2016/09/03 by Benjamin Schroeder, Benjamin B Schroeder, Schroeder, Benjamin B +19
Computer Science · Decision Sciences · Engineering · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #Bridging (networking) #Computer science #Data Analysis #Engineering #FOS: Physical sciences #Industrial engineering #Physics #Probabilistic and Robust Engineering Design #Statistical physics #Statistics and Probability (physics.data-an) #Thermochemical Biomass Conversion Processes #Weighting #physics.data-an
paper · pdf · doi:10.48550/arxiv.1609.00871
arxiv created 2016/09/03 · openalex publication_date 2016/09/03 · arxiv updated 2016/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
When performing large-scale, high-performance computations of multi-physics applications, it is common to limit the complexity of physics sub-models comprising the simulation. For a hierarchical system of coal boiler simulations a scale-bridging model is constructed to capture characteristics appropriate for the application-scale from a detailed coal devolatilization model. Such scale-bridging allows full descriptions of scale-applicable physics, while functioning at reasonable computational costs. This study presents a variation on multi-fidelity modeling with a detailed physics model, the chemical percolation devolatilization model, being used to calibrate a scale-briding model for the application of interest. The application space provides essential context for designing the scale-bridging model by defining scales, determining requirements and weighting desired characteristics. A single kinetic reaction equation with functional yield model and distributed activation energy is implemented to act as the scale-bridging model-form. Consistency constraints are used to locate regions of the scale-bridging model's parameter-space that are consistent with the uncertainty identified within the detailed model. Ultimately, the performance of the scale-bridging model with consistent parameter-sets was assessed against desired characteristics of the detailed model and found to perform satisfactorily in capturing thermodynamic trends and kinetic timescales for the desired application-scale. Framing the process of model-form selection within the context of calibration and uncertainty quantification allows the credibility of the model to be established.