The aging of operational reactors leads to increased mechanical vibrations in the reactor interior.The vibration of the incore sensors near their nominal locations is a new problem for neutronic field reconstruction.C...The aging of operational reactors leads to increased mechanical vibrations in the reactor interior.The vibration of the incore sensors near their nominal locations is a new problem for neutronic field reconstruction.Current field-reconstruction methods fail to handle spatially moving sensors.In this study,we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge.Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation,holding the magnitude and location information of the sensors.General convolutional neural networks were used to learn maps from observations to the global field.The proposed method reconstructed multi-physics fields(including fast flux,thermal flux,and power rate)using observations from a single field(such as thermal flux).Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications,particularly within an amplitude of 5 cm around the nominal locations,which led to average relative errors below 5% and 10% in the L_(2) and L_(∞)norms,respectively.展开更多
Storm surge is often the marine disaster that poses the greatest threat to life and property in coastal areas.Accurate and timely issuance of storm surge warnings to take appropriate countermeasures is an important me...Storm surge is often the marine disaster that poses the greatest threat to life and property in coastal areas.Accurate and timely issuance of storm surge warnings to take appropriate countermeasures is an important means to reduce storm surge-related losses.Storm surge numerical models are important for storm surge forecasting.To further improve the performance of the storm surge forecast models,we developed a numerical storm surge forecast model based on an unstructured spherical centroidal Voronoi tessellation(SCVT)grid.The model is based on shallow water equations in vector-invariant form,and is discretized by Arakawa C grid.The SCVT grid can not only better describe the coastline information but also avoid rigid transitions,and it has a better global consistency by generating high-resolution grids in the key areas through transition refinement.In addition,the simulation speed of the model is accelerated by using the openACC-based GPU acceleration technology to meet the timeliness requirements of operational ensemble forecast.It only takes 37 s to simulate a day in the coastal waters of China.The newly developed storm surge model was applied to simulate typhoon-induced storm surges in the coastal waters of China.The hindcast experiments on the selected representative typhoon-induced storm surge processes indicate that the model can reasonably simulate the distribution characteristics of storm surges.The simulated maximum storm surges and their occurrence times are consistent with the observed data at the representative tide gauge stations,and the mean absolute errors are 3.5 cm and 0.6 h respectively,showing high accuracy and application prospects.展开更多
随着工业化进程的加快和城市化的发展,大量污染物排入黄河流域,并被频繁检出,威胁生态系统和人类健康。为获取潜在生态环境风险污染物,该研究通过调研2000年1月1日−2022年12月31日Web of Science(WoS)和中国知网(CNKI)数据库中黄河流域...随着工业化进程的加快和城市化的发展,大量污染物排入黄河流域,并被频繁检出,威胁生态系统和人类健康。为获取潜在生态环境风险污染物,该研究通过调研2000年1月1日−2022年12月31日Web of Science(WoS)和中国知网(CNKI)数据库中黄河流域已报道的288篇污染物相关文献,使用多指标综合评分法筛选黄河流域的特征污染物,采用风险商值法获取水样和沉积物中的风险污染物。结果表明:①黄河流域共检出10类144种污染物,采用9类共13个筛选指标构建多指标综合评分法,对污染物各项指标进行评分,然后进行K-means聚类分析,按得分高低分为Ⅰ~Ⅵ级,选取得分较高的33种Ⅰ级和Ⅱ级高分值污染物作为黄河流域特征污染物,包括12种有机氯农药、10种多环芳烃、10种多氯联苯和1种邻苯二甲酸酯。②水样污染物浓度和沉积物含量前5种都是重金属、有机氯农药、邻苯二甲酸酯、多环芳烃以及药品和个人护理产品,而且二者顺序完全一致,且多数污染物的浓度之间存在显著相关性。③根据风险最大化原则,使用风险商值法(RQ)分别对水样和沉积物进行风险评估,将RQ≥0.1的污染物列为风险污染物,水样中共筛选出21种风险污染物,其中RQ≥1的高风险污染物有5种,包括硒、铅、苯并[a,h]蒽、苯并[a]蒽和邻苯二甲酸二丁酯。④沉积物中共筛选出19种风险污染物,其中有13种高风险污染物,包括8种多环芳烃(芘、蒽、荧蒽、苊、萘、芴、苯并[a]蒽、苯并[a,h]蒽)、4种重金属(汞、铅、硒、砷)和1种邻苯二甲酸酯(邻苯二甲酸二丁酯)。该研究对相关部门拟定黄河流域污染物监测方案和管控措施有重要参考意义。展开更多
基金partially supported by the Natural Science Foundation of Shanghai(No.23ZR1429300)the Innovation Fund of CNNC(Lingchuang Fund)+1 种基金EP/T000414/1 PREdictive Modeling with QuantIfication of UncERtainty for MultiphasE Systems(PREMIERE)the Leverhulme Centre for Wildfires,Environment,and Society through the Leverhulme Trust(No.RC-2018-023).
文摘The aging of operational reactors leads to increased mechanical vibrations in the reactor interior.The vibration of the incore sensors near their nominal locations is a new problem for neutronic field reconstruction.Current field-reconstruction methods fail to handle spatially moving sensors.In this study,we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge.Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation,holding the magnitude and location information of the sensors.General convolutional neural networks were used to learn maps from observations to the global field.The proposed method reconstructed multi-physics fields(including fast flux,thermal flux,and power rate)using observations from a single field(such as thermal flux).Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications,particularly within an amplitude of 5 cm around the nominal locations,which led to average relative errors below 5% and 10% in the L_(2) and L_(∞)norms,respectively.
基金The National Natural Science Foundation of China under contract No.42076214.
文摘Storm surge is often the marine disaster that poses the greatest threat to life and property in coastal areas.Accurate and timely issuance of storm surge warnings to take appropriate countermeasures is an important means to reduce storm surge-related losses.Storm surge numerical models are important for storm surge forecasting.To further improve the performance of the storm surge forecast models,we developed a numerical storm surge forecast model based on an unstructured spherical centroidal Voronoi tessellation(SCVT)grid.The model is based on shallow water equations in vector-invariant form,and is discretized by Arakawa C grid.The SCVT grid can not only better describe the coastline information but also avoid rigid transitions,and it has a better global consistency by generating high-resolution grids in the key areas through transition refinement.In addition,the simulation speed of the model is accelerated by using the openACC-based GPU acceleration technology to meet the timeliness requirements of operational ensemble forecast.It only takes 37 s to simulate a day in the coastal waters of China.The newly developed storm surge model was applied to simulate typhoon-induced storm surges in the coastal waters of China.The hindcast experiments on the selected representative typhoon-induced storm surge processes indicate that the model can reasonably simulate the distribution characteristics of storm surges.The simulated maximum storm surges and their occurrence times are consistent with the observed data at the representative tide gauge stations,and the mean absolute errors are 3.5 cm and 0.6 h respectively,showing high accuracy and application prospects.
文摘随着工业化进程的加快和城市化的发展,大量污染物排入黄河流域,并被频繁检出,威胁生态系统和人类健康。为获取潜在生态环境风险污染物,该研究通过调研2000年1月1日−2022年12月31日Web of Science(WoS)和中国知网(CNKI)数据库中黄河流域已报道的288篇污染物相关文献,使用多指标综合评分法筛选黄河流域的特征污染物,采用风险商值法获取水样和沉积物中的风险污染物。结果表明:①黄河流域共检出10类144种污染物,采用9类共13个筛选指标构建多指标综合评分法,对污染物各项指标进行评分,然后进行K-means聚类分析,按得分高低分为Ⅰ~Ⅵ级,选取得分较高的33种Ⅰ级和Ⅱ级高分值污染物作为黄河流域特征污染物,包括12种有机氯农药、10种多环芳烃、10种多氯联苯和1种邻苯二甲酸酯。②水样污染物浓度和沉积物含量前5种都是重金属、有机氯农药、邻苯二甲酸酯、多环芳烃以及药品和个人护理产品,而且二者顺序完全一致,且多数污染物的浓度之间存在显著相关性。③根据风险最大化原则,使用风险商值法(RQ)分别对水样和沉积物进行风险评估,将RQ≥0.1的污染物列为风险污染物,水样中共筛选出21种风险污染物,其中RQ≥1的高风险污染物有5种,包括硒、铅、苯并[a,h]蒽、苯并[a]蒽和邻苯二甲酸二丁酯。④沉积物中共筛选出19种风险污染物,其中有13种高风险污染物,包括8种多环芳烃(芘、蒽、荧蒽、苊、萘、芴、苯并[a]蒽、苯并[a,h]蒽)、4种重金属(汞、铅、硒、砷)和1种邻苯二甲酸酯(邻苯二甲酸二丁酯)。该研究对相关部门拟定黄河流域污染物监测方案和管控措施有重要参考意义。
