(110b) Multilevel Monte Carlo Sampling for Thin Film Deposition Under Uncertainty
AIChE Annual Meeting
Monday, November 11, 2019 - 12:46pm to 1:02pm
In our previous works, we applied MLMC to estimate the observables of classic chemical engineering systems subject to parametric uncertainty  and of a stochastic multiscale system of Chemical Vapour Deposition (CVD)  where the variability in the observables was due to noise rather than uncertainty . In both  and , we demonstrated that MLMC can be used to accurately estimate the expected values of the observables in a fraction of the time required for conventional Monte Carlo sampling. However, for stochastic multiscale systems, parametric uncertainty, rather than stochastic noise, is the greater contributor to the variability in the observables, and such uncertainty was not considered in . To the best of our knowledge, an application of MLMC for uncertainty quantification in a stochastic multiscale system is absent from the literature.
In our previous work , we relied on heuristics to obtain the expected values of the observables from the stochastic multiscale CVD model  and subsequently employed that data to construct PSE and PCE for efficient uncertainty quantification . It was found that the stochastic noise negatively affected the ability of PCE to approximate the expected values of the observables and that more data was necessary to improve the accuracy. In this work, using a methodology similar to , we apply MLMC to a stochastic multiscale model of a heterogeneous catalytic flow reactor system  to obtain its expected observables and use them to develop PSE and PCE expressions. The expressions are then employed to conduct parametric uncertainty quantification. The PSE and PCE expressions are also derived using traditional heuristics. The study compares the results of PSE and PCE derived using both techniques to the results of Monte Carlo sampling and establishes whether the use of MLMC instead of heuristics can decrease the variability in PSE sensitivities and PCE coefficients and consequently provide more accurate estimates of the probability distributions of the observables of the catalytic flow reactor system under uncertainty.
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