Optimal Sensor Placement for LDA Measurement and Health Monitoring in a Recursive H2 Combustion

Abstract The need for better combustion monitoring in gas turbines has become more acute with the latest technical requirements, standards, and policies in terms of efficiency, environment, safety, operation flexibility and operation costs. Within the research project “KI-PIRINHA”1 health monitoring of H2 combustion in recirculating combustor designs is investigated. The project focuses on improving the monitoring of thermal machines and, more generally, hydrogen facilities, hence the term machine protection. The idea is to detect conditions conducive to undesirable transient phenomena, such as detonation on ignition, and to have sufficient time to mitigate these unwanted conditions. For the implementation of a health monitoring system, we apply methods related to Design of Experiments. A reduced order model is created based on computational fluid dynamics (CFD) simulations. To optimize efficient information gain of validation experiments with LDA measurements, methods of sparse sensing are utilized on the flow field inside a conceptual combustion chamber. We present the mathematical foundations and the application on the specific flow field of a combustion chamber examined. Optimizing sensor placement is based on a dimensionality reduction of the flow field on to a tailored basis (Eigenmodes) and subsequent QR-decomposition of these modes. Without the need to fully reconstruct the complete system state, we apply minimization of the ℓ1-norm to find the fewest nonzero entries of the full measurement location index vector. These locations support either state reconstruction or discriminating a health status of the flow in the combustor and optimally inform the decision task for flow regime classification and identification of a precursor for detonation. The classification performance achieved leverages applications for health monitoring and emergency shut-down decisions and enable further research on sparse placement of future implementation of optic-acoustic health monitoring probes. We finish with a specific measurement plan as the Design of Experiment that optimizes information gain for model validation while obeying constraints, minimizes experiment duration and cost of in the development phase and thus leverages health monitoring and anomaly detection capabilities in operation.