Pub Date : 2026-01-03DOI: 10.1016/j.csite.2026.107647
Tze-Yin Lin , Kun-Yin Li
Thermal errors induced by inefficient cooling in rotary tables of five-axis machine tools significantly degrade machining accuracy and increase energy consumption, posing a critical challenge for high-precision and sustainable manufacturing. This study presents an integrated cooling system optimization framework for five-axis machine tool rotary tables, combining ISO 230–3:2020-based experimental measurements with multi-physics thermal–fluid–structural coupling analysis to accurately characterize heat generation and thermal deformation. Cooling channel geometry, operating parameters, and cooling loop configurations are systematically optimized using the Taguchi method and response surface methodology to achieve both thermal accuracy improvement and energy efficiency. The results demonstrate that the optimized cooling design and operating conditions reduce rotary table thermal errors by more than 12 % while simultaneously lowering coolant flow demand, power consumption, and associated carbon emissions by approximately 10 %. The proposed approach provides a practical and effective solution for enhancing thermal stability, machining accuracy, and energy efficiency in advanced CNC and five-axis machine tools used in aerospace, automotive, and high-value precision manufacturing applications.
{"title":"Cooling system optimization of five-axis machine tool rotary table for improved thermal accuracy and energy efficiency","authors":"Tze-Yin Lin , Kun-Yin Li","doi":"10.1016/j.csite.2026.107647","DOIUrl":"10.1016/j.csite.2026.107647","url":null,"abstract":"<div><div>Thermal errors induced by inefficient cooling in rotary tables of five-axis machine tools significantly degrade machining accuracy and increase energy consumption, posing a critical challenge for high-precision and sustainable manufacturing. This study presents an integrated cooling system optimization framework for five-axis machine tool rotary tables, combining ISO 230–3:2020-based experimental measurements with multi-physics thermal–fluid–structural coupling analysis to accurately characterize heat generation and thermal deformation. Cooling channel geometry, operating parameters, and cooling loop configurations are systematically optimized using the Taguchi method and response surface methodology to achieve both thermal accuracy improvement and energy efficiency. The results demonstrate that the optimized cooling design and operating conditions reduce rotary table thermal errors by more than 12 % while simultaneously lowering coolant flow demand, power consumption, and associated carbon emissions by approximately 10 %. The proposed approach provides a practical and effective solution for enhancing thermal stability, machining accuracy, and energy efficiency in advanced CNC and five-axis machine tools used in aerospace, automotive, and high-value precision manufacturing applications.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"78 ","pages":"Article 107647"},"PeriodicalIF":6.4,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1016/j.csite.2025.107620
Han-Taw Chen , Li-Yuan Hsu , Saman Rashidi , Wei-Mon Yan
This study conducts experimental and numerical studies on natural convection and ventilation characteristics in a factory with high heat-generating machinery. It also selects a turbulence flow model suitable for the factory. Finally, the effects of the partition configuration on the ventilation characteristics, temperature field, velocity field, and natural convection heat transfer coefficient in the factory are discussed. ANSYS Fluent 18 was used in this study. The results show that among all the turbulence flow models used in this study, the convection heat transfer coefficient predicted by the zero-equation model is closest to the result obtained by the existing empirical formula, and the root mean square error of the temperature is also small enough. Therefore, the zero-equation turbulence model is the most suitable model for this study. In addition, when the height of the partition increases, it will affect the surface temperature of the partition and increase the heat transfer coefficient on the heating block, with a maximum increase of 40 %. However, the increase in the height of the partition will cause a recirculation area and hot air accumulation under the partition. The increase in the partition spacing will reduce the partition and air temperatures, with the maximum temperature reduction of 15 K and 3 K, respectively. At the same time, it will increase the heat transfer coefficient on the heating block, with the maximum increase of 45 %, and help avoid the formation of recirculation areas and hot air accumulation under the partition. Therefore, this study recommends that the partitions of the factory should be set with a low height and a large spacing to achieve a better ventilation effect and improve the comfort of the working area under the partition.
{"title":"Experimental and numerical studies on heat transfer and ventilation characteristics in a factory with high heat-generating machinery","authors":"Han-Taw Chen , Li-Yuan Hsu , Saman Rashidi , Wei-Mon Yan","doi":"10.1016/j.csite.2025.107620","DOIUrl":"10.1016/j.csite.2025.107620","url":null,"abstract":"<div><div>This study conducts experimental and numerical studies on natural convection and ventilation characteristics in a factory with high heat-generating machinery. It also selects a turbulence flow model suitable for the factory. Finally, the effects of the partition configuration on the ventilation characteristics, temperature field, velocity field, and natural convection heat transfer coefficient in the factory are discussed. ANSYS Fluent 18 was used in this study. The results show that among all the turbulence flow models used in this study, the convection heat transfer coefficient predicted by the zero-equation model is closest to the result obtained by the existing empirical formula, and the root mean square error of the temperature is also small enough. Therefore, the zero-equation turbulence model is the most suitable model for this study. In addition, when the height of the partition increases, it will affect the surface temperature of the partition and increase the heat transfer coefficient on the heating block, with a maximum increase of 40 %. However, the increase in the height of the partition will cause a recirculation area and hot air accumulation under the partition. The increase in the partition spacing will reduce the partition and air temperatures, with the maximum temperature reduction of 15 K and 3 K, respectively. At the same time, it will increase the heat transfer coefficient on the heating block, with the maximum increase of 45 %, and help avoid the formation of recirculation areas and hot air accumulation under the partition. Therefore, this study recommends that the partitions of the factory should be set with a low height and a large spacing to achieve a better ventilation effect and improve the comfort of the working area under the partition.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"78 ","pages":"Article 107620"},"PeriodicalIF":6.4,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1016/j.csite.2026.107640
Maryam Mehdi, Nassreddine Hmidi, Ahmed Alami Merrouni
Operating temperature is a critical parameter influencing the efficiency and durability of photovoltaic (PV) systems, particularly in desert and semi-arid regions where intense solar irradiance, elevated ambient temperatures, and frequent soiling prevail. Excessive module heating not only reduces electrical conversion efficiency but also accelerates material degradation, making accurate temperature prediction essential for improving system performance, reliability, and lifespan. This study contributes to the advancement of efficient PV deployment in harsh climates by developing a machine learning (ML) model capable of accurately predicting PV module temperature under real outdoor conditions. The model is based on the Extreme Gradient Boosting (XGBoost) algorithm and is trained on a comprehensive, high-resolution dataset collected over one year in the hot semi-arid climate of Benguerir, Morocco. A key novelty of this work lies in its multi-technology and multi-condition modeling approach: it simultaneously predicts the operating temperature of two widely deployed PV technologies, polycrystalline silicon (pc-Si) and cadmium telluride (CdTe), while explicitly accounting for the impact of natural soiling, using both clean and soiled modules from each technology. For benchmarking, a multiple linear regression (MLR) model was developed using the same input features. Results show that the XGBoost model achieves high predictive accuracy across all configurations, with a coefficient of determination (R2) of 0.9869, significantly outperforming the MLR model (R2 = 0.8963). Seasonal and weather-specific evaluations further confirm the robustness of XGBoost, with relative deviations consistently within ±5 % for all module types and conditions. In contrast, the MLR model exhibits substantial errors, particularly during clear-sky periods in the wet season, where deviations exceeded −30 %. Year-long daily comparisons also reveal that XGBoost maintains stable performance across technologies, seasons, and soiling levels, highlighting its effectiveness as a predictive tool for PV thermal behavior in harsh climates. These findings underscore the potential of advanced AI-based modeling as a powerful and reliable tool for predicting PV thermal performance, aiding in better system design, performance optimization, and thermal management in challenging desert environments.
{"title":"Machine learning-based approach for predicting the PV modules temperature: A multi-technological assessment including soiling impact, toward a better solar plants’ operation under desert conditions","authors":"Maryam Mehdi, Nassreddine Hmidi, Ahmed Alami Merrouni","doi":"10.1016/j.csite.2026.107640","DOIUrl":"10.1016/j.csite.2026.107640","url":null,"abstract":"<div><div>Operating temperature is a critical parameter influencing the efficiency and durability of photovoltaic (PV) systems, particularly in desert and semi-arid regions where intense solar irradiance, elevated ambient temperatures, and frequent soiling prevail. Excessive module heating not only reduces electrical conversion efficiency but also accelerates material degradation, making accurate temperature prediction essential for improving system performance, reliability, and lifespan. This study contributes to the advancement of efficient PV deployment in harsh climates by developing a machine learning (ML) model capable of accurately predicting PV module temperature under real outdoor conditions. The model is based on the Extreme Gradient Boosting (XGBoost) algorithm and is trained on a comprehensive, high-resolution dataset collected over one year in the hot semi-arid climate of Benguerir, Morocco. A key novelty of this work lies in its multi-technology and multi-condition modeling approach: it simultaneously predicts the operating temperature of two widely deployed PV technologies, polycrystalline silicon (pc-Si) and cadmium telluride (CdTe), while explicitly accounting for the impact of natural soiling, using both clean and soiled modules from each technology. For benchmarking, a multiple linear regression (MLR) model was developed using the same input features. Results show that the XGBoost model achieves high predictive accuracy across all configurations, with a coefficient of determination (R<sup>2</sup>) of 0.9869, significantly outperforming the MLR model (R<sup>2</sup> = 0.8963). Seasonal and weather-specific evaluations further confirm the robustness of XGBoost, with relative deviations consistently within ±5 % for all module types and conditions. In contrast, the MLR model exhibits substantial errors, particularly during clear-sky periods in the wet season, where deviations exceeded −30 %. Year-long daily comparisons also reveal that XGBoost maintains stable performance across technologies, seasons, and soiling levels, highlighting its effectiveness as a predictive tool for PV thermal behavior in harsh climates. These findings underscore the potential of advanced AI-based modeling as a powerful and reliable tool for predicting PV thermal performance, aiding in better system design, performance optimization, and thermal management in challenging desert environments.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"78 ","pages":"Article 107640"},"PeriodicalIF":6.4,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1016/j.csite.2025.107630
Yu-shi Zhang, Ming Lü, Zhi Ning
{"title":"Technical Condition Evaluation of Armored Vehicle Diesel Engine Based on Deceleration Process","authors":"Yu-shi Zhang, Ming Lü, Zhi Ning","doi":"10.1016/j.csite.2025.107630","DOIUrl":"https://doi.org/10.1016/j.csite.2025.107630","url":null,"abstract":"","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"53 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1016/j.csite.2026.107641
Aliasghar Azma, Yakun Liu
Computational Fluid Dynamics (CFD) is commonly used to simulate the transport of heat in closed spaces. The resulting airflow and temperature predictions facilitate improved designs of Heating, Ventilation, and Air Conditioning (HVAC) systems. However, CFD is highly expensive to apply to large domains. This paper presents a novel approach that is a hybridization of artificial intelligence (AI) with CFD modeling, which improves computational speed and predictive accuracy. Specifically, CFD data from a forced air-conditioned room is used to train an Adaptive Network-based Fuzzy Inference System (ANFIS) with temperature taken to be the dependent variable. The trained ANFIS predicts the temperature distribution on a high-resolution mesh using partial CFD data without need for additional numerical modelling. Results for transient hot air inflow to an idealized ‘room’ demonstrate that ANFIS is a very useful adjunct to the CFD method with high accuracy achieved using coarse-grid CFD data. The proposed AI-CFD hybrid framework should enable fast, efficient HVAC system designs that are more sustainable through reducing energy consumption and computational overhead. Moreover, the framework could facilitate real-time energy monitoring of buildings.
{"title":"Enhancing CFD computational efficiency using hybrid data-driven and physics-based modeling","authors":"Aliasghar Azma, Yakun Liu","doi":"10.1016/j.csite.2026.107641","DOIUrl":"10.1016/j.csite.2026.107641","url":null,"abstract":"<div><div>Computational Fluid Dynamics (CFD) is commonly used to simulate the transport of heat in closed spaces. The resulting airflow and temperature predictions facilitate improved designs of Heating, Ventilation, and Air Conditioning (HVAC) systems. However, CFD is highly expensive to apply to large domains. This paper presents a novel approach that is a hybridization of artificial intelligence (AI) with CFD modeling, which improves computational speed and predictive accuracy. Specifically, CFD data from a forced air-conditioned room is used to train an Adaptive Network-based Fuzzy Inference System (ANFIS) with temperature taken to be the dependent variable. The trained ANFIS predicts the temperature distribution on a high-resolution mesh using partial CFD data without need for additional numerical modelling. Results for transient hot air inflow to an idealized ‘room’ demonstrate that ANFIS is a very useful adjunct to the CFD method with high accuracy achieved using coarse-grid CFD data. The proposed AI-CFD hybrid framework should enable fast, efficient HVAC system designs that are more sustainable through reducing energy consumption and computational overhead. Moreover, the framework could facilitate real-time energy monitoring of buildings.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"78 ","pages":"Article 107641"},"PeriodicalIF":6.4,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1016/j.csite.2026.107642
Ying Li , Zhen Li , Mengdan Huo , Yajun Li , Jian-ming Gao
Molten salt phase change material(PCM) has great potential as a substitute for thermal energy storage, however, their widespread industrial adoption has been limited by issues of leakage. In this study, a shape-stabilized phase change material (SSPCM) with high temperature range (250–800 °C) was successfully synthesized. The fly ash (FA) was employed as the supporting skeleton material, while a ternary sulfate salt composed of Na2SO4, K2SO4, and MgSO4 served as the PCMs. The results indicate that the composite S-FS-45/55 shows excellent chemical compatibility and maintains a stable morphology. The maximum latent heat of the composite reaches 63.10 J/g. After 500 thermal cycles, the composite S-FS-45/55 still maintains excellent chemical compatibility, with a latent heat retention rate of 92.55 %. The excellent leakage prevention performance of the SSPCMs may benefit from the reinforcement of the innate mullite-quartz skeleton of the FA during high-temperature sintering process. In addition, the thermal conductivity was increased from 0.33 W/(m·k) to 2.58 W/(m·k) after adding 7.5 wt% silicon carbide (SiC) in the composite. This study provides a new way for high-value utilization of FA and the design of thermal energy storage materials, demonstrating significant application potential, particularly in the fields of industrial waste heat recovery and clean energy technology.
{"title":"Fly ash based shape-stabilized phase change materials for high-temperature thermal energy storage with enhanced thermal conductivity","authors":"Ying Li , Zhen Li , Mengdan Huo , Yajun Li , Jian-ming Gao","doi":"10.1016/j.csite.2026.107642","DOIUrl":"10.1016/j.csite.2026.107642","url":null,"abstract":"<div><div>Molten salt phase change material(PCM) has great potential as a substitute for thermal energy storage, however, their widespread industrial adoption has been limited by issues of leakage. In this study, a shape-stabilized phase change material (SSPCM) with high temperature range (250–800 °C) was successfully synthesized. The fly ash (FA) was employed as the supporting skeleton material, while a ternary sulfate salt composed of Na<sub>2</sub>SO<sub>4</sub>, K<sub>2</sub>SO<sub>4</sub>, and MgSO<sub>4</sub> served as the PCMs. The results indicate that the composite S-FS-45/55 shows excellent chemical compatibility and maintains a stable morphology. The maximum latent heat of the composite reaches 63.10 J/g. After 500 thermal cycles, the composite S-FS-45/55 still maintains excellent chemical compatibility, with a latent heat retention rate of 92.55 %. The excellent leakage prevention performance of the SSPCMs may benefit from the reinforcement of the innate mullite-quartz skeleton of the FA during high-temperature sintering process. In addition, the thermal conductivity was increased from 0.33 W/(m·k) to 2.58 W/(m·k) after adding 7.5 wt% silicon carbide (SiC) in the composite. This study provides a new way for high-value utilization of FA and the design of thermal energy storage materials, demonstrating significant application potential, particularly in the fields of industrial waste heat recovery and clean energy technology.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"78 ","pages":"Article 107642"},"PeriodicalIF":6.4,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1016/j.csite.2026.107644
Suresh Vellaiyan , Bassam S. Aljohani , Khalid Aljohani , Muralidharan Kandasamy , Nguyen Van Minh
This study addresses fuel scarcity and emission control in compression-ignition engines by integrating water–diesel emulsification with a surface-area engineered hybrid nanocomposite. Unlike conventional water–diesel emulsions using single-phase nanoparticles, this approach employs a surface-area-enhanced MWCNT–Al2O3 nanocomposite to improve heat transfer and catalytic oxidation. Nitrogen sorption analysis of the proposed nanocomposite confirmed a type-IV isotherm with H3 hysteresis, a modal pore size of ∼3 nm, a cumulative mesopore volume of 0.12–0.13 cm3 g−1, and a BET surface area exceeding 180 m2 g−1. These features provide a large density of accessible reactive sites at ultra-low additive loading. Water–diesel emulsions containing 5 % (E5W) and 10 % (E10W) water were prepared using a non-ionic surfactant, and the nanocomposite was dispersed at 100 ppm into the 10 % emulsion (E10W–NC). Engine analysis showed that E5W reduced peak in-cylinder pressure (ICP) and brake thermal efficiency (BTE) by 1.3 % and 7.1 %, respectively, while E10W caused larger reductions of 2.5 % and 11.1 %, accompanied by higher fuel consumption. In contrast, E10W–NC recovered combustion intensity and efficiency. Compared with E10W, the E10W–NC fuel increased peak ICP and net heat-release rate by 3 % and 13.3 %, respectively, while improving BTE by 12.8 % and reducing fuel consumption by 11.6 %. At the same time, it lowered NOx, hydrocarbon, carbon monoxide, and smoke emissions by 3 %, 4.8 %, 5.8 %, and 5.6 %, respectively. Overall, the results demonstrate that surface-area architecture governs the effectiveness of water–diesel emulsions, offering a practical pathway to cleaner and more efficient CI engine operation without hardware modification.
{"title":"Surface-area engineered nanocomposite for cleaner compression ignition combustion with water–diesel emulsions","authors":"Suresh Vellaiyan , Bassam S. Aljohani , Khalid Aljohani , Muralidharan Kandasamy , Nguyen Van Minh","doi":"10.1016/j.csite.2026.107644","DOIUrl":"10.1016/j.csite.2026.107644","url":null,"abstract":"<div><div>This study addresses fuel scarcity and emission control in compression-ignition engines by integrating water–diesel emulsification with a surface-area engineered hybrid nanocomposite. Unlike conventional water–diesel emulsions using single-phase nanoparticles, this approach employs a surface-area-enhanced MWCNT–Al<sub>2</sub>O<sub>3</sub> nanocomposite to improve heat transfer and catalytic oxidation. Nitrogen sorption analysis of the proposed nanocomposite confirmed a type-IV isotherm with H3 hysteresis, a modal pore size of ∼3 nm, a cumulative mesopore volume of 0.12–0.13 cm<sup>3</sup> g<sup>−1</sup>, and a BET surface area exceeding 180 m<sup>2</sup> g<sup>−1</sup>. These features provide a large density of accessible reactive sites at ultra-low additive loading. Water–diesel emulsions containing 5 % (E5W) and 10 % (E10W) water were prepared using a non-ionic surfactant, and the nanocomposite was dispersed at 100 ppm into the 10 % emulsion (E10W–NC). Engine analysis showed that E5W reduced peak in-cylinder pressure (ICP) and brake thermal efficiency (BTE) by 1.3 % and 7.1 %, respectively, while E10W caused larger reductions of 2.5 % and 11.1 %, accompanied by higher fuel consumption. In contrast, E10W–NC recovered combustion intensity and efficiency. Compared with E10W, the E10W–NC fuel increased peak ICP and net heat-release rate by 3 % and 13.3 %, respectively, while improving BTE by 12.8 % and reducing fuel consumption by 11.6 %. At the same time, it lowered NO<sub>x</sub>, hydrocarbon, carbon monoxide, and smoke emissions by 3 %, 4.8 %, 5.8 %, and 5.6 %, respectively. Overall, the results demonstrate that surface-area architecture governs the effectiveness of water–diesel emulsions, offering a practical pathway to cleaner and more efficient CI engine operation without hardware modification.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"78 ","pages":"Article 107644"},"PeriodicalIF":6.4,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1016/j.csite.2025.107590
Mohsen Fallah, Zahra Mohammadi
{"title":"Development, modeling, and optimization of a solar-assisted hybrid building energy system incorporating photovoltaic panels, thermal collectors, and energy storage using transient simulation and ANN–GA methods: a case study","authors":"Mohsen Fallah, Zahra Mohammadi","doi":"10.1016/j.csite.2025.107590","DOIUrl":"https://doi.org/10.1016/j.csite.2025.107590","url":null,"abstract":"","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"8 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","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.csite.2025.107522
Cheng-Hung Huang, Kuan-Chieh Fang
A transient Inverse Conjugate Heat Transfer Problem (ICHTP) is experimentally investigated to estimate the spatially and temporally varying applied bottom heat flux in a three-dimensional plate-fin heat sink using infrared thermography. In this framework, the interface between the heat sink and the air domain is assumed to exhibit perfect thermal contact, thereby defining the problem as a transient conjugate heat transfer formulation. Unlike conventional inverse heat conduction problems, this approach necessitates the simultaneous solution of the continuity, momentum, and energy equations in the air domain, coupled with the heat conduction equation in the heat sink domain, significantly increasing its complexity. To the best of the authors’ knowledge, this work represents the first experimental investigation of an ICHTP aimed at estimating the unknown heat flux of a heat sink.
The accuracy of the estimated heat flux is verified experimentally under a prescribed inlet air velocity. Results indicate that, due to the singularity of the cost-function gradient at the terminal time, estimates near the final time must be discarded. For numerical simulations with error-free measurements and an inlet velocity of 5 m/s, highly accurate bottom-surface heat fluxes are recovered. The effect of measurement noise (σ = 0.3) is further examined in both numerical simulations and experimental evaluations. The average relative errors of the estimated heat fluxes are 2.82 % in the simulations and 9.6 % in the experiments, both achieved with only six iterations. The discrepancy arises because measurement noise in simulations can be precisely controlled, whereas experimental measurements inherently exhibit greater uncertainty. This underscores the inherent challenges associated with inverse problems and highlights the importance of obtaining accurate measurement data in the problem domain. Moreover, if the discrepancy principle is not employed as the stopping criterion, the estimation of heat flux deteriorates with additional iterations, despite the apparent reduction in temperature residuals between measured and estimated values.
{"title":"Experimental validation of an inverse method for bottom heat flux determination in a heat sink","authors":"Cheng-Hung Huang, Kuan-Chieh Fang","doi":"10.1016/j.csite.2025.107522","DOIUrl":"10.1016/j.csite.2025.107522","url":null,"abstract":"<div><div>A transient Inverse Conjugate Heat Transfer Problem (ICHTP) is experimentally investigated to estimate the spatially and temporally varying applied bottom heat flux in a three-dimensional plate-fin heat sink using infrared thermography. In this framework, the interface between the heat sink and the air domain is assumed to exhibit perfect thermal contact, thereby defining the problem as a transient conjugate heat transfer formulation. Unlike conventional inverse heat conduction problems, this approach necessitates the simultaneous solution of the continuity, momentum, and energy equations in the air domain, coupled with the heat conduction equation in the heat sink domain, significantly increasing its complexity. To the best of the authors’ knowledge, this work represents the first experimental investigation of an ICHTP aimed at estimating the unknown heat flux of a heat sink.</div><div>The accuracy of the estimated heat flux is verified experimentally under a prescribed inlet air velocity. Results indicate that, due to the singularity of the cost-function gradient at the terminal time, estimates near the final time must be discarded. For numerical simulations with error-free measurements and an inlet velocity of 5 m/s, highly accurate bottom-surface heat fluxes are recovered. The effect of measurement noise (σ = 0.3) is further examined in both numerical simulations and experimental evaluations. The average relative errors of the estimated heat fluxes are 2.82 % in the simulations and 9.6 % in the experiments, both achieved with only six iterations. The discrepancy arises because measurement noise in simulations can be precisely controlled, whereas experimental measurements inherently exhibit greater uncertainty. This underscores the inherent challenges associated with inverse problems and highlights the importance of obtaining accurate measurement data in the problem domain. Moreover, if the discrepancy principle is not employed as the stopping criterion, the estimation of heat flux deteriorates with additional iterations, despite the apparent reduction in temperature residuals between measured and estimated values.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"77 ","pages":"Article 107522"},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","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