PhD position

Prediction of Boundary Layer Transition Using Machine Learning

Starting date

1. February 2020

Duration of contract

3 years


up to German TVöD 13

Type of employment


"Cutting-edge research requires excellent minds – particularly more females – at all levels. Launch your mission with us and send in your application now!" Prof. Pascale Ehrenfreund - Chair of the DLR Executive Board

Future aircraft will use laminar flow wings in order to reduce the fuel consumption and the ecological footprint of aviation. For the design of laminar wings, state-of-the-art CFD codes currently use empirical criteria, database methods, transport equation models or concepts based linear stability theory (LST) for transition prediction. The different methods vary significantly in the amount of physics modelled, and usually the consideration of more boundary layer physics leads to a significant increase in computational costs. For the design of aircraft, however, a physics-based transition model is required at low computational cost.

A large number of transition studies using LST theory have been performed at DLR in the last two decades. These studies cover a variety of different configurations at different flight Reynolds numbers, Mach numbers, angles of attack, etc. Due to this, a huge data base of laminar boundary-layer flow data with corresponding linear stability results and transition locations exists, already covering a wide range of flow parameters relevant for laminar-turbulent transition for future aircraft. These data can be used for the development and training of new transition prediction concepts based on machine learning (ML), either for predicting directly the transition location or for estimating the local boundary-layer instability characteristics as an alternative to the database methods.

This research project/PhD Position is envisaged to comprise the following steps:

  • selection of the most promising ML-based concepts;
  • preparation of suitable training data sets;
  • implementation and training of ML-based approaches for prediction of  transition location and estimation local stability characteristics for subsequent use in N-factor methods;
  • comparison of the different concepts;
  • assessment of potential and limitations of ML-based transition prediction

Your qualifications:

  • Master degree or equivalent in engineering (preferably aerospace engineering), physics or computer sciences
  • a strong background in fluid mechanics/aerodynamics
  • good command of English and German
  • Practical experience in CFD simulation, transition prediction, and the use of machine learning is a clear advantage

Your benefits:

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 unparalled 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 (m/f/non-binary). Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.

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Technical contact

Dr. Stefan Hein
Institute of Aerodynamics and Flow Technology

Phone: +49 551 709-2687

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Vacancy 45847

HR department Göttingen

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DLR site Göttingen

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DLR Institute of Aerodynamics and Flow Technology

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