Engineering Papers⌕ Search

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.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Diffusive shock acceleration of energetic electrons and the method of generations

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.

Webb, G. M.↗
DOE OSTI2012

Uncertainty quantification of few group diffusion theory constants generated by the B1 theory-augmented Monte Carlo method

The purpose of this paper is to quantify uncertainties of fuel pin cell or fuel assembly (FA) homogenized few group diffusion theory constants generated from the B1 theory-augmented Monte Carlo (MC) method. A mathematical formulation of the first kind is presented to quantify uncertainties of the few group constants in terms of the two major sources of the MC method; statistical and nuclear cross section and nuclide number density input data uncertainties. The formulation is incorporated into the Seoul National Univ. MC code McCARD. It is then used to compute the uncertainties of the burnup-dependent homogenized two group constants of a low-enriched UO{sub 2} fuel pin cell and a PWR FA on the condition that nuclear cross section input data of U-235 and U-238 from JENDL 3.3 library and nuclide number densities from the solution to fuel depletion equations have uncertainties. The contribution of the MC input data uncertainties to the uncertainties of the two group constants of the two fuel systems is separated from that of the statistical uncertainties. The utilities of uncertainty quantifications are then discussed from the standpoints of safety analysis of existing power reactors, development of new fuel or reactor system design, and improvement of covariance files of the evaluated nuclear data libraries. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Score-based diffusion models for generating liquid argon time projection chamber images

For the first time, we show high-fidelity generation of Liquid Argon Time Projection Chamber (LArTPC-like) data using a generative neural network. This demonstrates that methods developed for natural images do transfer to LArTPC-produced images, which, in contrast to natural images, are globally sparse but locally dense. We present the score-based diffusion method employed. We evaluate the fidelity of the generated images using several quality metrics, including modified measures used to evaluate natural images, comparisons between high-dimensional distributions, and comparisons relevant to LArTPC experiments. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2016

Periodic surface structure bifurcation induced by ultrafast laser generated point defect diffusion in GaAs

The formation of high spatial frequency laser induced periodic surface structures (HSFL) with period <0.3 λ in GaAs after irradiation with femtosecond laser pulses in air is studied. We have identified a point defect generation mechanism that operates in a specific range of fluences in semiconductors between the band-gap closure and ultrafast-melt thresholds that produces vacancy/interstitial pairs. Stress relaxation, via diffusing defects, forms the 350–400 nm tall and ∼90 nm wide structures through a bifurcation process of lower spatial frequency surface structures. The resulting HSFL are predominately epitaxial single crystals and retain the original GaAs stoichiometry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1988

The role of gravity wave generated advection and diffusion in transport of tracers in the mesosphere

The roles of advection and turbulent diffusion in controlling tracer transport in the meridional plane of the mesosphere are compared using a version of the global primitive equation model for the middle atmosphere developed by Holton and Wehrbein (1980). The results of model sensitivity tests demonstrated that transport in the mesosphere for long-lived species with large-scale heights, such as H2O, CO, and NO(x), is primarily an advective process; on a global scale, small-scale turbulent diffusion produced by gravity wave breaking plays a secondary role. It is concluded that, for such tracers, the eddy diffusion coefficient used in one-dimensional chemical models of the mesosphere should be regarded as a parameterization of the transport by the meridional circulation, not as a parameterization of mixing by small-scale diffusion.

Holton, James R.↗
DOE OSTI2019

Uptake and diffusion of plasma-generated reactive nitrogen species through keratinized membrane

We propose a mathematical model for uptake and diffusion of air plasma-generated reactive nitrogen species (RNS) in a model keratinized membrane, such as a thin slice of bovine hoof or human nail material. An experimental system consisting of a surface microdischarge (SMD) in air was designed for the purpose of developing and validating a reaction-diffusion model to describe this system. Key variables such as membrane effective diffusivity, surface reaction rate coefficients, and other parameters are determined through comparison between the model predictions and experimental measurements. The model results yield spatial and temporal concentration profiles of RNS inside the keratinized membrane, leading to an improved understanding of transport and reaction of plasma generated RNS through the membrane. This work offers insights into possible mechanisms underlying plasma treatment of toenail fungus.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Geometry-complete diffusion for 3D molecule generation and optimization

Abstract Generative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively. Importantly, we demonstrate that GCDM’s generative denoising process enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Code and data are freely available on GitHub .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

Three Dimensional Compressible Turbulent Flow Computations for a Diffusing S-Duct With/Without Vortex Generators

Numerical investigations on a diffusing S-duct with/without vortex generators and a straight duct with vortex generators are presented. The investigation consists of solving the full three-dimensional unsteady compressible mass averaged Navier-Stokes equations. An implicit finite volume lower-upper time marching code (RPLUS3D) has been employed and modified. A three-dimensional Baldwin-Lomax turbulence model has been modified in conjunction with the flow physics. A model for the analysis of vortex generators in a fully viscous subsonic internal flow is evaluated. A vortical structure for modeling the shed vortex is used as a source term in the computation domain. The injected vortex paths in the straight duct are compared with the analysis by two kinds of prediction models. The flow structure by the vortex generators are investigated along the duct. Computed results of the flow in a circular diffusing S-duct provide an understanding of the flow structure within a typical engine inlet system. These are compared with the experimental wall static-pressure, static- and total-pressure field, and secondary velocity profiles. Additionally, boundary layer thickness, skin friction values, and velocity profiles in wall coordinates are presented. In order to investigate the effect of vortex generators, various vortex strengths are examined in this study. The total-pressure recovery and distortion coefficients are obtained at the exit of the S-duct. The numerical results clearly depict the interaction between the low velocity flow by the flow separation and the injected vortices.

Cho, Soo-Yong↗
NASA NTRS1985

Generation of Z mode radiation by diffuse auroral electron precipitation

The generation of Z mode waves by diffuse auroral electron precipitation is investigated assuming that a loss cone exists in the upgoing portion of the distribution due to electron interactions with the atmosphere. The waves are generated at frequencies above, but very near, the local electron cyclotron frequency omega(e) and at wave normal angles larger than 90 deg. In agreement with Hewitt et al. (1983), the group velocity is directed downward in regions where the ratio of the upper hybrid frequency omega(pe) to Omega(e) is less than 0.5, so that Z mode waves excited above a satellite propagate toward it and away from the upper hybrid resonance. Z mode waves are excited in a frequency band between Omega(e) and about 1.02 Omega(e), and with maximum growth rates of about 0.001 Omega(e). The amplification length is about 100 km, which allows Z mode waves to grow to the intensities observed by high-altitude satellites.

Dusenbery, P. B.↗
DOE OSTI2014

Diffusion and stochastic island generation in the magnetic field line random walk

The cross-field diffusion of field lines in stochastic magnetic fields described by the 2D+slab model is studied using a semi-analytic statistical approach, the decorrelation trajectory method. We show that field line trapping and the associated stochastic magnetic islands strongly influence the diffusion coefficients, leading to dependences on the parameters that are different from the quasilinear and Bohm regimes. A strong amplification of the diffusion is produced by a small slab field in the presence of trapping. The diffusion regimes are determined and the corresponding physical processes are identified.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Enhancing high-order harmonic generation by controlling the diffusion of the electron wave packet

We experimentally study the enhancement of high-order harmonic generation (HHG) driven by synthesized ω <#comment/> − <#comment/> 3 ω <#comment/> laser fields, where we control whether the ionization rate or the electron wave packet’s diffusion is the dominant enhancement mechanism. When minimizing the electron wave packet’s diffusion, the excursion times of the corresponding electron trajectories are reduced by a factor of 2 or more. This result is important for imaging techniques that use the returning electron wave packet to probe the remaining ion. Furthermore, we achieve a 10 × <#comment/> to 3800 × <#comment/> enhancement of the harmonic yield driven by the bichromatic fields relative to that of an optimized single-color field, showing that the bichromatic fields improve HHG’s capability as a light source. We also measure that the two-color field’s harmonics have half the divergence angle compared to their single-color counterpart, suggesting that the “short” electron trajectories play a more prominent role compared to their “long” trajectory counterparts, thus improving the wavefront of the emerging harmonic beam.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2013

Effect of electron energy distribution functions on plasma generated vacuum ultraviolet in a diffusion plasma excited by a microwave surface wave

Plasma generated vacuum ultraviolet (VUV) in diffusion plasma excited by a microwave surface wave has been studied by using dielectric-based VUV sensors. Evolution of plasma VUV in the diffusion plasma as a function of the distance from the power coupling surface is investigated. Experimental results have indicated that the energy and spatial distributions of plasma VUV are mainly controlled by the energy distribution functions of the plasma electrons, i.e., electron energy distribution functions (EEDFs). The study implies that by designing EEDF of plasma, one could be able to tailor plasma VUV in different applications such as in dielectric etching or photo resist smoothing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation

Here, we present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks (GANs), normalizing flows, and variational auto-encoders (VAEs), are usually considered as unsupervised learning models, because labeled data are usually unavailable for training. Despite the success of the generative models, there are several issues with the unsupervised training, e.g., requirement of reversible architectures, vanishing gradients, and training instability. To enable supervised learning in generative models, we utilize the score-based diffusion model to generate labeled data. Unlike existing diffusion models that train neural networks to learn the score function, we develop a training-free score estimation method. This approach uses mini-batch-based Monte Carlo estimators to directly approximate the score function at any spatial-temporal location in solving an ordinary differential equation (ODE), corresponding to the reverse-time stochastic differential equation (SDE). This approach can offer both high accuracy and substantial time savings in neural network training. Once the labeled data are generated, we can train a simple, fully connected neural network to learn the generative model in the supervised manner. Compared with existing normalizing flow models, our method does not require the use of reversible neural networks and avoids the computation of the Jacobian matrix. Compared with existing diffusion models, our method does not need to solve the reverse-time SDE to generate new samples. As a result, the sampling efficiency is significantly improved. We demonstrate the performance of our method by applying it to a set of 2D datasets as well as real data from the University of California Irvine (UCI) repository.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Ohmic contact formation process on low n-type gallium arsenide (GaAs) using indium gallium zinc oxide (IGZO)

Highlights: • We propose a method to fabricate non-gold Ohmic contact on low n-type GaAs with IGZO. • 0.15 A/cm{sup 2} on-current and 1.5 on/off-current ratio are achieved in the junction. • InAs and InGaAs formed by this process decrease an electron barrier height. • Traps generated by diffused O atoms also induce a trap-assisted tunneling phenomenon. - Abstract: Here, an excellent non-gold Ohmic contact on low n-type GaAs is demonstrated by using indium gallium zinc oxide and investigating through time of flight-secondary ion mass spectrometry, X-ray photoelectron spectroscopy, transmission electron microscopy, J–V measurement, and H [enthalpy], S [entropy], Cp [heat capacity] chemistry simulation. In is diffused through GaAs during annealing and reacts with As, forming InAs and InGaAs phases with lower energy bandgap. As a result, it decreases the electron barrier height, eventually increasing the reverse current. In addition, traps generated by diffused O atoms induce a trap-assisted tunneling phenomenon, increasing generation current and subsequently the reverse current. Therefore, an excellent Ohmic contact with 0.15 A/cm{sup 2} on-current density and 1.5 on/off-current ratio is achieved on n-type GaAs.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Computing Shapes Of Cascade Diffuser Blades

Computer program generates sizes and shapes of cascade-type blades for use in axial or radial turbomachine diffusers. Generates shapes of blades rapidly, incorporating extensive cascade data to determine optimum incidence and deviation angle for blade design based on 65-series data base of National Advisory Commission for Aeronautics and Astronautics (NACA). Allows great variability in blade profile through input variables. Also provides for design of three-dimensional blades by allowing variable blade stacking. Enables designer to obtain computed blade-geometry data in various forms: as input for blade-loading analysis; as input for quasi-three-dimensional analysis of flow; or as points for transfer to computer-aided design.

Tran, Ken↗
DOE OSTI2024

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Building and experimenting with an agent-based model to study the population-level impact of CommunityRx, a clinic-based community resource referral intervention

CommunityRx (CRx), an information technology intervention, provides patients with a personalized list of healthful community resources (HealtheRx). In repeated clinical studies, nearly half of those who received clinical “doses” of the HealtheRx shared their information with others (“social doses”). Clinical trial design cannot fully capture the impact of information diffusion, which can act as a force multiplier for the intervention. Furthermore, experimentation is needed to understand how intervention delivery can optimize social spread under varying circumstances. To study information diffusion from CRx under varying conditions, we built an agent-based model (ABM). This study describes the model building process and illustrates how an ABM provides insight about information diffusion through in silico experimentation. To build the ABM, we constructed a synthetic population (“agents”) using publicly-available data sources. Using clinical trial data, we developed empirically-informed processes simulating agent activities, resource knowledge evolution and information sharing. Using RepastHPC and chiSIM software, we replicated the intervention in silico, simulated information diffusion processes, and generated emergent information diffusion networks. The CRx ABM was calibrated using empirical data to replicate the CRx intervention in silico. We used the ABM to quantify information spread via social versus clinical dosing then conducted information diffusion experiments, comparing the social dosing effect of the intervention when delivered by physicians, nurses or clinical clerks. The synthetic population (N = 802,191) exhibited diverse behavioral characteristics, including activity and knowledge evolution patterns. In silico delivery of the intervention was replicated with high fidelity. Large-scale information diffusion networks emerged among agents exchanging resource information. Varying the propensity for information exchange resulted in networks with different topological characteristics. Community resource information spread via social dosing was nearly 4 fold that from clinical dosing alone and did not vary by delivery mode. This study, using CRx as an example, demonstrates the process of building and experimenting with an ABM to study information diffusion from, and the population-level impact of, a clinical information-based intervention. While the focus of the CRx ABM is to recreate the CRx intervention in silico, the general process of model building, and computational experimentation presented is generalizable to other large-scale ABMs of information diffusion.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1980

Low speed test of the aft inlet designed for a tandem fan V/STOL nacelle

An approximately .25 scale model of a Tandem Fan nacelle designed for a Type A V/STOL aircraft configuration was tested in a 10-by-10 foot wind tunnel. A 12 inch, tip driven, turbofan simulator was used to provide the suction source for the aft fan inlet. The front fan inlet was faired over for this test entry. Model variables consisted of a long aft inlet cowl, a short aft inlet cowl, a shaft simulator, blow-in door passages and diffuser vortex generators. Inlet pressure recovery, distortion, inlet angle of attack separation limits were evaluated at tunnel velocities from 0 to 240 knots, angles of attack from -10 to 40 degrees and inlet flow rates representative of throat Mach numbers of 0.1 to 0.6. High inlet performance and stable operation was verified at all design forward speed and angle of attack conditions. The short aft inlet configuration provided exceptionally high pressure recovery except at the highest combination of angle of attack and forward speed. The flow quality at the fan face was somewhat degraded by the addition of blow-in door passages to the long aft inlet configuration due to the pressure disturbances generated by the flow entering the diffuser through the auxiliary air passages.

Rhoades, W. W.↗