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The main objective of this work is to present an adjoint-based methodology to address combined optimization of drag force and cooling flow rate of an industrial vehicle. In order to cope with cooling effect, the volumetric flow rate is treated through a newly introduced cost function and the corresponding adjoint source term is derived. Also an alternative strategy is presented to tackle aerodynamic vehicle design improvement that relies on a so-called indirect force computation. The overall optimization is treated as a Multi-Objective problem and an original approach, called Optimize Both Favor One (OBFO), is introduced that allows selective emphasis on one or another objective without resorting to artificial cost function balancing. Finally, comparative results are presented to demonstrate the merit of the proposed methodology.

Aerodynamic vehicle design improvements require flow simulation driven iterative shape changes. The 3-D flow field simulations (CFD analysis) are not explicitly descriptive in providing the direction for aerodynamic shape changes (reducing drag force or increasing the down-force). In recent times, aerodynamic shape optimization using the adjoint method has been gaining more attention in the automotive industry. The traditional DOE (Design of Experiment) optimization method based on the shape parameters requires a large number of CFD flow simulations for obtaining design sensitivities of these shape parameters. The large number of CFD flow simulations can be significantly reduced if the adjoint method is applied. The main purpose of the present study is to demonstrate and validate the adjoint method for vehicle aerodynamic shape improvements.

The main objective of this work is to demonstrate the merits of the Adjoint method to provide comprehensive information for shape sensitivities and design directions to achieve low drag vehicle shapes. The adjoint method is applied to a simple 2D airfoil and a 3D vehicle shape. The discrete Adjoint equations in the flow solvers are used to investigate further potential shape improvements of the low drag vehicle shapes. The low drag vehicle used in this study was designed earlier using the conventional approach (i.e., extensive use of wind tunnel testing). The goal is to use the already low drag vehicle shape and reduce its drag even further using the adjoint methodology without using the time-consuming and the high cost of wind tunnel testing. In addition, the present study is intended to compare the results with the other computational techniques such as surface pressure gradients method.

Understanding the flow characteristics and, especially, how the aerodynamic forces are influenced by the changes in the vehicle body shape, are very important in order to improve vehicle aerodynamics. One specific goal of aerodynamic shape optimization is to predict the local shape sensitivities for aerodynamic forces. The availability of a reliable and efficient sensitivity analysis method will help to reduce the number of design iterations and the aerodynamic development costs. Among various shape optimization methods, the Adjoint Method has received much attention as an efficient sensitivity analysis method for aerodynamic shape optimization because it allows the computation of sensitivity information for a large number of shape parameters simultaneously.

Aerodynamic forces are the result of various complex viscous flow phenomena such as three-dimensional turbulent boundary layer on the body surfaces, longitudinal vortices induced by three-dimensional boundary layer separation, and high turbulence caused by flow separations. Understanding the flow characteristics and, especially, how the aerodynamic forces are influenced by the changes in the vehicle body shape, are very important in order to improve vehicle aerodynamics (particularly for low drag shapes). The present study was an attempt to provide insights for better understanding of the complex three-dimensional flow field around a vehicle by observing the limiting surface streamlines and the surface pressure gradients in the stream-wise and the transverse directions. The main objective of this work is to provide a comprehensive diagnostic analysis of the basic flow features in order to learn more about the flow separations in three-dimensions.

A methodology for measuring oil viscosity in an internal combustion engine has been developed that is based on measured values of oil pressure and oil temperature at a relatively low engine speed near idle. Engine oil pressure results from the resistance of the oil to flow under the pumping action of the oil pump. The resistance to flow, in turn, is a function of both the viscosity of the oil and the flow rate. At a constant oil flow rate, a higher oil viscosity will result in a higher oil pressure. Oil viscosity is an important factor in determining the ability of the oil to provide effective lubrication and, for example, can be used as an indicator of the need to change the oil. This report describes the operational principles of the methodology for determining engine oil viscosity and a proof of concept based on a simple vehicle test.

The growing applications of Computer-Aided Engineering (CAE) tools have been motivated by the need to create more effective product development processes. Computational Fluid Dynamics (CFD), as one of the CAE tools, has enjoyed growing popularity for analysis of many airflow situations, including road vehicle aerodynamics. In many cases, these applications have been limited by the level of predictive accuracy that is possible with CFD codes today. In the present exercise, simplified representations of three vehicle models (1:12 scale) were chosen to assess the overall level of predictability of the GMTEC CFD code, using detailed measurements that were made in a scale-model wind tunnel. The CFD computations used two turbulence models (standard k-εand RNG k-ε) and were matched to the experimental geometry and test conditions.