Hi guys, Welcome to the Advanced Video Search Engine, and you are watching Prediction of Drag for Rough Wall Boundary Layer Flows: Karen Flack. That video is uploaded by Cambridge University Press at 2022-01-24T05:54:46-08:00. We are promoting this video only for entertainment and educational purpose only. So, I hope you like & enjoy it. [jRrcIjJFZ0k]
| Name | Prediction of Drag for Rough Wall Boundary Layer Flows: Karen Flack |
| Video Uploader | Video From Cambridge University Press |
| Upload Date | This Video Uploaded At 24-01-2022 13:54:46 |
| Video Discription | The Leeds Institute for Fluid Dynamics is delighted to partner with the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge, the UK Fluids Network, and the Journal of Fluid Mechanics to deliver a regular webinar series on fluids-related topics on the first Friday of each month: https://www.cambridge.org/fmws Speaker: Karen Flack, United States Naval Academy, USA Significant progress has been made towards the understanding of rough-wall boundary layers and the subsequent drag penalty. Continued progress is promising since a larger range of parameter space can now be investigated experimentally and numerically. Recent advances in rapid prototyping techniques enables the generation of systematic variations of roughness scales and computationally efficient simulations with creative surface mapping techniques allows for experiments and computations to investigate similar complex roughness. While a universal drag prediction correlation is still elusive and may not be possible, predictive correlations for classes of surface roughness pertinent to engineering applications seem achievable. Three surface parameters based solely on surface statistics are showing promise in predictive correlations for a range of studies. These include a measure of surface elevation a slope parameter and the skewness of the surface elevation probability density function. Other candidate parameters that may be useful in a predictive correlation or a surface filter are the streamwise and spanwise correlation lengths. The challenges to represent this wide range of surface conditions and potential scales to characterize engineering roughness including biofouling in predictive correlations will be discussed. jRrcIjJFZ0k |
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