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The prediction of damage progression in composite wind turbine blades, especially under dynamic aeroelastic conditions, is usually a cumbersome multi-step process with significant manual user intervention.
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Manual titrations require constant user intervention to wash cuvettes, load and thermostat samples between measurements.
In addition, the domain connectivity procedures are completely automated such that no user intervention or manual input is necessary.
This way, the DSE loop iterations are efficiently performed, without user intervention, avoiding slow manual editions, or regeneration of the performance model.
Lower sample volume requirements and reduced user intervention time compared to the manual instruments have improved turnover of calorimetry experiments in a high-throughput format where 25 or more runs can be conducted per day.
The pace of this workflow is slowed by user intervention; the task for the computer at each step may be completed within seconds, but manual data entry and program execution might require several hours of concentrated human effort.
This process has to be automatic, without requiring any manual intervention from the user.
However, this approach requires manual intervention by the user.
These tools essentially automate several tasks that required manual intervention before, saving users lots of valuable time.
Independent of any predetermined threshold or manual intervention, LMD enables users to stop around the optimal time, based on the information collected during the stochastic process.
Manual intervention has a downside as well: Individual users have different perceptions about the "correct" spot shapes, so reproducibility between different operators of the software suffers.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com