A database of 590 axial compressor stator blade designs. The shape of the candidate blades is varied according to its 3D style (the type of Lean) and the number of stator blades. Machine Learning is used to identify which designs have corner separations visible in the downstream flow. Stator exit flow contour plots and 3D blade views are available by pressing the Plot Selected Tasks button. You can also inspect the 3D blade using Augmented Reality. More details can be found in this AIAA SciTech (2019) paper. Four versions of the demo are available:
In this demo, dbslice is used to visualise a collection of snapshots from a single simulation, rather than a collection of data from many different simulations. The time instance is changed by hovering over the points in the scatter plot. This triggers an update of the contour plot (1.4 million triangles in each snapshot) and the line cut. The data for the line is generated by the browser using an efficient quadtree-based interpolation method.
This is a stripped down demo of the Augmented Reality avaialble in the Compressor Stator Design Study demo above. When this AR marker is in the view of your webcam, a 3D model will be rendered as if sitting on top of the marker. dbslice uses the AR.js and A-Frame libraries to provide AR functionality. More details can be found in this AIAA SciTech (2019) paper.
A database of 99 computations of a 3-stage axial compressor, run at several operating conditions at 3 speeds, is stored. Filter the tasks using 'Design', 'Speed' and 'Pressure ratio'. Scatter plots showing pressure ratio and efficiency chractersitics update interactively as the filter is changed. For the selected Tasks, additional data for line, contour and 3D surface plots can be requested and plotted. More details can be found in this AIAA SciTech (2018) paper.
95 river level monitoring stations, covering 3 catchment areas and 30 rivers are availble for filtering. The location of each measurement station (Task) in the current selection is shown on a map. River level data, for the last 4 weeks, are obtained for the Task selection by making a request to the UK Environment Agency real-time data API; once received, dbslice reformats and plots the data. More details can be found in this AIAA SciTech (2018) paper.
An artificially generated 3D array of a single scalar. The database contains 100 such arrays (Tasks). For each Task, the mean and standard deviation of the scalar is stored, as well as the 'Simulation type' and 'Model type'. By filtering on these properties, the user selects a subset of Tasks for further plotting (line, contour and 3D surfaces). More details can be found in this AIAA SciTech (2017) paper.
Small building-block examples are available on bl.ocks.org.
Please send any comments, suggestions or issues related to these demos to