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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluation and Development of Phase Array Ultrasonic Testing (PAUT) System for Additively Manufactured Parts

This research focuses on the application of advanced ultrasonic testing techniques developed by The Phased Array Company (TPAC) for inspecting defects in additive manufacturing (AM) parts. Traditionally, X-ray computed tomography is the standard for inspecting AM components. Although, the long inspection and analysis time, along with relatively high cost make implementation difficult. Thus, an alternative nondestructive evaluation (NDE) approach is necessary to support quality assurance efforts within the field of AM. TPAC is recognized as a leader in ultrasonic testing innovation, deploying sophisticated algorithms such as Total Focusing Method (TFM) and Phased Wave Imaging (PWI) for ultrasonic data processing and interpretation. This work will explore how the TFM and PWI algorithms can assist defect detection within polymer AM parts. The AM field is seeking novel NDE methods to provide support within quality control and assurance efforts. Advanced ultrasonics inspection have the potential to fulfill this need.

36 MATERIALS SCIENCE↗
DOE OSTI2024

HSQC spectra of lignin isolated from poplar stems

Here we present a curated dataset of two-dimensional heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra of lignin isolated from stems of genetically engineered poplar through auxin signaling gene modification. The plants were grown in greenhouse with temperatures between 21 and 23 °C. Plants were harvested and the aboveground stems were cut off an approximately five-inch-long segment from the bottom end of the plant stem, debarked and air-dried for three weeks. The dried stem samples were Wiley milled (mesh size 20), Soxhlet-extracted with toluene/ethanol for 24 h to remove extractives. The extracted biomass was ball-milled in a Retsch PM100 planetary ball mill using a porcelain jar with ceramic balls at 600 rpm for 2 h (in 5 min on and 5 min off cycles to avoid excessive sample heating). The ball-milled materials were then subjected to enzymatic hydrolysis for 48 h followed by centrifugation and washing with deionized water. The solid residue was freeze-dried to recover the lignin. The dry stem lignin samples were dissolved in deuterated dimethyl sulfoxide (d6) and transferred into a 5 mm tube. 13C–1H HSQC experiments were performed in a Bruker Avance III HD 500 MHz NMR spectrometer operating at a frequency of 125.12 MHz for the 13C nucleus using a standard Bruker pulse sequence on a Prodigy platform cryoprobe. The NMR spectra were acquired under the following acquisition conditions: 230 ppm spectral width in F1 (13C) dimension with 256 data points and 12 ppm spectral width in F2 (1H) dimension with 2048 data points, a 90° pulse, a one bond C–H coupling constant of 145 Hz, a 1.0 s pulse delay, and 64 scans. Spectra were processed using the Bruker TopSpin 3.6 software. Additional meta data is embedded in the raw spectra figures.

HSQC, lignin, poplar, stems, CBI↗
DOE OSTI2024

HSQC spectra of lignin isolated from poplar roots

Here we present a curated dataset of two-dimensional heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra of lignin isolated from roots of a greenhouse grown natural population of an energy crop poplar (Populus trichocarpa). Dormant cuttings of field-grown poplar were grown in 6-liter pots in a peat-based media containing bark, perlite, vermiculite, dolomite lime and a wetting agent in an environmentally controlled greenhouse. Temperatures were between 21 and 23 °C, with supplemental lighting to support a 16-h day length using 1000-watt high-pressure sodium lights in greenhouse. Once established, all plants were cut-back, allowed to regrow and harvested at the same time following an eight-month long growth period. Plants were harvested and the belowground roots were washed off soils, blotted, dried in an oven at 70 °C for 3 days, and Wiley milled (mesh size 20). The roots were Soxhlet-extracted with toluene/ethanol for 24 h to remove extractives. The extracted roots were ball-milled in a Retsch PM100 planetary ball mill using a porcelain jar with ceramic balls at 600 rpm for 2 h (in 5 min on and 5 min off cycles to avoid excessive sample heating). The ball-milled materials were then subjected to enzymatic hydrolysis for 48 h followed by centrifugation and washing with deionized water. The solid residue was extracted twice with 96% (v/v) 1,4-dioxane/water mixture at room temperature overnight. The extracts were combined, rotary evaporated, and freeze-dried to recover lignin. The dry lignin samples were dissolved in deuterated dimethyl sulfoxide (d6) and transferred into a 5 mm tube. 13C–1H HSQC experiments were performed in a Bruker Avance III HD 500 MHz NMR spectrometer operating at a frequency of 125.12 MHz for the 13C nucleus using a standard Bruker pulse sequence on a Prodigy platform cryoprobe. The NMR spectra were acquired under the following acquisition conditions: 220 ppm spectral width in F1 (13C) dimension with 256 data points and 12 ppm spectral width in F2 (1H) dimension with 1024 data points, a 90° pulse, a one bond C–H coupling constant of 145 Hz, a 1.0 s pulse delay, and 64 scans. Spectra were processed using the Bruker TopSpin software. Additional meta data is embedded in the raw spectra figures.

HSQC, lignin, poplar, roots, CBI↗
DOE OSTI2022

Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders

Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble Kalman filter (MFEnKF) recently developed by the authors combines a full-order physical model and a hierarchy of reduced order surrogate models in order to increase the computational efficiency of data assimilation. The standard MFEnKF uses linear couplings between models, and is statistically optimal in case of Gaussian probability densities. This work extends the MFEnKF into to make use of a broader class of surrogate model such as those based on machine learning methods such as autoencoders non-linear couplings in between the model hierarchies. We identify the right-invertibility property for autoencoders as being a key predictor of success in the forecasting power of autoencoder-based reduced order models. We propose a methodology that allows us to construct reduced order surrogate models that are more accurate than the ones obtained via conventional linear methods. Numerical experiments with the canonical Lorenz'96 model illustrate that nonlinear surrogates perform better than linear projection-based ones in the context of multifidelity ensemble Kalman filtering. We additionality show a large-scale proof-of-concept result with the quasi-geostrophic equations, showing the competitiveness of the method with a traditional reduced order model-based MFEnKF.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The Target-selection Pipeline for the Dark Energy Spectroscopic Instrument

Abstract In 2021 May, the Dark Energy Spectroscopic Instrument (DESI) began a 5 yr survey of approximately 50 million total extragalactic and Galactic targets. The primary DESI dark-time targets are emission line galaxies, luminous red galaxies, and quasars. In bright time, DESI will focus on two surveys known as the Bright Galaxy Survey and the Milky Way Survey. DESI also observes a selection of “secondary” targets for bespoke science goals. This paper gives an overview of the publicly available pipeline ( desitarget ) used to process targets for DESI observations. Highlights include details of the different DESI survey targeting phases, the targeting ID ( TARGETID ) used to define unique targets, the bitmasks used to indicate a particular type of target, the data model and structure of DESI targeting files, and examples of how to access and use the desitarget code base. This paper will also describe “supporting” DESI target classes, such as standard stars, sky locations, and random catalogs that mimic the angular selection function of DESI targets. The DESI target-selection pipeline is complex and sizable; this paper attempts to summarize the most salient information required to understand and work with DESI targeting data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Search for the associated production of charm quarks and a Higgs boson decaying into a photon pair with the ATLAS detector

A search for the production of a Higgs boson and one or more charm quarks, in which the Higgs boson decays into a photon pair, is presented. This search uses proton-proton collision data with a centre-of-mass energy of $\sqrt{s}$ = 13 TeV and an integrated luminosity of 140 fb −1 recorded by the ATLAS detector at the Large Hadron Collider. The analysis relies on the identification of charm-quark-containing jets, and adopts an approach based on Gaussian process regression to model the non-resonant di-photon background. The observed (expected, assuming the Standard Model signal) upper limit at the 95% confidence level on the cross-section for producing a Higgs boson and at least one charm-quark-containing jet that passes a fiducial selection is found to be 10.6 pb (8.8 pb). The observed (expected) measured cross-section for this process is 5.3 ± 3.2 pb (2.9 ± 3.1 pb).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Uncertainty Aware Deep Learning for Particle Accelerators

Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics. However, their predictions can be arbitrarily inaccurate when the input samples are not similar to the training data. Implementation of distance aware uncertainty estimation can be used to detect these scenarios and provide a level of confidence associated with their predictions. In this paper, we present results from using Deep Gaussian Process Approximation (DGPA) methods for errant beam prediction at Spallation Neutron Source (SNS) accelerator (classification) and we provide an uncertainty aware surrogate model for the Fermi National Accelerator Lab (FNAL) Booster Accelerator Complex (regression).

Rajput, Kishansingh↗
DOE OSTI2025

Search for light sterile neutrinos with two neutrino beams at MicroBooNE

The existence of three distinct neutrino flavours, ν e , ν μ and ν τ , is a central tenet of the Standard Model of particle physics. Quantum-mechanical interference can allow a neutrino of one initial flavour to be detected sometime later as a different flavour, a process called neutrino oscillation. Several anomalous observations inconsistent with this three-flavour picture have motivated the hypothesis that an additional neutrino state exists, which does not interact directly with matter, termed as ‘sterile’ neutrino, ν s . This includes anomalous observations from the Liquid Scintillator Neutrino Detector (LSND)3 experiment and Mini-Booster Neutrino Experiment (MiniBooNE), consistent with ν μ → ν e transitions at a distance inconsistent with the three-neutrino picture. Here we use data obtained from the MicroBooNE liquid-argon time projection chamber in two accelerator neutrino beams to exclude the single light sterile neutrino interpretation of the LSND and MiniBooNE anomalies at the 95% confidence level (CL). Moreover, we rule out a notable portion of the parameter space that could explain the gallium anomaly. This is one of the first measurements to use two accelerator neutrino beams to break a degeneracy between ν e appearance and disappearance, which would otherwise weaken the sensitivity to the sterile neutrino hypothesis. We find no evidence for either ν μ → ν e flavour transitions or ν e disappearance that would indicate non-standard flavour oscillations. Our results indicate that previous anomalous observations consistent with ν μ → ν e transitions cannot be explained by introducing a single sterile neutrino state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

RAG for FLAG: AI Assistance for a Physics Code

Artificial intelligence (AI) has quickly become an important tool in scientific research, where significant efforts are underway to develop tools that will expedite the research process. One area of particular impact is scientific software, which can be particularly complex, and therefore time consuming to learn and use effectively. AI assistants are increasingly helping to streamline the process by performing tasks such as interactively answering user questions or suggesting solutions. Los Alamos National Laboratory (LANL) develops several advanced scientific codes, such as FLAG, which can be used to run multiphysics simulations. With this study, our goal was to develop an AI assistant for FLAG that could help make the process of understanding the software and running physics simulations more efficient. To develop an AI assistant for FLAG, we used a method called retrieval-augmented generation (RAG), which is a technique that uses information from relevant data sources to enhance the accuracy of large language models (LLMs). We used the FLAG user manual and other FLAG documentation as the knowledge base for the RAG system. When a user provides a query, RAG retrieves relevant sections from the knowledge base in response, then uses those excerpts to generate grounded and contextually rich answers. We found that our AI assistant was able to provide context aware answers and source references to user queries. To evaluate performance, we developed a set of 40 benchmark questions and compared the accuracy of the responses to those of two standard LLMs without retrieval. Our AI assistant significantly outperformed the standard LLMs at answering FLAG-related questions, with an 82.5% accuracy rate, compared to 47.5% for both of the standard LLMs. This has the potential to make the process of learning and using FLAG much easier, especially for new users. Ultimately, it supports LANL’s broader mission by empowering scientists and engineers to focus more on discovery and analysis rather than on navigating complex software systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Linear relationships for modeling CO 2 absorption in aqueous alkanolamine solutions in a thermodynamically consistent way

A thermodynamically consistent model for the carbon dioxide (CO 2 ) absorption in aqueous alkanolamine system is of great importance in the research and development of a CO 2 capture process. To facilitate the development of thermodynamic models, linear Gibbs free energy, enthalpy, and heat capacity relationships using well-known amines as reference are used to correlate the standard reference state properties of ionic species with those of molecular species in the electrolyte system, which has been approved to provide a reliable and consistent way to estimate required parameters when there is minimal or no appropriate experimental data available. Here, the proposed relationships have been applied to the development of an electrolyte nonrandom two liquid (NRTL) activity coefficient model for CO 2 absorption in aqueous 1-amino-2-propanol (A2P) solution, as an example to demonstration the methodology. With limited vapor–liquid equilibrium data and other thermodynamic properties, the parameters in the electrolyte NRTL model are identified with good accuracy.

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

Preliminary Insights Into the Feasibility of Determining the Purification Date of Enriched Uranium by Direct Measurement of the 230 Th/ 234 U Ratio Using an All-Faraday Detector Configuration on the Neoma MC-ICP-MS

Rationale: Mass spectrometric measurement of the 230 Th/ 234 U ratio to calculate the purification age of enriched uranium is typically conducted via a combination of ion counters and faraday detectors, thus requiring an inter-detector calibration scheme. Here, our aim is to understand whether the pursuit of a simplified measurement scheme involving only faraday detectors is feasible. Methods: We investigate the possibility of determining U-Th model ages for two enriched uranium standards (NBL U630 and U850) by direct measurement of the 230 Th/ 234 U ratio (without chromatographic separation or isotope dilution) on a ThermoFisher Scientific Neoma MC-ICP-MS utilizing both solution and laser ablation (LA)-based sampling techniques and an all-faraday detector configuration. Results: For the solution mode analyses conducted on aliquots containing sub μg/mL total U, we produce composite average 230 Th/ 234 U model dates of May 19, 1988 (± 351 days), and March 26, 1961 (± 2.5 years) using the directly measured 230 Th/ 234 U ratios for the NBL U630 and U850 uranium standards, which have certified purification dates of June 6, 1988 (± 190 days), and December 31, 1957 (± 36.5 days), respectively. The ages produced by LA-based sampling of dried residues of the same standards deposited onto cotton TexWipes are less accurate and of poorer precision (June 23, 2004 ± 8.7 years for U630 and December 21, 1965 ± 7.9 years for U850) but still yield meaningful information in regards to the purification date. Conclusions: We believe that further refinement of the all faraday detector measurement approach to include development of a more robust Th/U relative sensitivity factor determination, signal cutoff selection, and data processing protocols will allow for this approach to be confidently applied to enriched uranium materials with unknown purification histories. Potential advantages of the method include the reduced sample handling and infrastructure requirements as well as the ability to simultaneously generate a broad picture of the uranium isotopic composition in tandem with the U-Th age determination.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Oil-Pressure Based Apparatus for In-Situ High-Energy Synchrotron X-Ray Diffraction Studies During Biaxial Deformation

Background: Understanding biaxial loading response at the microstructural level is crucial in helping better design sheet manufacturing processes and calibrate/validate material deformation models. Objective: The objective of this work was to develop a low-cost testing apparatus to probe, with sufficient spatial resolution, the micro-mechanical response of a sheet material in-situ under biaxial loading conditions. Methods: The testing apparatus fabricated as a part of this study operates in a similar fashion to a standard bulge test and uses oil pressure to generate biaxial loading conditions. This biaxial testing apparatus was operated within a synchrotron beamline to characterize the mechanical response of a flash-processed steel sheet using in-situ high-energy X-ray diffraction (XRD) measurements. Further, the GSAS-II package was utilized to develop a workflow for the analysis of the large volume of diffraction data acquired. The workflow was then used to extract the peak position, width, and integrated intensity of the XRD peaks corresponding to the major body-centered cubic phase. Results: The equi-biaxial nature of the loading in the measured area was independently corroborated using experimental (XRD) and simulation (finite element analysis) methods. Furthermore, we discuss the evolution of elastic strain in the major body-centered cubic phase as a function of applied oil pressure and location on the steel sheet. Conclusions: A key advantage of the biaxial apparatus fabricated in this synchrotron study is demonstrated using the results obtained for the flash-processed steel sheet – i.e., mapping the lattice plane-dependent response to biaxial loading for a relatively large sample area in a spatially resolved manner.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evidence for $\text {W}\text {W}$ production from double-parton interactions in proton–proton collisions at $\sqrt{s} = 13 \,\text {TeV} $ = 13TeV

A search for WW production from double-parton scattering processes using same-charge electron-muon and dimuon events is reported, based on proton-proton collision data collected at a center-of-mass energy of 13 TeV. The analyzed data set corresponds to an integrated luminosity of 77.4 fb$^{-1}$, collected using the CMS detector at the LHC in 2016 and 2017. Multivariate classifiers are used to discriminate between the signal and the dominant background processes. A maximum likelihood fit is performed to extract the signal cross section. This leads to the first evidence for WW production via double-parton scattering, with a significance of 3.9 standard deviations. The measured inclusive cross section is 1.41 $\pm$ 0.28 (stat) $\pm$ 0.28 (syst) pb.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Simple and Scalable Process for Nanocellulose Production from Residues and Waste (CRADA Final Report)

This project successfully developed an innovative, cost-effective, and scalable method for converting low-cost agricultural waste feedstocks into nanocellulose—a sustainable material with wide-ranging industrial applications. It addressed the critical challenges of high production costs and limited global supply, which have hindered the widespread adoption of nanocellulose in industry. Through this work, significant advancements were made in optimizing resource use, improving process efficiency, and reducing costs. Notable achievements included a 50% reduction in water usage, 30% lower chemical consumption, and 20% energy savings, all while maintaining high-quality product standards. The technical feasibility of the process was further validated at a 10L scale in collaboration with the Advanced Biofuels and Bioproducts Development Unit (ABPDU), demonstrating scalability and replicability. Key challenges in reducing nanocellulose production costs were addressed by utilizing low-cost biomass feedstocks and implementing low-temperature conversion reactions, leading to lower capital and operating expenses. The project also mitigated financial risk by generating critical data on the feasibility, adaptability, and scalability of the conversion technology, paving the way for its commercial implementation. Public benefits include advancing the circular bioeconomy, reducing environmental impact, fostering job creation, and enabling a shift toward biobased materials as sustainable alternatives to fossil-based products. These outcomes align closely with national goals to reduce greenhouse gas emissions and promote sustainable technological innovation.

09 BIOMASS FUELS↗
DOE OSTI2025

The Poisson tensor completion non-parametric differential entropy estimator

We introduce the Poisson tensor completion (PTC) estimator, a non-parametric differential entropy estimator. The PTC estimator leverages inter-sample relationships to compute a low-rank Poisson tensor decomposition of the frequency histogram. Our crucial observation is that the histogram bins are an instance of a space partitioning of counts and thus can be identified with a spatial Poisson process. The Poisson tensor decomposition leads to a completion of the intensity measure over all bins—including those containing few to no samples—and leads to our proposed PTC differential entropy estimator. A Poisson tensor decomposition models the underlying distribution of the count data and guarantees non-negative estimated values and so can be safely used directly in entropy estimation. Our estimator is the first tensor-based estimator that exploits the underlying spatial Poisson process related to the histogram explicitly when estimating the probability density with low-rank tensor decompositions for the purpose of tensor completion. Furthermore, we demonstrate that our PTC estimator is a substantial improvement over standard histogram-based estimators for sub-Gaussian probability distributions because of the concentration of norm phenomenon.

42 ENGINEERING↗
DOE OSTI2025

Feasibility of an Accelerometer-Based Structural Health Monitoring System for the LANL Blast Tube

A modeling- and simulation-based study was conducted on the feasibility of implementing an accelerometer-based SHM system on the Los Alamos National Laboratory blast tube. A blast tube experiment was modeled using the Abaqus explicit finite element solver. A custom user subroutine was written to apply test-like pressure loading to the inside surface of the blast tube. The subroutine applies analytically defined pressure loads derived from tracer output taken from a Compressible Flow Computational Fluid Dynamics Solver model of the blast tube. Five unique versions of the model were created: an undamaged reference model at 65°F was used as the baseline and compared to equivalent models at 10°F and 100°F. These three models were compared to models with small damage at the reference temperature. The two types of damage considered were a radial (circumferential) crack in the main tube body and a longitudinal crack in the supports. Acceleration outputs were extracted from accelerometer bodies included in the model and were post processed using a variety of standard SHM techniques. Different potential features signaling failure were extracted and compared using statistical methods in the time and frequency domains. A method was identified that clearly shows that differences in structural response resulting from the modeled damage can be differentiated from the structural response resulting from changing environmental conditions. However, the amount of damage applied to create observable differences in the accelerometer data was so large that simpler methods of damage detection would be more cost effective in locating damage.

42 ENGINEERING↗
DOE OSTI2025

Real-Time Artificial Intelligence for Particle Reconstruction and Higgs Physics

With the discovery of the Higgs boson at the CERN LHC, the world's highest-energy particle accelerator complex, scientists have acquired an important tool to study the fundamental building blocks of the universe. Precision measurements of Higgs bosons produced with large momentum allow for unique insights into the structure of the interactions of the Higgs boson with other particles that may shed light on physics beyond the standard model. While experimentally challenging, exploring such interactions with novel artificial intelligence (AI) methods can advance our understanding of the Higgs sector, including the Higgs boson's self-interaction. Moreover, the LHC is undergoing a major upgrade to further increase its particle collision rate and thereby operate for an additional decade. The experimental detectors at the upgraded facility must process at least a factor of ten more data at rates of hundreds of terabytes per second all under challenging conditions. New AI techniques are required to reconstruct and select, or trigger on, the most physics-sensitive events in real-time to handle the resulting avalanche of data. The proposed research will achieve the goals of the LHC program at the CMS experiment by developing a sub-microsecond event reconstruction system using real-time AI algorithms that employ field-programmable gate array technologies. By harnessing sophisticated AI techniques, this research focuses on measuring the production of Higgs bosons at large momentum while enhancing particle reconstruction methods in the trigger and beyond. Overall, the proposed research has broader implications for the use of AI in resource-constrained, low-latency embedded applications across all fields of science.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗