DESIGN AND ECO-EFFICIENCY ASSESSMENT OF A PEOPLE MOVER AIRCRAFT IN COMPARISON TO STATE-OF-THE-ART NARROW BODY AIRCRAFT

The focus of the is paper is the question whether a People Mover aircraft can make its contribution towards the climate targets by replacing highly efficient narrow body aircraft where preliminary results indicate a significant disadvantage in terms of CO2 emissions per passenger seat. Therefore, the concept will be assessed on its economic performance and on its climate impact contribution

Integration of multi-physics analysis into the cabin design process using virtual reality

The design of an aircraft cabin and especially its disciplinary evaluation is a highly specialized process in the product development of an aircraft. Low-fidelity assessment of a cabin design is not commonly possible as there are many interactions with other disciplines and very detailed inputs are usually necessary in a particular form and format. In order to enable such high-fidelity evaluation processes for complex cabin systems in the early design stages of the cabin, the use of the Common Parametric Aircraft Configuration Schema (CPACS) is proposed in this paper for ensuring digital consistency among different disciplinary tools. Initially developed for the overall aircraft design, CPACS has been extended with a wide variety of definitions for the structural and cabin design. These support the efficient data exchange between the involved disciplines in a model-based design approach. This process is exemplified here by deriving different multi-fidelity and multi-physics simulation models from a central CPACS dataset. For multi-fidelity analysis, finite element models as well as statistical energy analysis models for the vibro-acoustic evaluation are demonstrated. In order to illustrate multi-physics capabilities, static structural and thermal variants for the finite element models are derived as well. A main advantage of using consistent data modelling is the fact, that all results from the different analyses can be fed back to a central visualisation platform. This enables the intuitive and collaborative exploration of the design as well as the results from the heterogeneous disciplines with all involved partners. The visualisation model is also derived from the central CPACS dataset and realised in this study in a virtual reality environment using the Unity game engine

Multi-fidelity Parametric Cabin Component Modeling Approach for Application-driven Geometry Generation

The aircraft cabin geometry is essential at many stages of the aircraft design process, ranging from preliminary design to detailed virtualisation. At each stage, for analysis purposes, a geometry model with an appropriate fidelity level is required. The aircraft cabin includes full-height components like closets, galley, and lavatory. This paper proposes a methodology, which derives CAD geometry for aircraft cabin full-height components from a set of design parameters at multiple distinct fidelity levels. Based on the complexity and for demonstration purposes, the galley model is selected.The galley's parametric description is based on data provided by the Common Parametric Aircraft Configuration Schema (CPACS), an established data model for aircraft design, and enhanced by component-specific parameters. The multi-fidelity model is the combination of low fidelity and high-fidelity models based on this description. The CAD geometry generation has been implemented using the Open Cascade Technology (OCCT) library. The multi-fidelity model provides consistent CAD geometry according to the model generation requirements of different disciplines based on the same set of parameters. The approach presented helps to accelerate multi-disciplinary design cycles, as tailored geometry with as little overhead as possible is available for disciplinary model generation. The CAD geometry generated in this model can be applied to experience aircraft cabin designs in virtual reality or to analysesthe dependencies between the aircraft cabin components and other systems. Furthermore, it can be used to validate the proposed enhanced cabin schema containing more detailed data about the cabin components

Streamlining Cross-Organizational Aircraft Development: Results from the AGILE Project

The research and innovation AGILE project developed the next generation of aircraft Multidisciplinary Design and Optimization processes, which target significant reductions in aircraft development costs and time to market, leading to more cost-effective and greener aircraft solutions. The high level objective is the reduction of the lead time of 40% with respect to the current state-of-the-art. 19 industry, research and academia partners from Europe, Canada and Russia developed solutions to cope with the challenges of collaborative design and optimization of complex products. In order to accelerate the deployment of large-scale, collaborative multidisciplinary design and optimization (MDO), a novel methodology, the so-called AGILE Paradigm, has been developed. Furthermore, the AGILE project has developed and released a set of open technologies enabling the implementation of the AGILE Paradigm approach. The collection of all the technologies constitutes AGILE Framework, which has been deployed for the design and the optimization of multiple aircraft configurations. This paper focuses on the application of the AGILE Paradigm on seven novel aircraft configurations, proving the achievement of the project’s objectives

Germline variation at 8q24 and prostate cancer risk in men of European ancestry

Chromosome 8q24 is a susceptibility locus for multiple cancers, including prostate cancer. Here we combine genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. We identify 12 independent risk signals for prostate cancer (p < 4.28 × 10−15), including three risk variants that have yet to be reported. From a polygenic risk score (PRS) model, derived to assess the cumulative effect of risk variants at 8q24, men in the top 1% of the PRS have a 4-fold (95%CI = 3.62–4.40) greater risk compared to the population average. These 12 variants account for ~25% of what can be currently explained of the familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Peer reviewe

Automatisierte Strukturauslegung im Flugzeugvorentwurf mit Python

Am Deutschen Zentrum für Luft- und Raumfahrt (DLR) wird an automatisierten Prozessketten für den Flugzeugvorentwurf geforscht. Ziel ist die Entwicklung und Bewertung von neuen Flugzeugkonzepten sowie die Verknüpfung unterschiedlicher Disziplinen im Vorentwurf wie Aerodynamik und Strukturauslegung. Das am DLR entwickelte CPACS-Datensatzformat [1] (Common Parametric Aircraft Configuration Schema, https://github.com/DLR-LY/CPACS) dient dabei als Austauschformat. Für die Auslegung der Rumpfstruktur zur Massenabschätzung hat das Institut für Bauweisen und Strukturtechnologie (BT) des DLR bereits das Tool TRAFUMO [2] (Transport Aircraft Fuselage Model) entwickelt, welches maßgeblich die skriptfähige Finite-Elemente-Software ANSYS nutzt. Um künftig deutliche Laufzeitreduktionen, weitere Anwendungsmöglichkeiten sowie mehr Schnittstellen mit anderen Tools zu ermöglichen wird aktuell eine alternative Open Source basierte Prozesskette mit Python entwickelt. Ausgehend vom CPACS-Parametersatz wird die Flugzeugoberfläche mittels Open Cascade erstellt und die Geometrie von Strukturkomponenten berechnet. Basierend auf eigens in Python entwickelten Tools wie einem FE-Präprozessor sowie einem FE-Konverter, wird ein FE-Modell der Flugzeugstruktur aufgebaut, welches in die Formate verschiedener FE-Solver exportiert werden kann. Zur Handhabung und Visualisierung dieser Datenmengen werden Module wie Numpy, Pandas und Mayavi verwendet. Die Dimensionierung der Struktur erfolgt zukünftig in Python auf Grundlage von Berechnungsergebnissen für verschiedene Lastfälle. Dabei können neben proprietären Solvern auch Open-Source-FE-Solver genutzt werden, wodurch die gesamte Prozesskette lizenzfrei läuft und der Datenaustausch vereinfacht sowie die Flexibilität erhöht wird. Weiterhin ermöglicht die modulare, objektorientierte Programmierung in Python neben der rein statischen Auslegung der Rumpfstruktur auch die Vorbereitung von Crashberechnungen mit komplexeren Anforderungen an das FE-Modell in einer Toolumgebung zu vereinen. Damit bietet die Open Source Prozesskette deutliche Vorteile im Vergleich zur Variante auf Basis bisher verwendeter kommerzieller Tools. Während der FrOSCon werden Einblicke in die Prozesskette gegeben, Schnittstellen zwischen den Modulen dargestellt sowie der Entwicklungsstand von einzelnen Kernmodulen wie dem FE-Präprozessor und dem FE-Konverter im Detail diskutiert sowie an Beispielanwendungen demonstriert. Literatur [1] B. Nagel, D. Böhnke, V. Gollnick, P. Schmollgruber, A. Rizzi, G. La Rocca, J.J. Alonso: „Communication in Aircraft Design: Can We Establish a Common Language?”, 28th Congress of the International Council of the Aeronautical Sciences (ICAS), Brisbane, Australia, 2012. [2] J. Scherer, D. Kohlgrüber, F. Dorbath, M. Sorour, „Finite element based Tool Chain for Sizing of Transport Aircraft in the Preliminary Aircraft Design Phase”, 62. DLRK, Stuttgart, Germany, 201

Towards surrogate-based aero-structural design optimization of an Unmanned Aerial Vehicle

In order to be able to assess also unconventional aircraft configurations, aircraft designers need to take into account physics-based analyses even during the early design stages. This highly multidisciplinary task requires the contributions and expertise of several different disciplinary specialists. This also applies to unmanned aerial vehicles, where improvements in performance yield a tactical advantage. In this paper, a partitioned design optimization process is presented, for the OptiMALE UAV configuration, originally introduced during the AeroStruct project and was further investigated during the AGILE project. The optimization will couple panel method aerodynamics and structural sizing to find the design with the maximum range. The process is set up in a modular fashion, using common data models as interfaces. The initial design is provided in the Common Parametric Aircraft Configuration Schema (CPACS), and serves as common input for the disciplinary model generators. The multidisciplinary analysis (MDA) process itself is implemented in Python as a Gauss-Seidel fixed point iteration, using comprehensive interfaces to the disciplinary analysis tools. The structural analysis and sizing is performed on a beam and shell model. For the aerodynamic analysis, a 3D potential method for subsonic flow applying the Green’s function method to the small perturbation potential flow equation after Morino has been implemented. The loads resulting from the converged MDA are used as inputs for a sizing optimization of the wing structural components using Lagrange. Finally, a mission simulation is performed using the updated massed to yield the range of the design. The optimization will be implemented in two steps. First, a design of experiments is performed on the wing design variables. Kriging is used to construct a metamodel from the DOE results, which provides gradients for a subsequent gradient-based optimization