Search Results

In the design of solar powered cars aerodynamic efficiency is extremely important. Due to the limited power and energy sources available, the aerodynamic design of the car must provide a low coefficient of drag. In order to identify the major drag source areas in the design and to improve the aerodynamics performance of the current NDSU solar car an extensive computational fluid dynamics (CFD) study was performed using ANSYS CFX 10.0. The study was set into two paths. The first path focused on modifying the current NDSU solar car design in order to reduce the drag force. The second path was to design a completely new solar car with better aerodynamics performance. Through the CFD analysis of all the designs considered major drag source areas were identified as the underbody, the dome, and the nose section of the car.

A computational study of separated flows over a NACA 0012 airfoil at transitional Reynolds numbers is performed. The transitional nature of the flowfield is incorporated into the computations through γ-Reθ transition model based on local variables. Fully turbulent and transitional computations are performed for steady airfoil flowfields and computed results are compared against experiments. The γ-Reθ transition model, in association with Menter’s SST turbulence model is used for the transitional solutions while, SST turbulence model alone is used for the fully turbulent solutions. Both steady and unsteady compressible flow analysis for transition onset prediction and flow separation characterization has been performed. Effects of free stream flow conditions on flow transition are examined.

An accurate air flow rate model is critical for high-quality air-fuel ratio control in Spark-Ignition engines using a Three-Way-Catalyst. Emerging Variable Valve Timing technology complicates cylinder air charge estimation by increasing the number of independent variables. In our previous study (SAE 2004-01-3054), an Artificial Neural Network (ANN) has been used successfully to represent the air flow rate as a function of four independent variables: intake camshaft position, exhaust camshaft position, engine speed and intake manifold pressure. However, in more general terms the air flow rate also depends on ambient temperature and pressure, the latter being largely a function of altitude. With arbitrary cam phasing combinations, the ambient pressure effects in particular can be very complex. In this study, we propose using a separate neural network to compensate the effects of altitude on the air flow rate.