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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A physics-constrained neural network for multiphase flows

The present study develops a physics-constrained neural network (PCNN) to predict sequential patterns and motions of multiphase flows (MPFs), which includes strong interactions among various fluid phases. To predict the order parameters, which locate individual phases in the future time, a neural network (NN) is applied to quickly infer the dynamics of the phases by encoding observations. The multiphase consistent and conservative boundedness mapping algorithm (MCBOM) is next implemented to correct the predicted order parameters. This enforces the predicted order parameters to strictly satisfy the mass conservation, the summation of the volume fractions of the phases to be unity, the consistency of reduction, and the boundedness of the order parameters. Then, the density of the fluid mixture is updated from the corrected order parameters. Finally, the velocity in the future time is predicted by another NN with the same network structure, but the conservation of momentum is included in the loss function to shrink the parameter space. The proposed PCNN for MPFs sequentially performs (NN)-(MCBOM)-(NN), which avoids nonphysical behaviors of the order parameters, accelerates the convergence, and requires fewer data to make predictions. Numerical experiments demonstrate that the proposed PCNN is capable of predicting MPFs effectively.

Mechanics↗
DOE OSTI2021

Investigation of turbulent inflow specification in Euler–Lagrange simulations of mid-field spray

The process of atomization of a liquid jet by a parallel high-speed gas stream results in a spray, whose downstream development is of considerable interest to several applications. The round jet spray can be spatially divided into (i) a near-field (near-nozzle) region of liquid atomization and (ii) a downstream mid-field region of fully-dispersed droplets. In order to accurately model mid-field droplet dispersion, this work aims at developing a rigorous and robust injection model for Euler–Lagrange spray simulations. Results from experiments are used to obtain the relevant droplet number density, size distribution, and mean and standard deviation velocity distributions of the injection model, systematically in a step-by-step process. Two-phase large eddy simulations are performed by stochastically generating the Lagrangian droplets at the inlet of the mid-field region. Number flux, diameter distribution, mean velocity, and other time-averaged statistics at several downstream locations are shown to agree well with the corresponding experimental data.

42 ENGINEERING↗
NASA NTRS2003

Fluid Physics of Foam Evolution and Flow

The grant supported theoretical, numerical and experimental work focused on the elucidation of the fluid physics of foam structure, evolution and flow. The experimental work concentrated on these subject areas: (a) Measurements of the speed of reconnections within a foam; (b) statistics of bubble rearrangements; and (c) three-dimensional reconstruction of the foam structure. On the numerical simulation and theory side our efforts concentrated on the subjects: (a) simulation techniques for 2D and 3D foams; (b) phase transition in a compressible foam; and (c) TCP structures.

Aref, H.↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2015

Feasibility of flow studies at NICA/MPD

In the light of recent developments in heavy ion physic, anisotropic flow measurements play a key role in a better understanding of the hot and dense barionic matter. In the presented article a short introduction to the proposed NICA/MPD project is given, as well as a brief description of the event-plane method used to estimate the elliptic flow of reconstructed and identified hadrons (p, π, Λ)

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Physical mechanisms for multiphase flow associated with hydrate formation

Many Arctic hydrate reservoirs such as those of the Prudhoe Bay and Kuparuk River area on the Alaska North Slope (ANS) are believed originally to be natural gas accumulations converted to hydrate accumulations after being placed in the gas hydrate stability zone (GHSZ) in response to ancient climate cooling. In this paper, the implications of a previously described mechanistic model for the transport of gaseous and aqueous phases are studied using a transient 1-D transport model during the conversion of a gas reservoir to a hydrate reservoir. The mechanistic model predicts/explains the vertical profile of hydrate saturation in “converted free gas” hydrate reservoirs. The initial gas phase saturation with depth is estimated from the profile of capillary entry pressure, which is estimated from grain size distributions measured in cores. The gas accumulation is assumed to be disconnected from its original source so that methane transport occurs only within it. As the base of the GHSZ descends through the sediment, hydrate forms within the GHSZ. The net volume reduction associated with hydrate formation creates a “sink” which drives flow of gaseous and aqueous phases to the hydrate formation zone. Mechanisms by which this fluid movement could have occurred are analyzed. Flow driven by saturation gradients plays a key role in creating reservoirs of large hydrate saturations, as observed in Mount Elbert stratigraphic test well in the Milne Point Unit of Alaska North Slope (ANS). Viscous-dominated pressure-driven flow of gaseous and aqueous phases cannot explain large hydrate saturations originated from large-saturation gas accumulations. The mode of hydrate formation for a wide range of rate of hydrate formation, the rate of descent of the base of GHSZ, and host sediment characteristics are analyzed and characterized based on dimensionless groups. The proposed transport model is also consistent with field data from hydrate-bearing sand units in Mount Elbert well. Finally, results show that not only the petrophysical properties of the host sediment but also the rate of hydrate formation and the rate of temperature cooling at the surface contribute greatly to the final hydrate saturation profiles.

58 GEOSCIENCES↗
DOE OSTI2023

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

The physical basis for solvent flow in organic solvent nanofiltration

Organic solvent nanofiltration (OSN) is an emerging membrane technology that could revolutionize chemical separations in numerous vital industries. Despite its significance, there remains a lack of fundamental understanding of solvent transport mechanisms in OSN membranes. Here, we use an extended Flory-Rehner theory, nonequilibrium molecular dynamic simulations, and organic solvent transport experiments to demonstrate that solvent flow in OSN membranes is driven by a pressure gradient. We show that solvent molecules migrate as clusters through interconnected pathways within the membrane pore structure, challenging the widely accepted diffusion-based view of solvent transport in OSN. We further reveal that solvent permeance is dependent on solvent affinity to the OSN membrane, which, in turn, controls the membrane pore structure. Our fundamental insights lay the scientific groundwork for the development of next-generation OSN membranes.

42 ENGINEERING↗
NASA NTRS1983

Symposium on Numerical and Physical Aspects of Aerodynamic Flows, 2nd, California State University, Long Beach, CA, January 17-20, 1983, Proceedings

The present conference covers topics concerning the measurement and calculation of interactive flows, together with problems posed by subsonic and transonic wings, missiles, and ships. Discussions are presented on the time-dependent finite difference simulation of unsteady interactive flows, Navier-Stokes equation methods, numerical solutions for spatially periodic boundary layers, the application of unsteady laminar tripple deck theory to viscous-inviscid interaction, the coupling of boundary layer and Euler equation solutions, and viscous-inviscid flow interactions. Also discussed are leading and trailing edge flows, three-dimensional wing flows, small disturbance calculations including entropy corrections, an inviscid computational method for tactical missiles, and boundary layer and flow separation characteristics of bodies of revolution at incidence.

Source record↗
NASA NTRS1985

Symposium on Numerical and Physical Aspects of Aerodynamic Flows, 3rd, California State University, Long Beach, CA, January 21-24, 1985, Proceedings

Among the topics discussed are hydrodynamic stability and turbulent transition, separated flow simulations employing finite difference methods, a quasi-simultaneous finite difference approach for strongly interacting flow, the Newton solution of coupled Euler and boundary layer equations, multielement airfoil aerodynamics, vorticity dynamics, the significance of the thin layer Navier-Stokes approximation, a comparison of interactive and Navier-Stokes calculations of separating boundary layer flows, and a Navier-Stokes analysis for the mechanism of shock wave/boundary layer interactions. Also discussed are transonic interactions of unsteady vortical flows, boundary layers on oscillating airfoils, a laser-interferometer skin friction meter, flow over surface-mounted semibluff bodies, computations for spinning and spin-stabilized supersonic projectiles, transition calculations in three-dimensional flows, the effect of projectile boattailing in transonic flow, flow characteristics of a body of revolution at incidence, the influence of the boundary layer on the leading edge flow of a swept wing, and an interactive scheme for three-dimensional transonic flows.

Source record↗
NASA NTRS1986

Numerical and physical aspects of aerodynamic flows III; Proceedings of the Third Symposium, California State University, Long Beach, CA, January 21-24, 1985

The present symposium considers hydrodynamic stability and turbulent transition, transition calculations in three-dimensional flows, a quasi-simultaneous finite difference approach for strongly interacting flows, the significance of the thin layer Navier-Stokes approximation, unsteady airfoil boundary layers, predictions and experiments on airfoils at low Reynolds numbers, and a comparison of interactive boundary layer and thin layer Navier-Stokes procedures. Also discussed are a viscous-inviscid interaction method for computing unsteady transonic separation, massive separation and dynamic stall on a cusped trailing edge airfoil, the computation of turbulent separated flows over wings, an iterative scheme for three-dimensional transonic flows, and the computation of three-dimensional flows with shock wave/boundary layer interaction. Many of the papers in this symposium were abstracted previously (cf., A85-42951).

Cebeci, T.↗