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A Power Regulation Strategy for Heat Pipe Cooled reactors Based on Deep Learning and Hybrid Data-Driven Optimization Algorithm

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SSRN 2023年

作者： Huang, Mengqi Peng, Changhong Du, Zhengyu Liu, Yu School of Nuclear Science and Technology University of Science and Technology of China Anhui Hefei230027 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Sichuan Chengdu610213 China

Heat pipe cooled reactors are suitable for deployment in remote or isolated areas as reliable small-scale power sources due to their good design characteristics. In order to cope with the real-time changing power demand in a dynamic environment, this study proposes a decision-making strategy for power regulation of heat pipe cooled reactors based on deep learning and hybrid data-driven optimization algorithm, which helps to achieve autonomous safe and efficient control of heat pipe cooled reactors under specific power demand. Firstly, an artificial neural network-based power prediction model for heat pipe cooled reactors is constructed;secondly, an evaluation criterion of power regulation schemes that integrates reactor safety and operational effectiveness is established based on utility theory, and finally, a hybrid data-driven optimization algorithm is proposed, which can quickly discover the power regulation scheme with the best utility under specific power demand. The strategy's effectiveness is verified by taking the power regulation process of the MegaPower heat pipe cooled reactor as an example. The results show that the strategy can make stable, accurate and near-optimal power regulation scheme decisions for arbitrary power demand within 20 seconds. © 2023, The Authors. All rights reserved.

关键词： Decision making

High-Order Coupling Method of Reaction Rate-Nuclide Density Used in Kylin-2 Code

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SSRN 2022年

作者： Hu, Yuying Liao, Hongkuan Yao, Dong Yu, Yingrui Zhou, Bingyan Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China China

The Projected Prediction-Correction(PPC) method is the most widely used method coupled transport calculation with burnup calculation nowadays, with linear rate(LR) interpolation for normal nuclides and logarithmic linear rate(LLR) interpolation for strong absorbent nuclides. This method has been verified by engineering, and also used in Kylin-2 code. By using PPC method, accurate results could be obtained in the case of detailed division of burnup steps, but the calculation efficiency is not high enough. In this study, the high-order coupling method of reaction rate-nuclide density and its substep calculation method are used to replace the coupling module of Kylin-2 code. By using the high-order coupling and substep calculation to replace parts of transport calculation, the amount of burnup steps is reduced, the calculation time is saved, and the calculation efficiency of Kylin-2 code is improved on the basis of maintaining the whole physical calculation accuracy as much as possible. © 2022, The Authors. All rights reserved.

关键词： Isotopes

A Study of Flow Measurement Uncertainty Quantification Based on Sample Chunked Bayesian Inference

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SSRN 2024年

作者： Lu, Zihan Zhu, Yudi Zhou, Xinzhi Yu, Junhui Dong, Chenlong Wang, Hailin College of Electronic Information Sichuan University Chengdu610000 China National Key Laboratory of Science and Technology on Reactor System Design Technology Nuclear Power Institute of China Chengdu610213 China

The accuracy of coolant flow measurement of reactor Primary Circuit has a direct impact on the safety and stability of nuclear reactors. However, the presence of errors in the measurement method and instrumentation, as well as the fluid-heat coupling field in the pipeline, leads to a certain degree of uncertainty in the flow measurement. In this paper, an inverse uncertainty quantification method named Sample Chunked Bayesian Inference is proposed based on the establishment of a mathematical model of measurement uncertainty. The proposed method divides the input sample into a number of small samples with single peaks, and then uses each of these as observation sample of Bayesian Inference. The experiments show that the method can effectively quantify the systematic uncertainty and random uncertainty in the measured data. Moreover, compared with the traditional method that use input sample directly as observation sample, the proposed method has higher quantization accuracy. © 2024, The Authors. All rights reserved.

关键词： Bayesian networks

Optimized Moderator design and Analysis of a Pin-Type Supercritical Carbon Dioxide reactor Based on reactor Monte Carlo Code

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Optimized Moderator Design and Analysis of a Pin-Type Superc...

作者： Zhao, Xingyu Liu, Minyun Cui, Rongyi Huang, Shanfang Wang, Kan Lu, Chuan Department of Engineering Physics Tsinghua University Beijing100084 China Department of Engineering Physics Tsinghua University Beijing100084 China China Nuclear Power Engineering Co. Ltd. Beijing100840 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China

This study analyzed an yttrium hydride (YH2) moderated supercritical carbon dioxide cooled reactor loaded with pin-type, beryllium oxide diluted oxide fuel elements to reduce the critical enrichment. The impact of the YH2 on the coolant void reactivity was studied along with a moderator zoning scheme to flatten the radial power distribution. The YH2 was added as hexagonal moderating rods at the center of the fuel assemblies. The core was modeled using the continuous-energy reactor Monte Carlo code (RMC) with the on-the-fly cross sections treatment. The results showed that the YH2 moderator increased the thermal fission and reduced the critical enrichment of the core with the same diluent volume fraction by more than 30%. The YH2 moderator significantly softened the neutron energy spectrum and reduced the neutron leakage upon core voiding, resulting in both a weaker positive spectral reactivity feedback and a weaker negative leakage reactivity feedback during core depressurization. For an UO2-loaded core, the YH2 gave a lower negative coolant void reactivity, while for a mixed oxide fuel (MOX)-loaded core with diluent volume fractions smaller than 35%, the spectral feedback was more important and the YH2 strongly reduced the positive coolant void reactivity to less than $1. Arranging the YH2 in the peripheral assemblies reduced the radial power peaking factor to 1.319. The study shows that the YH2 moderator can reduce the critical enrichment, make the core less sensitive to voiding, and can flatten the radial power distribution of a single-enrichment core through moderator zoning. [DOI: 10.1115/1.4056772] © 2023 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Carbon dioxide

Fuel Flow Modeling under Thermal Fluidics Considerations within Operational Domains of Molten Salt reactors 20

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Fuel Flow Modeling under Thermal Fluidics Considerations wit...

20th International Topical Meeting on Nuclear reactor Thermal Hydraulics, NURETH 2023

作者： Abuqudaira, Thabit M. Tsvetkov, Pavel V. Sabharwall, Piyush Department of Nuclear Engineering Texas A&M University College StationTX77843 United States Advanced Reactor Technology and Design Nuclear Science & Technology Idaho National Laboratory Idaho FallsID83415 United States

ISBN: (纸本)9780894487934

Molten Salt reactors (MSRs) offer significant versatility supported by their design flexibility and safety advantages due to high temperatures, design customization capabilities, strong negative reactivity feedback effects, and salt chemistry *** control schedules and different mass flow rates in the MSR primary systems impact neutronics and thermal hydraulics *** result is ***, it leads to adaptability in various applications when the customization of a MSR unit is ***, it calls for the quantification of dynamics and safety characteristics to assess the operational domain and make sure reactor stability and inherent safety characteristics are *** paper provides an in-depth transient analysis of the operational implications of MSRs having heat-generating fuel and heat-transporting salt mixed forming liquid fuel salts contained within primary systems.A coupling model was developed to simulate fuel flow in molten salt reactors.A zero-dimensional reactor kinetics model was used with a one-dimensional heat transfer model in the reactor *** temperature reactivity feedbacks of the fuel salt resulting from the thermal-hydraulics model were used in the reactor kinetics model to complete the coupled *** model was first validated with the experimental results from the Molten Salt reactor Experiment (MSRE).Then, it was applied to simulate the Molten Salt Breeder reactor (MSBR) response at steady state and *** developed model was shown to be a suitable tool for the dynamic analysis of molten salt reactors. © 2023 Proceedings of the 20th International Topical Meeting on Nuclear reactor Thermal Hydraulics, NURETH 2023. All rights reserved.

关键词： Molten salt reactor

Quantitative Calculation of Bubble Pinning to Grain Boundaries in Uo2: A Phase-Field Study

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SSRN 2023年

作者： Liu, Wenbo La, Yongxiao Jiang, Yanbo Yun, Di Sun, Zhipeng Department of Nuclear Science and Technology Xi’an Jiaotong University Xi’an710049 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China

A phase-field model was established to simulate the pinning effect of fission bubbles on moving grain boundaries (GBs) in UO2 nuclear fuel. It was found that the average velocity of the GB at different positions was obviously different during the movement of the GB with the participation of bubbles. The simulation results also showed that the maximum pinning force of bubbles to the GBs is related to the GB energy and the ratio of bubble diameter to bubble spacing (2r/L). The maximum pinning force decreases with the reduction of the 2r/L, which is consistent with the theoretical analysis results. In addition, the logarithm of the average grain size R has a linear relationship with the logarithm of the bubble volume fraction fv. © 2023, The Authors. All rights reserved.

关键词： Grain boundaries

Firefly Optimized Neuro-Dynamic Programming for Optimal Tracking Control of Multi-Player Nonlinear systems 42

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Firefly Optimized Neuro-Dynamic Programming for Optimal Trac...

42nd Chinese Control Conference, CCC 2023

作者： Wu, Qiuye Zhao, Bo Liu, Derong School of Automation Guangdong University of Technology Guangzhou510006 China School of Systems Science Beijing Normal University Beijing100875 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789887581543

This paper develops a novel optimal tracking control (OTC) approach via firefly optimized neuro-dynamic programming for multi-player nonlinear systems. By constructing an augmented system composed of the tracking error and the reference trajectory, the OTC problem is transformed into an optimal regulation problem. To obtain the OTC policy of each player, the solution of the coupled Hamilton-Jacobi equation is approximated by a critic neural network, whose weight vector is tuned by the firefly algorithm instead of the gradient descent algorithm to avoid the difficulty in manually selecting the initial weight values. According to the Lyapunov's direct method, the tracking error of the closed-loop system is ensured to be uniformly ultimately bounded. The effectiveness of the developed approach is demonstrated by numerical simulation. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Nonlinear systems

Adaptive Neural Network Observer-Based Fault Tolerant Control for Partially Unknown Nonlinear systems via Adaptive Dynamic Programming 13

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Adaptive Neural Network Observer-Based Fault Tolerant Contro...

13th IEEE International Conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2023

作者： Zeng, Chujian Zhao, Bo Liu, Derong School of Automation Guangdong University of Technology Guangzhou510006 China School of Systems Science Beijing Normal University Beijing100875 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518000 China

ISBN: (纸本)9798350315196

This article presents an adaptive neural network (NN) observer-based fault tolerant control (FTC) method for partially unknown nonlinear systems with actuator faults via adaptive dynamic programming. By using an adaptive NN observer, the system states, unknown system dynamics and faults are estimated accurately. An augmented system is constructed to transform the FTC problem into an optimal control problem. Through adopting a critic NN, the Hamilton-Jacobi-Bellman equation is solved for the augmented system. Then, the approximate optimal control policy is obtained, i.e., the FTC policy is acquired. The Lyapunov's direct method is utilized to ensure that the closed-loop nonlinear system is uniformly ultimately bounded. A simulation example shows the FTC performance of the proposed method. © 2023 IEEE.

关键词： Reinforcement learning

Latent Space Neural Architecture Search via LambdaNDCGloss-Based Listwise Ranker

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Latent Space Neural Architecture Search via LambdaNDCGloss-B...

2023 International Annual Conference on Complex systems and Intelligent science, CSIS-IAC 2023

作者： Xiao, Songyi Zhao, Bo Liu, Derong School of Automation Guangdong University of Technology Guangzhou510006 China School of Systems Science Beijing Normal University Beijing100875 China School of System Design and Intelligent Manufacturing Southern Unversity of Science and Technology Shenzhen518055 China

ISBN: (纸本)9798350309003

The rapid development of neural architecture search (NAS) promotes the research of efficient evaluation of candidate architectures. However, as one of highly efficient evaluation methods, the predictor is difficult to accurately predicting the absolute performance of architecture. Moreover, it is difficult for a predictor to obtain the high prediction performance in discrete architecture representation space. Thus, a LambdaNDCGloss-based listwise ranker is developed to accurately predict the architecture through ranking prediction. The LambdaNDCGloss assigns large weights to architectures with real top ranking during the training process. Then, architectures with discrete representation are embedded into the latent continuous space through graph variational autoencoder to further enhance the prediction performance of ranker. Moreover, a training data reconstruction method is proposed to divide the entire training set into several subsets, from where the ranker can learn the ranking distribution more easily in contrast to that of the entire training set. Extensive experiments on NAS benchmarks demonstrate that our approach outperforms the state-of-the-art methods. © 2023 IEEE.

关键词： Forecasting