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Aeroelastic Gust Modelling

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Smooth flying in turbulent air

Design of lighter aircraft promises to shrink aviation's carbon footprint, yet they need to be strong enough to cope with wind gusts. EU-funded researchers have developed virtual models with unprecedented accuracy to predict those loads quickly, allowing radical new aircraft designs to be conceptualised.

Transport and Mobility

Climate change is making turbulence more common in air travel. Even weak turbulence causes discomfort and injuries to flight attendants, but there is little pilots can do to make travel less bumpy: clear-air turbulence is neither visible to the naked eye nor can be detected on radars. Lighter aircraft will be more fuel efficient and emit less harmful carbon emissions, but can they also provide a smoother ride? To address this, designers need better tools to predict dynamic loads and optimise their design. Researchers initiated the EU-funded project AEROGUST to thoroughly investigate how gusts interact with aircraft. Improved understanding of gust encounters will lead to more lightweight designs, potentially reducing overconservative safety margins. Reduced need for wind tunnel testing “Developing new methods for encountering atmospheric turbulence is vital to the design and certification of aircraft and wind turbines,” notes Dr Ann Gaitonde, joint scientific coordinator of AEROGUST. In both cases, short wind blasts can create significant loads on the structures. To design safe structures, it is important to know how strong those loads can be at their worst. This enables aircraft or wind turbine makers to work out how tough the critical parts of the structures must be to cope with the maximum stresses they may experience during flight or operation respectively. “Currently, most of the experimental data concerning gusts is gathered during expensive wind tunnel tests quite late in the design process, when design options have already been narrowed down,” adds Dr Gaitonde. This makes it difficult to rapidly assess adaptations in the design of aerodynamic surfaces. Furthermore, there are few wind tunnels worldwide that can accurately reproduce the conditions for a full-size aircraft in flight. More accurate aircraft model designs before testing will lower the need for redesigning new models after the wind tunnel tests. This not only saves money and time, but also allows for novel design configurations to be explored, including the use of different, more flexible materials. Challenging current practices Computational fluid dynamics (CFD) enables high-performance computing for modern aerospace design. Using numerical analysis and data structures, it generates extremely accurate virtual models that offer unprecedented insight into what actually happens with airflow around the aircraft. However, CFD is too expensive to use for all the gust simulations needed. Until now, researchers have been modelling gust encounters with aircraft using simpler linear unsteady methods and steady experimental flow data. The AEROGUST team used unsteady CFD data to improve gust simulations and reduced-order models to decrease the computational cost of CFD based gust loads simulations. The inclusion of structural and aerodynamic non-linear effects in such models is an enabler for flexible and innovative structures. The enhanced simulation techniques developed in AEROGUST should help conceptualise novel designs and configurations, and create more eco-friendly aircraft. In addition, they should reduce the need for expensive wind tunnel testing and save time and costs at the design stage prior to building physical prototypes. This will ensure that the European aerospace industry remains competitive in the future. “If we can also predict the impact of gusts on wind turbines more accurately, the structures could be placed in more challenging regions such as the Arctic Circle and the tropics,” concludes Dr Dorian Jones, joint scientific coordinator of AEROGUST.


AEROGUST, design, gust, aircraft, wind tunnel, wind turbine, computational fluid dynamics (CFD), numerical simulation, reduced-order models, non-linear

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8 June 2020