Non-intrusive optical measurements of gas turbine engine inlet condensation using machine learning

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We demonstrate a novel application of supervised machine learning (ML) models to quantify the size, shape, number density, and distribution parameters of a water spray introduced at a gas turbine inlet. Only a limited set of laser scattering and extinction observations, acquired by pairs of photodetectors and cameras, are required for an accurate output. A phase Doppler particle analyzer as well as a conventional extinction inversion method are used to validate the particle size estimation, with the ML method converging closely to both. By measuring a water spray, where a spherical particle shape can be assumed, these size estimate validations could be made, which would have been difficult for a nonspherical particle measurement. By combining all the estimated parameters, the liquid volume fraction as well as the liquid flow rate is estimated and compared to a traceable ultrasonic flowmeter. To our knowledge, this is the first in situ condensation load measurement made at a gas turbine inlet without prior calibration. The ML approach is able to accurately estimate the liquid flow rate, with the majority of the estimates lying within the uncertainty bounds of the flowmeter and a root-mean-square difference of 0.8 L h−1 or 7.4%. Estimating the liquid flow rate using all the particle parameters demonstrates the method’s robustness and readiness for accurately measuring even nonspherical particles. The low number of required optical observations also makes this technique attractive for more generalized inlet particle measurements including sand, dust, and volcanic ash, in addition to condensation.