Real-time Bayesian Inversion in Resin Transfer Moulding using Neural Surrogates
Publication Date
July 19, 2024
Creators
Description
The aim of this study is to rapidly estimate the properties of fibrous reinforcements during the injection phase of Resin Transfer Moulding. There are five data sets associated with this study. The first data set was generated by simulating the resin injection process for 50,000 samples of reinforcement properties. This data was used to train a surrogate model to emulate the injection simulator. The remaining four data sets correspond to the lab experiments included within the paper. These show time series plots for resin pressure at each sensor within the tool, recorded by the data acquisition system.
Collection dates:
Surrogate training data were generated on 14/01/24. Experimental data were collected between 11/12/23 - 04/01/24.
Subjects
Subjects (JACS)
Subjects (LC)
Divisions
University of Nottingham, UK Campus::Faculty of Engineering
Data type
Surrogate training data are text files consisting of the inputs and outputs of the injection simulator. Experimental data recorded via the data acquisition system are in MATLAB data files.
Grant Number
EP/P006701/1
Data collection method
The surrogate training data were generated using the "Control volume FEM solver for 2D moving boundary problems" in Matlab (doi:10.5281/zenodo.10914584). The experimental data were collected via a data acquisition system, recording fluid pressure at each sensor within the tool at a rate of 10 per second.
Resource languages
English
Publisher
The University of Nottingham
Date Issued
July 19, 2024
Except where otherwise noted, this item's license is described as
File(s)![Thumbnail Image]()
![Thumbnail Image]()
Name
Y_9x9.txt
Description
Output data for training the surrogate model.
Size
944.94 MB
Format
Text
Checksum (MD5)
ddb5c0df63b69dd8229e6de5423ed410
Name
RT.data
Description
Data associated with race-tracking experiment.
Size
150.28 KB
Format
Unknown
Checksum (MD5)
4b218dcd6620c52daef079a0bdec4922