Introducing a cloud-based framework for creating, visualising, testing and automating complex simulation workflows

  • Stodieck, Olivia (Dapta Ltd)

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As performance expectations rise, products become more complex and so do engineering workflows and processes. In a recent industry report [1], it is estimated that more than half of R&D-driven organisations are using three or more different simulation software packages (not counting in-house tools), in many cases to support multidisciplinary simulation efforts. Engineers need tools that help them work with complex simulation workflows, so that they can focus on understanding the pertinent analysis results without worrying about computing resources, data transfers and data reformatting between applications. Besides performance and usability requirements, tools also need to be accessible anywhere in the world at any time. To fulfil these requirements, Dapta Ltd is developing a cloud-based framework, which is designed to be an all-in-one solution to create, visualise, test and automate complex simulation workflows. The aim is to reduce the time and effort spent on connecting tools and on performing multiphysics simulations. In this talk, we introduce the dapta web application [2] and provide an insight into its use with open-source software tools, such as Python, the finite element solver Calculix [3] and the multidisciplinary optimisation library OpenMDAO [4], amongst others. Examples include the design and aeroelastic analysis of a simple composite wing, for which python scripts and video-tutorials have been published previously [5]. We will also provide an overview of recently launched UK-based research projects, which will be contributing to the development of the app. REFERENCES [1] Big Compute 2021 State of Cloud HPC Report, url: [2] Launching the Dapta Trial, O. Stodieck, January 19, 2023, url: [3] CalculiX, A Free Software Three-Dimensional Structural Finite Element Program, url: [4] OpenMDAO, An open-source framework for efficient multidisciplinary optimization, url: [5] parametric_cgx_model, url: