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1. From Youth Awareness to Action: The Case of The EIT Climate-KIC Young Innovators Program in Cyprus. In United Nations Economic Commission for Europe (UNECE) Report

Author

Yiatros, Stylianos, Pastides, A, Karra, S., and Ioannou, Andri

Subjects
Problem-based learning, Entrepreneurial mindset, Social Sciences, Other Humanities, Climate crisis, Systems innovation, Challenge-based learning
Abstract

Sowing the seeds of ESD in K-12 school systems has been pivotal in creating the student move- ment of ‘climate strikes’ that have dominated international news across the globe. Students across Europe and the globe are aware of the climate emergency and are taking to the streets to demand action. Moving from awareness to action, Young Innovators, a pioneering program launched by EIT Climate-KIC, aims to empower young people to use systems thinking and climate innovation approaches in order to contribute to local and regional challenges faced by their community. The implementation of the pilot in high schools (12-18 year olds) in the Republic of Cyprus in 2020 is presented here, highlighting the effect this intervention had on teachers and students alike, as well as lessons learned for scaling up the program across ESD-related curricula in Cyprus.

Published
2022

2. The EGS Collab project: Status and Accomplishments

Author

Kneafsey, T, Blankenship, D, Dobson, P, White, M, Morris, JP, Fu, P, Schwering, PC, Ajo-Franklin, JB, Huang, L, Knox, HA, Strickland, C, Burghardt, J, Johnson, T, Neupane, G, Weers, J, Horne, R, Roggenthen, W, Doe, T, Mattson, E, Ajo-Franklin, J, Baumgartner, T, Beckers, K, Bonneville, A, Boyd, L, Brown, S, Burghardt, JA, Chai, C, Chakravarty, A, Chen, T, Chen, Y, Chi, B, Condon, K, Cook, PJ, Crandall, D, Doughty, CA, Elsworth, D, Feldman, J, Feng, Z, Foris, A, Frash, LP, Frone, Z, Gao, K, Ghassemi, A, Guglielmi, Y, Haimson, B, Hawkins, A, Heise, J, Hopp, C, Horn, M, Horne, RN, Horner, J, Hu, M, Huang, H, Im, KJ, Ingraham, M, Jafarov, E, Jayne, RS, Johnson, TC, Johnson, SE, Johnston, B, Karra, S, Kim, K, King, DK, Knox, H, Knox, J, Kumar, D, Kutun, K, Lee, M, Li, D, Li, J, Li, K, Li, Z, MacEira, M, MacKey, P, Makedonska, N, Marone, CJ, McClure, MW, McLennan, J, McLing, T, Medler, C, Mellors, RJ, Metcalfe, E, Miskimins, J, Moore, J, Morency, CE, Myers, T, Nakagawa, S, Newman, G, Nieto, A, Paronish, T, and Pawar, R

Subjects
Geochemistry & Geophysics
Abstract

The EGS Collab project, supported by the US Department of Energy, is addressing challenges in implementing enhanced geothermal systems (EGS). This includes improving understanding of the stimulation of crystalline rock to create appropriate flow pathways, and the ability to effectively simulate both the stimulation and the flow and transport processes in the resulting fracture network. The project is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory. Data and observations from the field test are compared to simulations to understand processes and to build confidence in numerical modeling of the processes. In Experiment 1, we examined hydraulic fracturing an underground test bed at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, at a depth of approximately 1.5 km. We drilled eight sub-horizontal boreholes in a well-characterized phyllite. Six of the boreholes were instrumented with many sensor types to allow careful monitoring of stimulation events and flow tests, and the other two boreholes were used for water injection and production. We performed a number of stimulations and flow tests in the testbed. Our monitoring systems allowed detailed observations and collection of numerous data sets of processes occurring during stimulation and during dynamic flow tests. Long-term ambient temperature and chilled water flow tests were performed in addition to many tracer tests to examine system behavior. Data were rapidly analyzed, allowing adaptive control of the tests. Numerical simulation was used to answer key experimental design questions, to forecast fracture propagation trajectories and extents, and to analyze and evaluate results. Many simulations were performed in near-real-time in conjunction with the field experiments, with more detailed process study simulations performed on a longer timeframe. Experiment 2 will examine hydraulic shearing in a test bed being built at the SURF at a depth of about 1.25 km in amphibolite under a different set of stress and fracture conditions than Experiment 1. Five sets of fracture orientations were considered in design, and three orientations seem to be consistently observed.

Published
2021

3. Creation of a Mixed-Mode Fracture Network at Mesoscale Through Hydraulic Fracturing and Shear Stimulation

Author

Schoenball, M, Ajo-Franklin, JB, Blankenship, D, Chai, C, Chakravarty, A, Dobson, P, Hopp, C, Kneafsey, T, Knox, HA, Maceira, M, Robertson, MC, Sprinkle, P, Strickland, C, Templeton, D, Schwering, PC, Ulrich, C, Wood, T, Ajo-Franklin, J, Baumgartner, T, Beckers, K, Bonneville, A, Boyd, L, Brown, S, Burghardt, JA, Chen, T, Chen, Y, Chi, B, Condon, K, Cook, PJ, Crandall, D, Dobson, PF, Doe, T, Doughty, CA, Elsworth, D, Feldman, J, Feng, Z, Foris, A, Frash, LP, Frone, Z, Fu, P, Gao, K, Ghassemi, A, Guglielmi, Y, Haimson, B, Hawkins, A, Heise, J, Horn, M, Horne, RN, Horner, J, Hu, M, Huang, H, Huang, L, Im, KJ, Ingraham, M, Jafarov, E, Jayne, RS, Johnson, TC, Johnson, SE, Johnston, B, Karra, S, Kim, K, King, DK, Knox, H, Knox, J, Kumar, D, Kutun, K, Lee, M, Li, K, Li, Z, Mackey, P, Makedonska, N, Marone, CJ, Mattson, E, McClure, MW, McLennan, J, McLing, T, Medler, C, Mellors, RJ, Metcalfe, E, Miskimins, J, Moore, J, Morency, CE, Morris, JP, Myers, T, Nakagawa, S, Neupane, G, Newman, G, Nieto, A, Paronish, T, Pawar, R, Petrov, P, Pietzyk, B, Podgorney, R, and Polsky, Y

Subjects
enhanced geothermal systems, Geochemistry, Geophysics, Geology, mesoscale, induced seismicity
Abstract

Enhanced Geothermal Systems could provide a substantial contribution to the global energy demand if their implementation could overcome inherent challenges. Examples are insufficient created permeability, early thermal breakthrough, and unacceptable induced seismicity. Here we report on the seismic response of a mesoscale hydraulic fracturing experiment performed at 1.5-km depth at the Sanford Underground Research Facility. We have measured the seismic activity by utilizing a 100-kHz, continuous seismic monitoring system deployed in six 60-m length monitoring boreholes surrounding the experimental domain in 3-D. The achieved location uncertainty was on the order of 1m and limited by the signal-to-noise ratio of detected events. These uncertainties were corroborated by detections of fracture intersections at the monitoring boreholes. Three intervals of the dedicated injection borehole were hydraulically stimulated by water injection at pressures up to 33MPa and flow rates up to 5L/min. We located 1,933 seismic events during several injection periods. The recorded seismicity delineates a complex fracture network comprised of multistrand hydraulic fractures and shear-reactivated, preexisting planes of weakness that grew unilaterally from the point of initiation. We find that heterogeneity of stress dictates the seismic outcome of hydraulic stimulations, even when relying on theoretically well-behaved hydraulic fractures. Once hydraulic fractures intersected boreholes, the boreholes acted as a pressure relief and fracture propagation ceased. In order to create an efficient subsurface heat exchanger, production boreholes should not be drilled before the end of hydraulic stimulations.

Published
2020

4. Protective effects of the mechanistic target of rapamycin against excess iron and ferroptosis in cardiomyocytes

Author

Takashi Matsui, Hiroko Aoyagi, Briana K. Shimada, Kate M. Horiuchi, Motoi Kobayashi, Hiroaki Kitaoka, Yuichi Baba, Tomohiro Suhara, Jonathan D. Woo, Jason K. Higa, and Karra S. Marh

Subjects
Male, 0301 basic medicine, Programmed cell death, Cell Survival, Physiology, Iron, Mice, Transgenic, Myocardial Reperfusion Injury, Apoptosis, Phenylenediamines, Biology, Ferric Compounds, Piperazines, 03 medical and health sciences, Physiology (medical), Animals, Humans, Myocytes, Cardiac, Mechanistic target of rapamycin, Cells, Cultured, PI3K/AKT/mTOR pathway, Sirolimus, Cyclohexylamines, Cell Death, TOR Serine-Threonine Kinases, Ferroptosis, Cell biology, Mice, Inbred C57BL, Death, 030104 developmental biology, Biochemistry, biology.protein, Reactive Oxygen Species, Cardiology and Cardiovascular Medicine, Carbolines, Signal Transduction, Research Article
Abstract

Clinical studies have suggested that myocardial iron is a risk factor for left ventricular remodeling in patients after myocardial infarction. Ferroptosis has recently been reported as a mechanism of iron-dependent nonapoptotic cell death. However, ferroptosis in the heart is not well understood. Mechanistic target of rapamycin (mTOR) protects the heart against pathological stimuli such as ischemia. To define the role of cardiac mTOR on cell survival in iron-mediated cell death, we examined cardiomyocyte (CM) cell viability under excess iron and ferroptosis conditions. Adult mouse CMs were isolated from cardiac-specific mTOR transgenic mice, cardiac-specific mTOR knockout mice, or control mice. CMs were treated with ferric iron [Fe(III)]-citrate, erastin, a class 1 ferroptosis inducer, or Ras-selective lethal 3 (RSL3), a class 2 ferroptosis inducer. Live/dead cell viability assays revealed that Fe(III)-citrate, erastin, and RSL3 induced cell death. Cotreatment with ferrostatin-1, a ferroptosis inhibitor, inhibited cell death in all conditions. mTOR overexpression suppressed Fe(III)-citrate, erastin, and RSL3-induced cell death, whereas mTOR deletion exaggerated cell death in these conditions. 2′,7′-Dichlorodihydrofluorescein diacetate measurement of reactive oxygen species (ROS) production showed that erastin-induced ROS production was significantly lower in mTOR transgenic versus control CMs. These findings suggest that ferroptosis is a significant type of cell death in CMs and that mTOR plays an important role in protecting CMs against excess iron and ferroptosis, at least in part, by regulating ROS production. Understanding the effects of mTOR in preventing iron-mediated cell death will provide a new therapy for patients with myocardial infarction. NEW & NOTEWORTHY Ferroptosis has recently been reported as a new form of iron-dependent nonapoptotic cell death. However, ferroptosis in the heart is not well characterized. Using cultured adult mouse cardiomyocytes, we demonstrated that the mechanistic target of rapamycin plays an important role in protecting cardiomyocytes against excess iron and ferroptosis. Listen to this article's corresponding podcast at http://ajpheart.podbean.com/e/mtor-prevents-ferroptosis-in-cardiomyocytes/.

Published
2018

5. Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing

Author

Mudunuru, M. K. and Karra, S.

Subjects
Computational Engineering, Finance, and Science (cs.CE), FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Physical sciences, Computational Physics (physics.comp-ph), Computer Science - Computational Engineering, Finance, and Science, Physics - Computational Physics, Machine Learning (cs.LG)
Abstract

This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simulations. QoIs include species decay, product yield, and degree of mixing. The ROMs for QoIs are applied to quantify and understand how the chemical species evolve over time. First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction-diffusion equations using a non-negative finite element formulation for different input parameters. Non-negative finite element formulation ensures that the species concentration is non-negative (which is needed for computing QoIs) on coarse computational grids even under high anisotropy. The reactive-mixing model input parameters are a time-scale associated with flipping of velocity, a spatial-scale controlling small/large vortex structures of velocity, a perturbation parameter of the vortex-based velocity, anisotropic dispersion strength/contrast, and molecular diffusion. Second, random forests, F-test, and mutual information criterion are used to evaluate the importance of model inputs/features with respect to QoIs. Third, Support Vector Machines (SVM) and Support Vector Regression (SVR) are used to construct ROMs based on the model inputs. Then, SVR-ROMs are used to predict scaling of QoIs. Qualitatively, SVR-ROMs are able to describe the trends observed in the scaling law associated with QoIs. Fourth, the scaling law's exponent dependence on model inputs/features are evaluated using $k$-means clustering. Finally, in terms of the computational cost, the proposed SVM-ROMs and SVR-ROMs are $\mathcal{O}(10^7)$ times faster than running a high-fidelity numerical simulation for evaluating QoIs.

Published
2019

6. Optimal design of 3D borehole seismic arrays for microearthquake monitoring in anisotropic media during stimulations in the EGS collab project

Author

Chen, Yu, Huang, Lianjie, Ajo-Franklin, J, Bauer, SJ, Baumgartner, T, Beckers, K, Blankenship, D, Bonneville, A, Boyd, L, Brown, ST, Burghardt, JA, Chen, T, Chen, Y, Condon, K, Cook, PJ, Dobson, PF, Doe, T, Doughty, CA, Elsworth, D, Feldman, J, Foris, A, Frash, LP, Frone, Z, Fu, P, Gao, K, Ghassemi, A, Gudmundsdottir, H, Guglielmi, Y, Guthrie, G, Haimson, B, Hawkins, A, Heise, J, Herrick, CG, Horn, M, Horne, RN, Horner, J, Hu, M, Huang, H, Huang, L, Im, K, Ingraham, M, Johnson, TC, Johnston, B, Karra, S, Kim, K, King, DK, Kneafsey, T, Knox, H, Knox, J, Kumar, D, Kutun, K, Lee, M, Li, K, Lopez, R, Maceira, M, Makedonska, N, Marone, C, Mattson, E, McClure, MW, McLennan, J, McLing, T, Mellors, RJ, Metcalfe, E, Miskimins, J, Morris, JP, Nakagawa, S, Neupane, G, Newman, G, Nieto, A, Oldenburg, CM, Pan, W, Pawar, R, Petrov, P, Pietzyk, B, Podgorney, R, Polsky, Y, Porse, S, Richard, S, Roberts, BQ, Robertson, M, Roggenthen, W, Rutqvist, J, Rynders, D, Santos-Villalobos, H, Schoenball, M, Schwering, P, Sesetty, V, Singh, A, Smith, MM, Sone, H, Strickland, CE, Su, J, Ulrich, C, Uzunlar, N, Vachaparampil, A, Valladao, CA, Vandermeer, W, Vandine, G, Vardiman, D, and Vermeul, VR

Subjects
Optimal design, Geochemistry & Geophysics, Enhanced geothermal systems, Geophysics, Hypocenter location, Focal mechanism, Anisotropic media, Microearthquake, Resources Engineering and Extractive Metallurgy, Geology, Borehole monitoring
Published
2019

7. EGS Collab project: Status, tests, and data

Author

Kneafsey, TJ, Dobson, PF, Ajo-Franklin, JB, Guglielmi, Y, Valladao, CA, Blankenship, DA, Schwering, PC, Knox, HA, White, MD, Johnson, TC, Strickland, CE, Vermuel, VR, Morris, JP, Fu, P, Mattson, E, Neupane, GH, Podgorney, RK, Doe, TW, Huang, L, Frash, LP, Ghassemi, A, Roggenthen, W, Bauer, SJ, Baumgartner, T, Beckers, K, Blankenship, D, Bonneville, A, Boyd, L, Brown, S, Brown, ST, Burghardt, JA, Chen, T, Chen, Y, Condon, K, Cook, PJ, Crandall, D, Doe, T, Doughty, CA, Elsworth, D, Feldman, J, Foris, A, Frone, Z, Gao, K, Gudmundsdottir, H, Guthrie, G, Haimson, B, Hawkins, A, Heise, J, Horn, M, Horne, RN, Horner, J, Hu, M, Huang, H, Im, KJ, Ingraham, M, Jayne, RS, Johnston, B, Karra, S, Kim, K, King, DK, Knox, H, Knox, J, Kumar, D, Kutun, K, Lee, M, Li, K, Lopez, R, Maceira, M, Mackey, P, Makedonska, N, Marone, CJ, McClure, MW, McLennan, J, McLing, T, Medler, C, Mellors, RJ, Metcalfe, E, Miskimins, J, Moore, J, Nakagawa, S, Neupane, G, Newman, G, Nieto, A, Oldenburg, CM, Pan, W, Paronish, T, Pawar, R, Petrov, P, and Pietzyk, B

Abstract

Copyright 2019 ARMA, American Rock Mechanics Association. The EGS (Enhanced Geothermal Systems) Collab project is performing stimulation and flow experiments in highly-monitored and well-characterized intermediate-scale (approximately10 to 20 meter) field test beds at a depth of approximately 1,500 meters in the Sanford Underground Research Facility (SURF) in the Black Hills of South Dakota. Our fracture stimulation and interwell flow tests are performed to better understand processes that control formation of effective subsurface heat exchangers that are critical to the development and success of EGS. Different EGS Collab stimulations will be performed under dissimilar stress conditions to produce data for model comparisons that better differentiate stimulation mechanisms and the evolution of permeability enhancement in crystalline rock. EGS Collab experiments provide a means of testing tools, concepts, and strategies that could later be employed under geothermal reservoir conditions at DOE’s Frontier Observatory for Research in Geothermal Energy (FORGE) and other enhanced geothermal systems. Key to the project is using numerical simulations in the experiment design and interpretation of

Published
2019

8. Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico

Author

Yuan, B., Tan, Y. J., Mudunuru, M. K., Marcillo, O. E., Delorey, A. A., Roberts, P. M., Webster, J. D., Gammans, C. N. L., Karra, S., Guthrie, G. D., and Johnson, P. A.

Subjects
Signal Processing (eess.SP), FOS: Computer and information sciences, Physics - Geophysics, Computer Science - Machine Learning, Statistics - Machine Learning, Physics - Data Analysis, Statistics and Probability, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Physical sciences, Machine Learning (stat.ML), Electrical Engineering and Systems Science - Signal Processing, Data Analysis, Statistics and Probability (physics.data-an), Machine Learning (cs.LG), Geophysics (physics.geo-ph)
Abstract

We present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimay\'{o} geyser (New Mexico, USA) under noisy environments. This geyser can be considered as a natural analog of $\mathrm{CO}_2$ intrusion into shallow water aquifers. By studying this geyser, we can understand upwelling of $\mathrm{CO}_2$-rich fluids from depth, which has relevance to leak monitoring in a $\mathrm{CO}_2$ sequestration project. ML methods such as Random Forests (RF) are known to be robust multi-class classifiers and perform well under unfavorable noisy conditions. However, the extent of the RF method's accuracy is poorly understood for this $\mathrm{CO}_2$-driven geysering application. The current study aims to quantify the performance of RF-classifiers to discern the geyser state. Towards this goal, we first present the data collected from the seismometer that is installed near the Chimay\'{o} geyser. The seismic signals collected at this site contain different types of noises such as daily temperature variations, seasonal trends, animal movement near the geyser, and human activity. First, we filter the signals from these noises by combining the Butterworth-Highpass filter and an Autoregressive method in a multi-level fashion. We show that by combining these filtering techniques, in a hierarchical fashion, leads to reduction in the noise in the seismic data without removing the precursors and eruption event signals. We then use RF on the filtered data to classify the state of geyser into three classes -- remnant noise, precursor, and eruption states. We show that the classification accuracy using RF on the filtered data is greater than 90\%.These aspects make the proposed ML framework attractive for event discrimination and signal enhancement under noisy conditions, with strong potential for application to monitoring leaks in $\mathrm{CO}_2$ sequestration., Comment: 16 pages,7 figures

Published
2018

9. Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications

Author

Hunter, A., Moore, B. A., Mudunuru, M. K., Chau, V. T., Miller, R. L., Tchoua, R. B., Nyshadham, C., Karra, S., Malley, D. O., Rougier, E., Viswanathan, H. S., and Srinivasan, G.

Subjects
Computational Engineering, Finance, and Science (cs.CE), FOS: Computer and information sciences, Statistics - Machine Learning, FOS: Mathematics, FOS: Physical sciences, Machine Learning (stat.ML), Numerical Analysis (math.NA), Mathematics - Numerical Analysis, Computational Physics (physics.comp-ph), Computer Science - Computational Engineering, Finance, and Science, Physics - Computational Physics
Abstract

In this paper, five different approaches for reduced-order modeling of brittle fracture in geomaterials, specifically concrete, are presented and compared. Four of the five methods rely on machine learning (ML) algorithms to approximate important aspects of the brittle fracture problem. In addition to the ML algorithms, each method incorporates different physics-based assumptions in order to reduce the computational complexity while maintaining the physics as much as possible. This work specifically focuses on using the ML approaches to model a 2D concrete sample under low strain rate pure tensile loading conditions with 20 preexisting cracks present. A high-fidelity finite element-discrete element model is used to both produce a training dataset of 150 simulations and an additional 35 simulations for validation. Results from the ML approaches are directly compared against the results from the high-fidelity model. Strengths and weaknesses of each approach are discussed and the most important conclusion is that a combination of physics-informed and data-driven features are necessary for emulating the physics of crack propagation, interaction and coalescence. All of the models presented here have runtimes that are orders of magnitude faster than the original high-fidelity model and pave the path for developing accurate reduced order models that could be used to inform larger length-scale models with important sub-scale physics that often cannot be accounted for due to computational cost., 25 pages, 8 figures

Published
2018

10. Effect of heated-air blanket on the dispersion of squames in an operating room

Author

He, X, Karra, S, Pakseresht, P, Apte, SV, and Elghobashi, S

Subjects
Operating Rooms, Hot Temperature, large-eddy simulation, Engineering, squames dispersion, Applied Mathematics, Humans, forced air warming, ultra-clean ventilation, Ventilation, Mathematical Sciences
Abstract

High-fidelity, predictive fluid flow simulations of the interactions between the rising thermal plumes from forced air warming blower and the ultra-clean ventilation air in an operating room (OR) are conducted to explore whether this complex flow can impact the dispersion of squames to the surgical site. A large-eddy simulation, accurately capturing the spatiotemporal evolution of the flow in 3 dimensions together with the trajectories of squames, is performed for a realistic OR consisting of an operating table (OT), side tables, surgical lamps, medical staff, and a patient. Two cases are studied with blower-off and blower-on together with Lagrangian trajectories of 3 million squames initially placed on the floor surrounding the OT. The large-eddy simulation results show that with the blower-off, squames are quickly transported by the ventilation air away from the table and towards the exit grilles. In contrast, with the hot air blower turned on, the ventilation airflow above and below the OT is disrupted significantly. The rising thermal plumes from the hot air blower drag the squames above the OT and the side tables and then they are advected downwards toward the surgical site by the ventilation air from the ceiling. Temporal history of the number of squames reaching 4 imaginary boxes surrounding the side tables, the OT, and the patient's knee shows that several particles reach these boxes for the blower-on case.

Published
2018

11. Effect of heated‐air blanket on the dispersion of squames in an operating room

Author

He, X., Karra, S., Pakseresht, P., Apte, S. V., and Elghobashi, S.

Subjects
Operating Rooms, Hot Temperature, squames dispersion, large‐eddy simulation, ultra‐clean ventilation, Humans, Part B ‐ Applications, forced air warming, Research Article ‐ Application, Ventilation
Abstract

High‐fidelity, predictive fluid flow simulations of the interactions between the rising thermal plumes from forced air warming blower and the ultra‐clean ventilation air in an operating room (OR) are conducted to explore whether this complex flow can impact the dispersion of squames to the surgical site. A large‐eddy simulation, accurately capturing the spatiotemporal evolution of the flow in 3 dimensions together with the trajectories of squames, is performed for a realistic OR consisting of an operating table (OT), side tables, surgical lamps, medical staff, and a patient. Two cases are studied with blower‐off and blower‐on together with Lagrangian trajectories of 3 million squames initially placed on the floor surrounding the OT. The large‐eddy simulation results show that with the blower‐off, squames are quickly transported by the ventilation air away from the table and towards the exit grilles. In contrast, with the hot air blower turned on, the ventilation airflow above and below the OT is disrupted significantly. The rising thermal plumes from the hot air blower drag the squames above the OT and the side tables and then they are advected downwards toward the surgical site by the ventilation air from the ceiling. Temporal history of the number of squames reaching 4 imaginary boxes surrounding the side tables, the OT, and the patient's knee shows that several particles reach these boxes for the blower‐on case.

Published
2018

12. Estimating Failure in Brittle Materials using Graph Theory

Author

Mudunuru, M. K., Panda, N., Karra, S., Srinivasan, G., Chau, V. T., Rougier, E., Hunter, A., and Viswanathan, H. S.

Subjects
Computational Engineering, Finance, and Science (cs.CE), FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Physical sciences, Computational Physics (physics.comp-ph), Computer Science - Computational Engineering, Finance, and Science, Physics - Computational Physics, Statistics - Computation, Computation (stat.CO), Machine Learning (cs.LG)
Abstract

In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI). High-fidelity models for brittle failure that accurately predict these QoI exist but are highly computationally intensive, making them infeasible to incorporate in upscaling and uncertainty quantification frameworks. The goal of this paper is to provide a fast heuristic to reasonably estimate quantities such as failure path and damage in the process of brittle failure. Towards this goal, we first present a method to predict failure paths under tensile loading conditions and low-strain rates. The method uses a $k$-nearest neighbors algorithm built on fracture process zone theory, and identifies the set of all possible pre-existing cracks that are likely to join early to form a large crack. The method then identifies zone of failure and failure paths using weighted graphs algorithms. We compare these failure paths to those computed with a high-fidelity model called the Hybrid Optimization Software Simulation Suite (HOSS). A probabilistic evolution model for average damage in a system is also developed that is trained using 150 HOSS simulations and tested on 40 simulations. A non-parametric approach based on confidence intervals is used to determine the damage evolution over time along the dominant failure path. For upscaling, damage is the key QoI needed as an input by the continuum models. This needs to be informed accurately by the surrogate models for calculating effective modulii at continuum-scale. We show that for the proposed average damage evolution model, the prediction accuracy on the test data is more than 90\%. In terms of the computational time, the proposed models are $\approx \mathcal{O}(10^6)$ times faster compared to high-fidelity HOSS., Comment: 20 pages, 10 figures

Published
2018
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13. Air flow and pollution in a real, heterogeneous urban street canyon: A field and laboratory study

Author

Karra, S., Malki-Epshtein, L., Neophytou, Marina K. A., and Neophytou, Marina K. A. [0000-0001-8393-2441]

Subjects
Flow visualization, Atmospheric Science, 010504 meteorology & atmospheric sciences, Meteorology, Planetary boundary layer, Flow (psychology), Airflow, 010501 environmental sciences, Wind direction, 01 natural sciences, Wind speed, Particle image velocimetry, Planar laser-induced fluorescence, Environmental science, 0105 earth and related environmental sciences, General Environmental Science
Abstract

In this work we investigate the influence of real world conditions, including heterogeneity and natural variability of background wind, on the air flow and pollutant concentrations in a heterogeneous urban street canyon using both a series of field measurements and controlled laboratory experiments. Field measurements of wind velocities and Carbon Monoxide (CO) concentrations were taken under field conditions in a heterogeneous street in a city centre at several cross-sections along the length of the street (each cross-section being of different aspect ratio). The real field background wind was in fact observed to be highly variable and thus different Intensive Observation Periods (IOPs) represented by a different mean wind velocity and different wind variability were defined. Observed pollution concentrations reveal high sensitivity to local parameters: there is a bias towards the side closer to the traffic lane; higher concentrations are found in the centre of the street as compared to cross-sections closer to the junctions; higher concentrations are found at 1.5 height from the ground than at 2.5 m height, all of which are of concern regarding pedestrian exposure to traffic-related pollution. A physical model of the same street was produced for the purpose of laboratory experiments, making some geometrical simplifications of complex volumes and extrusions. The physical model was tested in an Atmospheric Boundary Layer water channel, using simultaneously Particle Image Velocimetry (PIV) and Planar Laser Induced Fluorescence (PLIF), for flow visualisation as well as for quantitative measurement of concentrations and flow velocities. The wind field conditions were represented by a steady mean approach velocity in the laboratory simulation (essentially representing periods of near-zero wind variability). The laboratory investigations showed a clear sensitivity of the resulting flow field to the local geometry and substantial three-dimensional flow patterns were observed throughout the modelled street. The real-field observations and the laboratory measurements were compared. Overall, we found that lower variability in the background wind does not necessarily ensure a better agreement between the airflow velocity measured in the field and in the lab. In fact, it was observed that in certain cross sections, the airflow was more affected by the particular complex architectural features such as building extrusions and balconies, which were not represented in the simplified physical model tested in the laboratory, than by the real wind field variability. For wind speed comparisons the most favourable agreement (36.6% of the compared values were within a factor of 2) was found in the case of lowest wind variability and in the section with the most simple geometry where the physical lab model was most similar to the real street. For wind direction comparisons the most favourable agreement (45.5% of the compared values was within ±45°) was found in the case with higher wind variability but in the cross-sections with more homogeneous geometrical features. Street canyons are often simplified in research and are often modelled as homogenous symmetrical canyons under steady flow, for practical purposes; our study as a whole demonstrates that natural variability and heterogeneity play a large role in how pollution disperses throughout the street, and therefore further detail in models is vital to understand real world conditions.

Published
2017

14. Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems

Author

Mudunuru, M. K., Karra, S., Makedonska, N., and Chen, T.

Subjects
FOS: Computer and information sciences, FOS: Physical sciences, Machine Learning (stat.ML), Numerical Analysis (math.NA), Computational Physics (physics.comp-ph), Physics::Geophysics, Geophysics (physics.geo-ph), Physics - Geophysics, Computational Engineering, Finance, and Science (cs.CE), Statistics - Machine Learning, FOS: Mathematics, Mathematics - Numerical Analysis, Computer Science - Computational Engineering, Finance, and Science, Physics - Computational Physics
Abstract

Subsurface applications including geothermal, geological carbon sequestration, oil and gas, etc., typically involve maximizing either the extraction of energy or the storage of fluids. Characterizing the subsurface is extremely complex due to heterogeneity and anisotropy. Due to this complexity, there are uncertainties in the subsurface parameters, which need to be estimated from multiple diverse as well as fragmented data streams. In this paper, we present a non-intrusive sequential inversion framework, for integrating data from geophysical and flow sources to constraint subsurface Discrete Fracture Networks (DFN). In this approach, we first estimate bounds on the statistics for the DFN fracture orientations using microseismic data. These bounds are estimated through a combination of a focal mechanism (physics-based approach) and clustering analysis (statistical approach) of seismic data. Then, the fracture lengths are constrained based on the flow data. The efficacy of this multi-physics based sequential inversion is demonstrated through a representative synthetic example., Comment: 32 pages, 14 figures

Published
2016
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15. The Dispersion of Traffic Related Pollutants Across a Non-Homogeneous Street Canyon

Author

Karra, S., Malki-Epshtein, L., Neophytou, Marina K. A., and Neophytou, Marina K. A. [0000-0001-8393-2441]

Subjects
Canyon, Pollutant, Pollution, geography, Engineering, geography.geographical_feature_category, Meteorology, Street Canyon, business.industry, media_common.quotation_subject, Flow (psychology), Air pollution, Dispersion, medicine.disease_cause, Civil engineering, Air Pollution, Non homogeneous, medicine, General Earth and Planetary Sciences, Field Measurements, business, Dispersion (water waves), General Environmental Science, media_common, Street canyon
Abstract

Traffic emissions are measured at several locations along and across a non-homogeneous symmetrical street canyon in Nicosia, with a single off-centre traffic lane. Under several background wind conditions, CO concentrations were higher on the pavement near the traffic lane at various locations and heights within the street, as opposed to predicted flow regimes for street canyons in these winds. The levels of pollution experienced at ground level, depending greatly on street geometry and local wind conditions, are affected by the location of the traffic lanes, and thus might exceed safe pollution levels for long periods in time.

Published
2011
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16. Three-dimensional myocardial scarring along myofibers after coronary ischemia-reperfusion revealed by computerized images of histological assays

Author

Jason K. Higa, Ahmed Z Abdelkarim, Anthony Rosenzweig, Takashi Matsui, Monica Y. Katz, Hiroko Aoyagi, Yoichiro Kusakari, Scott Lozanoff, Karra S. Marh, Chunyang Xiao, and Toshinori Aoyagi

Subjects
Pathology, medicine.medical_specialty, Physiology, business.industry, myofiber, Coronary ischemia, Anterior Descending Coronary Artery, ischemia–reperfusion, medicine.disease, 3D Imaging, LV remodeling, Coronary arteries, animals, medicine.anatomical_structure, Fibrosis, Physiology (medical), Heart failure, Myocardial scarring, medicine, Myocardial infarction, medicine.symptom, business, Endocardium, Original Research
Abstract

Adverse left ventricular (LV) remodeling after acute myocardial infarction is characterized by LV dilatation and development of a fibrotic scar, and is a critical factor for the prognosis of subsequent development of heart failure. Although myofiber organization is recognized as being important for preserving physiological cardiac function and structure, the anatomical features of injured myofibers during LV remodeling have not been fully defined. In a mouse model of ischemia–reperfusion (I/R) injury induced by left anterior descending coronary artery ligation, our previous histological assays demonstrated that broad fibrotic scarring extended from the initial infarct zone to the remote zone, and was clearly demarcated along midcircumferential myofibers. Additionally, no fibrosis was observed in longitudinal myofibers in the subendocardium and subepicardium. However, a histological analysis of tissue sections does not adequately indicate myofiber injury distribution throughout the entire heart. To address this, we investigated patterns of scar formation along myofibers using three‐dimensional (3D) images obtained from multiple tissue sections from mouse hearts subjected to I/R injury. The fibrotic scar area observed in the 3D images was consistent with the distribution of the midcircumferential myofibers. At the apex, the scar formation tracked along the myofibers in an incomplete C‐shaped ring that converged to a triangular shape toward the end. Our findings suggest that myocyte injury after transient coronary ligation extends along myofibers, rather than following the path of coronary arteries penetrating the myocardium. The injury pattern observed along myofibers after I/R injury could be used to predict prognoses for patients with myocardial infarction., In a mouse model of ischemia–reperfusion injury induced by left anterior descending coronary artery ligation, three‐dimensional images of myocardial scarring obtained from tissue sections (solid yellow area) indicate that the scar sequentially extends from the base to the apex in the midcardium. At the apex, the scar formation tracks along the myofibers in an incomplete C‐shaped ring that converges to a triangular shape toward the end. These findings suggest that myocyte injury after transient coronary ligation extends along myofibers, rather than following the path of coronary arteries penetrating the myocardium.

Published
2014

17. Design of a long term hydraulic fracture and flow system

Author

Ingraham, M. D., King, D. K., Knox, H. A., Strickland, C. E., Vermeul, V. R., Guglielmi, Y., Cook, P., Doe, T., Ajo-Franklin, J., Bauer, S. J., Baumgartner, T., Beckers, K., Blankenship, D., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Chen, T., Chen, Y., Condon, K., Dobson, P. F., Doughty, C. A., Derek Elsworth, Feldman, J., Foris, A., Frash, L. P., Frone, Z., Fu, P., Gao, K., Ghassemi, A., Gudmundsdottir, H., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Herrick, C. G., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K., Johnson, T. C., Johnston, B., Karra, S., Kim, K., Kneafsey, T., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, K., Lopez, R., Maceira, M., Makedonska, N., Marone, C., Mattson, E., Mcclure, M. W., Mclennan, J., Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Morris, J. P., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Porse, S., Richard, S., Roberts, B. Q., Robertson, M., Roggenthen, W., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schoenball, M., Schwering, P., Sesetty, V., Singh, A., Smith, M. M., Sone, H., Su, J., Ulrich, C., Uzunlar, N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Wagoner, J. L., Wang, H. F., Weers, J., White, J., White, M. D., Winterfeld, P., Wood, T., Wu, H., Wu, Y. S., Wu, Y., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

19. Efforts and innovations to promote data sharing and data accessibility in the EGS collab experiments

Author

Fu, P., Weers, J., White, M., Knox, H., Schwering, P., Morris, J., Blankenship, D., Kneafsey, T., Egs Collab Teama, Ajo-Franklin, J., Baumgartner, T., Beckers, K., Bonneville, A., Boyd, L., Brown, S., Burghardt, J. A., Chai, C., Chakravarty, A., Chen, T., Chen, Y., Chi, B., Condon, K., Cook, P. J., Crandall, D., Dobson, P. F., Doe, T., Doughty, C. A., Elsworth, D., Feldman, J., Feng, Z., Foris, A., Frash, L. P., Frone, Z., Gao, K., Ghassemi, A., Guglielmi, Y., Haimson, B., Hawkins, A., Heise, J., Hopp, C., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K. J., Ingraham, M., Jafarov, E., Jayne, R. S., Johnson, T. C., Johnson, S. E., Johnston, B., Karra, S., Kim, K., King, D. K., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, D., Li, J., Li, K., Li, Z., Maceira, M., Mackey, P., Makedonska, N., Marone, C. J., Mattson, E., Mcclure, M. W., John McLennan, Mcling, T., Medler, C., Mellors, R. J., Metcalfe, E., Miskimins, J., Moore, J., Morency, C. E., Morris, J. P., Myers, T., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Paronish, T., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Pope, J., Porse, S., Primo, J. C., Reimers, C., Roberts, B. Q., Robertson, M., Rodriguez-Tribaldos, V., Roggenthen, W., Rutqvist, J., Rynders, D., Schoenball, M., Sesetty, V., Sherman, C. S., Singh, A., Smith, M. M., Sone, H., Sonnenthal, E. L., Soom, F. A., Sprinkle, D. P., Sprinkle, S., Strickland, C. E., Su, J., Templeton, D., Thomle, J. N., Ulrich, C., Uzunlar, N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Welch, N., White, J., White, M. D., Winterfeld, P., Wood, T., Workman, S., Wu, H., Wu, Y. S., Yildirim, E. C., Zhang, Y., Zhang, Y. Q., Zhou, Q., and Zoback, M. D.

20. The distribution, orientation, and characteristics of natural fractures for experiment 1 of the EGS collab project, Sanford underground research facility

Author

Ulrich, C., Dobson, P. F., Kneafsey, T. J., Roggenthen, W. M., Uzunlar, N., Doe, T. W., Neupane, G., Podgorney, R., Schwering, P., Frash, L., Singh, A., Ajo-Franklin, J., Bauer, S. J., Baumgartner, T., Beckers, K., Blankenship, D., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Chen, T., Chen, Y., Condon, K., Cook, P. J., Doughty, C. A., Elsworth, D., Feldman, J., Foris, A., Frone, Z., Fu, P., Gao, K., Ghassemi, A., Gudmundsdottir, H., Guglielmi, Y., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Herrick, C. G., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K., Ingraham, M., Johnson, T. C., Johnston, B., Karra, S., Kim, K., King, D. K., Knox, H., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, K., Lopez, R., Maceira, M., Makedonska, N., Marone, C., Mattson, E., Mcclure, M. W., Mclennan, J., Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Joseph Morris, Nakagawa, S., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Pawar, R., Petrov, P., Pietzyk, B., Polsky, Y., Porse, S., Richard, S., Roberts, B. Q., Robertson, M., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schoenball, M., Sesetty, V., Smith, M. M., Sone, H., Strickland, C. E., Su, J., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Weers, J., White, J., White, M. D., Winterfeld, P., Wood, T., Wu, H., Wu, Y. S., Wu, Y., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

21. Co-evolution of fracture permeability and friction in rocks from the egs collab experiment 1 site

Author

Yildirim, E. C., Im, K., Derek Elsworth, Ajo-Franklin, J., Bauer, S. J., Baumgartner, T., Beckers, K., Blankenship, D., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Chen, T., Chen, Y., Condon, K., Cook, P. J., Dobson, P. F., Doe, T., Doughty, C. A., Feldman, J., Foris, A., Frash, L. P., Frone, Z., Fu, P., Gao, K., Ghassemi, A., Gudmundsdottir, H., Guglielmi, Y., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Herrick, C. G., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Ingraham, M., Johnson, T. C., Johnston, B., Karra, S., Kim, K., King, D. K., Kneafsey, T., Knox, H., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, K., Lopez, R., Maceira, M., Makedonska, N., Marone, C., Mattson, E., Mcclure, M. W., Mclennan, J., Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Morris, J. P., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Porse, S., Richard, S., Roberts, B. Q., Robertson, M., Roggenthen, W., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schoenball, M., Schwering, P., Sesetty, V., Singh, A., Smith, M. M., Sone, H., Strickland, C. E., Su, J., Ulrich, C., Uzunlar, N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Weers, J., White, J., White, M. D., Winterfeld, P., Wood, T., Wu, H., Wu, Y. S., Wu, Y., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

22. The EGS collab project: Stimulation and simulation

Author

Kneafsey, T. J., Dobson, P. F., Ajo-Franklin, J. B., Valladao, C., Blankenship, D. A., Knox, H. A., Schwering, P., Morris, J. P., Smith, M., White, M. D., Johnson, T., Podgorney, R., Mattson, E., Neupane, G., Roggenthen, W., Doe, T., Bauer, S. J., Baumgartner, T., Beckers, K., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Chen, T., Chen, Y., Condon, K., Cook, P. J., Doughty, C. A., Elsworth, D., Feldman, J., Foris, A., Frash, L. P., Frone, Z., Pengcheng Fu, Gao, K., Ghassemi, A., Gudmundsdottir, H., Guglielmi, Y., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Herrick, C. G., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K., Ingraham, M., Johnston, B., Karra, S., Kim, K., King, D. K., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, K., Lopez, R., Maceira, M., Makedonska, N., Marone, C., Mcclure, M. W., Mclennan, J., Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Nakagawa, S., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Pawar, R., Petrov, P., Pietzyk, B., Polsky, Y., Porse, S., Richard, S., Roberts, B. Q., Robertson, M., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schoenball, M., Sesetty, V., Singh, A., Sone, H., Strickland, C. E., Su, J., Thomle, J., Ulrich, C., Uzunlar, N., Vachaparampil, A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Weers, J., White, J., Winterfeld, P., Wood, T., Wu, H., Wu, Y. S., Wu, Y., Yildirim, E., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

23. The role of numerical simulation in the design of stimulation and circulation experiments for the EGS Collab project

Author

White, M., Fu, P., Huang, H., Ghassemi, A., Kneafsey, T., Blankenship, D., Ajo-Franklin, J., Bauer, S. J., Baumgartner, T., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Carroll, S. A., Chen, T., Condon, C., Cook, P. J., Dobson, P. F., Doe, T., Doughty, C. A., Elsworth, D., Frash, L. P., Frone, Z., Gudmundsdottir, H., Guglielmi, Y., Guthrie, G., Haimson, B., Heise, J., Herrick, C. G., Horn, M., Horne, R. N., Hu, M., Huang, L., Johnson, T. C., Johnston, B., Karra, S., Kim, K., King, D. K., Knox, H., Kumar, D., Lee, M., Li, K., Maceira, M., Makedonska, N., Marone, C., Mattson, E., Mcclure, M. W., John McLennan, Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Morris, J. P., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Oldenburg, C. M., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Porse, S., Roggenthen, B., Rutqvist, J., Santos-Villalobos, H., Schwering, P., Sesetty, V., Singh, A., Smith, M. M., Snyder, N., Sone, H., Sonnenthal, E. L., Spycher, N., Strickland, C. E., Su, J., Suzuki, A., Ulrich, C., Uzunlar, N., Valladao, C. A., Vandermeer, W., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Weers, J., White, J., White, M. D., Winterfeld, P., Wu, Y. S., Wu, Y., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

24. Laboratory validation of fracture caging for hydraulic fracture control

Author

Frash, L. P., Arora, K., Gan, Y., Lu, M., Gutierrez, M., Fu, P., Morris, J., Hampton, J., Ajo-Franklin, J., Bauer, S. J., Baumgartner, T., Beckers, K., Blankenship, D., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Chen, T., Chen, Y., Condon, K., Cook, P. J., Dobson, P. F., Doe, T., Doughty, C. A., Derek Elsworth, Feldman, J., Foris, A., Frone, Z., Gao, K., Ghassemi, A., Gudmundsdottir, H., Guglielmi, Y., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Herrick, C. G., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K., Ingraham, M., Johnson, T. C., Johnston, B., Karra, S., Kim, K., King, D. K., Kneafsey, T., Knox, H., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, K., Lopez, R., Maceira, M., Makedonska, N., Marone, C., Mattson, E., Mcclure, M. W., Mclennan, J., Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Morris, J. P., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Porse, S., Richard, S., Roberts, B. Q., Robertson, M., Roggenthen, W., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schoenball, M., Schwering, P., Sesetty, V., Singh, A., Smith, M. M., Sone, H., Strickland, C. E., Su, J., Ulrich, C., Uzunlar, N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Weers, J., White, J., White, M. D., Winterfeld, P., Wood, T., Wu, H., Wu, Y. S., Wu, Y., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

25. The EGS collab hydrofracture experiment at the Sanford underground research facility - Campaign cross-borehole seismic characterization

Author

Schwering, P. C., Knox, H. A., Hoots, C. R., Linneman, D., Ajo-Franklin, J., Bauer, S. J., Baumgartner, T., Beckers, K., Blankenship, D. A., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Chen, T., Chen, Y., Condon, K., Cook, P. J., Dobson, P. F., Doe, T. W., Doughty, C. A., Elsworth, D., Feldman, J., Foris, A., Frash, L. P., Frone, Z., Fu, P., Gao, K., Ghassemi, A., Gudmundsdottir, H., Guglielmi, Y., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K., Ingraham, M., Johnson, T. C., Johnston, B., Karra, S., Kim, K., King, D. K., Kneafsey, T. J., Knox, J. M., Kumar, D., Kutun, K., Lee, M., Li, K., Lopez, R., Maceira, M., Makedonska, N., Marone, C., Mattson, E. D., Mcclure, M. W., Mclennan, J., Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Joseph Morris, Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R. K., Polsky, Y., Popejoy, J., Porse, S., Richard, S., Roberts, B. Q., Robertson, M., Roggenthen, W. M., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schoenball, M., Sesetty, V., Singh, A., Smith, M. M., Sone, H., Soom, F. A., Strickland, C. E., Su, J., Thomle, J. N., Ulrich, C., Uzunlar, N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Weers, J., White, J. A., White, M. D., Winterfeld, P., Wood, T., Wu, H., Wu, Y. S., Wu, Y., Yildirim, E. C., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M.

26. EGS Collab project experiment 1 overview and progress

Author

Kneafsey, T. J., Blankenship, D., Dobson, P. F., Knox, H. A., Johnson, T. C., Ajo-Franklin, J. B., Schwering, P. C., Morris, J. P., White, M. D., Podgorney, R., Roggenthen, W., Doe, T., Mattson, E., Valladao, C., Ajo-Franklin, J., Bauer, S. J., Baumgartner, T., Beckers, K., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Chen, T., Chen, Y., Condon, K., Cook, P. J., Doughty, C. A., Derek Elsworth, Feldman, J., Foris, A., Frash, L. P., Frone, Z., Fu, P., Gao, K., Ghassemi, A., Gudmundsdottir, H., Guglielmi, Y., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Herrick, C. G., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K., Ingraham, M., Johnston, B., Karra, S., Kim, K., King, D. K., Kneafsey, T., Knox, H., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, K., Lopez, R., Maceira, M., Makedonska, N., Marone, C., Mcclure, M. W., Mclennan, J., Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Pawar, R., Petrov, P., Pietzyk, B., Polsky, Y., Porse, S., Richard, S., Roberts, B. Q., Robertson, M., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schoenball, M., Schwering, P., Sesetty, V., Singh, A., Smith, M. M., Sone, H., Soom, F., Strickland, C. E., Su, J., Thomle, J., Ulrich, C., Uzunlar, N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Weers, J., White, J., Winterfeld, P., Wood, T., Wu, H., Wu, Y. S., Wu, Y., Yildirim, E., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

28. Natural fractures and their relationship to the EGS Collab Project in the underground of the Sanford Underground Research Facility (SURF)

Author

Roggenthen, W. M., Doe, T. W., Ajo-Franklin, J., Bauer, S. J., Baumgartner, T., Beckers, K., Blankenship, D., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Chen, T., Chen, Y., Condon, K., Cook, P. J., Dobson, P. F., Doe, T., Doughty, C. A., Elsworth, D., Feldman, J., Foris, A., Frash, L. P., Frone, Z., Fu, P., Gao, K., Ghassemi, A., Halldora Gudmundsdottir, Guglielmi, Y., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Herrick, C. G., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K., Ingraham, M., Johnson, T. C., Johnston, B., Karra, S., Kim, K., King, D. K., Kneafsey, T., Knox, H., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, K., Lopez, R., Maceira, M., Makedonska, N., Marone, C., Mattson, E., Mcclure, M. W., Mclennan, J., Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Morris, J. P., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Porse, S., Richard, S., Roberts, B. Q., Robertson, M., Roggenthen, W., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schoenball, M., Schwering, P., Sesetty, V., Singh, A., Smith, M. M., Sone, H., Strickland, C. E., Su, J., Ulrich, C., Uzunlar, N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Weers, J., White, J., White, M. D., Winterfeld, P., Wood, T., Wu, H., Wu, Y. S., Wu, Y., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

29. Hydraulic fracture modeling in support of EGS collab treatment designs

Author

Kutun, K., Miskimins, J. L., Beckers, K. F., Ajo-Franklin, J., Bauer, S. J., Baumgartner, T., Blankenship, D., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Chen, T., Chen, Y., Condon, K., Cook, P. J., Dobson, P. F., Doe, T., Doughty, C. A., Elsworth, D., Feldman, J., Foris, A., Frash, L. P., Frone, Z., Pengcheng Fu, Gao, K., Ghassemi, A., Gudmundsdottir, H., Guglielmi, Y., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Herrick, C. G., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K., Ingraham, M., Johnson, T. C., Johnston, B., Karra, S., Kim, K., King, D. K., Kneafsey, T., Knox, H., Knox, J., Kumar, D., Lee, M., Li, K., Lopez, R., Maceira, M., Makedonska, N., Marone, C., Mattson, E., Mcclure, M. W., Mclennan, J., Mcling, T., Mellors, R. J., Metcalfe, E., Morris, J. P., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Porse, S., Richard, S., Roberts, B. Q., Robertson, M., Roggenthen, W., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schoenball, M., Schwering, P., Sesetty, V., Singh, A., Smith, M. M., Sone, H., Strickland, C. E., Su, J., Ulrich, C., Uzunlar, N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Weers, J., White, J., White, M. D., Winterfeld, P., Wood, T., Wu, H., Wu, Y. S., Wu, Y., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

30. Design and fabrication of a remote-control hydraulic fracturing system

Author

Ingraham, M., Strickland, C., Vermeul, V., Roberts, B., Burghardt, J., Schwering, P., Knox, H., Ajo-Franklin, J., Bauer, S. J., Baumgartner, T., Beckers, K., Blankenship, D., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Chen, T., Chen, Y., Condon, K., Cook, P. J., Dobson, P. F., Doe, T., Doughty, C. A., Elsworth, D., Feldman, J., Foris, A., Frash, L. P., Frone, Z., Fu, P., Gao, K., Ghassemi, A., Halldora Gudmundsdottir, Guglielmi, Y., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Herrick, C. G., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K., Johnson, T. C., Johnston, B., Karra, S., Kim, K., King, D. K., Kneafsey, T., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, K., Lopez, R., Maceira, M., Makedonska, N., Marone, C., Mattson, E., Mcclure, M. W., Mclennan, J., Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Morris, J. P., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Porse, S., Richard, S., Roberts, B. Q., Robertson, M., Roggenthen, W., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schoenball, M., Sesetty, V., Singh, A., Smith, M. M., Sone, H., Strickland, C. E., Su, J., Ulrich, C., Uzunlar, N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Weers, J., White, J., White, M. D., Winterfeld, P., Wood, T., Wu, H., Wu, Y. S., Wu, Y., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

31. Analysis of pressure/flow response data from the EGS Collab Project

Author

Ingraham, M. D., Knox, H. A., Strickland, C. E., Vermeul, V. R., Burghardt, J. A., Ajo-Franklin, J., Bauer, S. J., Baumgartner, T., Beckers, K., Blankenship, D., Bonneville, A., Boyd, L., Brown, S. T., Chen, T., Chen, Y., Condon, K., Cook, P. J., Dobson, P. F., Doe, T., Doughty, C. A., Elsworth, D., Feldman, J., Foris, A., Frash, L. P., Frone, Z., Fu, P., Gao, K., Ghassemi, A., Halldora Gudmundsdottir, Guglielmi, Y., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Herrick, C. G., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K., Ingraham, M., Johnson, T. C., Johnston, B., Karra, S., Kim, K., King, D. K., Kneafsey, T., Knox, H., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, K., Lopez, R., Maceira, M., Makedonska, N., Marone, C., Mattson, E., Mcclure, M. W., Mclennan, J., Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Morris, J. P., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Porse, S., Richard, S., Roberts, B. Q., Robertson, M., Roggenthen, W., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schoenball, M., Schwering, P., Sesetty, V., Singh, A., Smith, M. M., Sone, H., Su, J., Ulrich, C., Uzunlar, N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Wagoner, J. L., Wang, H. F., Weers, J., White, J., White, M. D., Winterfeld, P., Wood, T., Wu, H., Wu, Y. S., Wu, Y., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

32. The EGS Collab project: Status and Accomplishments

Author

Kneafsey, T., Blankenship, D., Dobson, P., White, M., Morris, J. P., Fu, P., Schwering, P. C., Ajo-Franklin, J. B., Huang, L., Knox, H. A., Strickland, C., Burghardt, J., Johnson, T., Neupane, G., Weers, J., Horne, R., Roggenthen, W., Doe, T., Mattson, E., Ajo-Franklin, J., Baumgartner, T., Beckers, K., Bonneville, A., Boyd, L., Brown, S., Burghardt, J. A., Chai, C., Chakravarty, A., Chen, T., Chen, Y., Chi, B., Condon, K., Cook, P. J., Crandall, D., Dobson, P. F., Doughty, C. A., Elsworth, D., Feldman, J., Feng, Z., Foris, A., Frash, L. P., Frone, Z., Gao, K., Ghassemi, A., Guglielmi, Y., Haimson, B., Hawkins, A., Heise, J., Hopp, C., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Im, K. J., Ingraham, M., Jafarov, E., Jayne, R. S., Johnson, T. C., Johnson, S. E., Johnston, B., Karra, S., Kim, K., King, D. K., Knox, H., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, D., Li, J., Li, K., Li, Z., Maceira, M., Mackey, P., Makedonska, N., Marone, C. J., Mcclure, M. W., Mclennan, J., Mcling, T., Medler, C., Mellors, R. J., Metcalfe, E., Miskimins, J., Moore, J., Morency, C. E., Myers, T., Nakagawa, S., Newman, G., Nieto, A., Paronish, T., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Pope, J., Porse, S., Primo, J. C., Reimers, C., Roberts, B. Q., Robertson, M., Rodriguez-Tribaldos, V., Rutqvist, J., Rynders, D., Schoenball, M., Schwering, P., Sesetty, V., Sherman, C. S., Singh, A., Smith, M. M., Sone, H., Sonnenthal, E. L., Soom, F. A., Sprinkle, D. P., Sprinkle, S., Strickland, C. E., Su, J., Dennise Templeton, Thomle, J. N., Ulrich, C., Uzunlar, N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Welch, N., White, J., White, M. D., Winterfeld, P., Wood, T., Workman, S., Wu, H., Wu, Y. S., Yildirim, E. C., Zhang, Y., Zhang, Y. Q., Zhou, Q., and Zoback, M. D.

33. Fracture and flow designs for the collab/SIGMA-V project

Author

Knox, H., Fu, P., Morris, J., Guglielmi, Y., Vermeul, V., Ajo-Franklin, J., Strickland, C., Johnson, T., Cook, P., Herrick, C., Lee, M., Bauer, S. J., Baumgartner, T., Blankenship, D., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Carroll, S. A., Chen, T., Condon, C., Cook, P. J., Dobson, P. F., Doe, T., Doughty, C. A., Derek Elsworth, Frash, L. P., Frone, Z., Ghassemi, A., Gudmundsdottir, H., Guthrie, G., Haimson, B., Heise, J., Herrick, C. G., Horn, M., Horne, R. N., Hu, M., Huang, H., Huang, L., Johnson, T. C., Johnston, B., Karra, S., Kim, K., King, D. K., Kneafsey, T., Kumar, D., Li, K., Maceira, M., Makedonska, N., Marone, C., Mattson, E., Mcclure, M. W., Mclennan, J., Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Morris, J. P., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Oldenburg, C. M., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Porse, S., Roggenthen, B., Rutqvist, J., Santos-Villalobos, H., Schwering, P., Sesetty, V., Singh, A., Smith, M. M., Snyder, N., Sone, H., Sonnenthal, E. L., Spycher, N., Strickland, C. E., Su, J., Suzuki, A., Ulrich, C., Valladao, C. A., Vandermeer, W., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Wang, H. F., Weers, J., White, J., White, M. D., Winterfeld, P., Wu, Y. S., Wu, Y., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

34. EGS Collab Earth modeling: Integrated 3D model of the testbed

Author

Neupane, G., Podgorney, R. K., Huang, H., Mattson, E. D., Kneafsey, T. J., Dobson, P. F., Schoenball, M., Ajo-Franklin, J. B., Ulrich, C., Schwering, P. C., Knox, H. A., Blankenship, D. A., Johnson, T. C., Strickland, C. E., Vermeul, V. R., White, M. D., Roggenthen, W., Uzunlar, N., Doe, T. W., Ajo-Franklin, J., Baumgartner, T., Beckers, K., Blankenship, D., Bonneville, A., Boyd, L., Brown, S., Brown, S. T., Burghardt, J. A., Chen, T., Chen, Y., Condon, K., Cook, P. J., Crandall, D., Doe, T., Doughty, C. A., Elsworth, D., Feldman, J., Foris, A., Frash, L. P., Frone, Z., Fu, P., Gao, K., Ghassemi, A., Gudmundsdottir, H., Guglielmi, Y., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, L., Im, K. J., Ingraham, M., Jayne, R. S., Johnston, B., Karra, S., Kim, K., King, D. K., Kneafsey, T., Knox, H., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, K., Li, Z., Lopez, R., Maceira, M., Mackey, P., Makedonska, N., Marone, C. J., Mattson, E., Mcclure, M. W., Mclennan, J., Mcling, T., Medler, C., Mellors, R. J., Metcalfe, E., Miskimins, J., Moore, J., Morency, C. E., Morris, J. P., Nakagawa, S., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Paronish, T., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Pope, J., Porse, S., Roberts, B. Q., Robertson, M., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schwering, P., Sesetty, V., Sherman, C. S., Singh, A., Smith, M. M., Sone, H., Soom, F. A., Sprinkle, P., Su, J., Dennise Templeton, Thomle, J. N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Wagoner, J. L., Wang, H. F., Weers, J., Welch, N., White, J., Winterfeld, P., Wood, T., Workman, S., Wu, H., Wu, Y. S., Wu, Y., Yildirim, E. C., Zhang, Y., Zhang, Y. Q., Zhou, Q., and Zoback, M. D.

35. The EGS Collab hydroshear experiment at the Sanford underground research facility - Siting criteria and evaluation of candidate sites

Author

Dobson, P., Kneafsey, T., Morris, J., Singh, A., Zoback, M., Roggenthen, W., Doe, T., Neupane, G., Podgorney, R., Wang, H., Knox, H., Schwering, P., Blankenship, D., Ulrich, C., Johnson, T., White, M., Ajo-Franklin, J., Bauer, S. J., Baumgartner, T., Beckers, K., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Chen, T., Chen, Y., Condon, K., Cook, P. J., Doughty, C. A., Elsworth, D., Feldman, J., Foris, A., Frash, L. P., Frone, Z., Fu, P., Gao, K., Ghassemi, A., Gudmundsdottir, H., Guglielmi, Y., Guthrie, G., Haimson, B., Hawkins, A., Heise, J., Horn, M., Horne, R. N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K., Ingraham, M., Johnston, B., Karra, S., Kim, K., King, D. K., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, K., Lopez, R., Maceira, M., Makedonska, N., Marone, C., Mattson, E., Mcclure, M. W., John McLennan, Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Nakagawa, S., Newman, G., Nieto, A., Oldenburg, C. M., Pan, W., Pawar, R., Petrov, P., Pietzyk, B., Polsky, Y., Popejoy, J., Porse, S., Richard, S., Roberts, B. Q., Robertson, M., Rutqvist, J., Rynders, D., Santos-Villalobos, H., Schoenball, M., Sesetty, V., Smith, M. M., Sone, H., Soom, F. A., Strickland, C. E., Su, J., Thomle, J. N., Uzunlar, N., Vachaparampil, A., Valladao, C. A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V. R., Wagoner, J. L., Weers, J., White, J., Winterfeld, P., Wood, T., Wu, H., Wu, Y. S., Wu, Y., Yildirim, E. C., Zhang, Y., Zhang, Y. Q., Zhou, J., and Zhou, Q.

36. Fracture and flow designs for the collab/SIGMA-V project

Author

Knox, H., Fu, P., Morris, J., Guglielmi, Y., Vermeul, V., Ajo-Franklin, J., Strickland, C., Johnson, T., Cook, P., Herrick, C., Lee, M., Bauer, S. J., Baumgartner, T., Blankenship, D., Bonneville, A., Boyd, L., Brown, S. T., Burghardt, J. A., Carroll, S. A., Chen, T., Condon, C., Cook, P. J., Dobson, P. F., Doe, T., Doughty, C. A., Elsworth, D., Frash, L. P., Frone, Z., Ghassemi, A., Gudmundsdottir, H., Guthrie, G., Haimson, B., Heise, J., Horn, M., Horne, R. N., Hu, M., Huang, H., Huang, L., Johnson, T. C., Johnston, B., Karra, S., Kim, K., King, D. K., Kneafsey, T., Kumar, D., Li, K., Maceira, M., Makedonska, N., Marone, C., Mattson, E., Mcclure, M. W., John McLennan, Mcling, T., Mellors, R. J., Metcalfe, E., Miskimins, J., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Oldenburg, C. M., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Porse, S., Roggenthen, B., Rutqvist, J., Santos-Villalobos, H., Schwering, P., Sesetty, V., Singh, A., Smith, M. M., Snyder, N., Sone, H., Sonnenthal, E. L., Spycher, N., Su, J., Suzuki, A., Ulrich, C., Valladao, C. A., Vandermeer, W., Vardiman, D., Wagoner, J. L., Wang, H. F., Weers, J., White, J., White, M. D., Winterfeld, P., Wu, Y. S., Wu, Y., Zhang, Y., Zhang, Y. Q., Zhou, J., Zhou, Q., and Zoback, M. D.

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