PhD Defense by Justin R. Kizer

Event Details
  • Date/Time:
    • Tuesday May 31, 2016
      9:00 am - 11:00 am
  • Location: Weber Space Science and Technology Building (SST-II)
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Summary Sentence: AIRCRAFT CONCEPTUAL DESIGN ENABLED BY A SET-BASED APPROACH FOR THE EXPLORATION AND BOUNDING OF NON-HYPERCUBIC DESIGN SPACES

Full Summary: No summary paragraph submitted.

D. Thesis Defense

 

By

 

Justin R. Kizer

(Advisor: Prof. Dimitri N. Mavris)

9:00 AM, Tuesday, May 31, 2016

Weber Space Science and Technology Building (SST-II)

Collaborative Visualization Environment (CoVE)

 

AIRCRAFT CONCEPTUAL DESIGN ENABLED BY A SET-BASED APPROACH FOR THE EXPLORATION AND BOUNDING OF NON-HYPERCUBIC DESIGN SPACES

 

ABSTRACT:

 

With the increasing reliance upon advanced computational methods to provide analyses for multidisciplinary engineering problems, there is a need for the efficient exploration of experimental design spaces. Contemporary methods such as traditional Design of Experiments as well as advanced space-filling techniques have long provided a means to intelligently allocate resources throughout the design space. However, such methods operate with a governing assumption that the feasible space which they sample, defined by limits placed upon the design variables, can be generalized to a d-dimensional hypercube. Due to the presence of features such as embedded constraints, correlated design variables and numerical failures within computational models, such an assumption can be suspect. Furthermore, for problems that are computationally expensive and must be repeatedly explored, hypercubic design space exploration may yield an unacceptable waste of resources and poor understanding of the feasible experimental space.

 

This dissertation acknowledges that the aforementioned defining features present within an experimental design space of interest may yield feasible regions that are non-hypercubic in nature. To address this issue, a decision support methodology is presented to provide guidance for the exploration of general design spaces. Beginning with an initial sampling of the space of interest, this methodology provides hypercubic classification and informed strategies for further sampling. Additionally, a means to perform Set-Based Bounded Adaptive Sampling, enabled by machine learning techniques, is provided for the identification and exploitation of non-hypercubic feasible spaces.

 

An application of the methodology is demonstrated through conceptual design of an advanced aircraft concept, born to address aggressive performance goals set forth for the future of civil aviation. In this design problem of interest, the methodology is used to provide design space exploration for a Hybrid Wing-Body aircraft. Utilizing Experimental Design Space (EDS) as a modeling and simulation testbed, the methodology allowed for a more complete understanding of the feasible design space of interest. Less resource waste than attainable by contemporary sampling methods was additionally achieved. Ultimately, such an approach likely enables improved regression generation, optimization, visualization and additional exploration for this design problem as well as others of similar characteristics.   

 

Committee Members:

Professor Dimitri N. Mavris

Professor Graeme J. Kennedy

Dr. Jeff S. Schutte (GE Aviation)

Professor Daniel P. Schrage

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Phd Defense
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  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: May 25, 2016 - 5:17am
  • Last Updated: Oct 7, 2016 - 10:17pm