David Anthony Young

Lazarus: A SSTO Hypersonic Vehicle Concept Utilizing RBCC and HEDM Propulsion Technologies

14th AIAA/AHI Space Planes and Hypersonic Systems and Technologies Conference November 2006, Canberra, Australia

Artemis: A Reusable Excursion Vehicle Concept for Lunar Exploration

41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit July 10-13, 2005, Tucson, AZ.

Responsive Access Small Cargo Affordable Launch (RASCAL) Independent Performance Evaluation

13th International Space Planes and Hypersonics Systems and Technologies Conference May 2005, Capua, Italy

A Reduced Order Lunar Lander Model for Rapid Architecture Analysis

The conceptual design of an architecture for space exploration involves the evaluation of many concepts. These design spaces may encompass millions or billions of options when each trade is evaluated at the system, vehicle, subsystem, and component level. Various techniques are typically employed to select the conf iguration of systems that best meet s the requirements of the architecture. These include multi -attribute decision making techniques as well as optimization with the use of genetic algorithms and other stochastic methods. In order to speed up the evaluati on of these options, a set of reduced -order vehicle models can be used. These models evaluate the gross weight, dry weight, cost, and reliability of a vehicle given a set of programmatic and performance options in less than a second, versus the use of des ign codes that take on the order of minutes to hours to converge to a vehicle design. The use of such reduced -order models also enables other techniques that would otherwise take too long to run, such as Monte Carlo simulation to model uncertainty, as wel l as optimization of the vehicle and studies of sensitivities to changes in programmatic and performance inputs. A reduced -order lunar lander model is presented, utilizing response surface equations (RSEs) in place of detailed disciplinary simulations. While some fidelity is lost in approximating these disciplines with RSEs , this approach can be used to evaluate the relative impact of various trade studies at the subsystem, vehicle, and architecture levels. The propulsion system is modeled using a respo nse surface of the REDTOP -2 code . In a similar manner, the trajectory for lunar descent and ascent is simulated using Program to Optimize Simulated Trajectories (POST), and then approximated with a RSE for use in the reduced -order lunar lander model. The w eights and sizing model of the lunar lander is based on a combination of historical mass estimating relationships (MERs) , and physics based mass estimating relationships. Development and production c ost modeling is performed using the Cost Estimating Re lationships (CERs) from the NASA -Air Force Cost Model (NAFCOM). Because the reduced -order lunar lander model evaluates rapidly, stochastic optimization methods such as genetic algorithms can be used to find the performance inputs (such as thrust -to -weight ratios, propellant choices, and expansion ratios) that optimize the vehicle for smallest mass, highest reliability, or smallest development cost. A user -customizable Overall Evaluation Criterion (OEC) can be used to optimize the vehicle for a weighted com bination of multiple criteria. Within an architecture analysis, this quick turn -around is useful for rapidly designing the lunar lander to meet the mass constraints of the launch vehicles, and the cost and reliability constraints of the programmatics .

Tempest: Crew Exploration Vehicle Concept

41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit July 10-13, 2005, Tucson, AZ.

A Resource Allocation Method for Achieving Optimal Reliability in a Lunar Architecture

In January 2005, President Bush announced the Vision for Space Exploration. This vision involved a progressive expansion of human capabilities beyond Low Earth Orbit beginning with a return to the moon starting no later than 2020. Current design processes utilized to meet this vision employ performance based trade studies to determine the lowest cost, highest reliability solution. In these design processes, designers trade independent performance variables and then calculate the design discriminators, reliability and costs, of the different architectures. The methodology implemented in this paper focuses on a concurrent evaluation of the performance, cost, and reliabilities of lunar architectures. This process directly addresses the top level requirements early in the design process and allows the decision maker to evaluate the highest reliability, lowest cost lunar architectures without being distracted by the performance details of the architecture. To achieve this methodology of bringing optimal cost and reliability solutions to the decision maker, parametric performance, cost, and reliability models are created to model each vehicle element. These models were combined using multidisciplinary optimization techniques and response surface equations to create parametric vehicle models which quickly evaluate the performance, reliability, and cost of the vehicles. These parametric models, known as ROSETTA models, combined with a life cycle cost calculator provide the tools necessary to create a lunar architecture simulation. The integration of the tools into an integrated framework that can quickly and accurately evaluate the lunar architectures is presented. This lunar architecture selection tool is verified and validated against the Apollo lunar architectures. The results of this lunar architecture selection tool are then combined into a Pareto frontier to guide the decision maker to producing the highest reliability architecture for a given life cycle cost. The advantages of this method over traditional design processes are numerous. With this presented methodology, the decision maker can transparently choose a lunar architecture solution based upon the high level design discriminators. This method can achieve significant reductions in life cycle costs keeping the same architecture reliability as a traditional design process point solution. This methodology also allows the decision maker to choose a solution which achieves a significant reduction in failure rate while maintaining the same life cycle costs as the point solution of a traditional design process.