RHEOS.jl - A Julia Package for Rheology Data Analysis
RHEOS.jl - A Julia Package for Rheology Data Analysis
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RHEOS.jl - 用于流变数据分析的 Julia 软件包
DOI：
10.21105/joss.01700
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
A. Kabla
中科院分区：
文献类型：
--
作者：
J. Kaplan;A. Bonfanti;A. Kabla
流变学是变形和流动的科学，重点是不表现出简单的线性弹性或粘性牛顿行为的材料。流变学在食品和化妆品工业以及生物学和生物工程中常见的软粘弹性材料的经验表征中起着重要作用。由于数据分析和/或物理建模，存在广泛的理论工具来提取材料参数并解释它们。RHEOS（RHEology，开源）是一个软件包，旨在使流变数据的分析更简单，更快，更可重复。RHEOS目前仅限于广泛的线性粘弹性模型。该库的一个特殊优势是它能够处理含有分数导数的流变学模型，这些模型对生物材料的建模具有明显的实用性，但迄今为止仍然相对模糊，这可能是由于它们的数学和计算复杂性。RHEOS是用Julia编写的，这极大地帮助了我们实现目标，因为它提供了出色的计算效率和平易近人的语法。RHEOS有完整的文档记录，并具有广泛的测试范围。应该注意的是，RHEOS不是一个优化包。它建立在另一个优化包NLopt的基础上，通过添加大量特定于粘弹性数据探索的抽象和功能。
Rheology is the science of deformation and flow, with a focus on materials that do not exhibit simple linear elastic or viscous Newtonian behaviours. Rheology plays an important role in the empirical characterisation of soft viscoelastic materials commonly found in the food and cosmetics industry, as well as in biology and bioengineering. A broad range of theoretical tools exist to extract material parameters and interpret them thanks to data analysis and/or physical modelling. RHEOS (RHEology, Open-Source) is a software package designed to make the analysis of rheological data simpler, faster and more reproducible. RHEOS is currently limited to the broad family of linear viscoelastic models. A particular strength of the library is its ability to handle rheological models containing fractional derivatives which have demonstrable utility for the modelling of biological materials but have hitherto remained in relative obscurity-possibly due to their mathematical and computational complexity. RHEOS is written in Julia, which greatly assists achievement of our aims as it provides excellent computational efficiency and approachable syntax. RHEOS is fully documented and has extensive testing coverage. It should be noted that RHEOS is not an optimisation package. It builds on another optimisation package, NLopt, by adding a large number of abstractions and functionality specific to the exploration of viscoelastic data.
DOI：
10.1101/543330
发表时间：
2019
期刊：
--
影响因子：
--
作者：
Bonfanti A
通讯作者：
Bonfanti A