Multiscale Modeling
Multiscale modeling integrates computational techniques across different length and time scales to understand and predict complex chemical and material behaviors. This approach supports innovations in biofuels, materials design, catalysis, and environmental technology by linking atomic-level phenomena to industrial-scale processes, enabling more efficient and sustainable solutions worldwide.
The department specializes in multiscale modeling, employing a variety of computational methods to analyze systems from the atomic to the process scale. At the smallest scale, density functional theory and metadynamics are used to uncover fundamental reaction mechanisms in solvent environments, such as those critical to biofuels production, enabling net zero renewable energy development. Molecular dynamics simulations reveal how microstructural changes and defects evolve in polycrystalline materials, impacting industries like aerospace and electronics where material reliability is vital. At intermediate scales, mesoscale modeling explores larger structural and reaction behaviors, providing insights into catalyst design and materials engineering. Finally, reactor and process-scale models simulate entire industrial operations to optimize performance in chemical manufacturing, pharmaceuticals, and environmental processes such as carbon capture and water treatment. By connecting these scales, multiscale modeling enhances our ability to design safer, more efficient technologies with significant global impact.
Possible Careers
- Process modelling engineer
- Reactor design engineer
- Environmental modeling engineer
Current Research
Predictive Science: Bridging Molecular Behavior to Industrial Scale
Enter the digital frontier of engineering by leveraging advanced mathematical and computational tools to predict and optimize complex physical and biological systems. Our researchers in Multiscale Modeling bridge the gap between atomic behavior and industrial reality, using molecular modeling and large-scale mathematical simulations to understand phase behavior and transport processes. Predictive capabilities are applied across diverse fields, including the simulation and control of biophysical cardiac systems, optimizing geothermal energy harvesting, and developing sophisticated transport-reaction models for complex chemical processes. The focus is on the integration of data analytics and machine learning with classical engineering principles to create predictive digital twins essential for advanced process design and monitoring.