Fig 3 - uploaded by Matthias Ehrhardt
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Comparison of Steklov-Poincaré gradient (upper left) with TRACE Mesh Smoothing gradient (upper right) on a FEniCS computational mesh of the T106A (lower center)

Comparison of Steklov-Poincaré gradient (upper left) with TRACE Mesh Smoothing gradient (upper right) on a FEniCS computational mesh of the T106A (lower center)

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This paper describes the project GivEn that develops a novel multicriteria optimization process for gas turbine blades and vanes using modern "adjoint" shape optimization algorithms. Given the many start and shutdown processes of gas power plants in volatile energy grids, besides optimizing gas turbine geometries for efficiency, the durability unde...

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... (1) with Lamé parameters λ ≡ 0 and constant µ > 0, and a TRACE gradient, which is generated by solving a linear elasticity mesh smoothing system with Dirichlet boundaries being the lattice sensitivities D ad J(Ω ext,k ), for the isentropic total pressure loss coefficient in relative frame of reference based on dynamic pressure is portrayed in Fig. 3. We can see additional gain of regularity in the gradient through Steklov-Poincaré representation, in particular the pronounced rise in sensitivity at the trailing edge is handled by redistributing sensitivities at the pressure side in a smooth manner, thus guaranteeing better stability of the mesh morphing ...

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This paper describes the project GivEn that develops a novel multicriteria optimization process for gas turbine blades and vanes using modern "adjoint" shape optimization algorithms. Given the many start and shut-down processes of gas power plants in volatile energy grids, besides optimizing gas turbine geometries for efficiency, the durability und...