[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-128065-105":59,"doc-detail-128065-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","uncertainty-quantification-and-sensitivity-analysis-of-a-machine-learning-based-spill-fire-model-for-nuclear-power-plants","Uncertainty Quantification and Sensitivity Analysis of a Machine Learning-Based Spill Fire Model for Nuclear Power Plants","","Fuel spill fire constitutes a significant risk scenario for nuclear power plants, yet existing models show large uncertainties that limit accurate prediction of liquid fuel spread and burning behavior. The study develops a machine learning-based spill fire model that integrates uncertainty quantification and sensitivity analysis to strengthen risk assessment reliability. Experimental spill fire data for multiple spill sizes and fuels are used to identify dominant parameters via sensitivity analysis and Sobol indices. DAKOTA performs uncertainty quantification, while Monte Carlo sampling trains a model embedded with DAKOTA to estimate peak heat release-rate statistics and uncertainty, guiding where additional data is needed.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/uncertainty-quantification-and-sensitivity-analysis-of-a-machine-learning-based-spill-fire-model-for-nuclear-power-plants/128065/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/uncertainty-quantification-and-sensitivity-analysis-of-a-machine-learning-based-spill-fire-model-for-nuclear-power-plants/128065.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is uncertainty quantification important for spill fire modeling in nuclear power plants?","Question",{"text":113,"@type":114},"Existing spill fire models contain large uncertainties that hinder reliable predictions of how liquid fuel spreads and burns. Uncertainty quantification improves the credibility of risk assessment outputs.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How are dominant model parameters identified in this study?",{"text":118,"@type":114},"The approach collects prior experimental data for various spill sizes and fuels, then applies sensitivity analysis and Sobol indices to determine the most influential parameters.",{"name":120,"@type":111,"acceptedAnswer":121},"What role does DAKOTA and Monte Carlo sampling play?",{"text":122,"@type":114},"DAKOTA is used to perform uncertainty quantification and evaluate uncertainty propagation, while Monte Carlo sampling generates training data from DAKOTA-MLP simulations to produce ML-based statistics of peak heat release rate.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},128065,1785944586,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":81},5909887254083,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Uncertainty Quantification and Sensitivity Analysis of a Machine Learning-Based Spill  \nFire Model for Nuclear Power Plants  \nElvan Sahin 1*, Peter Henkes2, Brian Y. Lattimer 1, and Juliana P. Duarte2  \n1Virginia Polytechnic Institute and State University, Blacksburg/VA, 24061 2University of Wisconsin-Madison, Madison/WI, 53706  \n*Corresponding Author [[elvansahin@vt.edu](elvansahin@vt.edu)]  \n KEYWORD S  Spill Fire  \nMachine Learning Uncertainty Quantification Sensitivity Analysis  \n AB STRACT   \nFuel spill fire is a significant risk scenario in nuclear powerplants (NPPs) that requires accurate and reliable models to assess potential consequences. However, current models suffer from large uncertainties, making it challenging to predict the spread and burning behavior of liquid fuel in a spill fire accurately. To address these challenges, this study proposes a machine learning-based spill fire model that incorporates uncertainty quantification and sensitivity analysis to enhance the accuracy and reliability of risk assessment.  \nTo develop the spill fire model, past experimental data on spill fire for different spill sizes and fuels are collected. The spill is considered a continuous/fixed quantity in an unconfined area, and the dominant parameters are identified using sensitivity analysis and Sobol indices. The study also employs the DAKOTA software to perform uncertainty quantification on the model and evaluate the uncertainty propagation of the dominant parameters.  \nMoreover, the study uses the Monte Carlo method with a sampling size of 2048 to generate data from DAKOTA-MLP simulations. The data is used to train a machine learning model which is integrated with DAKOTA to obtain statistics of the spill fire peak heat release rate (HRR). The algorithm can also provide uncertainties associated with the lack of data or data uncertainty and, therefore, indicating regions where more data is needed  \nAdditionally, sensitivity analysis is carried out for four input parameters and one output parameter for each spill fire scenario. The results show that the fuel leakage rate or quantity, the slope of the spill surface, ignition delay time, substrate material, and fuel properties are the most critical parameters that impact the peak HRR of the spill fire.","cbCaieLvGvXC6X9M","https://ap.wps.com/l/cbCaieLvGvXC6X9M","pdf",32400,"English","# Method overview\n## Data collection and scenario setup\n## Parameter identification with sensitivity analysis\n## Uncertainty quantification with DAKOTA\n## Monte Carlo sampling and ML model training\n## Sensitivity results for key inputs","[{\"question\":\"Why is uncertainty quantification important for spill fire modeling in nuclear power plants?\",\"answer\":\"Existing spill fire models contain large uncertainties that hinder reliable predictions of how liquid fuel spreads and burns. Uncertainty quantification improves the credibility of risk assessment outputs.\"},{\"question\":\"How are dominant model parameters identified in this study?\",\"answer\":\"The approach collects prior experimental data for various spill sizes and fuels, then applies sensitivity analysis and Sobol indices to determine the most influential parameters.\"},{\"question\":\"What role does DAKOTA and Monte Carlo sampling play?\",\"answer\":\"DAKOTA is used to perform uncertainty quantification and evaluate uncertainty propagation, while Monte Carlo sampling generates training data from DAKOTA-MLP simulations to produce ML-based statistics of peak heat release rate.\"}]","Uncertainty Quantification and Sensitivity Analysis of a Machine Learning-Based Spill Fire Model for Nuclear Power Plants | PDF"]