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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Final Report on the Viability of Acoustic Techniques for Density and Mass Flow in Enrichment Plants

The primary purpose of this research is to study the viability of acoustic signatures and sensors that could support accurate, noninvasive, and unattended measurement of uranium hexafluoride (UF6) gas density and mass flow rate in situations representative of gaseous centrifuge enrichment plants (GCEP) under safeguards by the International Atomic Energy Agency. The measurement method under development can be readily retrofitted to the exterior of piping in existing systems, potentially facilitating acceptance by facility operators. The findings from the demonstration of a preliminary prototype design and first-generation analysis algorithms in this project will inform the safeguards community as to whether continued acoustic instrument and methods development are warranted. This project conducted two types of measurements, depending on the flow conditions of the gas. For static measurements, the focus is on understanding the acoustic properties at low pressure for non-flowing UF6. For flow measurements, the focus is on understanding the performance of the acoustic system in a simulation of a GCEP pipe using a surrogate gas under fully developed flow conditions. Testing with flowing UF6 was not considered because the cost of building a flowing UF6 system with flow similar to a GCEP was considerably greater than allocated project resources. This document summarizes the results of research conducted over the course of the project. Based on the results to date, the approach is seen to be promising with non-invasive acoustic measurements shown to be possible at the desired pressures. The project has demonstrated • 5% uncertainty of the UF6 gas density for static measurements at pressures relevant to GCEP operations over a 5-minute measurement window with a clear path forward to achieving considerably lower uncertainties over a two-hour period, • Measurements of flowing surrogate gases at pressures relevant to GCEP operations. However, additional research remains to be done to further develop the measurement approach and transition the technology for field use. These development needs include: • Understand the impact of flowing gas on gas density measurements, • Adding sensors and improving the measurement procedure, • Augmenting the data analysis methods by leveraging machine learning approaches, • Reevaluating uncertainty requirements for density and mass flow given observed Online Enrichment Monitor (OLEM) measurement uncertainties, • Designing a robust instrumentation package for field-testing, and • Evaluating measurement results to understand the potential impact on mass balance calculations within GCEP facilities.

42 ENGINEERING↗
DOE OSTI2025

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A review on nanomechanical resonators and their applications in sensors and molecular transportation

Nanotechnology has opened a new area in science and engineering, leading to the development of novel nano-electromechanical systems such as nanoresonators with ultra-high resonant frequencies. The ultra-high-frequency resonators facilitate wide-ranging applications such as ultra-high sensitive sensing, molecular transportation, molecular separation, high-frequency signal processing, and biological imaging. This paper reviews recent studies on dynamic characteristics of nanoresonators. A variety of theoretical approaches, i.e., continuum modeling, molecular simulations, and multiscale methods, in modeling of nanoresonators are reviewed. The potential application of nanoresonators in design of sensor devices and molecular transportation systems is introduced. The essence of nanoresonator sensors for detection of atoms and molecules with vibration and wave propagation analyses is outlined. The sensitivity of the resonator sensors and their feasibility in detecting different atoms and molecules are particularly discussed. Furthermore, the applicability of molecular transportation using the propagation of mechanical waves in nanoresonators is presented. An extended application of the transportation methods for building nanofiltering systems with ultra-high selectivity is surveyed. The article aims to provide an up-to-date review on the mechanical properties and applications of nanoresonators, and inspire additional potential of the resonators.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Initial testing of wave energy powered UV-C LED anti-biofouling system (CRADA 523)

3newable is requesting technical assistance for laboratory testing at the Marine and Coastal Research (MCRL) Laboratory of a UV-C LED anti-biofouling system. Ocean observing sensors provide critical data sources from deployed buoys and other platforms at sea. However, in addition to the availability of reliable power sources, biofouling of sensors and instruments is a limiting factor for operation and maintenance of deployed instruments. In collaboration with the Woods Hole Oceanographic Institute Ocean Observatories Initiative, 3newable has developed a novel solution to overcome these challenges and extend the deployment period of buoy-mounted sensors. The 3newable system co-locates wave energy converters to supply UV-C LED anti-biofouling units on powered buoy arrays. MCRL staff will implement an experiment to evaluate the efficacy of 3newable’s device under test for inhibiting growth of organisms on test coupons in a flow through seawater tank.

42 ENGINEERING↗
DOE OSTI2021

Fabrication and Preliminary Demonstration of Microwave Resonant Cavity Transducer Performance

We are investigating a microwave cavity-based transducer for in-core high-temperature fluid flow sensing in molten salt cooled reactors (MSCR) and sodium fast reactors (SFR). This sensor is a hollow metallic cylindrical cavity, which can be fabricated from stainless steel, and as such is expected to be resilient to radiation, high temperature and corrosive environment of MSCR and SFR. The principle of sensing consists of making one wall of the cylindrical cavity flexible enough so that dynamic pressure, which is proportional to fluid velocity, will cause membrane deflection. Membrane deflection causes cavity volume change, which leads to a shift in the resonant frequency. We have developed an initial design for proof-of-principle testing of the flow sensor performance in microwave K-band. A cylindrical resonator prototype was fabricated from brass for initial tests in water. The cavity size is matched to the flange of a standard WR-42 waveguide. Microwave field is coupled into the resonant cavity through a subwavelength-size aperture. A test article was developed, consisting of a piping Tee with bulkhead WR-42 microwave waveguide installed in leak-proof design. In the test article, the cylindrical cavity is positioned in the center of the pipe. A microwave waveguide circulator was installed in the setup to suppress microwave reflections at the cavity entrance. Preliminary spectral characterization of cavity spectral response was performed with microwave VNA. Applying mechanical pressure to cavity membrane showed a measurable shift in the microwave resonant frequency. We also investigate mechanical integrity of the flowmeter’s membrane through computer simulations. By calculating the stress on the plate due to deflection and, comparing the stress to the material ultimate tensile strength and yield strength, it can be estimated if the plate will fail. The stress on the plate was calculated with an analytic closed form solution model, and with COMSOL Structural Mechanics Module which does not involve any approximations. Both the analytic model and COMSOL model showed that maximum stresses on the plate, which are at the radial boundary of the plate, are three orders of magnitude smaller than the yield strength and ultimate tensile strength. This indicates that the sensor is at a low risk of mechanical failure.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Development of an Advanced Integrated Operations Concept for Hybrid Control Rooms

The U.S. Nuclear Industry has an urgent need to reduce the operations and maintenance costs to remain economically competitive in today’s energy market. Measures to improve efficiency in operations will need to leverage technology in a way that safely transforms how plants are operated. This work describes the development of an integrated operations concept that integrates data from existing Instrumentation and Control (I&C) infrastructure, upgraded I&C systems, new sensors, and field technologies such as computer-based procedures to provide operators with centralized, streamlined instructions. The concept was developed to allow for an operator to remotely supervise many plant activities and to dramatically streamline plant operations and maintenance. This report describes the design philosophy, the analysis used to inform design, the implementation in the Human Systems Simulation Laboratory, and illustrates the design concept.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Bees as Biosensors: Chemosensory Ability, Honey Bee Monitoring Systems, and Emergent Sensor Technologies Derived from the Pollinator Syndrome

This review focuses on critical milestones in the development path for the use of bees, mainly honey bees and bumble bees, as sentinels and biosensors. These keystone species comprise the most abundant pollinators of agro-ecosystems. Pollinating 70%–80% of flowering terrestrial plants, bees and other insects propel the reproduction and survival of plants and themselves, as well as improve the quantity and quality of seeds, nuts, and fruits that feed birds, wildlife, and us. Flowers provide insects with energy, nutrients, and shelter, while pollinators are essential to global ecosystem productivity and stability. A rich and diverse milieu of chemical signals establishes and maintains this intimate partnership. Observations of bee odor search behavior extend back to Aristotle. In the past two decades great strides have been made in methods and instrumentation for the study and exploitation of bee search behavior and for examining intra-organismal chemical communication signals. In particular, bees can be trained to search for and localize sources for a variety of chemicals, which when coupled with emerging tracking and mapping technologies create novel potential for research, as well as bee and crop management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

EPA and Idaho National Laboratory create first of its scale

Advancing the science and engineering of decontaminating water distribution systems and safely disposing of high-volumes of contaminated water are high priorities for the EPA. To help address these science gaps, EPA homeland security researchers have developed the first-of-its-scale Water Security Test Bed (WSTB). Water is an important component of everyday life. We rely on safe, clean water for our health and the health of the environment. Protecting this vital resource is critical to living safely and securely. The U.S. Environmental Protection Agency is responsible for working with water utilities to protect water systems from contamination and to clean up systems that become contaminated. Purposeful or inadvertent distribution system contamination can result in large amounts of infrastructure and water that must be cleaned. Water distribution systems contamination can be caused by, for example, natural disasters such as Superstorm Sandy or by individuals hoping to cause harm. The WSTB, constructed at the Department of Energy’s (DOE) Idaho National Laboratory, replicates a section of a typical municipal drinking water system including a piping system with roughly 445 feet of pipe and two fire hydrants laid out in an “L” shape using 40-year-old, eight-inch cement mortar lined, ductile iron pipes, excavated after twenty years of use for water conveyance. Researchers built the WSTB above ground for easy access during experiments, and to facilitate fast leak detection. Over the next several years, EPA and partner researchers will conduct experiments using simulants of various biological, chemical, and radioactive materials that simulate high toxic versions of these agents. At this “full” sized system, researchers will demonstrate approaches to contamination detection, sensor and model testing, infrastructure decontamination, water treatment and cyber testing developed at lab and pilot scale. Researchers are also able to verify if laboratory results that were effective in controlled conditions are as effective in full-scale testing. Having the ability to conduct bench scale studies in a real world setting can help address specific research gaps, and help water researchers understand conditions in which real world drinking water systems operate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Empirical profiling of cold hydrogen plumes formed from venting of LH 2 storage vessels

Liquid hydrogen (LH 2 ) storage is viewed as a viable approach to assure sufficient hydrogen capacity at commercial fuelling stations. Presently, LH 2 is produced at remote facilities and then transported to the end-use site by road vehicles (i.e., LH 2 tanker trucks). Venting of hydrogen to depressurize the transport storage tank is a routine part of the LH 2 delivery and site transfer process. The behavior of cold hydrogen plumes has not been well characterized because of the sparsity of empirical field data, which can lead to overly conservative safety requirements. Committee members of the National Fire Protection Association (NFPA) Standard 2 [1] formed the Hydrogen Storage Safety Task Group, which consists of hydrogen producers, safety experts, and computational fluid dynamics modellers, has identified the lack of understanding of hydrogen dispersion during LH 2 venting of storage vessels as a critical gap for establishing safety distances at LH 2 facilities, especially commercial hydrogen fuelling stations. To address this need, the National Renewable Energy Laboratory Sensor Laboratory, in collaboration with the NFPA Hydrogen Storage Task Group, developed a prototype Cold Hydrogen Plume Analyzer to empirically characterize the hydrogen plume formed during LH 2 storage tank venting. The prototype analyzer was field deployed during an actual LH 2 venting process. Critical findings included: hydrogen above the lower flammable limit (LFL) was detected as much as 2 m lower than the release point, which is not predicted by existing models; personal monitors detected hydrogen at ground level, although at levels below the LFL; a small but inconsistent correlation was found between oxygen depletion and the hydrogen concentration; a negligible to non-existent correlation was found between in-situ temperature measurements and the hydrogen concentration. The prototype analyzer is being upgraded for enhanced metrological capabilities, including improved real-time spatial and temporal profiling of hydrogen plumes and tracking of prevailing weather conditions. Additional deployments are planned to monitor plume behavior under different wind, humidity, and temperature conditions. The data will be shared with the Hydrogen Storage Task Group and ultimately will be used support theoretical models and code requirements prescribed in NFPA 2.

08 HYDROGEN↗
DOE OSTI2021

Integration of a DER Management System in Riverside. Final report

The tasks in this project covered various aspects, including algorithm development, algorithm integration into a commercial Active Network Management (ANM) platform, hardware-in-the-loop (HIL) testing in an industry-standard testing platform, pilot demonstration in Riverside, California, and also cost and benefit analysis. The DERMS platform in this project can host different algorithms developed on different platforms (e.g., MATLAB and Python) and it can interact with different hardware devices (e.g., different PV inverters, battery inverters, and different sensors). The DER control solution are based on an advanced model-free, layered, and clustered DER control paradigm. At the core of the DER control algorithms was the concept of Extremum Seeking (ES), which is a model-free probing-based control technique. The ES-based control algorithms were tested on major real-world inverters; both individually and in a cluster. It was shown that even legacy equipment (or when paired with a few additional advanced equipment) can support such advanced control. The monitoring algorithms utilize a heterogeneous set of legacy and advanced sensor measurements, such as behind-the-meter DER sensors, distribution-level Phase Measurement Units, distribution-substation Supervisory Control and Data Acquisition (SCADA), and line current sensors, with their limited availability; in order to infer practical network conditions. Sensor data are utilized to achieve resource forecasting, phase identification, and distribution system state estimation. The technology that was developed and demonstrated in this project could be transformational to utilities, including the smaller municipal utilities such as in Riverside, which may not have the resources to deploy advanced distribution system and DERMS solutions in order to support high penetration of solar power integration. This project created a real-world prototype to provide utilities with an assessment of smart grid monitoring and control technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advancing Industry 4.0: Multimodal Sensor Fusion for AI-Based Fault Detection in 3D Printing

Additive manufacturing, particularly fused deposition modeling, is transforming modern production by enabling rapid prototyping and complex part fabrication. However, its layer-by-layer process remains vulnerable to faults such as nozzle clogging, filament runout, and layer misalignment, which compromise print quality and reliability. Traditional inspection methods are costly, time-intensive, and often limited to post-process analysis, making them unsuitable for real-time intervention. In this current study, the authors developed a novel, low-cost, and portable faultdetection system that leverages multimodal sensor fusion and artificial intelligence for real-time monitoring in FDM-based 3D printing. The system integrates acoustic, vibration, and thermal sensing into a non-intrusive architecture, capturing complementary data streams that reflect both mechanical and process-related anomalies. Acoustic and thermal sensors operate in a fully contactless manner, while the vibration sensor requires minimal attachment such that it will not interfere with printer hardware, thereby preserving portability and ease of deployment. The multimodal signals are processed into spectrograms and time-frequency features, which are classified using convolutional neural networks for intelligent fault detection. The proposed system advances Industry 4.0 objectives by offering an affordable, scalable, and practical monitoring solution that improves faultdetection accuracy, reduces waste, and supports sustainable, adaptive manufacturing.

42 ENGINEERING↗
DOE OSTI2022

National Smart Manufacturing Strategic Plan: To Facilitate More Rapid Development, Deployment and Adoption of Smart Manufacturing Technologies

Smart manufacturing technologies provide real-time data and insight to improve the productivity, efficiency, and competitiveness of U.S. manufacturing, creating the potential for new jobs in the manufacturing sector. These technologies can support U.S. manufacturers’ ability to increase throughput and energy efficiency, and decrease waste, defects, and costs. A wide range of manufacturing and industrial subsectors, particularly energy-intensive and energy-dependent industries, have the potential to benefit from smart manufacturing technologies. There are technical improvements still needed to reduce the costs and barriers (trained workforce and upskilling, software-hardware integration, cost, and technical barriers to deployment of advanced sensors, computing, and communication technologies for existing manufacturing assets) to the adoption of smart manufacturing technologies, especially to small and medium-sized manufacturers, and subsequently, increase the overall adoption rate of smart manufacturing technologies. This report outlines the Department of Energy’s (DOE) strategic plan to accelerate the development and implementation of smart manufacturing technologies in the United States and the actions DOE has taken to use these technologies in smart manufacturing for the United States. DOE’s plan to facilitate more rapid development, deployment and widespread adoption of smart manufacturing technologies derive from the 2018 Strategy for American Leadership in Advanced Manufacturing. The strategy outlines a vision for America’s leadership in advanced manufacturing including the development of intelligent manufacturing systems to optimize manufacturing facilities and support the manufacturing of transformative materials. DOE’s Clean Energy Smart Manufacturing Innovation Institute (CESMII), a Manufacturing USA Institute, focuses on accelerating the development and adoption of advanced sensors, controls, platforms, and models needed for smart manufacturing. The objective is to enhance U.S. manufacturing productivity and global competitiveness through the research and development of these technologies. Smart manufacturing has the potential to improve the overall performance, energy productivity, and efficiency of manufacturing, fostering the economic competitiveness of the manufacturing sector.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Optimizing Transmission of Acoustic Signals to Monitor Internal Conditions of Canisters for Dry Storage of Commercial Spent Nuclear Fuel

Safe storage of spent nuclear fuel (SNF) is critical to the nuclear fuel cycle and the future of nuclear energy. In the United States, SNF is stored primarily via two methods regulated by the U.S. Nuclear Regulatory Commission: wet storage in SNF pools and dry storage in dry cask storage systems (DCSSs). After about five years of cooling in spent fuel pools, the fuel assemblies are transferred into DCSSs, and the systems are filled with helium and sealed by welding. Deterioration of conditions inside of a DCSS is reflected in changes in the internal gas properties; this motivates the development of acoustic techniques to monitor internal gas properties, over extended storage periods, using sensors mounted on the exterior of the storage packages. However, a major challenge in collecting acoustic signals is the impedance mismatch between the steel canister shell and the gas. Only a small fraction of the ultrasonic signal can be transmitted through the gas medium. This paper documents experimental studies conducted on a full-scale canister mock-up to capture the gas-borne signals. Damping materials were pasted on the outside, and blocking and unblocking tests were conducted to identify the gas-borne signal. The results show that the excitation frequency plays an important role in maximizing the gas-borne signals. The gas-borne signal was successfully detected at around the theoretical time-of-flight. A high signal-to-noise ratio was achieved in the measurements. Next, the acoustic impedance matching layers were introduced, and the gas signal was drastically improved compared with that using no AIM layers.

Spent nuclear fuel (SNF), Canisters, Internal cond↗
DOE OSTI2026

Microbial spies and bloggers: programming cells to convert environmental information into discernible signals

Microbes regulate their dynamic behaviors using the chemical and physical characteristics of their environment. The ability of microbes to continuously convert this physicochemical information into biochemical information and to use organic matter in the environment as a power source makes these organisms attractive as chassis for building sensors. However, most biosensors have severe limitations when considering applications in hard-to-image settings like soils, sediments, and wastewater. Emerging technologies at the interface of biomolecular design, microbiome engineering, and synthetic biology offer new tools to program cells and communities as biosensors for these settings. Here, in this review, we describe innovations in biosensor outputs that are enabling new applications in complex environments, including reporters that are read out using electrochemical, gas chromatography, hyperspectral imaging, and next-generation sequencing methods. We also discuss computational advances that are accelerating the diversification of sensing components by mining metagenomics data for new transcriptional regulators and by designing allosteric protein switches that directly regulate reporter outputs using analytes. We highlight emerging opportunities for programming undomesticated microbes in communities to function as distributed sensors in the environment. Finally, we discuss the need for responsible biosensor development and to modernize regulatory frameworks to support evidence-based assessment of environmental biosensors.

analyte↗
DOE OSTI2018

Directional Software Algorithms and Sensor Evaluations

This report evaluates the relative performance of two directional gamma-ray spectrometers and processing algorithms that are used to construct images and spatially resolved spectra. Polaris, which was developed by H3D Inc., uses 18 pixelated CZT crystals to construct gamma-ray images in either Compton camera(CC) or coded aperture (CA) mode. The other sensor that is referenced in this report incorporates a commercial high-purity germanium based imager, called GeGI, with a coded aperture mask and processing software developed by Oak Ridge National Laboratory (ORNL). H3D and the University of Michigan provided several algorithms that can be used to process data collected by Polaris in CC mode. This evaluation compares the performance of these algorithms with a Directional Unfolded Source Term (DUST) approach developed by Sandia National Laboratories (SNL). DUST differs from the other algorithms because its primary objective is synthesis of spatially-resolved gamma ray spectra as opposed to image reconstruction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Four-Stage Multi-Physics Simulations to Assist Temperature Sensor Design for Industrial-Scale Coal-Fired Boiler

The growth of renewable energy sources presents a pressing challenge to the operation and maintenance of existing fossil fuel power plants, given that fossil fuel remains the predominant fuel source, responsible for over 60% of electricity generation in the United States. One of the main concerns within these fossil fuel power plants is the unpredictable failure of boiler tubes, resulting in emergency maintenance with significant economic and societal consequences. A reliable high-temperature sensor is necessary for in situ monitoring of boiler tubes and the safety of fossil fuel power plants. In this study, a comprehensive four-stage multi-physics computational framework is developed to assist the design, optimization installation, and operation of the high-temperature stainless-steel and quartz coaxial cable sensor (SSQ-CCS) for coal-fired boiler applications. With the consideration of various operation conditions, we predict the distributions of flue gas temperatures within coal-fired boilers, the temperature correlation between the boiler tube and SSQ-CCS, and the safety of SSQ-CCS. With the simulation-guided sensor installation plan, the newly designed SSQ-CCSs have been employed for field testing for more than 430 days. The computational framework developed in this work can guide the future operation of coal-fired plants and other power plants for the safety prediction of boiler operations.

42 ENGINEERING↗
DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗