@article{wang_lin_dinh_2023, title={Data coverage assessment on neural network based digital twins for autonomous control system}, volume={182}, ISSN={["1873-2100"]}, DOI={10.1016/j.anucene.2022.109568}, abstractNote={In a recently developed Nearly Autonomous Management and Control (NAMAC) system, neural networks (NNs) are used to develop digital twins for diagnosis (DT-Ds). However, NNs are not usually considered extrapolation models and may result in large errors if they are applied to unseen data outside the training data (uncovered). In this study, we propose a data coverage assessment (DCA) to determine if the NN-based DT-Ds are extrapolated based on their epistemic uncertainty. The uncertainty quantification algorithms and uncertainty thresholds are selected based on the confusion matrix of classifying evaluation data into covered or uncovered data. To demonstrate the adaptability of the proposed framework, we applied it to a basic feedforward neural network and a more advanced recurrent neural network based on a more nonlinear database. Case studies show that the proposed framework can distinguish unseen data for both basic and advanced applications with proper uncertainty quantification algorithms and thresholds.}, journal={ANNALS OF NUCLEAR ENERGY}, author={Wang, Longcong and Lin, Linyu and Dinh, Nam}, year={2023}, month={Mar} }
 @article{lin_athe_rouxelin_avramova_gupta_youngblood_lane_dinh_2022, title={Digital-twin-based improvements to diagnosis, prognosis, strategy assessment, and discrepancy checking in a nearly autonomous management and control system}, volume={166}, ISSN={["1873-2100"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85115958204&partnerID=MN8TOARS}, DOI={10.1016/j.anucene.2021.108715}, abstractNote={The Nearly Autonomous Management and Control System (NAMAC) is a comprehensive control system that assists plant operations by furnishing control recommendations to operators in a broad class of situations. This study refines a NAMAC system for making reasonable recommendations during complex loss-of-flow scenarios with a validated Experimental Breeder Reactor II simulator, digital twins improved by machine-learning algorithms, a multi-attribute decision-making scheme, and a discrepancy checker for identifying unexpected recommendation effects. We assess the performance of each NAMAC component, while we demonstrate and evaluated the capability of NAMAC in a class of loss-of-flow scenarios.}, journal={ANNALS OF NUCLEAR ENERGY}, author={Lin, Linyu and Athe, Paridhi and Rouxelin, Pascal and Avramova, Maria and Gupta, Abhinav and Youngblood, Robert and Lane, Jeffrey and Dinh, Nam}, year={2022}, month={Feb} }
 @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} }
 @misc{lin_bao_dinh_2021, title={Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: A review}, volume={160}, ISSN={["1873-2100"]}, DOI={10.1016/j.anucene.2021.108362}, abstractNote={A nearly autonomous management and control (NAMAC) system is designed to furnish recommendations to operators for achieving particular goals based on NAMAC’s knowledge base. As a critical component in a NAMAC system, digital twins (DTs) are used to extract information from the knowledge base to support decision-making in reactor control and management during all modes of plant operations. With the advancement of artificial intelligence and data-driven methods, machine learning algorithms are used to build DTs of various functions in the NAMAC system. To evaluate the uncertainty of DTs and its impacts on the reactor digital instrumentation and control systems, uncertainty quantification (UQ) and software risk analysis is needed. As a comprehensive overview of prior research and a starting point for new investigations, this study selects and reviews relevant UQ techniques and software hazard and software risk analysis methods that may be suitable for DTs in the NAMAC system.}, journal={ANNALS OF NUCLEAR ENERGY}, author={Lin, Linyu and Bao, Han and Dinh, Nam}, year={2021}, month={Sep} }