Shape Optimization of Wind Turbine Blades Using the Continuous Adjoint Method and Volumetric NURBS on a GPU Cluster

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

doi:10.1007/978-3-319-89988-6_8

Cited by 9 publications

(14 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As mentioned in Vorspel et al (2017), this number depends on the optimization problem and optimizer settings, meaning that cross-setup comparison is difficult. c Tsiakas et al (2018) only gives the number of mesh nodes. d Reduced geometry where the root section was removed.…”

Section: High-fidelity Cfd-based Optimization Using the Adjoint Methodsmentioning

confidence: 99%

“…OpenFOAM with a continuous adjoint solver has also been used in 3-D. This was done by Vorspel et al (2016), who first performed a 2-D test case with two design variables. The 3-D test case consisted of an extruded airfoil with a spanwise length of five chords and a mesh of 2.4 × 10 6 cells with an y + of 2.5.…”

Section: High-fidelity Cfd-based Optimization Using the Adjoint Methodsmentioning

confidence: 99%

“…The above work does not model the rotation, which is important to get the correct local angle of attack along the blade and thus accurately compute the forces acting on the blade. Several 3-D adjoint-based optimization efforts model rotation effects, three of which studied the NREL Phase VI rotor Dhert et al, 2017;Vorspel et al, 2018), and another which studied the MEXICO rotor (Tsiakas et al, 2018). Economon et al 2013used a continuous adjoint formulation to perform single-point aerodynamic shape optimization using a compressible RANS model.…”

Section: High-fidelity Cfd-based Optimization Using the Adjoint Methodsmentioning

confidence: 99%

“…Finally, Tsiakas et al (2018) used a continuous adjoint approach that included the SA turbulence equations to optimize the MEXICO RWT. The flow was modeled by the incompressible RANS equations and solved in a co-moving frame of reference.…”

Section: High-fidelity Cfd-based Optimization Using the Adjoint Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Multipoint high-fidelity CFD-based aerodynamic shape optimization of a 10 MW wind turbine

Madsen¹,

Zahle²,

Sørensen³

et al. 2019

Wind Energ. Sci.

View full text Add to dashboard Cite

Abstract. The wind energy industry relies heavily on computational fluid dynamics (CFD) to analyze new turbine designs. To utilize CFD earlier in the design process, where lower-fidelity methods such as blade element momentum (BEM) are more common, requires the development of new tools. Tools that utilize numerical optimization are particularly valuable because they reduce the reliance on design by trial and error. We present the first comprehensive 3-D CFD adjoint-based shape optimization of a modern 10 MW offshore wind turbine. The optimization problem is aligned with a case study from International Energy Agency (IEA) Wind Task 37, making it possible to compare our findings with the BEM results from this case study and therefore allowing us to determine the value of design optimization based on high-fidelity models. The comparison shows that the overall design trends suggested by the two models do agree, and that it is particularly valuable to consult the high-fidelity model in areas such as root and tip where BEM is inaccurate. In addition, we compare two different CFD solvers to quantify the effect of modeling compressibility and to estimate the accuracy of the chosen grid resolution and order of convergence of the solver. Meshes up to 14×106 cells are used in the optimization whereby flow details are resolved. The present work shows that it is now possible to successfully optimize modern wind turbines aerodynamically under normal operating conditions using Reynolds-averaged Navier–Stokes (RANS) models. The key benefit of a 3-D RANS approach is that it is possible to optimize the blade planform and cross-sectional shape simultaneously, thus tailoring the shape to the actual 3-D flow over the rotor. This work does not address evaluation of extreme loads used for structural sizing, where BEM-based methods have proven very accurate, and therefore will likely remain the method of choice.

show abstract

Section: High-fidelity Cfd-based Optimization Using the Adjoint Methodsmentioning

confidence: 99%

Section: High-fidelity Cfd-based Optimization Using the Adjoint Methodsmentioning

confidence: 99%

Section: High-fidelity Cfd-based Optimization Using the Adjoint Methodsmentioning

confidence: 99%

Section: High-fidelity Cfd-based Optimization Using the Adjoint Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Multipoint high-fidelity CFD-based aerodynamic shape optimization of a 10 MW wind turbine

Madsen¹,

Zahle²,

Sørensen³

et al. 2019

Wind Energ. Sci.

View full text Add to dashboard Cite

show abstract

“…Esse poder computacional tem sido usado para programação de uso geral em muitasáreas da engenharia e da ciência. Na literatura, encontram-se trabalhos recentes relacionados com o uso de GPUs naárea da astronomia (Wang et al, 2015), dinâmica dos fluídos (Franco et al, 2015;Tsiakas et al, 2019), big data (Johnson et al, 2019), agricultura de precisão (Patrício and Rieder, 2018), imagens médicas (Lebedev et al, 2018), entre outros. Especificamente, ná area de simulações acústicas, alguns autores utilizaram GPUs para acelerar a solução da equação de onda (Mehra et al, 2012;Diaz et al, 2018;Wang et al, 2018).…”

Section: Introductionunclassified

Algoritmo em GPGPU para Acelerar a Determinação do Campo Acústico Produzido por Transdutor Ultrassônico Circular com Excitação Contínua

Duran

Sato

Silva

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

View full text Add to dashboard Cite

Este trabalho apresenta um algoritmo implementado em GPU, para calcular o campo acústico produzido por um transdutor ultrassônico com excitação contínua, emitindo em água. A pressão acústica em um ponto do espaço, na frente do transdutor, é calculada mediante a integral de Rayleigh, a qual utiliza o princípio de Huygens para considerar o campo de pressão como a soma da contribuição de um número infinito de fontes pontuais. Dado que a pressão em cada ponto do espaço pode ser calculada de forma independente, o algoritmo pode ser executado em paralelo, aproveitando a vantagem dos núcleos da GPU. Foi analisado o desempenho do algoritmo proposto realizando alguns testes na faixa de frequência de 0,25 a 5,0 MHz. A superfície de emissão foi discretizada com a finalidade de obter um determinado número de elementos finitos de área. Foi possível validar os campos acústicos simulados usando o valor teórico da pressão ao longo do eixo de simetria do transdutor. Adicionalmente, a análise de desempenho mostrou que a GPU foi 50 vezes mais rápida que a CPU, para os problemas mais demandantes.

show abstract

Adjoint variable‐based shape optimization with bounding surface constraints

Damigos

Papoutsis‐Kiachagias

Giannakoglou

2020

Numerical Methods in Fluids

Self Cite

View full text Add to dashboard Cite

Summary This article presents an algorithm for constraining shape deformations in adjoint‐based aerodynamic shape optimization. The algorithm considers known bounding surfaces and constrains the shape undergoing optimization not to intersect with them. For each and every node on the shape, its signed distance to the bounding surfaces is computed. The signed distance function returns a positive value, in case a node lies outside the bounds, or a negative one otherwise. It can, therefore, serve as an inequality constraint, the satisfaction of which ensures that this node does not violate the no‐intersection criteria. To increase the efficiency and make this imposition of constraints usable with any parameterization scheme, the resulting inequality constraints are transformed to a single‐equality constraint by means of a penalty function which is, then, summed over the shape to be optimized. The shape parameterization used in this article is spline‐based but this is not restrictive and any other parameterization type (node‐based, RBF, etc) can be used instead. The gradient of the objective function of the aerodynamic problem with respect to the design variables defined by the parameterization is computed using the continuous adjoint method. The method is demonstrated in the optimization of a 2D manifold case and two industrial‐like cases including the shape optimization of a U‐bend cooling duct, constrained to stay within a box, and the side mirror of a passenger car constrained to retain an almost undeformed reflector glass.

show abstract

Shape Optimization of Wind Turbine Blades Using the Continuous Adjoint Method and Volumetric NURBS on a GPU Cluster

Cited by 9 publications

References 8 publications

Multipoint high-fidelity CFD-based aerodynamic shape optimization of a 10 MW wind turbine

Multipoint high-fidelity CFD-based aerodynamic shape optimization of a 10 MW wind turbine

Algoritmo em GPGPU para Acelerar a Determinação do Campo Acústico Produzido por Transdutor Ultrassônico Circular com Excitação Contínua

Adjoint variable‐based shape optimization with bounding surface constraints

Contact Info

Product

Resources

About