Hydraulic investigations of thermal diffusion during heat treatment cycles: San Onofre Nuclear Generating Station Units 2 and 3
Explore the source record for details and available documents.
Isaacson, M. S.↗
SEARCH · Engineering Papers
Results for “generative diffusion”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Pulsed Gas Lasers Pumped by a Runaway Electron Initiated Discharge
The generation parameters are investigated in a runaway electron preionized diffuse discharge (REP DD). Laser generation is produced in different spectral bands from the IR to VUV range. New modes of the nitrogen laser operation are obtained. Ultimate efficiencies of N{sub 2}- and nonchain HF(DF)-lasers are achieved. A possibility of increasing the pulse durations of XeF-, KrF-, ArF- and VUV F{sub 2}- lasers (157 nm) in an oscillating REP DD is shown. The efficiencies of VUV- and UV-generation comparable with that of a laser pumped by a self-sustained volume discharge with preionization are gained.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Miniature UV lamp excited by subnanosecond voltage pulses
Energy, time, and spectral characteristics of emission of the second positive system of N{sub 2} molecules in gaseous nitrogen, Ar - N{sub 2} mixture, and air are investigated. An FPG-10 generator with voltage pulse FWHM of 200 and 400 ps and matched-load amplitudes of 14 and 6 kV, respectively, is used to excite gases. It is shown that excitation can be performed in two regimes using this generator. In the first regime a diffuse discharge is formed at atmospheric pressure, which opens ways to design miniature nanosecond UV lamps. A diffuse discharge is formed due to the generation of runaway electrons, with the aid of electrodes having a small radius of curvature and voltage pulses with a sharp leading edge. In the second regime an elevated average radiation power is obtained under excitation by a barrier discharge. However, the operating pressure is lower in this case, and the sizes of the emitting region and the UV pulse width significantly increase. (laser applications and other topics in quantum electronics)
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms
Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.
99 - GENERAL AND MISCELLANEOUS↗
COSMOLOGICAL SIMULATIONS OF GALAXY FORMATION WITH COSMIC RAYS
We investigate the dynamical impact of cosmic rays (CR) in cosmological simulations of galaxy formation using adaptive-mesh refinement simulations of a 10{sup 12} M {sub ☉} halo. In agreement with previous work, a run with only our standard thermal energy feedback model results in a massive spheroid and unrealistically peaked rotation curves. However, the addition of a simple two-fluid model for CRs drastically changes the morphology of the forming disk. We include an isotropic diffusive term and a source term tied to star formation due to (unresolved) supernova-driven shocks. Over a wide range of diffusion coefficients, the CRs generate thin, extended disks with a significantly more realistic (although still not flat) rotation curve. We find that the diffusion of CRs is key to this process, as they escape dense star-forming clumps and drive outflows within the more diffuse interstellar medium.
79 ASTRONOMY AND ASTROPHYSICS↗
Performance of vortex generators in a Mach 2.5 low-bleed full scale 45-percent-internal-contraction axisymmetric inlet
Steady-state and dynamic flow characteristics associated with two sets of vortex generators having different mixing criteria were determined. The inlet performance with and without these vortex generators is presented. The vortex generators were successful in eliminating separation, increasing area-weighted total pressure recovery, and decreasing distortion. Transmission times obtained from cross-correlations of the wall static pressures and the diffuser exit total pressure showed no effect of the upstream flow characteristics on the diffuser exit pressures when generators were used. Without generators, separation occurred and the upstream pressure characteristics had immediate effects on the diffuser exit pressure characteristics.
Neumann, H. E.↗
Modeling the formation of methane hydrate-bearing intervals in fine-grained sediments
Sediment grain size exerts a fundamental control on how methane hydrates are distributed within the pore space. Fine-grained muds are the predominant sediments in continental margins, and hydrates in these sediments have often been observed in semi-vertical veins and fractures. In several instances, these hydrate veins/fractures are found in discrete depth intervals a few tens meters thick within the gas hydrate stability zone (GHSZ) surrounded by hydrate-free sediments above and below. As they are not obviously connected with free gas occurring beneath the base of the GHSZ, these isolated hydrate-bearing intervals have been interpreted as formed by microbial methane generated in situ. To investigate further the formation of these hydrate deposits, we applied a time-dependent advection-diffusion-reaction model that includes the effects of sedimentation, solute diffusion, and microbial methane generation. The microbial methane generation term depends on the amount of metabolizable organic carbon deposited at the seafloor, which is degraded at a prescribed rate resulting in methane formation beneath the sulfate reduction zone. In the model, methane hydrate precipitates once the dissolved methane concentration is greater than solubility, or hydrate dissolves if concentration goes below solubility. If the deposition of organic carbon at the seafloor is kept constant in time, we found that the predicted amounts of hydrate formed in discrete intervals within the GHSZ are much less than those estimated from observations. We then investigated the effect of temporal variations in the deposition of organic carbon. If greater amounts of organic carbon are deposited during some time interval, methane generation is enhanced during burial in the corresponding sediment interval. With variations in organic carbon deposition that are consistent with observations in continental margin sediments, we were able to reproduce the methane hydrate contents estimated in discrete depth intervals. Our results support the suggestion that in situ microbial methane generation is the source for hydrates within fine-grained sediments.
Malinverno, Alberto↗
Diffusion Barrier Coatings for Liquid Metal Corrosion Resistance in Next-Generation Nuclear Reactors
The goal of this study is to characterize the chemical and physical interactions that occur between a ceramic diffusion barrier coating applied onto nuclear reactor cladding and liquid metal coolants used in Sodium-Cooled Fast Reactors (SFRs) and Lead-Cooled Fast Reactors (LFRs).
Hromiak, Alexander A. [Virginia Polytechnic Inst. ↗
Enabling probabilistic learning on manifolds through double diffusion maps
Here, we present a generative learning framework for probabilistic sampling that extends Probabilistic Learning on Manifolds (PLoM), which is designed to generate statistically consistent realizations of a random vector in a finite-dimensional Euclidean space, informed by a (representative) set of observations. In its original form, PLoM constructs a reduced-order probabilistic model by combining three main components: (a) kernel density estimation to approximate the underlying probability measure, (b) Diffusion Maps to characterize the manifold of the data, and (c) a reduced-order Itô Stochastic Differential Equation (ISDE) to sample from the learned distribution. However, its sampling dynamics are posed in the ambient space and the retained number of reduced coordinates is chosen by projection-reconstruction error. In practice, this often (i) requires more coordinates than the data’s intrinsic dimension to achieve stable sampling and (ii) lacks a smooth, basis-independent lifting back to the data domain; moreover, standard Diffusion Maps emphasize harmonic eigenfunctions and can miss non-harmonic latent structure. We address these limitations by decoupling geometry learning from sampling: a first Diffusion Maps pass identifies non-harmonic coordinates on which we formulate a full-order ISDE directly in the latent space, while Double Diffusion Maps captures multiscale geometric features and Geometric Harmonics (GH) learns a smooth lifting map to the ambient variables that is independent of the particular diffusion basis. This hybrid design preserves the system’s dynamical richness with a compact geometric representation and enables principled out-of-sample inference. The effectiveness and robustness of the proposed method are illustrated through two numerical studies: one based on data generated from two-dimensional Hermite polynomial functions and another based on high-fidelity simulations of a detonation wave in a reactive flow.
Double diffusion maps↗
Lateral charge transport from heavy-ion tracks in integrated circuit chips
A 256K DRAM has been used to study the lateral transport of charge (electron-hole pairs) induced by direct ionization from heavy-ion tracks in an IC. The qualitative charge transport has been simulated using a two-dimensional numerical code in cylindrical coordinates. The experimental bit-map data clearly show the manifestation of lateral charge transport in the creation of adjacent multiple-bit errors from a single heavy-ion track. The heavy-ion data further demonstrate the occurrence of multiple-bit errors from single ion tracks with sufficient stopping power. The qualitative numerical simulation results suggest that electric-field-funnel-aided (drift) collection accounts for single error generated by an ion passing through a charge-collecting junction, while multiple errors from a single ion track are due to lateral diffusion of ion-generated charge.
Zoutendyk, J. A.↗
Electron distributions in the inner Jovian magnetosphere: Voyager 1 observations
Using several improvements in the analysis of the observations of the Low Energy Charged Particle (LECP) experiment on Voyager 1, electron phase space densities in the inner Jovian magnetosphere (5 - 10 R(sub J) were first calculated at constant first and second invariants (represented by mu and K, respectively), based on the LECP measurements. The calculated electron phase space density profiles show that in the inner Jovian magnetosphere there exist evident time and longitude variations, energetic electron injections, and present radial transport and distributed losses. To study the radial and pitch angle diffusions of Jovian electrons, we have calculated the phase space densities in the K-L space. It is found that the electron population in the inner Jovian magnetosphere seems to consist of two components: electrons radially diffusing from a main external source and electrons generated from local sources. The radially diffusing electrons have a relatively time stationary and isotropic distribution, while the locally created electrons mainly concentrate around the equatorial plane and have relatively lower energies, in comparison with the inward diffusing electrons. Consequently, the sources of precipitation losses to the ionosphere must be primarily electrons transported from outer sources, and the major precipitations occur in the inner magnetosphere (L less than 7.5 R(sub J). In the inner Jovian magnetosphere (L = 5 to approximately 10 R(sub J)) it is estimated that for electrons with magnetic moment mu = 300 MeV/G, the diffusion coefficient D is roughly 10(exp -8) to approximately 10(exp -6) R(exp 2)(sub J)/s, and the lifetime against the diffusion losses is of the order of 10(exp 4) to approximately 10(exp 6) s.
Ye, G.↗
Generation and healing of porosity in high purity copper by high-pressure torsion
Highlights: • Direct characterization of severe plastic deformation induced and healed porosities. • HPT process of disk with pre-fabricated holes reveals healing of porosity. • Ultrafine grains coexisting with shear bands and pores exhibit high stability. • Excess vacancies and pores cause drag effect on the grain boundary motion. • Accelerated vacancy diffusion leads to the generation and growth of pores. - Abstract: Generation and healing of pores in metals under loading or during processing have long been important research topics, and their mechanisms are still debatable. In recent years, unexpected percolating networks of pores acting as ultra-fast diffusion paths were found in ultrafine-grained metals processed by severe plastic deformation. Herein, we conducted a systematic study of the mechanisms of generation and healing of pores in ultrahigh-purity copper (99.9995 wt%) during high-pressure torsion (HPT). In addition to a solid Cu disk, the disk with holes that were drilled before processing, resulting in nominal initial porosity of 4.48%, was processed by HPT. After HPT, the percolating porosities of 0.07 ± 0.01% and 0.04 ± 0.01% were found in the solid disk, and in the disk with pre-fabricated holes, respectively. Thus, higher initial porosity resulted in lower percolating porosity in the HPT-processed disk. Systemic characterization using scanning electron microscopy revealed that in both cases shear bands and ultra-fine grains are located in the regions with induced and healed pores. We discussed the mechanisms of porosity evolution in both cases. In the end, to verify the possibility of pores nucleation and growth by vacancy agglomeration, vacancy diffusivity during HPT processing was estimated. This work sheds a new light on the mechanisms of pores nucleation and evolution during severe plastic deformation.
36 MATERIALS SCIENCE↗
A new indirect measure of diffusion model error
We define a new indirect measure of the diffusion model error called the diffusion model error source. When this model error source is added to the diffusion equation, the transport solution for the angular-integrated intensity is obtained. This source represents a means by which a transport code can be used to generate information relating to the adequacy of diffusion theory for any given problem without actually solving the diffusion equation. The generation of this source does not relate in any way to acceleration of the iterative convergence of transport solutions. Perhaps the most well-known indirect measure of the diffusion model error is the variable-Eddington tensor. This tensor provides a great deal of information about the angular dependence of the angular intensity solution, but it is not always simple to interpret. In contrast, our diffusion model error source is a scalar that is conceptually easy to understand. In addition to defining the diffusion model error source analytically, we show how to generate this source numerically relative to the S{sub n} radiative transfer equations with linear-discontinuous spatial discretization. This numerical source is computationally tested and shown to reproduce the Sn solution for a Marshak-wave problem. (authors)
97 MATHEMATICS AND COMPUTING↗
A computer program for the simulation of heat and moisture flow in soils
A computer program that simulates the flow of heat and moisture in soils is described. The space-time dependence of temperature and moisture content is described by a set of diffusion-type partial differential equations. The simulator uses a predictor/corrector to numerically integrate them, giving wetness and temperature profiles as a function of time. The simulator was used to generate solutions to diffusion-type partial differential equations for which analytical solutions are known. These equations include both constant and variable diffusivities, and both flux and constant concentration boundary conditions. In all cases, the simulated and analytic solutions agreed to within the error bounds which were imposed on the integrator. Simulations of heat and moisture flow under actual field conditions were also performed. Ground truth data were used for the boundary conditions and soil transport properties. The qualitative agreement between simulated and measured profiles is an indication that the model equations are reasonably accurate representations of the physical processes involved.
Camillo, P.↗
Effects of size and concentration on diffusion-induced stress in lithium-ion batteries
Capacity fade of lithium-ion batteries induced by chemo-mechanical degradation during charge-discharge cycles is the bottleneck in design of high-performance batteries, especially high-capacity electrode materials. Stress generated due to diffusion-mechanical coupling in lithium-ion intercalation and deintercalation is accompanied by swelling, shrinking, and even micro-cracking. In this paper, we propose a theoretical model for a cylindrical nanowire electrode by combining the bond-order-length-strength and diffusion theories. It is shown that size and concentration have a significant influence on the stress fields in radial, hoop, and axial directions. This can explain why a smaller electrode with a huge volume change survives in the lithiation/delithiation process.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Stream interfaces and energetic ions 2: Ulysses test of Pioneer results
Ulysses measurements of energetic and solar wind particles taken near 5 AU between 20 and 30 degrees south latitude during a well-developed recurring corotating interaction region (CIR) show that the CIR's corotating energetic ion population (CEIP) associated with the trailing reverse shock starts within the CIR at the stream interface. This is consistent with an earlier result obtained by Pioneers 10 and 11 in the ecliptic plane between 4 and 6 AU. The Ulysses/Pioneer finding noteworthy since the stream interface is not magnetically connected to the reverse shock but lies 12-17 corotation hours from it. Thus, the finding to be inconsistent with the basic model that generates CEIP particles at the reverse shock and propagates them along field lines Eliminating the inconsistency probably entails an extension of the standard model. We consider two possible extensions cross-field diffusion and energetic particles generation closer to the sun in the gap between the stream interface and the reverse shock.
Intriligator, Devrie S.↗
ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model
Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here, we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as the target to enable the discovery of protein materials with superior mechanical properties.
59 BASIC BIOLOGICAL SCIENCES↗
Realistic galaxy image simulation via score-based generative models
ABSTRACT We show that a denoising diffusion probabilistic model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies. Our method is tested with Dark Energy Spectroscopic Instrument (DESI) grz imaging of galaxies from the Photometry and Rotation curve OBservations from Extragalactic Surveys (PROBES) sample and galaxies selected from the Sloan Digital Sky Survey. Subjectively, the generated galaxies are highly realistic when compared with samples from the real data set. We quantify the similarity by borrowing from the deep generative learning literature, using the ‘Fréchet inception distance’ to test for subjective and morphological similarity. We also introduce the ‘synthetic galaxy distance’ metric to compare the emergent physical properties (such as total magnitude, colour, and half-light radius) of a ground truth parent and synthesized child data set. We argue that the DDPM approach produces sharper and more realistic images than other generative methods such as adversarial networks (with the downside of more costly inference), and could be used to produce large samples of synthetic observations tailored to a specific imaging survey. We demonstrate two potential uses of the DDPM: (1) accurate inpainting of occluded data, such as satellite trails, and (2) domain transfer, where new input images can be processed to mimic the properties of the DDPM training set. Here we ‘DESI-fy’ cartoon images as a proof of concept for domain transfer. Finally, we suggest potential applications for score-based approaches that could motivate further research on this topic within the astronomical community.
79 ASTRONOMY AND ASTROPHYSICS↗