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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Construction of generalized quasilinear diffusion coefficient using neural networks with physical restrictions

The quasilinear diffusion coefficient (D QL ) derived from our machine learning framework shows comparable trends with the ground truth D QL obtained from GENRAY-CQL3D simulations. Additionally, for the strong absorption cases, the radial current drive profiles generated using the D QL from our model exhibit consistent behavior with those obtained from the original simulation. These findings indicate the potential of our surrogate modeling approach with physical restrictions to replicate key wave–plasma interaction characteristics while reducing computational costs. Traditionally, calculating D QL for wave–particle interactions relies on computationally intensive wave simulations coupled with Fokker–Planck solvers. To address this challenge, we developed a machine learning-based surrogate model with physical restrictions derived from cold plasma theory and bounce-averaged damping effects. First, we establish the propagation domain of Lower Hybrid Waves in the (N∥, ρ) space by identifying the accessibility limit and determining the upper and lower bounds of N∥ using the Potential Power Deposition (PPD) method. Subsequently, leveraging a database constructed using Latin hypercube sampling alongside the underlying physical restrictions (e.g. PPD), machine learning methods including U-Net and Recurrent Neural Networks are employed to design a physics-restricted machine learning framework capable of reconstructing D QL .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Probing many-body localization on a noisy quantum computer

A disordered quantum system of interacting particles exhibits localized behavior when the disorder is large compared to the interaction strength. Studying this phenomenon on a quantum computer with no, or limited, error correction is challenging because even weak coupling to a thermal environment destroys most signatures of localization. Fortunately, spectral functions of local operators are known to contain features that can survive the presence of noise. In these spectra, discrete peaks and a soft gap at low frequencies compared to the thermal phase indicate localization. Here, we present the computation of spectral functions on a trapped-ion quantum computer for a one-dimensional Heisenberg model with disorder. Further, we design an error-mitigation technique which is effective at removing the noise from the measurement allowing clear signatures of localization to emerge as the disorder increases. Furthermore, we show that spectral functions can serve as a robust and scalable diagnostic of many-body localization on current and future generations of quantum computers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Coupling particle scale model and SuperDEM–CFD for multiscale simulation of biomass pyrolysis in a packed bed pyrolyzer

An efficient biomass pyrolysis process requires a comprehensive understanding of the chemical and physical phenomena that occur at multi-length and time scales. In this study, a multiscale computational approach was developed and validated for biomass pyrolysis in a packed-bed reactor by integrating pyrolysis kinetics, a particle scale model, and SuperDEM-CFD in open-source code MFiX. A one-dimensional particle-scale model that discretizes the characteristic length of biomass particle into layers was developed to predict the intraparticle phenomena inside a single particle. The 1D model was validated by comparing it with a single biomass particle pyrolysis experiment. Here, a recently developed SuperDEM-CFD model was employed to simulate the non-spherical particle-particle contact and fluid-particle interaction. The coupled model was applied to simulate the pyrolysis of cubic biomass particles in a packed bed and validated by comparing with experimental data. Simulation with and without particle-scale model was compared, and the effect of the gas-solid heat transfer models was also investigated.

09 BIOMASS FUELS↗
DOE OSTI2023

High-Performance Electron Sources: Numerical Methods and Beam Dynamics at the Precision Frontier

Electron sources have a wide range of applications and there are many stakeholders that express continuing need for improvements and performance enhancements. Whether we consider ultra-cold, high-brightness, high-charge or high-average current source needs, there are some common themes from the point of view of the beam dynamics involved. These are the following ones: ability to model accurately the emission processes, including the presence of often complicated cathode and other boundary surfaces with a wide range of spatial scales; ability to deal accurately and efficiently with a large number of particles interacting pair-wise, including the stochastic part of these interactions with a wide range of spatial scales; and ability to propagate the particle distributions in time, including collisions with a wide range of temporal scales. These tasks require high precision and accuracy since the goal is usually generation, transport and preservation of very high-quality beams. This grant addressed one of the rem

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

A collision operator for describing dissipation in noncanonical phase space

The phase space of a noncanonical Hamiltonian system is partially inaccessible due to dynamical constraints (Casimir invariants) arising from the kernel of the Poisson tensor. When an ensemble of noncanonical Hamiltonian systems is allowed to interact, dissipative processes eventually break the phase space constraints, resulting in a thermodynamic equilibrium described by a Maxwell–Boltzmann distribution. However, the time scale required to reach Maxwell–Boltzmann statistics is often much longer than the time scale over which a given system achieves a state of thermal equilibrium. Examples include diffusion in rigid mechanical systems, as well as collisionless relaxation in magnetized plasmas and stellar systems, where the interval between binary Coulomb or gravitational collisions can be longer than the time scale over which stable structures are self-organized. Here, we focus on self-organizing phenomena over spacetime scales such that particle interactions respect the noncanonical Hamiltonian structure, but yet act to create a state of thermodynamic equilibrium. We derive a collision operator for general noncanonical Hamiltonian systems, applicable to fast, localized interactions. This collision operator depends on the interaction exchanged by colliding particles and on the Poisson tensor encoding the noncanonical phase space structure, is consistent with entropy growth and conservation of particle number and energy, preserves the interior Casimir invariants, reduces to the Landau collision operator in the limit of grazing binary Coulomb collisions in canonical phase space, and exhibits a metriplectic structure. We further show how thermodynamic equilibria depart from Maxwell–Boltzmann statistics due to the noncanonical phase space structure, and how self-organization and collisionless relaxation in magnetized plasmas and stellar systems can be described through the derived collision operator.

Boltzmann equation↗
DOE OSTI2023

Revisiting the empirical particle-fluid coupling model used in DEM-CFD by high-resolution DEM-LBM-IMB simulations: A 2D perspective

The work investigates the applicability of the unresolved Computational Fluid Dynamics and Discrete Element Method (CFDDEM) technique based on empirical equations for fluid-particle coupling. We first carry out a series of representative volume element simulations using the high-resolution particle-resolved Lattice Boltzmann method and Discrete Element Method (LBMDEM) coupled by an Immersed Moving Boundary (IMB) scheme. Then, we compare the results obtained by both LBMDEM and empirical equations used in unresolved CFDDEM with analytical solutions. It is found that the existing empirical equations used in solving fluid-particle interactions in 2D CFDDEM fail to accurately calculate the hydrodynamic force applied to solid particles. The underlying reason is that the existing empirical models are obtained based on 3D experimental results and thus are not applicable to 2D problems. Based on the simulation results, a new drag coefficient model is then proposed. The estimated drag forces using the new model are compared favourably with the simulated ones, indicating the good performance of the proposed model.

42 ENGINEERING↗
DOE OSTI2021

Progress on Cosmological Magnetic Fields

A variety of observations impose upper limits at the nano Gauss level on magnetic fields that are coherent on inter-galactic scales while blazar observations indicate a lower bound $\sim 10^{-16}$ Gauss. Such magnetic fields can play an important astrophysical role, for example at cosmic recombination and during structure formation, and also provide crucial information for particle physics in the early universe. Magnetic fields with significant energy density could have been produced at the electroweak phase transition. The evolution and survival of magnetic fields produced on sub-horizon scales in the early universe, however, depends on the magnetic helicity which is related to violation of symmetries in fundamental particle interactions. In conclusion, the generation of magnetic helicity requires new CP violating interactions that can be tested by accelerator experiments via decay channels of the Higgs particle.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Numerical Investigation of Enhanced Dehumidification Processes By Using Dielectrophoresis Principles in Moist Airflows

Dispersed particle-laden flows are encountered in many building and industrial applications, such as flow in a fluidized bed, hydrocarbon transportation in pipelines, and the fouling of air-cooled heat exchangers (Kuruneru et al., 2016; Ray et al., 2019; Wang et al., 2019). Computational fluid dynamic (CFD) models have been developed in recent years to depict particle-fluid and particle-particle interactions in laminar or turbulent flows with increasing accuracy and stability. One particular particle-laden system of interest for moisture control is electrically-enhanced condensation in air and water droplet flows. Electrically-enhanced condensation consists of the use of highly charged water droplets injected in the moist air. The droplets become electric seeds that attract polar water vapor molecules to their surfaces and promote condensation. The nucleation and growth of the charged droplets deplete the vapor phase near a droplet, which is compensated for by the dielectrophoresis flow and diffusion. Dielectrophoresis flow involves surrounding vapor at a distance of about 10 to 100 nm for droplets charged by an electrospray compared to ~2 nm for a single electron charge in a droplet. As the vapor molecules collapse on the surface of the droplets, their initial electrical charge decreases with time due to the neutralization of the ions. While the physics of this phenomena is well known, engineering models for predicting the condensation rates are not available. This work computationally investigates dehumidification of moist airflow in a converging rectangular duct. The objective is to develop an engineering model that predicts water vapor condensation by employing dielectrophoresis principles. We construct a Computational Fluid Dynamics (CFD) model of the duct with electrically-enhanced condensation. The model is implemented in the open-source software OpenFOAM. We utilize the Multi-Phase Particle-In-Cell (MP-PIC) method coupled with a Population Balance Equation (PBE) approach to simulate the particle-laden system. This methodology is an Eulerian-Lagrangian approach used to simulate the droplets' behavior in the humid air. The MP-PIC approach (Andrews and O'Rourke, 1996) mitigates the computational cost by parceling several fundamental particles with similar properties (such as types, sizes, and temperature) into one computational particle. Thus, the billions of particles can be substituted by millions of computational particles without significant loss of information. The PBE was considered with the Lagrangian frame to combine the particle distribution function used in MP-PIC (Kim et al., 2020). This approach preserves mass and energy conservation between the phases in the Eulerian and Lagrangian structures. The PBE in this procedure was directly linked to the discrete parcels, making the simulation of the particle distribution computationally efficient and robust. The MP-PIC-PBE approach used in the present work was applied to the dehumidification of air. Water droplets were injected in the air stream and forced to grow according to experimentally derived correlation. The experiments were conducted on a converging duct with the same geometry and boundary conditions used to build the CFD model. This approach enabled us to approximate the effect of dielectrophoresis phenomena on the droplet and air interface. This presentation will discuss the details of the new CFD model built for the duct, the implementation of the model in OpenFOAM CFD programming language, and the experimental validation of the newly developed model. The results revealed a moderate yet measurable increase in droplet diameter due to water vapor condensation at the vapor-liquid interface of the electrically charged droplets' surface. The seed water droplet particles grew in size by capturing the water vapor in the surrounding air. The OpenFOAM model predicted reductions of humidity in the air from 5 to 10 percent.

Yel Mahi, Maliha↗
DOE OSTI2022

LArQL: A phenomenological model for treating light and charge generation in liquid argon

Experimental data shows that both ionization charge and scintillation light in LAr depend on the deposited energy density ($dE/dx$) and electric field ($\mathcal{E}$). Moreover, free ionization charge and scintillation light are anticorrelated, complementary at a given ($dE/dx$, $\mathcal{E}$) pair. We present LArQL, a phenomenological model that provides the anticorrelation between light and charge and its dependence on the deposited energy as well as on the electric field applied. It modifies the Birks' charge model considering the contribution from the escape electrons at null and low electric fields, and reconciles with Birks' model prediction at higher fields. Deviations from current Birks' model are observed for LArTPCs operating at low $\mathcal{E}$ and for heavily ionizing particles. The LArQL model presents a satisfactory description at $dE/dx$ and field ranges for interacting particles in LArTPCs and fits well the available data. Improvements via data sets compilation and global fits are also interesting features of the model.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Wear in particle based CSP systems from particle abrasion and attrition at high temperature

High temperature particle-based receivers offer distinct advantages over conventional molten salt receivers due to their ability to achieve temperatures above 700 ºC, direct absorption of solar energy as they fall through a beam of concentrated sunlight, and the relative ease of storage (and retrieval using a secondary working fluid) in insulated storage tanks. The use of particles, however, also raises concerns with material degradation from the flow of hot or cold particles through discharge hoppers or along the inner receiver surfaces and other system components (e.g. tubes, valves etc.), depending on operating mode of the receiver. The flow of particles over surfaces may result in loss of material from abrasive wear, impact erosion from impingement under gravitational fall and particle attrition as particles fall and move on top of each other. In the present study, the performance of candidate materials and particles were evaluated through a series of abrasion erosion and particle attrition experiments at room temperature as well as at 800 °C. Candidate materials were subject to abrasive wear from particles at low particle to material velocities inside a resistance heated kiln, and analyzed for changes in mass and surface morphology using cross-sectional scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy (EDS) tests. The wear rate for different specimens was noted to be largely driven by the strength of chromia scales built on the specimens from exposure to high temperature. Particle attrition measurements explored the susceptibility of particles to breakdown from particle to particle interaction and the generation of fines from this process. At the low velocities expected in particle based CSP plants, the particles tested exhibited near negligible breakdown. However, changes in the particle hardness at 800 ºC resulted in a significantly higher particle breakdown to sizes <40 microns raising potential environmental concerns. In addition to particle breakdown, it was also noted that the sample had oxides from the stainless steel test setup mixed in with the particles. Similar oxides can be expected to turn up in the utility scale particle based CSP plants as well. The presence of oxide was also noted to affect the solar absorptivity of the mixture compared to a clean initial specimen, potentially resulting in a change in the overall efficiency of the CSP plant.

14 SOLAR ENERGY↗
DOE OSTI2022

Low-Energy Supernovae Severely Constrain Radiative Particle Decays

The hot and dense core formed in the collapse of a massive star is a powerful source of hypothetical feebly interacting particles such as sterile neutrinos, dark photons, axionlike particles (ALPs), and others. Radiative decays such as a→2γ deposit this energy in the surrounding material if the mean free path is less than the radius of the progenitor star. For the first time, we use a supernova (SN) population with particularly low explosion energies as the most sensitive calorimeters to constrain this possibility. These SNe are observationally identified as low-luminosity events with low ejecta velocities and low masses of ejected 56 Ni. Their low energies limit the energy deposition from particle decays to less than about 0.1 B, where 1 B(bethe) = 10 51 erg. For 1–500 MeV-mass ALPs, this generic argument excludes ALP-photon couplings G aγγ in the 10 -10 -10 -8 GeV -1 range.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Overview of Geant4 Physics

Geant4 is a powerful toolkit for simulating particle interactions across a wide energy range (0 to ~TeV). It offers comprehensive physics processes for various particle types, allowing users to define custom processes using the G4VProcess interface. Major categories include electromagnetic, hadronic, decay, parameterized processes, and transportation. A critical feature is the PhysicsList, which specifies particles and processes for simulations. Users can choose between G4VUserPhysicsList for simpler environments or G4VModularPhysicsList for more complex scenarios, leveraging pre-defined or custom G4VPhysicsConstructors. Additionally, Geant4 provides reference physics lists and the G4PhysicsListFactory for well-established applications, facilitating simulation development in diverse domains.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm 0.6$% and 84.1$\pm 0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Inverse design of equilibrium cluster fluids applied to a physically informed model

Inverse design strategies have proven highly useful for the discovery of interaction potentials that prompt self-assembly of a variety of interesting structures. However, often the optimized particle interactions do not have a direct relationship to experimental systems. In this work, we show that Relative Entropy minimization is able to discover physically meaningful parameter sets for a model interaction built from depletion attraction and electrostatic repulsion that yield self-assembly of size-specific clusters. Here, we then explore the sensitivity of the optimized interaction potentials with respect to deviations in the underlying physical quantities, showing that clustering behavior is largely preserved even as the optimized parameters are perturbed.

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

Physics-tailored machine learning reveals unexpected physics in dusty plasmas

Dusty plasma is a mixture of ions, electrons, and macroscopic charged particles that is commonly found in space and planetary environments. The particles interact through Coulomb forces mediated by the surrounding plasma, and as a result, the effective forces between particles can be nonconservative and nonreciprocal. Machine learning (ML) models are a promising route to learn these complex forces, yet their structure should match the underlying physical constraints to provide useful insight. Here, we demonstrate and experimentally validate an ML approach that incorporates physical intuition to infer force laws in a laboratory dusty plasma. Trained on 3D particle trajectories, the model accounts for inherent symmetries, nonidentical particles, and learns the effective nonreciprocal forces between particles with exquisite accuracy (R 2 > 0.99). We validate the model by inferring particle masses in two independent yet consistent ways. The model’s accuracy enables precise measurements of particle charge and screening length, identifying large deviations from common theoretical assumptions. Our ability to identify unknown physics from experimental data demonstrates how ML-powered approaches can guide new routes of scientific discovery in many-body systems. Furthermore, we anticipate our ML approach to be a starting point for inferring laws from dynamics in a wide range of many-body systems, from colloids to living organisms.

Science & Technology - Other Topics↗
DOE OSTI2025

Selection Algorithm for electron neutrino charged current interactions in SBND

The Short Baseline Neutrino (SBN) program at Fermilab is a joint proposal by three experimental collaborations primarily for the investigation of the cause behind the low-energy electron-like event excess observed by the MiniBoone experiment. This dissertation focuses on the near detector of the project, named the Short Baseline Near Detector (SBND), a Liquid Argon Time Projection Chamber apparatus which will conduct searches for sterile neutrinos in the mass range of 1 ${eV}^2/{c}^4$, as well as provide cross-section measurements for neutrino interactions in argon and perform other beyond the standard model studies.\\ \indent As is the case for all detectors in the program, the SBND will use the Booster Neutrino Beam as its source, which will provide it with both muon and electron neutrinos. Given that the ability to discern between the neutrino flavors will be crucial to the fulfillment of the detector's physics goals, the objective of this work is to provide the collaboration with a tool capable of doing so. As such, we here present the development process for an inclusive selection algorithm for the identification of electron neutrino charged current (CC) events regardless of their interaction channel. This is done through a combination of traditional techniques, such as the implementation of cuts on the reconstructed interaction properties, with the use of the Convolutional Visual Network, a machine learning algorithm capable of classifying particle interactions through the analysis of the topology of their final states. With this approach, we have developed a selection process that is capable of identifying $\nu_e$ CC interactions across a wide range of topologies with 34.4\% efficiency, as well as a purity of 91.2\%, making it especially promising for use in cross section studies.

Freire, Hector Moya [ABC Federal U.] (ORCID:000900↗
DOE OSTI2022

Recoil imaging for dark matter, neutrinos, and physics beyond the Standard Model

Recoil imaging entails the detection of spatially resolved ionization tracks generated by particle interactions. This is a highly sought-after capability in many classes of detector, with broad applications across particle and astroparticle physics. However, at low energies, where ionization signatures are small in size, recoil imaging only seems to be a practical goal for micro-pattern gas detectors. This white paper outlines the physics case for recoil imaging, and puts forward a decadal plan to advance towards the directional detection of low-energy recoils with sensitivity and resolution close to fundamental performance limits. The science case covered includes: the discovery of dark matter into the neutrino fog, directional detection of sub-MeV solar neutrinos, the precision study of coherent-elastic neutrino-nucleus scattering, the detection of solar axions, the measurement of the Migdal effect, X-ray polarimetry, and several other applied physics goals. We also outline the R&D programs necessary to test concepts that are crucial to advance detector performance towards their fundamental limit: single primary electron sensitivity with full 3D spatial resolution at the $\sim$100 micron-scale. These advancements include: the use of negative ion drift, electron counting with high-definition electronic readout, time projection chambers with optical readout, and the possibility for nuclear recoil tracking in high-density gases such as argon. We also discuss the readout and electronics systems needed to scale-up such detectors to the ton-scale and beyond.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Regulating phase behavior of nanoparticle assemblies through engineering of DNA-mediated isotropic interactions

Self-assembly of isotropically interacting particles into desired crystal structures could allow for creating designed functional materials via simple synthetic means. However, the ability to use isotropic particles to assemble different crystal types remains challenging, especially for generating low-coordinated crystal structures. Here, we demonstrate that isotropic pairwise interparticle interactions can be rationally tuned through the design of DNA shells in a range that allows transition from common, high-coordinated FCC-CuAu and BCC-CsCl lattices, to more exotic symmetries for spherical particles such as the SC-NaCl lattice and to low-coordinated crystal structures (i.e., cubic diamond, open honeycomb). The combination of computational and experimental approaches reveals such a design strategy using DNA-functionalized nanoparticles and successfully demonstrates the realization of BCC-CsCl, SC-NaCl, and a weakly ordered cubic diamond phase. The study reveals the phase behavior of isotropic nanoparticles for DNA–shell tunable interaction, which, due to the ease of synthesis is promising for the practical realization of non-close-packed lattices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗