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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Range-wide population assessments for subalpine fir indicate widespread disturbance-driven decline

Subalpine forests in western North America are threatened by rapid climate change, increased activity by endemic and exotic insects and diseases, and changing wildfire regimes. The interactive effects of these stressors have resulted in pronounced population declines in many subalpine tree species; however, a systematic assessment of the status and trends of subalpine forests is lacking. Subalpine fir (Abies lasiocarpa) is a widespread species across the western United States, with documented population declines in many parts of its distribution. Here we use subalpine fir as an initial leverage point to build a more complete understanding of subalpine forest baseline conditions and responses to environmental change. Specifically, we leverage the USDA Forest Service Forest Inventory and Analysis (FIA) database to (1) ask how subalpine fir populations are changing across the species’ distribution in the western US, (2) assess the drivers of recent subalpine fir population trends, and (3) explore whether those changes imply generalized species-wide and/or system-wide decline. We found that subalpine fir abundance and basal area are declining concurrently across ~ 62% of the species’ distribution, and increasing across ~ 19%. Range-wide, we estimated 25.02 ± 2.74 % subalpine fir mortality between 2000 and 2009 and 2010–2019 FIA inventory periods, with higher mortality concentrated in the eastern Oregon Cascades, central Idaho, and parts of southern Colorado. High regeneration density did not predict positive population trajectories, which were instead associated with higher rates of adult recruitment. While the importance of different mortality agents varied substantially between ecoregions, 83.4% of total range-wide mortality was related to fire or biological disturbance. Declining subalpine fir basal area coincided with declines in the basal area of other co-occurring tree species in 39% of subalpine forest area, and with increases in conspecific basal area in 22% of forest area. Fire disturbance was the single largest cause of subalpine fir mortality; however, even where subalpine fir fire mortality was high, mortality among other species was primarily caused by insects. In conclusion, our results suggest that subalpine fir declines across large portions of the western United States are driven by forest disturbance, and that declines in subalpine fir populations may be indicative of negative change in subalpine forest systems broadly.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Viscoelastic necking dynamics between attractive microgels

Hypothesis: Microgels can deform and interpenetrate and display colloid/polymer duality. The effective interaction of microgels in the collapsed state is governed by the interplay of polymer–solvent interfacial tension and bulk elasticity. A connecting neck is shown to mediate microgel interaction, but its temporal evolution has not been addressed. Here we hypothesize that the necking dynamics of attractive microgels exhibits liquid-like or solid-like behavior over different time and length scales. Experiments: We simulate the merging and pinching of attractive microgels with different crosslinking densities in explicit solvent using dissipative particle dynamics. The temporal coalescence dynamics of microgels is investigated and compared with simple liquid and polymeric droplets. We model the neck growth on long time scales using Maxwell model of polymer relaxation and compare the theoretical prediction with simulation data. The mechanical strength of the neck is characterized systematically via simulated pinch-off of microgels by steered molecular dynamics. Findings: We evidence a crossover in the coalescence dynamics reflecting the viscoelastic signature of microgels. In contrast to the common knowledge that viscoelastic materials respond elastically on short time scales, the early expansion of the microgel neck exhibits a linear behavior, similar to the viscous coalescence of liquid droplets. However, the late regime with arrested dynamics resembles sintering of solid particles. Through an analytical model relating microgel dynamics to neck growth, we show that the long-term behavior is governed by stress relaxation of the polymers in the neck region and predict an exponential decay in the rate of growth, which agrees favorably with the simulation. Different from coalescence, the thread thinning in microgel breakup primarily highlights its polymeric characteristics.

42 ENGINEERING↗
DOE OSTI2017

Facile synthesis of highly active reduced graphene oxide-CuI catalyst through a simple combustion method for photocatalytic reduction of CO{sub 2} to methanol

We report a facile combustion method synthesis of reduced graphene oxide/CuI composites as a photocatalyst, in which CuI nanoparticles were homogeneously distributed on the surface of reduced graphene oxide (rGO), showing a good visible light response. The rGO-supported and unsupported CuI hybrids were tested over the photocatalytic reduction of CO{sub 2} for methanol evolution in visible light. In the current study rGO-CuI composites have shown excellent yields (19.91 μmol g-cat{sup −1}). rGO provides a light-weight, charge complementary and two-dimensional material that interacts effectively with the CuI nanoparticles. - Graphical abstract: A facile reduced graphene oxide（rGO）/ CuI nanoparticles complexes hybrid material was prepared by a naked flame. This method can be simple and rapid preparation of composite materials which exhibit excellent catalytic performance. It is not only can improve the light absorption but also can be highly efficient reduction of CO{sub 2} to methanol in visible light. So the rGO-CuI composite will find potential applications as catalysts in organic photosynthetic reaction. Display Omitted - Highlights: • Reduced graphene oxide-CuI composites have been prepared by a facile method. • This method can produce reduced graphene oxide from graphene oxide without any reducing agent. • The absorption efficiency of rGO-CuI composites is significantly higher than rGO and CuI.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Near UV based LED fabricated with K2Ba3Si8O20:Eu2+ and energy transfer between Ce3+ and Eu2+

Highlights: • A kind of novel green-emitting phosphor K{sub 2}Ba{sub 3}Si{sub 8}O{sub 20}:Ce{sup 3+},Eu{sup 2+} has been prepared. • Energy transfer in this host is systematically investigated and their origins are to be revealed. • Commission on illumination value as well as thermal luminescence quenching properties are studied. • Results indicate that the phosphor can be served as a potential green emitting phosphor candidate for LED devices. - Abstract: K{sub 2}Ba{sub 3}Si{sub 8}O{sub 20}:Eu{sup 2+} as a new green-emitting phosphor was prepared and studied in this article. This material showed powerful absorption in near ultraviolet-visible regions from 350 to 450 and its green-emitting band peaked at 502 nm. Its photoluminescence emitting intensity affected by concentrations of K{sub 2}Ba{sub 3}Si{sub 8}O{sub 20}:Ce{sup 3+} and K{sub 2}Ba{sub 3}Si{sub 8}O{sub 20}:Eu{sup 2+} was also studied and consequently verified to be a dipole–dipole interaction. Effective energy transfer was observed in Ce{sup 3+},Eu{sup 2+} co-doped K{sub 2}Ba{sub 3}Si{sub 8}O{sub 20} phosphor, and the transfer efficiency was also obtained from the decay lifetime. The K{sub 2}Ba{sub 3}Si{sub 8}O{sub 20}:Eu{sup 2+} phosphor combined with a 380 nm near-UV chip was demonstrated to produce a diode which emitted green light. This article indicates that the phosphor prepared in this work could be used in manufacturing of white light-emitting diodes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Explaining drivers of housing prices with nonlinear hedonic regressions

Housing markets play a critical role in shaping the spatial and demographic evolution of urban areas. Simulating housing price dynamics can enhance projections of future urban development outcomes. However, traditional hedonic regressions for housing prices, which neglect nonlinear interactions among explanatory variables, often exhibit limited predictive performance. While machine learning (ML) methods can provide a more flexible representation of the relationships between predictors, they are often regarded as “black boxes” due to their complexity and lack of transparency. Interpretable ML techniques provide a promising route by combining the flexibility of ML methods with approaches to analyze the relationships between inputs and outputs. In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16 % more of housing price variance than a traditional linear regression model. The interpretable ML model also reveals more nuanced and realistic nonlinear relationships between housing sales price and predictors as well as interactive effects underlying Baltimore home price dynamics. For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Liquid lithium divertor analysis using coupled plasma material interaction model

A liquid lithium divertor can improve performance of future fusion devices by creating efficient power exhaust and improving the energy confinement via pumping of the hydrogen isotopes. In addition, significantly higher heat fluxes can be handled if controlled vapor shielding is used to redistribute the divertor heat flux over a wider area. Design and optimization of such a system calls for an analysis model which includes a strong two-way coupling between the plasma and divertor material. The incoming plasma heat and particle flux will affect the divertor surface temperature, which is a defining factor of the lithium evaporative and sputtered flux going into the plasma. Results of the coupled model based on the plasma edge code SOLPS-ITER and the computational fluid dynamics (CFD) code ANSYS-CFX will be presented for different configurations. An analytical slab flow model is used as a heat transfer boundary condition for SOLPS, defining particle flux from the wall via calculation of the surface temperature. At the final step, results of the SOLPS analysis are verified using a 3D CFD magnetohydrodynamics (MHD) analysis which uses heat and particle flux from SOLPS as a boundary condition. In addition to plasma heat flux, both analytical and CFD temperature models include several plasma material interaction effects, such as lithium evaporation, condensation and sputtering based on deuterium target flux. New adatom sputtering model based on the available experimental data is presented. Analytical model is expanded to include free surface axisymmetric configurations. Results of parametric studies of the divertor configurations with different lithium inlet temperature and velocity will be presented leading to the optimal design resulting in the lowest possible lithium contamination in the core.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Identification of a millisecond isomeric state in 129Cd81via the detection of internal conversion and Compton electrons

The decay of an isomeric state in the neutron-rich nucleus 129Cd has been observed via the detection of internal conversion and Compton electrons providing first experimental information on excited states in this nucleus. The isomer was populated in the projectile fission of a 238U beam at the Radioactive Isotope Beam Factory at RIKEN. From the measured yields of γ-rays and internal conversion electrons, a multipolarity of E 3 was tentatively assigned to the isomeric transition. A half-life of T1/2=3.6(2) msT1/2=3.6(2) ms was determined for the new state which was assigned a spin of (21/2+)(21/2+), based on a comparison to shell model calculations performed using state-of-the-art realistic effective interactions.

Isomeric decays↗
DOE OSTI2017

Observation of a γ -decaying millisecond isomeric state in 128 Cd 80

A new high-spin isomer in the neutron-rich nucleus 128 Cd was populated in the projectile fission of a 238 U beam at the Radioactive Isotope Beam Factory at RIKEN. A half-life of T1/2 = 6.3(8) ms was measured for the new state which was tentatively assigned a spin/parity of (15-). The experimental results are compared to shell model calculations performed using state-of-the-art realistic effective interactions and to the neighbouring nucleus 129 Cd. In the present experiment no evidence was found for the decay of a 18 + E6 spin-trap isomer, based on the complete alignment of the two-neutron and two-proton holes in the 0h 11/2 and the 0g 9/2 orbit, respectively, which is predicted to exist by the shell model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Emergence of an island of extreme nuclear isomerism at high excitation near 208 Pb

Metastable states with $T_{1/2}$ = 8(2) ms in 205Bi and $T_{1/2}$ = 0.22(2) ms in 204 Pb, with ≈ 8 MeV excitation energy and angular momentum ≥ 22 $\hbar$, have been established. These represent, by up to two orders of magnitude, the longest-lived nuclear states above an excitation energy of 7 MeV, ever identified in the nuclear chart. Additionally, the half-life of the 10.17 MeV state in 206 Bi has been determined to be 0.027(2) ms, the next highest value in this highly excited regime. These observations indicate the emergence of an island of extreme nuclear isomerism arising from core-excited configurations at high excitation in the vicinity of the doubly closed-shell nucleus 208 Pb. These results are expected to provide discriminating tests of the effective interactions used in current large-scale shell-model calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Intruder configurations in 29 Ne at the transition into the island of inversion: Detailed structure study of 28 Ne

Detailed γ-ray spectroscopy of the exotic neon isotope 28 Ne has been performed for the first time using the one-neutron removal reaction from 29 Ne on a liquid hydrogen target at 240 MeV/nucleon. Based on an analysis of parallel momentum distributions, a level scheme with spin-parity assignments has been constructed for 28 Ne and the negative-parity states are identified for the first time. The measured partial cross sections and momentum distributions reveal a significant intruder p-wave strength providing evidence of the breakdown of the N=20 and N=28 shell gaps. Only a weak, possible f-wave strength was observed to bound final states. Large-scale shell-model calculations with different effective interactions do not reproduce the large p-wave and small f-wave strength observed experimentally, indicating an ongoing challenge for a complete theoretical description of the transition into the island of inversion along the Ne isotopic chain.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

The evidence of N = 16 shell closure and β -delayed neutron emission from 25 F

We measured the β -delayed neutron emission from 25 F for the first time at the Facility for Rare Isotope Beams (FRIB). Using combined neutron and γ -ray detector systems of the FRIB Decay Station Initiator (FDSi), we observed β -decay transitions populating neutron unbound states between 4.2 and 8 MeV in 25 Ne. The experimental results led to the revision of the β -decay half-life and β -delayed neutron-emission probability of 25 F. The β -decay strength distribution of 25 F extracted from the data agrees with the shell-model predictions using the USDB and SDPF-M effective interactions. This result indicates that the spherical neutron shell gap persists in 25 F and 25 Ne.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Towards grounding nuclear physics in QCD

Exascale computing could soon enable a predictive theory of nuclear structure and reactions rooted in the Standard Model, with quantifiable and systematically improvable uncertainties. Such a predictive theory will help exploit experiments that use nucleons and nuclei as laboratories for testing the Standard Model and its limitations. Examples include direct dark matter detection, neutrinoless double beta decay, and searches for permanent electric dipole moments of the neutron and atoms. It will also help connect QCD to the properties of cold neutron stars and hot supernova cores. We discuss how a quantitative bridge between QCD and the properties of nuclei and nuclear matter will require a synthesis of lattice QCD (especially as applied to two- and three- nucleon interactions), effective field theory, and ab initio methods for solving the nuclear many-body problem. While there are significant challenges that must be addressed in developing this triad of theoretical tools, the rapid advance of computing is accelerating progress. In particular, we focus this review on the anticipated advances from lattice QCD and how these advances will impact few-body effective theories of nuclear physics by providing critical input, such as constraints on unknown low-energy constants of the effective (field) theories. We also review particular challenges that must be overcome for the successful application of lattice QCD for low-energy nuclear physics. We describe progress in developing few-body effective (field) theories of nuclear physics, with an emphasis on HOBET, a non-relativistic effective theory of nuclear physics, which is less common in the literature. We use the examples of neutrinoless double beta decay and the nuclear-matter equation of state to illustrate how the coupling of lattice QCD to effective theory might impact our understanding of symmetries and exotic astrophysical environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Changes in inundation drive carbon dioxide and methane fluxes in a temperate wetland

Wetlands cycle carbon by being net sinks for carbon dioxide (CO 2 ) and net sources of methane (CH 4 ). Daily and seasonal temporal patterns, dissolved oxygen (DO) availability, inundation status (flooded or dry/partially flooded), water depth, and vegetation can affect the magnitude of carbon uptake or emissions, but the extent and interactive effects of these variables on carbon gas fluxes are poorly understood. We characterized the linkages between carbon fluxes and these environmental and temporal drivers at the Old Woman Creek National Estuarine Research Reserve (OWC), OH. We measured diurnal gas flux patterns in an upstream side channel (called the cove) using chamber measurements at six sites (three vegetated and three non-vegetated). We sampled hourly from 7 AM to 7 PM and monthly from July to October 2022. DO concentrations and water levels were measured monthly. Water inundation status had the most influential effect on carbon fluxes with flooded conditions supporting higher CH 4 fluxes (0.39 μmol CH 4 m –2 s –1 ; –1.23 μmol CO 2 m –2 s –1 ) and drier conditions supporting higher CO 2 fluxes (0.03 μmol CH 4 m –2 s –1 ; 0.86 μmol CO 2 m –2 s –1 ). When flooded, the wetland was a net CO 2 sink; however, it became a source for both CH 4 and CO 2 when water levels were low. We compared chamber-based gas fluxes from the cove in flooded (July) and dry (August) months to fluxes measured with an eddy covariance tower whose footprint covers flooded portions of the wetland. The diurnal pattern of carbon fluxes at the tower did not vary with changing water levels but remained a CO 2 sink and a CH 4 source even when the cove where we performed the chamber measurements dried out. Furthermore, these results emphasize the role of inundation status on wetland carbon cycling and highlight the importance of fluctuating hydrologic patterns, especially hydrologic drawdowns, under changing climatic conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Peatland microbial community response to altered climate tempered by nutrient availability

Northern latitude peatlands contain large reserves of soil carbon (C) due in part to climatic constraints on decomposition, including low temperatures and water inundated soils. Under future climate change scenarios these peatlands will experience warmer temperatures, extended growing seasons, and a potential draw down of the water table, all of which improve conditions for decomposition, as well as photosynthesis. Increased photosynthesis may feedback to increase decomposition of soil C through increased root exudates to belowground decomposer communities, primarily as low molecular weight carbon compounds (LMWCC). In this study, we examine the interactive effects of climate, a combination of three temperatures and two moisture regimes, and root exudates on microbial decomposer function, measured as CO2 respiration, biomass, and potential enzyme activity. We had four substrate treatments: two common LMWCC (glycine or glucose + citric acid), chitin to simulate fungal necromass, and DI as the control. Our results support our first hypothesis that increasing temperature will increase C respiration across substrate and moisture treatments. Our second hypothesis was that compared to control and chitin, soluble substrates (i.e., LMWCC) will enhance respiration across all climate treatments. This was only partially supported. As expected, the two LMWCC substrate additions increased C respiration above the two other substrate additions at current recorded growing season average (12 °C, low treatment) and high (20 °C, med treatment) temperature treatments. Surprisingly, when the system was pushed to a higher temperature extreme (28 °C, high treatment), the low moisture controls respired more C than the other substrate × climate treatments. Potential enzyme activity and demand for phosphorus appear to explain these trends as opposed to changes to microbial biomass. Our results indicate that under projected future high temperatures, the peatland microbial community allocates additional labile C resources to enzyme production to meet nutrient demands, and as such, dampens C lost through respiration.

Keiser, Ashley D.↗
DOE OSTI2022

A legacy of fire emerges from multiple disturbances to most shape microbial and nitrogen dynamics in a deciduous forest

Healthy forests are vital components of terrestrial ecosystems for their raw materials, high biodiversity, cycling of nutrients, and potential to sequester carbon. However, these ecosystems are sensitive to disturbances, and anthropogenic activities pose a serious threat to forest ecosystems globally. For example, human activities have dramatically altered multiple historical disturbance regimes in forests, including suppressing fire, increasing the density of large herbivores, and reducing the size of canopy gaps, among other disturbances. Such disturbances can have dramatic impacts on microbially-mediated forest soil functions, but more research is needed to determine the collective impacts of these disturbances. In this study, we investigated the interactive effects of disturbances, namely the legacies of fire, large herbivore densities, and canopy gap creation, in a deciduous forest soil. We determined that forest floor and mineral soil carbon and nitrogen pools were shaped by multiple disturbances, but fire was more influential than the other disturbances. The abundance of several functionally-relevant microbial taxa were significantly impacted by fire, and the effect was more pronounced in the mineral soil than in the forest floor. Together, these findings demonstrate that multiple disturbances, especially a legacy of fire, exerts long-term control over soil carbon, nitrogen and microbial dynamics in a deciduous forest system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Mineral scaling on brass and aluminum surfaces with a range of wettability

Crystallization fouling, or mineral scaling, is fundamental to various surfaces in energy and environmental applications. This paper considers, for the first time, a systematic investigation of scaling of nonwetting aluminum and brass surfaces relative to their bare counterparts. Metallic surfaces with a range of nonwetting characteristics from hydrophilic to superhydrophobic and lubricant infused surfaces were fabricated using a facile etching method. Systematic dynamic flow scaling experiments are conducted on the six different surface types at three flow levels and two temperature levels. The interactive effects of the different parameters are analyzed relative to their correspondence to the nucleation theory. The study reveals that nonwetting metallic superhydrophobic or hydrophobic surfaces reduce fouling by over 50% compared to bare counterparts. Further, lubricant infused metallic surfaces demonstrate superior anti-scaling performance by over 60% and up to 90% fouling reduction. The study is the first to present fundamental insights into the engineering and anti-scaling performance of nonwetting brass and aluminum surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Gaining Perspective on Unconventional Well Design Choices through Play-level Application of Machine Learning Modeling

The recent development of unconventional oil and gas (O&G) reservoirs has led to an abundant hydrocarbon supply, both domestically and globally. However, there is a continued push to develop new and innovative approaches to improve exploration and extraction efficiencies and overall well productivity moving forward. Substantial improvements in unconventional O&G development are expected through optimized well completion and stimulation strategies aimed at maximizing well productivity. Optimizing well designs will require tailoring to the distinctive geologic conditions present for any newly placed well. To better evaluate the impact of well design attributes and their associated interactions on productivity in a major unconventional play, multivariate machine learning-based models that use empirical datasets were developed. A gradient boosted regression tree (GBRT) algorithm was applied. GBRT has been narrowly investigated for O&G applications but enables straightforward parametric importance and influence evaluation, as well as assessment of parameter interaction effects. Models were trained on well design and locational parameters that serve as a proxy for variable geologic conditions to estimate two types of productivity indicator response variables strongly correlated to estimated ultimate recovery (EUR). The dataset utilized consists of over 7,000 well observations that cover the majority of the productive region of the Marcellus Shale. Model performance was evaluated and algorithm parameters tuned by analyzing the goodness-of-fit for simulated results against observed data in a cross-validation approach. Models were found capable of 73–79 percent prediction accuracy on held out testing data of gas equivalent production and can be used to inform future well design and placement decisions for increasing EUR per well and improving overall field-level recovery. Study results indicate that Marcellus well performance improves most with upscaling perforated interval lengths and water and proppant volumes per foot; but relative productivity improvements are spatially dependent across the play. Finally, optimal combinations of water and proppant on well performance were found to vary depending on well location, emphasizing the utility of data-driven models capable of broad application across a play of interest for informing tailored well design approaches prior to their field deployment.

04 OIL SHALES AND TAR SANDS↗