Researchers from the University of Manchester, led by Charlie Fynn Perkins, Marcus Webb, and Fred J. Currell, have developed a novel computational method to simulate radiation chemistry processes. Their work, published in the Journal of Computational Physics, focuses on understanding the behavior of radiolytic species created by high-energy electrons in water, which has significant implications for various fields, including the energy sector.
The team introduced a hybrid approach that combines stochastic and deterministic methods to model the spatio-temporal evolution of radiolytic species. Initially, the conditions are determined stochastically, but the subsequent time evolution is calculated deterministically using a continuum representation derived from those initial conditions. This hybrid method, part of the Manchester Inhomogeneous Radiation Chemistry by Linear Expansions (MIRaCLE) toolkit, allows for efficient simulation of radiation chemistry processes.
One of the key advantages of this approach is its ability to converge to a high level of accuracy in a single run, often achieving a 1% accuracy level without requiring multiple simulations. This is particularly beneficial for calculating time-dependent G-values for various radiolytic products, such as e_{aq}^- and \dot{\mathrm{OH}}, at unprecedented dose rates. The researchers demonstrated that their method could perform calculations that would take years with conventional Monte Carlo approaches in just hours on a commercial laptop.
The study also addressed a known artifact of continuum modeling by introducing a correction term that mitigates this issue. This enhancement further improves the accuracy and reliability of the simulations. The researchers believe that their method provides a flexible and efficient platform for modeling long-timescale radiolysis, bridging the gap between Monte Carlo approaches and macroscopic reaction-diffusion schemes.
For the energy sector, this research has broad implications. Understanding radiation chemistry is crucial for nuclear safety, as it helps in predicting the behavior of radiolytic species in water-cooled reactors. It also has applications in environmental processing, where radiation is used to treat wastewater and other contaminants. Additionally, the insights gained from this research can be applied to electron microscopy and other technologies that involve high-energy electrons.
In summary, the researchers from the University of Manchester have developed a novel computational method that significantly improves the simulation of radiation chemistry processes. Their work provides a powerful tool for understanding and predicting the behavior of radiolytic species, with wide-ranging applications in the energy sector and beyond.
This article is based on research available at arXiv.