| Project Detail |
Bigger propulsion systems, less noise: a predictive model supports design
The larger aircraft propulsion systems that reduce CO2 emissions also decrease the space between airframe components, resulting in distortions of turbulent flow, airframe-propulsion system interaction vibration, and noise. Controlling these interactions could lead to new designs with radically reduced emissions. Funded by the European Research Council, the MORASINA project will return to basics to solve this problem. It will study interactions between rotating and stationary aerodynamic objects, elucidating the mechanisms of disrupted flow and turbulence and enabling the first mathematical formulation describing it. This will support the first holistic acoustic model predicting interaction noise. Expansion to a neural network approach will enable training and support design of next-generation aircraft.
The target of climate-neutral aviation has led to a strong increase in the size of new propulsion systems, resulting in their lowered distance to the airframe components. This causes new aerodynamic interactions with heavy distortion of the turbulent flow, determining unpredictable sources of noise. Mitigating this interaction noise would allow to deploy radically new aircraft configurations capable of reducing up to 20% of the current aviation emissions.
While studies from literature have tried to correct discrepancies larger than 10 dB from acoustic predictions by a-posteriori tuning the models to very specific flow patterns, recent results from my team have shed light on the physics behind the unpredictability of these noise sources. Results hinted that the geometrical deformation of the turbulent flow from its original pattern might explain the origin of interaction noise.
To solve this puzzle, with MORASINA I aim at first understanding how the flow and the turbulence are distorted in archetypal interactions between rotating and stationary aerodynamic objects. My objective is to discover the unknown mathematical formulation to model this distortion mechanism and to use it to create the first holistic acoustic model for predictions of interaction noise.
By innovatively describing the interaction mechanisms with mathematical functions related to the geometrical distortion of the flow, I will find an answer to whether different flow fields can be assimilated in a unique fundamental flow pattern. With this knowledge, I will create the first acoustic model based on a mathematical “flow twin” to accurately predict interaction noise.
For maximum impact on the society, I will extend the model to equipollent interaction mechanisms with a neural network approach trained on the results, allowing the use of the prediction framework for reducing interaction noise in the design of the next generation of zero-emission and silent aircraft. |
