COMPLAS 2021 is the 16th conference of the COMPLAS Series.
The COMPLAS conferences started in 1987 and since then have become established events in the field of computational plasticity and related topics. The first fifteen conferences in the COMPLAS series were all held in the city of Barcelona (Spain) and were very successful from the scientific, engineering and social points of view. We intend to make the 16th edition of the conferenceanother successful edition of the COMPLAS meetings.
The objectives of COMPLAS 2021 are to address both the theoretical bases for the solution of nonlinear solid mechanics problems, involving plasticity and other material nonlinearities, and the numerical algorithms necessary for efficient and robust computer implementation. COMPLAS 2021 aims to act as a forum for practitioners in the nonlinear structural mechanics field to discuss recent advances and identify future research directions.
Scope
COMPLAS 2021 is the 16th conference of the COMPLAS Series.
M. Kontou, X. Trompoukis, V. Asoutis, K. Giannakoglou
admos2023.
Abstract
In aerodynamic shape optimization, gradient-based algorithms usually rely on the adjoint method to compute gradients. Working with continuous adjoint offers a clear insight into the adjoint equations and their boundary conditions, but discretization schemes significantly affect the accuracy of gradients. On the other hand, discrete ad joint computes sensitivities consistent with the discretized flow equations, with a higher memory footprint though. This work bridges the gap between the two adjoint variants by proposing consistent discretization schemes (inspired by discrete adjoint) for the con tinuous adjoint PDEs and their boundary conditions, with a clear physical meaning. The capabilities of the new Think-Discrete-Do-Continuous adjoint are demonstrated, for in viscid flows of compressible fluids, in shape optimization in external aerodynamics.
Abstract In aerodynamic shape optimization, gradient-based algorithms usually rely on the adjoint method to compute gradients. Working with continuous adjoint offers a clear insight [...]