Implementation of a Physics-Based Decision-Making Framework for Evaluation of the Multidisciplinary Aircraft Uncertainty

Abstract

In today's business climate, aerospace companies are more than ever in need of rational methods and techniques that provide insights as to the best strategies which may be pursued for increased profitability and risk mitigation. However, the use of subjective, anecdotal decision-making remains prevalent due to the absence of analytical methods capable of capturing and forecasting future needs. Negotiations between airframe and engine manufacturers could benefit greatly from a structured environment that facilitates efficient, rational, decision-making. Creation of such an environment can be developed through a parametric physics-based, stochastic formulation that uses Response Surface Equations as meta-models to expedite the process. This paper describes the implementation of such an approach in order to demonstrate the types of insights that might be gained as an engine manufacturer tries to forecast the effects of the associated airframe uncertainties (structural, aerodynamic, etc. design changes) on engine related characteristics for the design of a hypothetical regional business jet.

A System of Systems View in Early Product Development : An Ontology-Based Approach

“…Additionally, [119] also illustrates how a dynamic dashboard can contribute to an interactive exploration of the design space. Finally, uncertainty in trade space analyses is also an important metric to consider, and it must be both managed and quantified for a design at an early stage to enable elaborate decision-making [120]. VA techniques can be used to provide visualizations of the uncertainty associated with different parts of a design or trade space under exploration.…”

Optimum Selection of "Number of Seats/Cargo Volume" for Transports in Uncertain Business Environment

“…Unfortunately, many times the relationship between actual responses and predictors are either too complex or they require considerable empirical adjustments to determine the behavior of the system in time. Moreover, due to some undesirable exponential behavior, they still would not give meaningful results in an acceptable period of time and quickly become impractical to use in time domain [4][5][6][7][8][9]. In contrast, decision making and/or predictions based on fuzzy logic systems are easier to implement and use and they do not exhibit any undesirable behavior in the mathematical formulation.…”

“…To effectively deal with uncertainties in systems, such as the case of the airplane design cycle, some well-known methods are available in the literature that have the capability to model uncertainties with more or less efficiency, among which response surface methodology (RSM) and probabilistic methods based on Monte Carlo simulations [4][5][6], are some examples. However, in these methods, the behavior of response governed by certain laws which can be approximated by a deterministic relationship between the response and the set of design variables.…”