Marie Sklodowska-Curie Early Stage Researchers - A systematic data assimilation model to predict thermoacoustic instabilities

University of Cambridge; Cambridge; On-site; GBP 18,000 - 23,000; Full time

Job summary

University of Cambridge invites applications for a Marie Curie ITN HyNOISE studentship in hydrogen-fuelled gas turbine injector research. The project aims to build quantitative thermoacoustic models by integrating experimental data, LES, and industrial test-rig data, with AXIOM data assimilation.

The successful candidate will develop mean-field flame models, apply Bayesian design, and contribute to optimizer tools for burner design.

Qualifications

• MSc or equivalent in aerospace engineering, mechanical engineering, physics or similar.
• Solid knowledge of Fluid Mechanics and Thermodynamics.
• Numerical analysis and CFD tools.
• Excellent English communication.

Skills

Fluid mechanics

Thermodynamics

Numerical analysis

CFD

English communication

Education

MSc or equivalent in aerospace engineering, mechanical engineering, physics or similar

Job description

This studentship is one of 15 across Europe in the Marie Curie ITN HyNOISE (https://cordis.europa.eu/project/id/101311721)The overall objective is to optimize the design of hydrogen-fuelled gas turbine injectors, so that combustors are quiet and not susceptible to thermoacoustic instabilities. This requires a quantitatively-accurate model of the thermoacoustic behaviour of the combustor. This model will be obtained by data assimilation from laboratory experiments (NTNU), large eddy simulations (TUB), and industrial experiments (RRD). This data will be assimilated into thermoacoustic models using UCAM's adjoint-accelerated inference and optimization method (AXIOM).The first intermediate objective is to extend AXIOM to typical hydrogen flame geometries using data from a laboratory scale combustor at NTNU with Bayesian model selection. The second intermediate objective is to extend AXIOM to industrial scale geometries using data from a real gas turbine fuel injector on an industrial test rig at RRD. Bayesian experimental design will be used to identify the experimental configurations that maximize the information that can be learned from a limited number of tests. The third intermediate objective is to develop and test several candidate mean field flame models with TUB and then to assimilate LES data into these models with AXIOM. The result will be a Bayesian framework that transfers data from experiments and numerical simulations into low order models. These models will steadily accumulate data and become more accurate over time. They can therefore be used for optimization and re-optimization, as more data become available in the future.Research Objectives-Assimilation of experimental data from a laboratory scale combustor into thermoacoustic models and flame models.-Assimilation of experimental data from a RRD industrial scale test rig into thermoacoustic models.-Development and testing of several candidate mean field flame models with TUB.-Assimilation of LES data into candidate mean field flame models and identification of optimal models.-Assimilation of experimental data from RRD industrial scale test rig into flame models.-Optimization of burner design to reduce susceptibility to thermoacoustic instabilityExpected Results-Detailed description of the acoustic field in premixed hydrogen combustion in canonical configurations.-Creation of a quantitatively accurate thermoacoustic model of the RRD industrial scale test rig containing an industrial burner, able to match experimental results from the RRD rig within quantified uncertainty bounds and able to extrapolate to different configurations within predicted uncertainty bounds.-Creation of a quantitatively accurate mean field flame model of a turbulent hydrogen flame, able to match the LES results on which it was trained to within quantified uncertainty bounds, and able to extrapolate to unseen LES data.-Optimal burner design derived from the quantitatively accurate models obtained from outcomes 1 & 2, and a numerical tool for optimizing the design at different conditions and combustor geometries.Secondments-Technical University of Berlin (Germany, ca. 2 months): application of developed model to improve mean fields reconstructions of non-measured quantities-ETH Zurich (Switzerland, ca. 2 months): to check capabilities of LOM to predict indirect noise.Home, EU, or OverseasAccording to the international mobility rules of the MSCA-DN program, the candidates must not have spent more than 12 months in the hosting country (UK), during the 36 months preceding the starting of the PhD.Applicants must have:-MSc or equivalent in the field of aerospace engineering, mechanical engineering, physics or similar-Solid knowledge of Fluid Mechanics and Thermodynamics, Computer Science and Informatics (Numerical Analysis; simulation, optimisation and modelling tools; Computational Fluid Dynamics (CFD))-Ability to work efficiently and self-reliantly in a diverse inter-disciplinary and multi-cultural environment-Ability to work in a team as well as independently-Ability to solve complex problems with adherence of strict deadlines-Proactive attitude-Excellent communication skills (both written and verbal) in English to derive the full benefit from the network training-As secondments and events are foreseen, applicants must be ready to travelTo apply for this studentship, please send your two page CV and transcripts (if available) to Matthew Juniper ([e-mail]) to arrive no later than 31 December 2026.Please note that any offer of funding will be conditional on securing a place as a PhD student. Candidates will need to apply separately for admission through the University's Graduate Admissions application portal; this can be done before or after applying for this funding opportunity. The applicant portal can be accessed via: www.graduate.study.cam.ac.uk/courses/directory/egegpdpeg. The final deadline for PhD applications is 13 May 2027, although it is advisable to apply earlier than this.The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.