An Imprecise Bayesian Approach to Thermal Runaway Probability

Marc Fischer, Alexis Vignes
Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, PMLR 147:150-160, 2021.

Abstract

In this pioneering work, an assessment of thermal runaway probability based on simplified chemical kinetics has been performed with imprecise Bayesian methods relying on several priors. The physical phenomenon is governed by two chemical kinetic parameters $A$ and $Ea$. We suppose that their values are considerably uncertain but also that we know the experimental profiles of a chemical species corresponding to their true values, thereby allowing us to compute likelihoods and posteriors corresponding to different levels of information. We are interested in the critical delay time $tc$ beyond which an explosion will certainly occur. The use of several priors allows us to see when the data truly dominate the prior with respect to the probability distribution of $tc$. It does not appear possible to do so in an orthodox precise Bayesian framework that reduces all forms of uncertainty to a single probability distribution.

Cite this Paper

BibTeX
@InProceedings{pmlr-v147-fischer21a, title = {An Imprecise Bayesian Approach to Thermal Runaway Probability}, author = {Fischer, Marc and Vignes, Alexis}, booktitle = {Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications}, pages = {150--160}, year = {2021}, editor = {Cano, Andrés and De Bock, Jasper and Miranda, Enrique and Moral, Serafı́n}, volume = {147}, series = {Proceedings of Machine Learning Research}, month = {06--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v147/fischer21a/fischer21a.pdf}, url = {https://proceedings.mlr.press/v147/fischer21a.html}, abstract = {In this pioneering work, an assessment of thermal runaway probability based on simplified chemical kinetics has been performed with imprecise Bayesian methods relying on several priors. The physical phenomenon is governed by two chemical kinetic parameters $A$ and $Ea$. We suppose that their values are considerably uncertain but also that we know the experimental profiles of a chemical species corresponding to their true values, thereby allowing us to compute likelihoods and posteriors corresponding to different levels of information. We are interested in the critical delay time $tc$ beyond which an explosion will certainly occur. The use of several priors allows us to see when the data truly dominate the prior with respect to the probability distribution of $tc$. It does not appear possible to do so in an orthodox precise Bayesian framework that reduces all forms of uncertainty to a single probability distribution.} }
Endnote
%0 Conference Paper %T An Imprecise Bayesian Approach to Thermal Runaway Probability %A Marc Fischer %A Alexis Vignes %B Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications %C Proceedings of Machine Learning Research %D 2021 %E Andrés Cano %E Jasper De Bock %E Enrique Miranda %E Serafı́n Moral %F pmlr-v147-fischer21a %I PMLR %P 150--160 %U https://proceedings.mlr.press/v147/fischer21a.html %V 147 %X In this pioneering work, an assessment of thermal runaway probability based on simplified chemical kinetics has been performed with imprecise Bayesian methods relying on several priors. The physical phenomenon is governed by two chemical kinetic parameters $A$ and $Ea$. We suppose that their values are considerably uncertain but also that we know the experimental profiles of a chemical species corresponding to their true values, thereby allowing us to compute likelihoods and posteriors corresponding to different levels of information. We are interested in the critical delay time $tc$ beyond which an explosion will certainly occur. The use of several priors allows us to see when the data truly dominate the prior with respect to the probability distribution of $tc$. It does not appear possible to do so in an orthodox precise Bayesian framework that reduces all forms of uncertainty to a single probability distribution.
APA
Fischer, M. & Vignes, A.. (2021). An Imprecise Bayesian Approach to Thermal Runaway Probability. Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, in Proceedings of Machine Learning Research 147:150-160 Available from https://proceedings.mlr.press/v147/fischer21a.html.

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