Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Network inference”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Indicator-directed Dynamic Power Management for Iterative Workloads on GPU-Accelerated Systems

Modern high-performance and warehouse computing centers show strong interest in minimizing system power consumption while satisfying customers’ quality of service (QoS). Dynamic voltage and frequency scaling (DVFS) is effective for achieving this goal. Nevertheless, automating the process online and making it transparent to users must address three major challenges: (1) Complexity — today’s hardware components (e.g., CPUs, GPUs, memory, network, etc.) can be configured in several or dozens of frequency/voltage states for satisfying divergent system demands. Given their combination and the emergence of heterogeneity, searching the optimal configuration in the design space online can be timing consuming. (2) QoS guarantee — user-defined objectives such as power constraint and performance target must be monitored, predicted and ensured at the best effort. (3) Adaptability — various known and unknown workloads run on systems. Workloads characteristics should be quickly determined and configurations dynamically adjusted in accord with workloads and QoS. In this work, we focus on applications exhibiting an interesting feature – iterative or periodic, which is common among conventional HPC and emerging machine learning workloads. We propose an online dynamic power-performance (ODPP) management framework to dynamically adjust GPU DVFS configurations to meet performance and power objectives and constraints, without any code annotation or intrusion. Particularly, ODPP extracts the performance and power indicators for applications from their resources utilization profiles in a short episode. It further automatically constructs an accurate model that infers from the indicators how the application's performance and power vary with GPU core and memory frequencies. Aided with the model, for both seen and unseen applications, ODPP can quickly determine the most appropriate DVFS configuration for their execution. We evaluate ODPP on an NVIDIA GPU using multiple exascale computing (ECP) and deep learning applications.

Zou, Pengfei↗
DOE OSTI2022

Regulators of early maize leaf development inferred from transcriptomes of laser capture microdissection (LCM)-isolated embryonic leaf cells

The superior photosynthetic efficiency of C 4 leaves over C 3 leaves is owing to their unique Kranz anatomy, in which the vein is surrounded by one layer of bundle sheath (BS) cells and one layer of mesophyll (M) cells. Kranz anatomy development starts from three contiguous ground meristem (GM) cells, but its regulators and underlying molecular mechanism are largely unknown. To identify the regulators, we obtained the transcriptomes of 11 maize embryonic leaf cell types from five stages of pre-Kranz cells starting from median GM cells and six stages of pre-M cells starting from undifferentiated cells. Principal component and clustering analyses of transcriptomic data revealed rapid pre-Kranz cell differentiation in the first two stages but slow differentiation in the last three stages, suggesting early Kranz cell fate determination. In contrast, pre-M cells exhibit a more prolonged transcriptional differentiation process. Differential gene expression and coexpression analyses identified gene coexpression modules, one of which included 3 auxin transporter and 18 transcription factor (TF) genes, including known regulators of Kranz anatomy and/or vascular development. In situ hybridization of 11 TF genes validated their expression in early Kranz development. We determined the binding motifs of 15 TFs, predicted TF target gene relationships among the 18 TF and 3 auxin transporter genes, and validated 67 predictions by electrophoresis mobility shift assay. From these data, we constructed a gene regulatory network for Kranz development. Our study sheds light on the regulation of early maize leaf development and provides candidate leaf development regulators for future study.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Masked Symbol Modeling for Demodulation of Oversampled Baseband Communication Signals in Impulsive Noise-Dominated Channels

Recent breakthroughs in natural language processing show that attention mech- anism in Transformer networks, trained via masked-token prediction, enables models to capture the semantic context of the tokens and internalize the grammar of language. While the application of Transformers to communication systems is a burgeoning field, the notion of context within physical waveforms remains under-explored. This paper addresses that gap by re-examining inter-symbol con- tribution (ISC) caused by pulse-shaping overlap. Rather than treating ISC as a nuisance, we view it as a deterministic source of contextual information embedded in oversampled complex baseband signals. We propose Masked Symbol Model- ing (MSM), a framework for the physical (PHY) layer inspired by Bidirectional Encoder Representations from Transformers methodology. In MSM, a subset of symbol-aligned samples is randomly masked, and a Transformer predicts the missing symbol identifiers using the surrounding “in-between” samples. Through this objective, the model learns the latent syntax of complex baseband waveforms. We illustrate MSM’s potential by applying it to the task of demodulating sig- nals corrupted by impulsive noise, where the model infers corrupted segments by leveraging the learned context. Our results suggest a path toward receivers that interpret, rather than merely detect communication signals, opening new avenues for context-aware PHY layer design.

Bedir, Oguz↗
DOE OSTI2024

Thinking Bayesian for plasma physicists

Bayesian statistics offers a powerful technique for plasma physicists to infer knowledge from the heterogeneous data types encountered. To explain this power, a simple example, Gaussian Process Regression, and the application of Bayesian statistics to inverse problems are explained. The likelihood is the key distribution because it contains the data model, or theoretic predictions, of the desired quantities. By using prior knowledge, the distribution of the inferred quantities of interest based on the data given can be inferred. Because it is a distribution of inferred quantities given the data and not a single prediction, uncertainty quantification is a natural consequence of Bayesian statistics. The benefits of machine learning in developing surrogate models for solving inverse problems are discussed, as well as progress in quantitatively understanding the errors that such a model introduces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Embedding Neural Thermal Scattering (NeTS) Modules in SERPENT for Higher Fidelity Advanced Reactor Analysis

When a neutron born in fission thermalizes to the order of $k$ $B$ $T$, it’s de-Broglie wavelength and energy approach the order of inter-atomic spacing and elementary lattice oscillations, respectively. $S$($a,β,t$) or the scattering law, uuantify these temperature-dependent crystallographic contributions to total cross section (or reaction rate). In a Monte Carlo analysis, cumulative distribution functions (CDFs) of $S$($a,β,t$) are loaded to memory from “A Compact ENDF” (ACE) files for stochastically selecting thermal scattered neutron trajectories. In this work, novel neural thermal scattering (NeTS) modules for $S$($a,β,t$) CDFs are designed, trained, serialized and embedded within SERPENT using Python’s limited C-API for on-the-fly deployment of crystalline graphite $S$($a,β,t$) sampling. Torchscript tracing and Numba just-in-time (JIT) compilation streamline neural inference on NVIDIA GPUs with CUDA libraries. Demonstrations of bare sphere thermalization of fast and thermal sources show excellent agreement between embedded NeTS in SERPENT and MCNP. With an explicit model of the reactor, NeTS can predict on-the-fly changes in TREAT neutron spectra as a function of local temperature, which can serve to improve transient and accident predictions in a multiphysics analysis framework. This framework can be further extended to account on-the-fly for changes in local graphitic microstructure to scattering cross sections, and outlines a novel coupling of modern machine learning with state-of-the-art reactor physics methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Low Temperature CO 2 Hydrogenation on Unsupported Mo 2 C Catalysts

CO 2 hydrogenation to methanol, a key reaction for decarbonizing the fuel and chemical industries, requires catalyst formulations that hydrogenate CO 2 selectively to methanol at temperatures where methanol conversion is not significantly equilibrium limited (<423 K). Herein we report continuous CO 2 hydrogenation at low temperatures (348-408 K, H 2 /CO 2 = 0.1-50, 5-35 bar) with high selectivity to methanol (up to ca. 80%) over unsupported β-Mo 2 C catalysts. Active site density quantification via titration with trifluoroacetic acid at reaction temperatures enables an assessment of site-specific rates. Methanation and reverse water gas shift (RWGS) occur concurrently with methanol synthesis during CO 2 hydrogenation over Mo 2 C. Reaction pathway analysis, product cofeeds, and reversibility formalisms show that all products form through primary reaction pathways from CO 2 , but secondary reactions of CO contribute significantly to rates of methanation. Dependences of forward rates on reactant and product concentration determined by independently varying the CO 2 , H 2 , CO, H 2 O, CH 3 OH, and CH 4 pressure in conjunction with reversibility formalisms reveal that all products form through H-assisted CO 2 activation and involve partially hydrogenated CO 2 -derived intermediates. Here, these inferences were verified by quantitative agreement between measured site-time yields and site-time yields predicted by closed form kinetic rate expressions in an integral reactor model over widely varying conditions (85-2000 kPa H 2 , 80-1500 kPa CO 2 , 0-45 kPa H 2 O, 0-21 kPa CO, 0-25 kPa CH 3 OH, 0-75 kPa CH 4 , 5-87 mol Mo s s mol CO 2 -1 ). Coverages calculated based on the kinetic model reveal that the Mo 2 C surface is covered with bidentate CO- and CO 2 -derived intermediates of the stoichiometry H 2 CO 2 and H 2 CO, indicating that H 2 and CO x do not compete for surface occupancy but instead adsorb cooperatively to form partially hydrogenated intermediates. Hydrogenation of the CO-derived H 2 CO** intermediate favors methanation, while hydrogenation of CO 2 -derived H 2 CO 2 ** favors methanol synthesis. Together, these findings demonstrate the ability of unsupported Mo 2 C to catalyze the hydrogenation of CO 2 to methanol at low temperatures and provide insight into the reaction network and mechanisms involved in its formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Advancing Diagnostic Model Evaluation to Better Understand Water Shortage Mechanisms in Institutionally Complex River Basins

Abstract Water resources systems models enable valuable inferences on consequential system stressors by representing both the geophysical processes determining the movement of water and the human elements distributing it to its various competing uses. This study contributes a diagnostic evaluation framework that pairs exploratory modeling with global sensitivity analysis to enhance our ability to make inferences on water scarcity vulnerabilities in institutionally complex river basins. Diagnostic evaluation of models representing institutionally complex river basins with many stakeholders poses significant challenges. First, it needs to exploit a large and diverse suite of simulations to capture important human‐natural system interactions as well as institutionally aware behavioral mechanisms. Second, it needs to have performance metrics that are consequential and draw on decision‐relevant model outputs that adequately capture the multisector concerns that emerge from diverse basin stakeholders. We demonstrate the proposed model diagnostic framework by evaluating how potential interactions between changing hydrologic conditions and human demands influence the frequencies and durations of water shortages of varying magnitudes experienced by hundreds of users in a subbasin of the Colorado River. We show that the dominant factors shaping these effects vary both across users and, for an individual user, across percentiles of shortage magnitude. These differences hold even for users sharing diversion locations, demand levels or water right seniority. Our findings underline the importance of detailed institutional representation for such basins, as institutions strongly shape how dominant factors of stakeholder vulnerabilities propagate through the complex network of users.

Hadjimichael, Antonia↗
DOE OSTI2021

A Reconfigurable Neural Network ASIC for Detector Front-End Data Compression at the HL-LHC

Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network (NN) autoencoder model can be implemented in a radiation-tolerant application-specific integrated circuit (ASIC) to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile. For our application, we consider the high-granularity calorimeter from the Compact Muon Solenoid (CMS) experiment at the CERN Large Hadron Collider. The advantage of the machine learning approach is in the flexibility and configurability of the algorithm. By changing the NN weights, a unique data compression algorithm can be deployed for each sensor in different detector regions and changing detector or collider conditions. To meet area, performance, and power constraints, we perform quantization-aware training to create an optimized NN hardware implementation. The design is achieved through the use of high-level synthesis tools and the hls4ml framework and was processed through synthesis and physical layout flows based on a low-power (LP)-CMOS 65-nm technology node. The flow anticipates 200 Mrad of ionizing radiation to select gates and reports a total area of 3.6 mm 2 and consumes 95 mW of power. The simulated energy consumption per inference is 2.4 nJ. Furthermore, this is the first radiation-tolerant on-detector ASIC implementation of an NN that has been designed for particle physics applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Turbulence model reduction by deep learning

A defining problem of turbulence theory is to produce a predictive model for turbulent fluxes. These have profound implications for virtually all aspects of the turbulence dynamics. In magnetic confinement devices, drift-wave turbulence produces anomalous fluxes via cross-correlations between fluctuations. In this work, we introduce an alternative, data-driven method for parametrizing these fluxes. The method uses deep supervised learning to infer a reduced mean-field model from a set of numerical simulations. We apply the method to a simple drift-wave turbulence system and find a significant new effect which couples the particle flux to the local gradient of vorticity. Notably, here, this effect is much stronger than the oft-invoked shear suppression effect. We also recover the result via a simple calculation. The vorticity gradient effect tends to modulate the density profile. In addition, our method recovers a model for spontaneous zonal flow generation by negative viscosity, stabilized by nonlinear and hyperviscous terms. We highlight the important role of symmetry to implementation of the new method.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties

Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Mass Detection for Heavy-Duty Vehicles using Gaussian Belief Propagation

Predicting vehicle mass is critical to accurately estimate energy use and emissions of commercial trucks. However, data from vehicle telematics is often not at sufficient temporal resolution or accuracy for use in model-based detection methods. In this work, a new statistical mass prediction technique is described for heavy-duty vehicles that incorporates the use Gaussian Belief Propagation (GBP) for probabilistic inference. Similar to Bayesian inference models, the GBP model typically requires less labeled training data than other contemporary machine learning techniques. First, a factor graph is constructed, and a set of Gaussian belief nodes with associated means and variances are fitted to the training data. To better handle noisy input data, the GBP mass prediction model utilizes a k-nearest factors (kNF) algorithm for probabilistic inference on unseen testing data. The proposed method is compared with a classical weighted k-nearest neighbors (kNN) regressor. This statistical kNF-GBP model works even with low-quantity, low-quality initial training data, while being capable of realtime mass estimation. Unlike the kNN regressor, the GBP model produces a measure of uncertainty with its predictions. The proposed method is validated using curve-sampled driving data collected from multiple cloud-connected Class 8 regional haul diesel trucks. Both the kNN regressor and the kNF-GBP mass prediction model were able to predict payload mass with coefficients of determination above 0.97 with minimal data preprocessing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. Finally, while primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Dynamic Earthquake Triggering in Southern California in High Resolution: Intensity, Time Decay, and Regional Variability

Abstract Earthquake triggering by seismic waves has been recognized as a phenomenon for nearly 30 years. However, our ability to study dynamic triggering has been limited by our ability to capture the triggering stresses accurately and record the resultant earthquakes. Here we use full waveforms from a dense seismic network and a modern, high‐resolution seismic catalog to measure triggering in Southern California from 2008 to 2017 based on interevent time ratios. We find that the fractional seismicity rate change, which we term triggering intensity or triggerability, as a function of peak strain change for the period of ∼20 s due to distant earthquakes is monotonically increasing and compatible with earlier measurements made with a disjoint data set from 1984 to 2008. A triggering strain of 1 microstrain is equivalent to the local productivity generated by an M 1.8 earthquakes. This result implies that a prediction of seismicity rate changes can be made based on recorded ground shaking using the same formalism as currently used for aftershock prediction. For a teleseismic event, this small level of triggering occurs throughout the region and thus aggregates to a regional effect. We find that the triggering rate decays after the triggerer follows an Omori‐Utsu law, but at a much slower rate than a typical aftershock sequence. The slow decay rate suggests that an ancillary process such as creep or fluid flow must be part of dynamic triggering. The prevalence of triggering in areas of creep or fluid involvement reinforces this inference. A triggering cascade of secondary earthquakes is insufficient to explain the data.

Miyazawa, Masatoshi↗
DOE OSTI2022

Automatic Generation of High-Performance Convolution Kernels on ARM CPUs for Deep Learning

In this work, we present FastConv, a template-based code auto-generation open source library that can automatically generate high-performance deep learning convolution kernels of arbitrary matrices/tensors shapes. FastConv is based on the Winograd algorithm, which is reportedly the highest performing algorithm for the time-consuming convolution layers of convolutional neural networks. ARM CPUs cover a wide range designs and specifications, from embedded devices to HPC-grade CPUs. The leads to the dilemma of how to consistently optimize Winograd-based convolution solvers for convolution layers of different shapes. FastConv addresses this problem by using templates to auto-generate multiple shapes of tuned kernels variants suitable for skinny tall matrices. As a performance portable library, FastConv transparently searches for the best combination of kernel shapes, cache tiles, scheduling of loop orders, packing strategies, access patterns, and online/offline computations. Auto-tuning is used to search the parameter configuration space for the best performance for a given target architecture and problem size. The experiments with layer-wise evaluation on the VGG--16 model confirms a 1.25x performance gains is got by tuning the Winograd library. Integrated comparison results shows 1.02x to 1.40x, 1.14x to 2.17x, and 1.22x and 2.48x speedup is achieved over NNPACK, Arm NN, and FeatherCNN on the Kunpeng 920 beside few cases. Furthermore, problem size performance portability experiments with various convolution shapes shows that FastConv achieves 1.2x to 1.7x speedup and 2x to 22x speedup over NNPACK and ARM NN inference engine using Winograd on Kunpeng 920 . CPU performance portability evaluation on the VGG--16 show an average speedup over NNPACK of 1.42x, 1.21x, 1.26x, 1.37x, 2.26x, and 11.02x is observed on Kunpeng 920, Snapdragon 835, 855, 888, Apple M1, and AWS Graviton2, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Geometry-aware framework for deep energy method: An application to structural mechanics with hyperelastic materials

Here, in this work, we introduce a novel physics-informed framework named the Geometry-Aware Deep Energy Method (GADEM) for solving structural mechanics problems on different geometries. As the weak form of the physical system equation (or the energy-based approach) has demonstrated clear advantages compared to the strong form for solving solid mechanics problems, GADEM employs the weak form and aims to infer the solution on multiple shapes of geometries. Integrating a geometry-aware framework into an energy-based method results in an effective physics-informed deep learning model in terms of accuracy and computational cost. Different ways to represent the geometric information and to encode the geometric latent vectors are investigated in this work. We introduce a loss function of GADEM which is minimized based on the potential energy of all considered geometries. An adaptive learning method is also employed for the sampling of collocation points to enhance the performance of GADEM. We present some applications of GADEM to solve solid mechanics problems, including a loading simulation of a toy tire involving contact mechanics and large deformation hyperelasticity. The numerical results of this work demonstrate the remarkable capability of GADEM to infer the solution on various and new shapes of geometries using only one trained model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine learning enhanced predictions of ICRF heating: Overcoming numerical limitations via data curation

In this work, we present the development of robust surrogate models for Ion Cyclotron Range of Frequencies (ICRF) and High-Harmonic Fast Wave (HHFW) heating predictions in fusion plasmas. Building upon our previous efforts to achieve real-time capable models, we identify the cause of the outliers found using TORIC in certain HHFW heating scenarios. The outliers are observed to be spurious ion Bernstein wave (IBW)-like modes caused by a wavelength control algorithm designed to address challenging scenarios with high perpendicular wavenumbers. The effect arises from the modulation in the perpendicular susceptibility, which can induce sign reversal and IBW-like propagation for scenarios featuring normalized ion Larmor radius λ i ≫ 1. We use TORIC with this algorithm disabled to generate a novel HHFW-NSTX database that is free of outliers. Surrogate models trained on this database, including Random Forest Regressor (RFR), Multi-Layer Perceptrons, and Gaussian Process Regressors (GPR), demonstrate the ability to accurately predict HHFW heating profiles, with regression scores of R 2 ∈[0.93−0.99]. Additionally we demonstrate that it is possible to generalize predictions beyond training data by the use of both RFR and GPR models, enabling the prediction of scenarios previously limited to the original model. GPR models also provide uncertainty quantification, offering insights into model confidence. This work introduces a comprehensive Verification, Validation, and Uncertainty Quantification methodology for surrogate modeling, applicable not only to ICRF heating but also to other RF heating challenges and fusion physics problems. Beyond accelerated inference, these models show effective extrapolation capabilities, providing an alternative for addressing numerical challenges.

Artificial neural networks↗
DOE OSTI2021

Investigation of Scale-Dependent Groundwater/Surface-water Exchange in Rivers by Gradient Self-Potential Logging: Numerical Modeling and Field Experiments

Exchanges of groundwater and surface-water are fundamental to a wide range of water-supply and water-quality management issues but challenging to map beyond the reach scale. Waterborne gradient self-potential (SP) measurements are directly sensitive to water flow through riverbed sediments and can be used to infer exchange locations, direction (gain versus loss), scale, and relative changes, but to date applications to river corridor hydrology are limited. Numerical modeling and field experiments were therefore performed herein, each emphasizing waterborne gradient SP logging for identifying and locating focused vertical groundwater discharge (surface-water gain) and recharge (surface-water loss) in a river. Two and three-dimensional numerical models were constructed to simulate the polarities, appearances, and peak amplitudes of streaming-potential and electric-field anomalies on a riverbed and in the surface-water that were attributable to steady-state vertical fluxes of groundwater through high-permeability conduits in the riverbed. Effects of varied hydraulic length-scale of exchange and surface-water depth were tested through numerical modeling. Modeling results aided in data acquisition and interpretation for three repeated field experiments performed along a 1.5–2.0 km reach of the Quashnet River in Cape Cod, Massachusetts, where focused, meter-scale groundwater discharges occur at discrete locations within otherwise ubiquitous and more diffuse groundwater upwelling conditions. Strong gradient SP anomalies were repeatedly measured in the Quashnet River at previously confirmed locations of focused groundwater discharge, showing the efficacy of waterborne gradient SP logging in identifying and characterizing groundwater/surface water exchange dynamics at multiple river network scales.

Engineering↗