Akshay Prasad
(Advisor: Prof. Mavris)

will propose a doctoral thesis entitled,

A METHODOLOGY TO ENABLE CONCURRENT TRADE SPACE

EXPLORATION OF SPACE CAMPAIGNS AND TRANSPORTATION SYSTEMS

On

Friday, August 20 at 8:00 a.m.

https://bluejeans.com/787485720/5800

Abstract
Space exploration campaigns detail the ways and means to achieve goals for our human spaceflight programs, which are significant strategic, financial, and programmatic investments over long timescales that need to be justified to decision makers prior to execution. To make an informed down-selection, many alternative campaign designs are presented at the conceptual-level as a set and sequence of individual missions to perform that meet goals and constraints, either technical or programmatic. Each mission is eventually executed by in-space transportation systems that deliver crew or cargo payloads to various destinations. Design of each transportation system is highly dependent on campaign goals and even small changes in subsystem design parameters at the can prompt significant changes in the overall campaign strategy. However, the current state of the art describes campaign and vehicle design process that are generally performed independently, which limits the ability to assess these sensitive impacts. The objective of this research is to establish a methodology for space exploration campaign design that represents transportation systems as a collection of subsystems and integrates its design process to enable concurrent trade space exploration.

In the past two decades, researchers have adopted terrestrial logistics and supply chain optimization process to the space campaign design problem by accounting for the challenges that accompany space travel. Fundamentally, a space campaign is formulated as a network design problem where destinations, such as orbits or surfaces of planetary bodies, are represented as nodes, routes between them as arcs, and the objective is to optimize the flow of commodities within using available transportation systems. Given the dynamic nature and the number of commodities involved, each campaign can be modeled as a time-expanded, generalized multi-commodity network flow and solved using a mixed integer programming algorithm. This approach enables rapid generation of campaign design alternatives at the conceptual level, where each one identifies the optimal set and sequence of missions, subject to set goals and constraints.

Representing transportation systems as a collection of subsystems introduces challenges in the design of each vehicle, with a high degree of coupling between each subsystem as well as the driving mission. Additionally, sizing of each subsystem can have many inputs and outputs linked across the system, resulting in a complex, multi-disciplinary analysis and optimization problem. By leveraging the ontology within the Dynamic Rocket Equation Tool, DYREQT, this problem can be solved rapidly by defining each system as a hierarchy of elements and subelements, the latter corresponding to external subsystem-level sizing models. Missions can be further decomposed to a set of events and mapped to those elements to synthesize the subsystems for each system and produce a numerical solution using the ideal rocket equation.

The proposed methodology is iterative in nature, where the flows of commodities in the logistics network are constrained by vehicle capabilities and the sizing and synthesis process is driven by the missions in the campaign. A converged solution is a campaign that meets set goals and is supported by transportation system sizing at the subsystem level, enabling the direct assessment of campaign level trades on the design of different transports within, and vice versa. An integrated trade study for a crewed Mars campaign will be performed with the long-stay surface mission in NASA’s Design Reference Architecture 5.0 as a basis. Surface stay duration serves as the campaign-level trade variable and propellant species for the Nuclear Thermal Propulsion systems is traded at the vehicle-level, showing the improvement over the state of the art.

Committee

• Prof. Dimitri N. Mavris – School of Aerospace Engineering, Georgia Institute of Technology (advisor)
• Prof. Glenn Lightsey – School of Aerospace Engineering, Georgia Institute of Technology
• Prof. Koki Ho – School of Aerospace Engineering, Georgia Institute of Technology
• Dr. Bradford E. Robertson – School of Aerospace Engineering, Georgia Institute of Technology
• Dr. Dale C. Arney – Space Mission Analysis Branch, NASA Langley Research Center