co9olguy
commented
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Team Name:
Coherence
Project Description:
Quantum Neural Network-based correlation for frictional pressure drop of non-azeotropic mixtures during cryogenic forced boiling
This work proposes a Quantum neural network-based methodology to estimate frictional pressure drop during boiling in mini-channels of non-azeotropic mixtures including nitrogen, methane, ethane, and propane. The methodology can assist in thermal analysis or design of heat exchangers used in cryogenic applications. The proposed model architecture includes the local quality, roughness, mass flux, and Reynolds number as inputs and frictional pressure drop as outputs. It will compare with one paper where my colleagues and I use the same data to create an ANN-based correlation for pressure drop estimation in microchannels [1].
[1] Barroso-Maldonado, J. M., Montañez-Barrera, J. A., Belman-Flores, J. M., and Aceves, S. M. (2019). ANN-based correlation for frictional pressure drop of non-azeotropic mixtures during cryogenic forced boiling. Applied Thermal Engineering, 149(August 2018), 492-501. https://doi.org/10.1016/j.applthermaleng.2018.12.082
Presentation:
https://youtu.be/f57pmrbClYc https://github.com/alejomonbar/QNN-for-Thermodynamic-correlation/blob/main/QNN.ipynb
Source code:
https://github.com/alejomonbar/QNN-for-Thermodynamic-correlation