研究员，博导，澳大利亚奋进学者（Australia Endeavor Fellowship）

通讯地址：江苏大学国家水泵中心大楼401室

电子邮件：longyan.wang@connect.qut.edu.au

教育背景

澳大利亚昆士兰科技大学博士（2013.9-2017.1） 机械工程专业

江苏大学硕士（2010.9-2013.6） 流体机械及工程专业

江苏大学学士（2006.9-2010.6） 工程热物理专业

工作经历

2020.6-至今  江苏大学研究员

2017.8-2020.5 江苏大学资格研究员

2018.7-2019.1 加拿大阿尔伯塔大学博士后

2016.10-2017.7 澳大利亚昆士兰科技大学博士后

研究方向

本课题组依托国家工程中心平台，探索人工智能在流体力学及流体机械中的应用，探索基于数据挖掘方法对旋转机械（主要包含风力机和潮流能水轮机）结构优化-设计诊断、控制策略-流场诊断、运行监测-故障诊断开展全方位研究。现拥有包含闭式循环水洞，开式风洞在内的基础实验设施及数据采集分析设备，在大型工作站及HPC超算辅助下，具有强大的数值计算处理能力。基于现有海外背景及长期研究合作关系，优秀学生可以进一步推荐赴国际知名高校联培或攻博。本人以一作通讯现已发表SCI论文四十余篇，基础领域方面研究有：

1. 流体机械结构设计优化方法与人工智能算法交叉研究

2. 基于数据挖掘和机器学习算法的尾流流场精细化方法及智能调控

3. 旋转机械设备运行监测及智能故障诊断

主持科研项目

2023-2024 基于人工智能的风电机组主轴轴承故障诊断及预测 国家外专项目

2021-2022 泵装置及系统运行状态智能诊断及故障分析 国家外专项目

2021-2022 基于深度学习方法的水翼动力学特性快速精确预测研究 江苏省博士后基金

2021-2023 风力机偏最优运行抑制尾流作用机理及其控制优化研究 国家自然科学基金

2019-2020 深海潜水器电力推进系统设计与关键技术的研究 流体工程装备技术研究院项目

2018-2023基于CNN的水翼动力学特性快速精确预测研究 江苏大学高级人才启动基金

2018-2020 基于尾流抑制作用的风力机组控制策略优化研究 中国博士后面上基金

2018-2021 尾流效应影响下的风力机组运行控制优化研究 江苏省自然科学基金

2018-2019 基于数据挖掘技术的风力机运行状态监测及诊断分析 澳大利亚奋进学者项目

代表性论文

中科院二区及以上SCI论文（近五年）

1.Jian Xu, Longyan Wang*. TurbineNet: Advancing tidal turbine blade hydrodynamic performance prediction with neural networks. Physics of Fluids, 2025(37): 027143.

2.Yuejiang Han, Longyan Wang*. The use of model-based voltage and current analysis for torque oscillation detection and improved condition monitoring of centrifugal pumps. Mechanical Systems and Signal Processing, 2025(222): 111781.

3.Longyan Wang*, Meng Chen. Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data. Energy, 2024(291): 130401.

4.Longyan Wang*, Qiang Dong. Effectiveness of cooperative yaw control based on reinforcement learning for in-line multiple wind turbines. Control Engineering Practice, 2024(153): 106124.

5.Jian Xu, Longyan Wang*. TurbineNet/FEM: Revolutionizing fluid-structure interaction analysis for efficient harvesting of tidal energy. Energy Conversion and Management, 2024(321): 119076.

6.Jian Xu, Longyan Wang*. DLFSI: A deep learning static fluid-structure interaction model for hydrodynamic-structural optimization of composite tidal turbine blade. Renewable Energy, 2024(224): 120179.

7.Jian Xu, Longyan Wang*. Deep learning enhanced fluid-structure interaction analysis for composite tidal turbine blades. Energy, 2024(296): 131216.

8.Zhaohui Luo, Longyan Wang*. Wind turbine dynamic wake flow estimation (DWFE) from sparse data via reduced-order modeling-based machine learning approach. Renewable Energy, 2024(237): 121552.

9.Zhaohui Luo, Longyan Wang*. A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements. Energy, 2024(294): 130772.

10.Zhaohui Luo, Longyan Wang*. A deep learning framework for reconstructing experimental missing flow field of hydrofoil. Ocean Engineering, 2024(293): 116605.

11.Tianshun Xia, Longyan Wang*. A novel generative approach to the parametric design and multi-objective optimization of horizontal axis tidal turbines. Physics of Fluids, 2024(36): 117118.

12.Tianshun Xia, Longyan Wang*. A novel generative–predictive data-driven approach for multi-objective optimization of horizontal axis tidal turbine. Physics of Fluids, 2024(36): 047103.

13.Meng Chen, Longyan Wang*. Super-resolution reconstruction framework of wind turbine wake : Design and application. Ocean Engineering, 2023(288): 116099.

14.Longyan Wang*, Jian Xu. A novel cost-efficient deep learning framework for static fluid–structure interaction analysis of hydrofoil in tidal turbine morphing blade. Renewable Energy, 2023(208): 367-384.

15.Jian Xu, Longyan Wang*. A cost-effective CNN-BEM coupling framework for design optimization of horizontal axis tidal turbine blades. Energy, 2023(282): 128707.

16.Zhaohui Luo, Longyan Wang*. Flow reconstruction from sparse sensors based on reduced-order autoencoder state estimation. Physics of Fluids, 2023(35): 075127.

17.Zhaohui Luo, Longyan Wang*. Reconstruction of missing flow field from imperfect turbulent flows by machine learning. Physics of Fluids, 2023(35): 085115.

18.Longyan Wang*, et al. Effectiveness of data-driven wind turbine wake models developed by machine/deep learning with spatial-segmentation technique. Sustainable Energy Technologies and Assessments, 2022(53): 102499.

19.Longyan Wang*, et al. Comparative study of decentralized instantaneous and wind-interval-based controls for in-line two scale wind turbines. Renewable Energy, 2022(189): 1218-1233.

20.Longyan Wang*, et al. A deep learning-based optimization framework of two-dimensional hydrofoils for tidal turbine rotor design. Energy, 2022(253): 124130.

21.Longyan Wang*, et al. A novel framework for cost-effectively reconstructing the global flow field by super-resolution. Physics of Fluids, 2021(33): 095105.

（五年以外）

22.Longyan Wang, Ming J Zuo*, et al. Optimizing wind farm layout by addressing energy-variance trade-off : a single-objective optimization approach. Energy, 2019(189): 116149.

23.Longyan Wang*, et al. Combined optimization of continuous wind turbine placement and variable hub height. Journal of Wind Engineering & Industrial Aerodynamics, 2018(180): 136–147.

24.Longyan Wang*, et al. Comparative study of discretization method and Monte Carlo method for wind farm layout optimization under Weibull distribution. Journal of Wind Engineering & Industrial Aerodynamics, 2018(180): 148–155.

25.Longyan Wang*, et al. Effectiveness of optimized control strategy and different hub height turbines on a real wind farm optimization. Renewable Energy, 2018(126): 819–829.

26.Longyan Wang, Yuantong Gu* et al. A computationally-efficient layout optimization method for real wind farms considering altitude variations. Energy, 2017(132): 147-159.

27.Longyan Wang, Yuantong Gu*, et al. Optimization of wind farm layout with complex land divisions. Renewable Energy, 2017(105): 30-40.

28.Longyan Wang, Yuantong Gu*, et al. Comparison of the effectiveness of analytical wake models for wind farm with constant or variable hub heights. Energy Conversion and Management, 2016(124): 189-202.

29.Longyan Wang*, et al. A novel control strategy approach to optimally design a wind farm layout. Renewable Energy, 2016(95): 10-21.

30.Longyan Wang, Andy Tan*, et al. Comparative study on optimizing the wind farm layout using different design methods and cost models. Journal of Wind Engineering & Industrial Aerodynamics, 2015(146): 1-10.

31.Longyan Wang*, et al. A new constraint handling method for wind farm layout optimization with lands owned by different owners. Renewable Energy, 2015(83): 151-161.

教学工作

CFD for Fluid Machinery 硕士研究生/博士研究生/留学生 2019-2024 英文授课

Dynamics （computer lab） 本科生 2016&2017 英文授课

荣誉奖励

澳洲奋进学者（Endeavor Research Fellowship Award）

昆士兰科技大学 Siganto奖章提名（Siganto Medalist Award Nominee）

昆士兰科技大学优秀博士论文（Outstanding Doctoral Thesis Award）

国家公派留学基金及昆士兰科技大学奖学金（CSC and QUT scholarship）

欢迎报考硕士、博士，共攀学术高峰。