Differentiable Reacting Flow Modeling Software
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Updated
Feb 6, 2023 - Jupyter Notebook
Differentiable Reacting Flow Modeling Software
This package utilises the Šesták–Berggren equation alongside the Arrhenius equation to make a simple and consistent way for a user to carry out the calculations and predictions required by accelerated stability studies.
A simple QML GUI application to calculate Arrhenius damage integral for various cases.
⛰️🌡️ A tool for empirical Arrhenius equation fitting for thermally-induced physicochemical processes
Arrhenius time-temperature superposition: fit accelerated-aging data, extrapolate service-life timeline, bootstrap time-to-failure CI
Self-heating thermal RC compact model for reliability-aware package/device analysis
Modular lithium-ion battery model including SOC/OCV modelling, CC/CV charging, cycle based degradation, calendar ageing, Arrhenius based capacity degradation and temperature dynamics modelling.
Does an IEEE C57.91 cumulative-ageing model beat "peak load" at ranking which distribution transformers fail? No — uplift +0.014 AUC against a pre-registered 0.05 threshold. A killing experiment run in two days, before building the product. Six of eight predictions hit; two informative misses.
Pharmaceutical shelf-life modelling: ICH Q1E kinetics, 95% confidence bounds, and Arrhenius extrapolation. Pure Python.
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