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Thermoacoustic heat engines convert heat into useful energy by generating acoustic waves from a heat source that can then be extracted as useful work. These engines are inexpensive, robust, versatile, and capable of extracting energy from a wide variety of heat sources ranging from waste heat from power plants to exhaust heat of vehicles. In this article, our investigation focuses on using simulation workflows to improve the performance of thermoacoustic engines. We begin with validating the workflows with published data for both traveling wave and standing wave thermoacoustic engines. Following that, we investigate the effect of changing the working fluid and the operating pressure to increase acoustic power. This study uses a coupled PowerFLOW™ and PowerTHERM™ methodology to simulate the buoyancy-driven flows that generate acoustic pressure waves. Good correlations were observed for both traveling and standing wave thermoacoustic engines.

Recent regulations on greenhouse gas (GHG) emission standards for heavy duty vehicles have prompted government agencies to standardize procedures to assess aerodynamic performance of Class 8 tractor-trailers. The coastdown test procedure is the primary reference method to assess vehicle drag and other valid alternatives include wind tunnel testing and computational fluid dynamics (CFD) simulations. While there have been many published studies comparing results between simulations and wind tunnel testing, it is less well understood how to compare results with coastdown testing. Both the wind tunnel and simulation directly measure aerodynamic drag forces in controlled conditions, while coastdown testing is conducted in an open road environment, aerodynamic forces are calculated from a road load equation, and variable wind and vehicle speed introduce additional complexity.

Aerodynamic performance assessment of automotive shapes is typically performed in wind tunnels. However, with the rapid progress in computer hardware technology and the maturity and accuracy of Computational Fluid Dynamics (CFD) software packages, evaluation of the production-level automotive shapes using a digital process has become a reality. As the time to market shrinks, automakers are adopting a digital design process for vehicle development. This has elevated the accuracy requirements on the flow simulation software, so that it can be used effectively in the production environment. Evaluation of aerodynamic performance covers prediction of the aerodynamic coefficients such as drag, lift, side force and also lift balance between the front and rear axle. Drag prediction accuracy is important for meeting fuel efficiency targets, prediction of front and rear lifts as well as side force and yawing moment are crucial for high speed handling.

In an environment of tougher engineering constraints to deliver tomorrow's aerodynamic vehicles, evaluation of aerodynamics early in the design process using digital prototypes and simulation tools has become more crucial for meeting cost and performance targets. Engineering needs have increased the demands on simulation software to provide robust solutions under a range of operating conditions and with detailed geometry representation. In this paper the application of simulation tools to wheel design in on-road operating conditions is explored. Typically, wheel and wheel cover design is investigated using physical tests very late in the development process, and requires costly testing of many sets of wheels in an on-road testing environment (either coast-down testing or a moving-ground wind-tunnel).

A correlation study was performed between static wind tunnel testing and computational fluid dynamics (CFD) for a small hatchback vehicle, with the intent of evaluating a variety of different wheel and tire designs for aerodynamic forces. This was the first step of a broader study to develop a tool for assessing wheel and tire designs with real world (rolling road) conditions. It was discovered that better correlation could be achieved when actual tire scan data was used versus traditional smooth (CAD) tire geometry. This paper details the process involved in achieving the best correlation of the CFD prediction with experimental results, and describes the steps taken to include the most accurate geometry possible, including photogrammetry scans of an actual tire that was tested, and the level of meshing detail utilized to capture the fluid effects of the tire detail.

The purpose of this paper is to describe some of the technology behind the Lattice Boltzmann approach to exterior airflow simulations as incorporated into the commercial CFD code, PowerFLOW®. The fundamental approach used is the Lattice Boltzmann Method (LBM) coupled with both a turbulence model to recover the dissipation of sub-grid eddy scales and a wall model to allow reduced resolution in the near-wall region. A description of LBM and both models is given. Comparisons to methods that directly solve the Navier-Stokes equations, such as finite volume or finite element methods (hereafter, collectively referred to as RANS methods) are also presented. A demonstration of the technology is presented by comparing numerical simulations with extensive experimental test data on Ford's standard calibration models. These models were originally described in SAE paper 940323 [1].