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Levels of contamination in fluid systems can be determined by a definition of a particle by a mathematical model, a method for calculating the tolerance limits of contamination, and an estimation of the probability that the contamination on the surface will migrate with the fluid in the system.
The fundamental non-Hermitian nature of the forms of the coupled-cluster (CC) theory widely used in quantum chemistry has usually been viewed as a negative, but the present paper shows how this can be used to an advantage. Specifically, the non-symmetric nature of the reduced one-particle density matrix (in the molecular orbital basis) is advocated as a diagnostic indicator of computational quality. In the limit of the full coupled-cluster theory [which is equivalent to full configuration interaction (FCI)], the electronic wave function and correlation energy are exact within a given one-particle basis set, and the symmetric character of the exact density matrix is recovered. The extent of the density matrix asymmetry is shown to provide a measure of “how difficult the problem is” (like the well-known T 1 diagnostic), but its variation with the level of theory also gives information about “how well this particular method works”, irrespective of the difficulty of the problem at hand. The proposed diagnostic is described and applied to a select group of small molecules, and an example of its overall utility for the practicing quantum chemist is illustrated through its application to the beryllium dimer (Be 2 ). Future application of this idea to excited states, open-shell systems, and symmetry-breaking problems and an extension of the method to the two-particle density are then proposed.
High power turbopumps are frequently used to supply propellants to the combustion chambers of rocket engines. Due to the high pressures and flow-rates required, turbopump components are subjected to harsh environments which include dynamic excitation due to random, sine, and acoustic vibration. Additionally, fluid-induced forces can couple with the dynamics of the structure resulting in flow induced instabilities (flutter). Structural response to these forms of excitation results in reduced fatigue life and increases the likelihood of an operational failure. Particle damping has been used successfully on vibration problems in the past by increasing the damping and therefore reducing the response to acceptable levels. Empirical methods have typically been employed to evaluate the performance of the particles in reducing the structural response. This report explores the use of finite element methods to estimate the effectiveness of particle damping in a typical non-rotating turbopump component. Axisymmetric harmonic models are used to estimate the increase in modal damping produced by the addition of particles in the cavity of an axisymmetric seal. Target modes of vibration are evaluated to quantify how the effective particle damping is altered by geometry changes in the seal design. A new method to predict the performance of particle dampers is developed and shown to provide more reasonable estimates of damping.
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.
Smoothed-particle hydrodynamics (SPH) is a mesh-free method used to simulate volumetric media in fluids, astrophysics, and solid mechanics. Visualizing these simulations is problematic because these datasets often contain millions, if not billions of particles carrying physical attributes and moving over time. Radial basis functions (RBFs) are used to model particles, and overlapping particles are interpolated to reconstruct a high-quality volumetric field; however, this interpolation process is expensive and makes interactive visualization difficult. Existing RBF interpolation schemes do not account for color-mapped attributes and are instead constrained to visualizing just the density field. To address these challenges, we exploit ray tracing cores in modern GPU architectures to accelerate scalar field reconstruction. We use a novel RBF interpolation scheme to integrate per-particle colors and densities, and leverage GPU-parallel tree construction and refitting to quickly update the tree as the simulation animates over time or when the user manipulates particle radii. We also propose a Hilbert reordering scheme to cluster particles together at the leaves of the tree to reduce tree memory consumption. Finally, we reduce the noise of volumetric shadows by adopting a spatially temporal blue noise sampling scheme. Our method can provide a more detailed and interactive view of these large, volumetric, time-series particle datasets than traditional methods, leading to new insights into these physics simulations.
Direct air capture (DAC), which removes CO 2 directly from ambient air, is a critical negative emission technology for mitigating global climate change. Efficiency and the source of energy are crucial considerations for DAC to enable negative emissions. Substantial technological progress has been made in DAC technologies, and promising opportunities exist for commercial-scale deployments. However, DAC technologies require high regeneration energy to release CO 2 from sorbents. Various approaches have been tested and optimized for different DAC systems. This review demonstrates that the work equivalent regeneration energy demand (supported by either the electric grid or fossil fuel combustion) ranges from 0.5–18.75 GJ/t-CO 2 for solid sorbent DAC systems and 0.62–17.28 GJ/t-CO 2 for liquid solvent DAC systems. The regeneration process is the energy-demanding process in DAC that is a key step for efficient operation. Potential methods to lower the regeneration energy demand include microwave, ultrasound, magnetic particle heating, and electric swing. Although the potential methods to date are still at the lab scale, significant work is being done to optimize DAC system processes.
Sampling equilibrium configurations of correlated systems of particles with long relaxation times (e.g., polymeric solutions) using conventional molecular dynamics and Monte Carlo methods can be challenging. This is especially true for systems with complicated, extended bond network topologies and other interactions that make the use and design of specialized relaxation protocols infeasible. We introduce a method based on Brownian dynamics simulations that can reduce the computational time it takes to reach equilibrium and draw decorrelated samples. Importantly, the method is completely agnostic to the particle configuration and the specifics of interparticle forces. In particular, we develop a mobility matrix that excites non-local, collective motion of N particles and can be computed efficiently in $\mathcal{O}(N)$ time. Particle motion in this scheme is computed by integrating the overdamped Langevin equation with an Euler–Maruyama scheme, in which Brownian displacements are drawn efficiently using a low-rank representation of the mobility matrix in position and wave space. We demonstrate the efficacy of the method with various examples from the realm of soft condensed matter and release a massively parallel implementation of the code as a plugin for the open-source package HOOMD-blue [J. A. Anderson et al., J. Comput. Phys. 227, 5342 (2008) and J. Glaser et al., Comput. Phys. Commun. 192, 97 (2015)] which runs on graphics processing units
Cryo-electron microscopy (cryo-EM) is a Nobel Prize-winning technique for deter-mining high-resolution 3D structures of biological macromolecules. A 3D structure is reconstructed from hundreds of thousands of noisy 2D projection images. However, existing 3D reconstruction methods are still time-consuming, and one of the major computational bottlenecks is to recover the unknown orientation of the particle in16each 2D image. The dominant methods typically exploit expensive global search on each image to estimate the missing orientations. Here, a novel end-to-end supervised learning method is introduced to directly recover the missing orientations from 2D cryo-EM images. A neural network is used to approximate the mapping from images to orientations. Furthermore, a robust loss function is proposed for optimizing the parameters of the network, which can handle both asymmetric and symmetric 3D structures. Experiments on synthetic datasets with various symmetry types confirm that the neural network is capable of recovering orientations from 2D cryo-EM images, and the results on one real cryo-EM dataset further demonstrate its potential in more challenging imaging conditions.
This workshop presentation discusses the design and implementation of numerical methods for the quantification of statistical uncertainty, including a-posteriori error bounds, for output quantities computed using CFD methods. Hydrodynamic realizations often contain numerical error arising from finite-dimensional approximation (e.g. numerical methods using grids, basis functions, particles) and statistical uncertainty arising from incomplete information and/or statistical characterization of model parameters and random fields. The first task at hand is to derive formal error bounds for statistics given realizations containing finite-dimensional numerical error [1]. The error in computed output statistics contains contributions from both realization error and the error resulting from the calculation of statistics integrals using a numerical method. A second task is to devise computable a-posteriori error bounds by numerically approximating all terms arising in the error bound estimates. For the same reason that CFD calculations including error bounds but omitting uncertainty modeling are only of limited value, CFD calculations including uncertainty modeling but omitting error bounds are only of limited value. To gain maximum value from CFD calculations, a general software package for uncertainty quantification with quantified error bounds has been developed at NASA. The package provides implementations for a suite of numerical methods used in uncertainty quantification: Dense tensorization basis methods [3] and a subscale recovery variant [1] for non-smooth data, Sparse tensorization methods[2] utilizing node-nested hierarchies, Sampling methods[4] for high-dimensional random variable spaces.
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The stable spin direction and the spin tune are two important concepts used when polarized beams are transferred along beam lines or circulating in an accelerator. One of the methods to calculate the stable spin direction and the spin tune of the reference particle of a polarized beam bunch is by integrating simultaneously the Lorentz Force $\vec{F}$ = q($\vec{E}$ +$\vec{v}$ x $\vec{B}$) and the BMT equation of the reference particle as it is transported by the magnets of a beam line or accelerator. A modified version of the Raytrace code was used to integrate the Lorentz equation and the BMT equation. Using the spin motion which is governed by the BMT equation one can compute both, the stable spin direction of the reference particle at any point along the reference orbit of an accelerator or a beam line, and the spin tune of the closed orbit in an accelerator. The simultaneous integration of the Lorentz force and of the BMT equation is the most accurate method to calculate the trajectory and the spin motion of a charged particle moving in an electromagnetic field, however this method requires the accurate description of the electromagnetic fields of the elements in which the charged particles are moving. An alternative method of calculating the stable spin direction and the spin tune of spin ½ charged particles along the reference trajectory of a beam line or an accelerator is the transformation of the spinor which is the wavefunction of spin ½ particles. This method requires the knowledge of the stable spin direction of each element of the beam line or the accelerator. This technical note describes the “spinor-method” to calculate the stable-spin- direction and the spin-tune of a spin ½ particle moving along the reference trajectory of a beam line or an accelerator.
The peculiar properties of nuclei in the so-called “island of inversion” around Z = 10 and N = 20 are the focus of current nuclear physics research. Recent studies showed that 28 F has a negative-parity ground state and thus lies within the southern shore of the island of inversion, and 29 F presents a halo structure in its ground state, but it is unclear which effects, such as deformation, shell evolution due to tensor forces, or couplings to the continuum, lead to this situation. Here, we investigate the role of quadrupole deformation and continuum effects on the single-particle structure of 28,29,31 F from a relativistic mean-field approach and show how both phenomena can lead to a negative-parity ground state in 28 F and halo structures in 29,31 F. We solve the Dirac equation in the complex-momentum (Berggren) representation for a potential with quadrupole deformation at the first order obtained from relativistic mean-field calculations using the NL3 interaction and calculate the continuum level densities using the Green’s function method. We extract single-particle energies and widths from the continuum level densities to construct the Nilsson diagrams of 28,29,31 F in the continuum and analyze the evolution of both the widths and occupation probabilities of relevant Nilsson orbitals in 28 F and find that some amount of prolate deformation must be present. In addition, we calculate the density distributions for bound Nilsson orbitals near the Fermi surface in 29,31 F and reveal that, for a quadrupole deformation 0.3 ≤ β 2 ≤ 0.45 (prolate), characteristic halo tails appear at large distances. Using the relativistic mean-field approach in the complex-momentum representation with the Green’s function method, we demonstrate that in neutron-rich fluorine isotopes, while in the spherical case the pf shells are already inverted and close to the neutron emission threshold, a small amount of quadrupole deformation can dramatically reduce the gap between positive- and negative-parity states and increase the role of continuum states, ultimately leading to the negative parity in the ground state of 28 F and the halo structures in 29,31 F.
Aerosols may be generated through the nebulizing of solutions and the evaporation of their solvent, leaving the dry solute particles. Attention is presently given to a method for the direct determination of the masses of chemical species in individual aerosol particles on a continuous, real-time basis, using mass spectrometry. After the aerosol particles are introduced into the ion source of a quadrupole mass spectrometer, the particles impinge on a hot rhenium filament in the mass spectrometer's ion source. The resulting vapor plume is ionized by electron bombardment, and a pulse of ions is generated by each particle. The intensities of different masses in the ion pulses can then be measured by the mass spectrometer.
Astronomical observations which reflect the optical and dynamical properties of interstellar and interplanetary grains are the primary means of identifying the shape, size, and the chemistry of extraterrestrial grain materials. Except for recent samplings of extraterrestrial particles in near-Earth orbit and in the stratosphere observations were the only method of deducing the properties of extraterrestrial particles. In order to elucidate the detailed characteristics of observed dust, the observations must be compared with theoretical studies, some of which are discussed in this volume, or compared with terrestrial laboratory experiments. The formation and optical characterization of simulated interstellar and interplanetary dust with particular emphasis on studying the properties on irregularly shaped particles were discussed. Efforts to develop the techniques to allow dust experiments to be carried out in low-Earth orbit were discussed, thus extending the conditions under which dust experiments may be performed.
The complexity of fission product distribution and composition within the SiC layer of tristructural isotropic (TRISO) coated particles, irradiation effects on the SiC structure as well as the variable nature of metallic fission product release, has led to the exploration of multiple micro- and nano-characterization techniques. The fine scale of the fission product precipitates necessitates unique learnings and application of electron microscopic techniques with irradiated fuel during the Advanced Gas Reactor (AGR)-1 experiment analysis. A summarized discussion on the specific advanced techniques with associated method development for TRISO coated particles is provided followed by the down selected techniques currently considered to provide the highest impact. Techniques considered are electron probe micro-analyzer (EPMA), scanning transmission electron microscopy (STEM), and Precession electron diffraction (PED). SiC grain boundary characteristics are evaluated due to the mobility of specific fission products through grain boundaries. PED was performed for understanding the role of grain boundary character on fission product transport. One set of comparative analyses between AGR-1 and AGR-2 particles leads to a finding that the AGR-1, high-Ag-retention particle, had statistically more coincidence site lattice (CSL)-related grain boundaries (but fewer low-angle grain boundaries), compared to the AGR-2 particle. This implies that CSL-related grain boundaries may have a direct influence on Ag retention. However, precipitates were found on a relatively small fraction of CSL-related grain boundaries in both particles. In another set of comparison between AGR-1 and AGR-2 particles that were subjected to safety testing, there is significant differences in average SiC grain boundary distributions, but the fission product distributions on these grain boundaries are very similar.
The complexity of fission product distribution and composition within the SiC layer of tristructural isotropic (TRISO) coated particles, irradiation effects on the SiC structure as well as the variable nature of metallic fission product release, has led to the exploration of multiple micro- and nano-characterization techniques. The fine scale of the fission product precipitates necessitates unique learnings and application of electron microscopic techniques with irradiated fuel during the Advanced Gas Reactor (AGR)-1 experiment analysis. A summarized discussion on the specific advanced techniques with associated method development for TRISO coated particles is provided followed by the down selected techniques currently considered to provide the highest impact. Techniques considered are electron probe micro-analyzer (EPMA), scanning transmission electron microscopy (STEM), and Precession electron diffraction (PED). SiC grain boundary characteristics are evaluated due to the mobility of specific fission products through grain boundaries. PED was performed for understanding the role of grain boundary character on fission product transport. One set of comparative analyses between AGR-1 and AGR-2 particles leads to a finding that the AGR-1, high-Ag-retention particle, had statistically more coincidence site lattice (CSL)-related grain boundaries (but fewer low-angle grain boundaries), compared to the AGR-2 particle. This implies that CSL-related grain boundaries may have a direct influence on Ag retention. However, precipitates were found on a relatively small fraction of CSL-related grain boundaries in both particles. In another set of comparison between AGR-1 and AGR-2 particles that were subjected to safety testing, there is significant differences in average SiC grain boundary distributions, but the fission product distributions on these grain boundaries are very similar.
The purpose of this project was to develop and demonstrate a novel method for the production of uranium oxide microsphere particles with tunable chemical compositions via a sol-gel process using a 3D-printer setup. These particles can serve several purposes in research and development as a forensic training tool or as standard reference materials. A key component of the project was to demonstrate the ability to control physical and chemical parameters of the particles created. First, we demonstrated the ability to employ an internal gelation sol-gel process to create individual uranium oxide particles. The particles were successfully dispensed using a unique 3D-printing setup onto a substrate to react and then were collected and thermally processed. A series of temperatures for the annealing process was tested on individual samples to investigate the effect on the sol-gel chemical composition and physical integrity. Next, we demonstrated the ability to control matrix composition of the particles by separately incorporating fission product isotopes as well as Np-237 into the sol-gel solution. It was shown by gamma-ray spectroscopy that these matrix elements were successfully retained during the gelation process. We studied the retention of the elements across a series of annealing temperatures. Additionally, we demonstrated the ability to quantitatively control the isotopic composition of the particles by altering the U-237/U-238 ratio to a controlled value. Finally, X-ray diffraction analysis (XRD) was used to investigate the oxidation state of the sol-gel after annealing at different temperatures.
Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles by encoding the protocol in the form of a neural network. We use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory.