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At least 721 records · Page 40

Human Factors and Behavioral Performance Exploration Measures Harmonized Across HERA, NEK, and ISS: Teams Risk

BACKGROUNDThe Human Factors and Behavioral Performance Exploration Measures (HFBP-EM) suite is a set of standardized measures to assess behavioral health and performance risk related to future exploration class missions, and to support reduction of the Human Research Program’s (HRP) Behavioral Medicine (BMed), Team, Sleep, and Human Systems Integration Architecture risks. HFBP-EM were collected during Human Exploration Research Analogs (HERA) campaigns 4 (C4) and 5 (C5), and during SIRIUS 17 and 19 missions in the Russian Ground Based Experiment Complex, NEK, to document the feasibility, flexibility, and acceptability of these measures in analogsof the spaceflight environment. A subset of the HFBP-EM suite was collected during spaceflight as part HRP’s Standard Measures in Spaceflight Project. Whenever possible, the HFBP-EM protocol and measures are kept the same across studies, however, differences across research settings (e.g., experimental manipulations, mission scenarios, mission length) and implementation of the measures require the data are harmonized to ensure comparable views across missions. The purpose of our project is to develop a harmonized database of HFBP-EM data from different settings, and to summarize the trajectory of behavioral health and performance within and between research settings. In this presentation, we will summarize the harmonized dataset and the trajectory of measures related to the Team Risk, including team performance, team cohesion, team processes, and psychological safety, over time and between and within settings.METHODSWe followed best practices for data harmonization. Characteristics of each research setting were assessed for harmonization potential, and we deemed NEK-SIRIUS 17 as inappropriate due to study aims, length, and data quality. Common variables of interest were identified. Study characteristics and key variables with which datafiles could be merged were defined as “meta-data.” HFBP-EM data from all settings were processed under a common format. We then created a harmonized team-level database designed to facilitate analyses that address HRP research gaps related to HRP’s Team risk. In this database, team cohesion, processes, performance, psychological safety, and group living were operationalized as the team mean of the crew responses for each data collection (e.g., on mission day 7). Data collected on the International Space Station (ISS) included a subset of scale items administered to participants. Data collected from ISS team members within +/- 7 days of the first data collection and every following 30 +/-4 days were aggregated to the team level. We generated figures that display the mean and variability of team constructs across research settings. We also generated plots of changes in team constructs with mission day and with percentage of the mission completed. Where possible, we compared data from spaceflight analogs with data from astronauts aboard the ISS. RESULTS AND DISCUSSIONThe team-level harmonized database was structured such that each row represents data for a team on a specific mission day. Team constructs (e.g., team cohesion) were included as columns (i.e., wide format) with repeated measures across mission days given in rows (i.e., long format). The database included data from 18 crews: five, 4-person American crews in HERA C4, four 4-person American crews in HERA C5, one 6-person multinational crew in NEK-SIRIUS 19, and eight, 2–11-person multinational crews aboard the ISS. Results provide insights into mission and campaign differences in team functioning and performance. For example, between campaign differences were observed for team processes—the interdependent team actions that orchestrate taskwork in pursuit of the team’s goals [1]. Greater between-team variability on team processes was detected during HERA C4 than during C5, and a downward trend was observed over the duration of C4, but not C5 or SIRIUS 19. This may be due to the campaign-level differences such as the sleep deprivation implemented during C4. Responses on the subset of team process items administered on the ISS indicate between-crew variability more like those observed in C4 than C5 and NEK, with some, but not all ISS crews demonstrating a downward trend over time. Additional findings will be presented. REFERENCESMarks, M. A., et al (2001) Academy of Management Review, 26(3), 356-376.

S T Bell↗

Possibility of an integrated transmission electron microscope: enabling complex in-situ experiments

Abstract Multimodal in-situ experiments are the wave of the future, as this approach will permit multispectral data collection and analysis during real-time nanoscale observation. In contrast, the evolution of technique development in the electron microscopy field has generally trended toward specialization and subsequent bifurcation into more and more niche instruments, creating a challenge for reintegration and backward compatibility for in-situ experiments on state-of-the-art microscopes. We do not believe this to be a requirement in the field; therefore, we propose an adaptive instrument that is designed to allow nearly simultaneous collection of data from aberration-corrected transmission electron microscopy (TEM), probe-corrected scanning transmission electron microscopy, ultrafast TEM, and dynamic TEM with a flexible in-situ testing chamber, where the entire instrument can be modified as future technologies are developed. The value would be to obtain a holistic understanding of the underlying physics and chemistry of the process-structure–property relationships in materials exposed to controlled extreme environments. Such a tool would permit the ability to explore, in-situ, the active reaction mechanisms in a controlled manner emulating those of real-world applications with nanometer and nanosecond resolution. If such a powerful tool is developed, it has the potential to revolutionize our materials understanding of nanoscale mechanisms and transients. Graphical Abstract

36 MATERIALS SCIENCE↗

IMAGINE: The neutron protein crystallography beamline at the high flux isotope reactor

IMAGINE is a high intensity, quasi-Laue neutron crystallography beamline developed at the 85 MW High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL). This state-of-the-art facility for neutron-diffraction enables neutron protein structures to be determined at or near atomic resolutions from crystals with volumes of < 1 mm 3 and unit cell edges of < 150 Å. The beamline features include elliptical focusing mirrors that deliver neutrons into a 2.0 × 3.2 mm 2 focal spot at the sample position, and variable short and long wavelength cutoff optics that provide automated exchange between multiple wavelength configurations. The beamline is equipped with a single-axis goniometer, neutron-sensitive cylindrical image plate detector and room temperature and cryogenic sample environments. This article describes the beamline components, the diffractometer and the data collection and data analysis protocols that are used, and outlines the protein deuteration, crystallization and conventional crystallography capabilities that are available to users at ORNL's neutron facilities. We also present examples of the scientific questions being addressed at this beamline and highlight important findings in enzyme chemistry that have been made possible by IMAGINE.

59 BASIC BIOLOGICAL SCIENCES↗

Mechanical ventilation and indoor air quality in recently constructed U.S. homes in marine and cold-dry climates

Data were collected to characterize whole-house mechanical ventilation (WHMV) and indoor air quality (IAQ) in 55 homes in the Marine climate of Oregon and the Cold-Dry climate of Colorado in the U.S. Sixteen homes were monitored for two weeks, with and without WHMV operating. Ventilation airflows; airtightness; time-resolved CO 2 , PM2.5 and radon; and time-integrated NO 2 , NO X and formaldehyde were measured. Participants provided information about IAQ-impacting activities, perceptions and ventilation use. All homes had operational cooktop ventilation and bathroom exhaust. Thirty homes had equipment that could meet the ASHRAE 62.2–2010 standard with continuous or controlled runtime and 34 had some WHMV operating as found. Thirty-five of 46 participants with WHMV reported they did not know how to operate it, and only half of the systems were properly labeled. Two-week homes had lower formaldehyde, radon, CO 2 and NO (NO X -NO 2 ) when operated with WHMV, and had faster PM 2.5 decays following indoor emission events. Overall IAQ satisfaction was similar in Oregon and Colorado, but more Colorado participants (19% vs 3%) felt their IAQ could be improved and more reported dryness as a problem (58% vs. 14%). The collected data indicate that there are benefits of operating WHMV, even when continuous use may not be needed because outdoor pollutant concentrations are low and indoor sources do not present substantial challenges.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

On falling film evaporator – A review of mechanisms and critical assessment of correlation on a horizontal tube bundle with updated development

Optimization of the energy efficiency and reduction of the greenhouse gas emission for the heat pump system is imperative to meet the net-zero goals at 2050. Chillers with falling film evaporator design not only possess better system performance but also contain less refrigerant inventory. Hence, accurate prediction of the evaporator performance is pivotal especially when charged with low-GWP refrigerant. Here, the study reviews the correlations for falling film evaporator with a horizontal tube bundle configuration. The major efforts of this study include four tasks: (a) literature review of the experimental studies and available empirical correlations; (b) comprehensive discussion of the falling film evaporation heat transfer mechanism; (c) development of a new rationally based correlation based on available literature; and (d) comparison of different correlations based on the existing data. The collected data includes 4114 data points from 8 sources, 6 refrigerants (R-600a, R-290, R-245fa, R-134a, R-1234ze(E), R-123), 5 types of the tubes (Plain, Turbo-GII-HP, GEWA-B, Low-fin, High-Flux), liquid Weber number from 2.2 × 10 –6 to 0.7, imposed vapor Weber number from 0 to 37.2, heat flux from 2.5 to 151.5 kW/m 2 , and film Reynolds number from 1 to 3159.8. The new correlation gives a MAD of 28.3%, and an R 2 of 0.86. Yet, the developed correlation considers various heat transfer mechanisms, including the transition point from falling film evaporation to the nucleate boiling, local evaporation, dry-out, mist flow, imposed flow, and enhanced tube effects.

42 ENGINEERING↗

Predicting future well performance for environmental remediation design using deep learning

Here in this study, we developed a deep learning (DL) framework with a multi-channel three-dimensional convolutional neural network (MC3D-CNN) to predict well performance and thereby assist future environmental remediation design. Such prediction of extraction well performance at designated locations is critical for configuring pump-and-treat (P&T) well network design and operation, setting reasonable target closure dates for overall remedying, and estimating remedy costs. The framework is developed with operational and monitoring data routinely collected during P&T remedy operations, including well extraction and injection rates as well as in situ contaminant concentrations. Traditionally, the collected data were rarely used for purposes other than assessing past well performance and the accuracy of the conceptual site model. However, recent advances in data-driven computational approaches enable better use of the large datasets to inform future well performance, enhance site characterization, and improve remediation planning. In this study, we established a DL framework to integrate transient three-dimensional contaminant plumes and multiple aquifer properties (e.g., hydraulic conductivity and hydrostratigraphic maps) to identify characteristic patterns controlling and representing extraction well mass recovery, aiming at providing future mass recovery estimates for existing wells and candidate wells at any proposed locations. We evaluated our framework by using a realistic synthetic dataset generated from a well-calibrated flow and transport model used in the 200 West Area of the U.S. Department of Energy’s Hanford Site in southeastern Washington state. The multi-channel feature in our framework allows integration of various types and temporal densities of training datasets for DL model development. Overall, we found that the trained DL model achieved an accuracy of over 90% in ranking extraction well performance in validation datasets, and over 80% in predicting high-performance-ranking well locations. This data-informed approach provides a flexible tool to support adaptive site management, streamline decision-making, and potentially reduce remediation time and costs. Our DL framework can be used as a filtering tool to improve the current P&T network optimization design by reducing the number of candidate well locations.

54 ENVIRONMENTAL SCIENCES↗

The energy footprint of automotive electronic sensors

We discuss how Information and communication technologies (ICT) have emerged as one of the leading technologies to reduce global emissions, particularly in the mobility sector. Automotive electronics, such as sensors/actuators and microcontrollers (commonly known as electronic control units (ECU) which control one or more of the electrical systems or subsystems in a vehicle), play a key role in ICT. Sensors/actuators are key in electronic ICT devices, starting with the data collection and data communication with the internet. The latest two big trends of electrification and automation in vehicles, are projected to increase the use of worldwide automotive sensors from 7.5 billion units in 2017 to 11.0 billion units by the year 2024. A representative state-of-the art automotive sensor system, (i.e., an ultrasonic backup system), including the ECUs controlling the vehicle electrical systems/subsystems, has been considered to estimate the energy footprint in terms of manufacturing and operational energy of global automotive sensors use. A widely used life cycle energy assessment method (i.e., cumulative energy demand) was used as both direct and indirect (including the extraction, manufacturing, and disposal of the raw and auxiliary materials) energy use can be considered for the energy footprint estimation. The embodied manufacturing energy impacts of the system was estimated to be 559 MJ/system, compared to the 417 MJ/system for lifetime system power and additional gasoline use. The share of purchased energy to the embodied energy where the upstream energy isn't included in the former case, is less than 10% and ~ 85% for the component manufacturing and vehicle operation energy uses, respectively. As the purpose of this ultrasonic backup system is to prevent rear crashes, an estimated 1.0 MJ/system is avoided from reduced lifetime vehicular repairs (from an estimated 11% chance of requiring a rear bumper replacement). While all of this is small compared to the overall automotive manufacturing and use energy, the 11 billion automotive sensors expected to be produced in 2024 could require 1540 PJ for manufacturing and those sensors would require an additional 780–1150 PJ for lifetime energy use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Production of $Υ(nS)$ mesons in Pb + Pb and $pp$ collisions at 5.02 TeV

A measurement of the production of vector bottomonium states, $Υ$(1S), $Υ$(2S), and $Υ$(3S), in Pb + Pb and $pp$ collisions at a center-of-mass energy per nucleon pair of 5.02 TeV is presented. The data correspond to integrated luminosities of 1.38 nb –1 of Pb + Pb data collected in 2018, 0.44 nb –1 of Pb + Pb data collected in 2015, and 0.26 fb –1 of $pp$ data collected in 2017 by the ATLAS detector at the Large Hadron Collider. The measurements are performed in the dimuon decay channel for transverse momentum ${p}_{T}^{μμ}$ < 30 GeV, absolute rapidity |$y^{μμ}$| < 1.5, and Pb + Pb event centrality 0–80%. The production rates of the three bottomonium states in Pb + Pb collisions are compared with those in $pp$ collisions to extract the nuclear modification factors as functions of event centrality, ${p}_{T}^{μμ}$, and $|y^{μμ}|$. In addition, the suppression of the excited states relative to the ground state is studied. The results are compared with theoretical model calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Comparison of pyrometry and thermography for thermal analysis of thermite reactions

This article examines the thermal behavior of a laser ignited thermite composed of aluminum and bismuth trioxide. Temperature data were collected during the reaction using a four-color pyrometer and a high-speed color camera modified for thermography. The two diagnostics were arranged to collect data simultaneously, with similar fields of view and with similar data acquisition rates, so that the two techniques could be directly compared. Results show that at initial and final stages of the reaction, a lower signal-to-noise ratio affects the accuracy of the measured temperatures. Both diagnostics captured the same trends in transient thermal behavior, but the average temperatures measured with thermography were about 750 K higher than those from the pyrometer. This difference was attributed to the lower dynamic range of the thermography camera’s image sensor, which was unable to resolve cooler temperatures in the field of view as well as the photomultiplier tube sensors in the pyrometer. Overall, while the camera could not accurately capture the average temperature of a scene, its ability to capture peak temperatures and spatial data make it the preferred method for tracking thermal behavior in thermite reactions.

36 MATERIALS SCIENCE↗

Dataset for Cruz-O'Byrne et al (2026): "Divergent biogeochemical responses in upland coastal forest soils to repeated flooding and shifts in water chemistry"

Hydrologic disturbances from accelerated sea-level rise and the increasing frequency and intensity of storms and tidal flooding are altering biogeochemical processes in upland coastal forests, transforming these ecosystems into wetlands. However, the initial effects of flooding on belowground biogeochemistry and the mechanisms driving greenhouse gas dynamics and soil organic matter stability during the early stages of this transition remain poorly understood. This dataset presents the results of a mesocosm experiment conducted in a controlled, highly instrumented laboratory environment, in which freshwater and brackish water pulses were applied to intact soil monoliths from a temperate upland coastal forest to examine how floodwater chemistry influences soil biogeochemistry and organo-mineral interactions. All data files are plain-text CSV (comma-separated value), and no special software is required to read them. Details about the content of each file are available in the document “Dataset_readme”. The dataset consists of the following data: • rcruzobyrne_moisture: Soil volumetric water content (VWC) • rcruzobyrne_GHG: Headspace greenhouse gas (GHG) concentration and fluxes • rcruzobyrne_methane_isotopes: Headspace methane isotope signature • rcruzobyrne_porewater: Porewater chemistry • rcruzobyrne_CDOM: Porewater colored dissolved organic matter (CDOM) • rcruzobyrne_FTIR: Soil Fourier-transform infrared (FTIR) spectroscopy Details of the experimental setup, data collection, and data analysis are provided in the manuscript by Cruz-O’Byrne et al (2026) Divergent biogeochemical responses in upland coastal forest soils to repeated flooding and shifts in water chemistry. Biogeochemistry. https://doi.org/10.1007/s10533-026-01340-0

EARTH SCIENCE > ATMOSPHERE > GREENHOUSE GAS↗

TRUST Testing Procedure Documentation

The TRUST testbed was designed and fabricated to conduct vibration testing and validate the behavior of hyper-elastic foams in a pre-loaded condition. The main goals of this testbed design were to ensure symmetry, proper instrumentation alignment and mounting, simplified geometry to make the data analysis validation process more straightforward, and to apply and monitor the applied pre-load. Based off of the test parameters, a shaker system was purchased. The baseplate was designed to mount directly to the bolt holes provided on the top of the shaker table. It was determined that the specimens will be cylindrical and vary in thickness from 2-10mm. The baseplate riser, center-mass, and testbed cap were designed to be cylindrical to make alignment about the center axis more straightforward. It was determined to fabricate the parts previously mentioned using Aluminum 6061-T6 due to its stiffness in order to reduce the likelihood of the stiff material impacting the data being collected for the foam specimens. The design concept used to apply the pre-load to the testbed was a through bolt/nut configuration. A through hole was included in each piece of the testbed, specimens, and instrumentation large enough to allow for 1/8” clearance between the through bolt and each of these pieces. This was imperative to avoid causing friction and complicating the validation model. A load cell was then purchased and used to measure the pre-load being applied to the testbed. It was also required to collect data for 3 axes at 3 different locations 120° apart on the center-mass. This configuration was required to be placed on the cylindrical face and toward the top and bottom of the center-mass. Slots were milled out in the middle of each sensor block with tight tolerances for mounting each of the accelerometers at 120° apart. This same approach was taken to mount 2 accelerometers on the baseplate at 180° apart. Holes were milled out of the corners of each accelerometer mounting slot to relieve the corner and allow for a flat edge at the back of each slot. Sensor blocks were mounted into the center-mass using 6-32 bolts at the required locations. This initial testbed design was fabricated at the TA53 machine shop.

42 ENGINEERING↗

EdgeAI: How to Use AI to Collect Reliable and Relevant Watershed Data

Focal Area(s): Focal areas are on data acquisition and assimilation enabled by AI, advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Science Challenge: The transformational science challenge that we address is the following: – Ensuring, in near real-time, that the data collected from distributed sensor networks is accurate and contains useful information to identify, quantify, and predict watershed and ecosystem dynamic responses to short- and long-term perturbations.

54 ENVIRONMENTAL SCIENCES↗

Quantitative Performance Assessment of Proxy Apps and Parents (Report for ECP Proxy App Project Milestone ADCD-504-11)

The ECP Proxy Application Project has an annual milestone to assess the state of ECP proxy applications. Our FY21 milestone (ADCD-504-11) proposed to: "Assess the performance and fidelity of proxy applications, including those in the ECP Proxy App Suite, relative to the ECP Application workload on heterogeneous platforms. Use proxy applications and selected ECP applications to assess the utility of critical elements of the Exascale toolchain, especially tools used to collect performance data. Identify gaps in coverage and/or common situations in which proxies may fail to adequately represent ECP applications." To satisfy this milestone, the following specific tasks were completed: Study ability of MACSio to represent I/O workloads of adaptive mesh codes; Prepare report on assessed performance and debugging tools; Perform cosine similarity study on Intel and P9 and submit paper; Complete LDMS/GPU subcontract, complete and receive report; Complete validation of LDMS data collection for GPUs and submit paper; and Perform cosine similarity study on GPUs.

97 MATHEMATICS AND COMPUTING↗

Evaluation of In-Situ AM Process Monitoring Techniques and Potential for Detecting Process Anomalies and Undesirable Microstructures

The US Department of Energy’s Advanced Materials and Manufacturing Technologies (AMMT) program is pursuing rapid qualification of new materials for fabrication of nuclear relevant components using advanced manufacturing techniques. Particular interest is placed on code-qualifying stainless steel (SS) 316H processed by laser powder bed fusion (LPBF) additive manufacturing. A paradigm that incorporates data from in-situ sensing during the printing, ex-situ characterization, and advanced artificial intelligence–based models was established under the Transformation Challenge Reactor (TCR) program to develop a pedigree for each fabricated component that could be tracked from the feedstock to the component’s release for application. Under the TCR program, the Peregrine software was developed as a tool for incorporating the vast amounts of in-situ and ex-situ characterization data collected; all data stored on a rapidly growing digital platform. The digital platform allows for users to link site-specific process anomalies to the macro- and microstructure. The platform will eventually be able to predict component performance, which will be crucial to qualifying materials and components in risk-averse industries such as those supporting and building nuclear reactors. Current in-situ process monitoring techniques that are already integrated with software like Peregrine are advantageous for identifying process anomalies including powder spatter, component edge swelling, recoating-build interactions, and so on. However, additional data are required to fully predict the resulting microstructures needed for identifying relationships to component performance. The rapid cooling rates observed in LPBF are some of the highest of any bulk manufacturing process, resulting in heterogenous microstructures and typically causing anisotropy in mechanical properties. Moreover, evolved residual thermal stresses are high, which can cause severe defects such as delamination or cracking. Therefore, other in-situ monitoring methods are warranted for exploration to measure and map the thermal history, and potentially the stress state, of each build. This report summarizes different in-situ monitoring strategies proposed for LPBF with a focus on the more developed sensor systems. Novel capabilities for measuring melt pool temperatures are also addressed to better inform modeling efforts.

36 MATERIALS SCIENCE↗

Accelerating the identification of novel secondary metabolites in bioenergy plant root exudates using MicroED

Small molecule metabolites drive inter- and intraspecies communication and dependencies in diverse biological systems, yet a large proportion of these important chemical compounds remain uncharacterized in plants and microbes. Approximately 90% of the metabolites in root exudate profiles are unknown compounds, despite the importance of root exudate composition in plant-microbe interactions. We need advanced analytical capabilities that will support rapid discovery and structural elucidation of metabolites from biological samples that may be limited in quantity and high in complexity. To fill this gap, this project aimed to develop an integrated workflow involving metabolite extraction, separation, and crystallization from plant root exudates followed by characterization using nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry, and microcrystal electron diffraction (MicroED). Using crude root exudates from sorghum, this project successfully developed higher throughput exudate fractionation strategies to obtain pure compounds for crystallization and identified crystals in multiple fractions that diffracted. Additional efforts to increase the throughput of high-quality crystal generation for MicroED, such as crystallization screening and crystallization chaperone exploration, will be needed to further advance root exudate metabolite identification. The overall optimized sample preparation process can then be integrated with the existing data collection and data analysis pipelines for MicroED at PNNL to facilitate more rapid natural product discovery.

59 BASIC BIOLOGICAL SCIENCES↗

ORNL Package Testing Program Software Quality Assurance Plan

The Oak Ridge National Laboratory (ORNL) Package Testing Program (PTP) uses commercial off-the-shelf (COTS) software in performing data collection of thermal test results for package designs that contain radioactive materials. Specifically, this software is used to collect temperature data from the furnace, packages, and ambient air to prepare and execute the thermal test specified in 10 CFR 71.73, “Thermal Test.” This software quality assurance (SQA) plan sets forth the guidelines, standards, and procedures that shall be used to provide SQA for PTP software applications. This is a living document that will be maintained for the lifecycle of the PTP program. The SQA plan follows the requirements set forth in ORNL Standards Based Management System (SBMS): Information Technology; Subject Area: Software Quality Assurance. When applicable to the requirements as described in ORNL SBMS, Software Quality Assurance, the software shall be listed in the ORNL Software Registration System (SRS). Exemptions to this SBMS are COTS and firmware that are not modified; spreadsheet applications and personal productivity tools that do not have a utility or safety application, research applications, legacy software, system software, vendor-supplied software used to interface with the vendor’s services, software used within the organization to facilitate processing or management of information, and software developed for applications not specific to the US Department of Energy (DOE).

97 MATHEMATICS AND COMPUTING↗

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗

Mechanical Ventilation and Indoor Air Quality in Recently Constructed U.S. Homes in Marine and Cold-Dry Climates Data from Building America Project

Data were collected to characterize whole-house mechanical ventilation (WHMV) and indoor air quality (IAQ) in 55 homes in the Marine climate of Oregon and Cold-Dry climate of Colorado in the U.S. Sixteen homes were monitored for two weeks, with and without WHMV operating. Ventilation airflows; airtightness; time-resolved CO2, PM2.5 and radon; and time-integrated NO2, NOX and formaldehyde were measured. Participants provided information about IAQ-impacting activities, perceptions and ventilation use. All homes had operational cooktop ventilation and bathroom exhaust. Thirty homes had equipment that could meet the ASHRAE 62.2-2010 standard with continuous or controlled runtime and 34 had some WHMV operating as found. Thirty-five of 46 participants with WHMV reported they did not know how to operate it, and only half of the systems were properly labeled. Two-week homes had lower formaldehyde, radon, CO2, and NO (NOX-NO2) when operated with WHMV; and also had faster PM2.5 decays following indoor emission events. Overall IAQ satisfaction was similar in Oregon and Colorado, but more Colorado participants (19 vs. 3%) felt their IAQ could be improved and more reported dryness as a problem (58 vs. 14%). The collected data indicate that there are benefits of operating WHMV, even when continuous use may not be needed because outdoor pollutant concentrations are low and indoor sources do not present substantial challenges.

air quality↗