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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

How to understand limitations of generative networks

Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustrate their benefits for distribution-shifted jets, calorimeter showers, and reconstruction-level events. In all cases, the classifier weights make for a powerful test of the generative network, identify potential problems in the density estimation, relate them to the underlying physics, and tie in with a comprehensive precision and uncertainty treatment for generative networks.

Physics↗
DOE OSTI2021

High Performance Superconducting Undulator Development

Initial Phase I work has involved a collaborative technical analysis of the superconducting magnet design. ANL has provided CMI with a history of the SCAPE prototype including the short 0.5m wet coil versions as well as thoughts on possible manufacturing methods.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Novel methods for TH-229 production through fast neutron irradiation of TH-23F0 and charged particle irradiation of Th-230 and Th-232. Final Report DE-SC0020140

The activities and results of research related to accelerator production of Pa and Th isotopes via bombardment of 230 Th and 232 Th targets are described herein. This research was supported through DOE grant DE-SC0020140 and was conducted in collaboration with Oak Ridge National Laboratory and Lawrence Berkeley National Laboratory. Cross section measurements, transport model and nuclear physics model calculations are presented. Highlights include the first ever measurements of 229 Pa, two publications, and graduation of a PhD student.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

GEANT4 Simulations on Faraday Cup Design for PIP-II Laser Wire Scanner System

The PIP-II accelerator upgrade at Fermilab represents a groundbreaking leap forward in high-energy physics research. This ambitious initiative involves enhancing Fermilab's accelerator complex by replacing the current linear accelerator with a warm front end (WFE) capable of accelerating H- beams up to 2.1 MeV. Subsequently, a superconducting linac further accelerates these beams up to 800 MeV. To precisely measure the transverse beam profile, a combination of traditional wire scanners at the WFE section and Laser wire scanners along the superconducting linac are planned for implementation. This investigation centers on refining the Faraday cup design for the PIP-II Laser wire scanners by utilizing GEANT4, a Monte Carlo simulation toolkit. Leveraging this method enables a comprehensive analysis of particle trajectories, energy deposition, secondary electron emission, backscattering, etc., facilitating optimization through adjustments to cup geometries, materials, and placement to maximize its efficiency.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Explainable and Differentiable Reinforcement Learning for Multi-objective Optimization in Particle Accelerators

Operating particle accelerators involves optimizing multiple goals simultaneously, which can be challenging due to trade-offs among objectives. While evolutionary algorithms like the genetic algorithm (GA) have been used for various Multi-Objective Optimization (MOO) tasks, they are not inherently suited for complex control problems. This talk highlights two variations of Reinforcement Learning (RL) for concurrently optimizing heat load and trip rates at the Continuous Electron Beam Accelerator Facility (CEBAF). The problem involves strict constraints on individual states, actions, and overall energy requirements of the beam. First, this talk highlights how differentiability can be harnessed through a Deep Differentiable Reinforcement Learning (DDRL) approach to address MOO issues within particle accelerators. We examine the DDRL method alongside Model Free Reinforcement Learning (MFRL), GA, and Bayesian Optimization (BO). The performance of these methods is assessed by generating a Pareto-front for two objectives. Our findings indicate that DDRL excels in handling high-dimensional problems more effectively than MFRL, BO, and GA. Next, we will show integration of explainable physics-based constraints into RL algorithms to enhance trans- parency and trust in decision-making processes by enabling users to verify that agents adhere to established physical principles. This surrogate function can be modeled using neural networks or sparse dictionary mod- els. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment provided but the surrogate model. In addi- tion, we find that the introduction of a mathematical functional dictionary based surrogate model enables our reinforcement learning algorithms to reliably converge for difficult high-dimensional accelerator controls environments.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2026

Final Technical Report for U.S.-Japan Hadronic Physics Exchange Program for Studies of Hadron Structure and QCD

Nuclear physics explores the fundamental properties of matter -- how protons and neutrons emerge as quantum systems of elementary particles, how they form the atomic nuclei, and how they give rise to the wide variety of phenomena and applications at biological, technical, and astronomical scales. It is a global scientific effort centered around large-scale experimental user facilities (particle accelerators and detectors), advanced theoretical methods and concepts, and computational techniques and resources. Exchange of knowledge and ideas, scientific collaboration, and workforce development on a global scale are essential for the future of the field. The nuclear physics program envisaged in the 2023 DOE/NSF NSAC Long-Range Plan and pursued at the U.S. National Labs has strong synergies with programs at other facilities worldwide and will realize significant benefits from international collaboration. Nuclear physics is also recognized for promoting international cooperation in the broadest sense through joint construction and operation of experimental equipment, personal contacts between scientists, and education and training. The U.S.-Japan Hadronic Physics Exchange Program (USJPHE) supported collaborative scientific research in hadronic physics and quantum chromodynamics. USJHPE focused on subject areas related to the programs at current and future experimental facilities in the U.S.\ and Japan and supported both experimental and theoretical studies. USJHPE particularly aimed to realize synergies between the hadronic physics programs at Jefferson Lab 12 GeV and J-PARC resulting from the complementarity of electromagnetic and hadronic probes in the multi-GeV energy range. Subject areas of common interest included the quark-gluon structure of hadrons and nuclei, meson and baryon spectroscopy, strangeness and hypernuclear physics, and other related topics. USJHPE also supported research in hadronic physics and nuclear-physics-enabled tests of fundamental symmetries related to the programs at Brookhaven National Lab, Fermilab, KEK, Spring-8, and university-based facilities in the U.S. and Japan. USJHPE especially promoted collaboration between the U.S. and Japanese nuclear physics communities in developing the physics program and instrumentation for the future Electron-Ion Collider. USJHPE was intended to provide travel grants to U.S.-based scientists (primary institutional affiliation with a U.S.\ university, national laboratory, or other research center) to visit Japanese institutions and conduct collaborative research there. The program supported senior researchers, postdoctoral fellows, and students. Continuing the setup of the preceding grant period, J-PARC served as the Japanese “hub” for U.S. physicists for short- and long-term visits, and JLab served as the corresponding U.S. “hub”. The program was officially managed through the U. of Connecticut in Storrs, CT. Support for Japanese physicists visiting the U.S. was provided through funds from Japanese funding agencies. The USJHPE program promoted the scientific exchange and the collaborative spirit in hadronic physics between the two countries.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Energetic Nonthermal Electrons within the Above-the-looptop Regions in Solar Flares: Acceleration, Feedback, and Quasiperiodic Pulsations

Solar flares are among the most dramatic events in the solar system, releasing substantial magnetic energy and accelerating a large number of electrons to high energies. Notably, in certain events, the above-the-looptop region may contain a significant population of nonthermal electrons, both in number and energy. For the first time, we adopt a novel numerical method that combines magnetohydrodynamics with energetic particles incorporating feedback from nonthermal electrons to investigate electron acceleration and transport in solar flares. We find that a large fraction of energetic electrons are accelerated via the current sheet and termination shock regions. Most energetic electrons are concentrated in the above-the-looptop region, carrying a sizable amount of the released energy. We observe that greater feedback of nonthermal electrons leads to steeper energy spectra. The energy density of the nonthermal electrons oscillates due to the periodic impact of magnetic islands into the above-the-looptop region, which may help explain the observed quasiperiodic pulsations. Our simulations provide new insights into the origin of nonthermal electrons and associated emissions in the above-the-looptop region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Method for forming a bed of stabilized magneto-caloric material

A method for forming a stabilized bed of magneto-caloric material is provided. The method includes aligning magneto-caloric particles within the casing while a magnetic field is applied to the magneto-caloric particles and then fixing positions of the magneto-caloric particles within the casing. A related stabilized bed of magneto-caloric material is also provided.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Porous thermally insulating compositions containing hollow silica particles

A thermally insulating composition comprising hollow spherical silica particles and a coating of a material having a thermal conductivity of less than 0.3 W/m·K on surfaces of said silica particles. In particular embodiments, the low conductivity coating material may be a polymer, such as polystyrene or polyvinylpyrrolidone, or the low conductivity coating material may be a quaternary ammonium salt of the Formula (1), i.e., R1R2R3R4N+A−, with at least one of R1, R2, R3, and R4 being an alkyl group containing at least ten carbon atoms (and A− is a counter anion), or the low conductivity coating material may be phenyl-C61-butyric acid methyl ester covalently bound to the hollow spherical silica particles. Also described herein is a method of thermally insulating a surface by applying a coating of the thermally insulating composition, described above, onto the surface.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Lipid-coated particles for treating viral infections

The present invention relates to lipid-coated particles for treating viral infections, including viral encephalitis infections. In particular, an antiviral compound can be disposed within the lipid-coated particle, thereby providing an antiviral carrier. Methods of making and using such carriers are described herein.

Negrete, Oscar↗
DOE OSTI2019

On the implicit immersed boundary method in coupled discrete element and lattice Boltzmann method

The coupled discrete element method and lattice Boltzmann method (DEMLBM) has increasingly drawn attention of researchers in geomechanics due to its mesoscopic nature since 2000. Immersed boundary method (IBM) and immersed moving boundary (IMB) are two popular schemes for coupling fluid particle in DEMLBM. This work aims at coupling DEM and LBM using the latest IBM algorithm and investigating its accuracy, computational efficiency, and applicability. Two benchmark tests, interstitial fluid flow in an ideal packing and single particle sedimentation in viscous fluid, are carried out to demonstrate the accuracy of IBM through semi-empirical Ergun equation, finite element method (FEM), and IMB. Then, simulations of particle migration with relatively large velocity in Poiseuille flow are utilized to address limitations of IBM in DEMLBM modeling. In addition, advantages and deficiencies of IBM are discussed and compared with IMB. It is found that the accuracy of IBM can be only guaranteed when sufficient boundary points are used and it is not suitable for geomechanical problems involving large fluid or particle velocity.

58 GEOSCIENCES↗
DOE OSTI2020

CFD-DEM and PR-DNS studies of low-temperature densely packed beds

Over the past few decades, granular media is gaining attention as a viable option for heat transfer fluids (HTFs). Several research efforts are studying the use of particle-based heat transfer fluids in a wide variety of applications. With this motivation, the current work focusses on analyzing the different heat transfer mechanisms in low-temperature mono-sized densely packed granular media. To study the heat transfer behavior of granular media at different scales, the current work employs a two-way coupled computational strategy. The motion of particles is solved using the Discrete Element Method (DEM) and the interstitial air is solved using a Finite-Volume (CFD) approach. The Open-Source library CFDEM Coupling® is used in the current study to join the Finite Volume PISO solver of OpenFOAM® and the DEM solver of LIGGGHTS®. Typically, particle-particle contact conduction and particle-air convection are the most popular closure models. But recent research identified a different heat transfer phenomenon in packed beds that cannot be identified by conduction or convection models. While closure models were developed to implement this on a CFD-DEM framework, they did not capture the effect of intra-particulate thermal gradients on this phenomenon. Hence the current work also employs Particle-Resolved Direct Numerical Simulations (PR-DNS) to gain valuable insights allowing for the modification of existing models. A new closure model is then proposed here and is implemented in the CFD-DEM framework. This model provides key insights into the different heat transfer mechanism of packed beds.

42 ENGINEERING↗
DOE OSTI2020

Photo-printing of faceted DNA patchy particles

Patchy particles with shape complementarity can serve as building blocks for assembling colloidal superstructures. Alternatively, encoding information on patches using DNA can direct assembly into a variety of crystalline or other preprogrammed structures. Here, we present a tool where DNA is used both to engineer shape and to encode information on colloidal particles. Two reactive oil emulsions with different but complementary DNA (cDNA) brushes are assembled into CsCl-like crystalline lattices. The DNA brushes are recruited to and ultimately localized at the junctions between neighboring droplets, which gives rise to DNA-encoded faceted patches. The emulsions are then solidified by ultraviolet (UV) polymerization, producing faceted patchy particles. The facet size and DNA distribution are determined by the balance between the DNA binding energy and the elastic deformation energy of droplets. This method leads to a variety of new patchy particles with directional interactions in scalable quantities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Automated Segmentation of Twin Boundaries in TRISO Silicon Carbide Using Deep Neural Networks

Coated particle fuels, such as the tristructural isotropic (TRISO) fuel particle, are essential for high-temperature gas reactor (HTGR) applications due to their efficiency and stability under normal and off-normal conditions. However, widespread commercialization and deployment of this technology for next-generation nuclear applications require robust quality assurance and quality control (QA/QC) methods linking fabrication, properties, and performance. Of the many important metrics for TRISO QA/QC, quantification of the silicon carbide (SiC) microstructure is critical because it correlates with fission product retention during irradiation. Previous work has shown extensive twinning of the SiC microstructure, which strongly affects microstructural metrics; however, twin grain boundaries are not expected play a significant role in fission product diffusion. This report summarizes the initial development, training, and testing of a machine learning image processing algorithm to detect twin grain boundaries in a backscattered electron image, which can be removed so that microstructural metrics can be recalculated for legacy data. Further development and deployment of this model will provide automated, scalable improvement of potential QA/QC methods for the SiC layer of TRISO particles.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Initial position optimization in molecular dynamics simulations for a Coulomb system

A new algorithm for molecular dynamics (MD) simulations is developed to optimize plasma particle distributions at given initial temperatures. By combining velocity scaling and reassignment, the method effectively eliminates the initial rise and oscillation in temperatures observed with randomly distributed positions. These rises and oscillations are undesired numerical artifacts observed in conventional plasma MD simulations, arising from unoptimized particle positions. The algorithm demonstrates temperature relaxation without initial rises or oscillations, as well as precise flow velocity relaxation, enabling accurate measurement of relaxation times. The code is accelerated using graphics processing units for parallel processing, enhancing the study of plasma dynamics. The proposed method for distributing physically valid particles in MD simulations enables accurate studies of intrinsic collision processes in plasmas, including the dynamics of strongly coupled plasmas, plasma–wave interactions, and transport phenomena in magnetized plasmas. The paper concludes with a discussion of potential applications and future enhancements to the algorithm.

Jo, Jawon (ORCID:0009000924193285)↗
DOE OSTI2024

Cross-correlation image analysis for real-time single particle tracking

Accurately measuring the translations of objects between images is essential in many fields, including biology, medicine, chemistry, and physics. One important application is tracking one or more particles by measuring their apparent displacements in a series of images. Popular methods, such as the center of mass, often require idealized scenarios to reach the shot noise limit of particle tracking and, therefore, are not generally applicable to multiple image types. More general methods, such as maximum likelihood estimation, reliably approach the shot noise limit, but are too computationally intense for use in real-time applications. These limitations are significant, as real-time, shot-noise-limited particle tracking is of paramount importance for feedback control systems. To fill this gap, we introduce a new cross-correlation-based algorithm that approaches shot-noise-limited displacement detection and a graphics processing unit-based implementation for real-time image analysis of a single particle.

Instruments & Instrumentation↗