Mechanical Engineer
Mechanical Engineer

Biography

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.

Research Interest

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

Education

  • 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

Publications

2024

  • Wang, D., B. Mitra, S. Nekkalapu, S. Datta, B. Matthew, R. Meyur, H. Wang, and S. Kincic. 2024. “Hy-DAT: A Tool to Address Hydropower Modelling Gaps Using Interdependency, Efficiency Curves, and Unit Dispatch Models.” https://arxiv.org/html/2402.18017v1.

2023

  • Wang, D., J. Bao, M. Zamarripa-Perez, B. Paul, Y. Chen, P. Gao, T. Ma, A. Noring, A. Iyengar, D. Miller, D. Schwartz, E. Eggleton, Q. He, O. Marina, B. Koeppel, and Z. Xu. 2023. “A coupled reinforcement learning and IDAES process modeling framework for automated conceptual design of energy and chemical systems.” Energy Advances. http://dx.doi.org/10.1039/D3YA00310H.

2021

  • Wang, D., S. Shi, Y. Fu, K. Song, X. Sun, A. Tentner, and Y. Liu. 2021. “Investigation of air-water two-phase flow characteristics in a 25.4 mm diameter circular pipe.” Progress in Nuclear Energy, 103813. https://doi.org/10.1016/j.pnucene.2021.103813.
  • Wang, D., Y. Fu, Y. Liu, J. D. Talley, T. Worosz, K. Hogan, and J. Buchannan. “A comprehensive uncertainty evaluation of double-sensor conductivity probe. Progress in Nuclear Energy 136: 103741. https://doi.org/10.1016/j.pnucene.2021.103741.
  • Wang, D., J. Bao, Z. Xu, B. Koeppel, O. A. Marina, A. Noring, M. Zamarripa, A. Iyengar, E. Eggleton, D. T. Schwartz, A. Burgard and D. Miller. “Machine Learning Tools Set for Natural Gas Fuel Cell System Design.” ECS Transactions 103 (1): 2283.