Are you a student eager to apply your theoretical knowledge and fresh perspectives to real-world challenges? At Saab, we believe that innovation thrives on new ideas, and your master thesis project could be the spark that ignites our next technological breakthrough.
Saab is continuously improving its aerodynamic design capability and computational fluid dynamics (CFD) methods. The need for turbulence-resolving methods such as hybrid RANS-LES are increasing in the design process. We are now looking for you who will contribute to the development of new methodology by exploring statistical tools with the aim to improve accuracy and reliability in turbulence-resolving simulations.
We recognize the immense value that students bring to our company. Your academic rigor, combined with your enthusiasm for cutting-edge technology, allows you to approach problems with a unique and insightful lens. At Saab, you’ll have the opportunity to collaborate with experienced engineers and specialists, gaining invaluable practical experience while making a tangible contribution to our growth and development.
Background
CFD tools are an essential part in today’s aircraft aerodynamic design. Traditional steady-state Reynolds-Averaged Navier-Stokes (RANS) methods are heavily utilized as they are fast and give quite accurate results for integrated aerodynamic properties. For challenging flight conditions at the boundaries of the flight envelope the flow is often separated, and RANS methods are inadequate for resolving such phenomena. In these cases the flow must be resolved in time to capture turbulent fluctuations and accurately predict the mean field. Full wall-resolved Large-eddy simulations (LES) remain computationally too costly for high Reynolds numbers so hybrid RANS-LES methods, where RANS is active in the near-wall region, are utilized instead. In LES-based turbulence-resolving CFD simulations, the solution must progress from arbitrary initial conditions to a statistically stationary state where valid sampling can begin. Sampling is then carried out for a large set of timesteps until the mean and variance of flow variables have converged. Detection of initial transient period and setting correct convergence criteria remain one of the most important considerations of a turbulence-resolving simulation.
Description of the Thesis Project
Traditionally the start and end times for sampling statistical data in turbulence-resolving simulations have been decided by the individual simulation engineer. This can lead to engineer-to-engineer variations, inconsistent levels of statistical accuracy and unnecessarily use of computational resources. This master thesis aims to explore statistical algorithms to automatically identify initial transients and to monitor data signals to identify statistical convergence. The statistical models you develop will be tested and tuned on synthesized signals and signals from turbulence-resolving simulations of research flow cases. The long-term goal with the tool is to integrate it with Saab’s in-house flow solver and to support engineers in the use of turbulence-resolving simulations.