Adaptive design of experiments for efficient and accurate estimation of aerodynamic loads

Abstract

Aerodynamic design, which aims at developing the outer shape of the aircraft while meeting several contrasting requirements, demands an accurate and reliable aerodynamic database. Computing forces and moments with the highest level of ?fidelity is a prerequisite, but practically limited by wall clock time and available computing resources. An e?fficient and robust approach is therefore sought after. This study investigates two design of experiments algorithms in combination with surrogate modelling. In traditional design of experiments, the samples are selected a priori before running the numerical explorative campaign. It is well-?known that this may result in either poor prediction capabilities or high computational costs. The second strategy employs an adaptive design of experiments algorithm. As opposed to the former, this is a self?-learning technique that iteratively: i) identi?fies the regions of the design space that are characterised by stronger non?linearities; and ii) select the new samples in order to maximise the information content
associated with the simulations to be performed during the next iteration. In this work, the Reynolds?-averaged Navier-?Stokes equations are solved around a complete aircraft confi?guration. A representative ?flight envelope is created taking the angle of attack and Mach number as design parameters. The adaptive strategy is found to perform better than the traditional counterpart. This is quantifi?ed in terms of the sum of the squared error between the surrogate model predictions and CFD results. For the pitch moment coe?fficient, which shows strong non?linearities, the error metric using the adaptive strategy is reduced by about one order of magnitude compared to the traditional approach. Furthermore, the proposed adaptive methodology, which is employed on a high performance computing facility, requires no extra costs or complications than a traditional methodology

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Identifiers

ORCID for Andrea Da Ronch: orcid.org/0000-0001-7428-6935

ORCID for Muhammad Abd Bari: orcid.org/0000-0003-2660-9124

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