Akshay Prasad
(Advisor: Prof. Mavris)

will defend a doctoral thesis entitled,

A METHODOLOGY TO ENABLE CONCURRENT TRADE SPACE

EXPLORATION OF SPACE CAMPAIGNS AND TRANSPORTATION SYSTEMS

On

Monday, November 7 at 10:30 a.m.
Join Zoom Meeting: https://gatech.zoom.us/j/94579406087?pwd=cXlwR3lTMlpIMm1UbWx1VDIxMjJBUT09

Meeting ID: 945 7940 6087

Passcode: 257802

Abstract
Space exploration campaigns detail the ways and means to achieve goals for our human spaceflight programs. Significant strategic, financial, and programmatic investments over long timescales are required to execute them, and therefore must be justified to decision makers. 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 meets the goals and constraints of the campaign, either technical or programmatic. Each mission is executed by in-space transportation systems that deliver crew or cargo payloads to various destinations. Design of each of these transportation systems is highly dependent on campaign goals and even small changes in subsystem design parameters which can prompt significant changes in the overall campaign strategy. However, the current state of the art describes campaign and vehicle design processes 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 processes 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, and routes between them as arcs, while 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.

Using NASA’s reference 3-element Human Landing System campaign, the process structure of the methodology is found with experimentation to be a non-linear Gauss Seidel driven iterative process for the vehicle and mission problems with vehicle initialization. The methodology is used in a demonstration of a concurrent vehicle and campaign-level trade study on NASA’s Design Reference Architecture 5.0. The LH2 Nuclear Thermal Propulsion (NTP) option is traded with NH3­ and H2O at the vehicle-level and the long-stay campaign is compared to a short-stay alternative. The subsequent impacts of the vehicle propellant species and surface stay duration on each other are detailed and the improvement of the state-of-the-art is shown using a baseline NTP architecture.

Committee

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