Organisation/Company Polytechnic University of Bari Research Field Engineering Aerospace engineering Engineering Mechanical engineering Engineering Simulation engineering Physics Applied physics Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 31 Dec 2026
- 23:59 (Europe/Rome) Country Italy Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Horizon Europe
- MSCA Is the Job related to staff position within a Research Infrastructure? No

Offer Description
About HyNOISE

HyNOISE is a Doctoral Network aimed to train doctoral candidates able to face current and future challenges in the prediction and control of direct and indirect noise generated by hydrogen flames. Four specific research objectives will be pursued:

- Unveil the dynamics and sound pressure level generated during unsteady ignition and flashback process
- Investigate the direct broadband sound generated due to the coupling between unsteady flame dynamics and flame-flame interactions, hydrodynamic and thermo
- diffusive instabilities, and turbulence.
- Understand physical mechanisms leading to the onset of thermoacoustic instabilities coupled with different modes at either with a specific focus on high frequencies
- Study the contribution of convective waves to combustion noise and thermoacoustic instabilities. Multiple hydrogen-air archetypes typical of air-breathing hydrogen combustion systems in both single and annular multi-injector configurations will be investigated. In a collaborative partnership between academic and industry partners who are major European gas turbine manufacturers,



HyNOISE bring relevant expertise together and to pursue solutions for hydrogen through integration and combination of unique and advanced theoretical, high-fidelity modelling and experimental expertise. This will lead to the development of new models and tools able to correctly predict hydrogen flame dynamics and its resulting pressure field, and, on this basis, developing control and mitigation methods to overcome current limitations in hydrogen combustion systems and design innovative, quiet, and stable combustion devices. The technologies developed will stimulate the green transition in the power generation and transport sectors, thus contributing to the achievement of the EU's challenging decarbonization goals.

About the host organization

University of Cambridge (UCAM) is one of the leading scientific research institutions worldwide and is regularly ranked in the top 5 universities. The Engineering Department is the highest-ranking department of its type in the UK. It is an integrated department, with several cross-disciplinary collaborations. The department has 180 members of staff, 700 post-doctoral researchers and a similar number of graduate students. A sizable fraction of the activity, involving 70 staff in the university, revolves around fluid mechanics, aeroacoustics and combustion, with a correspondingly large number of PhD students.

Individual Research Project (IRP)

The overall objective is to optimize the design of hydrogen-fuelled gas turbine injectors,



so that the combustors are quiet and not susceptible to thermoacoustic instabilities. This requires a quantitatively accurate model of the thermoacoustic system, which is the combination of a hydrogen flame with an acoustic network.

This model will be obtained by systematic assimilation of data 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.

Bayesian model selection (a component of AXIOM) will be used to find the optimal component models for this simple system. 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 (another component of AXIOM) 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 candidates mean field flame models with TUB and then to assimilate LES data into these models with AXIOM. This is an ambitious objective, but the risk it generates is mitigated by TUB's use of PINNs to achieve the same objective. 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 quantitatively accurate over time. They can therefore be used for optimization and re-optimization,

📌 PhD student – HyNOISE Doctoral Candidate (Roma)
🏢 Polytechnic University Of Bari
📍 Roma