iCASE Award Industrial Contribution (Airbus) Rich Simulation Driven Design Optimisation

Aerospace Technology Institute (ATI) collaborative research project “Next Wing” in collaboration with Airbus

The project aims to develop next generation wing structures for future passenger jets . The project is led by airbus and the QMUL team will develop novel wing topologies and advanced simulation and modelling approaches .

Aerospace Technology Institute (ATI) collaborative research project “Next Wing”

The development of a lightweight distributed aerospace transmission

Surface treatments for next generation quiet aerofoils

The project in collaboration with the University of Southampton and the University of Manchester is devoted to high-resolution modelling and experiments aimed to reduce aerofoil noise. Introducing ‘canopies’ into the turbulent boundary layer may produce significant reductions in the surface pressure variation near the trailing edge, and hence similar reductions in the far field noise.

Air Cleaning Technologies (ACT): design protocol

ACT is a multi-centred randomised control trial of two air disinfection technologies which have the potential to mitigate the airborne transmission of the Covid-19 virus within schools: Portable high efficiency particulate air (HEPA) filters Upper-room ultraviolet germicidal irradiation (ur-UVGI) 30 primary schools from across Bradford are trialling these technologies to assess both the feasibility and efficacy of using these in schools, see https://caer.org.uk/projects/air-cleaning-technologies-act/

PoLaRBEAR

SILOET II Programme

AMEDEO (Marie Curie ITN)

Multi-fidelity Acoustic Modelling

Uncertainty of the low-order jet noise modelling often comes from a certain number of assumptions made about the turbulent flow statistics. For example, such assumptions may involve modelling of the fourth-order velocity correlation functions based on the statistical quantities obtainable from RANS such as turbulent kinetic energy, energy dissipation rate and the jet meanflow velocity and its gradients. Since the experimental flow field measurements available are typically limited, inevitably, the low-order models have to rely on the far-field data for defining the rest of the calibration coefficients. Typically, the model calibration is performed by the model developer, who then has to work a ‘human optimiser’ in the sense of selecting the model parameters which correspond to the best fit to the experimental data available. As the low-order model becomes more complex to incorporate more physics, such as variable correlation amplitudes and space scales depending on the source directivity as well as cusp points of the correlation curves and negative correlation loops observed experimentally, more coefficients are required. In turn, this makes the whole process of model development very complicated and can also lead to sub-optimal solutions. This is where the automatic optimisation algorithms can help the developers by not only freeing up their time but also providing the model coefficients which are optimal over a given set of jet noise data. To guide the process of low-order models development, the high fidelity models, such as those based on Large Eddy Simulations, can be used to more accurately constrain the rage of variation of the calibration parameters. Such work which combines the high- and low-fidelity for aeroacoustics of high-speed jet flows is proposed as a new research project in our group to be considered either for EC or Newton Fellowship funding application. Interested candidates at postdoctoral level are encouraged to contact us directly.