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Thermo-Hydrodynamic Characteristics of Hybrid Nanofluids for Chip-Level Liquid Cooling in Data Centers: A Review of Numerical Investigations 认领 引用 被引量：1
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作者 Yifan Li Congzhe Zhu +2 位作者 Zhihan Lyu Bin Yang Thomas Olofsson 《Energy Engineering》 EI 2025年第9期3525-3553,共29页
The growth of computing power in data centers(DCs)leads to an increase in energy consumption and noise pollution of air cooling systems.Chip-level cooling with high-efficiency coolant is one of the promising methods t... The growth of computing power in data centers(DCs)leads to an increase in energy consumption and noise pollution of air cooling systems.Chip-level cooling with high-efficiency coolant is one of the promising methods to address the cooling challenge for high-power devices in DCs.Hybrid nanofluid(HNF)has the advantages of high thermal conductivity and good rheological properties.This study summarizes the numerical investigations of HNFs in mini/micro heat sinks,including the numerical methods,hydrothermal characteristics,and enhanced heat transfer technologies.The innovations of this paper include:(1)the characteristics,applicable conditions,and scenarios of each theoretical method and numerical method are clarified;(2)the molecular dynamics(MD)simulation can reveal the synergy effect,micro motion,and agglomeration morphology of different nanoparticles.Machine learning(ML)presents a feasiblemethod for parameter prediction,which provides the opportunity for the intelligent regulation of the thermal performance of HNFs;(3)the HNFs flowboiling and the synergy of passive and active technologies may further improve the overall efficiency of liquid cooling systems in DCs.This review provides valuable insights and references for exploring the multi-phase flow and heat transport mechanisms of HNFs,and promoting the practical application of HNFs in chip-level liquid cooling in DCs. 展开更多
关键词 Data centers chip-level liquid cooling hybrid nanofluid energy transport characteristic hydrodynamic performance numerical investigation
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Adaptive Wall-Based Attachment Ventilation:A Comparative Study on Its Effectiveness in Airborne Infection Isolation Rooms with Negative Pressure 认领 引用 被引量：5
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作者 Ying Zhang Ou Han +4 位作者 Angui Li Li’an Hou Thomas Olofsson Linhua Zhang Wenjun Lei 《Engineering》 SCIE EI CAS CSCD 2022年第1期130-137,共8页
The transmission of coronavirus disease 2019(COVID-19)has presented challenges for the control of the indoor environment of isolation wards.Scientific air distribution design and operation management are crucial to en... The transmission of coronavirus disease 2019(COVID-19)has presented challenges for the control of the indoor environment of isolation wards.Scientific air distribution design and operation management are crucial to ensure the environmental safety of medical staff.This paper proposes the application of adaptive wall-based attachment ventilation and evaluates this air supply mode based on contaminants dispersion,removal efficiency,thermal comfort,and operating expense.Adaptive wall-based attachment ventilation provides a direct supply of fresh air to the occupied zone.In comparison with a ceiling air supply or upper sidewall air supply,adaptive wall-based attachment ventilation results in a 15%–47%lower average concentration of contaminants,for a continual release of contaminants at the same air changes per hour(ACH;10 h-1).The contaminant removal efficiency of complete mixing ventilation cannot exceed 1.For adaptive wall-based attachment ventilation,the contaminant removal efficiency is an exponential function of the ACH.Compared with the ceiling air supply mode or upper sidewall air supply mode,adaptive wall-based attachment ventilation achieves a similar thermal comfort level(predicted mean vote(PMV)of0.1–0.4;draught rate of 2.5%–6.7%)and a similar performance in removing contaminants,but has a lower ACH and uses less energy. 展开更多
关键词 Ventilation efficiency Air change rate COVID-19 Attachment ventilation Air distribution Isolation ward
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Explainable Artificial Intelligence(XAI)for Material Design and Engineering Applications:A Quantitative Computational Framework 认领 引用 被引量：3
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作者 Bokai Liu Pengju Liu +1 位作者 Weizhuo Lu Thomas Olofsson 《International Journal of Mechanical System Dynamics》 EI CSCD 2025年第2期236-265,共30页
The advancement of artificial intelligence(AI)in material design and engineering has led to significant improvements in predictive modeling of material properties.However,the lack of interpretability in machine learni... The advancement of artificial intelligence(AI)in material design and engineering has led to significant improvements in predictive modeling of material properties.However,the lack of interpretability in machine learning(ML)-based material informatics presents a major barrier to its practical adoption.This study proposes a novel quantitative computational framework that integrates ML models with explainable artificial intelligence(XAI)techniques to enhance both predictive accuracy and interpretability in material property prediction.The framework systematically incorporates a structured pipeline,including data processing,feature selection,model training,performance evaluation,explainability analysis,and real-world deployment.It is validated through a representative case study on the prediction of high-performance concrete(HPC)compressive strength,utilizing a comparative analysis of ML models such as Random Forest,XGBoost,Support Vector Regression(SVR),and Deep Neural Networks(DNNs).The results demonstrate that XGBoost achieves the highest predictive performance(R2=0.918),while SHAP(Shapley Additive Explanations)and LIME(Local Interpretable Model-Agnostic Explanations)provide detailed insights into feature importance and material interactions.Additionally,the deployment of the trained model as a cloud-based Flask-Gunicorn API enables real-time inference,ensuring its scalability and accessibility for industrial and research applications.The proposed framework addresses key limitations of existing ML approaches by integrating advanced explainability techniques,systematically handling nonlinear feature interactions,and providing a scalable deployment strategy.This study contributes to the development of interpretable and deployable AI-driven material informatics,bridging the gap between data-driven predictions and fundamental material science principles. 展开更多
关键词 explainable artificial intelligence(XAI) high-performance concrete material informatics predictive modeling science cloud deployment
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