This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant  agreement No 860103.

ENODISE is an enabler project aimed at reducing aircraft gaseous and noise emissions by improving the integration of the propulsion system with the airframe. Complex aerodynamic and acoustic engine-airframe interactions are involved, which must be better understood to yield the expected gains. ENODISE will investigate the main propulsion-airframe integration issues at low TRL and build a solid basis of knowledge and methods based on simplified but representative configurations, permitting to assess a variety of integration concepts.

ENODISE will investigate the existence of local/global integration optima via an innovative experimental methodology combined with reduced order modelling and machine learning strategies. Selected configurations will be simulated using methods ranging from low-CPU to high-fidelity. The low-CPU techniques will be employed to verify if the experimentally observed optima can be obtained numerically, and the high-fidelity methods will contribute to the detailed investigation of the aeroacoustic mechanisms in addition to permitting a fine-tuning of the low-cost methods. The work being carried out on relatively low-cost generic configurations, this project will permit spanning a broad parameter space and testing optimization-based robust design methods.

Finally, if the interactions between the flow and acoustic field of the propulsion system with the airframe can be detrimental to aerodynamic performance or noise, they also offer opportunities to explore novel flow and acoustic control strategies, not yet explored in combination with installation effects. ENODISE will implement advanced materials and shape modifications to mitigate the adverse installation effects observed during the first phase of the project. The last objective of this project is thus the inclusion of innovative flow and acoustic control technologies in the optimization loop in order to derive better integration designs with minimal detrimental installation effects.