Physics of Prestall Propagating Disturbances in Axial Compressors and Their Potential as a Stall Warning Indicator
Axial compressors in aero engines are prone to suffering a breakdown of orderly flow when operating at the peak of the pressure rise characteristic. The damaging potential of separated flows is why a safe distance has to be left between every possible operating point and an operating point at which stall occurs. During earlier investigations of stall inception mechanisms, a new type of prestall instability has been found. In this study, it could be demonstrated that the prestall instability characterised by discrete flow disturbances can be clearly assigned to the subject of “Rotating Instabilities”. Propagating disturbances are responsible for the rise in blade passing irregularity. If the mass flow is reduced successively, the level of irregularity increases until the prestall condition devolves into rotating stall. The primary objective of the current work is to highlight the basic physics behind these prestall disturbances by complementary experimental and numerical investigations. Before reaching the peak of the pressure rise characteristic flow, disturbances appear as small vortex tubes with one end attached to the casing and the other attached to the suction surface of the rotor blade. These vortex structures arise when the entire tip region is affected by blockage and at the same time the critical rotor incidence is not exceeded in this flow regime. Furthermore, a new stall indicator was developed by applying statistical methods to the unsteady pressure signal measured over the rotor blade tips, thus granting a better control of the safety margin.
Prestall Instability in Axial Flow Compressors

 The aim of the present paper is to improve the physical understanding of discrete prestall flow disturbances developing in the tip area of the compressor rotor. For this purpose, a complementary instrumentation was used in a single-stage axial compressor. A set of pressure transducers evenly distributed along the circumference surface mounted in the casing near the rotor tip leading edges measures the time-resolved wall pressures simultaneously to an array of transducers recording the chordwise static pressures. The latter allows for plotting quasi-instantaneous casing pressure contours. Any occurring flow disturbances can be properly classified using validated frequency analysis methods applied to the data from the circumferential sensors. While leaving the flow coefficient constant, a continuously changing number of prestall flow disturbances appears to be causing a unique spectral signature, which is known from investigations on rotating instability. Any arising number of disturbances is matching a specific mode order found within this signature. While the flow coefficient is reduced, the propagation speed of prestall disturbances increases linearly, and meanwhile, the speed seems to be independent from the clearance size. Casing contour plots phase-locked to the rotor additionally provide a strong hint on prestall disturbances clearly not to be caused by a leading edge separation. Data taken beyond the stalling limit demonstrate a complex superposition of stall cells and flow disturbances, which the title “prestall disturbance” therefore does not fit to precisely any more. Different convection speeds allow the phenomena to be clearly distinguished from each other. Furthermore, statistical analysis of the pressure fluctuations caused by the prestall disturbances offer the potential to use them as a stall precursor or to quantify the deterioration of the clearance height between the rotor blade tips and the casing wall during the lifetime of an engine.
Distribution-free monitoring of univariate processes
We consider statistical process control (SPC) of univariate processes when observed data are not normally distributed. Most existing SPC procedures are based on the normality assumption. In the literature, it has been demonstrated that their performance is unreliable in cases when they are used for monitoring non-normal processes. To overcome this limitation, we propose two SPC control charts for applications when the process data are not normal, and compare them with the traditional CUSUM chart and two recent distribution-free control charts. Some empirical guidelines are provided for practitioners to choose a proper control chart for a specific application with non-normal data.