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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Vegetation Warming Experiment: Plant Physiology, Utqiagvik (Barrow), Alaska, 2017

Leaf gas exchange measurements on Petasites frigidus (L.) Fr. following a single season warming treatment. Data were collected in 2017 from 5 treatment warming chambers and paired control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. Data include CO2 response (ACi) curves, light response (AQ) curves, and dark-adapted respiration (Rdark) logged data measured at controlled leaf temperatures from 5-25 °C. The data package includes 4 data files in .csv format, 10 metadata files and the complete instrument output for all measurements. These data were collected as part of an experiment using Zero Power Warming (ZPW) chambers that delivered a single season warming treatment of ~4 °C above ambient air temperature. Four different plant species were targeted over four experimental years from 2017-2021. See related data packages for processed gas exchange data, leaf trait data (leaf mass per area, leaf nitrogen concentration), ambient and chamber environmental conditions, phenocamera images, thaw depth and GPS locations of chambers. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Vegetation Warming Experiment: Plant Physiology, Utqiagvik (Barrow), Alaska, 2018

Leaf gas exchange measurements on Arctagrostis latifolia (R. Br.) Griseb. following a single season warming treatment. Data were collected in 2018 from 5 treatment warming chambers and paired control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. Data include CO2 response (ACi) curves, light response (AQ) curves, and dark-adapted respiration (Rdark) logged data measured at controlled leaf temperatures from 5–25 °C. The data package includes 4 data files in .csv format, 10 metadata files and the complete instrument output for all measurements. These data were collected as part of an experiment using Zero Power Warming (ZPW) chambers that delivered a single season warming treatment of ~4 °C above ambient air temperature. Four different plant species were targeted over four experimental years from 2017–2021. See related data packages for processed gas exchange data, leaf trait data (leaf mass per area, leaf nitrogen concentration), ambient and chamber environmental conditions, phenocamera images, thaw depth and GPS locations of chambers. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Vegetation Warming Experiment: Plant Physiology, Utqiagvik (Barrow), Alaska, 2019

Leaf gas exchange measurements on Eriophorum angustifolium Honck. following a single season warming treatment. Data were collected in 2019 from 5 treatment warming chambers and paired control plots located on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. Data include CO2 response (ACi) curves, light response (AQ) curves, and dark-adapted respiration (Rdark) logged data measured at controlled leaf temperatures from 5–25 °C. The data package includes 4 data files in .csv format,, and include 4 data files, 10 metadata files and the complete instrument output for all measurements. These data were collected as part of an experiment using Zero Power Warming (ZPW) chambers that delivered a single season warming treatment of ~4 °C above ambient air temperature. Four different plant species were targeted over four experimental years from 2017–2021. See related data packages for processed gas exchange data, leaf trait data (leaf mass per area, leaf nitrogen concentration), ambient and chamber environmental conditions, phenocamera images, thaw depth and GPS locations of chambers. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Description of the TCERT Vetting Reports for Data Release 25

The Q1Q17 DR25 TCERT Vetting Reports are a collection of plots and diagnostics used by the Threshold Crossing Event Review Team (TCERT) to evaluate threshold crossing events (TCEs). While designation of Kepler Objects of Interest (KOIs) and classification of them as Planet Candidates (PCs) or False Positives (FPs) is completely automated via a robotic vetting procedure (the Robovetter) for the Q1Q17 DR25 planet catalog, as described in Thompson et al. (2017), these reports help to visualize the metrics used by the Robovetter and evaluate those robotic decisions for individual objects. For each Q1Q17 DR25 TCE, these reports include the following products: (a) the DV one-page summary, (b) selected pertinent diagnostics and plots from the full DV report, and (c) additional plots and diagnostics not included in the full DV report, including an alternate means of data detrending.

Kepler Objects of Interest↗
NASA NTRS1987

Photometric properties of lunar terrains derived from Hapke's equation

Hapke's bidirectional reflectance equations provide the most rigorous available description of photometric behavior in terms of physically meaningful parameters. The primary objective of this study was to derive Hapke parameters for the lunar surface from both disk-integrated and disk-resolved photometric data. Hapke's equation was fit to the disk-integrated phase curves and disk-resolved data for dark, average, and bright terrain classes using an iterative, nonlinear least squares algorithm described by Helfenstein. Parameters were initially determined from the disk-integrated data, and the result was applied as a first guess to the iterative solution of parameters for individual terrain classes. Plots are presented of the disk-resolved data normalized to corresponding brightnesses predicted from the disk-integrated solution under the same illumination and viewing geometries. Systematic trends in disk-resolved parameters can be identified. Values for single scattering albedo (w) of the dominantly anorthositic average and bright terrains are significantly larger than the value for the basaltic dark terrains (mare). Values for surge brightness parameters, particle phase function, and average topographic slope angle are also discussed.

Helfenstein, Paul↗
NASA NTRS1997

Internet Access to ISEE-1 and 2 Magnetometer Data

It is reported that the entire ISEE-1 and -2 magnetometer data are placed on-line, using an 8 Gbyte disk drive. The data are stored at 4-s and 60-s resolution. Also, an interactive world wide web page, which allows to plot, on request, any interval for which magnetometer data are available, is developed.

Source record↗
NASA NTRS2000

TAILSIM Users Guide

The TAILSIM program uses a 4th order Runge-Kutta method to integrate the standard aircraft equations-of-motion (EOM). The EOM determine three translational and three rotational accelerations about the aircraft's body axis reference system. The forces and moments that drive the EOM are determined from aerodynamic coefficients, dynamic derivatives, and control inputs. Values for these terms are determined from linear interpolation of tables that are a function of parameters such as angle-of-attack and surface deflections. Buildup equations combine these terms and dimensionalize them to generate the driving total forces and moments. Features that make TAILSIM applicable to studies of tailplane stall include modeling of the reversible control System, modeling of the pilot performing a load factor and/or airspeed command task, and modeling of vertical gusts. The reversible control system dynamics can be described as two hinged masses connected by a spring. resulting in a fifth order system. The pilot model is a standard form of lead-lag with a time delay applied to an integrated pitch rate and/or airspeed error feedback. The time delay is implemented by a Pade approximation, while the commanded pitch rate is determined by a commanded load factor. Vertical gust inputs include a single 1-cosine gust and a continuous NASA Dryden gust model. These dynamic models. coupled with the use of a nonlinear database, allow the tailplane stall characteristics, elevator response, and resulting aircraft response, to be modeled. A useful output capability of the TAILSIM program is the ability to display multiple post-run plot pages to allow a quick assessment of the time history response. There are 16 plot pages currently available to the user. Each plot page displays 9 parameters. Each parameter can also be displayed individually. on a one plot-per-page format. For a more refined display of the results the program can also create files of tabulated data. which can then be used by other plotting programs. The TAILSIM program was written straightforwardly assuming the user would want to change the database tables, the buildup equations, the output parameters. and the pilot model parameters. A separate database file and input file are automatically read in by the program. The use of an include file to set up all common blocks facilitates easy changing of parameter names and array sizes.

Hiltner, Dale W.↗
DOE OSTI2024

Spectral Efficiency and Tandem Performance Calculators [SWR-24-92]

The Spectral Efficiency and Tandem Performance Calculators allow users to test material types for use in tandem photovoltaic devices to better understand if a given material could work well. The Spectral Efficiency (SE) calculator takes a set of current vs voltage (I-V) and quantum efficiency (QE) data along with a spectrum input and plots the spectral efficiency of the cells represented by the datasets for the spectrum provided. The Tandem Performance calculator uses the calculated spectral efficiency of top and bottom cells and the transmission data of the top cell to calculate performance metrics for a tandem device in either a two or four terminal architecture. The SE and Tandem Performance calculators are based on the following publication: Yu, Z., Leilaeioun, M. & Holman, Z. Selecting tandem partners for silicon solar cells. Nat Energy 1, 16137 (2016).

Warren, Emily↗
DOE OSTI2026

ML-based Micro-CT SOFC Microstructure Models (from Kent 2026 Microstructural Augmentation paper)

Overview -------------------------- This repository contains datasets from the manuscript **"Enhanced Generalizability to Deep-Learning Quantification of 3D Microstructural Characteristics through Microstructurally Aware Augmentation of Scarce Data"** (*William F. Kent, Rochan Bajpai, Rachel C. Kurchin, William K. Epting, Harry W. Abernathy, Paul A. Salvador. Submitted 2026*). The methods are also described in the dissertation **Data Intensive Analysis of Solid Oxide Cell Microstructures** (*Doctoral dissertation, Carnegie Mellon University, 2025*). The datasets here are trained convolutional neural network (CNN) models for predicting key microstructural properties of solid oxide cell (SOC) electrodes from low-res, 2-channel 3D images, as well as some helpful code. The parameters for input images are provided in the paper. Sample data is provided in the file `Combined_anode_aug_dual_1k_examples` - that particular data was used to train `anode_all_aug.pth` and will work most accurately with that model. Please familiarize yourself with all caveats on accuracy and applicability, as detailed in the associated paper. Usage -------------------------- The basic usage is as follows, assuming `model_fn` is the path to the .pth file, and `X` is 2-channel input image(s) of the proper dimensions (either one image of shape `[2,12,24,24]`, or a batch of N input images of shape `[N,2,12,24,24]`): from CNN_inferencer import load_model_for_inference model = load_model_for_inference(model_fn) y_predicted = model(X) The model object automatically handles input scaling and output de-scaling based on the way the models were trained - in other words, pass in a 2-channel micro-CT image, and it will output microstructural property values in real units. ## Other model object attributes Note that model has useful attributes other than its forward pass model(X). * `model.output_descaler` - returns the output descaler object. Model does the de-scaling when generating inferences, but you may want to re-use this de-scaler on other values to e.g. compare predictions to ground truth from already-scaled training data. * `model.prop_names` - Gives the property names of the predicted y values, in order. Only exists if there's an output scaler as part of the model object, which there will be in the models provided here. ## Usage with sample data Here is a short script to use with the included sample data. from CNN_inferencer import display_predictions, load_model_for_inference, calculate_mape, parity_plot import h5py import numpy as np model_fn = 'anode_all_aug.pth' data_fn = 'Combined_anode_aug_dual_1k_examples.h5' N_samples = 200 figure_outdir = '.' model = load_model_for_inference(model_fn) with h5py.File(data_fn,'r') as f: XX = f['X'] #These are the 2-channel 3D images yy = f['y'] #These are the ground-truth microstructural properties, but they have been scaled for training - need to de-scale below N = XX.shape[0] #How many images total in the input data file #Run inferences on N_samples random samples from XX. #Run in a batch, much more efficient than one at a time. ii = np.random.choice(N,N_samples,replace=False) ii.sort() y_pred = model(XX[ii]) #Get the original/true (but normalized/scaled) values from the training dataset... #Because they were normalized, they are not in real units yet. So let's also de-scale them using model.output_scaler. y_true = model.output_scaler.transform(yy[ii]) #Let's display actual values for just 5 random ones for i in np.random.choice(N_samples,5,replace=False): display_predictions(y_true[i], y_pred[i], model.prop_names) #Make parity plots for each property (ground truth vs predicted values) #Also label each plot with the mean abs. percent error (MAPE) of the predicted values for i,key in enumerate(model.prop_names): mape = calculate_mape(y_true[:,i], y_pred[:,i]) parity_plot(y_true[:,i], y_pred[:,i], figure_outdir, key, extra_title=f' ({mape:.2f}% MAPE)')

3D microstructure↗
NASA NTRS1993

Long term trending of engineering data for the Hubble Space Telescope

A major goal in spacecraft engineering analysis is the detection of component failures before the fact. Trending is the process of monitoring subsystem states to discern unusual behaviors. This involves reducing vast amounts of data about a component or subsystem into a form that helps humans discern underlying patterns and correlations. A long term trending system has been developed for the Hubble Space Telescope. Besides processing the data for 988 distinct telemetry measurements each day, it produces plots of 477 important parameters for the entire 24 hours. Daily updates to the trend files also produce 339 thirty day trend plots each month. The total system combines command procedures to control the execution of the C-based data processing program, user-written FORTRAN routines, and commercial off-the-shelf plotting software. This paper includes a discussion the performance of the trending system and of its limitations.

Cox, Ross M.↗
NASA NTRS1994

Geotail MCA plasma wave data analysis

NASA Grant NAG 5-2346 supports the data analysis effort at The University of Iowa for the GEOTAIL Multi-Channel Analyzer (MCA) which is a part of the GEOTAIL Plasma Wave Instrument (PWI). At the beginning of this reporting period we had just begun to receive our GEOTAIL Sirius data on CD-ROMs. Much programming effort went into adapting and refining the data analysis programs to include the CD-ROM inputs. Programs were also developed to display the high-frequency-resolution PWI Sweep Frequency Analyzer (SFA) data and to include in all the various plot products the electron cyclotron frequency derived from the magnitude of the magnetic field extracted from the GEOTAIL Magnetic Field (MGF) data included in the GEOTAIL Sirius data. We also developed programs to use the MGF data residing in the Institute of Space and Astronautical Science (ISAS) GEOTAIL Scientific Data Base (SDB). Our programmers also developed programs and provided technical support for the GEOTAIL data analysis efforts of Co-lnvestigator William W. L. Taylor at Nichols Research Corporation (NRC). At the end of this report we have included brief summaries of the NRC effort and the progress being made.

Anderson, Roger R.↗
DOE OSTI2025

Processed Soil Respiration at the TRACE experimental Warming project, Aug 2015 - Sep 2017, Sabana, Luquillo, Puerto Rico

This data package contains processed measurements of soil carbon dioxide (CO₂) efflux collected using LI-COR LI-8100 soil respiration chambers at the Tropical Responses to Altered Climate Experiment (TRACE) located at the Sabana Field Research Station near Luquillo, Puerto Rico. The TRACE site is a mature, closed-canopy tropical wet forest within the Luquillo Experimental Forest. These data quantify soil surface CO₂ fluxes from both ambient (control) and experimentally warmed plots to evaluate how long-term soil warming affects belowground carbon cycling in tropical ecosystems. The data files include time-series tables of CO₂ flux (µmol CO₂ m⁻² s⁻¹), soil temperature (°C), and ancillary environmental variables, stored in comma-separated values (CSV) format and viewable with any text editor, spreadsheet, or statistical software (e.g., R, Python, Excel). Associated metadata describe plot identifiers, measurement intervals, and processing steps. These data were generated to address the research question: How does sustained soil warming influence soil respiration and carbon flux dynamics in tropical wet forests?

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Computer aided system engineering and analysis (CASE/A) modeling package for ECLS systems - An overview

An overview of the CASE/A-ECLSS series modeling package is presented. CASE/A is an analytical tool that has supplied engineering productivity accomplishments during ECLSS design activities. A components verification program was performed to assure component modeling validity based on test data from the Phase II comparative test program completed at the Marshall Space Flight Center. An integrated plotting feature has been added to the program which allows the operator to analyze on-screen data trends or get hard copy plots from within the CASE/A operating environment. New command features in the areas of schematic, output, and model management, and component data editing have been incorporated to enhance the engineer's productivity during a modeling program.

Dalee, Robert C.↗
NASA NTRS1981

Forward velocity effects on fan noise and the suppression characteristics of advanced inlets as measured in the NASA Ames 40 by 80 foot wind tunnel: Acoustic data report

Forward velocity effects on the forward radiated fan noise and on the suppression characteristics of three advanced inlets relative to a baseline cylindrical inlet were measured in a wind tunnel. A modified JT15D turbofan engine in a quiet nacelle was the source of fan noise; the advanced inlets were a CTOL hybrid inlet, an STOL hybrid inlet, and a treated deflector inlet. Also measured were the static to flight effects on the baseline inlet noise and the effects on the fan noise of canting the baseline inlet 4 deg downward to simulate typical wing mounted turbofan engines. The 1/3 octave band noise data from these tests are given along with selected plots of 1/3 octave band spectra and directivity and full scale PNL directivities. The test facilities and data reduction techniques used are also described.

Moore, M. T.↗
NASA NTRS2007

Advanced Systems for Monitoring Underwater Sounds

The term "Passive Acoustic Monitoring System" (PAMS) describes a developmental sensing-and-data-acquisition system for recording underwater sounds. The sounds (more precisely, digitized and preprocessed versions from acoustic transducers) are subsequently analyzed by a combination of data processing and interpretation to identify and/or, in some cases, to locate the sources of those sounds. PAMS was originally designed to locate the sources such as fish of species that one knows or seeks to identify. The PAMS unit could also be used to locate other sources, for example, marine life, human divers, and/or vessels. The underlying principles of passive acoustic sensing and analyzing acoustic-signal data in conjunction with temperature and salinity data are not new and not unique to PAMS. Part of the uniqueness of the PAMS design is that it is the first deep-sea instrumentation design to provide a capability for studying soniferous marine animals (especially fish) over the wide depth range described below. The uniqueness of PAMS also lies partly in a synergistic combination of advanced sensing, packaging, and data-processing design features with features adapted from proven marine instrumentation systems. This combination affords a versatility that enables adaptation to a variety of undersea missions using a variety of sensors. The interpretation of acoustic data can include visual inspection of power-spectrum plots for identification of spectral signatures of known biological species or artificial sources. Alternatively or in addition, data analysis could include determination of relative times of arrival of signals at different acoustic sensors arrayed at known locations. From these times of arrival, locations of acoustic sources (and errors in those locations) can be estimated. Estimates of relative locations of sources and sensors can be refined through analysis of the attenuation of sound in the intervening water in combination with water-temperature and salinity data acquired by instrumentation systems other than PAMS. A PAMS is packaged as a battery-powered unit, mated with external sensors, that can operate in the ocean at any depth from 2 m to 1 km. A PAMS includes a pressure housing, a deep-sea battery, a hydrophone (which is one of the mating external sensors), and an external monitor and keyboard box. In addition to acoustic transducers, external sensors can include temperature probes and, potentially, underwater cameras. The pressure housing contains a computer that includes a hard drive, DC-to- DC power converters, a post-amplifier board, a sound card, and a universal serial bus (USB) 4-port hub.

Lane, Michael↗
NASA NTRS2004

Software for Preprocessing Data from Rocket-Engine Tests

Three computer programs have been written to preprocess digitized outputs of sensors during rocket-engine tests at Stennis Space Center (SSC). The programs apply exclusively to the SSC E test-stand complex and utilize the SSC file format. The programs are the following: Engineering Units Generator (EUGEN) converts sensor-output-measurement data to engineering units. The inputs to EUGEN are raw binary test-data files, which include the voltage data, a list identifying the data channels, and time codes. EUGEN effects conversion by use of a file that contains calibration coefficients for each channel. QUICKLOOK enables immediate viewing of a few selected channels of data, in contradistinction to viewing only after post-test processing (which can take 30 minutes to several hours depending on the number of channels and other test parameters) of data from all channels. QUICKLOOK converts the selected data into a form in which they can be plotted in engineering units by use of Winplot (a free graphing program written by Rick Paris). EUPLOT provides a quick means for looking at data files generated by EUGEN without the necessity of relying on the PV-WAVE based plotting software.

Cheng, Chiu-Fu↗
NASA NTRS1984

LANDSAT instruments characterization

Work performed for the LANDSAT instrument characterization task in the areas of absolute radiometry, coherent noise analysis, and between-date smoothing is reported. Absolute radiometric calibration for LANDSAT-5 TM under ambient conditions was performed. The TM Radiometric Algorithms and Performance Program (TRAPP) was modified to create optional midscan data files and to match the TM Image Processing System (TIPS) algorithm for pulse determination. Several data reduction programs were developed, including a linear regression and its plotted result. A fast Fourier transformation study was conducted on the resequenced TM data. Subscenes of homogeneous water within scenes over Pensacola, Florida were used for testing the FFT on the resequenced data. Finally, a gain and pulse height stability study of LANDSAT 5 TM spectral bands was performed.

Lee, Y.↗
NASA NTRS1998

A Specimen Size Effect on the Fatigue Crack Growth Rate Threshold of IN 718

Fatigue crack growth rate (FCGR) tests were conducted on IN 718 in the solution annealed and aged condition at room temperature in accordance with E647-87. As part of each test, the FCGR threshold was measured using the decreasing Delta K method. A new heat of material was being tested and some of this material was sent to a different laboratory which wanted to use a specimen with a 127 mm width. Threshold data previously had been established on specimens with a width of 50.8 mm. As a check of the laboratory, tests were conducted at room temperature and R equal to 0.1 for comparison with the earlier data. The results were a threshold significantly higher than previously observed. Interchanging of specimen sizes and laboratories showed that the results were not due to a heat-to-heat or lab-to-lab variation. The results to be presented here are those obtained at the original laboratory. Growth rates were measured using the electric potential drop technique at R values of 0.1, 0.7, and 0.9. Compact tension specimen sizes with planer dimensions of 25.4 mm, 50.8 mm, and 127 mm were used. Crack growth rates at threshold were generally below 2.5 X 10(exp -8) mm / cycle. Closure measurements were made on some of the specimens by a manual procedure using a clip gage. When the crack growth rate data for the specimens tested at R equal to 0.1 were plotted as a function of applied Delta K, the thresholds varied with specimen width. The larger the width, the higher the threshold. The thresholds varied from 6.5 MPa-m(exp 1/2) for the 25.4 mm specimen to 15.4 MPa-m(exp 1/2) for the 127 mm specimen. At R equal to 0.7, the 25.4 mm and 50.8 mm specimens had essentially the same threshold, about 2.9 MPa-m(exp 1/2)while the 127 mm specimen had a threshold of 4.5 MPa-m(exp 1/2). When plotted as a function of effective Delta K, the R equal to 0.1 data are essentially normalized. Various aspects of the test procedure will be discussed as well as the results of analysis of the data using some different closure models.

Garr, K. R.↗