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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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A two-phase, common stimuli congiguration test approach was designed to satisfy both environmental reliability and thermal control design test requirements with the Instrument in a system configuration.
Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.
The Bonneville Power Administration (BPA) uses IRIG-B transmitted over microwave as its primary system time dissemination. Problems with accuracy and reliability have led to ongoing research into better methods. BPA has also developed and deployed a unique fault locator which uses precise clocks synchronized by a pulse over microwaves. It automatically transmits the data to a central computer for analysis. A proposed system could combine fault location timing and time dissemination into a Global Position System (GPS) timing receiver and close the verification loop through a master station at the Dittmer Control Center. Such a system would have many advantages, including lower cost, higher reliability, and wider industry support. Test results indicate the GPS has sufficient accuracy and reliability for this and other current timing requirements including synchronous phase angle measurements. A phasor measurement system which provides phase angle has recently been tested with excellent results. Phase angle is a key parameter in power system control applications including dynamic braking, DC modulation, remedial action schemes, and system state estimation. Further research is required to determine the applications which can most effectively use real-time phase angle measurements and the best method to apply them.
Diode JANTX1N5415 was tested to evaluate the reliability of these devices from different manufacturers, and to gain more knowledge of stress testing. The tests for determining power/temperature step stresses are described, and the test results are discussed. Failure analyses for power stress, and temperature stress are presented.
Structural models are usually tested by comparing their response with that of a reference structure (an actual structure or a more refined model) to a limited number of arbitrary loads. This test is not always reliable because the loads are arbitrary. An antioptimization-based method is proposed to test structural models. This method compares a structural model with a reference model or an actual structure under the worst loading case that maximizes the error in the model. Specifically, the method identifies the loading case that maximizes the difference between the responses of two models of the same structure using optimization. This method can be used to design experiments in order to validate a structural model. It can also be applied to identify damage in a structure by determining the load that maximizes the difference in the behavior of the damaged and the intact structure. The proposed method is illustrated by applying it to a plate and an automotive structure.
This study examined the feasibility of repeated self-administration of a newly developed battery of mental acuity tests. Researchers developed this battery to be used to screen the fitness for duty of persons in at-risk occupations (astronauts, race car drivers), or those who may be exposed to environmental stress, toxic agents, or disease. The menu under study contained cognitive and motor tests implemented on a portable microcomputer including: a five-test core battery, lasting six minutes, which had demonstrable reliabilities and stability from several previous repeated-measures studies, and also 13 new tests, lasting 42 minutes, which had appeared in other batteries but had not yet been evaluated for repeated-measures implementation in this medium. Sixteen subjects self-administered the battery over 10 repeated sessions. The hardware performed well throughout the study and the tests appeared to be easily self-administered. Stabilities and reliabilities of the test from the core battery were comparable to those obtained previously under more controlled experimental conditions. Analyses of metric properties of the remaining 13 tests produced eight additional tests with satisfactory properties. Although the average retest reliability was high, cross-correlations between tests were low, indicating factorial richness. The menu can be used to form batteries of flexible total testing time which are likely to tap different mental processes and functions.
Ariel II /UK-2/ international satellite environmental test program
As the design of Micro-Electro-Mechanical System (MEMS) devices matures and their application extends to critical areas, the issues of reliability and long-term survivability become increasingly important. This paper reviews some general approaches to addressing the reliability and qualification of MEMS devices for space applications.
A program to investigate the reliability characteristics of unencapsulated low-cost terrestrial solar cells using accelerated stress testing is described. Reliability (or parametric degradation) factors appropriate to the cell technologies and use conditions were studied and a series of accelerated stress tests was synthesized. An electrical measurement procedure and a data analysis and management system was derived, and stress test fixturing and material flow procedures were set up after consideration was given to the number of cells to be stress tested and measured and the nature of the information to be obtained from the process. Selected results and conclusions are presented.
Research on the reliability of terrestrial solar cells was performed to identify failure/degradation modes affecting solar cells and to relate these to basic physical, chemical, and metallurgical phenomena. Particular concerns addressed were the reliability attributes of individual single crystalline, polycrystalline, and amorphous thin film silicon cells. Results of subjecting different types of crystalline cells to the Clemson accelerated test schedule are given. Preliminary step stress results on one type of thin film amorphous silicon (a:Si) cell indicated that extraneous degradation modes were introduced above 140 C. Also described is development of measurement procedures which are applicable to the reliability testing of a:Si solar cells as well as an approach to achieving the necessary repeatability of fabricating a simulated a:Si reference cell from crystalline silicon photodiodes.
While NVIDIA has been the dominant provider of GPUs for HPC and ML, now AMD has several offerings of GPUs. This encourages programmers to try out AMD GPUs for new codes and also port existing codes over. Unfortunately, without understanding the floating-point differences between these GPU types, software development or porting can introduce bugs—and currently such an understanding is lacking. The magnitude of this open question becomes clear if one imagines the the number of floating-point precision choices (FP16, FP32, etc.), floating-point formats (standard floats, brain-float, etc.), and execution units available (elementary units, matrix/tensor cores, etc.) Questions such as rounding modes and subnormal support are also important. Most of these answers are unknown today or are hard to access. We provide the first testing-guided approach that answers a significant number of these questions. We also devise tests to reveal internal information (e.g., extra bits kept) to make sure that our findings are reliable. Many of our tests employ systematically generated random-programs, others apply fast-math flags and some involve fused multiplyadd. Especially for tensor/matrix cores, the tests have nontrivial logic that we present Our testing approach is reusable for the plethora of GPUs yet to be introduced. Our findings include up to 7 ulps of difference between NVIDIA and AMD for sin and cos at FP32 precision and 3 ulp at FP64. In our study of matrix cores (NVIDIA) and tensor cores (AMD), we have extensively characterized rounding modes (truncation versus round-to-nearest), the number of extra internal bits kept (whether 3 bits are kept or not), subnormal support for inputs and outputs across four different floating-point formats and across NVIDIA A100 and AMD MI250X GPUs. We believe that this wealth of data becoming available for the first time may help avoid significant porting bugs when migrating code across these platforms.
Between 2013 and 2015, full-scale crash tests were conducted on five aircraft at the Landing and Impact Research Facility (LandIR) at NASA Langley Research Center (LaRC). Two tests were conducted on CH-46E airframes as part of the Transport Rotorcraft Airframe Crash Testbed (TRACT) project, and three tests were conduced on Cessna 172 aircraft as part of the Emergency Locator Transmitter Survivability and Reliability (ELTSAR) project. Each test served to evaluate a variety of crashworthy systems including: seats, occupants, restraints, composite energy absorbing structures, and Emergency Locator Transmitters. As part of each test, the aircraft were outfitted with a variety of internal and external cameras that were focused on unique aspects of the crash event. A subset of three camera was solely used in the acquisition of photogrammetric test data. Examples of this data range from simple two-dimensional marker tracking for the determination of aircraft impact conditions to entire full-scale airframe deformation to markerless tracking of Anthropomorphic Test Devices (ATDs, a.k.a. crash test dummies) during the crash event. This report describes and discusses the techniques used and implications resulting from the photogrammetric data acquired from each of the five tests.
This is the preliminary report which is based on the flash memory reliability studies conducted during the first year (2008) of the multi-year NEPP task. The report outlines the technology and reliability challenges for advanced flash memory technologies, summarizes the test results and analyses of reliability characterizations performed on the selected flash memory devices during 2008, and provides the preliminary thoughts on the risk mitigation approaches on using advanced flash memory technologies for space applications.
Electronic or electromechanical failure-prone component pinpointing using various nondestructive testing methods
Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.
Report presents results of field tests of solar-cell-powered refrigerator/freezers for vaccines. Covers following topics: explanation of project; descriptions of refrigerator/freezer systems; account of installation experiences; performance data for 22 systems for which field-test data reported; summary of operational reliability; comments of users of some systems tested; and recommendations for design and future use. Photovoltaic systems store vaccines in remote regions where powerlines unavailable.
The advancement of domain reduction techniques has significantly enhanced the performance of solvers in mathematical programming. This paper delves into the impact of integrating convexification and domain reduction techniques within the Outer-Approximation method. We propose a refined convexification-based Outer-Approximation method alongside a Branch-and-Bound method for both convex and nonconvex Mixed-Integer Nonlinear Programming problems. These methods have been developed and incorporated into the open-source Mixed-Integer Nonlinear Decomposition Toolbox for Pyomo-MindtPy. Comprehensive benchmark tests were conducted, validating the effectiveness and reliability of our proposed algorithms. These tests highlight the improvements achieved by incorporating convexification and domain reduction techniques into the Outer-Approximation and Branch-and-Bound methods.
Traditional protection schemes face significant challenges when applied to microgrids with high penetrations of renewables with inverter-based resources (IBRs). The proliferation of advanced sensing and communication technologies has generated copious data, offering an opportunity to overcome these limitations using data-driven machine learning approaches. This work proposes a novel approach based on a support vector machine (SVM) for detecting faults within a 100% renewable microgrid. The approach encompasses a systematic offline training stage for the development of a linear SVM-based fault detection algorithm. This process covers offline data collection from the microgrid under study, the extraction of features such as positive- and negative-sequence components and the total harmonic distortion of the voltage and current measurements of the relays, and the design of the linear SVM-based classifier. During the online implementation, however, different classifiers can exhibit asynchronicity in detecting the fault inception at different subcycle-to-cycle period-level delays. To circumvent this asynchronicity issue, a separate algorithm is developed for each relay to estimate the fault inception time as close to the real fault time. The performance of the proposed SVM-based synchronized fault detection method is evaluated using online time-domain simulation studies on a microgrid test system. The results corroborate the reliability of the fault detection scheme when tested under various fault cases (fault types, locations, and impedances) and non-fault cases during both grid-tied and islanded operation modes.