SBIR-STTR Award

Extensive testing at substantial cost is a major part of military propulsion development programs. These programs involve numerous component and engine tests aimed at producing safe and reliable engines. Despite these efforts, engines still experience unexpected failures. Geometric variability of parts within design tolerances is a well-known source of uncertainty in overall reliability. USAF sustainment activities increasingly include geometric measurements of parts to quantify variations, but these data are generally ignored in the test process. In the proposed STTR program, the overall technical objective it to use the geometry and response data from test of one set of parts to describe the response behavior of a second population of parts of known, or formulated geometry. The University of Notre Dame will extend methods for parameterizing geometry and computation of response sensitivities for utilization of measured geometry data by the test community. APEX Turbine Testing Technologies will integrate these methods in GageMap, a commercially developed FEA post-processing product, together with response measurements, to describe the response for other geometries. This allows the USAF to identify geometric features that can produce higher responses. This can assist in focusing inspections, adjustment of tolerances, or perhaps culling parts as needed for fleet management.

High Cycle Fatigue (HCF) characterization and maintenance accounts for a significant portion of the overall life cycle cost of most military propulsion systems. A key variable that drives HCF margin is dynamic response which is directly related to the geometry of each part. This is especially true of integrally bladed rotors (IBRs, or blisks). It has been well established that HCF is a probabilistic phenomenon and only results in cracking for the most susceptible parts. Such susceptibility can include geometric features and/or damage such as foreign object damage (FOD). Nearly all of the effort put into HCF characterization testing is aimed at understanding dynamic responses of instrumented parts as a function of engine operation. Little, explicit attention is given to how variations in part-to-part geometry affects the dynamic response however. The proposed effort seeks to extend HCF assessment to include geometric variations by predicting the dynamic response of any part based entirely on its particular geometry. To do this, this proposal will employ a sensitivity based method to compute mode shapes based on deviation of the part from the nominal geometry, and the dynamic characteristics and response history of the nominal part. A machine learning routine will be implemented to parameterize the geometric variations and compute mode-shape sensitivities. Integration of response histories from HCF characterization testing and predicted dynamic characteristics of some part of interest will be done by extension of a commercially marketed product called GageMap. In the proposed STTR program, the University of Notre Dame will have primary responsibility for development of the machine learning, parameterization and sensitivity formulation. APEX Turbine will provide integration of these technologies with the GageMap product to describe the response behavior of parts in the fleet based on data and geometry of parts tested under development or during diagnostic test programs. An initial validation demonstration will be performed using data generated from a University of Notre Dame transonic rotor.