Application study of a correction method for a spacecraft thermal model with a Monte-Carlo hybrid algorithm
The correction of a thermal model for a thermally controlled satellite in ground test conditions is studied using a Monte Carlo hybrid algorithm. First, the global and local parameters are summarized according to sensitivity analyses on uncertain parameters, and then the model correction is treated as a parameter optimization problem to be solved with a hybrid algorithm. Finally, the correction of the thermal model is completed using a layered correction method. The sensitivity analysis showed that the effective emissivities across the multi-layer insulation (MLI) and the emissivities of the thermal control coating are global parameters, while the contact heat transfer coefficients are local parameters. After correction, the deviations between the calculated and test values were all within ±3°C. The final results prove that the method in this study is superior to traditional methods and satisfies the requirements for thermal model correction.
In determining the distances to stars within the Milky Way galaxy, one often uses photometric or spectroscopic parallax. In these methods, the type of each individual star is determined, and the absolute magnitude of that star type is compared with the measured apparent magnitude to determine individual distances. In this article, we define the term statistical photometric parallax, in which statistical knowledge of the absolute magnitudes of stellar populations is used to determine the underlying density distributions of those stars. This technique has been used to determine the density distribution of the Milky Way stellar halo and its component tidal streams, using very large samples of stars from the Sloan Digital Sky Survey. Most recently, the volunteer computing platform MilkyWay@home has been used to find the best fit model parameters for the density of these halo stars.