[Full Picture] A Machine-Learnt Wall Function for Rotating Diffusers | J. Turbomach.

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A Machine-Learnt Wall Function for Rotating Diffusers | J. Turbomach. | ASME Digital Collection

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Summary Analysis Research

Article summary:

1. This article presents a machine-learning wall function for rotating diffusers, which is designed to improve the accuracy of turbomachinery flow modeling.

2. The wall function is based on the observation of canonical flows and expressed as a polynomial of Reynolds number and turbulent kinetic energy.

3. The authors aim to prove that standard wall treatments can benefit from machine-learning modeling, particularly in rotating passages or swirled flows where the mathematical formulations of wall functions do not account for the distortion on the boundary layer due to centrifugal and Coriolis forces.

Article analysis:

This article provides an interesting approach to improving the accuracy of turbomachinery flow modeling through machine-learning techniques. The authors present a machine-learnt wall function for rotating diffusers, which is based on observations of canonical flows and expressed as a polynomial of Reynolds number and turbulent kinetic energy. The authors also provide evidence that this approach can be beneficial in certain cases, such as rotating passages or swirled flows where traditional wall functions may not be sufficient.

The article appears to be well researched and reliable, with no obvious biases or unsupported claims. It provides a detailed explanation of the methodology used and cites relevant research papers throughout. However, it does not explore any potential risks associated with using this approach or consider any counterarguments that may exist against its use. Additionally, it does not discuss any potential limitations or drawbacks associated with using this method compared to traditional approaches. As such, it would have been beneficial if these points had been explored further in order to provide a more balanced view on the topic.

Topics for further research:

Potential risks of machine-learning techniques in turbomachinery flow modeling Limitations of machine-learnt wall functions Advantages of traditional wall functions Comparison of machine-learning and traditional approaches in turbomachinery flow modeling Counterarguments against using machine-learning techniques in turbomachinery flow modeling Impact of Reynolds number and turbulent kinetic energy on machine-learnt wall functions