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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enabling fast-charging of lithium-ion batteries through printed electrodes

It has been well recognized that introducing secondary porous networks (SPNs) into the electrodes can effectively improve the electrochemical performance of lithium-ion batteries (LIBs), especially under fast-charging operations. However, the process complexity and high cost limit the commercial success of advanced electrodes with SPNs. To address this issue, we developed a facile screen-printing process to produce structured graphite electrodes with SPNs. The experimental results demonstrated that, by tuning the diameter and center-to-center (C2C) distance of emulsion dots on the stencil screen, the pore diameters and C2C pore distances of SPNs in screenprinted electrodes can be precisely controlled in the range of 100 mu m to 1 mm and 100 mu m to 3 mm respectively. In addition, the SPNs with hexagonal and square-shape pore alignments have also been imprinted onto the electrode coatings through adjusting the patterns of screen stencils. Used as anodes, the printed graphite electrodes demonstrated significantly reduced overpotential and voltage fluctuation under fast-charging operations from 2C to 6C. Coupled with LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) cathodes, the full cells with printed graphite anodes exhibited an unprecedently stable performance with almost no capacity decay up to 170 cycles when charged to 80 % SOC at 2C. Observations from electron microscopy showed plated lithium undetectable at the surface of printed graphite electrodes after numerous cycles. The electrochemical analysis on the voltage evolution during the cell rest period indicated the significantly delayed onset of lithium plating in the presence of printed graphite electrodes. In conclusion, all these results suggest that the significantly improved cell performance is associated with the shortened Li-ion diffusion distance, reduced polarization and suppressed Li plating in the printed electrodes with patterned SPNs.

25 ENERGY STORAGE↗
DOE OSTI2020

An open source fast fluid dynamics model for data center thermal management

Although computational fluid dynamics (CFD) has been widely adopted to improve data center thermal management, the high computational demand limits its applications, such as multivariate optimal design and operation. Fast fluid dynamics (FFD), which has been applied for fast airflow simulation, shows great potential. However, few research applied FFD for optimal design and operation of data center thermal management. This research improves the FFD model for data centers and conducts a comprehensive evaluation and demonstration. First, the FFD model is improved by solving the advection and diffusion equations together using an upwind scheme instead of a semi-Lagrangian advection solver in the conventional FFD model. Second, new features for data centers are added, such as a pressure correction method to simulate plenum airflow and dynamic boundary conditions for IT racks. The new FFD model is first validated with two indoor environment cases and the results show that the new FFD model has slightly better overall prediction accuracy and faster speed compared to the conventional FFD model. It is also observed that both FFD models achieve acceptable accuracy, except for a few localized disparities with experimental data, which might be due to simplified handling of turbulence viscosity near the boundaries. Furthermore, validation with a real data center shows that the FFD model achieves a similar level of accuracy as CFD when compared to the experimental measurements with some level of uncertainties. It is then demonstrated for data center optimal design and operation, which saves 53.4–58.8% of annual energy while still meeting the thermal requirements. In conclusion, with a much faster speed and comparable accuracy compared to CFD, the FFD model parallelized on a graphics processing unit is promising for practical model-based data center early design and operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Extended cycle life implications of fast charging for lithium-ion battery cathode

Enabling extreme fast charging (XFC, =10–15 min charging) requires a comprehensive understanding of its implications. While lithium plating is a key bottleneck for the anode, the full extent of limitations for the cathode are not well-understood, particularly in extended-cycle settings with well-defined battery designs and conditions. This article presents cycle-life implications of XFC on cathodes at multiple length scales, combining electrochemical analyses, degradation modeling, and post-test characterizations. The comprehensive test matrix includes 41 well-defined gr/NMC pouch cells under varied fast-charge rates (1–9C) and state-of-charges cycled up to 1000 times. Cathode issues remain minimal in early cycling, but begin to accelerate in later life, when distinct cracking is found and identified as a fatigue mechanism. The bulk structure of cathodes remains intact, but distinct particle surface reconstruction is observed; however, this shows less pronounced effect on cathode aging than does cracking.

25 ENERGY STORAGE↗
DOE OSTI2024

Levelized cost of charging of extreme fast charging with stationary LMO/LTO batteries

Extreme DC fast charging for electric vehicles (EVs) could be competitive with the internal combustion engine refueling experience and enable longer-distance travel, which could help with EV adoption and decarbonization, but these systems have high capital costs and extremely variable high-power demands. Behind-the-meter systems (BTMS) could support extreme-fast-charging (XFC) stations to increase nationwide adoption of EVs. Here, this study examines the optimal break-even levelized cost of charging (LCOC) across 96 BTMS scenarios to enable low-wait XFC stations providing 200 miles of charge in 10 min. This research simulates LCOC via synthetic XFC-capable EV loads, machine-learned battery life models from testing data, and nonlinear optimal controls, co-minimizing complex utility costs and battery replacements. An aggregate optimal BTMS design treating each EV load as equal likely gives an optimal LCOC per utility rate, the average of which is $\$$0.59/kWh. In addition, the sensitivity of optimal and off-optimal design factors, the long-life LMO/LTO chemistry, and optimized controls are analyzed. The battery control model, based on battery stressors to compare chemistries, optimizes LMO/LTO resting state of charge and cycle depth without compromising cost reduction, which enables greater flexibility in operation. The LCOC savings due to replacement reduction are small, up to $\$$0.035/kWh (6%), with an average of $\$$0.02/kWh (3.5%). Compared with gasoline stations, the aggregate XFC station design achieves comparable speed, experience of service, and cost at $\$$3.81/gal gasoline, showing that EVs can replace gasoline vehicles even for longer-distance travel.

25 ENERGY STORAGE↗
DOE OSTI2026

Theoretical background for a fast flow liquid metal divertor experiment

Tokamak Energy has developed a liquid metal experiment featuring a lithium loop that circulates lithium through the HIDRA stellarator at the University of Illinois at Urbana-Champaign. The loop includes a replaceable divertor module, designed to demonstrate fast, steady, open-surface liquid metal flow. The first-generation divertor module was deliberately kept simple—an open-surface chute—to enable modelling validation and establish a benchmark for future design iterations. This paper presents the theoretical foundations of the experimental design. Specifically, we identify module overflooding as the primary challenge and determine the limits of fluid velocity and magnetic field strength necessary to prevent it. The steady-state flow patterns are expected to exhibit relatively slow surface velocities, which do not fully align with the fast-flow concept. Nevertheless, the experiment is designed to achieve controllable, continuous open-surface flow within an operational fusion device, representing a significant step toward the development of liquid metal divertor technology.

Experiment↗
DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interface chemistry informs about cathode and anode degradation during fast charging

Here, the aim of this study is to examine the development and makeup of the Solid Electrolyte Interphase (SEI) and Cathode Electrolyte Interphase (CEI) in lithium-ion batteries during rapid charging. Using X-ray photoelectron spectroscopy (XPS) and depth profiling, we investigate the chemical modifications occurring on the electrode surfaces during the initial formation and subsequent fast charge-discharge cycles. Our research shows that the anode's SEI initially comprises a thin layer rich in ketones, which then transforms into a thicker layer dominated by carbonaceous compounds during rapid charging cycles. Following assembly, lithium fluoride (LiF) quickly becomes a key element of the SEI, and its presence continues to grow substantially during the formation cycle, remaining the primary component throughout subsequent cycles. Initially, the cathode forms a thin oxide layer rich in ketones, with no noticeable carbonaceous CEI. The CEI primarily comprises LiF, which experiences an increase during the formation cycle and retains a thin layer of carbon coating after the initial rapid discharge. These findings show how the interphase layers impact the performance and stability of lithium-ion batteries, especially during fast charging for electric vertical take-off and landing (eVTOL) vehicles.

Fast charging↗
DOE OSTI2023

Ultra-fast and sensitive magneto-optical hydrogen sensors using a magnetic nano-cap array

Magnetism in curved nano-geometries has opened a new path to design novel characteristics and phenomena that could greatly impact the future fundamental and applied studies of materials. Here, in this report, a hexagonally-packed array of magnetic nano-caps (or nano-patches, NP) exhibiting unique magnetic anisotropy and optimal nanostructure for fast hydrogen kinetics is explored as a sensing element for the spark-free magnetic-circular-dichroism (MCD) H 2 sensor. The MCD H 2 sensor outperforms the state-of-the-art optical sensors reported to date and satisfies the most challenging performance targets imposed by US Department of Energy. In particular, a Pd 67 Co 33 NP sensor exhibits response time of t 90 < 0.9 s, recovery time of t 10 < 9.0 s over the 1-100 mbar H 2 partial pressure range, and limit of detection (LOD) of ~1 ppm. The LOD improves to <700 ppb, when the signal-to-noise ratio is enhanced by stacking three NP arrays. Using a 30-nm TAF (Teflon AF 2400) polymer coating, the sorption kinetics of the Pd 67 Co 33 /TAF sensor are significantly accelerated, with t 90 < 0.4 s and t 10 < 2.8 s over the same pressure range. When the Pd 67 Co 33 /TAF sensor is further coated with a 100-nm poly(methyl methacrylate) (PMMA) layer, a rapid sorption time of t 90 < 0.5 s, LOD < 1 ppm and excellent sensor accuracy (<2.5% full scale) are maintained, while the sensor obtains strong selectivity against interference gases and moisture and a negligible aging effect. The MCD nano-cap sensor platform may have a great impact on the future deployment of H 2 fuel, H 2 environmental monitoring sensors, and fast proton-based magneto-ionic devices.

Fast and sensitive hydrogen sensors↗
DOE OSTI2024

Multi-principal element alloys for fast reactor cladding applications

Given the extensive list of multi-principal element alloys (MPEAs) within literature and the overwhelming number of alloys that can be made from only a handful of elements, this article proposes a methodology for prioritizing alloys for use as cladding within advanced reactors. This paper applies neutronic, mechanical, and chemical assessments to a collection of MPEAs available within literature for use as advanced reactor cladding. The results are compared to a reference design of HT9, a ferritic/martensitic steel, for employed in sodium-cooled fast reactor. Mechanical assessments determined pressure limits for a thin-walled tube pressure boundary, thus relating the material's mechanical properties to a minimum acceptable wall thickness. Neutronic analyses reveal a maximum allowable wall thickness that a given material must meet to provide a level of neutronic economy that is equivalent to than that of HT9. Lastly, each alloying element is compared to typical fission products found in a fast reactor fuels to mitigate deleterious phenomena such as fuel-cladding chemical interactions (FCCI). These analyses indicate that Mo-Nb-Ti-V-based alloys are likely to be advantageous and that the inclusion of elements with a high neutronic penalty (e.g., Hf) could be considered with minimal consequences.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

First evaluation of fast neutron imaging with LiInSe 2 semiconductors

Fast neutron imaging is a powerful tool to investigate elemental/isotopic compositions of objects, supporting both scientific studies as well as cargo scanning. Current neutron imaging systems are faced with challenges associated with timing, detection efficiency, and/or spatial resolution. Here, we report on the use of a semiconducting lithium indium diselenide neutron sensor coupled to a Timepix ASIC for fast neutron imaging. Using a 15 cm thick copper knife edge, the spatial resolution of the neutron imager was found to be 1.55 mm for 9 MeV neutrons. In conclusion, the experimental detection efficiency at 9 MeV was in general agreement with calculations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Characterization and pulse-shape discrimination of a multi-stacked structure of ZnS: Ag/PMMA for fast-neutron detection in high-flux environments

The Micro-Layered Fast Neutron Detector (MLFD) is a proton-recoil scintillator that incorporates the basic concept of a Hornyak button detector with an improved design using a layered structure of ZnS:Ag and PMMA (polymethyl methacrylate). The MLFD was predicted to have low sensitivity to gamma rays, hence pulse shape discrimination was performed to determine the gamma-ray contribution. Presented in this paper are the pulse-shape discrimination characteristics of the MLFD. Additionally, the charge integration technique was employed to discriminate between the neutrons and gamma rays using a fast digitzer. The MLFD was found to exhibit excellent neutron/gamma-ray discrimination, with the highest Figure-of-Merit (FoM) being 4.56. With PSD, the neutron signal is distinct from gamma-ray, Ĉerenkov and electronic noise signals. This clear separation was achieved due to the large differences in decay times of the neutron and gamma-ray-induced pulses in the ZnS:Ag phosphor. The MLFD can be employed in high gamma-ray exposure fields, up to at least 1287.7 mR/hr, without appreciable degradation in PSD performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Improvement in the spatial resolution for imaging with fast neutrons

In this paper, we report on the realization of an improved concept for the detection of fast neutrons using a specific setup of scintillators with the aim to overcome limitations in spatial resolution. While fast neutron imaging (FNI) is a technique to investigate very thick material layers in transmission mode, currently there is a limit in the spatial resolution at ~1 mm, which hinders the performance for the detection of features like cracks, material damage or pores in large objects ($\gg$1 cm 3 ). The improved concept presented here is based on the separation of the converter material from the scintillation layer in a suitable way, which was successfully tested and referenced to the standard devices at the NECTAR facility (FRM-2, MLZ, Garching) under realistic beamline conditions. The results imply a break-through in enabling detection of structures on the order of 0.2 to 0.5 mm (a more precise estimation is not possible due to other resolution determing factors in a realistic setup). Potential additional improvements are discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Digital pulse analysis for fast neutron recoil spectroscopy with a 4 He scintillation detector

Helium-4-based fast neutron scintillation detectors are an attractive alternative to pulse-shape discrimination-capable organic scintillators for fast neutron detection and spectroscopy, as the response of the detectors to gamma rays is intrinsically limited to low energy deposition. Consequently, the neutron recoil distribution can be measured with these detectors without the need for pulse shape analysis. In this work, the response of an Arktis S670 4 He scintillation detector to D-D, D-T, and 252 Cf neutrons was measured. The detector has a unique construction and readout mechanism, with multiple output channels observing the same scintillation event, and an analysis method was developed to aggregate the outputs from all channels into a single list. The D-D and D-T neutron responses were used to perform a two-point energy calibration, which yielded a near-zero intercept, suggesting that the 4 He scintillation medium behaves linearly to a higher energy than previously reported, and that a two-point calibration is sufficient for nuclear recoil energies below 9 MeV. As a result, the detector was measured to have 16.7-ns FWHM time resolution when using the developed custom analysis, a reduction of 4.9 ns when compared to the conventional pulse analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Reconstruction of fast neutron direction in segmented organic detectors using deep learning

A method for reconstructing the direction of a fast neutron source using a segmented organic scintillator-based detector and deep learning model is proposed and analyzed. Here, the model is based on recurrent neural network, which can be trained by a sequence of data obtained from an event recorded in the detector and suitably pre-processed. The performance of deep learning-based model is compared with the conventional double-scatter detection algorithm in reconstructing the direction of a fast neutron source. With the deep learning model, the uncertainty in source direction of 0.301 rad is achieved with 100 neutron detection events in a segmented cubic organic scintillator detector with a side length of 46 mm. To reconstruct the source direction with the same angular resolution as the double-scatter algorithm, the deep learning method requires 75% fewer events. Application of this method could augment the operation of segmented detectors operated in the neutron scatter camera configuration for applications such as special nuclear material detection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Studying the effect of the upper internal structure design on thermal stratification in sodium fast reactors

Thermal stratification and mixing in large reactor pools can pose significant issues with regard to operation and safety of nuclear plants. While significant work has been done in this area on light water reactors it is more critical for sodium reactors and there is far less data with regard to low Prandtl number fluids. Here this paper focuses on the effect of the upper internal structure on the formation of thermal stratification in a scaled sodium fast reactor upper plenum. Six different upper internal structure configurations were tested to observe the effect on thermal stratification. The degree of stratification was quantified using the stratification number. Each configuration was tested from low to high flow rates at a temperature difference of 50 and 100 °C. Over this broad array of testing, it was observed that higher flow rates, more porosity in the upper internal structure design, and a smaller temperature difference yielded a lower degree of stratification. This information can be used to aid in the design of the upper internal structure in a sodium fast reactor. Furthermore, this high resolved temperature data can be used to verify models under prototypic low Prandtl numbers.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Advances in solid-state NMR for the studies of mesoporous solids: fast magic spinning and dynamic nuclear polarization

Here, this review provides an up-to-date account of the development of two solid-state (SS)NMR methods for enhancing resolution and sensitivity, fast magic angle spinning (MAS) and dynamic nuclear polarization (DNP), and the resulting progress in surface science. We demonstrate the high resolution and efficiency that can be achieved by using two-dimensional homo- and heteronuclear correlation experiments with small rotors capable of MAS at rates exceeding 100 kHz. DNP has offered significant enhancements in signal sensitivity and allowed access to nuclei and experiments that are beyond the limits of conventional SSNMR. The continuing progress in fast MAS and DNP methodologies in recent years generated an unprecedented shift in SSNMR’s capabilities in the studies of surface and interface regions of solids, especially mesoporous supports and catalysts. We give numerous examples of recent applications and discuss the prospects for further improvements of both methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A status review on the thermal stratification modeling methods for Sodium-cooled Fast Reactors

The thermal stratification phenomenon plays a crucial role in the safety of various nuclear systems, including the Gen-III + Light Water Reactors (LWR) and the Gen-IV reactors. The phenomenon is of particular importance for the pool-type Sodium-cooled Fast Reactors (SFRs) because it may cause neutronic and thermal-hydraulic in- stabilities in the reactor core, or lead to damages of both the reactor vessel and in-vessel components due to the growth of thermal fatigue cracking. More significantly, thermal stratification could impede the establishment of the natural circulation during accidental scenarios and introduce uncertainties to the core safety of SFRs. Efforts for modeling of the thermal stratification in SFRs have been made for decades to prevent or mitigate the damage caused by the phenomenon. This paper gives a review of the advances that have been made in recent 10 years on the computational modeling methods for thermal stratification phenomenon in SFRs. These methods can be generally drawn into two categories. The first one is the system-level methods which provide fast-running but approximate calculations, and the second one is the CFD methods which provide high-resolution calculations at high computational expense. Finally, after introducing the efforts that have been made to improve the one- dimensional (1-D) models, the paper envisioned the possible research directions that could be pursued to enhance the modeling of thermal stratification in the near future.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Detecting onset of lithium plating during fast charging of Li-ion batteries using operando electrochemical impedance spectroscopy

Electrochemical plating of Li metal on the graphite electrode is the key limitation behind slow charging times of Li-ion batteries (LIBs) in electric vehicles (EVs). Currently, electrochemical methods to detect the onset of Li plating while a battery is fast charging are sparse. In this study, we use operando electrochemical impedance spectroscopy to reliably detect the onset of Li plating on graphite electrodes in three-electrode LIBs. An increase in the graphite solid-electrolyte interface (SEI) resistance indicates that Li plating has occurred. By cross-validating with a highly sensitive ex situ chemical titration, we determine that this technique can detect very small amounts of plated Li (<0.6% of the graphite electrode's capacity). We also offer physical explanations for the observed impedance behavior. Finally, we show that this technique can be applied to standard two-electrode LIB systems, making the method an important step toward safely implementing fast charging protocols for LIBs in EVs.

25 ENERGY STORAGE↗