Multi-Physical Modeling

Optimal design & robust control through multi-physical modeling.

Our work involves the development of state-of-art modeling and simulation tools for complex, heterogeneous systems. We apply these models for the optimal design and robust control of a variety of systems including HVAC systems, zero-energy buildings, automobiles, and robotic systems.

  • Researchers

  • News & Events


    See All News & Events for Multi-Physical Modeling
  • Internships

    • MD1887: Optimization and control of xEV and electric aircraft

      MERL is seeking a motivated and qualified individual to conduct research in modeling, control, simulation and analysis of electric system involved in xEV and electric aircraft. The ideal candidate should have solid backgrounds in some of the following areas: modeling, control, and simulation of electrical systems (including generators, motors, power electronics and batteries), aerodynamics, mission analysis, flight dynamics, and multi-disciplinary system design optimization. Demonstrated experience in software/language such as Modelica or Matlab/Simulink/Simscape is a necessity. Knowledge and experience of CarSim, NPSS, SUAVE, and FLOPS is a definite plus. Senior Ph.D. students in automotive, aerospace, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

    • MD1894: Topology Optimization for Electric Machines

      MERL is seeking a motivated and qualified intern to conduct research on topology optimization of electrical machines. The ideal candidate should have solid background and demonstrated research experience in mathematical optimization methods, in particular in topology optimization, robust optimization, and sensitivity analysis. Hands-on coding experiences with the implementation of topology optimization algorithms and finite-element simulation are desirable. Knowledge and experience with electric machine principle, design and finite-element analysis is a strong plus. Senior Ph.D. students in related expertise are encouraged to apply. The start date is flexible and typical duration is about 3 months.

    • MS1903: Bayesian Optimization and MPC for Net-Zero Energy Buildings

      MERL is looking for a highly motivated and qualified candidate to work on Bayesian Optimization and predictive control for net-zero energy buildings. The ideal candidate will have a strong understanding of control, optimization, and/or machine learning with expertise demonstrated via, e.g., publications, in at least one of: Bayesian optimization, (stochastic) model predictive control, reinforcement learning, controller tuning; additional understanding of energy systems is a plus. Hands-on programming experience with numerical optimization solvers and Python is preferred. PhD students are strongly preferred, as an expected outcome of the internship is a publication in a high-tier venue. The minimum duration of the internship is 12 weeks; start time is flexible.


    See All Internships for Multi-Physical Modeling
  • Recent Publications

    •  Qiao, H., Laughman, C.R., "Performance Enhancements for Zero-Flow Simulation of Vapor Compression Cycles", American Modelica Conference, October 2022.
      BibTeX TR2022-137 PDF
      • @inproceedings{Qiao2022oct,
      • author = {Qiao, Hongtao and Laughman, Christopher R.},
      • title = {Performance Enhancements for Zero-Flow Simulation of Vapor Compression Cycles},
      • booktitle = {American Modelica Conference},
      • year = 2022,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2022-137}
      • }
    •  Wang, B., Lin, C., Inoue, H., Kanemaru, M., "Topological Data Analysis for Electric Motor Eccentricity Fault Detection", Annual Conference of the IEEE Industrial Electronics Society (IECON), October 2022.
      BibTeX TR2022-130 PDF
      • @inproceedings{Wang2022oct2,
      • author = {Wang, Bingnan and Lin, Chungwei and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {Topological Data Analysis for Electric Motor Eccentricity Fault Detection},
      • booktitle = {Annual Conference of the IEEE Industrial Electronics Society (IECON)},
      • year = 2022,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2022-130}
      • }
    •  Bortoff, S.A., Laughman, C.R., "Estimation: A Key Technology for Digital Twins", Mitsubishi Sanden Gihou, October 2022.
      BibTeX TR2022-127 PDF
      • @article{Bortoff2022oct,
      • author = {Bortoff, Scott A. and Laughman, Christopher R.},
      • title = {Estimation: A Key Technology for Digital Twins},
      • journal = {Mitsubishi Sanden Gihou},
      • year = 2022,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2022-127}
      • }
    •  Qiao, H., Laughman, C.R., "A Low-Order Model for Nonlinear Dynamics of Heat Exchangers", International Refrigeration and Air Conditioning Conference (IRACC), September 2022.
      BibTeX TR2022-123 PDF
      • @inproceedings{Qiao2022sep2,
      • author = {Qiao, Hongtao and Laughman, Christopher R.},
      • title = {A Low-Order Model for Nonlinear Dynamics of Heat Exchangers},
      • booktitle = {International Refrigeration and Air Conditioning Conference (IRACC)},
      • year = 2022,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2022-123}
      • }
    •  Qiao, H., Laughman, C.R., "Dynamic Modeling of Oil Transport in Vapor Compression Systems", International Refrigeration and Air Conditioning Conference (IRACC), September 2022.
      BibTeX TR2022-122 PDF
      • @inproceedings{Qiao2022sep,
      • author = {Qiao, Hongtao and Laughman, Christopher R.},
      • title = {Dynamic Modeling of Oil Transport in Vapor Compression Systems},
      • booktitle = {International Refrigeration and Air Conditioning Conference (IRACC)},
      • year = 2022,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2022-122}
      • }
    •  Zhan, S., Wichern, G., Laughman, C.R., Chong, A., Chakrabarty, A., "Calibrating building simulation models using multi-source datasets and meta-learned Bayesian optimization", Energy and Buildings, DOI: 10.1016/​j.enbuild.2022.112278, Vol. 270, pp. 112278, September 2022.
      BibTeX TR2022-072 PDF
      • @article{Zhan2023jan,
      • author = {Zhan, Sicheng and Wichern, Gordon and Laughman, Christopher R. and Chong, Adrian and Chakrabarty, Ankush},
      • title = {Calibrating building simulation models using multi-source datasets and meta-learned Bayesian optimization},
      • journal = {Energy and Buildings},
      • year = 2022,
      • volume = 270,
      • pages = 112278,
      • month = sep,
      • doi = {10.1016/j.enbuild.2022.112278},
      • url = {https://www.merl.com/publications/TR2022-072}
      • }
    •  de Castro, M., Wang, Y., Vanfretti, L., Wang, H., Liu, D., Bortoff, S.A., Takegami, T., "Modeling, Simulation and Control of Turboelectric Propulsion Systems for More Electric Aircrafts using Modelica", AIAA Aviation Forum, DOI: 10.2514/​6.2022-3873, June 2022, pp. 3873.
      BibTeX TR2022-087 PDF
      • @inproceedings{deCastro2022jun,
      • author = {de Castro, Marcelo and Wang, Yebin and Vanfretti, Luigi and Wang, Hongyu and Liu, Dehong and Bortoff, Scott A. and Takegami, Tomoki},
      • title = {Modeling, Simulation and Control of Turboelectric Propulsion Systems for More Electric Aircrafts using Modelica},
      • booktitle = {AIAA Aviation Forum},
      • year = 2022,
      • pages = 3873,
      • month = jun,
      • doi = {10.2514/6.2022-3873},
      • url = {https://www.merl.com/publications/TR2022-087}
      • }
    •  Deshpande, V., Laughman, C.R., Ma, Y., Rackauckas, C., "Constrained Smoothers for State Estimation of Vapor Compression Cycles", American Control Conference (ACC), June 2022.
      BibTeX TR2022-063 PDF
      • @inproceedings{Deshpande2022jun,
      • author = {Deshpande, Vedang and Laughman, Christopher R. and Ma, Yingbo and Rackauckas, Chris},
      • title = {Constrained Smoothers for State Estimation of Vapor Compression Cycles},
      • booktitle = {American Control Conference (ACC)},
      • year = 2022,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2022-063}
      • }
    See All Publications for Multi-Physical Modeling
  • Videos