Ali Shirinzad, Khodr Jaber, Kecheng Xu, Pierre E. Sullivan
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引用次数: 0
摘要
粒子图像测速(PIV)是一种应用广泛的流量测量实验技术。近年来，开源PIV软件变得越来越流行，因为它为研究人员和从业者提供了增强的计算能力。图形处理单元(GPU)架构的软件开发需要仔细的算法设计和数据结构选择以获得最佳性能。PIV软件针对中央处理器(cpu)进行了优化，为专用GPU软件提供了另一种选择。本文提出了一种针对OpenPIV - python软件(Version 0.25.1, OpenPIV, Tel Aviv-Yafo, Israel)的改进算法，并在传统的CPU框架下实现。选择Python语言是因为它的多功能性和广泛的采用。在开发阶段，该算法还在超级计算集群、工作站和Google协作实验室上进行了测试。利用已知的速度场，该算法精确地捕获了时间平均流量、瞬时速度场和旋涡。
An Enhanced Python-Based Open-Source Particle Image Velocimetry Software for Use with Central Processing Units
Particle Image Velocimetry (PIV) is a widely used experimental technique for measuring flow. In recent years, open-source PIV software has become more popular as it offers researchers and practitioners enhanced computational capabilities. Software development for graphical processing unit (GPU) architectures requires careful algorithm design and data structure selection for optimal performance. PIV software, optimized for central processing units (CPUs), offer an alternative to specialized GPU software. In the present work, an improved algorithm for the OpenPIV–Python software (Version 0.25.1, OpenPIV, Tel Aviv-Yafo, Israel) is presented and implemented under a traditional CPU framework. The Python language was selected due to its versatility and widespread adoption. The algorithm was also tested on a supercomputing cluster, a workstation, and Google Colaboratory during the development phase. Using a known velocity field, the algorithm precisely captured the time-average flow, momentary velocity fields, and vortices.
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