اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدFluidSim: modular, object-oriented Python package for high-performance CFD simulations
83
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
The Python package fluidsim is introduced in this article as an extensible framework for Computational Fluid Mechanics (CFD) solvers. It is developed as a part of FluidDyn project (Augier et al., 2018), an effort to promote open-source and open-science collaboration within fluid mechanics community and intended for both educational as well as research purposes. Solvers in fluidsim are scalable, High-Performance Computing (HPC) codes which are powered under the hood by the rich, scientific Python ecosystem and the Application Programming Interfaces (API) provided by fluiddyn and fluidfft packages (Mohanan et al., 2018). The present article describes the design aspects of fluidsim, viz. use of Python as the main language; focus on the ease of use, reuse and maintenance of the code without compromising performance. The implementation details including optimization methods, modular organization of features and object-oriented approach of using classes to implement solvers are also briefly explained. Currently, fluidsim includes solvers for a variety of physical problems using different numerical methods (including finite-difference methods). However, this metapaper shall dwell only on the implementation and performance of its pseudo-spectral solvers, in particular the two- and three-dimensional Navier-Stokes solvers. We investigate the performance and scalability of fluidsim in a state of the art HPC cluster. Three similar pseudo-spectral CFD codes based on Python (Dedalus, SpectralDNS) and Fortran (NS3D) are presented and qualitatively and quantitatively compared to fluidsim. The source code is hosted at Bitbucket as a Mercurial repository bitbucket.org/fluiddyn/fluidsim and the documentation generated using Sphinx can be read online at fluidsim.readthedocs.io.
قيم البحث
اقرأ أيضاً
Huge data advent in high-performance computing (HPC) applications such as fluid flow simulations usually hinders the interactive processing and exploration of simulation results. Such an interactive data exploration not only allows scientiest to play
with their data but also to visualise huge (distributed) data sets in both an efficient and easy way. Therefore, we propose an HPC data exploration service based on a sliding window concept, that enables researches to access remote data (available on a supercomputer or cluster) during simulation runtime without exceeding any bandwidth limitations between the HPC back-end and the user front-end.
textsc{Pykat} is a Python package which extends the popular optical interferometer modelling software textsc{Finesse}. It provides a more modern and efficient user interface for conducting complex numerical simulations, as well as enabling the use of
Pythons extensive scientific software ecosystem. In this paper we highlight the relationship between textsc{Pykat} and textsc{Finesse}, how it is used, and provide an illustrative example of how it has helped to better understand the characteristics of the current generation of gravitational wave interferometers.
An open-source, Python-based Temporal Analysis of Products (TAP) reactor simulation and processing program is introduced. TAPsolver utilizes algorithmic differentiation for the calculation of highly accurate derivatives, which are used to perform sen
sitivity analyses and PDE-constrained optimization. The tool supports constraints to ensure thermodynamic consistency, which can lead to more accurate parameters and assist in mechanism discrimination. The mathematical and structural details of TAPsolver are outlined, as well as validation of the forward and inverse problems against well-studied prototype problems. Benchmarks of the code are presented, and a case study for extracting thermodynamically-consistent kinetic parameters from experimental TAP measurements of CO oxidation on supported platinum particles is presented. TAPsolver will act as a foundation for future development and dissemination of TAP data processing techniques.
The usage of the high-level scripting language Python has enabled new mechanisms for data interrogation, discovery and visualization of scientific data. We present yt, an open source, community-developed astrophysical analysis and visualization toolk
it for data generated by high-performance computing (HPC) simulations of astrophysical phenomena. Through a separation of responsibilities in the underlying Python code, yt allows data generated by incompatible, and sometimes even directly competing, astrophysical simulation platforms to be analyzed in a consistent manner, focusing on physically relevant quantities rather than quantities native to astrophysical simulation codes. We present on its mechanisms for data access, capabilities for MPI-parallel analysis, and its implementation as an in situ analysis and visualization tool.
We present a High-Level Python-based Hardware Description Language (ARGG-HDL), It uses Python as its source language and converts it to standard VHDL. Compared to other approaches of building converters from a high-level programming language into a h
ardware description language, this new approach aims to maintain an object-oriented paradigm throughout the entire process. Instead of removing all the high-level features from Python to make it into an HDL, this approach goes the opposite way. It tries to show how certain features from a high-level language can be implemented in an HDL, providing the corresponding benefits of high-level programming for the user.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
