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The Power of Shape
Published in Patrick Hossay, Automotive Innovation, 2019
The defining element of automotive aerodynamics is drag, defined formally as the resistive force to the flow of an object through a fluid. In this case, the fluid is air and the object is a car. So, drag is the force resulting from airflow that resists the forward movement of the car. At highway speeds, up to 75%–80% of a vehicle’s resistance to motion can be defined by aerodynamic drag.1 This makes drag a focal point of aerodynamic design in production cars, and a key to improved highway fuel economy.
Simulation study on energy saving of passenger car platoons based on DrivAer model
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019
Zhi-Fa Yang, Shu-Hong Li, Ai-Min Liu, Zhuo Yu, Huan-Jing Zeng, Shi-Wu Li
Automotive aerodynamics simulation studies often use extremely simplified vehicle models, such as the Ahmed model and the SAE (Society of Automotive Engineers) model, which are highly abstract in kind. The results obtained are quite different from actual vehicle behavior. Unlike the two models, the geometry of the DrivAer model is based on the geometry of two medium-sized vehicles—the Audi A4 and the BMW 3 Series (see Heft, Indinger, and Adams 2011). They merge the generated CAD (Computer Aided Design) geometry in a ratio model (1:1). Figure 1 shows the three models, and the DrivAer model is more consistent with the actual situation.
A Generic Performance Analysis Technique Applied to Different CFD Methods for HPC
Published in International Journal of Computational Fluid Dynamics, 2020
Marta Garcia-Gasulla, Fabio Banchelli, Kilian Peiro, Guillem Ramirez-Gargallo, Guillaume Houzeaux, Ismaïl Ben Hassan Saïdi, Christian Tenaud, Ivan Spisso, Filippo Mantovani
The problem considered in this work is the simulation of automotive aerodynamics using the DrivAer model, made publicly available by the Technical University of Munich .4 The model consists of 61M cells. The solver used is the simpleFoam based on the SIMPLE (Semi-Implicit Method for Pressure Linked Equations) algorithm implemented in OpenFOAM.
Engineering graduates professional formation: the connection between activity types and professional competencies
Published in European Journal of Engineering Education, 2022
David Lowe, Tom Goldfinch, Anthony Kadi, Keith Willey, Tim Wilkinson
The overall results of the initial semantic coding of student reflections are provided in Tables A3 and A4 in Appendix. For each of the different professional engagement activity types the results provide the normalised density within the associated reflections of terms associated with both each Bloom levels (B1 … B6) and each EA competency (E1.1 … E3.6). The data has been normalised by converting the density of terms to a z-score. To illustrate the coding of reflections, consider the following extract of a reflection, showing terms associated with EA competency 3.6 (teamwork) highlighted: From the humbling experience, it has to an extent, tempered my excitement for working in automotive aerodynamics in the specific context of a small team. If I were to go into a race team again, it would have to be with due consideration to the other roles and responsibilities required to be a part of the team. I do however understand that these ancillary roles are highly unlikely if entering a vehicle manufacturer’s aerodynamic division, or if entering the upper echelons of motorsports. Ultimately in this regard, I would be very remiss if I had to do the most fundamental roles in a future position, such as mopping and scrubbing the floors.To illustrate this calculation, an example of the pre-normalised density data is provided in Table A3 for Bloom level 1 terms (e.g. the column headed ‘Density/Bloom B1’). Looking at this raw data for B1, consider the value for activity type 2a (University-organised site visit). This shows that 0.25% of the total words contained in reflections on activity type 2a were Bloom level 1 terms. This converts to a z-score of −0.62, indicating that this proportion was much lower than the average proportion for all activity types. The tables also highlight those values which are at least one standard deviation above or below the average for that column (i.e. a |z-score| ≥ 1.0).