@article{bao_zhang_shorthill_chen_lawrence_2023, title={Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants}, volume={230}, ISSN={["1879-0836"]}, DOI={10.1016/j.ress.2022.108973}, abstractNote={• A comprehensive quantitative evaluation of common cause failures is performed for safety-critical digital control systems; • Both hardware and software common cause failures are tracked and identified; • Failure probabilities of digital control systems are estimated via a multiscale reliability analysis approach. • The impact of common cause failures to plant safety is evaluated. Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. This paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework's capabilities is documented in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety.}, journal={RELIABILITY ENGINEERING & SYSTEM SAFETY}, author={Bao, Han and Zhang, Hongbin and Shorthill, Tate and Chen, Edward and Lawrence, Svetlana}, year={2023}, month={Feb} }
 @article{iskhakov_dinh_chen_2021, title={Integration of neural networks with numerical solution of PDEs for closure models development}, volume={406}, ISSN={["1873-2429"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85107023012&partnerID=MN8TOARS}, DOI={10.1016/j.physleta.2021.127456}, abstractNote={The work is a continuation of a paper by Iskhakov A.S. and Dinh N.T. "Physics-integrated machine learning: embedding a neural network in the Navier-Stokes equations". Part I // arXiv:2008.10509 (2020) [1]. The proposed in [1] physics-integrated (or PDE-integrated (partial differential equation)) machine learning (ML) framework is furtherly investigated. The Navier-Stokes equations are solved using the Tensorflow ML library for Python programming language via the Chorin's projection method. The Tensorflow solution is integrated with a deep feedforward neural network (DFNN). Such integration allows one to train a DFNN embedded in the Navier-Stokes equations without having the target (labeled training) data for the direct outputs from the DFNN; instead, the DFNN is trained on the field variables (quantities of interest), which are solutions for the Navier-Stokes equations (velocity and pressure fields). To demonstrate performance of the framework, two additional case studies are formulated: 2D turbulent lid-driven cavities with predicted by a DFNN (a) turbulent viscosity and (b) derivatives of the Reynolds stresses. Despite its complexity and computational cost, the proposed physics-integrated ML shows a potential to develop a "PDE-integrated" closure relations for turbulent models and offers principal advantages, namely: (i) the target outputs (labeled training data) for a DFNN might be unknown and can be recovered using the knowledge base (PDEs); (ii) it is not necessary to extract and preprocess information (training targets) from big data, instead it can be extracted by PDEs; (iii) there is no need to employ a physics- or scale-separation assumptions to build a closure model for PDEs. The advantage (i) is demonstrated in the Part I paper [1], while the advantage (ii) is the subject of the current paper.}, journal={PHYSICS LETTERS A}, author={Iskhakov, Arsen S. and Dinh, Nam T. and Chen, Edward}, year={2021}, month={Aug} }