@article{chen_bao_dinh_2024, title={Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems}, volume={250}, ISSN={["1879-0836"]}, DOI={10.1016/j.ress.2024.110266}, abstractNote={The field of data-driven, neural-network-based machine learning (ML) has seen significant growth, with applications in various information and control systems. Despite promising real-world uses, the reliability of models remains questionable. Conventionally, reliability is assessed based on predictive fidelity, accuracy, and training effectiveness; however, quality developmental procedures and excellent training performance metrics do not guarantee operational reliability. Instead, an ML model's predictive performance depends on the training set's representativeness to the intended operational space. It is known that ML algorithms excel at interpolation but struggle with extrapolation tasks. Anomalies and feature drift can also reduce operational performance. Determining whether a new sample is an interpolation or extrapolation task involves out-of-distribution (OOD) detection for assessing its proximity to the existing training data. Thus, we present a real-time, model-agnostic individual prediction reliability evaluation method called Data Auditing for Reliability Evaluation (DARE) for applying OOD detection to the training dataset. We demonstrate on a feedforward neural network ML-integrated digital twin for predicting fuel centerline temperatures during loss-of-flow transients. DARE acts as a "data supervisor" in determining the model's applicability under different operating conditions. In this manner, we demonstrate how training data can serve as inductive evidence to support the reliability of ML predictions.}, journal={RELIABILITY ENGINEERING & SYSTEM SAFETY}, author={Chen, Edward and Bao, Han and Dinh, Nam}, year={2024}, month={Oct} }
 @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} }
 @article{bao_feng_dinh_zhang_2021, title={Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data}, volume={135}, ISSN={["1879-3533"]}, DOI={10.1016/j.ijmultiphaseflow.2020.103489}, abstractNote={Multiphase flow phenomena have been widely observed in the industrial applications, yet it remains a challenging unsolved problem. Three-dimensional computational fluid dynamics (CFD) approaches resolve of the flow fields on finer spatial and temporal scales, which can complement dedicated experimental study. However, closures must be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent-dispersion and wall-lubrication forces, play an important role in bubble distribution and migration in liquid-vapor two-phase flows. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions that usually cannot deliver a universal solution across a wide range of flow conditions. In this paper, a data-driven approach, named as feature-similarity measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low-fidelity data. Validation data (including relevant experimental data and validated fine-mesh CFD simulations results) are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper. These reveal that FSM can substantially improve the prediction of the coarse-mesh CFD model, regardless of the choice of interfacial closures. It demonstrates that data-driven methods can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.}, journal={INTERNATIONAL JOURNAL OF MULTIPHASE FLOW}, author={Bao, Han and Feng, Jinyong and Dinh, Nam and Zhang, Hongbin}, year={2021}, month={Feb} }
 @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} }
 @article{bao_zhao_zhang_zou_sharpe_dinh_2018, title={Safe reactor depressurization windows for BWR Mark I Station Blackout accident management strategy}, volume={114}, ISSN={["0306-4549"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85043374484&partnerID=MN8TOARS}, DOI={10.1016/j.anucene.2017.12.063}, abstractNote={In order to evaluate the effectiveness of reactor depressurization within accident mitigation strategy and how to avoid core damage during Station Black-Out accident in a BWR Mark I plant, a GOTHIC model has been developed to support characterization of reactor safety systems performance. The GOTHIC model provides seamless coupled simulations of the reactor coolant system and the containment system. In this study, the time intervals (also called "safe reactor depressurization windows") to initiate the reactor depressurization in order to optimize the early cooling strategy by injecting fire water and avoid clad failure are studied based on the decay heat removal capability of the reactor vessel coolant. This concept is instructive for the operation of the safety systems during the SBO accident mitigation. Sensitivity studies of several key parameters like reactor power, mass flow rates through RCIC system and fire water injection, and full open discharge coefficient of SRVs are performed to evaluate their impact on the safe reactor depressurization windows.}, journal={ANNALS OF NUCLEAR ENERGY}, author={Bao, Han and Zhao, Haihua and Zhang, Hongbin and Zou, Ling and Sharpe, Phil and Dinh, Nam}, year={2018}, month={Apr}, pages={518–529} }