Search OEDI Data

DCIF Westerly Granite AE Stress Effect Test (Task 3-1)

Directional Cooling-Induced Fracturing (DCIF) experiments were conducted on rectangular Westerly granite blocks (width=depth=4.0", height=2.0"). Liquid nitrogen was poured in a small, 1"-diameter copper cup attached to the top of the sample, and the resulting acoustic emissions (A...

Nakagawa, S. and Trzeciak, M. Lawrence Berkeley National Laboratory

Jul 08, 2021
15 Resources
0 Stars
Publicly accessible

Utah FORGE 2-2439: A Multi-Component Approach to Characterizing In-Situ Stress: Laboratory, Modeling and Field Measurement Workshop Presentation

This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the U.S DOE FORGE EGS Site: Laboratory, Modeling and Field Measurement project by Battelle [Columbus, OH], presented by Mark Kelley. The project's objective was to characterize stress in the U...

Kelley, M. and Bunger, A. Battelle Memorial Institute

Sep 08, 2023
1 Resources
0 Stars
Publicly accessible

DCIF (Directional Cooling-Induced Fracturing) Westerly Granite AE Borehole Damage Effect Test (Task 3-0)

Directional Cooling-Induced Fracturing (DCIF) experiments were conducted on three rectangular Westerly granite blocks (width=depth=4.0", height=2.0") which were preheated to 200, 400, and 600 degree C to induce damage (microcracks) with varying degrees. Liquid nitrogen was poured...

Nakagawa, S. Lawrence Berkeley National Laboratory

Jan 27, 2022
13 Resources
0 Stars
Publicly accessible

Utah FORGE 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements 2024 Annual Workshop Presentation

This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements project by The University of Pittsburgh, presented by Andrew Bunger. The project characterizes the stress in the Utah FORGE EGS...

Bunger, A. Energy and Geoscience Institute at the University of Utah

Sep 04, 2024
1 Resources
0 Stars
Publicly accessible

Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption

The overarching goal of the project is to create a highly efficient framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse advanced metering infrastructure (AMI) devices and phasor measurement units (PMUs) ...

Ayyanar, R. et al Arizona State University

Feb 01, 2025
12 Resources
0 Stars
Publicly accessible