Data from: Experimental Characterization of Power Losses in Electrodeless Plasma Thruster via Wall-Embedded Diagnostics (thermal data)

Data from: Experimental Characterization of Power Losses in Electrodeless Plasma Thruster via Wall-Embedded Diagnostics (thermal data) Authors: Elpidio Lettera, Marco Riccardo Inchingolo, Jaume Navarro-Cavallé, Mario Merino Contact email: [e-mail] Date: 2026-07-01 Keywords: Electric Propulsion, Plasma Physics, Plasma Plumes, Plasma-Wall Losses, Electron Cyclotron Resonance Thruster Version: 1.0.0 Digital Object Identifier (DOI): 10.5281/zenodo.21082822 License: This dataset is made available under the Open Data Commons Attribution License Abstract This dataset contains the post-processed experimental data employed in the MSc Thesis: “Experimental Characterization of Low-Frequency Oscillations and Power Losses in Electrodeless Plasma Thruster via Wall-Embedded Diagnostics” The data in this repository are obtained from measurements performed on a waveguide ECR thruster prototype as described in the reference. For further information on the experimental setup please refer to the MSc Thesis (Chapter 4). Dataset description The thermal data were acquired from a thermistor and a thermocouple embedded in the thruster wall. The thermistor signal provides the local transient thermal response of the probe, whose design is described in the reference (Subsection 3.2.1), while the thermocouple provides the associated wall-temperature measurement. These time-resolved temperature histories were used as input to a Weighted Least-Squares Estimator (WLSE) developed to estimate the plasma-to-wall power loss. The thermal signals were post-processed by removing the initial ignition transient of the thruster. The original acquisition time was converted into an elapsed-time coordinate by subtracting the time of the first thermistor sample retained after removal of the ignition transient; therefore, each test starts at $t = 0\,\mathrm{s}$. The thermocouple temperature was interpolated onto the thermistor time base and the two temperature signals were subsequently synchronized. The dataset presents the WLSE results in terms of estimated plasma-to-wall power loss and wall-loss fraction. The calibration-test data used in the WLSE procedure are provided according to the methodology described in the reference. For these tests, both the measured thermal histories and the calibrated power deposited on the thermal probe are reported. Additional supporting plume-diagnostic datasets are provided from electrostatic probe (i.e., Faraday Cup and Retarding Potential Analyzer) measurements. These data support the interpretation of the thruster operating conditions during the experimental campaign. Data Files The data files are in standard Matlab .mat format. A recent version of Matlab is recommended. The thermal measurements are subdivided according to the injected xenon mass flow rate. The file nomenclature follows the structure "ThermalData_[injected mass flow rate]SCCM.mat". Each file contains a structure array, where each element refers to an individual thermal test performed at the corresponding mass flow rate. Pset: Input power [W] N_test: Test number [-] t: Elapsed time [s] Tth: Thermistor temperature measurement [°C] Ttc: Thermocouple wall-temperature measurement [°C] The variables t, Tth and Ttc are stored as column vectors of equal length. Calibration data are provided in "ThermalData_Calibration.mat". This file follows the same structure-array layout as the thermal-test files, with one element associated with each calibration test, while excluding the input-power field Pset. The data file "CalibrationPower.mat" reports the Monte Carlo Ray-Tracing results used to quantify the calibrated power deposited on the thermal probe during the calibration tests. The data are stored as column vectors, with each row corresponding to one calibration test: N_test: Calibration-test number [-] PW: calibrated power deposited on the thermal probe [W] sigma_PW: uncertainty associated with PW [W] The WLSE results are collected in "PlasmaLossResults.mat". The data are arranged as column vectors, with each row referring to one thruster operating condition: mp: Injected xenon mass flow rate [SCCM] Pset: Input power [W] PT: Thruster power [W] Pwall: Estimated plasma-to-wall power loss [W] sigma_Pwall: Uncertainty associated with Pwall [W] eta_wall: Wall-loss fraction with respect to the thruster power [%] sigma_eta_wall: Uncertainty associated with eta_wall [%] The plume-characterization data are provided as electrostatic-probe datasets acquired using a Faraday Cup and a Retarding Potential Analyzer, complemented by efficiency estimates obtained for the same operating conditions. Faraday Cup data are organized according to the injected xenon mass flow rate. The file nomenclature follows the structure "FC_IonCurrentDensity_[injected mass flow rate]SCCM.mat". Each file contains a structure array, with one element associated with a single input power at the corresponding mass flow rate. Each structure-array element is organized as follows: Pset: Input power [W] alpha: Angular position with respect to the thruster axis [deg] j: Ion current density [A m^(-2)] The variables alpha and j are stored as column vectors of equal length. Mean-ion-energy estimates obtained from Retarding Potential Analyzer measurements are stored in "RPA_MeanIonEnergy.mat". The file follows a column-vector organization, with each row referring to one thruster operating condition: mp: Injected xenon mass flow rate [SCCM] PT: Thruster power [W] Ei: Mean ion energy [eV] The efficiencies, obtained as illustrated in Inchingolo et al., are reported in "EfficiencyData.mat". This file is arranged as column vectors of size 16×1, where each row corresponds to a given mass flow rate and thruster power value. The fields are defined as follows: mp: Injected xenon mass flow rate [SCCM] PT: Thruster power [W] X: Efficiency value [%] sigma_X: Uncertainty associated with X [%] Here, X denotes one of the efficiency quantities stored in the file: etau, etad, etae or etat, corresponding respectively to utilization, divergence, energy conversion and total efficiency. Acknowledgments This work and the corresponding dataset were supported by the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (Starting Grant ZARATHUSTRA, grant agreement No. 950466). Additional funding was provided by project PID2023-150052OB-I00, funded by MICIU/AEI/10.13039/501100011033 and by ERDF, EU.