Embedded Error Bayesian Calibration of Thermal Decomposition of Organic Materials
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Rice Univ., Houston, TX (United States)
Organic materials are an attractive choice for structural components due to their light weight and versatility. However, because they decompose at low temperatures relative to tradiational materials they pose a safety risk due to fire and loss of structural integrity. To quantify this risk, analysts use chemical kinetics models to describe the material pyrolysis and oxidation using thermogravimetric analysis. This process requires the calibration of many model parameters to closely match experimental data. Previous efforts in this field have largely been limited to finding a single best-fit set of parameters even though the experimental data may be very noisy. Furthermore the chemical kinetics models are often simplified representations of the true de- composition process. The simplification induces model-form errors that the fitting process cannot capture. In this work we propose a methodology for calibrating decomposition models to thermogravimetric analysis data that accounts for uncertainty in the model-form and experimental data simultaneously. The methodology is applied to the decomposition of a carbon fiber epoxy composite with a three-stage reaction network and Arrhenius kinetics. The results show a good overlap between the model predictions and thermogravimetric analysis data. Uncertainty bounds capture devia- tions of the model from the data. The calibrated parameter distributions are also presented. In conclusion, the distributions may be used in forward propagation of uncertainty in models that leverage this material.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1810364
- Report Number(s):
- SAND--2021-7958J; 697215
- Journal Information:
- Journal of Verification, Validation and Uncertainty Quantification, Journal Name: Journal of Verification, Validation and Uncertainty Quantification Journal Issue: 4 Vol. 6; ISSN 2377-2158
- Publisher:
- ASMECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Joint estimation of biogeochemical model parameters from multiple experiments: A bayesian approach applied to mercury methylation
Bayesian calibration of thermodynamic models for the uptake of CO2 in supported amine sorbents using ab initio priors