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2nd International Conference on Numerical Modelling in Engineering, NME 2019

作者： Jiang, L. Zhang, W. Wu, C.W. Zhang, L.P. Zhang, Y.X. Liu, Z.Y. National Key Laboratory of Science and Technology on Reactor System Design Technology Nuclear Power Institute of China Chengdu610041 China State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian116024 China

Flywheel, as a kinetic energy storage and conversion device, has promising applications due to its high efficiency, no pollution and long lifetime. In particular, with a three-layer structure packed with heavy metal alloy middle layer, a flywheel can achieve both a large rotational inertia and a high energy density. To guarantee structural strength safety of this type of multi-ring packed flywheel operating at high temperature, structural and thermal stress analysis for a flywheel with a tungsten alloy layer sandwiched between two stainless steel layers is carried out using finite element method (FEM). Based on plane stress assumption, flywheel stress characteristic along radius under assembly force, centrifugal force and thermal loading is demonstrated. The results show that, the rise of temperature causes inconsistent thermal expansion between neighbouring layers and results in a large thermal stress. Particularly, a high circumferential tensile stress occurs in the middle layer. It is also found that, the segments of the middle tungsten alloy layer can efficiently relieve the inconsistent thermal expansion and lower the thermal stress. Moreover, the number of segments significantly influences the efficiency of thermal stress releasing and the reserved gap between adjacent segments significantly improves the contact pressure between inner hub and tungsten alloy layer. This work may provide reference for structural design of the multi-ring packed flywheels under thermal loading. © Published under licence by IOP Publishing Ltd.

关键词： Kinetics

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Model Predictive Control for Steering system of Water-Jet Propulsion

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Journal of Shanghai Jiaotong university(science) 2020年第3期25卷 299-303页

作者： GONG Zhenghua SONG Chenwei LI Gangqiang CHEN Jianping XU Zijing YUAN Jingqi MOE Key Laboratory of Marine Intelligent Equipment and System Shanghai Jiao Tong UniversityShanghai 200240China Laboratory of Science and Technology on Waterjet Propulsion Shanghai 200011China Marine Design&Research Institute of China Shanghai 200011China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Water-jet propulsion is a widely applied ship propulsion *** steering control system has an important impact on manoeuvre *** this paper,transfer function model is firstly established on the basis of mechanism analysis of water-jet steering ***,by considering the variability of model parameters and input constraints in practical operation,a model predictive controller is designed for steering system ***,model based disturbance observer is employed in an attempt to reject environmental *** performance of the proposed model predictive control (MPC) scheme for a particular steering system is compared with that of conventional proportional integral derivative (PID) control *** results demonstrate that the proposed model predictive controller outperforms conventional PID controller,particularly in robustness,response delay and tracking accuracy.

关键词： water-jet propulsion steering system model predictive control(MPC) disturbance observer

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Evaluation and Analysis of RT Suspected Defects on reactor Pressure Boundary Weld

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IOP Conference Series: Earth and Environmental science 2021年第3期632卷

作者： Bo Yang Qiang Deng Cong Chen FengShou Zhang Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institution of China Sichuan ChengDu China

The control rod driving mechanism (CRDM) is one of the important equipments in the nuclear reactor, and its sealing shell is part of the pressure boundary of the nuclear reactor. The sealing shell is a pressure vessel made of two different material (austenitic stainless steel and nickel base alloy) cylinders welded by dissimilar metal welds. Product production process, in this dissimilar metal weld ray detection found suspected defects show. In this paper, the mechanism and process of the suspected defects are expounded through experimental verification, and it is proved that the suspected defects do not affect the quality of the weld.

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BREAKING THE DEGREES-OF-FREEDOM LIMIT OF HOLOGRAPHIC MIMO COMMUNICATIONS: A 3-D ANTENNA ARRAY TOPOLOGY

arXiv

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arXiv 2023年

作者： Yuan, Shuai S.A. Wu, Jie Xu, Hongjing Wang, Tengjiao Li, Da Chen, Xiaoming Huang, Chongwen Sun, Sheng Zheng, Shilie Zhang, Xianmin Li, Er-Ping Sha, Wei E.I. College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei230601 China Wireless Network Research Department Huawei Technologies Shanghai201206 China School of Information and Communications Engineering Xi’an Jiaotong University Xi’an710049 China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

The performance of holographic multiple-input multiple-output (MIMO) communications, employing two-dimensional (2-D) planar antenna arrays, is typically compromised by finite degrees-of-freedom (DOF) stemming from limited array size. The DOF constraint becomes significant when the element spacing approaches approximately half a wavelength, thereby restricting the overall performance of MIMO systems. To break this inherent limitation, we propose a novel three-dimensional (3-D) antenna array that strategically explores the untapped vertical dimension. We investigate the performance of MIMO systems utilizing 3-D arrays across different multi-path scenarios, encompassing Rayleigh channels with varying angular spreads and the 3rd generation partnership project (3GPP) channels. We subsequently showcase the advantages of these 3-D arrays over their 2-D counterparts with the same aperture sizes. As a proof of concept, a practical dipole-based 3-D array, facilitated by an electromagnetic band-gap (EBG) reflecting surface, is conceived, constructed, and evaluated. The experimental results align closely with full-wave simulations, and channel simulations substantiate that the DOF and capacity constraints of traditional holographic MIMO systems can be surpassed by adopting such a 3-D array configuration. © 2023, CC BY-NC-ND.

关键词： Energy gap

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Abnormal flow of α particles in heavy-ion collisions at intermediate energies

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Physical Review C 2021年第4期103卷 044607-044607页

作者： G. Qu (曲国峰) Y. Huang (黄宇) D. Peng (彭丹) Z. Xu (徐子虚) W. Lin (林炜平) H. Zheng (郑华) G. Tian (田国玉) R. Han (韩瑞) C. Ma (马春旺) M. Huang (黄美容) P. Ren (任培培) J. Han (韩纪锋 ) Z. Yang (杨振雷) X. Liu (刘星泉) R. Wada Key Laboratory of Radiation Physics and Technology of the Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu 610064China School of Physics Henan Normal University Xinxiang 453007 China School of Physics and Information Technology Shaanxi Normal University Xi'an 710119 China Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China College of Physics and Electronics information Inner Mongolia University for Nationalities Tongliao 028000 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu 610213 China Cyclotron Institute Texas A&M University College Station Texas 77843 USA

The experimentally observed abnormal α flow behavior from heavy-ion collisions at intermediate energies is investigated using the events of Ca40+Ca40 at 35 MeV/nucleon simulated with an improved antisymmetrized molecular dynamics model with specific consideration of the Fermi motion in the nucleon-nucleon collision process. Its possible origins in the processes from the fragment formation to the experimental flow extraction, i.e., dynamical process, sequential decay, experimental detection, and data analysis, are closely examined. It is found that the observed abnormal α flow behavior originates from the reconstruction of reaction planes in the flow extraction. How the abnormal α flow behavior is generated from the reaction plane reconstruction procedure is discussed.

关键词： Collective flow Low & intermediate energy heavy-ion reactions Models & methods for nuclear reactions

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Extension of Gen-Foam to Departure from Nucleate Boiling Prediction and Validation Against the Oecd/Nrc Psbt Benchmark

SSRN

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SSRN 2023年

作者： Niu, Yuhang Alessandro, Scolaro Fiorina, Carlo Qin, Hao Lazare, Gauthier Wu, Yingwei Tian, Wenxi Su, Guanghui H. Department of Nuclear Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Laboratory for Reactor Physics and System Behaviour École Polytechnique Fédérale de Lausanne – EPFL Lausanne1015 Switzerland Department of Nuclear Engineering Texas A&M University College StationTX77843 United States Department of Fluid Mechanics Energy and Environnement EDF R&D Chatou78400 France

The prediction of departure from nucleate boiling (DNB) has always been a crucial aspect of thermal-hydraulic codes for the analysis of Light Water reactors. In this paper, GeN-Foam, a multi-physics code developed based on OpenFOAM, has been enhanced to incorporate DNB prediction using a CHF look-up table as well as post-CHF flow and heat transfer models. To assess GeN-Foam's accuracy in modeling DNB conditions in typical pressurized water reactors (PWRs), a preliminary validation was conducted utilizing Phase II of the PSBT benchmark, including the steady-state fluid temperature (Test Series 1), steady-state DNB (Test Series 4), and transient DNB (Test Series 11T) benchmarks. This paper presents the closure correlations implemented in GeN-Foam, provides a description of the PSBT benchmarks, and includes a comparison between GeN-Foam's predictions and experimental data, as well as code-to-code verification with other participants. The results demonstrate that GeN-Foam exhibits good performance in simulating two-phase flow boiling conditions. Furthermore, GeN-Foam accurately predicts the DNB power and time of occurrence within a ±10% error range compared to measured values, preliminarily indicating its effectiveness in predicting DNB occurrence and two-phase flow boiling phenomena. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Determining impact parameters of heavy-ion collisions at low-intermediate incident energies using deep learning with convolutional neural networks

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Physical Review C 2022年第3期105卷 034611-034611页

作者： X. Zhang (张鑫) X. Liu (刘星泉) Y. Huang (黄宇) W. Lin (林炜平) H. Zheng (郑华) R. Wada A. Bonasera Z. Chen (陈志强) L. Chen (陈蕾) J. Han (韩纪锋) R. Han (韩瑞) M. Huang (黄美容) Q. Hu (胡强) Q. Leng (冷强钟) C. W. Ma (马春旺) G. Qu (曲国峰) P. Ren (任培培) G. Tian (田国玉) Z. Xu (徐子虚) Z. Yang (杨振雷) L. Zhang (张丽娜) Key Laboratory of Radiation Physics and Technology of the Ministry of Education Sichuan University Chengdu 610064 China School of Physics and Information Technology Shaanxi Normal University Xi'an 710119 China Cyclotron Institute Texas A&M University College Station Texas 77843 USA Laboratori Nazionali del Sud INFN via Santa Sofia 62 95123 Catania Italy Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China College of Physics and Electronics information Inner Mongolia University for Nationalities Tongliao 028000 China School of Physics Henan Normal University Xinxiang 453007 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu 610213 China School of Information Sciences and Engineering Lanzhou University Lanzhou 730000 China

A deep learning based method with the convolutional neural network (CNN) algorithm for determining the impact parameters is developed by using constrained molecular-dynamics model simulations, focusing on the heavy-ion collisions at the low-intermediate incident energies from several tens to one hundred MeV/nucleon in which the emissions of heavy fragments with the charge numbers larger than three become crucial. To make the CNN applicable in the task of the impact-parameter determination at the present energy range, specific improvements are made in the input selection, the CNN construction, and the CNN training. It is demonstrated from the comparisons of the deep CNN method and the conventional methods with the impact-parameter-sensitive observables that the deep CNN method shows better performance for determining the impact parameters, especially leading to the capability of providing better recognition of the central collision events. With a proper consideration of the experimental filter effect in both training and testing processes to keep consistency with the actual experiments, the good performance of the deep CNN method holds and performs significantly better in terms of predicting the impact parameters and recognizing the central collision events compared with conventional methods, demonstrating the superiority of the present deep CNN method. The deep CNN method with the consideration of the filter effect is applied in the deduction of nuclear stopping power. Higher accuracy for the stopping power deduction is achieved benefitting from the better impact-parameter determination using the deep CNN method, compared with using conventional methods. This result reveals the importance of selecting a reliable impact-parameter-determination method in the experimental deduction of the nuclear stopping power as well as other observables.

关键词： Low & intermediate energy heavy-ion reactions Convolutional neural networks Machine learning Nuclear data analysis & compilation

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Comparisons of Subgrid-Scale Models for OpenFoam Large- Eddy Simulation

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Journal of Physics: Conference Series 2021年第4期1802卷

作者： Zhipeng Feng Huanhuan Qi Xuan Huang Shuai Liu Jian Liu Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu Sichuan China

OpenFOAM is a free, open-source software package that can be used for the solutions of computational fluid dynamics and simulation of various fluid flow processes. Nevertheless, OpenFOAM still lacks default settings and a large number of different numerical schemes and turbulent models should be validated. In this paper, the unsteady flow around a cylinder (Re=3900) is calculated by the large eddy simulation of OpenFOAM. The predictions include the drag and lift coefficient, the pressure distribution around the cylinder, the velocity distribution and Reynolds stress distribution in the wake region, as well as the prediction of the recirculation length and separation angle. Thanks to several simulations, these five subgrid-scale (SGS) models are compared and studied: The Smagorinsky SGS model, wall adaptive local eddy visibility SGS model, dynamic SGS model with Lagrangian averaging, dynamic one equation eddy visibility model, one equation eddy visibility model. The numerical results are verified with the published experimental data.

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Limit load estimation for finite plates with semi-elliptical defects under tensile loading

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Journal of Physics: Conference Series 2021年第1期1777卷

作者： K K Shi X L Fu H Xie X J Shao J Tian Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu Sichuan China

The ASME code allows the designer to circumvent many of the problems arising in design by elastic analysis by performing plastic or limit analysis of the component. Unlike elastic analysis, these analyses take account of the redistribution of stress upon yield. In limit analysis, the calculation of the limit load for finite plate with different semi-elliptical defects is an important stage for mechanical safety evaluation. The elastic compensation technique proposed by Mackenzie can be used to provide the limit load of a structure with a finite element method and aims at producing a suitable stress filed for the lower bound theory. In the present study, it is assumed that the finite plate exists three kinds of semi-elliptical cracks with low aspect ratio a/c (a/c=0.1, 0.5 and 1.0), respectively. The improved elastic compensation technique realized by finite element method is applied in the limit load estimation. At last, the results show that the improved elastic compensation technique can be used to obtain the limit value for the finite plate with different semi-elliptical defects under tensile loading. Moreover, the improved elastic compensation technique can be easily implemented in any finite element method and enables the limit load for any complex structure to be obtained swiftly.

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Inverse design of composite pipe fittings using deep learning for lightweight structural optimization

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Composites Communications 2025年

作者： Qichao Gui Anchalee Duongthipthewa Limin Zhou School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen 518055 PR China Guangdong Provisional Key Laboratory of Functional Oxide Materials and Devices Southern University of Science and Technology Shenzhen 518055 PR China Shenzhen Key Laboratory of Intelligent Manufacturing for Continuous Carbon Fiber Reinforced Composites Southern University of Science and Technology Shenzhen 518055 PR China

The conventional design of composite pipe fittings is highly dependent on experimental testing and finite element simulations, which are both costly and time-intensive. To address these challenges, this study introduces a deep learning-based inverse design approach to optimize the stiffness characteristics of carbon fiber reinforced polymer (CFRP) composite pipe fittings. Two predictive models were developed: a Long-Short-Term Memory-Based (LSTMB) Model and a Multi-Head Attention-Based (MHAB) *** evaluations revealed that the MHAB model outperformed the LSTMB model in terms of predictive accuracy and generalization capability. Based on this, a population-based optimization algorithm was integrated to achieve the inverse design of the composite pipe fittings, ensuring efficient structural optimization while satisfying design constraints. The proposed method was validated through two optimization case studies, demonstrating its effectiveness in improving the efficiency and precision of composite pipe fitting design. This study highlights the potential of deep learning, particularly the Transformer framework, to accelerate the design and optimization of composite materials.

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