Hydrodynamic shape optimization of propulsor configurations using continuous adjoint approach

Dreyer, James J.; Martinelli, Luigi

doi:10.2514/6.2001-2580

Cited by 12 publications

(8 citation statements)

References 7 publications

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“…Beddhu et al (1998), Gentaz et al (1999), and Yeung et al (2000) perform simulations for forced motions, and Sato et al (1999) and Rhee and Stern (2001) perform simulations for motions in regular head waves. With capability for more practical geometries and conditions, coupled with continuing improvements in HPC resources, the promise of CFD-based optimization will soon be realized (Tahara et al, 2000;and Dreyer, 2002).…”

Section: Introductionmentioning

confidence: 99%

General-Purpose Parallel Unsteady Rans Ship Hydrodynamics Code: CFDSHIP-Iowa

Paterson¹,

Wilson²,

Stern³

2003

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Section: Introductionmentioning

confidence: 99%

General-Purpose Parallel Unsteady Rans Ship Hydrodynamics Code: CFDSHIP-Iowa

Paterson¹,

Wilson²,

Stern³

2003

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“…This is reported to be a reasonable approximation. 8 For cases in which I, and C can be represented as boundary integrals, we have…”

Section: Formulations Of the Adjoint Equation Methodsmentioning

confidence: 99%

Aerodynamic Design of Turbine Blades Using an Adjoint Equation Method

Wu¹,

Liu²,

Tsai³

2005

43rd AIAA Aerospace Sciences Meeting and Exhibit

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f i inviscid flux f vi viscous flux G Gradient for optimization I Cost function K ij Transformation functions between the physical domain the computational domain, K ij = ∂xi ∂ξj m f mass flow rate M Resultant cost function n i Unit normal vector in the ξ domain, pointing outward from the flow field N i Unit normal vector in the physical domain, pointing outward from the flow field R Gas constant s Entropy per unit mass S gen Entropy generation rate w Conservative flow variables, w = {ρ, ρu 1 , ρu 2 , ρu 3 , ρE} T x i Coordinates in the physical domaiñ β Mass-averaged exit flow angle Λ Weight of the penalty function ξ i Coordinates in the computational domain Ψ Co-state variables, Ψ = {ψ 1 , ψ 2 , ψ 3 , ψ 4 , ψ 5 } T

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“…However, its variation between the two design cycles is assumed to be negligible . This is reported to be a fair simplification (Dreyer 2002;Walther and Nadarajah 2013). Assuming viscous effects on the inlet and outlet boundaries to be negligible and variation of the total viscosity coefficientto be zero, the following expression can be derived for the second term on the right-hand side of Equation (6):…”

Section: Adjoint Equationsmentioning

confidence: 98%

Entropy minimization in turbine cascade using continuous adjoint formulation

Zeinalpour

Mazaheri

2015

Engineering Optimization

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A complete continuous adjoint formulation is presented here for the optimization of the turbulent flow entropy generation rate through a turbine cascade. The adjoint method allows one to have many design variables, but still afford to compute the objective function gradient. The new adjoint system can be applied to different structured and unstructured grids as well as mixed subsonic and supersonic flows. For turbulent flow simulation, the k-ω shear-stress transport turbulence model and Roe's flux function are used. To ensure all possible shape models, a mesh-point method is used for design parameters, and an implicit smoothing function is implemented to avoid the generation of non-smoothed blades. To analyse the capability of the presented algorithm, the shape of a turbine cascade blade is redesigned and a few physical observations are made on how the scheme improves the blade performance.

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Hydrodynamic shape optimization of propulsor configurations using continuous adjoint approach

Cited by 12 publications

References 7 publications

General-Purpose Parallel Unsteady Rans Ship Hydrodynamics Code: CFDSHIP-Iowa

General-Purpose Parallel Unsteady Rans Ship Hydrodynamics Code: CFDSHIP-Iowa

Aerodynamic Design of Turbine Blades Using an Adjoint Equation Method

Entropy minimization in turbine cascade using continuous adjoint formulation

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