The purpose of this paper is to document an efficient and accurate approach to generate aerodynamic tables using computational fluid dynamics. This is demonstrated in the context of a concept transport aircraft model.
Two designs of experiment algorithms in combination with surrogate modelling are investigated. An adaptive algorithm is compared to an industry-standard algorithm used as a benchmark. Numerical experiments are obtained solving the Reynolds-averaged Navier–Stokes equations on a large computational grid.
This study demonstrates that a surrogate model built upon an adaptive design of experiments strategy achieves a higher prediction capability than that built upon a traditional strategy. This is quantified in terms of the sum of the squared error between the surrogate model predictions and the computational fluid dynamics results. The error metric is reduced by about one order of magnitude compared to the traditional approach.
This work lays the ground to obtain more realistic aerodynamic predictions earlier in the aircraft design process at manageable costs, improving the design solution and reducing risks. This may be equally applied in the analysis of other complex and non-linear engineering phenomena.
This work explores the potential benefits of an adaptive design of experiment algorithm within a prototype working environment, whereby the maximum number of experiments is limited and a large parameter space is investigated.
Andrea Da Ronch, Marco Panzeri, M. Anas Abd Bari, Roberto d’Ippolito and Matteo Franciolini (2017) "Adaptive design of experiments for efficient and accurate estimation of aerodynamic loads", Aircraft Engineering and Aerospace Technology, Vol. 89 No. 4, pp. 558-569Download as .RIS
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