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Instead of a single model for one discipline, thermoelastic analysis involves at least a thermal model and a structural model. In many cases, these two analysis steps are followed by a RF or optical simulation to determine the impact on the performance of the instrument of the thermoelastic responses. Each analysis step is accompanied with uncertainties. Besides the thermal and structural analyses, the transfer of temperature data introduces additional uncertainties. A method based on factors of safety, as is used in structural analysis to cover uncertainties, is discussed. Although the method can be pragmatic, this approach is often lacking a physical basis, may not be compatible with the requirements for specific cases, and the values used for these factors are to a large extent ambiguous. Stochastic approaches are known to be powerful for estimating ranges in the responses due to variation of parameters. Although the Monte Carlo method is well known and robust, it is computationally expensive. Promising alternative methods to quantify uncertainties of the responses, among many others, are the Latin hypercube sampling (LHS) method and the Rosenblueth \(2k+1\) Point Estimates Moments (PEM) method. The significance of the random design variables on the random output is detected through a sensitivity analysis, i.e. by a regression analysis method or as an intermediate result of the Rosenblueth PEM method. The section concludes with several worked out examples.
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- Estimating Uncertainties in the Thermoelastic Analysis Process
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