Computational Capabilities for Predictions of Interactions at the Grain Boundary of Refractory Alloy Email Page
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CFD Research Corporation
Aluminum diffusion on Fe3 Al(100) surface. The ab initio<br/>trained ReaxFF force fields can also illustrate the kinetics<br/>of atomistic-scale processes with fair accuracy.
Aluminum diffusion on Fe3 Al(100) surface. The ab initio
trained ReaxFF force fields can also illustrate the kinetics
of atomistic-scale processes with fair accuracy.
Website:  CFD Research Corporation
Award Number:  FE0005867
Project Duration:  10/01/2010 – 09/30/2014
Total Award Value:  $1,249,996.00
DOE Share:  $999,971.00
Performer Share:  $250,025.00
Technology Area:  Plant Optimization Technologies
Key Technology:  Computational Materials Modeling

Project Description

The researchers will develop and validate ReaxFF potentials capable of naturally accounting for various types of grain boundaries and segregants (substitutional and interstitial) that will offer a compromise between high-level QM description and computational speed. This project will demonstrate the feasibility of the approach for analyzing alumina (Al2O3)-based refractory degradation at grain boundaries by evaluating predictions using existing ReaxFF potentials. Researchers will develop ReaxFF potentials for predicting interactions of chromia/alumina-based refractories with sulfur (S), iron oxide (FeO), and Al2O3. The resulting ReaxFF potentials will be validated against existing research literature for properties of interest. Finally, the proposed computational capabilities involving ReaxFF potentials and the MD simulator will be demonstrated to provide insight into the mechanism of segregation at the grain boundaries of refractories used in slagging gasifiers, where coal is converted to fuel gas under extreme conditions.

Project Benefits

The development of novel materials remains slow because it is driven by a trial-and-error experimental approach. Atomistic Molecular Dynamic (MD) design will accelerate the development of novel materials through the prediction of mechanical properties, corrosion, and segregation resistance of these materials. The success of MD simulations depends critically on the modeling of interatomic potentials. Existing potential models typically are not able to account for reactions, are not applicable for high-temperature simulations, or are only useful for modeling nano-scale clusters whose properties are different from bulk material properties.

The National Energy Technology Laboratory (NETL) has partnered with CFD Research Corporation and Pennsylvania State University (PSU) to address these deficiencies through development, demonstration, and validation of computational capabilities for predictive analysis of interactions at the grain boundary of refractory alloys currently being developed to withstand the high temperatures, pressures, and corrosive environments of advanced power plants. The simulation capabilities will include quantum mechanics (QM) -based reactive force field (ReaxFF) potentials integrated into an open-source MD code including the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) simulator developed by Sandia National Laboratories.

The anticipated impact of the project is accelerated development of new materials that can improve the efficiency of fossil fuel systems. Computer-aided development of materials will significantly accelerate time-to-market for new economically viable materials to be used in fossil fuel systems.

Goal and Objectives

The goal of the project is to provide a capability to assess degradation mechanisms and improve the reliability of refractory alloys for coal gasification and related processes. Specific objectives include (1) demonstrating the feasibility of the approach, (2) developing and validating ReaxFF potentials for chromia and Al2O3 based refractories, and (3) determining the mechanisms of grain boundary segregation in slagging gasifier refractories and identifying approaches to limit this segregation.

Contact Information

Federal Project Manager 
Patricia Rawls:
Technology Manager 
Susan Maley:
Principal Investigator 
Alex Vasenkov:


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