{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/36617"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/36617","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Thermal Characterization of Complex Aerospace Structures","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Hanuska, Alexander Robert"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Scott, Elaine P."],"committee_members":["Vick, Brian L.","Diller, Thomas E."],"year":1998,"date_issued":"1998-04-16","date_published":"1998-04-16","updated_at":"2026-07-22T22:20:21Z","subjects":["Parameter Estimation","Aerospace Structures","Genetic Algorithm"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-32298-85910"],"render_values":[{"text":"etd-32298-85910","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/36617","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Scott, Elaine P."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Vick, Brian L.","Diller, Thomas E."]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Hanuska, Alexander Robert"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:51:17Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:51:17Z","1999-04-24"]},{"key":"dc:date.issued","label":"Date","values":["1998-04-16"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Parameter Estimation","Aerospace Structures","Genetic Algorithm"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-32298-85910"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/36617"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Thermal Characterization of Complex Aerospace Structures"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Scott, Elaine P."],"dc:contributor.committeemember":["Vick, Brian L.","Diller, Thomas E."],"dc:contributor.department":["Mechanical Engineering"],"dc:creator":["Hanuska, Alexander Robert"],"dc:date.accessioned":["2014-03-14T20:51:17Z"],"dc:date.available":["2014-03-14T20:51:17Z","1999-04-24"],"dc:date.issued":["1998-04-16"],"dc:description.abstract":["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."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-32298-85910"],"dc:identifier.uri":["http://hdl.handle.net/10919/36617"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Parameter Estimation","Aerospace Structures","Genetic Algorithm"],"dc:title":["Thermal Characterization of Complex Aerospace Structures"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:21Z"}