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A distributed parallel computing-based CFD analysis of plate-type fuel assemblies in CARR reactors

INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2024年 153卷

作者： Yin, Xinli Chen, Guangliang Qian, Hao Li, Lei Su, Shaomin Jiang, Hongwei Zhang, Hanqi Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Harbin 150001 Peoples R China

Fine flow and heat transfer characteristics are critical to nuclear reactor safety. However, a comprehensive CFD calculation of the entire reactor core requires substantial computing resources, restricting the research domain's scope. To address this issue, we propose a distributed parallel (DP) computing scheme, tailored specifically for reactor cores using plate-type fuel assemblies. This study focuses on the thermal-hydraulic calculation of the large domain of a reactor core utilizing plate-type fuel assembly, based on the Computational Fluid Dynamics (CFD) method. This innovative solution enables the completion of extensive domain CFD calculations using modestly equipped personal workstations (8 cores, 128G RAM), which traditionally would require supercomputing platforms. To verify the distributed-parallel computing technique, it is successfully applied to all standard, full-length fuel assemblies in the China Advanced Research Reactor (CARR). In the result analysis, it was observed that detailed results could be obtained with reduced computational resources, demonstrating a significant breakthrough for CFD engineering analysis.

关键词： Plate-type fuel CFD Scheme design Thermal hydraulic distributed parallel computing

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Optimization algorithm of association rule mining for heavy-haul railway freight train fault data based on distributed parallel computing

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SCIENCE PROGRESS 2024年第4期107卷 368504241301181页

作者： Bai, Yanhui Li, Honghui Wang, Wengang Liu, Shufang Zhang, Ning Zhang, Chun Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing Peoples R China Beijing Jiaotong Univ Key Lab Engn Res Ctr High Speed Railway Network Management Minist Educ Beijing Peoples R China China Natl Energy Co Ltd Technol & Informat Dept Beijing Peoples R China Southwest Jiaotong Univ Sch Railway Safety Theory & Technol Chengdu Sichuan Peoples R China

With the continuous improvement in the efficiency of the heavy-haul railway freight transportation, the pressure on on-site maintenance is increasing. In-depth research on fault characteristics carries significant importance for fault scientific judgment and fault prevention. This study proposes an efficient association rule mining (ARM) algorithm, HM-RDHP, for analyzing fault data from heavy-haul railway freight trains. The algorithm introduces distributed parallel computing technology, integrating the MapReduce framework and HBase on the Hadoop platform to process large volumes of complex fault data efficiently. Experimental results show that the HM-RDHP algorithm can efficiently uncover hidden patterns and associations within the fault data of heavy-haul railway freight trains. The mined association rules provide a valuable reference model to aid in predictive maintenance and fault prevention strategies for freight train maintenance departments.

关键词： Heavy-haul railway freight train association rule mining DHP algorithm distributed parallel computing MapReduce

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Three-dimensional image registration using distributed parallel computing

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IET IMAGE PROCESSING 2018年第10期12卷 1713-1720页

作者： Song, Huajun Qiu, Peihua China Univ Petr East China Coll Informat & Control Engn Dongying Shandong Peoples R China Univ Florida Dept Biostat Gainesville FL 32611 USA

Three-dimensional (3D) images have become increasingly popular in practice. They are commonly used in medical imaging applications. In such applications, it is often critical to compare two 3D images, or monitor a sequence of 3D images. To make the image comparison or image monitoring valid, the related 3D images should be geometrically aligned first, which is called image registration (IR). However, IR for 3D images would take much computing time, especially when a flexible method is considered, which does not impose any parametric form on the underlying geometric transformation. Here, the authors explore a fast-computing environment for 3D IR based on the distributed parallel computing. The selected 3D IR method is based on the Taylor's expansion and 3D local kernel smoothing. It is flexible, but involves much computation. The authors demonstrate that this fast-computing environment can effectively handle the computing problem while keeping the good properties of the 3D IR method. The method discussed here is therefore useful for applications involving big data.

关键词： image registration image sequences parallel processing three-dimensional image registration distributed parallel computing 3D image sequences medical imaging applications image monitoring image comparison geometric transformation 3D IR method Taylor expansion 3D local kernel smoothing

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Optimization of reordering procedures in HOTRG for distributed parallel computing 32

Optimization of reordering procedures in HOTRG for distribut...

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32nd IEEE International parallel and distributed Processing Symposium (IPDPS)

作者： Yamada, Haruka Imakura, Akira Imamura, Toshiyuki Sakurai, Tetsuya Univ Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan RIKEN AICS Chuo Ku 7-1-26 Minatojima Minami Machi Kobe Hyogo 6500047 Japan JST CREST Kawaguchi Saitama Japan

ISBN: (纸本)9781538655559

Higher-order tensor renormalization group (HOTRG) is a coarse-graining algorithm for approximating the partition function in the field of elementary particle physics using a tensor network. Coarse-graining in HOTRG comprises an approximation step and a contraction step, and the contraction step is performed with tensor reorderings and matrix products. In this paper, we introduce a naive parallel implementation of HOTRG and propose optimal reordering procedures for a three-dimensional (3D) classical cubic lattice Ising model. Numerical experiments on the K computer show that the elapsed time of the proposed procedure is 6.88 times faster than the naive one for the reorderings.

关键词： Tensor network Higher-order tensor renormalization group (HOTRG) Optimization for reordering procedures 3D classical cubic lattice Ising model distributed parallel computing

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distributed parallel computing of the recursive eigenvalue search in the context of transfer matrix method for multibody systems

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ADVANCES IN MECHANICAL ENGINEERING 2016年第11期8卷 1-15页

作者： Gu, Junjie Rui, Xiaoting Chen, Gangli Zhou, Qinbo Yang, Haigen Nanjing Univ Sci & Technol Inst Launch Dynam Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Minist Educ Engn Res Ctr Wider & Wireless Commun Technol Nanjing Jiangsu Peoples R China

The modeling and solving a transcendental eigenvalue problem are important issues in the transfer matrix method for linear multibody systems. Based on the recursive eigenvalue search algorithm for transfer matrix method for linear multibody system, the distributed parallel approach for assembling overall transfer matrix and searching eigenvalues is proposed. This is achieved based on Message parallel Interface. The influence of the CPU core number as well as the distributed network environment on the final computational time is analyzed through numerical examples of both a non-uniform beam and a multiple launch rocket system. The results indicate that the computational time is significantly reduced by the proposed parallel computing method, so that the computational efficiency on optimization and design of complex multibody systems can be improved.

关键词： Multibody system dynamics transfer matrix method for multibody systems eigenvalue distributed parallel computing Message parallel Interface

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Meta-model Evolution with Self-Adaptation: An Empirical Development Approach for distributed parallel computing Framework 15

Meta-model Evolution with Self-Adaptation: An Empirical Deve...

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15th IEEE Int Conf on Trust, Security and Privacy in Comp and Commun / 10th IEEE Int Conf on Big Data Science and Engineering / 14th IEEE Int Symposium on parallel and distributed Proc with Applicat (IEEE Trustcom/BigDataSE/ISPA)

作者： Wu, Renke Huang, Linpeng Yu, Peng Shen, Kai Shi, Qiuwei Zhou, Haojie Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China Jiangnan Inst Comp Technol State Key Lab Mathemat Engn & Adv Comp Wuxi Peoples R China

ISBN: (纸本)9781509032051

distributed parallel computing platform performs well in processing big data. However, due to the platform's complexity and distributed characteristics, it is hard to design and achieve. For example, during the platform's design phase, variations are unpredictable. To address these issues, a highly structured object-oriented framework for systematic modeling, which has high flexibility, reusability and maintainability, is needed. In this paper, we present and implement an empirical development approach to achieve the specific modeling framework, based on meta-model evolution with hot-spots (variable parts) recognition. Our work expands this idea into the following directions. Firstly, we define a generic and stable meta-model. We give out concept models, object models and dynamic models of main objects. General data partitioning, job division and inter-node communication mechanism are designed. Secondly, based on the meta-model, we complete framework's incremental adaptation using an iterative process according to design variations. Practical experience of our empirical case study shows, the approach gets a relatively mature framework for design theory instruction. The integrated framework clearly expresses main objects and their relationship. This work promotes the development of parallel computing framework to have a qualitative leap, and eventually have more extensive application and popularization of the established framework.

关键词： big data processing platform distributed parallel computing development approach modeling meta-model evolution

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A parallel Sequential SBAS Processing Framework Based on Hadoop distributed computing

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REMOTE SENSING 2024年第3期16卷 466页

作者： Wu, Zhenning Lv, Xiaolei Yun, Ye Duan, Wei Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Chinese Acad Sci Key Lab Technol Geospatial Informat Proc & Applica Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Chinese Acad Sci Inst Software Beijing 100190 Peoples R China

With the rapid development of microwave remote sensing and SAR satellite systems, the use of InSAR techniques has been greatly encouraged due to the abundance of SAR data with unprecedented temporal and spatial coverage. Small Baseline Subset (SBAS) is a promising time-series InSAR method for applications involving deformation monitoring of the Earth's crust, and the sequential SBAS method is an extension of SBAS that allows long-term and large-scale surface displacements to be obtained with continuously auto-updating measurement results. As the Chinese LuTan-1 SAR system has begun acquiring massive SAR image data, the need for an efficient and lightweight InSAR processing platform has become urgent in various research fields. However, traditional sequential algorithms are incapable of meeting the huge challenges of low efficiency and frequent human interaction in large-scale InSAR data processing. Therefore, this study proposes a distributed parallel sequential SBAS (P2SBAS) processing chain based on Hadoop by effectively parallelizing and improving the current sequential SBAS method. P2SBAS mainly consists of two components: (1) a distributed SAR data storage platform based on HDFS, which supports efficient inter-node data transfer and continuous online data acquisition, and (2) several parallel InSAR processing algorithms based on the MapReduce model, including image registration, filtering, phase unwrapping, sequential SBAS processing, and so on. By leveraging the capabilities associated with the distributed nature of the Hadoop platform, these algorithms are able to efficiently utilize the segmentation strategy and perform careful boundary processing. These parallelized InSAR algorithm modules can achieve their goals on different nodes in the Hadoop distributed environment, thereby maximizing computing resources and improving the overall performance while comprehensively considering performance and precision. In addition, P2SBAS provides better computing and st

关键词： InSAR sequential SBAS Hadoop distributed parallel computing

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Graph computing Based distributed parallel Power Flow for AC/DC Systems with Improved Initial Estimate

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Journal of Modern Power Systems and Clean Energy 2021年第2期9卷 253-263页

作者： Wei Feng Chen Yuan Qingxin Shi Renchang Dai Guangyi Liu Zhiwei Wang Fangxing Li the University of Tennessee 1525 Coleman Road KnoxvilleTennessee 37909USA Global Energy Interconnection Research Institute North America San JoseCalifornia 95134USA

The sequential method is easy to integrate with existing large-scale alternating current(AC)power flow solvers and is therefore a common approach for solving the power flow of AC/direct current(DC)hybrid *** this paper,a highperformance graph computing based distributed parallel implementation of the sequential method with an improved initial estimate approach for hybrid AC/DC systems is *** proposed approach is capable of speeding up the entire computation process without compromising the accuracy of ***,the AC/DC network is intuitively represented by a graph and stored in a graph database(GDB)to expedite data *** the interconnection of AC grids via high-voltage direct current(HVDC)links,the network is subsequently partitioned into independent areas which are naturally fit for distributed power flow *** each area,the fast-decoupled power flow(FDPF)is employed with node-based parallel computing(NPC)and hierarchical parallel computing(HPC)to quickly identify system ***,to reduce the alternate iterations in the sequential method,a new decoupled approach is utilized to achieve a good initial estimate for the Newton-Raphson *** the improved initial estimate,the sequential method can converge in fewer ***,the proposed approach allows for significant reduction in computing time and is able to meet the requirement of the real-time analysis platform for power *** performance is verified on standard IEEE 300-bus system,extended large-scale systems,and a practical 11119-bus system in China.

关键词： AC/DC system distributed parallel computing graph computing initial estimate power flow analysis

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distributed-parallel proper orthogonal/dynamic mode decompositions of large flow data

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COMPUTER PHYSICS COMMUNICATIONS 2025年 313卷

作者： Shinde, Vilas Mississippi State Univ Dept Aerosp Engn Mississippi State MS 39759 USA

High-fidelity computational fluid dynamics (CFD) simulations produce large databases, which are typically stored on either centralized or distributed machines. Eigen/Singular value decompositions are some of the early-stage and most useful decompositions. The more popular proper orthogonal decomposition (POD) and dynamics mode decomposition (DMD) of fluid flows are essentially based on the eigen/singular value decomposition algorithms. Although there exist very efficient and parallel eigen/singular value solvers, most of them perform poorly when handling large data particularly in distributed settings, and often resort to a partial estimation of eigen/singular value spectra. In this paper, we present a memory-efficient and highly-scalable POD and DMD procedures in distributed-parallel settings, where the parallel DMD algorithm is an improved tall-and-skinny QR (TSQR) DMD algorithm. A Large Eddy Simulations (LES) database of a fully turbulent Shock Wave Boundary Layer Interaction (SBLI) at Mach 2.7 and Reynolds number of 54,600 based on the inflow boundary layer thickness is employed, first, to evaluate the performance and accuracy of the algorithms, and second, to elucidate some of the threedimensional coherent flow features of the SBLI pertaining to POD/DMD. The selected POD/DMD modes of the LES flowfields exhibit full 3D flow features, such as, the streamwise-elongated G & ouml;rtler-like vortices and high-frequency acoustic packets that are physically relevant to the SBLI dynamics.

关键词： Proper orthogonal decomposition Dynamic mode decomposition distributed parallel computing

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KNN-DBSCAN: a DBSCAN in high dimensions

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ACM TRANSACTIONS ON parallel computing 2025年第1期12卷 1-27页

作者： Chen, Youguang Ruys, William Biros, George UT Austin Oden Inst Austin TX 78712 USA

Clustering is a fundamental task in machine learning. One of the most successful and broadly used algorithms is DBSCAN, a density-based clustering algorithm. DBSCAN requires & varepsilon;-nearest neighbor graphs of the input dataset, which are computed with range-search algorithms and spatial data structures like KD-trees. Despite many efforts to design scalable implementations for DBSCAN, existing work is limited to low-dimensional datasets, as constructing & varepsilon;-nearest neighbor graphs can be expensive in high-dimensions. This article introduces a modified DBSCAN, using k-nearest neighbor (kNN) graphs to improve efficiency. We outline conditions for kNN-DBSCAN to match DBSCAN's results and present a parallel implementation using OpenMP and MPI for shared and distributed memory systems. Testing on datasets up to 32 dimensions, we achieve remarkable scalability. Our implementation clusters one billion 3D points in under one second on 28K cores at TACC's Frontera system. In a larger run, we cluster 65 billion points in 20 dimensions in under 40 seconds using 114,688 cores. Our method is up to 37x faster than state-of-the-art parallel DBSCAN on a 20-dimensional dataset with 4 million points. Code is available at https://***/ut-padas/knndbscan.

关键词： Density-based clustering nearest neighbor search minimum spanning tree distributed parallel computing performance analysis

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