COMPUTING DERIVATIVES IN NONLINEAR AEROELASTICITY USING ALGORITHM DIFFERENTIATION

Ruben Sanchez, Rafael Palacios

DOI Number: N/A

Conference number: IFASD-2017-149

This paper introduces a new method to calculate design sensitivities in high-ﬁdelity ﬂuid-structural interactions problems. As the intended application is on aeroelastic systems, characterized by many design variables and a small number of objective functions, gradients are computed from the fully-coupled adjoint equation. This is cast here as an iterated sequence for which convergence is guaranteed. The system is ﬁnally obtained using algorithm differentiation on the fully-coupled primal problem and its solution is then sought using a block Gauss-Seidel method, which preserves the partitioned structure of the primal coupled solver. This solution architecture has been implemented in the open-source SU2 software suite for the solution of industrial-scale aeroelastic optimization problems with viscous ﬂuids and structures with nonlinear geometric and material response.

Read the full paper here

adjoint sensititivities, algorithmic differentiation, ﬂuid-structure interaction, geometrically-nonlinear structures, multidisciplinary optimization, viscous ﬂows

In Categories: 1. IFASD 2017, 1.Events, Computational Aeroelasticity

The paper above was part of proceedings of a CEAS event and as such the author has signed a publication agreement to have their paper published in the repository. In the case this paper is found somewhere else CEAS always links to the other source. CEAS takes great care in making the correct content available to the reader. If any mistakes are found in the listings please contact us directly at papers@aerospacerepository.org and we will correct the listing promptly. CEAS cannot be held liable either for mistakes in editorial or technical aspects, nor for omissions, nor for the correctness of the content. In particular, CEAS does not guarantee completeness or correctness of information contained in external websites which can be accessed via links from CEAS’s websites. Despite accurate research on the content of such linked external websites, CEAS cannot be held liable for their content. Only the content providers of such external sites are liable for their content. Should you notice any mistake in technical or editorial aspects of the CEAS site, please do not hesitate to inform us.