Adjoint Based Optimisation for Coupled Conjugate Heat Transfer.
Abstract
Conjugate Heat Transfer (CHT) problems are typically solved using a partitioned approach
where separate solvers for the fluid and structure are loosely coupled through boundary
conditions. These boundary conditions need to be updated iteratively until the temperature
and heat flux are continuous between the two domains. In this thesis, CHT problems
are solved by coupling the in-house flow solver with the open-source heat conduction solver,
CalculiX. Three coupling algorithms are used, which involve a combination of Neumann,
Dirichlet, and Robin boundary conditions.
The interest in shape optimisation has increased the need for efficient optimisation
techniques. Consequently, gradient based optimisation using adjoint methods are preferred
due to the reduced computational cost of obtaining gradients. Currently, the use of adjoint
methods in CHT shape optimisation problems mostly favours the continuous adjoint
method which suffers from high developmental costs. This thesis advocates for the use
of the discrete adjoint via Automatic Differentiation (AD) as a cost-effective alternative
to the continuous adjoint. This is done by differentiating the fluid and solid solvers with
respect to the coupling boundary conditions using AD. A fully differentiated partitioned
coupling approach is achieved by differentiating the three coupling algorithms used. The
differentiation of the Robin boundary conditions results in two new differentiated coupling
algorithms and the accuracy of the differentiated coupling algorithms is demonstrated by
comparing with gradients obtained through finite differences.
The efficacy of the developed methods is then demonstrated on three CHT optimisation
problems: An inverse design problem related to a flat plate and a thermal optimisation of
the MarkII turbine blade and an internal cooling channel U-Bend. The gradient verification
and optimisation results revealed that the use of Robin boundary conditions in the flow
solver reduces computational runtime.
Authors
Imam-Lawal, Oluwadamilare RahmanCollections
- Theses [4209]