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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Saturation of fishbone instability through zonal flows driven by energetic particle transport in tokamak plasmas

Gyrokinetic and kinetic-MHD simulations are performed for the fishbone instability in the DIII-D discharge #178631, chosen for validation of first-principles simulations to predict the energetic particle (EP) transport in an ITER prefusion baseline scenario. Fishbone modes are found to generate zonal flows, which dominate the fishbone saturation. The underlying mechanisms of the two-way fishbone-zonal flows nonlinear interplay are discussed in details. Numerical and analytical analyses identify the fishbone-induced EP redistribution as the dominant generation mechanism for zonal flows. The zonal flows modify the nonlinear dynamics of phase space zonal structures, which reduces the amount of EPs able to resonate with the mode, leading to an early fishbone saturation. Simulation results including zonal flows agree quantitatively with DIII-D experimental measurements of the fishbone saturation amplitude and EP transport, supporting this novel saturation mechanism by self- generated zonal flows. Moreover, the wave-particle mode-locking mechanism is shown to determine quantitatively the fishbone frequency down-chirping, as evident in GTC simulation results in agreement with predictions from analytical theory. Finally, the
fishbone-induced zonal flows are possibly responsible for the formation of an ion-ITB in the DIII-D discharge. Based on the low EP transport and the large zonal flow shearing rates associated with the fishbone instability in gyrokinetic simulations of the ITER scenario, it is conjectured that high performance scenarios could be designed in ITER burning plasmas through fishbone-induced ITBs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Alfvén eigenmode-driven zonal modes saturate and heat thermal ions by cross-scale interactions

In scenarios where a sustained energetic particle source strongly drives toroidal Alfvén eigenmodes (TAE), and phase-space transport is insufficient to saturate TAE, this novel theory of TAE-zonal mode (ZM)-turbulence—self-regulated by cross-scale interactions (including collisionless ZF damping) – merits consideration. Zonal modes are driven by Reynolds and Maxwell stresses, without the onset of modulational instability. TAE evolution in the presence of ZMs conserves energy and closes the system feedback loop. The saturated zonal shears can be sufficient to suppress ambient drift-ion temperature gradient (ITG) turbulence, achieving an enhanced core confinement regime. The saturated state is regulated by linear and turbulent zonal flow drag. This regulation leads to bursty TAE spectral oscillations, which overshoot while approaching saturation. Heating by both collisional and collisionless ZM damping deposits alpha particle energy into the thermal plasma, achieving effective alpha channeling. This theory offers a mechanism for EP-induced transport barrier formation, and predicts a novel thermal ion heating mechanism.

ITB↗
DOE OSTI2019

Saturated random packing built of arbitrary polygons under random sequential adsorption protocol

Random packings and their properties are a popular and active field of research. Numerical algorithms that can efficiently generate them are useful tools in their study. This paper focuses on random packings produced according to the random sequential adsorption (RSA) protocol. Developing the idea presented by G. Zhang, where saturated random packings built of regular polygons were studied, we create an algorithm that generates strictly saturated packings built of any polygons. Then, the algorithm was used to determine the packing fractions for arbitrary triangles. In this work, the highest mean packing density, 0.552 814 ± 0.000 063, was observed for triangles of side lengths 0.63 : 1 : 1. Additionally, microstructural properties of such packings, kinetics of their growth, as well as distributions of saturated packing fractions and the number of RSA iterations needed to reach saturation were analyzed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Experiments on the dynamics and scaling of spontaneous-magnetic-field saturation in laser-produced plasmas

We report in laser-produced high-energy-density plasmas, large-scale strong magnetic fields are spontaneously generated by the Biermann battery effects when temperature and density gradients are misaligned. Saturation of the magnetic field takes place when convection and dissipation balance field generation. While theoretical and numerical modeling provide useful insight into the saturation mechanisms, experimental demonstration remains elusive. In this letter, we report an experiment on the saturation dynamics and scaling of Biermann battery magnetic field in the regime where plasma convection dominates. With time-gated charged-particle radiography and time-resolved Thomson scattering, the field structure and evolution as well as corresponding plasma conditions are measured. In these conditions, the spatially resolved magnetic fields are reconstructed, leading to a picture of field saturation with a scaling of $B ~ 1/L_T$ for a convectively dominated plasma, a regime where the temperature gradient scale ($L_T$) exceeds the ion skin depth.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Biological mechanisms may contribute to soil carbon saturation patterns

Increasing soil organic carbon (SOC) storage is a key strategy to mitigate rising atmospheric CO 2 , yet SOC pools often appear to saturate, or increase at a declining rate, as carbon (C) inputs increase. Soil C saturation is commonly hypothesized to result from the finite amount of reactive mineral surface area available for retaining SOC, and is accordingly represented in SOC models as a physicochemically determined SOC upper limit. However, mineral-associated SOC is largely microbially generated. In this perspective, we present the hypothesis that apparent SOC saturation patterns could emerge as a result of ecological constraints on microbial biomass—for example, via competition or predation—leading to reduced C flow through microbes and a reduced rate of mineral-associated SOC formation as soil C inputs increase. Microbially explicit SOC models offer an opportunity to explore this hypothesis, yet most of these models predict linear microbial biomass increases with C inputs and insensitivity of SOC to input rates. Synthesis of 54 C addition studies revealed constraints on microbial biomass as C inputs increase. Different hypotheses limiting microbial density were embedded in a three-pool SOC model without explicit limits on mineral surface area. As inputs increased, the model demonstrated either no change, linear, or apparently saturating increases in mineral-associated and particulate SOC pools. Taken together, our results suggest that microbial constraints are common and could lead to reduced mineral-associated SOC formation as input rates increase. We conclude that SOC responses to altered C inputs—or any environmental change—are influenced by the ecological factors that limit microbial populations, allowing for a wider range of potential SOC responses to stimuli. Understanding how biotic versus abiotic factors contribute to these patterns will better enable us to predict and manage soil C dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Machine learning and atomic layer deposition: Predicting saturation times from reactor growth profiles using artificial neural networks

In this work, we explore the application of deep neural networks to the optimization of atomic layer deposition (ALD) processes. In particular, we focus on a one-shot optimization problem, where we try to predict the optimal dose time that leads to saturation everywhere in the reactor based on thickness values measured at different points of an ALD reactor after a single trial growth. In order to tackle this problem, we introduce a dataset designed to train neural networks to predict saturation times based on these inputs for a cross-flow ALD reactor. Here, we then explore the predictive ability of artificial neural networks of different depths and sizes using a separate testing dataset to evaluate their accuracies. The results obtained show that networks trained using stochastic gradient descent methods can accurately predict saturation times without requiring any additional information on the surface kinetics. This provides a viable approach to minimize the number of experiments required to optimize new ALD processes in a known reactor, and it highlights the way machine learning can be leveraged for thin film growth and manufacturing. While the datasets and training procedure depend on the reactor geometry, the trained neural networks provide a general surrogate model connecting thickness values and trial dose times with optimal saturation times that can be reused for different ALD processes within the same reactor.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Research on Deformation Law of Deep Foundation Pit of Station in Core Region of Saturated Soft Loess Based on Monitoring

Saturated soft loess has a large pore structure, high compressibility, low strength, fluid plastic state, and poor engineering properties. It is still one of the key problems that engineering needs to solve. In order to study the influence of deep foundation pit excavation in the saturated soft loess area on the deformation of foundation pits, the deformation laws of ground settlement, enclosure structure, and supporting axial force were clarified based on the field tests on the deformation characteristics of deep foundation pits in areas with the high-water level in saturated soft loess, combined with geological conditions and on-site construction procedures. The results indicate the following: water supply and construction process were found to be the main factor in changing the surface settlement curve of deep foundation pits in saturated soft loess; increasing the construction speed of the pit bottom floor, inverted braces, floor frame beams, and sidewall frame beams to close the structure, which is conducive to restraining the deformation of the continuous underground wall and foundation pits in similar areas. In the initial stage of support layout, the axial force of steel support tends to increase too fast or even exceed the standard control value. Therefore, a reasonable preadding axial force is an effective means to control the deformation of the continuous underground wall and the axial force of the steel support. The current research results may provide a reference for constructing deep foundation pits in similar areas.

Wang, Xueyan↗
DOE OSTI2023

Machine Learning Application to Assess Occurrence and Saturations of Methane Hydrate in Marine Deposits Offshore India

Artificial Neural Networks (ANN) were used to assess methane hydrate occurrence and saturation in marine sediments offshore India. The ANN analysis classifies the gas hydrate occurrence into three types: methane hydrate in pore space, methane hydrate in fractures, or no methane hydrate. Further, predicted saturation characterizes the volume of gas hydrate with respect to the available void volume. Log data collected at six wells, which were drilled during the India National Gas Hydrate Program Expedition 02 (NGHP-02), provided a combination of well log measurements that were used as input for machine learning (ML) models. Well log measurements included density, porosity, electrical resistivity, natural gamma radiation, and acoustic wave velocity. Combinations of well logs used in the ML models provide good overall balanced accuracy (0.79 to 0.86) for the prediction of the gas hydrate occurrence and good accuracy (0.68 to 0.92) for methane hydrate saturation prediction in the marine accumulations against reference data. The accuracy scores indicate that the ML models can successfully predict reservoir characteristics for marine methane hydrate deposits. In conclusion, the results indicate that the ML models can either augment physics-driven methods for assessing the occurrence and saturation of methane hydrate deposits or serve as an independent predictive tool for those characteristics.

58 GEOSCIENCES↗
DOE OSTI2024

Efficient Dimension Reduction of Complex Three-dimensional CO2 Saturation using Deep Learning Models

In the domain of deep learning (DL), dimension reduction is crucial for enhancing training efficiency and mitigating overfitting, particularly when managing complex data such as three-dimensional (3D) saturation data. The 3D saturation data in the context of geological carbon storage (GCS) presents unique challenges due to its inherent sparsity and the abrupt transitions at plume boundaries, known as shock fronts. To address the challenges, we proposed a novel DL framework that integrates dimension reduction with advanced 3D reconstruction techniques. Our model leveraged latent variables derived from 2D average saturation data, offering a robust and efficient solution tailored to the intricate dynamics of 3D saturation fields. The proposed framework can extract the critical features of the high-dimensional data while reducing the variable numbers, which is more tractable for DL models and enhances the model robustness and accuracy. Therefore, it provides a novel approach for modeling and analyses in complex geological scenarios, which finds great potential applications in environmental monitoring and energy storage.

Wang, Hongsheng↗
DOE OSTI2024

Deep Learning-based Parameterization of Complex 3D CO2 Saturation Data in Large-scale Geological Carbon Storage

In deep learning (DL), dimension reduction plays a pivotal role in improving training efficiency and minimizing overfitting, especially when working with complex datasets like three-dimensional (3D) saturation data. In the context of geological carbon storage (GCS), 3D saturation data introduces unique challenges due to its sparse nature and sharp transitions at plume boundaries, known as shock fronts. To tackle these challenges, we developed a novel DL framework that combines dimension reduction with advanced 3D reconstruction techniques. Our approach utilizes latent variables derived from 2D average saturation fields to efficiently capture the essential features of high-dimensional data while reducing the number of variables. This enhances both the robustness and accuracy of DL models, making the framework more practical for real-world applications. By offering a tailored solution for modeling complex 3D saturation dynamics, this framework holds significant potential for environmental monitoring, energy storage, and other geological applications.

Wang, Hongsheng [University of Texas at Austin]↗
DOE OSTI2023

Comparison of Saturation Rules Used for Gyrokinetic Quasilinear Transport Modeling

Theory-based transport modeling has been widely successful and is built on the foundations of quasilinear theory. Specifically, the quasilinear expression of the flux can be used in combination with a saturation rule for the toroidal mode amplitude. Most transport models follow this approach. Saturation rules are heuristic and difficult to rigorously derive. We compare three common saturation rules using a fairly accurate quasilinear expression for the fluxes computed using local linear gyrokinetic simulation. We take plasma parameters from experimental H-mode profiles and magnetic equilibrium and include electrons, deuterium, and carbon species. We find that the various saturation rules provide qualitatively similar behavior. This may help to explain why the different theory-based transport models can all predict core tokamak profiles reasonably well. Comparisons with nonlinear local and global gyrokinetic simulations are discussed.

Parker, Scott E. (ORCID:000000030942630X)↗
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↗
NASA NTRS1972

Pseudo-saturating power converter

Relative performance of three basic configurations of electronic conversion units (saturating, pseudo-saturating, nonsaturating) requiring low power levels is compared. Pseudo-saturating mode exhibits best overall performance, nonsaturating a close second, saturating a poor third.

Radys, R. G.↗
NASA NTRS1979

Design, fabrication and delivery of a prototype saturator for ACPL

The design configuration and performance characteristics of a saturator developed to provide ground-based simulation for some of the experiments for ACPL-1 first flights of Spacelab are described, some difficulties encountered with the apparatus are discussed, and recommendations concerning testing of this type of instrument are presented. The saturators provide a means of accurately fixing the water vapor mixing ratio of an aerosol sample. Dew point temperatures from almost freezing to ambient room temperatures can be attained with high precision. The instruments can accommodate aerosol flow rates approaching 1000 cc/s. Provisions were made to inject aerosols upstream of these saturators, although downstream injection can be accomplished as well. A device of this type will be used in the ACPL-1 to condition various aerosols delivered concurrently to a CFD, expansion chamber, and static diffusion chamber used in zero gravity cloud-forming experiments. The saturator was designed to meet the requirements projected for the flight instrument.

Keyser, G.↗
NASA NTRS1978

Effective thermal conductivity of fully and partially saturated metal wicks

The purpose of the study is threefold: (1) to perform accurate experiments covering a wide range of porosities to determine the effective thermal conductivity of sintered fiber porous metals fully saturated with air, water, and water vapor; (2) to propose correlations for these cases, and to compare these correlations with existing analytical predictions, experimental correlations and data; and (3) to determine the effective thermal conductivity of porous metals partially saturated with water and water vapor, respectively. The experimental apparatus, based on the steady-state method of comparison, is designed and used for determining the effective thermal conductivity. The wicks are made of nickel 200, stainless steel 430, and copper, covering a porosity range 0.30-0.67. Based on the experimental results, a single correlation is derived for predicting the effective thermal conductivity of all wicks fully saturated with air, water, and water vapor. For two partially saturated wick specimens with both high porosity and large matrix thermal conductivity, a liquid-vapor transition zone is observed between the phases, which increases the total combined effective thermal conductivity of the specimen.

Kar, K.↗
NASA NTRS1983

Effects of control saturation on the command response of statically unstable aircraft

Hanson and Stengel (1981) have described the effects of saturating controls on the initial-condition response of statically unstable aircraft. In an analysis of the stability augmentation system, consideration was given to maximizing the region of stable response. The present investigation is concerned with an extension of the control saturation analysis to the problem of command response, taking into account the main problem of control design for satisfactory flying qualities (as perceived by the pilot). A model for examining the effects of control saturation on longitudinal stability and command response is developed and applied to an aircraft with a static instability. Three parameters prescribing longitudinal flying qualities are examined with respect to their ability to identify proper flying qualities in the presence of control saturation. The presented analysis provides also satisfactory guidelines for establishing command saturation/stability boundaries.

Hanson, G. D.↗
NASA NTRS1986

Saturation of cyclotron maser instability driven by an electron loss-cone distribution

The resonance diffusion of electrons in velocity space caused by the excited EM wave fields is considered to be the dominant saturation process of cyclotron maser instability that is driven by an electron loss-cone distribution. An upper bound of the saturation level is derived analytically. Since the resulting saturation level is low, the resonance diffusion is indeed responsible for the saturation of the cyclotron maser instability.

Kuo, S. P.↗
NASA NTRS1986

Resonant electron diffusion as a saturation process of the synchrotron maser instability

The theory of resonant electron diffusion as an effective saturation process of the auroral kilometric radiation has been formulated. The auroral kilometric radiation is assumed to be amplified by the synchrotron maser instability that is driven by an electron distribution of the loss-cone type. The calculated intensity of the saturated radiation is found to have a significantly lower value in comparison with that caused by the quasi-linear diffusion process as an alternative saturation process. This indicates that resonant electron diffusion dominates over quasi-linear diffusion in saturating the synchrotron maser instability.

Lee, M. C.↗