Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system

Lin, Linyu; Oncken, Joseph Eugene; Agarwal, Vivek; Permann, Cody J.; Gribok, Andrei V.; McJunkin, Timothy R.; Eggers, Shannon Leigh; Boring, Ronald Laurids

doi:10.1016/j.pnucene.2022.104527

Title: Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system

Journal Article · Mon Dec 12 00:00:00 EST 2022 · Progress in Nuclear Energy

DOI:https://doi.org/10.1016/j.pnucene.2022.104527· OSTI ID:1958721

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Idaho National Lab. (INL), Idaho Falls, ID (United States)

To support the reliable and resilient operation of modular reactors and microreactors, anticipatory control strategies have been proposed for achieving faster-than-real-time predictions and decision-making capabilities in anticipation of potential anomalies, including setpoint changes and cyber incidents. Here this work presents how anticipatory control strategies can be implemented via model predictive control (MPC) of a single heat pipe’s temperature. Considering the uncertainty in developing and applying MPC, this work evaluates MPC performance given three different model forms: a linear response surface model, an artificial neural network (ANN), and an autoregressive model with exogenous input (ARX). This work also evaluates the impacts of different input biases and variance on MPC performance in order to account for potential sensor reading variations due to cyber incidents. We observe that nonparametric models such as the ANN and ARX result in more fluctuated control actions compared to the MPC applied to the linear response surface model. However, when the cyber incidents are of a large magnitude, the linear response surface model produces smaller feasible regions than the nonparametric models under identical constraints.

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1958721

Alternate ID(s):: OSTI ID: 1961078

Report Number(s):: INL/JOU-22-67537-Rev001; TRN: US2312737

Journal Information:: Progress in Nuclear Energy, Vol. 156; ISSN 0149-1970

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (23)

Development and assessment of a nearly autonomous management and control system for advanced reactors Lin, Linyu; Athe, Paridhi; Rouxelin, Pascal Annals of Nuclear Energy, Vol. 150 https://doi.org/10.1016/j.anucene.2020.107861	journal	January 2021
Study of Heat Pipe Thermal Performance with Internal Modified Geometry Temimy, Alaa A. B.; Abdulrasool, Adnan A.; Hamad, F. A. Fluids, Vol. 6, Issue 7 https://doi.org/10.3390/fluids6070231	journal	June 2021
Multi-model predictive control method for nuclear steam generator water level Hu, Ke; Yuan, Jingqi Energy Conversion and Management, Vol. 49, Issue 5 https://doi.org/10.1016/j.enconman.2007.09.006	journal	May 2008
Forecast-Based Anticipatory Frequency Control in Power Systems Ganger, David; Zhang, Junshan; Vittal, Vijay IEEE Transactions on Power Systems, Vol. 33, Issue 1 https://doi.org/10.1109/TPWRS.2017.2705761	journal	January 2018
Sparse Identification of Nonlinear Dynamics with Control (SINDYc)**SLB acknowledges support from the U.S. Air Force Center of Excellence on Nature Inspired Flight Technologies and Ideas (FA9550-14-1-0398). JLP thanks Bill and Melinda Gates for their active support of the Institute of Disease Modeling and their sponsorship through the Global Good Fund. JNK acknowledges support from the U.S. Air Force Office of Scientific Research (FA9550-09-0174). Brunton, Steven L.; Proctor, Joshua L.; Kutz, J. Nathan IFAC-PapersOnLine, Vol. 49, Issue 18 https://doi.org/10.1016/j.ifacol.2016.10.249	journal	January 2016
Nonlinear modeling, estimation and predictive control in APMonitor Hedengren, John D.; Shishavan, Reza Asgharzadeh; Powell, Kody M. Computers & Chemical Engineering, Vol. 70 https://doi.org/10.1016/j.compchemeng.2014.04.013	journal	November 2014
State-Space Model Predictive Control Method for Core Power Control in Pressurized Water Reactor Nuclear Power Stations Wang, Guoxu; Wu, Jie; Zeng, Bifan Nuclear Engineering and Technology, Vol. 49, Issue 1 https://doi.org/10.1016/j.net.2016.07.008	journal	February 2017
A survey of industrial model predictive control technology Qin, S. Joe; Badgwell, Thomas A. Control Engineering Practice, Vol. 11, Issue 7, p. 733-764 https://doi.org/10.1016/S0967-0661(02)00186-7	journal	July 2003
Digital-twin-based improvements to diagnosis, prognosis, strategy assessment, and discrepancy checking in a nearly autonomous management and control system Lin, Linyu; Athe, Paridhi; Rouxelin, Pascal Annals of Nuclear Energy, Vol. 166 https://doi.org/10.1016/j.anucene.2021.108715	journal	February 2022
A neural network predictive control method for power control of small pressurized water reactors Xiao, Kai; Wu, Qiaofeng; Chen, Jie Annals of Nuclear Energy, Vol. 169 https://doi.org/10.1016/j.anucene.2021.108946	journal	May 2022
Application of model predictive control strategy based on fuzzy identification to an SP-100 space reactor Na, Man Gyun; Upadhyaya, Belle R. Annals of Nuclear Energy, Vol. 33, Issue 17-18 https://doi.org/10.1016/j.anucene.2006.09.011	journal	November 2006
Review on model predictive control: an engineering perspective Schwenzer, Max; Ay, Muzaffer; Bergs, Thomas The International Journal of Advanced Manufacturing Technology, Vol. 117, Issue 5-6 https://doi.org/10.1007/s00170-021-07682-3	journal	August 2021
Robust nonlinear model predictive control for nuclear power plants in load following operations with bounded xenon oscillations Eliasi, H.; Menhaj, M. B.; Davilu, H. Nuclear Engineering and Design, Vol. 241, Issue 2 https://doi.org/10.1016/j.nucengdes.2010.12.004	journal	February 2011
Sockeye: A One-Dimensional, Two-Phase, Compressible Flow Heat Pipe Application Hansel, Joshua E.; Berry, Ray A.; Andrs, David Nuclear Technology, Vol. 207, Issue 7 https://doi.org/10.1080/00295450.2020.1861879	journal	March 2021
Model predictive control: past, present and future Morari, Manfred; H. Lee, Jay Computers & Chemical Engineering, Vol. 23, Issue 4-5 https://doi.org/10.1016/S0098-1354(98)00301-9	journal	May 1999
A Takagi–Sugeno fuzzy power-distribution method for a prototypical advanced reactor considering pump degradation Yuan, Yue; Coble, Jamie Nuclear Engineering and Technology, Vol. 49, Issue 5 https://doi.org/10.1016/j.net.2017.06.004	journal	August 2017
The evaporation and condensation model with interface tracking Li, Mengnan; Bolotnov, Igor A. International Journal of Heat and Mass Transfer, Vol. 150 https://doi.org/10.1016/j.ijheatmasstransfer.2019.119256	journal	April 2020
Neurofuzzy approaches to anticipation: a new paradigm for intelligent systems Tsoukalas, L. H. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), Vol. 28, Issue 4 https://doi.org/10.1109/3477.704296	journal	January 1998
Quasi-min-max Fuzzy MPC of UTSG Water Level Based on Off-Line Invariant Set Liu, Xiangjie; Jiang, Di; Lee, Kwang Y. IEEE Transactions on Nuclear Science, Vol. 62, Issue 5 https://doi.org/10.1109/TNS.2015.2466658	journal	October 2015
Coupled Multiphysics Simulations of Heat Pipe Microreactors Using DireWolf Matthews, Christopher; Laboure, Vincent; DeHart, Mark Nuclear Technology, Vol. 207, Issue 7 https://doi.org/10.1080/00295450.2021.1906474	journal	July 2021
Constrained model predictive control: Stability and optimality Mayne, D. Q.; Rawlings, J. B.; Rao, C. V. Automatica, Vol. 36, Issue 6 https://doi.org/10.1016/S0005-1098(99)00214-9	journal	June 2000
Model predictive control technique combined with iterative learning for batch processes Lee, Kwang S.; Chin, In-Shik; Lee, Hyuk J. AIChE Journal, Vol. 45, Issue 10 https://doi.org/10.1002/aic.690451016	journal	October 1999
Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: A review Lin, Linyu; Bao, Han; Dinh, Nam Annals of Nuclear Energy, Vol. 160 https://doi.org/10.1016/j.anucene.2021.108362	journal	September 2021

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