English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Computationally efficient and accurate simulations of the flow overaxisymmetric bodies of revolution (ABR) has been an important desideratum forengineering design. In this article the flow field over an ABR is predictedusing machine learning (ML) algorithms, using trained ML models as surrogatesfor classical computational fluid dynamics (CFD) approaches. The flow field isapproximated as functions of x and y coordinates of locations in the flow fieldand the velocity at the inlet of the computational domain. The data requiredfor the development of the ML models were obtained from high fidelity Reynoldsstress transport model (RSTM) based simulations. The optimal hyper-parametersof the trained ML models are determined using validation. The trained ML modelscan predict the flow field rapidly and exhibits orders of magnitude speed upover conventional CFD approaches. The predicted results of pressure, velocityand turbulence kinetic energy are compared with the baseline CFD data, it isfound that the ML based surrogate model predictions are as accurate as CFDresults. This investigation offers a framework for fast and accuratepredictions for a flow scenario that is critically important in engineeringdesign.

Data-driven prediction of complex flow field over an axisymmetric body of revolution using Machine Learning