UCLA

Electrospray thrusters require significant improvements in operational lifetime for use inmulti-year spacecraft propulsion missions. The primary thruster lifetime-limiting mechanism is propellant overspray, in which wide-angle particles impinge on and saturate downstream electrodes instead of exiting through the electrode aperture and contributing to produced thrust. Electrospray particles are emitted within a small radial range, but diverge as they move downstream from emission to form a 3D plume, the edges of which contribute to overspray. In order to improve electrospray thruster designs towards minimizing overspray and optimizing operational lifetime, we need to understand what causes electrospray plume divergence.

This dissertation investigates electrospray plume divergence using the Discrete ElectrosprayLagrangian Interaction (DELI) Model to simulate electrospray particle dynamics. The governing equation for particle propagation includes the applied electrostatic force from the potential difference between the emitter and downstream electrode, the Coulomb forces between particles (including image charges), and the drag force. Each of these forces is investigated theoretically and computationally to determine its influence on plume divergence.None of the forces introduce radial divergence into a set of particles emitted straight down the axis of emission with no range in radial coordinate. However, electrospray particles are always emitted with some small range in radial coordinate due to hydrodynamic instabilities and minute asymmetries in the emitter. All three forces exacerbate existing radial divergence among a set of particles: the applied electric field has a radial component due to jet curvature and the electrode aperture; there is a radial component to Coulomb forces between particles with a difference in radial coordinate; and drag counters particle motion, keeping particles in a clustered state in which Coulomb forces are magnified.

Simulations compare the radial divergence of groups of particles with equal velocities andwith an upstream velocity gradient, in which upstream particles are moving faster than their forward neighbors. In the upstream velocity gradient case, faster particles catch up to their forward neighbors, magnifying the Coulomb interaction between the two in response to their increased proximity. We term this interaction a ‘traffic jam’ and correlate it with increased plume divergence through Coulomb interactions. We present two novel means of characterizing plume divergence: 1) a metric for positional divergence based on three standards of a Gaussian or Super-Gaussian fit to particle mass density distribution as a function of radial coordinate, and 2) emittance as a metric for positional and velocity divergence. We further describe how emittance can be used to identify when an electrospray plume has reached the steady state.

Machine learning is applied for the first time to electrospray particle dynamics data,produced by the DELI Model. Results demonstrate predictive abilities for downstream particle dynamic properties given particle properties at emission. Furthermore, a novel method is proposed for combining experimental electrospray particle data, computational plume evolution models, and machine learning algorithms to optimize diagnostic design. In summary, this dissertation presents a comprehensive consideration of electrospray plume divergence using computational and analytical models supported by experimental data. The origins and sources of growth of electrospray plume divergence are identified, new metrics for electrospray plume divergence are presented, and machine learning algorithms are developed to predict electrospray plume divergence.

In summary, this dissertation presents a comprehensive consideration of electrosprayplume divergence using computational and analytical models supported by experimental data. The origins and sources of growth of electrospray plume divergence are identified, new metrics for electrospray plume divergence are presented, and machine learning algorithms are developed to predict electrospray plume divergence.