Demonstration of a Probabilistic Technique for the Determination of Aircraft Economic Viability

Abstract

Over the past few years, modern aircraft design has experienced a paradigm shift from designing for performance to designing for affordability. This paper contains a probabilistic approach that will allow traditional deterministic design methods to be extended to account for disciplinary, economic, and technological uncertainty. The probabilistic approach was facilitated by the Fast Probability Integration (FPI) technique; a technique which allows the designer to gather valuable information about the vehicleÕs behavior in the design space. This technique is efficient for assessing multi-attribute, multiconstraint problems in a more realistic fashion. For implementation purposes, this technique is applied to illustrate how both economic and technological uncertainty associated with a Very Large Transport aircraft may be assessed. The assessment is evaluated with the FPI technique to determine the cumulative probability distributions of the design space, as bound by economic objectives and performance constraints. These distributions were compared to established targets for a comparable large capacity aircraft, similar in size to the Boeing 747-400. The conventional baseline configuration design space was determined to be marginally feasible and non-viable, motivating the infusion of advanced technologies, including laminar flow control, advanced materials, and advances in propulsion technology as to reflect 2005 entry into service. The resulting system benefits and penalties were qualitatively assessed with technology metric ÒkÓ factors. The infusion of technologies shifted the VLT design into regions of greater feasibility and viability. The study also demonstrated a method and relationship by which the impact of new technologies may be assessed in a more system focused approach. The impact of technology readiness and its associated risk was also addressed in this study and is not presented here.

Approximation of probabilistic distributions using selected direct simulations

“…Generality was shown in that the technique required little definition before it could be adapted to the example problems. 4. Numerical noise was shown to be an issue with iterative stochastic analyses.…”

“…This should be done in a fully iterative environment with an eye to computational expense. 4. Test the discrete probability sampling technique on problems other than trajectory.…”

A Method for Technology Selection Based on Benefit, Available Schedule and Budget Resources

“…Uncertainty also arises from various contributing factors including ambiguous customer requirements, analysis tool fidelity, manufacturing tolerances, daily fuel costs, etc. [9,10] Traditionally, uncertainty in knowledge about structural loads, mathematical models, economic assumptions, potential technological risks, etc., has been simulated deterministically through factors of safety and assumptions of reality [11]. This is the poor man's way of handling uncertainty.…”

“…The current research focuses on the enhancing the last step, technology selection under uncertainty. The reader is referred to References [10,17,18,19] for more detailed information regarding steps 1 through 7. STEP 8: TECHNOLOGY SELECTION -For any multiattribute, -constraint, or -objective problem, the selection of the "best" family of alternatives is inherently subjective and no single answer will fulfill all requirements.…”

An introduction to the ROSETTA modeling process for advanced space transportation technology investment

“…The Technology Identification, Evaluation, and Selection (TIES) methodology is a systematic aggregation of decision-making techniques and probabilistic methods developed at the Aerospace Systems Design Lab (ASDL) in the School of Aerospace Engineering at the Georgia Institute of Technology. Previous versions of the TIES method have been applied by the ASDL to commercial transport aircraft, rotorcraft, and uninhabited combat aerial vehicles 1,2,3 . An abbreviated version of this method-called the ATIES process 4 -consists of six major parts: baseline concept determination, technology identification, technology compatibility, technology impact, technology evaluation, and technology selection.…”