Publications

@conference{	Della2017_79	,

title = {An improved method for transport aircraft for high lift aerodynamic prediction.},

author = {P. {Della Vecchia} and F. Nicolosi and M. Ruocco and L. Stingo and A. {De Marco}},

url = {https://www.agile4.eu/cloud/index.php/s/Dsi6TAJbJx6kqmf},

year  = {2017},

date = {2017-10-01},

volume = {Paper 254},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {The aim of this work is the development of a methodology to predict lift characteristics for transport aircraft in the whole flight envelope, useful in the preliminary aircraft design stage. The purpose is an attempt to improve the classical methodologies for wing load distribution and lift prediction, considering the airfoils aerodynamic characteristics until stall and post stall conditions during the process, and modifying 2D characteristics in case of high lift devices to take into account 3D effects introduced by the devices themselves. The method is a modification of Nasa Blackwell procedure, capable to predict wing stall aerodynamic characteristics for both clean and flapped configuration. As far the high lift devices effect is concerned, the improved method works substituting clean airfoil aerodynamic characteristics with the flapped aerodynamics ones, and introducing a correction to evaluate the 3D effects induced by high lift devices geometrical discontinuities. The results of the developed method have been compared with CFD and experimental data showing good agreement, making available a fast and reliable method, useful in preliminary aircraft design.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Lombardi2017_107	,

title = {Automated Selection of Airliner Optimal On-board Systems Architecture within MDO Collaborative Environment.},

author = {R. Lombardi and M. Fioriti and L. Boggero and L. {Lo Verde} and N. Catino and A. Mirzoyan and R. D’Ippolito},

url = {https://www.agile4.eu/cloud/index.php/s/X9w6c4bX5mkdTqy},

year  = {2017},

date = {2017-10-01},

volume = {Paper 258},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {The test case described in the paper is a multidisciplinary design optimization (MDO) aimed at the identification of the best on-board system architecture on the basis of its production and operative costs. Other researcher have focused their attention in the selection of more suitable system architecture [1], however, in the present work, one of the key objective was to develop a collaborative multidisciplinary optimization environment able to propagate the effects of the chosen architecture on engine and actuator fairings and consequently on engine performance (fuel consumption) and aerodynamic properties (total drag). The collaborative MDO has been implemented using a commercial PIDO, Optimus by Noesis Solutions; this has been used to connect four different analysis tools (developed and operated by Politecnico di Torino, Leonardo Aircraft-Alenia and CIAM) adopting the communication protocols that have been developed within AGILE.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Prakasha2017_107	,

title = {Airframe – On Board System – Propulsion System Optimization for Civil Transport Aircraft: AGILE EU project.},

author = {P.S. Prakasha and P.D. Ciampa and L. Boggero and M. Fioriti and B. Aigner and A. Mirzoyan and A. Isyanov and K. Anisimov and A. Savelyev},

url = {https://www.agile4.eu/cloud/index.php/s/XBfJBF86rmLt9LK},

year  = {2017},

date = {2017-10-01},

volume = {Paper 270},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {As part of H2020 EU project “AGILE”, a Collaborative System of Systems Multidisciplinary Design Optimization research approach is presented in this paper. An approach to integrate airframe design analysis, as well as propulsion system, aircraft on-board systems, aerodynamics, structures and emission analysis in the early design process is presented. Moreover, the aim of this approach is to exploit the coupling parameters in an integrated analysis and optimization approach. 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 is shared between partners through a secured network of framework. The collaborative design process is implemented by making use of XML based standard Common Parametric Aircraft Configuration Scheme (CPACS), which is the basis for communication within distributed framework to exchange model information between the multi-disciplinary analysis modules and between partner organizations involved in the research activity. The framework is validated with a regional jet passenger reference aircraft. The Sensitivity of varying Engine By Pass Ratio, On-Board System Architectures (Conventional/More Electric/All Electric) is performed through disciplinary modules, effects propagated and its impact on overall aircraft performance in terms of Fuel Burn, Emission and Life Cycle Cost is presented.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Panzeri2017_97	,

title = {Uncertainty quantification and robust design optimization applied to aircraft propulsion systems.},

author = {M. Panzeri and A. Savelyev and K. Anisimov and R. D’Ippolito and A. Mirzoyan},

url = {https://www.agile4.eu/cloud/index.php/s/mbjdnkKXP3ksqgT},

year  = {2017},

date = {2017-10-01},

volume = {Paper 271},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {The standard way of formulating optimization problems applied to aircraft design is based on the assumption that the underlying system is deterministic, i.e., that the knowledge associated with the design variables and with the system dynamic is not characterized by uncertainty. However, in real conditions randomness impacts the formulation of the design process in multiple ways and the system outputs (i.e., the key performance indicators and the design constraints) are also affected by uncertainty. A system designed under deterministic assumptions may therefore have an unreliable behavior due to the fluctuations associated with the input random variables. This problem can be tackled by adopting a probabilistic approach and re-formulating the design optimization problem with an additional set of constraints associated with the robustness / reliability of the target system. This work addresses the problem of optimizing the geometry of a turbofan engine nacelle subject on reliability constraints. An advanced, machine-learning based framework is adopted in order to (a) investigate the system behavior through an adaptive design of experiments technique and (b) build accurate surrogate models of the system dynamics. These surrogate models are then employed to run a set of probabilistic studies at an affordable computational cost. The results of these investigations include (a) an extensive suite of analyses aimed at characterizing the uncertainty associated with the output quantities of interest; (b) a robust optimization of the engine nacelle geometry and (c) an assessment of the reliability of the optimized design. The improved performance and reliability of the design, together with the limited number of overall system evaluations required to run the analyses, demonstrate the effectiveness and the engineering applicability of the proposed approach.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Charbonnier2017_40	,

title = {Low Speed Take-Off Aerodynamic Analysis.},

author = {D. Charbonnier and J.B. Vos and P.S. Prakasha and A. Mirzoyan and A. Savelyev and P. {Della Vecchia}},

url = {https://www.agile4.eu/cloud/index.php/s/Rkn9mGDFQtWKxfy},

year  = {2017},

date = {2017-10-01},

volume = {Paper 272},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {In the frame of the EU funded H2020 project AGILE (Aircraft 3rd Generation MDO for Innovative Collab- oration of Heterogeneous Teams of Experts) detailed CFD simulations were made to analyze the high lift system of an optimized regional aircraft. The paper presents shortly how the different components of the aircraft were obtained and integrated in a model suitable to perform the simulations. High-fidelity RANS (Reynolds-Averaged Navier-Stokes) CFD calculations were carried out, with a focus on take-off conditions, and aerodynamic coefficients as well as flow field distributions were extracted. The results are discussed, and point out in one hand the reliability of high-fidelity CFD simulations to highlight detailed flow phenomena like flow separations, and in another hand the importance to consider low speed flow regimes (take-off or landing) in an aircraft design and optimization process.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Baalbergen2017_73	,

title = {Methods Supporting the Efficient Collaborative Design of Future Aircraft.},

author = {E. Baalbergen and E. Moerland and W. Lammen and P.D. Ciampa},

url = {https://www.agile4.eu/cloud/index.php/s/T6jj48BDXDHCGyX},

year  = {2017},

date = {2017-10-01},

volume = {Paper 844},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {The paper describes the need for and advantages of efficient and effective collaboration within the aircraft development supply chain. It discusses the barriers on the organisational, human and technical levels that hamper efficient collaboration. One of the focal points of the European Horizon2020 project AGILE is the creation of technical solutions for resolving the challenges that come with collaboration. In this light, the paper focuses on two methods being investigated and developed for supporting multidisciplinary teams from different organisations in collaborative aircraft design. The first method concerns the realisation of cross-organisational workflows for multidisciplinary design of aircraft. The workflows support the definition and smooth application of multiorganisation collaborative product development analyses. The second method concerns the deployment and management of surrogate models, which support efficient collaborative multidisciplinary aircraft design while dealing with intellectual property issues and computational speed limitations. After the introduction of the methods, two representative use cases which are successfully supported by the methods are highlighted. An important observation is that efficient collaboration is not straightforward when engineers from different and usually geographically dispersed organisations attempt to achieve a common design target. Once the collaboration methods are in place however, investigation of novel aircraft configurations is enabled by optimally leveraging the dedicated disciplinary knowledge of all involved experts.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Torrigiani2017_107	,

title = {MDO Architectures Comparison: from Analytical Test Case and Aerostructural Aircraft System Design Problems.},

author = {F. Torrigiani and P.D. Ciampa},

url = {https://www.agile4.eu/cloud/index.php/s/TTnBNfnSWwEA5gX},

year  = {2017},

date = {2017-10-01},

volume = {Paper 851},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {An aircraft system design problem is intrinsically a multidisciplinary problem. If the design configuration is unconventional, sound low-fidelity analysis methods are not available. Complex hi- fidelity tools are often the only solution to obtain reliable results, and for these reasons designers are deeply interested in the interactions and organization of these tools. Inside a Multidisciplinary Design Optimization (MDO) process, different architectures are possible. Analysis and comparison of six MDO architectures is the aim of this paper. The considered architectures are All-At-Once (AAO), Simultaneous Analysis and Design (SAND), Individual Discipline Feasible (IDF), Multidisciplinary Feasible (MDF), Collaborative Optimization (CO), Bi-Level Integrated System Synthesis (BLISS). The comparison is conducted on mathematical benchmark cases and on a simplified aerostructural aircraft design problem. Results expressed in a unified nomenclature are available as open source. Further, the CMDOWS (Common MDO Workflow Schema) developed in the AGILE project is used to translate the neutral description of the MDO problem into an executable implementation and it will be released as open source too. The aim is to promote the discussion on MDO architectures within the MDO research community.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Walther2017_44	,

title = {Knowledge-based airframe design using CPACS.},

author = {J.N. Walther and P.D. Ciampa},

url = {https://www.agile4.eu/cloud/index.php/s/tPfSynREB3fwBnZ},

year  = {2017},

date = {2017-10-01},

volume = {Paper 852},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {The CPACS data format [1, 2] has long been established as the primary means of data exchange in preliminary aircraft design projects within DLR. As described by Scherer et al. [3], it contains a wide range of options for describing the structural layout of a design including frames and stringers, floors, bulkheads, etc. Based on these descriptions, several finite element model generators comparable to the one described by Walther et al. [4] have been implemented, which can provide detailed computational structural models of a given design. However, all model generators require the information on the structural layout to be available in CPACS upfront. Within a larger aircraft design context, this necessitates the augmentation of the description of the structure to a given plain aircraft geometry. So far, this has been accomplished through a manual process, which not only results in an increased risk of errors, but also prohibits the exposure of parameters to a larger multidisciplinary optimization. In the presented paper, a newly developed knowledge-based airframe augmentation module will be introduced. Implemented in Python, it provides methods to automatically initialize a full structural layout on a given CPACS geometry, based on a manageable number of control parameters. In addition to an outline of the governing design rules, several application cases will also be given.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Ciampa2017_35	,

title = {AGILE Aircraft Development Process.},

author = {P.D. Ciampa and B. Nagel},

url = {https://www.agile4.eu/cloud/index.php/s/iL496Kp2K9WRJsR},

year  = {2017},

date = {2017-10-01},

volume = {Paper 876},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Lammen2017_90	,

title = {Collaborative design of aircraft systems – Multi-Level Optimisation of an aircraft rudder.},

author = {W. Lammen and B. {de Wit} and J. Vankan and H. Timmermans and T. {van der Laan} and P.D. Ciampa},

url = {https://www.agile4.eu/cloud/index.php/s/fkp8A3MML3xJ7Gs},

year  = {2017},

date = {2017-10-01},

volume = {Paper 898},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {The design and development of modern aircraft is a complex process involving many actors from different companies, e.g. the aircraft Original Equipment Manufacturer (OEM) and suppliers of (sub)systems and parts. The suppliers are responsible for their own system or part design, while the OEM is responsible for the overall aircraft design and the interfaces between the aircraft systems and parts. A system design that is optimal from system or part perspective may not be optimal from the global aircraft perspective. In order to avoid costly redesign iterations there is a need to optimize both the design of the overall aircraft and of its systems in an integrated way. This paper describes two methods for applying Multi-Level Optimization (MLO), in order to integrate the local system/part design optimization within the global aircraft design optimization. The design of an aircraft rudder is applied as use case. The use case addresses the coupling of a specific aircraft design analysis with a specific rudder design analysis and the global and local optimizations. First the MLO method Analytical Target Cascading (ATC) is applied to a theoretical example of fictive rudder design. Second a surrogate-based MLO approach is applied to a collaborative aircraft rudder design study involving multi-partner analysis tools. Both methods illustrate that applying MLO provides insight into the coupled design problem both for the OEM and for the supplier and reduces development time.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Zhang2017_68	,

title = {Disciplinary Data Fusion for Multi-Fidelity Aerodynamic Application.},

author = {M. Zhang and A. Jungo and N. Bartoli},

url = {https://www.agile4.eu/cloud/index.php/s/jgryrB5Lstra4pR},

year  = {2017},

date = {2017-10-01},

volume = {Paper 953},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {This paper presents some multi-fidelity activities in the field of aerodynamic in the ongoing EU-funded re- search project AGILE. Different computation tools relative to different level of fidelity are used in the project and the idea is to combine all the data in a global surrogate model to reduce the computational time in an optimization process for instance. The paper focuses on the data fusion tool embedded in the AGILE frame- work with the choice of the surrogate models, the choice of the tool’s fidelity and the choice of the sampling points via an iterative process. The paper will detail the different steps of the proposed approach to set up and operate the data fusion service proposed to the AGILE partners.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Lefebvre2017_95	,

title = {Overview of MDO enhancement in the AGILE project: A Clustered and Surrogate-Based MDA Use Case.},

author = {T. Lefebvre and N. Bartoli and S. Dubreuil and R. Lombardi and M. Panzeri and W. Lammen and M. Zhang and I. {van Gent} and P.D. Ciampa},

url = {https://www.agile4.eu/cloud/index.php/s/keD8gtoQZrpZxsB},

year  = {2017},

date = {2017-10-01},

volume = {Paper 956},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {This paper presents innovative methodological investigations performed as research activities in the field of MDO for conceptual aircraft in the ongoing EU-funded research project AGILE. The next generation of aircraft Multidisciplinary Design and Optimization processes is developed in AGILE, which targets significant reduc- tions in aircraft development costs and time to market, leading to cheaper and greener aircraft solutions. The paper introduces the AGILE project structure and recalls the achievements of the 1st year (Design Campaign 1 or DC-1) leading to a reference distributed MDO system. Design Campaign 2 (DC-2) is briefly described, investigating the ease of the optimization of complex workflows, characterized by a high degree of discipline interdependencies, high number of design variables in the context of multilevel and multipartner collaborative engineering projects. The paper focuses on an innovative approach where a complex workflow has been simplified and implemented by using surrogate models for clusters of disciplines to reduce the computational time. The paper will detail the different steps of the retained approach to set up and operate this test case, involving a team of surrogate specialists, and taking advantage of the AGILE framework.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	van2017_100	,

title = {Robust optimization of a rudder hinge system taking into account uncertainty in Airframe parameters.},

author = {T. {van der Laan} and L. Hootsmans and M. Panzeri and R. D’Ippolito},

url = {https://www.agile4.eu/cloud/index.php/s/JB6QHD6c6Zda3Jp},

year  = {2017},

date = {2017-10-01},

volume = {Paper 957},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {For a tier 1 supplier of aircraft components it is essential to develop components that can absorb changes in its requirements without having a negative influence on the supplier’s profit margin. In order to achieve this, the effect of design changes needs to be investigated early in the design process. To realize this vision the design process must be highly automated and incorporate solution finding techniques such as Design Of Experiments (DOE’s) and optimizations. In this paper a framework is presented that estimates the weight and cost of an aircraft rudder hinge system. This framework has been researched and developed within the context of the H2020 AGILE project to support 3rd generation multidisciplinary design optimization teams .The process is fully automated and packaged in the Process Integration and Design Optimization (PIDO) tool Optimus. This packaging allows the generation of DOE data with which robustness analyses can be performed. The robustness analysis presented in this paper is the response of the rudder hinge system to a change in design loads. It is shown that the best design found in a deterministic approach, so looking at one design point only, is not the best design available from a robustness point of view. In this way the value of robustness analysis in the design process of aircraft components is demonstrated.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	van2017_66	,

title = {CMDOWS: A proposed new standard to store and exchange MDO Systems.},

author = {I. {van Gent} and G. {La Rocca} and M.F.M. Hoogreef},

url = {https://www.agile4.eu/cloud/index.php/s/PXL5NzfeZjLrx46},

year  = {2017},

date = {2017-10-01},

volume = {Paper 969},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

abstract = {This paper proposes a new format to store and exchange multidisciplinary design optimization (MDO) sys- tems. Here, the generic term MDO system refers to the organized set of disciplinary tools, and their ex- changed data and the process connections that, all together, define an MDO computational setup. In the process leading to the formal specification of such a computational system, i.e. starting from a repository of disciplinary tools, down to the specification of the actual optimization problem and finally to the implementa- tion of a specific MDO architecture, the aforementioned set of tools, data and connections evolves, until the complete MDO system formulation (thus not yet executable) is reached. The proposed new standard, called CMDOWS (Common MDO Workflow Schema), has been developed to enable this process by providing a means to store and exchange any MDO system and its associated information in a neutral format. Further- more, CMDOWS provides the starting point to translate any MDO system formulation into an executable computational workflow, by means of a Process Integration and Design Optimization (PIDO) tool of choice. To the authors’ knowledge, such an exchange format does currently not exist, notwithstanding the enormous potential it would have for the exploitation of large-scale MDO in industry. CMDOWS is one of the outcomes of the EU project AGILE, where one of the main goals is to reduce the development time of distributed MDO workflows created by large and heterogeneous teams of experts. CMDOWS is an XML schema (XSD) that, in its set-up and structure, shows similarities with the Common Parametric Aircraft Configuration Schema (CPACS) developed by the German Aerospace Center (DLR), which is becoming a de-facto standard to store and exchange aircraft design and performance data. Whereas CPACS allows the user to store aircraft data in a standard format, CMDOWS enables the storing the specification of a full system of multidisciplinary tools, including the data and process links between its various operating blocks (e.g. disciplinary tools, objective and constraint functions, optimizers, convergers, etc.). The key aspect of this proposed format for MDO sys- tems is its neutral XML-based data representation, which is both human-readable and machine-interpretable, making any stored MDO system exchangeable between the design team members and the applications de- veloped to support the team in setting up the MDO system. The latter form of exchangeability is a key enabler for the creation of a versatile MDO framework that includes applications such as tool repositories, MDO system formulation platforms, visualization packages, and collaborative workflow execution platforms. The CMDOWS definition is available, including examples, through a publicly available software repository. Although the schema is under continuous development within the AGILE project, a case study demonstrating the use of CMDOWS version 0.6 in the AGILE MDO framework is presented in this paper. Based on this case study, it can be concluded that the current version of CMDOWS already provides a robust standard to exchange MDO systems between MDO framework applications. The schema will be extended to meet future developments and promote its adoption as a recognized standard in the broader MDO community.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Kaminski2017_40	,

title = {AGILE Academy-Student Collaborative MDA.},

author = {J. Kaminski and G. Torre and F. Tomasella and N.K. Rao},

url = {https://www.agile4.eu/cloud/index.php/s/gLkY4jgLH4Z8DTR},

year  = {2017},

date = {2017-10-01},

address = {Bucharest, Romania},

organization = {6th CEAS Air & Space Conference Aerospace Europe	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	van2017_66	b,

title = {Recent developments for MDO system formulation: KADMOS and CMDOWS.},

author = {I. {van Gent} and G. {La Rocca}},

url = {https://www.agile4.eu/cloud/index.php/s/Lr4MbCd2nHQWCWz},

year  = {2017},

date = {2017-10-01},

address = {Braunschweig, Germany},

organization = {1st European Workshop on MDO for Industrial Applications in Aeronautics – Challenges and Expectations	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Ciampa2017_71	,

title = {Management and reconfiguration of MDO workflow in the EU project AGILE.},

author = {P.D. Ciampa},

url = {https://www.agile4.eu/cloud/index.php/s/BwAXdGjTBigos3m},

year  = {2017},

date = {2017-10-01},

address = {Braunschweig, Germany},

organization = {1st European Workshop on MDO for Industrial Applications in Aeronautics – Challenges and Expectations	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Dubreuil2017_84	,

title = {Towards an Efficient Global Multidisciplinary Optimization based on surrogate models},

author = {S. Dubreuil and N. Bartoli and C. Gogu and T. Lefebvre},

url = {https://www.agile4.eu/cloud/index.php/s/bJtWtd96jCGxx7P},

year  = {2017},

date = {2017-10-01},

address = {Braunschweig, Germany},

organization = {1st European Workshop on MDO for Industrial Applications in Aeronautics – Challenges and Expectations	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Rizzi2017_65	,

title = {Towards the next generation collaborative MDO: The AGILE Project.},

author = {A. Rizzi},

url = {https://www.agile4.eu/cloud/index.php/s/LYnR7x3xo6aCPLf},

year  = {2017},

date = {2017-10-01},

address = {London, UK},

organization = {RAeS Aerodynamics CFD-MDO conference	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	van2017_76	,

title = {A Fully Automated Chain from MDAO Problem Formulation to Workflow Execution.},

author = {I. {van Gent} and R. Lombardi and G. {La Rocca} and R. D’Ippolito},

url = {https://www.agile4.eu/cloud/index.php/s/zE5P4L9Q67cyZGE},

year  = {2017},

date = {2017-09-01},

address = {Madrid, Spain},

organization = {EUROGEN 2017	,},

abstract = {In this paper, a methodology to connect the multidisciplinary design analysis and optimization (MDAO) problemformulation tool KADMOS and the commercial Process Integration and Design Optimization (PIDO) platform Optimusis presented. This capability has been developed in the context of the EU project AGILE. The aim of this developmentis to create a combined environment that gives the MDAO design team the ability to define and formalize an MDAOproblem and directly execute it with ease, without the need of the otherwise needed manual operations typically requiredto define the workflow in the PIDO system. The combination of problem formulation and PIDO platform execution havebeen tested on a small analytical MDAO problem to demonstrate its viability. Furthermore, a realistic aerostructuralMDAO system of industrial relevance was also used to demonstrate the scalability of the approach for a bigger and morecomplex MDAO system. Results indicate that a fully automated chain is indeed possible which will make it easier fordesign teams to define, execute and compare different MDAO problem definitions and architectures in the time usuallynecessary to implement one MDAO system. Future work will focus on extending the proven capabilities of the automatedchain to a wider variety of design problems and MDAO architectures.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Zhang2017_69	,

title = {Mixed Fidelity Aerodynamic and Aero-Structural Optimization for Wings},

author = {M. Zhang and T. Melin and J. Gong and M. Barth},

url = {https://www.agile4.eu/cloud/index.php/s/cPsX5AMZkSYRxLD},

year  = {2017},

date = {2017-09-01},

volume = {Paper 279},

address = {Warsaw, Poland},

organization = {7th EASN Workshop	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Ciampa2017_62	b,

title = {AGILE Project @ECN : The next generation of collaborative MDO.},

author = {P.D. Ciampa},

url = {https://www.agile4.eu/cloud/index.php/s/BBoPjPtspQ9CRfG},

year  = {2017},

date = {2017-07-01},

address = {Moscow, Russia},

organization = {IFAR-ECN Meeting	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Fioriti2017_102	,

title = {Automated Selection of the Optimal On-board Systems Architecture within MDO Collaborative Environment.},

author = {M. Fioriti and L. Boggero and S. Corpino and A. Isyanov and A. Mirzoyan and R. Lombardi and R. D’Ippolito},

url = {https://www.agile4.eu/cloud/index.php/s/q7cQtNJ3Hir3wqT},

year  = {2017},

date = {2017-06-01},

volume = {AIAA Paper 2017-3150},

address = {Denver, USA},

organization = {AIAA Aviation 2017	,},

abstract = {The on-board systems arehaving even more importance in aircraft design since thecontinuousresearch for a competitive, more optimized and less costly aircraft. In addition,the introduction of new technologies related to the More Electric Aircraft and AllElectric Aircraft concepts have raised theinterest on on-board systems discipline givingthe optionof analyzingdifferent architectures. The present paper would enhance the selection of the best on-board systems architecture introducing a new workflow, which is able to identifythe best architecture in terms of procurement and operating cost.Since the importance of fuel required providing the secondary power, the effect of each specific architecture on engine performanceis particularly considered including a detailed engine module. The workflow is implemented in Optimus framework within a collaborative andmultidisciplinary environment and it is open to be integrated with additional modulesincreasing the fidelity of the analysis. To explore the capability of the defined workflow,the H2020 AGILEregional jet isidentified astest case.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	van2017_111	,

title = {Composing MDO Symphonies : Graph-based Problem Formulation to Enable Automated Execution for Large MDO Systems.},

author = {I. {van Gent} and G. {La Rocca} and L. Veldhuis},

url = {https://www.agile4.eu/cloud/index.php/s/g2giD2wGQtJpMWN},

year  = {2017},

date = {2017-06-01},

volume = {AIAA Paper 2017-3663},

address = {Denver, USA},

organization = {AIAA Aviation 2017	,},

abstract = {This paper proposes a novel methodology and its software implementation, called KADMOS (Knowledge- and graph-based Agile Design for Multidisciplinary Optimization System), which aims at increasing the agility of aircraft design teams that perform collaborative multidisciplinary design analysis and optimization (MDAO) by means of graph manipulation techniques. By agility, the ease and flexibility to assemble, adapt and adjust MDAO computational systems is intended here, as necessary to better fit the iterative nature of the aircraft design process. KADMOS has been developed on the notion that a formal specification of an MDAO system is required before its actual implementation, especially to be able to compose large and complex systems in multidisciplinary design teams. This specification system is under development as part of the EU project AGILE where a new generation of aircraft MDAO systems is investigated to support collaboration of heterogeneous teams of experts. KADMOS improves the agility of the design team in three ways: 1) reducing the set-up time required to compose large and complex MDAO models, 2) enabling the systematic inspection and debugging of this model, and 3) manipulating the model for automated creation and reconfiguration of optimization strategies, including the accompanying executable workflow. This is achieved by means of a graph-based analysis system that combines different existing advantageous techniques for performing MDAO, such as the use of a single shared data schema containing a parametric representation of the aircraft, knowledge-based technologies, and simulation workflow (SWF) software packages. Two MDAO case studies will be presented in the paper. The first case study is based on a simple analytical problem, generally used in literature for MDAO benchmarking studies. The second case study concerns a detailed wing aerostructure design using a collection of wing design tools. While the simple and compact analytical problem is used in this paper to demonstrate the functionalities of the tool, the wing design case demonstrates the capability of KADMOS to support quick formulation, (re)configuration, and execution of MDAO workflows using distributed and heterogeneous sets of analysis tools.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Ciampa2017_62	,

title = {The AGILE Paradigm : the next generation of collaborative MDO.},

author = {P.D. Ciampa and B. Nagel},

url = {https://www.agile4.eu/cloud/index.php/s/noNXabMr5w957k5},

year  = {2017},

date = {2017-06-01},

volume = {AIAA Paper 2017-4137},

address = {Denver, USA},

organization = {AIAA Aviation 2017	,},

abstract = {The AGILE project is developing 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. 19 industry, research and academia partners from Europe, Canada and Russia are developing solutions to cope with the challenges of collaborative product development. In order to enable and to accelerate the deployment of collaborative, large-scale design and optimization frameworks for the development of complex products, a novel methodology, the so-called AGILE Paradigm, is currently under development. The main elements composing the AGILE Paradigm are the Knowledge Architecture (KA), and the Collaborative Architecture (CA). The technologies developed by the AGILE consortium have been used to implement the AGILE Paradigm, thus making it not only an abstract formalization of a methodology, but an applicable framework: the AGILE Development Framework (ADF). This paper introduces the AGILE Paradigm with all its conceptual elements and the framework’s key enablers. The paper addresses multiple use cases and achievement reached by the AGILE developments. All the technologies necessary to deploy the AGILE Paradigm will be accessible to the MDO community.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Ciampa2017_85	,

title = {A collaborative Architecture supporting AGILE Design of Complex Aeronautics Products.},

author = {P.D. Ciampa and E. Moerland and D. Seider and E. Baalbergen and R. Lombardi and R. D’Ippolito},

url = {https://www.agile4.eu/cloud/index.php/s/HWJrrw7JcL6mitY},

year  = {2017},

date = {2017-06-01},

volume = {AIAA Paper 2017-4138},

address = {Denver, USA},

organization = {AIAA Aviation 2017	,},

abstract = {The AGILE project is developing the next generation of aircraft Multidisciplinary Design and Optimization processes, which target significant reductions in aircraft development costs and time to market, leading to cost-effective and greener aircraft solutions. 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. This paper focuses on the Collaborative Architecture, which enables cross-organizational and cross-the-nation integration of distributed design competences of all the 19 project partners. The paper presents the Collaborative Architecture concepts, the underlying requirements, and the main CA deployment elements. Although the deployment of the CA is product independent, the implementation is presented for the AGILE reference use case, addressing the design and optimization of a transport aircraft.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	van2017_74	,

title = {Knowledge architecture supporting collaborative MDO in the AGILE Paradigm.},

author = {I. {van Gent} and P.D. Ciampa and B. Aigner and J. Jepsen and G. {La Rocca} and J. Schut},

url = {https://www.agile4.eu/cloud/index.php/s/AjHQXFC2Agdf5jz},

year  = {2017},

date = {2017-06-01},

volume = {AIAA Paper 2017-4139},

address = {Denver, USA},

organization = {AIAA Aviation 2017	,},

abstract = {The AGILE project is developing the next generation of development processes, anddeploying a collaborative MDO design system, called the AGILE development framework(ADF). Naturally, such a system contains a lot of implicit assumptions on how things shouldbe done and how to exploit different existing technologies. This collection of assumptionsand technologies is labeled the `AGILE Paradigm'. The two main building blocks of this paradigm are theCollaborative Architectureand theKnowledge Architecture. In essence,these building blocks aim to support large, heterogeneous teams of experts in performingcollaborative development in a streamlined and time-effective way. This paper has a focuson the definition of the Knowledge Architecture (KA) as a general conceptual framework which is independent of the aircraft-specific application in AGILE. The KA can be appliedto perform collaborative automated design in large, heterogeneous teams for any complexsystem (e.g. aircraft, automobiles, wind farms). The KA is structured with a multi-levelbackbone: Development Process layer, Automated Design layer, Design Competence layer.A fourth transverse layer impacting all other layers is the Data & Schemas layer. Eachlayer has its own set of assumptions and technologies, but more importantly, interfacesbetween the levels have to be created in order to have a fully interconnected developmentprocess from each design competence up to the top-level business process. The hierarchicallevels and interfaces are described in this paper as a generalized paradigm. In addition,four support platforms of the KA in the AGILE project are described in more detail:the development process environment, graph-based support in the design problem formulation, visualization of large, complex automated design processes, and design conceptsformalizations. Finally, a use case from the AGILE project is mapped on this paradigm todemonstrate the use of the KA and its support platforms in a realistic design case.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Lefebvre2017_81	,

title = {Methodological enhancements in the MDO process investigated in the AGILE project.},

author = {T. Lefebvre and N. Bartoli and S. Dubreuil and M. Panzeri and R. Lombardi and R. D’Ippolito and P. {Della Vecchia} and F. Nicolosi and P.D. Ciampa and K. Anisimov and A. Savelyev},

url = {https://www.agile4.eu/cloud/index.php/s/Hja8Rj28RP7NTNM},

year  = {2017},

date = {2017-06-01},

volume = {AIAA Paper 2017-4140},

address = {Denver, USA},

organization = {AIAA Aviation 2017	,},

abstract = {This paper presents methodological investigations performed in research activities in the field of MDO in overall aircraft design in the ongoing EU funded research project AGILE. AGILE is developing the next generation of aircraft Multidisciplinary Design and Optimization processes, which target significant reductions in aircraft development costs and time to market, leading to cheaper and greener aircraft solutions. The paper introduces the AGILE project structure and describes the achievements of the 1st year (Design Campaign 1) leading to a reference distributed MDO system. A focus is then made on the different novel optimization techniques studied during the 2nd year, all willing to ease the optimization of complex workflows, characterized by high degree of discipline interdependencies, high number of design variables in the context of multi-level and multi-partner collaborative engineering projects. Then the implementation of these methods in the enhanced MDO framework is discussed.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Daoud2017_98	,

title = {Aeroelastic Shape and Sizing Optimization of Aircraft Products supported by AGILE Design Paradigm.},

author = {F. Daoud},

url = {https://www.agile4.eu/cloud/index.php/s/zKyzMSMmMRL9XTC},

year  = {2017},

date = {2017-06-01},

volume = {AIAA Paper 2017-4141},

address = {Denver, USA},

organization = {AIAA Aviation 2017	,},

abstract = {Aircraft, and in particular military aircraft, are complex systems and the demand for high-performance flying platforms is constantly growing both for civil and military purposes. The development of aircraft is inherently multidisciplinary and the exploitation of the interaction between the disciplines driving the design opens the door for new (unconventional) aircraft designs, and consequently, for novel aircraft having increased performance. An important feature of modern aircraft development processes and procedures is to enable the engineers accessing complex design spaces also in the conceptual design phase in which key configuration decisions are made and frozen for later development phases. Pushing more MDO and numerical analysis capabilities into the early design phase will support the decision-making process through reliable physical information. It is worth mentioning that these design spaces are very large and can hardly be grasped and explored by humans without a structured approach and massive support of numerical analysis methods. The challenge is even larger when specialized competences are provided by several multidisciplinary teams that are distributed among different organizations. One one side there is the challenge to exchange consistent sets of data among automated simulation sub-processes. On another side there is the challenge of a coherent interpretation and understanding of the data generated. The first challenge is typically related to the lack of a collaborative platform connecting inter-organizations legacy processes. The second challenge is due to a lack of a common knowledge formalization through all the elements of the product development (disciplinary, process, development phases related). Many of the mentioned collaborative development challenges are addressed by AGILE (Aircraft 3rd Generation MDO for Innovative Collaboration of Heterogeneous Teams of Experts) [1], an EU H2020 funded research project coordinated by the German Aerospace Center (DLR). Within the AGILE project multiple use cases (aircraft designs) and MDO scenarios are formulated. This paper highlights the proper setup of industrial large scale optimisation elaborating the transition from aircraft to airframe level. Focus is on the transition from the conceptual aircraft solution to the detailed airframe design, based on a variable fidelity formulation for the disciplines involved in the exploration and optimization process. Section 2 at first summarizes the AGILE project and the overall methodology under development. Section 3 presents the aeroelastic use case and the main components integrated into the process. The final section presents the conclusion and provides an outlook on future developments.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Prakasha2017_108	,

title = {Collaborative System of Systems Multidisciplinary Design Optimization for Civil Aircraft : AGILE EU project.},

author = {P.S. Prakasha and P.D. Ciampa and L. Boggero and M. Fioriti and B. Aigner and A. Mirzoyan and A. Isyanov and K. Anisimov and I. Kursakov and A. Savelyev},

url = {https://www.agile4.eu/cloud/index.php/s/pxAsnnXp8a3krRE},

year  = {2017},

date = {2017-06-01},

volume = {AIAA Paper 2017-4142},

address = {Denver, USA},

organization = {AIAA Aviation 2017	,},

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 multi-disciplinary 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.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Keye2017_63	,

title = {Aero-Structural Optimization of the NASA Common Research Model.},

author = {S. Keye and T. Klimmek and M. Abu-Zurayk and M. Schulze and C. Ilic},

url = {https://www.agile4.eu/cloud/index.php/s/QZiJMqM2wgk5Xfo},

year  = {2017},

date = {2017-06-01},

volume = {AIAA Paper 2017-4145},

address = {Denver, USA},

organization = {AIAA Aviation 2017	,},

abstract = {A combined aerodynamic and structural, gradient-based optimization has been per-formed on the NASA/Boeing Common Research Model civil transport aircraft configuration. The computation of aerodynamic performance parameters includes a Reynolds-averaged Navier-Stokes CFD solver, coupling to a linear static structural analysis using thechnite element method to take into account aero-elastic effects. Aerodynamic performancegradients are computed using the adjoint approach. Within each optimization iteration, thewing's structure is sized via a gradient-based algorithm and an updated structure modelis forwarded for the performance analysis. In this pilot study wing profile shape is opti-mized in order to study engine installation effects. This setting was able to improve theaerodynamic performance by 4%.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Della2017_85	,

title = {Game theory and evolutionary algorithms applied to MDO in the AGILE European project.},

author = {P. {Della Vecchia} and L. Stingo and S. Corcione and D. Ciliberti and F. Nicolosi and A. {De Marco} and G. Nardone},

url = {https://www.agile4.eu/cloud/index.php/s/5y2JbL5YyyQF43G},

year  = {2017},

date = {2017-06-01},

volume = {AIAA Paper 2017-4330},

address = {Denver, USA},

organization = {AIAA Aviation 2017	,},

abstract = {In this paper, an optimization technique in aircraft design field, based on game theory and evolutionary algorithms to define the key variables for Multi-Disciplinary aircraft Optimization (MDO) into AGILE (Aircraft 3rd Generation MDO for Innovative Collaboration of Heterogeneous Teams of Experts) European project, is presented. This work represents one of the contributions given by UniNa (University of Naples “Federico II”) research group within the AGILE project, which is coordinated by the DLR and funded by EU through the project HORIZON 2020 that aims to create an evolution of MDO, promoting a novel approach based on collaborative remote design and knowledge dissemination among various teams of experts. Since the aircraft design field is very complex in terms of number of involved variables and the dimension of the space of variation, it is not feasible to perform an optimization process on all the design parameters; this leads to the need to reduce the number of the parameters to the most significant ones. A multi-objective optimization approach allows many different variables, which could be a constraint or an objective function for the specific investigation; thus, setting the constraints and objectives to reach, it is possible to perform an optimization process and control which parameters significantly affect the final result. Within AGILE project, UniNa research group aims to perform wing optimization processes in a preliminary design stage, coupling Nash game theory (N) with typical genetic evolutionary algorithm (GA), reducing computational time and allowing a more realistic association among objective functions and variables, to identify the main ones that significantly affect final result and that consequently must be considered by the partners of the AGILE consortium to perform MDO in the final part of project, applying the proposed optimization technique to novel aircraft configuration.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Bartoli2017_103	,

title = {An adaptive optimization strategy based on mixture of experts for wing aerodynamic design optimization.},

author = {N. Bartoli and T. Lefebvre and N. Bons and J. Martins and J. Morlie},

url = {https://www.agile4.eu/cloud/index.php/s/KXpkRfCj8EopYcW},

year  = {2017},

date = {2017-06-01},

volume = {AIAA Paper 2017-4433},

address = {Denver, USA},

organization = {AIAA Aviation 2017	,},

abstract = {In the _x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x000C_field of aircraft design, the last few decades have focused on the iterative improve-ment of conventional tube-and-wing designs to reduce cost, noise, and emission. Never-theless, the growing expectation in terms of environment impact for the next generationof aircraft pushes for more radical changes in the design. For unconventional aircraft con-_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x000C_gurations, the need to integrate more accurate data coming from higher _x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x000C_delity analysisearlier in the design process becomes more and more necessary. 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Therefore, these optimizations are oftendone at higher computational cost (gradient-based algorithms) in order to decrease thenumber of necessary function evaluations. In addition, the use of the adjoint method isoften implemented to accurately and e_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x000E_ciently compute derivatives for large numbers ofdesign variables. At the same time, new methods have been investigated to obtain opti-mized con_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x005F_x000C_gurations at a reasonable computational cost. The work presented in this paperfocuses on SEGOMOE algorithm, a solution to tackle this kind of optimization processof complex design problem through the use of an enrichment strategy approach based onmixture of experts surrogate models. Two aerodynamic shape optimization test cases, de-rived from cases developed by the Aerodynamic Design and Optimization Discussion Group(ADODG) are addressed: one with a single global minimum, and another one with severallocal minima. Both problems are nonlinearly constrained problems that involve a largenumber of design variables. Results are compared to gradient-based optimizers. A hybridapproach combining the advantages of both SEGOMOE and gradient-based optimizationis proposed and evaluated to reduce the number of function evaluations and to ensure theconvergence to the global optimum.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Gu2017_107	,

title = {An Application of Airframe Engine Integrated Optimization in Distributed Overall Aircraft Design Framework.},

author = {X. Gu and P.S. Prakasha and P.D. Ciampa and K. Anisimov and A. Savelyev},

url = {https://www.agile4.eu/cloud/index.php/s/SxJKe5HqayMFwX3},

year  = {2017},

date = {2017-06-01},

volume = {AIAA Paper 2017-4438},

address = {Denver, USA},

organization = {AIAA Aviation 2017	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Dubreuil2017_74	,

title = {Adaptive random field discretization for optimization under uncertainties.},

author = {S. Dubreuil and C. Gogu and N. Bartoli and T. Lefebvre},

url = {https://www.agile4.eu/cloud/index.php/s/RdxmfSkQscjsLFG},

year  = {2017},

date = {2017-06-01},

address = {Braunschweig, Germany},

organization = {WCSMO12	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Daoud2017_77	,

title = {High-Performance Aircraft Through Innovative Development Process and Methods.},

author = {F. Daoud and J. Dornwald and P. Ernstberger and H.P. Frantzen and R. Maierl and S. Deinert and O. Petersen},

url = {https://www.agile4.eu/cloud/index.php/s/EaW7WEwzWTKSXo9},

year  = {2017},

date = {2017-01-01},

address = {Grapevine, USA},

organization = {AIAA SciTech 2017	,},

abstract = {Aircraft, generally, and military aircraft in particular are complex systems and the demand for high-performance flying platforms is constantly growing regardless whether for civil or military purposes. The development of aircraft is inherently multidisciplinary and the exploitation of the interaction of the design-driving disciplines opens the door for new designs, and consequently, for new high-performance aircraft [1]. An important feature of modern aircraft development processes and procedures is to enable the engineers accessing these design spaces in the concept phase, where the key configuration decisions are made and frozen for later development phases. Furthermore, pushing more MDO and numerical analysis capabilities into the early design phase will support the decision-making process through reliable physical information. It is worth mentioning that these design spaces are very large and can hardly be grasped and explored by humans without a structured approach and massive support of numerical analysis methods. To this end, Airbus Defence and Space - Military Aircraft - established - in collaboration with universities and research institutes [2] [3] - an integrated multidisciplinary aircraft development process, based on flexible parametric geometry models connected to an innovative decentralized MDO platform. This radically different aircraft development process links the conceptual design phase to the preliminary and definition phase fulfilling following requirements addressed by repeatedly conducted technical gap analysis (see Figure 1): 1) Enable intermediate level analysis (fast and accurate methods), 2) Ensure model continuity through flexible parametric geometry models growing in fidelity over the development phases, 3) Enable fast analysis model generation, pushing more numerical analysis into the aircraft configuration assessment in the very early development phase, 3) Data continuity through using central multidisciplinary database over the complete Conceptual and Preliminary Design.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Zhang2016_72	,

title = {Disciplinary Data Fusion of Aerodynamic Data Base for Flight Simulation.},

author = {M. Zhang and A. Rizzi},

url = {https://www.agile4.eu/cloud/index.php/s/jZ37mBMiaYFzeJ3},

year  = {2016},

date = {2016-10-01},

address = {Stockholm, Sweden},

organization = {Swedish Aerospace Technology Congress	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Ciampa2016_64	,

title = {AGILE project: Towards the next generation in Collaborative MDO.},

author = {P.D. Ciampa and B. Nagel},

url = {https://www.agile4.eu/cloud/index.php/s/PEAjApiLxeeELb6},

year  = {2016},

date = {2016-10-01},

address = {Porto, Portugal},

organization = {6th EASN Workshop	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Lefebvre2016_71	,

title = {AGILE DC-1 MDO process using an efficient global optimization approach.},

author = {T. Lefebvre and N. Bartoli and R. Lafage and P.D. Ciampa},

url = {https://www.agile4.eu/cloud/index.php/s/nFaqZA9FSyWFxR4},

year  = {2016},

date = {2016-10-01},

address = {Porto, Portugal},

organization = {6th EASN Workshop	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Zhang2016_93	,

title = {Automated Meshing and Data Fusion applied to create Aerodataset for AGILE DC-1 Configuration.},

author = {M. Zhang and A. Rizzi and A. Jungo and J.B. Vos},

url = {https://www.agile4.eu/cloud/index.php/s/NQoGnc5iFWyEtXH},

year  = {2016},

date = {2016-10-01},

address = {Porto, Portugal},

organization = {6th EASN Workshop	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Fioriti2016_97	,

title = {Aircraft system architectures selection for aircraft design optimizaiton in an automated process.},

author = {M. Fioriti and L. Boggero and S. Corpino and R. Lombardi and M. Panzeri},

url = {https://www.agile4.eu/cloud/index.php/s/FYGYEPCkz4nNDCE},

year  = {2016},

date = {2016-10-01},

address = {Porto, Portugal},

organization = {6th EASN Workshop	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	Della2016_72	,

title = {The AGILE method applied to aircraft design at the University of Naples.},

author = {P. {Della Vecchia} and F. Nicolosi and A. {De Marco} and L. Stingo and G. Nardone},

url = {https://www.agile4.eu/cloud/index.php/s/JE7jjL8ee58ynqm},

year  = {2016},

date = {2016-10-01},

address = {Porto, Portugal},

organization = {6th EASN Workshop	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{	van2016_83	,

title = {A novel graph-based system for AGILE configurations and execution of MDO workflows.},

author = {I. {van Gent} and G. {La Rocca} and L. Veldhuis},

url = {https://www.agile4.eu/cloud/index.php/s/GzyADtpQswdnyns},

year  = {2016},

date = {2016-10-01},

address = {Porto, Portugal},

organization = {6th EASN Workshop	,},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}