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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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Test data from the 1/15th Wave Tank Tests of the Azura performed in 2017/2018 to validate the power performance and survivability of the Azura Design planned for deployment at the US Navy's Wave Energy Test Site. Raw and processed data included, along with test plan, test report, and summary data in the content model: "WEC Lab Testing content Model".
This file contains unprocessed drilling data tests on Sierra White Granite (SWG) using two new PDC bits. The tests were conducted at Sandia National Laboratories (SNL) in the Hard Rock Drilling Facility (HRDF). The collected data includes ROP data at rotational speeds of 80, 120, and 160 RPMs, with incremental weight on bit (WOB) up to 5100 lbs. The diameter of the 4-bladed and 5-bladed PDC bits was 3 3/4" and supplied by National Oilwell Varco (NOV). Five tests modes were conducted for both bits, which are as follow: (1) Rigid configuration, with no vibration compliance (2) Flywheel configuration (3) Torsional compliance configuration (4) Axial Compliance configuration (5) Combined Axial and Torsional compliance configuration Note: the WOB and torque in the drilling data should be calibrated by zeroing the WOB and torque when the drill bit tags the rock sample.
While accreditation programs require a study of uncertainty for each dosimeter model for which accreditation is sought (Torres 2005, DOE 1986), no one method is specifically recommended by accrediting bodies. Typically, the methods used to determine uncertainty have included the partial differentiation method described in the U.S. Guide to Uncertainty in Measurements (ANSI 1997) or the use of Monte Carlo techniques and probability distribution functions to generate simulated dose results. Each of these techniques has particular strengths and should be employed when the areas of uncertainty are required to be understood in detail. However, the uncertainty of dosimeter results can also be determined using a Model II One-Way Analysis of Variance technique and accreditation testing data (Cummings and Flood 2010). The strengths of this method are (1) the method is based in robust and well-accepted statistical techniques; (2) the method is straightforward and easily applied to dose algorithms, which are so complex as to render the use of other methods unwieldy; and (3) the method uses dosimeter performance data obtained during accreditation. The general method and criteria of inclusion of radiation types and fields are discussed.
Tracer testing under natural or forced gradient flow holds the potential to provide useful information for characterizing subsurface properties, through monitoring, modeling and interpretation of the tracer plume migration in an aquifer. Non-reactive tracer experiments were conducted at the Hanford 300 Area, along with constant-rate injection tests and electromagnetic borehole flowmeter (EBF) profiling. A Bayesian data assimilation technique, the method of anchored distributions (MAD) [Rubin et al., 2010], was applied to assimilate the experimental tracer test data with the other types of data and to infer the three-dimensional heterogeneous structure of the hydraulic conductivity in the saturated zone of the Hanford formation. In this study, the Bayesian prior information on the underlying random hydraulic conductivity field was obtained from previous field characterization efforts using the constant-rate injection tests and the EBF data. The posterior distribution of the conductivity field was obtained by further conditioning the field on the temporal moments of tracer breakthrough curves at various observation wells. MAD was implemented with the massively-parallel three-dimensional flow and transport code PFLOTRAN to cope with the highly transient flow boundary conditions at the site and to meet the computational demands of MAD. A synthetic study proved that the proposed method could effectively invert tracer test data to capture the essential spatial heterogeneity of the three-dimensional hydraulic conductivity field. Application of MAD to actual field data shows that the hydrogeological model, when conditioned on the tracer test data, can reproduce the tracer transport behavior better than the field characterized without the tracer test data. This study successfully demonstrates that MAD can sequentially assimilate multi-scale multi-type field data through a consistent Bayesian framework.
This submittal includes the field-test data collected during stress tests conducted in the Utah FORGE 16B(78)-32 wellbore to measure/characterize the stresses in the geothermal reservoir. The type of stress test performed is referred to as a mini-frac test or a micro-frac test. The test is a hydraulic fracture test that involves injecting a small volume of water into a short interval of the reservoir that is isolated by a straddle packer to create a fracture. The test provides information about the minimum and maximum horizontal stress at the test depths. A total of seven mini-frac tests were performed within the upper (vertical or nearly vertical) section of the 9-5/8-inch diameter uncased wellbore, between depths of 5,202 ft MD [5,202 ft TVD] and 5,980 ft MD [5966.18 ft TVD]. Two types of data were generated as part of the mini-frac tests. The first type of data is time-series data for each mini-frac test (e.g., injection-rate, test-interval pressure), and are contained in the "Mini-Frac Test Data.zip" file below. The second type is geophysical log data, attached below in the "Geophysical Logs.zip" file. Here, two types of image logs and acoustic logs were obtained before and after the mini-frac tests to examine each test depth for visual evidence of induced fracture(s) and to measure the azimuth (orientation) of any observed fractures. The attached Readme file includes the test depth information and contents of each data file included here. Analysis of the field-test data was conducted and is documented in the attached report below.
Fatigue testing campaigns are a common feature in the design and operation of advanced engineering systems in the aerospace and power generation sectors. The resulting data are typically of a high inherent technical and financial value. Presently, these data are typically transferred between departments and companies by way of ad-hoc solutions reliant on obsolete or proprietary technologies, including CSV files, MS Excel® files, and PDFs. In these circumstances there is significant potential for data loss, inconsistency, and error. To address these shortcomings, there is a need for a systematic means of transferring data between different digital systems. With this in mind, a series of CEN Workshops on engineering materials data have taken place with a view to developing technologies for representing and exchanging engineering materials data. Most recently, a CEN Workshop on the topic of fatigue test data has delivered data formats derived from the ISO 12106 standard for axial strain-controlled fatigue testing. This paper describes the methodology for developing the data formats and demonstrates their use in the scope of the INCEFA PLUS project on increasing safety in nuclear power plants by covering gaps in environmental fatigue assessment.
A computing system obtains test data for a particle source. The test data was generated by the particle source when the particle source was caused to emit particles. The test data comprises a first set of measurements of a first type and a second set of measurements of a second type. The computing system applies a data agnostic predictive model to the test data. The data agnostic predictive model is generated without a parametric analysis of variables of the first type and variables of the second type. The data agnostic predictive model outputs, based upon the test data, a value that is indicative of whether or not the test data is abnormal. Based upon the value, the computing system outputs an indication that the particle source was operating sub-optimally when emitting the particles.
This work quantifies the physical characteristics of infrasound signal and noise, assesses their temporal variations, and determines the degree to which these effects can be predicted by time-varying atmospheric models to estimate array and network performance. An automated detector that accounts for both correlated and uncorrelated noise is applied to infrasound data from three seismo-acoustic arrays in South Korea (BRDAR, CHNAR, and KSGAR), cooperatively operated by Korea Institute of Geoscience and Mineral Resources (KIGAM) and Southern Methodist University (SMU). Arrays located on an island and near the coast have higher noise power, consistent with both higher wind speeds and seasonably variable ocean wave contributions. On the basis of the adaptive F-detector quantification of time variable environmental effects, the time-dependent scaling variable is shown to be dependent on both weather conditions and local site effects. Significant seasonal variations in infrasound detections including daily time of occurrence, detection numbers, and phase velocity/azimuth estimates are documented. These time-dependent effects are strongly correlated with atmospheric winds and temperatures and are predicted by available atmospheric specifications. As a result, this suggests that commonly available atmospheric specifications can be used to predict both station and network detection performance, and an appropriate forward model improves location capabilities as a function of time.
Sensitivity tests apply a range of stimulus values to experimental subjects and record binary responses in order to estimate the distribution of threshold values in the subject population, where thresholds delineate responses from nonresponses. In many applications, such as explosives engineering, individual tests are expensive and are conducted in small runs. Scarcity of data results in nonexistence of estimates, or estimates with low precision. Here, we discuss various methods, such as combining test runs, covariate analysis, and penalized maximum likelihood, for enhancing precision and “mining more gold” from expensive test results.
The test data in the flowing pages below has been created to ensure the maintenance conducted on the current MSHLD systems is satisfactory per ASNT Level III review. This is diagnostic test data only and it is intended to be used as a reference document.
Static torque and no load test data for a 1hp, 300rpm axial-flux magnetically geared generator prototype developed by Texas A&M EMPE Lab.
The National Security Campus (NSC) collects a large amount of test data used The National Security Campus (NSC) collects a large amount of test data used to accept high value and high rigor product. The data has been used historically to support root cause analysis when anomalies are detected in down-stream processes. The opportunity to use the data for predictive failure analysis however, had never been exploited. The primary goal of the Test Data Monitor (TDM) software is to provide automated capabilities to analyze data in near-real-time and report trends that foreshadow actual product failures. To date, the aerospace industry as a whole is challenged at utilizing collected data to the degree that modern technology allows. As a result of the innovation behind TDM, Honeywell is able to monitor millions of data points through a multitude of SPC algorithms continuously and autonomously so that our personnel resources can more efficiently and accurately direct their attention to suspect processes or features. TDM’s capabilities have been recognized by our U.S. Department of Energy National Nuclear Security Administration (NNSA) sponsor for potential use at other sites within the NNSA. This activity supports multiple initiatives including expectations of the NNSA and broader corporate goals that center around data-based quality controls on production.
Access to high-quality, independent vehicle test data is critical to advancing energy-efficient transportation research. The Downloadable Dynamometer Database (D3) is a public repository of dynamometer test data on advanced-technology vehicles, generated at the Advanced Mobility Technology Laboratory (AMTL) at Argonne National Laboratory and hosted by the Transportation and Power Systems Division. The database has been made available to support researchers, students, and professionals engaged in energy-efficient vehicle research, development, and education. A wide range of vehicle categories has been tested (i.e., alternative fuel vehicles, conventional gasoline and diesel vehicles, all-electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles), as well as various drive cycles and test conditions documented in the accompanying D3 user presentation. Stakeholders can select a vehicle type, identify a vehicle of interest, and download the associated test data for use in their own analyses. Data downloaded from D3 must be accompanied by the required attribution: "This data is from the Downloadable Dynamometer Database and was generated at the Advanced Mobility Technology Laboratory (AMTL) at Argonne National Laboratory." These data are critical to vehicle modeling, validation, technology assessment, and educational use.
The Sandage-Loeb (SL) test is a unique method to probe dark energy in the ''redshift desert'' of 2∼<z∼<5, and thus it provides an important supplement to the other dark energy probes. Therefore, it is of great importance to quantify how the future SL test data impact on the dark energy constraints. To avoid the potential inconsistency in data, we use the best-fitting model based on the other geometric measurements as the fiducial model to produce 30 mock SL test data. The 10-yr, 20-yr, and 30-yr observations of SL test are analyzed and compared in detail. We show that compared to the current combined data of type Ia supernovae, baryon acoustic oscillation, cosmic microwave background, and Hubble constant, the 30-yr observation of SL test could improve the constraint on Ω{sub m} by about 80% and the constraint on w by about 25%. Furthermore, the SL test can also improve the measurement of the possible direct interaction between dark energy and dark matter. We show that the SL test 30-yr data could improve the constraint on γ by about 30% and 10% for the Q = γHρ{sub c} and Q = γHρ{sub de} models, respectively.
Static torque, no load, constant speed, and sinusoidal oscillation test data for a 10hp, 300rpm magnetically-geared generator prototype using either an adjustable load bank for a fixed resistance or an output power converter.
This dataset provides the following on road testing data: - Videos - In-vehicle dash camera videos during different testing scenarios. - Signal controller data - NTCIP log data and processed signal timing data from the corresponding signal controllers - Vehicle data - Vehicle data recorded during the testing, including GNSS, communication, CAN signals.
We review recent critical benchmark calculations with revised 235 U and 239 Pu evaluated data files.