Please use this identifier to cite or link to this item: 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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