Research data supporting "Theory of flow-induced covalent polymer mechanochemistry in dilute solutions"

This is the dataset and software supporting the study *Theory of flow-induced covalent polymer mechanochemistry in dilute solutions* by Etienne Rognin, Niamh Willis-Fox, Ronan Daly, Institute for Manufacturing, Department of Engineering, University of Cambridge, 17 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom. Contents -------- Main file ^^^^^^^^^ Use the Jupyter Notebook ``Supporting information.ipynb`` to run the data analysis and recreate the figures of the paper. Alternatively, open ``Supporting information.pdf`` to view the notebook outputs in a PDF reader without having to install and run the scripts. Raw data ^^^^^^^^ The folder ``bead-rod_dataset`` contains the results of bead-rod model simulations. For each simulation there is binary Python ``.npz`` file containing the data, and a text ``.json`` file containing metadata (such as date of the simulation, parameters...) The data is imported using ``np.load`` function which creates a Python dictionary for each simulation file. This dictionary contains the following labels: 1. ``t`` the time axis. 2. ``gradU`` the time series of velocity gradients used as forcing terms in the bead-rod simulation. 3. ``g_max`` the time series of the maximum tensile force, for each molecule of the simulation ensemble. 4. ``i_max`` the time series of the positions of the maximum force in the chain (not used in this study) 5. ``g_12`` the time series of the tensile force at the center of the chain, for each molecule. 6. ``A_average`` the time series of the average conformation tensor (second-order moment of the end-to-end vector). Used in section 4 for model validation. Note that the bead-rod algorithm and dimension normalization are described in a previous study (see Rognin et al. https://www.repository.cam.ac.uk/bitstream/1810/279443/1/multiscale_revision_clean.pdf) Other ^^^^^ The notebook ``JHTD_turbulence.ipynb`` has been used to extract data from the Johns Hopkins Turbulence Databases and is provided here for illustrative purposes only (it is not necessary to run this file). License ------- CC-BY-4.0 To view the full license, visit: https://creativecommons.org/licenses/by/4.0/legalcode Installation ------------ In the target directory, clone this repository:: git clone https://github.com/etiennerognin/flowmechanochem_dataset.git Usage ----- Run the notebook ``Supporting information.ipynb`` (you will need to have Jupyter installed, see https://jupyter.org/). The Python distribution will need to have packages listed in ``requirements.txt``.