Title:
Self-reconfigurable ship fluid-network modeling for simulation-based design
Self-reconfigurable ship fluid-network modeling for simulation-based design
| dc.contributor.advisor | Mavris, Dimitri N. | |
| dc.contributor.author | Moon, Kyungjin | en_US |
| dc.contributor.committeeMember | Costello, Mark | |
| dc.contributor.committeeMember | Ferrese, Frank | |
| dc.contributor.committeeMember | Schrage, Daniel P. | |
| dc.contributor.committeeMember | Weston, Neil R. | |
| dc.contributor.department | Aerospace Engineering | en_US |
| dc.date.accessioned | 2010-09-15T18:53:40Z | |
| dc.date.available | 2010-09-15T18:53:40Z | |
| dc.date.issued | 2010-05-21 | en_US |
| dc.description.abstract | Our world is filled with large-scale engineering systems, which provide various services and conveniences in our daily life. A distinctive trend in the development of today's large-scale engineering systems is the extensive and aggressive adoption of automation and autonomy that enable the significant improvement of systems' robustness, efficiency, and performance, with considerably reduced manning and maintenance costs, and the U.S. Navy's DD(X), the next-generation destroyer program, is considered as an extreme example of such a trend. This thesis pursues a modeling solution for performing simulation-based analysis in the conceptual or preliminary design stage of an intelligent, self-reconfigurable ship fluid system, which is one of the concepts of DD(X) engineering plant development. Through the investigations on the Navy's approach for designing a more survivable ship system, it is found that the current naval simulation-based analysis environment is limited by the capability gaps in damage modeling, dynamic model reconfiguration, and simulation speed of the domain specific models, especially fluid network models. As enablers of filling these gaps, two essential elements were identified in the formulation of the modeling method. The first one is the graph-based topological modeling method, which will be employed for rapid model reconstruction and damage modeling, and the second one is the recurrent neural network-based, component-level surrogate modeling method, which will be used to improve the affordability and efficiency of the modeling and simulation (M&S) computations. The integration of the two methods can deliver computationally efficient, flexible, and automation-friendly M&S which will create an environment for more rigorous damage analysis and exploration of design alternatives. As a demonstration for evaluating the developed method, a simulation model of a notional ship fluid system was created, and a damage analysis was performed. Next, the models representing different design configurations of the fluid system were created, and damage analyses were performed with them in order to find an optimal design configuration for system survivability. Finally, the benefits and drawbacks of the developed method were discussed based on the result of the demonstration. | en_US |
| dc.description.degree | Ph.D. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/34733 | |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.subject | Simulation-based design | en_US |
| dc.subject | Systems engineering | en_US |
| dc.subject | Graph theory | en_US |
| dc.subject | Neural networks | en_US |
| dc.subject.lcsh | Computer simulation | |
| dc.subject.lcsh | System analysis | |
| dc.subject.lcsh | Topological graph theory | |
| dc.title | Self-reconfigurable ship fluid-network modeling for simulation-based design | en_US |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Mavris, Dimitri N. | |
| local.contributor.corporatename | Daniel Guggenheim School of Aerospace Engineering | |
| local.contributor.corporatename | Aerospace Systems Design Laboratory (ASDL) | |
| local.contributor.corporatename | College of Engineering | |
| local.relation.ispartofseries | Doctor of Philosophy with a Major in Aerospace Engineering | |
| relation.isAdvisorOfPublication | d355c865-c3df-4bfe-8328-24541ea04f62 | |
| relation.isOrgUnitOfPublication | a348b767-ea7e-4789-af1f-1f1d5925fb65 | |
| relation.isOrgUnitOfPublication | a8736075-ffb0-4c28-aa40-2160181ead8c | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 | |
| relation.isSeriesOfPublication | f6a932db-1cde-43b5-bcab-bf573da55ed6 |
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