Sustainability Modeling of Integrated Energy-Environment Systems

Cheng – University of Idaho

Research Overview

Potential environmental impacts, carbon footprints, and economic feasibility are critical parameters for establishing a sustainable energy system. Securing resource and energy sustainability requires combining various resources for energy conversions. This approach reduces environmental impacts while enhancing efficiency and feasibility of integrated energy systems.

Computation tools will evaluate carbon footprints, environmental impacts, and economic feasibility of energy production from various resources based on their conversion technologies. Results will be extended to inform energy policy analysis as well as addressing related environmental and energy justice issues — helping discover sustainable energy solutions across energy, resources, economics, and environment perspectives.

Student Research Experience

Students will learn the correlation among natural resources, energy conversion, and environmental burden. They will learn the principle of sustainability modeling in the “Environmental Life Cycle Assessment” course and be mentored to use several simulation tools, including SuperPro Designer, GREET, and Open LCA to analyze different energy conversion pathways and interpret results.

Working with scientists in INL’s Digital Reactor Technology and Development Department using FORCE (Framework of Optimization of ResourCes and Economics) software will enrich students’ experiences. The participant will also be engaged in brainstorming sessions to extrapolate potential solutions to future energy systems.

Skills & Techniques

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    Russell – University of Idaho

    Research Overview

    Atomistic simulations are increasingly valuable for industrial needs to predict thermodynamic and kinetic properties of systems at the nanoscale. Design and fitting of interatomic potentials for application to nuclear materials is also an area of fundamental interest. Simulation and modeling can provide understanding of mechanisms that occur far from equilibrium, which can be difficult to directly observe by experiment.

    Accurate prediction of melting temperature and elastic properties at finite temperatures are challenging because interatomic potentials are typically fit to zero temperature electronic structure results. New interatomic potentials based on explainable artificial intelligence concepts are needed that combine physics based functional forms with advanced machine learning techniques to fit parameter sets.

    Student Research Experience

    Working in collaboration with INL’s Collaborative Computing Center, students will learn fundamentals of computational atomistic simulation and modeling. Students will obtain a user account on the NSF ACCESS cyberinfrastructure program and utilize the Bridges-2 supercomputer hosted by the Pittsburgh Supercomputing Center (PSC).

    Students will learn to use high performance computers, the LAMMPS code, visualization with VMD code, scripting, and post processing techniques. They will also learn fundamentals of machine learning to fit parameters and the physics behind interatomic potential design. Advanced students may learn programming languages and parallel computing techniques.

    Skills & Techniques