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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Numerical Modeling of Conjugate Heat Transfer in Fluid Network

Fluid network modeling with conjugate heat transfer has many applications in Aerospace engineering. In modeling unsteady flow with heat transfer, it is important to know the variation of wall temperature in time and space to calculate heat transfer between solid to fluid. Since wall temperature is a function of flow, a coupled analysis of temperature of solid and fluid is necessary. In cryogenic applications, modeling of conjugate heat transfer is of great importance to correctly predict boil-off rate in propellant tanks and chill down of transfer lines. In TFAWS 2003, the present author delivered a paper to describe a general-purpose computer program, GFSSP (Generalized Fluid System Simulation Program). GFSSP calculates flow distribution in complex flow circuit for compressible/incompressible, with or without heat transfer or phase change in all real fluids or mixtures. The flow circuit constitutes of fluid nodes and branches. The mass, energy and specie conservation equations are solved at the nodes where as momentum conservation equations are solved at the branches. The proposed paper describes the extension of GFSSP to model conjugate heat transfer. The network also includes solid nodes and conductors in addition to fluid nodes and branches. The energy conservation equations for solid nodes solves to determine the temperatures of the solid nodes simultaneously with all conservation equations governing fluid flow. The numerical scheme accounts for conduction, convection and radiation heat transfer. The paper will also describe the applications of the code to predict chill down of cryogenic transfer line and boil-off rate of cryogenic propellant storage tank.

Majumdar, Alok↗
DOE OSTI2018

Distribution Optimal Power Flow With Real-Time Price Elasticity

This letter investigates a new class of optimal power flow problem for distribution systems, where elastic loads respond to real-time nodal prices by adjusting their demands. A fixed point iteration algorithm is suggested to identify an equilibrium. A concise criterion is devised to judge convergence.

demand response↗
DOE OSTI2022

Adaptive Urban Traffic Signal Control for Multiple Intersections: An LQR Approach

Traffic congestion leads to severe problems especially in urban traffic networks. It increases the chance of accidents, energy waste, and social costs. In order to address these problems, an adaptive linear quadratic regulator (LQR) approach is developed for traffic signal control at multiple intersections in an urban area. The proposed method controls the green time of the traffic signals to reduce traffic congestion and smooth traffic flow. Real-world data from vision-based traffic sensors are used to build the traffic network model, which mimics the real-world traffic behavior. In addition, the proposed control utilizes recursive least square parameter estimation, which is capable of tracking dynamic changes in traffic conditions. Simulation of Urban MObility (SUMO) is used to analyze the efficacy of the proposed method. Results of the simulation show that the proposed method outperforms pretimed control in various aspects.

adaptive LQR control↗
NASA NTRS1993

Early Mars: The inextricable link between internal and external influences on valley network formation

The conditions under which the valley networks on the ancient cratered terrain on Mars formed are still highly debated within the scientific community. While liquid water was almost certainly involved, the exact mechanism of formation is uncertain. The networks most resemble terrestrial sapping channels, although some systems exhibit a runoff-dominated morphology. The major question in the formation of these networks is what, if anything, do they imply about early Martian climate? There are typically two major theories advanced to explain the presence of these networks. The first is that higher internal regolith temperatures, associated with a much higher heat flow 3.8 b.y. ago, would cause ground water to be closer to the surface than at present. Just how close to the surface ground water would have to exist in order to form these valley networks has recently been questioned. The second major theory is that early Mars had a much thicker atmosphere than at present, and an enhanced atmospheric greenhouse may have increased surface temperatures to near the freezing point of water. While recent calculations indicate that CO2 alone could not have produced the needed warming, the presence of other greenhouse gases may have contributed to surface warming.

Postawko, S. E.↗
NASA NTRS1996

Observing large-scale solar surface flows with GONG: Investigation of a key element in solar activity buildup

The Global Oscillation Network Group (GONG) solar telescope network has begun regular operations, and will provide continuous Doppler images of large-scale nearly-steady motions at the solar surface, primarily those due to supergranulation. Not only the Sun's well-known magnetic network, but also flux diffusion, dispersal, and concentration at the surface appear to be controlled by supergranulation. Through such magnetoconvective interactions, magnetic stresses develop, leading to solar activity. We show a Doppler movie made from a 45.5 hr time series obtained 1995 May 9-10 using data from three of the six GONG sites (Learmonth, Tenerife, Tucson), to demonstrate the capability of this system.

Beck, John G.↗
DOE OSTI2020

Voltage positioning using co-optimization of controllable grid assets in radial networks

With increasing penetration of solar PV, some distribution feeders are experiencing highly variable net-load flows and even reverse flows. To optimize distribution systems under such conditions, the scheduling of mechanical devices, such as OLTCs and capacitor banks, needs to take into account forecasted solar PV and actual grid conditions. However, these legacy switching assets are operated on a daily or hourly timescale, due to the wear and tear associated with mechanical switching, which makes them unsuitable for real-time control. Therefore, there is a natural timescale- separation between these slower mechanical assets and the responsive nature of inverter-based resources. In this paper, we present a network admissible convex formulation for holistically scheduling controllable grid assets to position voltage optimally against solar PV. An optimal hourly schedule is presented that utilizes mechanical resources to position the predicted voltages close to nominal values, while minimizing the use of inverter-based resources (i.e., DERs), making them available for control at a faster time-scale (after the uncertainty reveals itself). A convex, inner approximation of the OPF problem is adapted to a mixed-integer linear program that minimizes voltage deviations from nominal (i.e., maximizes voltage margins). Here, the resulting OPF solution respects all the network constraints and is, hence, robust against modeling simplifications. Simulation based analysis on IEEE distribution feeders validates the approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Connecting Pressure-Saturation and Relative Permeability Models to Fracture Properties: The Case of Capillary-Dominated Flow of Supercritical CO 2 and Brine

Fractures are potential pathways for subsurface fluids. In the context of geologic CO 2 sequestration, fractures could threaten the security of reservoirs since they are pathways for leakage. However, the constitutive equations describing the pressure-saturation and relative permeability-saturation, key inputs for flow modeling and prediction, are poorly understood for two-phase fracture flow at the field scale where each fracture might be an element of a complex fracture network or dual-porosity domain. This knowledge is required for the safe storage of CO 2 under potentially fractured caprocks. To address this, we numerically simulated two-phase viscous flow through horizontal heterogeneous fractures across a broad range of roughness and aperture correlation length. The numerical experiments, which solved the modified local cubic law, showed a weak to strong phase interference during the drainage process; that is, supercritical CO 2 displaces brine if the imposed pressure + local viscous force > local capillary force. The drainage process ended when no new brine cells can be further invaded by CO 2 . Afterward, we froze the saturation field for each individual phase and calculated the relative permeability by single-phase flow modeling. Thousands of simulated pressure-saturation and relative permeability curves enabled us to estimate and connect the parameters of the Van-Genuchten model and of the generalized ν-type model to fracture roughness and aperture correlation length. In conclusion, this empirical connection will be useful for assessing and predicting immiscible two-phase flow in horizontal rough fractures at the large scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A DLM/FD/IB method for simulating compound vesicle motion under creeping flow condition

In this article we present first a new distributed Lagrange multiplier/fictitious domain (DLM/FD) method for simulating fluid–particle interaction in Stokes flow. A conjugate gradient method driven by both pressure and distributed Lagrange multiplier, called one-shot method, has been developed to solve the discrete Stokes problem while enforcing the rigid body motion within the region occupied by the particle. The methodology is validated by comparing the numerical results of a neutrally buoyant particle of either a circular or elliptic shape with the associated Jeffery's solutions. We have successively combined the above methodology with an immersed boundary (IB) method and an elastic membrane modeled by a spring network to simulate the dynamics of a compound vesicle. In simple shear flow under creeping flow condition, the results are consistent with those obtained in literature. In Poiseuille flow, the compound vesicle motion is dominated by the motion of the vesicle membrane as expected and stays in the central region of the channel.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

A practical model for fluid flow in discrete-fracture porous media by using the numerical manifold method

Here in this study, a numerical manifold method (NMM) model is developed to analyze flow in porous media with discrete fractures in a non-conforming mesh. This new model is based on a two-cover-mesh system with a uniform triangular mathematical mesh and boundary/fracture-divided physical covers, where local independent cover functions are defined. The overlapping parts of the physical covers are elements where the global approximation is defined by the weighted average of the physical cover functions. The mesh is generated by a tree-cutting algorithm. A new model that does not introduce additional degrees of freedom (DOF) for fractures was developed for fluid flow in fractures. The fracture surfaces that belong to different physical covers are used to represent fracture flow in the direction of the fractures. In the direction normal to the fractures, the fracture surfaces are regarded as Dirichlet boundaries to exchange fluxes with the rock matrix. Furthermore, fractures that intersect with Dirichlet or Neumann boundaries are considered. Simulation examples are designed to verify the efficiency of the tree-cutting algorithm, the calculation's independency from the mesh orientation, and accuracy when modeling porous media that contain fractures with multiple intersections and different orientations. The simulation results show good agreement with available analytical solutions. Finally, the model is applied to cases that involve nine intersecting fractures and a complex network of 100 fractures, both of which achieve reasonable results. The new model is very practical for modeling flow in fractured porous media, even for a geometrically complex fracture network with large hydraulic conductivity contrasts between fractures and the matrix.

58 GEOSCIENCES↗
DOE OSTI2021

Recovering the effects of subgrid heterogeneity in simulations of radionuclide transport through fractured media

Groundwater flow and contaminant transport through fractured media can be simulated using Discrete Fracture Network (DFN) models which provide a natural description of structural heterogeneity. However, this approach is computationally expensive, with the large number of intersecting fractures necessitated by many real-world applications requiring modeling simplifications to be made for calculations to be tractable. Upscaling methods commonly used for this purpose can result in some loss of local-scale variability in the groundwater flow velocity field, resulting in underestimation of particle travel times, transport resistance and retention in transport calculations. In this paper, a transport downscaling algorithm to recover the transport effects of heterogeneity is tested on a synthetic Brittle Fault Zone model, motivated by the problem of large safety assessment calculations for geological repositories of spent nuclear fuel. We show that the variability in the local-scale velocity field which is lost by upscaling can be recovered by sampling from a library of DFN transport paths, accurately reproducing DFN transport statistic distributions and radionuclide breakthrough curves in an upscaled model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Distributary Channel Networks as Moving Boundaries: Causes and Morphodynamic Effects

In this study, we propose an exploratory model to describe the morphodynamics of distributary channel network growth on river deltas. The interface between deep channels and the shallow, unchannelized delta front deposits is modeled as a moving boundary. Steady flow over the unchannelized delta front is friction dominated and modeled by Laplace's equation. Shear stress along the network boundary produces nonlinear erosion rates at the interface, causing the boundary to move and network elements (channels and branches) to form. The model was run for boundary conditions resembling the Wax Lake Delta in coastal Louisiana, 20 parameterizations of sediment transport, and 3 parameterizations of discharge. In each case, the model produced a complex channel network with channel number, width, bifurcation angle, and channel shape depending on the sediment transport formula. For reasonable sediment transport parameters and gradually increasing water discharge, the model produced network characteristics and progradation rates similar to theWax Lake Delta. Lastly, this suggests that the model contains the processes responsible for network growth, despite its abstract formulation.

58 GEOSCIENCES↗
DOE OSTI2018

Delta size and plant patchiness as controls on channel network organization in experimental deltas

Understanding the feedbacks between water, sediment, and vegetation in deltas is an important part of understanding deltas as ecomorphodynamic systems. Here, we conducted a set of laboratory experiments using alfalfa (Medicago sativa) as a proxy for delta vegetation to investigate (1) the effects of plants on delta growth and channel network formation and (2) the timescales controlling delta evolution in the presence of plants. Experiments were conducted with fluctuating discharge (i.e. flood and base flow periods) and variable seeding densities. We found that when deltas were small, channels had no memory across flood cycles, as floods could completely fill the incised channel network. When deltas were large, the larger channel volume could remain underfilled to keep channel memory. Plant patches also helped to increase the number of channels and make a more distributive network. Patchiness increased over time to continually aid in bifurcation, but as vegetation cover and patch sizes increased, patches began to merge. Larger patches blocked the flow to enhance topset deposition and channel filling, even for the case of large deltas with a high channel volume. We conclude that both plant patchiness and delta size affect the development of the channel network, and we hypothesize that their influences are manifested through two competing timescales. The first timescale, T v , defines the time when the delta is large enough for channels to have memory (i.e., remain underfilled), and the second, T p , defines the time when vegetation patches merge, amplifying deposition and blocking channels. Finally, when run time is between these two timescales, the delta can develop a persistent distributary network of channels aided by bifurcation around plant patches, but once T p is reached, the channel network can again be destroyed by vegetation.

58 GEOSCIENCES↗
DOE OSTI2015

Criticism of generally accepted fundamentals and methodologies of traffic and transportation theory

It is explained why the set of the fundamental empirical features of traffic breakdown (a transition from free flow to congested traffic) should be the empirical basis for any traffic and transportation theory that can be reliable used for control and optimization in traffic networks. It is shown that generally accepted fundamentals and methodologies of traffic and transportation theory are not consistent with the set of the fundamental empirical features of traffic breakdown at a highway bottleneck. To these fundamentals and methodologies of traffic and transportation theory belong (i) Lighthill-Whitham-Richards (LWR) theory, (ii) the General Motors (GM) model class (for example, Herman, Gazis et al. GM model, Gipps’s model, Payne’s model, Newell’s optimal velocity (OV) model, Wiedemann’s model, Bando et al. OV model, Treiber’s IDM, Krauß’s model), (iii) the understanding of highway capacity as a particular stochastic value, and (iv) principles for traffic and transportation network optimization and control (for example, Wardrop’s user equilibrium (UE) and system optimum (SO) principles). Alternatively to these generally accepted fundamentals and methodologies of traffic and transportation theory, we discuss three-phase traffic theory as the basis for traffic flow modeling as well as briefly consider the network breakdown minimization (BM) principle for the optimization of traffic and transportation networks with road bottlenecks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1972

Subsonic, transonic, and supersonic nozzle flow by the inverse technique.

The inverse technique is used to obtain a mathematically and physically consistent solution of the flowfield in a nozzle from the mass generation surface through the supersonic region. The inverse method employs an assumed centerline function which is of the Cauchy type in that the values and the derivatives of the function are known. Since the Cauchy boundary conditions can give rise to numerical instabilities, the governing gasdynamic equations for rotational steady flow were transformed into a form which puts the geometry into a rectangular shape, and which spaces the network of interior points more finely in regions of the greatest gradients of the dependent variables. For arbitrarily specified centerline data, the solution of the governing flow equations may not exist, and if it does it may not depend continuously on the data.

Norton, D. J.↗
DOE OSTI2017

Congruent Bifurcation Angles in River Delta and Tributary Channel Networks

We show that distributary channels on river deltas exhibit a mean bifurcation angle that can be understood using theory developed in tributary channel networks. In certain cases, tributary network bifurcation geometries have been demonstrated to be controlled by diffusive groundwater flow feeding incipient bifurcations, producing a characteristic angle of 72°. We measured 25 unique distributary bifurcations in an experimental delta and 197 bifurcations in 10 natural deltas, yielding a mean angle of 70.4°±2.6° (95% confidence interval) for field-scale deltas and a mean angle of 68.3°±8.7° for the experimental delta, consistent with this theoretical prediction. The bifurcation angle holds for small scales relative to channel width length scales. Furthermore, the experimental data show that the mean angle is 72° immediately after bifurcation initiation and remains relatively constant over significant time scales. Although distributary networks do not mirror tributary networks perfectly, the similar control and expression of bifurcation angles suggests that additional morphodynamic insight may be gained from further comparative study.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2023

A Review of Physics-Informed Machine Learning in Fluid Mechanics

Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to the requirement of high temporal and spatial resolution. In this review, we (i) provide an introduction and historical perspective of ML methods, in particular neural networks (NN), (ii) examine existing PIML applications to fluid mechanics problems, especially in complex high Reynolds number flows, (iii) demonstrate the utility of PIML techniques through a case study, and (iv) discuss the challenges and opportunities of developing PIML for fluid mechanics.

42 ENGINEERING↗
DOE OSTI2021

Cyber-Power Co-Simulation for End-to-End Synchrophasor Network Analysis and Applications

The resiliency, reliability and security of the next generation cyber-power smart grid depend upon efficiently leveraging advanced communication and computing technologies. Also, developing real-time data-driven applications is critical to enable wide-area monitoring and control of the cyber-power grid given high-resolution data from Phasor Measurement Units (PMUs). North American Synchrophasor Initiative Network (NASPlnet) provides guidance for PMU data exchanges. With the advancement in networking and grid operation, it is necessary to evaluate the performance of different data flow architectures suggested by NASPInet and analyze the impact on applications. Therefore, we need a cyber-power co-simulation framework that supports very large-scale co-simulation capable of running in parallel, high-performance computing platforms and capturing real-life network behavior. This work presents an end-to-end automated and user-driven cyber-power co-simulation using NS3 to model communication networks, GridPACK to model the power grid, and HELICS as a co-simulation engine. Comparative analysis of latency in synchrophasor networks and a performance evaluation of a power system stabilizer application utilizing PMU data in an IEEE 39 bus test system is presented using this cosimulation testbed.

Mustafa, Hussain M.↗