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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders

In this work, the application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.

variational autoencoder↗
DOE OSTI2024

Studying the interaction between charm and light-flavor mesons

The two-particle momentum correlation functions between charm mesons ( D * ± and D ± ) and charged light-flavor mesons ( π ± and K ± ) in all charge combinations are measured for the first time by the ALICE Collaboration in high-multiplicity proton–proton collisions at a center-of-mass energy of s = 13 TeV . For DK and D * K pairs, the experimental results are in agreement with theoretical predictions of the residual strong interaction based on quantum chromodynamics calculations on the lattice and chiral effective field theory. In the case of D π and D * π pairs, tension between the calculations including strong interactions and the measurement is observed. For all particle pairs, the data can be adequately described by Coulomb interaction only, indicating a shallow interaction between charm and light-flavor mesons. Finally, the scattering lengths governing the residual strong interaction of the D π and D * π systems are determined by fitting the experimental correlation functions with a model that employs a Gaussian potential. The extracted values are small and compatible with zero. © 2024 CERN, for the ALICE Collaboration 2024 CERN

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Simulations of biomass pyrolysis using glued-sphere CFD-DEM with 3-D intra-particle models

The shape of biomass particles influences intra-particle heat transfer, particle-particle collisions, interphase momentum, heat transfer, and pyrolysis yield. In this research, the effects of these multi-scale phenomena are captured using a glued-sphere computational fluid dynamic discrete element method (CFD-DEM) with 3-D intra-particle models. The glued-spheres resolved the shapes of particles and intra-particle temperature and species distributions. The implementation was validated using the pyrolysis data of a spherical particle and a cylindrical particle. Then, the influences of biomass shapes and sizes on pyrolysis were investigated. The results revealed the significant influence of surface areas and mass distributions on interphase heat transfer and chemical reactions. The intra-particle heat transfer is neglectable for a Biot number smaller than 0.41. Finally, the pyrolysis of irregularly shaped biomass particles in a fixed bed, fluidized bed, and spouted bed was simulated. Analysis of conversion distributions showed the largest deviations in the fixed bed and the slowest conversion rate in the spouted bed. A good balance between uniform and fast conversion is achieved in the fluidized bed. The multi-scale method proposed in this research provides a tool for the evaluation of large irregular-shaped biomass particle pyrolysis in different types of reactors.

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

Lignin-Graft-Poly(lactic- co -glycolic) Acid Biopolymers for Polymeric Nanoparticle Synthesis

A lignin-graft-poly(lactic- co -glycolic) acid (PLGA) biopolymer was synthesized with two types of lignin (LGN), alkaline lignin (ALGN) and sodium lignosulfonate (SLGN), at different (A/S)LGN/PLGA ratios (1:2, 1:4, and 1:6 w/w). 1 H NMR and Fourier-transform infrared spectroscopy (FT-IR) confirmed the conjugation of PLGA to LGN. The (A/S)LGN-graft-PLGA biopolymers were used to form nanodelivery systems suitable for entrapment and delivery of drugs for disease treatment. The LGN-graft-PLGA NPs were generally small (100–200 nm), increased in size with the amount of PLGA added, monodisperse, and negatively charged (-48 to -60 mV). Small-angle scattering data showed that particles feature a relatively smooth surface and a compact spherical structure with a distinct core and a shell. The core size and shell thickness varied with the LGN/PLGA ratio, and at a 1:6 ratio, the particles deviated from the core–shell structure to a complex internal structure. The newly developed (A/S)LGN-graft-PLGA NPs are proposed as a potential delivery system for applications in biopharmaceutical, food, and agricultural sectors.

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

Fast jet tagging with MLP-Mixers on FPGAs

We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. MLP-Mixers excel in processing sequences of jet constituents, achieving state-of-the-art performance on datasets mimicking Large Hadron Collider conditions. By using advanced optimization techniques such as High-Granularity Quantization and Distributed Arithmetic, we achieve unprecedented efficiency. These models match or surpass the accuracy of previous architectures, reduce hardware resource usage by up to 97%, double the throughput, and half the latency. Additionally, non-permutation-invariant architectures enable smart feature prioritization and efficient FPGA deployment, setting a new benchmark for machine learning in real-time data processing at particle colliders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Benchmark eA generator for leptoproduction in high-energy lepton-nucleus collisions

The upcoming electron-ion collider (EIC) will address several outstanding puzzles in modern nuclear physics. Topics such as the partonic structure of nucleons and nuclei, the origin of their mass and spin, among others, can be understood via the study of high-energy electron-proton (ep) and electron-nucleus (eA) collisions. Achieving the scientific goals of the EIC will require a novel electron-hadron collider and detectors capable to perform high-precision measurements but also dedicated tools to model and interpret the data. To aid in the latter, we present a general-purpose e A Monte Carlo generator—BeAGLE. In this paper, we provide a general description of the models integrated into BeAGLE, applications of BeAGLE in eA physics, implications for detector requirements at the EIC, and the tuning of the parameters in BeAGLE based on available experimental data. Specifically, we focus on a selection of model and data comparisons in particle production in both ep and eA collisions, where baseline particle distributions provide essential information to characterize the event. In addition, we investigate the collision geometry determination in eA collisions, which could be used as an experimental tool for varying the nuclear density.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

VAIM-CFF: a variational autoencoder inverse mapper solution to Compton form factor extraction from deeply virtual exclusive reactions

We develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted CFFs, use of uncertainty quantification technique, and inclusion of prior physics information. We then use dimensionality reduction techniques such as principal component analysis to visualize the missing physics information tracked in the latent space of the VAIM framework. Through re-framing the extraction of CFFs as an inverse problem, we gain access to fundamental properties of the problem not comprehensible in standard fitting methodologies: exploring the limits of the information encoded in deeply virtual exclusive experiments.

Accelerator Physics↗
DOE OSTI2024

Shape-shifting Elephants: Multi-modal Transport for Integrated Research Infrastructure

Data Acquisition (DAQ) workloads form an important class of scientific network traffic that by its nature (1) flows across different research infrastructure, including remote instruments and supercomputer clusters, (2) has ever-increasing throughput demands, and (3) has ever-increasing integration demands---for example, observations at one instrument could trigger a reconfiguration of another instrument. Today's DAQ transfers rely on UDP and (heavily tuned) TCP, but this is driven by convenience rather than suitability. The mismatch between Internet transport protocols and scientific workloads becomes more stark with the steady increase in link capacities, data generation, and integration across research infrastructure.This position paper argues the importance of developing specialized transport protocols for DAQ workloads. It proposes a new transport feature for this kind of elephant flow: multi-modality involves the network actively configuring the transport protocol to change how DAQ flows are processed across different underlying networks that connect scientific research infrastructure. Multi-modality is a layering violation that is proposed as a pragmatic technique for DAQ transport protocol design. It takes advantage of programmable network hardware that is increasingly being deployed in scientific research infrastructure. The paper presents an initial evaluation through a pilot study that includes a Tofino2 switch and Alveo FPGA cards, and using data from a particle detector.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Release of the NDI 2.2.0

The Nuclear Data Interface 2.2.0 has been released. It includes the addition of charged particle dE/dx (stopping power) data, the ability to read in NDI-formatted binary data, corrections to TN data, and other minor changes and bug fixes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Gen 3 Particle Pilot Plant (G3P3) -- High-Temperature Particle System for Concentrating Solar Power (Phases 1 and 2)

The U.S. Department of Energy Solar Energy Technologies Office initiated the Generation 3 Concentrating Solar Power (CSP) program to achieve higher operating temperatures (>700 °C) to enable next-generation CSP high-temperature power cycles such as the supercritical CO 2 (sCO2) Brayton Cycle. Three teams were selected to pursue high-temperature gas, liquid, and solid pathways for the heat-transfer media. Phases 1 and 2, which lasted from 2018 – 2020, consisted of design, modeling, and testing activities to further de-risk each of the technologies and develop a design for construction, commissioning, and operation of a pilot-scale facility in Phase 3 (2021 – 2024). This report summarizes the activities in Phases 1 and 2 for the solid-particle pathway led by Sandia National Laboratories. In Phases 1 and 2, Sandia successfully de-risked key elements of the proposed Gen 3 Particle Pilot Plant (G3P3) by improving the design, operation, and performance of key particle component technologies including the receiver, storage bins, particle-to-sCO2 heat exchanger, particle lift, and data acquisition and controls. Modeling and testing of critical components have led to optimized designs that meet desired performance metrics. Detailed drawings, piping and instrumentation diagrams, and process flow diagrams were generated for the integrated system, and structural analyses of the assembled tower structure were performed to demonstrate compliance with relevant codes and standards. Instrumentation and control systems of key subsystems were also demonstrated. Together with Bridgers & Paxton, Bohannan Huston, and Sandia Facilities, we have completed a 100% G3P3 tower design package with stamped engineering drawings suitable for construction bid in Phase 3.

14 SOLAR ENERGY↗
DOE OSTI2023

Can We Lower the Voltage of a Silicon Module and Still Make It Just as Efficient?

The purpose of this experiment to conserve excess amounts of energy the best I can. I am trying to lower the voltage of a machine that collects data from cosmic particles that travel through it, without greatly affecting the results. By doing this, I am effectively saving energy and also creating a more efficient silicon module.

Mason, Eddie↗
DOE OSTI2023

A Novel Spatio-Temporal Regime Tracking Method for Impact Simulations

In this proposal, we present a novel method of tracking the rheological regimes activated during impact cratering events that will allow researchers to gain new insights into cratering mechanics. Rheology describes the stress-strain response of rocks to different conditions. Planetary impact cratering events often occur on too large of a scale to be feasibly captured in controlled experiments. Instead, these dynamic events are primarily studied using multi-physics codes equipped with complex material models that enable calculations of the impact event at scale. However, determining which physical processes are required for the problem of interest is challenging. Because the dominant rheological regimes change with space and time during crater formation, it is difficult to link numerical simulations with observable features of craters at the end of the event. The basis of this work is the implementation of numerical flags that track the activation of each rheological regime throughout impact simulations. We demonstrate this with the ‘Rock Model’ implemented in the CTH shock-physics code. We will use this rheology tracking method to ’zoom in’ on a specific region within an event and track the conditions the rock experiences over time. This work will develop community benchmarks to validate and distribute our implemented rheological models. We will focus on improving the melt models used by the planetary impact modeling community by developing an EOS-aware rheological transition from solid to melt. Through analysis of cell and tracer-particle based tracking data, we will study the effects of different rheologies on modeled outcomes, particularly on the volume and distributions of impacts melts. We will also use this method to link observable features with the rheological mechanisms responsible for them. The deliverables (peer-reviewed papers) from the proposed work are (i) tests of the implemented rheologic processes and demonstrations of the tracking flags; (ii) the first calculations of the spatio-temporal evolution of the dominant rheologies during impact cratering events; and (iii) application to delivery of impactor iron during basin-scale impacts.

58 GEOSCIENCES↗
DOE OSTI2025

Dense granular flows with MFIX-Exa

This report extends the linear spring dashpot collision model the discrete element method available in MFIX-Exa to include static a static tangential friction force. Additionally, two rolling friction models frequently used in the literature are also implemented. The governing equations are provided with an emphasis on the new terms. The new model is validated by comparison to existing experimental data of single particle oblique collisions. The model is then tested on three dense granular flow problems: the formation of static piles, the discharge from a flat-bottom hopper and the self-induced granular Rayleigh-Taylor instability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Collaborative Research: Enabling multi-scale studies of magnetic reconnection with interpretable data-driven models

The development of accurate reduced descriptions and improved closures for magnetic reconnection is an important and a long‐standing challenge in plasma physics. The four‐fluid approach, and associated closures, that were investigated have the potential to improve the accuracy of plasma fluid models, capturing physical effects which would otherwise require a kinetic description. If successful, this approach could have an important impact for the modeling of laboratory and space plasmas. The major goals of this project were to develop new machine learning (ML) tools based on sparse and symbolic regression techniques, and to extract interpretable and generalizable reduced models (e.g., in the form of partial differential equations - PDEs) from data generated by first principles plasma simulations. Preserving interpretability of such data‐driven models is key to addressing the long‐standing theoretical and numerical challenges. Prior proof‐of‐principle studies have demonstrated the enormous potential of this approach, by recovering the well‐established hierarchy of plasma equations (from Vlasov to MHD) from data produced by particle‐in‐cell (PIC) simulations. Our goal in this project was to extend and apply these new tools to construct better kinetic closures for magnetic reconnection; to derive better models of particle injection and acceleration by this fundamental plasma process; and to use this understanding to accelerate the development of multi‐scale plasma algorithms. While our immediate focus was on the problem of magnetic reconnection, the tools that were will developed are general and applicable to other areas of plasma physics, and more broadly to many‐body phenomena. We anticipate that the development of these multi‐scale models will have a significant impact across different areas of plasma science, from fusion to space and astrophysical plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Final Report: A Multi-Channel Fusion Product

The goal of this project was to measure charged fusion products from the d(d,p)t reaction in MAST-U plasmas as a function of time and position with good energy resolution using a system of up to six charged particle detectors. The data from this new diagnostic will make it possible to determine the neutral beam ion density profile as a function of R, z, and t with reduced model dependency and contribute new information to a global analysis of fast ion diagnostic data needed for the determination of the fast ion distribution function (velocity space tomography).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Online Machine Learning for Accelerating Molecular Dynamics Modeling of Cells

We developed a biomechanics-informed online learning framework to learn the dynamics with ground truth generated with multiscale modeling simulation. It was built on Summit-like supercomputers, which were also used to benchmark and validate our framework on one physiologically significant modeling of deformable biological cells. We generalized the century-old equation of Jeffery orbits to a new equation of motion with additional parameters to account for the flow conditions and the cell deformability. Using simulation data at particle-based resolutions for flowing cells and the learned parameters from our framework, we validated the new equation by the motions, mostly rotations, of a human platelet in shear blood flow at various shear stresses and platelet deformability. Our online framework, which surrogates redundant computations in the conventional multiscale modeling by solutions of our learned equation, accelerates the conventional modeling by three orders of magnitude without visible loss of accuracy.

multiscale modeling↗
DOE OSTI2024

Interpreting Transformers for Jet Tagging

Machine learning (ML) algorithms, particularly attention-based transformer models, have become indispensable for analyzing the vast data generated by particle physics experiments like ATLAS and CMS at the CERN LHC. Particle Transformer (ParT), a state-of-the-art model, leverages particle-level attention to improve jet-tagging tasks, which are critical for identifying particles resulting from proton collisions. This study focuses on interpreting ParT by analyzing attention heat maps and particle-pair correlations on the $\eta$-$\phi$ plane, revealing a binary attention pattern where each particle attends to at most one other particle. At the same time, we observe that ParT shows varying focus on important particles and subjets depending on decay, indicating that the model learns traditional jet substructure observables. These insights enhance our understanding of the model's internal workings and learning process, offering potential avenues for improving the efficiency of transformer architectures in future high-energy physics applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Measurement of 𝑑 2⁢ 𝜎/𝑑⁢|$\vec{q}$|⁢𝑑⁢𝐸 avail in charged current 𝜈 𝜇 -nucleus interactions at ⟨𝐸 𝜈 ⟩=1.86 GeV using the NOvA Near Detector

Double- and single-differential cross sections for inclusive charged-current 𝜈 𝜇 -nucleus scattering are reported for the kinematic domain 0 to 2 GeV/𝑐 in three-momentum transfer and 0 to 2 GeV in available energy, at a mean 𝜈 𝜇 energy of 1.86 GeV. The measurements are based on an estimated 995,760 𝜈 𝜇 charged-current (CC) interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole (2p2h) reactions is identified by the cross section excess relative to predictions for 𝜈 𝜇 -nucleus scattering that are constrained by a data control sample. Models for 2-particle-2-hole processes are rated by 𝜒 2 comparisons of the predicted-versus-measured 𝜈 𝜇 CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based València and SuSAv2 2p2h models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗