Droplets in Turbulence
Interface-resolved simulations of how turbulent eddies stretch, deform and break droplets.
Dr. Alex Morgan
Computational fluid dynamics researcher working on turbulence, multiphase flows and scientific machine learning.
Alex Morgan is a (fictional) research scientist studying turbulent and multiphase flows with high-fidelity simulation and data-driven reduced-order models. This is placeholder text for an academic portfolio template — replace it with your own biography.
News
All newsNew preprint on reduced-order models
Our preprint on operator-inference models for transitional boundary layers is now on arXiv.
Invited talk at the Placeholder Turbulence Symposium
I gave an invited talk on wall models for separated flows. Slides are on the talks page.
Grant awarded for wall-modelling research
Delighted to share that our group received a (fictional) three-year grant to continue our work on wall models.
Paper accepted in the Placeholder Journal of Fluid Mechanics
Our study on wall-modelled LES of separated channel flow has been accepted. Congratulations to the whole team!
Welcoming two new PhD students
Two new PhD students joined the group this autumn to work on multiphase flows and scientific machine learning.
Featured research
All projects
Wall Models for Separated Turbulent Flows
Making large-eddy simulation affordable for flows that separate, using pressure-gradient-aware wall models.
Physics-Informed Surrogates for Design
Neural-network surrogates that respect conservation laws, for fast aerodynamic design sweeps.
Selected publications
All publicationsWall-Modelled Large-Eddy Simulation of Separated Channel Flow
Placeholder Journal of Fluid Mechanics, vol. 982, pp. A12
Abstract
We present a wall-modelled large-eddy simulation framework for separated turbulent channel flow over periodic hills and show that a pressure-gradient-aware switch between equilibrium and non-equilibrium wall models improves reattachment prediction at modest cost. (Placeholder abstract.)
Droplet Breakup in Homogeneous Isotropic Turbulence at Moderate Weber Numbers
Example Journal of Multiphase Flow, vol. 161, no. 4, pp. 104455
Abstract
Interface-resolved simulations of droplets in forced turbulence are used to map breakup regimes across Weber and Reynolds numbers. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Physics-Informed Neural Surrogates for Rapid Airfoil Design Sweeps
Proceedings of the Placeholder Conference on Computational Engineering (PCCE), pp. 211-220
Abstract
We train physics-informed neural networks on sparse RANS data to predict pressure and skin-friction distributions across a family of airfoils, cutting the cost of design sweeps by two orders of magnitude. Sed do eiusmod tempor incididunt ut labore.