Improving chemical species tomography of turbulent flows using covariance estimation

Samuel J. Grauer, Paul J. Hadwin, Kyle J. Daun

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Chemical species tomography (CST) experiments can be divided into limited-data and full-rank cases. Both require solving ill-posed inverse problems, and thus the measurement data must be supplemented with prior information to carry out reconstructions. The Bayesian framework formalizes the role of additive information, expressed as the mean and covariance of a joint-normal prior probability density function. We present techniques for estimating the spatial covariance of a flow under limited-data and full-rank conditions. Our results show that incorporating a covariance estimate into CST reconstruction via a Bayesian prior increases the accuracy of instantaneous estimates. Improvements are especially dramatic in real-time limited-data CST, which is directly applicable to many industrially relevant experiments.

Original languageEnglish (US)
Pages (from-to)3900-3912
Number of pages13
JournalApplied optics
Issue number13
StatePublished - May 1 2017

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering


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