Particle Swarm Optimization for Multiple Dipole Modeling of Space Equipment

An advanced modeling algorithm based on particle swarm optimization (PSO) has been developed to solve multiple dipole modeling (MDM) problems in space applications. MDM is a method to represent spacecraft units as a set of equivalent magnetic dipoles able to reconstruct, in the far-field distance, the same magnetostatic field. This procedure allows preparing a magnetic model of the spacecraft during design and development phases. Moreover, it allows refined prediction of magnetic cleanliness for space missions with equipment susceptible to magnetic fields. Indeed, owing to the increase of missions requiring magnetostatic cleanliness, such characterization becomes increasingly important. To validate the PSO procedure, synthetic data have been initially used, generated using a software simulator. Algorithm performance has been tested through measured data acquired using the Mobile Coil Facility located at the European Space Research and Technology Centre in The Netherlands. Starting from measured data, the algorithm iteratively identifies the values of the unknowns, positions, and magnetic moments of the equivalent dipoles that best match the measured field. Since the problem is ill posed, several solutions are possible. To develop a reliable algorithm, some test cases have been analyzed where the expected solution is known. This allowed improving the algorithm leading to satisfying results.