Numerical simulations are carried out on the flow over a realistic generic car geometry, the DrivAer-fastback car model. Pure large eddy simulations (LES) and wall-modeled large eddy simulations (WMLES) are used and compared to numerical and experimental results to assess the validity of these approaches when solving the flow field around complex automotive geometries. Results show a 70% CPU time reduction when using the wall model. Drag coefficient results show the influence of the wall model on coarser meshes is positive, reducing the difference on those obtained using finer meshes. Pressure profiles exhibit mixed results. The wall model used works well in adverse pressure gradients and smooth geometry changes. Results worsen in sections

We introduce in this study an algorithm for the imaging of faults and of slip fields on those faults. The physics of this problem are modeled using the equations of linear elasticity. We define a regularized functional to be minimized for building the image. We first prove that the minimum of that functional converges to the unique solution of the related fault inverse problem. Due to inherent uncertainties in measurements, rather than seeking a deterministic solution to the fault inverse problem, we then consider a Bayesian approach. In this approach the geometry of the fault is assumed to be planar, it can thus be modeled by a three dimensional random variable whose probability density has to be determined knowing surface measurements. The randomness involved in the unknown slip is teased out by assuming independence of the priors, and we show how the regularized error functional introduced earlier can be used to recover the probability density of the geometry parameter. The advantage of the Bayesian approach is that we obtain a way of quantifying uncertainties as part of our final answer. On the downside, this approach leads to a very large computation since the slip is unknown. To contend with the size of this computation we developed an algorithm for the numerical solution to the stochastic minimization problem which can be easily implemented on a parallel multi-core platform and we discuss techniques aimed at saving on computational time. After showing how this algorithm performs on simulated data, we apply it to measured data.

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With the recent advances in Machine Learning, strategies based on data could be used to augment wall modeling in Large Eddy Simulation(LES). In this work, a wall model based on gradient boosted decision trees is presented. The model is trained to learn the boundary layer of a turbulent channel flow so that it can be used to make predictions for significantly different flows where the equilibrium assumptions are valid.The methodology of building the model is presented in detail. The experiments conducted to choose the data for training the model, as well as to choose the model input features are described. The trained model is tested a posteriori on a Turbulent channel flow and the flow over a wall mounted hump. The results from the tests are compared with that of an algebraic equilibrium wall model and the performance is evaluated. The results show that the model has succeeded in learning the boundary layer and performs as good as an algebraic wall stress model.

Portal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista Computers and Fluids.