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2026 世界仿真大会（2026GLOSIM）

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2026 世界仿真大会（Global Simulation Conference 2026）即将开幕，嘉宾阵容持续揭晓！今天将为大家介绍的嘉宾是 —— 中国汽车技术研究中心有限公司技术总监刘学龙先生。

他将出席2026世界仿真大会【智能仿真与数据治理专题论坛】，并围绕《汽车空气动力学数字化关键技术研究》（Research on Key Technologies for the Digitalization of Automotive Aerodynamics）这一主题，介绍空气动力学研发的数字化转型进展。

刘学龙，正高级工程师，现任中汽中心空气动力学技术总监，拥有15年工作经验，长期专注于汽车空气动力学高效节能及应用研究，主持和参与国家、省部级课题10余项，制定国家/行业标准3项，获省部级奖励3项，授权发明专利10余项。

Liu Xuelong, Professor-level Senior Engineer, currently serves as Technical Director of Aerodynamics at the China Automotive Technology and Research Center (CATARC). 

With 15 years of work experience, he has long been engaged in research on high-efficiency and energy-saving automotive aerodynamics and its applications. He has led and participated in more than 10 national and provincial/ministerial-level research projects, formulated 3 national/industry standards, received 3 provincial/ministerial-level awards, and holds more than 10 authorized invention patents.

当前运载装备节能、降噪需求推动空气动力学研发向数字化转型。传统模式依托风洞试验与 CFD 仿真，存在试验成本高、仿真算力消耗大、方案迭代慢、依赖人工经验等短板。流场测试技术可通过 PIV、PSP 等手段获取真实流场真值，用于仿真标定与数据集构建；CFD 数值仿真是方案评估主力，RANS 适用于快速评估，DES/LES 可解析复杂分离涡结构，但高精度计算耗时较长。智能预测技术基于机器学习、PINN 等方法构建代理模型，实现气动指标与流场快速推断；自动优化技术结合智能算法开展多目标寻优，大幅提升方案搜索效率。试验、仿真、智能预测、自动优化逐步融合，构建一体化气动研发体系，未来将朝着物理约束更强、虚实闭环、多物理场协同自主优化方向发展。

The current demand for energy saving and noise reduction in transport equipment is driving aerodynamics R&D toward digital transformation. 

The traditional approach relies on wind tunnel testing and CFD simulation, which suffers from shortcomings such as high testing costs, high simulation computing power consumption, slow design iteration, and dependence on manual experience. 

Flow field measurement techniques can obtain true flow field ground truth through methods such as PIV and PSP, which can be used for simulation calibration and dataset construction; CFD numerical simulation is the mainstay for design evaluation, with RANS suitable for rapid evaluation and DES/LES capable of resolving complex separated vortex structures, though high-fidelity computation is time-consuming. 

Intelligent prediction technology builds surrogate models based on methods such as machine learning and PINN to achieve rapid inference of aerodynamic metrics and flow fields; automatic optimization technology combines intelligent algorithms to carry out multi-objective optimization, greatly improving design search efficiency. Testing, simulation, intelligent prediction, and automatic optimization are gradually being integrated to build an integrated aerodynamic R&D system. 

In the future, development will move toward stronger physical constraints, virtual-real closed loops, and multi-physics collaborative autonomous optimization.