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


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  • Internships

    • MS1571: Data-based Dynamic Modeling of Vapor Compression Systems

      MERL is seeking a motivated and qualified individual to conduct research in dynamic modeling of vapor compression systems. Knowledge of data-based modeling techniques such as neural network and support vector regression is required. Experience in working with thermo-fluid systems is preferred. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. Senior Ph.D. students in applied mathematics, chemical/mechanical engineering and other related areas are encouraged to apply. The expected duration of the internship is 3 months and the start date is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • MS1466: Modelica-Based Control of HVAC Equipment

      MERL seeks a highly motivated intern to develop an interface between real-time control systems that are implemented in the Modelica language, and laboratory HVAC equipment that is controlled by Labview. The control algorithms are developed using our Modelica library of HVAC components, and are realized natively in the Modelica language using the Synchronous Library. They are run in real-time on a PC using the Modelica Device Drivers library, and communicate with the Labview system via UDP. The intern would be responsible for developing professional-grade code to mature this interface, and then conduct experiments to test new control algorithms in our laboratory. Expertise using software development tools, such as Microsoft Visual Studio and network protocols such as UDP, is necessary. Experience with Modelica is strongly preferred. Knowledge and experience of vapor compression systems is also strongly preferred. Knowledge of control theory, including classical feedback and finite state machines, along with related laboratory experience is required. On-site employment is preferred, although it may be possible to conduct this work remotely. Students enrolled in a Masters or Ph.D. degree program of study are encouraged to apply. The internship is expected to be 3-6 months in duration, preferably in the fall or winter, 2020.

    • MS1523: Transfer Learning for HVAC Modeling and Control

      The Multiphysical Systems (MS) team at MERL is seeking a highly motivated intern to conduct research on data-driven modeling and control of HVAC systems, with special emphasis on transfer learning. The ideal candidate is enrolled in a PhD program and is pursuing research in learning and control. The ideal candidate will have experience in (one or more of) parameter estimation of dynamical systems, transfer learning or meta-learning, Bayesian optimization, and must be fluent in Python. The expected duration of the (virtual) internship is 3 months in Summer 2021; start-date is flexible (after April 2021). This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.


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  • Recent Publications

    •  Shin, K.-H., Wang, B., "Semi-Analytical Modeling for Interior Permanent Magnet Synchronous Machines Considering Permeability of Rotor Core", International Conference on Electrical Machines and Systems (ICEMS), November 2020.
      BibTeX TR2020-149 PDF
      • @inproceedings{Shin2020nov,
      • author = {Shin, Kyung-Hun and Wang, Bingnan},
      • title = {Semi-Analytical Modeling for Interior Permanent Magnet Synchronous Machines Considering Permeability of Rotor Core},
      • booktitle = {International Conference on Electrical Machines and Systems (ICEMS)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-149}
      • }
    •  Zhang, S., Ye, F., Wang, B., Habetler, T.G., "Few-Shot Bearing Anomaly Detection via Model-Agnostic Meta-Learning", International Conference on Electrical Machines and Systems (ICEMS), November 2020.
      BibTeX TR2020-151 PDF
      • @inproceedings{Zhang2020nov2,
      • author = {Zhang, Shen and Ye, Fei and Wang, Bingnan and Habetler, Thomas G},
      • title = {Few-Shot Bearing Anomaly Detection via Model-Agnostic Meta-Learning},
      • booktitle = {International Conference on Electrical Machines and Systems (ICEMS)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-151}
      • }
    •  Wang, B., Hotta, A., "Contactless Eddy Current Sensing for Carbon Fiber Reinforced Polymer Defect Detection", Biennial IEEE Conference on Electromagnetic Field Computation (CEFC), November 2020.
      BibTeX TR2020-148 PDF
      • @inproceedings{Wang2020nov2,
      • author = {Wang, Bingnan and Hotta, Akira},
      • title = {Contactless Eddy Current Sensing for Carbon Fiber Reinforced Polymer Defect Detection},
      • booktitle = {Biennial IEEE Conference on Electromagnetic Field Computation (CEFC)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-148}
      • }
    •  Bhamidipati, S., Kim, K.J., Sun, H., Orlik, P.V., "Artificial Intelligence-Based Distributed Belief Propagation and Recurrent Neural Network Algorithm for Wide-Area Monitoring Systems", IEEE Network, DOI: 10.1109/MNET.011.1900322, Vol. 34, No. 3, pp. 64-72, May 2020.
      BibTeX TR2020-058 PDF
      • @article{Bhamidipati2020may,
      • author = {Bhamidipati, Sriramya and Kim, Kyeong Jin and Sun, Hongbo and Orlik, Philip V.},
      • title = {Artificial Intelligence-Based Distributed Belief Propagation and Recurrent Neural Network Algorithm for Wide-Area Monitoring Systems},
      • journal = {IEEE Network},
      • year = 2020,
      • volume = 34,
      • number = 3,
      • pages = {64--72},
      • month = may,
      • doi = {10.1109/MNET.011.1900322},
      • url = {https://www.merl.com/publications/TR2020-058}
      • }
    •  Zhang, S., Wang, B., Kanemaru, M., Lin, C., Liu, D., Habetler, T., "Model-Based Analysis and Quantification of Bearing Faults in Induction Machines", IEEE Transactions on Industry Applications, DOI: 10.1109/TIA.2020.2979383, Vol. 56, No. 3, pp. 2158-2170, May 2020.
      BibTeX TR2020-059 PDF
      • @article{Zhang2020may,
      • author = {Zhang, Shen and Wang, Bingnan and Kanemaru, Makoto and Lin, Chungwei and Liu, Dehong and Habetler, Thomas},
      • title = {Model-Based Analysis and Quantification of Bearing Faults in Induction Machines},
      • journal = {IEEE Transactions on Industry Applications},
      • year = 2020,
      • volume = 56,
      • number = 3,
      • pages = {2158--2170},
      • month = may,
      • doi = {10.1109/TIA.2020.2979383},
      • issn = {1939-9367},
      • url = {https://www.merl.com/publications/TR2020-059}
      • }
    •  Zhang, S., Zhang, S., Wang, B., Habetler, T., "Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review", IEEE Access, DOI: 10.1109/ACCESS.2020.2972859, Vol. 8, pp. 29857-29881, March 2020.
      BibTeX TR2020-034 PDF
      • @article{Zhang2020mar,
      • author = {Zhang, Shen and Zhang, Shibo and Wang, Bingnan and Habetler, Thomas},
      • title = {Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review},
      • journal = {IEEE Access},
      • year = 2020,
      • volume = 8,
      • pages = {29857--29881},
      • month = mar,
      • doi = {10.1109/ACCESS.2020.2972859},
      • issn = {2169-3536},
      • url = {https://www.merl.com/publications/TR2020-034}
      • }
    •  Bortoff, S.A., "Modeling Contact and Collisions for Robotic Assembly Control", American Modelica Conference 2020, DOI: 10.3384/ECP2016954, March 2020, pp. 54-63.
      BibTeX TR2020-032 PDF
      • @inproceedings{Bortoff2020mar,
      • author = {Bortoff, Scott A.},
      • title = {Modeling Contact and Collisions for Robotic Assembly Control},
      • booktitle = {American Modelica Conference 2020},
      • year = 2020,
      • pages = {54--63},
      • month = mar,
      • doi = {10.3384/ECP2016954},
      • url = {https://www.merl.com/publications/TR2020-032}
      • }
    •  Laughman, C.R., Bortoff, S.A., "Nonlinear State Estimation with FMI: Tutorial and Applications", American Modelica Conference 2020, March 2020.
      BibTeX TR2020-031 PDF Software
      • @inproceedings{Laughman2020mar,
      • author = {Laughman, Christopher R. and Bortoff, Scott A.},
      • title = {Nonlinear State Estimation with FMI: Tutorial and Applications},
      • booktitle = {American Modelica Conference 2020},
      • year = 2020,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2020-031}
      • }
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