Search NASA⌕ Search

SEARCH · Search NASA

Results for “generative flow model”

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 NTRS1995

Grid generation and surface modeling for CFD

When computing the flow around complex three dimensional configurations, the generation of the mesh is the most time consuming part of any calculation. With some meshing technologies this can take of the order of a man month or more. The requirement for a number of design iterations coupled with ever decreasing time allocated for design leads to the need for a significant acceleration of this process. Of the two competing approaches, block-structured and unstructured, only the unstructured approach will allow fully automatic mesh generation directly from a CAD model. Using this approach coupled with the techniques described in this paper, it is possible to reduce the mesh generation time from man months to a few hours on a workstation. The desire to closely couple a CFD code with a design or optimization algorithm requires that the changes to the geometry be performed quickly and in a smooth manner. This need for smoothness necessitates the use of Bezier polynomials in place of the more usual NURBS or cubic splines. A two dimensional Bezier polynomial based design system is described.

Connell, Stuart D.↗
DOE OSTI2023

A phased workflow to define permit‐ready locations for large volume CO 2 injection and storage

Abstract To‐date, only two UIC Class VI permits have been issued by the US Environmental Protection Agency. We illustrate a four‐phase workflow to first identify regional storage resources and then down‐select sites to yield permit‐ready locations that can accept and store large volumes of CO 2 . Specific permit requirements should guide objectives and define deliverables of respective workflow phases. In the first phase we used available regional data and screened structure and injection zones to locate resources that match CO 2 volumes planned to be captured. Available data were also used to assess presence and depth of usable groundwater, the key resource being protected via permitting. We then used advanced, closed‐form, analytical solutions (EASiTool) to estimate CO 2 injectivity into each hydrologically connected injection compartment. In the second phase we acquired and conditioned additional wireline logs and leased available seismic datasets. We interpreted the depositional systems from wireline well‐log character and mapped sandbody geometry to interpolate injection and confining‐zone distribution. Using available data, we mapped faults and locations of freshwater and overpressure (or other capacity‐limiting geologic parameters) in more detail. In the third phase, we used the augmented geologic data to develop a static model for the selected area, extracted the areas of highest interest, and generated and ran dynamic (flow) models. In a fourth phase, we reduced major uncertainties identified in earlier phases. Our case study indicates that to complete preparation of a permit application requires (1) improved lithologic characterization information (thicknesses and horizontal and vertical connectivity) and (2) better definition of poorly defined local faults. © 2023 The Authors. Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons Ltd.

58 GEOSCIENCES↗
DOE OSTI2025

CaloChallenge 2022: a community challenge for fast calorimeter simulation

Here, we present the results of the ‘Fast Calorimeter Simulation Challenge 2022’—the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few tens of thousand voxels. The 31 individual submissions span a wide range of current popular generative architectures, including variational autoencoders (VAEs), generative adversarial networks (GANs), normalizing flows, diffusion models, and models based on conditional flow matching. We compare all submissions in terms of quality of generated calorimeter showers, as well as shower generation time and model size. To assess the quality we use a broad range of different metrics including differences in one-dimensional histograms of observables, KPD/FPD scores, AUCs of binary classifiers, and the log-posterior of a multiclass classifier. The results of the CaloChallenge provide the most complete and comprehensive survey of cutting-edge approaches to calorimeter fast simulation to date. In addition, our work provides a uniquely detailed perspective on the important problem of how to evaluate generative models. As such, the results presented here should be applicable for other domains that use generative AI and require fast and faithful generation of samples in a large phase space.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2012

Slow Magnetosonic Waves and Fast Flows in Active Region Loops

Recent extreme ultraviolet spectroscopic observations indicate that slow magnetosonic waves are present in active region (AR) loops. Some of the spectral data were also interpreted as evidence of fast (approx 100-300 km/s) quasiperiodic flows. We have performed three-dimensional magnetohydrodynamic (3D MHD) modeling of a bipolar AR that contains impulsively generated waves and flows in coronal loops. The model AR is initiated with a dipole magnetic field and gravitationally stratified density, with an upflow-driven steadily or periodically in localized regions at the footpoints of magnetic loops. The resulting flows along the magnetic field lines of the AR produce higher density loops compared to the surrounding plasma by injection of material into the flux tubes and the establishment of siphon flow.We find that the impulsive onset of flows with subsonic speeds result in the excitation of damped slow magnetosonic waves that propagate along the loops and coupled nonlinearly driven fast-mode waves. The phase speed of the slow magnetosonic waves is close to the coronal sound speed. When the amplitude of the driving pulses is increased we find that slow shock-like wave trains are produced. When the upflows are driven periodically, undamped oscillations are produced with periods determined by the periodicity of the upflows. Based on the results of the 3D MHD model we suggest that the observed slow magnetosonic waves and persistent upflows may be produced by the same impulsive events at the bases of ARs.

Magnetohydrodynamics-Sun Activity↗
DOE OSTI2022

The channel-source hypothesis: Empirical evidence for in-channel sourcing of dissolved organic carbon to explain hysteresis in a headwater mountain stream

Catchment hydrologists have long puzzled over the question: How can catchments rapidly generate storm flows and pulses of solutes in response to storm events? Conceptual models viewing catchments as composed of discrete source areas generating flow at unique time scales and with unique chemical characteristics have been used to explain the observed changes in flow and water chemistry. Surprisingly, those conceptual models usually do not treat the stream channel as one of the potential source areas. We propose the channel-source hypothesis in which the stream itself should be considered as a potential source with the same rigour as other contributing areas. We pose this in the spirit of the scientific use of the word: a hypothesis is not a proven idea but rather a provisional supposition serving as the basis for further study. We suggest that the channel should be considered as a potential source for dissolved organic carbon (DOC). Channels store substantial amounts of organic matter, and stream ecologists have long studied stream carbon cycling. From those studies, we know that leaching and decomposition can generate DOC from particulate organic carbon (POC). Further, POC is stored in channel ‘dead-zones’—regions of low flow velocity - that can be activated as flow velocity increases, thus releasing accumulated DOC during storms. All catchments are different; there is no reason to assume that channel sources are always important, in every catchment, in every storm. Thus, the channel-source hypothesis does not replace existing conceptual models. Instead, it adds another potential mechanism that may explain DOC dynamics observed in streams. The channel-source hypothesis has substantial implications for catchment studies examining sources of DOC in stream water or using DOC as a tracer to determine the locations of, and proportional contributions of, different source areas for streamflow generation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Learning how structures form in drift-wave turbulence

Drift-wave turbulence produces anomalous transport via cross-correlations between fluctuations. This transport has profound implications for confinement, structure formation, and virtually all aspects of the non-linear turbulent dynamics. Herein, we use a data-driven method based on deep learning in order to study turbulent transport in the 2D Hasegawa–Wakatani system and infer a reduced mean-field model from numerical solution. In addition to the usual turbulent diffusion, we find an effect which couples the particle flux to the local gradient of vorticity, which tends to modulate the density profile. The direct coupling to the shear is relatively weak. In addition, the deep learning method finds a model for spontaneous zonal flow generation by negative viscosity, stabilized by non-linear and hyperviscous terms. We compare these results to analytic calculations using quasilinear theory and wave kinetics, finding qualitative agreement, though the calculations miss certain higher-order effects. A simplified, 1-D model for the evolution of the profile, flow, and intensity based on the deep learning results is solved numerically and compared to previous models for staircasing based on bistability. We see that the physics uncovered by the deep learning method provided simple explanations for the formation of zonal structures in the density, flow, and turbulence fields. We highlight the important role of symmetry in the deep learning method and speculate on the portability of the method to other applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Energy Prediction of the Chicago Metropolitan Area Using Distributed Transportation MBSE Framework

Energy consumption impact of vehicle powertrain is traditionally assessed using standard and regulatory drive cycles. In this paper, energy consumption is determined by studying all the trips occurring in the Chicago Metropolitan Area in a 24 hour period. The data is generated by combining Polaris, an agent based traffic flow model with SVTrip, a stochastic model that generates 1 Hz vehicle speed profiles and Autonomie, a detailed vehicle level modeling tool used to predict fuel consumption. Different scenarios are constructed to define trips for different timeframes and assumptions. Vehicle fleet distribution, vehicle technologies and automation levels are applied to represent an appropriate vehicle fleet for each scenario. Analysis of energy consumption is then discussed with a particular emphasis on the impact of powertrain technology and vehicle types.

Autonomie↗
DOE OSTI2025

A comparison of probabilistic generative frameworks for molecular simulations

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. Here, in this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) neural spline flows do best at capturing mode asymmetry present in low-dimensional data, (ii) conditional flow matching outperforms other models for high-dimensional data with low complexity, and (iii) denoising diffusion probabilistic models appear the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.

Artificial intelligence↗
NASA NTRS1995

Evaluation of grid generation technologies from an applied perspective

An analysis of the grid generation process from the point of view of an applied CFD engineer is given. Issues addressed include geometric modeling, structured grid generation, unstructured grid generation, hybrid grid generation and use of virtual parts libraries in large parametric analysis projects. The analysis is geared towards comparing the effective turn around time for specific grid generation and CFD projects. The conclusion was made that a single grid generation methodology is not universally suited for all CFD applications due to both limitations in grid generation and flow solver technology. A new geometric modeling and grid generation tool, CFD-GEOM, is introduced to effectively integrate the geometric modeling process to the various grid generation methodologies including structured, unstructured, and hybrid procedures. The full integration of the geometric modeling and grid generation allows implementation of extremely efficient updating procedures, a necessary requirement for large parametric analysis projects. The concept of using virtual parts libraries in conjunction with hybrid grids for large parametric analysis projects is also introduced to improve the efficiency of the applied CFD engineer.

Hufford, Gary S.↗
DOE OSTI2026

Conditional deep generative models for simultaneous simulation and reconstruction of entire events

We extend the particle-flow neural assisted simulations (arnassus) framework of fast simulation and reconstruction to entire collider events. In particular, we use two generative artificial intelligence tools, continuous normalizing flows and diffusion models, to create a set of reconstructed particle-flow objects conditioned on truth-level particles from CMS Open Simulations. While previous work focused on jets, our updated methods now can accommodate all particle-flow objects in an event along with particle-level attributes like particle type and production vertex coordinates. This approach is fully automated, entirely written in Python, and GPU-compatible. Using a variety of physics processes at the LHC, we show that the extended arnassus is able to generalize beyond the training dataset and outperforms the standard, public tool elphes.

Dreyer, Etienne [Weizmann Institute of Science, Re↗
NASA NTRS2016

Numerical Simulation of a Complete Low-Speed Wind Tunnel Circuit

A numerical simulation of the complete circuit of the NASA Langley 14 x 22-ft low-speed wind tunnel is described. Inside the circuit, all turning vanes are modeled as well as the five flow control vanes downstream of the 1st corner. The fan drive system is modeled using an actuator disk for the fan blades coupled with the fan nacelle. All the surfaces are modeled as viscous walls except the turning vanes, which were modeled as inviscid surfaces. NASA Langley's TetrUSS unstructured grid software was used for grid generation and flow simulation. Two turbulence models were employed in the present study, namely, the one-equation Spalart-Allmaras model and the shear stress transport (SST) model of Menter. The paper shows the flow characteristics in the circuit and compares the results with experimental data where available.

Nayani, Sudheer N.↗
DOE OSTI2024

Convolutional L2LFlows: generating accurate showers in highly granular calorimeters using convolutional normalizing flows

Abstract In the quest to build generative surrogate models as computationally efficient alternatives to rule-based simulations, the quality of the generated samples remains a crucial frontier. So far, normalizing flows have been among the models with the best fidelity. However, as the latent space in such models is required to have the same dimensionality as the data space, scaling up normalizing flows to high dimensional datasets is not straightforward. The prior L2LFlows approach successfully used a series of separate normalizing flows and sequence of conditioning steps to circumvent this problem. In this work, we extend L2LFlows to simulate showers with a 9-times larger profile in the lateral direction. To achieve this, we introduce convolutional layers and U-Net-type connections, move from masked autoregressive flows to coupling layers, and demonstrate the successful modelling of showers in the ILD Electromagnetic Calorimeter as well as Dataset 3 from the public CaloChallenge dataset.

Instruments & Instrumentation↗
NASA NTRS1991

Computational Fluid Dynamics Symposium on Aeropropulsion

Recognizing the considerable advances that have been made in computational fluid dynamics, the Internal Fluid Mechanics Division of NASA Lewis Research Center sponsored this symposium with the objective of providing a forum for exchanging information regarding recent developments in numerical methods, physical and chemical modeling, and applications. This conference publication is a compilation of 4 invited and 34 contributed papers presented in six sessions: algorithms one and two, turbomachinery, turbulence, components application, and combustors. Topics include numerical methods, grid generation, chemically reacting flows, turbulence modeling, inlets, nozzles, and unsteady flows.

Source record↗
NASA NTRS2015

Effect of Notches on Creep-Fatigue Behavior of a P/M Nickel-Based Superalloy

A study was performed to determine and model the effect of high temperature dwells on notched low cycle fatigue (NLCF) and notch stress rupture behavior of a fine grain LSHR powder metallurgy (PM) nickel-based superalloy. It was shown that a 90 second dwell applied at the minimum stress (min dwell) was considerably more detrimental to the NLCF lives than similar dwell applied at the maximum stress (max dwell). The short min dwell NLCF lives were shown to be caused by growth of small oxide blisters which caused preferential cracking when coupled with high concentrated notch root stresses. The cyclic max dwell notch tests failed mostly by a creep accumulation, not by fatigue, with the crack origin shifting internally to a substantial distance away from the notch root. The classical von Mises plastic flow model was unable to match the experimental results while the hydrostatic stress profile generated using the Drucker-Prager plasticity flow model was consistent with the experimental findings. The max dwell NLCF and notch stress rupture tests exhibited substantial creep notch strengthening. The triaxial Bridgman effective stress parameter was able to account for the notch strengthening by collapsing the notched and uniform gage geometry test data into a singular grouping.

Dwell Notch Fatigue↗
NASA NTRS2010

Reduced Order Modeling Incompressible Flows

The details: a) Need stable numerical methods; b) Round off error can be considerable; c) Not convinced modes are correct for incompressible flow. Nonetheless, can derive compact and accurate reduced-order models. Can be used to generate actuator models or full flow-field models

Helenbrook, B. T.↗
DOE OSTI2022

1D Modeling for the Development of a Digital Twin for Hydropower Systems with Applications for a Testing Rig

Modeling and simulation constitute two important parts in constructing a sensible digital twin to mimic the dynamics of hydropower systems. Because of the physical nature of a hydropower system, the basic modeling should cover the water flow systems from the reservoir to the penstock (inlet water pipes), penstock to hydro turbine, and hydro turbine to generator and from the linkage of the hydropower systems to the grid. This report describes initial attempts to model these dynamic components, including the formulation of the linearized state space model for hydro turbine systems using the well-known six-coefficients linearization method and the formulation of the voltage and power dynamical models of a synchronous generator. To validate the accuracy of the proposed model, data collected from Norwegian University of Science and Technology were used to obtain relevant parameters for the model, and the desired simulation results were obtained. Additionally, neural network modeling and learning were also developed and applied to model the generation torque and water flow rate subsystems to demonstrate a potential learning scheme, which can be used in the development of a digital twin.

13 HYDRO ENERGY↗
DOE OSTI2021

GOTHIC Aerosol Source Depletion Studies

Pacific Northwest National Laboratory has continued work to develop an aerosol-laden flow modeling capability with the Generation of Thermal Hydraulic Information in Containment (GOTHIC TM ) computer code to perform simulations for thermal hydraulic conditions and aerosol transport and deposition in spent fuel casks. This report describes our recent work to expand our model that was originally developed in 2019 to allow for thermal characterization, carrier gas flow determination, and tracking of particulate behavior throughout the entire canister volume. This was achieved through conversion of decay heat source in all fuel tubes within the model from heaters to thermal conductors in GOTHIC as well as remeshing the internal volume of the canister. These model improvements allowed for simulations of base case scenarios for comparison of similar efforts by our collaborators with alternative tools at Sandia and Oak Ridge. Several characteristics of the GOTHIC code were elucidated by this effort and were documented for consideration in how the code is used for this effort moving forward. For instance, treatment of particle size distributions, initial spatial distributions, mesh gradients, and minimum volume concentrations were identified as important issues for consideration. Ultimately, the current version of the code is capable of tracking temperatures, flow rates, and particle behavior throughout the canister internal volume. This report presents predicted depletion times and preferred deposition patterns within the canister. Additionally, this report documents code performance and insights for further model development and use for comparisons to other codes and for predicting system behavior in experiments being performed and planned by our collaborators.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1972

The role of fluctuating forces in the generation of compressor noise

The results of a theoretical and experimental study on the role of aerodynamically-induced fluctuating forces in the sound generation by axial-flow compressors are presented. Analytical models for the generation and radiation of sound by rotor/stator combinations were developed. For the experimental substantiation of the analytical results, the technology was developed to measure fluctuating forces directly on rotating airfoils using miniature differential-pressure sensors and FM telemetry. Under the assumption of full coherence of the force field on the blade, radiated sound power was predicted from the force measurements on blades and compared with measured sound power. Both broadband radiation from a single rotating airfoil and discrete frequency radiation due to interaction of multi-bladed stator/rotor configurations were investigated. The results indicate the necessity to obtain information on the details of the force field for accurate prediction of the radiated sound spectrum.

Heller, H. H.↗