大涡模拟技术在航空湍流研究中的应用

A review of large eddy simulation of aviation turbulence

航空湍流是威胁飞行安全的重要因素,它的形成机制复杂,一直是航空气象研究中的重点难点问题之一。近年来,随着大涡模拟(large eddy simulation,LES)技术的发展,LES已经成为研究航空湍流问题的重要方法。本文围绕在飞机起降阶段的飞机尾涡和低空湍流以及巡航阶段的对流湍流、山地波湍流和晴空湍流这五类航空湍流对飞机颠簸造成的影响,综述了LES技术在相关方向的研究进展,并对LES技术应用中亟需解决的问题及未来的重点研究方向进行了总结与展望。整体来看,航空湍流的LES模拟研究已经取得较大进展,高分辨率的LES可以更加明确航空湍流的来源和生命周期,显著提升了对航空湍流的机制认知和诊断预警能力。但在机制方面,对各种复杂湍流过程的相互影响机制仍然不够清楚;在数值模式技术方面,LES模拟与模式初始状态、边界条件和模式参数化方案的敏感性问题仍然有待解决。未来,LES与中尺度区域模式变网格嵌套技术、动态网格技术、集合预报与概率预报技术的发展,以及与深度学习等方法的结合都将进一步有效提升LES在航空湍流模拟方面的计算效率和预报能力。

Aviation turbulence is an essential factor threatening flight safety.However,due to its complex mechanism,which study has been one of the key issues faced by the aviation industry.In recent years,with the development of large eddy numerical simulation(LES),which has become an important method to solve aviation turbulence problems.This study reviews the research progress of LES technology in the past few decades,focusing on the impact of aircraft trailing vortex wakes and low-level turbulence during the takeoff and landing phase,as well as convective induced turbulence,mountain wave turbulence,and clear air turbulence during the cruise phase on aircraft turbulence.It also summarizes and prospects the urgent problems to be solved in the application of LES technology and future key research directions.Overall,LES simulation research on aviation turbulence has got much achievement,which can clarify the source and lifecycle of aviation turbulence more clearly,significantly improving the mechanism cognition,quantitative diagnosis,prediction and warning capabilities. However, in terms of mechanism, the interaction mechanism of various complex turbulent processes is still unclear;in terms of numerical model techniques, the predictive skill of LES of aviation turbulence is still limited by errors in initial conditions, boundary conditions, and the models themselves (e.g., parameterizations, dynamical methods). In the future, the development of nesting and dynamic grid technology between LES and mesoscale regional models, high-resolution ensemble prediction methods and probability prediction approaches, as well as the combination with deep learning methods will further improve the computational efficiency and prediction ability of LES on aviation turbulence simulation and forecasting.