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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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Disclosed herein are structures comprising a titanium, zirconium, or hafnium powder particle with titanium carbide, zirconium carbide, or hafnium carbide (respectively) nano-whiskers grown directly from and anchored to the powder particle. Also disclosed are methods for fabrication of such structures, involving heating the powder particles and exposing the particles to an organic gas.
There are numerous factors that can have an impact on the degradation behavior of batteries, such as the number of recharge cycles or the charge rate. Here, we investigate the influence of operating temperature on the structural degradation of the microstructure in lithium-ion positive electrodes. For that purpose, the microstructure is characterized for cathodes which have been cycled for 200 cycles under 6C (10-minute) charging at different operating temperatures, namely, 20°C, 30°C, 40°C, and 50°C. For each operating condition scanning electron microscopy (SEM) images of cross-sectioned Li x Ni 0.5 Mn 0.3 Co 0.2 O 2 (NMC532) electrodes have been analyzed, to determine structural descriptors such as global particle porosity, crack size/length/width distribution, and porosity and specific surface area distribution of individual particles. Additionally, a stereological method has been deployed to investigate the local particle porosity as a function of distance to the particle center. Results show that particle porosity increases with increasing cycling temperature. Particle porosity is greatest at the particle center and decreases along the particle radius to the exterior. Particle surface area is similar across the four cycling-temperature aging conditions.
This paper describes the experience with the calibration,reconstruction and evaluation of the timing capabilities of the CMSHGCAL prototype in the beam tests in 2018. The calibrationprocedure includes multiple steps and corrections ranging from tensof nanoseconds to a few hundred picoseconds. The timing performanceis studied using signals from positron beam particles with energiesbetween 20 GeV and 300 GeV. The performance is studied as afunction of particle energy against an external timing reference aswell as standalone by comparing the two different halves of theprototype. The timing resolution is found to be 60 ps forsingle-channel measurements and better than 20 ps for full showersat the highest energies, setting excellent perspectives for theHGCAL calorimeter performance at the HL-LHC.
Pacific Northwest National Laboratory (PNNL), in collaboration with Flibe Energy, Inc., demonstrated the fabrication of metal organic frameworks (MOFs) into engineered beads to manage off-gases released from molten salt reactors. Among the many MOF options, SBMOF 1 was selected as an ideal sorbent to manage off-gases released from a molten salt reactor because its pore size is optimal. The SBMOF 1 was synthesized and fabricated into engineered particles using three different methods, with and without polymers. All the engineered particles were exposed to Co-60 radiation (1,000 kGy) to demonstrate their radiation stability. Single column breakthrough experiments were performed at different temperatures, activation conditions, flow rates, and off-gas compositions to optimize the Xe capacity at the breakthrough point and at saturation. The Xe loading in SBMOF 1 was compared with that of standard solid sorbent (Nucon carbon) under identical conditions. Based on the single column experiments, SBMOF 1 outperforms the Nucon carbon at all the temperatures and reduces the mass (to as little as 55%) of sorbent needed to achieve the same performance as carbon. The bed volume is also reduced, down to 48% of that of Nucon carbon, when the MOF is used. These results clearly demonstrate improvement in the amount of adsorbent needed and show a reduction in bed volume compared to traditional sorbent material.
Computer code for calculating omnidirectional vehicle encountered trapped particle fluxes
We investigate the formation of galaxies and larger structure with a simulation modeling two gravitationally coupled fluids representing dark matter and baryons. The baryon gas dynamics are calculated with a smoothed particle hydrodynamics (SPH) method, and the physics modeled includes thermal pressure, shock heating, and radiative cooling. We simulate a 16 Mpc periodic cube with 64(exp 3) particles in each fluid and 10% baryon mass fraction. We confirm, for the first time experimentally, disk formation as a natural consequence of hierarchical clustering in a large-scale cosmological environment. The majority of isolated galaxies exhibit centrifugally supported disks. A power-law relation between cold baryonic mass and maximum rotation velocity is found, M varies as nu(sub rot)(exp alpha) with alpha = 2.5 after correcting for differential numerical resolution. Both the spatial and velocity distributions of the simulated galaxies are biased with respect to the dark matter. A counts-in-cells analysis indicates that an unphysical degree of merging in the central cluster is likely responsible for the antibias signal in the correlation function. A robust, scale-dependent velocity bias is measured. The ratio of galaxy to dark matter pairwise velocity dispersions on a scale of 1 Mpc is 0.7. The amplitude is only mildly dependent on redshift or mass cutoff and scales with separation as r(exp 0.2). The degree to which these results depend on numerical parameters is discussed. Mass resolution plays a key role in controlling the resulting fraction of cold, dense baryons. The mass fraction associated with galaxies decreases by a factor of approximately greater than 3 when the mass per particle is increased by a factor 8. Photoionization and energy input from supernova will have to be included to determine more carefully the fraction of highly dissipated material and the characteristics of the stellar component of galaxies.
Micrometeorites are constantly arriving at the Earth's surface, however, they are quickly diluted by the natural and anthropogenic back-ground dust. The successful collection of micromete-orites requires either the employment of a separation technique (e.g. using magnets to separate metal-bearing micrometeorites from deepsea sediments [e.g. 1,2] and dissolved pre-historic limestones and salts [e.g. 3,4]), or an approach that limits contamination by terrestrial dust (e.g. collecting from ice, snow and well water in polar regions - locations where the terrestrial dust flux is so low that micrometeorites repre-sent the major dust component [e.g. 5-7]). We have recently set up a micrometeorite collection station on Kwajalein Island in the Republic of the Marshall Is-lands in the Pacific Ocean, using high volume air samplers to collect particles directly from the atmosphere. Collecting at this location exploits the considerably reduced anthropogenic background; Kwajalein is >1000 miles from the nearest continent and for much of the year, trade winds blow from the northeast at 15 to 20 knots providing a continuous stream of oceanic aerosol for sampling. By collecting directly from the atmosphere, the terrestrial age of the particles, and hence weathering they experience, is minimal. We therefore anticipate that the Kwajalein col-lection may include particles that are highly susceptible to weathering and either not preserved well or not found at all in other collections. In addition, this collection method allows for particle arrival times to be constrained so that collections can be timed to correlate with celestial events (e.g. meteor showers). Here we describe the collections and their preparation and report on the initial results.
Kinetic plasma processes, such as magnetic reconnection, collisionless shocks, and turbulence, are fundamental to the dynamics of astrophysical and laboratory plasmas. Simulating these processes often requires particle-in-cell (PIC) methods, but the computational cost of fully kinetic simulations can necessitate the use of artificial parameters, such as a reduced speed of light and ion-to-electron mass ratio, to decrease expense. While these approximations can preserve overall dynamics under specific conditions, they introduce nontrivial impacts on particle collisionality that are not yet well understood. In this work, we develop a method to scale particle collisionality in simulations employing an artificial speed of light and/or an artificial ion-to-electron mass ratio. By introducing species-dependent scaling factors, we independently adjust inter- and intra-species collision rates to better replicate the collisional properties of the physical system. Our approach maintains the fidelity of electron and ion transport properties while preserving critical relaxation rates, such as energy exchange timescales, within the limits of weakly collisional plasma theory. Furthermore, we demonstrate the accuracy of this scaling method through benchmarking tests against theoretical relaxation rates and connecting to fluid theory, highlighting its ability to retain key transport properties. Existing collisional PIC implementations can be easily modified to include this scaling, which will enable deeper insights into the behavior of marginally collisional plasmas across various contexts.
Current experimental and computational techniques must be improved in order to advance the prediction capability of the longitudinal vortical flows shed by underwater vehicles. The generation, development, and breakdown mechanisms of the shed vortices at high Reynolds numbers are not fully understood. The ability to measure hull separated vortices associated with vehicle maneuvering does not exist at present. The existing point-by-point measurement techniques can only capture approximately the large 'mean' eddies but fail to meet the dynamics of small vortices during the initial stage of generation. A new technique, which offers a previously unavailable capability to measure the unsteady cross-flow distribution in the plane of the laser light sheet, is called Particle Displacement Velocimetry (PDV). PDV consists of illuminating a thin section of the flowfield with a pulsed laser. The water is seeded with microscopic, neutrally buoyant particles containing imbedded fluorescing dye which responds with intense spontaneous fluorescence with the illuminated section. The seeded particles in the vortical flow structure shed by the underwater vehicle are illuminated by the pulse laser and the corresponding particle traces are recorded in a single photographic frame. Two distinct approaches were utilized for determining the velocity distribution from the particle traces. The first method is based on matching the traces of the same particle and measuring the distance between them. The direction of the flow can be identified by keeping one of the pulses longer than the other. The second method is based on selecting a small window within the image and finding the mean shift of all the particles within that region. The computation of the auto-correlation of the intensity distribution within the selected sample window is used to determine the mean displacement of particles. The direction of the flow is identified by varying the intensity of the laser light between pulses. Considerable computational resources are required to compute the auto-correction of the intensity distribution. Parallel processing will be employed to speed up the data reduction. A few examples of measured unsteady vortical flow structures shed by the underwater vehicles will be presented.
The diffusive shock acceleration of energetic electrons at a plane shock is studied by means of the method of generations developed by Petukhov et al. (1986), where the particles are binned according to the number of particle acceleration cycles. The method emphasizes the cyclic character of the shock acceleration process, and takes into account the particle energy increment per cycle, the energy loss per cycle, and the escape probability of particles downstream of the shock. In the limit of a small fractional momentum gain per cycle, the method reproduces the known steady-state semianalytic solution for constant diffusion coefficients on both sides of the shock. It is demonstrated that the method may also be applied to time-dependent shock-acceleration problems.
The feasibility of developing a method for making monodisperse, unagglomerated spherical particles greater than 50 nm in diameter was demonstrated. Carbonaceous particles were made by pyrolyzing ethylene with a pulsed CO2 laser, thereby creating a non-equilibrium mixture of carbon, hydrogen, hydrocarbon vapors, and unpyrolyzed ethylene. Via a complex series of reactions, the carbon and hydrocarbon vapors quickly condensed into the spherical particles. By cooling and dispersing them in a supersonic expansion immediately after their creation, the hot newly-formed spheres were prevented from colliding and coalescing, thus preventing the problem of agglomeration which as plagued other investigators studying laser-simulated particle formation. The cold particles could be left suspended in the residual gases indefinitely without agglomerating. Their uniform sizes and unagglomerated nature were visualized by collecting the particles on filters that were subsequently examined using electron microscopy. It was found the mean particle size can be coarsely controlled by varying the initial ethylene pressure, and can be finely controlled by varying the fluence (energy/unit area) with which the laser irradiates the gas. The motivating application for this research was to manufacture particles that could be used as laser Doppler velocimetry (LDV) seeds in high-temperature high-speed flows. Though the particles made in this program will not evaporate until heated to about 3000 K, and thus could serve as LDV seeds in some applications, they are not ideal when the hot atmosphere is also oxidizing. In that situation, ceramic materials would be preferable. Research performed elsewhere has demonstrated that selected ceramic materials can be manufactured by laser pyrolysis of appropriate supply gases. It is anticipated that, when the same gases are used in conjunction with the rapid cooling technique, unagglomerated spherical ceramic particles can be made with little difficulty. Such particles would also be valuable to manufacturers of ceramic or abrasive products, and this technique may find its greatest commercial potential in those areas.
Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. In conclusion, the improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.
The particle-in-cell (PIC) method is widely used to model the self-consistent interaction between discrete particles and electromagnetic fields. It has been successfully applied to problems across plasma physics including plasma based acceleration, inertial confinement fusion, magnetically confined fusion, space physics, astrophysics, high energy density plasmas. In many cases the physics involves how relativistic particles (those with high relativistic γ factors) are generated and interact with plasmas. However, when relativistic particles stream across the grid, both in “vacuum” and in plasma, many numerical issues may arise which can lead to unphysical results. We present a detailed analysis of how discretized Maxwell solvers used in PIC codes can lead to numerical errors to the fields that surround particles that move at relativistic speeds across the grid. Expressions for the axial electric field as integrals in k space are presented that reveal two types of errors. The first arises from errors to the numerator of the integrand and leads to unphysical fields that are antisymmetric about the particle. Furthermore, the second arises from errors to the denominator of the integrand and lead to Cherenkov like radiation in “vacuum”. These fields are not anti-symmetric, extend behind the particle, and cause the particle to accelerate or decelerate depending on the solver and parameters. The unphysical fields are studied in detail for two representative solvers - the Yee solver and the FFT based solver. Although the Cherenkov fields are absent, the space charge fields are still present in the fundamental Brillouin zone for the FFT based solvers. In addition, the Cherenkov fields are present in higher order zones for the FFT based solvers. Comparison between the analytical solutions and PIC simulation results are presented. A solution for eliminating these unphysical fields by modifying the k operator in the axial direction is also presented. Using a customized finite difference solver, this solution was successfully implemented into OSIRIS [1]. Results from the customized solver are also presented. Additionally, this solution will be useful for a beam of particles that all move in one direction with a small angular divergence.
Monte Carlo method for computing particle and gamma radiation transfer for radiation shielding
The structural and chemical evolution of battery electrodes at the nanoscale plays an important role in affecting the cell performance. Nano-resolution X-ray microscopy has been demonstrated as a powerful technique for characterizing the evolution of battery electrodes under operating conditions with sensitivity to their morphology, compositional distribution and redox heterogeneity. In real-world batteries, the electrode could deform upon battery operation, causing challenges for the image registration which is necessary for several experimental modalities, e.g. XANES imaging. To address this challenge, this work develops a deep-learning-based method for automatic particle identification and tracking. This approach was not only able to facilitate image registration with good robustness but also allowed quantification of the degree of sample deformation. The effectiveness of the method was first demonstrated using synthetic datasets with known ground truth. The method was then applied to an experimental dataset collected on an operating lithium battery cell, revealing a high degree of intra- and interparticle chemical complexity in operating batteries.
Traditional probabilistic methods for the simulation of advection-diffusion equations (ADEs) often overlook the entropic contribution of the discretization, e.g., the number of particles, within associated numerical methods. Many times, the gain in accuracy of a highly discretized numerical model is outweighed by its associated computational costs or the noise within the data. Herein, we address the question of how many particles are needed in a simulation to best approximate and estimate parameters in one-dimensional advective-diffusive transport. To do so, we use the well-known Akaike Information Criterion (AIC) and a recently-developed correction called the Computational Information Criterion (COMIC) to guide the model selection process. Random-walk and mass-transfer particle tracking methods are employed to solve the model equations at various levels of discretization. Numerical results demonstrate that the COMIC provides an optimal number of particles that can describe a more efficient model in terms of parameter estimation and model prediction compared to the model selected by the AIC even when the data is sparse or noisy, the sampling volume is not uniform throughout the physical domain, or the error distribution of the data is non-IID Gaussian.
A fast and efficient method is employed to trace charged particles through realistic magnetospheric electric and magnetic fields, greatly reducing computer simulation times. The method is applicable for particles having arbitrary charge, energy, or pitch angle but which conserve the first two adiabatic invariants. An efficient method is used to classify drift orbits, which greatly facilitates identification of the last closed drift path or other drift boundaries. The time-independent evolution of the bounce-averaged phase space density along convective drift orbits is calculated. These three tools can be used to quantitatively describe convective evolution of the particle distribution from the tail, an essential step in understanding the production of unstable distributions in the magnetosphere.
We develop methods for investigating protein drift-diffusion dynamics in heterogeneous cell membranes and the roles played by geometry, diffusion, chemical kinetics, and phase separation. Our hybrid stochastic numerical methods combine discrete particle descriptions with continuum-level models for tracking the individual protein drift-diffusion dynamics when coupled to continuum fields. We show how our approaches can be used to investigate phenomena motivated by protein kinetics within dendritic spines. The spine geometry is hypothesized to play an important biological role regulating synaptic strength, protein kinetics, and self-assembly of clusters. We perform simulation studies for model spine geometries varying the neck size to investigate how phase-separation and protein organization is influenced by different shapes. We also show how our methods can be used to study the roles of geometry in reaction-diffusion systems including Turing instabilities. Furthermore, our methods provide general approaches for investigating protein kinetics and drift-diffusion dynamics within curved membrane structures.