Modern high-performance computers enable researchers to study energy problems of unprecedented complexity with predictive accuracy. This provides a clear picture even in situations where experimentation is expensive, time-consuming, or dangerous. In addition, cutting-edge modeling and simulation extends across multiple length and time scales and incorporates large amounts of existing data from experiment or theory. Cloud infrastructures and machine-learning for high-throughput screening allow researchers to quickly identify the most promising solutions for a given problem in energy-related fields and beyond.

Areas of Research Focus

At the University of Illinois, diverse and cutting-edge computational researchers join their forces to develop predictive models for energy solutions of the future. Among them are projects on:

• Improving existing or developing novel technologies and materials for solar-energy harvesting, thermoelectrics, and hydrogen storage.
• Increasing energy efficiency by improving processing capabilities or high-temperature properties of materials for automotive, aerospace, reactor, and other applications.
• Improving thermal-mechanical systems and energy management systems by advanced modeling.
• Energy analysis investigating current and future energy technologies, e.g. low-carbon infrastructure and potential energy-use reductions.
• Photoconversion efficiency of perovskite solar cell materials.
• Sodium-cooled fast reactors.
• Spent nuclear fuel.
• Process simulation.
• Fluid mechanics and mass transfer.
• Hydrocarbon biomarker analysis and modeling.
• Thermal maturation and basin modeling utilizing geochemical markers.