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DOE OSTI2014

Smart Grid Information Clearinghouse (SGIC)

Since the Energy Independence and Security Act of 2007 was enacted, there has been a large number of websites that discusses smart grid and relevant information, including those from government, academia, industry, private sector and regulatory. These websites collect information independently. Therefore, smart grid information was quite scattered and dispersed. The objective of this work was to develop, populate, manage and maintain the public Smart Grid Information Clearinghouse (SGIC) web portal. The information in the SGIC website is comprehensive that includes smart grid information, research & development, demonstration projects, technical standards, costs & benefit analyses, business cases, legislation, policy & regulation, and other information on lesson learned and best practices. The content in the SGIC website is logically grouped to allow easily browse, search and sort. In addition to providing the browse and search feature, the SGIC web portal also allow users to share their smart grid information with others though our online content submission platform. The Clearinghouse web portal, therefore, serves as the first stop shop for smart grid information that collects smart grid information in a non-bias, non-promotional manner and can provide a missing link from information sources to end users and better serve users’ needs. The web portal is available at www.sgiclearinghouse.org. This report summarizes the work performed during the course of the project (September 2009 – August 2014). Section 2.0 lists SGIC Advisory Committee and User Group members. Section 3.0 discusses SGIC information architecture and web-based database application functionalities. Section 4.0 summarizes SGIC features and functionalities, including its search, browse and sort capabilities, web portal social networking, online content submission platform and security measures implemented. Section 5.0 discusses SGIC web portal contents, including smart grid 101, smart grid projects, deployment experience (i.e., use cases, lessons learned, cost-benefit analyses and business cases), in-depth information (i.e., standards, technology, cyber security, legislation, education and training and demand response), as well as international information. Section 6.0 summarizes SGIC statistics from the launch of the portal on July 07, 2010 to August 31, 2014. Section 7.0 summarizes publicly available information as a result of this work.

Rahman, Saifur↗
DOE OSTI2024

Heterogeneous Integration Technologies for High-temperature, High-density, Low-profile Power Modules of Wide Bandgap Devices in Electric Drive Applications (Final Technical Report)

The goal of this project is to develop packaging technologies for making high-temperature, high-density, and low-profile wide-bandgap (WBG) power electronics modules for electric drives. These modules are aimed at enabling the DOE’s University Consortium to reach its 2025 inverter targets of ≥ 100 kW/L and ≤ 2.7 $/kW. The specific objectives are to: design and fabricate SiC half-bridge power modules with double-sided cooling and parasitic inductances < 5 nH, heat flux density > 400 W/cm 2 , and working junction temperature of 200 o C; design, fabricate, and deliver a gate driver with double-sided cooled modules for the construction of a 100 kW/L inverter at Oak Ridge National Lab; and design and prototype intelligent gate drivers with integrated current sensor and a low-profile DC-DC power supply with air-core transformer for testing power modules at 200 o C junction temperature. We followed an iterative technical approach of design, simulation, fabrication, and testing of various versions of modules, current sensors, and power supply. The state-of-the-art silicon carbide devices rated at 1.2 kV and 149 A were packaged by sintered-silver bonding on an aluminum nitride direct-bond-copper substrate for high thermal conductivity, high working temperature, and high joint reliability. Porous silver posts were used to interconnect the device’s source pads to the other direct-bond-copper substrate for low mechanical stresses, ease of manufacturing, and double-sided cooling. A current sensor based on package parasitic inductance was developed to measure switching current. A dynamic feedback scheme was developed to compensate the effect of parasitic resistance and temperature variation. A constant-current class-E dc-dc converter with air-core transformer was developed. Air-core transformer was used due to the unavailability of magnetic core at high temperatures. Gate driver and power supply were integrated with the double-side cooled, high temperature SiC power modules for testing the modules at 200 o C junction temperature. Double-pulse and continuous testing of the integrated technologies validated the design and fabrication of the three component technologies. Throughout the project, we overcame the challenge for design verification caused by low prototyping yield, which then helped train the graduate students, the future workforce, to learn the engineering know-how for low-cost manufacturing of reliable products. Below is a summary of the major accomplishments of this project: development of a prototyping process for fabricating double-side cooled (1200 V, 149 A) SiC phase-leg modules capable of working to 200 o C Tj; simulation and experimental verification of the improvement of thermo-mechanical reliability of the double-side cooled SiC phase-leg module by using rigid encapsulant; design and experimental validation of a current sensor based on package parasitic inductance and a compensation solution for eliminating the effect of parasitic resistance; design and experimental validation of a low-profile power supply with six-output air-core transformer for gate driver; functional demonstration of a SiC phase-leg module integrated with its gate driver, current sensor, and an air-core power supply at 200 o C Tj in a double-pulse switching test setup and Buck converter continuous test setup; successful completion of six PhD and two MS students who are or will work at Apple Inc., Tesla Inc., Wolfspeed Inc., Microchip Inc., Monolithic Power Systems Inc., and LG Magna Inc.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2020

Patch Plate Materials Compatibility Assessment

Lunar dust proved to be a greater problem during the Apollo missions than was originally anticipated. The highly angular, charged dust particles stuck to seals, radiators, and visors; clogged mechanisms; and abraded space suits. As reported by Apollo 12 astronaut Pete Conrad "We must have had more than a hundred hours suited work with the same equipment, and the wear was not as bad on the training suits as it is on these flight suits in just the eight hours we were out.". Dust clinging to surfaces was also transport-ed into habitable spaces leading to lung and eye irritation of the astronauts. The Apollo astronauts were on the Lunar surface less than 24 hours and experienced many dust related problems. With the Artemis program, we are planning longer stays on the surface, with more activities that have the potential to put the astronauts and equipment in contact with greater quantities of Lunar dust. The success of these missions will depend on our understanding of material interactions with Lunar dust and the development of ways to mitigate dust effects in cases where exposure to dust will lead to failure of components, unacceptable loss of power or thermal control, unacceptable loss of visibility, or health issues. Through the Lunar Surface In-novation Initiative (LSII), we are initiating a Patch Plate Materials Compatibility Assessment project. The overall goal of the three year project is to develop passive approaches to mitigate Lunar dust adhesion to surfaces for technologies that are currently at TRL levels 2-3 to bring them to TRL level 5 through ground-based assessment, culminating in a demonstration flight experiment on a Commercial Lunar Payload Services (CLPS) lander in 2022-2023. This paper discusses the detailed technical objectives and approach for this project. References: Gaier, J.R. "The Effects of Lunar Dust on EVA Systems During the Apollo Missions," NASA/TM-2005-213610/REV1, (2005), Apollo 12 Technical Crew Debriefing, December 1, 1969, pp. 10-54.

Miller, S. K. R.↗
DOE OSTI2025

Searches for New Long-Lived Particles and Upgrade to the ATLAS Inner Detector (Final Technical Report)

The search for new fundamental particles is one of the defining goals of the Large Hadron Collider (LHC). The discovery of the Higgs Boson by the ATLAS and CMS collaborations provided the capstone of the Standard Model of particle physics, but outstanding questions remain. Why does the Higgs boson have a mass of 125 GeV when its natural mass would be many orders of magnitude larger? Is there a universal symmetry which unites all three forces described by the Standard Model? Can that symmetry be extended to include gravity? Is dark matter, evidenced by astronomical observations, made of a particle that interacts via Standard Model forces with the rest of matter? Together, these motivations provide compelling arguments that new physical processes await discovery. This project addressed some outstanding questions about the fundamental particles and their interactions with the ATLAS experiment at the Large Hadron Collider. In particular, the project improved the discovery potential for new, long- lived particles produced via electroweak processes in proton-proton collisions and set world-leading limits on their existence for certain values of their potential mass and lifetime. To achieve this, the project developed new data analysis methods, developed new triggers to select events with new long-lived particles during data-taking of the ATLAS experiment, and analyzed the largest proton–proton collision dataset ever produced. The project also supported significant development of the data acquisition software for the upgrade to the ATLAS inner detector, the Inner TracKer (ITk). The upgrade of the ATLAS inner detector is essential to the success of the entire Phase II physics program on ATLAS. Personnel supported by the project provided support for integration, assembly, and testing of the inner two layers of the ITk pixel system during its prototype and pre-production phase. Four PhD students and two post-doctoral scholars were supported by the grant and received invaluable scientific training as part of the research endeavor. The students and postdocs gained essential professional skills in the areas of advanced data analysis techniques, statistical analysis of data and simulation, programming in C++ and Python, hardware and instrumentation development, and presentation and collaboration skills. Additionally, approximately ten undergraduate students supported through other funding sources participated in research activities synergistic with the goals of this project, receiving essential mentorship from the personnel supported by this project.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Corrective Action Investigation Plan for Corrective Action Unit 541: Small Boy Nevada National Security Site and Nevada Test and Training Range, Nevada with ROTC 1

Corrective Action Unit (CAU) 541 is co-located on the boundary of Area 5 of the Nevada National Security Site and Range 65C of the Nevada Test and Training Range, approximately 65 miles northwest of Las Vegas, Nevada. CAU 541 is a grouping of sites where there has been a suspected release of contamination associated with nuclear testing. This document describes the planned investigation of CAU 541, which comprises the following corrective action sites (CASs): 05-23-04, Atmospheric Tests (6) - BFa Site; 05-45-03, Atmospheric Test Site - Small Boy. These sites are being investigated because existing information on the nature and extent of potential contamination is insufficient to evaluate and recommend corrective action alternatives (CAAs). Additional information will be obtained by conducting a corrective action investigation before evaluating CAAs and selecting the appropriate corrective action for each CAS. The results of the field investigation will support a defensible evaluation of viable CAAs that will be presented in the investigation report. The sites will be investigated based on the data quality objectives (DQOs) developed on April 1, 2014, by representatives of the Nevada Division of Environmental Protection; U.S. Air Force; and the U.S. Department of Energy (DOE), National Nuclear Security Administration Nevada Field Office. The DQO process was used to identify and define the type, amount, and quality of data needed to develop and evaluate appropriate corrective actions for CAU 541. The site investigation process also will be conducted in accordance with the Soils Activity Quality Assurance Plan, which establishes requirements, technical planning, and general quality practices to be applied to this activity. The potential contamination sources associated with CASs 05-23-04 and 05-45-03 are from nuclear testing activities conducted at the Atmospheric Tests (6) - BFa Site and Atmospheric Test Site - Small Boy sites. The presence and nature of contamination at CAU 541 will be evaluated based on information collected from field investigations. Radiological contamination will be evaluated based on a comparison of the total effective dose at sample locations to the dose-based final action level. The total effective dose will be calculated as the total of separate estimates of internal and external dose. Results from the analysis of soil samples will be used to calculate internal radiological dose. Thermoluminescent dosimeters placed at the center of each sample location will be used to measure external radiological dose. Appendix A provides a detailed discussion of the DQO methodology and the DQOs specific to each CAS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART)

This report contains key findings from a project titled Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART), which was carried out through a collaborative effort of a team of researchers from Texas A&M Engineering Experiment Station, Temple University, and Quanta Technology, LLC. The in-kind support came from OSIsoft (acquired by AVEVA), which provided their PI Historian software to demonstrate the use case of streaming PMU data. The first section of the report describes the project goals and objectives related to the development of Machine Learning (ML) models capable of detecting and classifying events by processing phasor measurements captured in the field by Phasor Measurement Units (PMUs). The data for this study was contributed by the utilities/ISOs from the Western and Eastern interconnects and ERCOT, further referred to as Interconnect B (IC B), Interconnect A (IC A), and Interconnect C (IC C), respectively. The approach that the BDSMART Research Team proposed and the key research tasks defined by the team are outlined in this section. The next section describes the technical approach. We first discuss the data constraints related to the PMU measurements and data interpretation constraints imposed by the data contributors. They provided neither the topological information of the grid nor PMU placement locations and captured recorded data at very few locations in the system with the reporting rate of either 30 or 60 fps. The recordings are mostly positive sequence voltage, frequency, and ROCOF, and in some limited cases, three-phase voltages and currents. We then reflect on the bad data issues that stem from poor recording practices and vague definitions of the PMU status bits to supposedly be used for bad data identification. Finally, the data discovery points to imprecise time stamps with incomplete event start/end time, as well as inconsistent and incomplete event labeling, which combined make the implementation of the data models using supervising learning quite challenging. Following the data discovery study, we hypothesize that because the IC B data has the most complete labels, we should focus our model development on that data and then test it on data from other interconnects. We also define the common metrics used to evaluate the results from the ML algorithm tests. We concluded this section by summarizing the common ML models we used and explaining how we implemented and tested them. The issues from this section are expanded in the Training Dataset Report from this project. The final section of this report deals with the accomplishments and conclusions. As the accomplishments, we formulate the problem we are solving and what is achieved by solving the problem. We then reflect on each of the analytics tools we developed and point out the performance of each tool when applied to solving the mentioned problems. We reference this work for further details to the papers we published on each tool. In the conclusions, we give recommendations on how to improve future PMU recording practices to facilitate the ML algorithm implementation and guidance for the future standardization work aimed at clarifying the ambiguities associated with the PMU status bits. We finally list future tasks that can bring about further improvements in the proposed algorithms. The issues from this section are expanded in the Training, and Test Dataset Report filed at the project completion date.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

OSIRIS-REx, Returning the Asteroid Sample

This paper addresses the technical aspects of the sample return system for the upcoming Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) asteroid sample return mission. The overall mission design and current implementation are presented as an overview to establish a context for the technical description of the reentry and landing segment of the mission.The prime objective of the OSIRIS-REx mission is to sample a primitive, carbonaceous asteroid and to return that sample to Earth in pristine condition for detailed laboratory analysis. Targeting the near-Earth asteroid Bennu, the mission launches in September 2016 with an Earth reentry date of September 24, 2023.OSIRIS-REx will thoroughly characterize asteroid Bennu providing knowledge of the nature of near-Earth asteroids that is fundamental to understanding planet formation and the origin of life. The return to Earth of pristine samples with known geologic context will enable precise analyses that cannot be duplicated by spacecraft-based instruments, revolutionizing our understanding of the early Solar System. Bennu is both the most accessible carbonaceous asteroid and one of the most potentially Earth-hazardous asteroids known. Study of Bennu addresses multiple NASA objectives to understand the origin of the Solar System and the origin of life and will provide a greater understanding of both the hazards and resources in near-Earth space, serving as a precursor to future human missions to asteroids.This paper focuses on the technical aspects of the Sample Return Capsule (SRC) design and concept of operations, including trajectory design and reentry retrieval. Highlights of the mission are included below.The OSIRIS-REx spacecraft provides the essential functions for an asteroid characterization and sample return mission: attitude control propulsion power thermal control telecommunications command and data handling structural support to ensure successful rendezvous with Bennu characterization of Bennus properties delivery of the sampler to the surface, and return of the spacecraft to the vicinity of the Earth sample collection, performed by the Touch-and-Go Sample Acquisition Mechanism (TAGSAM), to acquire a regolith sample from the surface Earth re-entry and SRC recovery. Following sample collection, OSIRIS-REx drifts away from Bennu until the Asteroid Departure Maneuver is commanded on March 4, 2021, sending OSIRIS-REx on a ballistic return cruise to Earth. No additional large deterministic maneuvers are required to return the SRC to Earth. During the cruise, tracking and trajectory correction maneuvers (TCMs) are performed as necessary to precisely target the entry corridor. As OSIRIS-REx approaches Earth, the reentry plans are reviewed starting about a year before arrival, and preparations begin. The spacecraft is targeted away from the Earth until 7 days before entry. The final two trajectory correction maneuvers bring the spacecraft on target toward the Utah Test and Training Range (UTTR), with sufficient time for contingency resolution. The SRC releases 4 hours prior to atmospheric entry interface and, using the Stardust capsule heritage design, employs a traditional drogue and main parachute descent system for a soft touchdown.

reentry↗
NASA NTRS2005

Youth Technology Academy

One of the most important aspects of the YTA Program is that students, working with Tri-C instructors, NASA engineers, and technicians, begin to build relationships with professionals with whom they might not normally have contact. These professionals serve as role models for students and help them to apply math, science and problem-solving skills in real time to construct a competition-ready robot, allowing them to experience the satisfaction and challenges of an engineering or technical career by working diligently to solve problems that may never have been thought possible to solve. Transcending school boundaries, YTA is available to all students who are interested and qualified but who may otherwise never get an opportunity to participate because their school does not offer a robotics program. YTA fills an educational void by offering an exciting engineering and technology experience to a greater number of students than have been able to participate in the past. Our students have been working hard and see the long-term rewards for their efforts. The YTA Team 1270 was a finalist at the 2005 FIRST Robotics Florida Regional and took home second- place honors. Mark Poljak, nominated by the students of YTA Team 1270, received the Woodie Flowers Award at the FIRST 2005 Buckeye Regional competition. This award celebrates effective communication in the art and science of engineering and design. The YTA Team was also invited, as one of fifty-four teams, to participate in this year s FIRST VEX Robotics tournament representing Cuyahoga Community College and NASA GRC (Team 26), held at the FIRST Robotics National Competition in Atlanta, GA. Because of our efforts, community colleges from around the country have taken notice and have asked us to become part of the executive board of the RoboEducators, whose mission is to bring technology training to high school students in preparation for the future workforce.

Source record↗
DOE OSTI2026

Multiphysics and Multiscale Simulation Methods for Electromagnetic Energy Assisted Fossil Fuel to Hydrogen Conversion (Final Scientific/Technical Report)

This report summarizes the technical accomplishments of the four-year research project “Multiphysics and Multiscale Simulation Methods for Electromagnetic Energy Assisted Fossil Fuel to Hydrogen Conversion” (Award No. DE-FE0032092), conducted at Howard University and the University of Houston (subawardee) from September 2021 to August 2025. The project successfully achieved all four major objectives: 1. 3D Structural Characterization – Developed 3D optical imaging and mechanical sectioning methods to characterize catalyst distribution and support morphology in nickel foam substrates. Successfully reconstructed 3D geometries and imported them into COMSOL Multiphysics for electromagnetic simulations. 2. EM Hotspot Simulation – Created all-frequency stable electromagnetic formulations and 3D nodal discontinuous Galerkin (NDG) methods for coupled electromagnetic-thermal-fluid problems in multiscale catalytic media. Demonstrated stable solutions from DC to microwave frequencies. 3. Multiphysics Coupling – Developed multiscale simulation methods coupling FEM electromagnetic solvers with thermal transport equations. Reactive molecular dynamics (ReaxFF MD) simulations were performed to investigate catalytic reaction mechanisms at the atomistic level. Demonstrated electromagnetic-thermal co-simulation capabilities for porous catalyst structures. 4. System Optimization – Designed and optimized EM-assisted catalytic systems using nickel foam and carbon foam structures, demonstrating significant temperature increases due to microwave heating. Observed and characterized plasma generation in carbon fiber catalysts. Investigated multiple reaction chamber geometries for improved microwave energy deposition. The project produced significant scientific contributions including 15+ peer-reviewed publications, trained multiple Ph.D. students and undergraduate researchers, and advanced the understanding of microwave-assisted hydrogen production from fossil fuels.

08 HYDROGEN↗
NASA NTRS2022

Ideas on the NASA Senior Review and Mission Extension Process

Extended mission budgets fund a significant part of our community, particularly young scientists who in a relatively short time are exposed to a broad range of experiences and opportunities available in space science and other STEM fields. In particular, mission-funded science offers a relatively stable funding stream that enables meaningful mentoring efforts and interactions, including apprenticeships ranging from project management, to mission operations, to data processing and analysis, to hardware and software development, to STEM-related education and outreach. It moreover provides professional contacts that fuel diversity in mission leadership and in the science and technical workforce at-large. Last but not least, it sustains the scientific productivity of NASA’s missions, which constitute major national investments meriting their full exploitation. Budgets cuts, however, are exercising pressure on the science and operations of the extended mission fleet despite being only a small portion of the overall Division budget. The history of Heliophysics has demonstrated that maintaining the operation of missions beyond their prime phase invariably leads to a much deeper knowledge of the original mission science goals, together with new applications often not foreseen during their original design. Their operation as part of the Heliophysics System Observatory (HSO)---a coordinated, 1eliophysics, multipoint observing system of the heliosphere, is a prime example. Some of the first activities undertaken by the Parker Solar Probe and Solar Orbiter missions required recreating the larger context of their new observations by using the diversity of HSO mission capabilities. The marginal cost of maintaining this valuable, working science resource, in the form of both continuing high quality data access and expertise, is small compared to the cost of the original missions or of new missions to achieve similar objectives, making the operation of extended missions, the best “science per dollar” investment in Heliophysics. We argue that reducing both extended mission operations and science budgets will have a ripple effect across the community, including reducing employment and training opportunities for young scientists, to lowering the quality/impact of the Heliophysics Systems Observatory. Instead, we suggest that the proposal process for mission extension should be re-examined. We offer several ideas for reducing costs of extended missions toward maintaining science output and streamlining the management process.

A. Vourlidas↗
DOE OSTI2023

Controlling Molecular Structure and Spin with Multiconfigurational Quantum Chemistry (Final Technical Report)

For many first-row transition metal complexes, structure-property relationships can be obtained from high-level molecular geometry optimizations and subsequent electronic structure studies. The project funded under this award utilized newly implemented fully internally contracted (FIC) nuclear gradients for extended multi-state (XMS) complete active space second-order multireference perturbation theory (CASPT2) to explore geometry changes in first-row transition metal coordination complexes. At the start of the project, only one full geometry optimization using FIC-CASPT2 analytical gradients had been reported (J. Chem. Theory Comput., 2016, 12 (8), 3781), and many open-questions regarding the performance and achievable accuracy in applying such computations to larger complexes persisted. Key deliverables in the report include the implementation of the numerical Hessian and subsequent vibrational analysis in the BAGEL program package. This includes both full Hessian vibrational analysis (FHVA) and a partial Hessian vibrational analysis (PHVA). The latter of which allows us to reduce the significant cost of computing the vibrational frequencies in a large molecule by focusing on the modes of highest interest. The gradient code also proved useful in the development of an approach to improve the Hubbard-U correction by removing bias in predicting spin-splitting in Fe(II) complexes via plane-wave density functional theory (DFT). Finally, we showed for three families of complexes (spin-crossover (SCO) complexes, metallocorroles complexes, and those with metal-metal bonds) that CASPT2 can result in good molecular geometries and established best practices in undertaking this work. We are now using this approach in collaborative projects with experimental groups, which would not have been possible at the start of this project. A common theme also arose through this work that static and dynamic correlation must be recovered in a balanced way to yield quantitative results. By systematically varying how both effects were included, we were able to make key chemical insights. This work supported the training of two postdoctoral scholars, one graduate student, and one undergraduate student in applying high-level wavefunction based methods to challenging systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Targeted Technology Transfer to US Independents

The Petroleum Technology Transfer Council (PTTC) is a unique not-for-profit network that focuses on transferring Exploration and Production (E&P) technology to the domestic oil and natural gas producing industry. PTTC connects producers, technology providers and innovators, academia, research and development (R&D) consortiums and governments. Local affordable workshops delivered by Regional Lead Organizations (RLOs), which are typically a university or geological survey, are a primary tool. PTTC also maintains a website network, issues a national newsletter, provides a column in a major trade publication, and exhibits at major industry events. It also encourages industry to ask technology-related questions, striving to find relevant answers that will save questioners significant time. Working since late 1993, the PTTC network has a proven track record of providing industry with technology insights they can apply. Volunteers at the regional and national level provide key guidance regarding where to focus technical effort and help connect PTTC with industry. At historical funding levels, PTTC had been able to hold well more than 100 workshops per year, drawing 6,000+ attendees. As funding decreased in the early 2000s, the level of activity decreased and PTTC sought a merger with the American Association of Petroleum Geologists (AAPG), becoming an AAPG-managed organization at the start of FY08. This relationship with AAPG was terminated by mutual consent in May 2011 and PTTC once again operates independently. Chris Hall, California continued to serve as Chairman of the Board of Directors until December 2013. At the time PTTC reorganized into a RLO led organization with Mary Carr and Jeremy Viscomi as co-Executive Directors. Jerry Anderson became the Chairman of the PTTC Board of Directors and Chris Hall continues to serve on the Board. Workshop activity stabilized at 55-65 workshops per year averaging 3,100 attendees. FY14 represented the fifth year in a multi-year contract with the Department of Energy (DOE) for providing technology transfer services. This report summarizes activity and results during for five years, FY10 through FY14. In FY12 changes occurred in responsibilities of consultants serving HQ, because funding was reduced below the threshold level of $500,000 audits were no longer required and consultant time was reduced on the primary contract. Contracts for Permian Carbon Capture Utilization and Storage (CCUS) training, and providing tech transfer services to the Research Partnership to Secure Energy for America (RPSEA) provided work that enabled HQ to retain services of regular consultants. Both CCUS and RPSEA were five year contracts with PTTC, and providing services for these DOE funded contracts provided synergy for PTTC and the oil and gas industry. With further decreases in DOE funding the regions conducted workshops with no PTTC funding starting in June FY11. Since 2011 the number of workshops has declined from 79 in FY10 and FY11 to 49 in FY12, and risen to 54 in FY13 and 63 in FY14. The attendee's numbers dipped slightly below 3,000 per year in FY 10, FY12, and FY13, but rose to over 3,800 in FY 11 and 3105 in FY14. Quantitative accomplishments: PTTC has maintained its unique structure of a national organization working through Regional Lead Organizations (RLOs) to deliver local, affordable workshops. During the contract period PTTC consolidated from 10 to five regions to increase efficiency, and because no active RLO's would be maintained in the Central and Eastern Gulf Coast regions. RLO's for the regions are located at: Eastern - West Virginia University, (Illinois Geol. Survey., W. Michigan Univ. FY10-12); Midwest created in FY13 - Illinois Geological Survey, W. Michigan University; Midcontinent - University of Kansas, expanded to Houston, TX (2013-14); Rocky Mountain - Colorado School of Mines; Texas/SE New Mexico (FY10-FY11) - Bureau of Economic Geology, Univ. of Texas at Austin; West Coast - Conservation Committee of California O&G Producers.

02 PETROLEUM↗
DOE OSTI2024

Novel ceramic capacitors with ultrahigh energy density and efficiency (Final Technical Report)

Antiferroelectric ceramics are a special class of material that have shown great potential as the dielectric in electrical capacitors due to their high energy- and power-density. During each charge-discharge cycle, the ceramic undergoes transformation to a ferroelectric phase and resumes its antiferroelectric phase. The hysteresis associated with the transitions leads to a mediocre energy efficiency and service lifetime of antiferroelectric capacitors and, hence, their almost absence in commercial products. Under the support of this research project, we first formulated a universal lattice-compatibility theory that included electrostatic polarization energy along with elastic energy and thermal energy to understand the origin of the hysteresis in antiferroelectric oxides. Guided by this compatibility theory, we conducted high-throughput density functional theory (DFT) calculations to assess chemical modifiers and their effect on crystal structures of 400+ PbZrO 3 -based compositions. Down-selected compositions were experimentally validated for their suppressed hysteresis and higher energy efficiency. The verified low-hysteresis compositions were then expanded to an antiferroelectric ceramic library with nearly 500 new compositions (more than 1,500 samples) using high-throughput experiments involving ceramic synthesis and property screening. The large quantity of data generated (theory and experimental) in these tasks were processed by machine-learning techniques and identified trends were fed to the next iteration. In the end, we successfully discovered four compositions with near-zero hysteresis, yielding a world-record energy efficiency of 98.2% at an energy density of 3.0 J/cm 3 . Furthermore, our antiferroelectric ceramic capacitor reaches 79.5 million charge-discharge cycles lifetime, a factor of 80 enhancement over previous antiferroelectric ceramics with large hysteresis. These research accomplishments have not only met the milestones set in the SOPO, but also led to two patent filings, three journal publications (one of them was in Advanced Materials, impact factor 29.4), and nine oral presentations at various venues. Through the course of the project, three postdocs, four Ph.D. students, and one M.S. student were trained. In short, our project established a new methodology in searching next-generation functional ceramics on the fundamental side and discovered several high-efficiency antiferroelectric compositions for capacitors on the applied side. Once fabricated into the multilayer form for commercial applications, these ceramic capacitors can potentially enable the high temperature high power density DC-link capacitors that are critical for the next generation inverters in electric vehicles. The project also significantly contributed to the nation’s workforce development in the STEM fields.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data-driven analysis of dipole strength functions using artificial neural networks

Here, we present a data-driven analysis of dipole strength functions across the nuclear chart, employing an artificial neural network to model nuclear dipole responses. We train the network on a dataset of experimentally measured dipole strength functions for 216 different nuclei. To assess its predictive capability, we test the trained model on an additional set of 10 new nuclei, where experimental data exist. We demonstrate that the artificial neural network not only accurately reproduces known data but also identifies potential inconsistencies in experimental datasets, indicating which results may warrant further review or possible rejection. For nuclei where experimental data are sparse or unavailable, the network confirms theoretical calculations, reinforcing its utility as a predictive tool in nuclear physics. Finally, utilizing the predicted electric dipole polarizability, we extract the value of the symmetry energy at saturation density and find it consistent with results from the literature.

artificial neural networks↗
DOE OSTI2025

Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm

Eels (Anguilla spp.), including American eels (Anguilla rostrata), European eels (Anguilla anguilla), and Japanese eels (Anguilla japonica), are species of critical management and regulatory concern due to their vulnerability to various stressors during downstream migrations. Accurate and efficient detection of migrating eels can improve our understanding of fish behaviors and fish-hydraulic structure interactions, thereby facilitating the design, operation, and optimization of more effective downstream passage facilities from both biological and economic perspectives. However, a real-time, automated framework for detecting migrating eels in real-world applications is currently lacking. Leveraging imaging sonar as a reliable technology for fish passage monitoring, field data are acquired using imaging sonar and then converted to single sonar frames/images for subsequent analysis. In this study, a framework based on the You Only Look Once Version 8 (YOLOv8)-based convolutional neural network is proposed for multi-object detection of eels and non-eel fish using the sonar images after image subtraction and additional wavelet denoising. The results from both training and testing phases demonstrate that the framework's ability can successfully detect both eels and non-eel fish in preprocessed sonar images, achieving F1-scores and mAP@0.50 exceeding 0.84. Additionally, the incorporation of wavelet denoising during preprocessing slightly improve detection performance. Furthermore, the transferability of this framework from eel to lamprey detection is demonstrated to be feasible given the similar morphological characteristics of these two species. Overall, the proposed framework achieves accurate and efficient detection of migrating eels, providing reliable and real-time information that can help conserve vulnerable eel and eel-like populations.

Deep learning↗
NASA NTRS2011

Full-Scaled Advanced Systems Testbed: Ensuring Success of Adaptive Control Research Through Project Lifecycle Risk Mitigation

The National Aeronautics and Space Administration's Dryden Flight Research Center completed flight testing of adaptive controls research on the Full-Scale Advance Systems Testbed (FAST) in January of 2011. The research addressed technical challenges involved with reducing risk in an increasingly complex and dynamic national airspace. Specific challenges lie with the development of validated, multidisciplinary, integrated aircraft control design tools and techniques to enable safe flight in the presence of adverse conditions such as structural damage, control surface failures, or aerodynamic upsets. The testbed is an F-18 aircraft serving as a full-scale vehicle to test and validate adaptive flight control research and lends a significant confidence to the development, maturation, and acceptance process of incorporating adaptive control laws into follow-on research and the operational environment. The experimental systems integrated into FAST were designed to allow for flexible yet safe flight test evaluation and validation of modern adaptive control technologies and revolve around two major hardware upgrades: the modification of Production Support Flight Control Computers (PSFCC) and integration of two, fourth-generation Airborne Research Test Systems (ARTS). Post-hardware integration verification and validation provided the foundation for safe flight test of Nonlinear Dynamic Inversion and Model Reference Aircraft Control adaptive control law experiments. To ensure success of flight in terms of cost, schedule, and test results, emphasis on risk management was incorporated into early stages of design and flight test planning and continued through the execution of each flight test mission. Specific consideration was made to incorporate safety features within the hardware and software to alleviate user demands as well as into test processes and training to reduce human factor impacts to safe and successful flight test. This paper describes the research configuration, experiment functionality, overall risk mitigation, flight test approach and results, and lessons learned of adaptive controls research of the Full-Scale Advanced Systems Testbed.

Pavlock, Kate M.↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗