hydropower-fatigue-damage-reduction
Code for EPFL PTMH & SDSC's paired-hydro project
https://github.com/till-m/hydropower-fatigue-damage-reduction
Science Score: 44.0%
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Low similarity (11.9%) to scientific vocabulary
Repository
Code for EPFL PTMH & SDSC's paired-hydro project
Basic Info
- Host: GitHub
- Owner: till-m
- License: mit
- Language: Jupyter Notebook
- Default Branch: master
- Size: 37.8 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
hydropower-fatigue-damage-reduction
This repository contains code to model stress in hydraulic machines and trajectory optimization code developed during the EPFL PTMH & SDSC's paired-hydro project.
System Requirements/Environment
The provided Dockerfile specifies an environment to run this code in. Alternatively, you can launch a renku session by clicking the Start button on this projects renku page. Toy data is tracked using git LFS. Please run git lfs pull to ensure these files are present. Specific dependencies are otherwise specified in requirements.txt. The software has been tested using the associated Docker image.
Hardware
Due to the underlying deep learning model, this software runs faster if GPUs are present. The demo-script demo.ipynb is however designed to run solely CPU resources as well.
Installation
To install, clone the repository and install the dependencies, e.g. via pip by running pip install -r requirements.txt. This should not take more than several minutes.
Use and Demo
A demo notebook is present as demo.ipynb and shows the end-to-end process of training a model and running the optimization. On a normal machine with no GPU present, this notebook can take up to several hours to execute.
However, you can also execute standalone scripts main.py and optimize_dijkstra.py to train and optimize respectively.
Training the model
To train the model, ensure appropriate training data is present in the input directory (IN_FOLDER which can be set in CONFIG.py), then run main.py. Training the model should take a few hours on e.g. a NVIDIA P100 GPU. We have added some toy data to showcase the expected structure of the input files.
Dijkstra-based trajectory optimization
To run the optimization procedure, execute optimize_dijkstra.py. Using a NVIDIA P100 GPU, the optimization procedure can take a few days to complete on the original grid spacing. On a more coarse, grid, it can be significantly faster. An example of how to adjust the grid size is given in the optimization file.
Owner
- Login: till-m
- Kind: user
- Location: Zurich, CH
- Company: @SwissDataScienceCenter
- Repositories: 5
- Profile: https://github.com/till-m
Data Scientist @ SDSC / ETH Zurich
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Muser" given-names: "Till" - family-names: "Krymova" given-names: "Ekaterina" - family-names: "Morabito" given-names: "Alessandro" - family-names: "Seydoux" given-names: "Martin" - family-names: "Vagnoni" given-names: "Elena" orcid: https://orcid.org/0000-0002-9856-4554 title: "Fatigue Damage Reduction in Hydropower Startups with Machine Learning" doi: 10.5281/zenodo.14628160 version: v1.0.0 date-released: 2025-01-10 url: "https://github.com/till-m/hydropower-fatigue-damage-reduction"
GitHub Events
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- Release event: 1
- Watch event: 1
- Push event: 2
- Create event: 3
Last Year
- Release event: 1
- Watch event: 1
- Push event: 2
- Create event: 3
Issues and Pull Requests
Last synced: 7 months ago
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- Total pull requests: 0
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