Estimation of the nitrogen ionization reaction rate using electric arc shock tube data and Bayesian model analysis

K. Miki*, M. Panesi, E. E. Prudencio, S. Prudhomme

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


In this paper, we apply a Bayesian analysis to calibrate the parameters of a model for atomic nitrogen ionization using experimental data from the electric arc shock tube (EAST) wind-tunnel at NASA. We use a one-dimensional plasma flow solver coupled with a radiation solver for the simulation of the radiative signature emitted in the shock-heated air plasma as well as a Park's two-temperature model for the thermal and chemical non-equilibrium effects. We simultaneously quantify model parameter uncertainties and physical model inadequacies when solving the statistical inverse problem. Prior to the solution of such a problem, we perform a sensitivity analysis of the radiative heat flux in order to identify important sources of uncertainty. This analysis clearly shows the importance of the direct ionization of atomic nitrogen as it mostly influences the radiative heating. We then solve the statistical inverse problem and compare the calibrated reaction rates against values available in the literature. Our calculations estimate the reaction rate of the atomic nitrogen ionization to be (3.7 ± 1.5) 10 11 cm 3 mol -1 s -1 at 10 000 K, a range consistent with Park's estimation. Finally, in order to assess the validity of the estimated parameters, we propagate their uncertainties through a statistical forward problem defined on a prediction scenario different from the calibration scenarios and compare the model predictions against other experimental data.

Original languageEnglish (US)
Article number023507
Issue number2
StatePublished - Feb 2012
Externally publishedYes

ASJC Scopus subject areas

  • Condensed Matter Physics


Dive into the research topics of 'Estimation of the nitrogen ionization reaction rate using electric arc shock tube data and Bayesian model analysis'. Together they form a unique fingerprint.

Cite this