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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Final Report: NASA X-HAB Space Habitat Research Group Iowa State University

This document serves as the Final Report for Iowa State University's Space Habitat group to fulfill the specifications of NASA's eXploration Systems and Habitation (X-Hab) 2019 Academic Innovation Challenge for the 'Implementation of Advanced Sorbents in a Carbon Dioxide Management Unit' portion of the challenge. The scope of this document includes a description of the current Carbon Dioxide management systems implemented on ISS, a description of the group’s design, a description of the operational environment and scenarios, risks and mitigations, performance and testing results of the system, outreach, and future work.

Smalls, Nevin↗
NASA NTRS2020

Amazonia Disasters: Assessing Methods for Gold Mining-related Deforestation Detection in Amazonia Using NASA Earth Observations

Artisanal and small-scale gold mining (ASGM) is responsible for a large fraction of deforestation and disturbance in Amazonia. These activities cause severe impacts on the rainforest ecosystem and socioeconomic state of the region. NASA DEVELOP partnered with the Asociación para la Conservación de la Cuenca Amazónica (ACCA), NASA SERVIR Science Coordination Office, and the Spatial Informatics Group to enhance ASGM-related deforestation detection methods. ACCA currently uses the Omnibus Q-test Change Point Detection Algorithm to identify changes in Synthetic Aperture Radar (SAR) monthly-aggregated temporal data from the Sentinel-1 satellite. The team determined the algorithm's accuracy by comparing a stratified random sample of change points against data from January 2019 to June 2020 identified using PlanetScope and Landsat 8 Operational Land Imager (OLI) Earth observations through Collect Earth Online. Our results indicated a users' accuracy of 55% for temporal change detection and producer's and user's accuracies of 99% and 97%, respectively, for detecting when change did not occur. Of the labeled change points, only 19% were due to mining activity. This research can help our partners have a more accurate understanding of where illegal gold mining may be taking place and inform decisions to remediate this activity.

DEVELOP Project Summary↗
NASA NTRS2020

Amazonia Disasters: Assessing Methods for Gold Mining-Related Deforestation Detection in Amazonia Using NASA Earth Observations

Artisanal and small-scale gold mining (ASGM) is responsible for a large fraction of deforestation and disturbance in Amazonia. These activities cause severe impacts on the rainforest ecosystem and socioeconomic state of the region. NASA DEVELOP partnered with the Asociación para la Conservación de la Cuenca Amazónica (ACCA), NASA SERVIR Science Coordination Office, and the Spatial Informatics Group to enhance ASGM-related deforestation detection methods. ACCA currently uses the Omnibus Q-test Change Point Detection Algorithm to identify changes in Synthetic Aperture Radar (SAR) monthly-aggregated temporal data from the Sentinel-1 satellite. The team determined the algorithm's accuracy by comparing a stratified random sample of change points against data from January 2019 to June 2020 identified using PlanetScope and Landsat 8 Operational Land Imager (OLI) Earth observations through Collect Earth Online. Our results indicated a users' accuracy of 55% for temporal change detection and producer's and user's accuracies of 99% and 97%, respectively, for detecting when change did not occur. Of the labeled change points, only 19% were due to mining activity. This research can help our partners have a more accurate understanding of where illegal gold mining may be taking place and inform decisions to remediate this activity.

DEVELOP Tech Paper↗
NASA NTRSDate not supplied

Using Federated Learning to Overcome Data Gravity in Space

Humans intend to take longer missions to outer space. Understanding the impact that space has on human health is paramount to the success of these missions. Controlled experiments with model organisms are run to infer the impact of space conditions on human health, but the data these experiments generate are too large to transfer to Earth for building models. The same is true for space-relevant data generated on Earth. Ideally, these datasets should be combined to improve statistical power and model accuracy without having to transfer data. Federated learning is such a method which trains an algorithm across decentralized computing systems, each of which has their own local copy of training and testing data. In this research, made possible by NASA@Work, the AI for Life in Space group at NASA demonstrates the use of federated learning to train an ensemble of causality inference models on a combination of data residing on the International Space Station (ISS) and in the cloud. Our work leverages CRISP, a causal inference platform developed during the 2020 Frontier Development Lab’s “Astronaut Health Challenge.” We also leverage the OpenFL federated learning library which was collaboratively developed at Intel and UPenn. We used publicly available data from the NASA Ames Life Sciences Data Archive to identify features in ionizing radiation experiments as causal of changes in cardiac blood velocity. This research demonstrates, for the first time, the possibility of running machine learning algorithms on datasets separated by astronomical distances. In this experiment, all the data were generated in terra, half of which were transferred to the ISS and analyzed on the Spaceborne Computer. In the future, our research will leverage federated learning on data generated in situ on the ISS with data generated terrestrially to predict the impact of spaceflight on mammalian female reproductive capacity.

James Casaletto↗
NASA NTRSDate not supplied

Model Assisted Probability of Detection for NASA Space Missions

Model assisted probability of detection (MAPOD) uses data from simulations to improve a traditional probability of detection (POD) study. This could include extending the parameter space to reduce uncertainty or substituting experimental data with simulated data to reduce the time and cost of a POD study. In the past MAPOD was difficult due to limited computational resources, but recent innovations in simulation tools and high-performance computing have made this type of high-degree-of-freedom modelling possible, and complex structures have made it necessary. This presentation will summarize the work done by the computational nondestructive evaluation (CNDE) group at NASA Langley Research Center (LaRC) to complete a MAPOD study for phased array ultrasound testing (PAUT) of a friction stir welding (FSW) method to be used on Space Launch System (SLS) structures. The three critical needs for a MAPOD study are a validated and verified model of the inspection technique for the structure being inspected, some experimental POD data, and an uncertainty model for both the model and the experimental data. PAUT was simulated using Extende CIVA’s UT module. The model was validated using laboratory inspection data from NASA Marshall Space Flight Center (MSFC) for a Hit/Miss POD for FSW in 2219-T87 aluminum panels representative of those used in the SLS. This model was then used to simulate flaw sizes that were originally omitted from the original POD study. The results of this new MAPOD study will be presented along with a discussion of the methods and processes used to analyze the original data, selected simulation parameters, and development of the uncertainty model used for the statistical analysis. The goal of this effort is not just to improve the POD study but to demonstrate the value of MAPOD and provide a roadmap for application of MAPOD on future projects.

Elizabeth Gregory↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRSDate not supplied

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗
NASA NTRS2023

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As the space economy continues to expand through increasingly easy access to advanced and inexpensive technology, space missions themselves have become more ambitious with exploration targets growing ever distant while simultaneously requiring larger guidance and communication budgets. These conflicting desires of distance and control drive the need for advanced on-board intelligent decision making to reduce communication and control limitations by automating as many mission functions as possible in-situ. While the amount of research on such Artificial Intelligence and Machine Learning (AI/ML) software modules has grown exponentially, the capacity to experimentally validate such software modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available bleeding-edge computational platforms via what is programmatically referred to as the BrainStack on the TechEdSat (TES-n) flight series. This on-orbit computational platform provides an evaluation laboratory where advanced software experiments are pre-loaded into memory prior to launch, then executed as payloads during mission operations with results reported back and program tweaks or new training sets uploaded as needed. Processors selected as part of the BrainStack are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and, more recently, neuromorphic processors, in LEO operations. Neuromorphic processors are of particular interest due to their superior power efficiency over GPUs in intelligent automation applications. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on TES-13, January 13, 2022, and continues to operate in orbit despite no significant modifications to harden the processor against the space environment. The Intel Loihi Gen-1 on TES-13 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. The processor is packaged in the Kapoho Bay USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by an Intel Pentium single-board computer to handle scheduling of the software application payloads and communications with the satellite’s primary computer. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the continually evolving BrainStack in the upcoming three TES-n/NOW flights. The Kapoho Point unit will incorporate eight Loihi-2 processors, enabling neural networks of up to one million neurons and one billion synapsis. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space automation applications may be initially tested.

Artificial Intelligence↗
NASA NTRSDate not supplied

Continuation of Solar Absorptivity Degradation of Spacecraft Materials Due to UV and Charged Particles in the Gateway Environment

Gateway is a lunar orbiting platform that will be assembled in space similar to the International Space Station. Modules will arrive following different transit orbits, some of which have an extended duration in the Van Allen Belts. This means the spacecraft is exposed to significant charged particle radiation, after which it must operate in cislunar space for 15 years. Charged particle and UV degradation of potential Gateway materials was identified as a knowledge gap by the Passive Thermal Control System (PTCS) Group as end-of-life solar absorptivity greatly impacts thermal performance. Multiple rounds of ground testing have been completed at NASA Goddard Space Flight Center in which potential Gateway materials were subjected to expected worst-case transit charged particle fluence (9.86x1015 protons/cm2 at 2.5 keV and 3.1x1016 electrons/cm2 at 10 keV) and up to 5093 Equivalent Solar Hours (ESH) of UV exposure. This report builds on previous data from Zinecker et al. 2022 which presented the first round of ground testing. Additional materials have been tested and solar absorptivity degradation along with lessons learned are shared. Comparisons to previous results are also presented.

Brandon Hoffmann↗
NASA NTRS2024

Continuation of Solar Absorptivity Degradation of Spacecraft Materials Due to UV and Charged Particles in the Gateway Environment

Gateway is a lunar orbiting platform that will be assembled in space similar to the International Space Station. Modules will arrive following different transit orbits, some of which have an extended duration in the Van Allen Belts. This means the spacecraft is exposed to significant charged particle radiation, after which it must operate in cislunar space for 15 years. Charged particle and UV degradation of potential Gateway materials was identified as a knowledge gap by the Passive Thermal Control System (PTCS) Group as end-of-life solar absorptivity greatly impacts thermal performance. Multiple rounds of ground testing have been completed at NASA Goddard Space Flight Center in which potential Gateway materials were subjected to expected worst-case transit charged particle fluence (9.86 x 10 15 protons/cm 2 at 2.5 keV and 3.1x10 16 electrons/cm 2 at 10 keV) and up to 5093 Equivalent Solar Hours (ESH) of UV exposure. This report builds on previous data from Zinecker et al. 2022. Round 1a was published in that paper and Rounds 2a, 2b, and 2c are discussed here with some comparisons to Round 1a. Additional materials have been tested and solar absorptivity degradation is presented along with comparisons to previous results.

Brandon Hoffmann↗
DOE OSTI2017

Achieving reasonable confidence in the validation of control room designs and modifications: A summary of the 2015 NEA experts' workshop - 124

Defining and achieving reasonable confidence in the validation of nuclear power plant control room designs is a complex challenge for many reasons; not the least of which is that validation is a test of strength rather than a test of truth. In 2015, the Nuclear Energy Agency, Committee for Safety of Nuclear Installations' Working Group on Human and Organizational Factors (WGHOF) hosted an experts' workshop to identify and critically examine potential means to address the challenge of achieving reasonable confidence in control room design validations. The experts' principal task was to answer four challenge questions. (1) What are the critical considerations in defining validation objectives and how do these impact achieving reasonable confidence? (2) What methods, approaches, resources, or rationales might be used for deriving performance requirements, selecting measures, and establishing acceptance criteria so as to support reasonable confidence? (3) What methods might be used to develop scenarios that maximize the amount and relevance of information in support of the validation conclusions and achieving reasonable confidence? (4) How should the validation results be aggregated and analyzed to determine the final validation conclusions? This paper provides an overview of the experts' responses to these challenges and briefly touches upon their implications for future directions. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2004

Student Interns Work on Mars

The exceptional imagery and data acquired by the Mars Exploration Rovers since their January 2004 landing have captured the attention of scientists, the public, and students and teachers worldwide. One aspect of particular interest lies with a group of high school teachers and students actively engaged in the Athena Student Interns Program. The Athena Student Interns Program (ASIP) is a joint effort between NASA s Mars Public Engagement Office and the Athena Science Investigation that began in early 1999 as a pilot student-scientist research partnership program associated with the FIDO prototype Mars rover field test . The program is designed to actively engage high school students and their teachers in Mars exploration and scientific inquiry. In ASIP, groups of students and teachers from around the country work with mentors from the mission s Athena Science Team to carry out an aspect of the mission.

Bowman, C. D.↗
NASA NTRS2023

An Accelerated Life Testing Dataset for Lithium-Ion Batteries With Constant and Variable Loading Conditions

The dataset repository is organized into three main folders, each containing one group of life cycled battery packs. Within each folder individual battery packs own their dedicated csv file for continuous data logging, which are named with their respective battery pack number. The folders are named: - regular_alt_batteries: Containing one csv file for each battery pack cycled at the same load level or load range throughout lifetime - recommissioned_batteries: Containing one csv file for each battery pack cycled at different load levels at varying life stages - second_life_batteries: Containing one csv file for each second life battery pack cycled at constant current througout the second life

Li-ion Battery↗
DOE OSTI2017

Improvements to PHISICS/RELAP5-3D{sup C} Capabilities for Simulating HTGRs

Accurate, high-resolution full core simulations of prismatic high temperature reactors for burnup and transient analysis remain a challenge. The cores are geometrically large, but some features are quite small (tri-structural isotropic (TRISO) particles, burnable poison pellets). The treatment of both neutron scattering in graphite and resonance capture are complex and not adequately captured using the methods traditionally used in High Temperature Gas-cooled Reactors (HTGR) and commonly used in Light Water Reactors (LWR). For transient analysis, temperature feedback is a function of the TRISO fuel form, but full core models cannot resolve phenomena at this scale without averaging over space and energy. For burnup calculations, a second level of heterogeneity must be resolved to capture the local effects of fuel and burnable poisons, while accurately propagating their effects through and between blocks. The HTGR Methods and Simulation group has been utilizing the INL-developed PHISICS/RELAP5-3D{sup C} (P/R), code suite for participation in the Organization for Economic Cooperation and Development (OECD) Modular High Temperature Gas Cooled Reactor (MHTGR) Benchmark, funded by the US Department of Energy (DOE) under the Advanced Reactor Technologies (ART) program. The MHTGR-350 benchmark consists of several lattice, steady-state and transient exercises, and the P/R code has been successfully applied to most of the cases defined. In addition to this code-to-code verification activity, it is planned that P/R will be validated using data from several Loss of Forced Cooling (LOFC) tests performed at the High Temperature Test Reactor (HTTR) in a joint effort with Japan Atomic Energy Agency (JAEA) under the Civil Nuclear Energy Working Group (CNWG) project. These verification and validation simulations will identify code and model discrepancies, sensitivities, and establish the current state-of-the-art in HTGR simulation. The Department of Astronautical, Electrical and Energy Engineering (DIAEE), 'Sapienza' University has been involved in the development of P/R since 2014 through various graduate and PhD student projects. In the framework of this collaboration the P/R optimization for application on the OECD MHTGR-350 and HTTR models will be carried out.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Group Structure Optimization Using the PyGroup Code

Group constants are used as coefficients in the energy domain discretized Linear Boltzmann Equation (LBE), in which the Continuous-Energy (CE) fluxes are also integrated over energy bins and transformed into group fluxes. This procedure, based on the Multi-Group (MG) theory, is widely applied in radiation transport and diffusion simulations Careful preparation of MG cross section data, or group constants, is thus a prerequisite for success in acquiring high-fidelity LBE solutions. Generation of MG cross section data itself is a complicated multi-step task, since group constants are indispensably coupled with the solution of the LBE, i.e. the flux spectrum in the problem to be solved. Theoretically, a MG cross section library is only valid for the problem that it is built upon. Nevertheless, in practice MG cross section libraries are prepared for similar type of problems, such as thermal reactors, fast reactors, etc., to facilitate the neutronic simulations. Generally, CE point-wise cross section data set, generated by both theoretical models and experimental data, is first converted to Fine-Group (FG) libraries using a pre-defined or precalculated flux spectra, then, Broad-Group (BG) libraries are collapsed from the fine-group libraries using problem-dependent flux spectra. Proper treatments of energy and spatial self-shielding and resonance cross section are required in the CE-to-MG converting process Previously, we have developed a computer code, YGROUP, for fine-to-broad group cross section collapsing. To maintain high accuracy of the resulting BG libraries, YGROUP provides users several options on collapsing strategies and weighting schemes for group binning and evaluation of BG cross section data in users' specific problems.. An approach based on Particle Swarm Optimization was applied to automatically determine the optimal group structure. We also developed a hybrid weighting scheme to further improve BG library performance for Special Nuclear Materials (SNM) detection applications. In this work, a new computer code, PyGroup, has been developed to implement the PSO approach with an improved simulation-driven fitness function design that includes both response and forward/adjoint flux errors. Together with YGROUP, and deterministic transport code TITAN/PENMSHXP, the PyGroup code package is tested on a Dual Range Coincident Counter (DRCC) model. In this paper, we demonstrated the capability of the PyGroup code for group structure optimization using the DRCC detector model. In the future, an iteration scheme can be implemented to determine the number of broad groups based on a user-defined tolerance. we will also apply PyGroup to more complicated reactor models, and investigate physics-based fitness function designs to further reduce computational cost. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Development of a frequency regulation duty-cycle for standardized energy storage performance testing

The US DOE Protocol for uniformly measuring and expressing the performance of energy storage systems, first developed in 2012 through inclusive working group activities, provides standardized methodologies for evaluating an energy storage system’s ability to supply specific services to electrical grids. This article elaborates on the data and decisions behind the duty-cycle used for frequency regulation in this protocol. Analysis of a year of publicly available frequency regulation control signal data from a utility was considered in developing the representative signal for this use case. Moreover, this showed that signal standard deviation can be used as a metric for aggressiveness or rigor. From these data, we select representative 2 h long signals that exhibit nearly all of dynamics of actual usage under two distinct regimens, one for average use and the other for highly aggressive use. Our results were combined into a 24-h duty-cycle comprised of average and aggressive segments. The benefits and drawbacks of the selected duty-cycle are discussed along with its potential implications to the energy storage industry.

25 ENERGY STORAGE↗
NASA NTRS1976

The plume impingement test program at AEDC utilizing the S-2 ullage motors (November 1973), section 1

Proposed experiments for analyzing rocket plumes are reported. Two groups of experiments were studied: (1) those that would help define some of the parameters that characterize the plume and (2) those that would enable evaluation of some of the contamination effects of the plume environment on various items of interest. The items investigated, the purpose of the investigation, are given in tabular form.

Source record↗
NASA NTRS1989

Results of the Round Robin on opening-load measurement conducted by ASTM Task Group E24.04.04 on Crack Closure Measurement and Analysis

An experimental Round Robin on the measurement of the opening load in fatigue crack growth tests was conducted on Crack Closure Measurement and Analysis. The Round Robin evaluated the current level of consistency of opening load measurements among laboratories and to identify causes for observed inconsistency. Eleven laboratories participated in the testing of compact and middle-crack specimens. Opening-load measurements were made for crack growth at two stress-intensity factor levels, three crack lengths, and following an overload. All opening-load measurements were based on the analysis of specimen compliance data. When all of the results reported (from all participants, all measurement methods, and all data analysis methods) for a given test condition were pooled, the range of opening loads was very large--typically spanning the lower half of the fatigue loading cycle. Part of the large scatter in the reported opening-load results was ascribed to consistent differences in results produced by the various methods used to measure specimen compliance and to evaluate the opening load from the compliance data. Another significant portion of the scatter was ascribed to lab-to-lab differences in producing the compliance data when using nominally the same method of measurement.

Phillips, Edward P.↗