In response to Navy STTR topic N23A-T010, Interphase Materials (IPM) in collaboration with the Virginia Tech Applied Research Corporation, propose the development of INTELLIPHASE, a sensor fusion and data analysis and software package, for the monitoring and prediction of the antifouling lifespan of TBTO in sonar radar domes. Sonar domes are necessary for optimal sonar performance, but require Tributyltin Oxide (TBTO) to prevent fouling from reducing performance. The lifetime and performance of TBTO throughout a sonar domes lifetime is not well understood, but a method of measuring TBTO in sonar domes has recently been developed. There is a need to record data from TBTO measurements and pertinent information on the sonar domes as well as develop predictive algorithms to predict antifouling lifetime and inform future design decisions. IPM has been developing INTELLIPHASE software for prediction of biofouling in cooling water for HVAC and power plan applications. This platform can be adapted to the specific requirements and data associated with sonar domes. IPM is partnering with VT-ARC to assist in the development of the ontological framework. In Phase I, IPM and VT-ARC will train machine learning models for the prediction of fouling and TBTO depletion in sonar domes as well as design an ontology-driven GUI framework.
Benefit: The proposed technology will enable the monitoring and prediction of fouling and Tributyltin Oxide (TBTO) depletion on sonar domes for surface vessels. This will provide detailed data and insights into the performance of TBTO across the entire lifetime of sonar domes across different environments and use cases. These data insights can be used to predict the presence of fouling on sonar domes which can be used to optimize maintenance schedules as well as inform the design of new sonar domes with TBTO. The fouling prediction software can also be expanded to other fouling applications such as cooling water in HVAC systems and power plants to optimize maintenance schedules and prevent fouling related losses for cost reductions.
Keywords: fouling, fouling, sonar domes, prediction, sensor fusion, Machine Learning
