Computer Aided Design (CAD) is a powerful tool for designing
parametric geometry. However, many CAD models of current
configurations are constructed in previous generations of CAD
systems, which represent the configuration simply as a collection of
surfaces instead of as a parametrized solid model. But since many
modern analysis techniques take advantage of a parametrization, one
often has to re-engineer the configuration into a parametric
model. The objective here is to generate an efficient, robust, and
accurate method for fitting parametric models to a cloud of
points. The process uses a gradient-based optimization technique,
which is applied to the whole cloud, without the need to segment or
classify the points in the cloud a priori.
First, for the points associated with any component, a variant of
the Levenberg-Marquardt gradient-based optimization method (ILM) is
used to find the set of model parameters that minimizes the
least-square errors between the model and the points. The
efficiency of the ILM algorithm is greatly improved through the use
of analytic geometric sensitivities and sparse matrix techniques.
Second, for cases in which one does not know a priori the
correspondences between points in the cloud and the geometry model's
components, an efficient initialization and classification algorithm
is introduced. While this technique works well once the
configuration is close enough, it occasionally fails when the
initial parametrized configuration is too far from the cloud of
points. To circumvent this problem, the objective function is
modified, which has yielded good results for all cases tested.
This technique is applied to a series of increasingly complex
configurations. The final configuration represents a full transport
aircraft configuration, with a wing, fuselage, empennage, and
engines. Although only applied to aerospace applications, the
technique is general enough to be applicable in any domain for which
basic parametrized models are available.
","abstract_html":"<p>Computer Aided Design (CAD) is a powerful tool for designing</p> <p> parametric geometry. However, many CAD models of current</p> <p> configurations are constructed in previous generations of CAD</p> <p> systems, which represent the configuration simply as a collection of</p> <p> surfaces instead of as a parametrized solid model. But since many</p> <p> modern analysis techniques take advantage of a parametrization, one</p> <p> often has to re-engineer the configuration into a parametric</p> <p> model. The objective here is to generate an efficient, robust, and</p> <p> accurate method for fitting parametric models to a cloud of</p> <p> points. The process uses a gradient-based optimization technique,</p> <p> which is applied to the whole cloud, without the need to segment or</p> <p> classify the points in the cloud a priori.</p> <p> First, for the points associated with any component, a variant of</p> <p> the Levenberg-Marquardt gradient-based optimization method (ILM) is</p> <p> used to find the set of model parameters that minimizes the</p> <p> least-square errors between the model and the points. The</p> <p> efficiency of the ILM algorithm is greatly improved through the use</p> <p> of analytic geometric sensitivities and sparse matrix techniques.</p> <p> Second, for cases in which one does not know a priori the</p> <p> correspondences between points in the cloud and the geometry model's</p> <p> components, an efficient initialization and classification algorithm</p> <p> is introduced. While this technique works well once the</p> <p> configuration is close enough, it occasionally fails when the</p> <p> initial parametrized configuration is too far from the cloud of</p> <p> points. To circumvent this problem, the objective function is</p> <p> modified, which has yielded good results for all cases tested.</p> <p> This technique is applied to a series of increasingly complex</p> <p> configurations. The final configuration represents a full transport</p> <p> aircraft configuration, with a wing, fuselage, empennage, and</p> <p> engines. Although only applied to aerospace applications, the</p> <p> technique is general enough to be applicable in any domain for which</p> <p> basic parametrized models are available.</p>","abstract_has_math":false,"creators":["Jia, Pengcheng"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Mechanical and Aerospace Engineering","degree_department":null,"school":null,"contributors":["John F. Dannenhoffer","Sinéad C. Mac Namara"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-06-30T07:00:00Z","date_published":"2017-06-30T07:00:00Z","updated_at":"2026-07-24T04:55:19Z","subjects":["Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://surface.syr.edu/etd/673","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["John F. Dannenhoffer","Sinéad C. Mac Namara"]},{"key":"dc:creator","label":"Author","values":["Jia, Pengcheng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical and Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://surface.syr.edu/etd/673"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Computer Aided Design (CAD) is a powerful tool for designing
parametric geometry. However, many CAD models of current
configurations are constructed in previous generations of CAD
systems, which represent the configuration simply as a collection of
surfaces instead of as a parametrized solid model. But since many
modern analysis techniques take advantage of a parametrization, one
often has to re-engineer the configuration into a parametric
model. The objective here is to generate an efficient, robust, and
accurate method for fitting parametric models to a cloud of
points. The process uses a gradient-based optimization technique,
which is applied to the whole cloud, without the need to segment or
classify the points in the cloud a priori.
First, for the points associated with any component, a variant of
the Levenberg-Marquardt gradient-based optimization method (ILM) is
used to find the set of model parameters that minimizes the
least-square errors between the model and the points. The
efficiency of the ILM algorithm is greatly improved through the use
of analytic geometric sensitivities and sparse matrix techniques.
Second, for cases in which one does not know a priori the
correspondences between points in the cloud and the geometry model's
components, an efficient initialization and classification algorithm
is introduced. While this technique works well once the
configuration is close enough, it occasionally fails when the
initial parametrized configuration is too far from the cloud of
points. To circumvent this problem, the objective function is
modified, which has yielded good results for all cases tested.
This technique is applied to a series of increasingly complex
configurations. The final configuration represents a full transport
aircraft configuration, with a wing, fuselage, empennage, and
engines. Although only applied to aerospace applications, the
technique is general enough to be applicable in any domain for which
basic parametrized models are available.
"]},{"key":"dc:title","label":"Title","values":["FITTING A PARAMETRIC MODEL TO A CLOUD OF POINTS VIA OPTIMIZATION METHODS"]}]}],"canonical_facts":{"dc:contributor":["John F. Dannenhoffer","Sinéad C. Mac Namara"],"dc:creator":["Jia, Pengcheng"],"dc:description.abstract":["Computer Aided Design (CAD) is a powerful tool for designing
parametric geometry. However, many CAD models of current
configurations are constructed in previous generations of CAD
systems, which represent the configuration simply as a collection of
surfaces instead of as a parametrized solid model. But since many
modern analysis techniques take advantage of a parametrization, one
often has to re-engineer the configuration into a parametric
model. The objective here is to generate an efficient, robust, and
accurate method for fitting parametric models to a cloud of
points. The process uses a gradient-based optimization technique,
which is applied to the whole cloud, without the need to segment or
classify the points in the cloud a priori.
First, for the points associated with any component, a variant of
the Levenberg-Marquardt gradient-based optimization method (ILM) is
used to find the set of model parameters that minimizes the
least-square errors between the model and the points. The
efficiency of the ILM algorithm is greatly improved through the use
of analytic geometric sensitivities and sparse matrix techniques.
Second, for cases in which one does not know a priori the
correspondences between points in the cloud and the geometry model's
components, an efficient initialization and classification algorithm
is introduced. While this technique works well once the
configuration is close enough, it occasionally fails when the
initial parametrized configuration is too far from the cloud of
points. To circumvent this problem, the objective function is
modified, which has yielded good results for all cases tested.
This technique is applied to a series of increasingly complex
configurations. The final configuration represents a full transport
aircraft configuration, with a wing, fuselage, empennage, and
engines. Although only applied to aerospace applications, the
technique is general enough to be applicable in any domain for which
basic parametrized models are available.
"],"dc:identifier":["https://surface.syr.edu/etd/673"],"dc:subject":["Engineering"],"dc:title":["FITTING A PARAMETRIC MODEL TO A CLOUD OF POINTS VIA OPTIMIZATION METHODS"],"thesis:degree_discipline":["Mechanical and Aerospace Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T04:55:19Z"}