Oct 2026· Nonimaging Optics: Efficient Design for Illumination and Concentration XXI· 0 citations
Abstract
Linear LED light fixtures are very popular in many interior lighting applications. Some high efficacy linear LED light fixtures result in undesirable glare for the occupants in a lighted space. Traditionally, light absorbing baffles or optical diffusers are used to minimize glare. Such solutions negatively affect the fixture efficacy. Therefore, the objective of this study is to explore custom designed baffle optics that can reduce the glare while minimally affecting the fixture efficacy. The strategy used here transferred the luminous flux above 50˚ to lower angles without wasting them. This basically alters the intensity distribution of the light output of the fixture. An additional benefit of this method is that the Lambertian output beam distribution can be modified to produce a batwing shaped intensity distribution. As a result, the batwing shaped intensity distribution produces uniform illuminance on the horizontal task plane and the lower light intensity above 50˚ mitigating the glare. The study began with an optical ray tracing analysis using a commercial ray tracing software, namely, LightTools. This allowed for designing a suitable baffle that enabled reshaping the light fixture output distribution to the desired shape. It further helped analyze the UGR (glare metric), and the fixture efficiency. Next, the light intensity distribution (IES files) was imported into AGi32 software to analyze the task plane illuminance uniformity an d the fixture UGR value. Once a satisfactory baffle design was realized, it was 3D-printed using a FDM printer using PLA matte white filament material. The printed baffles were assembled and installed in a 2 feet long linear LED light fixture, and the fixture was tested in the photometry lab to compare the measured fixture efficiency, UGR value, intensity distribution a nd the task plane illuminance distribution when mounted at a height above the task plane. The measured and the ray tracing results showed good agreement. The details of the study and the results are explained in this manuscript.
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StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
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