SOFTWARE FRAMEWORKS
- MOOSE: The Multiphysics Object-Oriented Simulation Environment.
- BISON: A Finite Element-Based Nuclear Fuel Performance Code.
- PETSc: Portable, Extensible Toolkit for Scientific Computation.
- MKL: Math Kernel Library for Intel-Based Systems.
- libMesh: An adaptive finite element library.
- GitHub: The world's leading software development platform.
- Gmsh: An open source 3D finite element mesh generator with a built-in CAD engine and post-processor.
FUNDED PROJECTS
Project 6: Advanced Computational Center for Entry System Simulation (ACCESS)
Sponsor: NASA
Role: Co-PI
Period: 10/2021-09/2026
Brief: Entry, descent, and landing technologies must continue to improve to meet the challenges of placing large payloads on other worlds, such as Mars. Accurate modeling and simulation of atmospheric entry systems are critical for the design and planning of these missions. The ACCESS institute will advance the analysis and design of NASA entry systems by developing a fully integrated, interdisciplinary simulation capability. ACCESS will focus on thermal protection systems, which protect spacecraft from aerodynamic heating, as well as prediction of the extreme environments experienced during entry. It will develop game-changing capabilities through the use of high-fidelity, validated physics models. This advancement will be enabled by innovative numerical algorithms, high-performance computing, and uncertainty quantification methods, with the goal of enabling computational entry system reliability assessments. The project is led by Dr. Iain Boyd of the University of Colorado Boulder.
Project 5: Microstructure-Based Thermomechanical Homogenization Using a Meshfree Framework
Sponsor: NASA Kentucky EPSCoR
Role: PI
Period: 01/2021-12/2021
Brief: Composite materials have been widely used in aerospace industry. Computational multiscale models can be applied to study the performance of composite materials at macroscale while accounting for microstructural effects from mesoscale. The goal of this project is to perform computational homogenization to determine microstructure sensitive effective thermomechanical properties including material failure properties of thermal protection materials using a novel nonlocal meshfree framework.
Project 4: AI Institute: Planning: Novel Neural Architectures for 4D Materials Science
Sponsor: NSF
Role: Co-I
Period: 09/2020-08/2023
Brief: High-fidelity predictive modeling of complex materials under extreme conditions (high temperature, high stress, corrosive environment etc.) is crucial for accelerating material design and optimization to address the pressing challenges in our world. This project will aim to leverage both fundamental and use-inspired artificial intelligence (AI) research, coupled with cutting-edge experiments, to revolutionize and transform traditional materials science and engineering (MSE). The novel approach, rooted in the fundamental principle in MSE, that microstructure controls properties, focuses on the development of novel neural architectures that naturally capture the physical causal relations across key microstructural features at multiple length and time scales for predictive modeling and optimal material design. The methodologies and experimental frameworks for constructing novel physics-based learning models developed in this project will be applied to a variety of compelling problems in complex material systems including ceramics, metals and metallic alloys, composites, and porous materials. It is expected that this project will impact many areas including aerospace, microelectronics, petroleum industry, and consumer products.
Project 3: Developing Computational Tools for Modeling Fracture of Electrode Particles Used in Lithium-Ion Batteries
Sponsor: University of Kentucky Energy Research Prioritization Partnership
Role: PI
Period: 06/2020-12/2020
Brief: Layered lithium transition-metal oxide materials are the most promising candidates for lithiumion battery (LIB) positive electrodes. In particular, lithium nickel manganese cobalt oxide (NMC) has received considerable attention due to its greater specific capacity compared to conventional lithium cobalt oxide. To fully understand the microcracking behavior of NMC particles, it is critical to develop an agglomerate level computational model which can take into account the particle level microstructural effects. The goal of this project is to develop predictive computational tools to model the cracking of NMC particles in LIB applications.
Project 2: Development of Peridynamics Capabilities for Modeling of Metallic Nuclear Fuels
Sponsor: Los Alamos National Laboratory
Role: PI
Period: 06/2019-04/2020
Brief: Metallic nuclear fuels exhibit extremely complex behavior during irradiation, which include but are not limited to, thermal expansion, swelling due to gas and solid products of nuclear fission, phase transformation, creep and plastic flow, damage and cracking. Based on current available modeling and simulation capabilities of peridynamics module in MOOSE framework for oxide fuel, the overall goal of this project is to further develop the peridynamics capabilities for modeling of metallic nuclear fuels.
Project 1: Multi-physics Simulations of RIA and LOCA Events with Post-Event Fuel Migration Experiments
Sponsor: Laboratory Directed Research & Development (LDRD) program, Idaho National Laboratory
Role: Co-PI (10/2016-08/2018), University PI (02/2019-09/2019)
Period: 10/2016-09/2019
Brief: The nuclear fuels performance is heavily affected by the fracture of fuel pellets. The complex network of cracks causes the fuel outside diameter to expand, enhancing the thermal conductance across the pellet/cladding gap and decreasing the thermal conductivity within the fuel. This project aims to develop MOOSE-based capabilities needed to model fragmentation and large relative motion and interaction of fuel fragments in a Loss of Coolant Accident scenario using Peridynamics theory.
SPONSORS
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