当前位置:网站首页>How to get started with MOOSE platform - an example of how to run the official tutorial
How to get started with MOOSE platform - an example of how to run the official tutorial
2022-08-04 06:19:00 【nuomi666】
This article only introduces how to run the official examples given by the MOOSE platform (Examples and Tutorials | MOOSE), how to install the MOOSE platform can refer to the official tutorial (Install MOOSE | MOOSE), this article is based on the Ubuntu 20.04 virtual machine of the windows10 system.
To run these examples, you can use the app you built yourself, or you can compile the app in the official example folder. In order to run through the examples as soon as possible, you can directly compile the app in the example folder here.
1. Switch the moose environment
First open Ubuntu, type
conda activate moose
If you haven't built the moose environment, you can refer to (Conda MOOSE Environment |MOOSE),
At this point the beginning of the command line base will be changed to moose
2. Compile the app
Take Ee01 as an example, it is recommended that you put the original file inCopy once in the same directory, switch the working directory to the copied folder ~\projects\moose\examples\ex01_inputfile_copy, and then run make -j 4 to compile the app in this directory, where the number after j is how many threads to compileMeaning, -j4 is 4 threads.
cd ~/projects/moose/examples/ex01_inputfile_copymake -j 4
The process of compilation
The compilation process is slow, just wait patiently for completion.
3. Running example
After the app is compiled, we can use the generated appname-opt file to run the corresponding executable file (name.i). Here we use the app:ex01-opt just compiled in the ex01_inputfile_copy directory to run ex01.i, enter the command
./ex01-opt -i ./ex01.i
Wait patiently for the results, the model information to be solved
Framework Information:MOOSE Version: git commit cddfe1453b on 2021-12-14LibMesh Version:PETSc Version: 3.15.1SLEPc Version: 3.15.1Current Time: Tue May 10 13:34:47 2022Executable Timestamp: Tue May 10 13:32:14 2022Parallelism:Num Processors: 1Num Threads: 1Mesh:Parallel Type: replicatedMesh Dimension: 3Spatial Dimension: 3Nodes: 3774Elems: 2476Num Subdomains: 1Nonlinear System:Num DOFs: 3774Num Local DOFs: 3774Variables: "diffused"Finite Element Types: "LAGRANGE"Approximation Orders: "FIRST"Execution Information:Executioner: SteadySolver Mode: Preconditioned JFNK
The process of solving, residual output
4. Viewing results
In this example, the final output file is ex01_out.e, in Exodus II format, which can be used with Paraview (Download | ParaView) view, or use the Peacock that comes with the Moose platform (Peacock | MOOSE).
After using Paraview to open, the first step is to check the variable name to be viewed in the Properties tab at the bottom left of the default interface, and the second step is to click the Apply button.
The third step is to select the displayed variables at the top of the interface. The fourth step is to display the type of solution domain (surface, mesh, or node, etc.), and for transient models, you can also adjust the time step.
The final result display:
5. Remarks
During the initial compilation, an error code occurred
MAKEFILE:11:***MISSING SEPARATOR.STOP.
Open the script file Makefile for viewing later, and find that it is blank. It should be caused by an operation error that deleted the content in the previous use process.
Solution: Open the GitHub repository of MOOSE official website, find the link of the damaged file, and use the GitHub file downloader (GitHub File Acceleration), download the appropriate file, and then replace the corrupted file.
边栏推荐
- MAE 论文《Masked Autoencoders Are Scalable Vision Learners》
- 【CV-Learning】卷积神经网络预备知识
- 光条中心提取方法总结(一)
- 多层LSTM
- MNIST手写数字识别 —— 从零构建感知机实现二分类
- Deep Learning Theory - Initialization, Parameter Adjustment
- Qt日常学习
- tensorRT教程——tensor RT OP理解(实现自定义层,搭建网络)
- PyTorch
- The use of the attribute of the use of the animation and ButterKnife
猜你喜欢
深度学习理论 —— 初始化、参数调节
Vision Transformer 论文 + 详解( ViT )
【Copy攻城狮日志】“一分钟”跑通MindSpore的LeNet模型
双向LSTM
【CV-Learning】Image Classification
【CV-Learning】Object Detection & Instance Segmentation
target has libraries with conflicting names: libcrypto.a and libssl.a.
MAE 论文《Masked Autoencoders Are Scalable Vision Learners》
Use of double pointers
【CV-Learning】卷积神经网络
随机推荐
fill_between in Matplotlib; np.argsort() function
MNIST手写数字识别 —— Lenet-5首个商用级别卷积神经网络
Deep Learning Theory - Initialization, Parameter Adjustment
[Deep Learning Diary] Day 1: Hello world, Hello CNN MNIST
Dictionary feature extraction, text feature extraction.
MNIST手写数字识别 —— ResNet-经典卷积神经网络
MNIST手写数字识别 —— 从感知机到卷积神经网络
【Copy攻城狮日志】飞浆学院强化学习7日打卡营-学习笔记
The pipeline mechanism in sklearn
动手学深度学习_卷积神经网络CNN
Copy攻城狮5分钟在线体验 MindIR 格式模型生成
tensorRT教程——使用tensorRT OP 搭建自己的网络
Usage of Thread, Handler and IntentService
SQL注入详解
机器学习——分类问题对于文字标签的处理(特征工程)
TensorFlow2 study notes: 8. tf.keras implements linear regression, Income dataset: years of education and income dataset
Halcon缺陷检测
【Copy攻城狮日志】“一分钟”跑通MindSpore的LeNet模型
TensorFlow2 study notes: 4. The first neural network model, iris classification
postgres recursive query