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RWTH Publication No: 849257 2022   |
TITLE |
Probabilistic Constrained Bayesion Inversion for Transpiration Cooling |
AUTHORS |
Ella Steins, Tan Bui-Thanh, Michael Herty, Siegfried Müller |
ABSTRACT |
To enable safe operations in applications such as rocket combustion chambers, the
materials require cooling to avoid material damage. Here, transpiration cooling is a
promising cooling technique. Numerous studies investigate possibilities to simulate and
evaluate the complex cooling mechanism. One naturally arising question is the amount
of coolant required to ensure a safe operation. To study this, we introduce an approach
that determines the posterior probability distribution of the Reynolds number using
an inverse problem and constraining the maximum temperature of the system under
parameter uncertainties. Mathematically, this chance inequality constraint is dealt with by
a generalized Polynomial Chaos expansion of the system. The posterior distribution will
be evaluated by different Markov Chain Monte Carlo based methods. A novel method for
the constrained case is proposed and tested among others on two-dimensional transpiration
cooling models. |
KEYWORDS |
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DOI |
10.1002/fld.5135 |
PUBLICATION |
Numerical Methods in Fluids, Volume94, Issue12, December 2022, Pages 2020-2039 |