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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Calculation of Hydraulic Conductivity for a Porous Medium with Staggered Array of Solid Grains - 20087

For a porous medium with periodic array of solid grains. the flow of an incompressible viscous fluid in the pore space is considered with an aim of calculating the permeability of the medium. The solid grains are of ellipsoidal shape. For this purpose, a unit-cell boundary-value problem (Stokes problem) is solved numerically. With the assumption of periodicity of the medium structure and the fluid properties (in fact constant), the flow field and pressure distributions are determined numerically with inertial effects ignored. By applying the theory of homogenization to the viscous flow through pore space, a microscale boundary-value problem is defined. After solving the problem, the permeability is determined by taking the micro-cell average of the velocity components, Various porous structures are chosen to allow a wide range of porosity values. The permeability increases mildly for porosity values in the lower range. But it increases sharply for porosity values in the upper range of the porosity. The permeability of the present study agrees with the Kozeny-Carman relation excellently for smaller porosity. Certain discrepancy between the calculated permeability in this study and the Kozeny-Carman relation appears and increases with porosity for larger porosity. It is believed that the discrepancy is due to the replacement of the pores by a bundle of capillaries, which is unrealistic, in the Kozeny-Carman relation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

On the effects of reactant stratification and wall curvature in non-premixed rotating detonation combustors

The optimization of non-premixed rotating detonation combustors (RDCs) requires improved understanding of the coupled effects of reactant stratification, fluid property gradients, and complex shock-wave interactions on the detonation wave structure within annular geometries. In the current work, simultaneous orthogonal views of chemiluminescence and hydroxyl planar laser-induced fluorescence (PLIF) are utilized to establish the existence of a dual-wave system characterized by leading and trailing detonation waves that are closely coupled by the local flow physics. These features are persistent over a wide range of mass flow rates and are consistent with prior observations of non-premixed rotating detonations in annular geometries. The detailed instantaneous time sequences are compared with a 3D reactive unsteady Reynolds averaged Navier-Stokes (URANS) simulation to more clearly elucidate the in-situ combustion dynamics and the sensitivity to reactant inlet conditions. It is found that the dual-wave system results from unburned reactants that survive the leading detonation wave in the injector near field and are consumed within a trailing azimuthal reflected-shock combustion (ARSC) zone. By contrast, the injector far field is characterized by rapid mixing due to a sudden drop to subsonic conditions, and the bifurcated detonation wave structure collapses into a stronger, single-wave detonation front with higher overall pressure ratio as compared with the dual-wave system. While each RDC will have different inflow, mixing, and combustion characteristics, the underlying interactions between the stratified reactants and azimuthal wave dynamics identified through the combination of advanced MHz-rate diagnostics and 3D numerical simulations have important implications for the study of detonation wave stability, mode transition, and combustion efficiency in non-premixed annular RDCs.

3D URANS↗
DOE OSTI2023

Control methods for mitigating flow oscillations in a supercritical CO 2 recompression closed Brayton cycle

A dynamic model of a 10 MWe supercritical CO 2 (sCO 2 ) recompression closed Brayton power generation cycle is used to investigate control methods for mitigating oscillations in flow conditions and power demand load, especially as the load ramp rate is increased and the load setpoint is more closely tracked. The focus is on control of the main compressor inlet temperature (MCIT) since oscillations are prompted by the strong nonlinearities in sCO 2 fluid properties near the critical point and by changes in system inventory for efficient load management. In this study, the oscillatory behavior during load ramping is first shown and then two potential control solutions for substantially reducing oscillations are given: 1) the use of the main compressor inlet guide vanes (IGV) and 2) bypassing a portion of the sCO 2 around the cooler to maintain the MCIT. These control methods reduce the effect of cycle feedback from interactions between the MCIT and load. When implementing either control method, a load setpoint ramp of 7.5%/min was maintainable while achieving closer load setpoint tracking with limited oscillations. Finally, the impact of sensor noise on the control was examined since derivative action was used in the IGV and cooler sCO 2 bypass control. Finally, while some effect was evident, signal noise was not problematic to the control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Well–based monitoring of CO 2 geological sequestration operations in saline aquifers: Critical insights into key questions

Geological carbon sequestration in saline aquifers is one of the most promising strategies to help mitigate emissions of CO 2 to the atmosphere. Significant challenges in ensuring the security of the sequestration process rest in the evolution and expansion of the CO 2 plume in the subsurface. The ability to track the movement of the injected CO 2 poses another challenge. Critical questions related to the integrity of the sequestration operations in saline aquifers relate to plume characteristics that can we monitor using well-based variables. We addressed this and related questions using an integrated modeling framework through a numerical investigation of carbon sequestration in saline aquifers during long-term and post-injection periods. This modeling paradigm incorporates the effect of structural, geological, and petrophysical characteristics. That way, we can account for critical physicochemical processes, rock-fluid interactions, and lithology dependencies. The well fluid variables investigated include fluid composition, pH, fluid density, and ion activity. We learned that fluid property analytics and diagnostics can be powerful tools to estimate the movement of CO 2 and its storage in different trapping mechanisms. These analytics can help optimize operational aspects and simplify reservoir-scale models while still reflecting the complex nature of the CO 2 interactions underground and offering insights into plume evolution.

58 GEOSCIENCES↗
DOE OSTI2023

Bridging adsorption behavior of confined CH 4 -CO 2 binary mixtures across scales

An accurate understanding of the competitive adsorption of CH 4 -CO 2 binary mixtures in nano-confined systems is critical for engineering CO 2 storage in shale gas reservoirs. Due to difficulties in making reliable experimental observations in nano-scale, atomistic simulations (ASs), such as the Grand Canonical Monte Carlo (GCMC) method, provide a viable approach to studying the adsorption behavior of confined fluids. ASs are, however, limited in the size of the compositional domain due to the high computational cost. This work proposes a framework that combines AS and the lattice Boltzmann (LB) method to bridge the physics of confined fluids across scales. The Peng–Robinson equation of state (PR-EoS) produces fugacity coefficients, which serve as input for conducting multi-component GCMC simulations. These GCMC simulations explore the competitive adsorption behavior of CH 4 -CO 2 in nano-slits at various composition, pressure, and channel-width conditions. Both components generate adsorption layers with high densities near the walls with CO 2 preferentially adsorbing compared to CH 4 on the organic walls of carbon sheets. At the mesoscale, a pseudopotential model represents the intermolecular forces in multi-component, multiple-relaxation-time LB simulations. The LB simulations are in good agreement with the GCMC results, allowing us to obtain values for tunable LB parameters. We then extend the use of LB to simulate adsorption behavior in complex networks with nano-sized channels. The phase behavior and fluid properties in the complex geometries of nano-channels differ from nano-slits and bulk systems. Furthermore, the bridging of physics from GCMC (microscale) to LB (mesoscale) via the macroscale PR-EoS connects the adsorption behavior of binary systems across scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Analysis of gas storage and transport in Eagle Ford shale using pressure pulse decay measurements with He, Kr and CO 2

Detailed characterization of gas transport and storage in low-permeability and organic-rich shales is associated with an array of challenges due to the complex morphology of the pore space, representing a broad range of pore sizes, combined with the heterogeneous fabric of the shale matrix. These factors, and their interplay during gas transport and sorption, complicate a) the analysis of shale samples at the laboratory scale (~ up to a ft. In length), and b) the prediction of natural gas production and carbon sequestration potential at larger scales. Here, in this work, tandem experiments with inert (helium – He) and adsorbing (krypton - Kr and carbon dioxide - CO 2 ) gases were performed and analyzed to develop an efficient workflow for characterizing and modeling transport and sorption at the laboratory scale. In particular, pressure pulse-decay (PPD) measurements were conducted on an Eagle Ford shale core sample at room temperature using He, Kr, and CO 2 . PPD measurements with He (a non-sorbing gas) were employed to probe the overall porosity, including natural fractures, microcracks, mesopores, and micropores. A triple-porosity model (TPM) was adopted to interpret the gas transport in the shale sample: The pore space is represented by three interacting continua, including macropores (larger fractures), mesopores (including microcracks), and micropores. To facilitate the application of the TPM, a modified analytical approach is introduced to extract effective transport parameters (in terms of the characteristic time for transport at relevant porosity levels) directly from the PPD measurements with He. To validate the modeling approach, model parameters extracted from two He PPD measurements are demonstrated to provide excellent agreement with a 3rd He PPD measurement performed at a higher pressure. The effective transport parameters, extracted from the He experiments, were subsequently converted for application to the Kr and CO 2 PPD experiments, by accounting for relevant transport modes and differences in fluid properties. Excess adsorption isotherms were extracted from the equilibrium pressures of the Kr and CO 2 PPD experiments using the He pore volume as a baseline. These adsorption isotherms were then integrated into the TPM to predict combined gas transport and sorption for Kr and CO 2 , with transport coefficients translated from the He measurements. The predictions for Kr and CO 2 are demonstrated to be in excellent agreement with the experimental observations. This, in turn, demonstrates that the proposed analytical approach provides for an effective characterization of mass transfer rates in shales, that can be applied directly in a TPM representation of mass transfer and sorption.

58 GEOSCIENCES↗
DOE OSTI2021

A screening model for predicting injection well pressure buildup and plume extent in CO 2 geologic storage projects

We present a new screening model for predicting injection well pressure buildup and CO 2 plume migration for CO 2 geologic sequestration projects. The model requires only limited information and is quite accurate when compared to detailed simulation results. Such models can assist project developers during the early days of project planning (e.g., for 45Q related projects), and also help regulators perform simple checks against detailed numerical models. The screening model consists of two correlations: one for injectivity index in terms of the slope of the CO 2 fractional flow curve, and the second for total storage efficiency within the footprint of plume as a function of gravity number and slope of the CO 2 fractional flow curve. Using these two correlations, as well as a knowledge of some basic reservoir characteristics and estimates of fluid properties from standard correlations, the injection-well pressure buildup and CO 2 plume extent in the formation can be readily estimated. Finally, the new correlations show a good match with the results of the underlying simulations, and also provide good agreement with independent calculations for two example problems.

42 ENGINEERING↗
DOE OSTI2020

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (“residual”) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at , but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. As a result, we also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Modeling Nanoconfinement Effects Using Active Learning

Predicting the spatial configuration of gas in nanopores of is relevant in applications such as fluid flow forecasting and hydrocarbon reserves estimation. For example, shale reservoirs have suffered from computationally intractable multiscale problems, since fluid properties such as viscosity, density, and adsorption must be calculated by using expensive molecular dynamics (MD) simulations within each nanopore, whereas flow through these connected nanopores must be simulated at the micrometer scale. We utilize machine learning techniques to quickly and accurately model nanoscale confinement effects as an important step toward bridging the nano and micro scales. Our workflow is based on building and training physics-based deep-neural-networks models by learning from a database of MD calculations. The model accounts for the adsorption phenomenon by predicting the statistical distribution of gas inside nanopores. Because large databases of MD calculations are expensive to create, we investigate active learning (AL) as a data set construction strategy. In this workflow, new data are selected based on the model uncertainty via the query-by-committee approach. We show that our workflow obtains accurate models that generalize to real scanning electron microscopy geometries with 1/10th of the number of MD calculations required vs random data set generation. Our method enables the possibility of modeling nanoconfinement effects at the mesoscale, where complex connected sets of nanopores affect flow.

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

Low Melting Temperature Gallium–Indium Liquid Metal Anode for Solid-State Li-Ion Batteries

Solid-state Li-ion batteries are attracting attention for their enhanced safety features, higher energy density, and broader operational temperature range compared to systems based on liquid electrolytes. However, current solid-state Li-ion batteries face performance challenges, such as suboptimal cycling and poor rate capabilities, often due to inadequate interfacial contact between the solid electrolyte and electrodes. To address this issue, we incorporated a gallium–indium (Ga–In) liquid metal as the anode in a solid-state Li-ion battery setup, employing Li 6 PS 5 Cl as the solid electrolyte. Operating at room temperature, this configuration achieved an initial capacity of 389 mAh g –1 and maintained 88% of this capacity after 30 cycles at a 0.05 C rate. It also demonstrated a capacity retention of 66% after 500 cycles at a 0.5 C rate. In comparison to solid anode materials, such as tin, the Ga–In liquid metal exhibited superior cycling stability and rate capacity, which is due to the self-healing and fluid properties of the alloy that ensure stable interfacial contact with solid electrolytes. In situ X-ray diffraction (XRD) and ex situ scanning electron microscope (SEM) analyses revealed that indium does not directly participate in the lithiation/delithiation process. Instead, it helps maintain the alloy’s low melting point, facilitating its return to a liquid state after delithiation. In a comparative analysis of stack pressure during cycling in cells utilizing Ga–In liquid metal and tin, the Ga–In liquid metal cell demonstrated an ability to buffer pressure increases associated with deformation. In conclusion, these findings suggest a promising approach for enhancing solid-state batteries by integrating liquid metal anodes, which improve interfacial contact and stability.

Alloys↗
DOE OSTI2021

Detection of Kardar–Parisi–Zhang hydrodynamics in a quantum Heisenberg spin-1/2 chain

Classical hydrodynamics is a remarkably versatile description of the coarse-grained behaviour of many-particle systems once local equilibrium has been established. The form of the hydrodynamical equations is determined primarily by the conserved quantities present in a system. Some quantum spin chains are known to possess, even in the simplest cases, a greatly expanded set of conservation laws, and recent work suggests that these laws strongly modify collective spin dynamics, even at high temperature. In this work, by probing the dynamical exponent of the one-dimensional Heisenberg antiferromagnet KCuF 3 with neutron scattering, we find evidence that the spin dynamics are well described by the dynamical exponent z = 3/2, which is consistent with the recent theoretical conjecture that the dynamics of this quantum system are described by the Kardar–Parisi–Zhang universality class. This observation shows that low-energy inelastic neutron scattering at moderate temperatures can reveal the details of emergent quantum fluid properties like those arising in non-Fermi liquids in higher dimensions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Correlation Function Approach for Diffusion in Confined Geometries

This paper describes a formalism for extracting spatially varying transport coefficients from simulations of a molecular fluid in a nano channel. This approach is applied to self-diffusion of a Lennard-Jones fluid confined between two parallel surfaces. A numerical grid is laid over the domain confining the fluid, and fluid properties are projected onto the grid cells. The time correlation functions between properties in different grid cells are calculated and can be used as the basis for a fitting procedure for extracting spatially varying diffusion coefficients from the simulation. Results for the Lennard-Jones system show that transport behavior varies sharply near the liquid-solid boundary and that the changes depend on the details of the liquid-solid interaction. A quantitative difference between the reduced and detailed models is discussed. It is found that the difference could be associated with assumptions about the form of the transport equations at molecular scales in lieu of problems with the method itself. The study suggests that this approach to fitting molecular simulations to continuum equations may guide the development of appropriate coarse-grained equations to model transport phenomena at nanometer scales.

Nanoscale flow, Transport, molecular simulation↗
DOE OSTI2023

Prediction of the Inter-Tube Flow Mode Transitions in the Evaporators of Multi-Effect Thermal Desalination Plants

Water is one of the most stressed resources on the planet. The limited availability of fresh water and the high cost of transportation have led to an increased interest in water desalination technologies. The two main categories of desalination techniques are membrane desalination and thermal desalination. Membrane technologies include pressure driven and electrical driven membranes. On the other hand, thermal desalination includes: multi-effect desalination (MED), multi-stage flash (MSF) desalination, and mechanical vapor compression desalination. Multi-effect desalination plants are usually made of a series of evaporators (also known as effects). In each effect, hot steam flows inside the tubes and evaporates the seawater that falls on the outside of the tubes. The vapor formed at each effect flows to the next effect and acts as the heating medium for the falling seawater. The prevailing flow mode of the falling seawater (i.e. droplet, jet, or sheet) influences heat and mass transfer as well as dry out in the evaporators of Multi-Effect Desalination (MED) plants. The objective of this paper is to predict and discuss the prevailing falling film flow modes in the evaporators of MED plants, under different operating conditions. The paper demonstrates the transitional Reynolds numbers between the main falling film modes for seawater. This closes a gap in the literature where there is a dearth of mode transition data for seawater. The effect of fluid properties and tube geometry on the transitions is discussed in details. As a result, the accuracy of the predicted transitional Reynolds numbers is evaluated via uncertainty quantification techniques.

availability↗
DOE OSTI2021

NRAP-Open-IAM: Open Wellbore Component (V.2.0)

The Open Wellbore component of NRAP-Open-IAM is applicable to the calculation of Area of Review at a carbon storage site and fast estimation of dense gas dispersion from multiple continuous CO 2 surface leakage sources for risk assessment. The Open Wellbore Component of NRAP-Open-IAM is a lookup table model based on the drift-flux approach. The National Research Assessment Partnership’s second-generation integrated assessment model, NRAP-Open-IAM, is open-source software written in Python for use in performance and quantitative risk assessment of geologic carbon storage (GCS) systems. The look-up table for open well leakage is an updated version the previous model which provides finer resolution of reservoir depth and more accurate fluid properties than the previous open well look-up table, especially in calculating the gas (CO 2 -rich) phase properties. The component model input parameters include the reservoir transmissivity, aquifer transmissivity, brine salinity, well radius, and well top and bottom depths. The reservoir transmissivity and brine salinity were set to values consistent with the reservoir component, and the well top and bottom depths varied with location. The possible outputs from the Open Wellbore component are leakage rates of CO 2 and brine to either an aquifer or atmosphere, depending on the depth of the well top. Several python test scripts are presented that demonstrate that the Open Wellbore component works correctly. Use of the open wellbore model in NRAP-Open-IAM may result in a large CO 2 leakage rates, comparable to the leakage rates of CO 2 blow out.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Automated Control for Nuclear Thermal Propulsion Start-Up using MOOSE-based Applications

This report presents a Griffin/Bison/RELAP-7 numerical model of a prototypical NTP system that features fuel assemblies arranged in rings, and which was designed to simulate rapid startup transients. The physics modeled include full-core neutronics, assembly-wise heat conduction, and conjugate heat transfer, with the balance of plant mainly imposed through boundary conditions. In addition, various forms of automated reactivity control were deployed by using the MOOSE to autonomously drive the model and simulate the reactor transitioning from assumed initial conditions to nominal power in a fraction of a minute. To generate the cross-sections of the neutronics model, and in an effort to simultaneously account for the tremendous axial temperature gradients in the reactor and to limit the number of state points required for cross-section generation, the average component temperatures and hydrogen densities in the cooling channels were correlated to the average fuel and moderator temperatures, and fixed axial profiles were derived for nominal conditions and then used during the transient. With this approximation, a tractable cross-section library tabulated with fuel/moderator temperatures and CD angles was generated using Serpent. The full-core SPH correction procedure and the CD decusping technology in Griffin, respectively, ensure preservation of the multiplication factor and reaction rates at state points, along with a reasonably accurate reactivity worth between tabulated CD angles, despite using a coarse mesh. Feedback from other physics was calculated by modeling one representative fuel assembly per ring, along with the corresponding fuel and moderator cooling channels. To limit power overshoots during startup, another layer of multiphysics coupling was added to the model in order to automatically control the drums. Two different technologies presented herein showed outstanding performance in this regard: (1) a novel hybrid PID controller based on both power and reactivity signals, and (2) a PGC that relies on kinetics parameters and reactivity coefficients to predict future behavior and adjust the desired signal accordingly. A challenging benchmark was devised, featuring a power demand curve that exponentially increases by a factor of 500 within 30 seconds, then levels out after that. Both control approaches create a simulated power curve that closely follows the power demand curve and limits power overshoots to 1% or less. While the former approach requires more tuning of the internal parameters, the latter requires additional knowledge of the reactivity feedback coefficients and rates of change of the corresponding variables, including fuel and moderator temperature, which could be difficult to dynamically measure for a real NTP system. Fortunately, some inaccuracy in these quantities will not drastically degrade the PGC performance. Subsequently, a more realistic startup sequence was considered, in which the mass flow rate and outlet pressures are ramped up to model bootstrap and thrust build-up phases prior to reaching steady-state conditions, demonstrating the ability of the hybrid PID and PGCs to handle such transients, with both types of controllers exhibiting very similar behavior. Nevertheless, a significant chamber temperature overshoot was observed, caused by the demanded power signal and assumed mass flow rate. This issue could be mitigated by deploying a reactor controller that follows the chamber temperature signal and actuates both the control valves and drums (rather than using a power signal based solely on the drums to control reactivity). Enhancement of the hydrogen fluid properties available in MOOSE, as well as a better understanding of prototypical initial conditions, are also needed to further enhance this startup model. Finally, a study was performed to model decay heat post-shutdown, and to prepare for extending this model to predict shutdown behavior and post-shutdown pulsed cooling requirements.

33 ADVANCED PROPULSION SYSTEMS↗