자료유형  

 학위논문

Control Number  

0017164206

International Standard Book Number  

9798896077923

Dewey Decimal Classification Number  

629.1

Main Entry-Personal Name  

Davis, McKenna.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, Los Angeles., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

285 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

General Note  

Advisor: Wirz, Richard E.

Dissertation Note  

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

Summary, Etc.  

요약Electrospray thrusters require significant improvements in operational lifetime for use inmulti-year spacecraft propulsion missions. The primary thruster lifetime-limiting mechanismis propellant overspray, in which wide-angle particles impinge on and saturate downstreamelectrodes instead of exiting through the electrode aperture and contributing to producedthrust. Electrospray particles are emitted within a small radial range, but diverge as theymove downstream from emission to form a 3D plume, the edges of which contribute tooverspray. In order to improve electrospray thruster designs towards minimizing oversprayand optimizing operational lifetime, we need to understand what causes electrospray plumedivergence.This dissertation investigates electrospray plume divergence using the Discrete ElectrosprayLagrangian Interaction (DELI) Model to simulate electrospray particle dynamics. Thegoverning equation for particle propagation includes the applied electrostatic force from thepotential difference between the emitter and downstream electrode, the Coulomb forcesbetween particles (including image charges), and the drag force. Each of these forces is investigatedtheoretically 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 instabilitiesand minute asymmetries in the emitter. All three forces exacerbate existing radial divergenceamong a set of particles: the applied electric field has a radial component due to jetcurvature and the electrode aperture; there is a radial component to Coulomb forces betweenparticles with a difference in radial coordinate; and drag counters particle motion, keepingparticles 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 theirforward neighbors. In the upstream velocity gradient case, faster particles catch up to theirforward neighbors, magnifying the Coulomb interaction between the two in response to theirincreased proximity. We term this interaction a 'traffic jam' and correlate it with increasedplume divergence through Coulomb interactions. We present two novel means of characterizingplume divergence: 1) a metric for positional divergence based on three standards of aGaussian or Super-Gaussian fit to particle mass density distribution as a function of radialcoordinate, and 2) emittance as a metric for positional and velocity divergence. We furtherdescribe how emittance can be used to identify when an electrospray plume has reached thesteady state.Machine learning is applied for the first time to electrospray particle dynamics data,produced by the DELI Model. Results demonstrate predictive abilities for downstreamparticle dynamic properties given particle properties at emission. Furthermore, a novelmethod is proposed for combining experimental electrospray particle data, computationalplume evolution models, and machine learning algorithms to optimize diagnostic design.In summary, this dissertation presents a comprehensive consideration of electrosprayplume divergence using computational and analytical models supported by experimentaldata. The origins and sources of growth of electrospray plume divergence are identified, new metrics for electrospray plume divergence are presented, and machine learning algorithmsare developed to predict electrospray plume divergence.In summary, this dissertation presents a comprehensive consideration of electrosprayplume divergence using computational and analytical models supported by experimentaldata. The origins and sources of growth of electrospray plume divergence are identified, new metrics for electrospray plume divergence are presented, and machine learning algorithmsare developed to predict electrospray plume divergence.

Subject Added Entry-Topical Term  

Aerospace engineering.

Index Term-Uncontrolled  

Computational fluid dynamics

Index Term-Uncontrolled  

Data science

Index Term-Uncontrolled  

Electrospray

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Spacecraft propulsion

Added Entry-Corporate Name  

University of California, Los Angeles Aerospace Engineering 0279

Host Item Entry  

Dissertations Abstracts International. 86-04B.

Electronic Location and Access  

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Control Number  

joongbu:657448