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At least 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Advances in Geophysical Monitoring Methods To Support Groundwater Remedies

Monitoring plays an important role in supporting remedial investigations and selection of remedies for groundwater contamination and for assessing performance during implementation of remedies. When a groundwater plume is near the interface between groundwater and surface water, the dynamics of groundwater flow at this interface needs to be considered in the design and implementation of a remedy. For this and other aquifer situations, groundwater flow dynamics may be difficult to evaluate through groundwater head measurements due to a sparse monitoring-well network, a relatively flat hydraulic gradient, or the presence of induced gradients from pumping operations or groundwater mounds. Geophysical approaches, coupled with other targeted measurements or the use of an ionic tracer, provide temporal data that can quantify groundwater flow dynamics. Recent developments include a flux-monitoring tool that measures fluid conductivity, bulk conductivity, and temperature at the interface between groundwater and surface water. Joint interpretation of these local data with temporal two- or three-dimensional geophysical data improves quantification of groundwater and mass flux at a targeted location. In addition, geophysical design improvements that enable the use of numerical simulations to evaluate implementation configurations and expected data resolution for geophysical methods identify threshold conditions for successful monitoring and identify the most cost-effective configurations to meet monitoring objectives.

Strickland, Christopher E.↗
DOE OSTI2019

Advances in Geophysical Monitoring Methods to Support Groundwater Remedies - 19096

Monitoring plays an important role in supporting remedial investigations and selection of remedies for groundwater contamination and for assessing performance during implementation of remedies. When a groundwater plume is near the interface between groundwater and surface water, the dynamics of groundwater flow at this interface needs to be considered in the design and implementation of a remedy. For this and other aquifer situations, groundwater flow dynamics may be difficult to evaluate through groundwater head measurements due to a sparse monitoring-well network, a relatively flat hydraulic gradient, or the presence of induced gradients from pumping operations or groundwater mounds. Geophysical approaches, coupled with other targeted measurements or the use of an ionic tracer, provide temporal data that can quantify groundwater flow dynamics. Recent developments include a flux-monitoring tool that measures fluid conductivity, bulk conductivity, and temperature at the interface between groundwater and surface water. Joint interpretation of these local data with temporal two- or three-dimensional geophysical data improves quantification of groundwater and mass flux at a targeted location. In addition, geophysical design improvements that enable the use of numerical simulations to evaluate implementation configurations and expected data resolution for geophysical methods identify threshold conditions for successful monitoring and identify the most cost-effective configurations to meet monitoring objectives. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2001

Electron Heat Flux in Pressure Balance Structures at Ulysses

Pressure balance structures (PBSs) are a common feature in the high-latitude solar wind near solar minimum. Rom previous studies, PBSs are believed to be remnants of coronal plumes and be related to network activity such as magnetic reconnection in the photosphere. We investigated the magnetic structures of the PBSs, applying a minimum variance analysis to Ulysses/Magnetometer data. At 2001 AGU Spring meeting, we reported that PBSs have structures like current sheets or plasmoids, and suggested that they are associated with network activity at the base of polar plumes. In this paper, we have analyzed high-energy electron data at Ulysses/SWOOPS to see whether bi-directional electron flow exists and confirm the conclusions more precisely. As a result, although most events show a typical flux directed away from the Sun, we have obtained evidence that some PBSs show bi-directional electron flux and others show an isotropic distribution of electron pitch angles. The evidence shows that plasmoids are flowing away from the Sun, changing their flow direction dynamically in a way not caused by Alfven waves. From this, we have concluded that PBSs are generated due to network activity at the base of polar plumes and their magnetic structures axe current sheets or plasmoids.

Yamauchi, Yohei↗
NASA NTRS2012

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

Yao, Weigang↗
NASA NTRS1995

Adaptive Optimization of Aircraft Engine Performance Using Neural Networks

Preliminary results are presented on the development of an adaptive neural network based control algorithm to enhance aircraft engine performance. This work builds upon a previous National Aeronautics and Space Administration (NASA) effort known as Performance Seeking Control (PSC). PSC is an adaptive control algorithm which contains a model of the aircraft's propulsion system which is updated on-line to match the operation of the aircraft's actual propulsion system. Information from the on-line model is used to adapt the control system during flight to allow optimal operation of the aircraft's propulsion system (inlet, engine, and nozzle) to improve aircraft engine performance without compromising reliability or operability. Performance Seeking Control has been shown to yield reductions in fuel flow, increases in thrust, and reductions in engine fan turbine inlet temperature. The neural network based adaptive control, like PSC, will contain a model of the propulsion system which will be used to calculate optimal control commands on-line. Hopes are that it will be able to provide some additional benefits above and beyond those of PSC. The PSC algorithm is computationally intensive, it is valid only at near steady-state flight conditions, and it has no way to adapt or learn on-line. These issues are being addressed in the development of the optimal neural controller. Specialized neural network processing hardware is being developed to run the software, the algorithm will be valid at steady-state and transient conditions, and will take advantage of the on-line learning capability of neural networks. Future plans include testing the neural network software and hardware prototype against an aircraft engine simulation. In this paper, the proposed neural network software and hardware is described and preliminary neural network training results are presented.

Simon, Donald L.↗
NASA NTRS2005

An Intense Terminal Epoch of Widespread Fluvial Activity on Early Mars: 2. Increased Runoff and Paleolake Development

To explain the much higher denudation rates and valley network development on early Mars (more than approximately 3.6 Gyr ago), most investigators have invoked either steady state warm/wet (Earthlike) or cold/dry (modern Mars) end-member paleoclimates. Here we discuss evidence that highland gradation was prolonged, but generally slow and possibly ephemeral during the Noachian Period, and that the immature valley networks entrenched during a brief terminal epoch of more erosive fluvial activity in the late Noachian to early Hesperian. Observational support for this interpretation includes (1) late-stage breaching of some enclosed basins that had previously been extensively modified, but only by internal erosion and deposition; (2) deposition of pristine deltas and fans during a late stage of contributing valley entrenchment; (3) a brief, erosive response to base level decline (which was imparted as fretted terrain developed by a suite of processes unrelated to surface runoff) in fluvial valleys that crosscut the highland-lowland boundary scarp; and (4) width/contributing area relationships of interior channels within valley networks, which record significant late-stage runoff production with no evidence of recovery to lower-flow conditions. This erosion appears to have ended abruptly, as depositional landforms generally were not entrenched with declining base level in crater lakes. A possible planetwide synchronicity and common cause to the late-stage fluvial activity are possible but remain uncertain. This increased activity of valley networks is offered as a possible explanation for diverse features of highland drainage basins, which were previously cited to support competing warm, wet and cold, dry paleoclimate scenarios.

Rossman III, Irwin P.↗
DOE OSTI2023

Experimental flow-through a single fracture with monolayer proppant at reservoir conditions: A case study on Caney Shale, Southwest Oklahoma, USA

Global energy systems are undergoing a crucial transition to clean energy sources, thereby moving away from fossil-fuel based energy with the ultimate aim of reducing carbon dioxide emissions. It is essential to continue to advance in exploring and developing shale gas resources as this will underpin the global supply of natural gas that is needed to support the energy transition. The challenge hereby is that the slow fluid flow of the shale matrix due to its very low permeability requires large-scale volume fracturing to produce a conductive fracture network that enhances the connectivity between the shale formation and the wellbore. This work examines the influence of a thin proppant layer on a single fracture using a flow-through experiment, and also explores whether proppant embedment in the Caney shale is affected by the rock mineralogy, surface roughness, fluids, confining stress, time, temperature and bedding. The experiment was conducted for a duration of nine days (216 h), experimental temperature was varied from ambient temperature to reservoir temperature of 125 °C (257 °F) and confining stress was varied from 367 psi (2.53MPa) to a maximum of 4011.82 psi (27.66 MPa). Here, we find that the conductivity of the fracture is primarily influenced by the layer of proppant used, surface roughness, mineralogy, fluids, temperature and closure stress.

Caney shale↗
NASA NTRS1981

Landsat hydrobiological classification for an inland fresh water marsh within Everglades National Park

The considered investigation is concerned with the application of Landsat Multispectral Scanner (MSS) data to the classification of vegetative communities and the establishment of flow vectors for the Shark River Slough in Everglades National Park, Florida. A systematic array of 'ground truth' was established utilizing comprehensive hydrologic field data and conventional high altitude infrared aerial photography. A control network was defined that represented all hydrobiological zones (those wetland vegetative communities that directly influence the rate of overland sheet flow) in the Shark River Slough. These data were then directly applied to the Landsat imagery utilizing an interactive multispectral processor which generated hydrographic maps of the slough and defined the surface radiance characteristics of each hydrobiological system. It was found that the application of Landsat imagery for hydrologic applications in a wetlands area, such as the Shark River Slough in Everglades National Park, is definitely a viable tool for resource management.

Rose, P. W.↗
DOE OSTI2021

Analysis of a new multispecies tumor growth model coupling 3D phase-fields with a 1D vascular network

In this study, we present and analyze a mathematical model for tumor growth incorporating ECM erosion, interstitial flow, and the effect of vascular flow and nutrient transport. The model is of phase-field or diffused-interface type in which multiple phases of cell species and other constituents are separated by smooth evolving interfaces. The model involves a mesoscale version of Darcy’s law to capture the flow mechanism in the tissue matrix. Modeling flow and transport processes in the vasculature supplying the healthy and cancerous tissue, one-dimensional (1D) equations are considered. Since the models governing the transport and flow processes are defined together with cell species models on a three-dimensional (3D) domain, we obtain a 3D–1D coupled model.

3D–1D coupled blood flow models↗
DOE OSTI2023

pyflowline: a mesh-independent river network generator for hydrologic models

River networks are crucial in hydrologic and Earth system models. Accurately representing river networks in spatially distributed hydrologic models requires considering the model's spatial discretization and computational mesh. However, current methods of generating river networks for hydrologic models do not typically support unstructured meshes. Unstructured meshes offer numerous advantages over traditional, structured meshes. To overcome this limitation, we developed PyFlowline, a Python package that generates mesh-independent river networks. With PyFlowline, hydrologic modelers can generate conceptual river networks and their topological relationships for both structured and unstructured meshes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Modular DC circuit breaker with integrated energy storage for future DC networks

A T-breaker is an all-in-one solution for dc microgrid fault protection, power flow control, and power quality improvement. A T-breaker features a modular multilevel “T” structure with integrated energy storage devices. The two horizontal arms of the T-breaker realize fault current breaking, load voltage compensation, and power flow control; and the vertical arm of the T-breaker realizes shunt compensation. The configuration provides excellent voltage scalability and relaxes the requirements on the switching signal synchronization during fault transients. The local energy storage in sub-modules eases the fault energy dissipation requirement placed on the traditionally-adopted surge arrestors. The modular multilevel structure also offers immense control flexibility for all types of targeted functions of the provided T-breaker.

Wang, Jin↗
DOE OSTI2023

Combustion control using spiking neural networks

A system that controls a combustion engine stores network vectors in a memory that represent diverse and distinct spiking neural networks. The system decodes the network vectors and trains and evaluates the spiking neural networks. The system duplicates selected network vectors and crosses-over the duplicated network vectors that represent modified spiking neural networks. The system mutates the crossed-over duplicated network vectors by randomly modifying one or more portions of the crossing-over duplicated network vectors. The system meter exhaust gas into an intake manifold when an engine temperature exceeds a threshold, an engine load exceeds a threshold, an engine's rotation-per-minute rate exceeds a threshold, and a fuel flow exceeds a threshold. The system modifies fuel flow into an engine's combustion chamber on a cycle-to-cycle basis by the trained spiking neural network.

Puente, Brian P. Maldonado↗
NASA NTRS2015

Regolith and Environment Science and Oxygen and Lunar Volatile Extraction (RESOLVE): Lunar Advanced Volatile Analysis (LAVA) Capillary Fluid Dynamic Restriction Effects on Gas Chromatography

The Resource Prospector (RP) mission with the Regolith and Environment Science and Oxygen Lunar Volatile Extraction (RESOLVE) payload aims to show the presence of water in lunar regolith, and establish a proving ground for NASAs mission to Mars. One of the analysis is performed by the Lunar Advanced Volatiles Analysis (LAVA) subsystem, which consists of a fluid network that facilitates the transport of volatile samples to a gas chromatograph and mass spectrometer (GC-MS) instrument. The understanding of fluid dynamics directed from the GC to the MS is important due to the influence of flow rates and pressures that affect the accuracy of and prevent the damage to the overall GC-MS instrument. The micro-scale capillary fluid network within the GC alone has various lengths and inner-diameters; therefore, determination of pressure differentials and flow rates are difficult to model computationally, with additional complexity from the vacuum conditions in space and lack of a lunar atmosphere. A series of tests were performed on an experimental set-up of the system where the inner diameters of the GC transfer line connecting to the MS were varied. The effect on chromatography readings were also studied by applying these lines onto a GC instrument. It was found that a smaller inner diameter transfer line resulted in a lower flow rate, as well as a lower pressure differential across the thermal conductivity detector (TCD) unit of the GC and a negligible pressure drop across the mock-up capillary column. The chromatography was affected with longer retention times and broader peak integrations. It was concluded that a 0.050 mm inner diameter line still proved most suitable for the systems flow rate preferences. In addition, it was evident that this small transfer line portrayed some expense to GC signal characteristics and the wait time for steady-state operation.

RESOLVE↗
DOE OSTI2025

Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble

In this work we explore the capability of physics-informed neural networks (PINNs) to discover multiple solutions. Many real-world phenomena governed by nonlinear differential equations (DEs), such as fluid flow, exhibit multiple solutions under the same conditions, yet capturing this solution multiplicity remains a significant challenge. A key difficulty lies in providing appropriate initial conditions or guesses, as widely used time-marching schemes and Newton’s method are highly sensitive to these choices when solving complex computational problems. While machine learning models, particularly PINNs, have shown promise in solving DEs, their ability to capture multiple solutions remains underexplored. In this work, we propose a simple and practical approach using PINNs to learn and discover multiple solutions. We first demonstrate that PINNs, when combined with random initialization and deep ensemble method—originally developed for uncertainty quantification—can effectively uncover multiple solutions to nonlinear ordinary and partial DEs. Although training large ensembles of PINNs may appear computationally demanding, this can be done efficiently using vectorization techniques supported by modern deep learning frameworks, allowing many networks to be trained simultaneously. Our approach highlights the critical role of initialization in shaping solution diversity, addressing an often-overlooked aspect of machine learning for scientific computing. Furthermore, we propose utilizing PINN-generated solutions as initial conditions or initial guesses for conventional numerical solvers to enhance accuracy and efficiency in capturing multiple solutions. Extensive numerical experiments, including the Allen–Cahn equation and cavity flow, where our approach successfully identifies both stable and unstable solutions, validate the effectiveness of our method. These findings establish a general and efficient framework for addressing solution multiplicity in nonlinear DEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Understanding Hydraulic Fracturing: A Multi-Scale Problem

Despite the impact that hydraulic fracturing has had on the energy sector, the physical mechanisms that control its efficiency and environmental impacts remain poorly understood in part because the length scales involved range from nano-meters to kilo-meters. We characterize flow and transport in shale formations across and between these scales using integrated computational, theoretical, and experimental efforts. At the field scale, we use discrete fracture network modeling to simulate production at a well site whose fracture network is based on a site characterization of a shale formation. At the core scale, we use triaxial fracture experiments and a finite-element discrete-element fracture propagation model with a coupled fluid solver to study dynamic crack propagation in low permeability shale. We use lattice Boltzmann pore-scale simulations and microfluidic experiments in both synthetic and real micromodels to study pore-scale flow phenomenon such as multiphase flow and mixing. A mechanistic description and integration of these multiple scales is required for accurate predictions of production and the eventual optimization of hydrocarbon extraction from unconventional reservoirs.

03 NATURAL GAS↗
NASA NTRS2008

A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System: Implementation and Basic Experiments

A three-dimensional variational data assimilation scheme for the Regional Ocean Modeling System (ROMS), named ROMS3DVAR, has been described in the work of Li et al. (2008). In this paper, ROMS3DVAR is applied to the central California coastal region, an area characterized by inhomogeneity and anisotropy, as well as by dynamically unbalanced flows. A method for estimating the model error variances from limited observations is presented, and the construction of the inhomogeneous and anisotropic error correlations based on the Kronecker product is demonstrated. A set of single observation experiments illustrates the inhomogeneous and anisotropic error correlations and weak dynamic constraints used. Results are presented from the assimilation of data gathered during the Autonomous Ocean Sampling Network (AOSN) experiment during August 2003. The results show that ROMS3DVAR is capable of reproducing complex flows associated with upwelling and relaxation, as well as the rapid transitions between them. Some difficulties encountered during the experiment are also discussed.

California coastal ocean↗