PNNL

Dewei Wang joined Pacific Northwest National Laboratory in 2020, and his research involves solid oxide cell modeling, machine learning applications in energy and chemical system design and power grid modeling, reduced-order model development, and two-phase flow measurement and modeling.

• Deep learning
• Reinforcement learning
• Multiscale modeling
• Reduced-order model
• Two-phase flow
• Two-fluid model
• Thermal-hydraulics measuring and visualization

• PhD in Mechanical Engineering, Virginia Polytechnic Institute & State University, 2019
• BS in Engineering Theoretical and Applied Mechanics, University of Science and Technology of China, 2013

2025

• Wang D., T. Chen, and K. Mahapatra. 2025. "Interpretability, Explainability and Trust Metrics in Anomaly Detection Method for Power Grid Sensors." In IEEE PES Grid Edge Technologies Conference & Exposition (Grid Edge 2025), January 21-23, 2025, San Diego, CA, 1-5. Piscataway, New Jersey:IEEE. PNNL-SA-192629. doi:10.1109/GridEdge61154.2025.10887434

2024

• Wang D., B. Mitra, S. Nekkalapu, S. Datta, B. Mathew, R. Meyur, and H. Wang, et al. 2024. "Hy-DAT: A Tool to Address Hydropower Modeling Gaps Using Interdependency, Efficiency Curves, and Unit Dispatch Models." In IEEE Green Technologies Conference (GreenTech 2024), April 3-5, 2024, Springdale, AR, 91-95. Piscataway, New Jersey:IEEE. PNNL-SA-183732. Hy-DAT: A Tool to Address Hydropower Modeling Gaps Using Interdependency, Efficiency Curves, and Unit Dispatch Models">doi:10.1109/GreenTech58819.2024.10520554Hy-DAT: A Tool to Address Hydropower Modeling Gaps Using Interdependency, Efficiency Curves, and Unit Dispatch Models

2023

• Wang D., J. Bao, M. Zamarripa-Perez, B. Paul, Y. Chen, P. Gao, and T. Ma, et al. 2023. "A coupled reinforcement learning and IDAES process modeling framework for automated conceptual design of energy and chemical systems." Energy Advances 2, no. 10:1735-1751. PNNL-SA-178647. doi:10.1039/d3ya00310h

2021

• Wang D., J. Bao, Z. Xu, B.J. Koeppel, O.A. Marina, A. Noring, and M. Zamarripa-Perez, et al. 2021. "Machine Learning Tools Set for Natural Gas Fuel Cell System Design." In 17th International Symposium on Solid Oxide Fuel Cells (SOF-XVII) July 18, 2021 - July 23, 2021 Stockholm, Sweden. ECS Transactions, 103, Paper No. 2283. PNNL-SA-162378. doi:10.1149/10301.2283ecst