fluid dynamics

Katerina Nik

Professor Nik studies partial differential equations that describe phenomena in biological growth processes, fluid dynamics, and mechanical engineering.

Particle Approximations For Partially Observed Fluid Dynamics Models

Professor Dan Crisan, Mathematics, Imperial College London

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KAUST

Fluid dynamics models are ubiquitous in a multitude of applications. One of the most important applications of fluid dynamics models is numerical weather prediction. Modern numerical weather prediction combines sophisticated nonlinear fluid dynamics models with increasingly accurate high-dimensional data. This process is called data assimilation and it is performed every day at all major operational weather centers across the world. Data assimilation (DA) requires massive computing capabilities as realistic atmosphere-ocean models typically have billions of degrees of freedom. I will give a short overview of the ongoing research that aims to drastically decrease the required DA computational effort by reducing the dimension of the models involved and using stochastic perturbations to account for the unresolved scales. The incorporation of observation data is done by using particle approximations suitably adapted to solve high-dimensional problems.

Cohomology in fluids

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B4 L5 R5209

We present a topological analysis of the vorticity formulation in describing fluid dynamics. Despite its widespread use in fluid mechanics, this formulation is insufficient at describing fluid dynamics on a non-simply-connected domain. What is missing is an equation of motion for fluid's cohomology component, which exhibits fascinating dynamics previously under explored. Using geometric language, we derive the new equation of motion and establish new conservation laws, as Casimir invariants in Hamiltonian mechanics, for fluids on domains with general topology. Significantly, we present the first physically correct vortex method on curved surfaces with genus and boundaries.

Better racing car design through an industry partnership

1 min read ·
The Formula 1 race track is the ultimate testbed for a KAUST researcher's latest work.

Harnessing the particle-tracking power of the algorithm

1 min read ·
A simple camera system paired with a sophisticated image-processing algorithm can achieve faster and more accurate reconstructions of particle flow.
KAUST-CEMSE-Matteo-Parsani

Matteo Parsani

Professor Parsani develops robust, variable order and self-adaptive algorithms for solving fluid flow problems in complex geometries. The application domains currently driving Parsani's research are compressible computational aerodynamics, computational aeroacoustics for noise reduction in vehicles and molecular communication.

Lisandro Dalcin

Lisandro Dalcin obtained a degree in Electromechanical Engineering from National Technological University, Concepción del Uruguay, Argentina in 2001 and a PhD degree in Engineering Sciences, Computational Mechanics from National University of the Littoral, Santa Fe, Argentina in 2008. Research Interest Lisandro Dalcin's research interest include ​​Parallel and High Performance Computing, Scientific Software Development, Finite Element Methods and Computational Fluid Dynamics.​ Education Profile ​​PhD in Engineering Sciences, Computational Mechanics, National University of the Littoral, Santa

Irving Enrique Reyna Nolasco

Research Interests Irving Enrique Reyna Nolasco's research interests include numerical methods for partial differential equations, large eddy simulations, high order entropy stable schemes, machine learning, complex systems, and non-linear dynamics. Why did you choose your field of research? As a graduate student, I have always found satisfaction in developing scientific software and libraries for the simulation and visualization of different phenomena. I enjoy applying scientific computing tools and mathematical concepts to relevant problems in physics. By the end of my bachelor studies, I

Diego Rojas Blanco

Diego Rojas is a Ph.D. candidate in Earth Science & Engineering. He is studying under the supervision of Professor Matteo Parsani. Diego works on the development of entropy stable algorithms for the simulation of turbulent flow. Algorithms of this kind have new and exciting properties related to the reliability and scalability of our simulations so that we can use the power of modern supercomputers to simulate fluid flow problems to higher resolution. Research Interest Diego Rojas research interest include Computational Fluid Dynamics (CFD), Scientific Visualization, and High Performance

Daniele Boffi

Professor Boffi is a leading international expert in mixed finite elements and the approximation of eigenvalue problems arising from partial differential equations.