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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Evaluating the impact of wildfire smoke on solar photovoltaic production

There are growing needs to understand how extreme weather events impact the electrical grid. Renewable energy sources such as solar photovoltaics are expanding in use to help sustainably meet electricity demands. Wildfires and, notably, the widespread smoke resulting from them, are one such extreme event that can impair the performance of solar photovoltaics. However, isolating the impact that smoke has on photovoltaic energy production, separate from ambient conditions, can be difficult. In this work, we seek to understand and quantify the impacts of wildfire smoke on solar photovoltaic production within the Western United States. Our analysis focuses on the construction of a random forest regression model to predict overall solar photovoltaic production. The model is used to separate and quantify the impacts of wildfire smoke in particular. To do so, we fuse historical weather, solar photovoltaic energy production, and PM2.5 particulate matter (primary smoke pollutant) data to train and test our model. The additional weather data allows us to capture interactions between wildfire smoke and other ambient conditions, as well as to create a more powerful predictive model capable of better quantifying the impacts of wildfire smoke on its own. We find that solar PV energy production decreases 8.3% on average during high smoke days at PV sites as compared to similar conditions without smoke present. Finally, this work allows us to improve our understanding of the potential impact on photovoltaic-based energy production estimates due to wildfire events and can help inform grid and operational planning as solar photovoltaic penetration levels continue to grow.

14 SOLAR ENERGY↗
DOE OSTI2020

CMDV (CM)4 Project - University of Washington contribution. Final report

The original goal of this project was to diagnose and improve CLUBB, the turbulence and cloud fraction parameterization used in DOE’s E3SM model. For this purpose, we planned to use large-eddy simulation (LES) and ARM observations from the NE Pacific Ocean and the SGP site, in coordination with ARM’s LASSO program. It was determined that for these cloud regimes, many of the turbulent ‘moment closures’ which underlie CLUBB’s mathematical formulation are inconsistent with LES, which is an appropriate benchmark for testing this. The research assistant found that these closures could be more accurately formulated using machine learning using the LES as a training dataset. However the resulting parameterization proved to quickly drift away from physical plausibility. A novel machine-learning based boundary layer parameterization called MARBLE based on matching the time evolution of a parameterized cloud-topped boundary layer to reanalysis was then developed and published.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Kansas City, Missouri, Streetlight Electric Vehicle Charging: Strategies and challenges for site selection of streetlight electric vehicle infrastructure in Kansas City, Missouri (Final Report)

Public streetlight charging, whether on streets in central business districts or residential areas, provides easy charging access for apartment residents and homeowners alike. While most electric vehicle (EV) drivers charge at home, they do so in garages or on driveways they own. For renters and residents of multifamily housing (MFH), however, this may not be an option. EVs have a lower cost of ownership compared to conventional vehicles, and a used EV may be an affordable option for a lower-income household. But without easy access to charging, even a low-cost used EV may not be an option for a prospective buyer. An affordable curbside charging network has the potential to expand EV adoption into neighborhoods that have to date seen minimal interest and uptake of the technology and associated charging infrastructure. Streetlight charging networks can provide an economical, scalable, and effective approach to providing equitable and convenient charging. Metropolitan Energy Center (MEC) is dedicated to the mission of creating resource efficiency, environmental health, and economic vitality in the Kansas City region and beyond. Since 1983, MEC has provided resources, outreach, and training to make alternative fuels and energy efficiency commonplace. MEC led a streetlight charging pilot project that installed limited EV charging infrastructure on the streetlight system in Kansas City, Missouri, to demonstrate and test the benefits of curbside charging for EVs at existing on-street parking locations. The project aimed to cost-effectively expand the charging network in Kansas City to support residential charging and provide infrastructure in one or more charging deserts throughout the city. This pilot evaluates the impact and overall success of streetlight charging based on community feedback, utilization of charging infrastructure, technical feasibility, and cost. The project has pursued a data- and community-driven site selection process designed to identify sites with high demand and high opportunity for EV charging. This project was funded by the U.S. Department of Energy (DOE) and awarded to MEC through a competitive proposal process. The novelty and complexity of this project required an organization that could facilitate collaboration across levels of government, community members, and industry partners. For the past 25 years, through Kansas City Regional Clean Cities, MEC has worked with numerous public and private fleets on a variety of projects to improve the environmental performance and efficiency of the regional vehicle fleet. To advance affordable, efficient, and clean transportation efforts, DOE Clean Cities and Communities coalitions create local networks of public and private sector stakeholders and engage communities. Rooted within their local communities, the coalitions serve as experts and ambassadors, bringing to bear the collective knowledge, experience, and practical know-how of the entire network from within DOE, its national laboratories, and diverse stakeholders in the field. MEC and its project partners made in-kind contributions to leverage federal dollars for the benefit of the Kansas City community. Findings from this project will help determine the best applications for streetlight charging technologies to maximize funding impact and serve community needs. The team evaluated locations based on expected charging demand, technical feasibility, safety considerations, and enhanced charging network siting needs. Throughout the project, the team gathered feedback and evaluated ways to make public charging for EVs available to all community members. The insights will help Kansas City and other communities streamline future efforts to support EV drivers through public charging in the city right-of-way. Furthermore, this project will inform citywide guidance for future installations. MEC is committed to a transparent and publicly accessible approach that encourages the collaborative evaluation of streetlight charging. The project has engaged the community to proactively identify and evaluate the benefits and impacts of streetlight charging. It was a priority for the project to ensure the benefits of this pilot are distributed equitably to all members of the Kansas City community and that new charging opportunities and associated resources are available in diverse neighborhoods across the city. The charging infrastructure supports an affordable curbside charging network that will enable more drivers to choose EVs and provide easy charging access for all community members interested in driving an EV. The community feedback received through this project informed future resources and opportunities to make EVs more accessible to all members of the Kansas City community. MEC worked with several community partners on this project, including Missouri University of Science and Technology (MST), Pennsylvania State University (Penn State), the National Renewable Energy Laboratory (NREL); the city of Kansas City, Missouri; Evergy; Black and McDonald (B&M); LilyPad EV; EVNoire; and Westside Housing Organization (WHO). Project partners contributed to the cost match required for DOE grants through capital expenditures, personnel, and other in-kind contributions. Detailed descriptions of project team organizations can be found in Appendix A. Project Partners. Analysts at NREL and MST/PennState developed site maps based on demand and equity considerations. MEC conducted outreach to community members to garner input on project design and site selection, and received approval from the Missouri Public Service Commission (PSC) for Evergy’s EV charging station ownership. MEC worked with all partners to gather additional siting criteria and developed a site selection evaluation checklist, and partners conducted site visits to proposed installation sites. Next, B&M, Evergy, and the city executed all site agreements, conducted site-specific engineering design, acquired associated permits, and issued notices to proceed site by site or in small batches. Finally, from January to April 2023, the project team installed 23 EV charging stations built on Kansas City’s streetlight system in six council districts. Evergy will own, operate, and monitor the stations for 10 years, sharing charging data with MEC for at least 1 year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

NRAP-Open-IAM Multisegmented Wellbore Reduced-Order Model: Improvement and Quality Assurance

The multisegmented wellbore model (MSW) semi-analytically estimates the amount of CO 2 and brine leakage from a leaking legacy well by segmenting it into intervals to simulate site-specific stratigraphic and hydrogeologic properties. The model is a component of the National Risk Assessment Partnership Open-Source Integrated Assessment Model (NRAP-Open-IAM), which was developed to perform risk assessment for geologic CO 2 storage. The new wellbore leakage model, which uses deep learning networks for a caprock segment, was developed to enhance the analytical MSW. The model was trained and validated using a synthetic data set of Subsurface Transport Over Multiple Phases (STOMP) multiphase flow simulations from various geological, well attribute, and operational conditions to ensure its quality. The results demonstrate that the model is more accurate than the existing model in predicting the transport of two-phase fluids (brine and injected CO 2 ) through the well. This report provides a detailed explanation of the model development and quality assurance.

58 GEOSCIENCES↗
DOE OSTI2026

High-Fidelity Building Emulator

This dataset provides high-fidelity time series data for an emulated commercial office building sited in the Chicago, IL area during a Typical Meteorological Year (TMY). This dataset consists of air-side HVAC measurements and control inputs, and it includes normal operations as well as various implemented faults (with associated ground truth measurements) implemented on selected days. This data could be used to quantify and compare the impacts of different faults, and it could also be used as training or validation data for machine learning algorithms (e.g., reduced-order modelling, fault detection and diagnosis).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A Machine Learning Approach for Hourly Traffic Prediction Used in EV-Charging Sites

Reliable forecasting of hourly traffic volumes on highways is critical for planning and operating electric-vehicle charging infrastructure without overloading the grid. In this work, we develop and evaluate a station-specific machine-learning approach based on NeuralProphet, enhanced with conditional seasonality to better distinguish weekday, weekend, and holiday patterns. For each station, the model automatically retrieves the same calendar day from the prior years as an AR-Net initialization, fits trend and Fourier-based seasonality components, and then applies short-term auto-regressive corrections. We train and test on 2021 and 2022 TMAS data, respectively, and validate performance over the whole year. We chose to demonstrate how the model performs on a typical weekday (3/15/2022), weekend (3/27/2022), and a special holiday (12/25/2022). Our results yield MAPE of 7.4%, 23.6%, and 32.0%, respectively. Over the entire year 2022, the overall MAPE was 17%. This demonstrates that station-specific models with conditional seasonality can achieve accurate, scalable hourly forecasts for EV-charging load planning.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Prediction of Distributed River Sediment Respiration Rates Using Community-Generated Data and Machine Learning

River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi-scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature-rich (i.e., 100+ possible input variables) data set. Here, we present results from a two-tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud-based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger-scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger-scale features to generate data-driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Microbiome dynamics in the congregate environment of U.S. Army Infantry training

Within military training and operational environments, individuals from diverse backgrounds share common spaces, follow structured routines and diets, and engage in physically demanding tasks. While there has been interest in leveraging microbiome features to predict and improve military health and performance, the longitudinal convergence of microbiomes in such constrained environments has not been established. To assess the degree of microbiome convergence, we performed shotgun metagenomic sequencing on swab samples from a military trainee cohort. Samples were taken across four different body sites, three timepoints, and two spatially distinct platoons. We observed evidence of convergence in one platoon, whereby similarity in microbiome composition increased over time, with numerous differentially abundant species. We found no indication of strain transfer between individuals, suggesting that convergence was influenced by external environmental factors, diet, and lifestyle. Microbial shifts observed in the convergence process included a decrease in fungal species, such as Malassezia restricta in nasal cavities, and a decrease in Prevotella species at inguinal regions across time. Shifts in multiple Corynebacterium species were also observed with varying magnitudes depending on the body site. Overall, we provide preliminary evidence of convergence of host microbial communities in military-associated environments that were distinguishable using shotgun metagenomic sequencing approaches. The data presented here on microbiome convergence, dynamics, and stability may inform risk-based mitigation in congregate military settings facilitating development of targeted microbial, dietary, or other interventions to optimize health and performance of military populations.

Biological and medical sciences↗
DOE OSTI2020

Predicting metal–metal interactions. II. Accelerating generalized schemes through physical insights

Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution. In this work, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Swimming and the human microbiome at the intersection of sports, clinical, and environmental sciences: A scoping review of the literature

The human microbiota is comprised of more than 10–100 trillion microbial taxa and symbiotic cells. Two major human sites that are host to microbial communities are the gut and the skin. Physical exercise has favorable effects on the structure of human microbiota and metabolite production in sedentary subjects. Recently, the concept of “athletic microbiome” has been introduced. To the best of our knowledge, there exists no review specifically addressing the potential role of microbiomics for swimmers, since each sports discipline requires a specific set of techniques, training protocols, and interactions with the athletic infrastructure/facility. Therefore, to fill in this gap, the present scoping review was undertaken. Four studies were included, three focusing on the gut microbiome, and one addressing the skin microbiome. It was found that several exercise-related variables, such as training volume/intensity, impact the athlete’s microbiome, and specifically the non-core/peripheral microbiome, in terms of its architecture/composition, richness, and diversity. Swimming-related power-/sprint- and endurance-oriented activities, acute bouts and chronic exercise, anaerobic/aerobic energy systems have a differential impact on the athlete’s microbiome. Therefore, their microbiome can be utilized for different purposes, including talent identification, monitoring the effects of training methodologies, and devising ad hoc conditioning protocols, including dietary supplementation. Microbiomics can be exploited also for clinical purposes, assessing the effects of exposure to swimming pools and developing potential pharmacological strategies to counteract the insurgence of skin infections/inflammation, including acne. In conclusion, microbiomics appears to be a promising tool, even though current research is still limited, warranting, as such, further studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Quantifying subsurface parameter and transport uncertainty using surrogate modelling and environmental tracers

Here, we combine physics-based groundwater reactive transport modelling with machine-learning techniques to quantify hydrogeological model and solute transport predictive uncertainties. We train an artificial neural network (ANN) on a dataset of groundwater hydraulic heads and 3 H concentrations generated using a high-fidelity groundwater reactive transport model. Using the trained ANN as a surrogate model to reproduce the input–output response of the high-fidelity reactive transport model, we quantify the posterior distributions of hydrogeological parameters and hydraulic forcing conditions using Markov chain Monte Carlo calibration against field observations of groundwater hydraulic heads and 3 H concentrations. We demonstrate the methodology with a model application that predicts Chlorofluorocarbon-12 (CFC-12) solute transport at a contaminated field site in Wyoming, United States. Our results show that including 3 H observations in the calibration dataset reduced the uncertainty in the estimated permeability field and infiltration rates, compared to calibration against hydraulic heads alone. However, predictive uncertainty quantification shows that CFC-12 transport predictions conditioned to the parameter posterior distributions cannot reproduce the field measurements. We found that calibrating the model to hydraulic head and 3 H observations results in groundwater mean ages that are too large to explain the observed CFC-12 concentrations. The coupling of the physics-based reactive transport model with the machine-learning surrogate model allows us to efficiently quantify model parameter and predictive uncertainties, which is typically computationally intractable using reactive transport models alone.

58 GEOSCIENCES↗
DOE OSTI2020

Development of the Mobile Systems for Conditioning of Disused Sealed Radioactive Sources in Serbia - 20105

Sealed radioactive sources (SRS) are being used worldwide in the field of medicine, agriculture, industry and research. They can be found in mobile as well as stationary devices. SRS contains radioactive material that is (a) permanently sealed in a capsule or (b) closely bounded and in a solid form. The capsule or material of an SRS should be strong enough to maintain leak tightness under the conditions of use and purpose for which the source was designed, also in case of accidents. In this case only emitted radiation is utilized. Firstly, the hazard from external radiation has to be considered, but the possibility of contamination due to fracture of the capsule should not be disregarded. The radioactive sources are composed of the radiating isotope contained in the filling medium, the single or double isotope holder that partially or totally surrounds the filling medium, the outer cover that contains the parts mentioned above and the capsule closed airtightly by welding or using some other method. The capsule must be tested for leakage periodically. If the SRS is no longer needed (e.g. replaced by a different technique) or it becomes useless for the intended application (e.g. the activity becomes too weak, the equipment containing the source works poorly or becomes obsolete, the source is damaged or leaking) it is considered disused. Disused sealed radioactive sources (DSRS) are typically conditioned and disposed if a facility is available. If the disposal option is not available, conditioned DSRS should be stored under proper conditions. In some cases, the radionuclide(s) in DSRS can be recovered/recycled or the DSRS can be repurposed for other applications. Conditioning of DSRS ensures containment of the radioactive material, provides confinement for leaking sources, provides sufficient radiation shielding, reduces storage/disposal volume by allowing consolidation of multiple sources into a single storage/disposal container, facilitates transport operations and contributes to safety and security as well. Typically, conditioning technologies are deployed either as permanently installed stationary systems in centralized or mobile on-site waste processing facilities, or in a mobile configuration. Centralized stationary facilities provide a single processing location for multiple users that requires transport of the waste to the facility. On the other hand, mobile systems may be provided for the selection and application of the optimum technology for a specific waste stream (such as DSRS) by bringing the process to the point where the waste is generated. In addition, mobile systems could offer additional flexibility by sharing equipment among multiple waste generating sites for processing campaigns that vary in duration, from very short periods to several years. The term 'mobile processing system' refers to any radioactive waste processing system or component which is designed to be transportable and which is not considered permanently installed. Two mobile system for conditioning of disused sealed radioactive sources are developed in the Public Company Nuclear Facilities of Serbia. Development of these mobile systems was supported by SRB9005 national project via Technical Cooperation of the IAEA. The first mobile system, built inside the 20 feet ISO container, will be used for conditioning of DSRS category 3 to 5. The second mobile system, built inside the 7 m long vehicle (Iveco Daily Van), will be used for dismantling of ionizing smoke detectors mostly with Am-241 sources. Designs of the mobile systems were defined in cooperation with two companies from Belgium (Belgoprocess and Leniko) and a Croatian company Ekoteh as well as with the support of the IAEA experts. The generic safety assessment and operational procedures for the mobile systems are developed. Based on safety assessment the acceptance criteria and operational limits and conditions are established. Operational procedures include: (a) equipment and material requirements, (b) assembling procedure of the mobile unit, (c) procedure for acceptance of devices for dismantling and conditioning, (d) dismantling procedure for devices to recover the DSRS, (e) characterization of DSRS, (f) encapsulation procedure of DSRS, (g) disassembling procedure of the mobile unit, and (h) keeping records, identification and traceability. In addition, radiation safety, health safety, security and emergency preparedness plans are prepared. The generic safety assessment and operational procedures could be updated with site specific requirements, DSRS inventory, and different needs for future customers. Developed mobile systems could be used in all situations when it is feasible to perform conditioning of DSRS on the spot in the county and worldwide. Development of these mobile units was just the first step to create the Reference Center for Radioactive Waste Treatment and Disused Radioactive Sources Conditioning for Small Facilities which can become a regional training center in the future, and/or as a tool for comprehensive national search and secure programmes. In the next phases development of e-learning platforms and blended learning packages as well as application for the IAEA Qualified Technical Centre (QTC) for the management of DSRS is foreseen. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Conduct of Operations and Nuclear Criticality Safety Standards

Since the beginning of the nuclear age in 1943, there have been 22 criticality accidents in process facilities worldwide in which fissionable materials were processed by hand for various purposes. Of these 22 process criticality accidents, 16 involved faulty or flawed conduct of operations. Most of these process accidents (17 of 22) occurred before 1970, mostly due to the implementation of formal conduct of operations—such as the use of operating procedures and training—and the use of consensus standards for nuclear criticality safety (NCS). Obviously, the nuclear facilities applied lessons learned from their accident history, and the rate of criticality accidents was significantly reduced as a result. Over the years, it has been emphasized in NCS training courses and other venues in the United States that the consensus standards, such as ASA N6.1-1964, were the key contributors to this reduction in accidents. The formality of operations required to implement and apply the standards at a facility is also a crucial prerequisite that must be sufficiently robust to ensure NCS. This prerequisite is crucial to success fully implement the NCS consensus and other safety standards at a site.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Advancing Cross-Disciplinary Understanding of Land-Atmosphere Interactions

The evolution of disciplinary silos and increasingly narrow disciplinary boundaries have together resulted in one-sided approaches to the study of land-atmosphere interactions—a field that requires a bi-directional approach to understand the complex feedbacks and interactions that occur. The integration of surface flux and atmospheric boundary layer measurements is therefore essential to advancing our understanding. The Land-Atmosphere 2021 workshop (held virtually, June 10-11, 2021) involved almost 300 participants from around the world and promoted cross-discipline collaboration by way of talks from invited speakers, moderated discussions, breakout sessions, and a virtual poster session. The workshop focused on five main theme areas: “big picture” overview, instrumentation and remote sensing, modeling, water, and aerosols and clouds. In talks and breakout groups, there were frequent calls for more AmeriFlux sites to be instrumented for boundary layer height measurements, and for the development of some “super sites” where profiling instruments would be deployed. There was further agreement on the need for the standardization of various datasets. There was also a consensus that funding agencies need to be willing to support the sorts of large projects (including associated instrumentation) which can drive interdisciplinary work. Early-career scientists, in particular, expressed enthusiasm for working across disciplinary boundaries but noted that there need to be more financial support and training opportunities so they would be better prepared for interdisciplinary work. Investment in these career development opportunities would enable today's cohort of early-career scientists to advance the frontiers of interdisciplinary work over the next couple of decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A quantitative risk assessment framework for fault reactivation in underground hydrogen storage: Coupled simulation and deep learning approach

Underground hydrogen storage (UHS) is emerging as a critical solution for large-scale energy storage. However, like all subsurface fluid injection activities, UHS poses the risk of injection-induced fault reactivation. Accurate risk assessment is essential to ensuring the safety and efficiency of UHS operations. This study presents the development of deep-learning surrogate models for fault reactivation prediction in UHS, trained on a comprehensive database of fully coupled fluid flow-geomechanics simulations. Our findings reveal that analytical models often yield unreliable estimates, with errors up to 54% in the allowable injection pressure, potentially leading to a 40% reduction in UHS operational capacity. The developed surrogate models were incorporated into a quantitative risk assessment (QRA) framework, enabling probabilistic evaluation of fault reactivation risk while accounting for uncertainties in the input variables. Site-specific features, such as horizontal stress gradients, fault’s dip and strike angles, and operational parameters like bottom-hole injection pressure and well-fault distance, were identified as the primary drivers of fault reactivation across various stress regimes. Whereas other hydraulic, geological, and poroelastic reservoir properties were found to have a secondary impact. Notably, we observed that the risk of fault reactivation for a critically oriented fault with a static friction coefficient greater than 0.55 remains below 10% in a normal faulting stress regime. However, the risk significantly increases as the stress regime transitions from normal to strike-slip and ultimately to reverse faulting conditions. These findings underscore the importance of rigorous site characterization and comprehensive QRA evaluations to optimize UHS performance and minimize geomechanical risks.

25 ENERGY STORAGE↗
DOE OSTI2020

FIU Projects 4 & 5: DOE-FIU Science and Technology Workforce Development Program

The DOE-FIU Science and Technology Workforce Development Program has been designed to build upon the existing DOE/FIU relationship by creating a “pipeline” of minority engineers specifically trained and mentored to enter the Department of Energy workforce in technical areas of need. The main objective of the program is to provide interested students with a unique opportunity to integrate course work, DOE field work, and research work at FIU into a well-structured academic program that leads to entry into DOE EM’s Pathways Program. Students selected as DOE EM Fellows perform research at FIU and at DOE sites, national laboratories, and DOE contractors. Graduation and completion of this fellowship leads to employment opportunities with DOE EM, DOE contractors, DOE national laboratories, other federal agencies, and private industry as well as the pursuit of post-master or post-doctoral positions at DOE national labs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

A Scalable and Cost-Effective Solution to the U.S. Housing Crisis: A Case Study on Locally Manufactured Modular Multifamily Housing

This case study assesses waste management efficiencies in modular buildings compared to traditional construction methods. We focus on the modular 1-bedroom Model/Z unit by Model Z Modular, LLC. As part of the collaboration between Model Z Modular and the National Renewable Energy Laboratory, we analyzed waste metrics from design through construction, contrasting these findings against conventional stick-built and site-built multifamily buildings. The Model/Z unit is part of a strategic effort to address affordable housing shortages in South Los Angeles, where household income challenges are pronounced. The unit is produced in a state-of-the-art 150,000 sq. ft. modular manufacturing facility located within the city it is serving, and has so far supported the production of over 1,500 affordable housing units. Model/Z units have been used in projects with as many as 195 units, achieving large economies of scale and time. Our analysis demonstrates that modular construction reduces waste compared to traditional methods. This reduction is achieved through precise prefabrication techniques, the implementation of new framing methods, the concentration of workforce expertise, and streamlined logistics, which optimize material use and greatly reduce on-site handling. Additionally, local manufacturing minimizes transportation needs, improving overall project efficiency. The Model/Z unit exemplifies a scalable solution for enhancing housing affordability by optimizing resource utilization and minimizing associated costs. These methods support the delivery of high-quality units at reduced expenses, addressing critical urban housing shortages effectively. This case study underscores Model Z Modular's commitment to producing affordable, quality housing while fostering economic opportunities through job creation and training programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Raman Microscopy Analysis of Wyoming CarbonSAFE Pilot Well Thin Sections for Mineralogy and Organic Matter Characterization

Scanning confocal Raman microspectroscopy (RMS) was used to analyze thin sections for inorganic mineral content and for evaluation of organic material (OM). Thin sections were selected from a stratigraphic test well core in the Powder River Basin, Wyoming under the Wyoming CarbonSAFE project. The well was analyzed with a variety of rock and fluid characterization techniques to determine the feasibility of a commercial-scale CO2 storage site. RMS complements other analyses, including traditional petrography, SEM, porosity and permeability, and XRD For this aspect of the study, RMS is especially important in the evaluation of OM in sealing lithologies. At prospective geologic CO2 storage sites, it is imperative to assess the unconventional oil and gas potential of seals to ensure that any future development would not compromise the integrity of the seals. Whole-slide mineralogical surveys were performed on thin sections from various shale and sandstone formations. Surveys were analyzed with Direct Classical Least Squares to identify and quantify minerals. The location and concentration of minerals was color-coded and overlaid on optical images for visualization of the distribution of minerals. Dense hyperspectral Raman mapping of OM was performed on five thin sections. Eleven spectral parameters diagnostic of organic type and thermal maturity were used to train a Partial Least Squares (PLS) calibration against a set of artificially matured samples spanning the pre- to mid-oil window. The PLS was applied to the study set and a post-mature set. Additionally, the PLS was applied to each point in hyperspectral maps for visualization of trends in maturity across sample sets and discrimination of organic matter types within a given map. In inorganic surveys on thin sections, a total of 14 unique inorganic minerals were identified in Raman spectra including quartz, dolomite, calcite, hematite and anhydrite. Shale thin sections tended to be dominated by organic material. OM was often observed mixed with inorganic minerals. Sand- and mudstones were dominated by inorganic minerals. The PLS extrapolated the post-mature set to reflectances >1.2%. The study set ranged from very immature to postmature in the median of map fit-peak parameters. However, point maturity maps indicate that matrix OM in all study samples is immature and that discreet organic particles selected for mapping, which may be inertinites, bias medians towards more-mature. The work demonstrates the capabilities of RMS to perform both whole-slide mineralogy and OM analysis with applications to formation evaluation in oil & gas, carbon sequestration and mining. Here, analysis of sealing formations in the well indicates high levels of immature organic matter that would not be a viable target for future oil production that could compromise the CO2 storage site. The work brings together diverse disciplines from geology and petrography to analytical chemistry, big data and microscopy.

Myers, Grant↗