Pub Date : 2026-01-01DOI: 10.1016/j.csite.2025.107577
Yongdong Pu , Yuechan Liu , Chao Sun
Based on structural optimization of JAG-type corrugated plates and active heat transfer enhancement via pulsating flow, this study conducts a numerical simulation of shell-side condensation heat transfer in JAG-type plate and shell heat exchangers under pulsating flow conditions. The flow and heat transfer performance of the condensation process were studied by changing the frequency (10Hz–30Hz) and amplitude (0.1–0.3) of the pulsating flow, and the irreversibility of the heat transfer process was analyzed in combination with the entransy dissipation theory. The results demonstrate that pulsating flow technology can effectively disrupt the continuity of the liquid film in steady-state flow through periodic velocity perturbations, thereby enhancing turbulent mixing at the phase change interface and achieving a balance between heat transfer enhancement and reduced heat transfer capacity loss. Both the condensation heat transfer coefficient and the two-phase frictional pressure drop increase with higher pulsating flow frequencies and amplitudes. However, the overall heat transfer performance exhibits a decreasing trend as the pulsating flow parameters increase, indicating the existence of an optimal parameter range. Furthermore, entransy dissipation analysis confirms that the application of pulsating flow contributes to reducing irreversibility during the condensation heat transfer process.
{"title":"Numerical simulation of active enhancement of condensation heat transfer by pulsating flow in JAG-type corrugated plate heat exchangers","authors":"Yongdong Pu , Yuechan Liu , Chao Sun","doi":"10.1016/j.csite.2025.107577","DOIUrl":"10.1016/j.csite.2025.107577","url":null,"abstract":"<div><div>Based on structural optimization of JAG-type corrugated plates and active heat transfer enhancement via pulsating flow, this study conducts a numerical simulation of shell-side condensation heat transfer in JAG-type plate and shell heat exchangers under pulsating flow conditions. The flow and heat transfer performance of the condensation process were studied by changing the frequency (10Hz–30Hz) and amplitude (0.1–0.3) of the pulsating flow, and the irreversibility of the heat transfer process was analyzed in combination with the entransy dissipation theory. The results demonstrate that pulsating flow technology can effectively disrupt the continuity of the liquid film in steady-state flow through periodic velocity perturbations, thereby enhancing turbulent mixing at the phase change interface and achieving a balance between heat transfer enhancement and reduced heat transfer capacity loss. Both the condensation heat transfer coefficient and the two-phase frictional pressure drop increase with higher pulsating flow frequencies and amplitudes. However, the overall heat transfer performance exhibits a decreasing trend as the pulsating flow parameters increase, indicating the existence of an optimal parameter range. Furthermore, entransy dissipation analysis confirms that the application of pulsating flow contributes to reducing irreversibility during the condensation heat transfer process.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"77 ","pages":"Article 107577"},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813772","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.2025.107561
Pan Zhang , Usama S. Altimari , Krunal Vaghela , V. Vivek , Sarbeswara Hota , Devendra Singh , Mahesh Manchanda , Shirin Shomurotova , Prakhar Tomar , Mohammad Mahtab Alam , Heyder Mhohamdi
This study presents an advanced hybrid evaluation approach for predicting chemical concentration (C) in adsorption-based water treatment processes using a combination of tree-based machine learning models and Massively Parallel Hyperparameter Tuning. The objective is to accurately model the nonlinear relationships between spatial input parameters (x and y) and concentration outputs within a complex porous-material system. Three ensemble learning algorithms—Random Forest (RF), Gradient Boosting (GB), and Extra Trees (ET)—were systematically optimized and assessed to determine their suitability for high-precision concentration prediction. The tuning framework enabled extensive exploration of hyperparameter space, significantly enhancing model performance. Among the tested models, Extra Trees (ET) demonstrated outstanding predictive capability, achieving an R2 value of 0.99924, along with the lowest MAPE (2.52675E-02) and MAE (5.59418E-03). These metrics confirm the ET model's exceptional ability to capture subtle nonlinear trends and complex interactions inherent to adsorption-driven systems. In comparison, RF and GB also achieved strong performance but fell short of ET in both accuracy and robustness for the data analysis. The results highlight the effectiveness of parallelized hyperparameter optimization in improving predictive reliability for chemically intricate processes. This work underscores the value of machine-learning-assisted modeling for water treatment applications and provides a scalable framework that can support process design, operational decision-making, and further mechanistic integration in environmental and chemical engineering.
本研究提出了一种先进的混合评估方法，用于预测基于吸附的水处理过程中的化学浓度(C)，该方法使用基于树的机器学习模型和大规模并行超参数调谐相结合。目标是准确地模拟复杂多孔材料系统中空间输入参数（x和y）与浓度输出之间的非线性关系。本文对随机森林（RF）、梯度增强（GB）和额外树（ET）三种集成学习算法进行了系统优化和评估，以确定它们对高精度浓度预测的适用性。该调优框架可以对超参数空间进行广泛的探索，显著提高了模型性能。在被测试的模型中，Extra Trees （ET）表现出出色的预测能力，R2值为0.99924,MAPE （2.52675E-02）和MAE （5.59418E-03）最低。这些指标证实了ET模型捕捉细微非线性趋势和吸附驱动系统固有的复杂相互作用的卓越能力。相比之下，RF和GB也取得了较强的性能，但在数据分析的准确性和稳健性方面都不及ET。结果突出了并行超参数优化在提高化学复杂过程预测可靠性方面的有效性。这项工作强调了机器学习辅助建模在水处理应用中的价值，并提供了一个可扩展的框架，可以支持过程设计、操作决策以及环境和化学工程中的进一步机械集成。
{"title":"Advanced hybrid evaluation of water treatment using porous materials for adsorption separation via machine learning and mechanistic models","authors":"Pan Zhang , Usama S. Altimari , Krunal Vaghela , V. Vivek , Sarbeswara Hota , Devendra Singh , Mahesh Manchanda , Shirin Shomurotova , Prakhar Tomar , Mohammad Mahtab Alam , Heyder Mhohamdi","doi":"10.1016/j.csite.2025.107561","DOIUrl":"10.1016/j.csite.2025.107561","url":null,"abstract":"<div><div>This study presents an advanced hybrid evaluation approach for predicting chemical concentration (C) in adsorption-based water treatment processes using a combination of tree-based machine learning models and Massively Parallel Hyperparameter Tuning. The objective is to accurately model the nonlinear relationships between spatial input parameters (x and y) and concentration outputs within a complex porous-material system. Three ensemble learning algorithms—Random Forest (RF), Gradient Boosting (GB), and Extra Trees (ET)—were systematically optimized and assessed to determine their suitability for high-precision concentration prediction. The tuning framework enabled extensive exploration of hyperparameter space, significantly enhancing model performance. Among the tested models, Extra Trees (ET) demonstrated outstanding predictive capability, achieving an R<sup>2</sup> value of 0.99924, along with the lowest MAPE (2.52675E-02) and MAE (5.59418E-03). These metrics confirm the ET model's exceptional ability to capture subtle nonlinear trends and complex interactions inherent to adsorption-driven systems. In comparison, RF and GB also achieved strong performance but fell short of ET in both accuracy and robustness for the data analysis. The results highlight the effectiveness of parallelized hyperparameter optimization in improving predictive reliability for chemically intricate processes. This work underscores the value of machine-learning-assisted modeling for water treatment applications and provides a scalable framework that can support process design, operational decision-making, and further mechanistic integration in environmental and chemical engineering.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"77 ","pages":"Article 107561"},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784661","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.2025.107593
Sihao Chen , Yaoxun Feng , Jiaan Gu , Jiangbo Li
The advantages of a dedicated outdoor air-conditioning system (DOAS) include lower operating energy consumption and better indoor air quality. The global operation optimization of DOASs has rarely been a focus of previous research, resulting in the inapparent energy-saving effect. In this paper, the machine learning-based model predictive control (MLB-MPC) with dynamic constraint conditions was proposed for optimizing the DOAS to obtain the highest operation efficiency. The support vector regression (SVR) was used to predict the total energy consumption of the DOAS and its disturbing factors. The particle swarm optimization was utilized to search for optimal setpoints of the crucial operating parameters. The dynamic constraint conditions were applied to ensure the cooling capacity of the DOAS. The case study based on Modelica simulation demonstrated that: 1) The proposed MLB-MPC with dynamic constraint conditions can ensure robustness for the DOAS, e.g., the indoor temperature and humidity of different air-conditioning areas were stably controlled in ranges 25.4 °C–26.1 °C and 57 %–65 %, respectively, achieving a good indoor heat comfort. 2) The root mean squared errors using SVR for the outdoor wet-bulb temperature and the outdoor dew-point temperature were 0.21 °C and 0.36 °C, respectively; the mean absolute percentage errors using SVR for the cooling load and the DOAS energy consumption were 2.73 % and 3.12 %, respectively, obtaining high prediction accuracies. 3) The system performances were significantly improved after optimization, e.g., during the cooling season, the DOAS's COP enhanced from 3.14 to 3.91, with an improvement of 24.5 %, demonstrating a significant energy-saving effect. The proposed global optimization control would provide a valuable reference for optimizing the operation efficiency of DOASs.
{"title":"Global optimization control based on dynamic constraint conditions of a dedicated outdoor air-conditioning system using machine learning and model predictive control","authors":"Sihao Chen , Yaoxun Feng , Jiaan Gu , Jiangbo Li","doi":"10.1016/j.csite.2025.107593","DOIUrl":"10.1016/j.csite.2025.107593","url":null,"abstract":"<div><div>The advantages of a dedicated outdoor air-conditioning system (DOAS) include lower operating energy consumption and better indoor air quality. The global operation optimization of DOASs has rarely been a focus of previous research, resulting in the inapparent energy-saving effect. In this paper, the machine learning-based model predictive control (MLB-MPC) with dynamic constraint conditions was proposed for optimizing the DOAS to obtain the highest operation efficiency. The support vector regression (SVR) was used to predict the total energy consumption of the DOAS and its disturbing factors. The particle swarm optimization was utilized to search for optimal setpoints of the crucial operating parameters. The dynamic constraint conditions were applied to ensure the cooling capacity of the DOAS. The case study based on Modelica simulation demonstrated that: 1) The proposed MLB-MPC with dynamic constraint conditions can ensure robustness for the DOAS, e.g., the indoor temperature and humidity of different air-conditioning areas were stably controlled in ranges 25.4 °C–26.1 °C and 57 %–65 %, respectively, achieving a good indoor heat comfort. 2) The root mean squared errors using SVR for the outdoor wet-bulb temperature and the outdoor dew-point temperature were 0.21 °C and 0.36 °C, respectively; the mean absolute percentage errors using SVR for the cooling load and the DOAS energy consumption were 2.73 % and 3.12 %, respectively, obtaining high prediction accuracies. 3) The system performances were significantly improved after optimization, e.g., during the cooling season, the DOAS's <em>COP</em> enhanced from 3.14 to 3.91, with an improvement of 24.5 %, demonstrating a significant energy-saving effect. The proposed global optimization control would provide a valuable reference for optimizing the operation efficiency of DOASs.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"77 ","pages":"Article 107593"},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145822870","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.2025.107562
Liang Zhao , Gang Wang , Jiaquan Li , Kai Jiao , Lin Lu
Nanoencapsulated phase-change material containing urea was synthesized by an interfacial polymerization method using SiO2 as shell material. The physicochemical properties of the nanocapsules were characterized through various techniques. The nanocapsules were evaluated for acid and alkali resisting performance, wear resistance, cyclohexane recycling and drilling fluid circulation. The melting temperature, latent heat, encapsulation ratio and yield of nanocapsule were determined to be 133.4 °C, 183.5 J/g, 84.7 % and 93.7 %, respectively. After 100 melting-freezing cycles, the melting latent heat and freezing latent heat still maintained at 175 J/g and 164.3 J/g. The inclusion of these nanocapsules did not alter the fundamental properties of the drilling fluid, yet it reduced the peak temperature. Besides, the as-prepared nanocapsule showed a good reusability, and the difference in cooling effect between the two cooling experiments did not exceed 1.5 °C. Therefore, the urea@SiO2 nanocapsule is found to be a promising candidate for thermal regulation of deep well drilling.
{"title":"Preparation of medium temperature phase-change nanocapsules for thermal regulation of deep well drilling","authors":"Liang Zhao , Gang Wang , Jiaquan Li , Kai Jiao , Lin Lu","doi":"10.1016/j.csite.2025.107562","DOIUrl":"10.1016/j.csite.2025.107562","url":null,"abstract":"<div><div>Nanoencapsulated phase-change material containing urea was synthesized by an interfacial polymerization method using SiO<sub>2</sub> as shell material. The physicochemical properties of the nanocapsules were characterized through various techniques. The nanocapsules were evaluated for acid and alkali resisting performance, wear resistance, cyclohexane recycling and drilling fluid circulation. The melting temperature, latent heat, encapsulation ratio and yield of nanocapsule were determined to be 133.4 °C, 183.5 J/g, 84.7 % and 93.7 %, respectively. After 100 melting-freezing cycles, the melting latent heat and freezing latent heat still maintained at 175 J/g and 164.3 J/g. The inclusion of these nanocapsules did not alter the fundamental properties of the drilling fluid, yet it reduced the peak temperature. Besides, the as-prepared nanocapsule showed a good reusability, and the difference in cooling effect between the two cooling experiments did not exceed 1.5 °C. Therefore, the urea@SiO<sub>2</sub> nanocapsule is found to be a promising candidate for thermal regulation of deep well drilling.</div></div>","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"77 ","pages":"Article 107562"},"PeriodicalIF":6.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784672","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 : 2025-12-31DOI: 10.1016/j.csite.2025.107634
F. Noh-Pat, M. Gijón-Rivera, C.I. Rivera-Solorio, M. Jiménez-Xamán
{"title":"Numerical analysis of the thermal performance of a lightweight insulating roof integrated with a phase change material","authors":"F. Noh-Pat, M. Gijón-Rivera, C.I. Rivera-Solorio, M. Jiménez-Xamán","doi":"10.1016/j.csite.2025.107634","DOIUrl":"https://doi.org/10.1016/j.csite.2025.107634","url":null,"abstract":"","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"72 1","pages":"107634"},"PeriodicalIF":6.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894211","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 : 2025-12-31DOI: 10.1016/j.csite.2025.107632
Feng Cao, Xiang Chen, Shian Hu, Yuting Huang, Guangcai Gong
{"title":"Correlation-complementary model of building envelope effects on indoor temperature","authors":"Feng Cao, Xiang Chen, Shian Hu, Yuting Huang, Guangcai Gong","doi":"10.1016/j.csite.2025.107632","DOIUrl":"https://doi.org/10.1016/j.csite.2025.107632","url":null,"abstract":"","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"21 1","pages":"107632"},"PeriodicalIF":6.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894209","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 : 2025-12-31DOI: 10.1016/j.csite.2025.107635
Jiaqi Xu, Tao Fang, Xitao Han, Junhan Duan, Ruijie Liu
{"title":"Considering District Shading in Solar Potential Optimization of Residential Nearly Zero Energy Buildings (NZEBs): A Case Study of Jinan City","authors":"Jiaqi Xu, Tao Fang, Xitao Han, Junhan Duan, Ruijie Liu","doi":"10.1016/j.csite.2025.107635","DOIUrl":"https://doi.org/10.1016/j.csite.2025.107635","url":null,"abstract":"","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"52 1","pages":"107635"},"PeriodicalIF":6.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894210","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 : 2025-12-31DOI: 10.1016/j.csite.2025.107636
Shanshan Liu, Wenling Jiao, Yanyu Zhang
{"title":"Frost Formation Criterion Framework for Ambient Air Vaporizers Integrating Inter-fin Mainstream Air Temperature Correction","authors":"Shanshan Liu, Wenling Jiao, Yanyu Zhang","doi":"10.1016/j.csite.2025.107636","DOIUrl":"https://doi.org/10.1016/j.csite.2025.107636","url":null,"abstract":"","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"53 1","pages":"107636"},"PeriodicalIF":6.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894213","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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