GEMS: A Python Library for Automation of Multidisciplinary Design Optimization Process Generation

Gallard, François; Vanaret, Charlie; Guénot, Damien; Gachelin, Vincent; Lafage, Rémi; Pauwels, B.; Barjhoux, Pierre-Jean; Gazaix, Anne

doi:10.2514/6.2018-0657

Cited by 36 publications

(23 citation statements)

References 20 publications

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“…Algorithm 1 was implemented using the Generic Engine for MDO Scenarios (GEMS) (Gallard et al, 2018) in Python. The tool offers an efficient way to test multilevel formulations, with built-in classes that facilitate optimization problems manipulations (Gallard et al, 2019).…”

Section: Implementation Details and Comparison Solversmentioning

confidence: 99%

A bi-level methodology for solving large-scale mixed categorical structural optimization

Barjhoux

Diouane

Grihon

et al. 2020

Struct Multidisc Optim

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In this paper, large scale structural optimization problems involving both non-ordinal categorical and continuous design variables are investigated. The aim is to minimize the weight of a truss structure with respect to the cross-section areas, with optimal materials and cross-section type selection. The targeted structure counts more than one hundred elements. The proposed methodology consists of using a bi-level decomposition involving two problems, named master and slave. For given categorical choices, the slave addresses the continuous variables of our optimization problem. The master consists of minimizing a first order like approximation of the slave problem with respect to the categorical design variables. Such approximation helps to drastically reduce the combinatorial raised by the

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Section: Implementation Details and Comparison Solversmentioning

confidence: 99%

A bi-level methodology for solving large-scale mixed categorical structural optimization

Barjhoux

Diouane

Grihon

et al. 2020

Struct Multidisc Optim

Self Cite

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“…Step 5 is handled by the MDO library GEMS [7], [8], which usage along with MARILib is briefly introduced in the present paper.…”

Section: Fig 2 Optimization Loopmentioning

confidence: 99%

“…GEMS is a MDO framework, which offers many possibilities to build, run and post process complex multidisciplinary design problems based on a catalog of MDO formulations [7] [8].…”

Section: Interfacing With the Gems Mdo Librarymentioning

confidence: 99%

A Multidisciplinary Airplane Research Integrated Library With Applications To Partial Turboelectric Propulsion

Druot

Belleville

Roches

et al. 2019

AIAA Aviation 2019 Forum

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The aim of this paper is to present a new model library developped as a new test case to benchmark optimization algorithms and Multidisciplinary Design Optimization (MDO) formulations. It introduces the MARILib software (Multidisciplinary Airplane Research Integrated Library). MARILib's first objective is to provide models for research on a wide range of aircraft concepts, from business jets to super jumbos, and also some unconventional configurations, through an Overall Aircraft Design (OAD) approach. The second objective is to share a new benchmark test case for MDO strategies, which does not raise intellectual property issues. The third objective is to describe a case study for educational and training purposes. MARILib will be available as Open Source.

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“…The relation between MDO-Driver and MDO frameworks such as OpenM-DAO [7], GEMS [5], or KADMOS [6], is not that of a competitor, but rather of a "super-executor", which controls parallel execution and HPC resource allocation. For example, if it is desired to implement a gradient-based MDF architecture, then the actual tool behind the top row of the cybermatrix, as described in the previous section, can be OpenMDAO.…”

Section: Realization On An Hpc Clustermentioning

confidence: 99%

Cybermatrix: A Novel Approach to Computationally and Collaboration Intensive Multidisciplinary Optimization for Transport Aircraft Design

Ilić

Merle

Ronzheimer

et al. 2019

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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This paper presents an approach to multidisciplinary optimization (MDO) of transport aircraft that attempts to strike a balance between two broad classes of MDO approaches: those arising from the formal optimization background, and those coming from the aircraft design background. It starts from the observation that any kind of numerical design process can be viewed as an approximation of a formal optimization process, where Jacobians of cost functions may be inexact and are often not explicitly computed. Based on that, a specific MDO problem representation and a highly parallel process assembly and execution protocol (the "cybermatrix" protocol) is defined, as well as one possible realization on high-performance computing (HPC) resources. The approach is applied to an optimization of a long-range transport aircraft, employing disciplinary subprocesses for high-fidelity aerodynamic design

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GEMS: A Python Library for Automation of Multidisciplinary Design Optimization Process Generation

Cited by 36 publications

References 20 publications

A bi-level methodology for solving large-scale mixed categorical structural optimization

A bi-level methodology for solving large-scale mixed categorical structural optimization

A Multidisciplinary Airplane Research Integrated Library With Applications To Partial Turboelectric Propulsion

Cybermatrix: A Novel Approach to Computationally and Collaboration Intensive Multidisciplinary Optimization for Transport Aircraft Design

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