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Article summary:
1. Near-wall modeling is a challenging aspect of computational fluid dynamic computations, and wall functions are usually used as a compromise between accuracy and speed.
2. This article presents an attempt to create an alternative data-driven wall function using artificial neural networks (ANNs).
3. The ANN has been implemented in a Python environment, using scikit-learn and tensorflow libraries, and the training examples have been collected from LES simulations or open databases.
Article analysis:
This article provides a detailed assessment of a machine-learnt adaptive wall-function in a compressor cascade with sinusoidal leading edge. The authors present an attempt to create an alternative data-driven wall function using artificial neural networks (ANNs), which have proven to be powerful in solving complex nonlinear problems. The ANN has been implemented in a Python environment, using scikit-learn and tensorflow libraries, and the training examples have been collected from LES simulations or open databases.
The article is well written and provides sufficient detail on the methodology used for the assessment of the machine-learnt adaptive wall-function. However, there are some potential biases that should be noted when assessing the trustworthiness and reliability of this article. Firstly, there is no discussion on possible risks associated with using ANNs for this purpose, such as overfitting or lack of generalizability due to limited datasets used for training. Secondly, there is no mention of any counterarguments or unexplored points of consideration that could challenge the findings presented in this article. Finally, it would be beneficial if more evidence was provided to support the claims made by the authors regarding the effectiveness of their proposed approach.
In conclusion, while this article provides useful insights into the assessment of a machine-learnt adaptive wall-function in a compressor cascade with sinusoidal leading edge, it should be read with caution due to potential biases mentioned above.