Updates and improvements to the satellite drag coefficient Response Surface Modeling toolkit
نویسندگان
چکیده
For satellites in the Low Earth Orbit (LEO) region, drag coefficient is a primary source of uncertainty for orbit determination and prediction. Researchers at Los Alamos National Laboratory (LANL) have created so-called Response Surface Modeling (RSM) toolkit to provide community with resource simulating modeling satellite coefficients complex geometries (modeled using triangulated facets) free molecular flow (FMF) regime. The fits an interpolation surface non-parametric Gaussian Process Regression (GPR) over data computed numerical Test Particle Monte Carlo (TPMC) method. fitted response provides substantial computational benefit approaches calculating coefficients. In this work, RSM further developed into versatile software extended capabilities. capabilities are specifically expanded include quantification adaptation automatic development regression models non-stationary components (e.g. rotating solar panels). Furthermore, uses Python 3.x C programming languages open package OSI approved GPL license. To assist end user, new has been user-friendly installation process provided extensive documentation. analysis two different conceptual performed during work: simple cube CubeSat consisting body 2 panels. During creation model each atmospheric species, it found that cube’s minimum Root Mean Squared Error (RMSE) 0.00211 maximum RMSE 0.00350. 0.00304 0.00498. These results overall conducive well performing model.
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ژورنال
عنوان ژورنال: Advances in Space Research
سال: 2022
ISSN: ['0273-1177', '1879-1948']
DOI: https://doi.org/10.1016/j.asr.2022.02.044