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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

In Situ Decommissioning Sensor Network, Meso-Scale Test Bed - Phase 3 Fluid Injection Test Summary Report

The DOE Office of Environmental management (DOE EM) faces the challenge of decommissioning thousands of excess nuclear facilities, many of which are highly contaminated. A number of these excess facilities are massive and robust concrete structures that are suitable for isolating the contained contamination for hundreds of years, and a permanent decommissioning end state option for these facilities is in situ decommissioning (ISD). The ISD option is feasible for a limited, but meaningful number of DOE contaminated facilities for which there is substantial incremental environmental, safety, and cost benefits versus alternate actions to demolish and excavate the entire facility and transport the rubble to a radioactive waste landfill. A general description of an ISD project encompasses an entombed facility; in some cases limited to the blow-grade portion of a facility. However, monitoring of the ISD structures is needed to demonstrate that the building retains its structural integrity and the contaminants remain entombed within the grout stabilization matrix. The DOE EM Office of Deactivation and Decommissioning and Facility Engineering (EM-13) Program Goal is to develop a monitoring system to demonstrate long-term performance of closed nuclear facilities using the ISD approach. The Savannah River National Laboratory (SRNL) has designed and implemented the In Situ Decommissioning Sensor Network, Meso-Scale Test Bed (ISDSN-MSTB) to address the feasibility of deploying a long-term monitoring system into an ISD closed nuclear facility.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1976

Economic optimization of the energy transport component of a large distributed solar power plant

A solar thermal power plant with a field of collectors, each locally heating some transport fluid, requires a pipe network system for eventual delivery of energy power generation equipment. For a given collector distribution and pipe network geometry, a technique is herein developed which manipulates basic cost information and physical data in order to design an energy transport system consistent with minimized cost constrained by a calculated technical performance. For a given transport fluid and collector conditions, the method determines the network pipe diameter and pipe thickness distribution and also insulation thickness distribution associated with minimum system cost; these relative distributions are unique. Transport losses, including pump work and heat leak, are calculated operating expenses and impact the total system cost. The minimum cost system is readily selected. The technique is demonstrated on six candidate transport fluids to emphasize which parameters dominate the system cost and to provide basic decision data. Three different power plant output sizes are evaluated in each case to determine severity of diseconomy of scale.

Turner, R. H.↗
DOE OSTI2022

Investigation of two concrete thermal energy storage system configurations for continuous power production

Two Modelica concrete thermal energy storage (CTES) models are built to analyze potential CTES system designs. One design is the single pipe network design wherein a heat transfer fluid (HTF) flows in one direction during heat deposition and the opposite direction during heat removal. All pipes in the network are used for either deposition (charging) or removal (discharging). The second design is a dual pipe network design in which a network of HTF pipes carries charging fluid and a separate piping network carries discharging HTF. Operation of these two configurations to produce constant steam is evaluated. Here, results indicate that the designs are likely appropriate for different applications with the single pipe network appearing appropriate for batch energy applications and the dual pipe network for continuous energy applications.

25 ENERGY STORAGE↗
DOE OSTI2021

Carbon dioxide capture system and method with mass transfer contactor

A carbon dioxide capture system, fluid contactor and method are disclosed. In embodiments, a gas-liquid contactor unit is disposed along a process fluid flow axis and includes a contactor network of flow diversion barriers with flow voids for movement of process fluids therebetween. A plurality of heat exchange channels are provided in the flow diversion barriers to transport a heat exchange fluid through the contactor network. A heat exchange feed channel is provided to deliver feed of the heat exchange fluid to the heat exchange channels at multiple feed locations spaced along the flow axis. At least one heat exchange bypass channel may extend beyond the multiple feed locations to deliver a portion of the feed of the heat exchange fluid to additional heat exchange channels located downstream from the multiple feed locations for the heat exchange channels.

Panaccione, Charles↗
DOE OSTI2023

Embedding hard physical constraints in neural network coarse-graining of three-dimensional turbulence

In recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences. A major challenge in deep learning of partial differential equations is enforcing physical constraints and boundary conditions. In this work, we propose a general framework to directly embed the notion of an incompressible fluid into convolutional neural networks, and apply this to coarse-graining of turbulent flow. These physics-embedded neural networks leverage interpretable strategies from numerical methods and computational fluid dynamics to enforce physical laws and boundary conditions by taking advantage the mathematical properties of the underlying equations. Here, we demonstrate results on three-dimensional fully developed turbulence, showing that this technique drastically improves local conservation of mass, without sacrificing performance according to several other metrics characterizing the fluid flow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Bioreactors including enzyme-embedded multicomponent polymers

According to one general embodiment, a membrane, includes a polymeric network configured to separate a first fluid and a second fluid, where the first and second fluids are different; and a plurality of enzymatic reactive components embedded within the polymeric network. According to another embodiment, a bioreactor includes a lattice of three dimensional structures, each structure including a membrane having: a polymeric network configured to separate a first fluid and a second fluid, where the first and second fluids are different; and a plurality of enzymatic reactive components embedded within the polymeric network.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Emergent Nonthermal Fluid from Jets in the Massive Schwinger Model Using Tensor Networks

We analyze the correlation between the energy, momentum, and spatial entanglement produced by two luminal jets in the massive Schwinger model. Using tensor network methods, we show that for m/g > 1/π, in the vicinity of the strong- to weak-coupling transition, a nearly perfect and chargeless effective fluid behavior appears around the midrapidity region with a universal energy-pressure relationship. The evolution of energy and pressure is strongly correlated with the rise of the spatial entanglement entropy, indicating a key role of quantum dynamics. Some of these observations may be used to analyze high multiplicity jet fragmentation events, energy-energy and energy-charge correlators at current collider energies.

Ab initio calculations↗
DOE OSTI2015

Modeling of Propagation of Interacting Cracks Under Hydraulic Pressure Gradient

A robust and reliable numerical model for fracture initiation and propagation, which includes the interactions among propagating fractures and the coupling between deformation, fracturing and fluid flow in fracture apertures and in the permeable rock matrix, would be an important tool for developing a better understanding of fracturing behaviors of crystalline brittle rocks driven by thermal and (or) hydraulic pressure gradients. In this paper, we present a physics-based hydraulic fracturing simulator based on coupling a quasi-static discrete element model (DEM) for deformation and fracturing with conjugate lattice network flow model for fluid flow in both fractures and porous matrix. Fracturing is represented explicitly by removing broken bonds from the network to represent microcracks. Initiation of new microfractures and growth and coalescence of the microcracks leads to the formation of macroscopic fractures when external and/or internal loads are applied. The coupled DEM-network flow model reproduces realistic growth pattern of hydraulic fractures. In particular, simulation results of perforated horizontal wellbore clearly demonstrate that elastic interactions among multiple propagating fractures, fluid viscosity, strong coupling between fluid pressure fluctuations within fractures and fracturing, and lower length scale heterogeneities, collectively lead to complicated fracturing patterns.

hydraulic fracturing↗
DOE OSTI2021

Understanding Discrete Fracture Networks Through Spectral Graph Theory

Discrete Fracture Network models (DFNs) are used to simulate fluid flow and particle transport through fracture networks in low permeability rock. Understanding these processes are essential in many subsurface applications, such as environmental restoration of contaminated fractured media, CO 2 sequestration, detection of low-level nuclear tests, and hydrocarbon extraction. Compared with other models, DFNs allow for incorporation of a wider range of network characteristics but have substantially greater computation cost. These networks can be represented with graphs, allowing the use of graph theory tools to study the networks. I used Python to simulate flow and transport on a range of DFNs and analyzed these networks using methods from network analysis and spectral graph theory. My purpose was to find ways to gain insight about flow and transport on DFNs using these graph representations, bypassing the computationally intensive meshing typically required. My work is still in progress, but I have discovered several interesting trends and patterns that I believe could be useful towards my goal. If I am able to bring these results to fruition, they will aid subsurface geologists in extracting flow and transport information about fracture networks more efficiently.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Transient thermal analysis of fluid systems

Computer program performs transient thermal analysis of any 2-node to 200-node-thermal network, which transports heat by fluid flow convection. Program can be modified to add conduction along tubes and radiation.

Chandler, G. D.↗
DOE OSTI2026

WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs With Physics‐Informed Neural Networks

Accurate representation of pumping wells is essential for reliable reservoir characterization and simulation of operational scenarios in subsurface flow models. Physics-informed neural networks (PINNs) are emerging as a promising alternative to numerical models for reservoir modeling, offering seamless integration of monitoring data and governing physical equations. However, existing PINN-based studies face major challenges in capturing fluid pressure near wells when using a source/sink term, particularly during the early stages after pumping begins. We address this problem by introducing WellPINN, a workflow in which an initially trained PINN infers fluid pressure across the entire reservoir domain using a large equivalent well radius. This initial PINN solution is then locally refined around the well by a set of subdomain PINNs that are trained for smaller equivalent well radii. Continuity across these subdomain interfaces as well as at the initial condition is ensured by hard-constraining each PINN on its subdomain boundary. Our results demonstrate WellPINN as the first workflow of its kind to focus on accurate inference of fluid pressure from pumping rates throughout the entire injection period, significantly advancing the potential of PINNs for inverse modeling and operational scenario simulations. All data and code for this paper are openly available at https://doi.org/10.20350/DIGITALCSIC/17260.

58 GEOSCIENCES↗
DOE OSTI2019

Basin-wide gradients in dolomite d26Mg and 87Sr/86Sr identify the structural centre of the Williston Basin as a source of magnesium for dolomitization of the Middle Devonian Winnipegosis Formation

Dolomitized carbonates in the Williston Basin are common below the Middle Devonian Prairie Evaporite Formation, indicating a plausible role for brine-reflux as the source of Mg for dolomitization. However, the Lower Member Winnipegosis Formation, which directly underlies the evaporite sequence, exhibits dolomite 87Sr/86Sr ratios that are higher than expected for Middle Devonian seawater, instead suggesting that the dolomitizing fluids ascended from the deep basin where present-day formation waters are known to be more radiogenic. This interpretation is supported in this study by Mg isotope data showing a basin-scale gradient of increasing d26Mg values from the deep center (–2.03‰) to the edge (–1.34‰) of the Williston Basin along the lower Member Winnipegosis bed. Dolomite favors light isotopes of Mg when it forms, thus driving dolomitizing solutions, and the produced dolomite, towards heavier isotopic compositions in the direction of fluid flow. However, dolomite is also Mg-deficient when it forms, and its initial Mg isotope composition is susceptible to alteration by fluid-rock interactions occurring later in the depositional history of the deposit. We argue that the Mg isotope gradient in the Lower Member Winnipegosis was created by an ascending flow of isotopically light Mg-bearing fluid that differentially altered an earlier formed brine-reflux dolomite, with more alteration occurring deeper in the basin and less towards to edges. Alteration of pre-existing dolomite by an Mg isotope exchange mechanism requires large supplies of Mg. This fact, and the direction of fluid flow point to the underlying crystalline basement as an important Mg source for dolomitization in the Williston Basin. We speculate that ultramafic rocks supplied the Mg when they were hydrothermally altered during an anomalous heating event that affected the Williston Basin in the Late Devonian / Early Carboniferous. Seismic pumping drove these fluids upwards through fracture networks in the crust, and eventually into the deep center of the Williston Basin along vertically-oriented down-to-the-basement faults, pressuring deep aquifers like the Winnipegosis, which resulted in fluids migrating up the stratigraphic dip of the beds towards the edges of the basin. Plotting and contouring the dolomite d26Mg data on a map of the Williston Basin reveals the pathways taken by the migrating fluids. One prominent pathway extending outwards from the center of the basin towards the northeast bears a strong resemblance to petroleum migration fairways deduced for the underlying Late Ordovician Red River Formation. This study demonstrates that Mg isotopic mapping of large dolomite bodies is a useful tool for deciphering fluid flow histories in sedimentary basins and may assist in locating migrated petroleum and Mississippi Valley Type ore deposits.

Magnesium Isotopes, Strontium Isotopes, Dolomitiza↗
NASA NTRS2017

Adaptive Time Stepping for Transient Network Flow Simulation in Rocket Propulsion Systems

Fluid and thermal transients found in rocket propulsion systems such as propellant feedline system is a complex process involving fast phases followed by slow phases. Therefore their time accurate computation requires use of short time step initially followed by the use of much larger time step. Yet there are instances that involve fast-slow-fast phases. In this paper, we present a feedback control based adaptive time stepping algorithm, and discuss its use in network flow simulation of fluid and thermal transients. The time step is automatically controlled during the simulation by monitoring changes in certain key variables and by feedback. In order to demonstrate the viability of time adaptivity for engineering problems, we applied it to simulate water hammer and cryogenic chill down in pipelines. Our comparison and validation demonstrate the accuracy and efficiency of this adaptive strategy.

Majumdar, Alok K.↗
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 OSTI2022

Fluid – Fluid Interfacial Area and Its Impact on Relative Permeability - A Pore Network Modeling Study

Abstract Relative permeability (kr) is commonly modeled as an empirical function of phase saturation. Although current empirical models can provide a good match of one or two measured relative permeabilities using saturation alone, they are unable to predict relative permeabilities well when there is hysteresis or when physical properties such as wettability change. Further, current models often result in relative permeability discontinuities that can cause convergence and accuracy problems in simulation. To overcome these problems, recent research has modeled relative permeability as a state function of both saturation (S) and phase connectivity (X). Pore network modeling (PNM) data, however, shows small differences in relative permeability for the same S-X value when approached from a different flow direction. This paper examines the impact of one additional Minkowski parameter (Mecke and Arns, 2005), the fluid-fluid interfacial area, on relative permeability to identify if that satisfactorily explains this discrepancy. We calculate the total fluid-fluid interfacial areas (IA) during two-phase (oil/water) flow in porous media using pore network modeling. The area is calculated from PNM simulations using the areas associated with corners and throats in pore elements of different shapes. The pore network is modeled after a Bentheimer sandstone, using square, triangular, and circular pore shapes. Simulations were conducted for numerous primary drainage and imbibition cycles at a constant contact angle of 0° for the wetting phase. Simultaneous measurements of capillary pressure, relative permeability, saturation, and phase connectivity are made for each displacement. Fluid-fluid interfacial area is calculated from the PNM capillary pressure, the fluid location in the pore elements, and the pore element dimensional data. The results show that differences in the relative permeability at the same (S,X) point is explained well by differences in the fluid-fluid interfacial area (IA). That is, for a larger change in IA at these intersection points, the permeability difference is greater. That difference in relative permeability approaches zero as the difference in IA approaches zero. This confirms that relative permeability can be modeled better as a unique function of S, X, and IA. The results also show that an increase in IA restricts flow decreasing the nonwetting (oil) phase permeability. This decrease is caused by an increase in the throat area fraction compared to the corner area as the total area IA increases. The wetting phase relative permeability, however, shows the inverse trend, in that its relative permeability is greater when IA becomes larger owing to a greater fraction of the total area associated with the corners. The area IA, however, impacts the nonwetting phase relative permeability more than the wetting phase relative permeability. Corner flow improves the wetting phase relative permeability because the wetting phase is continuous there. Finally, a sensitivity analysis shows that relative permeability a is more sensitive to change in S than they are for IA for the case studied implying that if only two parameters are used to model relative permeability it is better to choose S and X.

Engineering↗
NASA NTRS2017

Fluid Transient Analysis During Priming of Evacuated Line

Pressure surges are critical in the design of spacecraft propellant feed lines. The pressure transients that occur during priming of feed lines are very important in the design and analysis of liquid propulsion systems. During the start-up of the propulsion system of a spacecraft, the process of filling of an evacuated pipeline is called priming. Priming can generate severe pressure peaks due to the slam (water hammer) of the propellant against a closed thruster valve. The downstream conditions strongly affect the pressure surge. In space systems, satellites, or interplanetary probes, the propellant lines are vacuum-pumped or filled with low pressure helium or nitrogen before the launch. Before operations, these lines are primed with a vaporizing liquid, sometimes in the presence of a non-condensable gas (NCG), which produces water hammer phenomena. The objective of the current study is to use a finite volume based network flow solver (Generalized Fluid System Simulation Program, GFSSP) for the numerical simulation of Priming in (a) a straight feedline and (b) a flow network. The geometrical configurations and dimensions for the pipe and other components used for the current study are identical to experimental study of Prickett et al.

Bandyopadhyay, Alak↗
DOE OSTI2016

Optimizing Intermittent Water Supply in Urban Pipe Distribution Networks

In many urban areas of the developing world, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between free-surface and pressurized, resulting in complex dynamical features with important consequences for water suppliers and users. Here, we develop a computational model of transition, transient pipe flow in a network, accounting for a wide variety of realistic boundary conditions. We validate the model against several published data sets and demonstrate its use on a real pipe network. The model is extended to consider several optimization problems motivated by realistic scenarios. Here, we demonstrate how to infer water flow in a small pipe network from a single pressure sensor, and show how to control water inflow to minimize damaging pressure transients.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil

Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in the design of commercial air transportation vehicles. Computational fluid dynamics models of transonic flow for aerospace applications are computationally expensive to solve because of the high degrees of freedom as well as the coupled nature of the conservation laws. While these issues pose a bottleneck for the use of such models in aerospace design, computational costs can be significantly minimized by constructing special, structure-preserving surrogate models called reduced-order models. In this work, we propose a machine learning method to construct reduced-order models via deep neural networks and we demonstrate its ability to preserve accuracy with a significantly lower computational cost. In addition, our machine learning methodology is physics-informed and constrained through the utilization of an interpretable encoding by way of proper orthogonal decomposition. Application to the inviscid transonic flow past the RAE2822 airfoil under varying freestream Mach numbers and angles of attack, as well as airfoil shape parameters with a deforming mesh, shows that the proposed approach adapts to high-dimensional parameter variation well. Notably, the proposed framework precludes the knowledge of numerical operators utilized in the data generation phase, thereby demonstrating its potential utility in the fast exploration of design space for diverse engineering applications. Comparison against a projection-based nonintrusive model order reduction method demonstrates that the proposed approach produces comparable accuracy and yet is orders of magnitude computationally cheap to evaluate, despite being agnostic to the physics of the problem.

97 MATHEMATICS AND COMPUTING↗