Search

Induced magnetospheres form around planetary bodies with atmospheres through the interaction of the solar wind with their ionosphere. Induced magnetospheres are highly dependent on the solar wind conditions and have only been studied with single spacecraft missions in the past. This gap in knowledge could be addressed by a multi-spacecraft plasma mission, optimized for studying global spatial and temporal variations in the magnetospheric system around Venus, which hosts the most prominent example of an induced magnetosphere in our solar system. The MVSE mission comprises four satellites, of which three are identical scientific spacecraft, carrying the same suite of instruments probing different regions of the induced magnetosphere and the solar wind simultaneously. The fourth spacecraft is the transfer vehicle which acts as a relay satellite for communications at Venus. In this way, changes in the solar wind conditions and extreme solar events can be observed, and their effects can be quantified as they propagate through the Venusian induced magnetosphere. Additionally, energy transfer in the Venusian induced magnetosphere can be investigated. The scientific payload includes instrumentation to measure the magnetic field, electric field, and ion-electron velocity distributions. This study presents the scientific motivation for the mission as well as requirements and the resulting mission design. Concretely, a mission timeline along with a complete spacecraft design, including mass, power, communication, propulsion and thermal budgets are given. This mission was initially conceived at the Alpbach Summer School 2022 and refined during a week-long study at ESAs Concurrent Design Facility in Redu, Belgium, Comment: 23 pages, 5 figures, Submitted to Acta Astronautica

We use Lyapunov-like functions and convex optimization to propagate uncertainty in the initial condition of nonlinear systems governed by ordinary differential equations. We consider the full nonlinear dynamics without approximation, producing rigorous bounds on the expected future value of a quantity of interest even when only limited statistics of the initial condition (e.g., mean and variance) are known. For dynamical systems evolving in compact sets, the best upper (lower) bound coincides with the largest (smallest) expectation among all initial state distributions consistent with the known statistics. For systems governed by polynomial equations and polynomial quantities of interest, one-sided estimates on the optimal bounds can be computed using tools from polynomial optimization and semidefinite programming. Moreover, these numerical bounds provably converge to the optimal ones in the compact case. We illustrate the approach on a van der Pol oscillator and on the Lorenz system in the chaotic regime., Comment: 6 pages, 2 figures, 2 tables. Accepted to American Control Conference 2023