At the Center for Methane Emissions Research and Innovation (CMERI), we leverage Digital Twin technology to transform how we understand, measure, and mitigate methane emissions. A Digital Twin is a dynamic virtual model that mirrors real-world physical systems by integrating sensor data, simulations, and machine learning together. By creating high-fidelity digital replicas of the emission source, its local environment (e.g. industrial facilities, natural gas infrastructure, etc.) and atmospheric conditions, we enable real-time analysis, predictive insights, and optimized decision-making. At CMERI, we believe that Digital Twins are a game-changing tool for methane emissions research and other industries with similar problems. By advancing modeling frameworks, behavior-matching techniques, and smart sensing integration, we are paving the way for data-driven, real-time, and scalable methane management solutions.
Our research is focused on three key research thrusts:
Digital Twin Frameworks and Modeling -- Developing robust digital twin frameworks requires an interdisciplinary approach that combines fluid dynamics, atmospheric modeling, and data-driven simulations. Our research focuses on: (1) Scalable digital twin architectures that integrate real-time and historical data for methane emission tracking. (2) Physics-based models that simulate methane transport, dispersion, and interactions with environmental variables. (3) Computationally efficient solvers to enable large-scale simulations for real-time methane monitoring applications. By refining digital twin models, we enhance their predictive accuracy, allowing industries to anticipate methane leakage scenarios and optimize mitigation strategies.
Behavior Matching for Emission Detection -- Understanding and predicting methane emissions require more than just raw data—it requires behavioral insights. Insights that are gained through a parameter estimation like process (i.e. behavior matching) of the Digital Twin output to the observed output from the physical system. This problem is a high dimensional one, and the ability to do this in real-time is a challenge. Therefore, we are working on developing advanced behavior-matching methodologies that: (1) Correlate observed methane plumes with potential sources using machine learning and inverse modeling techniques. (2) Characterize emission patterns under varying environmental and operational conditions. (3) Improve detection reliability by distinguishing between transient leaks, continuous emissions, and background methane levels. These methodologies strengthen our ability to match real-world emissions to their digital twin counterparts, improving the accuracy of emission inventories and regulatory reporting.
Smart Sensing for Real-Time Analytics and Feedback -- Smart sensing is essential for making digital twins not just predictive but also adaptive. Our research focuses on: (1) Real-time data assimilation from drone-based, stationary, and mobile methane sensors. (2) Edge computing and AI-enhanced analytics to provide immediate insights on emission dynamics. (3) Self-optimizing sensor networks that adapt placement and sensitivity based on emission patterns. By integrating smart sensing technologies, our digital twins not only enhance methane source identification but also improve the measurement and quantification process itself. This leads to more accurate emissions reporting, better mitigation strategies, and a deeper understanding of methane’s environmental impact.
Digital Twin Frameworks and Modeling
Updates
- Fall 2024 12/5/2024 -- Dr. Hollenbeck gave a Digital Twin lecture from the environmental sensing perspective to the ME190 (ME152) course on Digital Twins, "Smart Environmental Sensing with Digital Twins"
Current Projects
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See PI YangQuan Chen's work developing methane quantification strategies utilizing machine learing based big data and Digital Twins. For more information on this task see the Mission page.
Selected Publications
- Hollenbeck, D., Zulevic, D., & Chen, Y. (2021). Advanced leak detection and quantification of methane emissions using suas. Drones, 5(4), 117.
- Hollenbeck, D., Zulevic, D., & Chen, Y. (2022, June). A modified near-field gaussian plume inversion method using multi-sUAS for emission quantification. In 2022 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1620-1625). IEEE.
- Hollenbeck, D., Zulevicl, D., & Chen, Y. (2022, November). Single and Multi-sUAS Based Emission Quantification Performance Assessment Using MOABS/DT: A Simulation Case Study. In 2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) (pp. 1-5). IEEE.
- Hollenbeck, D., & Chen, Y. (2020, September). Characterization of ground-to-air emissions with sUAS using a digital twin framework. In 2020 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 1162-1166). IEEE
Behavior Matching
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Smart Sensing
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last updated 3/17/2025 by Dr. Derek Hollenbeck
