Published December 2, 2024 | Version 0.1.0
Dataset Open

Resultant force on grains of a real sand dune

Authors/Creators

  • 1. ROR icon Stanford University
  • 2. UNICAMP - University of Campinas

Contributors

  • 1. ROR icon Stanford University
  • 2. UNICAMP - University of Campinas

Description

This repository contains the dataaset used to train a convolutional neural network (CNN) designed to measure the resultant forces acting on a barchan dune. The model has been trained using numerical data to predict force distributions based on the morphological features of dunes.
 
In our research, we developed a groundbreaking method that leverages deep learning to estimate the forces acting on real barchan dunes at grain scale. This method combines image data from subaqueous experiments with numerical simulations, offering a novel approach to granular mechanics measurement. The trained CNN model is capable of predicting force distributions on dunes from their morphological features, even when applied to dune configurations not seen during training.

Files

experiments.zip

Files (16.1 GB)

Name Size Download all
md5:89e661af2a574c22cb21cf733b437b76
485.8 MB Download
md5:5ea7eac100859cb09f34b0cabfc9647e
372.6 MB Download
md5:a6a25056ee778763327ebe3464119a30
485.8 MB Download
md5:a2da3e08d4c023e8fb4087b76ef3660d
372.6 MB Download
md5:7594e9747ddf102479480474156ce73c
485.8 MB Download
md5:05e64982bb272fd11e8c59ae15e7a57b
372.6 MB Download
md5:3d84e0edb048f7cc31db0ce14bdfae17
485.8 MB Download
md5:8fca23d57311ea3a82d75d1ce32a22b4
372.6 MB Download
md5:0dba0a226e015f56d5353c9e511d919d
6.8 GB Preview Download
md5:67db8e7d17e085d3d3a91d1e7299af48
5.8 GB Preview Download

Additional details

Software

Repository URL
https://github.com/las-unicamp/measuring_forces_on_sand_dunes
Programming language
Python