Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Network monitoring”

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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Estimating Interplanetary Magnetic Field Conditions at Mercury's Orbit From MESSENGER Magnetosheath Observations Using a Feedforward Neural Network

Abstract Mercury's small magnetosphere is embedded in the dynamic and intense solar wind environment characteristic of the inner heliosphere. Both the magnitude and orientation of the interplanetary magnetic field (IMF) significantly influence the solar wind‐magnetospheric interaction at Mercury, driving phenomena such as magnetic reconnection. The MErcury Surface, Space Environment, Geochemistry and Ranging (MESSENGER) spacecraft provided in‐situ magnetic field measurements of the solar wind, the magnetosheath, and the magnetosphere along each orbit. However, it is a challenge to directly assess the IMF's impact on Mercury's plasma environment due to the temporal separation between observations within the solar wind and the magnetosphere, especially in the absence of an upstream monitor. Here, we present a feedforward neural network (FNN) trained on a subset of magnetosheath observations to estimate the strength and orientation of the IMF upstream of the bow shock. Utilizing magnetosheath magnetic field, cylindrical spatial coordinates, and heliocentric distance measurements, the FNN predicts upstream IMF conditions with an score of 0.70 and mean averaged error of 5.3 nT, thereby greatly decreasing the temporal separation between IMF estimates and magnetospheric measurements throughout the MESSENGER mission. This approach yields IMF estimates for all magnetosheath data measured by MESSENGER, providing a useful tool for future investigations of the IMF impact on Mercury's magnetosphere. This method will be integrable with the dual‐spacecraft BepiColombo magnetosheath measurements, providing useful estimates of upstream IMF conditions particularly during the extended periods in which neither spacecraft sample the solar wind. Our results demonstrate the utility of machine learning techniques on advancing space science research.

Bowers, Charles F.↗
DOE OSTI2024

Assessing the design of integrated methane sensing networks

Abstract While methane is the second largest contributor to global warming after carbon dioxide, it has a larger warming effect over a much shorter lifetime. Despite accelerated technological efforts to radically reduce global carbon dioxide emissions, rapid reductions in methane emissions are needed to limit near-term warming. Being primarily emitted as a byproduct from agricultural activities and energy extraction, methane is currently monitored via bottom–up (i.e. activity level) or top–down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected and emission rates are challenging to characterize with current monitoring frameworks. In this paper, we study the design of a layered monitoring approach that combines bottom–up and top–down approaches as an integrated sensing network. By recognizing that varying meteorological conditions and emission rates impact the efficacy of bottom–up monitoring, we develop a probabilistic approach to optimal sensor placement in its bottom–up network. Subsequently, we derive an inverse Bayesian framework to quantify the improvement that a design-optimized integrated framework has on emission-rate quantifications and their uncertainties. We find that under realistic meteorological conditions, the overall error in estimating the true emission rates is approximately 1.3 times higher, with their uncertainties being approximately 2.4 times higher, when using a randomized network over an optimized network, highlighting the importance of optimizing the design of integrated methane sensing networks. Further, we find that optimized networks can improve scenario coverage fractions by more than a factor of 2 over experimentally-studied networks, and identify a budget threshold beyond which the rate of optimized-network coverage improvement exhibits diminishing returns, suggesting that strategic sensor placement is also crucial for maximizing network efficiency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Monitoring covariance in multivariate time series: Comparing machine learning and statistical approaches

Abstract In complex systems with multiple variables monitored at high‐frequency, variables are not only temporally autocorrelated, but they may also be nonlinearly related or exhibit nonstationarity as the inputs or operation changes. One approach to handling such variables is to detrend them prior to monitoring and then apply control charts that assume independence and stationarity to the residuals. Monitoring controlled systems is even more challenging because the control strategy seeks to maintain variables at prespecified mean levels, and to compensate, correlations among variables may change, making monitoring the covariance essential. In this paper, a vector autoregressive model (VAR) is compared with a multivariate random forest (MRF) and a neural network (NN) for detrending multivariate time series prior to monitoring the covariance of the residuals using a multivariate exponentially weighted moving average (MEWMA) control chart. Machine learning models have an advantage when the data's structure is unknown or may change. We design a novel simulation study with nonlinear, nonstationary, and autocorrelated data to compare the different detrending models and subsequent covariance monitoring. The machine learning models have superior performance for nonlinear and strongly autocorrelated data and similar performance for linear data. An illustration with data from a reverse osmosis process is given.

Weix, Derek↗
DOE OSTI2024

WM2024 Symposia Student Poster - ARG-US System Remote Area Monitoring at INL Experimental Proxy Design and Early Results

The patent-pending ARG-US system uses customizable high-tech sensors and monitoring systems in conjunction with a remote network connection to provide easy and quick access to the status of nuclear and radiological facilities and storage containers. In a partnership with the Argonne National Laboratory and the Idaho National Laboratory, the research team are finding new ways to customize and test the limitations the ARG-US system. This presentation is for the student poster session.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Evaluation of the PhaseNet Model Applied to the IMS Seismic Network

Producing a complete and accurate set of signal detections is essential for automatically building and characterizing seismic events of interest for nuclear explosion monitoring. Signal detection algorithms have been an area of research for decades, but still produce large quantities of false detections and misidentify real signals that must be detected to produce a complete global catalog of events of interest. Deep learning methods have shown promising capabilities in effectively characterizing seismic signals for complex tasks such as identifying phase arrival times. We use the PhaseNet model, a UNet-based Neural Network, trained on local distance data from northern California to predict seismic arrivals on data from the International Monitoring System (IMS) global network. We use an analyst-curated bulletin generated from this data set to compare the performance of PhaseNet to that of the Short-Term Average/Long-Term Average (STA/LTA) algorithm. We find that PhaseNet has the potential of outperforming traditional processing methods and recommend the training of a new model with the IMS data to achieve optimal performance.

58 GEOSCIENCES↗
DOE OSTI2025

Resilient Entanglement Distribution in a Multihop Quantum Network

The evolution of quantum networking requires architectures capable of dynamically reconfigurable entanglement distribution to meet diverse user needs and ensure tolerance against transmission disruptions. We introduce multihop quantum networks to improve network reach and resilience by enabling quantum communications across intermediate nodes, thus broadening network connectivity and increasing scalability. We present multihop two-qubit polarization-entanglement distribution within a quantum network at the Oak Ridge National Laboratory campus. Our system uses wavelength-selective switches for adaptive bandwidth management on a software-defined quantum network that integrates a quantum data plane with classical data and control planes, creating a flexible, reconfigurable mesh. Our network distributes entanglement across six nodes within three subnetworks, each located in a separate building, optimizing quantum state fidelity and transmission rate through adaptive resource management. Additionally, we demonstrate the network's resilience by implementing a link recovery approach that monitors and reroutes quantum resources to maintain service continuity despite link failures—paving the way for scalable and reliable quantum networking infrastructures.

Alshowkan, Muneer [Oak Ridge National Laboratory (↗
DOE OSTI2025

Network of networks: Time series clustering of AmeriFlux sites

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluating factors influencing infrasonic signal detection and automatic processing performance utilizing a regional network

Physical and deployment factors that influence infrasound signal detection and assess automatic detection performance for a regional infrasound network of arrays in the Western U.S. are explored using signatures of ground truth (GT) explosions (yields). Despite these repeated known sources, published infrasound event bulletins contain few GT events. Arrays are primarily distributed toward the south-southeast and south-southwest at distances between 84 and 458 km of the source with one array offering azimuthal resolution toward the northeast. Events occurred throughout the spring, summer, and fall of 2012 with the majority occurring during the summer months. Depending upon the array, automatic detection, which utilizes the adaptive F-detector successfully, identifies between 14% and 80% of the GT events, whereas a subsequent analyst review increases successful detection to 24%–90%. Combined background noise quantification, atmospheric propagation analyses, and comparison of spectral amplitudes determine the mechanisms that contribute to missed detections across the network. This analysis provides an estimate of detector performance across the network, as well as a qualitative assessment of conditions that impact infrasound monitoring capabilities. Finally, the mechanisms that lead to missed detections at individual arrays contribute to network-level estimates of detection capabilities and provide a basis for deployment decisions for regional infrasound arrays in areas of interest.

58 GEOSCIENCES↗
DOE OSTI2023

Spatiotemporal analysis of 3D human iPSC-derived neural networks using a 3D multi-electrode array

While there is a growing appreciation of three-dimensional (3D) neural tissues (i.e., hydrogel-based, organoids, and spheroids), shown to improve cellular health and network activity to mirror brain-like activity in vivo , functional assessment using current electrophysiology techniques (e.g., planar multi-electrode arrays or patch clamp) has been technically challenging and limited to surface measurements at the bottom or top of the 3D tissue. As next-generation MEAs, specifically 3D MEAs, are being developed to increase the spatial precision across all three dimensions (X, Y, Z), development of improved computational analytical tools to discern region-specific changes within the Z dimension of the 3D tissue is needed. In the present study, we introduce a novel computational analytical pipeline to analyze 3D neural network activity recorded from a “bottom-up” 3D MEA integrated with a 3D hydrogel-based tissue containing human iPSC-derived neurons and primary astrocytes. Over a period of ~6.5 weeks, we describe the development and maturation of 3D neural activity (i.e., features of spiking and bursting activity) within cross sections of the 3D tissue, based on the vertical position of the electrode on the 3D MEA probe, in addition to network activity (identified using synchrony analysis) within and between cross sections. Then, using the sequential addition of postsynaptic receptor antagonists, bicuculline (BIC), 2-amino-5-phosphonovaleric acid (AP-5), and 6-cyano-5-nitroquinoxaline-2,3-dione (CNQX), we demonstrate that networks within and between cross sections of the 3D hydrogel-based tissue show a preference for GABA and/or glutamate synaptic transmission, suggesting differences in the network composition throughout the neural tissue. The ability to monitor the functional dynamics of the entire 3D reconstructed neural tissue is a critical bottleneck; here we demonstrate a computational pipeline that can be implemented in studies to better interpret network activity within an engineered 3D neural tissue and have a better understanding of the modeled organ tissue.

3D culture↗
DOE OSTI2025

Fracture Network Quantification during CO2 Injection

This is the presentation prepared for the ARMA 2025 (59th US Rock Mechanics/Geomechanics Symposium) Conference held in Santa Fe, New Mexico, June 8-11, 2025. Accurate mapping and quantification of these networks are essential to ensure the integrity of CO2 storage reservoirs, understand and reduce potential leakage, and maintain long-term environmental safety. This study presents a novel machine learning-driven approach, integrated with geomechanical analysis, to quantify fracture networks and assess their spatial distribution during CO2 injection. This paper combines microseismic monitoring data with principles of hydraulic diffusivity and geomechanical analysis to characterize reservoir scale fracture network. The novelty of our approach lies in its capacity to assimilate time-dependent pressure data and microseismicity into a cohesive framework, which not only identifies microseismic triggering fronts but also tracks fracture distribution during active injection. Besides, leveraging image log data and analysis our approach also provides another angle of the insights to solidate the fracture networks understanding and geomechanical impacts. Key results from our study include the detection of over 100 distinct fracture clusters across the injection site, with fracture orientations strongly correlated with the prevailing in-situ stress field.

CO2 storage and sequestration↗
DOE OSTI2025

Fracture Network Quantification during CO2 Injection

This is the conference paper accompanying an oral presentation at the ARMA 2025 (59th US Rock Mechanics/Geomechanics Symposium) Conference held in Santa Fe, New Mexico, June 8-11, 2025. Accurate mapping and quantification of these networks are essential to ensure the integrity of CO2 storage reservoirs, understand and reduce potential leakage, and maintain long-term environmental safety. This study presents a novel machine learning-driven approach, integrated with geomechanical analysis, to quantify fracture networks and assess their spatial distribution during CO2 injection. This paper combines microseismic monitoring data with principles of hydraulic diffusivity and geomechanical analysis to characterize reservoir scale fracture network. The novelty of our approach lies in its capacity to assimilate time-dependent pressure data and microseismicity into a cohesive framework, which not only identifies microseismic triggering fronts but also tracks fracture distribution during active injection. Besides, leveraging image log data and analysis our approach also provides another angle of the insights to solidate the fracture networks understanding and geomechanical impacts. Key results from our study include the detection of over 100 distinct fracture clusters across the injection site, with fracture orientations strongly correlated with the prevailing in-situ stress field.

CO2 storage and sequestration↗
DOE OSTI2026

Secure Time Synchronization in Power Grids and Network HIL Synchrophasor Testing

Reliable and secure time synchronization underpins the monitoring and control functions of modern power grids. As GPS-based timing infrastructures grow more susceptible to spoofing and jamming, their vulnerabilities pose escalating risks to grid stability. This work investigates a secure, resilient timing framework that can serve as an alternative or redundant source for power grids, with a particular focus on synchrophasorbased applications. A candidate timing system architecture is evaluated to guarantee trustworthy time dissemination, even in degraded conditions. A network hardware-in-the-loop testing of two synchrophasors validates the concepts, demonstrating enhanced timing integrity, improved detection of timing anomalies, and sustained observability during adverse timing events.

Wu, Ori [ORNL] (ORCID:0000000326723410)↗
DOE OSTI2024

Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations

Abstract Long-term environmental monitoring is critical for managing the soil and groundwater at contaminated sites. Recent improvements in state-of-the-art sensor technology, communication networks, and artificial intelligence have created opportunities to modernize this monitoring activity for automated, fast, robust, and predictive monitoring. In such modernization, it is required that sensor locations be optimized to capture the spatiotemporal dynamics of all monitoring variables as well as to make it cost-effective. The legacy monitoring datasets of the target area are important to perform this optimization. In this study, we have developed a machine-learning approach to optimize sensor locations for soil and groundwater monitoring based on ensemble supervised learning and majority voting. For spatial optimization, Gaussian process regression (GPR) is used for spatial interpolation, while the majority voting is applied to accommodate the multivariate temporal dimension. Results show that the algorithms significantly outperform the random selection of the sensor locations for predictive spatiotemporal interpolation. While the method has been applied to a four-dimensional dataset (with two-dimensional space, time, and multiple contaminants), we anticipate that it can be generalizable to higher-dimensional datasets for environmental monitoring sensor location optimization.

Siddiquee, Masudur R.↗
DOE OSTI2026

Oak Ridge Computing Academy: An HPC cluster deployment and management pilot

The High Performance Computing Technologies (HPCT) course is a hands-on High Performance Computing (HPC) cluster deployment and management training program offered as part of the International School for Advanced Studies (SISSA) and the International Center for Theoretical Physics (ICTP) Master in High Performance Computing (MHPC) specialization. Here, this training program introduces students to key concepts in cluster configuration. which include networking, software stack provisioning, job scheduling, and monitoring. The publicly available course materials feature several examples and underlying methods that are broadly applicable to cluster deployment and management. This paper discusses the design of a new workforce development program at the Oak Ridge National Laboratory that is based on HPCT, the Oak Ridge Computing Academy (ORCA). The ORCA pilot program was hosted by the Oak Ridge Leadership Computing Facility (OLCF) in Summer 2025. As a part of this discussion, HPCT and ORCA course contents and infrastructure are outlined, ORCA participant experiences are detailed, and potential opportunities for improvement are discussed.

Education↗
DOE OSTI2023

Radionuclide measurements of the international monitoring system

The International Monitoring System (IMS) is a unique global network of sensors, tuned to measure various phenomenology, with the common goal of detecting a nuclear explosion anywhere in the world. One component of this network collects measurements of radioactive particulates and gases (collectively known as radionuclides) present in the atmosphere; through this, compliance with the Comprehensive Nuclear-Test-Ban Treaty (CTBT) can be verified. The radionuclide sub-network consists of 120 sensors across 80 locations, supported by 16 measurement laboratories. All radionuclide stations make use of a form of γ-ray spectroscopy to measure radionuclides from samples; this remains largely unchanged since the network was first established 25 years ago. Advances in sampling and spectroscopy systems can yield improvements to the sensitivity of the network to detect a nuclear explosion. This paper summarises the status of the IMS radionuclide network, the current suite of technology used and reviews new technology that could enhance future iterations, potentially improving the verification power of the IMS.

Goodwin, Matthew A.↗
DOE OSTI2022

High-performance piezoelectric composites via β phase programming

Polymer-ceramic piezoelectric composites, combining high piezoelectricity and mechanical flexibility, have attracted increasing interest in both academia and industry. However, their piezoelectric activity is largely limited by intrinsically low crystallinity and weak spontaneous polarization. Here, we propose a Ti 3 C 2 T x MXene anchoring method to manipulate the intermolecular interactions within the all-trans conformation of a polymer matrix. Employing phase-field simulation and molecular dynamics calculations, we show that OH surface terminations on the Ti 3 C 2 T x nanosheets offer hydrogen bonding with the fluoropolymer matrix, leading to dipole alignment and enhanced net spontaneous polarization of the polymer-ceramic composites. We then translated this interfacial bonding strategy into electrospinning to boost the piezoelectric response of samarium doped Pb (Mg1/3Nb 2/3 )O 3 -PbTiO 3 /polyvinylidene fluoride composite nanofibers by 160% via Ti 3 C 2 T x nanosheets inclusion. With excellent piezoelectric and mechanical attributes, the as-electrospun piezoelectric nanofibers can be easily integrated into the conventional shoe insoles to form a foot sensor network for all-around gait patterns monitoring, walking habits identification and Metatarsalgi prognosis. This work utilizes the interfacial coupling mechanism of intermolecular anchoring as a strategy to develop high-performance piezoelectric composites for wearable electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Insights on the dissolution of water in an albite melt at high pressures and temperatures from a direct structural analysis

The water dissolution mechanism in silicate melts under high pressures is not well understood. Here we present the first direct structure investigation of a water-saturated albite melt to monitor the interactions between water and the network structure of silicate melt at the molecular level. In situ high-energy X-ray diffraction was carried out on the NaAlSi 3 O 8 -H 2 O system at 800 °C and 300 MPa, at the Advanced Photon Source synchrotron facility. The analysis of the X-ray diffraction data was augmented with classical Molecular Dynamics simulations of a hydrous albite melt, incorporating accurate water-based interactions. The results show that metal–oxygen bond breaking at the bridging sites occurs overwhelmingly at the Si site upon reaction with H 2 O, with subsequent Si–OH bond formation and negligible Al–OH formation. Furthermore, we see no evidence for the dissociation of the Al 3+ ion from the network structure upon breaking of the Si–O bond in the hydrous albite melt. The results also indicate that the Na + ion is an active participant in the modifications of the silicate network structure of the albite melt upon water dissolution at high P–T conditions. We do not find evidence for the Na + ion dissociating from the network structure upon depolymerization and subsequent formation of NaOH complexes. Instead, our results show that the Na + ion persists as a structure modifier with a shift away from Na–BO bonding to an increase in the extent of Na-NBO bonding, in parallel with pronounced depolymerization of the network. Our MD simulations show that the Si–O and Al–O bond lengths are expanded by about 6% in the hydrous albite melt compared to those of the dry melt at high P–T conditions. The changes in the network silicate structure of a hydrous albite melt at high pressure and temperature, as revealed in this study, must be considered in the advancement of water dissolution models of hydrous granitic (or alkali aluminosilicate) melts.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Design and evaluation of a cyber‐physical testbed for improving attack resilience of power systems

Abstract A power system is a complex cyber‐physical system whose security is critical to its function. A major challenge is to model, analyse and visualise the communication backbone of the power systems concerning cyber threats. To achieve this, the design and evaluation of a cyber‐physical power system (CPPS) testbed called Resilient Energy Systems Lab (RESLab) are presented to capture realistic cyber, physical, and protection system features. RESLab is architected to be a fundamental platform for studying and improving the resilience of complex CPPS to cyber threats. The cyber network is emulated using Common Open Research Emulator (CORE), which acts as a gateway for the physical and protection devices to communicate. The physical grid is simulated in the dynamic time frame using Power World Dynamic Studio (PWDS). The protection components are modelled with both PWDS and physical devices including the SEL Real‐Time Automation Controller (RTAC). Distributed Network Protocol 3 (DNP3) is used to monitor and control the grid. Then, the design is exemplified and the tools are validated. This work presents four case studies on cyberattack and defence using RESLab, where we demonstrate false data and command injection using Man‐in‐the‐Middle and Denial of Service attacks and validate them on a large‐scale synthetic electric grid.

Sahu, Abhijeet↗