WeChat Mini Program
Old Version Features

SW2D-Lemon: A New Software for Upscaled Shallow Water Modeling

Advances in Hydroinformatics(2022)

Univ Montpellier

Cited 1|Views7
Abstract
We present a new multi-OS platform named SW2D-LEMON (SW2D for Shallow Water 2D) developed by the LEMON research team in Montpellier. SW2D-LEMON is a multi-model software focusing on shallow water-based models. It includes an unprecedented collection of upscaled (porosity) models used for shallow water equations and transport-reaction processes. Porosity models are obtained by averaging the two-dimensional shallow water equations over large areas containing both a water and a solid phase. The size of a computational cell can be increased by a factor 10–50 compared to a 2D shallow water model, with CPU times reduced by 2–3 orders of magnitude. Applications include urban flood simulations as well as flows over complex topography. Besides the standard shallow water equations (the default model), several porosity models are included in the platform: (i) Single Porosity, (ii) Dual Integral Porosity, and others are currently under development such as (iii) Depth-dependent Porosity model. Various flow processes (friction, head losses, wind, momentum diffusion, precipitation/infiltration) can be included in a modular way by activating specific execution flags. We recall here the governing equations as well as numerical aspects and present the software features. Several examples are presented to illustrate the potential of SW2D.
More
Translated text
Key words
Shallow water equations, Urban floods, Software, Upscaling, Finite volumes
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
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文介绍了一种新的多操作系统平台SW2D-LEMON,该平台专注于浅水模型的多模型软件,包含一系列创新的孔隙率模型,可显著提升计算效率并适用于城市洪水模拟和复杂地形流动。

方法】:SW2D-LEMON通过在较大区域对二维浅水方程进行平均来获取孔隙率模型,允许计算单元的大小增加10-50倍,从而减少CPU时间2-3个数量级。

实验】:研究展示了SW2D-LEMON在不同孔隙率模型(包括单孔隙率模型和双积分孔隙率模型等)的应用实例,并使用标准浅水方程作为默认模型,通过激活特定的执行标志来包含各种流动过程。具体的数据集名称和实验结果在文中未明确提及。