Döpking, Sandra
[Author]
;
Matera, Sebastian
[Contributor];
Reuter, Karsten
[Contributor]
Error aware analysis of multi-scale reactivity models for chemical surface reactions ; An Adaptive and a Multilevel Adaptive Sparse Grid approach to address global uncertainty and sensitivity
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Media type:
E-Book;
Electronic Thesis;
Doctoral Thesis
Title:
Error aware analysis of multi-scale reactivity models for chemical surface reactions ; An Adaptive and a Multilevel Adaptive Sparse Grid approach to address global uncertainty and sensitivity
Contributor:
Döpking, Sandra
[Author]
imprint:
Freie Universität Berlin: Refubium (FU Berlin), 2022
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
In the field of heterogeneous catalysis, first- principle-based microkinetic modeling has been proven to be an essential tool to provide a deeper understanding of the microscopic interplay between reactions. It avoids the bias of being fitted to experimental data, which allows us to extract information about the materials’ properties that cannot be drawn from experimental data. Unfortunately, the catalytic models draw information from electronic structure theory (e.g. Density Functional Theory) which contains a sizable error due to intrinsic approximations to make the computational costs feasible. Although the errors are commonly accepted and known, this work will analyse how significant the impact of these errors can be on the model outcome. We first explain how these errors are propagated into a model outcome, e.g., turnover-frequency (TOF), and how significant the outcome is impacted. Secondly, we quantify the propagation of single errors by a local and global sensitivity analysis, including a discussion of their dis-/advantages for a catalytic model. The global approach requires the numerical quadrature of high dimensional integrals as the catalytic model often depends on multiple parameters. This, we tackle with a local and dimension-adaptive sg! (sg!) approach. sg!s have shown to be very useful for medium dimensional problems since their adaptivity feature allows for an accurate surrogate model with a modest number of points. Despite the models’ high dimensionality, the outcome is mostly dominated by a fraction of the input parameter, which implies a high refinement in only a fraction of the dimensions (dimension-adaptive). Additionally, the kinetic data shows characteristics of sharp transitions between "non-active" and "active" areas, which need a higher order of refinement (local-adaptive). The efficiency of the adaptive sg! is tested on different toy models and a realistic first principle model, including the Sensitivity Analysis. Results show that for catalytic models, a local derivative-based ...