Lea Harris
(Advisor: Prof. Mavris)

will propose a doctoral thesis entitled,

On

Monday, August 30 at 10 a.m. EDT

 Collaborative Visualization Environment (CoVE)
Weber Space Science and Technology Building (SST II)

Virtual Attendee Option: https://bluejeans.com/9027537002

Meeting ID:  902 753 700 2

The evolution of complex aerospace vehicles traced with the progression of novel missions and state of the art technology has forged a continuous need for adapted or new vehicles. This sequence introduces more uncertainties into the process of design. With more sources and unknowns, the uncertainty of early design decision-making becomes less distinguishable and difficult to address. A matter of reducing and mitigating risks due to these uncertainties is a significant complication in requirements definition and conceptual design phases for novel vehicle definitions. The inaccessibility and inapplicable nature of historical data, along with sparsity of experts for each next generation of vehicles, makes this task even harder. This leaves the industry with a gap in managing the compounding uncertainties in setting performance constraints and risk mitigation strategies without accessible data or expertise.

A prime example of lessons learned can be found in the Space Transportation System Vehicle (STS-1). The program required significant risk mitigation when the aerodynamic loading on the wings of the orbiter introduced 30-40% higher structural loads than designed to withstand.  Since then, the field of design has evolved significantly into the state-of-the-art multidisciplinary design and integrated modeling and simulation methods. The basis the existing conceptual design process sets enables uncertainty reduction through knowledge of performance and quantitative decision-making.

To address the aspects of uncertainty often overlooked or ignored, this research proposes an augmentation of the existing conceptual design environment. The focus will be to enable uncertainty characterization of the design assumptions, and modeling qualities, for uncertainty-informed risk mitigation that reduces the risks of rework due to variation in the design and under-predicted phenomena from reduced modeling approximations. The methodology proposed within this research hypothesizes an uncertainty threshold scenario-based approach for dependent performance enveloping and margin allocation.  It is proposed to demonstrate this method on the case study of approximating the dynamic modal deflections of a publicly available Launch Vehicle model under the approximation between low-fidelity dynamics and full-scale flexible body dynamics.

• Prof. Dimitri Mavris – School of Aerospace Engineering (advisor)
• Prof. Daniel Schrage – School of Aerospace Engineering
• Prof. Graeme J. Kennedy – School of Aerospace Engineering
• Dr. Adam Cox – Research Engineer II, School of Aerospace Engineering
• Mr. Bob Jurenko – MDA/TCM Senior Technical Advisor (sponsor)