As engineering systems grow in complexity so too must the design tools that we use evolve and allow for decision makers to efficiently ask questions of their model and obtain meaningful answers. The process of whitespace exploration has recently been developed to aid in engineering design and provide insight into a design space where traditional design exploration methods may fail. In an effort to further the research and development of whitespace exploration algorithms, a software package called Thalia has been created to allow for automated data collection and experimentation with the whitespace exploration methodology.
In this work, whitespace exploration is defined and the current state of the art of whitespace exploration algorithms is reviewed. The whitespace exploration library Thalia along with a collection of benchmarking cases are described in detail. A set of experiments on the benchmark cases are run and analyzed to further understand the behavior of the algorithm and outline initial performance results which can later be used for comparison to aid in improving the methodology.
","abstract_html":"<p>As engineering systems grow in complexity so too must the design tools that we use evolve and allow for decision makers to efficiently ask questions of their model and obtain meaningful answers. The process of whitespace exploration has recently been developed to aid in engineering design and provide insight into a design space where traditional design exploration methods may fail. In an effort to further the research and development of whitespace exploration algorithms, a software package called Thalia has been created to allow for automated data collection and experimentation with the whitespace exploration methodology.</p> <p>In this work, whitespace exploration is defined and the current state of the art of whitespace exploration algorithms is reviewed. The whitespace exploration library Thalia along with a collection of benchmarking cases are described in detail. A set of experiments on the benchmark cases are run and analyzed to further understand the behavior of the algorithm and outline initial performance results which can later be used for comparison to aid in improving the methodology.</p>","abstract_has_math":false,"creators":["Daniel, Jason Lloyd"],"institution":null,"degree_name":"MS in Aerospace Engineering","degree_level":null,"degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["David D. Marshall"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-12-01T08:00:00Z","date_published":"2017-12-01T08:00:00Z","updated_at":"2026-07-24T01:31:56Z","subjects":["optimization","design","Computational Engineering","Systems Engineering and Multidisciplinary Design Optimization"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2017.109"],"render_values":[{"text":"10.15368/theses.2017.109","href":"https://doi.org/10.15368/theses.2017.109","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1791","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["David D. Marshall"]},{"key":"dc:creator","label":"Author","values":["Daniel, Jason Lloyd"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2017-12-08T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["optimization","design","Computational Engineering","Systems Engineering and Multidisciplinary Design Optimization"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1791","10.15368/theses.2017.109"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["As engineering systems grow in complexity so too must the design tools that we use evolve and allow for decision makers to efficiently ask questions of their model and obtain meaningful answers. The process of whitespace exploration has recently been developed to aid in engineering design and provide insight into a design space where traditional design exploration methods may fail. In an effort to further the research and development of whitespace exploration algorithms, a software package called Thalia has been created to allow for automated data collection and experimentation with the whitespace exploration methodology.
In this work, whitespace exploration is defined and the current state of the art of whitespace exploration algorithms is reviewed. The whitespace exploration library Thalia along with a collection of benchmarking cases are described in detail. A set of experiments on the benchmark cases are run and analyzed to further understand the behavior of the algorithm and outline initial performance results which can later be used for comparison to aid in improving the methodology.
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In this work, whitespace exploration is defined and the current state of the art of whitespace exploration algorithms is reviewed. The whitespace exploration library Thalia along with a collection of benchmarking cases are described in detail. A set of experiments on the benchmark cases are run and analyzed to further understand the behavior of the algorithm and outline initial performance results which can later be used for comparison to aid in improving the methodology.
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