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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

MULTI-TECHNIQUE CHARACTERIZATION OF SUPERCONDUCTING MATERIALS FOR PARTICLE ACCELERATOR APPLICATIONS

We investigated the performance limitations of superconducting radio-frequency (SRF) cavities and materials using multiple experimental techniques. In particular, this study focuses on understanding the surface properties of nitrogen-doped Nb cavities and super?conducting thin films with higher Tc such as Nb3Sn. The main goal of this work is to use different techniques to better understand each aspect of the complex loss mechanism in superconductors to further improve the already highly efficient SRF cavities. Nitrogen doping applied to a Nb SRF cavity significantly improves the quality factor Q0 compared to a conventional Nb cavity, at an expense of reduced maximum accelerating gradient. The early quench mechanism was analyzed by using temperature maps before and during the quenching. The temperature maps revealed insignificant heating before the quench, and we concluded that nitrogen doping reduces critical magnetic fields in local regions, leading to premature quenching. To understand the origin of the increasing Q0 with the rf field, the density of states (DOS) of cold spots from nitrogen-doped and standard cavities were measured and analyzed using scanning tunneling microscopy. The results suggested that nitrogen doping reduces the spatial inhomogeneity of superconducting properties and shrinks the metallic suboxide layers, which tunes the DOS in such a way as to produce the field-induced reduction in the surface resistance. To characterize SRF thin films, an experimental setup for measuring a coplanar waveguide (CPW) resonator was developed and tested. A surface impedance measurement of the Nb film showed good agreement with the BCS calculation. The preliminary results from measurements of Nb3Sn and NbTiN films are also presented here. The nonlinear Meissner effect was investigated in Nb3Sn film CPW resonators by mea?suring the resonant frequency as a function of a parallel magnetic field. Contrary to a conventional quadratic dependence of the penetration depth ?(B) on the applied magnetic field B, as expected in s-wave superconductors, nearly a linear increase of ?(B) with B was observed. It was concluded that this behavior of ?(B) is due to weakly linked grain bound?aries on the polycrystalline Nb3Sn films, which can mimic the NLME expected in a clean d-wave superconductor.

Makita, Junki↗
DOE OSTI2023

Using heterogeneous GPU nodes with a Cabana-based implementation of MPCD

In this study, the Kokkos based library Cabana, which has been developed in the Co-design Center for Particle Applications (CoPA), is used for the implementation of Multi-Particle Collision Dynamics (MPCD), a particle-based description of hydrodynamic interactions. Cabana allows for a function portable implementation, which has been used to study the interplay between CPU and GPU usage on a multi-node system as well as analysis of said interplay with performance analysis tools. As a result, we see most advantages in a homogeneous GPU usage, but we also discuss the extent to which heterogeneous applications might be more performant, using both CPU and GPU concurrently.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hydration of alumina (Al 2 O 3 ) toward advancing aluminum particles for energy generation applications

Transforming metal particle combustion may require alteration of the metal oxide passivation shell surrounding the metal core. One approach for aluminum (Al) relies on the hydrated form of the metal oxide to incite surface reactions. Here, this study explores the conditions required for hydrating alumina (Al 2 O 3 ) particles and then extends those conditions toward hydration of the Al 2 O 3 passivation layer surrounding an Al core particle. By raising the pH of the water slurry to 11 and controlling temperature and time in slurry (i.e., aging), aluminum hydroxide Al(OH) 3 formation from Al 2 O 3 particles was optimized. The procedure was then extended to Al nanoparticles (nAl). Even though heating and extended aging time in slurry proved advantageous for Al 2 O 3 particles, these conditions were discarded for nAl particles because they favored formation of AlOOH, a less desirable hydrate. Through microscopy, spectroscopy, X-ray diffraction, and thermal analyses, results indicate that for a pH range of 11.26–11.56, the original Al 2 O 3 shell on Al particles transformed into Al(OH) 3 . Results indicate a feasible path forward towards producing new shell chemistries that may result in more directed energy from metal particle combustion.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Design and Development of a Particle Flow Control Mechanism for Particle-Based CSP System Applications

Within particle-based Concentrated Solar Power Systems (CSP), particles are the heat transfer medium, and therefore, the heat transfer effectiveness of these systems is reliant on their ability to effectively control particle mass flow rate. The vast majority of approaches for particle flow control in high temperature CSP is based on a slide-gate approach where a gate is linearly actuated across a tear-drop shaped opening. The slide-gate mechanism has a sensitivity of 0.354 g/s-mm, which is defined as the mass flow rate over the position of the gate. In the present work, we have designed and tested a new particle flow control mechanism with the potential for faster response times and different sensitivities than a slide gate approach enabling more control of particle flow. The new device enables flow control through moving jaws diagonally to change the outlet area. The new devices sensitivity value follows a quadratic curved as compared to the slide-gate’s linear slope over the same jaw movement. A larger sensitivity value is directly related to a quicker response time when utilizing the same actuator.

14 SOLAR ENERGY↗
DOE OSTI2023

Massively-parallel Lagrangian particle code and applications

Massively-parallel, distributed-memory algorithms for the Lagrangian particle hydrodynamic method (Samulyak et al., 2018) have been developed, verified, and implemented. The key component of parallel algorithms is a particle management module that includes a parallel construction of octree databases, dynamic adaptation and refinement of octrees, and particle migration between parallel subdomains. The particle management module is based on the p4est (parallel forest of k-trees) library. The massively-parallel Lagrangian particle code has been applied to a variety of fundamental science and applied problems. A summary of Lagrangian particle code applications to the injection of impurities into thermonuclear fusion devices and to the simulation of supersonic hydrogen jets in support of laser-plasma wakefield acceleration research has also been presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Cabana: A Performance Portable Library for Particle-Based Simulations

Particle-based simulations are ubiquitous throughout many fields of computational science and engineering, spanning the atomistic level with molecular dynamics (MD), to mesoscale particle-in-cell (PIC) simulations for solid mechanics, device-scale modeling with PIC methods for plasma physics, and massive N-body cosmology simulations of galaxy structures, with many other methods in between (Hockney & Eastwood, 1989). While these methods use particles to represent significantly different entities with completely different physical models, many low-level details are shared including performant algorithms for short- and/or long-range particle interactions, multi-node particle communication patterns, and other data management tasks such as particle sorting and neighbor list construction. Cabana is a performance portable library for particle-based simulations, developed as part of the Co-Design Center for Particle Applications (CoPA) within the Exascale Computing Project (ECP) (Alexander et al., 2020). The CoPA project and its full development scope, including ECP partner applications, algorithm development, and similar software libraries for quantum MD, is described in (Mniszewski et al., 2021). Cabana uses the Kokkos library for on-node parallelism (Edwards et al., 2014; Trott et al., 2022), enabling simulation on multi-core CPU and GPU architectures, and MPI for GPU-aware, multi-node communication. Cabana provides particle simulation capabilities on almost all current Kokkos backends, including serial execution, OpenMP (including OpenMP-Target for GPUs), CUDA (NVIDIA GPUs), HIP (AMD GPUs), and SYCL (Intel GPUs), providing a clear path for the coming generation of accelerator-based exascale hardware. Cabana builds on Kokkos by providing new particle data structures and particle algorithms resulting in a similar execution policy-based, node-level programming model that is intended to be used in addition to the core Kokkos library within an application. Cabana is designed as an application and physics agnostic, but particle-specific toolkit which can either be used to generate a new application, or to be used as needed in existing applications at various levels of invasiveness including through interfaces that wrap user memory in existing data structures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Data Science and Machine Learning Platform Supporting Large Particle Accelerator Control and Diagnostics Applications Final Report: SBIR Initial Phase II DE-SC0022583

The Machine Learning Data Platform (MLDP) is a product providing full-stack support for data science, Machine Learning, and Artificial Intelligence (ML/AI) applications at particle accelerator and large experimental physics facilities. It supports ML/AI applications from front-end, high-speed acquisition of heterogeneous, time-series data, through data archiving and management, to back-end analysis. The MLDP embodies a “data-science ready” platform for data analysis and ML/AI applications in diagnosis, modelling, control, and optimization of these facilities. It provides data scientists and applications a consistent, datacentric interface to archive data standardizing implementation and deployment of ML/AI algorithms to different operations configurations within the same facility, or between facilities. Being an open-source, public-domain project, the MLDP is intended for broadest possible impact by increasing accessibility and minimizing the required expertise for installation and operation. The MLDP can also be deployed at user facilities for experimental data collection, archiving, and analysis. It is capable of acquisition and archiving of heterogeneous data from experimental equipment (e.g., images, arrays, structures, etc.) along with system hardware configurations (e.g., scalars, tables), control system process variables, and any metadata required for provenance. Thus, the MLDP can manage experimental data through its entire lifecycle, from acquisition and archiving, through analysis and investigation, to release and final publication.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Computational fluid dynamics modeling and analysis of silica nanoparticle synthesis in a flame spray pyrolysis reactor

Flame Spray Pyrolysis (FSP) is a method for large-scale production of nanoparticles and nanoscale powders employed in a wide range of industrial applications. Particle size and morphology are complex functions of the physicochemical phenomena occurring in the FSP reactor. An extensive study of FSP-related phenomena can be utilized to develop effective strategies for achieving desired particle size/morphology and scaling up the overall yield of an FSP system. In this work, a computational fluid dynamics (CFD) model of an FSP reactor is developed to simulate the coupling of key phenomena involved in the particle synthesis process: liquid spray breakup and evaporation, mixing, combustion, and particle formation/growth of silica nanoparticles. Herein, the particle sizes and their distributions from the CFD simulations are validated against experimental data. Subsequently, the simulations are utilized to investigate the impact of process parameters on the resultant flame dynamics and particle growth. Firstly, the CFD results show that the particle sizes are strongly correlated with the precursor concentration in the solvent. At lower precursor concentrations, the spread of the distribution is relatively insensitive to the value of the concentration. At higher concentrations, the spread is higher as the collision probability between particles is higher. Secondly, increasing the pilot flow rate increases the length of the pilot flames impacting the local ignition location of the spray flame. Lastly, it is shown that the dispersion gas flow rate strongly influences the spray flame shape. This shape can be used for control of particle growth as it helps determine the regions of high temperature and the residence time of the particles in the high temperature region enabling the design and process optimization of the FSP reactor.

42 ENGINEERING↗
DOE OSTI2025

Symplectic neural network and its application to charged particle dynamics in electromagnetic fields

Recently, machine learning models have shown many successes in various applications in science and technology. In this work, we focus on the charged particle dynamics, with the development of a class of symplectic neural networks, including a linear version, SympMat, and a nonlinear version, HénonNet. Both are designed to preserve the structure of Hamiltonian systems. We show that they can be used to model relevant Hamiltonian systems of interest in plasma physics and astrophysics, for linear and nonlinear charged particle dynamics, with the potential to bridge multi-scale simulations. These symplectic neural networks are adapted to the applications in plasma simulations and particle-wave interaction with parametric dependence and periodicity, where we have investigated their performance and accuracy. In particular, SympMat is shown to outperform the traditional Boris particle pusher down to the sub-gyroperiod scale in the case of charged particles in uniform magnetic fields. HénonNet successfully predicts the hot electron distribution, which is validated against theoretical results. These results highlight the potential of symplectic neural networks as a trajectory integrator for particle-in-cell simulations or a fast surrogate to replace conventional numerical schemes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Characterization of solid particle candidates for application in thermal energy storage and concentrating solar power systems

Thermal energy storage (TES) enables concentrating solar power to remain competitive in the renewable energy mix by firming up intermittent solar resource and providing grid services such as load shifting. Free from siting constraints, stand-alone TES systems show promise as a low-cost alternative to traditional pumped-storage hydropower or compressed air energy storage. At the core of all TES technologies is a storage medium, the selection of which governs many aspects of system design and operation. Although the majority of commercial installations utilize molten salts, solid particles can demonstrate stability over wider temperature ranges. This amounts to increased energy storage densities and corresponding reductions in system cost which is essential in achieving low-cost energy storage. In this work, eight solid particle candidates are systematically identified and screened for application in a specific particle-TES system. The five most promising candidates (CARBO CP and HSP, calcined flint clay (CFC), brown fused alumina (BFA), and silica sand) are further characterized by size and morphology for fluidization suitability, flowability for particle transport, and thermal stability. Calcined flint clay and brown fused alumina are eventually down-selected due to thermal instability at the target operational temperature of 1200 °C. Although the physical characteristics of CARBO outperform silica sand in all categories examined, the marginal performance gains are considered insufficient to justify the additional media cost so silica sand is selected as the leading candidate. Within the silica sand (α-quartz) space, the high end of Geldart Group B particles is identified to satisfy the target fluidization regime for the application of interest without compromising particle flowability. Here, in focused testing, Silica 460 is shown to exhibit sufficient stability through long-duration (500-hour) thermal and cyclic testing (1200 °C), 10-hour testing at 1400 °C, and in contact with candidate refractory containment materials. Finally, an average heat capacity of 1.1 J/g∙ °C is measured over 300-1200 °C with a quartz inversion enthalpy (ΔH α-β ) of 10.7J/g.

14 SOLAR ENERGY↗
DOE OSTI2026

Design of a SiC-Si moving packed-bed particle-to-sCO 2 heat exchanger for high temperature concentrating solar power applications

Particle-based concentrating solar power systems integrated with sCO 2 power cycles offer high thermal efficiencies but require durable heat exchangers to transfer heat from high-temperature particles to the sCO 2 working fluid. Here, this study presents the design and optimization of a silicon carbide-silicon moving packed-bed heat exchanger for fabrication via binder jetting additive manufacturing. The heat exchanger was designed to withstand a 20 MPa sCO 2 pressure and operate at particle inlet temperatures up to 750 °C. The final design features 152 sCO 2 channels distributed across 19 plates, with elliptical corners and a minimum wall thickness of 3 mm. Flow restrictors at the sCO 2 channel inlets significantly improved flow uniformity, reducing thermal stresses and achieving a structural reliability of 99 % under representative operating conditions. The heat exchanger delivers a thermal duty of 9 kW and a volumetric power density of approximately 1 MW/m 3 in the channel region. Sensitivity studies confirmed the heat exchanger’s robustness under varying operating conditions, demonstrating its viability as a high-performance alternative to metallic heat exchangers for particle-based high-temperature concentrating solar power applications.

Barua, Bipul [Argonne National Laboratory (ANL), A↗
DOE OSTI2026

Modeling and Experimental Validation of a Direct-Contact Counter-Flow Fluidized Bed Heat Exchanger for Thermal Energy Storage (TES) Applications

Particle-based thermal energy storage (TES) systems are an emerging energy storage technology. The technological advances have reduced costs, making TES more competitive and reliable in the marketplace but an efficient and reliable operation is heavily dependent on coherent heat transfer between air to particles or vice versa. The particle-based TES technologies provide an intermediate system that can store energy for short (0-10 h), long (10-200 h) and seasonal (> 200 h) timescales. The TES systems store energy by converting electricity to thermal energy; electricity can be directly sourced intermittent generation technologies and/or the grid, helping manage peak loads and other mismatches in supply and demand. The overall efficiency of the TES system depends on the performance of system components (particle storage silos and particle transfer mechanism etc.). The particle heat exchanger is one of the key system components that affects the system efficiency. The pressurized fluidized bed heat exchanger (PFB HX) performance is challenging to predict due to the chaotic behavior of particle and fluid interaction. This research presents a computational study of a novel direct-contact, counter-flow and air-to-particles PFB HX, that contributes in advancing the particle-based long-duration TES technologies. For the current analysis an unsteady Eulerian-Eulerian CFD model was developed and validated against experiments performed at the National Laboratory of the Rockies for two particle sizes (600 ..mu..m and 825 ..mu..m ). Following validation, parametric simulations were conducted to evaluate the effects of interphase drag models (Syamlal-O'Brien and Gidaspow), particle size, bed height and the influence of a frictional-viscosity term on hydrodynamics and heat transfer between the air & particles. The key findings from the analysis are: (1) for the studied operating window Syamlal-O'Brien provides superior agreement with measured gas temperatures (errors generally < 10%) while Gidaspow shows large deviations for the coarse particle case; (2) model predictions are most sensitive in the lower 0.2 m above the air distributor where bubble initiation and local mixing dominate interphase heat transfer; (3) representation of the distributor (number of inlet ports) materially affects predicted local mixing and temperature stratification; and (4) the Eulerian-Eulerian framework reproduces bulk thermal trends but shows regime dependent limitations for coarse particles, motivating mesoscale informed closures for scale-up analysis for future studies. These results provide validated guidance for drag selection and distributor design in particle-based thermal energy storage applications. Collectively, the validated model and parametric results quantify key drivers of PHB-HX performance and provide practical guidance for design and optimization. The results provide confidence in the model predictability and provide a step forward to improve on heat exchange performance. The demonstrated performance and modeling approach support the deployment and further development of this novel PHB-HX concept for robust, particle-based long-duration thermal energy storage systems.

25 ENERGY STORAGE↗