Bayesian mass averaging

Mass averaging is pivotal in turbomachinery. In both experiments and CFD simulations mass averages of flow quantities may be taken circumferentially, radially, and often both. These are critical for arriving at 1D or 2D performance metrics that shape our understanding of losses and their control. Such understanding empowers design advances, affords anomaly detection, informs maintenance and diagnostic efforts. This paper presents a new statistical framework for obtaining mass averages of flow quantities in turbomachinery, tailored for rig tests, heavily instrumented engines, and cruise-level engines. The proposed Bayesian framework is tailored for computing mass averages of pressures and temperatures, given their values from circumferentially scattered rakes. Two variants of this methodology are offered: (i) for the case where massflow rate distributions can not be experimentally determined and computational fluid dynamics (CFD) based profiles are available, and (ii) where experimental massflow rate distributions are available. In scope, this framework addresses limitations with existing measurement budget calculation practices and with a view towards facilitating principled aggregation of uncertainties over measurement chains.