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https://ir.swu.ac.th/jspui/handle/123456789/12963
ชื่อเรื่อง: | Towards parallel, 192 channel, 40MS/s/ch data acquisition for optical tomography: A system for aero-engine exhaust emission diagnostics |
ผู้แต่ง: | Fisher E. Tsekenis S.-A. Yang Y. Ouypornkochagorn T. Chighine A. Polydorides N. Wright P. McCann H. |
Keywords: | Absorption spectroscopy Aircraft engines Demodulation Engines Exhaust systems (engine) Integrated circuit design Optical tomography Optical variables measurement Parallel processing systems Semiconductor lasers Chemical species Direct memory access Distributed data acquisition Industrial environments Microprocessor control Microprocessor memory TDLAS Tunable diode laser absorption spectroscopy Data acquisition |
วันที่เผยแพร่: | 2017 |
บทคัดย่อ: | To investigate novel engine and fuel designs for greener aviation, instrumentation is required that can spatially and temporally resolve gas concentrations within aero-engine exhausts. This paper presents work towards a parallel, high-speed, distributed data acquisition (DAQ) system that employs in-situ demodulation of tunable diode laser absorption spectroscopy (TDLAS) signals. We briefly describe how this sits within a wider tomographic instrument, the electrical system of this scalable design and preliminary characterization. Being remote from the end-user (approx. 60m) and deployed within an industrial environment, we have used a hierarchical, embedded strategy. This uses photodiode pre-amplification, filtering, digitization, signal demodulation, Ethernet packaging and microprocessor control implemented both on a multi-node, distributed basis and with the DAQ physically mounted on the same mechanical 'ring' as the tomographic imaging array. Results show agreement with design but indicate that the first-generation interrupt-based direct-memory-access (DMA) between FPGA fabric memory and microprocessor memories is the predominant bottleneck. © 2017 IEEE. |
URI: | https://ir.swu.ac.th/jspui/handle/123456789/12963 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044288782&doi=10.1109%2fICSENS.2017.8234310&partnerID=40&md5=459d9973733dda9487fb64ca601aee3e |
ISSN: | 19300395 |
Appears in Collections: | Scopus 1983-2021 |
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