The hysteresis of saturation-capillary pressure （S-p） relations was investigated in a fine sandy medium under consecutive drainage-imbibition cycles, which resulted from scheduled water level fluctuations. A drainag...The hysteresis of saturation-capillary pressure （S-p） relations was investigated in a fine sandy medium under consecutive drainage-imbibition cycles, which resulted from scheduled water level fluctuations. A drainage-imbibition cycle starts with a drainage process and ends with an imbibition process in sequence. The saturation and capillary pressure were measured online with time domain reflectometry （TDR） probes and T5 tensiometers, respectively. Results show that the relation between the degree of hysteresis and the number of the drainage-imbibition cycles is not obvious. However, the degree decreases with the increase of the initial water saturation of the imbibition processes in these drainage-imbibition cycles. The air-entry pressure of a sandy medium is also found to be constant, which is independent of the drainage-imbibition cycles and the initial water saturation of the drainage process. In all the imbibition processes, parameter a of the van Genuchten （VG） model decreases with the increase of the initial water saturation, which corresponds positively to the magnitude of the hysteresis.展开更多
High-Entropy Alloys(HEAs)exhibit significant potential across multiple domains due to their unique properties.However,conventional research methodologies face limitations in composition design,property prediction,and ...High-Entropy Alloys(HEAs)exhibit significant potential across multiple domains due to their unique properties.However,conventional research methodologies face limitations in composition design,property prediction,and process optimization,characterized by low efficiency and high costs.The integration of Artificial Intelligence(AI)technologies has provided innovative solutions for HEAs research.This review presented a detailed overview of recent advancements in AI applications for structural modeling and mechanical property prediction of HEAs.Furthermore,it discussed the advantages of big data analytics in facilitating alloy composition design and screening,quality control,and defect prediction,as well as the construction and sharing of specialized material databases.The paper also addressed the existing challenges in current AI-driven HEAs research,including issues related to data quality,model interpretability,and cross-domain knowledge integration.Additionally,it proposed prospects for the synergistic development of AI-enhanced computational materials science and experimental validation systems.展开更多
基金Project(41072182) supported by the National Natural Science Foundation of ChinaProject(2010Z1-E101) supported by the Science and Technology Program of Guangzhou City,China+1 种基金Project(200809095) supported by the Special Funds for Environmental Nonprofit Research,ChinaProject(8151027501000008) supported by the Natural Science Foundation of Guangdong Province,China
文摘The hysteresis of saturation-capillary pressure （S-p） relations was investigated in a fine sandy medium under consecutive drainage-imbibition cycles, which resulted from scheduled water level fluctuations. A drainage-imbibition cycle starts with a drainage process and ends with an imbibition process in sequence. The saturation and capillary pressure were measured online with time domain reflectometry （TDR） probes and T5 tensiometers, respectively. Results show that the relation between the degree of hysteresis and the number of the drainage-imbibition cycles is not obvious. However, the degree decreases with the increase of the initial water saturation of the imbibition processes in these drainage-imbibition cycles. The air-entry pressure of a sandy medium is also found to be constant, which is independent of the drainage-imbibition cycles and the initial water saturation of the drainage process. In all the imbibition processes, parameter a of the van Genuchten （VG） model decreases with the increase of the initial water saturation, which corresponds positively to the magnitude of the hysteresis.
文摘High-Entropy Alloys(HEAs)exhibit significant potential across multiple domains due to their unique properties.However,conventional research methodologies face limitations in composition design,property prediction,and process optimization,characterized by low efficiency and high costs.The integration of Artificial Intelligence(AI)technologies has provided innovative solutions for HEAs research.This review presented a detailed overview of recent advancements in AI applications for structural modeling and mechanical property prediction of HEAs.Furthermore,it discussed the advantages of big data analytics in facilitating alloy composition design and screening,quality control,and defect prediction,as well as the construction and sharing of specialized material databases.The paper also addressed the existing challenges in current AI-driven HEAs research,including issues related to data quality,model interpretability,and cross-domain knowledge integration.Additionally,it proposed prospects for the synergistic development of AI-enhanced computational materials science and experimental validation systems.
