Development of a Universal Post-processing Tool for Computational Fluid Dynamics Datasets CFD Post-Processing Tool Development, Design and Optimization of a Lightweight and Portable CFD Post-Processing Tool Based on VTK and QT

Zihao Pan,Jiawei Chen,Mark David Butala

EITCE '23 Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering（2024）

Cited 0|Views4

Abstract

Visualization in scientific computing has greatly improved the speed and quality of scientific computation, achieved further modernization of scientific computation tools and environments, and brought fundamental changes to scientific research work. We have developed a general post-processing tool for computational fluid dynamics (CFD) based on VTK and QT, which is used to visually represent the output files generated by CFD solvers. The tool utilizes the VTK graphics rendering library to develop a general CFD file reading and visualization interface for data visualization, and implements general CFD post-processing algorithms such as extracting contours, extracting iso-surfaces, generating streamline, etc. In addition, some auxiliary display functions have been added, such as displaying bounding boxes, adjusting color mapping tables, rotating perspective, etc. Besides, a simple but effective graphical user interface has been developed using QT.

Translated text

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper