Obtaining high-fidelity accurate data for turbulent flows using numerical simulations and laboratory experiments is challenging considering the cost and technical restrictions. Accurate prediction of turbulent flow is the recognised bottleneck to improved understanding and more efficient design in many areas of science and engineering. Specifically for the wall-bounded turbulent flows and turbulent boundary layers, the computational cost of scale-resolving approaches such as direct numerical simulation (DNS) and large eddy simulation (LES) grows significantly with the Reynolds number. Therefore, it is not cost-effective to apply these methods when performing different “outer-loop” problems such as uncertainty quantification (UQ), data-driven methods, Bayesian optimization, etc for various industrial/engineering applications, as several simulations are required corresponding to the samples from design parameters. A solution can be to use multifidelity models which aim at combining the data of different fidelities such that the overall computational cost of an outer-loop problem becomes affordable by relying more on the inexpensive simulations, such as Reynolds-averaged Navier-Stokes (RANS) simulations and reduced-order models, while retaining the accuracy of the predictions over the space of design parameters on par with the case of having many high-fidelity simulations.
The project is aimed at development and application of novel data-driven multifidelity models for various engineering applications which rely on the data for wall-bounded turbulent flows. The main approach is based on the Gaussian processes within a Bayesian formalism, and applications can be diverse, within disciplines such as aerodynamics, hydrodynamics, biomechanical and process engineering. The actual application will be decided based on the candidate’s interest, however some examples are flow control/drag reduction/shape optimization in aerodynamics, optimization of turbulent static mixers, and development of predictive models for bio-flows. For flow simulation, various approaches such as RANS, LES, hybrid RANS-LES, and DNS will be considered using both commercial and open-source software packages.
The student will be supervised by a team of academics with complementary expertise in disciplines like UQ and data science, turbulence modelling, and computation. The student will have the possibility of overseas placements and also collaboration with industrial partners.
Qualifications of the applicant:
The successful candidate is expected to be interested in numerical methods and computation, familiar with scientific programming, and self-driven to derive, implement and test different multifidelity models. The applicant is expected to hold or close to obtaining a Masters-level degree in one of these disciplines: engineering, scientific computing, computer science, applied mathematics and computational fluid dynamics (CFD). A good knowledge of Python (and/or Fortran/C++), turbulence and CFD is necessary. The University has a long-standing commitment to workforce diversity and we strongly encourage applications from women, minorities and all who contribute to that diversity.
Previous experience in at least one of the following is desirable:
● Turbulence modelling
● Object-oriented programming (OOP)
● Data science/machine learning/uncertainty quantification/Bayesian inference.
● Familiarity with libraries such as GPy, pyTorch, scikit-learn, PyMC3, etc.
How to apply:
The closing date for application is December 31, 2022, but the interviews will be performed continuously until the closing date. Before submitting a full application to the University of Manchester, the applicants are encouraged to send their CV, copies of their transcript, and a motivation letter (max 1 page) to Dr Rezaeiravesh and Prof Revell at email@example.com and firstname.lastname@example.org with ‘MFid-PhD’ in the subject line.
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.
We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).