A Research Software Engineering Workflow for Computational Science and Engineering

Marić, Tomislav; Gläser, Dennis; Lehr, Jan-Patrick; Papagiannidis, Ioannis; Lambie, Benjamin; Bischof, Christian; Bothe, Dieter

doi:10.48550/arxiv.2208.07460

Cited by 5 publications

(3 citation statements)

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“…Unfortunately, in the majority of cases, executing this workflow is a timeconsuming process, which is largely attributed to a lack of workflow automation. Workflow orchestration tools are extremely popular in a number of industries for creating more automated workflows, however as discussed in the work of Maric et al they are not commonly employed in the field of computational engineering [112]. One reason for this is the manual steps performed at the different stages of the workflow, which is largely attributed to a dependence on the graphical user interfaces (GUI) of software packages.…”

Section: Obstaclesmentioning

confidence: 99%

“…This means that in many cases, the generation of synthetic data can only be achieved on a specific computer by an individual who knows the necessary steps. This inevitably leads to repetition of work between colleagues, resulting in a waste of both time and resources [112].…”

Section: Obstaclesmentioning

confidence: 99%

“…Such a platform would also greatly improve the ability to reproduce data, something which Maric et al say is rarely adhered to, especially in academia [112]. This is because reproduction of the computing environment used to generate data is challenging, and not all details used throughout the workflow are available in publication.…”

Section: Workflow Platformmentioning

confidence: 99%

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Simulation driven machine learning methods to optimise design of physical experiments and enhance data analysis for testing of fusion energy heat exchanger components

Lewis

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Plasma facing components (PFCs) must be designed to routinely withstand the harsh environment of a fusion device, where temperatures at the core of the plasma exceed 150,000,000 °C. The heat by induction to verify extremes (HIVE) experimental facility was established to replicate the thermal loads a PFC is subjected to during normal operation of a fusion device.To maximise its impact on the design of PFCs, HIVE must deliver smarter testing and improved component insight. Currently, the experimental parameters required to deliver a certain response to the component are decided at the point of testing through a combination of previous experience, intuition, and trial & error, which is both time-consuming and unreliable. To assess a PFC’s suitability, knowledge of its mechanical performance while operating at high temperatures is desirable, however HIVE only records pointwise temperature measurements on the component’s surface using thermocouples. Currently, HIVE has no method of inferring a component’s mechanical response using the temperature measure-ments.Both the challenges of smarter testing and improved component insight can be achieved through the identification of inverse solutions. A popular approach to solving engineering inverse problems is surrogate assisted optimisation, where a machine learning model is trained using finite element (FE) simulation data. Much of the work in literature use single value surrogate models on quite simplistic problems, however HIVE is a real-world, multi-physics problem which requires full field (FF) surrogate models to solve its multitude of inverse problems.The development of a method which can easily construct FE data driven FF surrogates would be invaluable for a variety of tasks in engineering, as well as solving inverse problems. In this work, it demonstrates that it can provide a much more robust and comprehensive method of characterising a PFC’s strengths and limitations, enabling more informed decisions to be made during its design cycle.

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Section: Obstaclesmentioning

confidence: 99%

Section: Obstaclesmentioning

confidence: 99%