Heliostats are arrays of mirrors that together focus sunlight on a small target. Such solar concentrators are typically used to drive heat exchangers in solar-thermal power plants. Sometimes, heliostats are also used for daylighting, and in very high temperature solar furnaces.
To be efficient, heliostat systems must maximize solar energy reflected by each mirror. So they track the sun's changing position intra-day, and over seasons. A bit of straightforward spherical geometry solves the tracking problem, but another, much harder problem emerges, literally from the shadows.
As the sun moves around, individual mirrors partially block each other, and cast shadows onto each other. This causes a drop in collection efficiency, and also makes it hard to conserve real estate, as we can't pack mirrors densely. The trick is to simultaneously avoid shadowing, and decrease real-estate usage.
Traditional optimization techniques optimize overall heliostat densities and placement patterns. Yet, we found further efficiencies, by choosing to optimize individual mirror positions. This is a more complex optimization problem, given the non-convex, non-smooth nature of the search space. We created advanced geometric, light and energy models, and developed algorithms that optimize individual mirror positions using these models. The result was the ability to create solar power efficiencies which are considerably higher than achievable using heliostats in regular patterns.
Our deep expertise in applied and computational physics is helpful in designing better products, and devising better sensing and control methods
We have 17 years of expertise creating accurate physics simulations for diverse, interconnected phenomena. These simulations can be used to design better products, better processes and sensing and control techniques.