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Modelling the effect of computation sampling on insight error in computational fluid dynamics scientific simulation

Modelling the effect of computation sampling on insight error in computational fluid dynamics... The purpose of this paper is to collect data from humans as they generate insights from the visualised results of computational fluid dynamics (CFD) scientific simulation. The authors hypothesise the behaviour of their insight errors (IEs) and proceed to quantify the IEs provided by the crowd participants. They then use the insight framework to model the behaviours of the errors. Using the crowd responses and models from the framework, they test the hypotheses and use the results to validate the framework for the speedup of CFD applications.Design/methodology/approachThe authors use a randomised between-subjects experiment with blocking. CFD grid resolution is the independent variable while IE is the dependent variable. The experiment has one treatment factor with five levels. In case varying timestamps has an effect on insight variance levels, the authors block the responses by timestep. In total, 150 participants are randomly assigned to one of five groups and also randomly assigned to one of five blocks within a treatment. Participants are asked to complete a benchmark and open-ended task.FindingsThe authors find that the variances of insight and perception errors have a U-shaped relationship with grid resolution, that similar to the previously studied visualisation applications, the IE framework is valid for insights generated from CFD results and grid resolution can be used to predict the variance of IE resulting from observing CFD post-processing results.Originality/valueTo the best of the authors’ knowledge, no other work has measured IE variance to present it to simulation users so that they can use it as a feedback metric for selecting the ideal grid resolution when using grid resolution to speedup CFD simulation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Engineering, Design and Technology Emerald Publishing

Modelling the effect of computation sampling on insight error in computational fluid dynamics scientific simulation

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1726-0531
DOI
10.1108/jedt-05-2020-0161
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to collect data from humans as they generate insights from the visualised results of computational fluid dynamics (CFD) scientific simulation. The authors hypothesise the behaviour of their insight errors (IEs) and proceed to quantify the IEs provided by the crowd participants. They then use the insight framework to model the behaviours of the errors. Using the crowd responses and models from the framework, they test the hypotheses and use the results to validate the framework for the speedup of CFD applications.Design/methodology/approachThe authors use a randomised between-subjects experiment with blocking. CFD grid resolution is the independent variable while IE is the dependent variable. The experiment has one treatment factor with five levels. In case varying timestamps has an effect on insight variance levels, the authors block the responses by timestep. In total, 150 participants are randomly assigned to one of five groups and also randomly assigned to one of five blocks within a treatment. Participants are asked to complete a benchmark and open-ended task.FindingsThe authors find that the variances of insight and perception errors have a U-shaped relationship with grid resolution, that similar to the previously studied visualisation applications, the IE framework is valid for insights generated from CFD results and grid resolution can be used to predict the variance of IE resulting from observing CFD post-processing results.Originality/valueTo the best of the authors’ knowledge, no other work has measured IE variance to present it to simulation users so that they can use it as a feedback metric for selecting the ideal grid resolution when using grid resolution to speedup CFD simulation.

Journal

Journal of Engineering, Design and TechnologyEmerald Publishing

Published: Mar 3, 2021

Keywords: CFD; Visualisation; Simulation; Sampling; Insight

References