div><div>Phase change materials (PCMs) are gaining significant attention for their efficiency in thermal energy storage. Recent research shows that PCMs can enhance heat storage systems' effectiveness when used in photovoltaic (PV) panels. By adding nanoparticles, thermal conductivity and heat transmission are improved. This study aimed to review the recent advancements and future challenges of PCMs based on metallic, carbonic, ceramic, and hybrid nanomaterials. The up-to-date references were taken from the Google search engine. Results indicated that metallic nanoparticles like copper (20 nm) can increase thermal conductivity by up to 46.3 % and diffusivity by 44.9 % with minor changes in phase transition temperatures. While carbonic materials like expanded graphite (EG) show latent heat retention trade-offs, they are 40 times more conductive than pure paraffin. Ceramic nanoparticles, such as Al<sub>2</sub>O₃ and Fe<sub>3</sub>O₄, enhance structural stability and reduce super-cooling, with Fe<sub>3</sub>O₄ composites showing a 60 % conductivity increase. Hybrid systems validated by predictive machine learning techniques integrate conductivity, nucleation, and thermal stability, using materials like graphene-WO₃ nano-fluids and SiO₂-CeO₂-paraffin. These developments highlight nanomaterials' potential to improve paraffin's low conductivity while balancing nanoparticle integration to maintain energy density. Challenges remain in addressing trade-offs like restricted natural convection and decreased latent heat (up to 35 % at high filler loadings). Structural modifications, such as radial fins combined with Al<sub>2</sub>O₃ nanoparticles, result in a 28.3 % faster melting rate, compensating for convection losses. Real-world applications demonstrate scalability, with Cu-paraffin composites achieving a 1.7 % efficiency gain and Gr-Ag hybrids extending operation by three hours. Environmentally friendly methods, such as plant-derived iron oxide nanoparticles, prioritize sustainability without compromising functionality. Future research should focus on scalable synthesis, optimal filler interactions, and durability testing to meet global demands for effective, sustainable thermal energy storage solutions.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"30 ","pages":"Article 101418"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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