TY - GEN
T1 - Early Prediction and Classification of Heat Transfer Degradation in Coolant Channels using Kramers-Moyal Coefficients
AU - Ross, Molly
AU - Chu, Xu
AU - Bindra, Hitesh
N1 - Publisher Copyright:
© 2023 Proceedings of 18th International Probabilistic Safety Assessment and Analysis, PSA 2023. All Rights Reserved.
PY - 2023
Y1 - 2023
N2 - The use of in-situ sensing information in advanced reactors is important for informed assessment of reactor operation, maintenance, and safety. To perform an accurate plant risk assessment, such sensing information must be properly analyzed to identify significant physical phenomena. One such phenomena is flow laminarization, which occurs when upward helium flows, such as those in an HTGR coolant channel, are stabilized through flow acceleration due to heating. This flow stabilization can lead to degradation of heat transfer. While the presence of laminarization can be observed through the wall heat transfer coefficient once it has occurred, there are not adequate methods of predicting flow laminarization directly from temporal sensor information, such as velocity and temperature. The fluctuation levels of these signals can provide some insight into the characterization of the flow; however, the direct measurement of the fluctuations often does not provide enough information to predict the degradation of heat transfer due to laminarization. Further probabilistic analysis of these signals through the Kramers-Moyal expansion method can provide some insight into the impact of potential laminarization on degraded heat transfer in reactor coolant channels. Experimentally validated direct numerical simulations are performed to obtain velocity and temperature time series at various locations within a vertical flow channel. The Kramers-Moyal coefficients are calculated at multiple locations along the channel and compared for multiple flow conditions, including cases where laminarization is predicted to occur and where laminarization is not expected to occur. The Kramers-Moyal coefficients are then used to define laminarization criteria, which can be used to predict degraded heat transfer from live sensing data within the reactor. This approach provides a probabilistic framework for predicting reactor behavior from real-time sensing data, improving the operation and maintenance of the reactor.
AB - The use of in-situ sensing information in advanced reactors is important for informed assessment of reactor operation, maintenance, and safety. To perform an accurate plant risk assessment, such sensing information must be properly analyzed to identify significant physical phenomena. One such phenomena is flow laminarization, which occurs when upward helium flows, such as those in an HTGR coolant channel, are stabilized through flow acceleration due to heating. This flow stabilization can lead to degradation of heat transfer. While the presence of laminarization can be observed through the wall heat transfer coefficient once it has occurred, there are not adequate methods of predicting flow laminarization directly from temporal sensor information, such as velocity and temperature. The fluctuation levels of these signals can provide some insight into the characterization of the flow; however, the direct measurement of the fluctuations often does not provide enough information to predict the degradation of heat transfer due to laminarization. Further probabilistic analysis of these signals through the Kramers-Moyal expansion method can provide some insight into the impact of potential laminarization on degraded heat transfer in reactor coolant channels. Experimentally validated direct numerical simulations are performed to obtain velocity and temperature time series at various locations within a vertical flow channel. The Kramers-Moyal coefficients are calculated at multiple locations along the channel and compared for multiple flow conditions, including cases where laminarization is predicted to occur and where laminarization is not expected to occur. The Kramers-Moyal coefficients are then used to define laminarization criteria, which can be used to predict degraded heat transfer from live sensing data within the reactor. This approach provides a probabilistic framework for predicting reactor behavior from real-time sensing data, improving the operation and maintenance of the reactor.
KW - Data-informed risk assessment
KW - Kramers-Moyal Coefficients
KW - Laminarization
UR - http://www.scopus.com/inward/record.url?scp=85184354720&partnerID=8YFLogxK
U2 - 10.13182/PSA23-41258
DO - 10.13182/PSA23-41258
M3 - Conference contribution
AN - SCOPUS:85184354720
T3 - Proceedings of 18th International Probabilistic Safety Assessment and Analysis, PSA 2023
SP - 649
EP - 658
BT - Proceedings of 18th International Probabilistic Safety Assessment and Analysis, PSA 2023
PB - American Nuclear Society
T2 - 18th International Probabilistic Safety Assessment and Analysis, PSA 2023
Y2 - 15 July 2023 through 20 July 2023
ER -