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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.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Electrochemical CO 2 Utilization: Scalable System Operation for Formic Acid Production

In recent years, research into the electrochemical reduction of CO 2 has grown exponentially. Formic acid (FA) has become a particular value product from CO 2 due to its versatility [1], optimal atom economy [2], and thermodynamic favorable [3]. FA can be produced electrochemically via a direct two-electron transfer process involving CO 2 with a proton source, requiring less energy input and fewer reaction steps than the conventional Kemira process. However, product selectivity, electrode stability, and low faradic efficiency due to increased resistance under constant current, remain key issues for CO 2 reduction to FA [4]. University of Kentucky’s Center for Applied Energy Research (UK CAER) is currently investigating reactor designs to address the challenges associated with electrochemical CO 2 conversion to FA [5]. The current reactor system employs: (1) an organic-based charge carrier which shuttles charge directly to the catalyst to enable CO 2 reduction to FA; (2) novel electrode materials to mitigate large voltages and improve conductivity; and (3) a flow system which not only allows for the volumetric scale-up of both charge carrier and catalyst, but also decouples the charger carrier re-energized and FA production processes to protect the catalyst’s stability due to overpotential. Through careful adjustment of electrode, charge carrier, and electrolyte selection, cell resistances have decreased by over 15%, facilitating conduction necessary for effective CO 2 reduction to FA. The system uses a highly specific engineered catalyst, which has produced over 100 mM FA at a rate of over 10 mM FA/hour following the aforementioned electrode/charge carrier changes. System design considerations (flow rate, bulk volume) and modeling of the system will also be discussed.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Cascade CO 2 electroreduction enables efficient carbonate-free production of ethylene

CO 2 electroreduction provides a route to convert waste emissions into chemicals such as ethylene (C 2 H 4 ). However, the direct transformation of CO 2 -to-C 2 H 4 suffers from CO 2 loss to carbonate, consuming up to 72% of energy input. A cascade approach—coupling a solid-oxide CO 2 -to-CO electrochemical cell (SOEC) with a CO-to-C 2 H 4 membrane electrode assembly (MEA)—would eliminate CO 2 loss to carbonate. However, this approach requires a CO-to-C 2 H 4 MEA with energy efficiency well beyond demonstrations to date. Focusing on the MEA, in this study we find that an N-tolyl substituted tetrahydro-bipyridine film improves the stabilization of key reaction intermediates, while an SSC ionomer enhances CO transport to the Cu surface, enabling a C 2 H 4 faradaic efficiency of 65% at 150 mA cm –2 for 110 h. Demonstrating a cascade SOEC-MEA approach, we achieve CO 2 -to-C 2 H 4 with a ~48% reduction in energy intensity compared with the direct route. We further reduce the energy intensity by coupling CO electroreduction (CORR) with glucose electrooxidation.

10 SYNTHETIC FUELS↗
DOE OSTI2022

Particulates for additive manufacturing techniques

A method of making a particulate for an additive manufacturing technique includes receiving particulate at a chemical vapor deposition (CVD) reactor, flowing a hydrocarbon gas into the CVD reactor, decomposing the hydrocarbon gas in the CVD reactor, and depositing a carbonaceous coating on the particulate using a product of the decomposed hydrocarbon gas. The coating deposited over the particulate has a reflectivity that is lower than the reflectivity the underlying particulate body to reduce an energy input requirement for purposes of fusing the particulate into a layer of an article using an additive manufacturing technique. In embodiments, the coated particulate can be received at an additive manufacturing apparatus and fused into a layer of an article as a metallic-carbon composite using a high-density energy source.

42 ENGINEERING↗
DOE OSTI2026

Innovating Distributed Embedded Energy Prize (InDEEP): A Lessons Learned Report

The U.S. Department of Energy's Water Power Technologies Office (WPTO) launched the Innovating Distributed Embedded Energy Prize (InDEEP) in March 2023 to accelerate innovation in Distributed Embedded Energy Conversion Technologies (DEEC-Tec) for ocean wave energy. Administered by the National Laboratory of the Rockies (NLR) with technical support from Sandia National Laboratories (SNL), InDEEP focused on the development of small, distributed, and embeddable energy converters (DEECs) and their integration into scalable DEEC-Tec metamaterials for marine renewable energy applications. Spanning three phases over two years, InDEEP awarded approximately $2.3 million to teams from academia, industry, and startups. Phase I emphasized conceptual design. Phase II moved into the prototyping of individual DEECs. Phase III required integration into functional DEEC-Tec metamaterial prototypes. Across 60 submissions, teams explored a wide range of energy conversion mechanisms - including piezoelectric, variable-capacitance, ionic, and inductive methods. Note, the prize did not include the design nor demonstration of ocean wave energy conversion systems. Rather, the prize only required participants to design and demonstrate individual DEECs and corresponding DEEC-Tec metamaterials. This prize utilized a mix of novel and proven techniques to attract participants from outside marine energy, including an engagement leaderboard, robust recruitment, technical expert mentorship, and a suite of technical trainings. Key insights from the competition emphasized that DEEC-Tec metamaterials must be intentionally designed to produce beneficial emergent behaviors - advantages that go beyond simply combining multiple DEEC units. Top-performing teams showed that thoughtful design of system architecture, coordinated deformation, and systems adaptabilities could unlock meaningful performance gains both at the DEEC system level and DEEC-Tec metamaterial system level. A critical realization was that many DEEC-Tec metamaterials could benefit from being designed to accept lower-frequency energy inputs and shift those into higher-frequencies per each DEEC making up the respective DEEC-Tec metamaterial. Other important takeaways included the need for rigorous and quantitative performance testing, effective integration of power conditioning electronics, and the pivotal role of material science in enabling innovative, adaptive DEEC-Tec-based energy conversion designs. InDEEP also helped establish a growing DEEC-Tec community of practitioners, attracting participants from beyond traditional marine energy sectors. Through a strong support infrastructure, InDEEP fostered early-stage innovation and laid a foundation for future DEEC-Tec-based ocean wave energy conversion solutions - positioning DEEC-Tec as a promising pathway toward scalable, resilient ocean wave energy conversion. Through focused R&D of individual DEECs and their integration into DEEC-Tec metamaterials, alongside a growing, multidisciplinary community catalyzed by InDEEP, there is a strong opportunity to drive a disruptive shift in ocean wave energy conversion design and development. This convergence of novel architectures, emergent behaviors, and collaborative innovation positions DEEC-Tec as a transformative approach, moving the field from rigid, centralized energy conversion-based designs to resilient, modular systems highly adaptable for real-world ocean wave energy conversion applications.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Efficient Automated Driving Strategies Leveraging Anticipation and Optimal Control

Automated vehicles and advanced driver assistance systems bring computation, sensing, and communication technologies that exceed human abilities in some ways. For example, automated vehicles may sense a panorama all at once, do not suffer from human impairments and distractions, and could wirelessly communicate precise data with neighboring vehicles. Prototype and commercial deployments have demonstrated the capability to relieve human operators of some driving tasks up to and including fully autonomous taxi rides in some areas. The ultimate impact of this technology’s large-scale market penetration on energy efficiency remains unclear, with potential negative factors like road use by empty vehicles competing with positive ones like automatic eco-driving. Fundamentally enabled by historic and look-ahead data, this dissertation addresses the use of automated driving and driver assistance to optimize vehicle motion for energy efficiency. Facets of this problem include car following, co-optimized acceleration and lane change planning, and collaborative multi-agent guidance. Optimal control, especially model predictive control, is used extensively to improve energy efficiency while maintaining safe and timely driving via constraints. Techniques including chance constraints and mixed integer programming help overcome uncertainty and non-convexity challenges. Extensions of these techniques to tractor trailers on sloping roads are provided by making use of linear parameter-varying models. To approach the wheel-input energy eco-driving problem over generally shaped sloping roads with the computational potential for closed-loop implementation, a linear programming formulation is constructed. Distributed and collaborative techniques that enable connected and automated vehicles to accommodate their neighbors in traffic are also explored and compared to centralized control. Using simulations and vehicle-in-the-loop car following experiments, the proposed algorithms are benchmarked against others that do not make use of look-ahead information.

Dollar, Robert Austin↗
DOE OSTI2024

Utilization of an Advanced Sensor network to determine fuel heating value and Real-Time net unit heat rate during transient operation

Coal-fired utility boilers are being increasingly used as variable electricity generation to resolve the imbalance in the energy market from the expansion of intermittent renewable energy. The frequent transient operation required to meet residual energy demand has created a challenge for coal-fired units to operate efficiently. This work utilizes an advanced sensor network (ASN) to calculate net unit heat rate (NUHR) of a coal-fired boiler in real time through combustion calculations and statistical correlations to provide the tools for optimizing dynamic operation. Real-time heating values that were necessary to determine fuel input energy to calculate accurate NUHR were found using both fundamental and data-driven methods. Real-time NUHR shows distinct shifts that reflect changes in process conditions that will improve the ability to optimize transient operation. Data-driven heating value correlations had 24% lower root mean square error (RMSE) than the fundamental combustion calculation approach when compared to daily retrospective proximate analysis. Furthermore, the data-driven method RMSE improved by 7% with the inclusion of ASN data. Future work is to validate by comparing unit performance with and without the inclusion of NUHR as a control parameter for the dynamic neural network.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Respiratory energy demands and scope for demand expansion and destruction

Photosynthesis is the primary energy input to plants, but most plant metabolic processes are powered much or all of the time by ATP and NAD(P)H that come from respiratory oxidation of photosynthetically produced carbohydrates, that is, “dark respiration” (Amthor, 1994). The processes of growth, nutrient uptake and assimilation, active transport, and maintenance are thus all clients of respiration and compete for their share of a respiratory energy budget that is capped by photosynthate income. The economic principle of opportunity cost applies to these clients’ competing demands: spending respiratory energy on process A means missing the benefit of investing that energy in process B (Mahmoudabadi et al., 2019). This principle is key to assessing prospects for crop improvement by metabolic engineering. As de Lorenzo (2015) put it: “metabolism…frames and ultimately resolves whether a given genetic program (existing…or engineered) can be deployed or not.” Foreign or reconfigured native processes bolted on to a crop-plant’s metabolic chassis must compete for respiratory energy with native ones without crashing the energy economy. A proviso on plant carbon budgets is that photosynthetic carbon fixation (“source activity”) can in certain cases increase to meet increased carbon demand (“sink activity”), that is, budget envelopes are not always fixed (Smith et al., 2018). However, as there is some consensus that productivity is most often limited or co-limited by carbon supply (Ainsworth and Long, 2005; Körner, 2015; Sonnewald and Fernie, 2018), we make this our basal assumption in the analyses below.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Emergent microrobotic oscillators via asymmetry-induced order

Abstract Spontaneous oscillations on the order of several hertz are the drivers of many crucial processes in nature. From bacterial swimming to mammal gaits, converting static energy inputs into slowly oscillating power is key to the autonomy of organisms across scales. However, the fabrication of slow micrometre-scale oscillators remains a major roadblock towards fully-autonomous microrobots. Here, we study a low-frequency oscillator that emerges from a collective of active microparticles at the air-liquid interface of a hydrogen peroxide drop. Their interactions transduce ambient chemical energy into periodic mechanical motion and on-board electrical currents. Surprisingly, these oscillations persist at larger ensemble sizes only when a particle with modified reactivity is added to intentionally break permutation symmetry. We explain such emergent order through the discovery of a thermodynamic mechanism for asymmetry-induced order. The on-board power harvested from the stabilised oscillations enables the use of electronic components, which we demonstrate by cyclically and synchronously driving a microrobotic arm. This work highlights a new strategy for achieving low-frequency oscillations at the microscale, paving the way for future microrobotic autonomy.

42 ENGINEERING↗
DOE OSTI2025

A Business Case Evaluation of Gas Switching Reforming (GSR) Technology: A Promising Technology for Natural Gas Reforming with Integrated CO2 Capture

Hydrogen is essential in the transition to sustainable energy, and developing low-carbon production methods is a key research focus. Traditional steam methane reforming (SMR) dominates the hydrogen industry but contributes substantially to CO2 emissions. In response, Gas Switching Reforming (GSR) has emerged as a novel process that integrates carbon capture and utilizes process heat more efficiently. Unlike other reforming methods, GSR consolidates oxidation and reduction reactions within a single reactor, which minimizes external energy inputs and simplifies scaling. Like conventional steam methane reforming (SMR), GSR can be integrated with water-gas shift and pressure swing adsorption units for pure hydrogen production. This work presents a comprehensive business case analysis of GSR technology based on experimental results in Technology Readiness Level 3, Life Cycle Assessment (LCA) and Techno-Economic (TEA) evaluation incorporating ASPEN Plus process modeling considering different configurations and energy scenarios. The TEA incorporates data from kinetic experiments from various catalysts to evaluate the GSR process under various conditions. The goal of this work is to evaluate GSR’s potential to serve as a low-carbon alternative to SMR, focusing on global warming potential and additional impact categories to evaluate a wide spectrum of environmental impacts. Comparative assessments were conducted with SMR, chemical loop reforming (CLR), and proton exchange membrane (PEM) electrolysis to explore trade-offs across environmental metrics. The environmental impact assessment of this work encompasses the entire hydrogen production lifecycle from raw material extraction to plant decommissioning, using a cradle-to-gate boundary. Preliminary findings highlight that GSR, when integrated with low-carbon energy sources, could significantly reduce environmental impacts, making it a promising candidate for low-carbon hydrogen infrastructure. The insights from this business case evaluation aim to guide industry in scale-up and commercialization of this promising clean energy technology.

03 NATURAL GAS↗
DOE OSTI2021

Mexico and U.S. Power Systems Under Variations in Natural Gas Prices

This study examines the impact of natural gas prices on the power systems of Mexico and the United States. For this, we develop an integrated modeling framework by soft linking three different techno-economic bottom-up models of the power and energy systems, one partial equilibrium model of the natural gas sector, and a partial equilibrium model of the Mexican energy sector. Our results show several interesting results: high natural gas prices raise the use of carbon-intensive technologies in the short-term and boost renewable investments at longer time intervals, increasing emissions in earlier periods and reducing them thereafter. Regarding system costs, because of more capital-intensive green power and lower expenditures in raw energy carriers, capital costs rise and operating costs decrease in the long haul. Furthermore, we see an increase in natural gas demand when its price is low, reducing long-term capital and operating costs through cheaper energy inputs in natural gas facilities and a lower share of capital-intensive renewable facilities in the power system. Concerning emissions, low natural-gas prices decrease coal use in the United States, reducing anthropogenic emissions until the last stages of the optimization period. For Mexico, they show heterogeneous results across models. Policymakers can use this study's results to understand the influence of natural gas prices in the Mexican and United States energy sectors.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Analysis of Nuclear Fuel Cycle Data

Electricity generated using nuclear power accounted for 18.9% of all electricity consumed in the United States in 2021, putting it in third place behind natural gas (38%) and coal (22%) power plants. Nuclear power plants boast a significantly higher uptime or capacity factor—90% and above—compared to 49.1% for coal fired power plants and 56.6% for natural gas power plants. Renewable energy sources, such as solar photovoltaic (PV) and wind electricity, have lower capacity factors: 24.9% and 36.3%, respectively. In addition, nuclear power is cleaner than both coal and natural gas fired power plants. With the passing of the 2022 Inflation Reduction Act, significant tax credits will be claimed by producers of hydrogen with well-to-gate greenhouse gas (GHG) emissions below 0.45 kg CO 2e /kg H 2 . This has sparked interest in using clean sources of electricity, including nuclear power, to generate H 2 via water electrolysis. As uranium is a primary fuel for modern nuclear power plants, the upstream emissions from nuclear fuel production greatly impact the GHG emissions related to all nuclear power end use. Therefore, it is important to accurately determine the upstream emissions associated with the nuclear fuel cycle of nuclear power production in the United States. In this analysis, the nuclear fuel cycle was separated into distinct steps to allow better understanding of the chemical and energy inputs at each step of the fuel cycle. This also provides details of the GHG emissions at each step in the nuclear fuel cycle. The transportation distance for each step of the fuel cycle was updated to account for the locations of uranium processing facilities along the supply chain of the current U.S. nuclear power plants. Finally, all the updated values were incorporated into Argonne National Laboratory’s Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Forecasting Solar-Thermal Systems Performance under Transient Operation Using a Data-Driven Machine Learning Approach Based on the Deep Operator Network Architecture

Modeling and prediction of the dynamic behavior of thermal systems operating under intermittent energy input and variable load requirements represent one of the greatest challenges in the development of efficient and reliable renewable-based power generation technologies. In this work, a data-driven machine learning modeling framework was developed based on a modified version of the Deep Operator Network architecture where the time coordinate in the trunk net is replaced with historical data of the predicting quantity. The modeling framework can be used to accurately predict the performance of renewable-based energy conversion technologies including wind- and solar-based power plants. This novel framework was applied on a solar-thermal system that consists of a solar collection loop using a flat plate collector, a power generation loop comprising an Organic Rankine Cycle, and a thermal energy storage tank connecting both loops. Variable solar irradiance, air temperature, and power load profiles were used by the Deep Operator Network to predict the State-of-Charge and the efficiency of the thermal system for several days. The results were compared with the State-of-Charge and efficiency functions calculated using a physics-based model. For a simple operation scenario, characterized by a clear sky solar irradiance profile and constant load, the standard deviation in the State-of-Charge prediction by Deep Operator Network is below 0.9% during a seven-day prediction time horizon. For the most realistic operation scenario that considers real solar irradiance and a rough load profile, the maximum standard deviation in the predictions for the State-of-Charge and efficiency are below 6.8% and 2.5%, respectively. A comparison between Deep Operator Network and Long Short Term Memory network was also performed. In general, both networks predict very well the State-of-Charge for different data density conditions; however, a higher accuracy, with a standard deviation below 2.0%, is obtained by the Deep Operator Network during three and half days using sparser training data of 20-minute points. The same accuracy for the State-of-Charge prediction with the Long Short Term Memory network is achieved only for 14 h. Average standard deviations for the State-of-Charge prediction of 1.1% with the Deep Operator Network and 1.5% with the Long Short Term Memory network are obtained for a four-day prediction time using a denser training data of 5-minute points.

DeepONet↗
DOE OSTI2023

Global cascade of kinetic energy in the ocean and the atmospheric imprint

Here, we present an estimate for the ocean's global scale transfer of kinetic energy (KE), across scales from 10 to 40,000 km. Oceanic KE transfer between gyre scales and mesoscales is induced by the atmosphere’s Hadley, Ferrel, and polar cells, and the intertropical convergence zone induces an intense downscale KE transfer. Upscale transfer peaks at 300 gigawatts across mesoscales of 120 km in size, roughly one-third the energy input by winds into the oceanic general circulation. Nearly three quarters of this “cascade” occurs south of 15°S and penetrates almost the entire water column. The mesoscale cascade has a self-similar seasonal cycle with characteristic lag time of ≈27 days per octave of length scales; transfer across 50 km peaks in spring, while transfer across 500 km peaks in summer. KE of those mesoscales follows the same cycle but peaks ≈40 days after the peak cascade, suggesting that energy transferred across a scale is primarily deposited at a scale four times larger.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Energy Principles of Scientific Breakeven in an Inertial Fusion Experiment

Fusion “scientific breakeven” (i.e., unity target gain G target , total fusion energy out > laser energy input) has been achieved for the first time (here, G target ∼ 1.5 ). This Letter reports on the physics principles of the design changes that led to the first controlled fusion experiment, using laser indirect drive, on the National Ignition Facility to produce target gain greater than unity and exceeded the previously obtained conditions needed for ignition by the Lawson criterion. Key elements of the success came from reducing “coast time” (the time duration between the end of the laser pulse and implosion peak compression) and maximizing the internal energy delivered to the “hot spot” (the yield producing part of the fusion fuel). The link between coast time and maximally efficient conversion of kinetic energy into internal energy is explained. The energetics consequences of asymmetry and hydrodynamic-induced mixing were part of high-yield big radius implosion design experimental and design strategy. Herein, it is shown how asymmetry and mixing consolidate into one key relationship. It is shown that mixing distills into a kinetic energy cost similar to the impact of implosion asymmetry, shifting the threshold for ignition to higher implosion kinetic energy—a factor not normally included in most statements of the generalized Lawson criterion, but the key needed modifications clearly emerge. Published by the American Physical Society 2024

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Property-structure-process relationships in dissimilar material repair with directed energy deposition: Repairing gray cast iron using stainless steel 316L

Directed energy deposition (DED) based remanufacturing leverages the flexibility of additive manufacturing to add value to broken or worn components. DED offers the ability to repair cast iron, a material difficult to repair with traditional welding techniques. Despite this, development of appropriate DED process conditions for bimetallic cast iron structures lags low- and medium-carbon steel repair. Thermal stresses and porosity generated by high-temperature deposition on cast iron often lower mechanical properties and hinder the qualification process. In this report scanning speed, powder mass flow rate, and stepover width are studied in multiple-track structures deposited on gray cast iron. Dilution and residual stresses are found to be highly dependent on the selected process parameters. Samples with a higher volumetric energy input, e.g., slower scanning speeds and higher powder feed rates, showed improved density and lower residual stresses but suffered lower dilution into the substrate. The presented conditions further the development of additive manufacturing technologies for automotive repair.

42 ENGINEERING↗
DOE OSTI2020

Asphaltene-induced spontaneous emulsification: Effects of interfacial co-adsorption and viscoelasticity

Asphaltenes are a class of high molecular weight aromatic compounds found in crude oil. They adsorb onto toluene-water interfaces and induce a spontaneous emulsification phenomenon, whereby stable water-in-oil emulsions form without the need of an external energy input. This work aims to control and understand the factors affecting spontaneous droplet formation in the presence of asphaltene adsorption. This is particularly useful for crude oil refining, where the presence of a stable emulsion hampers the efficiency of downstream processing operations. We explore the effect of the addition of copolymers designed as crude oil flow improvers as a means to control the extent of emulsion formation. We find that the polymers competitively adsorb onto the toluene-water interface and diminish spontaneous emulsification. Additionally, we conduct fluorescence microscopy experiments and measurements of the interfacial energy to determine the mechanism of spontaneous emulsification in asphaltene systems. We conclude that an emulsion forms via the diffusion of molecular water into the oil phase and subsequent binding with asphaltene aggregates, leading to the nucleation of micrometer-sized water droplets. We find that the polymer forms complexes with the dissolved asphaltenes, possibly hampering the ability of diffused water to bind to the asphaltenes and reducing the extent of spontaneous emulsification. Finally, we investigate the role of interfacial shear and dilatational viscoelasticity to better understand which fundamental interfacial properties are important in the emulsification of asphaltene-laden systems. We find that the rate of formation of an interfacial microstructural network is inversely correlated with the extent and rate of spontaneous emulsification.

42 ENGINEERING↗
DOE OSTI2024

Geological Thermal Energy Storage (GeoTES) Charged with Solar Thermal Technology Using Depleted Oil/Gas Reservoirs and Carnot-Battery Technique Using Shallow Reservoirs: Preprint

Geological thermal energy storage (GeoTES) utilizes the underground reservoirs to storage and dispatch energy per given demand schedule over a time scale up to seasons of a year. The energy input can be of various sources/forms; in this paper, we investigate 1) the GeoTES technology with solar thermal hybridization and using depleted oil/gas reservoirs; 2) the GeoTES technology with heat pumps charged by excess renewable electricity and using low-temperature shallow reservoirs. For each GeoTES technology, we carry out the suitability analysis of candidate reservoirs, develop initial techno-economic models, and validate the model with selected case study. The paper provides an overview of our technical progress on the topics of concern and aims to promote a wider acceptance of the GeoTES technologies in the future energy market.

Carnot battery↗