SEARCH · Search NASA
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.
This final report details work done by GTI Energy, in partnership with University of Texas at Austin – Center for Electromechanics (UTCEM), Argonne National Laboratory (ANL), and Quantum Fuel Solutions (Quantum) to successfully design, fabricate, and test a linear free-piston hydrogen expander controlled by linear motors which also generates electrical power during the process.
The rapid digitalization of nuclear power plants (NPPs) and the deployment of advanced and small modular reactors (A/SMRs) have expanded the cybersecurity attack surface within the nuclear sector. This evolution introduces unique challenges beyond those faced in general information technology (IT), operational technology (OT) and industrial control system (ICS) security, due to nuclear power’s regulatory rigor, safety-critical nature, and operational needs. A pressing workforce gap persists; cybersecurity graduates typically lack nuclear-specific context and retraining them for industry readiness requires 12–18 months, creating a significant burden. This paper addresses this gap by defining the domains of knowledge that nuclear cybersecurity specialists must master, spanning cybersecurity, nuclear engineering, OT/ICS security, and regulatory governance. We propose a curricular framework integrating technical, regulatory, and applied learning components to accelerate workforce readiness. Our approach builds on existing findings that current curricula inadequately integrate nuclear engineering and cybersecurity, shifting the discourse from why specialization is needed to what knowledge must be taught. The recommendations have implications for workforce development and long-term resilience of the nuclear energy sector.
Oak Ridge National Laboratory (ORNL) is participating with Georgetown University (GU) as a subrecipient in response to the National Institutes of Health (NIH), National Center for Advancing Translational Sciences (NCATS) Clinical and Translational Science Award (CTSA) (U54 Clinical Trial Optional) funding opportunity announcement. This Cooperative Research and Development Agreement is put in to place to facilitate the development and implementation of clinical interventions that demonstrably improve human health is currently a complex, recursive, and inefficient process that leads to delays of years or decades before discoveries in biomedical research result in health benefits for patients and communities. NCATS conducts and supports research in the science of translation, to discover the mechanistic and operational principles of the intervention development and dissemination process, thereby providing the scientific foundation for improvements in translational efficiency that will accelerate the realization of interventions that improve human health. Under NCATS’ leadership, the CTSA Program supports a national network of medical research institutions called hubs. GU is the lead institution in one of the NIH hubs that was created as a result of a previous NIH CTSA. The missions of the GU have historically included the advancement of health through research in the clinical and biomedical sciences, the education of future leaders in medical and nursing practice and academia, and the provision of compassionate and scientifically competent patient care and service to the Washington, DC community and the nation. GU is the lead institution for the Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS), a multi-institutional partnership of medical research institutions forged from a desire to promote clinical research and translational science. Through multiple collaborations among these institutions, GHUCCTS is transforming clinical research and translational science in order to bring new scientific advances to health care. Oak Ridge National Laboratory is the Department of Energy's (DOE) largest science and energy laboratory. Managed since April 2000 by a partnership of the University of Tennessee and Battelle, ORNL was established in 1943 as a part of the secret Manhattan Project to pioneer a method for producing and separating plutonium. During the 1950s and 1960s, ORNL became an international center for the study of nuclear energy and related research in the physical and life sciences. With the creation of DOE in the 1970s, ORNL's mission broadened to include a variety of energy technologies and strategies. Today the laboratory supports the nation with a peacetime science and technology mission that is just as important as, but very different from, its role during the Manhattan Project. ORNL is home to the world's premier center for high performance supercomputing to enable scientific discovery. ORNL has extensive expertise in various areas of computer science that are uniquely situated to support GU. Additionally, ORNL’s leading computational user facilities present a unique opportunity to leverage the largest scale machines for open science in support of the stated mission of the NCATS CTSA. ORNL's partnership with GU will offer unparalleled opportunity in data analytics, deep-learning, artificial intelligence, and urban dynamics.
This project evaluated and developed new mathematical and algorithmic techniques capable of handling (in real-time) the growing amounts of data generated by modern fusion research. While existing numerical linear algebra (NLA) methods provide the backbone to classical data analysis and algorithms, these methods fundamentally do not port to distributed architectures nor do they allow low-latency data reduction for control. Motivated by the needs for modern fusion reactors, this project explored and implemented new numerical methods to characterize plasma dynamics, respond in real-time to discharge evolution, and to process massive-scale data accurately and rapidly more fully. This project links expertise in multiple-sensor diagnostics of tokamak plasma dynamics from Columbia University’s Plasma Physics Laboratory with expertise in massive-scale data reduction and extreme data control algorithms at Columbia University’s Data Science Institute. This interdisciplinary project (i) applied machine learning methods, (ii) implemented a properly-trained neural-network for very fast processing of high-speed plasma videography, and (ii) developed the applied mathematical methods, based on randomized-NLA (rNLA) routines, for data analysis, reduction, and real-time control. The Columbia University High Beta Tokamak-Extended Pulse (HBT-EP) facility provided data to test new algorithms and partnership with Columbia University's Data Sciences Institute evaluated the broader use of new algorithms for many challenging control applications.
The Energy Technology Innovation Partnership Project (ETIPP) worked with the University of Hawaii at Manoa to determine the technological and market feasibility of shallow geothermal heat exchanger technology for building cooling and energy efficiency applications.
Our first primary objective is to provide industrial assessments to clients in Oklahoma, Arkansas, Kansas, and north and northwest Texas, including the Texas Panhandle (about 10% of the state of Texas). This geographical region extends about 400 miles north and south and about 600 miles east and west. In addition, the partnership links two universities, Oklahoma State University (OSU) and Wichita State University (WSU) within our region together in a significant collaboration focused on improving our region’s industrial competitiveness. Our second primary objective is to produce competent, motivated energy engineers. Student training will be a combination of classroom work, individual mentoring, and on-the-job training. The focus will be on energy conservation technologies, in conjunction with increased productivity, the economics of the same, and client relationships including client recruiting, on-site assessment, reporting, and client-related communications. Finally, we will work to provide resources and expertise to complement the first two primary objectives. The effectiveness and efficiency of our IAC will be measured, internally, by four broad criteria: (1) client benefits, (2) student development and acceptance after graduation, (3) regional/national contribution to lowering energy use and reducing pollutants, and (4) compliance with DOE/FM requirements. Client-related benefits include client contacts, timeliness of assessments and reports, the value of recommendations provided, and the value of recommendations implemented. Student development includes many students trained and the level of training provided in energy, waste, and productivity-related technologies as well as teamwork and communication skills. The contribution to lower national energy use and waste production is a natural outcome of the program. Compliance with DOE/FM requirements includes timeliness of reports, participation in Best Practices activities, contributions to EERE goals and objectives, and other requirements as communicated to the IAC.
Scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) have teamed up with the University of Hawai‘i at Manoa (UH Manoa) through the U.S. Department of Energy’s Energy Technology Innovation Partnership Project to evaluate the technological and market feasibility of shallow geothermal heat exchanger (GHE) technology. UH requested this analysis to evaluate opportunities in building cooling, energy efficiency, and emissions reduction applications in Hawai‘i. UH has an abundance of geologic and geothermal data and is looking to the national labs’ expertise to execute this analysis. UH is also interested in investigating policy, regulatory, and business conditions advantageous for implementation of a pilot project and more broad deployment of this technology in Hawai‘i. In many locations around the world, the demands for heating and cooling are roughly balanced over the course of the year, so GHEs do not cause significant long-term changes in subsurface temperature. This is not the case in Hawai’i, where the demand for heating is very small, meaning that, over time, GHEs will add heat to the subsurface. If temperatures increase significantly, GHE systems will not work as designed. Regional groundwater flow has the potential to sweep heated water away from boreholes, thereby maintaining the functionality of the GHE system. Significant regional groundwater flow requires two things: a sufficiently large driving hydraulic head gradient (usually closely related to surface topography), and sufficient porosity and permeability to enable groundwater to flow in large enough quantities to enable near-borehole temperatures to be maintained at ambient values. Hawai‘i’s volcanic terrain offers ample surface topographic variation. The lava itself shows an extremely large range of porosity and permeability, so sites with large enough values of these properties must be selected. Numerical modeling of coupled groundwater and heat flow can be used to determine how large is large enough. Primarily, closed-loop systems have been investigated. Other options considered are open-loop systems and using cool seawater as the chilling source. Project work investigated the feasibility of GHE technology at two scales. At the island scale, GIS layers of various attributes relevant for GHE were combined to develop an overall favorability map for employing GHE in Hawai‘i. At the local scale, a hydrogeologic model for the subsurface component of a closed-loop system was developed for the Stan Sheriff Center at the UH Manoa campus. This site is considered promising because the rock below and immediately downgradient of the borefield is highly permeable, consisting of a subsurface karst system (limestone containing high-permeability open channels), which is underlain by a thick, high-permeability fractured basalt. Moreover, the site is near the base of the Ko‘olau Range, providing a large hydraulic head gradient. Thus, groundwater flow through the site is expected to be large, enabling efficient removal of heated groundwater. A full-GHE-system model of the site was also developed, with a simplified representation of the subsurface, in which groundwater flow is not considered and heat transfer is purely by conduction. Using the building cooling load data provided by UH, simulation results show that with groundwater flow present, a GHE can operate successfully for at least 10 years, but with no groundwater flow, the subsurface begins to heat up after only one year of operation, making the GHE unviable within 2-6 years. The team also developed a techno-economic model for this site to compare the cost of cooling using a GHE system with the costs of operating the current air-conditioning system. The GHE system is advantageous economically if favorable tax incentives and interest rates can be obtained.
Liquid Ion Solutions LLC (DBA RoCo Global) in partnership with Carnegie Mellon University and Carbon Capture Scientific LLC, has performed lab-scale development and evaluation of novel additives that lower the viscosity of water-lean amine solvents for post-combustion carbon dioxide capture. This project focuses on developing additives that minimize the formation of long-range electrostatic and hydrogen bonding (HB) networks, decreasing the solvent viscosity, improving diffusion, and improving the process economics. The project objectives included: 1) performing computer simulation to understand the molecular interactions of the additive molecules in water-lean CO 2 capture solvents, 2) design and synthesis of HB disruptors additives, 3) performance testing with additive molecules on model amine solvents, and 4) demonstration of the effectiveness of the optimized additives in the presence of synthetic flue gas. To meet the abovementioned objectives, the project team utilized a holistic approach that combines molecular simulation, experimental testing, and economic analysis studies. The project team developed ab initio molecular model and then perform computer simulation to develop relationship between hydrogen bonding, viscosity, and performed quantitative analysis of additive on the viscosity of the solvent. The team completed computational comparative study on a range of organic functional groups such as ethers, esters, cyclic carbonates, alkanes, and ammonium salts for their effect on viscosity gaining key insights into molecular interactions and the impact of various functional groups and molecular shapes on viscosity. Assisted with molecular simulation insights, the project team conducted additive synthesis and testing, including a proof-of-concept study, additive screening, optimization, and synthetic flue gas testing. The experimental proof-of-concept study proved that the hydrogen bonding acceptors result in significant decrease of viscosities. Detailed additive screening (exploring various functionalities and molecular structures) has been performed. Several promising additives showed excellent reduction in viscosity (30-41%) at 5% additive loading, and over 50% viscosity reduction at 10% additive loading for the model solvents. The team also performed complex screening studies on additive loadings and mixing effect among additives using the design of experiments. Based on multiple screening experiments, one additive-solvent candidate was down-selected for synthetic flue gas testing. A 100-hour continuous absorption/desorption study was conducted under simulated flue gas using a lab-scale continuous capture and separation system. No degradation (for both solvent and additive) was observed based on the GC results of the solvent samples collected from the continuous study. The team conducted preliminary engineering analyses and cost-benefit analyses to quantify the potential economic benefits of the additive approach for solvent viscosity reduction. Based on the experimental data, CO 2 capture cost savings from the capital and operating cost savings are estimated at $\$$4.7/tonne and $\$$0.3/tonne CO 2 captured, respectively. Considering the additive cost, the net benefit is estimated to be between $\$$4.32~$\$$4.86/tonne CO 2 captured.
Abstract The production of freshwater from desalinating abundant saline water on the planet is increasingly considered a climate change adaptation measure. Yet, there are challenges associated with the high cost, intensive energy demand, and environmental implications of desalination. Effective integration of solar energy generation and freshwater production can address both issues. This review article highlights recent key advances in such integration achieved in a joint-research university-national laboratory partnership under the auspices of the United States Department of Energy and parallel efforts worldwide. First, an overview of current and emerging desalination technologies and associated pretreatment, brine treatment, and valorization technologies that together can result in zero-liquid-discharge systems is presented, and their technological readiness levels are evaluated. Then, advanced modeling techniques and new software platforms that enable optimization of solar-desalination applications with the dual objective of cost and environmental impact minimization are discussed.
Included here are the mechanical, electrical, and plumbing design report and drawings for the proposed community geothermal system at an affordable housing complex in Wallingford, Connecticut. The report and drawings were developed by LN Consulting, in partnership with the University of Connecticut, which completed the energy modeling that formed the basis of the design work. The drawings can be used as a basis for a Request for Proposals to procure entities to complete construction-ready design documents.
Barr, in partnership with the University of North Dakota, Microbeam Technologies Inc., Envergex LLC, and MLJ Consulting, worked to develop a transformational technology that controls the formation of alkali aerosols found in coal ash. This technology mitigates ash deposition by injecting sorbents into the boiler and capturing volatile species responsible for boiler fouling, the primary cause for boiler outages. In this session, we’ll explore the science behind this technology and see some real-world results of its implementation.
Barr, in partnership with the University of North Dakota, Microbeam Technologies Inc., Envergex LLC, and MLJ Consulting, worked to develop a transformational technology that controls the formation of alkali aerosols found in coal ash. This technology mitigates ash deposition by injecting sorbents into the boiler and capturing volatile species responsible for boiler fouling, the primary cause for boiler outages. In this session, we’ll explore the science behind this technology and see some real-world results of its implementation.
The overarching goal of the proposed work was to set up a partnership between the University of Southern Mississippi (USM) and Oak Ridge National Laboratory (ORNL) to develop novel approaches to measure the backbone rigidity of conjugated polymers (CPs) and understand the critical role of sidechains on the backbone conformation and the materials macroscopic property. The backbone rigidity greatly influences the electronic properties of CPs, which ultimately determines the functionality and performance of these materials. Improvements in the electronic properties of CPs would allow for enhanced charge transport in semiconductor devices, improved photovoltaic performance, recycling of waste heat in thermoelectrics, and discovery of new phenomena that will enable the next generation of energy technologies. Although significant progress has been made to optimize the optical and electronic properties of CPs, largely through Edisonian methodologies, it remains a challenge to experimentally characterize conjugated backbone conformation (chain rigidity, torsion, planarity, and short-range order) and relate these to the fundamental optical and electronic properties (electronic coupling, charge transport, etc.). This has left fundamental gaps in our knowledge of the most basic structure/property relationships within these systems, precluded the study of fundamental physical phenomena, and constrained the design and realization of new electronic and device functionalities. Thus, the major goal of this work is to use novel deuteration methodologies via systematic synthetic approaches, and neutron scattering techniques to comprehensively characterize the structural and dynamic properties of CPs in contrast-matching solvents. Our work would, for the first time, elucidate the relationship between backbone rigidity and macroscopic properties. They will also allow a rational formulation of design principles for next-generation CPs that are resilient to disorder through precise control of the delocalized electrons along the polymer backbone. Overall, this project will advance our understanding of the structure, dynamics, and fundamental physics of these materials, which is crucial for enabling the prediction, design, control, and manipulation of current and emerging material electronic properties.
This project was performed primarily as a partnership between three Universities – The University of Toledo (UToledo), Montana State University (MSU) and The University of North Carolina at Chapel Hill (UNC). North Carolina State University also contributed to the project through a sub-subcontract. The project developed fundamental and applied knowhow for cultivation systems that are not restricted by (i) proximate availability of flue gas or other high concentration CO 2 sources and (ii) the energy and infrastructure burden to deliver CO 2 over long distances. To overcome these constraints, this project focused on cultivation of alkaliphilic algal cultures in high pH, high alkalinity media (pH>10, alkalinity >100mEq) and demonstrated high biomass productivity, improvement of biomass composition for fuel production, and culture stability. The high media alkalinity ensured availability of sufficient HCO 3 - in solution for high rates of carbon fixation while the high pH of the media facilitated rapid scavenging of atmospheric CO 2 . Overall, the direct air capture rates (DAC) in our system were sufficient to achieve high biomass productivities using atmospheric CO 2 alone. The project also developed and validated toolkits related to metabolic flux modeling and microbiome characterization.
Improving our representation of convective cell processes requires better quantification of convective clouds throughout their entire life cycle. This includes gaining a clearer understanding of the controls on key convective cloud properties, such as updraft intensity, particle size distributions, rainfall rates, and hydrometeor species. Our inability to improve convective cloud process modeling stems, in part, from a limited understanding of convective cell properties, particularly given how rapidly these storms evolve. This lack of detailed observations in the most intense and organized convective storms is especially significant, as large errors remain in representing these clouds, which are critical for severe weather prediction and Earth system model performance. Cloud and precipitation radars are essential tools for studying cloud microphysics and dynamics, particularly in deeper convective clouds. The recent U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s third Mobile Facility (AMF3) Bankhead National Forest (BNF) deployment provides a unique opportunity to investigate important land–atmosphere interactions, as well as the environmental controls on deep convective cloud processes, in a location favorable for frequent convection. We operate the X/Ka-band Scanning ARM Cloud Radar (X/Ka SACR) using a scan strategy optimized to capture these rapidly evolving clouds and their properties, thereby improving studies of deep convective cloud processes. This effort is strengthened by a complementary and coordinated partnership with ongoing university and multi-agency radar activities collocated in north Alabama—a unique opportunity to examine clouds and precipitation from a lifetime-centric perspective.
This is the third Systems Engineering (SE) class I have taken through the partnership between Georgetown University and Lawrence Livermore National Laboratory (LLNL). In my previous jobs, I had minimal exposure to SE and was intrigued about the methodical approach to completing projects at LLNL.
The maintenance costs can represent about 15%–60% of the cost of produced goods depending on the type of goods transported. To comply with stringent emissions regulations, diesel engines are incorporated with complex after-treatment systems that demand increased maintenance. The availability of alternative fuels such as natural gas and propane has fostered the natural gas and propane powertrain systems as well as electrification options for heavy- and medium-duty vehicles. A critical barrier to adopting alternative fuel vehicles has been the lack of knowledge on comparative vehicle maintenance/repair costs with conventional diesel. Moreover, the region of operation, the type of vehicle operation, and seasonal temperature changes also affect the duty cycle which impacts the maintenance and repair costs. This study focuses on estimating the cost-per-mile for heavy-duty vehicles using machine learning models such as random forest, xgboost, neural networks, and a super-learner model. The super-learner model achieved an error as low as 0.0068 $/mile for mean absolute error and 0.0086 $/mile for root mean square error with a coefficient of determination/R-Squared of 97.28%. Specifically, the paper investigates the data collected from the maintenance and repair costs associated with delivery trucks using diesel and natural gas fuels. Since the availability of data is the major constraint, we leveraged the data collected by West Virginia University and the partnership with fleet companies. This allows for additional information related to maintenance costs and fleet-specific maintenance practices of alternative fuel vehicles. This study promotes clean fuel technologies and enables fleet management companies to adopt alternative fuel vehicles in case of similar or lower cost of maintenance compared to diesel vehicles resulting in reduced emissions and total cost of ownership.
We developed multiple machine learning methods for the detection and classification of new wireless communication waveforms, which is critical for targeted attacks in wireless networks and electronic warfare. Our machine learning models are capable of dynamically detecting security threats in near real time through our advanced open set recognition (OSR) approach. This model has demonstrated significant improvements in the detection of unknown waveforms, thereby enhancing the security and reliability of mission critical communications. Our approach to detecting uncertain security threats is novel; we advanced OSR techniques by incorporating domain knowledge of wireless signals. Specifically, we combined time and frequency domain model features to enhance the model’s performance. Utilizing an OSR approach eliminates the need for training data to be distributed similarly to the deployment environment and removes the requirement for the training set to contains all possible threat classes. This is crucial because it is often infeasible to determine and characterize all potential security threats in advance. Our model were trained on simulated data, generated in partnership with the University at Albany, State of New York. The data set contained a diverse array of wireless signals, including those with additive white Gaussian noise and multipath signals, with and without line of sight. This comprehensive training set allowed us to optimize our models to detect unknown waveforms under various challenging scenarios, such as low signal-to-noise ratios. By training on various waveforms, varying signal-to-noise ratio, and different sample sizes under normal conditions, our models were fine tuned to perform effectively in challenging environments.