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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Insights into elevated temperature tensile deformation mechanisms and kink banding in additively manufactured tungsten

Additive manufacturing (AM) potentially enables fabrication and repair of plasma facing components (PFCs) of tungsten. However, deformation mechanisms in AM-fabricated tungsten under service-relevant thermomechanical conditions remain unknown majorly due to challenges in achieving crack-free W. This study reveals the deformation mechanisms operative under tension at 800 °C and 1200 °C in the electron beam melting powder bed fusion (EBM-PBF) fabricated W. Textured columnar grains with mixed <001 >/<111 >|| build-direction were observed. Strong anisotropy in tensile properties persisted across test temperatures, and all specimens exhibited extensive deformation-induced banding phenomena. Grain boundary character analysis of the deformed specimens indicated that these deformation bands were kink bands with tilt grain boundaries prominently present at the band-matrix interface. Interestingly, the material within the kink bands rotated toward higher resolved shear stress, providing insights into the origins of kink band formation in body-centered cubic refractory metals. Intragranular misorientation axis analysis revealed that plasticity was dominated by {110} <111 > slip at 800 °C, whereas additional (213)[11⁢̄1] and (112)[11⁢̄1] slip systems activated at 1200 °C. A dense low-angle boundary networks observed near fractured surface indicated the onset of dynamic recrystallization. Results reveal plasticity governing mechanisms in AM-fabricated W under power plant-relevant conditions, and provide insights into kink band attributes in refractory metals.

Mayes, Riley [ORNL] (ORCID:000900089307010X)↗
DOE OSTI2022

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Structure of the master regulator Rns reveals an inhibitor of enterotoxigenic Escherichia coli virulence regulons

Enteric infections caused by the gram-negative bacteria enterotoxigenic Escherichia coli (ETEC), Vibrio cholerae, Shigella flexneri, and Salmonella enterica are among the most common and affect billions of people each year. These bacteria control expression of virulence factors using a network of transcriptional regulators, some of which are modulated by small molecules as has been shown for ToxT, an AraC family member from V. cholerae. In ETEC the expression of many types of adhesive pili is dependent upon the AraC family member Rns. We present here the 3 Å crystal structure of Rns and show it closely resembles ToxT. Rns crystallized as a dimer via an interface similar to that observed in other dimeric AraC’s. Furthermore, the structure of Rns revealed the presence of a ligand, decanoic acid, that inhibits its activity in a manner similar to the fatty acid mediated inhibition observed for ToxT and the S. enterica homologue HilD. Together, these results support our hypothesis that fatty acids regulate virulence controlling AraC family members in a common manner across a number of enteric pathogens. Furthermore, for the first time this work identifies a small molecule capable of inhibiting the ETEC Rns regulon, providing a basis for development of therapeutics against this deadly human pathogen.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Investigation of partitionless growth of ε -Al 60 Sm 11 phase in Al-10 at% Sm liquid

Recent experiments on devitrification of Al 90 Sm 10 amorphous alloys revealed an unusual polymorphic transformation to a complex cubic crystal structure called the ε-Al 60 Sm 11 phase. Molecular dynamics simulations of the growth of the stoichiometric ε-phase seed in contact with an undercooled Al-10 at% Sm liquid are performed to elucidate the microscopic process of transformation. The as-grown crystal and undercooled liquid possess similar local order around Al atoms whereas a rigid network defined by the Sm sub-lattice develops during the growth. Using a template-cluster alignment method, we define an order parameter to characterize the structural evolution in the system. Estimates of the attachment rate is ${R}_{{\rm{a}}}=8.70\times {10}^{-4}$ Å-2 ns-1 and detachment rate is ${R}_{{\rm{d}}}=3.83\times {10}^{-4}$ Å-2 ns-1 at the interface between ε-Al 60 Sm 11 and Al-10 at% Sm liquid at 800 K.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Visualizing Interfacial Jamming Using an Aggregation-Induced-Emission Molecular Reporter

With the interfacial jamming of nanoparticles (NPs), a load-bearing network of NPs forms as the areal density of NPs increases, converting the assembly from a liquid-like into a solid-like assembly. Unlike vitrification, the lineal packing of the NPs in the network is denser, while the remaining NPs can remain in a liquid-like state. It is a challenge to determine the point at which the assemblies jam, since both jamming and vitrification lead to a solid-like behavior of the assemblies. Herein, we show a real-time fluorescence imaging method to probe the evolution of the interfacial dynamics of NP surfactants at the water/oil interface using aggregation-induced emission (AIE) as a reporter for the transition of the assemblies into the jammed state. The AIEgens show typical fluorescence behavior at densities at which they can move and rotate. However, when aggregation of these fluorophores occurs, the smaller intermolecular separation distance arrests rotation, and a significant enhancement in the fluorescence intensity occurs.

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

Effect of TiN Addition on 3Y-TZP Ceramics with Emphasis on Making EDM-Able Bodies

In this study, to produce electrically conductive ceramics, rapid hot press (RHP) sintering of 3 mol.% yttria-stabilized tetragonal zirconia polycrystal (3Y-TZP) and 3Y-TZP/TiN composites with TiN amounts of 25, 35, and 45 vol.% was performed at 1300, 1350, and 1400 °C. Interestingly, the toughness and hardness were improved in the presence of TiN up to 35 vol.% and maximum fracture toughness and hardness of 5.40 ± 0.05 MPa m{sup 1/2} and 14.50 ± 0.06 GPa, respectively, were obtained. However, the bending strength was decreased which could be attributed to the rather weak interfaces of nitride and oxide phases. Regarding the zirconia matrix, the effect of grain size on fracture toughness of the samples has been studied using x-ray diffraction and field emission scanning electron microscope (FESEM) analysis. It was also found that electrical resistivity decreased to the value of 6.88 × 10{sup −6} Ω m at 45 vol.% of TiN. It seems the TiN grains form a network to impose conductivity on the ZrO{sub 2} body; however, below 35 vol.% TiN, due to lack of percolation effect, this conductivity could not be maintained according to FESEM studies. Finally, electrically conductive samples were successfully machined by electrical discharge machining (EDM).

36 MATERIALS SCIENCE↗
DOE OSTI2017

Promising Proton Conductor for Intermediate-Temperature Fuel Cells: Li 13.9 Sr 0.1 Zn(GeO 4 ) 4

Commercialization of fuel cell technologies hinges on the development of solid electrolytes of sufficient ionic conductivity at intermediate temperatures (200–600 °C). Here we report a novel proton conductor derived from Li 13.9 Sr 0.1 Zn(GeO 4 ) 4 (LSZG), demonstrating the highest protonic conductivity (0.034 S cm –1 at 600 °C) among all known proton conducting ceramics, which is much higher than those of several well-known oxygen ion conducting electrolytes (e.g., ~0.009 and 0.018 S cm –1 , respectively, for zirconia- and ceria-based oxide electrolyte at 600 °C). Interestingly, after fully replacing the mobile Li + ions by H + through proper ion exchange, the H+ conductivity increases from 0.034 to 0.048 S cm –1 at 600 °C. A simple but effective ab initio molecular dynamics simulation study suggests a unique H + /Li + transport mechanism: the proton in LSZG moves freely in the Li+ interstitial space within the 3D Li+ transport network (i.e., 4c and 4a sites, as the occupancies of the Li1 and Li 2 sites are 55% and 16%, respectively). In particular, a solid oxide fuel cell (SOFC) based on an LSZG electrolyte (~40 μm thick) demonstrates high open circuit voltage (~1.1 V) and good peak power density (377 mW cm –2 ) at 600 °C. The cell performance may be further improved if the electrode–electrolyte interface can be optimized. The new transport mechanism and excellent proton conductivity suggest that the LSZG represents an important family of electrolyte materials, which may be used as a proton-conducting membrane for intermediate-temperature SOFCs and hydrogen production or separation.

25 ENERGY STORAGE↗
DOE OSTI2026

Measuring pH Changes Inside a Bipolar Membrane Junction

Here, the local pH environment within bipolar membrane (BPM) junctions is complex and not well understood, yet it is important to control for advancing the performance of BPM-based electrochemical systems. We report a voltammetric strategy using an ultrathin Ni mesh pH probe to spatially resolve pH changes in the BPM junction during model BPM electrolyzer operation. Under reverse bias, we observe depletion of OH – at the anion-exchange layer (AEL) interface, with a degree diminishing with increasing distance from the AEL. These gradients correlate with current-dependent water dissociation (WD) and are modulated by the electric field and the surface charge state of the catalyst. By correlating spatial pH profiles with the surface-charging behavior of WD catalysts, we explore a mechanism of catalyst-mediated H + and OH – transfer facilitated by hydrogen-bonding networks. These findings highlight the role of local chemistry and electrostatics in BPM performance and offer new methods to probe and engineer catalytic junctions in electrochemical energy devices.

Hou, Shujin [University of California, Berkeley, C↗
DOE OSTI2016

A Nonrelational Data Warehouse for the Analysis of Field and Laboratory Data From Multiple Heterogeneous Photovoltaic Test Sites

A nonrelational, distributed computing, data warehouse, and analytics environment (Energy-CRADLE) was developed for the analysis of field and laboratory data from multiple heterogeneous photovoltaic (PV) test sites. This data informatics and analytics infrastructure was designed to process diverse formats of PV performance data and climatic telemetry time-series data collected from a PV outdoor test network, i.e., the Solar Durability and Lifetime Extension global SunFarm network, as well as point-in-time laboratory spectral and image measurements of PV material samples. Using Hadoop/HBase for the distributed data warehouse, Energy-CRADLE does not have a predefined data table schema, which enables ingestion of data in diverse and changing formats. For easy data ingestion and data retrieval, Energy-CRADLE utilizes Hadoop streaming to enable Python MapReduce and provides a graphical user interface, i.e., py-CRADLE. By developing the Hadoop distributed computing platform and the HBase NoSQL database schema for solar energy, Energy-CRADLE exemplifies an integrated, scalable, secure, and user-friendly data informatics and analytics system for PV researchers. Furthermore, an example of Energy-CRADLE enabled scalable, data-driven, analytics is presented, where machine learning is used for anomaly detection across 2.2 million real-world current-voltage (I–V) curves of PV modules in three distinct Koppen–Geiger climatic zones.

14 SOLAR ENERGY↗
DOE OSTI2016

Bioelectronic interfaces by spontaneously organized peptides on 2D atomic single layer materials

Self-assembly of biological molecules on solid materials is central to the “bottom-up” approach to directly integrate biology with electronics. Inspired by biology, exquisite biomolecular nanoarchitectures have been formed on solid surfaces. We demonstrate that a combinatorially-selected dodecapeptide and its variants self-assemble into peptide nanowires on two-dimensional nanosheets, single-layer graphene and MoS 2 . The abrupt boundaries of nanowires create electronic junctions via spatial biomolecular doping of graphene and manifest themselves as a self-assembled electronic network. Furthermore, designed peptides form nanowires on single-layer MoS 2 modifying both its electric conductivity and photoluminescence. The biomolecular doping of nanosheets defined by peptide nanostructures may represent the crucial first step in integrating biology with nano-electronics towards realizing fully self-assembled bionanoelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Synergistic Coupling Effect of Electronic Conductivity and Interphase Compatibility on High-Voltage Na 3 V 2 (PO 4 ) 2 F 3 Cathodes

Na 3 V 2 (PO 4 ) 2 F 3 (NVPF) has been considered an up-and-coming cathode material candidate for sodium (Na) ion batteries in light of its high specific capacity and working voltage. However, an erratic cathode/electrolyte interface layer is inevitably formed, accompanied by continuous electrolyte decomposition on the NVPF surface, when the voltage exceeds 4.2 V vs Na + /Na. Herein, the interphase features of NVPF are obviously enhanced owing to the ameliorated electronic conductivity obtained by combining it with carbon nanotubes (CNT). The NVPF with 3 wt % CNT (NVPF@3% CNT) reduces the Na + diffusion kinetic energy barrier and electron transport resistance. Furthermore, the conducting network formed by CNT with sturdy structure strength can promptly accommodate the volumetric changes during sequential Na + extraction/insertion and thus effectively improve the long-term cyclic performance of NVPF/hard carbon full cells. The initial discharge capacity approaches 105 mA h g –1 at 0.5C, and it retains 94% capacity retention after 200 cycles at the temperature of –10 °C. The cathode/electrolyte interphase characterization results further demonstrate that the interphase layer on the NVPF@3% CNT cathode is thinner and more compact compared with pristine samples. Here, this research provides a competitive strategy to facilitate the interfacial compatibility between the NVPF and electrolytes and accelerate the commercialization of high-performance Na-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Compliant Intramedullary Stems for Joint Reconstruction

The longevity of current joint replacements is limited by aseptic loosening, which is the primary cause of non-infectious failure for hip, knee, and ankle arthroplasty. Aseptic loosening is typically caused either by osteolysis from particulate wear, or by high shear stresses at the bone-implant interface from over-constraint. Our objective was to demonstrate feasibility of a compliant intramedullary stem that eliminates over-constraint without generating particulate wear. The compliant stem is built around a compliant mechanism that permits rotation about a single axis. We first established several models to understand the relationship between mechanism geometry and implant performance under a given angular displacement and compressive load. We then used a neural network to identify a design space of geometries that would support an expected 100-year fatigue life inside the body. We additively manufactured one representative mechanism for each of three anatomic locations, and evaluated these prototypes on a KR-210 robot. The neural network predicts maximum stress and torsional stiffness with 2.69% and 4.08% error respectively, relative to finite element analysis data. We identified feasible design spaces for all three of the anatomic locations. Simulated peak stresses for the three stem prototypes were below the fatigue limit. Benchtop performance of all three prototypes was within design specifications. Our results demonstrate the feasibility of designing patient- and joint-specific compliant stems that address the root causes of aseptic loosening. Guided by these results, we expect the use of compliant intramedullary stems in joint reconstruction technology to increase implant lifetime.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Performance Evaluation of an Intelligent Sensor Platform for Radiation Monitoring Applications

Accurate, rugged, and reliable radiation detection systems are important for area and environmental monitoring applications. The desire for spectroscopic capability has increased in monitoring aspects of the nuclear fuel cycle to provide fast characterization of the radiation profile of a situation, such as the planned or unplanned release of material. The reduction or elimination of having to conduct sampling for laboratory analysis can result in significant cost savings for an industry, government agency, or regulatory body. A new system, comprised of a NaI:Tl scintillator and a G-M tube, has been designed and tested, taking into account the following end-user requirements: ease-of-use, capability to network and supervise multiple units, compact form factor, low power consumption, versatility, and stability. The detector sizes were selected to accommodate a dose rate up to 1 Sv/hr. Several algorithms and analysis routines have been developed to incorporate these key needs without sacrificing on accuracy, dynamic range, nuclide identification, and sensitivity. This presentation will introduce the major hardware and software components of the platform, as well as the user interface and data analysis workflow. Key features of the hardware include an environmentally robust housing, low power signal processing electronics, patented LED-based gain stabilization, and an embedded processor for unattended instrument management and data analysis. New and improved algorithms for determining scintillator gamma dose rates, total integrated dose, and nuclide identification will be introduced. The two detector elements were modeled with MCNP and validated experimentally. The results of the radiological testing shall be presented including energy resolution, throughput, dose response, and minimum detectable activities. The dose response has been evaluated in simulations and with measurements to ensure accurate response with respect to energy spectrum of the dose field and direction of the incident radiation. Spectroscopy evaluations, such as nuclide detection probability and false alarm rates, were performed and comparisons to existing algorithms shall also be presented. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual Decoding

Decoding visual experiences from fMRI offers a powerful avenue to understand human perception and develop advanced brain-computer interfaces. However, current progress often prioritizes maximizing reconstruction fidelity while overlooking interpretability, an essential aspect for deriving neuroscientific insight. To address this gap, we propose MoRE-Brain, a neuro-inspired framework designed for high-fidelity, adaptable, and interpretable visual reconstruction. MoRE-Brain uniquely employs a hierarchical Mixture-of-Experts architecture where distinct experts process fMRI signals from functionally related voxel groups, mimicking specialized brain networks. The experts are first trained to encode fMRI into the frozen CLIP space. A finetuned diffusion model then synthesizes images, guided by expert outputs through a novel dual-stage routing mechanism that dynamically weighs expert contributions across the diffusion process. MoRE-Brain offers three main advancements: First, it introduces a novel Mixture-of-Experts architecture grounded in brain network principles for neuro-decoding. Second, it achieves efficient cross-subject generalization by sharing core expert networks while adapting only subject-specific routers. Third, it provides enhanced mechanistic insight, as the explicit routing reveals precisely how different modeled brain regions shape the semantic and spatial attributes of the reconstructed image. Extensive experiments validate MoRE-Brain’s high reconstruction fidelity, with bottleneck analyses further demonstrating its effective utilization of fMRI signals, distinguishing genuine neural decoding from over-reliance on generative priors. Consequently, MoRE-Brain marks a substantial advance towards more generalizable and interpretable fMRI-based visual decoding.

Wei, Yuxiang [Georgia Institute of Technology]↗
DOE OSTI2022

Mitigate: An Adaptive Network Data Anonymization Tool Using Condensation-Based Differential Privacy

Modern network devices collect a large amount of data that can be analyzed to identify bottlenecks, anomalies, cyber-attacks, etc. Therefore, there is often a need to analyze such collections of network data quite often by an external expert or by the research community. However, these collections of data contain sensitive, proprietary information. In order for the network data to be shared, it must first be anonymized. The overall objective of this project is to develop an innovative privacy management tool to anonymize network data and achieve sufficient privacy, acceptable data utility, and efficient data analysis at the same time. No existing anonymization methods can achieve all of these at the same time. The core of this technology is a differential private clustering algorithm that provides strong privacy protection, preserves data properties important for subsequent analysis, and allows the party receiving the anonymized data to conduct analysis directly on anonymized data without the need of decryption or any extra processing. The research carried out was to design, implement and verify a solution to this problem by completing the following tasks: 1) developing the core technology; 2) developing a context based method that automatically recommends fields that must be anonymized; 3) conducted experiments showing superior results using our approach compared to existing tools, and 4) developed an intuitive but basic user interface. The research that was conducted generated novel algorithmic techniques that utilize state-of-the-art methods such as condensation, differential privacy preservation, clustering, automated tuning based on contextual awareness, and recommendation techniques to specify columns to users for anonymization leading to optimal privacy that allows research analysis on the dataset. Experiments were conducted to evaluate the efficacy of these novel algorithmic techniques by performing analysis on original non-anonymized datasets, then conducting analysis on the same yet anonymized datasets and comparing the results of the analyses. Overall, the anonymized analysis results were within 1% of the original results, verifying that the generated technology not only guarantees a high level of privacy but also enables research analysis as if it were conducted on the original dataset. Potential applications of this technology include anonymization of any type of structured network datasets that contain sensitive identifiers, such as IP addresses, that can be used in multiple applications. For example, to create an AI or machine learning model for cyber security, e.g., to detect attacks, or for performance analysis, e.g., identify bottlenecks or predict performance. In addition, a market analysis that was conducted for potential applications of this technology identified a broader range of applications of our anonymization technology beyond the network sector that includes healthcare, banking, insurance, securities, finance (FISB), data brokering, cloud services, ad sales, and government.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Grid Integration of Small-Scale Photovoltaic Systems in Secondary Distribution Network—A Review

The relative share of renewable energy, specifically the solar photovoltaic (PV), is increasing exponentially in the world electric energy sector. This is a cumulative result of reduction in the cost of solar panels, improvement in the panel efficiency, and advancement in the associated power electronics. Among different types of PV plants, installation of small-scale rooftop PV is growing rapidly due to direct end-user benefits and lucrative governmental incentives. There are various standards developed in regards to grid integration of PVs and other distributed generations (DGs). Different power converter topologies are developed to interface the PV panel with the utility grid. To keep up with the stringent regulations imposed by the standards, various control strategies and grid synchronization methods have been developed. This review article amalgamates and summarizes all of the aforementioned aspects of a grid-integrated PV system including various standards, power stage architectures, grid synchronization methods, operation under extreme events, and control methodologies, pertaining to small-scale PV plants. This article will help freshman researchers to gain some familiarity with the topic and introduce them to some of the key issues encountered in this field.

14 SOLAR ENERGY↗
DOE OSTI2022

PythonFOAM: In-situ data analyses with OpenFOAM and Python

Here, we outline the development of a general-purpose Python-based data analysis tool for OpenFOAM. Our implementation relies on the construction of OpenFOAM applications that have bindings to data analysis libraries in Python. Double precision data in OpenFOAM is cast to a NumPy array using the NumPy C-API and Python modules may then be used for arbitrary data analysis and manipulation on flow-field information. We highlight how the proposed wrapper may be used for an in-situ online singular value decomposition (SVD) implemented in Python and accessed from the OpenFOAM solver PimpleFOAM. Here, 'in-situ' refers to a programming paradigm that allows for a concurrent computation of the data analysis on the same computational resources utilized for the partial differential equation solver. In addition, to demonstrate parallel deployments, we deploy a distributed SVD, which collects snapshot data across the ranks of a distributed simulation to compute the global left singular vectors. Crucially, both OpenFOAM and Python share the same message passing interface (MPI) communicator for this deployment which allows Python objects and functions to exchange NumPy arrays across ranks. Subsequently, we provide scaling assessments of this distributed SVD on multiple nodes of Intel Broadwell and KNL architectures for canonical test cases such as the large eddy simulations of a backward facing step and a channel flow at friction Reynolds number of 395. Finally, we demonstrate the deployment of a deep neural network for compressing the flow-field information using an autoencoder to demonstrate an ability to use state-of-the-art machine learning tools in the Python ecosystem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Aperture size distribution, length, and preferential location of bed-parallel veins in shale

Bed-parallel, calcite-filled veins (BPVs) are common in shale formations, and although they have been widely described in other studies, little is known about their population aperture size distribution. To address this knowledge gap, we analyzed BPV sizes in outcrops and cores from the Vaca Muerta Formation, Neuquén Basin, Argentina; in two cores from the Marcellus Formation, Appalachian Basin, northeast Pennsylvania; and in one core from the Wolfcamp Shale, Delaware Basin, West Texas. Nine out of ten aperture size populations follow a negative exponential distribution, with one following a weak power law. Bed-parallel vein size distribution and intensity vary among formations and within the same shale. We define three groups of distributions: (1) Vaca Muerta outcrops, with the highest BPV intensity and the largest BPVs (cumulative frequency of 4.9 BPVs per meter [BPVs/m] for apertures 0.265 mm to 8.7 cm); (2) Vaca Muerta cores with a similar BPV intensity overall but with no apertures wider than 1.2 cm; and (3) Vaca Muerta, Wolfcamp, and Marcellus cores with the fewest BPVs (cumulative frequency up to 0.63 BPVs/m) and very few wider than 1 cm. Aperture and length in two outcrop data sets are weakly positively correlated and follow power laws with exponents of 0.44 and 0.49. Mechanical interfaces at boundaries between different lithologies exert a strong control on BPV location, with 65–75% of observed interfaces having BPVs along them. Only 25–30% of the BPVs occur at observed material interfaces, however, and unless subtle, unobserved mechanical layering is present, other factors must also control location. BPV intensity and organic richness (TOC) from Vaca Muerta well logs are correlated in some instances but not in others, indicating TOC is not always a good proxy for BPV location or intensity. Furthermore, these findings provide useful information for modeling of hydraulic fracture treatments where BPVs may influence development of the stimulated fracture network, for example by limiting height growth.

58 GEOSCIENCES↗