@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{hanna_dinh_youngblood_bolotnov_2020, title={Machine-learning based error prediction approach for coarse-grid Computational Fluid Dynamics (CG-CFD)}, volume={118}, ISSN={["1878-4224"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85072240790&partnerID=MN8TOARS}, DOI={10.1016/j.pnucene.2019.103140}, abstractNote={Computational Fluid Dynamics (CFD) is one of the modeling approaches essential to identifying the parameters that affect Containment Thermal Hydraulics (CTH) phenomena. While the CFD approach can capture the multidimensional behavior of CTH phenomena, its computational cost is high when modeling complex accident scenarios. To mitigate this expense, we propose reliance on coarse-grid CFD (CG-CFD). Coarsening the computational grid increases the grid-induced error thus requiring a novel approach that will produce a surrogate model predicting the distribution of the CG-CFD local error and correcting the fluid-flow variables. Given sufficiently fine-mesh simulations, a surrogate model can be trained to predict the CG-CFD local errors as a function of the coarse-grid local flow features. The surrogate model is constructed using Machine Learning (ML) regression algorithms. Two of the widely used ML regression algorithms were tested: Artificial Neural Network (ANN) and Random Forest (RF). The proposed CG-CFD method is illustrated with a three-dimensional turbulent flow inside a lid-driven cavity. We studied a set of scenarios to investigate the capability of the surrogate model to interpolate and extrapolate outside the training data range. The proposed method has proven capable of correcting the coarse-grid results and obtaining reasonable predictions for new cases (of different Reynolds number, different grid sizes, or larger geometries). Based on the investigated cases, we found this novel method maximizes the benefit of the available data and shows potential for a good predictive capability.}, journal={PROGRESS IN NUCLEAR ENERGY}, author={Hanna, Botros N. and Dinh, Nam T. and Youngblood, Robert W. and Bolotnov, Igor A.}, year={2020}, month={Jan} }
 @article{bao_dinh_lane_youngblood_2019, title={A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation}, volume={349}, ISSN={["1872-759X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85064505536&partnerID=MN8TOARS}, DOI={10.1016/j.nucengdes.2019.04.023}, abstractNote={Over the past decades, several computer codes have been developed for simulation and analysis of thermal-hydraulics and system response in nuclear reactors under operating, abnormal transient, and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-driven framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of computational mesh size (i.e., nodalization) and constitutive correlations (e.g., wall functions and turbulence models) for low-fidelity, coarse-mesh thermal-hydraulic simulation, in order to achieve accuracy comparable to that of high-fidelity simulation. Using results from high-fidelity simulations and experimental data with many fast-running low-fidelity simulations, an error database is built and used to train a machine learning model that can determine the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS framework is designed as a modularized six-step procedure and accomplished with state-of-the-art methods and algorithms. A mixed-convection case study was performed to illustrate the entire framework.}, journal={NUCLEAR ENGINEERING AND DESIGN}, author={Bao, Han and Dinh, Nam T. and Lane, Jeffrey W. and Youngblood, Robert W.}, year={2019}, month={Aug}, pages={27–45} }