The reduction of energy consumption of cars is always a significant issue in automotive design. Turbulent flow around a car body is very difficult to simulate accurately due to the complexity of the flow conditions around the body, such as complex flow separation and laminar to turbulent flow transition. In particular, flow over the Ahmed body with a rear angle of 25o is considered a challenging problem for the RANS approach with two-equation turbulence models. In this study, we aim to analyze the Kelvin-Helmholtz instability associated with this flow with a URANS approach. Methodology for utilizing the URANS method is fully discussed. The predicted velocity profiles and drag coefficient are com-pared with experimental results. Three turbulence models, such as the k-ε, k-ω and SST models, are assessed and validated with experimental data. The aim of the study is to evaluate the performance of these models for the study of the Kel-vin-Helmholtz instability over the Ahmed body and for car bodies generally us-ing experimental data for their validations. It is found that the URANS approach with the turbulence models with proper numerical treatment can perform as well as or even better than the LES. And the SST model shows the best performance compared with other turbulence models.

This paper presents two novel automated optimization approaches. The first one proposes a framework to optimize wind turbine blades by integrating multidisciplinary 3D parametric modeling, a physics-based optimization scheme, the Inverse Blade Element Momentum (IBEM) method, and 3D Reynolds-averaged Navier–Stokes (RANS) simulation; the second method introduces a framework combining 3D parametric modeling and an integrated goal-driven optimization together with a 4D Unsteady Reynolds-averaged Navier–Stokes (URANS) solver. In the first approach, the optimization toolbox operates concurrently with the other software packages through scripts. The automated optimization process modifies the parametric model of the blade by decreasing the twist angle and increasing the local angle of attack (AoA) across the blade at locations with lower than maximum 3D lift/drag ratio until a maximum mean lift/drag ratio for the whole blade is found. This process exploits the 3D stall delay, which is often ignored in the regular 2D BEM approach. The second approach focuses on the shape optimization of individual cross-sections where the shape near the trailing edge is adjusted to achieve high power output, using a goal-driven optimization toolbox verified by 4D URANS Computational Fluid Dynamics (CFD) simulation for the whole rotor. The results obtained from the case study indicate that (1) the 4D URANS whole rotor simulation in the second approach generates more accurate results than the 3D RANS single blade simulation with periodic boundary conditions; (2) the second approach of the framework can automatically produce the blade geometry that satisfies the optimization objective, while the first approach is less desirable as the 3D stall delay is not prominent enough to be fruitfully exploited for this particular case study.

The demand in solving complex turbulent fluid flows has been growing rapidly in the automotive industry for the last decade as engineers strive to design better vehicles to improve drag coefficients, noise levels and drivability. This paper presents the implementation of an arbitrary hybrid turbulence modeling (AHTM) approach in OpenFOAM for the efficient simulation of common automotive aerodynamics with unsteady turbulent separated flows such as the Kelvin–Helmholtz effect, which can also be used as an efficient part of aerodynamic design optimization (ADO) tools. This AHTM approach is based on the concept of Very Large Eddy Simulation (VLES), which can arbitrarily combine RANS, URANS, LES and DNS turbulence models in a single flow field depending on the local mesh refinement. As a result, the design engineer can take advantage of this unique and highly flexible approach to tailor his grid according to his design and resolution requirements in different areas of the flow field over the car body without sacrificing accuracy and efficiency at the same time. This paper presents the details of the implementation and careful validation of the AHTM method using the standard benchmark case of the Ahmed body, in comparison with some other existing models, such as RANS, URANS, DES and LES, which shows VLES to be the most accurate among the five examined. Furthermore, the results of this study demonstrate that the AHTM approach has the flexibility, efficiency and accuracy to be integrated with ADO tools for engineering design in the automotive industry. The approach can also be used for the detailed study of highly complex turbulent phenomena such as the Kelvin–Helmholtz instability commonly found in automotive aerodynamics. Currently, the AHTM implementation is being integrated with the DAFoam for gradient-based multi-point ADO using an efficient adjoint solver based on a Sparse Nonlinear optimizer (SNOPT).

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To evaluate novel turbine designs, the wind energy sector extensively depends on computational fluid dynamics (CFD). To use CFD in the design optimization process, where lower-fidelity approaches such as blade element momentum (BEM) are more popular, new tools to increase the accuracy must be developed as the latest wind turbines are larger and the aerodynamics and structural dynamics become more complex. In the present study, a new concurrent aerodynamic shape optimization approach towards multidisciplinary design optimization (MDO) that uses a Reynolds-averaged Navier–Stokes solver in conjunction with a numerical optimization methodology is introduced. A multidisciplinary design optimization tool called DAFoam is used for the NREL phase VI turbine as a baseline geometry. Aerodynamic design optimizations in terms of five different schemes, namely, cross-sectional shape, pitch angle, twist, chord length, and dihedral optimization are conducted. Pointwise, a commercial mesh generator is used to create the numerical meshes. As the adjoint approach is strongly reliant on the mesh quality, up to 17.8 million mesh cells were employed during the mesh convergence and result validation processes, whereas 2.65 million mesh cells were used throughout the design optimization due to the computational cost. The Sparse Nonlinear OPTimizer (SNOPT) is used for the optimization process in the adjoint solver. The torque in the tangential direction is the optimization’s merit function and excellent results are achieved, which shows the promising prospect of applying this approach for transient MDO. This work represents the first attempt to implement DAFoam for wind turbine aerodynamic design optimization.