物理信息神经网络在清洁燃烧中的应用：通往可持续航空推进的路径
Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion
AI总结：
本文综述了物理信息神经网络（PINNs）在清洁燃烧中的应用，通过将物理定律融入数据驱动学习，以降低计算成本并提升预测性能，从而推动可持续航空推进技术的发展。
摘要：
实现清洁燃烧系统对于解决环境影响、脱碳需求和可持续性问题至关重要。传统的燃烧建模技术通过计算流体力学结合精确化学动力学，在计算成本和湍流-化学相互作用的准确表示方面面临障碍。物理信息神经网络（PINNs）作为一种新框架，将物理定律与数据驱动学习相结合，展现出作为替代方法的巨大潜力。通过将守恒方程直接整合到其训练过程中，PINNs在数据集有限的情况下也能实现对复杂燃烧现象的无网格精确建模。本综述考察了该方法如何应用于清洁燃烧系统，同时重点关注其在航空航天应用中的影响，包括火焰动力学、湍流燃烧、排放预测以及推进系统中的不稳定性管理。下一代航空发动机依赖PINNs来降低计算成本，同时提高预测性能并实现实时控制方法。本分析最后探讨了当前的障碍和未来路径，同时展示了PINNs如何革新航空推进系统中可持续且高效的燃烧技术。
英文摘要：
Achieving clean combustion systems is crucial in terms of solving environmental impacts, decarbonization needs and sustainability matters. Traditional combustion modeling techniques via computational fluid dynamics with accurate chemical kinetics face obstacles in computational cost and accurate representation of turbulence-chemistry interactions. Physically Informed Neural Networks (PINNs) as a new framework, merges physical laws with data-driven learning and shows great potential as an alternative methodology. By directly integrating conservation equations into their training process, PINNs achieve accurate mesh-free modeling of complex combustion phenomena despite having limited data sets. This review examines how this approach applies to clean combustion systems while focusing on their impact in aerospace applications including flame dynamics, turbulent combustion, emission prediction, and instability management in propulsion systems. Next-generation aerospace engines rely on PINNs to reduce computational costs while increasing predictive performance and enabling real-time control methods. This analysis concludes by exploring current barriers and future paths, while demonstrating how PINNs can revolutionize sustainable and efficient combustion technologies in aerospace propulsion systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
