Sparse Regression for the Breathing Mode Instability: Extracting Governing Equations from Hall Effect Thrusters Simulation Data

Bayón Buján, Borja

Publication:
Sparse Regression for the Breathing Mode Instability: Extracting Governing Equations from Hall Effect Thrusters Simulation Data

dc.contributor.author	Bayón Buján, Borja
dc.contributor.departamento	UC3M. Departamento de Ingeniería Aeroespacial	es
dc.contributor.funder	European Commission	es
dc.contributor.tutor	Merino Martínez, Mario
dc.date.accessioned	2023-09-25T11:37:04Z
dc.date.available	2023-09-25T11:37:04Z
dc.date.issued	2023
dc.date.submitted	2023-07-06
dc.description.abstract	The development of efficient and reliable electric space propulsion systems relies on accurate modeling and identification of their underlying dynamics. Traditional approaches to model identification often involve intricate physical analysis or making extensive assumptions, limiting their applicability and scalability. In this thesis an algorithm for accurate modeling and identification of electric space propulsion systems is presented. The algorithm, based on sparse regression and model parsimony, allows automatic data-driven identification of models for space plasma thrusters. It incorporates statistical techniques, physical constraints, and trajectory-based information for robust system identification. The algorithm is demonstrated using PIC/fluid simulation data from a Hall Effect Thruster for several operating points. Models of varying complexity are obtained, focusing on physical explainability and coefficient variation with operating point. The resulting equations for average ion and neutral densities align well with existing models. Point-wise density models exhibit location dependency in the discharge chamber. The algorithm showcases its general applicability to other electric propulsion systems.	en
dc.description.degree	Máster en Ingeniería Espacial	es
dc.description.sponsorship	This work has been carried out as part of the ZARATHUSTRA project, which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 950466).	en
dc.identifier.uri	https://hdl.handle.net/10016/38438
dc.language.iso	eng	en
dc.relation.projectID	info:eu-repo/grantAgreement/EC/950466
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 España	*
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/	*
dc.subject.eciencia	Aeronáutica	es
dc.subject.eciencia	Física	es
dc.subject.eciencia	Ingeniería Mecánica	es
dc.title	Sparse Regression for the Breathing Mode Instability: Extracting Governing Equations from Hall Effect Thrusters Simulation Data	en
dc.type	master thesis	*
dspace.entity.type	Publication