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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Investigating the Thermal Behavior of Polymers by Modulated Differential Scanning Calorimetry (MDSC) – A Review

Modulated Differential Scanning Calorimetry (MDSC) is a thermoanalytical technique that detects changes in intrinsic heat flow by subjecting a material to two simultaneous heating rates, one linear and one sinusoidal. In this way, MDSC overcomes the limitations of its predecessor- conventional DSC-allowing for complex and overlapping thermal transitions to be resolved without sacrificing sensitivity. The advantages of MDSC lend credence to its extensive use in applications that require superior analytical accuracy, such as the food and drug industries.

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

Unraveling the Genetics of Two Key Biomass Traits that Differentiate Upland and Lowland Tetraploid Switchgrass Ecotypes, Colonization by Mycorrhizal Fungi and Frost Tolerance

The overall objectives of the proposal were to dissect the genetics of the ability of switchgrass to interact with arbuscular mycorrhizal fungi (AMF) and to tolerate subzero temperatures, and to investigate the effect of AMF colonization on freezing tolerance and biomass production. In order to achieve this, we generated genetic maps in an F2 mapping population derived from a cross between the lowland genotype AP13 and the upland genotype VS16. In addition to providing a framework for identification of quantitative trait loci (QTL), the genetic maps were used to validate and improve the switchgrass genome assembly. We mapped phenotypic quantitative trait loci (QTL) for AMF colonization and cold tolerance in the F 2 population. We demonstrated that identification of QTL for AMF colonization was achieved only if AMF genus-specific colonization levels were used as phenotypes. No QTL were obtained when the overall level of AMF colonization was used as a trait. We conducted RNASeq on a subset of the F2 progeny following cold acclimation, and developed statistical methods to conduct expression QTL (eQTL) analyses in a polyploid organism. A preliminary analysis of the results identified eQTL for a range of genes that were differentially regulated in the lowland AP13 compared to the upland VS16 under cold acclimation. Once sequencing of the transcriptomes of a temporal replicate of the mapping population that was cold-acclimated using the same conditions has been completed, a final eQTL analysis will be conducted.

09 BIOMASS FUELS↗
DOE OSTI2021

Uncertainty Quantification of Geophysical Inversion Using Stochastic Partial Differential Equations (LDRD #218329)

This report summarizes work completed under the Laboratory Directed Research and Development (LDRD) project "Uncertainty Quantification of Geophysical Inversion Using Stochastic Differential Equations." Geophysical inversions often require computationally expensive algorithms to find even one solution, let alone propagating uncertainties through to the solution domain. The primary purpose of this project was to find more computationally efficient means to approximate solution uncertainty in geophysical inversions. We found multiple computationally efficient methods of propagating Earth model uncertainty into uncertainties in solutions of full waveform seismic moment tensor inversions. However, the optimum method of approximating the uncertainty in these seismic source solutions was to use the Karhunen-Love theorem with data misfit residuals. This method was orders of magnitude more computationally efficient than traditional Monte Carlo methods and yielded estimates of uncertainty that closely approximated those of Monte Carlo. We will summarize the various methods we evaluated for estimating uncertainty in seismic source inversions as well as work toward this goal in the realm of 3-D seismic tomographic inversion uncertainty.

58 GEOSCIENCES↗
DOE OSTI2022

Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology

Modeling real-world phenomena to any degree of accuracy is a challenge that the scientific research community has navigated since its foundation. Lack of information and limited computational and observational resources necessitate modeling assumptions which, when invalid, lead to model-form error (MFE). The work reported herein explored a novel method to represent model-form uncertainty (MFU) that combines Bayesian statistics with the emerging field of universal differential equations (UDEs). The fundamental principle behind UDEs is simple: use known equational forms that govern a dynamical system when you have them; then incorporate data-driven approaches – in this case neural networks (NNs) – embedded within the governing equations to learn the interacting terms that were underrepresented. Utilizing epidemiology as our motivating exemplar, this report will highlight the challenges of modeling novel infectious diseases while introducing ways to incorporate NN approximations to MFE. Prior to embarking on a Bayesian calibration, we first explored methods to augment the standard (non-Bayesian) UDE training procedure to account for uncertainty and increase robustness of training. In addition, it is often the case that uncertainty in observations is significant; this may be due to randomness or lack of precision in the measurement process. This uncertainty typically manifests as “noisy” observations which deviate from a true underlying signal. To account for such variability, the NN approximation to MFE is endowed with a probabilistic representation and is updated using available observational data in a Bayesian framework. By representing the MFU explicitly and deploying an embedded, data-driven model, this approach enables an agile, expressive, and interpretable method for representing MFU. In this report we will provide evidence that Bayesian UDEs show promise as a novel framework for any science-based, data-driven MFU representation; while emphasizing that significant advances must be made in the calibration of Bayesian NNs to ensure a robust calibration procedure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Journey Over Destination: Differentiable Sensor Placement Enhances Generalization [Poster]

The challenge of reconstructing spatial fields that change over time from limited sensor data has been a focal point for many research studies. Various machine learning methods have been used in attempts to address this complex issue, including convolutional neural networks. All of the proposed methods share a common requirement that the user needs to manually determine the sensor positions. This requirement remains a limiting factor in the ongoing quest for efficient learning and accurate field reconstruction. This study aims to present a method that enables a model to optimize sensor positions via backpropagation, thereby facilitating the model’s exploration of the spatial domain and enhancing sensor positioning effectively. Indexing naturally incorporates discrete decisions. This operation is nondifferentiable which is a requirement for the application of gradient-based optimization methods. We showcased its effectiveness by training an attention-based neural network, which achieved top-tier performance on two separate datasets. To our knowledge, this represents the first fully end-to-end differentiable workflow for enhancing sensor placement within a neural network model.

58 GEOSCIENCES↗
DOE OSTI2023

Differential Scanning Calorimetry Calibration and Heat Capacity

The differential scanning calorimeter (DSC) 404 F3 is a machine that heats up to 1500 degrees Celsius, and collects/evaluates thermal properties such as heat capacity, crystallization, glass transition, and more. The first part of the project is based around calibrating the DSC 404 F3 using the given procedures and manuals. This ensures that the DSC 404 F3 is working properly and does not have any issues moving forward. After the calibration is complete, the second part is to start testing. There will be tests conducted on known metals, such as steel and aluminum, in which there is data to compare it to. If the data collected is not accurate compared to its known values, further troubleshooting will need to ensue to ensure that the following process does not give wrong readings. Then, testing will begin for High Entropy Alloys(HEA) and the interesting issue with these is that there is no data to compare these to. The goal of finding the values of the HEA is to find a compatible alloy that we can use in future projects.

Szczech, Sebastian↗
DOE OSTI2023

Differential Scanning Calorimetry Calibration and Heat Capacity

The Differential Scanning Calorimeter(DSC) is used to find multiple properties ofmaterials such as heat capacity, melting points, crystallization, and more. Fermilabowns the NETZSCH DSC 404 F3 Pegasus to test novel material for a high poweredtargetry components. The DSC heats up to 1500 C, uses a platinum furnace, and Al 2 O 3 crucibles. In order for the DSC to produce consistent and correct data,calibration of the machine was necessary. Testing with the given calibration kit ofknown metals. Once calibrated, testing on novel materials for their specific heatcapacity ensues.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Quasi-Differential Neutron Scattering Measurements of 181 Ta and Teflon from 1.5 to 20 MeV [Slides]

This presentation covers motivation for the Experiments Performed. Secondly, it touches on upgrades to the Experimental Apparatus for Measurements. Thirdly, it presents quasi-Differential Experimental Methodology a) Pulse Shape Discrimination b) Validation Measurement of Carbon. The presentation concludes with Preliminary Results and Future Studies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Measurement of Differential Neutral Current Elastic $ν_μ$-Argon Scattering Cross Sections with MicroBooNE

The MicroBooNE experiment is an 85 ton active mass liquid-argon time projection chamber located in the Fermilab Booster Neutrino Beamline. MicroBooNE’s ability to detect low-energy protons allows us to study single proton events with momenta as low as 300 MeV/c. We present a measurement of the flux-averaged neutral-current elastic differential cross section for neutrinos scattering on argon as functions of four-momentum transfer squared, Q 2 , proton kinetic energy, momentum and angle with respect to the beam direction. Our measured cross section as a function of Q 2 goes as low Q 2 = 0.10 GeV2 . This is our first step towards extracting the strange quark contribution to the axial form factor, which is not only the least-constrained contribution to the neutral-current elastic scattering cross section but is also crucial for understanding the strange quark contribution to the proton spin.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

First Extraction of Single Differential Cross-Sections on Argon for CC1$\mu$2p0$\pi$ Event Topologies in the MicroBooNE Detector

The precise measurement of cross-sections for a variety of interactions is critical to the success of forthcoming flagship neutrino experiments. Of special interest are neutrino interactions that leave the nucleus in a 2-particle 2-hole state (2p2h). This note will present the first cross-section measurement for the production of 2p2h states on argon. Using MicroBooNE data collected from the first three years of operation, we select events corresponding to a charged-current $ν_µ$ interaction that left the argon nucleus in a 2p2h state. These interactions produce a topology with one muon and two protons in the final state (CC1$\mu$2p0$\pi$). This analysis targets a single differential cross-section measurement for CC1$\mu$2p0$\pi$ event topologies in a variety of kinematic variables. Comparisons are made to a set of theoretical models that explore different cross-section modeling configurations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Single differential $\nu_\mu$ charged-current cross section with the MicroBooNE detector using the Cosmic Ray Tagger

We perform a single differential $\nu_\mu$ charged current (CC) inclusive cross section measurement in MicroBooNE using improved detector response simulation, reconstruction, and with improved cosmic ray-induced background rejection using an external Cosmic Ray Tagger (CRT). The measurement uses data recorded from Fermilab’s Booster Neutrino Beamline (BNB) between December 2017 and July 2018, corresponding to an integrated protons on target of 7.6e18). We present the results in a similar manner as our previous $\nu_\mu$ charged current (CC) inclusive cross section to allow for direct comparison. The direct comparison highlights two improvements in this measurement over the previous measurement: firstly, the drastically reduced cosmic contamination achieved when using the CRT, and secondly the significantly improved detector modeling, with associated reduced systematic uncertainties. It is shown in this analysis that the outcome is an improved measurement (σ = 0.800 ± 0.030 (stat) ± 0.101 (sys) · 10 -38 cm 2 ) with much smaller uncertainties but a result consistent with that of the previous analysis (σ previous = 0.693 ± 0.010 ± 0.165 · 10 -38 cm 2 ).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

First Measurements of Differential Cross Sections In Kinematic Imbalance Variables With The MicroBooNE Detector

Making high-precision measurements of neutrino oscillation parameters requires an unprecedented understanding of neutrino-nucleus scattering. In this presentation, we present the first muon neutrino charged current double-differential cross sections in kinematic imbalance variables. These variables characterize the imbalance in the plane transverse to an incoming neutrino. We use events with a single muon above 100 MeV/c, a single final state proton above 300 MeV/c, and no recorded final state pions. Thus, these variables act as a direct probe of nuclear effects such as final state interactions, Fermi motion, and multi-nucleon processes. We also present a complementary ongoing analysis using electron neutrinos. This channel is of the utmost importance for the extraction of neutrino oscillation parameters by making high-precision measurements. Our measurements allow us to constrain systematic uncertainties associated with neutrino oscillation results performed by near-future experiments of the Short Baseline Neutrino (SBN) program, as well as by future large-scale experiments like DUNE.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Multi-Differential Charged Current $\nu_{\mu}$ - Argon Cross Section without Pions in the Final State Measurement in MicroBooNE

MicroBooNE, an 85-tonne liquid argon time projection chamber (LArTPC) detector is on-axis to the Booster Neutrino Beam (BNB) beamline facility at Fermi National Accelerator Laboratory. MicroBooNE is elucidating neutrino interactions with argon through cross-section measurements to refine interaction models and reduce uncertainties. In this poster, we present the status of the single and double multi-differential charged current (CC) cross section with zero pions in the final state (CC-0$\pi$) as a function of muon momentum ($0.1<p_\mu<2.0\,\mathrm{GeV/c}$) and the cosine of the muon angle ($-1<\cos\theta_\mu<1$). We present the details of the event selection and cross section extraction along with a set of tests using fake data to establish the robustness of the analysis methodology. We also discuss prospects for a future combined measurement with the Gd-H$_2$O target at the ANNIE experiment, to explore MicroBooNE’s proton multiplicity alongside ANNIE’s neutron multiplicity.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Measurement of the Triple Differential Muon-Antineutrino Charged-Current Inclusive Cross Section in the NOvA Near Detector

NOvA is a long-baseline accelerator-based internationally collaborated neutrino experiment based in the USA. NOvA uses an intense neutrino beam produced at Fermilab’s accelerator complex to make physics measurements of neutrino oscillations, neutrino cross sections, and other high quality neutrino analyses. For its physics goals, NOvA uses two functionally-identical detectors. The Near Detector (ND) is situated at Fermilab, 1 km from the neutrino target and the Far Detector (FD) is located at Ash River, MN, a distance of 810 km from the neutrino source. The ND sees high intensity of the neutrino beam due to its close proximity to the neutrino target. This gives us a unique opportunity for high-precision neutrino cross-section measurements. In this talk, we present our latest results of the muon antineutrino charge current inclusive cross section measurement in the NOvA ND. The new measurement is a triple differential cross section in antimuon kinematic phase-space and in the total energy of all observable final state hadrons, also known as the available energy. We compare different GENIE configuration to our data. We also compare our data results to various neutrino generator predictions, for example, comparisons to GENIE, NuWro, NEUT, and GiBUU neutrino generators are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Triple Differential Muon Antineutrino Charge Current Inclusive Cross Section Measurement in NOvA

NOvA is a long-baseline accelerator-based neutrino experiment based in the USA. For its physics goals, NOvA uses two functionally identical detectors. The Near Detector is situated at Fermilab, 1 km from the neutrino target, and the Far Detector is located at Ash River, MN, a distance of 810 km from the neutrino source. The ND sees the high intensity of the neutrino beam due to its close proximity to the neutrino target, giving us a unique opportunity to do high-precision neutrino cross-section measurements. In this talk, we present our latest results of the muon antineutrino charge current inclusive cross-section measurement in the NOvA ND. The new measurement is a triple differential cross section in antimuon kinematic phase-space and in the total energy of all observable final state hadrons. We also compare our data results to various neutrino generator predictions, for example, comparisons to GENIE, NuWro, GiBUU, and NEUT neutrino generators are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Earthquake Relocation in Rock Valley, NV Using Absolute and Differential Times

In this brief report we document algorithmic choices and updates to our code related to the earthquake relocation portion of our tomographic imaging algorithm. We show results of these improvements by relocating over 40,000 events located within 20-30 km of the Rock Valley Direct Comparison (RV/DC) site using both absolute and differential arrival times within the context of two different 3-D Earth models. Accurate hypocentral locations and Earth models are important to the ultimate goals of the RV/DC program, which will co-locate a chemical explosion with a shallow earthquake within Rock Valley, southern Nevada, to investigate differences between the source types and improve our analysis algorithms for both types (Snelson et al., 2022). Our improvements to our relocation algorithms comprise just one step toward achieving these goals

58 GEOSCIENCES↗
DOE OSTI2024

Non-Intrusive Parallel-in-Time Solvers for Partial Differential Equations (Final Report)

Many time-dependent problems and simulations are often modeled using Partial Differential Equations. Traditional modeling approaches that use sequential time-stepping are reaching a bottleneck in optimizing efficiency. The Center of Applied Science and Computing at Lawrence Livermore National Laboratory extensively works on parallelizing these algorithms to leverage the increasing computational power from the growing number of processors in computer hardware. In particular, they aim to design non-intrusive algorithms that can generalize to a variety of problems and sizes without requiring additional information from or modifications on the original problems. Multigrid Reduction in Time (MGRIT) is a parallel-in-time algorithm that is designed to be non-intrusive. This project focuses on increasing the efficiency of MGRIT by approximating the coarse-grid operator using machine learning approaches as a means to find the most non-intrusive, or general, solution.

97 MATHEMATICS AND COMPUTING↗