Kavli Affiliate: Long Zhang
| First 5 Authors: Yiming Lu, Xu Zhu, Long Zhang, Hua Zhou,
| Summary:
Gas sampling methods have been crucial for the advancement of combustion
science, enabling analysis of reaction kinetics and pollutant formation.
However, the measured composition can deviate from the true one because of the
potential residual reactions in the sampling probes. This study formulates the
initial composition estimation in stiff chemically reacting systems as a
Bayesian inference problem, solved using the No-U-Turn Sampler (NUTS).
Information loss arises from the restriction of system dynamics by low
dimensional attracting manifold, where constrained evolution causes initial
perturbations to decay or vanish in fast eigen-directions in composition space.
This study systematically investigates the initial value inference in
combustion systems and successfully validates the methodological framework in
the Robertson toy system and hydrogen autoignition. Furthermore, a gas sample
collected from a one-dimensional hydrogen diffusion flame is analyzed to
investigate the effect of frozen temperature on information loss. The research
highlights the importance of species covariance information from observations
in improving estimation accuracy and identifies how the rank reduction in the
sensitivity matrix leads to inference failures. Critical failure times for
species inference in the Robertson and hydrogen autoignition systems are
analyzed, providing insights into the limits of inference reliability and its
physical significance.
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