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Thermodynamic Analysis and Optimization of Nuclear Closed Air Brayton Cycle 1

23rd Pacific Basin Nuclear Conference, PBNC 2022

作者： Fang, Huawei Yi, Jingwei Zhang, Xiaoyu Liang, Tiebo Qian, Yiran Tang, Xin Chen, Weixiong Key Laboratory of Nuclear Reactor System Design Technology Nuclear Power Institute of China Sichuan Chengdu China State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong University Shaanxi Xi’an China

ISBN: (数字)9789811988998

ISBN: (纸本)9789811988981

In order to improve the flexibility and adaptability of micro nuclear power systems, air is considered as an optional working fluid of nuclear Brayton cycle. It has ability to avoid shortage of working fluid under extreme conditions, and ensure the system operating with tight supply. Compared with the traditional open cycles, the closed cycle has advantages of higher cycle efficiency, smaller equipment size and higher power density. Few of closed air power cycle in nuclear power system has been studied by now. In this paper, cycle parameters were analyzed and designed, and optimal cycle performance was compared between cycle constructions. Four closed air Brayton cycles are proposed and modeled in EBSILON 15.0, including simple regenerative cycle (SRC), intercooling regenerative cycle (IRC), reheat regenerative cycle (RRC) and intercooling-reheat regenerative cycle (IRRC). Thermodynamic analysis of the key parameters is conducted, including pressure ratio, turbine inlet temperature, compressor inlet temperature and pressure. In addition, the genetic algorithm (GA) is applied for multi-parameters optimization for the maximal cycle efficiency. Result shows that with the increase of pressure ratio, the efficiency of SRC increases firstly and then decreases. With the increase of turbine inlet temperature, the pressure ratio corresponding to the maximal cycle efficiency increases gradually. Specific work gradually increases with pressure ratio. In higher turbine inlet temperature, the rising velocity of specific work is faster. Cycle efficiency increases with compressor inlet pressure, and this trend tend to be slower in high pressure region. With the increasing of compressor inlet pressure, optimal pressure ratio decreases and finally approaches to 2.0. The cycle minimal cycle pressure was selected as 6 MPa. Maximal efficiency and corresponding pressure ratio increase with turbine inlet temperature increasing and decrease with compressor inlet temperature increasing. Co

关键词： Air

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A Cost-Sensitive Multi-scale Feature Multi-order Fusion Network for Bearing Fault Diagnosis Under Data Imbalance Conditions

A Cost-Sensitive Multi-scale Feature Multi-order Fusion Netw...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Deng, Shuaiqing Lei, Zihao Wen, Guangrui Su, Yu Liu, Zimin Meng, Zhangxuan Zhang, Zhifen National Key Laboratory of Aviation Power System and Plasma Technology Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an710049 China Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong University Xi’an710049 China

ISBN: (纸本)9789819639724

Railroad transportation plays a vital role in the national economy, and fault diagnosis of its key components is of great significance in ensuring its safe operation. However, due to its long-term operation in a healthy state, it is tough to obtain fault signals, and this class imbalance data seriously degrades the diagnostic capability of intelligent diagnostic algorithms, leading to increased misclassification of minority classes. To address this problem, we propose a cost-sensitive multi-scale feature multi-order fusion network for bearing fault diagnosis under data imbalance conditions. Firstly, a lightweight multi-scale convolutional layer is employed to effectively extract the multi-scale features of the signal. Then the multi-order feature fusion is performed by a hornet to fully utilize the multi-scale information and enhance the ability to mine the limited diagnostic information of the minority class. And the excellent high-dimensional discriminative features are extracted through residual blocks. In addition, an improved cost-sensitive strategy is designed through the posterior probability distribution to rebalance the contribution of each class to the training process for improving the overall diagnostic performance of the model. Finally, the effectiveness and superiority of the proposed method are comprehensively verified by several sets of experiments with different imbalance rates. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Railroad transportation

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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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Research of SSAE-GPC in Coordinated Control system of Nuclear Power Plant 5th

Research of SSAE-GPC in Coordinated Control System of Nuclea...

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5th International Symposium on Software Reliability, Industrial Safety, Cyber Security and Physical Protection of Nuclear Power Plant, ISNPP 2020

作者： Deng, Zhi-Guang Wu, Qian Zhu, Bi-Wei Lv, Xin Zhu, Jia-Liang Xu, Si-Jie Wang, Xue-Mei National Key Laboratory of Science and Technology on Reactor System Design Technology Chengdu610213 China

ISBN: (纸本)9789811634550

For the problem of poor control effect of conventional PID controllers in complex systems, this paper fully combines the advantages of deep learning in feature extraction, regression prediction, and predictive control in dealing with multivariable, strong coupling and other issues. Firstly, the predictive model controller is constructed through the stacked sparse auto-encoder (SSAE) deep network, and then the stepped generalized predictive control (SGPC) is used as the rolling optimization controller. Thus, the SSAE-SGPC neural network predictive controller is built. Finally, a simulation experiment is carried out in the dual-input dual-output multi-variable coordinated control system of nuclear power plant. Through a series of simulations such as set value disturbance, internal disturbance and external disturbance, compared with conventional PID control, SSAE-GPC controller has better control effect. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Controllers

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MSC-APFNN: Fault Diagnosis Deep Transfer Network for Rotating Machine based on Multi-scale Convolutional Extraction and Adaptive Pruning Fuzzy Inference system

MSC-APFNN: Fault Diagnosis Deep Transfer Network for Rotatin...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zhou, Yanrun Lei, Zihao Wen, Guangrui Su, Yu Zhang, Zhifen National Key Lab of Aerospace Power System and Plasma Technology Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an710049 China Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an710049 China

ISBN: (纸本)9789819639687

Intelligent fault diagnosis on mechanical transmission system of the train is of great importance to ensure the efficient operation of high-speed trains. However, due to the complex and changeable operation conditions (OCs), it is difficult to ensure the uniformity of the data distribution obtained by real-time acquisition, which sets a higher standard for the design of intelligent algorithms. Empowered by deep transfer learning (DTL) methods, the fuzzy inference system provides an effective solution to the abovementioned problem. In this paper, an Adaptive Pruning Fuzzy Inference system (APFIS) is proposed predicated on the fuzzy theory and the basic structure of fuzzy neural network, along with the hybrid model MSC-APFNN constructed by combining the multi-scale convolutional network with the proposed APFIS. This model can be employed as a backbone for the application to transfer diagnostic detection of rotating systems based on adversarial networks. Experimental results demonstrate that the proposed model achieves excellent diagnostic results under variable operating conditions, which provide a novel insight into the optimization of the traditional convolutional and fully-connected deep network structure and offer a promising avenue for the research of fuzzy decision-making in the context of fault diagnostics for high-speed trains. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Fuzzy neural networks

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Assessment of Coupled Effect of Steam Pipes on Seismic Analysis of reactor Coolant system 7

Assessment of Coupled Effect of Steam Pipes on Seismic Analy...

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7th International Conference on Environmental Science and Civil Engineering, ESCE 2021

作者： Li, Lijuan Liu, Zhenyu Li, Hui Ye, Xianhui Ai, Honglei National Key Laboratory of Science and Technology on Reactor System Design Technology Chengdu Sichuan China

reactor coolant system is connected with auxiliary pipes, surge pipes and steam pipes. In order to facilitate the dynamic analysis of reactor coolant system, it is necessary to decouple the pipes connected with the reactor coolant system. Although some international standards stipulating requirements for the decoupling of subsystems from the main system, in order to further explore the influence of decoupling steam pipes, the seismic response of reactor coolant system with or without steam pipes is simulated by using ANSYS program. Firstly, the modal analysis of reactor coolant system is carried out to analysis the influence of steam pipes on the resonant frequency of reactor system. Then, the standardized seismic dynamic analysis of reactor system with or without steam pipes is carried out. The nozzle load (inlet and outlet nozzle load of main device, such as reactor pressure vessel, steam generator, pump) and support load (of main device such as reactor pressure vessel, steam generator, pump) are output and compared. According to nozzle load and support load, it can be seen that the steam pipes have little influence on the seismic analysis of reactor coolant system, i.e. steam pipes can be decoupled in the seismic analysis of reactor coolant system. © Published under licence by IOP Publishing Ltd.

关键词： Steam generators

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Research of Advanced Control Algorithm in Primary Loop Control system 5th

Research of Advanced Control Algorithm in Primary Loop Contr...

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5th International Symposium on Software Reliability, Industrial Safety, Cyber Security and Physical Protection of Nuclear Power Plant, ISNPP 2020

作者： Deng, Zhi-Guang Zhu, Bi-Wei Wu, Qian He, Peng Xiang, Mei-Qiong Xu, Tao Qing, Yue National Key Laboratory of Science and Technology on Reactor System Design Technology Chengdu610213 China

ISBN: (纸本)9789811634550

The primary loop system of nuclear power plant is a large-scale control system with serious coupling between loops and complex objects. In order to solve the problem of integrated control of primary loop core power, stabilizer pressure and water level, and steam generator water level, this paper applies improved predictive control algorithms to each sub-control system on the basis of mechanism modeling. For the strong non-linear problem of water level control, a multi-model predictive controller is constructed through linearization at each typical operating point. The simulation results show that the application of advanced predictive control algorithm to the primary loop control system has achieved good control performance, and it also lays a solid foundation for subsequent practical engineering applications. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water levels

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EXPERIMENTAL STUDY ON FLOW-INDUCED VIBRATION CHARACTERISTICS OF A SINGLE ROD IN AXIAL FLOW 30

EXPERIMENTAL STUDY ON FLOW-INDUCED VIBRATION CHARACTERISTICS...

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30th International Conference on Nuclear Engineering, ICONE 2023

作者： Pu, Xingwu Chen, Deqi Liu, Hanzhou Wang, Ningyuan Zi, Binbin Deng, Jian Key Laboratory of Low-grade Energy Utilization Technologies and Systems Chongqing University Ministry of Education Chongqing400044 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610041 China

ISBN: (纸本)9784888982566

In order to study the characteristics of single rod flow-induced vibration in axial flow, the vibration response of single rod flow-induced vibration in axial flow with different constraints at both ends and different flow velocity was measured with a Laser Doppler Vibrometer. Based on spectral analysis and statistical analysis, the transient vibration response was analyzed in depth. The results show that the vibration amplitude increases with the increase of the flow velocity, and the two ends of the constraints have a significant influence on the flow-induced vibration response. The root means square amplitude value of the simply-supported single rod is 35% larger than that of the fixed one at 3 m/s. Spectrum analysis shows that with the increase of flow velocity, the dominant frequency decreases from 17.5 Hz at 1.5 m/s to 13.8 Hz at 3.0 m/s under simply-supported constraints, while the dominant frequency basically fluctuates around 13.0 Hz under fixed support constraints. The vibration frequency of simply-supported constraints are distributed between 6 Hz and 28 Hz, while those of fixed constraints are mainly distributed between 12 Hz and 16 Hz. The statistical results show that in the X direction of the simply-supported constraints, the geometric center point of the rod is shifted, which indicates that the rod is buckling. © 2023 by JSME.

关键词： Nuclear reactors

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NUMERICAL SIMULATION OF THERMAL CHARACTERISTICS OF BOILING TWO-PHASE FLOW IN A SPIRAL TUBE 30

NUMERICAL SIMULATION OF THERMAL CHARACTERISTICS OF BOILING T...

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30th International Conference on Nuclear Engineering, ICONE 2023

作者： Hongcheng, Dong Guangchao, Yang Guo, Chen Xiaofei, Yu Simian, Qin Shanshan, Bu Deqi, Chen Key Laboratory of Low-grade Energy Utilization Technologies and Systems Chongqing University Ministry of Education Chongqing400044 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610200 China

ISBN: (纸本)9784888982566

Helical coil once-through tube steam generator (HCOTSG) has the advantages of compact structure and higher heat transfer efficiency due to its special structure. In order to study the flow boiling heat transfer characteristics in HCOTSG, this paper used the FLUENT commercial software, based on the Critical heat flux(CHF) wall boiling model, to conduct numerical simulation in the spiral tube under the given wall temperature boundary conditions, and verified its reliability by comparing the relevant experimental research. On this basis, the flow boiling heat transfer process in the spiral tube was simulated under different flow parameters, and the velocity field, temperature field and the distribution of vapor-liquid two-phases in the spiral tube were studied, It is found that under the condition of constant wall temperature boundary, the influence of different inlet temperature only exists in the single-phase water region near the inlet, and has little influence on the boiling two-phase region and single-phase steam region. The void fraction decreases at the same height in the boiling two-phase region with the increase of inlet flow velocity, which is due to the decrease of the heating time when the fluid reaches the same height. © 2023 by JSME.

关键词： Heat flux

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Intelligent Fault Diagnosis Method of Train Axle Box Bearing Based on Improved Deep Reinforcement Learning

Intelligent Fault Diagnosis Method of Train Axle Box Bearing...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Meng, Zhangxuan Lei, Zihao Deng, Shuaiqing Gu, Shulong Zhang, Zhifen Su, Yu Wen, Guangrui National Key Lab of Aerospace Power System and Plasma Technology Xi’an Jiaotong University Xi’an 710049 China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an 710049 China Key Laboratory of Modern Design and Rotor Bearing System of the Ministry of Education Xi’an Jiaotong University Xi’an 710049 China

ISBN: (纸本)9789819639687

Axlebox bearings are the key components of rail transit train bogies, which are crucial to the operation of the train. However, the conditions of transit are relatively complex, coupled with the fact that the optimization of the deep neural network of the existing fault diagnosis methods requires a large number of manual modifications and experience of experts, which makes it difficult to effectively diagnose the faults of axlebox bearings. To address the above issues, this paper proposes an improved deep reinforcement learning strategy based on Dueling Double Deep Q-Network (D3QN) and Prioritized Experience replay (PER) for the fault diagnosis of axlebox bearings. Firstly, D3QN combines Double DQN and Dueling DQN to solve the problem of overestimation of the Q-value of Deep Q Network (DQN) and improve the stability of training. Secondly, by introducing the prioritized experience replay and the elevated reward mechanism, the efficiency of experience utilization and the speed of model convergence is improved. Finally, the proposed method is validated by using subway train transmission system fault simulation datasets of Beijing Jiaotong University (BJTU). The results show that compared with the original DQN method, PER-DR3QN has significant improvement in training efficiency, accuracy and stability. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Deep reinforcement learning

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