Collaborative System of Systems Multidisciplinary Design Optimization for Civil Aircraft:AGILE EU project

Abstract

As part of H2020 EU project "AGILE", A Collaborative System of Systems Multidisciplinary Design Optimization research approach is presented in this paper. This approach relies on physics-based analysis to evaluate the correlations between the airframe design, as well as propulsion, aircraft systems, aerodynamics, structures and emission, from the early design process, and to exploit the synergies within a simultaneous optimization process. Further, the disciplinary analysis modules from multiple organizations, involved in the optimization are integrated within a distributed framework. The disciplinary analysis tools are not shared, but only the data are distributed among partners through a secured network of framework. In order to enable and to accelerate the deployment of collaborative, large scale design and optimization frameworks, the "AGILE Paradigm", a novel methodology, has been formulated during the project. The main elements composing the AGILE Paradigm are the Knowledge Architecture (KA), and the Collaborative Architecture (CA). The first formalizes the overall product development process in a multi-level structure. The latter formalizes the collaborative process within the entire supply chain, and defines how the multiple stakeholders interact with each other.The current paper is focused on the application of using the AGILE Paradigm to solve system of stystems MDO on a regional jet transport aircraft. The focus of the current research paper is: 1) Creation of a system of systems frame work using AGILE Paradigm to support multidisciplinary distributive analysis capability. The framework involves physics based modules such as : Airframe synthesis, aerodynamics, structures, aircraft systems , propulsion system design, nacelle design, nacelle airframe integration, aircraft mission simulation,costs and emissions. 2) Validate the frame work with case study of a regional jet reference aircraft. 3) Assess the sensitivity and coupling of design parameters, local disciplinary optimizataion and its effect on global optimization objectives or constraints. The effects of varying Bypass Ratio (BPR) of engine, offtake effects due to degree of electrification and nacelle effects are propagated through the AGILE MDO framework and presented.

Design Optimization of Unmanned Aerial Vehicles : A System of Systems Approach

“…Consequently, design optimization as an integrated activity of the conceptual stage has been shown to offer the possibility to speed up the process through the concurrent analysis of several disciplines like for example in the development of multidisciplinary UAV design frameworks (Tyan et al, 2017;Sepulveda et al, 2019). Moreover, design optimization frameworks can enable the use of models from the preliminary design like for example a representation of the flight controls (Perez et al, 2006;Mieloszyk and Goetzendorf-Grabowski, 2017) or a description of the onboard sub-systems (Allison et al, 2012;Prakasha et al, 2017). As far as the operational environment of the product is concerned, design optimization frameworks can be enhanced with a model that will enable to consider certain product interactions in one or more specific scenarios like for instance in the case of airport approach operations (Subramanian and DeLaurentis, 2016).…”

“…Overall, it has been frequently shown that design optimization as a process can permeate many layers of the design, and a typical example of this in UAV development is to have concurrent airframe and sub-system evaluations in a suitable computational environment (Prakasha et al, 2017;Fioriti et al, 2018). Moreover, it can be seen that a design optimization framework offers information on properties such as robustness, adaptability, flexibility, and safety, and to this date, this can be achieved by using a set of appropriate process inputs like probabilistic and uncertainty constraints (Gavel et al, 2008;Nguyen et al, 2015).…”

“…For each one of the candidate SoS combinations, the SS are analyzed first by using dedicated analysis models, and then, the demands from the CS level (couplings) are exported so that they can be used in the sizing of the CS. Given this information, the performance of the CS is analyzed right after, and eventual backwards demands from the SS level (couplings) are resolved by means of an iterative loop as in the work of Prakasha et al, (2017) andVan Tooren et al (2011). At the end of the process the performance as well as the "soft" constraints are saved in order to be used later in the SoS simulation.…”

Applying a distributed collaborative MDAO environment to study the power matching of the propulsion system and the on-board electrified systems for advanced regional and short- to medium-range jetliners

“…These specific architectures have been selected here because of their similarity with the ones adopted by the newest civil transport aircraft (i.e. Boeing 787 and Airbus A350) and they have been already studied in (1,10,27) for different purposes. Therefore, the studied architectures could be considered the most promising for future use.…”

A clustered and surrogate-based MDA use case for MDO scenarios in AGILE project

“…5) A large-scale system-of-systems application was also studied, coupling Aircraft -Engine -On-board systems (OBS) -Emissions in a distributed framework approach with the involvement of disciplinary services from the other partners. [14] (a) Hi-Fi multi-level optimization formulation (b) Rudder optimization…”