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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Energy resolution of the LZ detector for high-energy electronic recoils

The LUX-ZEPLIN (LZ) detector is a dual-phase liquid xenon time projection chamber (TPC) installed at the Sanford Underground Research Facility (Lead, South Dakota) at a depth of 1478 meters. Although the main objective of LZ is the direct detection of dark matter, its low background environment allows for the search of other rare processes, such as the neutrinoless double beta decay of xenon isotopes 134 Xe and 136 Xe with the respective Q-values of 826 keV and 2458 keV. The sensitivity of the detector to these decays is directly determined by the energy resolution, which, in turn, is degraded by non-uniformities in detector response. In this work, we present a novel method to correct, in the data, the non-uniformity of the light collected by an array of photosensors in a scintillation detector. This method is based on the knowledge of the light response functions of individual photosensors. With these techniques, we report, at a very early phase of the detector operations, a state-of-the-art energy resolution (σ/μ) of (0.67 ± 0.01)% at 2614 keV for the fiducial volume of 5.6 tonnes of liquid xenon.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

α -decay energies of superheavy nuclei: Systematic trends

Background: New superheavy nuclei are often identified through their characteristic α -decay energies, which requires accurate calculations of Q α values. While many Q α predictions are available, little is known about their uncertainties, and this makes it difficult to carry out extrapolations to as-yet-unknown systems. Purpose: This work aims to analyze several models, compare their predictions to available experimental data, and study their performance for the unobserved α-decay chains of 296 120 and 298 120, which are of current experimental interest. Our quantified results will also serve as a benchmark for future, more sophisticated statistical studies. Methods: We use nuclear superfluid density functional theory (DFT) with several Skyrme energy density functionals (EDFs). To estimate systematic model uncertainties, we employ uniform model averaging. Results: We investigated the Q α values for even-even nuclei from Fm to Z = 120 . For well-deformed nuclei between Fm and Ds, we find excellent consistency between different model predictions, and a good agreement with experimental results. For transitional nuclei beyond Ds, intermodel differences grow, resulting in an appreciable systematic error. In particular, our models underestimate Q α for the heaviest nucleus 294 Og. Conclusions The robustness of DFT predictions for well-deformed superheavy nuclei supports the idea of using experimental Q α values, together with theoretical predictions, as reasonable ( Z, A ) indicators. Unfortunately, this identification method is not projected to work well in the region of deformed-to-spherical shape transition as one approaches N = 184 . The use of Q α values in the identification of new superheavy nuclei will benefit greatly from progress in developing both new spectroscopic-quality EDFs and more sophisticated statistical techniques of uncertainty quantification.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Hydraulic Response to Thermal Stimulation Efforts at Raft River Based on Stepped Rate Injection Testing

The injection well stimulation project at the Raft River geothermal field tests the effect of long-term cold water injection and high pressure injection on well injectivity, improvements to which could reduce operating costs. The primary data for analysis and interpretation of the injection test are step-rate flow tests run before each new phase of the injection. These tests were analyzed using a combination of standard pump-test analytical solution methods and methods developed expressly for the observed conditions. The stepped rate injection tests, combined with long-term flow and pressure response data suggest that the well is located within a fractured formation of low transmissivity but high storativity. These calculated parameters appeared to increase with pressure during the first injection test and the higher values were reproduced during the second stepped rate test. Calculated transmissivity and storativity are on the order of 4E-5 m cm 2 and 1E-4 m Pa -1 , respectively. The apparent pressure dependence of fitted hydraulic parameters may reflect near-well fracture compliance that increased the effective radius of the wellbore during the first test. While the type curve fit analysis also suggests that the reservoir behaves as a uniformly fractured reservoir with a radial flow regime, the hydraulic parameters indicate that condition may exist only a very limited distance (<10 m) from the well. Longer-term pressure response suggests that flow in the system effectively reaches steady state in a period of less than a day, which may reflect pressure stabilization resulting from pressure-dependent permeability or a region of much higher permeability located with a few meters of the well. The transmissivity estimates obtained from this analysis, converted to approximate fracture density and aperture, provide useful constraints on the distance to which the thermal front may migrate from the well during the cold water injection phase of the stimulation project. We estimate that the cooling front will migrate less than a tenth of a kilometer over an approximately one-year injection period. Here, the effects of that cooling, however, may be substantial, because increases in permeability have maximum effect nearest the well.

cold water injection↗
DOE OSTI2026

Challenging conventional assumptions in PV: a high-throughput open-air approach to low-cost perovskite module production

Perovskite solar modules (PSMs) offer a promising pathway to low-cost photovoltaics, yet their commercialization is challenged by manufacturing scalability, device uniformity, additive costs, interlayer complexity, and module stability. This study introduces a comprehensive technoeconomic analysis of single junction PSM's and projections for tandem perovskite-Si modules that integrate all materials and manufacturing steps, module performances, projected lifetimes, and manufacturing costs across scales. Here, we highlight an open-air manufacturing approach to fabricate all active layers of serially interconnected PSMs, including electrodes and charge transport layers, enabling high-throughput production without inert or vacuum environments. The analysis reveals two orders of magnitude throughput enhancement and cost reductions of 24% in all-open-air production, escalating to over 60% at 1 GW factory capacity compared to conventional methods. Levelized cost of energy (LCOE) projections for utility-scale installations over 30 years, accounting for module replacement and recycling, demonstrate the potential to achieve the 2030 US target of $0.03 per kWh with realistic 7–11-year PSM lifetimes, outperforming incumbent silicon-based modules. Neither four terminal (4T) nor two terminal (2T) tandem-Si PSMs improve over single junction perovskite or silicon LCOE regardless of higher efficiencies at any modeled lifetime. Addressing PSM technical challenges with a cost-modeling framework guides commercialization efforts and provides a convincing pathway for challenging incumbent Si-based PV.

14 SOLAR ENERGY↗
DOE OSTI2021

Single-Site Metal Organic Complexes on Oxide Supports for Selective Alkane Functionalization

High levels of reaction selectivity for heterogeneous catalysis are generally difficult to achieve with traditional metal nanoparticle catalysts, due to the variety of metal binding sites available. In this project, we have developed a metal-ligand coordination strategy to form transition metal single-sites on high surface area supports as heterogeneous single-site catalysts. Metal centers can be atomically dispersed in the binding pockets of organic ligands on oxide supports. The systems have to be carefully designed so that tendencies for metal cluster formation or binding to surface defect sites are overcome by the attractive coordination environment provided by the organic ligand. This was confirmed by a comprehensive set of characterization methods, including XAS, XPS, XRD, TEM, and CO adsorption. Design and tuning of metal-support and support-ligand interactions were crucial for high structural uniformity. We demonstrate that supported metal-ligand SSCs are effective, recyclable catalysts for alkene hydrosilylation reactions. Compared with commercial homogeneous catalysts, they exhibit improved yield and selectivity, less metal aggregation and side reactions, and stronger tolerance with functionalized substrates. These results give some preliminary indication that coordination with organic ligands can improve activity and selectivity in metal/oxide heterogeneous catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Lapse-Rate Feeback: The Key to Reconciling TOA and Surface Attributions of Surface Warming

The impact of climate feedbacks on surface warming is conventionally evaluated using a decomposition of the top-of-atmosphere (TOA) energy budget. Alternatively, the climate feedback analysis can also be carried out using the surface energy budget. However, the two perspectives do not provide the same interpretation of process contributions to surface warming, particularly when executing a spatial analysis. The TOA energy budget is equal to the sum of the surface and atmospheric energy budgets. Using the CMIP5 RCP 8.5 model projections, we show that the major discrepancies between the surface and TOA climate feedback attributions of surface warming are due to non-negligible changes in the atmospheric energy budget that differ from their counterparts at the surface. Individual radiative and non-radiative processes cause vertically non-uniform energy flux perturbations, in response to an external forcing, that naturally lead to a vertically non-uniform temperature change. The TOA lapse-rate feedback is the manifestation of these multiple processes that produce a vertically non-uniform warming response such that it accounts for the asymmetry between the changes in the atmospheric and surface energy budgets. Using the climate feedback-response analysis method, we can decompose the lapse-rate feedback into contributions by individual processes. The negative lapse-rate feedback in the tropics is due to greater moist convection and condensational heating along with ocean heat storage. On the other hand, the positive lapse-rate feedback in polar regions is primarily due to the surface albedo and water vapor feedbacks. Combining the process contributions that are hidden within the lapse-rate feedback with their respective direct impacts on the TOA energy budget allows for a very consistent picture of process contributions to surface warming and its inter-model spread as that given by the surface energy budget approach. With the modified approach, both perspectives indicate water vapor feedback is the largest contributor in the tropics, while surface albedo feedback is the greatest contributor in polar regions. Dynamics and ocean heat storage are the main suppressors of surface warming, except over Antarctica. Both perspectives show there is large inter-model uncertainty in the contributions of water vapor, clouds, albedo, and dynamics plus ocean heat storage.

Sergio A Sejas↗
NASA NTRS2012

Mechanical and Combustion Performance of Multi-Walled Carbon Nanotubes as an Additive to Paraffin-Based Solid Fuels for Hybrid Rockets

Paraffin-based solid fuels for hybrid rocket motor applications are recognized as a fastburning alternative to other fuel binders such as HTPB, but efforts to further improve the burning rate and mechanical properties of paraffin are still necessary. One approach that is considered in this study is to use multi-walled carbon nanotubes (MWNT) as an additive to paraffin wax. Carbon nanotubes provide increased electrical and thermal conductivity to the solid-fuel grains to which they are added, which can improve the mass burning rate. Furthermore, the addition of ultra-fine aluminum particles to the paraffin/MWNT fuel grains can enhance regression rate of the solid fuel and the density impulse of the hybrid rocket. The multi-walled carbon nanotubes also present the possibility of greatly improving the mechanical properties (e.g., tensile strength) of the paraffin-based solid-fuel grains. For casting these solid-fuel grains, various percentages of MWNT and aluminum particles will be added to the paraffin wax. Previous work has been published about the dispersion and mixing of carbon nanotubes.1 Another manufacturing method has been used for mixing the MWNT with a phenolic resin for ablative applications, and the manufacturing and mixing processes are well-documented in the literature.2 The cost of MWNT is a small fraction of single-walled nanotubes. This is a scale-up advantage as future applications and projects will require low cost additives to maintain cost effectiveness. Testing of the solid-fuel grains will be conducted in several steps. Dog bone samples will be cast and prepared for tensile testing. The fuel samples will also be analyzed using thermogravimetric analysis and a high-resolution scanning electron microscope (SEM). The SEM will allow for examination of the solid fuel grain for uniformity and consistency. The paraffin-based fuel grains will also be tested using two hybrid rocket test motors located at the Pennsylvania State University s High Pressure Combustion Lab.

Larson, Daniel B.↗
DOE OSTI2025

Fast-RF-Shimming: Accelerate RF shimming in 7T MRI using deep learning

Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) offers an elevated signal-to-noise ratio (SNR), enabling exceptionally high spatial resolution that benefits both clinical diagnostics and advanced research. However, the jump to higher fields introduces complications, particularly transmit radiofrequency (RF) field ($B^{+}_{1}$) inhomogeneities, manifesting as uneven flip angles and image intensity irregularities. These artifacts can degrade image quality and impede broader clinical adoption. Traditional RF shimming methods, such as Magnitude Least Squares (MLS) optimization, effectively mitigate $B^{+}_{1}$ inhomogeneity, but remain time-consuming. Recent machine learning approaches, including RF Shim Prediction by Iteratively Projected Ridge Regression and other deep learning architectures, suggest alternative pathways. Although these approaches show promise, challenges such as extensive training periods, limited network complexity, and practical data requirements persist. In this paper, we introduce a holistic learning-based framework called Fast-RF-Shimming, which achieves a 5000 ​× ​speed-up compared to the traditional MLS method. In the initial phase, we employ random-initialized Adaptive Moment Estimation (Adam) to derive the desired reference shimming weights from multi-channel $B^{+}_{1}$ fields. Next, we train a Residual Network (ResNet) to map $B^{+}_{1}$ fields directly to the ultimate RF shimming outputs, incorporating the confidence parameter into its loss function. Finally, we design Non-uniformity Field Detector (NFD), an optional post-processing step, to ensure the extreme non-uniform outcomes are identified. Comparative evaluations with standard MLS optimization underscore notable gains in both processing speed and predictive accuracy, which indicates that our technique shows a promising solution for addressing persistent inhomogeneity challenges.

Deep learning↗
DOE OSTI2021

Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data

The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affects the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 93.7% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in ν μ CC interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Advanced surface plasma nitriding for development of corrosion resistant and accident tolerant fuel cladding: Final Report

Although various surface coating techniques have been proposed to increase oxidation and corrosion resistance of fuel cladding materials, the de-bonding of the coating layer with the original cladding matrix under exposure to coolants makes such approaches unsuitable for reactor applications. Furthermore, the feasibility of techniques for large scale processing on cladding tubes remains another technological bottleneck. This project aims to develop a hollow cathode plasma nitriding technique to solve the above issues. Starting with alloys of DOE interest, the team will apply an advanced surface plasma nitriding technique to convert alloy surface layers into nitride layers for better structural integrity and compatibility with both coolants and nuclear fuels. The technique can form uniform nitride layers on both inner and outer surfaces of cylindrical hollow tubes. Both treated and untreated samples will be irradiated by using Fe self ions or dual beams of Fe ions and He ions. Various microstructural characterizations and mechanical property measurements will be performed. For outside surfaces, samples will be tested in high temperature water and liquid molten salt to evaluate their corrosion resistance and compatibility with coolants for LWR and fast reactor applications. For inside surfaces, solid-to-solid diffusion couple experiments will be used to study interface reactions of nitrided samples with uranium, to test whether a nitrided layer can slow down fuel-cladding interactions. The project will impact both the development of advanced methods for manufacturing and the development of advanced reactor in-core structural materials. The proposed technique can form nitride layers on alloys of irregular shapes without an edging effect - an issue encountered in traditional plasma nitriding processes, and is suitable for surface processing of other in-core components.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1994

The Grand Tour via Geodesic Interpolation of 2-frames

Grand tours are a class of methods for visualizing multivariate data, or any finite set of points in n-space. The idea is to create an animation of data projections by moving a 2-dimensional projection plane through n-space. The path of planes used in the animation is chosen so that it becomes dense, that is, it comes arbitrarily close to any plane. One of the original inspirations for the grand tour was the experience of trying to comprehend an abstract sculpture in a museum. One tends to walk around the sculpture, viewing it from many different angles. A useful class of grand tours is based on the idea of continuously interpolating an infinite sequence of randomly chosen planes. Visiting randomly (more precisely: uniformly) distributed planes guarantees denseness of the interpolating path. In computer implementations, 2-dimensional orthogonal projections are specified by two 1-dimensional projections which map to the horizontal and vertical screen dimensions, respectively. Hence, a grand tour is specified by a path of pairs of orthonormal projection vectors. This paper describes an interpolation scheme for smoothly connecting two pairs of orthonormal vectors, and thus for constructing interpolating grand tours. The scheme is optimal in the sense that connecting paths are geodesics in a natural Riemannian geometry.

Asimov, Daniel↗
DOE OSTI2019

The HANDE-QMC Project: Open-Source Stochastic Quantum Chemistry from the Ground State Up

Building on the success of Quantum Monte Carlo techniques such as diffusion Monte Carlo, alternative stochastic approaches to solve electronic structure problems have emerged over the past decade. The full configuration interaction quantum Monte Carlo (FCIQMC) method allows one to systematically approach the exact solution of such problems, for cases where very high accuracy is desired. The introduction of FCIQMC has subsequently led to the development of coupled cluster Monte Carlo (CCMC) and density matrix quantum Monte Carlo (DMQMC), allowing stochastic sampling of the coupled cluster wave function and the exact thermal density matrix, respectively. In this Article, we describe the HANDE-QMC code, an open-source implementation of FCIQMC, CCMC and DMQMC, including initiator and semistochastic adaptations. We describe our code and demonstrate its use on three example systems; a molecule (nitric oxide), a model solid (the uniform electron gas), and a real solid (diamond). An illustrative tutorial is also included.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Advanced sorbents for modular oxygen production for REMS gasifiers

Under a DOE funded effort, Thermosolv LLC has been developing a sorbent-based oxygen production technology for gasification and oxy-combustion applications. The sorbents utilize oxygen-storage properties of certain perovskites to (1) selectively adsorb oxygen at moderate temperature from compressed air and (2) release the adsorbed oxygen into a vacuum or a sweep gas such as CO2 and/or steam. Through cyclic operations of multiple sorbent beds such a process can be made continuous. Pressure drop across the sorbent bed and other similar design considerations dictate that the sorbent be in the form of a high surface-area-to-volume pellet and yet still possess sufficient crush strength and integrity. Process efficiency is determined by the sorption and desorption kinetics and sorbent capacity. Typical process involves cycle times in the order of 100 seconds, a time too short to fully utilize the full volume of the sorbent and the sorption capacity of the sorbent pellets. As a part of this project, Thermosolv LLC undertook the development of oxygen sorbents as a high-surface area supported sorbent on a light-weight inert support to (1) reduce the cost, (2) increase the productivity, and (3) reduce the overall weight of the reactor. Using a previously developed perovskite (LSCF 1991) with its well-characterized performance, composite sorbent pellets each consisting of an inert core coated by a thin layer of the functional perovskite material were produced. Several low-density, inexpensive inert solids supports in the 1/8”-1/4” size were selected to keep the pressure drop across the sorbent bed in the acceptable range. A number of commercial technologies including spray coating, dip coating, incipient impregnation of porous substrate followed by thermal annealing at various temperatures and duration were employed and tested to produce robust composite supported sorbent pellets. A sense of optimum coating thickness was developed by testing sorbent pellets of various increasing diameter pellets. LSCF-1991extrudates ranging in size from 1/32” to 3/16” were tested in a TGA and in a fixed-bed pressure swing test set-up for sorption/desorption cycles of interest. The data show that for the operational conditions of interest the coating thickness for a supported sorbent pellet needs to be approximately 1/32” (about 0.8 mm). Subsequent work thereby concentrated on developing composite pellets of various substrates, shapes and sizes with coating layer of about this size range. Candidate support materials were chosen based on cost, inertness, mechanical strength, thermal expansion and chemical stability with sorbent material, and in a size range to give an acceptable pressure drop in fixed-bad reactor configurations. Coating application methods used for application of the sorbent onto the support included precipitation, spray coating and dip coating from sorbent slurries and sol gels. For all coated supports where we could successfully apply a uniform coating of desired thickness with an acceptable handling performance in terms of exfoliation, TGA-based cyclic sorption/desorption testing was performed to determine cycling capacity of oxygen. Among nearly twenty different support materials tested, best adhesion performance was obtained from stainless saddle supports. In the bench-scale fixed-bad tests stainless steel saddle support composite pellets with a coating thickness in the 0.6 mm or so range, the composite sorbent pellet performance approached up to 95% of that of the 2 mm parent material pellets. In a parallel approach to reducing the cost of the sorbent, alternate sorbent formulations replacing/reducing the amount of cobalt in the LSCF family were also investigated. Successful formulations that could match the performance of LSCF 1991 were identified based on TGA cyclic tests as LSCF 1919 and LSF 1910. In the range of operational envelop of cycle times and other relevant process operating conditions, the reduced Co formulations showed comparable performance in the bench-scale fixed-bad cyclic operations. As a part of this project, we also attempted substitution of La and Sr with Ca and Ba, and Co with Mn, Ni and Cu with little success, but the overall project goal of reducing sorbent cost in terms of raw material and manufacturing expenses was successful.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2019

Larger increases in more extreme local precipitation events in as climate warms

Global climate models project that extreme precipitation events will increase in intensity during the 21st century at large spatial scales, with several studies suggesting that the most extreme events will exhibit the highest rate of intensification. The identification of the causes of this phenomenon nevertheless remains tenuous, in part because some studies have used methods that may not be appropriate for diagnosing long-term changes in intensity. Using a large ensemble of North American regional climate simulations, we confirm statistical evidence that intensification occurs more rapidly with warming for more extreme events of hourly-to-daily durations at finer regional scales. The larger increases in more extreme events are found to be primarily due to atmospheric circulation changes. Thermodynamically induced changes are found to have relatively uniform effects across extreme events and regions. In contrast, dynamical changes that are reflected in vertical velocity weaken moderate precipitation events over western interior regions of North America, and enhance them elsewhere. The weakening effect decreases and even reverses for more extreme precipitation events, whereas there is further intensification over other parts of North America, creating an “intense gets intenser” pattern over most of the continent.

Li, Chao↗
DOE OSTI2025

D–MOPH–25: diverse MOF–molecule pairs for Henry’s constants prediction

Computational methods like grand-canonical Monte Carlo simulations and machine learning (ML) have accelerated metal–organic frameworks (MOF) exploration but are typically limited to a narrow range of adsorbates due to data availability and force field constraints. In this study, we introduce a dataset of diverse MOF–molecule pairs for Henry’s constant prediction, D–MOPH–25, which systematically explores a diverse chemical space by combining 113 molecular adsorbates with over 5000 MOF structures through an active learning process. D–MOPH–25 constitutes the most diverse adsorbate dataset used in any ML study of molecular adsorption in MOFs to date. Our workflow builds a benchmark for predicting Henry’s constants at 300 K, leveraging conformal prediction for uncertainty quantification. Assessment through Shannon entropy and uniform manifold approximation and projection confirms the comprehensiveness of D–MOPH–25 while highlighting the importance of robust classification to filter out unphysical data points in regression tasks. Although future enhancements in model architecture and sampling criteria could improve predictive performance, our dataset already spans the target space using only 2.31% of total possibilities. This comprehensive dataset facilitates assessment of model generalizability across adsorbate species and can establish a foundation for high-throughput MOF screening and ML-driven separation processes.

active learning↗
DOE OSTI2021

Lower-hybrid drift waves and their interaction with plasmas in a 3D symmetric reconnection simulation with zero guide field

We investigate lower-hybrid drift waves (LHDW) in symmetric magnetic reconnection with zero guide field using three-dimensional particle-in-cell simulations. The long-wavelength mode with kρiρe∼1 develops in the bifurcated electron current layer around the X-line within the width of the electron meandering motion from the mid-plane, where ρi(e) is the ion (electron) gyroradius. The short-wavelength mode with kρe∼1 develops in the separatrix region downstream of the electron outflow jet, producing electron vortices in the background flow frame. Electrons follow the E × B drift with corrections from the diamagnetic drift and are heated inside the vortices with diverging electric fields. In the vortices, ions have comparable E × B and inertia drifts, which together mostly cancel the diamagnetic drift. Toward the center of diverging field vortices, ions are decelerated, leading to a decrease in the perpendicular temperature, while the loss of low-energy ions results in an increase in the parallel temperature. Parallel electric fields exist as a combination of the LHDW wave field projected to the magnetic field direction and the penetration of whistler waves that are mainly outside of the LHDW layer. The magnetic flux tube is twisted in the vortices. The twist may potentially lead to slippage reconnection, as indicated by the non-uniform parallel potential variation across field lines, while the periodic variations of the twisting directions are a limiting factor.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Improvements to Stitching Controls for Manufacturing Advanced Stitched Composites

Stitched composites have been extensively studied and have been shown to have benefits over unstitched composites for stiffened structures, including improved damage tolerance, reduced weight, and fewer fasteners. These benefits make stitched composites attractive from a performance perspective, however, the nature of stitching the preforms prior to infusion includes additional manufacturing steps and equipment compared to conventional resin infusion. Stitching of composite material structures at NASA Langley Research Center (LaRC) is performed at the Integrated Structural Assembly of Advanced Composites (ISAAC) facility using control algorithms developed by the lead author. Two deficiencies have been identified in the current control algorithms, and mitigations for these deficiencies were developed within the Hi-Rate Composite Aircraft Manufacturing (HiCAM) project and are presented herein. First, decoupling stitch translation and rotation takes more time for stitching since two robot operation control steps are executed for each stitch. Therefore, a method was developed to determine when to decouple the translation and rotation for stitching, which reduces stitch time by decoupling the operation control steps only when necessary to execute the stitch properly. Second, a correction to the sideslip stitching approach that eliminates a significant portion of the pre- and post-stitching activities was developed. The developed method adjusts the sideslip stitch angle based on the rotations of previous stitches to ensure that the width of the stitching seam remains constant along its length. Constant seam width eliminates observed overlap of the insertion and catcher thread portions for highly curved paths, which maintains the desired uniformity of through-the-thickness architecture along the seam and eliminates the local degradation of properties where the insertion needle is too close to, or crosses, the catcher needle portion of the seam.

stitching↗
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗