Aircraft Multidisciplinary Design Optimization Under Both Model and Design Variables Uncertainty

Abstract

Low-fidelity analytical models are often used at the conceptual aircraft design stage. Because of uncertainties on these models and their corresponding input variables, deterministic optimization may achieve under-design or overdesign. Therefore it is important to already consider these uncertainties at the conceptual design stage in order to avoid inefficient design and then costly time over runs due to re-design. This paper presents a procedure for reliable and robust optimization of an aircraft at the conceptual design phase. Uncertainties on model and design variables are taken into account in a probabilistic setting. More precisely, at each point of the optimization process uncertainties are modeled by an adaptive normal law strategy in order to fit the historical aircraft database. The statistical parameters are adjusted depending on the available information at the current point of the optimization process. To improve computational cost, response surface approximations are constructed to represent reliability constraints. The developed methodology is applied to the conceptual design of a short range aircraft. Compared to standard deterministic optimization without design margins, the result shows a modest increase on weight, which allows however to ensure a desired reliability and robustness of the design compared to the unreliable and sensitive deterministic optimum.

Aircraft derivative design optimization considering global sensitivity and uncertainty of analysis models

“…Moreover, RBDO and PBDO were implemented to consider errors associated with low fidelity analysis tools of aircraft conceptual design [41][42][43][44][45][46]. Nuefeld et al (2011) and Jaeger et al (2013) implemented RBDO to handle uncertainty from the fidelity of the analysis model [43,45]. In this research, analysis results using the low fidelity analysis tools and a historical database were compared to derive error terms for uncertainty-based design optimization methods.…”

“…Nuefeld et al (2011) and Jaeger et al (2013) implem from the fidelity of the analysis model [43,45]. In this research, analysis results and a historical database were compared to derive error terms for uncertaintyAdditionally, RBDO and PBDO results were compared with each other.…”

“…Moreover, [43,45]. In this research, analysis results using the low fidelity analysis tools and a historical database were compared to derive error terms for uncertainty-based design optimization methods.…”

“…GSA method was performed to find the important design variables for the RBDO and PBDO were implemented to consider errors associated with low conceptual design [41][42][43][44][45][46]. Nuefeld et al (2011) and Jaeger et al (2013) implem from the fidelity of the analysis model [43,45].…”

An integrated multidisciplinary design optimization method for computer numerical control machine tool development

“…Jaeger et al 15 proposed an effective aircraft MDO method with uncertain design model and design variables, which provides an effective method for dealing with this kind of problem under uncertainty conditions. Chen et al 16 proposed a multidisciplinary collaborative optimization method for a flight missile system development by using artificial neural network.…”