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Abstract for ECCOMAS 2024

Key Words: Composite structures, VV&UQ platform, Parametric Finite-Element model

The Aerospace Industry is facing the challenge of developing and integrating disruptive technological innovations by 2035-2050. It means that disruptive design or designs with limited in-service experience need to be integrated in shorter times with high levels of safety and reliability. Composite structures play a major role in this transition, but have to adapt to new designs, new threats (cryogenic temperatures), new material (bio-sourced) and new manufacturing and assembly processes. A key enabler for the reduction of development cycle time and cost is the integration of more simulations in the certification test pyramid. As a result, the simulation of composite structures must reach a high level of credibility.

VV&UQ builds and assesses credibility of models through methodologies ([1], [2]) which bring to play several components like Numerical Simulation tools and Mathematical algorithms, High Performance Computing submission and monitoring, and so forth. The notions of traceability (of both functional capabilities (like models) and data), data visualisation and standardized taxonomy to describe the manipulated quantities and methods are also significantly contributing to the credibility and the decision-making process. Note that some components may already exist and can be re-used off-the-shelf (UQ methodologies, FE software).

The present work focuses on an efficient software implementation of the VV&UQ process to support the above challenges. VIMSEO (Virtual testing Integration platform for decision-Making Support) is a Python library (still under construction) developed from the translation of VV&UQ processes into software requirements. Some of them are listed hereafter:

  • The integration of state-of-the-art simulation models and methodologies for Uncertainty Quantification requires interfacing experts in these domains.
  • Decision-makers must also interface to the platform to validate the process establishing credibility. Thus, relevant data visualisation and an effective User Interface is necessary.
  • Traceability of models and associated analysis methods is also necessary both for credibility assessment and further use of the models and methodologies for optimisation, calculation of design values and structural sizing. The platform should then be closely linked with databases.
  • Model standardization through a global taxonomy or ontology and a semantic layer is also a key feature since it allows to seamlessly change models and analysis methodologies, and also use the verified models in other frameworks like MultiDisciplinary Optimisation (MDO) processes, without re-implementing the model.

While generic, the VIMSEO platform is extended for applications to composite structures [3] in a plug-in integrating Composite Damage Models (OPFM 1 and PG 2) at coupons and element levels. Several parametric FE models are integrated under a consistent model template, compatible with the MDO platform GEMSEO 3. Each FE model integrated in the platform is decomposed in bricks allowing to inter-change different load cases and material definition while making sure that we keep the same numerical model (FE method and material law). The latter constitutes the Travelling Model 4. We will focus on the model Verification and Validation bricks, including an example of stochastic validation.

References:

  • [1] ASME V&V 10.3 Validation Metrics
  • [2] ASME V&V 10-2019 Standard Verification and Validation in Computational Solid Mechanics
  • [3] J. Fatemi, G. Poort, Towards Qualification of Composite Launcher Structures by Simulation, 17th ECCSMET Proceedings, 28-30 March 20203, Toulouse.

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  2. P. Maimí, P.P. Camanho, J.A. Mayugo, and C.G. Dávila. A continuum damage model for composite laminates: part i – constitutive model. Mechanics of Materials, 39(10):897–908, 2007. URL: https://www.sciencedirect.com/science/article/pii/S0167663607000543, doi:https://doi.org/10.1016/j.mechmat.2007.03.005. ↩

  3. François Gallard, Charlie Vanaret, Damien Guénot, Vincent Gachelin, Rémi Lafage, Benoît Pauwels, Pierre-Jean Barjhoux, and Anne Gazaix. Gems: a python library for automation of multidisciplinary design optimization process generation. In 01 2018. doi:10.2514/6.2018-0657. ↩

  4. Vicente Romero. Comparison of several model validation conceptions against a "real space" end-to-end approach. SAE International, 4:396–420, 2011. URL: https://www.jstor.org/stable/10.2307/26273778. ↩

  5. Stefan Riedmaier, Benedikt Danquah, Bernhard Schick, and Frank Diermeyer. Unified framework and survey for model verification, validation and uncertainty quantification. Computational Methods in Engineering, 28:2655–2688, 2020. doi:10.1007/s11831-020-09473-7. ↩