A Model-Based Methodology for the Integration of a System Architecture in a Digital Aircraft Design Process

Due to the current trend towards sustainable, environmentally friendly, and digitally networked aircraft, it is necessary to integrate new revolutionary technology. This increases complexity and necessitates the investigation of novel aircraft designs and configurations early, quickly, and cost-effectively. Innovative concepts and approaches are necessary to handle this complexity. Model-based Systems Engineering (MBSE) is a fundamental approach to support and manage complex system development. Notable benefits are achieved compared to more traditional document-based methods. Therefore, researchers at the DLR Institute of System Architectures in Aeronautics are developing a process to fully digitalize and virtualize an aircraft. This enables it to be completely represented as a virtual product, allowing for the rapid implementation, visualization, and validation of novel design concepts. This work extends the digital design process with a model-based methodology for developing and integrating the functional system architecture. An application use case on passenger service functions serves as a proof of concept (PoC) during the methodology evaluation. At the beginning, the system is analyzed and it's architecture is modeled using the Systems Modeling Language (SysML). The system requirements are defined and all model elements are linked together. This improves the traceability and enables early error detection as well as the validation of requirements. The system model is then linked to existing models. Model integration allows the system architecture to be configured with cabin design parameters from CPACS and the architecture data to be used for geometrical cabin design. To illustrate advantages of architecture integration, multidisciplinary optimization is investigated based on the interaction between the different models. A trade-off analysis is performed using multidisciplinary design parameters regarding electrical power distribution and cable length. The interactions and effects between the design domains are therefore identified and analyzed

Model-based design and multidisciplinary optimization of complex system architectures in the aircraft cabin

The aviation industry is currently facing major challenges due to environmental and socio-economic trends toward sustainable and digitalized aviation. Revolutionary, more powerful and efficient technologies must be rapidly integrated into aircraft,while aircraft manufacturers must demonstrate the required safety. To support the implementation of new concepts, the DLR Institute of System Architectures in Aeronautics is researching methods for end-to-end digitalization from the preliminary design phase to assembly and production. In this context, Model-Based Systems Engineering (MBSE) and Multidisciplinary Design Optimization are important approaches for the development of complex systems. This paper presents a method for the end-to-end use of digital models for multidisciplinary optimization of system architectures. The Systems Modeling Language (SysML) is used to represent the system architecture. The focus is on the cabin and cabin systems, since theyare highly coupled to other aircraft systems and have dynamic, customer-specific configuration requirements. The system architecture in SysML is instantiated and configured by the interface to the aircraft fuselage and cabin design parameter sets in the Common Parametric Configuration Schema. The subsequent coupling of the generated system architecture model with the cabin system design model developed in Matlab allows a multidisciplinary optimization of the system properties. A sensitivity analysis is performed using the Passenger Service Unit as an example. The effects of different cabin configurations on the system architecture are investigated and interdisciplinary synergies are identified and analyzed. The results of this analysis are discussed in this paper

An Approach for Linking Heterogenous and Domain-Specific Models to Investigate Cabin System Variants

This paper presents an approach to link heterogeneous and domain-specific models. The background of this research is the complete investigation and comparison of cabin system variants, where many different aspects have to be represented. These include functional requirements, safety regulations, and geometric properties (e.g. installation space). However, these cannot always be validated or represented with just one model, as different levels of detail are required. Therefore, different discipline models have to be created, which in turn increases the complexity as a whole. Furthermore, the system to be represented by the models, such as the aircraft cabin, is already complex in itself. The many dependencies among each other and subsystems make it difficult to integrate new variants or technologies (e.g. liquid hydrogen) into the existing system architecture. The approach presented here therefore shows how the data and models of the different disciplines can interact with each other in order to be able to investigate variants holistically. This is demonstrated using the design of hatrack variants for a commercial aircraft

Permanently updated 3D‑model of actual geometries of research environments

This report describes the approach to create permanently updated 3D models of research aircraft and laboratory facilities. Therefore, optical metrology scans the research environment in its raw or as-delivered condition. The result is a virtual model of the actual geometry and, in comparison to reference data (e.g. CAD-data), the smallest inaccuracies can be identi- fied and analyzed. The exact position of non-rigid components, like riser ducts, electronics or isolation, can be determined in the models. Further changes to the layout of these facilities are permanently digitized and added to the virtual model of the environment. This can be a new recording of the entire facility or of individual areas that are affected by the changes. The individual, newly recorded models are then integrated into the existing model. This creates an always up-to-date 3D model of the research environment, which is added to its digital twin and can be observed there. In combination with CAD data, future conversion and installation measures are planned in advance and analyzed virtually in relation to the up-to-date geometry and installation space data. In addition, the virtual models of the aircraft cabins can be used to support the lengthy approval and certification process at an early stage

Modellbasierte Auslegung und multidisziplinäre Design Optimierung komplexer Systemarchitekturen in der Flugzeugkabine

Die ökologischen und sozio-ökonomischen Trends zur nachhaltigen und digitalisierten Luftfahrt stellen diese Industriebranche aktuell vor große Herausforderungen. Revolutionäre, leistungsfähigere und effiziente Technologien müssen schnell ins Flugzeug integriert werden während Flugzeughersteller die erforderliche Sicherheit nachweisen müssen. Um die Umsetzung neuer Konzepte zu unterstützen, forscht das DLR-Institut für Systemarchitekturen in der Luftfahrt an Methoden zur durchgängigen Digitalisierung von der Vorentwurfsphase bis hin zur Montage und Produktion. Dabei sind das Model-based Systems Engineering (MBSE) und die Multidisziplinäre Design Optimierung (MDO) wichtige Ansätze der Entwicklung komplexer Systeme. Diese Arbeit stellt eine Methode für den durchgängigen Einsatz digitaler Modelle zur multidisziplinären Optimierung von Systemarchitekturen vor. Zur Abbildung der Systemarchitektur wird die Systems Modeling Language (SysML) verwendet. Dabei liegt der Fokus auf der Kabine und den Kabinensystemen, da diese einen hohen Integrationsgrad im Flugzeug nachweisen und mit dynamischen, kundenspezifischen Konfigurationsanforderungen verbunden sind. Die Systemarchitektur in SysML wird durch die automatisierte Schnittstelle zu den Flugzeugrumpf- und Kabinenentwurfsparametersets im Common Parametric Configuration Schema (CPACS) instanziiert und konfiguriert. Die anschließende Kopplung des generierten Systemarchitekturmodells mit dem in Matlab entwickeltem Auslegungsmodell für Kabinensysteme ermöglicht die Durchführung einer multidisziplinären Optimierung der Systemeigenschaften. Dabei wird eine Sensitivitätsanalyse am Beispiel der Passenger Service Unit (PSU) durchgeführt. Die Effekte verschiedener Kabinenkonfigurationen auf die Systemarchitektur werden untersucht und interdisziplinäre Synergien identifiziert und analysiert. Die Ergebnisse dieser Analyse werden in dieser Arbeit diskutiert und Erweiterungen in zukünftigen Arbeiten vorgestellt

A Model-Based Approach for Evaluating and Validating the Sustainability of Production Systems

As an important contributor to the global transportation system, the aeronautics industry has an important role to play in promoting sustainability and accelerating the development of sustainable products. This paper aims to investigate how innovative technologies, engineering methods, and digitalization can support the achievement of sustainability goals in the aeronautics industry. This work argues that the adoption of Model-Based Systems Engineering (MBSE) improves the definition and validation of sustainability key performance indicators (KPIs) and stakeholder requirements while designing and planning both products and production systems. By using modeling languages such as SysML, the production of customizable aircraft becomes more flexible and modular, enabling the industry to adapt to changing product configurations and resource availability. Moreover, this paper demonstrates how MBSE can depict relationships within the production system and can ensure KPIs traceability for both product components and industrial processes. The data and relationships captured through MBSE can be used to plan and optimize production processes, and to simulate them to analyze sustainability aspects and identify energy-saving opportunities. By employing a comprehensive and systematic approach, MBSE enables the aeronautics industry to address sustainability concerns across all stages of the lifecycle from design to production, considering different fidelity levels. This paper presents a methodology for linking model-based production system architectures to real production systems. It highlights how sustainability data obtained from executed processes and resources can be utilized for model-based requirements validation and support the optimization of sustainable conceptual design

Entwicklung eines modularen Planungsalgorithmus für die Multifidelitätsanalyse der Kabinenmontageprozesse von der Vormontage bis zur Endmontagelinie

Das wachsende öffentliche Bewusstsein für Umweltfragen sowie ökonomische Ziele führen dazu, dass die Luftfahrtbranche neue Ansätze für Flugzeugkonfigurationen betrachten muss. Solche neuartigen Konzepte sind für OEM mit neuen Herausforderungen verbunden. Um die Umsetzung solcher Konzepte möglichst früh ganzheitlich zu bewerten und damit die Markteinführung und Produktentwicklung zu beschleunigen, müssen daher Produktionsfaktoren bereits in der Designphase berücksichtigt werden. Das Ziel besteht darin, eine Flugzeugproduktion möglichst schnell an grundlegende und Last-Minute Änderungen anzupassen. Um dies umzusetzen, müssen verschiedene Phasen im Produktionsprozess betrachtet werden, von der Top-Level Planung bis hin zur Umsetzung in der Montage. Diese verschiedenen Phasen sind komplex und benötigen verschiedene Detaillierungsgrade. So betrachten Top-Level Prozesse vornehmlich logistische und Supply-Chain relevante Aspekte, sodass ein grober Detaillierungsgrad ausreicht. Für die Prozessplanung von Montageprozessen wird aber das Wissen über lokale Ressourcen (z.B. Roboter) und deren Verfügbarkeiten benötigt, was einen höheren Detaillierungsgrad voraussetzt. Daher ist es nötig, eine modulare und semantische Anbindung verschiedener Disziplinen zu ermöglichen. In dieser Arbeit wird diese digitale Durchgängingkeit erweitert, um eine modulare und durchgängige Fabrikplanung zu ermöglichen. Damit lassen sich Fabrikplanungsalgorithmen mit der Prozessplanung und dem Design verknüpfen, wodurch eine flexible Produktion ermöglicht wird. Diese Methodik wird anschließend am Beispiel der Fabrikplanung für den Kabinenmontageprozesses vorgestellt. Durch die Verknüpfung von Fabrikplanung mit anderen heterogenen Disziplinen leistet diese Arbeit einen wertvollen Beitrag zur Erschaffung eines digitalen Fadens in der Flugzeugproduktion

Erweiterung einer virtuellen Entwicklungsumgebung für komplexe Kabinensysteme durch funktionale Architekturmodelle

Model-based systems engineering (MBSE) is a fundamental approach for the end-to-end use of digital models in the development of complex systems. The aviation industry in particular, where system complexity is constantly increasing, needs new concepts and methods to overcome ecological and socio-economic challenges. Therefore, domain-specific models are needed for the design and evaluation of systems to support the various system investigations, such as requirements management, installation space optimization, or failure analyses. An end-to-end coupling and linking of these mostly heterogeneous systems offer many advantages (e.g. shorter development times) over working with isolated digital sub-models, natural language documents, and purely physical prototypes. In addition, digitalization allows global and interdisciplinary collaboration of multiple teams of experts on the same virtual product. Since this approach is particularly promising for the configuration of aircraft cabins, a virtual development platform is developed at the German Aerospace Center (DLR) for the conceptual design of the aircraft cabin and its systems. As a result, virtual prototypes of cabin configurations are quickly generated to allow new concepts to be visualized and investigated at an early design stage. Extending the conceptual cabin system design process with a functional system architecture and executable system architecture models promotes information traceability, early failure detection, and requirements verification. The methodology used for this purpose is presented in this paper. The systems modeling language (SysML) is used to build a model for the functional depiction of cabin systems and to link it to existing models of the conceptual cabin design process. The modeling is performed exemplarily for the passenger service functions. Subsequently, the results are automatically transferred to the virtual development platform to experience the generated cabin concept.AlternativeReviewe

Erweiterung der virtuellen Entwurfsplattform zur Abbildung einer funktionalen Systemarchitektur komplexer Kabinensysteme

Model-based Systems Engineering (MBSE) ist ein grundlegender Ansatz für den durchgängigen Einsatz digitaler Modelle bei der Entwicklung komplexer Systeme. Insbesondere die Luftfahrtindustrie, wo die Systemkomplexität stetig zunimmt, braucht neue Konzepte und Methoden, um ökologische und sozioökonomische Herausforderungen zu bewältigen. Die durchgängige Nutzung verknüpfter, digitaler Systemmodelle und die Interaktion von Modellen und physischen Systemen bieten viele Vorteile gegenüber der Arbeit mit isolierten digitalen Teilmodellen, natürlichsprachlichen Dokumenten und rein physischen Prototypen. Zudem erlaubt die Digitalisierung die globale und interdisziplinäre Zusammenarbeit mehrerer Expertenteams an demselben virtuellen Produkt. Da dieser Ansatz für die Auslegung von Flugzeugkabinen besonders vielversprechend ist, wird beim DLR eine virtuelle Entwicklungsplattform für die Auslegung und Darstellung der Flugzeugkabine und ihrer Systeme aufgebaut. Damit werden virtuelle Prototypen von Kabinenkonfigurationen schnell erzeugt, um neue Konzepte frühzeitig visualisieren und untersuchen zu können. Die Erweiterung des Auslegungsprozesses der Kabinensysteme um eine funktionale Systemarchitektur und um ausführbare Systemarchitekturmodelle fördert die Nachverfolgbarkeit von Informationen, die frühe Fehlererkennung und die Überprüfung von Anforderungen. Die hierfür genutzte Methodik wird in diesem Bericht vorgestellt. Dazu werden mit der Systems Modeling Language (SysML) Modelle zur funktionalen Abbildung von Systemen aufgebaut und mit vorhandenen Modellen digital verknüpft. Die Modellierung wird exemplarisch für die Passagierservicefunktionen durchgeführt. Die Ergebnisse werden automatisch an vorhandene Modelle der virtuellen Entwicklungsplattform übergeben. Mit Hilfe von virtueller Realität können in einer immersiven Umgebung frühzeitig Erkenntnisse über neue Kabinenkonfigurationen gewonnen und die Kommunikation zwischen den am Auslegungsprozess beteiligten Experten gefördert werden

Optimal robot positioning for sustainable process execution

In this paper, the methodology to implement an inference algorithm based on the robot parameters for process execution on different Key Performance Indicators (KPI’s) is presented. This work is part of holistic objective to implement a system to perform virtual validation of automated process with robots, where the impact of relative position of robot to required task and vice-versa could be inferred virtually. For the above implementation, it is proposed to develop the digital model of the necessary infrastructure in the simulation environment. To start with, computational data from simulation is stored to a database, which is then analysed and appropriate regression based inference model is formulated. To ensure that the digital model imitates the real-time system, the feedback data from hardware execution is used to improve the parameters of regression model. With this implementation, the digital model would represent the digital twin (DT) of the hardware under consideration. The whole execution is performed on the pre-assembly cell at the Institute for System Architectures in Aeronautics, Hamburg. Use case for the digital twin implementation is the pre-assembly of overhead structural truss that assists in realizing the modularized cabin assembly process. Implementation of simulation model for cabin assembly is a two-fold approach, where the robot localization for reachability is computed followed by computation of joint trajectories. From the obtained trajectories, the energy and time consumed by the robot for a given task is calculated. The computed information is then stored in a database, which is then fed to an inference algorithm. Implementation of the algorithm is desired based on the time taken for path computation, and using this algorithm the optimal robot location and joint angles for any new unknown task could be com- puted