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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

DESS (Differential Equation System Solver) [SWR-24-48]

The Differential Equation System Solver (DESS) is a Rust crate implementing fixed-step and adaptive-step solvers and designed especially for modeling physical systems. Seven explicit ordinary differential equation (ODE) solver methods have been added so far: Euler’s, Heun’s, Midpoint, Ralston’s, Classic Runge-Kutta, Bogacki-Shampine, and Cash-Karp. These comprise five fixed-step methods and two adaptive-step methods. Few solver packages are implemented in the Rust ecosystem and none are intended specifically for physical system modeling, so the goal of DESS is to create a Rust ODE solver crate designed to easily specify and model physical systems with modular, configurable solver options. In addition to allowing users to directly input equations to solve, DESS allows users to optionally specify and define relationships between nodes in their system, which the package then translates into a system of equations via the Rust macro system, leading to simpler and more intuitive code.

Steuteville, Robin↗
DOE OSTI2022

Differentially Private DBSCAN

SAND2022-15185 O Differentially Private DBSCAN is a differential private clustering algorithm used for running experiments and conducting data analysis. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Smith, MichaelReed↗
DOE OSTI2021

GNN Parameterized Differential Operators v.0.9

GNN Parametrized Differential Operators is an example code and layer implementation for using the parameterized differential operator used in conference submissions. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-2984 O

Potter, KevinMatthew↗
DOE OSTI2025

A multi-language auto differentiation package (mAD) v1.0

This is a multi-language auto differentiation package. It includes multiple language versions of the package. It can be included/used as a module/class in users' application program to enable differentiability of the program. It is simple, transparent, extensible, efficient compared with the other AD package such as Enzyme or Pytorch.

Qiang, Ji [Lawrence Berkeley National Laboratory (↗
DOE OSTI2019

Pseudo-differential representation of the metaplectic transform and its application to fast algorithms

The metaplectic transform (MT), also known as the linear canonical transform, is a unitary integral mapping that is widely used in signal processing and can be viewed as a generalization of the Fourier transform. For a given function $ψ$ on an $N$-dimensional continuous space q , the MT of $ψ$ is parameterized by a rotation (or more generally, a linear symplectic transformation) of the 2$N$-dimensional phase space ( q,p ), where p is the wavevector space dual to q . In this study, we derive a pseudo-differential form of the MT. For small-angle rotations, or near-identity transformations of the phase space, it readily yields asymptotic differential representations of the MT, which are easy to compute numerically. Rotations by larger angles are implemented as successive applications of $K \gg 1$ small-angle MTs. The algorithm complexity scales as $O(KN^3N_p)$ where $N_p$ is the number of grid points. Here, we present a numerical implementation of this algorithm and discuss how to mitigate the associated numerical instabilities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Measurement of differential cross sections and W + / W − cross-section ratios for W boson production in association with jets at s = 8 TeV with the ATLAS detector

This paper presents a measurement of the W boson production cross section and the W+/W- cross-section ratio, both in association with jets, in proton-proton collisions at sqrt(s) = 8 TeV with the ATLAS experiment at the Large Hadron Collider. The measurement is performed in final states containing one electron and missing transverse momentum using data corresponding to an integrated luminosity of 20.2 fb^-1. Differential cross sections for events with at least one or two jets are presented for a range of observables, including jet transverse momenta and rapidities, the scalar sum of transverse momenta of the visible particles in the event, and the transverse momentum of the W boson. For a subset of the observables, the differential cross sections of positively and negatively charged W bosons are measured separately. In the cross-section ratio of W+/W- the dominant systematic uncertainties cancel out, improving the measurement precision by up to a factor of nine. The observables and ratios selected for this paper provide valuable input for the up quark, down quark, and gluon parton distribution functions of the proton.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Measurement of fiducial and differential W + W − production cross-sections at s = 13 TeV with the ATLAS detector

A measurement of fiducial and differential cross-sections for W+W- production in proton-proton collisions at sqrt(s)=13 TeV with the ATLAS experiment at the Large Hadron Collider using an integrated luminosity of 36.1fb-1 is presented. Events with one electron and one muon are selected, corresponding to the decay of the diboson system as WW->e nu mu nu. To suppress top-quark background, events containing jets with a transverse momentum exceeding 35 GeV are not included in the measurement phase space. The fiducial cross-section, six differential distributions and the cross-section as a function of the jet-veto transverse momentum threshold are measured and compared with several theoretical predictions. Constraints on anomalous weak boson self-interactions are also presented in the framework of a dimension-six effective field theory.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Unified differentiable digital twin for the IOTA/FAST facility

As the design complexity of modern accelerators grows, there is more interest in using advanced simulations that have fast execution time or produce insights about accelerator state. One notable example of additional information are gradients of physical observables with respect to design parameters produced by differentiable simulations. The IOTA/FAST facility has recently begun a program to implement and experimentally validate a unified start-to-end differentiable digital twin to serve as a virtual accelerator test stand, allowing for rapid prototyping of new software and experiments with minimal beam time costs. In this contribution we will discuss our plans and progress. Specifically, we will cover the selection and benchmarking of both physics and ML codes, the development of generic interfaces between device models and surrogate or physics-based sections, and the export of the parameters through either a deterministic event loop or a fully asynchronous EPICS soft input/output controller. We will also discuss challenges in model calibration and uncertainty quantification, as well as future plans to support larger proton accelerators like PIPII and Booster.

Kuklev, Nikita [Fermilab]↗
DOE OSTI2023

DPFEHM: a differentiable subsurface physics simulator

The Earth’s subsurface is a key resource that provides energy via fossil fuels and geothermal sources, stores drinking water, and is used in the fight against climate change via carbon sequestration. Simulating the physical processes that occur in the Earth’s subsurface with computers enables better use of this resource. DPFEHM is a Julia package that includes computer models with a focus on the Earth’s subsurface, especially fluid flow, which is critical for the aforementioned applications. DPFEHM is able to solve the groundwater flow equations (single phase flow), Richards equation (air/water), the advection-dispersion equation, and the 2d wave equation. One of the key features of DPFEHM is that it supports automatic differentiation, so it can be integrated into machine learning workflows using frameworks such as Flux or PyTorch. The automatic differentiation capabilities give it the same performance as adjoint methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

High Temperature Heat Exchanger Design and Fabrication for Systems with Large Pressure Differentials

The project’s main purpose was to design, build and test a compact heat exchanger for supercritical carbon dioxide (sCO 2 ) power cycle recuperators. The compact recuperator is required to operate at high temperature and high pressure differentials, 169 bar (~2,500 psi), between streams of sCO 2 . Additional project tasks included building a hot air-to-sCO 2 Heater heat exchanger (HX) and design, build and operate a test loop to characterize the recuperator and heater heat exchangers. A novel counter-current microtube recuperator was built to meet the high temperature high differential pressure criteria and tested. The compact HX design also incorporated a number of features that optimize material use, improved reliability and reduced cost. The air-to-sCO 2 Heater HX utilized a cross flow, counter-current, micro-tubular design. This compact HX design was incorporated into the test loop and exceeded design expectations. The test loop design to characterize the prototype Brayton power cycle HXs was assembled, commissioned and operated during the program. Both the prototype recuperator and Heater HXs were characterized. Measured results for the recuperator confirmed the predictions of the heat transfer models developed during the project. Heater HX data analysis is ongoing.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2017

The Differential Cross Section and Λ Recoil Polarization from γδ -> Κ0(ρ)

Presented is the analysis of the differential cross section and Λ recoil polarization from the reaction γδ -> Κ0(ρ). This work measured these observables over beam energies from 0.90 GeV to 3.0 GeV. These measurements are the first in this channel to cover such a wide range of energies. The data were taken using the CEBAF Large Acceptance Spectrometer (CLAS) at Jefferson Laboratory (JLAB) along with a tagged photon beam. This analysis was completed by identifying events of interest that decayed into the final state topology of π-π+,π-&rho'(ρ). Through conservation of energy and momentum, the Κ0, Λ and missing mass of the spectator proton were reconstructed. Utilizing the same analysis techniques, the observables were measured on two different experiments with good agreement. Photoproduction of strange mesons from the neutron are difficult to measure, consequently there are only a few measurements of this kind. Despite that, these reactions supply essential complementary data to those on the proton. The differential cross sections and the recoil polarization extracted, span the region where new nucleon resonances have been found from studies of the reaction γρ -> Κ+Λ. Comparisons between the Κ+Λ and Κ0Λ cross section demonstrate that possible interference terms near 1900 MeV are less pronounced in the latter. This unexpected result inspired a partial wave analyses (PWA) to be fitted to the data. The fit solution shows that this measurement fostered an improvement on the knowledge of observed resonance parameters, necessary to understanding these excited states. The study of nucleon resonances is a key motivating factor since the resonance masses can be calculated from the theory of the strong nuclear force, called quantum chromodynamics, or QCD.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Mitigate: An Adaptive Network Data Anonymization Tool Using Condensation-Based Differential Privacy

Modern network devices collect a large amount of data that can be analyzed to identify bottlenecks, anomalies, cyber-attacks, etc. Therefore, there is often a need to analyze such collections of network data quite often by an external expert or by the research community. However, these collections of data contain sensitive, proprietary information. In order for the network data to be shared, it must first be anonymized. The overall objective of this project is to develop an innovative privacy management tool to anonymize network data and achieve sufficient privacy, acceptable data utility, and efficient data analysis at the same time. No existing anonymization methods can achieve all of these at the same time. The core of this technology is a differential private clustering algorithm that provides strong privacy protection, preserves data properties important for subsequent analysis, and allows the party receiving the anonymized data to conduct analysis directly on anonymized data without the need of decryption or any extra processing. The research carried out was to design, implement and verify a solution to this problem by completing the following tasks: 1) developing the core technology; 2) developing a context based method that automatically recommends fields that must be anonymized; 3) conducted experiments showing superior results using our approach compared to existing tools, and 4) developed an intuitive but basic user interface. The research that was conducted generated novel algorithmic techniques that utilize state-of-the-art methods such as condensation, differential privacy preservation, clustering, automated tuning based on contextual awareness, and recommendation techniques to specify columns to users for anonymization leading to optimal privacy that allows research analysis on the dataset. Experiments were conducted to evaluate the efficacy of these novel algorithmic techniques by performing analysis on original non-anonymized datasets, then conducting analysis on the same yet anonymized datasets and comparing the results of the analyses. Overall, the anonymized analysis results were within 1% of the original results, verifying that the generated technology not only guarantees a high level of privacy but also enables research analysis as if it were conducted on the original dataset. Potential applications of this technology include anonymization of any type of structured network datasets that contain sensitive identifiers, such as IP addresses, that can be used in multiple applications. For example, to create an AI or machine learning model for cyber security, e.g., to detect attacks, or for performance analysis, e.g., identify bottlenecks or predict performance. In addition, a market analysis that was conducted for potential applications of this technology identified a broader range of applications of our anonymization technology beyond the network sector that includes healthcare, banking, insurance, securities, finance (FISB), data brokering, cloud services, ad sales, and government.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High Energy Quasi - Differential Neutron Emission Measurements of 181 Ta and 19 F [Slides]

This presentation begins with the motivation for performing 181 Ta and 19 F high energy quasi-differential and neutron emission measurements. It then provides a discussion of significant pulse digitization upgrade. The Quasi-Differential methodology and importance for validating nuclear data are provided. Preliminary results from 181 Ta and 19 F are displayed. The presentation concludes with a discussion and future studies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Methodology of the Extraction of Multi-Differential Cross Sections of Charged-Current $\nu_\mu$-Argon Interactions in MicroBooNE Using Wire-Cell $\nu_\mu$CC Selection

Neutrino physics experiments rely on accurate nuclear-interaction models that are in part guided by experimental observations. Multi-differential cross section measurements are particularly valuable for exploring the underlying physics described in these models, making them a significant step forward in the field. This note presents methodology in preparation to unfold neutrino flux-averaged double and triple-differential cross sections of the inclusive muon neutrino charged-current interaction on Argon. This work builds upon the existing framework and methodology presented in recent energy dependent cross section measurements at MicroBooNE using the Wire-Cell tomographic event reconstruction. The signal definition, choice of binning, and handling of estimated detector uncertainties are discussed, and the MicroBooNE simulation model is validated over the multi-dimensional phase space of muon momentum, muon polar angle, and visible hadronic energy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Measurement of Triple-Differential Inclusive Muon Neutrino Charged-Current Cross Sections on Argon with the MicroBooNE Detector

We report the first measurement of the differential cross section $d^2σ(E_ν)/dcos(θ_µ)dP_µ$for inclusive muon neutrino charged-current scattering on argon. This measurement utilizes data from6.5×10 20 protons on target of exposure collected using the MicroBooNE liquid argon time projection chamber located on-axis in the Fermilab Booster Neutrino Beam with a mean neutrino energy of approximately 0.8 GeV. In this note, the mapping from reconstructed kinematics to truth quantities, particularly from reconstructed to true neutrino energy, is validated by comparing the distribution of reconstructed hadronic energy in data to that of the model prediction in different muon scattering angle bins after conditional constraint from the muon momentum distribution in data. The success of this validation gives confidence that the missing energy in the MicroBooNE detector is well-modeled within its uncertainties in simulation, enabling the unfolding to an energy-dependent triple-differential measurement over muon momentum, muon scattering angle, and neutrino energy. The unfolded measurement covers an extensive phase space, providing a wealth of information useful for future LArTPC experiments measuring neutrino oscillations. Comparisons with model predictions show the best agreement with Neut 5.4.0.1 at low energy, and with NuWro 19.02.01 at higher energies, particularly at forward muon scattering angles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Multi-Differential Semi-Inclusive Charged-Current Muon Neutrino Cross Sections on Helium-4 in MINERvA and the Helium-to-CH Target Cross Section Ratio

A comprehensive international effort has been underway to elucidate the properties and behaviors of neutrinos. A major source of systematic uncertainties in studying neutrino-induced interactions comes from neutrino-nucleus cross-section models, highlighting the need for more precise statistical measurements. MINERvA, an on-axis neutrino-nucleus scattering experiment located at the Fermi National Accelerator Laboratory, was established to produce neutrino cross-section measurements with many different nuclei. The helium target is the lightest nucleus to be measured by MINERvA. Using the NuMI medium energy muon neutrino data set, we present preliminary results and summarize the extraction of the charged current (CC) muon neutrino - helium-4 semi-inclusive ($\nu_{\mu}$+$^{4}\textrm{He}$$\rightarrow$$\mu^{-}$+N$\pi$+M$\textit{p}$) multi-differential cross-section extraction as a function of transverse ($P_{T}$) and longitudinal ($P_{L}$) muon momentum with respect to the neutrino beamline. We define the final state topology as CC-N$\pi$M$\textit{p}$, with at least two reconstructed tracks: a muon and a combination of N protons and M pions, where $N + M > 0$. To probe the dependence on the size of the nucleus in neutrino-induced interaction phenomena we present a differential cross-section ratio of helium-4 to MINERvA's hydrocarbon target (\textrm{CH}) as a function of transverse muon momentum.

Nguyen, Christian↗
DOE OSTI2024

Machine-learning based model reduction for partial differential equations

We develop a novel synergistic approach between model reduction and machine learning. The specific goal of this project is to aid in the construction of reduced order models for basis functions that are custom-made to represent the solution of partial differential equations. Partial differential equations (PDEs) are one of the main mathematical tools for describing physical phenomena. However, due to either efficiency or necessity, for many real-world problems, we are interested in constructing reduced order models (ROMs) which focus only on the explicit computation of subsets of the active spatio-temporal scales in the problem, while treating the interaction with the rest of the scales approximately. The task of accurate representation of such interactions (usually called memory terms) constitutes a vast area of research known as model reduction. PI Stinis has significant expertise in the construction of ROMs for complex systems. In addition, in recent work with the project key participant Qadeer, they have utilized machine learning to acquire custom-made basis functions (CBFs) to expand the solutions of PDEs. In the proposed work, we will merge the two concepts by constructing ROMs for subsets of the CBFs needed to represent the solution of a PDE. Specifically, we will use the Mori-Zwanzig model reduction formalism to construct ROMs for subsets of CBFs for nonlinear PDEs of various complexity, as well as investigate the usage of CBFs in the spectral vanishing viscosity method for problems that can form shocks in finite time. The outcome of the research is aimed to be proof-of-concept about a novel synergistic approach between model reduction and machine learning, thus advancing the field of scientific machine learning. Such a capability will benefit the efficient modeling of physical systems appearing in various areas of interest to the DOE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Model-form Error Correction using Universal Differential Equations for an Agent-Based Model of Infectious Disease

This report demonstrates universal differential equations (UDEs) as an approach to bridge the gap between ordinary differential equations (ODE) models and agent-based models (ABMs). Using UDE models as surrogates for ABMs allows us to preserve the foundational ODE that represents global disease dynamics while coupling it with a neural network model to approximate functions for the local behaviors of the ABM.

59 BASIC BIOLOGICAL SCIENCES↗