使用声学透镜的全息热映射
Holographic Thermal Mapping Using Acoustic Lenses
AI总结：
本文提出机器学习辅助的单一逆问题框架设计声学全息透镜，利用压力相位与振幅将热目标转换为全息表示，并通过压力、热测量及体积声热分析验证其可编程热映射能力。
摘要：
声学全息透镜（AHL）在声操控方面展现出巨大潜力。当由单个声学源照射时，这些透镜存储所需波前的相位和振幅分布，以重建聚焦超声（FUS）压力场、诱导局部加热，并在聚合物等声热材料中实现时间和空间热效应。超声能量由换能器通过AHL传输并聚焦到特定的聚焦体积中。随后，它通过聚合物链中的内摩擦转化为热量，使聚合物在焦点位置温度升高，而在其他地方几乎没有或完全没有影响。AHL的发展使这种独特能力成为可能，其利用将衰减压力场转化为可编程热模式。然而，AHL的声热动力学在很大程度上尚未得到探索。我们采用机器学习辅助的单一逆问题方法来快速高效地设计AHL。该过程通过利用两个潜在函数（压力相位和振幅）将热信息转换为全息表示。对压力和热测量进行了实验验证。对实验样品进行体积声热分析，以提供所得模式动力学的知识，以及全息FUS热映射在非均匀介质复杂体积中精确温度控制的适用性。所提出框架为仅使用外表面测量来预测和评估材料内部热变化提供了坚实基础，因为它能够仅与表面温度数据建立关联。
英文摘要：
Acoustic holographic lenses (AHLs) show great potential for sound manipulation. These lenses store the phase and amplitude profile of the desired wavefront when illuminated by a single acoustic source to reconstruct focused ultrasound (FUS) pressure fields, induce localized heating, and achieve temporal and spatial thermal effects in acousto-thermal materials like polymers. The ultrasonic energy is transmitted and focused by AHL from a transducer into a particular focal volume. It is then converted to heat by internal friction in the polymer chains, causing the temperature of the polymer to rise at the focus locations while having little to no effect elsewhere. This one-of-a-kind capability is made possible by the development of AHLs to make use of the translation of attenuated pressure fields into programmable heat patterns. However, the acousto-thermal dynamics of AHLs are largely unexplored. We use a machine learning-assisted single inverse problem approach for rapid and efficient AHL designs. The process involves the conversion of thermal information into a holographic representation through the utilization of two latent functions; pressure phase and amplitude. Experimental verification is performed for pressure and thermal measurements. The volumetric acousto-thermal analysis of experimental samples is performed to offer knowledge of the obtained pattern dynamics, as well as the applicability of holographic FUS thermal mapping for precise temperature control in complex volumes of heterogeneous media. The proposed framework provides a solid foundation for anticipating and assessing thermal changes within materials using only outer surface measurements since it can correlate with surface temperature data alone.
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