The twelve selected projects fall under four areas of interest (AOIs). Each is described below:

AOI 1: Quantum for Energy Systems and Technologies

Harnessing Quantum Information Science for Enhancing Sensors in Harsh Fossil Energy EnvironmentsThe University of California (Riverside, CA) plans to utilize real-time quantum dynamics simulations and quantum optimal control algorithms to (1) harness near-surface nitrogen vacancy (NV) centers to detect chemical analytes in harsh fossil energy environments and (2) design optimally constructed electromagnetic fields for initializing these near-surface NV center spins for efficient sensor performance and detection. Together, these objectives will leverage quantum information science to enable new sensing modalities for the extremely sensitive monitoring (i.e., below classical measurement limits) of critical operating parameters of fossil energy infrastructures in harsh environments.

DOE Funding: $500,000; Non-DOE Funding: $0; Total Value: $500,000

Ultra-low Disorder Graphene Quantum Dot-Based Spin Qubits for Cyber Secure Fossil Energy Infrastructure The University of Texas at El Paso (El Paso, TX) aims to enhance the performance of graphene quantum dots (GQD) qubit platforms by significantly reducing local disorder for their application in highly secure quantum communication systems intended for use in cyber-resilient energy infrastructure. Researchers will (1) develop a simple strategy to define disorder-free GQDs in graphene nanoribbons (GNRs) and (2) extend the strategy to establish a high-fidelity, high-speed, and noise-free qubit array. The focus of the project is to develop a novel GQD fabrication technique by combining two novel techniques—nanotomy and oxidation lithography—to define GQDs with ultralow disorder and investigate their applicability and superiority as qubits.

DOE Funding: $499,546; Non-DOE Funding: $0; Total Value: $499,546

AOI 2: Novel Sensors and Controls for Flexible Generation

High-Accuracy and High-Stability Fiber-Optic Temperature Sensors for Coal Fired Advanced Energy SystemsMichigan State University (East Lansing, MI) plans to develop a revolutionary “gas”-based fiber-optic temperature sensor technology with the accuracy and long-term stability needed for temperature control and condition monitoring of next-generation coal-fired advanced energy systems. The challenges presented by the harsh environments in advanced coal-fired power plants will be addressed by a new temperature sensing mechanism. Instead of a “solid” that is being used by nearly all current sensors, the sensing material to be developed in this project is a “gas”. Gas properties can be inherently stable under extreme temperatures and pressures.

DOE Funding: $496,475; Non-DOE Funding: $0; Total Value: $496,475

Boride-based Ceramic Super-High Temperature Thermocouples in Harsh EnvironmentsMorgan State University (Baltimore, MD) aims to develop novel, durable, low-cost, and ceramic-based super high-temperature thermocouples (up to 2000°C) for use in high-temperature (750–1800°C) and pressure (1000 pounds per square inch and above) coal-based energy systems under high corrosion and erosion conditions. Thermocouple performance will be evaluated in oxygen, carbon oxide, and sulfide atmospheres at high pressure and temperature. In addition, the effects of heat flow, flow rate, and mass flux found in coal power generation on the performance of the thermocouples will be investigated. The physical behaviors and long-term stability of the thermocouples will be evaluated as well.

DOE Funding: $500,000; Non-DOE Funding: $0; Total Value: $500,000

Robust Heat-Flux Sensors for Coal-Fired Boiler Extreme EnvironmentsUniversity of Maryland (College Park, MD) aims to develop robust heat flux measurement systems capable of operating in the challenging high-temperature, corrosive environments within the boilers of coal-fired power plants. The heat-flux sensors will utilize thermoelectric effects to directly transduce the heat-flux input to analog electrical voltage signals and will be constructed from dedicated materials that can withstand oxidative atmospheres at temperatures from 700–1200°C and maintain adequate performance under these conditions for prolonged periods.

DOE Funding: $500,000; Non-DOE Funding: $0; Total Value: $500,000

Wireless High Temperature Sensor Network for Smart Boiler SystemsUniversity of Massachusetts, Lowell (Lowell, MA) plans to develop a new wireless high-temperature sensor network for real-time continuous boiler condition monitoring in harsh environments. The network will enable network-based automatic temperature sensing and data collection. When combined with artificial intelligence algorithms, the network will enable the construction of smart boiler systems with boiling condition management and optimization for significant energy savings and improved reliability. The network will consist of wireless radio frequency high-temperature sensors with integrated attached antennas for wireless internet-based continuous remote monitoring.

DOE Funding: $497,999; Non-DOE Funding: $0; Total Value: $497,999

Passive Wireless Sensors for Temperature and Corrosion Monitoring of Coal Boiler Components under Flexible OperationWest Virginia University (Morgantown, WV) proposes to develop an inexpensive, wireless high-temperature sensor to directly monitor both temperature and corrosion of metal components commonly used in coal-fired boilers in real time. This work will focus on fabricating and testing harsh environment, chipless radio-frequency identification (RFID) sensors that will operate between 25–1300°C in high steam and/or combustion gas environments. Sensor arrays in which each RFID sensor will be specifically designed with a specified frequency band to spatially differentiate the testing site on the metal specimen will also be evaluated.

DOE Funding: $500,000; Non-DOE Funding: $0; Total Value: $500,000

AOI 3: Machine Learning for Computational Fluid Dynamics

Development and Evaluation of a General Drag Model for Gas-Solid Flows via Physics-Informed Deep Machine Learning Florida International University (Miami, FL) plans to develop, test, and validate a general drag model for multiphase flows in assemblies of non-spherical particles via a physics-informed deep machine learning approach, using an artificial neural network (ANN) that can be used as a powerful tool in the fossil energy industry and many other industries in which gas-solid multiphase flows are encountered. Once implemented in computational fluid dynamics (CFD) code, such as NETL’s open-source CFD software MFiX, the model is expected to accurately predict a (non-spherical) particle’s drag coefficient and flow fields by simulating gas-particle flows for a wide range of parameters including Reynolds number, Stokes number, solid volume fractions, particle densities, particle orientations, and particle aspect ratios.

DOE Funding: $500,000; Non-DOE Funding: $0; Total Value: $500,000

Developing Drag Models for Non-spherical Particles through Machine LearningJohns Hopkins University (Baltimore, MD) aims to employ a unique combination of Direct Numerical Simulation (DNS) and high-resolution experiments, a capability to reduce the number of parameters, and machine learning-based data processing to develop a drag model that has high accuracy and breadth of regimes to which it can be applied. DNS and high-resolution experiments will provide comprehensive datasets of dense non-spherical particles free-falling in the air. The treatment of collision and the particular numerical scheme selected for DNS allow for an arbitrarily large density ratio between two phases, a challenge that remains unsolved in many other methods. The validated drag model will be implemented so that it can be connected to MFiX-DEM by leveraging existing software links between MFiX and TensorFlow.

DOE Funding: $500,000; Non-DOE Funding: $0; Total Value: $500,000

Unsupervised Learning Based Interaction Force Model for Nonspherical Particles in Incompressible FlowsThe Ohio State University (Columbus, OH) plans to develop a neural network-based force model for non-spherical particles immersed in an incompressible fluid phase from low to moderate Reynolds and low to high Stokes numbers, which can be coupled in a CFD / Discrete Element Method model (DEM). To achieve this objective, particle-resolved direct numerical simulation using the Lattice-Boltzmann method, coupled with immersed boundary method will be used to explore the geometrical (shape factor) and physical (viscosity, density, etc.) spaces to generate the sample points of the gas-solid coupling (drag and lifting) force. After model training and validation, the neural network will be linked to MFiX.

DOE Funding: $500,000; Non-DOE Funding: $0; Total Value: $500,000

A General Drag Model for Assemblies of Non-spherical Particles Created with Artificial Neural Networks The University of Texas at San Antonio (San Antonio, TX) plans to develop a more accurate ANN-based method for modeling the momentum exchange in fluid-solid multiphase mixtures to significantly improve the accuracy and reduce the uncertainty of multiphase numerical codes and, in particular, of MFIX, by developing and providing a general and accurate method for determining the drag coefficients of assemblies of non-spherical particles for wide ranges of Reynolds numbers, Stokes numbers, and fluid-solid properties and characteristics. The research team will achieve this goal by conducting numerical computations with a validated in-house CFD code and using artificial intelligence methods to develop an ANN that will be implemented in TensorFlow and linked with the MFIX code.

DOE Funding: $499,982; Non-DOE Funding: $0; Total Value: $499,982

AOI 4: Fast, Efficient, and Reliable Fossil Power with Integrated Energy Storage

Techno-Economic and Deployment Analysis of Fossil Fuel-Based Power Generation with Integrated Energy StorageUniversity of North Carolina at Charlotte (Charlotte, NC) plans to analyze four energy storage options (technologies) and six sub-options, and determine their impact on the operation and economics of a representative coal-fired power plant. A coal-fired steam plant was selected for the analysis because it may provide the greatest benefits from the integration of energy storage and can be used as a foundation for other fossil fuel facilities. The savings due to the integrated energy storage resulting from improved operating efficiency, improved system reliability, reduced carbon dioxide and other pollutant emissions, lower operating costs, more efficient plant participation in frequency control, and increased participation in the ancillary services market will be considered.

DOE Funding: $499,998; Non-DOE Funding: $0; Total Value: $499,998