Towards chemical species tomography of carbon dioxide for aviation turbine emissions
This thesis sets out to examine the proposal that, by using tomography and gas sensing techniques to detect and image gas concentration in fast moving flows, engineers can improve the combustion diagnostics and emissions performance of gas turbines, enabling a better understanding of combustion and design optimisation of greener engines. The key factor is the combination of tomography with Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensing technology, implemented simultaneously along many beams, to image the gas concentration distribution in the exhaust plume of a gas turbine, in a plane perpendicular to the plume flow direction. The target gas species is carbon dioxide, CO2, and the absorption feature chosen is at a wavelength of 1997.2 nm. The narrow spectral absorption properties of such small molecules present a considerable challenge for a multi-beam tomographic implementation. Moreover, the design, oriented to harsh and industrial environments, presents key challenges for the design of robust optics and electronics for the collection of reliable data. The development of a 126-beam tomography system required the investigation of recently developed TDLAS techniques and their compatibility with data acquisition (DAQ) system firmware strategies to be implemented by custom DAQ electronics. A novel FPGA-based single channel TDLAS CO2 detection system has been designed and built to demonstrate the feasibility for the replication of 126-channels in the full system. Further proof-of-concept experiments carried out at full scale have produced tomographic images of phantom CO2 distributions that demonstrate the utility of the CST technique.