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Scale-bridging models are created to capture desired characteristics of high-fidelity models within low-fidelity model-forms for the purpose of allowing models to function at required spacial and/or temporal scales. The development, analysis, and application of scale-bridging models will be the focus of this dissertation. The applications dictating scales herein are large-scale computational fluid dynamics codes. Three unique scale-bridging models will be presented. First, the development and validation of a multiple-polymorph, particle precipitation modeling framework for highly supersaturated CaCO3 systems will be presented. This precipitation framework is validated against literature data, as well as explored for additional avenues of validation and potential future applications. Following this will be an introduction to the concepts of validation and uncertainty quantification and an approach for credible simulation development based upon those concepts. The credible simulation development approach is demonstrated through a spring-mass-damper pedagogical example. Bayesian statistical methods are commonly applied to validation and uncertainty quantification issues and the well-known Kennedy O'Hagan approach towards model-form uncertainty will be explored thoroughly using a chemical kinetics pedagogical example. Additional issues and ideas surrounding model-form uncertainty such as the identification problem will also be considered. Bayesian methods will then be applied towards the creation of a scale-bridging model for coal particle heat capacity and enthalpy modeling. Lastly, an alternative validation and uncertainty quantification technique, known as consistency testing, will be utilized to create a scale-bridging model for coal particle devolatilization. The credibility of the devolatilization scale-bridging model due to the model development process is assessed and found to have benefited from the use of validation and uncertainty quantification practices. |
