Stochastic modelling and spacecraft thermal analysis The thermal analysis of spacecraft in orbit is currently a computationally expensive task. Monte Carlo raytracing is typically used to determine the parameters for the radiative heat transfer from the Sun and planet, and between different parts of the spacecraft. These parameters are then added to a mathematical model representing the conductive heat transfer, and iterative finite difference solvers are used to calculate temperatures within the spacecraft. In addition, there are many variables that influence these temperatures, such as the properties of the materials used to build the spacecraft, and the orbit and attitude of the spacecraft. Some or all of these variables may change significantly over the lifetime of the mission, and this variation needs to be taken into account at different points in the spacecraft design and development process. Finding, for instance, the critical design cases (hottest, coldest, etc.) may involve running many parametric studies. In general, any optimisation process for the spacecraft design, or the correlation of the spacecraft model against test data, will require further parametric studies.
Stochastic techniques involve applying probability functions to select values for these variables at random from a given range, and using statistical methods to determine the influence of the variable and the accuracy of the result. Software tools now exist to automate the process of selecting the values for the variables, and providing statistical feedback to the engineer to help arrive at the important analysis cases using fewer parametric runs than traditional methods.
