The Aircraft and Its Manufacturing System: From Early Requirements to Global Design

Chan, Anouck; Pires, Anthony Fernandes; Polacsek, Thomas; Roussel, Stéphane

doi:10.1007/978-3-031-07472-1_10

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…The objective is to reduce the environmental impact associated with fuel consumption, waste generation, and emissions throughout the aircraft system's activities and operations. This requires collaborative efforts involving not only the aircraft design domain but also other industrial domains such as manufacturing, supply chain management, maintenance, and certification [13,32,40].…”

Section: General Aircraft Design Contextmentioning

confidence: 99%

High-dimensional multidisciplinary design optimization for aircraft eco-design

SAVES

2024

Preprint

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Nowadays, there is a significant and growing interest in improving the efficiency of vehicle design processes through the development of tools and techniques in the field of MDO. Specifically, in aerostructure design, aerodynamic and structural variables influence each other and have a joint effect on quantities of interest like weight or fuel consumption and, as such, MDO arises as a powerful tool for automatically making interdisciplinary trade-offs. In the aircraft design context, the process generally involves mixed continuous and categorical design variables. For instance, the size of an aircraft’s structural parts can be described using continuous variables, while discrete variables may include either integer variables, like the number of panels, or categorical variables, like cross-sections or material choices. The objective of this Philosophiae Doctor (Ph.D) thesis is to propose an efficient approach for optimizing a multidisciplinary black-box model when the optimization problem is constrained and involves a large number of mixed integer design variables (typically 100 variables). The targeted optimization approach, called EGO, is based on a sequential enrichment of an adaptive surrogate model and, in this context, GP surrogate models are one of the most widely used in engineering problems to approximate time-consuming high fidelity models. EGO is a heuristic BO method that performs well in terms of solution quality. However, like any other global optimization method, EGO suffers from the curse of dimensionality, meaning that its performance is satisfactory on lower dimensional problems, but deteriorates as the dimensionality of the optimization search space increases. For realistic aircraft design problems, the typical size of the design variables can even exceed 100 and, thus, trying to solve directly the problems using EGO is ruled out. The latter is especially true when the problems involve both continuous and categorical variables increasing even more the size of the search space. In this Ph.D thesis, effective parameterization tools are investigated, including techniques like partial least squares regression, to significantly reduce the number of design variables. Additionally, Bayesian optimization is adapted to handle discrete variables and high-dimensional spaces in order to reduce the number of evaluations when optimizing innovative aircraft concepts such as the “DRAGON” hybrid airplane to reduce their climate impact.

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Section: General Aircraft Design Contextmentioning

confidence: 99%

High-dimensional multidisciplinary design optimization for aircraft eco-design

SAVES

2024

Preprint

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Requirements engineering in industry 4.0: State of the art and directions to continuous requirements engineering

Barcelos,

Antonino,

Nakagawa

2024

Systems Engineering

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The 4th Industrial Revolution, also known as Industry 4.0, intends to transform manufacturing processes into smart factories with full digitalization and intelligent, decentralized, and flexible production. In this scenario, Industry 4.0 systems (i.e., software‐intensive systems that automate smart factories) have required rigorous and continuous development, but smart factory companies often have difficulty dealing with Requirements Engineering (RE) where requirements continuously change and emerge at runtime to support the changeability of complex production processes. Such requirements encompass engineering (e.g., mechanical, electrical, electronic, production/manufacturing) and business areas and involve the vertical and horizontal integration of heterogeneous manufacturing systems. There is also a lack of a panorama of how Industry 4.0 projects have performed with RE activities. The main goal of this paper is to present the state‐of‐the‐art research concerning RE in Industry 4.0 and draw attention to the next most urgent steps. For this, we selected and examined studies that address RE for Industry 4.0, noting that much of this literature is recent but does not fully address the complexity and dynamism of the requirements for Industry 4.0. Grounded on these studies and our academic and industry experience, we highlight the need for Continuous Requirements Engineering (CRE) for Industry 4.0.Significance and Practitioner Points: The main implications of this paper are: (i) For researchers: It offers the state of the art of RE in the context of Industry 4.0 and points out several important open issues that require an urgent investigation through new research topics; and (ii) For practitioners: It provides directions for new or even existing Industry 4.0 projects on how to deal with RE activities aiming to overcome the several challenges to perform them.

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