SHINE: Origin and Evolution of Compressible Fluctuations in the Solar Wind and Their Role in Solar Wind Heating and Acceleration

了解导致日冕加热、太阳风加速和湍流输送的原因，是提高空间天气模型预测能力的关键，从而有助于防止可能伤害宇航员和/或我们的技术基础设施的灾难性太阳事件。该项目将测试新颖的、物理知识丰富的机器学习技术，因此，它将为更广泛的等离子体物理社区（从空间物理学到天体物理学，从聚变到激光等离子体相互作用）和研究复杂系统的科学家提供有价值的概念验证方法。该项目还将对教育产生重大影响，通过支持早期职业女性教员，博士后研究员，并为本科生提供参与太阳物理学研究的机会。alfvsamn和可压缩模之间的相互作用为加热等离子体和增强湍流级联提供了重要的机制。然而，这两种效应如何影响等离子体的宏观特性尚不清楚，事实上，在最先进的全球太阳风模型中，它们被忽略了。拟议的项目将通过研究可压缩性的作用，包括由可压缩波动介导的波粒相互作用，在太阳风动力学和热特性上的作用，来缩小这样的知识差距。通过研究太阳风中的可压缩效应和动力学效应，以及开发一种新的波浪驱动太阳风模型，该项目将提供波浪和湍流在波浪驱动风模型中的作用的见解。本项目的科学目标是：(1)表征不同类型湍流太阳风中可压缩波动随径向距离的演变；(2)确定什么相空间动力学导致质子加热，并量化不同类型湍流（平衡与不平衡）的加热速率；(3)将动力学可压缩效应纳入全球波驱动太阳风模型。为了实现我们的科学目标，我们将(1)分析帕克太阳探测器、太阳神号和Wind数据中波动的径向演化，(2)进行混合pic数值模拟，(3)基于知识机器学习方法开发一种新的全球太阳风模型。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Understanding what leads to coronal heating, solar wind acceleration, and turbulence transport, is key to improve the predictive capabilities of space weather models and, thus, to help preventing catastrophic solar events that can harm astronauts and/or our technological infrastructure. This project will test novel, physics-informed machine learning techniques and, thus, it will provide valuable proof-of-concept methods for the broader plasma physics community (from space physics to astrophysics, to fusion to laser-plasma interactions) and for scientists investigating complex systems. This project will also have significant impact in education, by supporting an early career woman faculty member, postdoctoral researcher, and by providing an opportunity for undergraduate students to be involved in heliophysics research. Interactions between Alfvén and compressible modes can provide an important mechanism to heat the plasma and to enhance the turbulent cascade. However, how these two effects impact the macroscopic properties of the plasma is not yet well understood and, indeed, they are neglected in state-of-the-art global solar wind models. The proposed project will close such a knowledge gap by investigating the role of compressibility, including wave-particle interactions mediated by compressible fluctuations, on solar wind dynamics and thermal properties. By investigating compressible and kinetic effects in the solar wind, and by developing a novel wave-driven solar wind model, this project will provide insights on the role of waves and turbulence in wave-driven wind models. The science objectives of this project are: (1) to characterize the evolution of compressible fluctuations with radial distance and in different types of turbulent solar wind; (2) to determine what phase-space dynamics leads to proton heating and to quantify the heating rate in different types of turbulence (balanced vs. imbalanced); (3) to incorporate kinetic compressible effects into a global wave-driven solar wind model. To achieve our science objectives, we will (1) analyze the radial evolution of fluctuations from Parker Solar Probe, Helios, and Wind data, (2) perform hybrid-PIC numerical simulations and (3) develop a novel global solar wind model based on knowledge-based machine learning methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.