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NGCM-0052: A data-mining approach to predicting surface roughness drag in turbulent flows

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  • Full or part time
    Dr A S Sharma
  • Application Deadline
    Applications accepted all year round

Project Description

In most practical applications, turbulent boundary layers will be in the rough-wall regime. The fluid must move over the roughness, and this increased effort results in higher energy consumption in these flows. Despite decades of sustained research, we are still unable to make a priori predictions of skin-friction based on the surface topology, which is exactly what is required to design energy efficient systems. Such predictions are also necessary for improved meteorological and climate models.

Existing predictive models use simple measure like the root-mean-square and skewness of the surface roughness height to predict rough-surface drag, but these methods are not applicable universally for a wide range of irregular surface patterns. Therefore, there is need to systematically identify other key parameters that are required for this prediction.

In this project, we aim to explore a new systematic approach to predicting skin-friction drag from surface roughness. We will create a comprehensive database of flows over rough surfaces using state-of-the-art direct numerical simulations. Such a comprehensive database does not currently exist.

This will allow us to use modern data-mining techniques to relate the surface properties to the overall drag. Firstly, clustering algorithms will reveal the different roughness regimes that exist.
Some roughness profiles that appear different when characterised by the many possible parameters, produce similar drag. In essence, only a small region of the high-dimensional parameter space is occupied by actual rough flows. Exploiting this, the project will also explore modern nonlinear dimensionality reduction techniques to determine an optimal low-dimensional representation of the high-dimensional roughness parameter space and its mapping to drag. The code produced, specifically suited to large turbulence simulation data sets, will be made freely available to the community to apply to similar problems.

This new capability will reveal the dominant roughness characteristics and their true functional relation to drag for the first time. Using simulation will allow the measurement of the flow field near the rough surface in great detail. This will allow the discovery of new relationships between surface drag, roughness characteristics and the flow field structure and the events that are responsible for momentum flux.

If you wish to discuss any details of the project informally, please contact Dr Ati Sharma, AFM research group, Email: [email protected]

This project is run through participation in the EPSRC Centre for Doctoral Training in Next Generation Computational Modelling ( For details of our 4 Year PhD programme, please see

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