Use Of Signal Processing Tools Research Articles

Analysis of a civil aircraft wing transonic shock buffet experiment

The physical mechanism governing the onset of transonic shock buffet on swept wings remains elusive, with no unequivocal description forthcoming despite over half a century of research. This paper elucidates the fundamental flow physics on a civil aircraft wing using an extensive experimental database from a transonic wind tunnel facility. The analysis covers a wide range of flow conditions at a Reynolds number of around . Data at pre-buffet conditions and beyond onset are assessed for Mach numbers between 0.70 and 0.84. Critically, unsteady surface pressure data of high spatial and temporal resolution acquired by dynamic pressure-sensitive paint is analysed, in addition to conventional data from pressure transducers and a root strain gauge. We identify two distinct phenomena in shock buffet conditions. First, we highlight a low-frequency shock unsteadiness for Strouhal numbers between 0.05 and 0.15, based on mean aerodynamic chord and reference free stream velocity. This has a characteristic wavelength of approximately 0.8 semi-span lengths (equivalent to three mean aerodynamic chords). Such shock unsteadiness is already observed at low-incidence conditions, below the buffet onset defined by traditional indicators. This has the effect of propagating disturbances predominantly in the inboard direction, depending on localised separation, with a dimensionless convection speed of approximately 0.26 for a Strouhal number of 0.09. Second, we describe a broadband higher-frequency behaviour for Strouhal numbers between 0.2 and 0.5 with a wavelength of 0.2 to 0.3 semi-span lengths (0.6 to 1.2 mean aerodynamic chords). This outboard propagation is confined to the tip region, similar to previously reported buffet cells believed to constitute the shock buffet instability on conventional swept wings. Interestingly, a dimensionless outboard convection speed of approximately 0.26, coinciding with the low-frequency shock unsteadiness, is found to be nearly independent of frequency. We characterise these coexisting phenomena by use of signal processing tools and modal analysis of the dynamic pressure-sensitive paint data, specifically proper orthogonal and dynamic mode decomposition. The results are scrutinised within the context of a broader research effort, including numerical simulation, and viewed alongside other experiments. We anticipate our findings will help to clarify experimental and numerical observations in edge-of-the-envelope conditions and to ultimately inform buffet-control strategies.

Cyclostationary Modelling of Reciprocating Compressors and Application to Valve Fault Detection

The use of signal processing tools in the condition monitoring of machines is a topic of growing potential and interest in industry. In fact with economical and nonintrusive instrumentation, signal processing can provide useful information about the identification of the physical phenomena (observability), and the detection, and the isolation of faults (diagnostics). For rotating and reciprocating machines, classical maintenance techniques are commonly based on oil, gas and temperature analyses (diesel engines), pressure (pumps) or electrical measurements (asynchronous machines); more recently, acoustic emission has shown an interesting potential. But no doubt, the most used technique is still vibration analysis which provides the best sensitivity to the dynamics and kinematics of such machines. The choice of a vibrationmonitoring analysis procedure should be made in accordance with the operational characteristic of the machine. Conventional approaches, such as Fourier or ARMA analyses, are based on the assumptions that vibration signals are stationary (having time invariant statistics) and ergodic (statistical indicators can be computed from time averages); this is usually a legitimate assumption for rotating machines. However, when coming to reciprocating machines such as IC engines and compressors, the dissipation of energy is strongly unsteady due to shocks and abrupt pressure changes, thus resulting in vibration signals that are transient and nonstationary (having time variant statistics). Even though no theory can universally model all nonstationary processes, some dedicated tools such as envelope analysis and time-frequency representation are efficient enough to follow energy changes and to detect intermittent faults. When applied to reciprocating machines, these representations show that the energy changes are periodic, in synchronisation with the machine operating cycle, so that the process can be considered as cyclostationary. Even though cyclostationarity is a concept that has appeared in wireless communication engineering, its application to machine condition monitoring has only recently witnessed fruitful successes. However, taking advantage of the cyclostationarity of a machine requires the introduction of specific signal preprocessing algorithms, especially to cancel out any rotational velocity fluctuations. In fact, to maintain the same number of samples for all recorded cycles of a machine, time signals are usually resampled according to an angular reference locked to the machine kinematics; therefore, all statistical averages necessary to compute monitoring indicators can be performed in a cyclic manner (having synchronous statistics). Many condition monitoring applications have shown that, compared to conventional approaches, cyclostationary modelling and cyclic signal processing yield extra information that greatly helps to better characterise physi-

Identification of vibration sources

The problem addressed is the identification of force distributions exciting structures from the knowledge of measured flexural displacements. The method, called “Force Analysis Technique” (FAT), is based on the computation of the force distribution, the second member of the equation of motion, which is discretized by a finite difference scheme. Then, the main difficulty associated with this problem comes from the data uncertainties that produce noise in the results. It is shown that the use of Signal Processing tools (spatial windows, wavenumber filters,…) permits the technique to be regularized. The goals of this paper are to formulate briefly the application of FAT and to define the tools adapted for regularizing the approach. Experimental results are presented in order to validate the technique and apply it to industrial problems.