A Novel Semi-Automatic Method to Optimize Multi-Lamp High Flux Solar Simulators

Extensive research is being conducted on photon energy driven reactions which include wastewater treatment, H2 production, and advanced material aging under diffuse or concentrated light. Solar simulators are a convenient tool for the latter research applications because they allow emulation of solar radiation under well controlled laboratory conditions. Most concentrated light simulators are comprised of multiple light sources with ellipsoidal /parabolic mirrors. Thus, they require accurate characterization of various system parameters such as peak flux and flux density distribution. They also require optimization to ensure the system can operate at its theoretical maximum flux and provide the necessary radiating energy. However, this process is either manual or semi-automated and demands tedious expert intervention. This study utilizes the High Flux Solar Simulator (HFSS) facility at TAMUQ comprised of seven short-arc xenon lamps of 6 kW each. We present intermediate results of our attempts to create an automated system used to optimize alignment of the light sources (four degrees of freedom each) given variable system parameters. An automated data acquisition program was developed which included image capture, target movement, flux readings, etc. The irradiance was then characterized using the flux mapping method using an in-house algorithm used to calculate flux parameters. Monte-Carlo ray tracing was used to generate training data. This data was used to train a supervised machine learning algorithm based on a typical convolutional neural network model. Finally the model was tested against the same Monte-Carlo ray tracing used initially as well as experimental data from the HFSS. The proposed methodology is expected to facilitate initial deployment of HFSS. It will also assist on the dynamic control of reactor conditions i.e. emulating variable overcast or daily sunlight variability.