@article{crowder_lee_gupta_han_bodda_ritter_2022, title={Digital Engineering for Integrated Modeling and Simulation for Building-Piping Systems Through Interoperability Solutions}, ISSN={["1943-748X"]}, DOI={10.1080/00295639.2022.2055705}, abstractNote={Abstract Designing piping systems for nuclear power plants involves engineers from multiple disciplines (i.e., thermal hydraulics, mechanical engineering, and structural/seismic) and close coordination with the contractors who build the plant. Any design changes during construction need to be carefully communicated and managed with all stakeholders in order to assess risks associated with the design changes. To allow the quick assessment of building and piping design changes through a streamlined building-piping coupled analysis, this paper presents a novel interoperability solution that converts bidirectionally between building information models (BIMs) and pipe stress models. Any design changes during construction that are shown in an as-built BIM are automatically converted into a pipe stress model. Any further design changes due to building-piping interaction analyses are converted back to the BIM for the contractor and other designers to access the latest model. Two case studies are presented to illustrate the bidirectional conversion that allows an integrated coupled analysis of the building-piping system to account for their interactions.}, journal={NUCLEAR SCIENCE AND ENGINEERING}, author={Crowder, Nicholas and Lee, Joomyung and Gupta, Abhinav and Han, Kevin and Bodda, Saran and Ritter, Christopher}, year={2022}, month={May} }
 @article{lee_lin_athe_dinh_2021, title={Development of the Machine Learning-based Safety Significant Factor Inference Model for Diagnosis in Autonomous Control System}, volume={162}, ISSN={["1873-2100"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85109449538&partnerID=MN8TOARS}, DOI={10.1016/j.anucene.2021.108443}, abstractNote={As a critical component to the autonomous control system, Digital Twin for Diagnosis (DT-D) is a virtual replica of physical systems for an accurate understanding of reactor states. Since the physical damage state cannot be measured directly in transient or accident conditions, safety significant factor (SSF) is introduced as a surrogate index for physical damage states to support safety-related decision making. This study develops a machine learning (ML) based SSF inference model (SSFIM) using the Recurrent Neural Network (RNN) with acceptable accuracy, generalization capability, effectiveness, and robustness against sensor errors. To demonstrate the capability of the ML-based SSFIM, case studies are implemented on a plant simulator for Experimental Breeder Reactor – II. For partial loss of flow accident scenarios, the SSFIM is able to infer the peak fuel centerline temperature with minimally one sensor. Meanwhile the SSFIM is also found to be robust against manipulated sensor drifts and/or random noises.}, journal={ANNALS OF NUCLEAR ENERGY}, author={Lee, Joomyung and Lin, Linyu and Athe, Paridhi and Dinh, Nam}, year={2021}, month={Nov} }
 @article{lin_lee_poudel_mcjunkin_dinh_agarwal_2021, title={Enhancing the Operational Resilience of Advanced Reactors with Digital Twins by Recurrent Neural Networks}, DOI={10.1109/RWS52686.2021.9611796}, abstractNote={Because of a lack of operation data during abnormal and accident scenarios, along with the existence of uncertainty in the evaluation model for transient and accident analysis, the established abnormal and emergency operating procedures can be biased in characterizing the reactor states and ensuring operational resilience. To improve state awareness and ensure operational flexibility for minimizing effects on the system due to anomaly, digital twin (DT) technology is suggested to support operator's decision-making by effectively extracting and using knowledge of the current and future plant states from the knowledge base. To demonstrate DT's capability for recovering the complete states of reactors and for predicting the future reactor behaviors, this paper develops and assesses both the diagnosis and prognosis DTs in a nearly autonomous management and control system for an Experimental Breeder Reactor-II simulator during different loss-of-flow scenarios.}, journal={2021 RESILIENCE WEEK (RWS)}, author={Lin, Linyu and Lee, Joomyung and Poudel, Bikash and McJunkin, Timothy and Dinh, Nam and Agarwal, Vivek}, year={2021} }