System Level Uncertainty Assessment for Collaborative RLV Design
A.C. Charania, John E. Bradford, John R. Olds, Matthew Graham
April 12, 2002
Abstract
A collaborative design process utilizing Probabilistic Data Assessment (PDA) is showcased. Given the limitation of financial resources by both the government and industry, strategic decision makers need more than just traditional point designs, they need to be aware of the likelihood of these future designs to meet their objectives. This uncertainty, an ever-present character in the design process, can be embraced through a probabilistic design environment. A conceptual design process is presented that encapsulates the major engineering disciplines for a 3rd Generation Reusable Launch Vehicle (RLV). Toolsets consist of aerospace industry standard tools in disciplines such as trajectory, propulsion, mass properties, cost, operations, safety, and economics. Variations of the design process are presented that use different fidelities of tools. The disciplinary engineering models are used in a collaborative engineering framework utilizing Phoenix Integration’s ModelCenter© and AnalysisServer© environment. These tools allow the designer to join disparate models and simulations together in a unified environment wherein each discipline can interact with any other discipline. The design process also uses probabilistic methods to generate the system level output metrics of interest for a RLV conceptual design. The specific system being examined is the Advanced Concept Rocket Engine 92 (ACRE-92) RLV. Previous experience and knowledge (in terms of input uncertainty distributions from experts and modeling and simulation codes) can be coupled with Monte Carlo processes to best predict the chances of program success.
Introduction
Any envisioned future with ubiquitous Earth-To-Orbit (ETO) space transportation systems will rely on revolutionary improvements in the development and integration of technologies. Uncertainty exists as to the impact of these technologies upon concepts such as Reusable Launch Vehicles (RLVs). Such variability exists in both the immaturity of candidate technologies and limitations of current design codes to model reality (both in terms of physics and economics) to a sufficient degree. Given the limitation of financial resources by both the government and industry, strategic decision makers need more than just traditional point designs, they need to be aware of the likelihood of these future designs to meet their objectives. Such methods are important to the eventual prioritization of advanced space transportation technological investments. In particular, NASA’s 2nd Gen RLV/Space Launch Initiative (SLI) Advanced Engineering Environment (AEE) project has been evaluating such RLV airframe and propulsion technologies and transportation architectures. As part of this project, SpaceWorks Engineering, Inc. (SEI) has been developing probabilistic methods to augment AEE’s conceptual RLV design and analysis competency.
OBJECTIVE: The metrics used to evaluate the impact of technologies on a conceptual RLV system can be composed from various disciplines (i.e. performance, operations, cost, economics, safety and reliability) representing both a system’s technical feasibility and economic viability. Uncertainty, an ever-present character in the design process, can also be embraced through a probabilistic design environment1,2. The objective is to probabilistically quantify the impact of these technologies on the output metrics of interest from the full design process, notionally referred to here as Probabilistic Data Assessment (PDA). Robust design methods such as PDA allow quantitative assessment of risk. Monte Carlo simulation techniques can be used to place uncertainty distributions on internal design parameters3,4. The resultant outputs are cumulative and frequency probability distributions rather than simple deterministic values. Confidence intervals can be placed upon output metrics of interest to determine the 80% or 95% likelihood of meeting a target (e.g. payload capability, gross weight, launch price).
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