An efficient approach for limited-data chemical species tomography and its error bounds

N. Polydorides; S. A. Tsekenis; H. McCann; V. -D. A. Prat; P. Wright

doi:10.1098/rspa.2015.0875

An efficient approach for limited-data chemical species tomography and its error bounds

N. Polydorides, S. A. Tsekenis, H. McCann, V. -D. A. Prat, P. Wright

School of Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

We present a computationally efficient reconstruction method for the limited-data chemical species tomography problem that incorporates projection of the unknown gas concentration function onto a low-dimensional subspace, and regularization using prior information obtained from a simple flow model. In this context, the contribution of this work is on the analysis of the projection-induced data errors and the calculation of bounds for the overall image error incorporating the impact of projection and regularization errors as well as measurement noise. As an extension to this methodology, we present a variant algorithm that preserves the positivity of the concentration image.

Original language	English
Article number	20150875
Number of pages	17
Journal	Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume	472
Issue number	2187
Early online date	1 Mar 2016
DOIs	https://doi.org/10.1098/rspa.2015.0875
Publication status	Published - 31 Mar 2016

Access to Document

10.1098/rspa.2015.0875Licence: Creative Commons: Attribution (CC-BY)

20150875.fullFinal published version, 1.5 MBLicence: Creative Commons: Attribution (CC-BY)

TRANSFER: FLITES: Fibre-Laser Imaging of gas Turbine Exhaust Species
Mccann, H. (Principal Investigator)
EPSRC
1/03/13 → 28/02/17
Project: Research

Nick Polydorides
- School of Engineering - Personal Chair in Computational Engineering
Person: Academic: Research Active

Cite this

@article{61ed307d63e4424f9d6bd3987316f0f9,

title = "An efficient approach for limited-data chemical species tomography and its error bounds",

abstract = "We present a computationally efficient reconstruction method for the limited-data chemical species tomography problem that incorporates projection of the unknown gas concentration function onto a low-dimensional subspace, and regularization using prior information obtained from a simple flow model. In this context, the contribution of this work is on the analysis of the projection-induced data errors and the calculation of bounds for the overall image error incorporating the impact of projection and regularization errors as well as measurement noise. As an extension to this methodology, we present a variant algorithm that preserves the positivity of the concentration image.",

author = "N. Polydorides and Tsekenis, \{S. A.\} and H. McCann and Prat, \{V. -D. A.\} and P. Wright",

year = "2016",

month = mar,

day = "31",

doi = "10.1098/rspa.2015.0875",

language = "English",

volume = "472",

journal = "Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences",

issn = "1364-5021",

publisher = "Royal Society Publishing",

number = "2187",

}

TY - JOUR

T1 - An efficient approach for limited-data chemical species tomography and its error bounds

AU - Polydorides, N.

AU - Tsekenis, S. A.

AU - McCann, H.

AU - Prat, V. -D. A.

AU - Wright, P.

PY - 2016/3/31

Y1 - 2016/3/31

N2 - We present a computationally efficient reconstruction method for the limited-data chemical species tomography problem that incorporates projection of the unknown gas concentration function onto a low-dimensional subspace, and regularization using prior information obtained from a simple flow model. In this context, the contribution of this work is on the analysis of the projection-induced data errors and the calculation of bounds for the overall image error incorporating the impact of projection and regularization errors as well as measurement noise. As an extension to this methodology, we present a variant algorithm that preserves the positivity of the concentration image.

AB - We present a computationally efficient reconstruction method for the limited-data chemical species tomography problem that incorporates projection of the unknown gas concentration function onto a low-dimensional subspace, and regularization using prior information obtained from a simple flow model. In this context, the contribution of this work is on the analysis of the projection-induced data errors and the calculation of bounds for the overall image error incorporating the impact of projection and regularization errors as well as measurement noise. As an extension to this methodology, we present a variant algorithm that preserves the positivity of the concentration image.

U2 - 10.1098/rspa.2015.0875

DO - 10.1098/rspa.2015.0875

M3 - Article

SN - 1364-5021

VL - 472

JO - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences

JF - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences

IS - 2187

M1 - 20150875

ER -

An efficient approach for limited-data chemical species tomography and its error bounds

Abstract

Access to Document

Fingerprint

Projects

TRANSFER: FLITES: Fibre-Laser Imaging of gas Turbine Exhaust Species

Profiles

Nick Polydorides

Cite this