Application of Stochastic Technique to Spacecraft Thermal and Multidisciplinary Simulations

Mareschi, Vincenzo; Perotto, Valter; Gorlani, Matteo; Lazzeri, Danilo; Pin, Olivier

doi:10.4271/2004-01-2274

Cited by 4 publications

(2 citation statements)

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“…are derived from ECSS 2,6 . For what concerns the parameters that are typical of a re-entry vehicle the inaccuracies specifically defined are reported in the following table.…”

Section: A Inaccuracy Of Input Parametersmentioning

confidence: 99%

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Stochastic Thermal Sensitivity/Uncertainty Analysis Of Expert Re-Entry Vehicle Demonstrator

Gorlani¹,

Cappadona²,

Ernesto

2010

40th International Conference on Environmental Systems

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Nowadays, the increasing complexity and performances requested to products have lead modern engineering to develop and provide much more optimised components. In the context of design and analysis disciplines, one of the main barriers is the uncertainty of predictions that numerical modelling and simulation provide. Some techniques have been already standardised to cope with inaccuracies and relevant results uncertainties. In thermal problems they rely and involve the adoption of temperature margins, the implementation of sensitivity analyses and the execution of tests. In last years the application of the stochastic approach, as an advanced method for the adoption of novel techniques or just for improving the efficiency and productivity of the traditional ones, has been successfully introduced and proved its usefulness. The paper presents the results of the uncertainty/sensitivity analysis performed for the Thales Alenia Space thermal model of Expert re-entry vehicle demonstrator with Blue Engineering support using the stochastic approach based on the Monte Carlo Simulation method. Emphases are given to some goals reached thanks to the application of the stochastic method to the thermal analysis that are impossible to be obtained with the traditional approach. In particular the assessment of the confidence level related to the Expert Electronic Equipments and TPS performances and of the driving parameters. Nomenclature∆T ij = temperature deviation due to random parameter j on zone i ∆T ik = temperature deviation due to modeling parameter k on thermal zone i GMM = geometrical mathematical model MLI = multi layer insulation OA = over all the mission P xx,yy = percentile at xx,yy % RSS = root sum squared SERR = systematic error TD = up to touch down T i,nominal = extreme temperature of the zone i in nominal case T ij = extreme temperature of the zone i when the parameter j is changed T ik = extreme temperature of the zone i due to systematic parameter k TAS-I = Thales Alenia Space Italy TMM = thermal mathematical model TOML = thermal hot outer mold line 2 TPS = thermal protection system UFP = uncertainty of prediction UM = uncertainty margin µ = mean σ = standard deviation

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“…are derived from ECSS 2,6 . For what concerns the parameters that are typical of a re-entry vehicle the inaccuracies specifically defined are reported in the following table.…”

Section: A Inaccuracy Of Input Parametersmentioning

confidence: 99%

“…The analysis adopted for the project and described in the paper is based on the stochastic approach proposed by BLUE and TAS-I Turin through an ESA project 6 . It has been adopted for the following benefits:…”

Section: Stochastic Approachmentioning

confidence: 99%