Get the latest weekly fluid news direct to your inbox.

Sign up for our free newsletter now.
logo
menu

Groundbreaking insights into accurately simulating turbulent, multiphase flows

Using the HLRS Hazel Hen machine, RWTH Aachen University researchers were able to run a DNS simulation on a system of 45,000 particles at the Kolmogorov scale. To the team's knowledge, this is the direct-particle simulation for the largest number of particles at this scale to date, and serves as a benchmark for how other researchers studying this process can get more realistic simulation results. Image courtesy of L. Schneiders, M. Meinke, and W. Schröder. RWTH Aachen University, AIA
Using the HLRS Hazel Hen machine, RWTH Aachen University researchers were able to run a DNS simulation on a system of 45,000 particles at the Kolmogorov scale. To the team's knowledge, this is the direct-particle simulation for the largest number of particles at this scale to date, and serves as a benchmark for how other researchers studying this process can get more realistic simulation results. Image courtesy of L. Schneiders, M. Meinke, and W. Schröder. RWTH Aachen University, AIA

A team of scientists from RWTH Aachen University have reached a new landmark in modelling turbulence, something which could have a significant impact on a host of fluid handling applications.

Although most people would associate turbulence with air travel, it is in fact a far more complicated concept. The researchers from RWTH Aachen University’s Institute of Aerodynamics (AIA) have been using the Cray XC40 Hazel Hen supercomputer at the High-Performance Computing Centre in Stuttgart to study turbulent multiphase flows, that is, the movement of two materials in different states (for example a liquid and a gas), or materials in the same state that for chemical reasons mustn’t be mixed, for instance oil and gas.

At present, models are used to measure particle motion in a flow as the computational cost of more realistic simulations would be too high. The RWTH Aachen team wants to improve computational methods, accounting for the small interactions that have a big impact on turbulent flow and providing more accurate simulations than currently possible with the current models.

"We wanted to figure out a more detailed method that is necessary for us to understand these particle-laden flows when the particles are extremely small," said Prof. Dr. Wolfgang Schröder, AIA Director and collaborator on the team's project. "These particles actually define the efficiency of the overall combustion process, and that is our overall objective because, from an engineering perspective, we want to make the models that describe these types of processes more accurate."

The team’s research focuses on coal power plants. They have used Hazel Hen to run a DNS simulation on a system of 45,000 particles. As far they know, it is the largest simulation of particles to this scale to date, and could serve as a benchmark for other researchers to get more accurate simulations using the same process.

Now, according to a press release from the Gauss Centre for Supercomputing, the researchers are working on methods to integrate the data they received from the DNS simulations into simpler, less computationally intensive methods so they can be performed without the use of supercomputers. As well as aiding further research it could also be hugely beneficial to industry, not just for coal power stations, but any fluid handling applications that encounter turbulent, multiphase flows.

The team has recently published a study in the Journal of Fluid Mechanics detailing its roadmap towards better modelling of multiphase flows. 

Using the HLRS Hazel Hen machine, RWTH Aachen University researchers were able to run a DNS simulation on a system of 45,000 particles at the Kolmogorov scale. To the team's knowledge, this is the direct-particle simulation for the largest number of particles at this scale to date, and serves as a benchmark for how other researchers studying this process can get more realistic simulation results. Image courtesy of L. Schneiders, M. Meinke, and W. Schröder. RWTH Aachen University, AIA