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In the paper some methods are presented, with the corresponding practical examples, related to MonteCarlo (MC) techniques for thermal model/test correlation purposes. The MonteCarlo techniques applied to model correlation are intended to be used as an alternative to empirical ‘manual’ correlation techniques, gradients methods, matrix methods based on least square fit minimization. First of all, Design Of Experiments (DoE) tools are used to determine the model response to uncertain parameters and the confidence level of such a response. A sensitivity map is built, allowing the design of the test to maximize the response of the system to the uncertain parameters. Techniques derived from the extreme statistics are used to extrapolate data beyond test limits, with a sufficient confidence in the queue behaviour.

The satellite AGILE (Astro-rivelatore Gamma a Immagini LEggero, “Light Gamma Ray Imaging Detector”) is a promising instrument for near-earth space research of the Italian Space(ASI) during the years 2007-2009: its scientific instrumentation has optimal imaging capabilities in both the gamma-ray energy range (30 MeV - 30 GeV) and hard X-ray range (15 - 45 keV). It will study the phenomena occurring in the high energy spectrum, such as: Active Galactic Nuclei, Gamma Ray Bursts, Gamma-ray Galactic Diffuse Emission, and more. The satellite was designed and built in years 2004-2006; this paper describes the design of the thermal control system of the satellite, with a survey of the flight prediction. As an example of uncertainty reduction, MLI performance characterization by test was done in an early phase of the AIV phase (i.e. well before the system level test), to meet stringent payload requirements in terms of temperature gradients and temperature stability.

In the paper several application techniques of MonteCarlo (MC) method applied to thermal analysis of space vehicles are presented. Although these methods are widely used in other engineering domains, their introduction to the thermal one is quite recent and not fully developed in the industrial practice. This paper aims at showing that, even without demanding computation resources (all what presented has been obtained with a single processor PC) MonteCarlo analysis techniques, in a preliminary design phase, can support and integrate engineering judgment of the thermal designer. In particular, it is exploited the applicability of the method to reduced thermal models, with a clear advantage in terms of computation time. An original approach is proposed, and results are shown. The papers shows the applicability of the MC method to the case when experimental data of the uncertain parameters are available, using the bootstrap re-sampling techniques.