Master Student Mathematics, Computer Science or Engineering (f/m/x)

The C²A²S²E department develops numerical methods for multidisciplinary simulation and optimization of air vehicles. Besides physical modeling of complex flows and the development of modern numerical algorithms, our research activities cover the software-based integration of all relevant disciplines, the development of efficient optimization strategies as well as surrogate modeling based on higher fidelity methods. The DLR hybrid computational fluid dynamics code TAU is developed by our department. TAU is routinely applied to a wide range of flows in industries, research institutes and academia. Currently, its successor, CODA, is designed and developed at our department in cooperation with Airbus and ONERA.

The offered master thesis focuses on numerical flow simulations (using computational fluid dynamics, CFD) and data-driven turbulence modeling for flows with significant effects of streamline curvature. Examples are vortical flows (wing tip vortices, delta-wing based aircrafts) and the flow around aircraft wings with deployed slat and flap in high-lift configuration. The first part of the work is to design and to provide a set of numerical test cases with a parametrization of the effects of curvature. This includes mesh generation using the software-tool Pointwise and the computation of numerical flow solutions for different RANS turbulence models using the DLR TAU code. The second part is a combined numerical-theoretical investigation of the numerical results and to assess/improve a new approximative model for the so-called Richardson number to characterize the effects of curvature on the turbulence. The third step is to use the improved Richardson number for feature detection using machine learning methods.

  • studies in Mathematics, Computer Science or Engineering (Aerospace engineering, mechanical engineering, or equivalent)
  • knowledge in Numerical Methods and/or CFD
  • good English skills
  • knowledge in Turbulence Modeling is a plus
  • ideally knowledge/Interest in Programming (Python)
  • knowledge in Machine Learning is preferred

Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalleled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (f/m/x).Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.

DLR - Helmholtz / Deutsches Zentrum für Luft- und Raumfahrt

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Deadline: 2023-04-10
Location: Germany, Göttingen
Categories: Aerospace Engineering, Computer Engineering, Computer Sciences, Machine Learning, Mathematics, Mechanical Engineering, Programming, Student Assistants,


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