Intelligent Modeling and Control of Flow in Unmanned Aerial Systems   (Submission Deadline: Sep. 30, 2025)

(无人飞行系统流动智能建模与调控)

Summary:

This special topic focuses on the core theme of "Intelligent Modeling and Control of Flow in Unmanned Aerial Systems (UAS)", deeply integrating artificial intelligence (AI) technology with disciplines such as fluid mechanics and aircraft design, and conducting academic discussions and technical exchanges around four key research directions.

In the field of intelligent turbulence modeling, it focuses on breaking through the adaptability limitations of traditional turbulence models in complex flow scenarios. Through intelligent algorithms such as machine learning and deep learning, the evolution laws of turbulence are explored, and high-precision and high-generalization turbulence prediction models are constructed to provide theoretical support for the stable operation of unmanned aerial systems in complex airflow environments.

In the direction of intelligent flow field prediction, aiming at the dynamic and nonlinear characteristics of the flow field during the flight of unmanned aerial vehicles (UAVs), intelligent prediction methods combining data-driven approaches and physical constraints are developed. These methods realize real-time and accurate prediction of key parameters such as flow field distribution and pressure gradient under different flight conditions, providing technical guarantee for flight state perception and risk early warning.

In terms of multidisciplinary optimization design of aircraft, with the goal of improving the aerodynamic performance, structural efficiency and energy utilization of unmanned aerial vehicles, it integrates multidisciplinary design variables including aerodynamic layout, structural strength and power system. With the help of intelligent optimization algorithms (e.g., genetic algorithms, reinforcement learning), multi-objective collaborative optimization is achieved, promoting the development of unmanned aerial vehicles towards higher efficiency, lighter weight and lower energy consumption.

In the field of intelligent flow control, it explores the integrated technology of active flow control (e.g., synthetic jets, plasma actuation) and intelligent decision-making algorithms. By real-time sensing of the flow field state and dynamically adjusting control strategies, undesirable flow phenomena (e.g., separated flow, vortices) are suppressed, thereby improving the handling stability and maneuverability of unmanned aerial vehicles.

This special topic aims to build an interdisciplinary communication platform, gather domestic and international research achievements in related fields, promote the innovative application of intelligent technology in the flow modeling and control of unmanned aerial systems, and provide key technical support for the efficient and safe application of unmanned aerial systems in civil scenarios (e.g., logistics transportation, environmental monitoring) and military scenarios (e.g., reconnaissance, combat support).

本专题聚焦“无人飞行系统流动智能建模与调控”核心主题，深度融合人工智能技术与流体力学、飞行器设计等学科，围绕四大关键研究方向展开学术探讨与技术交流。在湍流智能建模领域，重点突破传统湍流模型对复杂流动场景的适应性局限，通过机器学习、深度学习等智能算法挖掘湍流演化规律，构建高精度、高泛化性的湍流预测模型，为无人飞行系统在复杂气流环境下的稳定运行提供理论支撑；在流场智能预测方向，针对无人飞行器飞行过程中流场的动态性、非线性特征，开发基于数据驱动与物理约束结合的智能预测方法，实现对不同飞行工况下流场分布、压力梯度等关键参数的实时、精准预测，为飞行状态感知与风险预警提供技术保障；在飞行器多学科优化设计方面，以提升无人飞行器气动性能、结构效率与能源利用率为目标，整合气动布局、结构强度、动力系统等多学科设计变量，借助智能优化算法（如遗传算法、强化学习）实现多目标协同优化，推动无人飞行器向高效化、轻量化、低能耗方向发展；在流动智能调控领域，探索基于主动流动控制（如合成射流、等离子体激励）与智能决策算法的融合技术，通过实时感知流场状态动态调整调控策略，抑制不良流动现象（如分离流、涡旋），提升无人飞行器的操纵稳定性与机动性能。专题旨在搭建跨学科交流平台，汇聚国内外相关领域研究成果，推动智能技术在无人飞行系统流动建模与调控中的创新应用，为无人飞行系统在民用（如物流运输、环境监测）、军用（如侦察、作战支援）等场景的高效、安全应用提供关键技术支撑。