Parametric Identification of Cavitation Instabilities in a 4-Bladed Rocket Engine Inducer

The onset of cavitation and the instabilities it induces in the flowfield are the most stringent fluid dynamic limitations to the suction performance, power density and reliability of the inducers and turbopumps used in liquid propellant rocket engines for primary space propulsion. These considerations justify the need for careful analysis of these phenomena. To this purpose, the present article illustrates the application of Bayesian estimation method recently developed by some of the authors to the identification and characterization of rotating cavitation in a four-bladed axial inducer, using the unsteady pressure readings of a single transducer flush-mounted on the casing just behind the leading edges of the impeller blades. The typical trapezoidal pressure distribution in the blade channels is parameterized and modulated in time and space for reproducing the expected pressure generated by N-lobed rotating cavitation. The Fourier spectrum of the pressure so obtained in the rotating frame is transformed in the stationary frame, frequency broadened to better approximate the experimental results, and parametrically fitted to the measured auto-correlation spectra by maximum likelihood estimation with equal and independent Gaussian errors. Each form of instability generates a characteristic distribution of sidebands in addition to its fundamental frequency. The identification uses this information for effective detection, discrimination and characterization of multiple simultaneous flow instabilities/perturbations. The same information also allows for effectively bypassing the aliasing limitations of traditional cross-correlation methods in the discrimination of multiple-lobed azimuthal instabilities from dual-sensor measurements on the same axial station of the machine. The method returns both the estimates of the model parameters and their standard deviations, providing the information needed for the assessment of the accuracy and statistical significance of the results. The results are consistent with the available reference data obtained from traditional pressure cross-correlation techniques and confirm the differences in the occurrence of azimuthal flow instabilities typically observed in three- and four-bladed inducers. The identifications also proved capable of characterizing instabilities occurring simultaneously with intensities differing by up to an order of magnitude, down to signal-to-noise ratios on the order of 4, thus demonstrating to be significantly superior in terms of resolution and sensitivity w.r.t. traditional spectral analysis methods. The proposed approach represents therefore a promising tool for improving the sensitivity and cost-effectiveness of experimental research and/or operation monitoring of flow instabilities in high-performance turbopumps.