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The Utah Frontier Observatory for Research in Geothermal Energy (FORGE) attempted a stimulation at well 16A(78)-32 during April and May 2022. We recorded telluric and magnetotelluric (MT) data before, during, and after the well stimulation experiment using the FORGE Telluric Monitoring (FTM) array to constrain transients in the Earth's electrical structure caused by the stimulation.
Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This project (1) developed and demonstrated data-driven process controls at full-scale facilities for five promising WRRF process technologies that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) created a Machine Learning (ML) Toolkit and an implementation guide of new process control approaches that walks users through each step of the ML workflow and illustrates the steps through case study examples.
The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.
As the use additive manufacturing (AM) processes continues to grow in critical industries, improved quality assurance methods are becoming increasingly sought after for qualification and certification of AM components. Traditional nondestructive evaluation of printed components is often unable to supply the required confidence in print quality to justify qualification and certification, but the layer-by-layer nature of AM provides unprecedented opportunities for in situ quality inspection. This document summarizes recent developments in process monitoring research specifically related to Directed Energy Deposition (DED). Particular attention is given to three aspects of the highlighted manuscripts: (1) the type of sensors used, (2) features extracted from each sensor modality, and (3) analysis of extracted features for AM quality assessment. Based on the review of the state-of-the-art, several observations have been made. First, none of the reviewed works have applied their trained models to real part geometries, with many of the works relying on single track experiments, thin-walled structures, and cubes. Similarly, there have not been any works demonstrating model generalizability, i.e., a model trained on data from one build allows for fruitful analysis of data from another build. Many works used machine learning techniques to distinguish different process regimes (i.e., normal, keyholing, lack-of-fusion), but very few papers have investigated stochastic variation in an already “optimized” process. Sensor fusion approaches are also limited in the DED sensing literature, but the few works that have employed such techniques have demonstrated the benefits. Finally, registration of in situ data to the build coordinate system is of paramount importance to producing industrially relevant in situ monitoring systems. Data registration allows direct correlations between process anomalies detected in the process monitoring data to localized departures in part quality, but such techniques are generally lacking in the current literature.
Albedo (precipitating/splash) electrons, created by galactic cosmic rays (GCR) interaction with the upper atmosphere move upwards away from the surface of the earth. In the past validation work these particles were often considered to have negligible contribution to astronaut radiation exposure on the International Space Station (ISS). Estimates of astronaut exposure based on the available Computer Aided Design (CAD) models of ISS consistently underestimated measurements onboard ISS when the contribution of albedo particles to exposure were neglected. Recent measurements of high energy electrons outside ISS Japan Experimental Module (JEM) using Exposed Facility (EF), Space Environment Data Acquisition Equipment - Attached Payload (SEDA-AP) and Standard DOse Monitor (SDOM), indicates the presence of high energy electrons at ISS altitude. In this presentation the status of these energetic electrons is reviewed and mechanism for the creation of these particles inside/outside South Atlantic Anomaly (SAA) region explained. In addition, limited dosimetric evaluation of these electrons at 600 MeV and 10 GeV is presented.
Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.
Context. The response of imaging atmospheric Cherenkov telescopes to incident γ -ray-initiated showers in the atmosphere changes as the telescopes age due to exposure to light and weather. These aging processes affect the reconstructed energies of the events and γ -ray fluxes. Aims. This work discusses the implementation of signal calibration methods for the Very Energetic Radiation Imaging Telescope Array System (VERITAS) to account for changes in the optical throughput and detector performance over time. Methods. The total throughput of a Cherenkov telescope is the product of camera-dependent factors, such as the photomultiplier tube gains and their quantum efficiencies, and the mirror reflectivity and Winston cone response to incoming radiation. This document summarizes different methods to determine how the camera gains and mirror reflectivity have evolved over time and how we can calibrate this changing throughput in reconstruction pipelines for imaging atmospheric Cherenkov telescopes. The implementation is validated against seven years of observations with the VERITAS telescopes of the Crab Nebula, which is a reference object in very-high-energy astronomy. Results. Regular optical throughput monitoring and the corresponding signal calibrations are found to be critical for the reconstruction of extensive air shower images. The proposed implementation is applied as a correction to the signals of the photomultiplier tubes in the telescope simulation to produce fine-tuned instrument response functions. This method is shown to be effective for calibrating the acquired γ -ray data and for recovering the correct energy of the events and photon fluxes. At the same time, it keeps the computational effort of generating Monte Carlo simulations for instrument response functions affordably low.
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The US DOE Office of Electricity's Energy Storage Program's joint R&D work with the Cordova Electric Cooperative (CEC) has deployed several advanced grid technologies that are providing benefits today to Cordova Alaska's electricity users. Advanced grid technologies deployed through DoE co-funded R&D include a 1MW Battery Energy Storage System (BESS), and enhanced monitoring including Phasor Measurement Units (PMU's) to help better understand the operational impacts of the added BESS. This paper will highlight key accomplishments to-date in deploying and using advanced grid technologies, and then outline the next phase of work that will use these technologies to implement an operating scheme to reconfigure the utility's distribution system and utility resources including BESS to provide emergency back-up power to a critical load: the Cordova Community Medical Center (CCMC). This paper will include additional insights on the use of utility resources to support critical loads via case study examples by the National Rural Electric Cooperative Assoc. (NRECA).
Aquifer thermal energy storage (ATES) is a concept that can help to address heating and cooling needs through the use of the subsurface as a seasonal thermal energy storage (STES) system. Over 2800 ATES systems have been deployed with storage temperatures typically below 25 °C and only a few with higher temperatures (>40 °C), which would increase the energy density and utility of the stored thermal fluids. Until now, only a few high-temperature aquifer thermal energy storage (HT-ATES) projects have been initiated and are still in operation. These HT-ATES projects have encountered a range of technical and non-technical challenges. This study reviews ten such projects: four in Germany and six in the Netherlands. The non-technical issues include public acceptance, a lack of regulatory framework for these systems, managing overlapping uses of the subsurface, managing changes with the providers and off-takers of thermal energy, and obtaining financing to implement these projects. Common technical issues include geological factors such as incomplete characterization of the subsurface and reservoir heterogeneity; geochemical issues such as mineral scaling, corrosion, and biofouling; lower than expected thermal recovery; and issues with system design and reliability. This review highlights benefits and challenges faced by HT-ATES projects with the goal to use the lessons learned to improve the siting, design, development, and operation of such systems. Recommendations include improved initial subsurface site characterization, use of coupled process models to optimize system design and predict system performance, cascaded uses of stored thermal energy to better utilize the stored heat, monitoring networks to provide feedback on system performance, and expanded system scale to allow for continued operation even when maintenance of some system components is required. Techno-economic modeling and risk analysis could be used to optimize such HT-ATES project design and identify key factors that will affect sustained economic viability. In addition, design flexibility is important for these systems to allow for changing conditions regarding the supply and demand of thermal energy. Adopting these findings should improve the performance and reduce the risks for future HT-ATES projects worldwide.
Monitoring ultralow nitrogen dioxide (NO 2 ) concentrations is crucial for air quality management and public health. However, the existing NO 2 gas sensors have several defects, like high cost and power consumption, and exhibit poor selectivity. This study addresses these challenges by presenting a novel hexadecafluorinated iron phthalocyanine-reduced graphene oxide (FePcF 16 -rGO) covalent hybrid sensor for NO 2 detection. This innovative approach, which overcomes the limitations of fabrication cost, energy efficiency, and gas selectivity, is a significant step forward in gas sensor technology. The sensor demonstrates exceptional sensitivity toward ultralow NO 2 concentrations (15.14% response for 100 ppb) with a rapid 60 s UV light-induced recovery. Additionally, the sensor exhibits high selectivity for NO 2 , achieving a limit of detection (LOD) of 8.59 ppb. This approach paves the way for developing cost-effective, energy-efficient, and miniature NO 2 monitoring devices for improved environmental monitoring and enhanced safety in workplaces where NO 2 exposure is a concern.
As access to computational resources continues to increase, free-energy calculations have emerged as a powerful tool that can play a predictive role in drug design. Yet, in a number of instances, the reliability of these calculations can be improved significantly if a number of precepts, or good practices are followed. For the most part, the theory upon which these good practices rely has been known for many years, but often overlooked, or simply ignored. In other cases, the theoretical developments are too recent for their potential to be fully grasped and merged into popular platforms for the computation of free-energy differences. The current best practices for carrying out free-energy calculations will be reviewed demonstrating that, at little to no additional cost, free-energy estimates could be markedly improved and bounded by meaningful error estimates. In energy perturbation and nonequilibrium work methods, monitoring the probability distributions that underlie the transformation between the states of interest, performing the calculation bidirectionally, stratifying the reaction pathway and choosing the most appropriate paradigms and algorithms for transforming between states offer significant gains in both accuracy and precision. In thermodynamic integration and probability distribution (histogramming) methods, properly designed adaptive techniques yield nearly uniform sampling of the relevant degrees of freedom and, by doing so, could markedly improve efficiency and accuracy of free energy calculations without incurring any additional computational expense.
The Global Energy and Water Cycle Exchanges (GEWEX) project was created more than 30 years ago within the framework of the World Climate Research Programme (WCRP). The aim of this initiative was to address major gaps in our understanding of Earth’s energy and water cycles given a lack of information about the basic fluxes and associated reservoirs of these cycles. GEWEX sought to acquire and set standards for climatological data on variables essential for quantifying water and energy fluxes and for closing budgets at the regional and global scales. In so doing, GEWEX activities led to a greatly improved understanding of processes and our ability to predict them. Such understanding was viewed then, as it remains today, essential for advancing weather and climate prediction from global to regional scales. GEWEX has also demonstrated over time the importance of a wider engagement of different communities and the necessity of international collaboration for making progress on understanding and on the monitoring of the changes in the energy and water cycles under ever increasing human pressures. Here, this paper reflects on the first 30 years of evolution and progress that has occurred within GEWEX. This evolution is presented in terms of three main phases of activity. Progress toward the main goals of GEWEX is highlighted by calling out a few achievements from each phase. A vision of the path forward for the coming decade, including the goals of GEWEX for the future, are also described.
The effect of temperature on water vapour desorption isotherms can be described simply using the Clausius-Clapeyron equation and the isosteric energy of adsorption, the latter conventionally estimated using two (or more) isotherms obtained at different temperatures. This approach has some disadvantages, however, and this article puts forward a simple alternative methodology for characterising this isosteric energy experimentally. Our method is based on monitoring the relative humidity of a cementitious material heated under sealed conditions. The evolution of the relative humidity as a function of temperature makes it possible to directly evaluate the isosteric energy. In practice, the tests are performed using a cell of the type that is used for self-desiccation studies.
Correlations between solar activity and atmospheric processes have been investigated for more than 30 years, and the reality of the strong correlations found has been well accepted. However, it remains problematic to establish mechanisms capable of coupling the physical manifestations of solar variability to the lower atmosphere. There has been to date a lack of long-term satellite measurements of particles of sufficient energy to penetrate the Earth's atmosphere. The NOAA Space Environment Laboratory has maintained energetic particle detectors on board the NOAA/TIROS and GOES series of satellites. These instruments monitor the flux of energetic protons to energies greater than about 800 MeV. Measurements of particles above 350 MeV began October 1978, and continue to date. The instruments are briefly described, along with the current program to reduce the data. The goal is to provide a data base that will help to assess the significance of variations of atmospheric and ionospheric properties due to energetic particle precipitation.
Conference presentation at International Meeting for Applied Geoscience & Energy (IMAGE), Houston, Texas, August 25–28, 2025. The Energy & Environmental Research Center (EERC) is leading applied research on electromagnetic (EM) monitoring methods at an active carbon storage site in North Dakota. Injection operations at the site began in February 2024, with a permitted injection rate of up to 2.7 million tonnes of CO 2 annually using six injection wells. CO 2 is captured on-site and injected into the Broom Creek Formation, a predominantly sandstone reservoir and saline aquifer located at a depth of approximately 1800 meters. The EERC led acquisition of multiple active- and passive-source EM techniques between August and October of 2024 to provide a thorough understanding of the resistivity profile at the site.
Across the Department of Energy’s Environmental and Legacy Management sites, complex groundwater plumes exist that will require long-term monitoring to ensure remedial actions that have been put in place remain effective decades into the future. The current monitoring paradigm predominantly consists of groundwater well sampling, whereby samples are collected, concentrations analyzed, and plume anomalies are detected after they have occurred. The Advanced Long Term Environmental Monitoring Systems (ALTEMIS) program is a multi-lab, multi-institution team of researchers that is deploying spatially integrative technologies (i.e., real-time in situ sensor networks), coupled with artificial intelligence and machine learning, to establish a more proactive monitoring paradigm. Within this approach, plume anomalies can be predicted, and corrective actions can be established prior to the occurrence, offering a more cost-effective and robust approach to long-term monitoring. The team has deployed a variety of different in situ sensing technologies at the Savannah River Site’s F-Area Hazardous Waste Management Facility around the F-Area Seepage Basins, which are unlined basins that received 7 billion liters of acidic low-level radioactive waste from the 1950s until the late 1980s. The technologies and techniques that the team is deploying are intended to ensure that the remedial actions that have been taken by the site remain effective decades into the future. Foundational to this approach is a robust, integrated data management and analysis plan to ensure accurate and timely reporting from the variety of sensor systems that are in place. This report will outline the data management plan that has been implemented by the ALTEMIS team at the Savannah River Site and will serve as a blueprint as the technology is translated to new sites across the DOE Complex.
This paper addresses a method of non-intrusive local inspection of surface and sub-surface conditions, interfaces, laminations and seals in both space vehicle and ground operations with an integrated suite of imaging sensors during pre-launch operations. It employs an advanced Raman spectrophotometer with additional spectrophotometers and lidar mounted on a flying robot to constantly monitor the space hardware as well as inner surface of the vehicle and ground operations hardware. This paper addresses a team of micro flying robots with necessary sensors and photometers to monitor the entire space vehicle internally and externally. The micro flying robots can reach altitude with least amount of energy, where astronauts have difficulty in reaching and monitoring the materials and subsurface faults. The micro flying robot has an embedded fault detection system which acts as an advisory system and in many cases micro flying robots act as a Supervisor to fix the problems. As missions expand to a sustainable presence in the Moon, and extend for durations longer than one year in lunar outpost, the effectiveness of the instrumentation and hardware has to be revolutionized if NASA is to meet high levels of mission safety, reliability, and overall success. The micro flying robot uses contra-rotating propellers powered by an ultra-thin, ultrasonic motor with currently the world's highest power weight ratio, and is balanced in mid-air by means of the world's first stabilizing mechanism using a linear actuator. The essence of micromechatronics has been brought together in high-density mounting technology to minimize the size and weight. The robot can take suitable payloads of photometers, embedded chips for image analysis and micro pumps for sealing cracks or fixing other material problems. This paper also highlights advantages that this type of non-intrusive techniques offer over costly and monolithic traditional techniques.