Comparison between aerodynamic designs obtained by human driven and automatic procedures

Puente, Ricardo; Corral, Roque; Parra, Jorge O.

doi:10.1016/j.ast.2017.11.011

Cited by 8 publications

(2 citation statements)

References 34 publications

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“…Unfortunately, due to the failure of Moore's law, it becomes difficult for the traditional central processing unit (CPU) to deal with more and more complex problems. Recently, the graphics processing unit (GPU) gradually catches the focuses of researchers for its powerful floating-point computing capacity [19][20][21]. In 2007, NVIDIA developed a general-purpose parallel computing platform and programming model, namely the Compute Unified Device Architecture (CUDA) [22], which dramatically simplified the GPU programming and popularized the GPU parallel computing to wider fields.…”

Section: Introductionmentioning

confidence: 99%

GPU acceleration of a 2D compressible Euler solver on CUDA-based block-structured Cartesian meshes

Feng

Liang

Liu

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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A novel block-structured Cartesian mesh method is developed that is well designed for the graphics processing unit (GPU) acceleration of flow simulations. The size of the mesh block is set based on the thread hierarchy in Compute Unified Device Architecture which is an easy-to-use GPU programming model. The mesh method is implemented in a finite-volume compressible flow solver, where the two-dimension steady Euler equations are solved by using the AUSM + scheme in spatial discretization and third-order total-variation-diminishing Runge-Kutta method in time discretization. For the GPU implementation, the redundant computation, data structure reorganization and Structure-of-Arrays (SoA) mesh layout are utilized to reduce the frequency and data size for information transfer between host and device. Two test cases, the supersonic flow over a circular cylinder and a NACA0012 airfoil, are presented to validate numerical approaches and valuate computational performance. Additionally, numerical experiments are carried out on a test case at different mesh sizes and execution configurations to investigate properties of the proposed method.

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Section: Introductionmentioning

confidence: 99%

GPU acceleration of a 2D compressible Euler solver on CUDA-based block-structured Cartesian meshes

Feng

Liang

Liu

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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“…To further increase the CFD as a design tool, we use an optimization algorithm to automate the design process. A recent study of turbomachinery aerodynamics demonstrated that the CFD-based design optimization approach reduced the duration of design process by 85%, while achieving a same quality compared with a sophisticated human-supervised design tool [2]. Given this advantage, CFD-based design optimization is becoming increasing popular.…”

Section: Introductionmentioning

confidence: 99%

Aerothermal Optimization of Internal Cooling Passages Using a Discrete Adjoint Method

Mader

Martins

et al. 2018

2018 Joint Thermophysics and Heat Transfer Conference

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Aerothermal optimization is a powerful technique for the design of internal cooling passages because it maximizes heat transfer and simultaneously minimizes pressure loss. Moreover, the optimization is fully automatic, which reduces the duration of design process compared with a human-supervised design approach. Existing optimization studies commonly rely on gradient-free methods, which can only handle a small number of design variables. However, cooling passage designs use complex geometry configurations (e.g., serpentine channels with rib-roughened surfaces) to enhance heat transfer; what is needed is to parameterize the passage using a large number of design variables. To address this need, we perform aerothermal optimization using a gradient-based optimization algorithm along with the discrete adjoint method to compute derivatives. The benefit of using the adjoint method is that its computational cost is independent of the number of design variables. In this paper, we focus on optimizing a U-bend duct, which is representative of a simplified, rib-free turbine internal cooling passage. The duct geometry is parameterized using 135 design variables, which gives us sufficient design freedom for geometric modification. We construct a Pareto front for heat transfer enhancement and total pressure loss reduction by running multi-objective optimizations. We also compare our optimization results with those from the gradient-free methods and demonstrate that we achieve better pressure loss reduction and heat transfer enhancement. The above results show that our gradient-based optimization framework functions as desired and has the potential to be a useful tool for turbine aerothermal designs with full internal cooling configurations.

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Aerodynamic Performance of Multi-stage Axial Compressor Considering Cold-to-Hot Deformation Effect

Kim

Son

2018

Int. J. Aeronaut. Space Sci.

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Comparison between aerodynamic designs obtained by human driven and automatic procedures

Cited by 8 publications

References 34 publications

GPU acceleration of a 2D compressible Euler solver on CUDA-based block-structured Cartesian meshes

GPU acceleration of a 2D compressible Euler solver on CUDA-based block-structured Cartesian meshes

Aerothermal Optimization of Internal Cooling Passages Using a Discrete Adjoint Method

Aerodynamic Performance of Multi-stage Axial Compressor Considering Cold-to-Hot Deformation Effect

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