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DOE OSTI · 2278831

Data-Driven Model Predictive Control for Temperature Management of Heat Pipe Microreactor

Lin, Linyu·Oncken, Joseph Eugene·Agarwal, Vivek
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Abstract

A data-driven model predictive control (MPC) was developed to enable the self-regulating capability of heat pipe (HP) nuclear microreactors. The MPC can proactively respond to potential disturbances of HP microreactors using three approaches for system identifications: linear state-space model, feedforward neural network, and recurrent neural networks with long short-term memory units. We present numerical results of data-driven MPCs to control the temperatures of selected HPs in a 37-HP test article. Our results show qualitatively that all data-driven MPCs produced similar control actions, while quantitatively, with artificial neural nets (especially feedforward neural nets), MPC can better follow drastic changes in setpoints with small errors.

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22 GENERAL STUDIES OF NUCLEAR REACTORSmachine learningmicroreactormodel predictive control

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Lin, Linyu, Oncken, Joseph Eugene, Agarwal, Vivek. 2023-08-03. Data-Driven Model Predictive Control for Temperature Management of Heat Pipe Microreactor. https://doi.org/10.13182/npichmit23-40517

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