Adam Donato

Bridging the Continuum Between Molecular Modeling and Computational Fluid Dynamics for Adsorption Processes

This project is focused on developing a multi-scale computational model for adsorption that bridges the continuum from molecular modeling to bulk fluid flow.  Some specific areas that this model will see applications in are adsorption-based carbon capture, improved catalytic activity of gas for automobiles, and subsurface transport.  The primary objective is to understand the physics behind adsorption so that the principles developed can be widely applied. This model will serve to fill a gap in our understanding of adsorption in a manner that can only be accomplished through computational techniques.  The majority of research into adsorption has relied on either Nano-scale theory or macro-scale empiricism. Unfortunately, Nano-scale theory is not well-suited to analyze the macro-scale heat and mass transfer and macro-scale empirical measurements of adsorption are notoriously difficult due to the fact that adsorption is primarily an internal process. A multi-scale computational adsorption model will resolve this problem by combining the Nano and macro scales.

Development of Real-Time Predictions for the Flow and Reaction Dynamics Inside of the Hostile Environment of a Gasification System

Integrated gasification combined cycles offer an economical and competitive means of producing electricity and thermal energy with significantly reduced emission levels.  But, the gasification process itself is inherently hostile to sensors, and thus difficult to monitor and optimize.  This project will focus on determining real-time activity inside of the gasification system by comparing actual sensor reading to predictions from a computational transient transport model.  This "virtual sensor" data will then be used to improve the performance on the gasification system in real-time.

Publications

• C-12-01: A. Donato, R. Pitchumani, and M. Shahnam, "A Computationally Efficient Approach to Un-certainty Quantification in Multiphase Systems," NETL 2012 Conference on Multiphase Flow Science, NRCCE, May 22-24, 2012, Morgantown, WV.
• C-13-03: A. Donato and R. Pitchumani, “Quantifying Uncertainty in Computational Knowledge Engineering Rapidly (QUICKER),” Paper No. V&V2013-2235, Presented at theASME 2013 Verification and Validation Symposium, TRACK 2 Uncertainty Quantification, Sensitivity Analysis, and Prediction, May 22-24, 2013, Las Vegas, NV.
• C-13-09: A. Donato and R. Pitchumani, “A Comparison of QUICKER with Conventional Meta-modeling Methods for Input Uncertainty Propagation,”NETL 2013 Multiphase FlowScience Conference, August 6-7, 2013, Morgantown, WV.
• C-14-10: A. Donato and R. Pitchumani, “Stochastic, quantum-based approach to molecular modeling of zeolites,” Paper No. 25598, Session on Basic Research in Colloids,Surfactants and Nanomaterials, 247th ACS National Meeting, March 16-20, 2014, Dallas, TX.
• J-14-07: A. Donato and R. Pitchumani, “QUICKER: Quantifying Uncertainty In Computational Knowledge Engineering Rapidly—A Rapid Methodology for UncertaintyQuantification,” Powder Technology, 216,54–65, 2014.