Store separation from aircraft and spacecraft has historically been a critical and in some cases fatal issue for the aerospace industry. Given the severity of the issue much effort has been spent on the development of processes to identify failure flight conditions for store separation. The processes currently used for identifying potential failure conditions however are both resource intensive and iterative processes. A potential remedy to reducing resource use and improve turn around time in this process is the implementation of a mode based reduced order model (ROM) for modeling store separation. The objective of this study was to first identify the leading modes that can best be used to model a store separating from an aircraft. To obtain these modes, two algorithms were used; Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD). The computational fluid dynamic (CFD) solver Ansys Fluent was employed to obtain flow field data around a representative vehicle and store. Preliminary validation of the numerical results was initially preformed and the results showed good comparison of surface pressures and free-stream vorticity. The validated data-set was then used to identify which modal method, POD or DMD, better resolves the known dominate structures of the flow field. The results of this analysis showed the superiority of POD in identifying both free-stream and surface pressure structures. A final representative case of store separation was obtained at a flight speed of mach 0.8. POD was then used to obtain leading modes that were used to reconstruct a ROM of the flow field. This ROM was successful in predicting the store’s trajectory both inside and out of the training flight profile.
","abstract_html":"<p>Store separation from aircraft and spacecraft has historically been a critical and in some cases fatal issue for the aerospace industry. Given the severity of the issue much effort has been spent on the development of processes to identify failure flight conditions for store separation. The processes currently used for identifying potential failure conditions however are both resource intensive and iterative processes. A potential remedy to reducing resource use and improve turn around time in this process is the implementation of a mode based reduced order model (ROM) for modeling store separation. The objective of this study was to first identify the leading modes that can best be used to model a store separating from an aircraft. To obtain these modes, two algorithms were used; Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD). The computational fluid dynamic (CFD) solver Ansys Fluent was employed to obtain flow field data around a representative vehicle and store. Preliminary validation of the numerical results was initially preformed and the results showed good comparison of surface pressures and free-stream vorticity. The validated data-set was then used to identify which modal method, POD or DMD, better resolves the known dominate structures of the flow field. The results of this analysis showed the superiority of POD in identifying both free-stream and surface pressure structures. A final representative case of store separation was obtained at a flight speed of mach 0.8. POD was then used to obtain leading modes that were used to reconstruct a ROM of the flow field. This ROM was successful in predicting the store’s trajectory both inside and out of the training flight profile.</p>","abstract_has_math":false,"creators":["Peters, Nicholas"],"institution":null,"degree_name":"Master of Science in Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-05-01T07:00:00Z","date_published":"2020-05-01T07:00:00Z","updated_at":"2026-07-27T19:26:28Z","subjects":["DMD","POD","modal analysis","store separation","Aerodynamics and Fluid Mechanics","Aerospace Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/520","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Peters, Nicholas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["DMD","POD","modal analysis","store separation","Aerodynamics and Fluid Mechanics","Aerospace Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/520"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Store separation from aircraft and spacecraft has historically been a critical and in some cases fatal issue for the aerospace industry. Given the severity of the issue much effort has been spent on the development of processes to identify failure flight conditions for store separation. The processes currently used for identifying potential failure conditions however are both resource intensive and iterative processes. A potential remedy to reducing resource use and improve turn around time in this process is the implementation of a mode based reduced order model (ROM) for modeling store separation. The objective of this study was to first identify the leading modes that can best be used to model a store separating from an aircraft. To obtain these modes, two algorithms were used; Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD). The computational fluid dynamic (CFD) solver Ansys Fluent was employed to obtain flow field data around a representative vehicle and store. Preliminary validation of the numerical results was initially preformed and the results showed good comparison of surface pressures and free-stream vorticity. The validated data-set was then used to identify which modal method, POD or DMD, better resolves the known dominate structures of the flow field. The results of this analysis showed the superiority of POD in identifying both free-stream and surface pressure structures. A final representative case of store separation was obtained at a flight speed of mach 0.8. POD was then used to obtain leading modes that were used to reconstruct a ROM of the flow field. This ROM was successful in predicting the store’s trajectory both inside and out of the training flight profile.
"]},{"key":"dc:title","label":"Title","values":["DMD and POD Modal Analysis for Store Separation"]}]}],"canonical_facts":{"dc:creator":["Peters, Nicholas"],"dc:description.abstract":["Store separation from aircraft and spacecraft has historically been a critical and in some cases fatal issue for the aerospace industry. Given the severity of the issue much effort has been spent on the development of processes to identify failure flight conditions for store separation. The processes currently used for identifying potential failure conditions however are both resource intensive and iterative processes. A potential remedy to reducing resource use and improve turn around time in this process is the implementation of a mode based reduced order model (ROM) for modeling store separation. The objective of this study was to first identify the leading modes that can best be used to model a store separating from an aircraft. To obtain these modes, two algorithms were used; Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD). The computational fluid dynamic (CFD) solver Ansys Fluent was employed to obtain flow field data around a representative vehicle and store. Preliminary validation of the numerical results was initially preformed and the results showed good comparison of surface pressures and free-stream vorticity. The validated data-set was then used to identify which modal method, POD or DMD, better resolves the known dominate structures of the flow field. The results of this analysis showed the superiority of POD in identifying both free-stream and surface pressure structures. A final representative case of store separation was obtained at a flight speed of mach 0.8. POD was then used to obtain leading modes that were used to reconstruct a ROM of the flow field. This ROM was successful in predicting the store’s trajectory both inside and out of the training flight profile.
"],"dc:identifier":["https://commons.erau.edu/edt/520"],"dc:subject":["DMD","POD","modal analysis","store separation","Aerodynamics and Fluid Mechanics","Aerospace Engineering"],"dc:title":["DMD and POD Modal Analysis for Store Separation"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Aerospace Engineering"]},"updated_at":"2026-07-27T19:26:28Z"}