DC13: Efficient optimization of airfoil trailing edge noise reduction through resolvent sensitivity

(MSCA-DN FairCFD, DC13)

We are seeking to recruit an outstanding Doctoral (PhD) Candidate to join the new MSCA Doctoral Network “FairCFD”. The doctoral research will be carried out at Technische Universität Berlin (Germany) under the supervision of Dr. Simon Demange. The position is funded for 36 months and includes secondments with international academic partners in France and Italy. For more details about the network’s philosophy and actions, see the FairCFD website.

Within the FairCFD Doctoral Network, you will benefit from a unique three-fold experience:

  • Contribute to innovation in renewable energy by developing advanced and efficient CFD strategies.
  • Participate in a network-wide interdisciplinary effort to define and promote sustainable numerical practices in scientific computing.
  • Join a vibrant European network of 15 doctoral candidates, with access to high-level training, cutting-edge events, and exposure to both academic and industrial environments.

Scientific background

Airfoil trailing-edge (TE) noise arises from the scattering of turbulent boundary-layer pressure fluctuations at the trailing edge. It is a dominant source of wind turbine noise in the audible range [1] and a major barrier to the expansion of onshore wind energy.

Simulating this phenomenon is highly challenging due to the wide range of scales involved, from turbulent flow structures to their acoustic emissions. As a result, direct numerical approaches become prohibitively expensive for realistic configurations.

Analyses based on the linearized Navier–Stokes equations provide a powerful alternative. By extracting and modelling the coherent flow structures that drive surface pressure fluctuations, they offer a physically interpretable and computationally efficient framework for predicting TE noise [2,3,4]. This approach further enables the systematic design and optimization of noise-reduction strategies for next-generation wind turbine blades [5].

Selected references:

  • Oerlemans, P. Sijtsma, and B. Méndez López, “Location and quantification of noise sources on a wind turbine”, Journal of Sound and Vibration 299, 869 (2007) 10.1016/j.jsv.2006.07.032
  • Demange, Z. Yuan, S. Jekosch, A. Hanifi, A. V. G. Cavalieri, E. Sarradj, T. L. Kaiser, K. Oberleithner “Resolvent model for aeroacoustics of trailing edge noise” (2024) Theor. Comp. Fluid Dyn., Vol 38; 10.1007/s00162-024-00688-z
  • Demange, K. Oberleithner, Z. Yuan, A. Hanifi, A. V. G. Cavalieri, “Flow Structures Driving Broadband Trailing-Edge Noise: A Resolvent-Based Model” (2026) AIAA Journal 64:3, 1414-1426; doi:10.2514/1.J065730
  • Yuan, S. Demange, K. Oberleithner , A. V. G. Cavalieri, A. Hanifi, “Coherent structures driving broadband trailing-edge noise: Spanwise wavenumber selection and low-order modeling” (2026) Phys. Rev. Fluids 11, 034606; doi:10.1103/kxbq-ynzd
  • S. Müller, J.M. Reumschussel, T.L. Kaiser, S.J. Knetchtel, K. Oberleithner “Combining Bayesian optimization with adjoint-based gradients for efficient control of flow instabilities” (2024) Proceedings of the 2024 CTR Summer Program.

Your research program

This project aims to develop a physics-based framework for the analysis and optimization of airfoil trailing-edge noise, combining resolvent analysis with data-efficient optimization strategies. Building on recent developments of the open-source FELiCS code at Technische Universität Berlin, the PhD will focus on identifying the coherent flow structures responsible for noise generation and on exploiting this understanding to design effective noise-reduction strategies.

The research will address the following key objectives:

  • Identification of noise-generating mechanisms: Characterize the dominant coherent structures in turbulent boundary layers that contribute to trailing-edge noise, using compressible resolvent analysis as a reduced-order modelling framework.
  • Modelling of turbulence effects in linear analysis: Extend the resolvent formulation to account for turbulence through appropriate linearized closures, and assess their impact on the prediction of flow–acoustic coupling and amplification mechanisms.
  • Sensitivity analysis and control-oriented modelling: Derive sensitivities of the resolvent response to mean-flow modifications and boundary perturbations, in order to uncover effective control mechanisms and guide design strategies.
  • Physics-informed optimization: Develop an optimization framework combining resolvent-based models with gradient-enhanced Bayesian strategies, enabling efficient exploration of design parameters for noise reduction.
  • Validation and application to realistic configurations: Apply the methodology to wind-turbine airfoils, and validate the predictions against high-fidelity simulations (LES) and/or experimental data, with the goal of quantifying achievable noise reductions.

Work environment

For the main part of your work, you will be hosted in Technische Universität Berlin. The Technische Universität Berlin is one of the largest technical universities in Germany, hosting more than 30.000 students, almost 20% being internationals. TUB also has a leading position in terms of gender aspects among German universities. The Institute of Fluid Dynamics and Technical Acoustics (ISTA) within the Department of Mechanical Engineering and Transport Systems performs teaching and research related to experimental and numerical fluid dynamics, acoustics and combustion. It currently employs about 100 members of staff. Besides fundamental research, development work is being performed for major companies in power generation as well as in transportation.

Integration within the FairCFD Network

Within the FairCFD Doctoral Network, you will contribute primarily to WP1 (Efficient physics-based numerical methods) and WP4 (Efficient optimization for complex problems). Your research will be closely connected to complementary activities across the network.

You will engage in regular scientific exchanges with other doctoral candidates working on related flow configurations or using alternative modelling approaches. This interaction is designed to foster a broader understanding of complex flow phenomena and to benchmark different methodologies across applications.

Two secondments (short research stays with partner institutions) are planned to strengthen specific aspects of the project:

  1. ONERA (France): implementation and assessment of turbulence models within the compressible linear stability and resolvent framework developed in FELiCS.
  2. University of Salerno (UNISA, Italy): derivation and implementation of resolvent-based sensitivity analysis for control and optimization.

Interdisciplinary task: co-designing numerical frugality 

Beyond your individual research program described above, you will contribute along with all other FairCFD doctoral candidates to a network-wide multidisciplinary effort (WP5) addressing the environmental and societal dimensions of numerical simulation.
Each DC will participate in the definition of practical metrics for numerical frugality (computational cost, energy use, resource impact) and contribute data from their simulations to a collective meta-analysis. This initiative will be supported by interdisciplinary experts and accompanied by a dedicated DC in social sciences, who will lead a qualitative study on evolving practices in simulation across the network. Together, we aim to make concrete, informed recommendations for sustainable scientific computing.

Network Training Program — More Than Just a PhD

As a Doctoral Network funded by Marie Sklodowska-Curie Actions (MSCA-DN), FairCFD will offer you rich and engaging training experience, including

  • Four one-week training events; (i) an induction week devoted to team-building, open-science practices and sustainability issues, (ii) an Essential Skills Accelerator event combining aiming to equip DCs with essential technical and transferable skills, (iii) a Hackathon event where DCs will collaborate in teams to solve complex physics problem and compare various simulation strategies in terms of precision and sobriety, and (iv) a Career and Leadership Development Forum Aiming to equip DCs with transferable skills essential for their future careers.
  • Five Online courses combining technical training with state-of-the art simulation methods ranging from physics-based approaches to data-driven ones, exposition to industrial applications, along with Social, ethical and environmental aspects of decision-making in modelling practices.
  • Involvement in the organization of scientific events, including a mini-symposium as part of a large-audience scientific conference, a scientific symposium allowing to share the output in terms of new methods, innovation, and applications to industrial processes, and a Societal colloquium to deliver the outputs of the multidisciplinary tasks of the network.

This program is designed to support your growth as a researcher, innovator, and engaged citizen, fully equipped to lead the next generation of responsible simulation science. See our website for more details.

Where to apply

The candidates should send a CV, cover letter, BSc and MSc degrees (certified copies plus translation in English) and two letters of recommendation. Copies of the reference publications can be sent upon request. Personal interviews might be asked.

All applications must be mailed here with subject: “[FairCFD] Application to DC13”.

This call will remain open until May 31st.

Note that applications within the FairCFD network are limited to 2 DC positions per candidate. If you want to apply to 2 positions please indicate this in your application and indicate your preference. Applications not respecting this rule will be discarded.

Requirements

Research Field

Engineering » Mechanical engineering

Education Level

Master Degree or equivalent

Skills/Qualifications

  • Master’s degree (or equivalent) in fluid mechanics, applied mathematics, scientific computing, or related fields.
  • Strong background in fluid mechanics, numerical methods, PDEs, and/or data-driven modeling.
  • Interest in interdisciplinary research and open science.

Additional Information

Benefits

The successful candidates will receive an attractive gross salary of 7.400€ per month in accordance with the MSCA regulations for Doctoral Researchers. The exact (net) salary will be confirmed upon appointment and is dependent on local tax regulations and on the country correction factor (to allow for the difference in cost of living in different EU Member States). The salary includes a living allowance, a mobility allowance, and a family allowance (if applicable). The guaranteed PhD funding is for 36 months (i.e., EC funding, additional funding is possible, depending on the local Supervisor, and in accordance with the regular PhD time in the country of origin).

Eligibility criteria

According to the international mobility rules of the MSCA-DN program, the candidates must not have spent more than 12 months in the hosting country (Germany), during the 36 months preceding the starting of the PhD. Apart from this rule, worldwide applications are expected and encouraged.

Selection process

The application process is officially open on April 1st 2026 and will remain active up to May 1st . Meanwhile, additional information can be obtained by contacting the supervisors along with the DN coordinating team. For this sake, please contact us by e-mail using this contact link, mentioning “[FairCFD] Application to DC13” in the subject of the e-mail.

Additional comments

A start date will be negotiated with the successful candidate. Ideally start dates would be between August 2026 and September 2026.