Pub Date : 2026-03-01Epub Date: 2026-01-22DOI: 10.1016/j.ijft.2026.101566
Tangwei Liu , Dingding Yan , Xiaoyu Zhong , Wanglin Ouyang , Jeevan Kafle
Accurate reconstruction of subsurface temperature fields in layered media underpins exploration and development of deep geothermal resources. Traditional inverse computation methods improve numerical stability of finite‐difference schemes but still require careful regularization and layer‐by‐layer marching. In contrast, Physics‐Informed Neural Networks (PINNs) directly integrate governing equations, interface continuity, and boundary observations in a single mesh‐free optimization, dramatically reducing sensitivity to noise and eliminating the need for manual layer strategies. Through numerical experiments on two-dimensional multilayered domains, we show that PINNs method maintains robustness under realistic measurement noise, and deliver comparable accuracy without bespoke regularization parameters. Our results demonstrate that PINNs not only simplify the inverse workflow but also outperform classical layer‐marching approaches in accuracy, stability, and computational efficiency.
{"title":"A class of Cauchy problems for the Poisson equation from steady-state heat conduction in multilayered media","authors":"Tangwei Liu , Dingding Yan , Xiaoyu Zhong , Wanglin Ouyang , Jeevan Kafle","doi":"10.1016/j.ijft.2026.101566","DOIUrl":"10.1016/j.ijft.2026.101566","url":null,"abstract":"<div><div>Accurate reconstruction of subsurface temperature fields in layered media underpins exploration and development of deep geothermal resources. Traditional inverse computation methods improve numerical stability of finite‐difference schemes but still require careful regularization and layer‐by‐layer marching. In contrast, Physics‐Informed Neural Networks (PINNs) directly integrate governing equations, interface continuity, and boundary observations in a single mesh‐free optimization, dramatically reducing sensitivity to noise and eliminating the need for manual layer strategies. Through numerical experiments on two-dimensional multilayered domains, we show that PINNs method maintains robustness under realistic measurement noise, and deliver comparable accuracy without bespoke regularization parameters. Our results demonstrate that PINNs not only simplify the inverse workflow but also outperform classical layer‐marching approaches in accuracy, stability, and computational efficiency.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"32 ","pages":"Article 101566"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078821","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 : 2026-03-01Epub Date: 2026-02-11DOI: 10.1016/j.ijft.2026.101579
Charity Mokom , Chika Oliver Ujah , Christian O. Asadu , Peter A. Olubambi
The fourth industrial revolution (4IR) era is characterized by the adoption of Innovative and emerging renewable technologies, such as solar photovoltaic (PV), which are pivotal in driving the transition to green energy. This study examines the synergies between solar PV and 4IR technology, such as AI and blockchain, enabling the development of distributed energy systems, intelligent grids, and sophisticated energy trading platforms. Recent technological advances in PV, such as bifacial, flexible, floating, perovskite and tandem cells, improve energy conversion efficiency and system durability, minimising dependency on conventional fuels and limiting CO2 emissions. The integration of energy storage solutions, such as batteries, pumped hydro, compressed air, and hydrogen systems, mitigates variability and ensures grid stability. The role of solar PV in providing rural areas with electricity has increased accessibility to green energy, thereby strengthening communities and local economies. Furthermore, PV promotes circular economy initiatives by facilitating reuse and recycling, and improves grid-integrated PV systems. Leveraging these prospects could see solar PV emerge as a reliable, mainstream energy source, aligning seamlessly with worldwide sustainability targets and championing decarburization alongside energy equality. Although there are barriers relating to finance, infrastructure, and regulation, introducing policy innovations could speed up the deployment of PV systems. This study emphasizes the necessity of further research, favourable regulations, and collaborative efforts between various stakeholders to realize the utmost capabilities of solar PV in developing a robust, sustainable approach to energy.
{"title":"Development of solar photovoltaic as the mainstream source of energy in the fourth industrial revolution (4IR)","authors":"Charity Mokom , Chika Oliver Ujah , Christian O. Asadu , Peter A. Olubambi","doi":"10.1016/j.ijft.2026.101579","DOIUrl":"10.1016/j.ijft.2026.101579","url":null,"abstract":"<div><div>The <strong>fourth industrial revolution (</strong>4IR) era is characterized by the adoption of Innovative and emerging renewable technologies, such as solar photovoltaic (PV), which are pivotal in driving the transition to green energy. This study examines the synergies between solar PV and 4IR technology, such as AI and blockchain, enabling the development of distributed energy systems, intelligent grids, and sophisticated energy trading platforms. Recent technological advances in PV, such as bifacial, flexible, floating, perovskite and tandem cells, improve energy conversion efficiency and system durability, minimising dependency on conventional fuels and limiting CO<sub>2</sub> emissions. The integration of energy storage solutions, such as batteries, pumped hydro, compressed air, and hydrogen systems, mitigates variability and ensures grid stability. The role of solar PV in providing rural areas with electricity has increased accessibility to green energy, thereby strengthening communities and local economies. Furthermore, PV promotes circular economy initiatives by facilitating reuse and recycling, and improves grid-integrated PV systems. Leveraging these prospects could see solar PV emerge as a reliable, mainstream energy source, aligning seamlessly with worldwide sustainability targets and championing decarburization alongside energy equality. Although there are barriers relating to finance, infrastructure, and regulation, introducing policy innovations could speed up the deployment of PV systems. This study emphasizes the necessity of further research, favourable regulations, and collaborative efforts between various stakeholders to realize the utmost capabilities of solar PV in developing a robust, sustainable approach to energy.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"32 ","pages":"Article 101579"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398210","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}
Thermal management plays a key role in ensuring efficient operation across EVs, aerospace, power plants, and high-performance computing. Excessive heat flux and rising operating temperatures can significantly compromise the reliability and efficiency of devices such as CPUs, GPUs, and stretchable electronics. This review addresses thermal stress issues in modern electronic systems and highlights advanced cooling strategies, with a particular focus on microchannel heat exchangers (MCHS). Further this review emphasizes on evaluating cooling technologies for better energy efficiency and clean-energy support, highlighting nanofluids and PCMs for effective transient heat control. This study presents a recent progress in computational approaches including molecular dynamics simulations (MDS), computational fluid dynamics (CFD), and machine learning techniques such as genetic algorithms and artificial neural networks for optimizing microchannel geometries and enhancing heat transfer efficiency. Special attention is given to emerging microchannel designs, fluid flow behavior, and cooling performance, including concepts such as the Zwieback–Fung effect and fractal like branching channels. The review concludes by outlining future research directions toward the development of highly efficient and cost-effective cooling solutions for next-generation high-power electronic systems.
{"title":"Integrating CFD, molecular dynamics, and AI techniques for thermal management of heat transfer devices in microelectronic chip cooling using nano-enhanced PCMs and hybrid cooling systems","authors":"Monica Jayaram Indhe , Anirban Sur , Ashok Kumar Yadav , Ashok Kumar Dewangan , Ashu Yadav","doi":"10.1016/j.ijft.2026.101587","DOIUrl":"10.1016/j.ijft.2026.101587","url":null,"abstract":"<div><div>Thermal management plays a key role in ensuring efficient operation across EVs, aerospace, power plants, and high-performance computing. Excessive heat flux and rising operating temperatures can significantly compromise the reliability and efficiency of devices such as CPUs, GPUs, and stretchable electronics. This review addresses thermal stress issues in modern electronic systems and highlights advanced cooling strategies, with a particular focus on microchannel heat exchangers (MCHS). Further this review emphasizes on evaluating cooling technologies for better energy efficiency and clean-energy support, highlighting nanofluids and PCMs for effective transient heat control. This study presents a recent progress in computational approaches including molecular dynamics simulations (MDS), computational fluid dynamics (CFD), and machine learning techniques such as genetic algorithms and artificial neural networks for optimizing microchannel geometries and enhancing heat transfer efficiency. Special attention is given to emerging microchannel designs, fluid flow behavior, and cooling performance, including concepts such as the Zwieback–Fung effect and fractal like branching channels. The review concludes by outlining future research directions toward the development of highly efficient and cost-effective cooling solutions for next-generation high-power electronic systems.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"32 ","pages":"Article 101587"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147398211","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 : 2026-03-01Epub Date: 2026-01-03DOI: 10.1016/j.ijft.2026.101547
S Raghu , K.M Niranjan , Venkanagouda M Goudar , N Neelima , K Vinutha , J.K Madhukesh
The precise forecasting of thermal and mass transportation properties in a non-Newtonian nanofluid circulation is vital for the design and development of efficient thermal management systems, especially in micro-scale electronic devices, polymer processing, and biomedical equipment. In this context, the current work inspects the thermal and mass distribution of Casson nanofluid composed of SWCNT nanoparticles and sodium alginate-based liquid over an exponential stretching surface in the presence of inclined magnetic field, chemical reaction, slip impact, and non-uniform heat source/sink physical phenomena. The effective transport properties of these nanofluids strongly depend on their molecular structure, necessitating the use of topological indices. Similarity transformations are utilized to alter the governing partial differential equations (PDEs) to a system of ordinary differential equations (ODEs), and solutions are obtained using Runge Kutta Fehlberg - 4th 5th scheme and the shooting technique. The outcomes of the numerical calculations are presented and visualized with the aid of graphs. To improve the predictive capability, a Multi-Task Neural Network is developed and offers improved generalization across a wide range of parameter values. The numerical outcomes show that improving Casson, inclination angle, and magnetic parameter values slows down the velocity, while thermal slip declines the temperature profile. The improvement in the rate of heat and mass transfer improves up to 2.3753% and 10.5201% in the presence of Casson nanofluid for changes in inclination angle and magnetic field. The outcomes of the neural network model show strong agreement among the numerical and MTNN predictions, with a total loss of 0.000, and R2 for Cf, Nu, and Sh tasks are found to be 0.9999, 0.9997, and 0.9994, respectively, indicating a perfect fit of the data for predicted and target values with excellent convergence and effective numerical stability.
{"title":"Thermal and mass transfer prediction of Casson based nanofluid flow over an exponential stretching sheet using a Multi-Task Neural Network approach","authors":"S Raghu , K.M Niranjan , Venkanagouda M Goudar , N Neelima , K Vinutha , J.K Madhukesh","doi":"10.1016/j.ijft.2026.101547","DOIUrl":"10.1016/j.ijft.2026.101547","url":null,"abstract":"<div><div>The precise forecasting of thermal and mass transportation properties in a non-Newtonian nanofluid circulation is vital for the design and development of efficient thermal management systems, especially in micro-scale electronic devices, polymer processing, and biomedical equipment. In this context, the current work inspects the thermal and mass distribution of Casson nanofluid composed of SWCNT nanoparticles and sodium alginate-based liquid over an exponential stretching surface in the presence of inclined magnetic field, chemical reaction, slip impact, and non-uniform heat source/sink physical phenomena. The effective transport properties of these nanofluids strongly depend on their molecular structure, necessitating the use of topological indices. Similarity transformations are utilized to alter the governing partial differential equations (PDEs) to a system of ordinary differential equations (ODEs), and solutions are obtained using Runge Kutta Fehlberg - 4<sup>th</sup> 5<sup>th</sup> scheme and the shooting technique. The outcomes of the numerical calculations are presented and visualized with the aid of graphs. To improve the predictive capability, a Multi-Task Neural Network is developed and offers improved generalization across a wide range of parameter values. The numerical outcomes show that improving Casson, inclination angle, and magnetic parameter values slows down the velocity, while thermal slip declines the temperature profile. The improvement in the rate of heat and mass transfer improves up to 2.3753% and 10.5201% in the presence of Casson nanofluid for changes in inclination angle and magnetic field. The outcomes of the neural network model show strong agreement among the numerical and MTNN predictions, with a total loss of 0.000, and <em>R</em><sup>2</sup> for <em>Cf, Nu</em>, and <em>Sh</em> tasks are found to be 0.9999, 0.9997, and 0.9994, respectively, indicating a perfect fit of the data for predicted and target values with excellent convergence and effective numerical stability.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"32 ","pages":"Article 101547"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024814","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 : 2026-03-01Epub Date: 2026-01-05DOI: 10.1016/j.ijft.2026.101550
Lilik Hasanah , Fasya Nur Afifah , Roer Eka Pawinanto , Muhammad Iqbal , Gilang Gumilar , Muhammad Yusuf , Widyaningrum Indrasari , Ida Hamidah , Jumril Yunas , Budi Mulyanti
Passive micromixers are essential components in microfluidic systems, enabling efficient fluid mixing under laminar flow conditions without external energy input. This study numerically investigates the mixing performance and flow mechanisms of passive micromixers incorporating Koch fractal obstacle arrays. Four fractal geometries—Secondary Snowflakes Fractal (SSF), Tertiary Snowflakes Fractal (TSF), Rounded Secondary Snowflakes Fractal (RSSF), and Rounded Tertiary Snowflakes Fractal (RTSF) are analyzed in both same-side and different-side configurations using COMSOL Multiphysics over a wide Reynolds number range (Re = 0.1–100). Model validation against benchmark obstacle-based micromixers from the literature shows good agreement in mixing efficiency and pressure drop, confirming the reliability of the numerical framework. The results demonstrate that Koch fractal obstacles enhance mixing through flow splitting, stretching, and chaotic advection. Among all configurations, the different-side rounded tertiary snowflake fractal (DSRTSF) exhibits the most stable and consistently high mixing performance, achieving a maximum mixing efficiency of 97.70% at a Reynolds number (Re) of 0.1. Performance index analysis further reveals that rounded fractal geometries provide a favorable balance between mixing efficiency and pressure drop. These findings offer practical design guidelines for high-performance fractal obstacle-based passive micromixers in lab-on-a-chip and microfluidic applications.
{"title":"A stable high mixing performance of Koch fractal array obstacle-based micromixer","authors":"Lilik Hasanah , Fasya Nur Afifah , Roer Eka Pawinanto , Muhammad Iqbal , Gilang Gumilar , Muhammad Yusuf , Widyaningrum Indrasari , Ida Hamidah , Jumril Yunas , Budi Mulyanti","doi":"10.1016/j.ijft.2026.101550","DOIUrl":"10.1016/j.ijft.2026.101550","url":null,"abstract":"<div><div>Passive micromixers are essential components in microfluidic systems, enabling efficient fluid mixing under laminar flow conditions without external energy input. This study numerically investigates the mixing performance and flow mechanisms of passive micromixers incorporating Koch fractal obstacle arrays. Four fractal geometries—Secondary Snowflakes Fractal (SSF), Tertiary Snowflakes Fractal (TSF), Rounded Secondary Snowflakes Fractal (RSSF), and Rounded Tertiary Snowflakes Fractal (RTSF) are analyzed in both same-side and different-side configurations using COMSOL Multiphysics over a wide Reynolds number range (<em>Re</em> = 0.1–100). Model validation against benchmark obstacle-based micromixers from the literature shows good agreement in mixing efficiency and pressure drop, confirming the reliability of the numerical framework. The results demonstrate that Koch fractal obstacles enhance mixing through flow splitting, stretching, and chaotic advection. Among all configurations, the different-side rounded tertiary snowflake fractal (DSRTSF) exhibits the most stable and consistently high mixing performance, achieving a maximum mixing efficiency of 97.70% at a Reynolds number (<em>Re</em>) of 0.1. Performance index analysis further reveals that rounded fractal geometries provide a favorable balance between mixing efficiency and pressure drop. These findings offer practical design guidelines for high-performance fractal obstacle-based passive micromixers in lab-on-a-chip and microfluidic applications.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"32 ","pages":"Article 101550"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981356","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: 2025-12-28DOI: 10.1016/j.ijft.2025.101540
Vo Long Hai , Nguyen Duc Nam , Nguyen Minh Phu
This study presents a numerical investigation into the coupled heat and mass transfer phenomena and the associated thermodynamic irreversibilities (entropy generation) of humid air flowing over a flat plate under vacuum conditions (40 kPa to 100 kPa). A comprehensive mathematical model, incorporating the continuity, momentum, energy, and species transport equations, was established and solved using the finite difference method. The model's accuracy was validated by comparing the numerical local heat transfer coefficient results, which demonstrated excellent agreement with theoretical predictions. Key findings indicate that lower operating pressures significantly enhance mass transfer efficiency: the local mass transfer coefficient approached its maximum value of approximately 4.5 × 10−4 m/s at 40 kPa. This enhancement translates directly to water production, with the hourly condensation rate increasing from about 0.11 kg/m2-h at 100 kPa to approximately 0.19 kg/m2-h at 40 kPa. Examination of irreversibilities showed that thermal irreversibility is the predominant contributor to entropy generation. At 100 kPa, the local thermal irreversibility peaked at approximately 50 W/m3-K (compared to concentration irreversibility up to 10 W/m3-K and viscous irreversibility around 1.6 × 10−3 W/m3-K. On average, the total irreversibility increased with operating pressure, ranging from approximately 0.6 to 0.85 W/m3-K as the pressure rose from 40 kPa to 100 kPa. These quantitative insights offer valuable understanding for optimizing the design and improving the performance of humid air systems operating under vacuum by focusing on reducing thermal energy dissipation.
{"title":"Heat and mass transfer and irreversibilities of humid air flow over a flat plate operating under vacuum","authors":"Vo Long Hai , Nguyen Duc Nam , Nguyen Minh Phu","doi":"10.1016/j.ijft.2025.101540","DOIUrl":"10.1016/j.ijft.2025.101540","url":null,"abstract":"<div><div>This study presents a numerical investigation into the coupled heat and mass transfer phenomena and the associated thermodynamic irreversibilities (entropy generation) of humid air flowing over a flat plate under vacuum conditions (40 kPa to 100 kPa). A comprehensive mathematical model, incorporating the continuity, momentum, energy, and species transport equations, was established and solved using the finite difference method. The model's accuracy was validated by comparing the numerical local heat transfer coefficient results, which demonstrated excellent agreement with theoretical predictions. Key findings indicate that lower operating pressures significantly enhance mass transfer efficiency: the local mass transfer coefficient approached its maximum value of approximately 4.5 × 10<sup>−4</sup> m/s at 40 kPa. This enhancement translates directly to water production, with the hourly condensation rate increasing from about 0.11 kg/m<sup>2</sup>-h at 100 kPa to approximately 0.19 kg/m<sup>2</sup>-h at 40 kPa. Examination of irreversibilities showed that thermal irreversibility is the predominant contributor to entropy generation. At 100 kPa, the local thermal irreversibility peaked at approximately 50 W/m<sup>3</sup>-K (compared to concentration irreversibility up to 10 W/m<sup>3</sup>-K and viscous irreversibility around 1.6 × 10<sup>−3</sup> W/m<sup>3</sup>-K. On average, the total irreversibility increased with operating pressure, ranging from approximately 0.6 to 0.85 W/m<sup>3</sup>-K as the pressure rose from 40 kPa to 100 kPa. These quantitative insights offer valuable understanding for optimizing the design and improving the performance of humid air systems operating under vacuum by focusing on reducing thermal energy dissipation.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101540"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926542","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.2025.101520
Muhammad Farooq , Faisal Zia , Rashid Nawaz , Alamgeer Khan , Ilker Ozsahin , Hijaz Ahmad , Waleed Mohammed Abdelfattah
This study is motivated by the need to understand complex thermal and hydrodynamic behaviors of couple stress fluids, which commonly occur in lubrication systems, microfluidic devices, and polymeric material processing. Its significance lies in modeling non-isothermal couple stress fluid flow through an inclined Poiseuille channel bounded by two heated parallel plates, a configuration relevant to advanced heat and mass transfer applications. The aim is to determine the velocity profile, temperature distribution, volumetric flow rate, average velocity, and shear stress for the incompressible fluid. To achieve this, the highly nonlinear coupled ordinary differential equations governing the system are solved using the Optimal Homotopy Asymptotic Method and the Homotopy Perturbation Method, which provide accurate approximate solutions without linearization. The major findings show excellent agreement between the two approaches, confirming their validity, while parametric studies reveal how physical factors such as couple stress effects, plate inclination, and thermal gradients influence the flow. The specific applications of this work include lubrication processes, thermal energy devices, and fluid transport systems requiring precise control of flow and heat transfer.
{"title":"Analysis of Plane Poiseuille flow of non-isothermal couple stress fluid between two parallel inclined plates using two reliable methods","authors":"Muhammad Farooq , Faisal Zia , Rashid Nawaz , Alamgeer Khan , Ilker Ozsahin , Hijaz Ahmad , Waleed Mohammed Abdelfattah","doi":"10.1016/j.ijft.2025.101520","DOIUrl":"10.1016/j.ijft.2025.101520","url":null,"abstract":"<div><div>This study is motivated by the need to understand complex thermal and hydrodynamic behaviors of couple stress fluids, which commonly occur in lubrication systems, microfluidic devices, and polymeric material processing. Its significance lies in modeling non-isothermal couple stress fluid flow through an inclined Poiseuille channel bounded by two heated parallel plates, a configuration relevant to advanced heat and mass transfer applications. The aim is to determine the velocity profile, temperature distribution, volumetric flow rate, average velocity, and shear stress for the incompressible fluid. To achieve this, the highly nonlinear coupled ordinary differential equations governing the system are solved using the Optimal Homotopy Asymptotic Method and the Homotopy Perturbation Method, which provide accurate approximate solutions without linearization. The major findings show excellent agreement between the two approaches, confirming their validity, while parametric studies reveal how physical factors such as couple stress effects, plate inclination, and thermal gradients influence the flow. The specific applications of this work include lubrication processes, thermal energy devices, and fluid transport systems requiring precise control of flow and heat transfer.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101520"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926541","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: 2025-12-19DOI: 10.1016/j.ijft.2025.101533
Abdul Aabid , Sher Afghan Khan , Yasir Javed
Sudden expansion phenomena are prevalent in defense and automotive applications, where flow separation at the blunt base of structures such as fuselages, missiles, and rockets leads to low-pressure recirculation zones, significantly reducing base pressure and increasing drag. This study presents active control methods using microjets to regulate base pressure, employing experimental and machine learning approaches. Experiments were conducted using duct diameters of 16 mm, 18 mm, 22 mm, and 25 mm, level of expansion, the Nozzle pressure ratio ranging from 3 to 11, Mach numbers (1.25, 1.3, 1.48, 1.6, 2.0, and 3.0), and length-to-diameter ratios (10–1) were varied to evaluate their impact on flow evolution and base pressure. Active control was achieved using micro-jets of 0.5 mm radius, positioned at 90° intervals along a pitch circle with a radius of 0.65 times the nozzle exit diameter. Micro-jets significantly increased base pressure under favorable pressure-gradient conditions for the Mach numbers 1.25, 1.3, 1.48, 1.6, and 2.0. At Mach M = 3, the control is ineffective as the NPRs are such that the flow from the nozzle remained over-expanded. Furthermore, machine learning (ML) algorithms were utilized to predict base pressure outcomes and optimize control strategies. These algorithms demonstrated high predictive accuracy, as evidenced by low error rates, indicating their reliability in high-speed flow-control applications. The findings reveal that base pressure is strongly influenced by nozzle pressure ratio, Mach number, L/D ratio, and duct area ratio. The study presents cost-effective, energy-efficient methods to enhance base pressure, offering critical insights into the aerodynamic optimization of high-speed systems. This comprehensive approach integrates experimental techniques and ML–based predictions to achieve optimal results in flow control.
{"title":"Base pressure control through micro jets at supersonic mach numbers using experimental and machine learning approach","authors":"Abdul Aabid , Sher Afghan Khan , Yasir Javed","doi":"10.1016/j.ijft.2025.101533","DOIUrl":"10.1016/j.ijft.2025.101533","url":null,"abstract":"<div><div>Sudden expansion phenomena are prevalent in defense and automotive applications, where flow separation at the blunt base of structures such as fuselages, missiles, and rockets leads to low-pressure recirculation zones, significantly reducing base pressure and increasing drag. This study presents active control methods using microjets to regulate base pressure, employing experimental and machine learning approaches. Experiments were conducted using duct diameters of 16 mm, 18 mm, 22 mm, and 25 mm, level of expansion, the Nozzle pressure ratio ranging from 3 to 11, Mach numbers (1.25, 1.3, 1.48, 1.6, 2.0, and 3.0), and length-to-diameter ratios (10–1) were varied to evaluate their impact on flow evolution and base pressure. Active control was achieved using micro-jets of 0.5 mm radius, positioned at 90° intervals along a pitch circle with a radius of 0.65 times the nozzle exit diameter. Micro-jets significantly increased base pressure under favorable pressure-gradient conditions for the Mach numbers 1.25, 1.3, 1.48, 1.6, and 2.0. At Mach <em>M</em> = 3, the control is ineffective as the NPRs are such that the flow from the nozzle remained over-expanded. Furthermore, machine learning (ML) algorithms were utilized to predict base pressure outcomes and optimize control strategies. These algorithms demonstrated high predictive accuracy, as evidenced by low error rates, indicating their reliability in high-speed flow-control applications. The findings reveal that base pressure is strongly influenced by nozzle pressure ratio, Mach number, L/D ratio, and duct area ratio. The study presents cost-effective, energy-efficient methods to enhance base pressure, offering critical insights into the aerodynamic optimization of high-speed systems. This comprehensive approach integrates experimental techniques and ML–based predictions to achieve optimal results in flow control.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101533"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926540","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: 2025-12-30DOI: 10.1016/j.ijft.2025.101539
Eman Abdelhafez , Nabeel Abu Shaban , Mohammad Hamdan , Maher Al-Maghalseh
Efficient thermal energy storage (TES) is essential for enhancing the reliability and sustainability of solar thermal systems, particularly under fluctuating solar radiation conditions. This study investigates the predictive performance of different data-driven approaches—Multiple Linear Regression (MLR), Multilayer Perceptron (MLP), and Radial Basis Function (RBF) neural networks—for estimating stored thermal energy in a solar thermal tank. Experimental data were obtained from a controlled solar simulator setup that incorporated halogen-lamp irradiation, a flat-plate collector, and nanoparticle-enhanced water as the storage medium. Eight independent variables—including collector inlet and outlet temperatures, tank and ambient temperatures, flow rate, solar radiation, nanoparticle concentration, and specific heat capacity—were used as model inputs. Results show that the MLP model significantly outperformed both MLR and RBF, achieving the highest correlation coefficient (R = 0.647), and the lowest RMSE (346.35) and MBE (152.49), demonstrating superior accuracy and generalization. By contrast, MLR exhibited limited predictive power due to its linear assumptions, while RBF suffered from high testing error and poor generalization. These findings underscore the suitability of neural network models, particularly MLP, for capturing the nonlinear dynamics of TES systems, providing a robust framework for system optimization and improved energy management strategies.
{"title":"Comparative data-driven modeling of thermal energy storage using artificial neural networks and multiple linear regression","authors":"Eman Abdelhafez , Nabeel Abu Shaban , Mohammad Hamdan , Maher Al-Maghalseh","doi":"10.1016/j.ijft.2025.101539","DOIUrl":"10.1016/j.ijft.2025.101539","url":null,"abstract":"<div><div>Efficient thermal energy storage (TES) is essential for enhancing the reliability and sustainability of solar thermal systems, particularly under fluctuating solar radiation conditions. This study investigates the predictive performance of different data-driven approaches—Multiple Linear Regression (MLR), Multilayer Perceptron (MLP), and Radial Basis Function (RBF) neural networks—for estimating stored thermal energy in a solar thermal tank. Experimental data were obtained from a controlled solar simulator setup that incorporated halogen-lamp irradiation, a flat-plate collector, and nanoparticle-enhanced water as the storage medium. Eight independent variables—including collector inlet and outlet temperatures, tank and ambient temperatures, flow rate, solar radiation, nanoparticle concentration, and specific heat capacity—were used as model inputs. Results show that the MLP model significantly outperformed both MLR and RBF, achieving the highest correlation coefficient (<em>R</em> = 0.647), and the lowest RMSE (346.35) and MBE (152.49), demonstrating superior accuracy and generalization. By contrast, MLR exhibited limited predictive power due to its linear assumptions, while RBF suffered from high testing error and poor generalization. These findings underscore the suitability of neural network models, particularly MLP, for capturing the nonlinear dynamics of TES systems, providing a robust framework for system optimization and improved energy management strategies.</div></div>","PeriodicalId":36341,"journal":{"name":"International Journal of Thermofluids","volume":"31 ","pages":"Article 101539"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926538","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: 2025-12-11DOI: 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":"2026-01-01","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}
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