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 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.
The 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.
The 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.
To 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.
As the field of action-oriented research becomes increasingly diffuse and diverse, this paper seeks to identify common ground across the multiple modalities of action…
As the field of action-oriented research becomes increasingly diffuse and diverse, this paper seeks to identify common ground across the multiple modalities of action research and collaborative management research through articulating and exploring a general empirical method that is grounded in the recognizable structure of human knowing. This method is grounded in: attention to observable data (experience), envisaging possible explanations of that data (understanding), and preferring as probable or certain the explanations, which provide the best account for the data (judgment). Engaging this method requires the dispositions to perform the operations of attentiveness, intelligence, and reasonableness, to which responsibility is added when we seek to take action. This paper seeks to provide insight into the multiple modalities of action research and collaborative management research and to illustrate how each modality engages the recognizable operations of human knowing.