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arXiv 2024年

作者： Zhang, Yu Zheng, Kefeng Liu, Fei Zhang, Qingfu Wang, Zhenkun School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong

Symbolic regression (SR) methods have been extensively investigated to explore explicit algebraic Reynolds stress models (EARSM) for turbulence closure of Reynolds-averaged Navier-Stokes (RANS) equations. The deduced EARSM can be readily implemented in existing computational fluid dynamic (CFD) codes and promotes the identification of physically interpretable turbulence models. The existing SR methods, such as genetic programming, sparse regression, or artificial neural networks, require user-defined functional operators, a library of candidates, or complex optimization algorithms. Recently, large language models (LLMs), trained on large amounts of publicly available source code, have drawn great attention for their abilities to generate computer programs with more general free-text inputs and problem descriptions, and provide wider possibilities with novel insights. In this work, a novel framework using LLMs to automatically discover algebraic expressions for correcting the RSM is proposed. The direct observation of Reynolds stress and the indirect output of the CFD simulation are both involved in the training process to guarantee data consistency and avoid numerical stiffness. Constraints of functional complexity and convergence are supplementally imposed in the objective function on account of the tremendous flexibility of LLMs. The evolutionary search is employed for global optimization. The proposed method is performed for separated flow over periodic hills at Re = 10, 595. The generalizability of the discovered model is verified on a set of 2D turbulent separated flow configurations with different Reynolds numbers and geometries. It is demonstrated that the corrective RANS can improve the prediction for both the Reynolds stress and mean velocity fields. Compared with algebraic models discovered by other works, the discovered model performs better in accuracy and generalization capability. The proposed approach provides a promising paradigm for using LLMs to im

关键词： Navier Stokes equations

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The Instability Marsh Phenomenon and Marginal Stability Boundary Distortion of Density Wave Oscillation in Parallel Channels

SSRN

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SSRN 2024年

作者： Sun, Ducheng Ma, Zaiyong Lu, Q. Liu, C.W. Pan, L.M. School of Energy and Power Engineering Shandong University No. 17923 Jingshi Road Jinan250061 China Key Laboratory of Low-grade Energy Utilization Technologies and Systems Ministry of Education Chongqing400044 China Department of Nuclear Engineering and Technology Chongqing University Chongqing400044 China Science and Technology on Reactor System Design Technology Laboratory Chengdu610041 China

Density wave oscillation (DWO) in parallel channels is an important problem that has been widely studied. The marginal stability boundary (MSB) is often found to be "L " shaped on the N pch -N sub plane, but some very different shapes were also reported. In this paper, the instability marsh region which could occur in the traditional instable region but having both instability and stability sections was presented and discussed, which is found to be an explanation of the distortion of MSB. The analysis of the instability marsh region showed that the evolution of instability marsh should be due to the stability change when operation condition changes, which can be estimated with the analysis of the two-phase and single phase pressure drop variation, as well as Ma’s stability criterion for different heat flux profiles. Variation of instability marsh with different heat flux profile, inlet and outlet resistance coefficient, mass flow and pressure were obtained and analyzed, and the effects of these parameters on instability marsh variation were found to be well interpreted with stability change. The calculation and analysis of instability marsh showed a clear evolution figure of the distortion of MSB under different conditions, and related research could be helpful in better understanding the flow instability in parallel channels. © 2024, The Authors. All rights reserved.

关键词： Stability criteria

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Multi-objective Evolution of Heuristic Using Large Language Model

arXiv

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arXiv 2024年

作者： Yao, Shunyu Liu, Fei Lin, Xi Lu, Zhichao Wang, Zhenkun Zhang, Qingfu Department of Computer Science City University of Hong Kong Hong Kong School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shen Zhen China

Heuristics are commonly used to tackle various search and optimization problems. design heuristics usually require tedious manual crafting with domain knowledge. Recent works have incorporated Large Language Models (LLMs) into automatic heuristic search, leveraging their powerful language and coding capacity. However, existing research focuses on the optimal performance on the target problem as the sole objective, neglecting other criteria such as efficiency and scalability, which are vital in practice. To tackle this challenge, we propose to model the heuristic search as a multiobjective optimization problem and consider introducing additional practical criteria beyond optimal performance. Due to the complexity of the search space, conventional multiobjective optimization methods struggle to effectively handle LLM-based multi-objective heuristic search. We propose the first LLM-based multi-objective heuristic search framework, Multi-objective Evolution of Heuristic (MEoH), which integrates LLMs in a zero-shot manner to generate a nondominated set of heuristics to meet multiple design criteria. We design a new dominance-dissimilarity mechanism for effective population management and selection, which incorporates both code dissimilarity in the search space and dominance in the objective space. MEoH is demonstrated in two well-known combinatorial optimization problems: the online Bin Packing Problem (BPP) and the Traveling Salesman Problem (TSP). The results indicate that a variety of elite heuristics are automatically generated in a single run, offering more trade-off options than the existing methods. It successfully achieves competitive or superior performance while improving efficiency up to 10 times. Moreover, we also observe that the multi-objective search introduces novel insights into heuristic design and leads to the discovery of diverse heuristics. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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Improved and Stable Data-Based Tracking Control via Approximate Iterative Q-Learning

Improved and Stable Data-Based Tracking Control via Approxim...

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IEEE International Conference on Control science and systems Engineering (CCSSE)

作者： Zhantao Liang Weiming Liu Mingming Ha Derong Liu School of Automation Guangdong University of Technology Guangzhou China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Intelligence Science and Technology and Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China

In this paper, to avoid computing the feedward control input and to completely eliminate the tracking error, an improved cost function with respect to the tracking error is proposed, under which the optimal tracking controller can stabilize the tracking error system. Then, the approximate value-iteration-based Q-learning is given to solve the neuro-optimal control policy. Also, the finite error bound of the approximate Q-function is established. In addition, for guaranteeing the admissibility of the obtained control policies, some stability conditions for this data-based iterative Q-learning are developed. When the stability conditions are satisfied, it is ensured that the iterative control policies derived from the critic network and the trained action network with approximation errors will stabilize the tracking error system. Finally, a simulation example is employed to implement the data-based Q-learning algorithm and verify the present theoretical results.

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Microstructure and impact toughness of 16MND5 reactor pressure vessel steel manufactured by electrical additive manufacturing

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Journal of Iron and Steel Research International 2020年第8期27卷 992-1004页

作者： Xi-kou He Chang-sheng Xie Li-jun Xiao Ying Luo Di Lu Zheng-dong Liu Xi-tao Wang Institute for Special Steels China Iron and Steel Research InstituteBeijing 100081China Collaborative Innovation Center of Steel Technology University of Science and Technology BeijingBeijing 100083China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of ChinaChengdu 610041SichuanChina Southern Additive Science and Technology Co. Ltd.Foshan 528225GuangdongChina

Electrical additive manufacturing can improve manufacturing efficiency and reduce the cost of 16MND5 reactor pres-sure vessel steel. Impact tests were conducted to compare the impact toughness of 16MND5 steels manufactured by the electrical additive manufacturing and conventional forging, respectively. It is found that the impact toughness of electrical additive manufacturing specimen was slightly higher than that of conventional forging specimen. The characterizations of microstructure show that there were large ferrites and carbides in electrical additive manufacturing specimen. The fracture mechanisms of electrical additive manufacturing specimen were that microvoids or microcracks were prone to nucleate at the large ferrite/bainite interface and large carbide/bainitic ferrite interface, where the stress concentration was high. In addi-tion, the block size and high-angle grain boundaries played a vital role in hindering crack propagation of electrical additive manufacturing specimen, helping to improve the impact energy and leading to a low ductile–brittle transition temperature. The results suggest that the electrical additive manufacturing technology was an effective method to enhance the impact toughness of 16MND5 steel.

关键词： Electrical additive manufacturing 16MND5 steel Impact toughness Fracture High-angle grain boundary

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Infection Risk in a Restaurant Environment Due to Airborne Diseases 5th

Infection Risk in a Restaurant Environment Due to Airborne D...

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5th International Conference on Building Energy and Environment, COBEE 2022

作者： Bale, Rahul Li, Chungang Fukudome, Hajime Yumino, Saori Iida, Akiyoshi Tsubokura, Makoto RIKEN Center for Computaitonal Science Kobe Japan Graduate School of System Informatics Kobe Univeristy Kobe Japan Department of Mechanical Engineering Toyohashi Institue of Technology Toyohashi Japan Kajima Corporation Tokyo Japan Numerical Flow Design Tokyo Japan

ISBN: (纸本)9789811998218

An activity wherein the usage of masks and social distancing, to prevent the spread of COVID-19 may not be possible, is not possible is during social gatherings over a meal or a drink. Droplets generated during a casual conversation during a meal can lead to the transmission of COVID, due to which restaurants and bars can turn out to be vectors for its transmission. Therefore, it is necessary to investigate the dispersion of respiratory droplets in such an environment. To that end, we carry out numerical simulations of droplets dispersion in a typical Japanese restaurant. The role of ventilation and air-conditioning systems on dispersion will be discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Simulation of Nuclear reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning

Simulation of Nuclear Reactor Accident Scenarios using Physi...

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Electronic Information Engineering and Computer science (EIECS), 2021 International Conference on

作者： Yang Yu Yufei Xie Wenlin Wang Guohua Wu Haitao Lan Enbo Lin Ping An Zibin Sun Haichuan Zhang Yixian Wu National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R 2 : 0.735).

关键词： Radioactive materials Accuracy Monte Carlo methods Transfer learning Neural networks Robustness Data models Fuels Inductors Accidents

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Achieving Privacy-Preserving and Scalable Graph Neural Network Prediction in Cloud Environments

Achieving Privacy-Preserving and Scalable Graph Neural Netwo...

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IEEE International Conference on Communications (ICC)

作者： Yanli Yuan Dian Lei Chuan Zhang Ximeng Liu Zehui Xiong Liehuang Zhu School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Guangdong Provincial Key Laboratory Novel Security Intelligence Technologies Guangdong China College of Mathematics and Computer Science Fuzhou University Fuzhou China School of Information Systems Singapore Management University Singapore Information System and Technology Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Graph neural networks (GNNs) have been widely applied in various graph analysis tasks. To provide more convenient and faster predictive services, many enterprises are choosing to deploy GNNs in cloud environments. However, given the increasing privacy concerns about GNNs models and graph data, as well as the need to quickly generate embeddings for new nodes in real-world applications, a critical issue in this emerging paradigm is to ensure the security and scalability of GNN predictions. In this paper, we propose a privacy-preserving and scalable GNN prediction scheme, named PS-GNN, to address the privacy issues in cloud environments. Specifically, PS-GNN utilizes a customized array structure to store graph data and employs secret sharing to preserve the confidentiality of both the GNN model and graph data. Besides, the scalability of PS-GNN is achieved by aggregating feature information from local node neighborhoods in parallel. Through a detailed analysis, we demonstrate the security of PS-GNN. Extensive experiments on real-world datasets demonstrate that PS-GNN outperforms existing schemes in terms of computational and communication overhead, and reaches state-of-the-art performance on large graphs.

关键词： Privacy Data privacy Scalability Computational modeling Predictive models Graph neural networks Data models

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Effective Information Selection Method on Spatiotemporal Information Infrastructure with Photogrammetry 48

Effective Information Selection Method on Spatiotemporal Inf...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Sugawara, Ayaki Sonoda, Ayu Nishi, Hiroaki Keio University Graduate School of Science and Technology 3-14-1 Hiyoshi Kohoku Kanagawa Yokohama223-8522 Japan Keio University Faculty of Science and Technology Department of System Design 3-14-1 Hiyoshi Kohoku Kanagawa Yokohama223-8522 Japan

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In recent years, there has been an increase in the demand for three-dimensional data of geospatial information in various fields, such as automatic driving and disaster prevention. Beyond5G (above 5th generation network), which features wideband, low latency, reliable connection, allows for the easily collection of spatial information of many points and times from camera-equipped vehicles, such as self-driving cars and drones. Spatiotemporal information at any location and at any time can be provided depending on users' requests. However, there are instances where the target point's spatiotemporal information and the request's target time have not been collected. Therefore, appropriate data completion is required to provide this service seamlessly. To create 3D models using photogrammetry, images are required;therefore, multiple videos that captured the streets near our university were prepared and split into frames. Then, those images were stored in our local storage. The information of the recording date, weather, file path to the image, and location (latitude and longitude) were saved to our database per frame. When users request spatiotemporal information, this system commences searching for images that meet the requirements. When the number of matched images are sufficient, the 3D model is created. When it is insufficient, the data are complemented appropriately. © 2022 IEEE.

关键词： Disaster prevention

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Modeling of Body Pressure Distribution on Alternative Pressure Mattress

Modeling of Body Pressure Distribution on Alternative Pressu...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Masamichi Kosuge Masaki Takahashi School of Science for Open and Environmental Systems Graduate School of Science and Technology Keio University Yokohama Japan Department of System Design Engineering Faculty of Science and Technology Keio University Yokohama Japan

Recently, an alternative pressure mattress has been proposed to prevent pressure ulcers. In this study, we aim to design a control system for body pressure distribution using the mattress. This study focuses on modeling the dynamics between the body pressure distribution and the mattress. As modeling based on first principles is challenging, we identify the model from experimental data. However, when high-dimensional data such as body pressure distribution are directly used for identification, the model accuracy can deteriorate. Therefore, we need to determine the suitable model structure or dataset. Herein, we examined how the prediction accuracy of body pressure distribution varies with the dataset and model structure. We also proposed a method for representing the model as the dynamics of the parameters of the Gaussian Mixture Model to suppress the estimated parameters. To the best of our knowledge, this study is the first to propose the body pressure model as dynamics of the parameters of the Gaussian Mixture Model.

关键词： Automation Shape Predictive models Control systems Approximation error Data models History

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