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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Σ3(111) Grain Boundaries Accelerate Hydrogen Insertion into Palladium Nanostructures

Grain boundaries (GBs) are frequently implicated as key defect structures facilitating metal hydride formation, yet their specific role remains poorly understood due to their structural complexity. Here, we investigate hydrogen insertion in Pd nanostructures enriched with well-defined Σ3(111) GBs (Pd GB ) synthesized via electrolysis-driven nanoparticle assembly. In situ synchrotron X-ray diffraction reveals that Pd GB exhibits dramatically accelerated hydriding and dehydriding kinetics compared with ligand-free and ligand-capped Pd nanoparticles with similar crystallite sizes. Strain mapping using environmental transmission electron microscopy shows that strain is highly localized at GBs and intensifies upon hydrogen exposure, indicating preferential hydrogen insertion along GB sites. Density functional theory calculations provide mechanistic insight supporting these findings, showing that hydrogen insertion near Σ3(111) GBs is energetically more favorable and that tensile strain lowers insertion barriers. Furthermore, these results provide atomic-level insights into the role of GBs in hydride formation and suggest new design strategies for GB-engineered Pd-based functional materials.

Anions↗
DOE OSTI2023

Amazon windthrow disturbances are likely to increase with storm frequency under global warming

Abstract Forest mortality caused by convective storms (windthrow) is a major disturbance in the Amazon. However, the linkage between windthrows at the surface and convective storms in the atmosphere remains unclear. In addition, the current Earth system models (ESMs) lack mechanistic links between convective wind events and tree mortality. Here we find an empirical relationship that maps convective available potential energy, which is well simulated by ESMs, to the spatial pattern of large windthrow events. This relationship builds connections between strong convective storms and forest dynamics in the Amazon. Based on the relationship, our model projects a 51 ± 20% increase in the area favorable to extreme storms, and a 43 ± 17% increase in windthrow density within the Amazon by the end of this century under the high-emission scenario (SSP 585). These results indicate significant changes in tropical forest composition and carbon cycle dynamics under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Discovering Blind Geothermal Systems in the Great Basin Region: An Integrated Geologic and Geophysical Approach for Establishing Geothermal Play Fairways: All Phases

Most geothermal resources in the Great Basin region of the western USA are blind, and thus the discovery of new commercial-grade systems requires synthesis of favorable characteristics for geothermal activity. The geothermal play fairway concept involves integration of multiple parameters indicative of geothermal activity to identify promising areas for new development. This project integrated multiple datasets to apply the play fairway concept and assess geothermal potential in a large region of the Great Basin in Nevada. It is therefore referred to as the Nevada play fairway project. This project was a strong collaborative effort between several organizations, led by the Nevada Bureau of Mines and Geology at the University of Nevada, Reno, but with key support from the U.S. Geological Survey, ATLAS Geosciences, Inc,, Hi-Q Geophysical, Inc., Lawrence Berkeley National Laboratory, Utah Geological Survey, and Innovative Geothermal Ltd. In Budget Period 1 of this project, available data for nine geologic, geochemical, and geophysical parameters were initially synthesized to produce a new detailed geothermal potential map of 96,000 km2 from west-central to eastern Nevada. These parameters were grouped into subsets and individually weighted to delineate rankings for local permeability, intermediate permeability, regional permeability, and thermal potential, which collectively defined geothermal play fairways (i.e., most likely locations for significant geothermal fluid flow). This initial work was aimed at reducing the risks in regional exploration and therefore facilitating discovery of new commercial-grade systems in blind settings, as well as in areas with surface expressions of geothermal activity. Budget Period 2 of the project involved detailed analysis of some of the most promising areas identified in Phase 1. Twenty-four highly prospective areas, including both known undeveloped systems and previously undiscovered potential blind systems, were identified for further analysis. After reconnaissance of these areas, five of the most promising sites were selected for detailed studies. Multiple techniques were employed in the detailed studies, including geologic mapping, shallow temperature surveys, gravity surveys, Lidar, geochemical studies, seismic reflection analysis, and 3D modeling. The goal of the detailed studies was to identify specific areas with the highest likelihood for high permeability and thermal fluids, such that drill sites could be targeted. Three main sets of predictive maps were generated for each detailed study area: 1) play fairway maps, 2) play fairway error maps, and 3) direct evidence maps. Local- and intermediate-scale permeability models were revised to reflect results of the detailed geologic, geophysical, and geochemical analyses. Budget Period 3 of the project involved more detailed geophysical analyses and temperature-gradient (TG) drilling in southeastern Gabbs Valley and northern Granite Springs Valley, deemed the two most promising sites, with the goal of providing preliminary validation of the play fairway methodology. In southeastern Gabbs Valley, the collocation of a favorable structural setting (displacement transfer zone and fault intersections), Quaternary faults, intersecting and terminating gravity gradients, magnetic low, shallow (2 m) temperature anomaly, low resistivity anomaly, and promising geothermometry from nearby water wells provided evidence for a blind system. Drilling of six TG holes defines an apparent geothermal system at this locality with temperatures as high as 124°C at 152 m. This system is blind, with no surface hot springs, fumaroles, or paleo-geothermal deposits. For northern Granite Springs Valley, a favorable structural setting (termination of a major Quaternary normal fault), terminating gravity gradient, magnetic gradient, newly discovered sinter deposits, nearby warm water wells, previously drilled TG holes in the vicinity, and promising geothermometry suggest a hidden system. Drilling of six new TG holes yields temperatures of ~96°C at ~250 m, suggesting the presence of a geothermal system. Major lessons learned in the course of this project include: 1) initially identified sites commonly include multiple favorable structural settings at a finer scale; 2) promising sites in Cenozoic basins cannot be recognized without detailed geophysical surveys; and 3) play fairway analysis should be refined as the exploration program vectors into the most promising sites and finer-scale data are acquired. In addition to producing copious amounts of data, this project resulted in 16 published papers, 10 abstracts, more than 40 presentations across the U.S. and abroad (including several keynote addresses), 2 Masters theses, and 7 media reports.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Liquid state properties of SEI components in dimethoxyethane

The solid-electrolyte interphase (SEI) layer is a critical constituent of battery technology, which incorporates the use of lithium metals. Since the formation of the SEI is difficult to avoid, the engineering and harnessing of the SEI are absolutely critical to advancing energy storage. One problem is that much fundamental information about SEI properties is lacking due to the difficulty in probing a chemically complex interfacial system. One such property that is currently unknown is the dissolution of the SEI. This process can have significant effects on the stability of the SEI, which is critical to battery performance but is difficult to probe experimentally. Here, we report the use of ab initio computational chemistry simulations to probe the solution state properties of SEI components LiF, Li 2 O, LiOH, and Li 2 CO 3 in order to study their dissolution and other solution-based characteristics. Ab initio molecular dynamics was used to study the solvation structures of the SEI with a combination of radial distribution functions, discrete solvation structure maps, and vibrational density of states, which allows for the determination of free energies. From the change in free energy of dissolution, we determined that LiOH is the most likely component to dissolve in the electrolyte followed by LiF, Li 2 CO 3 , and Li 2 O although none were favored thermodynamically. Furthermore, this indicates that dissolution is not probable, but Li 2 O would make the most stable SEI with regard to dissolution in the electrolyte.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Imaging how thermal capillary waves and anisotropic interfacial stiffness shape nanoparticle supracrystals

Abstract Development of the surface morphology and shape of crystalline nanostructures governs the functionality of various materials, ranging from phonon transport to biocompatibility. However, the kinetic pathways, following which such development occurs, have been largely unexplored due to the lack of real-space imaging at single particle resolution. Here, we use colloidal nanoparticles assembling into supracrystals as a model system, and pinpoint the key role of surface fluctuation in shaping supracrystals. Utilizing liquid-phase transmission electron microscopy, we map the spatiotemporal surface profiles of supracrystals, which follow a capillary wave theory. Based on this theory, we measure otherwise elusive interfacial properties such as interfacial stiffness and mobility, the former of which demonstrates a remarkable dependence on the exposed facet of the supracrystal. The facet of lower surface energy is favored, consistent with the Wulff construction rule. Our imaging–analysis framework can be applicable to other phenomena, such as electrodeposition, nucleation, and membrane deformation.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Mapping Interfacial Solution Forces that Drive Fluorescent Nanoparticle Incorporation into Crystals

Interfacial solution structure governs processes ranging from catalytic and electrochemical reactions to particle aggregation and crystallization. Here, we design bio-inspired nanocomposites by mapping interfacial forces and controlling nanoparticle incorporation during crystal growth. Our analytical model contains measured kinetic barriers for surface approaches and equilibrium binding constants with the crystal surface. We validate this model using fluorescent silica nanoparticles and calcite. Our results show that surface chemistry dictates incorporation pathways: methoxy groups are least favorable, hydroxyls reduce kinetic barriers by interacting favorably with hydration layers, carboxylates bind strongly but must overcome a kinetic barrier, and amines outperform all kinetically and thermodynamically. This framework resolves the interplay between interfacial solution structures, particle forces and dynamics, and growth kinetics, enabling predicative control of nanocomposite formation and multiplexed mixed-particle systems.

Zhang, Mingyi [Pacific Northwest National Laborato↗
DOE OSTI2023

Sustainable materials acceleration platform reveals stable and efficient wide-bandgap metal halide perovskite alloys

The vast chemical space of emerging semiconductors, like metal halide perovskites, and their varied requirements for semiconductor applications have rendered trial-and-error environmentally unsustainable. Here, in this work, we demonstrate RoboMapper, a materials acceleration platform (MAP), that achieves 10-fold research acceleration by formulating and palletizing semiconductors on a chip, thereby allowing high-throughput (HT) measurements to generate quantitative structure-property relationships (QSPRs) considerably more efficiently and sustainably. We leverage the RoboMapper to construct QSPR maps for the mixed ion FA 1-y Cs y Pb(I 1-x Br x ) 3 halide perovskite in terms of structure, bandgap, and photostability with respect to its composition. We identify wide-bandgap alloys suitable for perovskite-Si hybrid tandem solar cells exhibiting a pure cubic perovskite phase with favorable defect chemistry while achieving superior stability at the target bandgap of ~1.7 eV. RoboMapper’s palletization strategy reduces environmental impacts of data generation in materials research by more than an order of magnitude, paving the way for sustainable data-driven materials research.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mapping the Ultrafast Mechanistic Pathways of Co Photocatalysts in Pure Water through Time‐Resolved X‐ray Spectroscopy

Abstract Nanosecond time‐resolved X‐ray (tr‐XAS) and optical transient absorption spectroscopy (OTA) are applied to study 3 multimolecular photocatalytic systems with [Ru(bpy) 3 ] 2+ photoabsorber, ascorbic acid electron donor and Co catalysts with methylene ( 1 ), hydroxomethylene ( 2 ) and methyl ( 3 ) amine substituents in pure water. OTA and tr‐XAS of 1 and 2 show that the favored catalytic pathway involves reductive quenching of the excited photosensitizer and electron transfer to the catalyst to form a Co II square pyramidal intermediate with a bonded aqua molecule followed by a Co I square planar derivative that decays within ≈8 μs. By contrast, a Co I square pyramidal intermediate with a longer decay lifetime of ≈35 μs is formed from an analogous Co II geometry for 3 in H 2 O. These results highlight the protonation of Co I to form the elusive hydride species to be the rate limiting step and show that the catalytic rate can be enhanced through hydrogen containing pendant amines that act as H−H bond formation proton relays.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Superior Capacitive Energy-Storage Performance in Pb-Free Relaxors with a Simple Chemical Composition

Chemical design of lead-free relaxors with simultaneously high energy density (W rec ) and high efficiency (η) for capacitive energy-storage has been a big challenge for advanced electronic systems. The current situation indicates that realizing such superior energy-storage properties requires highly complex chemical components. Herein, we demonstrate that, via local structure design, an ultrahigh W rec of 10.1 J/cm 3 , concurrent with a high η of 90%, as well as excellent thermal and frequency stabilities can be achieved in a relaxor with a very simple chemical composition. By introducing 6s 2 lone pair stereochemical active Bi into the classical BaTiO 3 ferroelectric to generate a mismatch between A- and B-site polar displacements, a relaxor state with strong local polar fluctuations can be formed. Through advanced atomic-resolution displacement mapping and 3D reconstructing the nanoscale structure from neutron/X-ray total scattering, it is revealed that the localized Bi enhances the polar length largely at several perovskite unit cells and disrupts the long-range coherent Ti polar displacements, resulting in a slush-like structure with extremely small size polar clusters and strong local polar fluctuations. This favorable relaxor state exhibits substantially enhanced polarization, and minimized hysteresis at a high breakdown strength. This work offers a feasible avenue to chemically design new relaxors with a simple composition for high-performance capacitive energy-storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Mapping ammonia-diesel combustion on a single-cylinder 107 mm bore diesel engine retrofitted for ammonia port-fuel injection

Ammonia is garnering significant interest from the international maritime sector as an alternative fuel. It is attractive as a hydrogen carrier and as a fuel because it has a higher volumetric energy density compared with gaseous or liquid hydrogen, making it easier to store and transport without requiring high pressures or cryogenic storage. Ammonia has significant toxicity concerns, but safe handling procedures have already been established because it is one of the most widely produced chemicals worldwide for use as a fertilizer. Barriers to consuming NH 3 as a fuel in engines include (1) less favorable ignition energy and flame speed compared with conventional fuels; (2) emissions challenges, including potentially high NH 3 , NO X , and N 2 O emissions; and (3) fuel delivery and handling challenges. Although NH 3 has been used to fuel compression-ignition marine engines in limited demonstration projects, technical barriers still exist. The use of NH 3 as a fuel in smaller-bore, high-speed auxiliary engines for large vessels and for smaller inland and coastal marine applications remains unaddressed. This work investigates a late-injection diesel pilot ignition dual-fuel NH 3 strategy using a single-cylinder, high-speed Cummins four-stroke diesel engine platform with a 107 mm bore and 1.1 L displacement per cylinder. The engine was modified for port fuel injection of heated gaseous anhydrous NH 3 . The diesel fuel injection system and the combustion geometry were unmodified to represent a retrofit application, which would minimize additional hardware to maximize diesel fuel displacement with NH 3 . Furthermore, the results show the applicability of a late injection diesel pilot strategy to overcome the challenging fuel properties of NH 3 over the engine operating envelope. Mapping results focusing on emissions are presented, and comparisons are made to a conventional diesel combustion baseline.

Ship engines↗
DOE OSTI2024

Elucidating the Role of Carbon Conductive Additive in the Processing and Electrochemical Behavior of Surface-Modified Si Anodes

In the present work, we explore the impact of conducting carbon on the processing of silicon (Si)-rich electrodes, their resulting cycling performance, and parasitic side reactions. We employed three different carbon additives, Super C45 (carbon black), Super C65 (carbon black), and TIMCAL C-NERGY KS 6L (graphite), with varying sizes and morphologies to determine how these parameters influence the structure and the electrochemical behavior of the cast electrodes. Raman mapping indicates that the improved performance achieved using C45 can be explained by the homogeneous distribution of Si and carbon, enabling the formation of continuous electrical pathways throughout the electrode. Further, this finding aligns with the results of ζ potential measurements, which indicate that C45 can maintain more stable dispersions in N-methyl-2-pyrrolidone (NMP) compared with the other carbon additives. However, electrodes with C45 as the conducting agent exhibited increased parasitic side reactions, as evidenced by the leakage current obtained from voltage hold experiments. These side reactions can have adverse effects on the calendar life of the electrode. Furthermore, the Raman maps of the electrodes reveal heterogeneities in Si and carbon distribution with lower carbon content, especially with only 2% carbon. This drastically impacts the cyclability of the electrodes, with portions of the low-carbon electrodes that remain unable to undergo cycling even at slow rates (0.2 Ah g –1 ). Additionally, we found that slower formation rates are favored in the context of parasitic side reactions, although slower formation rates can potentially create thicker passivating films that could hinder Li + -ion transport. Therefore, we emphasize the need for an intricate balance in materials selection and cycling protocol for optimal electrode processing and electrochemical behavior.

25 ENERGY STORAGE↗
DOE OSTI2026

Experimental Investigation of Uranium and Iron Condensation from High-Temperature Plasma Conditions

We used a plasma flow reactor (PFR) to generate synthetic fallout nanoparticles from vapor-phase condensation of two different input concentrations of uranium and iron analytes (U/Fe = 1:1 and 1:2). Synthetic fallout from complex chemical matrices (e.g., mixtures of U and Fe) has not been generated in this setup before and allows for the observation of relative condensation and fractionation of nuclear debris. Experiments were conducted under two different temperature histories with variations in particle flow patterns along the PFR. Transmission electron microscopy (TEM) observation and analysis of the nanoparticles revealed variations in speciation of uranium oxides (UO 2 and α-UO 3 ) depending on the competition between flow mixing and oxygen sequestration by iron. A ternary metal oxide, UFeO 4 , was observed in addition to iron oxides (e.g., FeO and Fe 3 O 4 ), which suggests that fallout models should account for chemical speciation of ternary metal oxides (i.e., UFeO 4 ) and their relative condensation behaviors in addition to those of singular metal oxides (e.g., FeO and UO 2 ). X-ray Energy Dispersive Spectroscopy (EDS) elemental maps showed that some particles had Fe-rich cores surrounded by U-rich regions. This suggests that either condensed U oxides coagulate onto molten Fe oxides or that the increase in iron analyte concentration might drive the system toward a higher degree of supersaturation, leading to earlier formation of iron oxide particles and providing an energetically favored pathway for nucleation of uranium oxides around iron oxide particles.

and nuclear chemistry↗
DOE OSTI2022

Improved Architectures and Training Algorithms for Deep Operator Networks

Operator learning techniques have recently emerged as a powerful tool for learning maps between infinite-dimensional Banach spaces. Trained under appropriate constraints, they can also be effective in learning the solution operator of partial differential equations (PDEs) in an entirely self-supervised manner. In this work we analyze the training dynamics of deep operator networks (DeepONets) through the lens of Neural Tangent Kernel theory, and reveal a bias that favors the approximation of functions with larger magnitudes. To correct this bias we propose to adaptively re-weight the importance of each training example, and demonstrate how this procedure can effectively balance the magnitude of back-propagated gradients during training via gradient descent. We also propose a novel network architecture that is more resilient to vanishing gradient pathologies. Taken together, our developments provide new insights into the training of DeepONets and consistently improve their predictive accuracy by a factor of 10-50x, demonstrated in the challenging setting of learning PDE solution operators in the absence of paired input-output observations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Intricate Genetic Programs Controlling Dormancy in Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB) displays the remarkable ability to transition in and out of dormancy, a hallmark of the pathogen’s capacity to evade the immune system and exploit susceptible individuals. Uncovering the gene regulatory programs that underlie the phenotypic shifts in MTB during disease latency and reactivation has posed a challenge. We develop an experimental system to precisely control dissolved oxygen levels in MTB cultures in order to capture the transcriptional events that unfold as MTB transitions into and out of hypoxia-induced dormancy. Using a comprehensive genome-wide transcription factor binding map and insights from network topology analysis, we identify regulatory circuits that deterministically drive sequential transitions across six transcriptionally and functionally distinct states encompassing more than three-fifths of the MTB genome. The architecture of the genetic programs explains the transcriptional dynamics underlying synchronous entry of cells into a dormant state that is primed to infect the host upon encountering favorable conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Sequential Fracture Activation and Stress Evolution During EGS Stimulation at Utah FORGE Revealed by Waveform Cross‐Correlation

Mapping fracture networks in Enhanced Geothermal Systems (EGS) is essential for optimizing reservoir performance, yet complex fracture evolution during stimulation remains difficult to resolve. This study examines the evolution of microseismicity and fracture networks during stage 3 of the 2022 EGS stimulation at the Utah Frontier Observatory for Research in Geothermal Energy site. We map the fracture network represented by 20 clusters of seismic events identified by waveform similarities with cross-correlation. We characterize their geometric properties such as strike, dip, length, and width, and analyze the time evolution of activated fractures. The results reveal a systematic fracture evolution: early activation of pre-existing natural fractures, complex network development during peak injection, and continued activation of less favorably oriented fractures post-injection. Magnitude calibration using the Principal Component Analysis of cross-correlated waveforms improves relative amplitude measurements, refining estimations of the Gutenberg-Richter b-values with spatial variations in b-values suggesting stress re-distribution across the stimulated area. Analysis of the stress state of selected fractures further shows that fractures requiring higher excess pore pressure primarily activate at the end of injection and post-injection, highlighting stress transfer due to pore pressure as a dominant triggering mechanism. These findings provide insights into fracture propagation, stress evolution, and seismic hazard assessment in EGS reservoirs.

Asirifi, Richard [Texas A & M Univ., College Stati↗
DOE OSTI2024

Datasets and U-Net Model for "A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma"

This dataset has results and the model associated with the publication Ciulla et al., (2024). It contains a U-Net semantic segmentation model (unet_model.h5) and associated code implemented in tensorflow 2.0 for the model training and identification of oil and gas well symbols in USGS historical topographic maps (HTMC). Given a quadrangle map (7.5 minutes), downloadable at this url: https://ngmdb.usgs.gov/topoview/, and a list of coordinates of the documented wells present in the area, the model returns the coordinates of oil and gas symbols in the HTMC maps. For reproducibility of our workflow, we provide a sample map in California and the documented well locations for the entire State of California (CalGEM_AllWells_20231128.csv) downloaded from https://www.conservation.ca.gov/calgem/maps/Pages/GISMapping2.aspx. Additionally, the locations of 1,301 potential undocumented orphaned wells identified using our deep learning framework or the counties of Los Angeles and Kern in California, and Osage and Oklahoma in Oklahoma are provided in the file found_potential_UOWs.zip. The results of the visual inspection of satellite imagery in Osage County is in the file visible_potential_UOWs.zip. The dataset also includes a custom tool to validate the detected symbols in the HTMC maps (vetting_tool.py). More details about the methodology can be found in the associated paper: Ciulla, F., Santos, A., Jordan, P., Kneafsey, T., Biraud, S.C., and Varadharajan, C. (2024) A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma. Accepted for publication in Environmental Science and Technology. The geographical coordinates provided correspond to the locations of potential undocumented orphaned oil and gas wells (UOWs) extracted from historical maps. The actual presence of wells need to be confirmed with on-the-ground investigations. For your safety, do not attempt to visit or investigate these sites without appropriate safety training, proper equipment, and authorization from local authorities. Approaching these well sites without proper personal protective equipment (PPE) may pose significant health and safety risks. Oil and gas wells can emit hazardous gasses including methane, which is flammable, odorless and colorless, as well as hydrogen sulfide, which can be fatal even at low concentrations. Additionally, there may be unstable ground near the wellhead that may collapse around the wellbore. This dataset was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

Artificial Intelligence↗
DOE OSTI2023

Potential Benefits in Remapping the Special Flood Hazard Area: Evidence from the U.S. Housing Market

A typical U.S. homebuyer's understanding of whether a property faces flood risk is based on whether the property is located inside the National Flood Insurance Program's (NFIP) Special Flood Hazard Area (SFHA). The SFHA boundary, however, may bias homebuyers’ perceptions of flood risk relative to unobserved true risk because the SFHA is an incomplete, and sometimes inaccurate, representation of flood hazards. Using a national dataset of property transactions, flood hazard data from the First Street Foundation, state-level measures of flood disclosure laws, and a spatially restricted triple-difference design, we distinguish capitalization effects of policy-driven (SFHA) versus quasi-objective (First Street) indicators of flood hazard. We identify these effects by assessing thousands of local spatial interactions between property SFHA designations, measures of quasi-objective flood hazard, and being in a state that mandates flood history disclosures. Being inside the SFHA generates a risk discount, but the signal is muted relative to underlying hazard exposure. Further, the SFHA signal can result in inefficient discounts for properties erroneously mapped in the SFHA. Disclosure requirements about flood history accentuate price effects for hazardous properties and result in more adequate risk internalization. In the absence of disclosures, however, we find either no or a weak risk signal for houses outside the SFHA that face flood hazard. Our results highlight potential benefits of updating SFHA boundaries to include all houses that may experience flooding, and argue in favor of requiring flood-related disclosures from sellers to improve the market's ability to internalize flood risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. As a result, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.

15 GEOTHERMAL ENERGY↗