The question of how solar storms affect Earth holds both fundamental scientific importance and considerable societal relevance, particularly with regard to protecting infrastructure from the most intense solar events. Current models typically employ a fluid representation of electrons, primarily due to algorithmic and computational limitations. The main objective of the TerraVirtualE project is to develop a model of Earth’s near-space environment based on a particle-based description of both ions and electrons.
TerraVirtualE aims to utilise a Particle-in-Cell (PIC) model, in which ions and electrons are treated explicitly as particles. This approach will enable a detailed investigation of the role of electrons in the transfer of energy and matter from the solar wind to Earth’s near-space environment.
This endeavour is made possible by the Energy-Conserving Semi-Implicit Method (ECsim) algorithm, developed by the Principal Investigator, Professor Giovanni Lapenta. ECsim conserves the total energy of particles and electromagnetic fields in PIC simulations—a crucial element for analysing energy flow from the solar wind. In addition, the algorithm’s energy conservation properties significantly improve numerical stability, greatly enhancing ECsim’s capacity to simulate large-scale systems, such as planetary atmospheres.
Building on this foundation, we will demonstrate that a comprehensive particle-based representation of planetary space, incorporating both electrons and ions, is viable using the ECsim algorithm (Objective 1). To enhance the physical realism of solar wind dynamics within the heliosphere, these PIC simulations will be coupled with the heliospheric model in EUHFORIA (Objective 2). Furthermore, we will show that machine learning algorithms can be used to cluster in situ observations of the solar wind and analyse the outputs of PIC simulations (Objective 4).
