Automotive lighting presents a challenging and interesting illumination design space. In addition to non-trivial intensity distribution test specifications, automotive lighting is also increasingly important for communicating stylistic details. Light guides, in which light is introduced at one or both ends and then is extracted at roughly orthogonal angles to the path curve, allow a great deal of aesthetic freedom, and are therefore well suited to automotive lighting tasks that need to convey a brand’s image-for instance, daytime running lamps, tail lamps and turn indicators. Light guides can also help minimize the number of sources and parts needed to create a desired light distribution; or may facilitate a more favorable source placement from a packaging point of view, making them even more attractive for these lighting solutions. Optical designs for automotive light guides are challenging and consequently time consuming. This problem is exacerbated by increasing complexity due to new styling demands, higher expectations for perceived uniformity from multiple viewing directions, and the desire to reduce required source power. In this paper, we describe a new software approach to automatically creating and optimizing light guides with prismatic extractors in a CATIA environment . Often the light guide path curve has a complex shape, and the light guide’s extractor surface must be oriented such that it is opposite the desired light direction. The cross section of the light guide is often partially circular, but other shapes may also be used. Additionally, extractors, which can be bumps or holes, need to be created and oriented appropriately on the surface, and key geometrical parameters that are related to optical performance must be easily changed. Our approach leverages the powerful CATIA environment to construct the light guide geometry. Light sources, ray trace simulations, optical material properties, and optical sensors are also added directly into the CATIA model. Furthermore, many types of optical analysis can be performed after Monte Carlo rays are traced. For instance, one can examine the intensity distribution and select specifications against which to measure test points. Luminance camera, ray history and ray file sensors are also available. Using Monte Carlo ray trace results, the light guide prism geometry is automatically optimized to achieve a desired spatially uniform (or deliberately non-uniform) light distribution. At the same time, the angular distribution is optimized by adjusting prism face angles to point light towards defined angular centroid targets. We employ a binning concept that splits the light guide into sections along its length. Prisms in each bin are associated with the light distribution nearby and are adjusted so that the light from the associated bin has a specified relative flux within a cone, as well as a specified centroid pointing direction. In many cases, the source is only on one end of the light guide; however, in some situations, sources at both ends are needed. Other design considerations include fillet radii, prism face curvature, and draft angles. We provide design examples in the paper.
The optical design of a solar concentrator is based not on understanding and evaluating a point solution in time, but
instead on the integrated performance over a band of time. Important additional factors are to evaluate different
locations in the world and different seasons. Here we construct a software tool for modeling bands of time and use it to
study different types of passive, low-concentration, CPC-profile solar collectors. Extruded trough geometry is shown to
have superior performance to an annulus, and a cylindrically curved CPC profile had better pointing tolerance than the
Uniformity remains a central topic in illumination system design and mixing rods provide an effective means to
providing uniformity. Typically, flux enters one end of a mixing rod and the flux exiting the other end provides
improved spatial and/or angular uniformity. We investigate the use of mixing rods with rippled surface structures to
provide enhanced uniformity.
In displays such as backlights and signage, it is often desirable to produce a particular spatial luminance distribution of
light. This work demonstrates an iterative optimization technique for determining the density of light extractors
required to produce desired luminance distributions.
In this paper, we investigate how to improve the uniformity of the spatial distribution of the illuminance at the output plane for angle-to-area-converting, light-piping systems through the introduction of cyclical surface features. A superposition approach is used for studying uniformity. Improvements in uniformity for square-to-circle and rectangle-to-circle lightpipe configurations are demonstrated for a short package length.
Angle-to-area converters are a key topic of illumination design, and much work has been done in this area over the last 30 years. However, relatively little work exists in the literature in which these converters have been designed using optimization techniques. The present work takes a fresh look at some angle-to-area conversion problems using optimized, circularly and non-circularly symmetric surfaces.
Computers are routinely used to design illumination systems. Automating the design process is enhanced through the use of optimization procedures. This paper describes some of the underlying illumination optimization fundamentals: parameterization, merit functions, and optimization algorithms. Numerous interesting examples of illumination design problems that benefit from optimization are shown. These examples illustrate illumination optimization through use of ray aiming, computing illuminance using flux tubes, and computing illuminance using Monte Carlo simulations.
Use of NURBS surfaces to create facets on a reflecting surface will be considered. Specifically, the design of a reflector that generates a circular illuminance pattern will be investigated. Important considerations are: choice of variables used to represent a NURBS surface, total number of variables, parameterization and/or knot vector specification, and where to use algorithmic vs. optimization approaches.