- This tool allows one to develop correlations by training a special neural network, DimNet.
- The obtained correlations are explicit and algebraic, while inheriting the powerful predictive capability of neural networks.
- DimNet is a special neural network, primarily designed to generate explicit & algebraic correlations for complex thermal-hydraulic problems, but can also be applied to other modeling problems.
- DimNet is equivalent to a piecewise function of power-law-like equations.
- One can train a DimNet with experimental/simulation data and then convert the trained network to an explicit, power-law-like piecewise function.
DimNet is a feed-forward neural network that consists of an log-transformed input layer, two hidden layers (with activation functions of ReLU and Exp, respectively) and a linear output layer, as shown in the figure below.
- Construct a DimNet, and train it with your own dataset.
- Convert the trained DimNet to an explicit, algebraic, piecewise function.
Simply download the zip file and unzip to a folder where you can run Python (Make sure you've installed all the Dependencies!)
- The code is written in Python 3. Anaconda is the recommended Python platform since it installs all basic dependencies (numpy, pandas, joblib, etc.)
- Additional: PyTorch, scikit-learn
- The tutorials are run in Jupyter Notebook, which is included in Anaconda.
Please first follow the Tutorials to get familiar with the package (the instructions and considerations are provided in comments), then create your own jobs by modifying the provided examples
- Example_1D: friction factor of flow in smooth tubes
- Example_2D: friction factor of flow in rough tubes
This code is licensed under the Apache v2 license. Feel free to use all or portions for your research or related projects so long as you provide the following citation information:
Lin, L., Gao, L., Kedzierski, M., Hwang, Y. 2022. A general model for flow boiling heat transfer in microfin tubes based on a new neural network architecture. Energy and AI. 8: 100151. https://doi.org/10.1016/j.egyai.2022.100151
Feel free to contact me should you have any questions/comments: Lingnan Lin, Ph.D. Email: lingnan dot lin at nist dot gov