Anisotropic powder was prepared with precursor （NdDy）-（FeCoNbCu）-B sintered magnets by hydrogen decrepitation, desorption, and subsequent annealing treatment. The hydrogen desorption was performed in magnetic fiel...Anisotropic powder was prepared with precursor （NdDy）-（FeCoNbCu）-B sintered magnets by hydrogen decrepitation, desorption, and subsequent annealing treatment. The hydrogen desorption was performed in magnetic fields of 0, 1, 3, and 5 T. The orientation of tetragonal phase grains of the powder was evaluated from the hysteresis loops measured by extraction magnetometer. Residual hydrogen content of the powder was evaluated by thermal-magnetic analysis. The powder with Hcj, Br, and （BH）max of 1138 kA.m^-1, 1.029 T, and 172.5 kJ.m^-3, respectively, was achieved under the condition of the magnetic field of 3 T. Magnetic properties of the powder, especially, the remanence of the powder, are enhanced upon magnetic fields, which is due to better orientation of powder particles and less residual hydrogen in the powder resulted from the magnetic field during the hydrogen desorption process.展开更多
镍基690合金广泛用于压水堆核电站核岛主设备关键部件及焊缝,高温高压水环境应力腐蚀开裂(SCC)是其潜在的失效机理。由于SCC行为影响因素多达二十余种,因此存在参数化模型预测精度不高的问题。本研究通过融合随机森林机器学习算法(rando...镍基690合金广泛用于压水堆核电站核岛主设备关键部件及焊缝,高温高压水环境应力腐蚀开裂(SCC)是其潜在的失效机理。由于SCC行为影响因素多达二十余种,因此存在参数化模型预测精度不高的问题。本研究通过融合随机森林机器学习算法(random forest,RF)与基于领域知识的MRP-386参数化模型,建立了镍基690合金SCC裂纹扩展速率KBRF(knowledge-based random forest)预测模型。结果表明,领域知识的引入增强了KBRF模型的鲁棒性,准确性较MRP-386参数化模型和RF等机器学习模型显著提高。模型将应用于中国压水堆核电站镍基690合金部件及焊缝在反应堆冷却剂中的应力腐蚀裂纹扩展工程预测。展开更多
基金the French Embassy in Beijing for provision of a collaborative research grant as part of a co-research program under the frame of LIA-LAS2M between Northwestern Polytechnic University-Xi'an,China and CNRS-Grenoble,France
文摘Anisotropic powder was prepared with precursor （NdDy）-（FeCoNbCu）-B sintered magnets by hydrogen decrepitation, desorption, and subsequent annealing treatment. The hydrogen desorption was performed in magnetic fields of 0, 1, 3, and 5 T. The orientation of tetragonal phase grains of the powder was evaluated from the hysteresis loops measured by extraction magnetometer. Residual hydrogen content of the powder was evaluated by thermal-magnetic analysis. The powder with Hcj, Br, and （BH）max of 1138 kA.m^-1, 1.029 T, and 172.5 kJ.m^-3, respectively, was achieved under the condition of the magnetic field of 3 T. Magnetic properties of the powder, especially, the remanence of the powder, are enhanced upon magnetic fields, which is due to better orientation of powder particles and less residual hydrogen in the powder resulted from the magnetic field during the hydrogen desorption process.
文摘镍基690合金广泛用于压水堆核电站核岛主设备关键部件及焊缝,高温高压水环境应力腐蚀开裂(SCC)是其潜在的失效机理。由于SCC行为影响因素多达二十余种,因此存在参数化模型预测精度不高的问题。本研究通过融合随机森林机器学习算法(random forest,RF)与基于领域知识的MRP-386参数化模型,建立了镍基690合金SCC裂纹扩展速率KBRF(knowledge-based random forest)预测模型。结果表明,领域知识的引入增强了KBRF模型的鲁棒性,准确性较MRP-386参数化模型和RF等机器学习模型显著提高。模型将应用于中国压水堆核电站镍基690合金部件及焊缝在反应堆冷却剂中的应力腐蚀裂纹扩展工程预测。
基金Natural Science Foundation of Jiangsu Province(BK2018117,BK20180210)Scientific Research and Innovation Project of China General Nuclear Power Group(3100129119)National Science and Technology Major Project of China(2019ZX06005003)。
