This study proposes a multi-objective optimization framework integrating dynamic simulation, a Backpropagation Neural Network, and the Non-dominated Sorting Genetic Algorithm III to improve energy efficiency, thermal comfort, and affordability of rural houses in cold regions. Using a representative dwelling in Shangqiu, Henan, 21 key variables were identified through sensitivity analysis, and 30,000 samples were generated by Latin Hypercube Sampling. The BPNN achieved high prediction accuracy (R2 = 0.967 for EUIth, 0.988 for PPD). NSGA-III optimization produced 100 Pareto-optimal retrofit schemes, among which three representative solutions—comfort-oriented, balanced, and energy-saving—were selected using TOPSIS. Compared with the baseline, energy consumption decreased by 46.4–67.2 %, and thermal comfort improved by up to 67.5 %, with retrofit costs between ¥36,000–52,000, within the economic capacity of rural households. The findings demonstrate that enhancing envelope insulation and reducing window U-values effectively achieve energy savings and thermal stability, offering quantitative guidance for low-carbon, cost-effective rural housing retrofits.
{"title":"Multi-objective optimization of rural residential buildings in cold regions using BPNN and NSGA-III: A case study of Shangqiu","authors":"Zhongcheng Duan , Binhao Li , Yidi Zhao , Mingxue Chen","doi":"10.1016/j.csite.2026.107654","DOIUrl":"10.1016/j.csite.2026.107654","url":null,"abstract":"<div><div>This study proposes a multi-objective optimization framework integrating dynamic simulation, a Backpropagation Neural Network, and the Non-dominated Sorting Genetic Algorithm III to improve energy efficiency, thermal comfort, and affordability of rural houses in cold regions. Using a representative dwelling in Shangqiu, Henan, 21 key variables were identified through sensitivity analysis, and 30,000 samples were generated by Latin Hypercube Sampling. The BPNN achieved high prediction accuracy (R<sup>2</sup> = 0.967 for EUI<sub>th</sub>, 0.988 for PPD). NSGA-III optimization produced 100 Pareto-optimal retrofit schemes, among which three representative solutions—comfort-oriented, balanced, and energy-saving—were selected using TOPSIS. Compared with the baseline, energy consumption decreased by 46.4–67.2 %, and thermal comfort improved by up to 67.5 %, with retrofit costs between ¥36,000–52,000, within the economic capacity of rural households. The findings demonstrate that enhancing envelope insulation and reducing window U-values effectively achieve energy savings and thermal stability, offering quantitative guidance for low-carbon, cost-effective rural housing retrofits.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"78 ","pages":"Article 107654"},"PeriodicalIF":6.4,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902614","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}
High-performance thermal management system (TMS) using environmentally benign refrigerants is critical to the safe, efficient operation of electric vehicles (EVs) and to reducing environmental impact. An indirect TMS based on an R290 vapor-injection (VPI) heat pump was developed in this study, and a simulation platform for the TMS was established based on AMESim software to evaluate system performance. We further propose a hybrid multi-objective optimization (MOO) strategy that couples deep reinforcement learning with non-dominated sorting genetic algorithm II (DRL-NSGA) to determine the optimal operating parameters for system performance and energy efficiency. A theoretical analysis based on hypervolume drift confirms that the algorithm's “warm-start” strategy accelerates convergence and enhances solution diversity. The results demonstrate that, compared to conventional MOO methods, DRL-NSGA produces Pareto fronts with broader coverage and higher solution quality while reducing computational time by more than 60 %. Under the optimized settings, the system's coefficient of performance (COP) was enhanced by up to 7.7 %, with cooling and heating capacities increasing by up to 7.2 % and 5 %, respectively. Furthermore, the time required to reach the target outlet air temperature was significantly reduced by 132 s under cooling and 429 s under heating conditions. The proposed synergistic optimization algorithm offers an effective and efficient solution to MOO in EV thermal management.
{"title":"Optimization study of an R290 electric vehicle thermal management system using a synergistic deep reinforcement learning and genetic algorithm","authors":"Congqing Xu , Xianzhen Ruan , Jianghong Wu , Yuhang Chen , Mengliang Yao","doi":"10.1016/j.csite.2026.107649","DOIUrl":"10.1016/j.csite.2026.107649","url":null,"abstract":"<div><div>High-performance thermal management system (TMS) using environmentally benign refrigerants is critical to the safe, efficient operation of electric vehicles (EVs) and to reducing environmental impact. An indirect TMS based on an R290 vapor-injection (VPI) heat pump was developed in this study, and a simulation platform for the TMS was established based on AMESim software to evaluate system performance. We further propose a hybrid multi-objective optimization (MOO) strategy that couples deep reinforcement learning with non-dominated sorting genetic algorithm II (DRL-NSGA) to determine the optimal operating parameters for system performance and energy efficiency. A theoretical analysis based on hypervolume drift confirms that the algorithm's “warm-start” strategy accelerates convergence and enhances solution diversity. The results demonstrate that, compared to conventional MOO methods, DRL-NSGA produces Pareto fronts with broader coverage and higher solution quality while reducing computational time by more than 60 %. Under the optimized settings, the system's coefficient of performance (COP) was enhanced by up to 7.7 %, with cooling and heating capacities increasing by up to 7.2 % and 5 %, respectively. Furthermore, the time required to reach the target outlet air temperature was significantly reduced by 132 s under cooling and 429 s under heating conditions. The proposed synergistic optimization algorithm offers an effective and efficient solution to MOO in EV thermal management.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"78 ","pages":"Article 107649"},"PeriodicalIF":6.4,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894196","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.2026.107646
Mohammadreza Hasandust Rostami
Atmospheric water harvesting (AWH) systems have emerged as a critical solution to address global water scarcity, particularly in arid and semi-arid regions where traditional water sources are limited. With over 2 billion people facing water stress worldwide, developing sustainable and energy-efficient water extraction technologies is urgently needed. However, existing AWH methods often suffer from high energy consumption, low yields, or geographical limitations, creating a significant research gap for optimized passive systems. This study investigates an innovative passive AWH system utilizing subsurface geothermal cooling with optimized coil configurations. Four key parameters were analyzed: coil cross-section (rectangular, circular, square, hexagonal), number of coils (8–64), soil type (clay, sandy loam, peat), and coil length (10–40 m). The system employed rectangular stainless-steel coils buried in clay soil at 4.5 m depth, leveraging stable geothermal temperatures for efficient condensation. Results demonstrated that rectangular coils achieved 12.3 % higher water yield than circular designs due to superior surface area and drainage. Clay soil enhanced production by 38.5 % over peat, while increasing coil length from 10 m to 40 m linearly boosted output by 300 %. The system maintained 85.4–85.8 % energy savings compared to vapor compression cycles, with peak nighttime efficiency reaching 87.3 %. Economically, large-scale configurations (64 coils) achieved a payback period of just 1.2 years. These findings validate passive AWH systems as scalable, low-energy solutions for water-scarce regions, with geometric and soil optimizations significantly improving performance.
{"title":"Parametric analysis of an energy-efficient atmospheric water harvesting system (AWHs): Optimizing underground coil configurations for economic water production","authors":"Mohammadreza Hasandust Rostami","doi":"10.1016/j.csite.2026.107646","DOIUrl":"10.1016/j.csite.2026.107646","url":null,"abstract":"<div><div>Atmospheric water harvesting (AWH) systems have emerged as a critical solution to address global water scarcity, particularly in arid and semi-arid regions where traditional water sources are limited. With over 2 billion people facing water stress worldwide, developing sustainable and energy-efficient water extraction technologies is urgently needed. However, existing AWH methods often suffer from high energy consumption, low yields, or geographical limitations, creating a significant research gap for optimized passive systems. This study investigates an innovative passive AWH system utilizing subsurface geothermal cooling with optimized coil configurations. Four key parameters were analyzed: coil cross-section (rectangular, circular, square, hexagonal), number of coils (8–64), soil type (clay, sandy loam, peat), and coil length (10–40 m). The system employed rectangular stainless-steel coils buried in clay soil at 4.5 m depth, leveraging stable geothermal temperatures for efficient condensation. Results demonstrated that rectangular coils achieved 12.3 % higher water yield than circular designs due to superior surface area and drainage. Clay soil enhanced production by 38.5 % over peat, while increasing coil length from 10 m to 40 m linearly boosted output by 300 %. The system maintained 85.4–85.8 % energy savings compared to vapor compression cycles, with peak nighttime efficiency reaching 87.3 %. Economically, large-scale configurations (64 coils) achieved a payback period of just 1.2 years. These findings validate passive AWH systems as scalable, low-energy solutions for water-scarce regions, with geometric and soil optimizations significantly improving performance.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"78 ","pages":"Article 107646"},"PeriodicalIF":6.4,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894198","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.2026.107647
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}
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