Continuous adjoint‐based shape optimization of a turbomachinery stage using a 3D volumetric parameterization

Trompoukis, Xenofon; Τσιάκας, Κωνσταντίνος; Asouti, Varvara; Giannakoglou, Kyriakos C.

doi:10.1002/fld.5187

Cited by 11 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the design variables b i and setting the multipliers of δUn δb i to zero leads to a new set of PDEs, the so-called field adjoint equations (FAE), and the adjoint boundary conditions (ABC). In an inviscid flow, the FAE are written as, [10].…”

Section: The Think-discrete-do-continuous Adjointmentioning

confidence: 99%

On the Discretization of the Continuous Adjoint to the Euler Equations in Aerodynamic Shape Optimization

Kontou,

Trompoukis,

Asoutis

et al. 2023

XI International Conference on Adaptive Modeling and Simulation

View full text Add to dashboard Cite

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 adjoint 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 continuous adjoint PDEs and their boundary conditions, with a clear physical meaning. The capabilities of the new Think-Discrete-Do-Continuous adjoint are demonstrated, for inviscid flows of compressible fluids, in shape optimization in external aerodynamics.

show abstract

Section: The Think-discrete-do-continuous Adjointmentioning

confidence: 99%

On the Discretization of the Continuous Adjoint to the Euler Equations in Aerodynamic Shape Optimization

Kontou,

Trompoukis,

Asoutis

et al. 2023

XI International Conference on Adaptive Modeling and Simulation

View full text Add to dashboard Cite

show abstract

“…In this work, a free-form deformation technique based on volumetric NURBS, tailored to peripheral blade rows, is used, Trompoukis et al (2023). This not only controls the shape under consideration but also deforms the volume mesh.…”

Section: Shape Parameterization and Mesh Deformationmentioning

confidence: 99%

Unsteady CFD analysis and shape optimization of a hydraulic turbine

Asouti,

Trompoukis,

C. Giannakoglou

et al. 2023

European Conference on Turbomachinery Fluid Dynamics and Thermodynamics

View full text Add to dashboard Cite

This paper presents the CFD-based constrained shape optimization of the runner of a hydraulic turbine for minimum pressure pulsations between the guide vanes and the runner. The GPU-accelerated flow solver PUMA, which solves the (U)RANS equations coupled with the Spalart-Allmaras turbulence model and a free-form deformation tool based on volumetric NURBS are used. A control lattice encapsulates the runner's blade and the coordinates of (some of) its control points are the design variables; this results in 180 design variables. The optimization is carried out by means of an evolutionary algorithm assisted by surrogate evaluation models and the principal component analysis, in order to minimize the computational cost. During the one-and two-operating point optimizations, all CFD simulations rely on the mixing plane model; however, at the end, the so-optimized solution is re-evaluated using the sliding plane technique.

show abstract

“…To support shape optimization, PUMA offers a set of shape parameterization tools based on volumetric Non-Uniform Rational B-Splines (NURBS) [23,24]. These are also used to adapt the CFD volume mesh to new boundaries emerging during the optimization.…”

Section: Shape Parameterization Mesh Displacement and Geometric Uncer...mentioning

confidence: 99%

Radial Basis Function Surrogates for Uncertainty Quantification and Aerodynamic Shape Optimization under Uncertainties

Asouti,

Kontou,

Giannakoglou

2023

Fluids

View full text Add to dashboard Cite

This paper investigates the adequacy of radial basis function (RBF)-based models as surrogates in uncertainty quantification (UQ) and CFD shape optimization; for the latter, problems with and without uncertainties are considered. In UQ, these are used to support the Monte Carlo, as well as, the non-intrusive, Gauss Quadrature and regression-based polynomial chaos expansion methods. They are applied to the flow around an isolated airfoil and a wing to quantify uncertainties associated with the constants of the γ−R˜eθt transition model and the surface roughness (in the 3D case); it is demonstrated that the use of the RBF-based surrogates leads to an up to 50% reduction in computational cost, compared with the same UQ method that uses CFD computations. In shape optimization under uncertainties, solved by stochastic search methods, RBF-based surrogates are used to compute statistical moments of the objective function. In applications with geometric uncertainties which are modeled through the Karhunen–Loève technique, the use on an RBF-based surrogate reduces the turnaround time of an evolutionary algorithm by orders of magnitude. In this type of applications, RBF networks are also used to perform mesh displacement for the perturbed geometries.

show abstract

Continuous adjoint‐based shape optimization of a turbomachinery stage using a 3D volumetric parameterization

Cited by 11 publications

References 36 publications

On the Discretization of the Continuous Adjoint to the Euler Equations in Aerodynamic Shape Optimization

On the Discretization of the Continuous Adjoint to the Euler Equations in Aerodynamic Shape Optimization

Unsteady CFD analysis and shape optimization of a hydraulic turbine

Radial Basis Function Surrogates for Uncertainty Quantification and Aerodynamic Shape Optimization under Uncertainties

Contact Info

Product

Resources

About