Overview

• Digital Twin – a myth, the next hype ?

• Digital Twin Synthesis: Overall Concept to strategically address new data flows, technologies – supporting concrete use cases

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• Digital Twin for product approach – a first sketch of ideas ?

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With consolidated revenues of € 70.5 billion in 2019, Airbus is a global leader in aeronautics, space and related services. Airbus offers the most comprehensive range of passenger airliners. Airbus is also a European leader, providing tanker, combat, transport and mission aircraft, as well as one of the world’s leading space companies. In helicopters, Airbus provides the most efficient civil and military rotorcraft solutions worldwide. In 2019, it generated approximately 86% of its total revenues in the civil sector and 14% in the defence sector. As of 31 December 2019, Airbus’ active headcount was 134,931 employees.

Are you interested in an integrated view from Airbus Transformation drivers including mega trends and demographic overview, to the main Human Resources (HR) current practices and current evolutions? If yes, this book is just for you! The book delves into the main assets of the company, its Human Capital and how to secure its continuous positive growth and its full engagement through the Airbus transition as a new worldwide Industry 4.0 and as a new technology company.

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The ambition of the Global Workforce Forecast is to provide all employees with relevant data, information and analysis to better understand, anticipate and prepare the evolution of our company competencies. As employees worldwide, you are actors of this opportunity and you are encouraged to engage your own relevant development actions such as learning, mobility, knowledge management. As leaders, you could maximise usage of the HR levers (recruitment, employment marketing, learning, knowledge management…) presented here with the actors that could support your projects.

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Abstract

Aeronautical industry faces the increase of system complexity and reliability-based constraints, and in the meantime tries to reduce costs, to maximize the system performance and to improve the safety. The recent development of MBSE facilitates the transfer of models and enables to simulate the system behavior early in the development phase. In this context, the thesis aims to shape a collaborative and adaptive software environment to carry out uncertainty management on multidisciplinary aeronautical systems. Case Studies examine the implementation of a systematic CPM throughout the design process of a new commercial aircraft. This work raises the point of uncertainty-based optimization complexity and investigates different solutions to face this issue.

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Most of the analytical models tackled in the thesis derive from a set of regressions suited to an Airbus commercial aircraft. While Cameo Systems Modeler supports a modular modeling, ModelCenter bridges the gap between descriptive and analytical models while ensuring a great traceability. The multi-levels simulation enabled by ModelCenter helps identifying the critical parameters from the early steps of the design process. This holistic and data-driven approach drives the product development process by eliminating non-value-added activities. The variety of sensitivity analysis tools suits any type of system complexity.

The software environment supports the implementation of an uncertainty-based multidisciplinary optimization. Non-dominated Sorting Genetic Algorithm NSGA-II highlights the tradeoff between performance optimization and cost reduction and its influence on the optimal design. Reliability-based constraints reduce the solution space and affects the final design of the aircraft by shifting the Pareto-front away from the best objective values. ModelCenter provides effective tools to face the high level of complexity of optimization under uncertainty. While the parallelization of simulations on virtual machines enhances the computational performance, DOE screening enables reducing the design space by eliminating irrelevant inputs. The conversion of multi-objective into single objective function focuses the search for optimal on a part of the global Pareto-front and significantly shortens the computing time. However, this solution requires setting up a hierarchy between the objectives and thus leaves behind non-dominated design solutions.

Although the results show the ability of this software environment to design complex systems under uncertainty, it is difficult to extrapolate a general uncertainty-based multidisciplinary design optimization workflow for various aeronautical systems at Airbus. Each design under uncertainty depends on the model complexity, the size of the design space as well as the available computational resources. Improvements of the Case Studies models are possible by refining both performance and cost functions. While models linking them to the design parameters are difficult to set up, a precise definition may capture the complete product life cycle in the design process under uncertainty.

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访谈介绍了Airbus“城市空中交通”（Urban Air Mobility）的构想。

H1 RESULTS 2015

ROADSHOW PRESENTATION

INVESTOR MEETING

PARIS AIRSHOW 2015

Tom Enders Fabrice Bregier Harald Wilhelm

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CEO – Airbus Group CEO – Airbus CFO – Airbus Group & Airbus

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Airbus Q1 2015 RESULTS 2015 ROADSHOW PRESENTATION