Publications

2016

Fioriti, Marco; Boggero, Luca; Corpino, Sabrina; Lombardi, Riccardo; Panzeri, Marco

Aircraft system architectures selection for aircraft design optimization in an automated process Conference

EASN 2016 - 6th EASN International Conference on Innovation in European Aeronautics Research 2016.

Abstract | Links | BibTeX

Jungo, Aidan; Zhang, Mengmeng; Vos, Jan; Rizzi, Arthur

Automated Meshing and Data Fusion applied to create Aerodataset for AGILE DC-1 and beyond Conference

EASN 2016 - 6th EASN International Conference on Innovation in European Aeronautics Research 2016.

Links | BibTeX

Della Vecchia, Pierluigi ; Nicoloso, Fabrizio; De Marco, Agostino ; Stingo, Luca; Nardone, Giovanni

The AGILE method applied to aircraft design at University of Naples Conference

EASN 2016 - 6th EASN International Conference on Innovation in European Aeronautics Research 2016.

Abstract | Links | BibTeX

Anisimov, Kirill; Savelyev, Andrey

Aerodynamic Optimization of Airplane Propulsion System within the Framework of AGILE Project Conference

ICAS 2016 - 30th Congress of the International Council of the Aeronautical Sciences 2016.

Abstract | Links | BibTeX

Dubreuil, Sylvain; Bartoli, Nathalie; Gogu, Christian; Lefebvre, Thierry

Propagation of Modeling Uncertainty by Polynomial Chaos Expansion in Multidisciplinary Analysis Journal Article

Journal of Mechanical Design, 138 (11), pp. 11, 2016.

Abstract | Links | BibTeX

Jungo, Aidan; Vos, Jan; Zhang, Mengmeng; Rizzi, Arthur

Benchmarking New CEASIOM with CPACS Adoption for Aerodynamic Analysis and Flight Simulation Conference

READ/SCAD 2016 - Research & Education in Aircraft Design/Symposium on Collaboration in Aircraft Design 2016.

Abstract | Links | BibTeX

@conference{Jungo2016,
title = {Benchmarking New CEASIOM with CPACS Adoption for Aerodynamic Analysis and Flight Simulation},
author = {Aidan Jungo and Jan Vos and Mengmeng Zhang and Arthur Rizzi},
editor = {CEAS Technical Committee Aircraft Design},
url = {http://read.meil.pw.edu.pl/wp-content/uploads/Abstracts/SCAD/Abstract_M_Zahn_SCAD.pdf},
year = {2016},
date = {2016-09-12},
organization = {READ/SCAD 2016 - Research & Education in Aircraft Design/Symposium on Collaboration in Aircraft Design},
abstract = {Designing an aircraft is a very complex engineering task. The complexity needs to be handled by decomposition,
as a hierarchy of different levels. In general the aircraft design process is divided into three phases: the conceptual
design phase, the preliminary design phase, and the detailed design. The product fidelity, the model complexity
as well as the designed time increase exponentially from the previous phase to the next one. A variety of
multidisciplinary design optimization (MDO) methods have been developed that enable a formalized design
optimization process in the preliminary design phase. In this paper we will focus on the early design stages,
i.e., the conceptual design as well as the preliminary design, and find out way to improve the prediction fidelity
in the early design.

Managing the interconnections from the multi-disciplinary design groups is complex and error-prone. Adoption
of a standardized, data-centric scheme for storage of all data improves consistency and reduces risks of miscon-
ceptions and errors in the process. It requires an initial effort to make interfaces between analysis modules and
the data archive. The CPACS (Common Parametric Aircraft Configuration Schema), developed by DLR,
was adopted for the New CEASIOM framework, and is described in the following section. A design loop runs
several analysis modules in sequence. The RCE integration environment and workflow manager records the se-
quence and manages the data transport and translation as well as logging the process. RCE, developed by DLR,
makes it easy to set up and run a workflow also using modules in which the engineers are not discipline-experts.

CEASIOM (www.ceasiom.com) is a framework system that integrates discipline-specific tools like CAD, mesh
generation, CFD, stability and control analysis, and structural analysis, all for the purpose of aircraft conceptual
design. The New CEASIOM is under development within EU Project AGILE (www.agile-project.eu), by
adopting the CPACS XML data-format for representation of all design data pertaining to the aircraft under
development. All the modules are available to be integrated via RCE with CPACS as I/O.
In this paper we will report the current status of the development for New CEASIOM, includes the following
modules:
• CPACS file visualizer and editor CPACSupdater (Matlab);
• Multi-fidelity CFD solvers: Tornado (VLM, Level 1), Edge-Euler & SU2-Euler (Level 2), Edge-RANS
& SU2-RANS (Level 3)
• Data fusion tool: aerodynamic coefficients fusion from variable fidelity CFD tools above to compile
complete aero-table for flight analysis and simulation
• Automatic unstructured (Euler) & hybrid (RANS) mesh generation by sumo
• Stability and Control analysis module SDSA },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

Designing an aircraft is a very complex engineering task. The complexity needs to be handled by decomposition,
as a hierarchy of different levels. In general the aircraft design process is divided into three phases: the conceptual
design phase, the preliminary design phase, and the detailed design. The product fidelity, the model complexity
as well as the designed time increase exponentially from the previous phase to the next one. A variety of
multidisciplinary design optimization (MDO) methods have been developed that enable a formalized design
optimization process in the preliminary design phase. In this paper we will focus on the early design stages,
i.e., the conceptual design as well as the preliminary design, and find out way to improve the prediction fidelity
in the early design.

Managing the interconnections from the multi-disciplinary design groups is complex and error-prone. Adoption
of a standardized, data-centric scheme for storage of all data improves consistency and reduces risks of miscon-
ceptions and errors in the process. It requires an initial effort to make interfaces between analysis modules and
the data archive. The CPACS (Common Parametric Aircraft Configuration Schema), developed by DLR,
was adopted for the New CEASIOM framework, and is described in the following section. A design loop runs
several analysis modules in sequence. The RCE integration environment and workflow manager records the se-
quence and manages the data transport and translation as well as logging the process. RCE, developed by DLR,
makes it easy to set up and run a workflow also using modules in which the engineers are not discipline-experts.

CEASIOM (www.ceasiom.com) is a framework system that integrates discipline-specific tools like CAD, mesh
generation, CFD, stability and control analysis, and structural analysis, all for the purpose of aircraft conceptual
design. The New CEASIOM is under development within EU Project AGILE (www.agile-project.eu), by
adopting the CPACS XML data-format for representation of all design data pertaining to the aircraft under
development. All the modules are available to be integrated via RCE with CPACS as I/O.
In this paper we will report the current status of the development for New CEASIOM, includes the following
modules:
• CPACS file visualizer and editor CPACSupdater (Matlab);
• Multi-fidelity CFD solvers: Tornado (VLM, Level 1), Edge-Euler & SU2-Euler (Level 2), Edge-RANS
& SU2-RANS (Level 3)
• Data fusion tool: aerodynamic coefficients fusion from variable fidelity CFD tools above to compile
complete aero-table for flight analysis and simulation
• Automatic unstructured (Euler) & hybrid (RANS) mesh generation by sumo
• Stability and Control analysis module SDSA

Fioriti, Marco; Boggero, Luca; Corpino, Sabrina

Preliminary Sub-Systems Design integrated in an MDO Framework. Conference

READ/SCAD 2016 - Research & Education in Aircraft Design/Symposium on Collaboration in Aircraft Design 2016.

Abstract | Links | BibTeX

Stingo, Luca; Nardone, Giovanni

Development of an Innovative Collaborative Framework for Aircraft Design Conference

READ/SCAD 2016 - Research & Education in Aircraft Design/Symposium on Collaboration in Aircraft Design 2016.

Abstract | Links | BibTeX

Fioriti, Marco; Boggero, Luca; Corpino, Sabrina

Systems driven aircraft design Research activities at PoliTo Conference

READ/SCAD 2016 - Research & Education in Aircraft Design/Symposium on Collaboration in Aircraft Design 2016.

Links | BibTeX

Cooper, Dave; Jespen, Jonas

Pyndl: Increasing Classical KBE’s Reach with a Python-Gendl Bridge Conference

ADCP 2016 - Advanced Design Concepts and Practice Workshop 2016.

Abstract | Links | BibTeX

Savelyev, Andrey; Anisimov, Kirill; Kazhan, Egor; Kursakov, Innocentiy; Lysenkov, Alexandr

Computational Study of Engine External Aerodynamics as a part of Multidiciplinary Optimization Procedure Conference

ICMAR 2016 - 18th International Conference on the Methods of Aerophysical Research 2016.

Abstract | Links | BibTeX

Dubreuil, Sylvain; Bartoli, Nathalie; Gogu, Christian; Lefebvre, Thierry

Multidisciplinary optimization by surrogate models: handling epistemic uncertainties by polynomial chaos expansion Conference

ECCOMAS Congress 2016.

Abstract | Links | BibTeX

@conference{Dubreuil2016b,
title = {Multidisciplinary optimization by surrogate models: handling epistemic uncertainties by polynomial chaos expansion},
author = {Sylvain Dubreuil and Nathalie Bartoli and Christian Gogu and Thierry Lefebvre },
editor = {ECCOMAS},
url = {https://www.eccomas2016.org/proceedings/pdf/7623.pdf},
year = {2016},
date = {2016-06-05},
organization = {ECCOMAS Congress},
abstract = {We are interested in multidisciplinary optimization (MDO) of industrial structures such as aircraft. Performing MDO based on numerical high fidelity tools is challenging due to the cost of such solvers. Among all MDO architectures (see [1]), multidisciplinary feasible method (MDF) is known to be efficient and simple to implement. Unfortunately, its cost can be unaffordable as it requires a multidisciplinary analysis (MDA) of the system at each design evaluation. A way to tackle this issue is to replace each expert discipline solver by surrogate model [2, 3]. In addition to numerical cost reduction, this approach can be seen as an uncoupled architecture where each discipline is responsible for the training of its surrogate model (no MDA is performed for the computation of the surrogate). Nevertheless, let us assume that surrogate model evaluation returns the mean predicted value and its associated error approximation. This last information can be a probabilistic distribution (consider kriging surrogate model as an example) or some partial dispersion knowledge (see [2]). Finally, let us assume that outputs of surrogate models can be modeled by random variables (RV) whose distributions depend on the coupling variables.
As mentioned in [3], propagation of such model uncertainty in MDA context is challenging and only few studies have been proposed so far. To this respect, our main contribution is to carry out a functional representation of these model errors based on polynomial chaos expansion (PCE). As in usual application of PCE, each model error (ME) is expanded on a polynomial chaos basis with coefficients a ME,k,i for i=0,...,P-1 and k=1,...,n (where P is the size of the polynomial basis and n is the number of disciplines). But, as the probability distribution of the model error is a function of the coupling variables (which are RV), one has to consider that a ME,k,i are also RV. We propose to expand these RV with respect to the independent RV of the underlying random space and to perform some fixed point iterations over the coefficients of the coupling variables PCE. As a result, a functional representation of the joint probability distribution of the coupling variables is obtained. From this functional representation, it is then straightforward to compute the PCE of the objective function for a given design. Moreover, PCE approach allows an analytical computation of Sobol' sensitivity indices [4]. These indices are used to determine which surrogate model must be improved to reduce the uncertainty on the objective function. At the end, the proposed methodology allows to quantify the uncertainty on the objective function due to the use of surrogate models. Then, the problem of finding a reliable or robust optimum is equivalent to MDO under aleatory uncertainties. The proposed method has been successfully tested on different test cases and will be experimented on more realistic ones. This work is supported by the european project H2020 AGILE.

References
[1] J.R.R.A Martins and A.B. Lambe, Multidisciplinary Design Optimization: A Survey of Architecture. AIAA Journal, Vol. 51, No. 9 (2013), pp. 2049-2075.
[2] L. Jaeger, C. Gogu, S. Segonds and C. Bes, Aircraft Multidisciplinary Design Optimization under Both Model and Design Variables Uncertainty. Journal of Aircraft, Vol. 50, No. 2 (2013), pp. 528-538.
[3] C. Liang, S. Mahadevan and S. Sankararaman, Stochastic Multidisciplinary Analysis Under Epistemic Uncertainty. Journal of Mechanical Design, Vol. 137 (2015), pp. 021404.1-021404.12
[4] B. Sudret, Global sensitivity analysis using polynomial chaos expansions. Reliability Engineering and System Safety, Vol. 93 (2008), pp. 964-979 },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

We are interested in multidisciplinary optimization (MDO) of industrial structures such as aircraft. Performing MDO based on numerical high fidelity tools is challenging due to the cost of such solvers. Among all MDO architectures (see [1]), multidisciplinary feasible method (MDF) is known to be efficient and simple to implement. Unfortunately, its cost can be unaffordable as it requires a multidisciplinary analysis (MDA) of the system at each design evaluation. A way to tackle this issue is to replace each expert discipline solver by surrogate model [2, 3]. In addition to numerical cost reduction, this approach can be seen as an uncoupled architecture where each discipline is responsible for the training of its surrogate model (no MDA is performed for the computation of the surrogate). Nevertheless, let us assume that surrogate model evaluation returns the mean predicted value and its associated error approximation. This last information can be a probabilistic distribution (consider kriging surrogate model as an example) or some partial dispersion knowledge (see [2]). Finally, let us assume that outputs of surrogate models can be modeled by random variables (RV) whose distributions depend on the coupling variables.
As mentioned in [3], propagation of such model uncertainty in MDA context is challenging and only few studies have been proposed so far. To this respect, our main contribution is to carry out a functional representation of these model errors based on polynomial chaos expansion (PCE). As in usual application of PCE, each model error (ME) is expanded on a polynomial chaos basis with coefficients a ME,k,i for i=0,...,P-1 and k=1,...,n (where P is the size of the polynomial basis and n is the number of disciplines). But, as the probability distribution of the model error is a function of the coupling variables (which are RV), one has to consider that a ME,k,i are also RV. We propose to expand these RV with respect to the independent RV of the underlying random space and to perform some fixed point iterations over the coefficients of the coupling variables PCE. As a result, a functional representation of the joint probability distribution of the coupling variables is obtained. From this functional representation, it is then straightforward to compute the PCE of the objective function for a given design. Moreover, PCE approach allows an analytical computation of Sobol' sensitivity indices [4]. These indices are used to determine which surrogate model must be improved to reduce the uncertainty on the objective function. At the end, the proposed methodology allows to quantify the uncertainty on the objective function due to the use of surrogate models. Then, the problem of finding a reliable or robust optimum is equivalent to MDO under aleatory uncertainties. The proposed method has been successfully tested on different test cases and will be experimented on more realistic ones. This work is supported by the european project H2020 AGILE.

References
[1] J.R.R.A Martins and A.B. Lambe, Multidisciplinary Design Optimization: A Survey of Architecture. AIAA Journal, Vol. 51, No. 9 (2013), pp. 2049-2075.
[2] L. Jaeger, C. Gogu, S. Segonds and C. Bes, Aircraft Multidisciplinary Design Optimization under Both Model and Design Variables Uncertainty. Journal of Aircraft, Vol. 50, No. 2 (2013), pp. 528-538.
[3] C. Liang, S. Mahadevan and S. Sankararaman, Stochastic Multidisciplinary Analysis Under Epistemic Uncertainty. Journal of Mechanical Design, Vol. 137 (2015), pp. 021404.1-021404.12
[4] B. Sudret, Global sensitivity analysis using polynomial chaos expansions. Reliability Engineering and System Safety, Vol. 93 (2008), pp. 964-979