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Probabilistic inference of reaction rate parameters from summary statistics

Journal Article · · Combustion Theory and Modelling
 [1];  [1]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
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Here, this investigation tackles the probabilistic parameter estimation problem involving the Arrhenius parameters for the rate coefficient of the chain branching reaction H + O2 → OH + O. This is achieved in a Bayesian inference framework that uses indirect data from the literature in the form of summary statistics by approximating the maximum entropy solution with the aid of approximate bayesian computation. The summary statistics include nominal values and uncertainty factors of the rate coefficient, obtained from shock-tube experiments performed at various initial temperatures. The Bayesian framework allows for the incorporation of uncertainty in the rate coefficient of a secondary reaction, namely OH + H2 → H2O + H, resulting in a consistent joint probability density on Arrhenius parameters for the two rate coefficients. It also allows for uncertainty quantification in numerical ignition predictions while conforming with the published summary statistics.
Research Organization:
Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Grant/Contract Number:
AC04-94AL85000
OSTI ID:
1465801
Report Number(s):
SAND--2017-2451J; 666595
Journal Information:
Combustion Theory and Modelling, Journal Name: Combustion Theory and Modelling Journal Issue: 4 Vol. 22; ISSN 1364-7830
Publisher:
Taylor & FrancisCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (1)

Parameter Estimation for RANS Models Using Approximate Bayesian Computation preprint January 2020

Figures / Tables (13)

Table 1(p. 4)table Table 1
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Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Arrhenius parameters
Bayesian inference
maximum relative entropy
reaction rates
shock-tube experiments
uncertainty quantification