Thermal Characterization of Complex Aerospace Structures
Predicting the performance of complex structures exposed to harsh thermal environments is a crucial issue in many of today's aerospace and space designs. To predict the thermal stresses a structure might be exposed to, the thermal properties of the independent materials used in the design of t...
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Virginia Tech
2014
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| Online Access: | http://hdl.handle.net/10919/36617 http://scholar.lib.vt.edu/theses/available/etd-32298-85910/ |
| Summary: | Predicting the performance of complex structures exposed to harsh
thermal environments is a crucial issue in many of today's aerospace
and space designs. To predict the thermal stresses a structure might
be exposed to, the thermal properties of the independent materials
used in the design of the structure need to be known. Therefore, a
noninvasive estimation procedure involving Genetic Algorithms was
developed to determine the various thermal properties needed to
adequately model the Outer Wing Subcomponent (OWS), a structure located at the trailing edge of the High Speed Civil Transport's (HSCT) wing tip.
Due to the nature of the nonlinear least-squares estimation method
used in this study, both theoretical and experimental temperature
histories were required. Several one-dimensional and two-dimensional finite element models of
the OWS were developed to compute the transient theoretical
temperature histories. The experimental data were obtained from
optimized experiments that were run at various surrounding
temperature settings to investigate the temperature dependence of the
estimated properties. An experimental optimization was performed to
provide the most accurate estimates and reduce the confidence
intervals.
The simultaneous estimation of eight thermal properties, including the
volumetric heat capacities and out-of-plane thermal conductivities of
the facesheets, the honeycomb, the skins, and the torque tubes, was
successfully completed with the one-dimensional model and the results used to
evaluate the remaining in-plane thermal conductivities of the
facesheets, the honeycomb, the skins, and the torque tubes with the
two-dimensional model. Although experimental optimization did not eliminate all
correlation between the parameters, the minimization procedure based
on the Genetic Algorithm performed extremely well, despite the high
degree of correlation and low sensitivity of many of the parameters. === Master of Science |
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