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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Optimization on a Network-based Parallel Computer System for Supersonic Laminar Wing Design

A set of Computational Fluid Dynamics (CFD) routines and flow transition prediction tools are integrated into a network based parallel numerical optimization routine. Through this optimization routine, the design of a 2-D airfoil and an infinitely swept wing will be studied in order to advance the design cycle capability of supersonic laminar flow wings. The goal of advancing supersonic laminar flow wing design is achieved by wisely choosing the design variables used in the optimization routine. The design variables are represented by the theory of Fourier series and potential theory. These theories, combined with the parallel CFD flow routines and flow transition prediction tools, provide a design space for a global optimal point to be searched. Finally, the parallel optimization routine enables gradient evaluations to be performed in a fast and parallel fashion.

Garcia, Joseph A.↗
DOE OSTI2021

Model fusion with physics-guided machine learning: Projection-based reduced-order modeling

The unprecedented amount of data generated from experiments, field observations, and large-scale numerical simulations at a wide range of spatiotemporal scales has enabled the rapid advancement of data-driven and especially deep learning models in the field of fluid mechanics. Although these methods are proven successful for many applications, there is a grand challenge of improving their generalizability. This is particularly essential when data-driven models are employed within outer-loop applications like optimization. In this work, we put forth a physics-guided machine learning (PGML) framework that leverages the interpretable physics-based model with a deep learning model. Leveraging a concatenated neural network design from multi-modal data sources, the PGML framework is capable of enhancing the generalizability of data-driven models and effectively protects against or inform about the inaccurate predictions resulting from extrapolation. We apply the PGML framework as a novel model fusion approach combining the physics-based Galerkin projection model and long- to short-term memory (LSTM) network for parametric model order reduction of fluid flows. We demonstrate the improved generalizability of the PGML framework against a purely data-driven approach through the injection of physics features into intermediate LSTM layers. Our quantitative analysis shows that the overall model uncertainty can be reduced through the PGML approach, especially for test data coming from a distribution different than the training data. Moreover, we demonstrate that our approach can be used as an inverse diagnostic tool providing a confidence score associated with models and observations. The proposed framework also allows for multi-fidelity computing by making use of low-fidelity models in the online deployment of quantified data-driven models.

42 ENGINEERING↗
DOE OSTI2021

Rotational and reflectional equivariant convolutional neural network for data-limited applications: Multiphase flow demonstration

This article deals with approximating steady-state particle-resolved fluid flow around a fixed particle of interest under the influence of randomly distributed stationary particles in a dispersed multiphase setup using convolutional neural network (CNN). The considered problem involves rotational symmetry about the mean velocity (streamwise) direction. Thus, this work enforces this symmetry using SE(3)-equivariant, special Euclidean group of dimension 3, CNN architecture, which is translation and three-dimensional rotation equivariant. This study mainly explores the generalization capabilities and benefits of a SE(3)-equivariant network. Accurate synthetic flow fields for Reynolds number and particle volume fraction combinations spanning over a range of [86.22, 172.96] and [0.11, 0.45], respectively, are produced with careful application of symmetry-aware data-driven approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

EGS Collab Experiment 1: Common Discrete Fracture Network

This package includes data and models that support hydraulic fracture stimulation and fluid circulation experiments in the Sanford Underground Research Facility (SURF). A paper by Schwering et al. (2020) describes the deterministic basis for developing a "common" discrete fracture network (CDFN) model of significant natural fractures in EGS Collab Testbed 1 on the 4850-Level of SURF. The ReadMe for this model shows drift, wells, scanlines, fracture data, interpreted fractures, and geophysical visualizations. There is also a summary of the data that was used in this experiment and includes results from reviewing core, televiewer (TV) logs, core-TV depth/feature registration, and from mapping weeps in the 4850-Level drift. The CDFN is intended to be a baseline model of the pre-stimulated testbed (though some observations from stimulation helped inform the model).

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Coupled Investigation of Fracture Permeability Impact on Reservoir Stress and Seismic Slip Behavior (Final Technical Report)

Enhanced Geothermal Systems (EGS) produce clean energy by circulating fluid through hot rock deep underground and bringing that heat to the surface to generate electricity. For this process to work reliably, fluids must be able to move efficiently through networks of natural or engineered fractures in the rock. Enhancing and maintaining subsurface permeability over time is essential for sustainable energy production. However, fluid injection changes the underground temperature, pressure, rock stress, and chemistry, which can alter permeability and sometimes trigger earthquakes. Predicting these interconnected processes remains a key challenge. To address this, we combined high-temperature laboratory experiments with high-fidelity simulations to better understand how fractures in geothermal reservoirs evolve over time. Our experiments measured how fractures respond to stress, slip, slip rate, and chemical reactions under geothermal conditions. These data were integrated into coupled thermal-hydrological-mechanical-chemical and earthquake (THMC+E) models tailored to the Utah FORGE site. The validated modeling framework improves predictions of reservoir performance and seismic response and helps guide operational decisions. This work reduces technical risk and strengthens the scientific foundation needed to make geothermal energy a reliable and scalable clean energy resource.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Data for Monitoring Fracture Saturation with Internal Transportable Seismic Sources and Twin Neural Networks

Seismic coda-wave analysis is a well-developed method for detecting subtle physical changes in complex media by measuring arrival times in the late-arriving energy from multiply-scattered or reflected waves. However, a challenge arises when multiply-scattered waves are not sufficiently separated in time from the direct arrivals to provide a clear coda wave train. Additional complications for monitoring changes in fracture systems arise when the signals originate from unsynchronized internal sources, such as natural induced seismicity, from acoustic emission, or from transportable intra-fracture sources (chattering dust), that generate uncontrolled signals that vary in arrival time, amplitude and frequency content. Here, we use a twin neural network (also known as a Siamese neural network) for dimensionality reduction to analyze signals from chattering dust to classify the fluid saturation state of a synthetic fracture system. The twin neural network with shared weights generates a low-dimensional representation of the data input by minimizing contrastive loss, serving as the input to a multiclass classifier that accurately classifies whether multiple fractures in a fracture system are fully saturated or partially saturated, or whether a change in saturation has occurred in different fractures in the system. These results show that information buried in unresolved codas from uncontrolled sources can be extracted using machine learning to monitor the evolution of fracture systems caused by physical and chemical processes even when the scattered and direct wave fields overlap.

58 GEOSCIENCES↗
DOE OSTI2018

An exploration of pressure dynamics using differential equations defined on a fractal geometry

Pressure tests have been used to understand the behavior of naturally fractured reservoirs (NFRs) by establishing parameters such as permeability, connectivity of fractures, system compressibility, fracture network parameters, and others associated with the physical properties of the reservoir. The complex fracture network of a NFR produces anomalous fluid flow, which is shown by the pressure behavior at the well. A plausible model to explain this behavior is to assume that it arises from a fractal fracture system. However, to estimate the parameters that define this fractal fracture system, fluid flow models based on the modification of Darcy’s law are used. On the other hand, in this work, a fractal structure with known fractal dimension: Sierpinski gasket (SG) is proposed. The objective of this work is to solve numerically the diffusion equation on SG, and establish a comparison with flow models used in the oil industry to investigate the fractality assumption. This study is focused on the qualitative behavior of the solution of the diffusion equation defined on a SG supported by the Kigami’s theory of differential equations on fractals.

42 ENGINEERING↗
DOE OSTI2016

Comparison of CO 2 trapping in highly heterogeneous reservoirs with Brooks-Corey and van Genuchten type capillary pressure curves

Geological heterogeneities affect the dynamics of carbon dioxide (CO 2 ) plumes in subsurface environments in important ways. Previously, we showed how the dynamics of CO 2 plumes are influenced by the multiscaled sedimentary architecture in deep brine fluvial-type reservoirs. The results confirm that representing small-scale features and the corresponding heterogeneity in saturation functions, along with hysteresis in saturation functions, are all critical to understanding capillary trapping processes. Here, we show that when heterogeneity and hysteresis are represented, the two conventional approaches for defining saturation functions, Brooks-Corey and van Genuchten, represent fundamentally different physical systems. The Brooks-Corey approach represents heterogeneity in entry pressures, and leads to trapping by capillary pinning. The van Genuchten approach represents a network of pores transporting the nonwetting fluid, across rock types, with negligible capillary entry pressure, and leads to capillary retardation. We conclude these differences significantly affect the large-scale characteristics of CO 2 plumes (i.e., their mass, shape, and position).

58 GEOSCIENCES↗
DOE OSTI2017

Numerical modeling of fluid flow in a fault zone: a case of study from Majella Mountain (Italy)

We present the preliminary results of a numerical model of fluid flow in a fault zone, based on field data acquired at the Majella Mountain, (Italy). The fault damage zone is described as a discretely fractured medium, and the fault core as a porous medium. Our model utilizes dfnWorks, a parallelized computational suite, developed at Los Alamos National Laboratory to model the damage zone and characterizes its hydraulic parameters. We present results from the field investigations and the basic computational workflow, along with preliminary results of fluid flow simulations at the scale of the fault.

58 GEOSCIENCES↗
DOE OSTI2019

Thicknesses of Chemically Altered Zones in Shale Matrices Resulting from Interactions with Hydraulic Fracturing Fluid

Hydraulic fracturing of unconventional shale reservoirs increases the fracture network surface area to access hydrocarbons from the low permeability rock matrix. Porosity and permeability of the matrix, through which hydrocarbons migrate to fractures, are important for determining production efficiency and can be altered by chemical interactions between shale and hydraulic fracturing fluids (HFFs). Here, we present results from an experimental study that characterizes the thickness of the alteration zone in the shale matrix after shale–HFF interactions. Experiments were conducted with whole cores submerged in HFF both with and without added barium and sulfate to promote barite scale formation. After 3 weeks of reaction at 77 bar and 80°C, the cores were characterized using X-ray microtomography, synchrotron X-ray fluorescence microprobe imaging, and synchrotron X-ray absorption spectroscopy. Our results show that the thickness of the altered zone depends on shale mineralogical composition and varies for different chemical reactions. Reactions between the low-carbonate Marcellus shale and HFF, pyrite (FeS 2 ) oxidation manifests as both a thick zone of sulfur oxidation (>0.5 cm) and a thinner zone of iron oxidation (100–150 $μ$m). Carbonate dissolution extended 100–200 μm into the matrix, with the resulting observable secondary porosity localized at the shale–fluid interface where mineral grains were removed by either dissolution or mechanical erosion. In solutions oversaturated with respect to barite, barite precipitates were observed in the reaction fluid and at the shal–HFF interface. In contrast, the carbonate dissolution zone in the high-carbonate Eagle Ford was only 30–40 μm thick, within which a uniform texture of increased porosity was observed. Pyrite oxidation in the Eagle Ford was evident from an iron oxidation zone (150–200 μm thick), while sulfur oxidation was minor and hard to observe. Barite precipitation extended 1–2 mm into the matrix when the initial HFF was oversaturated with respect to barite, filling shale microcracks down to the submicrometer length scale. Our findings provide a scientific basis to predict the extent of chemical alteration in shale reservoirs during hydraulic fracturing and its impacts on hydrocarbon production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Symbolic regression development of empirical equations for diffusion in Lennard-Jones fluids

Symbolic regression (SR) with a multi-gene genetic program has been used to elucidate new empirical equations describing diffusion in Lennard-Jones (LJ) fluids. Some examples include equations to predict self-diffusion in pure LJ fluids and equations describing the finite-size correction for self-diffusion in binary LJ fluids. The performance of the SR-obtained equations was compared to that of both the existing empirical equations in the literature and to the results from artificial neural net (ANN) models recently reported. It is found that the SR equations have improved predictive performance in comparison to the existing empirical equations, even though employing a smaller number of adjustable parameters, but show an overall reduced performance in comparison to more extensive ANNs.

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

Accelerating Scientific Simulations with Bi-Fidelity Weighted Transfer Learning

High-fidelity modeling is an essential design tool for many engineering applications. However, for complex systems, computational cost can be a limiting factor. Analyzing parameter sensitivity, uncertainty quantification, and design optimization require many model evaluations. Surrogate models are often used to develop the relationship between model parameters and quantities of interest. However, in the case of complex systems, surrogate models require several degrees of freedom and, thus, a large number of data points to determine the correct dependencies. For many applications, this may be prohibitively expensive. The reduction of computational requirements can be achieved by leveraging low-fidelity models. Low-fidelity models represent the system at a coarser resolution with the advantage of computational efficiency. Therefore, a bi-fidelity modeling paradigm, which augments the accuracy of a low-fidelity model in a computationally efficient manner by invoking limited runs of a high-fidelity model, can be leveraged to sufficiently balance the accuracy and computational requirements. In this work, a bi-fidelity weighted transfer learning method using neural networks was applied to a computational fluid dynamics heat transfer modeling problem. The transfer learning advantage was investigated as a function of hyperparameters. Our main finding is that the use of a bi-fidelity modeling paradigm achieves accuracy close to that of a high-fidelity Gaussian process model while significantly reducing computational cost. The bi-fidelity model achieves comparable performance with 90 high-fidelity samples-that is, 60% less than the samples needed to achieve similar accuracy without the use of bi-fidelity modeling,

Borowiec, Katarzyna↗
DOE OSTI2022

A Brief Overview of Reactive Transport Codes Used in CO 2 Applications

Reactive transport modelling consists of computational and numerical models that describe the coupled physical, chemical, mechanical, and biological processes interacting with each other over a broad range of spatial and temporal scales. This report reviews some of the reactive transport codes available in the literature for carbon dioxide (CO 2 ) applications and can assist the scientific community with the applicability of each code to solve a specific problem. Approaches implemented in these codes can be categorized in three groups: (i) continuum-scale, (ii) pore-scale, and (iii) hybrid-scale approaches. Theoretical foundations, numerical implementations, and application examples using the models are described. In the continuum approach, flow and transport are formulated in terms of a representative control volume of the medium and coupled with geochemical reactions. The medium is characterized by bulk parameters such as porosity, permeability, or reactive surface area. In the pore-scale approach, each point of space in the pore network is occupied by either a fluid or solid phase. The pore-scale approach requires an exact knowledge of the spatial and temporal phase distribution. In the hybrid-scale approach, media are described by multiple characteristic length-scales, with some regions using a pore-scale approach while others used a continuum approach.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2010

Evidence for Hesperian Impact-Induced Hydrothermalism on Mars

Several hydrated silicate deposits on Mars are observed within craters and are interpreted as excavated Noachian material. Toro crater (71.8 deg E, 17.0 deg N), located on the northern edge of the Syrtis Major Volcanic Plains, shows spectral and morphologic evidence of impact-induced hydrothermal activity. Spectroscopic observations were used to identify extensive hydrated silicate deposits, including prehnite, chlorites, smectites, and opaline material, a suite of phases that frequently results from hydrothermal alteration in terrestrial craters and also expected on Mars from geochemical modeling of hydrothermal environments. When combined with altimetry and high-resolution imaging data, these deposits appear associated predominantly with the central uplift and with portions of the northern part of the crater floor. Detailed geologic mapping of these deposits reveals geomorphic features that are consistent with hydrothermal activity that followed the impact event, including vent-like and conical mound structures, and a complex network of tectonic structures caused by fluid interactions such as fractures and joints. The crater age has been calculated from the cumulative crater size-frequency distributions and is found to be Early Hesperian. The evidence presented here provides support for impact-induced hydrothermal activity in Toro crater, that extends phyllosilicate formation processes beyond the Noachian era.

Marzo, Giuseppe A.↗
DOE OSTI2026

Monitoring of Liquid Metal Reactor Heater Zones with Recurrent Neural Network Learning of Temperature Time Series

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F 1 score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F 1 scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively.

gated recurrent unit↗
NASA NTRS1981

A pulse-forming network for particle path visualization

A procedure is described for visualizing nonsteady fluid flow patterns over a wide velocity range using discrete nonluminous particles. The paramount element responsible for this capability is a pulse-forming network with variable inductance that is used to modulate the discharge of a fixed amount of electrical energy through a xenon flashtube. The selectable duration of the resultant light emission functions as a variable shutter so that particle path images of constant length can be recorded. The particles employed as flow markers are hydrogen bubbles that are generated by electrolysis in a water tunnel. Data are presented which document the characteristics of the electrical circuit and establish the relation of particle velocity to both section inductance and film exposure.

Mcalister, K. W.↗
DOE OSTI2016

Interplay between structure and transport properties of molten salt mixtures of ZnCl 2 –NaCl–KCl: A molecular dynamics study

In this paper, molten mixtures of network-forming covalently bonded ZnCl 2 and network-modifying ionically bonded NaCl and KCl salts are investigated as high-temperature heat transfer fluids for concentrating solar power plants. Specifically, using molecular dynamics simulations, the interplay between the extent of the network structure, composition, and the transport properties (viscosity, thermal conductivity, and diffusion) of ZnCl 2 –NaCl–KCl molten salts is characterized. The Stokes-Einstein/Eyring relationship is found to break down in these network-forming liquids at high concentrations of ZnCl 2 (>63 mol. %), while the Eyring relationship is seen with increasing KCl concentration. Further, the network modification due to the addition of K ions leads to formation of non-bridging terminal Cl ions, which in turn lead to a positive temperature dependence of thermal conductivity in these melts. Finally, this new understanding of transport in these ternary liquids enables the identification of appropriate concentrations of the network formers and network modifiers to design heat transfer fluids with desired transport properties for concentrating solar power plants.

14 SOLAR ENERGY↗