Uncertainty Simulation Of Thermal Structural Dynamics And Model Validation Method Research

The computation and simulation has become the third pillar along with theory and experiment inscientific research. Although the computation and simulation has been widely applied to various fieldsof engineering, the third pillar of computation and simulation is just now beginning to be constructed.The true accuracy and confidence of simulation model has become an important topic with the highsimulation requirements in engineering design.Model validation is the process of determining the degree to which a model is an accuraterepresentation of the real world from the perspective of the intended uses of the model. It is notmerely a process of assessing the accuracy of a simulation model, but also a process to improve thepredictive precision through the model validation results. The typical thermal dynamical structuresimulation analysis for the hypersonic vehicle and the model validation method are studied in thiswork. The main contents are summarized as follows:(1) The thermal conductivity of the material is expressed as a polynomial function of temperature,and genetic algorithm is used to identify the coefficients of the polynomial in order to get a moreaccurate temperature distribution to provide guidance for the thermal structure design. The Bayesianframework for model validation is achieved to the example of model validation thermal challengeproblem presented in Sandia National Laboratories. The basic theories of Bayesian analysis anduncertainty quantification are introduced and several model updating methods are emphasized andcompared in model validation. Finally, the Bayesian model updating method is applied to modelvalidation thermal challenge problem, and more accurate prediction results are obtained than thosefrom the initial model. The results demonstrate that the model predictive precision can be significantlyimproved when utilizing Bayesian model updating method in model validation.(2) Finite element model updating based on sensitivity analysis and genetic algorithm respectivelyare used to identify the parameters coupled with mass, stiffness and damping matrixes simultaneouslyfor unsymmetrical damping system. A new finite element model updating method is presented usingeffective modal mass based on sensitivity analysis and genetic algorithm respectively. The simulationresults show that the two updating method using the effective modal mass which providing moreuseful information and can both be used to dynamic model updating.(3) The kernel density estimation method combined with kernel principal component analysis issuccessfully used to solve the structural dynamic model validation challenge problem presented bySandia National Laboratories. The confidence level method is introduced and the optimum sample variance is determined using an improved method in kernel density estimation to increase thecredibility of model validation and as a numerical example, the static frame model validationchallenge problem presented by Sandia National Laboratories is chosen. The researches demonstratethat the kernel density estimation combined with kernel principal component analysis and theconfidence level methods are effective approach to solve the model validation problem with smallsamples.(4) The coupled thermoelastic vibration governing equations are derived based on the differentialequations of Fourier heat conduction and transverse vibrations of Euler beam. Mixed aleatory andepistemic uncertainty quantification is described using p-box solution with double-loop Monte Carlosampling techniques. The distribution of coupled natural frequencies is performed when consideringthe material uncertainty with mixed aleatory and epistemic. The researches demonstrate that the meanand standard deviation of coupled nature frequency of beam are interval, and are both increasing asincreased of the input parameter uncertainties.(5) Model-form probability belongs to epistemic uncertainty which is usually determined based onexpert opinion or experience but is described by interval uncertainty and its optimal value isdetermined through the maximum entropy approach. A new interval adjustment factor approach ispresented to model-form uncertainty quantification. The new method is validated through a nonlinearsingle degree of freedom vibration system for nature frequency, and the flutter velocity prediction of atwo degrees of freedom airfoil subject to unsteady aerodynamics. The studies demonstrate that thenew interval adjustment factor approach is feasible to model prediction for combination withmodel-form, aleatory and epistemic of parameter uncertainty.(6) The temperature distribution of C/C composite panel structure is determined through transientheat conduction analysis before thermal modal and thermal flutter analysis in different moments.Thermal structural dynamics model updating is performed in deterministic framework and theprediction for thermal flutter velocity with updated model is performed. Uncertainty quantification forthermal modal and thermal flutter analysis considered mixed aleatory and epistemic uncertaintiesfrom the material of C/C composite. Quantification of margins and uncertainties technology isachieved to quantitative assessment and certification for thermal flutter velocity based on the aboveuncertainty analysis results.