I am currently a Master's student in Chemical Engineering, jointly trained at Shanghai University of Electric Power and Shanghai Jiao Tong University.
My research focuses on bridging multiscale simulations with artificial intelligence, aiming to develop reliable models for fluid dynamics, reactor design, and digital twins in chemical processes.

📧 Contact: dwt_ai@163.com

I am particularly interested in:

• Understanding multiphase flow and erosion through CFD/DEM simulations.
• Applying deep learning to accelerate simulation, prediction, and optimization in chemical engineering.
• Building digital twins that integrate physical models with data-driven intelligence for safer and greener processes.

Developed an automated workflow combining CFD simulations with surrogate modeling, enabling efficient multi-objective optimization of stirred tank performance.
(Manuscript under review)

Constructed a large-scale dataset and designed a hybrid AI framework for reconstructing flow fields and tracing leakage sources under turbulent conditions.
(Towards applications in industrial safety and emergency response)

Proposed a graph-based neural network to predict NO conversion and pressure drop in fixed-bed reactors, outperforming traditional methods.
Published in Chemical Engineering Journal, 2025

Experienced in Python and PyTorch, with practical expertise in CFD modeling (Fluent/PyFluent), 3D modeling (SpaceClaim, Blender), and scientific visualization (PyVista, Unity3D).

• 3rd Prize, Baidu PaddlePaddle AI Co-creation Competition (2024)
• Innovation Pioneer Award, 1st National AI Application Contest in Chemical Industry (2024)
• 2nd Prize, National Digital Innovation Design Contest (2019)
• Special Prize, Provincial Round of the National Digital Innovation Design Contest (2019)
• National Scholarship for Encouragement (2019)
• First-class Scholarship, Jiangsu University of Technology (multiple times during undergraduate studies)
• Honors for “Outstanding Student / Advanced Individual / Excellent Student Leader”

我目前就读于上海电力大学与上海交通大学联合培养硕士，研究方向是化工过程的多尺度模拟与人工智能建模。
我的研究聚焦于将 多尺度模拟 与 人工智能 相结合，致力于开发适用于 流体动力学、反应器设计与化工过程数字孪生 的可靠模型。

📧 邮箱: dwt_ai@163.com

我的研究兴趣包括：

• 基于 CFD/DEM 的多相流与冲蚀机理研究
• 应用深度学习加速化工过程的模拟、预测与优化
• 构建融合物理模型与数据驱动智能的化工过程数字孪生

开发了一个自动化工作流，将 CFD 模拟与代理模型结合，实现了搅拌槽性能的高效多目标优化。
相关论文已投稿，正在审稿中

构建了一个大规模时序数据集，并提出混合 AI 框架，用于在湍流条件下重建流场与泄漏源追踪。
研究目标面向工业安全与应急响应应用

提出基于图神经网络的模型，实现了对固定床反应器中 NO 转化率与压降的高精度预测，效果优于传统方法。
成果已发表于《Chemical Engineering Journal》，2025

熟练掌握 Python 与 PyTorch，具备 CFD 建模（Fluent/PyFluent）、三维建模（SpaceClaim、Blender）以及科学可视化（PyVista、Unity3D）的实践经验。

• 百度飞桨 AI 共创计划三等奖（2024）
• 全国首届化工行业人工智能应用创新大赛 创新先锋奖（2024）
• 全国数字化创新设计大赛 国家二等奖（2019）
• 全国数字化创新设计大赛 省特等奖（2019）
• 国家励志奖学金（2019）
• 江苏理工学院一等奖学金（本科期间多次获得）
• 校级荣誉：“三好学生 / 先进个人 / 优秀班干部”