Thermal simulation solves complexities of LED headlight designs

  • 11-Oct-2013 03:26 EDT
LED_Headlight_Designs_7x4.tif

FloEFD Simulation images of an Audi A3 headlight showing the velocity isosurfaces providing fresh air to the headlight system for cooling and evaporation. (Upper images courtesy of Mentor Graphics; lower images courtesy of Audi AG)

LEDs enable automotive designers to be more creative and differentiate the brand or vehicle model through individualistic and impressive designs. When designing automotive lighting, engineers start from a basic idea of what the headlight should look like and the physical space limitations in the car. Then they model the headlight. With the basic shell and elements they need to include, they will conduct an optical simulation to ensure that the headlight fulfills regulations such as not being too bright for oncoming traffic, etc. They also can see whether the headlight looks good as an important part of the “face” of the car.

The simulation provides the basic specifications for aspects of the optics that have to be included in the headlight design, such as reflectors and lenses. When the headlight’s external appearance is completed, the functionality has to be simulated. The basic product concept must be validated, reconciling mechanical and aesthetic ideals with the realities of thermal behavior.

At this point, the engineers often simulate single components such as an array of LEDs with heat sink and fan to check that the temperatures are in an acceptable range and the right materials and components are selected.

But because a small component is working doesn’t mean that it will work in the headlight in the same way. Once it’s placed in the housing, the airflow is more limited and more strongly influenced by the other parts, such as reflectors and lenses.

The key to successful LED system design is to transfer the active device’s heat efficiently from its own PN junction to the ambient. The path involves both the printed circuit board (PCB) that mounts the LED and the enclosure or heat sink. The designer must confirm that housings and shrouds participate efficiently in carrying heat away from the LED.

The LEDs may not be cooled if temperatures increase or the airflow suddenly short-circuits when air that is sucked in by the fan and heated, when passing the heat sink on which the LEDs are mounted, is accidently guided back to the intake of the fan by some other geometries in the headlight assembly. De-condensation or de-icing of the headlight’s front screen (also called the lens) is now often done by the hot air that is a result of cooling the LEDs and blown onto the lens to heat it. LED headlights do not have bulbs that radiate a lot of the heat onto the lens, so the warm/hot air has to be forced onto the lens to heat it.

Concurrent CFD applications, such as FloEFD, automate the most demanding steps associated with preparing and running a simulation. The CFD tool embedded in a standard MCAD environment such as CATIA V5 allows designers to develop models and test the heat dispersion properties of an emerging luminaire design. The design engineer can use the dimensions and physical characteristics of the proposed design stored within the MCAD application by directly using the MCAD geometry. The CFD software detects and assigns grids to the solids and flow spaces, creating an optimized computing mesh. It aids the designer in setting boundary conditions and automatically provides solution-control settings that ensure convergence when the solver runs.

The simplest, most stable, and fastest mesh for a CFD solver is a Cartesian mesh. The errors caused by the skewness of cells are eliminated because of the rectangular cells, but there is the general problem of mesh realization at the model’s surfaces. Commonly, the surface in these cases is either meshed with a tetrahedral cell type (hybrid mesh) or the mesh creates a step-like surface that isn’t practical for free-form surfaces. One promising solution is the immersed-body mesh with a special cell technology separating the fluid from the solid part within a cell and creating subgrid control volumes. This partial cell approach recognizes the geometry with an excellent degree of detail, and little to no effort is required to create an optimal mesh even for the most complex geometry. It also can be fully automated.

Partial cells enable complex surface to be realized by a simple Boolean operation that cuts the cell intersected by the solid surface into multiple subcell control volumes, each of which can have either different phases (for example, liquid-solid-gas) or same phase but different properties (such as steel-copper-aluminum)—all contained within one partial cell. With this technology, it is possible to drastically reduce the amount of cells needed to represent accurately a complex geometry, maintain the solver stability, and reduce numerical error. Additional technologies and engineering models, such as a porous media definition for filter and other pressure-loss-causing geometries that need not be resolved in every detail, improve the usability of this solution approach and further reduce the cell count. In this way, simulation is brought toward the design engineer with increased ease of use but also with high accuracy.

A headlight consists of many parts from small to large, from simple shapes such as a screw or sheet of metal or plastic to complex surfaces such as the reflectors and housing with a lot of faceted surfaces or pins, ribs, and clips, to assemble the headlight into the car. Using a traditional CFD tool to mesh these geometries would take days. But with a tool such as FloEFD that implements the partial-cell methodology, it can be meshed within a few hours automatically. With two to three runs of meshing, finding the optimal settings for the mesh in all areas of the headlight can take one to two days when running meshing during breaks or overnight. With one to two days for the simulation and a few hours to set up a model, it takes approximately three to four days for a complete simulation from loading the geometry the first time to a converged result. And once that is done, every change in the geometry, such as a different heat sink or fan or boundary condition, takes 30 minutes to an hour to apply the changes to the model and then run the simulation again; in one to two days, the results are done. Four to seven simulations of different scenarios can be done in the same amount of time that it would take to do one simulation with traditional CFD.

Boris Marovic, Product Marketing Manager for Automotive & Transportation, Mentor Graphics, wrote this article for Automotive Engineering International.

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