Parameter optimization of the reduced-order scrape-off-layer model DIV1D using Markov-Chain Monte Carlo sampling

Accurate and efficient modeling of scrape-off layer (SOL) dynamics is essential for controlling divertor detachment in future fusion reactors. The reduced-order SOL model DIV1D provides a fast alternative to high-fidelity codes such as SOLPS-ITER, but it contains fitting parameters that are traditionally tuned manually and with limited knowledge about their posterior distribution. This paper introduces a Bayesian framework employing Markov Chain Monte Carlo (MCMC) sampling to fit DIV1D to mapped SOLPS-ITER solutions. The framework quantifies parameter uncertainties through posterior likelihood distributions, revealing parameter correlations and multi-modal behavior. A Sobol sensitivity analysis, extended with a novel adaptive formulation, provides additional insight into parameter influence and interactions. Application to SOLPS-ITER simulations of TCV shows improved fits compared to the benchmark parameter set, while results across a SOLPS-ITER density ramp on TCV highlight systematic trends and parameter correlations, suggesting the potential of density-dependent adaptive fitting. Extension of the method to SOLPS-ITER simulations of AUG demonstrates its robustness and adaptability. Overall, the Bayesian MCMC framework reduces manual workload and enables reproducible and interpretable parameter estimation, leading to improved reduced-order SOL modeling capabilities.