空泡份额是核反应堆两相流中关注的重点参数之一,是检验压水堆系统级程序计算能力的重要物理量。为加速我国压水堆软件自主化进程,中国广核集团有限公司开发了压水堆热工水力系统分析软件LOCUST。为支持LOCUST在冷却剂丧失事故(Loss of ...空泡份额是核反应堆两相流中关注的重点参数之一,是检验压水堆系统级程序计算能力的重要物理量。为加速我国压水堆软件自主化进程,中国广核集团有限公司开发了压水堆热工水力系统分析软件LOCUST。为支持LOCUST在冷却剂丧失事故(Loss of Coolant Accident,LOCA)等事故分析中的应用,本文基于瑞典通用电机公司开展的FRIGG空泡份额实验,利用LOCUST对不同功率分布、入口过冷度以及质量流速的实验工况进行计算验证。评估结果表明,所有工况空泡份额相对误差的平均值为10.60%,单个工况最大相对误差的平均值不超过16.62%,部分误差可能是由流型转换判定、高估液相相变换热等原因引起。本文总体验证了LOCUST在不同功率分布、入口过冷度以及质量流速工况下对空泡份额的计算能力,为后续软件模型改进提供了参考。展开更多
Landslide dam failures can cause significant damage to both society and ecosystems.Predicting the failure of these dams in advance enables early preventive measures,thereby minimizing potential harm.This paper aims to...Landslide dam failures can cause significant damage to both society and ecosystems.Predicting the failure of these dams in advance enables early preventive measures,thereby minimizing potential harm.This paper aims to propose a fast and accurate model for predicting the longevity of landslide dams while also addressing the issue of missing data.Given the wide variation in the survival times of landslide dams—from mere minutes to several thousand years—predicting their longevity presents a considerable challenge.The study develops predictive models by considering key factors such as dam geometry,hydrodynamic conditions,materials,and triggering parameters.A dataset of 1045 landslide dam cases is analyzed,categorizing their longevity into three distinct groups:C1(<1 month),C2(1 month to 1 year),and C3(>1 year).Multiple imputation and knearest neighbor algorithms are used to handle missing data on geometric size,hydrodynamic conditions,materials,and triggers.Based on the imputed data,two predictive models are developed:a classification model for dam longevity categories and a regression model for precise longevity predictions.The classification model achieves an accuracy of 88.38%while the regression model outperforms existing models with an R^(2) value of 0.966.Two real-life landslide dam cases are used to validate the models,which show correct classification and small prediction errors.The longevity of landslide dams is jointly influenced by factors such as geometric size,hydrodynamic conditions,materials,and triggering events.Among these,geometric size has the greatest impact,followed by hydrodynamic conditions,materials,and triggers,as confirmed by variable importance in the model development.展开更多
文摘空泡份额是核反应堆两相流中关注的重点参数之一,是检验压水堆系统级程序计算能力的重要物理量。为加速我国压水堆软件自主化进程,中国广核集团有限公司开发了压水堆热工水力系统分析软件LOCUST。为支持LOCUST在冷却剂丧失事故(Loss of Coolant Accident,LOCA)等事故分析中的应用,本文基于瑞典通用电机公司开展的FRIGG空泡份额实验,利用LOCUST对不同功率分布、入口过冷度以及质量流速的实验工况进行计算验证。评估结果表明,所有工况空泡份额相对误差的平均值为10.60%,单个工况最大相对误差的平均值不超过16.62%,部分误差可能是由流型转换判定、高估液相相变换热等原因引起。本文总体验证了LOCUST在不同功率分布、入口过冷度以及质量流速工况下对空泡份额的计算能力,为后续软件模型改进提供了参考。
基金support of the National Natural Science Foundation of China(U42107189,20A20111).
文摘Landslide dam failures can cause significant damage to both society and ecosystems.Predicting the failure of these dams in advance enables early preventive measures,thereby minimizing potential harm.This paper aims to propose a fast and accurate model for predicting the longevity of landslide dams while also addressing the issue of missing data.Given the wide variation in the survival times of landslide dams—from mere minutes to several thousand years—predicting their longevity presents a considerable challenge.The study develops predictive models by considering key factors such as dam geometry,hydrodynamic conditions,materials,and triggering parameters.A dataset of 1045 landslide dam cases is analyzed,categorizing their longevity into three distinct groups:C1(<1 month),C2(1 month to 1 year),and C3(>1 year).Multiple imputation and knearest neighbor algorithms are used to handle missing data on geometric size,hydrodynamic conditions,materials,and triggers.Based on the imputed data,two predictive models are developed:a classification model for dam longevity categories and a regression model for precise longevity predictions.The classification model achieves an accuracy of 88.38%while the regression model outperforms existing models with an R^(2) value of 0.966.Two real-life landslide dam cases are used to validate the models,which show correct classification and small prediction errors.The longevity of landslide dams is jointly influenced by factors such as geometric size,hydrodynamic conditions,materials,and triggering events.Among these,geometric size has the greatest impact,followed by hydrodynamic conditions,materials,and triggers,as confirmed by variable importance in the model development.
