A 60% efficient solar microconcentrator for electricity and hot water. The aim of this project is to develop a microconcentrator for deployment on house roofs that will produce both solar hot water and solar electricity with a combined efficiency above 60%. The system will have a low profile and will be nearly invisible from the street. The system will track the sun. Concentration will be accomplished by a mixture of refraction and reflection. About 20% of the sunlight will be converted to elect ....A 60% efficient solar microconcentrator for electricity and hot water. The aim of this project is to develop a microconcentrator for deployment on house roofs that will produce both solar hot water and solar electricity with a combined efficiency above 60%. The system will have a low profile and will be nearly invisible from the street. The system will track the sun. Concentration will be accomplished by a mixture of refraction and reflection. About 20% of the sunlight will be converted to electricity using lines of tiny solar cells, with the balance being converted to heat which is removed by cooling fluid and stored in hot water tanks.Read moreRead less
A novel physical-digital approach for the assessing a large critical asset. This project aims to deliver an artificial intelligence-enabled decision-making tool to maintain and manage the floating covers of vast lagoons that treat raw sewage. The cover harvests the biogas released from the anaerobic digestion of sewage for electric power generation that exceeds the plant’s requirement. The approach involves an innovative thermographic technique and exploits transfer learning to adapt neural netw ....A novel physical-digital approach for the assessing a large critical asset. This project aims to deliver an artificial intelligence-enabled decision-making tool to maintain and manage the floating covers of vast lagoons that treat raw sewage. The cover harvests the biogas released from the anaerobic digestion of sewage for electric power generation that exceeds the plant’s requirement. The approach involves an innovative thermographic technique and exploits transfer learning to adapt neural networks trained on lab-scale and synthetic data to field implementation. The outcome is a machine learning framework to optimise biogas harvesting and renewable energy generation, and to avoid structural failure, that is capable of continuous improvement to take into account improved data and/or modelling capabilities.Read moreRead less