Pub Date : 2026-01-01DOI: 10.1016/j.ijft.2025.101534
Yogesh K. , Varatharaj K. , Tamizharasi R.
This study investigates the synergistic influence of magnetohydrodynamics, thermal radiation and porous medium on the transport of Casson–Jeffrey hybrid nanofluid over both linear and nonlinear stretching sheets. The governing equations are solved numerically using the Runge–Kutta with Shooting method and the outcomes are validated with a multilayer perceptron artificial neural network in neural network time series. The key controlling parameters include magnetic number, Casson parameter, Jeffrey parameter, buoyancy ratio, radiation parameter, Brownian motion, thermophoresis, Prandtl number and heat generation. The results demonstrate that increasing the magnetic parameter from 0.1 to 0.4 enhances the skin friction magnitude by approximately 12% for linear stretching and 15% for nonlinear stretching. Similarly, raising the radiation parameter from 0.1 to 0.4 increases skin friction magnitude by about 10% in the linear case and 25% in the nonlinear case. In contrast, the Nusselt number decreases when the Brownian motion parameter rises from 0.1 to 0.4, leading to an almost 11% reduction in both linear and nonlinear flows. Thermophoresis effects further suppress the heat transfer rate, showing a 5% decline when its value increases from 0.1 to 0.4. Neural network validation confirms the accuracy of the solver, with regression coefficients very close to unity and mean square error values as low as . These findings underline the physical importance of magnetic, radiative, and porous medium effects in hybrid nanofluid transport and demonstrate the effectiveness of artificial intelligence tools for predictive modeling. Future research can extend this framework to unsteady, three-dimensional and experimentally validated configurations.
{"title":"Numerical and artificial neural network time-series modeling of Casson–Jeffrey nanofluid flow over linear and nonlinear stretching surfaces in porous media","authors":"Yogesh K. , Varatharaj K. , Tamizharasi R.","doi":"10.1016/j.ijft.2025.101534","DOIUrl":"10.1016/j.ijft.2025.101534","url":null,"abstract":"<div><div>This study investigates the synergistic influence of magnetohydrodynamics, thermal radiation and porous medium on the transport of Casson–Jeffrey hybrid nanofluid over both linear and nonlinear stretching sheets. The governing equations are solved numerically using the Runge–Kutta with Shooting method and the outcomes are validated with a multilayer perceptron artificial neural network in neural network time series. The key controlling parameters include magnetic number, Casson parameter, Jeffrey parameter, buoyancy ratio, radiation parameter, Brownian motion, thermophoresis, Prandtl number and heat generation. The results demonstrate that increasing the magnetic parameter from 0.1 to 0.4 enhances the skin friction magnitude by approximately 12% for linear stretching and 15% for nonlinear stretching. Similarly, raising the radiation parameter from 0.1 to 0.4 increases skin friction magnitude by about 10% in the linear case and 25% in the nonlinear case. In contrast, the Nusselt number decreases when the Brownian motion parameter rises from 0.1 to 0.4, leading to an almost 11% reduction in both linear and nonlinear flows. Thermophoresis effects further suppress the heat transfer rate, showing a 5% decline when its value increases from 0.1 to 0.4. Neural network validation confirms the accuracy of the solver, with regression coefficients very close to unity <span><math><mrow><mi>R</mi><mo>≈</mo><mn>0</mn><mo>.</mo><mn>9999</mn></mrow></math></span> and mean square error values as low as <span><math><mrow><mn>1</mn><mo>.</mo><mn>84</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>6</mn></mrow></msup></mrow></math></span>. These findings underline the physical importance of magnetic, radiative, and porous medium effects in hybrid nanofluid transport and demonstrate the effectiveness of artificial intelligence tools for predictive modeling. Future research can extend this framework to unsteady, three-dimensional and experimentally validated configurations.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101534"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926539","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}
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}
This study investigates the thermal performance of four innovative insulation materials phase change materials (PCM), aerogel, vacuum insulated panels (VIP), and autoclaved aerated concrete (AAC)for cold-climate buildings in Varzaqan, Iran. Using 24 years of hourly climate data, five wall configurations (uninsulated reference, PCM, aerogel, VIP, and AAC) were simulated in EnergyPlus, with PCM behavior modeled via an enthalpy-temperature phase change routine. Key indicators included annual heating energy demand, wall surface temperature stability, time lag, and comfort hours. Results show that VIP achieved the greatest reduction in annual heating demand (36.6%), followed by aerogel (29.5%), AAC (24.1%), and PCM (21.4%). PCM and AAC provided substantial thermal inertia, delaying heat transfer by150–180 min and90–120 min, respectively, thereby enhancing night-time comfort. In contrast, VIP and aerogel maintained nearly constant surface temperatures (fluctuations <3ºC). Hybrid configurations offered the most favorable outcomes: a VIP+PCM wall reduced annual demand by 40.3% and achieved∼6510 comfort hours (74% of the year). Passive solar gains, when integrated into the analysis, improved PCM effectiveness by ∼12% in sunny winter days, while AAC showed moderate benefit and VIP remained largely unaffected. Sensitivity analysis highlighted VIP’s vulnerability to vacuum loss ∼12% performance degradation and AAC’s dependence on moisture, whereas aerogel and PCM proved more robust. Overall, the findings underscore the complementarity of ultra-low conductivity materials (VIP, aerogel) and high thermal mass/storage materials (PCM,AAC). While economic and practical barriers remain, hybrid approaches represent a promising pathway to significantly reducing heating energy demand and improving thermal comfort in cold climates.
{"title":"Evaluation of Thermal Performance of Innovative Insulation Materials for Energy-Efficient Buildings in Cold Climates","authors":"Ali Maboudi Reveshti , Farid Hosseini Mansoub , Jhila Nasiri Reveshti , Karim Farajeyan Bonab","doi":"10.1016/j.ijft.2025.101544","DOIUrl":"10.1016/j.ijft.2025.101544","url":null,"abstract":"<div><div>This study investigates the thermal performance of four innovative insulation materials phase change materials (PCM), aerogel, vacuum insulated panels (VIP), and autoclaved aerated concrete (AAC)for cold-climate buildings in Varzaqan, Iran. Using 24 years of hourly climate data, five wall configurations (uninsulated reference, PCM, aerogel, VIP, and AAC) were simulated in EnergyPlus, with PCM behavior modeled via an enthalpy-temperature phase change routine. Key indicators included annual heating energy demand, wall surface temperature stability, time lag, and comfort hours. Results show that VIP achieved the greatest reduction in annual heating demand (36.6%), followed by aerogel (29.5%), AAC (24.1%), and PCM (21.4%). PCM and AAC provided substantial thermal inertia, delaying heat transfer by150–180 min and90–120 min, respectively, thereby enhancing night-time comfort. In contrast, VIP and aerogel maintained nearly constant surface temperatures (fluctuations <3ºC). Hybrid configurations offered the most favorable outcomes: a VIP+PCM wall reduced annual demand by 40.3% and achieved∼6510 comfort hours (74% of the year). Passive solar gains, when integrated into the analysis, improved PCM effectiveness by ∼12% in sunny winter days, while AAC showed moderate benefit and VIP remained largely unaffected. Sensitivity analysis highlighted VIP’s vulnerability to vacuum loss ∼12% performance degradation and AAC’s dependence on moisture, whereas aerogel and PCM proved more robust. Overall, the findings underscore the complementarity of ultra-low conductivity materials (VIP, aerogel) and high thermal mass/storage materials (PCM,AAC). While economic and practical barriers remain, hybrid approaches represent a promising pathway to significantly reducing heating energy demand and improving thermal comfort in cold climates.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"32 ","pages":"Article 101544"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024815","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 : 2026-01-01DOI: 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 : 2026-01-01DOI: 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 : 2026-01-01DOI: 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 : 2026-01-01DOI: 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}
Pub Date : 2025-12-17DOI: 10.1016/j.ijft.2025.101532
Bin Chen, Yutong Lei, Jiayun Ding
The advancement and implementation of low-carbon buildings are crucial for global climate change mitigation and sustainable development. However, conventional single-energy systems often suffer from limited efficiency and high carbon emissions, highlighting the need for integrated and efficient multi-output energy solutions. This study proposes a novel cogeneration system for simultaneous electricity, hydrogen, and heat production based on photovoltaic power generation, with operational parameters for electrolysis and fuel processes determined through parametric analysis. Energy and environmental assessments were conducted to evaluate system performance. The results show that the system achieves a peak solar power output of 125.68 kW/h, an alkaline electrolysis hydrogen production rate of 708.9 mol/h, and a proton exchange membrane fuel cell power generation of 10.3 kW. The overall system efficiency reaches 0.90, representing improvements of 30.19% and 74.77% compared to standalone alkaline electrolysis and fuel cell systems, respectively. Additionally, the system can reduce CO₂ emissions by 352,451 kg annually, demonstrating significant potential for enhancing energy efficiency and supporting decarbonization in the building sector.
{"title":"Energy and environmental analysis of a hydrogen energy cogeneration system based on photovoltaic power generation for low-carbon building","authors":"Bin Chen, Yutong Lei, Jiayun Ding","doi":"10.1016/j.ijft.2025.101532","DOIUrl":"10.1016/j.ijft.2025.101532","url":null,"abstract":"<div><div>The advancement and implementation of low-carbon buildings are crucial for global climate change mitigation and sustainable development. However, conventional single-energy systems often suffer from limited efficiency and high carbon emissions, highlighting the need for integrated and efficient multi-output energy solutions. This study proposes a novel cogeneration system for simultaneous electricity, hydrogen, and heat production based on photovoltaic power generation, with operational parameters for electrolysis and fuel processes determined through parametric analysis. Energy and environmental assessments were conducted to evaluate system performance. The results show that the system achieves a peak solar power output of 125.68 kW/h, an alkaline electrolysis hydrogen production rate of 708.9 mol/h, and a proton exchange membrane fuel cell power generation of 10.3 kW. The overall system efficiency reaches 0.90, representing improvements of 30.19% and 74.77% compared to standalone alkaline electrolysis and fuel cell systems, respectively. Additionally, the system can reduce CO₂ emissions by 352,451 kg annually, demonstrating significant potential for enhancing energy efficiency and supporting decarbonization in the building sector.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"32 ","pages":"Article 101532"},"PeriodicalIF":0.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981467","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 : 2025-12-12DOI: 10.1016/j.ijft.2025.101531
Irfan Ahmad Sheikh , Emad Elnajjar , Mahmoud Elgendi
Flow control is essential in various engineering applications and environmental contexts to ensure safety, improve efficiency, and enhance overall performance. This study examines the influence of slot configurations at turbulent flow separation points on a circular cylinder and their ability to passively control vortex shedding at a high Reynolds number (Re) = 3.6 × 10⁶. An unsteady Reynolds-Averaged Navier–Stokes (URANS) simulation using a realizable k–ε turbulence model with standard wall treatment was employed to evaluate the aerodynamic behavior of two slot geometries, straight and curved, under identical flow conditions. The results reveal that the introduction of slots substantially modifies the wake structure and aerodynamic loading, increasing the mean drag coefficient from 0.379 for the smooth cylinder to 0.99 and 1.5 for the straight and curved slot configurations, respectively. Similarly, the lift coefficient amplitude increased nearly tenfold, from ±0.1 to approximately ±1 for the curved-slotted cylinder. These findings confirm that slot-induced flow reattachment and momentum exchange enhance vortex coherence and wake stability, providing a robust passive flow-control mechanism. The proposed configuration demonstrates strong potential for integration into bluff-body-based systems such as bladeless wind turbines and tidal energy harvesters, where enhanced lift and controlled drag can improve energy capture efficiency and structural performance.
{"title":"Passive control of turbulent flow around a circular cylinder using slots at separation points","authors":"Irfan Ahmad Sheikh , Emad Elnajjar , Mahmoud Elgendi","doi":"10.1016/j.ijft.2025.101531","DOIUrl":"10.1016/j.ijft.2025.101531","url":null,"abstract":"<div><div>Flow control is essential in various engineering applications and environmental contexts to ensure safety, improve efficiency, and enhance overall performance. This study examines the influence of slot configurations at turbulent flow separation points on a circular cylinder and their ability to passively control vortex shedding at a high Reynolds number (<em>Re</em>) = 3.6 × 10⁶. An unsteady Reynolds-Averaged Navier–Stokes (URANS) simulation using a realizable k–ε turbulence model with standard wall treatment was employed to evaluate the aerodynamic behavior of two slot geometries, straight and curved, under identical flow conditions. The results reveal that the introduction of slots substantially modifies the wake structure and aerodynamic loading, increasing the mean drag coefficient from 0.379 for the smooth cylinder to 0.99 and 1.5 for the straight and curved slot configurations, respectively. Similarly, the lift coefficient amplitude increased nearly tenfold, from ±0.1 to approximately ±1 for the curved-slotted cylinder. These findings confirm that slot-induced flow reattachment and momentum exchange enhance vortex coherence and wake stability, providing a robust passive flow-control mechanism. The proposed configuration demonstrates strong potential for integration into bluff-body-based systems such as bladeless wind turbines and tidal energy harvesters, where enhanced lift and controlled drag can improve energy capture efficiency and structural performance.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101531"},"PeriodicalIF":0.0,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798227","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 : 2025-12-11DOI: 10.1016/j.ijft.2025.101522
Praveen Cheekatamarla , Vishaldeep Sharma , Hongbin Sun
The increasing prevalence of counterfeit and incompatible refrigerants presents significant risks to Heating, Ventilation, Air Conditioning, and Refrigeration (HVAC&R) systems, including compromised equipment performance, safety hazards, and non-compliance. This article details the development of a novel, cost-effective, and portable detection device designed to accurately verify refrigerants. The device utilizes a controlled gas sampling and analysis system within a sealed chamber, ensuring precise measurements while maintaining safety through a purging mechanism. The system features a high-sensitivity sensor integrated with an onboard control module that analyzes gas composition in real-time, providing feedback within a 2-minute duration. Laboratory validation demonstrated the device’s high accuracy (>95 % based on correct identification of compliant vs. non-compliant blends) in detecting unauthorized refrigerant blends. The projected cost of the product stands at ∼ $150, based on the retail pricing of individual components. Laboratory validation demonstrated the device’s high accuracy (>95 % for composition identification, 100 % rejection of tested counterfeit/incorrect blends) in detecting unauthorized refrigerant blends with a response time <2 min. The device correctly identified authentic R-454A/B/C blends and reliably rejected R-407F and closely related counterfeit mixtures. Key advantages include affordability, ease of use, rapid response time, and compatibility with a wide range of refrigerants. This solution supports compliance with regulatory frameworks, enhances safety in HVAC&R operations, and mitigates the risks associated with counterfeit refrigerants.
{"title":"Innovative approach to counterfeit and noncompliant refrigerant detection: A cost-effective, portable solution","authors":"Praveen Cheekatamarla , Vishaldeep Sharma , Hongbin Sun","doi":"10.1016/j.ijft.2025.101522","DOIUrl":"10.1016/j.ijft.2025.101522","url":null,"abstract":"<div><div>The increasing prevalence of counterfeit and incompatible refrigerants presents significant risks to Heating, Ventilation, Air Conditioning, and Refrigeration (HVAC&R) systems, including compromised equipment performance, safety hazards, and non-compliance. This article details the development of a novel, cost-effective, and portable detection device designed to accurately verify refrigerants. The device utilizes a controlled gas sampling and analysis system within a sealed chamber, ensuring precise measurements while maintaining safety through a purging mechanism. The system features a high-sensitivity sensor integrated with an onboard control module that analyzes gas composition in real-time, providing feedback within a 2-minute duration. Laboratory validation demonstrated the device’s high accuracy (>95 % based on correct identification of compliant vs. non-compliant blends) in detecting unauthorized refrigerant blends. The projected cost of the product stands at ∼ $150, based on the retail pricing of individual components. Laboratory validation demonstrated the device’s high accuracy (>95 % for composition identification, 100 % rejection of tested counterfeit/incorrect blends) in detecting unauthorized refrigerant blends with a response time <2 min. The device correctly identified authentic R-454A/B/C blends and reliably rejected R-407F and closely related counterfeit mixtures. Key advantages include affordability, ease of use, rapid response time, and compatibility with a wide range of refrigerants. This solution supports compliance with regulatory frameworks, enhances safety in HVAC&R operations, and mitigates the risks associated with counterfeit refrigerants.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101522"},"PeriodicalIF":0.0,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798225","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号