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709 records · Page 40
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DOE OSTI2022

Foam Fracturing Study for Stimulation Development of Enhanced Geothermal Systems

The large thermal gradients and high subsurface temperatures of the western region of the U.S. hold great potential for the implementation of enhanced geothermal systems (EGS). The development of these potential EGS resources requires stimulation of the reservoir to enhance permeability and it has been widely reported that a substantial amount of water will be required should conventional hydraulic stimulation be used. This presents a huge challenge and a high risk to the geothermal development because the water stress1 in these areas is already high or extremely high. The use of foam, a gas/liquid mixture predominantly composed of gas, in fracturing is considered and explored in this project as a potential approach to address water concerns with hydraulic stimulation in the development of EGS. This project, led by Oak Ridge National Laboratory (ORNL) in collaboration with Temple University, was awarded in an open lab call in 2018, and was part of the DOE GTO waterless stimulation initiative. The goal of the project was to demonstrate the feasibility of foam fracturing for EGS development through two primary tasks: Task 1: Laboratory study of the effectiveness of foam fracturing for representative geological materials, including cyclic pressurization using foam (led by ORNL) and Task 2: High temperature foam material selection and characterization (led by Temple University). In FY19, ORNL finished the critical review on serval issues associated with foam fracturing and the implementation of the proposed tasks in a lab study (Wang, et al., 2019), and completed the foam fracturing testing using cement as a model material (Wang, et al, 2020a). The work at ORNL was geared up to develop a brand-new foam testing system in FY20. The purchase of main components for the new system was finished in the first half of the FY20. The assembly of the foam testing system and foam fracturing testing were completed in the second half of the FY20 (Wang, et al., 2021a). Task 1 required the development of a test system which can be used to perform hydraulic fracturing of geological specimens with both water and foamed liquids at pressure up to 6,000 psi (41.4 MPa). The system possesses several capabilities that conventional injection systems lack for hydraulic fracturing. In addition to its ability to generate foam with controlled quality, it is capable of cycling pressure levels between specified values up to frequencies of 50 Hz. The latter capability was developed to evaluate the hypothesis that cyclic loading of samples would produce enhanced fracturing. The system consists of two sections: one for foam generation and another for foam injection. The foam is generated through separate control and pressurization of liquid and gas phases with controlled flow rates. The injection section is equipped with a low-flow Coriolis flow that monitors the density of foam to ensure the injection is in the range of target foam quality2. Experimental results of foam fracturing are reported for cylindrical granite specimens using water and aqueous N2 foam as the fracturing fluids. All experiments were performed for unconfined conditions. The effects of injection mode (i.e., monotonic vs cyclic pressurization) on breakdown pressure and failure response sample were investigated using water alone as a fracturing fluid and foams with a range of compositions. It was found that in the case of monotonic injection, the breakdown pressure of granite specimens tended to be slightly higher when fracturing with foam. Additionally, with a foam quality of 90%, the water use can be reduced by 50 to 84%, depending on hole size. On the other hand, it was observed that the breakdown pressure can be brought down to 70% of the monotonic breakdown pressure by using low cycle fatigue. Finally, discussions are presented regarding injectivity and water use reduction.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Quasi-Steady Simulation of Glaze Ice Accretion and Heat Transfer in the Supercooled Large Droplet Regime in Atmospheric Turbulent Air Flow

Abstract To avoid the use of a computationally intensive full unsteady simulation while providing an accurate solution, a quasi-steady simulation has been performed to study glaze and mixed ice accretion in the supercooled large droplet (SLD) regime in turbulent flow of atmospheric air. We have attempted to find the minimum time-step necessary to adequately simulate the icing process on an airfoil surface. Based on node displacement, a mesh morphing scheme has been adopted in the computations to account for the moving boundaries that are caused by the continuous ice buildup. We have modeled ice accretion on an airfoil surface for a time period of 232 s using several time steps. At each time-step we solved the steady-state conservation equations for the air and droplet phases and then used the results as initial conditions for the next time position. The magnitude of the time-step ranged from a least accurate two-shot simulation (where the time-step is 116 s) to a most accurate 2320-shot simulation (where the time-step is 0.1 s). In-between these two extreme time steps, we have performed a three-shot simulation (where the time-step is 77.33 s), a four-shot simulation (where the time-step is 58 s), a six-shot simulation (where the time-step is 38.67 s), a 46-shot simulation (where the time-step is 5 s), and a 232-shot simulation (where the time-step is 1 s). We have done so to find out the degree of accuracy (or inaccuracy) of the multishot simulation approach and to find out the appropriate time-step needed for a successful and valid quasi-steady simulation. A valid quasi-steady simulation needs to use a time-step that is small enough to reproduce the full time-dependent solution within a very small error band. We have found that both the 1 s and the 0.1 s time steps produce virtually identical results. This is the primary litmus test that proves the validity of the quasi-steady-state assumption. The results adopted in this paper are thus all based on the more conservative 0.1 s time-step. In the process of performing the simulations, remeshing was required in order to maintain the grid density in zones of high curvature to be able to capture the full physics in those zones. After successfully modeling glaze ice accretion over the airfoil surface using the 0.1 s quasi-steady simulation approach, the effects of supercooled large droplets (SLDs) impacting the surface have been examined and presented in terms of the variation of the local collection efficiency, the water film thickness, and the heat transfer rate. Examination of the variation of the angle that the ice horn makes with the airfoil chord line demonstrated a 20% improvement in angle prediction when the time-step is reduced from 116 s to 0.1 s. The analysis also reveals a 12% or 8.5% increase in the maximum collection efficiency, βmax, depending on whether the value of the liquid water content (LWC) has been doubled from 0.5 g/m3 to 1 g/m3, or the value of the freestream velocity has been doubled from 75 m/s to 150 m/s, respectively. Because of the need to monitor the local collection efficiency and convective heat fluxes at each shot (regardless of the number of shots employed), the approach adopted here was found to be effective in successively and successfully reproducing the curvature of the glaze ice horn.

Engineering↗
DOE OSTI2024

Do not forget the electrons: Extending moderately-sized nuclear networks for multidimensional hydrodynamic codes

Context.Nuclear networks are widely used coupled with hydrodynamical simulations of explosive scenarios to account for the change of nuclear species and energy generation rate due to nuclear reactions. In this way, there is a feedback mechanism between the hydrodynamical state and the nuclear processes. Unfortunately, the timescale of nuclear reactions is orders of magnitude smaller than the dynamical timescale that drives hydrodynamical simulations. Therefore, these nuclear networks are usually very small, reduced in most cases to a dozen elements, especially when simulations are carried out in more than one dimension. Aims.We present here an extended nuclear network, with 90 species, designed for being coupled with hydrodynamic simulations, which includes neutrons, protons, electrons, positrons, and the corresponding neutrino and anti-neutrino emission. This network is also coupled with temperature, making it extremely robust and, together with its size, unique of its kind. The inclusion of electron captures on free protons makes the network very appropriate for multidimensional studies of Type Ia supernova explosions, especially when the exploding object is a massive white dwarf. Methods.We perform several tests that are relevant to simulate explosive scenarios, such as Type Ia supernovae and core-collapse supernovae. We compare the results of the 90 nuclei network with a standardα-chain network with 14 elements to evaluate the differences in the energy generation rate. We also evaluate the relevance of including the electrons in the network in terms of generated yields and how it affects the pressure of a degenerate fluid such as that of white dwarfs. The results obtained with the 90-nuclei network have been verified with a much larger 2000-nuclei network built from REACLIB (WinNet), in terms of nuclear energy generation rate, pressure, and produced yields. Results.The results obtained with the proposed medium-sized network compare fairly well, to a few percent, with those computed withWinNetin scenarios reproducing the gross physical conditions of current Type Ia supernova explosion models. In those cases where the carbon and oxygen fuel ignites at high density, the high-temperature plateau typical of the nuclear statistical equilibrium regime is well defined and stable, allowing large integration time steps. We show that the inclusion of electron captures on free protons substantially improves the estimation of the electron fraction of the mixture. Therefore, the pressure is better determined than in networks where electron captures are excluded, which will ultimately lead to more reliable hydrodynamic models. Explosive combustion of helium at low density, occurring near the surface layer of a white dwarf, is also better described with the proposed network, which gives nuclear energy generation rates much closer toWinNetthan typical reduced alpha networks. Conclusions.A nuclear network withN= 90 species, including electrons, aimed at multidimensional calculations of supernova explosions is described and verified. The proposed network is suitable for the study of Type Ia supernova explosions because it provides better values of pressure and electron abundance than other existing networks with smaller or even a similar size but without including electron capture processes.

Astronomy & Astrophysics↗
DOE OSTI2022

Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning

Drought is a pressing issue for the Colorado River Basin (CRB) due to the social and economic value of water resources in the region and the significant uncertainty of future drought under climate change. Here, we use climate simulations from various Earth System Models (ESMs) to force the Variable Infiltration Capacity hydrologic model and project multiple drought indicators for the sub-watersheds within the CRB. We apply an unsupervised machine learning (ML) based on Non-Negative Matrix Factorization using K-means clustering (NMFk) to synthesize the simulated historical, future, and change in drought indicators. The unsupervised ML approach can identify sub-watersheds where key changes to drought indicator behavior occur, including shifts in snowpack, snowmelt timing, precipitation, and evapotranspiration. While changes in future precipitation vary across ESMs, the results indicate that the Upper CRB will experience increasing evaporative demand and surface-water scarcity, with some locations experiencing a shift from a radiation-limited to a water-limited evaporation regime in the summer. Large shifts in peak runoff are observed in snowmelt-dominant sub-watersheds, with complete disappearance of the snowmelt signal for some sub-watersheds. The work demonstrates the utility of the NMFk algorithm to efficiently identify behavioral changes of drought indicators across space and time and to quickly analyze and interpret hydro climate model results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Mechanical Behavior of Neutron Irradiated Refractory Multi-Principal Element Alloys Processed via Spark Plasma Sintering

The search for advanced materials capable of withstanding the extreme conditions of Generation IV reactors is a critical area in materials science research. These reactors operate under severe environments, including high temperatures, corrosion, stress, and irradiation damage. Consequently, there is a need for innovative alloy systems to ensure the reliability and longevity of proposed Generation IV reactor components. Refractory multi-principal-element alloys (RMPEA) have emerged as a promising candidate due to their exceptional properties. These alloys, characterized by their composition of multiple principal elements in near-equiatomic ratios, exhibit superior resistance to irradiation damage, reduced void swelling, enhanced microstructural stability, and minimal irradiation-induced hardening. While initial studies on RMPEAs have shown promising results, most research has been limited to thin films, nanocrystalline microstructures, and ion irradiation, which do not accurately represent the behavior of bulk materials. To address this gap, our research focused on the neutron irradiation of bulk RMPEAs. We aim to conduct comprehensive post-irradiation examinations (PIE) of RMPEAs irradiated at the Advanced Test Reactor at Idaho National Laboratory. The RMPEAs were synthesized using spark plasma sintering (SPS) with mechanically alloyed metallurgical powder. The RMPEA specimens are a MoNbTi alloy system with additions of -Zr, and -ZrV. Furthermore, PIE consisted of mechanical testing and advanced materials characterization. The mechanical testing consisted of sub-sized tensile testing, micro- and nano- indentation. Microstructural characterization included scanning electron microscopy and transmission electron microscopy. Mechanical testing coupled with advanced microscopy techniques provides insight into phase morphology and its effects on the mechanical properties of the RMPEA specimens. The results indicate that both pristine and irradiated RMPEA specimens exhibited brittle behavior during tensile testing, which can be attributed to their heterogeneous microstructure. The SPS manufacturing process did not include any post treatment, which resulted in a heterogeneous microstructure. Energy-dispersive X-ray spectroscopy revealed the presence of intermetallic such as laves phases within the microstructure. Specifically, Ti-rich precipitates were observed in the MoNbTi specimen, while Mo-rich precipitates were found in the MoNbTiZrV specimen. Nano-hardness testing of pristine samples showed that the laves phases exhibited higher hardness values compared to the matrix phase, suggesting that precipitate hardening is likely the dominant hardening mechanism in these specimens. The results from this work will be used to build a finite element model to predict mechanical behavior of future MPEA compositions. Thus, enabling for a streamlined approach to developing novel MPEAs for the nuclear industry.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

The Snow Albedo Evolution (SALVO) Campaign at the North Slope of Alaska Field Campaign Report

The springtime surface-albedo transition in the Alaskan Arctic, along with the forces that determine its duration and nature, was the focus of the Snow ALbedo eVOlution (SALVO) campaign. The SALVO team investigated the reasons and durations of the stages of spring melt, during which albedo values decrease from 0.8 to 0.1, signifying the year’s largest and most significant radiative energy change. SALVO II (2022, 2024) built on findings from the successful SALVO I (2019) melt season campaign and previous research on the melt transition period conducted by Grenfell and Perovich (2004). SALVO used the ARM NSA observatory, thus aligning with a core principle of the ARM Decadal Vision to “provide comprehensive and impactful field measurements to support scientific advancement of atmospheric process understanding.” SALVO fieldwork was conducted in the spring near the ARM NSA Central Facility in Utqiagvik, Alaska (Figure 1). Comprehensive data sets were collected in 2019, 2022, and 2024, which included spectral and broadband albedos, snow and meltwater depths, snow stratigraphy, snow grain size data, and aerial imagery. In April of each project year, before the snow began to melt, we established survey lines at three or four locations: inland tundra at NSA E12, coastal tundra at the NSA C1 site, Elson Lagoon, and offshore on the Chukchi Sea sea ice (2022 only). We set up a 200-meter-long line, marked every five meters, to enable repeated measurements of surface albedo and snow depth at each location. During most site visits, the SALVO II team dug at least one snow pit to assess the characteristics of the vertical snow layers, including the types and sizes of snow grains, snow density, and the distribution of liquid water. We evaluated the characteristics of flowing or pooled water at the bottom of the snowpack. Watching snow melt in the Arctic has been compared by some to being as exciting as watching paint dry, but it is far more exciting and dynamic than that. Initially, changes happen slowly while the snow cover remains above 80%, but then reductions in the albedo of the landscape, from about 0.8 to 0.4, combined with rising spring temperatures, begin to accelerate the melt. It happens so quickly that no matter how hard the field team works, they cannot keep up with documenting the changes. At first, liquid water is scarce—only a few wet layers of snow in the snowpack. But then, water becomes ubiquitous, transporting melt energy and creating an extremely heterogeneous albedo and melt landscape. At the end of each field season, the SALVO team is exhausted, relieved to see the snow gone, and joyful to play in the melt ponds on the sea ice under the midnight sun. Overall, the 2024 SALVO field campaign was a highlight, reflecting numerous lessons learned from 2019 and 2022. Daily measurements were taken more than 10 times at each site, resulting in a comprehensive time series of snow conditions and albedo. The team deployed using snowmachines and then on foot when snowmachine travel was no longer permitted on the melting tundra. Instrument mounts and packaging were optimized for quick deployment, regardless of the mode of transportation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Multi-Instrument Cloud Condensation Nuclei Spectrum Product (Final Technical Report)

A wealth of observational data exists on the characteristics of atmospheric particulate matter, over multiple years, at the DOE ARM Southern Great Plains (SGP) Central Facility site. This site is located in a region of the country that frequently experiences weather extremes, and that is removed from many local sources of pollution but is affected by transported smoke, dust, and urban emissions. The relationships between particulate matter, cloud formation and evolution, and precipitation are therefore of strong interest, and are being explored via modeling on a variety of scales. These models require as input detailed information on the characteristics of particles capable of serving as the nuclei for cloud formation. Sufficient data exist to be able to put together a picture of the nature of the total aerosol and the cloud condensation nuclei (CCN) subset, and their variability, through merged data products. This study was aimed at exploiting the multiple measurement types at SGP to develop the first such multi-year estimates. Further, the resulting data were analyzed to understand temporal patterns ranging from hourly to seasonal, thereby gaining insights into the particle sources affecting the atmosphere in this region. DOE-funded datasets that were analyzed in this study include total particle number concentrations, submicron aerosol scattering coefficients, dry aerosol size distributions, and more recently, time-resolved submicron aerosol chemical composition. Data are also available for the number concentrations of particles that are activated in a cloud condensation nucleus instrument at a series of setpoint supersaturations, providing direct observations of the number concentrations of “CCN”. This variable is the quantity that is generally desired for inclusion in numerical models that seek to represent and predict the impacts of varying aerosol characteristics on the formation and microphysical properties of clouds. One limitation of the use of direct CCN observations is that they are not available for supersaturations larger than about 1%, which is insufficient for deep convection and may be insufficient even for shallow convection, depending on the nature of the available CCN and the dynamics of the cloud. We developed a data-based approach to representing the full aerosol size spectrum with size-dependent hygroscopicity, and used this to extrapolate CCN spectra beyond the limited measurements. Five years of SGP aerosol data (2009 -2013) were analyzed. As a side product of our work, we identified and communicated several previously-unflagged data quality issues. The resulting merged aerosol distributions, along with fits for seasonal averages, were published and submitted to the ARM archive as a special value-added product (VAP; submitted as a PI product). CCN spectra were computed for the same data period and will similarly be published and submitted to the archive for use by the community. We also note that our methodologies and findings have been discussed at several Joint ARM User Facility/Atmospheric System Research (ASR) Principal Investigators Meetings and that recent ARM/ASR aerosol data reporting strategies have included similar ideas for data merging, indicating that this work has had a lasting impact on ARM aerosol data acquisition and reporting. The proposed work advances the science of the interactions of aerosols, clouds and precipitation, with direct application to improve representation of such interactions for clouds in regional and global climate models. The archived data will continue to serve research studies in the future.

54 ENVIRONMENTAL SCIENCES↗