维度分析与人工智能相遇,用于非牛顿式滴滴生成
Farnoosh Hormozinezhad1, Claire Barnes2, Alexandre Fabregat1
1Departament d'Enginyeria Mecanica, Universitat Rovira i Virgili, Tarragona, Spain.
Lab on a chip
|February 18, 2025
概括
本研究引入了一种混合机器学习模型,用于预测产生非牛顿滴滴的流量. 该模型准确预测液滴大小,有助于制药和材料科学中的应用.
科学领域:
- 流体动力学 流体动力学
- 微流体学 微流体学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 非牛顿滴在制药,食品加工和药物输送中至关重要.
- 预测这些复杂流体的滴滴形成是具有挑战性的,因为多相相互作用和不同的特性.
研究的目的:
- 开发一种新的混合机器学习架构,用于预测非牛顿式滴滴生成中的流量.
- 为了适应切割率依赖的粘度和估计弹性性质.
主要方法:
- 整合维度分析与机器学习.
- 开发一种混合模型,根据滴滴尺寸和流体粘度曲线预测分散和连续相位流速.
主要成果:
- 对于未见的数据,实现了高达0.82的R平方值,证明了强大的预测能力.
- 该模型准确地预测了特定大小的液滴的流量,即使流体特性与训练数据的偏差.
结论:
- 开发的模型可以在各种不同粘度曲线的非牛顿系统中推广.
- 这为优化滴滴生成提供了强大的工具,并推进了微流体学中的机器学习应用,以实现高效的实验设计.
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