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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Oscillating surge wave energy converter using a novel above-water power takeoff with belt-arc speed amplification

We investigate the performance of a novel power takeoff (PTO) featuring belt-arc speed amplification for oscillating surge wave energy converters (OSWECs), aiming to address the challenges of extremely low rotary speed and large torque under the low-frequency ocean wave excitations. The belt-arc design significantly increases the rotary speed of the generator, enabling generator downsizing and decreasing the powertrain friction losses. The design also allows for placing the generator above water, eliminating the need for high Ingress Protection ratings for the generator and powertrain, and potentially leading to substantial reductions in capital and maintenance costs. Using the linear potential wave theory, the dynamics of the integrated system are analyzed, and key parameters are identified. To validate the numerical analysis, a 1:10 scale model is designed, fabricated, and tested in a wave tank. Performance evaluations are conducted under regular and irregular wave conditions, with quantified effects of parameter tuning. The results reveal an optimal wave-to-electric efficiency of 48% under regular wave excitation and 20% under irregular excitation. Furthermore, these findings underscore the effectiveness of the proposed novel PTO design in addressing the challenges of low rotation speed and large torque inherent in OSWECs, demonstrating its ability to efficiently convert wave power into electricity.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Response of the Current Climate to Land‐Ocean Contrasts in Parameterized Cumulus Entrainment

Abstract Cumulus entrainment substantially regulates the earth's climate but remains poorly constrained in global climate models. Recent studies have shown that cumulus bulk entrainment (or dilution) is particularly sensitive to continentality, with the entrainment rate in simulated maritime cumuli nearly double that of continental cumuli. This study examines the impacts of such land–ocean entrainment contrasts on the current climate using 21‐year simulations with the Geophysical Fluid Dynamics Laboratory's High‐Resolution Atmospheric Model (HIRAM). In response to a 25% reduction in the HIRAM entrainment parameter c 0 over land, precipitation over tropical land regions increases by up to 40%. Along with directly facilitating enhanced convective precipitation, this c 0 reduction induces an increase in soil moisture, which may contribute to a further enhancement of convective precipitation over land. A 25% c 0 reduction over the oceans leads to more widespread modifications of convection patterns, with the strongest signal in the tropical Pacific. Deep convection shifts upstream (eastward) there, inducing enhanced large‐scale ascent over the central Pacific with compensating subsidence and reduced humidity and precipitation over the western Pacific (WP). Land–ocean variations in c 0 project onto the Pacific Walker circulation, with the 25% land reduction strengthening it by 4% and the 25% ocean reduction weakening it by 14%. These changes are driven by variations in convective and large‐scale stratiform heating over the Pacific. While reduced c 0 over land enhances diabatic heating in the Maritime Continent to strengthen the Walker circulation, reduced c 0 over the oceans decreases diabatic heating in the WP to weaken the Walker circulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Peak Flow Timing

Abstract Global climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Gear Test Assembly - Report on Experimental Testing (FY2020)

The Gear Test Assembly (GTA) has completed Campaign #2 of in-sodium testing at the Mechanisms Engineering Test Loop (METL). Campaign #1 was conducted in 2019 and completed 19,600 cycles, correlating to 9,800 fuel assembly maneuvers. Campaign #1 was terminated following a shaft seizure caused by a failed bearing in the sodium gearbox. Campaign #2 was conducted in the spring of 2020 and completed 2,768 cycles, correlating to 1,384 fuel assembly maneuvers. Campaign #2 was terminated following elevated vibration measurements coming from the sodium gearbox. Following cleaning and disassembly, a bearing was found to have failed in the sodium gearbox. Campaign #1 used tapered roller bearings made of 52100 steel with a heat treatment from the manufacturer that allows for operation to 350°C. Campaign #2 used the same tapered roller bearings, but with no heat treatment. A new dynamic shaft seal design was used in Campaign #2 that led to significant reductions in argon consumption and graphite debris generation. A modified external gearbox system led to no external bearing failures and allowed for higher torque output. Finally, modifications to the Carbonation System led to a more effective carbonation process and all-around reduction in turnaround time.

42 ENGINEERING↗
DOE OSTI2024

Integration Development and Testing of Rear Transition Monitor for Beam Current Monitoring System

Addressing baseline effects in accelerator environments is crucial for accurate data acquisition and analysis, since baseline effects can obscure signal clarity and impact the reliability of beam current monitoring systems. There are many potential contributors to baseline noise, such as variations in beam dynamics, electromagnetic interference from nearby equipment, or RF interference. Previous applications of noise reduction systems don t sufficiently filter sources of asynchronous noise, so a new algorithm was implemented. A simulation dataset was created to replicate beam conditions and a Red Pitaya FPGA was used to collect data through the streaming application. A Python script was developed to implement noise reduction algorithms and efforts were made to integrate real-time data streaming with the Redis platform and Acnet Front End infrastructure.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Applying Time-Parallelization to Turbulent Flows

Parallelization of the temporal domain is explored for the solution of turbulent flows. Multigrid reduction-in-time (MGRIT) is used to advance the large-scale fluid dynamics in time sequentially on the coarsest space-time grid but propagate the information in time parallel on all other levels. The goal of this process is to accurately and efficiently resolve the coarse-scale turbulence structure and use that to drive the fine-scales of the turbulent flow. The extra forcing from nonlinear multigrid facilitates the coupling and interaction between fine and coarse scales, through which the multiscale nonlinear physics is properly captured. Adaptive mesh refinement is employed to finely resolve only the regions with strong gradients, which provides further computational efficiency. The underlying computational fluid dynamics solver is a fourth-order finite-volume scheme with the standard 4-stage Runge-Kutta method. An advanced approach is devised and implemented to enable MGRIT to solve highly turbulent flows successfully. Furthermore, the method is applied to solve a Taylor-Green vortex problem and a doubleshear-layer turbulent mixing flow. Results are promising, validating that MGRIT with the filtering approach has the potential to efficiently solve general turbulent flows.

Computational Fluid Dynamics↗
DOE OSTI2025

Effect of Solvents on Lignin–Surface Interactions via Molecular Dynamics Simulations

Lignin, an essential building block of lignocellulosic biomass, is a potential abundant source of aromatic monomers for the polymer and chemical industry. Reductive catalytic fractionation (RCF) is one promising process that can produce high yields of phenolic monomers and oligomers from lignin under different catalytic conditions. An important choice in optimizing RCF is the selection of solvent; however, detailed insights into solvent effects on lignin behaviors and interactions remain limited. Here, in this work, we perform all-atom molecular dynamics simulations to study the solvation of lignin, solvent-mediated conformational changes, and the interaction of solvated lignin oligomers with model surfaces. We focus on the behavior of an oligomeric lignin model compound in methanol, ethanol, a binary mixture of ethanol and water, and water at both the RCF reaction temperature (473 K) and room temperature. Analysis of structural features of lignin suggests that these three organic solvent systems favorably solvate lignin, resulting in a more extended conformation suitable for catalytic conversion to valuable chemicals. We further introduce model palladium (Pd) and carbon (C) surfaces to understand how solvent choice impacts adsorption onto a representative catalytic surface and support, and to quantify the competition among the reactant and solvent molecules for the surface. Unbiased simulations suggest strong adsorption of lignin on both Pd and C surfaces at 473 K, with notable solvent-mediated differences in adsorption energies. Additionally, our findings indicate that lignin adsorption is promoted by the entropy change resulting from the displacement of solvent molecules from the surface. This study provides a molecular perspective of adsorption of lignin onto varying surfaces, which is a step towards understanding and optimizing the catalytic conversion of lignin into valuable chemicals.

adsorption↗
DOE OSTI2021

Disentangling Ferroelectric Wall Dynamics and Identification of Pinning Mechanisms via Deep Learning

Field-induced domain-wall dynamics in ferroelectric materials underpins multiple applications ranging from actuators to information technology devices and necessitates a quantitative description of the associated mechanisms including giant electromechanical couplings, controlled nonlinearities, or low coercive voltages. While the advances in dynamic piezoresponse force microscopy measurements over the last two decades have rendered visualization of polarization dynamics relatively straightforward, the associated insights into the local mechanisms have been elusive. This work explores the domain dynamics in model polycrystalline materials using a workflow combining deep-learning-based segmentation of the domain structures with nonlinear dimensionality reduction using multilayer rotationally invariant autoencoders (rVAE). The former allows unambiguous identification and classification of the ferroelectric and ferroelastic domain walls. Here, the rVAE discovers the latent representations of the domain wall geometries and their dynamics, consequently providing insight into the intrinsic mechanisms of polarization switching, that can further be compared to simple physical models. The rVAE disentangles the factors affecting the pinning efficiency of ferroelectric walls, offering insights into the correlation of ferroelastic wall distribution and ferroelectric wall pinning.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating particle-in-cell kinetic plasma simulations via reduced-order modeling of space-charge dynamics using dynamic mode decomposition

We present a data-driven reduced-order modeling of the space-charge dynamics for electromagnetic particle-in-cell (EMPIC) plasma simulations based on dynamic mode decomposition (DMD). The dynamics of the charged particles in kinetic plasma simulations such as EMPIC is manifested through the plasma current density defined along the edges of the spatial mesh. We showcase the efficacy of DMD in modeling the time evolution of current density through a low-dimensional feature space. Not only do such DMD based predictive reduced-order models help accelerate EMPIC simulations, they also have the potential to facilitate investigative analysis and control applications. Here, we demonstrate the proposed DMD-EMPIC scheme for reduced-order modeling of current density and speedup in EMPIC simulations involving electron beam under the influence of magnetic field, virtual cathode oscillations, and backward wave oscillator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Sulfite Is Not Required for N 2 Reduction Catalyzed by Mo-Nitrogenase

Mo-nitrogenase catalyzes the reduction of dinitrogen (N 2 ) to two ammonia (NH 3 ) at the active-site FeMo-cofactor. Substrate activation requires the accumulation of three or four electrons and protons as two Fe-bound hydrides and is coupled to obligatory H 2 release through reductive hydride elimination. Subsequent delivery of four or five additional electrons and protons to the bound N 2 yields two NH 3 molecules. Increasing evidence suggests that at least one belt sulfide within FeMo-cofactor is dynamically involved in the catalytic cycle. A recent report further proposed that sulfite (SO 3 2− ) is required for N 2 reduction, with sulfite binding required for NH 3 release and a subsequent six-electron reduction of the bound sulfite to regenerate the resting cofactor. To test this proposal, we conducted turnover studies of Mo-nitrogenase under sulfite-free conditions using a reduced viologen as reductant and protein preparations devoid of dithionite or sulfite. Under these conditions, nitrogenase effectively catalyzed both N 2 reduction and proton reduction, exhibiting steady-state turnover under N 2 for 6 min, with a turnover number exceeding 150, approaching that observed with dithionite as reductant. The same H 2 -formed/N 2 -reduced ratio was observed whether dithionite or the viologen species was used as reductant. Further, EPR spectroscopic analyses showed that the FeMo-cofactor returned to its resting state after multiple catalytic cycles in the absence of sulfite. Finally, physiological bypass of sulfite formation does not affect the capacity for diazotrophic growth of the model nitrogen-fixing organism Azotobacter vinelandii . Finally, these results demonstrate that sulfite is not required for Mo-nitrogenase-catalyzed N 2 reduction either in vitro or in vivo .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Alternating salt and freshwater floods of coastal soils impact soil structure, hydraulic properties, and oxygen dynamics

Coastal soils are increasingly impacted by hydrologic intensification in the form of rising sea level and flooding from storm surges and precipitation. Alternating saltwater (SW) and freshwater (FW) exposure has the potential to disperse colloids, which can lead to disintegration of soil structure, clogging of pore spaces, and reduction in ecologically and biogeochemically important functions like infiltration or gas exchange. To investigate how hydrologic intensification affects soil structure and oxygen dynamics, we conducted a series of laboratory-based flood simulations. Intact soil cores of the A and B horizons (22 total) from the toe slope of an upland coastal forest along the western shore of Chesapeake Bay. We subjected the cores to 24 h of saturation with either FW or alternating brackish SW and FW, with a 24-h draining event in between flood events. Oxygen diffusion into soil during draining was reduced by up to 30% for soils flooded alternating SW-FW compared to soils flooded only with FW. We attributed the reduced oxygen diffusion to clogging of smaller pores by colloids, with colloid redistribution observed as an increase in specific surface area down the core profile of up to 59%. After three SW-FW floods (six floods total), there were significant changes in pore size distribution, significant redistribution of colloids, and the A horizon became sodic. We concluded that a small number of SW flooding events can induce a measurable change in soil physical properties that directly impacts the biogeochemical dynamics.

Rod, Kenton A. [Pacific Northwest National Laborat↗
DOE OSTI2022

Pathway to Zero Energy Windows: Advancing Technologies and Market Adoption

Improving the performance of windows has the potential to reduce U.S. annual energy use by 1.7% and CO2 emissions by 1.9% in 2050. Beyond static windows, dynamic technologies could reduce U.S. annual energy use by 1.6 quads and CO2 emissions by 68 million metric tons. In addition, the widespread adoption of static and dynamic technologies would substantially reduce peak electricity demand from buildings. Beyond energy use and CO2 emissions reductions, improving window performance will increase occupant comfort and well-being. In a new report, Pathway to Zero Energy Windows: Advancing Technologies and Market Adoption, the U.S. Department of Energy (DOE) outlines multiple avenues for technology development, deployment, and adoption to increase the impact that windows can have on decarbonizing America's buildings.

building energy efficiency↗
DOE OSTI2022

Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations

Abstract Analysis of the temperature‐ and stimulus‐dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature‐induced phase transition in BaTiO 3 is explored using the machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions.

Valleti, Sai Mani Prudhvi↗
DOE OSTI2025

Spin-State and Reorganization Energy Considerations for Metal-Centered Photoredox Catalysis

Transition-metal complexes featuring metal-centered excited states have recently emerged as mechanistically distinct platforms for selective photochemistry, including photoredox catalysis. Among these, Co(III) complexes have demonstrated productive photoinduced electron transfer via the 3 T 1 metal-centered state. In contrast, photoreactivity from the 5 T 2 metal-centered state in Fe(II) polypyridyl complexes remains limited. Building on our prior report concerning reactivity associated with the 5 T 2 state in [Fe(tren(py) 3 )] 2+ (tren(py) 3 = tris(2-pyridylmethyliminoethyl)-amine), we introduced stronger-field ligands in an effort to increase excited-state energies of Fe(II) polypyridyl complexes and enhance reactivity. Despite achieving nanosecond-scale excited-state lifetimes and favorable thermodynamic driving forces, no photoreactivity was observed. Reinvestigation of the observations previously reported for [Fe(tren(py) 3 )] 2+ revealed interactions between the metal complex and the substrate in their respective ground states that mimicked dynamic quenching of the chromophore, prompting a reassessment of mechanistic considerations inherent in leveraging reductive chemistry from the 5 T 2 excited state of Fe(II). Our analysis indicates that electron transfer from the 5 T 2 excited state of a low-spin d6 metal is subject to significant barriers both in terms of reorganization energies and spin conservation that undermines its ability to act as an electron donor for photoredox catalysis. In contrast, ligand fields that are sufficient to stabilize the 3 T 1 excited state have available to them numerous spin-allowed and, in certain cases, near-barrierless pathways to engage in excited-state electron transfer (both oxidative and reductive depending on the identity of the metal). These results highlight the critical role of spin-state changes and their associated reorganization energy requirements in metal-centered photoredox catalysis.

charge transfer↗
DOE OSTI2025

Visualizing degradation mechanisms in a gas-fed CO 2 reduction cell via operando X-ray tomography

We utilize operando X-ray computed tomography, coupled with real-time electrochemical analysis, to reveal the underlying failure mechanisms of membrane electrode assemblies (MEAs) for electrochemical CO 2 reduction (eCO 2 R). Through operando imaging, we can obtain unprecedented insights into the dynamic behavior of the MEA under different operating conditions, revealing critical changes in interface interactions, phase distribution, and structural integrity over time. Our findings identify phenomena giving rise to the transition from CO 2 R to the hydrogen evolution reaction (HER), as evidenced by shifts in cathode potential and CO 2 R selectivity. The formation of inhomogeneous precipitates at the gas diffusion electrode disrupts the CO 2 supply and reduces the active sites for eCO 2 R, resulting in a shift toward H2 production during low current density operation. Additionally, under high current density conditions, rapid water crossover up to the microporous layer/gas diffusion layer promotes the transition from CO 2 R to HER, further shifting cell potential toward anodic direction. Oscillating voltage conditions reveal the dissolution and regrowth of precipitates, providing direct visualization of the competing selectivity of CO 2 R and HER. This work offers new insight into the degradation mechanisms of MEAs, with implications for the design of more durable CO 2 R systems.

Lee, Sol A [California Institute of Technology (Ca↗
DOE OSTI2025

Effect of Transverse Compression on the Dielectric Breakdown of Power Cable with Ethylene Propylene Rubber Insulation

Understanding of the failure mechanisms of submarine dynamic power cables (SDPC) is critical for innovative design to meet the 2035 cost reduction target of U.S. DOE Floating Offshore Wind Shot. This is important because the current design suffers a significant failure rate in the field. This project explored a multiphysics approach and conducted systematic electro-thermo-mechanical (ETM) experimental study on the power cores extracted from a 15 kV power cable. The power cable had three cores of copper conductor and ethylene propylene rubber (EPR) insulation. An ETM testing system with transverse compression capability was developed. Mechanical loading induced dielectric breakdown. Increasing temperature from room temperature (RT, 22 degrees Celsius) to 90 degrees Celsius resulted in the 67% decrease in the failure mechanical load as defined by the dielectric breakdown. With the same temperature rise, the creep mechanical load was reduced by 70% at a given dielectric breakdown time. The post-test measurement revealed an impressive recovery of electrical resistance, although the dielectric breakdown occurred in the ETM test that would not be captured without the use of ETM testing.

Wang, Hong [ORNL] (ORCID:0000000201730545)↗
DOE OSTI2020

Development and Clustering of Rate-Oriented Load Metrics for Customer Price-Plan Analysis

One of the few methods electric utilities can use to motivate and change customer energy consumption is through retail rate structures. Utilities are increasingly moving toward more dynamic rate plans to encourage energy conservation, utilization of onsite renewable generation, peak demand reduction and flattening of demand profiles. This paper creates a set of rate-oriented load metrics that are the determinants of customers' bills under four unique rate plans. These metrics are not only indicative of which rate structure can provide customer bill reductions based on their load profile characteristics, but also convey useful information about load consumption behavior. With these metrics, utilities can analyze their customers and identify classes that are rewarded under each rate plan. This can help inform utilities whether the customers rewarded under each rate plans are meeting their original objectives. To develop these customer classes, we calculate these rate-oriented load metrics for each customer and perform k-means clustering. The analysis is conducted on a set of 300 customer profiles, examining four different rate plans, different numbers of clusters, customer bills and cluster load profile characteristics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Interpreting and Accelerating Transformers for Jet Tagging

Attention-based transformers are ubiquitous in machine learning applications from natural language processing to computer vision. In high energy physics, one central application is to classify collimated particle showers in colliders based on the particle of origin, known as jet tagging. In this work, we study the interpretatbility and prospects for acceleration of Particle Transformer (ParT), a state-of-the-art model, leverages particle-level attention to improve jet-tagging performance. We analyzing ParT's attention maps and particle-pair correlations in the eta-phi plane, revealing intriguing features, such as a binary attention pattern that identifies critical substructure in jets. These insights enhance our understanding of the model's internal workings and learning process and hint at ways to improve its efficiency. Along these lines, we also explore low-rank attention, attention alternatives, and dynamic quantization to accelerate transformers for jet tagging. With quantization, we achieve a 50% reduction in model size and a 10% increase in inference speed without compromising accuracy. These combined efforts enhance both the performance and the interpretability of transformers in high-energy physics, opening avenues for more efficient and physics-driven model designs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗