Methods and systems for airfoil prognostics using physics-informed machine learning

A computing system, comprising:

• a processor;
• and
• a memory storing executable instructions that, when executed by the processor, cause the processor to execute:
• an impending fault simulator configured to:
• receive input of a nominal airfoil geometry of an airfoil,
• simulate impending faults in the airfoil by perturbing the nominal airfoil geometry to generate perturbed airfoil geometries, and
• input the perturbed airfoil geometries into one or more physics-informed neural network (PINN) models to generate respective predicted airfoil lift coefficients for the perturbed airfoil geometries;
• and
• a health status evaluator configured to generate a predicted future health status for the airfoil under one or more operational conditions based on the predicted airfoil lift coefficients for the perturbed airfoil geometries, thereby leveraging physics-informed machine learning for efficiently generating airfoil prognostics, wherein
• the health status evaluator further includes an extreme operation condition identifier configured to use a fault probing strategy to search for plausible fault probing conditions, and use the plausible fault probing conditions to simulate impending faults and generate the predicted future health status for the airfoil under the one or more operational conditions;
• the health status evaluator is configured to generate the predicted future health status for the airfoil for each of a plurality of mission-specific requirements;
• and
• the extreme operation condition identifier is configured to generate the predicted future health status for the airfoil for each of the plurality of mission-specific requirements.