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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

RANS simulations of neutral atmospheric boundary layer flow over complex terrain with comparisons to field measurements

Micro-scale Reynolds-averaged Navier-Stokes (RANS) simulations of the neutral atmospheric boundary layer (ABL) over complex terrain and a comparison of the results with conditionally averaged met-tower data are presented. A robust conditional sampling procedure for the meteorological tower (met-tower) data to identify near-neutral conditions based on a criterion for the turbulence intensity is developed. The conditionally averaged wind data on 14 met-towers are used for the model validation. The ABL flow simulations are conducted over complex terrain which includes a prominent hill using the OpenFOAM-based simulator for on/offshore wind farm applications (SOWFA) with the k-ϵ and the SST k-ω turbulence models. The discretization of the production term in the transport equation for the turbulent kinetic energy (TKE) is modified to greatly reduce the commonly observed nonphysical near surface TKE peak. The driving inflow is generated through an iterative approach using a precursor method to reproduce the measured wind statistics at the reference tower. Both of the RANS models are able to capture the flow behavior windward of the hill. The SST k-ω model predicts more intense flow separation than the k-ϵ model downstream of the steepest sections of the hill. The wind statistics predicted at the location of the met-towers by both of the RANS models are fairly consistent. Overall, the comparisons of the direction, mean, and standard deviation of the wind between the simulations and the tower data show reasonable agreement except for the differences of the mean wind speeds at four met-towers located closer to the main ridge of the hill in a region of strong terrain variations.

17 WIND ENERGY↗
DOE OSTI2021

Machine learning enables national assessment of wind plant controls with implications for land use

Summary As deployment of wind energy continues to expand, computationally efficient tools for predicting wind plant performance over a wide range of layout designs, technology innovations, and spatial locations are increasingly important for policy and investment decisions. We demonstrate two approaches to training a surrogate model to predict annual energy production (AEP) of parameterized wind plant layouts: one using a Gaussian process (GP) and the other using a fully convolutional neural network (FCNN). We leverage the powerful FCNN architecture by encoding wind plant design parameters and output response surface as an image. The FCNN produces more accurate results than the GP with mean absolute errors equivalent to 1% and 1.9% of plant rated power, respectively, although the GP performs well under limited training data and provides useful uncertainty information. We also evaluate a surrogate model for wake steering, enabling a nationwide assessment of the impact of plant control strategies and plant layout decisions. Across two million locations, we find that wake steering strategies boost AEP with relative gains upwards of 3%. Gains are most pronounced at sites without a dominant wind direction and where layout optimization is less fruitful. Additionally, we perform a nationwide sensitivity analysis showing that wake steering can mitigate wake losses from higher density plant layouts. Our results suggest that regions which have not been previously viable for wind deployment due to moderate wind resources are especially well enhanced by wake steering strategies that could help overcome land constraints and inflexible layout options, potentially identifying new deployment opportunities.

17 WIND ENERGY↗
DOE OSTI2022

What can surface wind observations tell us about interannual variation in wind energy output?

The past decade of wind power growth was supported by capacity factor improvements and associated cost reductions. But are higher capacity factors a technology success story or, as suggested by recent research, has the influence of technology been overstated by ignoring positive surface wind speed trends? The answer could influence estimates of wind energy's cost and even future deployment rates. We find that US surface wind speed observations imply a 2.6% improvement in capacity factors from 2010 to 2019. Yet newer vintages of wind plants have recorded capacity factors that are ~25% larger than plants built close to 2010. It follows that technological factors and improved site quality, not higher wind speeds, drove most of the improvement in capacity factors. Additionally, we match hundreds of meteorological stations to nearby (< 25 km) wind plants and compare annual estimated generation, based on a function of surface wind speed observations, to annual recorded generation. Researchers rely on this publicly available surface data because measurements co-located with wind plants are generally considered proprietary. Our analysis addresses a research gap: interannual variation in observed surface wind speeds is rarely compared to observed data at wind plant locations and turbine heights. We find that despite its common use for this purpose, generation estimates based on publicly available surface observational data provide a poor proxy for interannual variability in recorded wind generation. These findings suggest that caution is generally needed when researchers use surface wind speed measurements to investigate long-term wind energy trends.

17 WIND ENERGY↗
DOE OSTI2025

A Comparison of Pre‐Construction and Operational Wake Loss Estimates for Land‐Based Wind Plants

The overall bias between pre‐construction energy yield assessment (EYA) estimates of wind plant energy production and the achieved operational production is improving in the wind industry, but uncertainty remains high for individual wind plants. Wake effects within wind plants are one of the largest sources of energy loss considered in the EYA process, and previous work shows wake loss estimates to be a major source of disagreement among wind energy consultants who perform EYAs. To better understand the accuracy of wake loss predictions, we compare overall operational wake loss estimates based on supervisory control and data acquisition data to pre‐construction estimates provided by six wind energy consultants for five land‐based wind plants in North America. By augmenting existing approaches for quantifying operational wake losses, we estimate wake losses during the period of record for which operational data are available as well as the expected long‐term wake losses, based on historical reanalysis weather data, to which the EYA estimates are compared. To account for power variations at different turbine locations caused by terrain‐induced wind resource heterogeneity, we correct the operational wake loss estimates using predicted freestream wind speed variations from the Wind Systems Engineering Reynolds‐averaged Navier–Stokes (RANS) tool. We identify long‐term corrected operational wake losses between 1.9% and 6.4% for the five plants, with a mean loss of 4%. For the project deemed most acceptable for operational wake loss assessment, which is located in the simplest terrain and isolated from neighboring plants, the mean EYA wake loss estimate is within 0.7 percentage points of the operational value of 6.4%. For most of the remaining plants, results suggest that wake losses are generally overpredicted by 2.6–6.3 percentage points. However, operational wake losses may be underestimated for many of these projects because of spatial wind resource variations not captured by the RANS model, external wake effects that are unaccounted for in the estimation process, and wind plant blockage effects. To better understand factors that contribute to the observed wake losses, we investigate operational wake losses as a function of wind direction and wind speed. As expected, wake losses are generally concentrated near wind directions that are aligned with rows of closely spaced turbines and at below‐rated wind speeds; however, for some projects, the energy produced by the wind plant exceeds the estimated potential energy of the plant without wake interactions for certain wind directions and wind speeds, suggesting inaccurate assumptions in the wake loss estimation method for those plants. Lastly, we compare predicted and operational wake losses for individual wind turbines, finding that even when overall wake losses are predicted accurately, large uncertainty exists at the turbine level.

17 WIND ENERGY↗
DOE OSTI2020

Factors Affecting Bait Site Visitation: Area of Influence of Baits

Baiting is a fundamental strategy for the global management of wild pigs (Sus scrofa); however, little information exists on how anthropogenic bait affects wild pig movements on a landscape. We investigated factors that are important in determining the spatial area of attraction for wild pigs to bait (‘area of influence’ of a bait site) using data from Global Positioning System (GPS) collars and locations of bait sites. We monitored movements of wild pigs in 2 distinct study areas in the United States from February to September 2016 and used locational data using GPS collars to analyze the influence of habitat quality (dependent on site), home range size, number of bait sites in the home range, distance to a bait site, and sex in relation to movement in time and space. We determined the average area of influence by calculating the area of a circle with the radius as the average maximum distance travelled by wild pigs to reach a bait site. The average area of influence for our bait sites was 6.7 km 2 (or a radius of approximately 1.5 km), suggesting a bait spacing of approximately 1.5 km would be adequate to capture visitation by most wild pigs and a spacing of 3 km could allow substantial visitation while minimizing redundant effort depending on the spatial structure of the populations. Eighty percent of wild pigs first visited bait sites within 8.9 days after bait deployment; and they visited earlier when their home range size was larger. As the number of bait sites in an individual's home range increased, individual pigs visited more bait sites, and the probability of a visit increased dramatically up to approximately 5 bait sites and much less thereafter. Wild pigs travelled farther distances to visit bait sites in lower quality habitat. Our results support the hypothesis that habitat quality can mediate the efficacy of baiting programs for wildlife by influencing their movement patterns and motivation to use anthropogenic resources. Our results suggest wild pigs will travel extensively within their home range to visit bait sites, and that in lower quality habitat, most animals will find bait sites more quickly. Determining the area of influence of bait sites can increase the efficacy of planning and monitoring management programs. Overall, our study provides new information to help managers plan baiting designs to attract the greatest number of pigs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Evaluation of common trap types for capturing wild pigs

Abstract Wild pigs ( Sus scrofa ) are one of the most ecologically harmful invasive vertebrates globally. Due to their negative impacts, wild pig populations are managed via trapping, yet studies investigating various trap designs have been limited in duration, location, and number of trappers. Using data from professional trappers across the southeastern U.S., we evaluated the performance of 3 wild pig trap types (corral, drop, passive net), and the effect of forage season (barren, green‐up, masting) on trapping success. Specifically, we compared trap effectiveness (estimated proportion of targeted wild pigs caught per capture event), trap night effort (number of days to first catch), and the amount of bait and time used per individual take over the lifetime of each trap location across the 3 trap types and forage seasons. Across >850 capture events by 31 trappers, all 3 trap types evaluated were effective at capturing entire social groups of wild pigs. Few differences were observed among trap types, although drop traps in masting season captured a greater estimated proportion of wild pigs per capture event compared to other trap type‐season combinations. Our data also revealed that passive net traps required slightly more bait/wild pig captured than drop traps but there was no difference in the time/wild pig captured among traps. Our findings demonstrate that when operated by wildlife professionals, multiple designs of wild pig traps can be effective at removing targeted groups of wild pigs. Further, our results offer insights regarding the performance of wild pig trap types across seasons that can be used to inform wild pig trapping programs.

Taylor, Charles R. [Warnell School of Forestry and↗
DOE OSTI2022

LC-MEMENTO: A Memory Model for Accelerated Architectures

With the advent of heterogeneous architectures, in particular, with the ubiquity of multi-GPU systems, it is becoming increasingly important to manage device memory efficiently in order to reap the benefits of the additional core count. To date, such responsibility mainly falls on the programmer where device-to-host data communication (and vice versa), if not done properly, may incur costly memory transfer operations and synchronization. The problem may be compounded by additional requirement to maintain system-wide memory consistency that may involve expensive synchronization overhead. In this paper, we present Location Consistency Memory Model for Enhanced Transfer Operations (LC-MEMENTO). This framework considers incorporating runtime techniques for multi-GPU memory management to support relaxed synchronization semantics and memory transfer operations automatically. Specifically, we implement a relaxed form of a memory consistency model based on the Location Consistency (LC) in an Asynchronous Many-Task Runtime (ARTS) and demonstrate that, this memory model enables additional optimization opportunities for the three representative applications encompassing different computational patterns (scientific computation, graphs, data streaming, etc.).

Memory Models, Accelerators, Adaptive Optimization↗
DOE OSTI2020

Flavor broken QCD 3 at large N

We examine the vacuum structure of QCD 3 with flavor group U (f)×U (N f –f) in the limit N → ∞ with g 2 N =fixed. We find that, generically, the resolution of critical points into a series of first order phase transitions persists at special locations in the phase diagram. In particular, the number of Grassmannians that one traverses and their locations in the phase diagram is a function of f.

1/N Expansion↗
DOE OSTI2020

$T\overline{T}$, black holes and negative strings

String theory on AdS 3 has a solvable single-trace irrelevant deformation that is closely related to $T\overline{T}$. For one sign of the coupling, it leads to an asymptotically linear dilaton spacetime, and a corresponding Hagedorn spectrum. For the other, the resulting spacetime has a curvature singularity at a finite radial location, and an upper bound on the energies of states. Beyond the singularity, the signature of spacetime is flipped and there is an asymptotically linear dilaton boundary at infinity. We study the properties of black holes and fundamental strings in this spacetime, and find a sensible picture. The singularity does not give rise to a hard ultraviolet wall for excitations -one must include the region beyond it to understand the theory. The size of black holes diverges as their energy approaches the upper bound, as does the location of the singularity. Fundamental strings pass smoothly through the singularity, but if their energy is above the upper bound, their trajectories are singular. From the point of view of the boundary at infinity, this background can be thought of as a vacuum of Little String Theory which contains a large number of negative strings.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Updates to the Regional Seismic Travel Time (RSTT) Model: 1. Tomography

Abstract A function of global monitoring of nuclear explosions is the development of Earth models for predicting seismic travel times for more accurate calculation of event locations. Most monitoring agencies rely on fast, distance-dependent one-dimensional (1D) Earth models to calculate seismic event locations quickly and in near real-time. RSTT (Regional Seismic Travel Time) is a seismic velocity model and computer software package that captures the major effects of three-dimensional crust and upper mantle structure on regional seismic travel times, while still allowing for fast prediction speed (milliseconds). We describe updates to the RSTT model using a refined data set of regional phases (i.e., Pn, Pg, Sn, Lg) using the Bayesloc relative relocation algorithm. The tomographic inversion shown here acts to refine the previous RSTT public model ( rstt201404um ) and displays significant features related to areas of global tectonic complexity as well as further reduction in arrival residual values. Validation of the updated RSTT model demonstrates significant reduction in median epicenter mislocation (15.3 km) using all regional phases compared to the iasp91 1D model (22.1 km) as well as to the current station correction approach used at the Comprehensive Nuclear-Test-Ban Treaty Organization International Data Centre (18.9 km).

58 GEOSCIENCES↗
DOE OSTI2025

Investigating the Relationship Between Bolide Entry Angle and Apparent Direction of Infrasound Signal Arrivals

Infrasound sensing offers critical capabilities for detecting and geolocating bolide events globally. However, the observed back azimuths, directions from which infrasound signals arrive at stations, often differ from the theoretical expectations based on the bolide’s peak brightness location. For objects with shallow entry angles, which traverse longer atmospheric paths, acoustic energy may be emitted from multiple points along the trajectory, leading to substantial variability in back azimuth residuals. This study investigates how the entry angle of energetic bolides affects the back azimuth deviations, independent of extrinsic factors such as atmospheric propagation, station noise, and signal processing methodologies. A theoretical framework, the Bolide Infrasound Back-Azimuth EXplorer Model (BIBEX-M), was developed to compute predicted back azimuths solely from geometric considerations. The model quantifies how these residuals vary as a function of source-to-receiver distance, revealing that bolides entering at shallow angles, e.g., 10°, can produce average residuals of 20°, with deviations reaching up to 46° at distances below 1000 km, and remaining significant even at 5000 km (up to 8°). In contrast, bolides with steeper entry angles, e.g., > 60°, show smaller deviations, typically under 5° at 1000 km and diminishing to less than ~1° beyond 5000 km. These findings attest to the need for careful interpretation when evaluating signal detections and estimating bolide locations. This work is not only pertinent to bolides but also to other high-energy, extended-duration atmospheric phenomena such as space debris and reentry events, where similar geometric considerations can influence infrasound arrival directions.

Acoustics↗
DOE OSTI2020

Root identification in minirhizotron imagery with multiple instance learning

In this study, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed. Root and soil characteristics vary from location to location, and thus, supervised machine learning approaches that are trained with local data provide the best ability to identify and segment roots in minirhizotron imagery. However, labeling roots for training data (or otherwise) is an extremely tedious and time-consuming task. This paper aims to address this problem by labeling data at the image level (rather than the individual root or root pixel level) and train algorithms to perform individual root pixel level segmentation using MIL strategies. Three MIL methods (multiple instance adaptive cosine coherence estimator, multiple instance support vector machine, multiple instance learning with randomized trees) were applied to root detection and compared to non-MIL approaches. The results show that MIL methods improve root segmentation in challenging minirhizotron imagery and reduce the labeling burden. In our results, multiple instance support vector machine outperformed other methods. The multiple instance adaptive cosine coherence estimator algorithm was a close second with an added advantage that it learned an interpretable root signature which identified the traits used to distinguish roots from soil and did not require parameter selection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Settling of two-way momentum and energy coupled particles subject to Boussinesq and non-Boussinesq heating

This work establishes a procedure to accurately compute heat transfer between an Eulerian fluid and Lagrangian point-particles. Recent work has focused on accurately computing momentum transfer between fluid and particles. The coupling term for momentum involves the undisturbed fluid velocity at the particle location which is not directly accessible in the simulation. Analogously, in the context of thermal coupling, the undisturbed fluid temperature at the particle location is not directly accessible in simulations and must be estimated. In this paper, we develop a scheme to accurately estimate the undisturbed fluid temperature of a point-particle exchanging thermal energy with a surrounding fluid. Furthermore, the temperature disturbance is correlated with the enhanced temperature curvature in the vicinity of the particle and is formally valid in the low heating, low convection limit. We conduct extensive verification of the correction procedure for a settling particle subject to radiation. This setup allows the simultaneous testing of thermal and momentum corrections. By considering equations of drag and Nusselt number extended to finite Péclet and Boussinesq numbers, we establish a large range over which the correction procedure can be applied.

42 ENGINEERING↗
DOE OSTI2023

Uncovering acoustic signatures of pore formation in laser powder bed fusion

Abstract We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a low-dimensional model to accurately predict the location of keyhole pores formed during additive manufacturing processes. Acoustic measurements were sampled at 100 kHz during single-layer laser powder bed fusion (LPBF) experiments, and spatio-temporal registration of pore locations was obtained from post-build radiography. Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom $$\varvec{k}$$ k -means clustering (NMF $$\varvec{k}$$ k ) to learn the underlying spectral patterns associated with pore formation. NMF $$\varvec{k}$$ k returned a library of basis signals and matching coefficients to blindly construct a feature space based on the PSD estimates in an optimized fashion. Moreover, the NMF $$\varvec{k}$$ k decomposition led to the development of computationally inexpensive machine learning models which are capable of quickly and accurately identifying pore formation with classification accuracy of supervised and unsupervised label learning greater than 95% and 90%, respectively. The intrinsic data compression of NMF k , the relatively light computational cost of the machine learning workflow, and the high classification accuracy makes the proposed workflow an attractive candidate for edge computing toward in-situ keyhole pore prediction in LPBF.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Assessment of potential impact of magnetic fields from a subsea high-voltage DC power cable on migrating green sturgeon, Acipenser medirostris

Abstract Empirical evidence suggests that marine animals perceive and orient to local distortions in the earth’s natural magnetic field. Magnetic fields (MFs) generated by electrified underwater cables may produce similar local distortions in the earth’s main field. Concern exists that these distortions may impact migration movements of MF-sensitive animals. The Trans Bay Cable (TBC) is a ± 200-kV, 400-megawatt, 85-km high-voltage direct current transmission line buried through San Francisco Bay (37° 56′ 8.81″ N, 122° 27′ 0.19″ W). Detections of adult green sturgeon implanted with acoustic transmitters were used from six cross-bay receiver arrays from 2006 to 2015 to investigate how inbound and outbound migration movements through lower portions of their route to/from upstream breeding grounds are related to the TBC’s energization status (off/on) and other local environmental variables. Here, we assess how these variables impacted transit success, misdirection from the migration route, transit times, and migration path locations within stretches between the Bay’s mouth and the start of the Sacramento River. Overall, there was varied evidence for any effect on migration behavior associated with cable status (off/on). A higher percentage of inbound fish successfully transited after the cable was energized, but this effect was nonsignificant in models including temperature. Outbound fish took longer to transit after cable energization. Inbound and outbound migration path locations were not significantly influenced by cable energization, but results suggest a potential subtle relationship between energization and both inbound and outbound paths. Overall, additional migration-based studies are needed to investigate the impact of anthropogenic cables on marine species.

Wyman, Megan T. (ORCID:0000000256844603)↗
DOE OSTI2023

Projections of winter polynyas and their biophysical impacts in the Ross Sea Antarctica

Abstract This study investigates winter polynyas in the southern Ross Sea, Antarctica where several polynyas are known to form. Coastal polynyas are areas of lower sea ice concentration and/or thickness along the coast that are otherwise surrounded by more extensive, thicker sea ice pack. Polynyas are also locations where organisms can exploit both the ice substrate and pelagic resources. Using a self organizing map algorithm, we identify polynya events in the Community Earth System Model Version 2 Large Ensemble (CESM2-LE). The neural network algorithm is able to identify polynya events without imposing an ice concentration or thickness threshold, as is often done when identifying polynyas. The CESM2-LE produces a wintertime polynya feature comparable in size and location to the Ross Sea polynya, and during polynya events there are large turbulent heat fluxes and export of sea ice from the Ross Sea. In the CESM2-LE polynya event frequency is projected to decrease sharply in the later twentyfirst century, leading to increasing sea ice concentrations and thicknesses in the region. The drivers of the polynya frequency decline are likely both large scale circulation changes and local atmosphere and ocean feedbacks. If declines in wintertime polynya frequency over the twentyfirst century do occur they may impact Antarctic Bottom Water formation and local net primary productivity. Thus, better understanding potential local and unexpected sea ice changes in the Ross Sea is important for both assessing climate system impacts and ecological impacts on the Ross Sea ecosystem, which is currently protected by an internationally recognized marine protected area.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Volcano infrasound: progress and future directions

Over the past two decades (2000–2020), volcano infrasound (acoustic waves with frequencies less than 20 Hz propagating in the atmosphere) has evolved from an area of academic research to a useful monitoring tool. As a result, infrasound is routinely used by volcano observatories around the world to detect, locate, and characterize volcanic activity. It is particularly useful in confirming subaerial activity and monitoring remote eruptions, and it has shown promise in forecasting paroxysmal activity at open-vent systems. Fundamental research on volcano infrasound is providing substantial new insights on eruption dynamics and volcanic processes and will continue to do so over the next decade. The increased availability of infrasound sensors will expand observations of varied eruption styles, and the associated increase in data volume will make machine learning workflows more feasible. More sophisticated modeling will be applied to examine infrasound source and propagation effects from local to global distances, leading to improved infrasound-derived estimates of eruption properties. Future work will use infrasound to detect, locate, and characterize moving flows, such as pyroclastic density currents, lahars, rockfalls, lava flows, and avalanches. Infrasound observations will be further integrated with other data streams, such as seismic, ground- and satellite-based thermal and visual imagery, geodetic, lightning, and gas data. The volcano infrasound community should continue efforts to make data and codes accessible and to improve diversity, equity, and inclusion in the field. In summary, the next decade of volcano infrasound research will continue to advance our understanding of complex volcano processes through increased data availability, sensor technologies, enhanced modeling capabilities, and novel data analysis methods that will improve hazard detection and mitigation.

58 GEOSCIENCES↗
DOE OSTI2025

Isolated vesicles in submarine pumice: insights from the 2019 Volcano F eruption, Kingdom of Tonga

No reliable method can distinguish between subaerial and submarine pumice, but recent work shows that highly vesicular (total porosity, φ > 65%) submarine pumice from the 2012 Havre and 2021 Fukutoku Oka-no-Ba eruptions contains abundant isolated porosity (connectivity, C < 0.6). This differs from textural measurements of subaerial pumice with similarly high vesicularities, where high connectivities (C > 0.8) are often measured. To investigate the implications of this under-studied texture for bubble nucleation, growth, and coalescence dynamics at submarine eruptions, we analyze the textural characteristics of rafted pumice clasts from the 2019 submarine eruption of Volcano F, located along the Tonga-Kermadec Arc. We examined clasts collected while floating near the vent (50–200 km) and distally on the shores of Fiji (900 km). Using helium pycnometry and X-ray tomography (XRT), we quantified porosity and connectivity in 45 pumice lapilli, classifying them by vesicle macrotexture and edge morphology. Microvesicular clasts, the majority of which have breadcrust and cauliflower textures, exhibit the lowest connectivities (C = 0.61 ± 0.2), while macrovesicular clasts have high connectivities with C ~ 1. We propose that isolated porosity abundance varies according to clast location in the eruption column post-fragmentation. Microvesicular clasts erupted on the edges of the column and experienced high levels of seawater ingestion and subsequent quenching of the entire clast. Macrovesicular clasts, in contrast, erupted in the center of the eruption column and were thus thermally insulated, allowing magmatic bubbles in the molten pumice to nucleate, grow, and coalesce for longer. Our results and interpretation imply that low connectivity, microvesicular clasts preserve pre and syn-eruptive bubble processes at the 2019 Volcano F eruption.

Permeability↗