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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Machine Learning Approach Toward Improving QA/QC of Coated Particle Fuels

Coated particle fuels, such as the tristructural isotropic (TRISO) fuel particle, have been studied and developed for decades for high-temperature gas reactor (HTGR) applications because of their efficiency and relative stability under off-normal conditions. Critical to this development is a strong understanding of the relationship between fuel fabrication, properties and performance, as is how the former can be adjusted and improved to optimize the latter. Accordingly, wide-scale implementation of coated nuclear particle fuels requires thorough and robust quality assurance/quality control (QA/QC) methods for fabrication, characterization, and deployment

Conry, Bryan [ORNL] (ORCID:0000000236089604)↗
DOE OSTI2026

Calibrated particle identification for Belle II

We present several efforts aimed at improving charged particle identification at the Belle II experiment. We define an ablation test to quantify and evaluate the impact of each sub-detector on the global particle identification performance. We demonstrate that the performance of the identification scheme can be improved via a simple calibration of the sub-detector likelihoods through a set of per hypothesis, per sub-detector weights. Finally, we present preliminary results on an improved definition of the likelihoods contributed by the electromagnetic calorimeter. A set of multiclass boosted decision trees is trained to exploit the shape of energy depositions of different particle species. In simulated $B\bar{B}$ events, the pion-to-electron and muon fake rates are reduced by 55% and 31% respectively at low-medium momentum.

Hohmann, Marcel [Univ. of Melbourne, Parkville, VI↗
DOE OSTI2021

Impacts of Anisotropic Porosity on Heat Transfer and Off-Gassing during Biomass Pyrolysis

The pore structure of biogenic materials imbues the ability to deliver water and nutrients through a plant from root to leaf. This anisotropic pore granularity can also play a significant role in processes such as biomass pyrolysis that are used to convert these materials into useful products like heat, fuel, and chemicals. Evolutions in modeling of biomass pyrolysis as well as imaging of pore structures allow for further insights into the concerted physics of phase change-induced off-gassing, heat transfer, and chemical reactions. In this work, we report a biomass single particle model which incorporates these physics to explore the impact of implementing anisotropic permeability and diffusivity on the conversion time and yields predicted for pyrolysis of oak and pine particles. Simulation results showed that anisotropic permeability impacts predicted conversion time more than 2 times when the Biot number is above 0.1 and pyrolysis numbers (Py 1 , Py 2 ) are less than 20. Pore structure significantly impacts predicted pyrolytic conversion time (>8 times) when the Biot number is above 1 and the pyrolysis number is below 1, i.e., the “conduction controlled” regime. Therefore, these nondimensional numbers reflect that when internal heat conduction limits pyrolysis performance, internal pyrolysis off-gassing further retards effective heat transfer rates as a closely coupled phenomenon. Overall, this study highlights physically meaningful opportunities to improve particle-scale pyrolysis modeling and experimental validation relevant to a variety of feedstock identities and preparations, guiding the future design of pyrolyzers for efficient biomass conversion.

09 BIOMASS FUELS↗
DOE OSTI2020

Improved Limits on Millicharged Particles Using the ArgoNeuT Experiment at Fermilab

A search for millicharged particles, a simple extension of the standard model, has been performed with the ArgoNeuT detector exposed to the Neutrinos at the Main Injector beam at Fermilab. The ArgoNeuT liquid argon time projection chamber detector enables a search for millicharged particles through the detection of visible electron recoils. We search for an event signature with two soft hits (MeV-scale energy depositions) aligned with the upstream target. For an exposure of the detector of 1.0×10 20 protons on target, one candidate event has been observed, compatible with the expected background. This search is sensitive to millicharged particles with charges between 10 -3 $e$ and 10 -1 $e$ and with masses in the range from 0.1 to 3 GeV. This measurement provides leading constraints on millicharged particles in this large unexplored parameter space region.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Intubation Containment System for Improved Protection From Aerosolized Particles During Airway Management

Worldwide efforts to protect front line providers performing endotracheal intubation during the COVID-19 pandemic have led to innovative devices. Authors evaluated the aerosol containment effectiveness of a novel intubation aerosol containment system (IACS) compared with a recently promoted intubation box and no protective barrier. In a simulation center at the authors’ university, the IACS was compared to no protective barrier and an intubation box. Aerosolization was simulated using a commercial fog machine and leakage of aerosolize mist was visually assessed. The IACS appeared to contain the aerosolized mist, while the intubation box allowed for mist to contact the laryngoscopist and contaminate the clinical space through arm port holes and the open caudal end. Both devices protected the laryngoscopist better than no protective barrier. The IACS with integrated sleeves and plastic drape appears to offer superior protection for the laryngoscopist and assistant providers from aerosolized particles.

aerosols↗
DOE OSTI2022

Performance in beam tests of irradiated Low Gain Avalanche Detectors for the ATLAS High Granularity Timing Detector

The High Granularity Timing Detector (HGTD) will be installed in the ATLAS detector to mitigate pile-up effects during the High Luminosity (HL) upgrade of the Large Hadron Collider (LHC) at CERN. The design of the HGTD is based on the use of Low Gain Avalanche Detectors (LGADs), with an active thickness of 50 μm, that allow to measure with high-precision the time of arrival of particles. The HGTD will improve the particle-vertex assignment by measuring the track time with a resolution ranging from approximately 30 ps at the beginning of the HL-LHC operations to 50 ps at the end. Performances of several unirradiated, as well as neutron- and proton-irradiated, LGAD sensors from different vendors have been measured in beam test campaigns during the years 2018 and 2019 at CERN SPS and DESY. This paper presents the results obtained with data recorded by an oscilloscope synchronized with a beam telescope which provides particle position information within a resolution of a few μm. Collected charge, time resolution and hit efficiency are presented. In addition to these properties, the charge uniformity is also studied as a function of the position of the incident particle inside the sensor pad.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Spouted bed with venturi draft tube and method for improved gas-solid contacting

A Venturi draft tube spouted bed and method are disclosed that enables scale-up with small particles and improves heat and mass transfer by increasing material turnover rate. A Venturi-style eductor has been incorporated into the spouted bed draft tube to provide suction at the bottom of the bed to better entrain material and reduce the propensity for dead zones at the bottom of the narrow conical section.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Mesoscale fractal whey protein particles derived from microscale linear-shaped protein assemblies (Part 1): Manufacturing method and particle characteristics

Whey protein isolates (WPI) are widely used in processed foods for their versatile functional properties. Modifying the structural properties of proteins by assembling them into mesoscale or microscale particles may improve their functionality and broaden their applications. This study aims to manufacture and characterize mesoscale whey protein particles (WPP) derived from WPI. Two types of WPP, WPP1 (0.05 mL/min) and WPP2 (0.25 mL/min), were prepared through a multistep approach involving liquid antisolvent (LAS) precipitation, heat treatment, and microfluidization. Liquid antisolvent precipitation was performed by injecting a 20% (wt/vol) WPI dispersion (pH 7) into an ethanol-glycerol mixture (75:25, vol/vol) under laminar flow, followed by heat treatment at 80°C for 20 min as a particle hardening step. This process produced stable fiber- and ribbon-shaped whey protein assemblies (WPA), which served as precursors to WPP. Subsequent microfluidization (150 MPa, 6 passages) reduced the size of WPA, yielding mesoscale WPP with irregular morphologies and a more uniform size distribution, as revealed by microscopy and dynamic light scattering. ζ-Potential and fluorescence labeling indicated higher surface charge and surface hydrophobicity of WPP compared with untreated WPI. The WPP showed internal mass fractal and surface fractal structures at larger length scales, analyzed using small-angle X-ray scattering. Fourier transform infrared spectroscopy demonstrated an increased fraction of intermolecular β-sheets in WPP, suggesting that hydrogen bonding contributed to their formation. Gel electrophoresis confirmed that disulfide bonds served as the primary cross-links stabilizing the WPP structure. Furthermore, turbidity measurements showed that WPP exhibited superior colloidal phase stability compared with untreated WPI and maintained high colloidal stability under both acidic and neutral pH conditions.

Antisolvent precipitation↗
DOE OSTI2024

Probing Thermal Transport in Fluidized Bed Using Modulated Photothermal Radiometry

Abstract In concentrated solar power (CSP) applications, fluidized bed is a promising approach for high heat transfer coefficient (HTC) solar receivers and heat exchangers. However, the complexity of multiphase mixing has made it difficult to characterize and analyze the heat transfer mechanism. This paper presents an experimental study on simultaneously characterizing heat transfer in both the near-wall and the bulk regions of a fluidized bed using modulated photothermal radiometry (MPR). The MPR is a non-contact frequency-domain technique using an intensity-modulated laser as the heat source and surface infrared emission as thermometry. The thermal penetration depth of the laser heating is varied by controlling its modulation frequency, and thus the measurement can resolve the near-wall and the bulk thermal resistances. With the MPR technique, we measured fluidized silica sands with a mean size of 164 μm in a vertical channel of 6 mm depth. Our results show that the near-wall thermal resistance is substantially increased with increasing gas velocity, which partially offsets the benefit of higher HTC brought by stronger particle mixing during the fluidization. We also used the MPR to quantify the improvement in particle-wall heat transfer in an inclined channel. We found that an 8° inclination towards the heat exchanging side led to a lower near-wall thermal resistance and a higher HTC at high gas velocities. This work demonstrates that the MPR technique is a useful tool to quantify the important near-wall thermal resistance from a bulk particle bed, which not only advances our understanding of heat transfer in fluidized beds, but may also contribute to the design of fluidized bed heat exchangers with higher HTC.

14 SOLAR ENERGY↗
DOE OSTI2023

Semi-supervised graph neural networks for pileup noise removal

Abstract The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton–proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mitigation algorithms are designed to remove this noise from pileup particles and improve the performance of crucial physics observables. This study implements a semi-supervised graph neural network for particle-level pileup noise removal, by identifying individual particles produced from pileup. The graph neural network is firstly trained on charged particles with known labels, which can be obtained from detector measurements on data or simulation, and then inferred on neutral particles for which such labels are missing. This semi-supervised approach does not depend on the neutral particle pileup label information from simulation, and thus allows us to perform training directly on experimental data. The performance of this approach is found to be consistently better than widely-used domain algorithms and comparable to the fully-supervised training using simulation truth information. The study serves as the first attempt at applying semi-supervised learning techniques to pileup mitigation, and opens up a new direction of fully data-driven machine learning pileup mitigation studies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Understanding the Chemomechanical Function of the Silver–Carbon Interlayer in Sheet-type All-Solid-State Lithium–Metal Batteries

All-solid-state batteries with lithium metal anodes hold great potential for high-energy battery applications. However, forming and maintaining stable solid–solid contact between the lithium anode and solid electrolyte remains a major challenge. One promising solution is the use of a silver–carbon (Ag–C) interlayer, but its chemomechanical properties and impact on interface stabilities need to be comprehensively explored. Here, we examine the function of Ag–C interlayers in addressing interfacial challenges using various cell configurations. Experiments show that the interlayer improves interfacial mechanical contact, leading to a uniform current distribution and suppressing lithium dendrite growth. Furthermore, the interlayer regulates lithium deposition in the presence of Ag particles via improved Li diffusivity. The sheet-type cells with the interlayer achieve a high energy density of 514.3 Wh L –1 and an average Coulombic efficiency of 99.97% over 500 cycles. Furthermore, this work provides insights into the benefits of using Ag–C interlayers for enhancing the performance of all-solid-state batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6)

Abstract Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC ( R BC , the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use R BC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust R BC to make the modeled BC mixing state index ( χ mode ) match the one predicted by the ML model ( χ ML ). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χ mode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χ ML (66%). The R BC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs.

Meteorology & Atmospheric Sciences↗
DOE OSTI2025

Intralattice-bonded phase-engineered ultrahigh-Ni single-crystalline cathodes suppress strain evolution

Single crystallization remains a debated strategy for advancing Ni-rich cathode materials. While it mitigates particle cracking and improves tap density by eliminating particle boundaries, extended diffusion pathways introduce volumetric and lattice distortions, compromising electrochemical and structural stability. These challenges hinder the commercialization of high-Ni single-crystal cathodes, calling for a reassessment of their viability. Here, in this study, we report a structural design: intralattice-bonded phase single-crystal LiNi 0.92 Co 0.03 Mn 0.05 O 2 (IBP-SC92). This architecture maintains structural integrity while shortening diffusion pathways, resulting in almost zero electrochemical degradation during cycling. The robust structure and fast ion transport mitigate lattice strain, as confirmed by multiscale high-resolution diffraction and imaging techniques, preventing intragranular cracks and irreversible phase transitions. As a result, IBP-SC92 shows outstanding cycling stability, with nearly 100% capacity retention after 100 cycles in half cells and 94.5% retention after 1,000 cycles in full cells. This redefined single-crystal cathode represents a significant step towards the industrial adoption of high-energy-density materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Reinforcement Learning for Load-balanced Parallel Particle Tracing

We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a work donation algorithm, (2) a high-order workload estimation model, and (3) a communication cost model. First, we design an RL-based work donation algorithm. Our algorithm monitors workloads of processes and creates RL agents to donate data blocks and particles from high-workload processes to low-workload processes to minimize program execution time. The agents learn the donation strategy on the fly based on reward and cost functions designed to consider processes' workload changes and data transfer costs of donation actions. Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations. Third, we design a communication cost model that considers both block and particle data exchange costs, helping RL agents make effective decisions with minimized communication costs. We demonstrate that our algorithm adapts to different flow behaviors in large-scale fluid dynamics, ocean, and weather simulation data. Our algorithm improves parallel particle tracing performance in terms of parallel efficiency, load balance, and costs of I/O and communication for evaluations with up to 16,384 processors.

Distributed and parallel particle tracing↗
DOE OSTI2024

Effect of post-deposition heat treatment on microstructure and mechanical properties of NASA HR-1 cold spray coatings

The primary goal of this work was to examine the impact of heat treatment on the evolution of microstructure and mechanical properties of NASA HR-1 cold spray coatings. Coatings were produced employing a high pressure cold spray system using N2 and He process gases and the effect of process gas and deposition temperature on the microstructure and mechanical properties of the coatings was examined. Microstructural characterization was performed using optical microscopy, scanning electron microscopy, micro X-ray computed tomography, and electron backscatter diffraction. NASA HR-1 coatings produced with He process gas exhibited improved plastic deformation and resulted in the lowest porosity compared to those deposited with N2 process gas. All coatings showed brittle failure with limited ductility in the as-deposited condition. Heat treatment of the NASA HR-1 cold spray coatings was performed at 550°C and 950°C for 1 h to improve the mechanical strength of the coatings. Heat treatment improved the particle bonding and enhanced the mechanical properties of the cold spray coatings. Heat treatment performed at 550°C resulted in higher mechanical strength of coatings, whereas the heat treatment performed at 950°C resulted in recrystallized microstructure and improved ductility of the coatings. However, heat treatment at 950°C resulted in forming the Eta (η) phase and Ni-Ti intermetallics in all the NASA HR-1 cold spray coatings. Here, the overall results suggest that using He as process gas contributed to reduced porosity and enhanced mechanical properties of the cold spray coatings.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Anisotropic Thermally Conductive Composites Enabled by Acoustophoresis and Stereolithography

Opportunities to improve thermal management in electronic devices are currently hindered by processing constraints that limit thermal conductivity in polymer-matrix composites. Active patterning of filler particles is a promising route to improve conductivity while retaining processability by improving particle contact density and directing heat along optimized pathways. Here, we employ acoustic patterning to align and compact filler particles into stripes during stereolithographic 3D printing. This approach produces polymer-based composite materials with highly efficient embedded heat transport pathways which reach 95 vol% particle utilization (relative to the parallel conduction upper limit). These composites exhibit anisotropic thermal conductivity up to 300% higher than unpatterned composites, with in-plane anisotropy ratios of up to 350%. Combining this high conductivity with 3D printing enables materials with engineered heat networks that optimize transport from hot spots to heatsinks while maintaining low viscosity for fast particle patterning and for infiltration around electronic components. Finally, numerical simulations of acoustic assembly of particles with varied geometry, when compared to experimentally characterized particle packing, illuminate pathways for further improving conductivity by optimizing particle geometry for alignment and stacking of particles with maximum contact surface area.

36 MATERIALS SCIENCE↗