Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Georgia Tech”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Real-time Measurements of Complex Transition Metal Oxide Nanostructure Growth (Year 3 - Final Technical Report-GaTech-MIT)

The project, a collaboration between the Ross Lab at MIT and the Filler Lab at Georgia Tech, aimed to combine in situ microscopy and spectroscopy measurements to answer fundamental questions about the physics and chemistry governing the bottom-up vapor-solid-liquid (VLS) growth of one-dimensional (1-D) functional oxides. This work supported one PhD student and 1 postdoc. One paper has been published and 5 others are in preparation. To date, this work has been presented at 5 conferences.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Metal nitride materials for solar-thermal ammonia production [Slides]

Solar Thermal Ammonia Production has the potential to synthesize ammonia in a green, renewable process that can greatly reduce the carbon footprint left by the conventional Haber-Bosch reaction. Co 3 Mo 3 N has been identified as a potential candidate for ammonia production. It is synthesized via oxide precursor synthesis followed by nitridation under 10% H 2 /N 2 . The synthesis method can be extended to other candidate nitrides. The Co 3 Mo 3 N → Co 6 Mo 6 N reduction is demonstrated on TGA with rapid kinetics. The formation of NH 3 is qualitatively observed, but not quantitatively determined. The material retains crystal structure, but no secondary phases are observed in XRD. Partial re-nitridation back to CMN331 of ~35% of max nitridation is observed. Reaction parameters in TGA differ from experimental conditions in the literature. Experiments at Georgia Tech better mimic re-nitridation conditions with more sensitive, quantitative analytical techniques (GC-MS). The ASU NH 3 synthesis/re-nitridation reactor is under development and will permit experiments (reduction/re-nitridation) under precisely controlled T, pH 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Autonomous System Subversion Tactics: Prototypes and Recommended Countermeasures

One of the fielding requirements for Advanced and Small Modular Reactors (AR/SMR) is the ability to support remote and autonomous operations. Autonomous Control Systems (ACS) are found on platforms such as Autonomous Space Vehicles, Cruise Missiles, and advanced driver-assistance systems. Each of these ACS implementations depends upon a set of decision support subsystems responsible for supporting Autonomous Mission Managers (names vary based upon field and author preferences). These Autonomous Mission Managers receive inputs from system sensors (e.g., LIDAR collection from an automobile travelling down a street; transients from a nuclear reactor), and perform a set of classifications (e.g., Red Traffic Light; Small Pedestrian at 10m; Load Rejection; Single Coolant Pump Trip), and then use these classifications in combination with recommendation algorithms to achieve platform goals (e.g., Stop the Vehicle at the Traffic Light, Avoid the Small Pedestrian; Trip the Reactor to prevent a Safety Event). The design, implementation, and fielding of an ACS capability will alter the cyber-attack surface such that existing risk management plans will need to be updated to include how to protect and defend against data-science and decision-support-system attack classes. These attack classes would include protection of the design and training environments where algorithm selection and testing and training data would be obvious attack vectors. These attack classes would also require an informed set of detection and response procedures to identify anomalous behaviors and document best practices for anomaly assessment and vulnerability mitigation and remediation. Last year we published a Cyber Threat Assessment Methodology for Autonomous and Remote Operations for AR/SMRs along with a companion publication on Cyber Attack and Defense Use Cases. The focus of the methodology was on describing and enumerating ACS processes, components, and functions such that security engineers could: evaluate subversion options against the target; identify threat actor attributes and capabilities derived from each subversion option; and identify security controls and response countermeasures. The Use Cases document offered detailed methodology examples including an assessment of a Military Base SMR, an Autonomous System Decision Loop, and implementation of AR/SMR Machine Learning algorithms. Our proposal at the end of last year was to focus on implementation of subversion prototypes related to the last Use Case area: AR/SMR Machine Learning (ML) Algorithms. We included six attack scenarios in our Use Cases paper: a Poisoning Attack against ML functions implemented using an FPGA; a Trojaning Attack against ML classifiers exploiting the excitability of Nuclear Engineers; a Backdooring Attack against ML Training environments to ensure persistence of an attack vector; a False Positive Evasion Attack against multi-factor Access Control Systems using clever inputs; an Inference Attack against ML models by an Insider with access to the Operational environment; and an Adversarial Reprogramming Attack against a Material Access Control Video Surveillance System. At the beginning of this year these six attack scenarios were provided to our research teams at Georgia Tech and Idaho State University and each team successfully implemented a subversion attack against a ML implementation to include transient misclassifications. While this is a notable outcome from this type of research, this paper offers the reader insight into not only how to structure and execute these types of attacks, but into the thought process behind how the researcher investigated the problem space, performed initial algorithm implementation, and the trial-and-error behind arriving at the successful subversion prototypes. We include in this paper a set of associated Scenarios on how these subversion prototypes could be implemented and an initial set of guidance for AR/SMR architects, Nuclear Regulators, and Cyber Defenders to implement awareness and defense capabilities into their current operational portfolios.

42 ENGINEERING↗
DOE OSTI2022

Development of Novel Materials for Direct Air Capture of CO 2 : MIL-101(Cr)-Amine Sorbents Evaluation Under Realistic Direct Air Capture Conditions (Final Report)

The overarching goal of this project is to evaluate the CO 2 adsorption properties of a small family of metal-organic framework (MOFs) materials functionalized with amines at sub-ambient conditions. Our goal is to develop capabilities to measure CO 2 adsorption at conditions more relevant to the weather of the planet. For this purpose, Georgia Tech is constructing a “sub-ambient adsorption facility” in partnership with ZCP Sorbent Development, LLC, aimed specifically at rapidly and deeply characterizing the performance of DAC candidate materials in this important operational range (adsorption at -20 to 20 °C and RH of 0-100%). Here, we use the sub-ambient lab instrumentation designed or adapted to study the behavior of the pristine metal organic framework (MOF) MIL-101(Cr) and the MOF in the presence of amines ranging from small molecules (e.g. TREN, tris(2-aminoethylamine)) to oligomers (e.g. PEI, poly(ethyleneimine)). Any DAC sorbent must be amenable to deployment in practical contactors for gas-solid contacting (traditional pellet-based fixed beds are impossible at scale). To this end, we developed and tested these DAC materials in the forms of composite polymer/MOF fibers and custom 3D-printed monolith structures containing MOF DAC sorbents. The proposed studies advance these materials from technology readiness level (TRL) 2 to TRL 3.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

A Case Study of Tunable White LED Lighting with Networked Lighting Controls (Emory University Cognitive Empowerment Program)

Emory University and Georgia Institute of Technology (Georgia Tech) partnered to create the Charlie and Harriet Schaffer Cognitive Empowerment Program (CEP) facility in northeast Atlanta. Together with the funders they are building a program to help individuals experiencing mild cognitive impairment (MCI) to maintain their physical and cognitive health, and independence as long as possible. In addition to applying effective strategies and therapies, the two research groups are investigating the responses of the MCI members to treatments that involve acoustical conditions, exercise and movement, and lighting changes that may support retention or relearning of skills. Care partners and family members receive support and instruction to improve home and work life, promoting joy, purpose, and wellness in the family groups. The lighting system uses tunable-white LEDs, employing luminaires with both warm and cool-color emitters that can be dimmed separately to produce any white correlated color temperature (CCT) between 2700 K and 6500 K. All luminaires were dimmable to achieve subdued or lively surroundings for different treatments, time-of-day, and mood. A central networked digital control system was employed to allow tuning of multiple spaces together (for example, bright, cool morning light could be programmed for extra stimulation, or lighting in all spaces at the end of the day could be reduced in both light output and CCT to promote relaxation and not interfere with the melatonin cycle of occupants). Almost all spaces were equipped with individual room control of dimming and color temperature with touch screens to allow users to tune the lighting as desired, but each room’s controls could also be specially programmed through the server in case the research staff were investigating lighting settings on learning, for example. The server incorporates a timeclock, and it is able to send signals to switch off all lighting after occupancy hours, or enable occupancy sensors to control the lighting. The bulk of the construction work was completed in January 2020. It was clear from an initial walk-through that although the lighting system produced the expected high light level with low-glare qualities of light, that there were issues and inconsistencies to be resolved. These were noted in an initial punchlist visit and expected to be resolved when the tech representatives from the agency visited with the electrical contractor in the following few weeks. What followed was 2.5 years of identifying unexpected lighting performance in terms of light output, color, scheduling, and occupancy. This report documents the issues encountered in the effort to get the lighting and controls systems to operate as intended. It concludes with guidance for design professionals and manufacturers to help avoid problematic complexity in future projects.

42 ENGINEERING↗
DOE OSTI2023

High Inlet Temperature Combustor for Direct-Fired Supercritical Oxy-Combustion

It is envisioned that supercritical CO 2 (sCO 2 ) electric power plant efficiencies can exceed 52% with 99% carbon capture using direct-fired oxy-combustion. Such efficiencies would be competitive with Natural Gas Combined Cycles (NGCC) and operate with nearly zero carbon emissions. To achieve high plant efficiencies, turbine inlet conditions must approach 1,200 °C at 250 bar. Such conditions, while desirable from a systems perspective, exceed the current state of the art in turbine design and materials qualification. The team of Southwest Research Institute ® (SwRI ® ), Georgia Institute of Technology (Georgia Tech), Spectral Energies, LLC (Spectral), GE Global Research (GE-GRC), and the University of Central Florida (UCF) sought to demonstrate an intermediate step toward these high efficiency plants with the development of a 1 MWth, subscale direct-fired oxy-fuel combustor. The efforts focused largely on the development of an auto-ignition based combustion system capable of providing the targeted 1200 °C and the pilot-scale plant to operate the combustor. While the project was stopped short of the commissioning and demonstration work, the advances made in this effort will reduce risks associated with chemical kinetics, thermal management, water separation, flue gas cleanup, materials selection, and corrosion in future demonstration efforts for the direct-fired oxy-fuel combustion cycles. The following report documents the design, analysis, and installation efforts completed during the project periods of performance.

03 NATURAL GAS↗
DOE OSTI2023

Advanced Algal Biofoundries for the Production of Polyurethane Precursors

The primary goal of the BEEPs project was to develop a process that could accelerate the development of algae as bioproduction platforms, from initial chemical product concept to an economically viable market supply. Under this program we elected to develop strains of algae that could generate polyurethane precursors, while simultaneously developing basic genetic tools to enable improved algal production systems. This program was specifically designed to incorporate National Laboratories as a means to utilize the expertise and facilities for new bio-production platforms. To that end, we designed a program to collaborate with the Agile BioFoundry at Lawrence Berkeley National Laboratory (LBNL) and computational platforms at Pacific Northwest National Laboratory (PNNL). In addition to these National Laboratory partners, we also had academic partners from UC Davis and Georgia Tech, as well as commercial partners Algenesis Materials and BASF. To achieve these goals, we initially focused on developing the genetic tools and high throughput screening technologies necessary to generate and assess production of polymer precursors (succinic acid) in algae and cyanobacteria, including advanced promoters and biosensors. In parallel, we computationally identified potential production bottlenecks and then used the developed genetic tools to increase production rates and yields. Constant feedback of data was used in conjunction with machine learning, high-throughput cell sorting, and synthetic biology, for additional targeted metabolic engineering. Multiple rounds of tool design, building, testing, and learning were supplied to partners at PNNL and LBNL to develop new models and tools that could expedite bioproduction platform development and increase yield performance. We targeted, and achieved, the FOA requirement yield metric of 20 g/L, as a milestone and deliverable from at least one of our engineered strains for the production of succinic acid.

09 BIOMASS FUELS↗
DOE OSTI2024

Robust Combined Heat and Hybrid Power (CHHP) for High Electrical Efficiency Cogeneration

Georgia Tech (Prime Recipient), the University of Texas at El Paso (UTEP) and the National Energy Technology Laboratory (NETL) investigated a hybrid fuel cell/ gas turbine system concept as a combined heat and hybrid power (CHHP) system for both robust and high power-to-process heat ratio cogeneration. The novelty of the proposed system entailed the distinct, elevated electrical efficiencies it maintains while simultaneously supporting a broad span of heating needs (e.g., supply temperatures) demanded across variable heat loads. The scope included: 1) leveraging a pioneering national lab facility configured for dynamic system operability development of hybrid fuel cell/gas turbine cycles; 2) enabling technology development to adjust and modulate the quality and quantity of thermal supply to bottoming heat loads via novel extreme temperature gas bypass valves. Hybrid fuel cell/gas turbine systems have primarily been reduced-to-practice in a constrained (e.g., initial proof-of-concept) manner and have still demonstrated considerable electrical efficiencies. However, these pre-pilot systems have focused upon electrical efficiencies and electrical power generation as the exclusive energy demand. Such hybrid systems had not been extensively researched or developed for flexible and variable operation consisting of both power and heat demands; however, these variable combined power and heat demands are characteristic of many types of manufacturers such as animal/poultry processing, bakeries and milk/flour/pastry manufacturing, textile mills, and electrochemical processing. Commercially, developing the system into a working combined heat and power system benefits these types of manufacturers by allowing them to meet their power and heat demands at a lower cost, higher efficiency, and/or through onsite generation. Therefore, the technical scope of this project was largely to study and facilitate these hybrid systems as combined heat and hybrid power (CHHP) systems that include dynamic operability for variable heat and power loads and/or grid dynamics for various types of manufacturers. Simulation results were used to predict the performance of the CHHP system and conceptually develop it to achieve desired dynamic operability. Experimentally, the primary goal was to design, manufacture, and experiment upon a high-temperature bypass valve. Experimental data included air mass flow rates through the valve orifice when the valve was changed to variable extent between fully closed and fully open. The experimental data was then used to create a semi-empirical computational model of the bypass valve. Concluded simulation goals for the research included developing computational heat exchanger models for the hybrid system inclusive of the bottoming heat exchanger and the recuperative heat exchanger, and then combining the computational recuperator model with the computational valve model. Afterwards, the computational models were then integrated to predict the dynamic operation of hybrid fuel cell/gas turbine cycles throughout a design space and reporting such. The scope stated in the preceding paragraph was packaged into five specific goals: 1) enabling the simulation of dynamic combined heat and power through the creation of computational, modular heat exchanger models; 2) simulation and exploration of the CHHP system’s performance by integrating the heat exchanger models with the national lab’s pre-existing hybrid system (computational) simulation, but without the recuperator bypass valve concept in order to initially determine how the (baseline) system behaves and can be controlled in order to meet variable heat and power demands; 3) development and initial deployment of the high-temperature recuperator bypass valve technology in order to confirm and characterize the approach; 4) usage of the experimental data for the valve to create a semi-empirical computational model for the bypass valve which could then be combined with the heat exchanger computational models; 5) repeat of the second task of simulating and exploring the system’s performance, but this time including the bypass valve to resolve its efficacy. Tasks were successfully completed, and the general notion of flexibly operating, high electrical efficiency CHHP was further corroborated. Supportive details are provided in the report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Modifications to Solar Titan-130 Combustion Systems for Efficient, High Turndown Operation

The project team of Southwest Research Institute® (SwRI®), Solar Turbines Incorporated (Solar), the Electric Power Research Institute (EPRI), the University of California, Irvine (UCI), and the Georgia Institute of Technology (Georgia Tech) investigated methods to allow higher efficiency part-load operation of a Solar Titan 130 gas turbine. The objective was to develop a low-emission combustion system capable of sustaining combustion and avoiding lean blowout during high turndown operation, which would allow the gas turbine to operate as efficiently as possible at part load. Currently, electric utility markets are beginning to experience substantial increases in renewable energy generation. Some of these renewable energy sources have highly variable output in an uncontrolled manner. In order to maintain grid stability, there is a need for power plants to ramp up power to the grid rapidly to make up for drops in renewable generation. This is often termed spinning reserve, but the size of this reserve may need to increase as renewable penetration into the electric utility market increases. Small combined heat and power (CHP) power plants provide a promising option for meeting this spinning reserve requirement. In order to operate in spinning reserve while still meeting the heat requirements for the CHP, the gas turbine needs to operate efficiently at very low loads. Efficient, high turndown operations in this engine are limited by the lean flammability limit of the premixed combustion system. This project sought enhance the lean operability range of the Titan 130 combustor. First, the project team participated in a brainstorming activity and ultimately selected two concepts to explore: fuel augmentation with hydrogen (H2) to improve the stability at lean operating conditions and modifications to the fuel nozzle to improve the emissions performance at lean operating conditions. Analytical and laboratory investigations were accomplished by UCI to investigate the efficacy of H2 addition at improving lean blow out (LBO) limits and the resulting emissions. These investigations used a variety of chemical reactor network (CRN) and CFD models, validated against laboratory data, to model the impact of H 2 and inform the experimental efforts accomplished by SwRI and Solar. Ultimately, both the CRN and CFD models yielded generally good agreement with the experimental data below a particular temperature threshold. Atmospheric tests of a full-scale T130 annular combustor were performed at SwRI facilities in San Antonio, Texas, to investigate the use of H 2 addition. For these tests, the T130 combustion system remained largely unchanged; minor modifications were performed to the fuel ducting to allow for the safe use of H 2 . The test ultimately demonstrated that the addition of H 2 to the fuel mixture significantly increased the AFR ratio at which the combustor could operate. This improvement to the LBO limit should allow for less use of compressor bleed and less throttling needed by the inlet guide vanes (IGV). This in turn could result in more efficient operation of the gas turbine at lower load points. The second modification explored in this work was a direct modification to the T130 injector. The project team hypothesized that modifications to the pilot of the T130 injector could provide lower emissions at high turn-down operations. These modifications were manufactured and explored by the team at Solar. High pressure rig tests, originally slated to occur at SwRI, were ultimately accomplished by Solar to maintain overall project budget and mitigate cost growth attributable to supply chain issues and inflation. The pressurized rig tests ultimately showed that the SwRI Project No. 18.24153 - DE-EE0008415 Page 2 Final Technical Report January 24, 2024 modifications did not significantly alter the performance of the combustion system at the high turn-down conditions; both the modified injectors and the baseline configuration exhibited elevated emissions comparted to the full-load operating condition. A final set of studies performed by EPRI investigated the benefit-cost of flexible CHP as well as a grid interconnection study for the California Independent System Operator (CAISO) grid. These studies considered: traditional CHP with no spinning reserve available for on-demand grid support, 50% flexible CHP where 50% of the machine’s capacity is consumed by on-site baseload operations while providing an additional 50% capacity for on-demand grid support, and 70% flexible CHP where 70% of capacity is consumed on-site by baseload operations and 30% is available for on-demand grid support. In all cases, the analyses showed a benefit-to-cost ratio greater than unity implying a positive net present value for all configurations. However, the traditional CHP showed the most economic benefit. These results are sensitive to several factors, many of which are not fully known and may vary over time. Thus site owners must be convinced that taking up the increased costs and risks from flexible CHP would be worth implementing. As the grid in California and across the country transition to incorporate larger renewable energy generation, flexible CHP can provide much needed operating reserves and dispatchability. Alternative fuel options, such as hydrogen blending and biofuels, may also lower carbon intensities of CHP. Flexible CHP should be examined in the evolving market to understand innovative business models, changes market rules and services, and new technologies.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Preparing Underrepresented Minorities for STEM Careers in Energy

The purpose of the grant was to increase the participation of underrepresented minorities and women in STEM disciplines that are critical for the energy industry. The grant was implemented by the Fort Valley State University’s (FVSU) Cooperative Developmental Energy Program (CDEP). FVSU-CDEP enrolled two cohorts of six academically talented freshmen students into its accelerated five-year 3+2 dual degree STEM program. In the 3+2 dual degree format, students enroll at FVSU for three years and earn bachelor’s degrees in biology, chemistry, or mathematics and transfer to a partnering university for two years to earn second bachelor’s degrees in engineering, geology/geophysics, or health physics. FVSU-CDEP partnering universities consist of Georgia Tech, Grand Valley State University, Penn State University, University of Alabama, University of Arkansas, University of Nevada-Las Vegas, and the University of Texas-Austin. Cohort I enrolled at FVSU fall semester of 2017 and cohort II enrolled fall semester of 2020. All six of the students in cohort I earned bachelor’s STEM degrees at FVSU and transferred to a partnering university. Five of the six who transferred to a partnering university earned the second bachelor’s degree. One student transferred from a partnering university to a non-partner university to continue pursuit of the second degree. All students in cohort I had internships, attended professional meetings, and made presentations. Cohort II enrolled the fall-semester of 2020 in the midst of COVID epidemic. Cohort II was only funded for one year, 2020/2021. The DOE Office of Economic Impact and Diversity informed the PI that there would not be any funding for the fifth and final year (2021/2022) of the project. All six of the students in cohort II earned bachelor’s STEM degrees from FVSU and transferred to one of CDEP’s partnering universities. One of the students in cohort II graduate with an MS degree spring of 2023. Currently, four are pursuing graduate degrees at partnering universities and one withdrew from a partnering school because health reasons. Students in cohort II opportunities to participate in internship were negatively impact by the COVID epidemic (2020-2022). Of the 12 scholarship participants, 8 were females. Fifty percent of the males pursued second degrees in Engineering while only twenty-five percent of the females chose Engineering as their second degree.

42 ENGINEERING↗
DOE OSTI2024

A Highly Efficient and Affordable Hybrid System for Hydrogen and Electricity Production (Final Project)

The pursuit of clean, secure, and sustainable energy has sparked significant interest in fuel cells for power generation and electrolyzer cells for hydrogen production. Among all types of fuel and electrolyzer cells, solid oxide cells (SOCs) have emerged as promising candidates due to their high efficiency and versatility. However, conventional oxygen-ion conductive SOCs face several challenges related to their performance and durability associated with their high-temperature operation (≥ 800 ºC). This has led to a growing interest in intermediate-temperature (≤ 650 ºC) proton-conducting solid oxide cells (p-SOCs) as potential alternatives. In collaboration between Phillips 66 and Georgia Tech, this project aims to achieve a 1 kW p-SOCs system to demonstrate the commercial viability of efficient SOC systems. This report addresses four primary areas and key challenges we overcame: (1) development of efficient and durable proton-conducting electrolyte (e.g., BaHf 0.1 Ce 0.7 Yb 0.2 O 3-δ ) and electrode/catalyst materials, (2) large area cell fabrication (10 x 10 cm 2 ), (3) scalable stack design and building (250 W and 1 kW), and (4) demonstration of a 1 kW prototype system. Notably, significant challenges faced during the large area cell fabrication process were addressed by achieving cell flatness, improving fabrication yield, and ensuring electrode/electrolyte interfacial adhesion. Stack designs were also developed, focusing on reducing contact resistance and optimizing stack components (e.g., sealants). These efforts resulted in the achievement of high performance and durability with promising outputs of 250 W and 1 kW. Furthermore, the integration of these stacks into a fuel-powered system was explored, with refinements made to heat management, as well as to pressure and heating conditions. The results demonstrated the potential applicability of our p-SOC technology in commercial energy storage and power generation systems. Additionally, the report discusses techno-economic analysis and a market transformation plan, aiming to evaluate and advance the commercial feasibility of this technology.

25 ENERGY STORAGE↗
DOE OSTI2024

Supporting ARPA-E Power Grid Optimization (Final Report)

Pacific Northwest National Laboratory (PNNL), Arizona State University (ASU), Georgia Institute of Technology (Georgia Tech), Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), The University of Texas at Austin (UT), and the University of Wisconsin-Madison (UW-M) supported the ARPA-E Grid Optimization (GO) Competition by providing a common problem formulation, data format, datasets, evaluation mechanism, scoring, rules, and results that resulted in the awarding of $\$9.24$ million dollars to teams from academia, industry, and national labs for solving three sets of increasingly difficult non-linear, security- constrained AC Optimal Powerflow (AC-OPF) optimization problems in order to increase the efficiency of the US Electric Grid. It is estimated that a 1% increase in efficiency can save $\$1$ billion. Current industry practices typically use a linear DC model (DC-OPF) in order solve the OPF problem within the time constraints of the operation schedule. The GO Competition challenges the best power engineers, mathematicians, and computer scientists to make possible operational decisions based on accurate physical models. To accomplish this, the GO Competition created a series of Challenges and funded teams to produce the best solver. Challenge 1 was to solve the security constrained Alternating Current Optimal Power Flow (ACOPF) problem. Challenge 2 extended that to by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment (UC). Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. While Challenge 3 was being developed, the entrants were invited to find better solutions to the Challenge 2 synthetic datasets with no restrictions on time, hardware, or algorithms. The Challenge 2 solutions turned out to be very good. Challenge 3 expanded the Challenge 2 problem further by using multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead. These problems included active bid-in demand and topology optimization. Together the Challenges used nearly 30 million CPU hours. Since each team was working on the same problem, using the same data, and running on the same hardware, fair comparisons could be drawn as to the best solver. The datasets were varied enough, however, that the best solver for one dataset was not necessarily the best at another, so cumulative scores were used. The process was managed by the PNNL maintained website https://GOCompetition.energy.gov, where Entrants could find information about the problem, the data, the rules, submit their solver for evaluation, and see the scores of all the competing teams on a Leaderboard. Interest was world-wide but only American teams were eligible for prizes. The Competition has produced 34 journal articles 115 papers and been cited over 500 times in the literature, including 12 dissertations (4 from foreign countries; Columbia (2), Germany, and Italy) and 3 from the DOE ExaScale project. Software developed by Pearl Street Technologies for Challenges 1 and 2 is now deployed by Southwest Power Pool (SPP) and Midcontinent Independent Service Operator (MISO). Other teams have received inquiries from venture capitalists. Google DeepMind has thanked the Competition for making the datasets developed for the Competition public. They are using it to train machine learning models. The larger datasets have billions of unknowns to be solved for, but only a small percent matter in the final solution. Knowing what unknowns are important can dramatically speedup the solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Synthesizing, Compounding, and Characterizing a Heat Labile Polyurethane Foam

Savannah River National Laboratory (SRNL) is part of the National Laboratory system within US Department of Energy (DOE) • Located within the DOE’s Savanah River Site near Aiken, SC • Operated by Battelle Savannah River Alliance (BSRA) LLC, a consortium of Battelle Laboratories, SRNL, University of South Carolina, Clemson University, South Carolina State University, University of Georgia, and Georgia Tech University.

Kranjc, Mark D.↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

Danovo Energy Solution's presented its paper named: Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events at the 2026 Georgia Tech Fault & Disturbance Analysis Conference. The full paper can be found at OSTI ID# 3169150 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danova Energy Solutions]↗
DOE OSTI2022

Real-time Measurements of Complex Transition Metal Oxide Nanostructure Growth (Final Technical Report)

This is the final technical report for a collaborative project between the Ross Lab at MIT and the Filler Lab at Georgia Tech. The project aimed to combine in situ microscopy and spectroscopy measurements to answer fundamental questions about the physics and chemistry governing the bottom-up vapor-solid-liquid (VLS) growth of one-dimensional (1-D) functional oxides. This work supported one PhD student and 1 postdoc. One paper has been published and 5 others are in preparation. To date, this work has been presented at 5 conferences.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multiphysics Modeling in Support of NASA Nuclear Thermal Propulsion Designs

This presentation provides and overview of INL and some of the work we do, but focuses on analysis and methods at INL being performed in support of NASA nuclear thermal propulsion. This presentation will be given at department seminars at Texas A&M University and at Georgia Tech.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Real-Time Health Monitoring for Gas Turbine Components Using Online Learning and High-Dimensional Data (Final Report)

Capital-intensive turbomachinery, such as gas turbines and combined cycle plants, are constantly being monitored for performance anomalies, faults, and physical degradation. Although these power-generating assets are equipped with hundreds of sensors, existing monitoring tools can only handle moderate-sized data. As a result, only a handful of aggregate metrics are used to monitor machine health. At the same time, developing advanced tools suitable for large datasets have been restricted by the lack of appropriate data. The objective of this proposal was to demonstrate a Big Data analytics framework for fault detection and diagnosis in gas turbine applications. We develop a predictive analytics framework methodology guided by these experimental data, industrial data from our collaborators, and physics-based models with engineering domain knowledge. Our analytics framework consists of four key components: (1) a data curation process that addresses data storage, data quality assessments, and integrity checks, (2) a feature engineering component that utilizes statistical methods and transformation algorithms guided by physics-based models to extract high-fidelity fault features that can be leveraged for fault detection and classifying fault severities, (3) a Machine Learning-based fault detection and diagnostics algorithms for detecting operational and hardware faults in the combustion and the turbines section. We utilize two industry-class gas turbine component test rigs to generate first of its kind data for critical gas turbine faults with varying severity levels. Advanced gas turbine test facilities will be interrogated using state-of-the-art instrumentation techniques to build fault signatures and data trends for key combustor and turbine faults. Data generated from a combustor test rig (Georgia Tech) and a turbine test rig (Penn State) during both normal operation and with seeded faults serve as the basis for the Big Data sets. The test conditions in the two test facilities include common, critical events that occur in the operation. Utilizing the combustor test rig, we examine two common combustor faults: lean blowout and centerbody degradation. For the turbine section we develop analytic models for monitoring cooling faults in the gas turbine.

20 FOSSIL-FUELED POWER PLANTS↗