The development of high-speed PIV techniques and their application to jet noise measurement

This thesis describes the design, development and deployment of a high-speed jet flow measurement system. The apparatus was created in response to the need to collect a large quantity of statistically-converged aerodynamic data from a series of commercial turbofan engine models. This acquisition was performed in conjunction with acoustic measurements as part of the ED CoJeN project to investigate jet noise production, and associated noise reduction techniques. Particle Image Velocimetry is a well established flow measurement technique, but its application outside of the laboratory can be limited by a relatively low sample rate and' the need to operate in a hostile environment. This thesis presents a multiple camera technique - used as the basis for the j et measurement system - that is capable of acquiring both time-series PIV data at MHz rates, and continuous, statistically independent measurements at up to 14 Hz. The resultant PIV measurement rig was therefore capable of acquiring time-averaged velocity and turbulence data from the whole of a 110 scale coaxial engine exhaust plume (down to 4m or 20D) in no more than 1 hour. The -500aC Mach:5 0.9 jets were also scanned volumetrically in order to check the spatial alignment of the nozzle and flow streams,.and all PIV measurements were synchronised to simultaneous LDA acquisition, thus enabling the data to be validated. Finally, the cameras were used to acquire novel6-frame time-series data at:5 330 kHz, which was used to calculate time-space correlations within the exhaust. By providing a highly automated and completely remote-controlled system, the exhaust measurements could be repeated over 3 operating conditions and 2 nozzle geometries, thereby providing a comprehensive description of the flow field. The data, having been systematically post-processed, has been shown to agree well with concurrent measurements, and it will now be used to validate CFD models of coaxial jet flow. By improving the quality of computational flow prediction in this way, the time taken to design and test quieter jet engines will be significantly reduced.