Abstract

The original or modified Rayleigh Plesset equation (RPE) is often used to analyze cavitation bubble dynamics. The prediction accuracy of these equations is governed by the initial values of the physical parameters. However, even for higher fidelity models, deviations from experimental measurements are observed due to the models' underlying assumptions. Here, we present a novel state-observer data assimilation technique designed to fuse time-resolved cavitation bubble diameter measurements with a governing model to yield enhanced spatiotemporal prediction of the cavitation bubble dynamics. This technique places an observer variable in the original or modified RPE and uses a proportional–integral–derivative (PID) control law on the difference between the predicted and measured cavitation bubble diameter. The data-assimilated modeling most accurately estimates the bubble diameter and far-field pressure as the deviation of bubble diameter and far-field pressure predictions from measurements decrease by up to 90% and 60%, respectively. Although the assimilated model is not a substitude for high fidelity models, this technique overcomes the inherent model assumptions, and make the model’s outputs more robust with respect to the physical parameters' initial values.

Graphic abstract

中文翻译：

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数据同化以模拟空化气泡动力学

摘要

原始或修改后的瑞利普莱塞特方程（RPE）通常用于分析空化气泡动力学。这些方程的预测精度由物理参数的初始值控制。但是，即使对于更高保真度的模型，由于模型的基本假设，也观察到与实验测量值的偏差。在这里，我们提出了一种新颖的状态观测器数据同化技术，该技术旨在将时间分辨的空化气泡直径测量值与控制模型融合，以产生对空化气泡动力学增强的时空预测。该技术将观察者变量放置在原始RPE或修改后的RPE中，并对预测和测量的空化气泡直径之间的差异使用比例积分微分（PID）控制律。数据辅助建模最准确地估计气泡直径和远场压力，因为气泡直径和远场压力预测值与测量值的偏差分别减少了多达90％和60％。尽管同化模型不是高保真模型的附属品，但该技术克服了固有的模型假设，并使模型的输出相对于物理参数的初始值更稳定。

图形摘要