Nuclear Engineering and Design 390 (2022) 111724 Available online 10 March 2022 0029-5493/© 2022 Elsevier B.V. All rights reserved. A newly proposed data assimilation framework to enhance predictions for reflood tests Nguyen Huu Tiep a, b, c, * , Kyung-Doo Kim b, * , Jaeseok Heo b , Chi-Woong Choi b , Hae-Yong Jeong a a Department of Quantum and Nuclear Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea b Korea Atomic Energy Research Institute (KAERI), 111, Daedeok-daero 989beon-gil, Yuseong-gu, Daejeon 34057, Republic of Korea c Institute for Nuclear Science and Technology (INST), Vietnam Atomic Energy Institute (VINATOM), 179 Hoang Quoc Viet Str., Cau Giay Dist., Hanoi 100000, Vietnam A R T I C L E INFO Keywords: Monte Carlo sampling Uncertainty analysis Data assimilation ABSTRACT Data assimilation is the process that can enhance the predictions by adjusting the input parameters inside their uncertainties range. In this study, the predicted results of the reflood tests, which were simulated by the SPACE code, were examined using the data assimilation technique. Crucially, a newly proposed data assimilation framework, STARU (Sampling meThod for highly non-lineAR system Uncertainty analysis), was developed. To propose the new data assimilation framework, we combined a new accuracy evaluation method with the Monte Carlo sampling algorithm to enhance the predictions for such a complex system as reflooding phenomena (many input parameters and non-linear responses). Subsequently, the outcomes of STARU were compared with the performances of data assimilation in PAPIRUS, a toolkit with many functions and modules for uncertainty analysis, which was developed by Heo and Kim (2015). Conversely, STARU only focused on solving problems with many parameters and highly non-linear systems in which implementation of the Monte Carlo sampling method is required because of the complexity of the systems. The main objectives of developing STARU are to reduce the computation time and increase the acceptance rate of the data assimilation process for complex systems. Consequently, we found that STARU effectively enhanced the data assimilation results for the reflood tests with higher acceptance rates and faster convergence. Furthermore, better improvements and more stable convergence of the system states were also observed, which effectively facilitated the search process for the most sensitive physical models in the predictions. In the future, STARU may be suitable for implementation in the other complex problems to calibrate the models, examine the uncertainty propagation, and study the sensitivity analysis. 1. Introduction A crucial postulated accident in a pressurized water reactor (PWR) is a large break LOCA (LB LOCA), the water inventory is rapidly reduced due to the high-pressure difference between the primary coolant system and the containment. The fuel rods may undergo a very high tempera- ture because of a lack of water to remove the decay heat. To cool down the reactor core, the Emergency Core Cooling System (ECCS) system, which is designed to remove residual heat from the reactor fuel rods, is necessary in the PWR. When the reactor coolant system pressure is decreased under safety injection set points, the ECCS injects subcooled water into the reactor core to cool the fuel rods. The phenomenon in which this reactor core is filled with ECCS water is the reflooding process that is a complex process of many complicated phenomena. Understanding the heat transfer and flow phenomena of the reflooding phase is essential. Therefore, recently, many efforts have focused on experimental and analytical comprehensive research, especially concentrating on the rod bundle reflood phenomena. The literature research on reflood experiments and simulation in recent decades was well-reviewed in Yang et al. (2021). However, obtaining adequately accurate predictions for the complex phenomena under reflooding conditions is still challenging despite uti- lization of state-of-the-art thermal–hydraulic codes such as RELAP5/ MOD3.3 (Choi and No, 2012), MARS (Seo et al., 2015), and COBRA-TF (Jin et al., 2020). For instance, Choi and No (2012) specified that the maximum deviation of the RELAP5 original model predicted quenching times with the experimental data for the FLECHT SEASET (F-S) reflood tests (Loftus et al., 1981) was about 150 s and these predicted quenching times were significantly earlier than the measured data. Similar * Corresponding authors. E-mail addresses: tiepngh@sejong.ac.kr, tiepngh@kaeri.re.kr (N. Huu Tiep), kdkim@kaeri.re.kr (K.-D. Kim). Contents lists available at ScienceDirect Nuclear Engineering and Design journal homepage: www.elsevier.com/locate/nucengdes https://doi.org/10.1016/j.nucengdes.2022.111724 Received 9 November 2021; Received in revised form 24 February 2022; Accepted 28 February 2022