Comparative study on gradient-free optimization methods for inverse source-term estimation of radioactive dispersion...

In this study, we rigorously assess the performance of three gradient-free optimization algorithms-Ensemble Kalman Inversion (EKI), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA)-for estimating source terms in diverse radionuclide release scenarios. Our analysis encompasses both single and multiple sources with varying radionuclide compositions, delving into the influence of decay constants and radioactivity on source estimation accuracy. Although estimating a single radionuclide from a single source exhibits outstanding results, estimating multiple radionuclides from a single source proves more arduous due to the limited information available for discerning gamma dose rates. Contrary to expectations, increasing the number of observation stations does not consistently improve the likelihood of finding accurate solutions in ill-posed inverse problems. Impressively, under our simulation settings, EKI demonstrates competitive performance in terms of convergence, accuracy, and runtime compared to PSO and GA, with GPU parallelization further bolstering computational efficiency. We explore strategies for enhancing source term estimation, including incorporating prior information, applying uncertainty removal techniques, and optimizing observation placement. Additionally, this study underscores the intricate role of relative error in determining multi-radionuclide estimation accuracy from gamma dose measurements. By employing the Gaussian plume model under steady-state conditions, our research lays the groundwork for future applications of Lagrangian dispersion models with real-time data integration. The insights gleaned from our study promise to advance environmental radioactivity monitoring and catalyze the development of cutting-edge, real-time source estimation technologies in full-scale systems.