A Novel Integrated Multi-Scale Framework for Modeling Combustion Processes in Alternative Fuels: Integrating Bayesian Uncertainty Quantification, Sensitivity Analysis, and Empirical Validation

This conceptual paper introduces a novel framework for modeling combustion in alternative fuels, integrating multi-scale approaches from quantum chemistry to macroscopic engine performance. We propose a hybrid model combining detailed kinetic mechanisms with Bayesian inference for uncertainty quantification and advanced sensitivity analysis using Saltelli sampling. Supported by real-world data on hydrogen, biodiesel, and ethanol combustion, the framework incorporates rigorous mathematical derivations, Python-based simulations with multiple-chain MCMC, and falsifiability assessments. Quantitative validation metrics such as RMSE and R² demonstrate superior performance compared to deterministic models. This approach enables predictive, reproducible evaluations of sustainable fuels, drawing from interdisciplinary fields including chemical engineering, statistical mechanics, and computational fluid dynamics. All data and codes are self-contained within this manuscript and supplementary materials.