Conference Agenda

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Session Overview
8.09: System Codes Development & Validation
Tuesday, 17/Mar/2020:
3:30pm - 5:00pm

Session Chair: Jeong Ik Lee, KAIST, Korea, Republic of (South Korea)
Location: R-2013

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Solid-to-Fluid Radiative Heat Transfer Modeling for System Analysis Module

Ishak Johnson1, Rui Hu2, Ling Zou2, Per Peterson1

1University of California, Berkeley; 2Argonne National Laboratory

System Analysis Module (SAM) is a system-level thermal hydraulics code being developed at Argonne National Laboratory for advanced nuclear reactor analysis. In addition to a wide range of interests from the advanced reactor design community, SAM has also been adopted by the United States Nuclear Regulatory Commission’s suite of codes purposed for advanced reactor licensing. Nevertheless, the code is still under active development and new capabilities are being added to address various modeling and simulation challenges for advanced reactor analysis. One such phenomenon important to the thermal behavior of some advanced reactor concepts is radiative heat transfer (rad HT). Conditions, such as high temperatures and long optical paths, increase the radiative contributions from solids and coolants alike. This paper discusses the development of rad HT modeling in SAM and describes the new capabilities provided for thermal analysis. Depending on the geometry and temperatures of the system at hand, as well as the coolant in question, thermal transfer due to thermal radiation will vary dramatically. Therefore, the ability to model variable radiative systems was maintained as a priority during development of SAM rad HT modeling. This newly developed simulation feature provides a flexible solid-to-fluid radiative heat transfer framework necessary for SAM to perform accurate analysis for advanced reactor designs. Test cases are also presented and shown to match analytical solutions, which demonstrate the rad HT model’s efficacy.

Critical heat flux prediction with machine learning for the narrow rectangular channel under the steady-state flow condition

Huiyung Kim, Dongjin Hong, Geunsik Kim, Euiyoung Cha, Byongjo Yun

Pusan National University

Machine learning is a useful tool for analyzing complex data in the engineering field. For this, it requires sufficient data representing concerned phenomena. However, available CHF experimental data for narrow rectangular channel are obtained under limited flow condition. In this case, existing correlations for the CHF prediction can be used to generate pseudo data for the training of the neural network. Examples are Mirshak, Kaminaga, Kureta-Akimoto and Tanaka correlations which are applicable to the narrow rectangular channels. Since the applicable range of each correlation is different each other, the pseudo CHF data generated by these correlations can cover wide range of flow conditions. The neural network for the CHF prediction consists of a pre-training part and a prediction part. The pre-training part is composed of a 3-layer deep belief network (DBN) structure and the prediction part is composed of a 5-layer convolution neural network (CNN). The DBN is a stacked structure of restricted Boltzmann machines (RBMs) which is one of the unsupervised learning networks. The developed neural network is expected to improve the learning performance through pre-training with DBN. The trained neural network predicts the pseudo test-data within 4.26% of root-mean-squared (RMS) error and it indicates that the learning is successful and verified. The neural network trained by above method requires a validation because only the pseudo data is used for the training. The trained neural network was validated experimental CHF data that has never been used in the training and verification of the neural network. Finally, the validation showed that the trained neural network predicts the experimental CHF data within 15.28% of RMS error.

Thermal Analysis of fuel bundle weight simulator for IPHWR- A Numerical Approach

Madhuri Bhadauria, Ravi Kumar, Arup K Das

Indian Institute of Technology Roorkee

During LOCA , the pressure tube (PT) heat-up due to decay heat available that leads to ballooning or sagging of pressure tube depending upon the internal pressure. It has been found that the thermal -structural behaviour of fuel channel sagging behaviour underheat-up for 700MWe Indian PHWR has been not discussed in open literature. Hence for understanding the behaviour of PT for Indian PHWR under LOCA, a series of experiments will be carried out using weight bundle simulator of capacity 190 kW (approx.3% decay heat of time averaged maximum channel power is 6.5MW). An experimental facility is presently being developed in India at Indian Institute of Technology Roorkee (IIT R) to study scenario of Loss of Coolant Accident (LOCA) with un-availability of emergency core cooling system (ECCS) for IPHWR . The test facility aims to estimate the thermo-mechanical deformation of pressure tube. In order to stimulate same weight as that of nuclear reactor fuel pin bundle, weight bundle simulator are accommodated whose fabrication and testing are done to ensure its thermal integrity. Previous work done on sagging does not include fuel bundle instead directly uses joule heating of pressure tube so it becomes a prime matter of concern to simulate actual condition as that of nuclear reactor by making use of weight bundle simulator to transfer heat to pressure tube through radiation. In this paper various aspects for the development of experimental facility are being discussed. A numerical simulation of channel behaviour under heat-up condition is being carried out in Ansys 19.0 using solver mechanical APDL. It helps in designing the experimental facility by providing the information beforehand about the transient heat transfer rate required for determining sagging behaviour of full length channel.

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