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DOE OSTI2020

Energy Performance Validation of a Gaseous Air Cleaning Technology for Commercial Buildings

The U.S. Department of Energy (DOE) Building Technologies Office Commercial Building Integration group conducts evaluations of new energy efficiency technologies as part of the High Impact Technology Catalyst program. The subject of this field validation project is an air cleaning technology that removes gaseous contaminants from indoor air to reduce outdoor air ventilation requirements. Reducing the volume of outdoor air required reduces heating, ventilating, and air conditioning (HVAC) loads and can provide significant energy savings for some buildings in certain climates. The technology, called the HLR (HVAC Load Reduction), was developed by enVerid. The technology uses a solid regenerable sorbent material that was developed by DOE's National Energy Technology Laboratory and licensed to the vendor for this application. The sorbent material was originally developed to adsorb carbon dioxide (CO2); the vendor continued to develop the sorbent and the overall system so that it can also remove volatile organic compounds (including aldehydes), carbon monoxide, ozone, and particulate matter 2.5 micrometers in diameter or smaller (PM2.5) from the air. This material has low regeneration temperature requirements, and its sorption properties are unaffected by humidity levels, both of which are beneficial for building applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

A systematic analytical framework for multi-source municipal solid waste characterization for energy recovery

Advancing municipal solid waste (MSW) management from disposal-oriented practices toward circular, value-driven systems requires standardized methodologies capable of identifying material composition and resource recoverable potential at the point of generation. Despite extensive research, MSW characterization remains fragmented due to inconsistences in sampling methodologies, waste sorting categories, and temporal coverage across previous studies which limit cross-site comparability, reproducibility, and constrain the reliable evaluation of potential resource recovery pathways. This lack of consistency has hindered the development of a unified framework for MSW characterization and resource assessment. This study introduces a standardized, field-validated protocol for MSW sampling and composition analysis that ensures consistent, traceable data across diverse waste sources. The protocol integrates randomized spatial sampling, systematic material sorting, and controlled subsampling for multi-site and multi-season field campaigns. Validation included MSW collection from residential, grocery, restaurant, and school MSW streams across five U.S. states, including Maryland, Idaho, Virginia, Ohio, and Mississippi, to demonstrate the protocol’s ability to identify source-based composition patterns relevant to resource recovery applications. Grocery and restaurant streams were dominated by food waste and high-moisture organics, while school waste contained higher paper content and residential waste showed greater heterogeneity. Aggregation into energy-relevant fractions highlighted practical recovery pathways via anaerobic digestion or gasification, supporting data-driven planning, policy, and circular economy strategies for sustainable waste management across waste sources.

09 BIOMASS FUELS↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗

DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data: Preprint

advanced metering infrastructure↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, Volt/VAr optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder-head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure (AMI)↗

NASA NTRSDate not supplied

In-Situ Scanning Electron Microscope Experiments for Microscale Mechanical Testing and Validated Modeling of Fiber Reinforced Thermoplastics

A novel, in-situ, scanning electron microscope (SEM) mechanical testing capability for materials at the microscale which provides experimental validation to a machine learning (ML) toolset for full-field validation of physics-based micromechanics models is being developed by researchers at NASA Glenn Research Center. These are enabling technologies for the integration of multiscale digital twins for materials into system level models which will result in the improved performance, material discovery, reduced production cost and time, rapid characterization, and prognostic structural health monitoring (SHM) for materials and structures for extreme environments in support of NASA space exploration missions. In order to bridge the material structure-to-system gap for digital twins, physics-based models must be experimentally validated at multiple length scales. Seminal microscale experiments, conducted at the Air Force Research Laboratory (AFRL), were limited to transverse compression of single-layer, unidirectional thermoset polymer matrix composite (PMC) micropillar specimens [1]. The early phases of the current project followed those initial results and setup to reproduce the compression testing of PMC material on the custom-built piezoelectric actuated micromechanical testing rig built by MicroTesting Solutions LLC. In this work, samples of thermoplastic PMC material were first machined into 3 mm cubes, and then further machining and final milling was done using a Focused Ion Beam (FIB). The initial experiment was done on a pillar roughly 20 µm x 20 µm x 40 µm tall. Additional pillars were milled with final sizes ranging from 20 µm x 20 µm x 40 µm tall to 40 µm x 40 µm x 65 µm tall. A speckle pattern for in-situ full-field measurements using Digital Image Correlation (DIC) was applied with platinum, which was coated on the surface, and then the FIB was used to mill away some of the coating to produce an irregular pattern of Pt on the pillar surface. The samples were loaded into the custom testing rig and placed into the SEM and loaded under compression until failure. Images were collected in the SEM during testing. Post-processing of the images was conducted using DIC to obtain full-field displacement and strain measurements elucidating the role of the matrix as well as fiber-fiber interaction at the microscale within the composite subjected to compression loading well into the non-linear regime of the material. Moreover, the evolution of fiber-matrix debonding and matrix cracking is observed in-situ at the microscale. This data, along with images segmented with a newly developed ML toolset [2], was used to create and validate physics-based micromechanics models. An image of the failed micropillar is shown in Figure 1. The techniques developed in the initial compression experiment was tailored to the validation needs of the models and expanded to include different sized samples as well as possibly tension and fatigue.

Laura Wilson↗

DOE OSTI2025

Casing Annulus Monitoring of CO 2 Injection Using Wireless Autonomous Distributed Sensor Networks

Effective and secure carbon subsurface storage, involving the deep underground injection of CO 2 into geological formations where it is permanently trapped, is paramount to mitigating CO 2 emissions (Figure I). Ensuring the integrity of these storage sites and detecting potential leakage through the casing annulus necessitates robust monitoring. This work provides the first integrated demonstration of a wireless casing-annulus monitoring architecture that can operate in highly attenuating cement-brine environments relevant to CO 2 storage. This project focused on developing and validating a novel sensor system for integration with autonomous monitoring near the cement reservoir interface. The goal was a fully integrated Technology Readiness Level (TRL) 4/5 field validation of a distributed wireless intelligent sensor system providing real-time, direct subsurface formation measurements to enhance fluid movement monitoring in the cemented casing annulus. Achieving this objective required the development and integration of 1) wireless autonomous microsensor technology by California Institute of Technology (Caltech); 2) sensor packaging and emplacement technology by Research Triangle Institute (RTI); and 3) smart well completions using wireless active casing collars and NOV pipe by the Sandia National Lab (SNL). The collaboration with the Caltech team in this project aimed to develop millimeter-scale radio frequency identification (RFID) sensors capable of detecting CO 2 , pH, and/or methane levels. These sensors are engineered to be impervious to fluids, allowing them to be mixed with cement and installed within the casing annulus. They operate using RFID protocols at frequencies of 902–928 MHz for both power and communication. A Sandia National Laboratories’ team engaged their expertise in the development of a Smart Collar system designed for the wireless data collection from these RFID sensors embedded in the cement annulus and transmission of this information to the ground surface via IntelliPipe/IntelliServ NOV drill pipe. This is accomplished through inductive coupling at the collar, which facilitates data transfer through each segment of the pipe. Because the system cannot transmit a direct current signal to power the Smart Collar, both power and communication were implemented using alternating current and electromagnetic signals at varying frequencies. Furthermore, the developed microsensor technology had to be demonstrated and validated in comparison with reference transducer measurements in a field test site at The University of Texas at Austin (UT-Austin). Although the full sensor suite did not reach field-deployment readiness, the system-level integration achieved in this project establishes a validated pathway for future incorporation of advanced microsensors.

47 OTHER INSTRUMENTATION↗

NASA NTRS2002

Global Positioning System Simulator Field Operational Procedures

Global Positioning System (GPS) simulation is an important activity in the development or qualification of GPS signal receivers for space flight. Because a GPS simulator is a critical resource it is highly desirable to develop a set of field operational procedures to supplement the basic procedures provided by most simulator vendors. Validated field procedures allow better utilization of the GPS simulator in the development of new test scenarios and simulation operations. These procedures expedite simulation scenario development while resulting in scenarios that are more representative of the true design, as well as enabling construction of more complex simulations than previously possible, for example, spacecraft maneuvers. One difficulty in the development of a simulation scenario is specifying various modes of test vehicle motion and associated maneuvers requiring that a user specify some (but not all) of a few closely related simulation parameters. Currently this can only be done by trial and error. A stand-alone procedure that implements the simulator maneuver motion equations and solves for the motion profile transient times, jerk and acceleration would be of considerable value. Another procedure would permit the specification of some configuration parameters that would determine the simulated GPS signal composition. The resulting signal navigation message, for example, would force the receiver under test to use only the intended C-code component of the simulated GPS signal. A representative class of GPS simulation-related field operational procedures is described in this paper. These procedures were developed and used in support of GPS integration and testing for many successful spacecraft missions such as SAC-A, EO-1, AMSAT, VCL, SeaStar, sounding rockets, and by using the industry standard Spirent Global Simulation Systems Incorporated (GSSI) STR series simulators.

Kizhner, Semion↗

NASA NTRS2024

Retrievals of Aerosol Optical Depth Over the Western North Atlantic Ocean During ACTIVATE

Aerosol optical depth was retrieved from two airborne remote sensing instruments, the Research Scanning Polarimeter (RSP) and Second Generation High Spectral Resolution Lidar (HSRL-2), during the National Aeronautics and Space Administration (NASA) Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). The field campaign offers a unique opportunity to evaluate an extensive 3-year dataset under a wide range of meteorological conditions from two instruments on the same platform. However, a long-standing issue in atmospheric field studies is that there is a lack of reference datasets for properly validating field measurements and estimating their uncertainties. Here we address this issue by using the triple collocation method, in which a third collocated satellite dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS) is introduced for comparison. HSRL-2 is found to provide a more accurate retrieval than RSP over the study region. The error standard deviation of HSRL-2 with respect to the ground truth is 0.027. Moreover, this approach enables us to develop a simple, yet efficient, quality control criterion for RSP data. The physical reasons for the differences in two retrievals are determined to be cloud contamination, aerosols near the surface, multiple aerosol layers, absorbing aerosols, non-spherical aerosols, and simplified retrieval assumptions. These results demonstrate the pathway for optimal aerosol retrievals by combining information from both lidars and polarimeters for future airborne and satellite missions.

Aerosol optical depth↗

NASA NTRS2003

Retrieval of Hydrometeor Drop Size Distributions from TRMM Field Campaign Profiler Doppler Velocity Spectra Observations

Consistent with the original proposal and work plan, this project focused on estimating the raindrop size distributions (DSDs) retrieved from vertically pointing Doppler radar profilers and analyzing the relationship of the retrieved DSDs with the dynamics of the precipitation processes. The first phase of this project focused on developing the model to retrieve the DSD from the observed Doppler velocity spectra. The second phase used this model to perform DSD retrievals from the profiler observations made during the TRMM Ground Validation Field Campaigns of TEFLUN-B, TRMM-LBA, and KWAJEX. The third phase of this project established collaborations with scientists involved with each field campaign in order to validate the profiler DSD estimates and to enable the profiler retrievals to be used in their research. Through these collaborations, the retrieved DSDs were placed into context with the dynamical processes of the observed precipitating cloud systems.

Williams, Christopher R.↗

DOE OSTI2020

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Preliminary Regional Assessment of Potential Storage Complexes, Including Capacities and Costs, Based on Application of NRAP and SCO2T (Deliverable 3.3) Storage Complexes

The Recipient will assist with the field validation of one or more risk assessment toolsets through: 1) collaborative research; 2) sharing of relevant datasets, information, and technical insights from field efforts; and 3) other modeling and simulation toolsets as they are developed. Collaborative research could include, but is not limited to: 1) demonstrating use of field data for improved leakage pathway characterization, uncertainty reduction, and risk management and uncertainty workflows; 2) testing and validation of a specific National Risk Assessment Partnership (NRAP) tool using site specific data; and 3) improved modeling or reduced-order modeling of geophysical monitoring.

42 ENGINEERING↗

DOE OSTI2023

Late-Stage Research Development and Demonstration Sub-Activities Updates

Oak Ridge National Laboratory (ORNL), in collaboration with the Pacific Northwest National Laboratory (PNNL), the National Renewable Energy Laboratory (NREL), the Lawrence Berkeley National Laboratory (LBNL), and the Hummingbird Firm (a specialized consulting firm focused on promoting diversity, equity, and inclusion considerations), has initiated a national initiative known as the Heat Pump (HP) and Heat Pump Water Heater (HPWH) Field Validation Partnership. This effort involves active participation from numerous critical entities involved in research and market transformation within the field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Late-Stage Research Development and Demonstration Sub-activities Updates

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2000

Three Conservation Applications of Astronaut Photographs of Earth: Tidal Flat Loss (Japan), Elephant Impacts on Vegetation (Botswana), and Seagrass and Mangrove Monitoring (Australia)

NASA photographs taken from low Earth orbit can provide information relevant to conservation biology. This data source is now more accessible due to improvements in digitizing technology, Internet file transfer, and availability of image processing software. We present three examples of conservation-related projects that benefited from using orbital photographs. (1) A time series of photographs from the Space Shuttle showing wetland conversion in Japan was used as a tool for communicating about the impacts of tidal flat loss. Real-time communication with astronauts about a newsworthy event resulted in acquiring current imagery. These images and the availability of other high resolution digital images from NASA provided timely public information on the observed changes. (2) A Space Shuttle photograph of Chobe National Park in Botswana was digitally classified and analyzed to identify the locations of elephant-impacted woodland. Field validation later confirmed that areas identified on the image showed evidence of elephant impacts. (3) A summary map from intensive field surveys of seagrasses in Shoalwater Bay, Australia was used as reference data for a supervised classification of a digitized photograph taken from orbit. The classification was able to distinguish seagrasses, sediments and mangroves with accuracy approximating that in studies using other satellite remote sensing data. Orbital photographs are in the public domain and the database of nearly 400,000 photographs from the late 1960s to the present is available at a single searchable location on the Internet. These photographs can be used by conservation biologists for general information about the landscape and in quantitative applications.

Lulla, Kamlesh P.↗

NASA NTRS2012

Recent Progress on Sonic Boom Research at NASA

Sonic boom research conducted at NASA through the Supersonics Project of the Fundamental Aeronautics Program is oriented toward understanding the potential impact of sonic boom noise on communities from new low-boom supersonic aircraft designs. Encompassing research in atmospheric propagation, structural response, and human response, NASA research contributes to knowledge in key areas needed to support development of a new noise-based standard for supersonic aircraft certification. Partnerships with several industry, government, and academic institutions have enabled the recent execution of several acoustic field studies on sonic booms. An overview of recent activities funded by NASA includes: focus boom model development and experimental validation, field experiments of structural transmission of sonic booms into large buildings, and low boom community response testing.

Loubeau, Alexandra↗

NASA NTRS2003

EOS Aqua AMSR-E Arctic Sea Ice Validation Program: Arctic2003 Aircraft Campaign Flight Report

In March 2003 a coordinated Arctic sea ice validation field campaign using the NASA Wallops P-3B aircraft was successfully completed. This campaign was part of the program for validating the Earth Observing System (EOS) Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea ice products. The AMSR-E, designed and built by the Japanese National Space Development Agency for NASA, was launched May 4, 2002 on the EOS Aqua spacecraft. The AMSR-E sea ice products to be validated include sea ice concentration, sea ice temperature, and snow depth on sea ice. This flight report describes the suite of instruments flown on the P-3, the objectives of each of the seven flights, the Arctic regions overflown, and the coordination among satellite, aircraft, and surface-based measurements. Two of the seven aircraft flights were coordinated with scientists making surface measurements of snow and ice properties including sea ice temperature and snow depth on sea ice at a study area near Barrow, AK and at a Navy ice camp located in the Beaufort Sea. Two additional flights were dedicated to making heat and moisture flux measurements over the St. Lawrence Island polynya to support ongoing air-sea-ice processes studies of Arctic coastal polynyas. The remaining flights covered portions of the Bering Sea ice edge, the Chukchi Sea, and Norton Sound.

Cavalieri, D. J.↗

DOE OSTI2024

Field-Scale Testing of a High-Efficiency Membrane Reactor (MR)—Adsorptive Reactor (AR) Process for H2 Generation and Pre-Combustion CO2 Capture

The study objective was to field-validate the technical feasibility of a membrane- and adsorption-enhanced water gas shift reaction process employing a carbon molecular sieve membrane (CMSM)-based membrane reactor (MR) followed by an adsorptive reactor (AR) for pre-combustion CO2 capture. The project was carried out in two different phases. In Phase I, the field-scale experimental MR-AR system was designed and constructed, the membranes, and adsorbents were prepared, and the unit was tested with simulated syngas to validate functionality. In Phase II, the unit was installed at the test site, field-tested using real syngas, and a technoeconomic analysis (TEA) of the technology was completed. All project milestones were met. Specifically, (i) high-performance CMSMs were prepared meeting the target H2 permeance (>1 m3/(m2.hbar) and H2/CO selectivity of >80 at temperatures of up to 300 °C and pressures of up to 25 bar with a <10% performance decline over the testing period; (ii) pelletized adsorbents were prepared for use in relevant conditions (250 °C < T < 450 °C, pressures up to 25 bar) with a working capacity of >2.5 wt.% and an attrition rate of <0.2; (iii) TEA showed that the MR-AR technology met the CO2 capture goals of 95% CO2 purity at a cost of electricity (COE) 30% less than baseline approaches.

42 ENGINEERING↗