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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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NASA NTRS2010

Experimental Space Shuttle Orbiter Studies to Acquire Data for Code and Flight Heating Model Validation

In an experimental study to obtain detailed heating data over the Space Shuttle Orbiter, CUBRC has completed an extensive matrix of experiments using three distinct models and two unique hypervelocity wind tunnel facilities. This detailed data will be employed to assess heating augmentation due to boundary layer transition on the Orbiter wing leading edge and wind side acreage with comparisons to computational methods and flight data obtained during the Orbiter Entry Boundary Layer Flight Experiment and HYTHIRM during STS-119 reentry. These comparisons will facilitate critical updates to be made to the engineering tools employed to make assessments about natural and tripped boundary layer transition during Orbiter reentry. To achieve the goals of this study data was obtained over a range of Mach numbers from 10 to 18, with flight scaled Reynolds numbers and model attitudes representing key points on the Orbiter reentry trajectory. The first of these studies were performed as an integral part of Return to Flight activities following the accident that occurred during the reentry of the Space Shuttle Columbia (STS-107) in February of 2003. This accident was caused by debris, which originated from the foam covering the external tank bipod fitting ramps, striking and damaging critical wing leading edge heating tiles that reside in the Orbiter bow shock/wing interaction region. During investigation of the accident aeroheating team members discovered that only a limited amount of experimental wing leading edge data existed in this critical peak heating area and a need arose to acquire a detailed dataset of heating in this region. This new dataset was acquired in three phases consisting of a risk mitigation phase employing a 1.8% scale Orbiter model with special temperature sensitive paint covering the wing leading edge, a 0.9% scale Orbiter model with high resolution thin-film instrumentation in the span direction, and the primary 1.8% scale Orbiter model with detailed thin-film resolution in both the span and chord direction in the area of peak heating. Additional objectives of this first study included: obtaining natural or tripped turbulent wing leading edge heating levels, assessing the effectiveness of protuberances and cavities placed at specified locations on the orbiter over a range of Mach numbers and Reynolds numbers to evaluate and compare to existing engineering and computational tools, obtaining cavity floor heating to aid in the verification of cavity heating correlations, acquiring control surface deflection heating data on both the main body flap and elevons, and obtain high speed schlieren videos of the interaction of the orbiter nose bow shock with the wing leading edge. To support these objectives, the stainless steel 1.8% scale orbiter model in addition to the sensors on the wing leading edge was instrumented down the windward centerline, over the wing acreage on the port side, and painted with temperature sensitive paint on the starboard side wing acreage. In all, the stainless steel 1.8% scale Orbiter model was instrumented with over three-hundred highly sensitive thin-film heating sensors, two-hundred of which were located in the wing leading edge shock interaction region. Further experimental studies will also be performed following the successful acquisition of flight data during the Orbiter Entry Boundary Layer Flight Experiment and HYTHIRM on STS-119 at specific data points simulating flight conditions and geometries. Additional instrumentation and a protuberance matching the layout present during the STS-119 boundary layer transition flight experiment were added with testing performed at Mach number and Reynolds number conditions simulating conditions experienced in flight. In addition to the experimental studies, CUBRC also performed a large amount of CFD analysis to confirm and validate not only the tunnel freestream conditions, but also 3D flows over the orbiter acreage, wing leading edge, and controlurfaces to assess data quality, shock interaction locations, and control surface separation regions. This analysis is a standard part of any experimental program at CUBRC, and this information was of key importance for post-test data quality analysis and understanding particular phenomena seen in the data. All work during this effort was sponsored and paid for by the NASA Space Shuttle Program Office at the Johnson Space Center in Houston, Texas.

Wadhams, T. P.↗
NASA NTRS2018

NASA NeMO-Net

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low- resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

NASA↗
NASA NTRS2018

Enriching the Twitter Stream Increasing Data Mining Yield and Quality Using Machine Learning

Social media data streams are important sources of real-time and historical global information for science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are exploring the Twitter data stream for its potential in augmenting the validation program of NASA Earth science missions, specifically the Global Precipitation Measurement (GPM) mission. We have implemented a tweet processing infrastructure that outputs classified precipitation tweets. Inputs are "passive" tweets, along with a smaller number of tweets from "active" participants, i.e., those knowingly contributing to our effort. The "active" tweets, presumably of higher quality, enrich the Twitter stream. "Active" sources include data scraped from other social media (e.g., public Facebook posts) and data from existing crowdsourcing programs (e.g., mPING reports). In addition, there is likely relevant precipitation information in images and documents that are the end points of links often included in tweets. Information derived from these "active" sources could then be tweeted into the Twitter stream, thus enriching its quality. The objective of our current work is to mine these tweet­ linked images and documents, using neural networks, to increase the information content and quality related to precipitation. For images, we classified them as either precipitation-related or not. For training and validation, we used images obtained via the Google custom search API. We created two models: (1) by training a simple Convolutional Neural Network and (2) by using transfer learning principles to adapt a pre-trained object recognition model. For documents, both those linked to tweets and the tweet contents, we trained Hierarchical Attention Networks to determine precipitation occurrence, type, and intensity. For training and validation, we used a keyword-filtered tweet data set labelled with ground truth data from Dark Sky (an API to retrieve weather-related labels) and the National Severe Storms Laboratory's Multi­ Radar/Multi-Sensor (MRMS) system. Our results demonstrated the efficacy of our machine learning approaches for enriching the Twitter stream, to derive information potentially useful for validation of earth science satellite data.

Albayrak, Arif↗
NASA NTRS2009

Evaluating Failures and Near Misses in Human Spaceflight History for Lessons for Future Human Spaceflight

There have been a number of studies done in the past drawn on lessons learned with regard to human loss-of-life events. Generally, the systemic causes and proximate causes for fatal events have both been examined in considerable detail. However, an examination of near-fatal accidents and failures that narrowly missed being fatal could be equally useful, not only in detecting causes, both proximate and systemic, but also for determining what factors averted disaster, what design decisions and/or operator actions prevented catastrophe. Additionally, review of risk factors for upcoming or future programs will often look at trending statistics, generally focusing on failure/success statistics. Unfortunately, doing so can give a skewed or misleading view of past reliability or a reliability that cannot be presumed to apply to a new program. One reason for this might be that failure/success criteria aren't the same across programs, but also that apparent success can hide systemic faults that, under other circumstances, can be fatal to a program with different parameters. A program with a number of near misses can look more reliable than a consistently healthy program with a single out-of-family failure and provide very misleading data if it is not examined in detail. This is particularly true for a manned space program where failure/success includes more than making a particular orbit. Augmenting reliability evaluations with this near miss data can provide insight and expand on the limitations of a strictly pass/fail evaluation. Even more importantly, a thorough understanding of these near miss events can identify conditions that prevented fatalities. Those conditions may be key to a programs reliability, but, without insight to the repercussions if such conditions were not in place, their importance may not be readily clear. As programs mature and political and fiscal responsibilities come to the fore, often there is considerable incentive to eliminate unnecessary conservatism, design margin, redundancy, operational support, testing, training, or safety oversight. An evaluation that demonstrates how these features and capabilities averted disaster can ensure processes that saved lives or missions are not discarded without appropriate review and understanding. Close examination of accidents that almost were can also highlight differences in design from one program to another, either justifying reliability comparisons or negating them. It can also provide considerable insight into how those saving factors were developed and implemented so that similar methods can be used to ensure appropriate life-saving and mission saving factors can be developed, even for a dissimilar space program. The lessons are appropriate for seasoned manned space programs and agencies, but crucial for untried agencies and organizations that are interested in sending man into space. The large body of publicly available near miss and accident data available provide invaluable insight into programmatic, technical, and even political issues that can be addressed before they impact safety. In this paper, we examine a number of these near misses and accidents and steps a program, agency, or potential spacefaring company might take to improve their chances of success and avoid mission or safety disasters using this data.

Barr, Stephanie↗
NASA NTRS1986

An IBM PC-based math model for space station solar array simulation

This report discusses and documents the design, development, and verification of a microcomputer-based solar cell math model for simulating the Space Station's solar array Initial Operational Capability (IOC) reference configuration. The array model is developed utilizing a linear solar cell dc math model requiring only five input parameters: short circuit current, open circuit voltage, maximum power voltage, maximum power current, and orbit inclination. The accuracy of this model is investigated using actual solar array on orbit electrical data derived from the Solar Array Flight Experiment/Dynamic Augmentation Experiment (SAFE/DAE), conducted during the STS-41D mission. This simulator provides real-time simulated performance data during the steady state portion of the Space Station orbit (i.e., array fully exposed to sunlight). Eclipse to sunlight transients and shadowing effects are not included in the analysis, but are discussed briefly. Integrating the Solar Array Simulator (SAS) into the Power Management and Distribution (PMAD) subsystem is also discussed.

Emanuel, E. M.↗
DOE OSTI2024

A Deep Multimodal Representation Learning Framework for Accurate Molecular Properties Prediction

Drug discovery is a complex and challenging process, requiring the optimization of candidate compounds to identify those with the potential to become safe and effective drugs. Predicting molecular properties is an indispensable step in the drug discovery pipeline. Traditionally, this process is costly and time-intensive, involving multiple rounds of experiments and clinical trials, rendering it impractical for every candidate compound. Deep learning techniques have emerged as a promising approach to drug discovery to reduce the cost and time required to identify novel drugs. However, prevalent research in deep learning models focused on predicting molecular properties has primarily fixated on single-modal models, which utilize a single modality of data, neglecting the potential benefits of combining different data modalities. To overcome this limitation, we introduce MRL-Mol: a deep \textbf{M}ultimodal \textbf{R}epresentation \textbf{L}earning framework for accurate \textbf{Mol}ecular properties prediction. MRL-Mol harnesses three data modalities: sequence, graph, and image, augmenting the depth of comprehension. Leveraging a large-scale unlabeled dataset~($\sim$1M unique molecules), we pretrain MRL-Mol to extract inter- and intra-modal information. Our study demonstrates the superior performance of MRL-Mol in predicting molecular properties across six benchmark datasets, including both classification and regression tasks. Notably, MRL-Mol outperforms other state-of-the-art molecular properties prediction models. These findings suggest that by combining information from multiple data modalities, MRL-Mol can comprehend molecules better than single-modal deep learning models and identify molecular properties with better accuracy.

Yang, Yuxin↗
NASA NTRS2007

Review of Remote Sensing Needs and Applications in Africa

Remote sensing data has had an important role in identifying and responding to inter-annual variations in the African environment during the past three decades. As a largely agricultural region with diverse but generally limited government capacity to acquire and distribute ground observations of rainfall, temperature and other parameters, remote sensing is sometimes the only reliable measure of crop growing conditions in Africa. Thus, developing and maintaining the technical and scientific capacity to analyze and utilize satellite remote sensing data in Africa is critical to augmenting the continent's local weather/climate observation networks as well as its agricultural and natural resource development and management. The report Review of Remote Sensing Needs and Applications in Africa' has as its central goal to recommend to the US Agency for International Development an appropriate approach to support sustainable remote sensing applications at African regional remote sensing centers. The report focuses on "RS applications" to refer to the acquisition, maintenance and archiving, dissemination, distribution, analysis, and interpretation of remote sensing data, as well as the integration of interpreted data with other spatial data products. The report focuses on three primary remote sensing centers: (1) The AGRHYMET Regional Center in Niamey, Niger, created in 1974, is a specialized institute of the Permanent Interstate Committee for Drought Control in the Sahel (CILSS), with particular specialization in science and techniques applied to agricultural development, rural development, and natural resource management. (2) The Regional Centre for Maiming of Resources for Development (RCMRD) in Nairobi, Kenya, established in 1975 under the auspices of the United Nations Economic Commission for Africa and the Organization of African Unity (now the African Union), is an intergovernmental organization, with 15 member states from eastern and southern Africa. (3) The Regional Remote Sensing Unit (RRSU) in Gaborone, Botswana, began work in June 1988 and operates under the Agriculture Information Management System (AIMS), as part of the Food, Agriculture and Natural Resources (FANR) Directorate, based at the Southern Africa Development Community (SADC) Secretariat.

Brown, Molly E.↗
NASA NTRS1990

Flight data acquisition methodology for validation of passive ranging algorithms for obstacle avoidance

The automation of low-altitude rotorcraft flight depends on the ability to detect, locate, and navigate around obstacles lying in the rotorcraft's intended flightpath. Computer vision techniques provide a passive method of obstacle detection and range estimation, for obstacle avoidance. Several algorithms based on computer vision methods have been developed for this purpose using laboratory data; however, further development and validation of candidate algorithms require data collected from rotorcraft flight. A data base containing low-altitude imagery augmented with the rotorcraft and sensor parameters required for passive range estimation is not readily available. Here, the emphasis is on the methodology used to develop such a data base from flight-test data consisting of imagery, rotorcraft and sensor parameters, and ground-truth range measurements. As part of the data preparation, a technique for obtaining the sensor calibration parameters is described. The data base will enable the further development of algorithms for computer vision-based obstacle detection and passive range estimation, as well as provide a benchmark for verification of range estimates against ground-truth measurements.

Smith, Phillip N.↗
NASA NTRS1983

High angle-of-attack aerodynamics of a strake-canard-wing V/STOL fighter configuration

High angle-of-attack aerodynamic data are analyzed for a strake-canard-wing V/STOL fighter configuration. The configuration represents a twin-engine supersonic V/STOL fighter aircraft which uses four longitudinal thrust-augmenting ejectors to provide vertical lift. The data were obtained in tests of a 9.39 percent scale model of the configuration in the NASA Ames 12-Foot Pressure Wind Tunnel, at a Mach number of 0.2. Trimmed aerodynamic characteristics, longitudinal control power, longitudinal and lateral/directional stability, and effects of alternate strake and canard configurations are analyzed. The configuration could not be trimmed (power-off) above 12 deg angle of attack because of the limited pitch control power and the high degree of longitudinal instability (28 percent) at this Mach number. Aerodynamic center location was found to be controllable by varying strake size and canard location without significantly affecting lift and drag. These configuration variations had relatively little effect on the lateral/directional stability up to 10 deg angle of attack.

Durston, D. A.↗
NASA NTRS2007

Mars Science Laboratory Entry Capsule Aerothermodynamics and Thermal Protection System

The Mars Science Laboratory (MSL) spacecraft is being designed to carry a large rover (greater than 800 kg) to the surface of Mars using a blunt-body entry capsule as the primary decelerator. The spacecraft is being designed for launch in 2009 and arrival at Mars in 2010. The combination of large mass and diameter with non-zero angle-of-attack for MSL will result in unprecedented convective heating environments caused by turbulence prior to peak heating. Navier-Stokes computations predict a large turbulent heating augmentation for which there are no supporting flight data1 and little ground data for validation. Consequently, an extensive experimental program has been established specifically for MSL to understand the level of turbulent augmentation expected in flight. The experimental data support the prediction of turbulent transition and have also uncovered phenomena that cannot be replicated with available computational methods. The result is that the flight aeroheating environments predictions must include larger uncertainties than are typically used for a Mars entry capsule. Finally, the thermal protection system (TPS) being used for MSL has not been flown at the heat flux, pressure, and shear stress combinations expected in flight, so a test program has been established to obtain conditions relevant to flight. This paper summarizes the aerothermodynamic definition analysis and TPS development, focusing on the challenges that are unique to MSL.

Edquist, Karl T.↗
NASA NTRS1979

Aerodynamic characteristics of a large-scale semispan model with a swept wing and an augmented jet flap with hypermixing nozzles

The aerodynamic characteristics of the augmentor wing concept with hypermixing primary nozzles were investigated. A large-scale semispan model in the Ames 40- by 80-Foot Wind Tunnel and Static Test Facility was used. The trailing edge, augmentor flap system occupied 65% of the span and consisted of two fixed pivot flaps. The nozzle system consisted of hypermixing, lobe primary nozzles, and BLC slot nozzles at the forward inlet, both sides and ends of the throat, and at the aft flap. The entire wing leading edge was fitted with a 10% chord slat and a blowing slot. Outboard of the flap was a blown aileron. The model was tested statically and at forward speed. Primary parameters and their ranges included angle of attack from -12 to 32 degrees, flap angles of 20, 30, 45, 60 and 70 degrees, and deflection and diffuser area ratios from 1.16 to 2.22. Thrust coefficients ranged from 0 to 2.73, while nozzle pressure ratios varied from 1.0 to 2.34. Reynolds number per foot varied from 0 to 1.4 million. Analysis of the data indicated a maximum static, gross augmentation of 1.53 at a flap angle of 45 degrees. Analysis also indicated that the configuration was an efficient powered lift device and that the net thrust was comparable with augmentor wings of similar static performance. Performance at forward speed was best at a diffuser area ratio of 1.37.

Aiken, T. N.↗
NASA NTRS1970

XB-70 aerodynamic, geometric, mass, and symmetric structural mode data

XB-70-1 mass, structural, and aerodynamic data were updated to reflect as closely as possible the characteristics of the airplane at three specific flight conditions which were actually flown; a nominal Mach number of 0.90 at an altitude of 25,000 feet (two cases) and a nominal Mach number of 1.6 at an altitude of 40,000 feet (one case). In-flight response characteristics at a number of points on the vehicle were obtained by exciting a pair of shaker vanes on the nose of the airplane. Data were recorded with the basic stability augmentation system (SAS) operating both alone and together with the identical location of accelerometer and force (ILAF) structural mode control system. Detailed total vehicle weight, mass characteristics, structural frequencies, generalized masses, all aerodynamic data used in the present analyses, and a description of the actual mode shapes are tabulated and presented.

Wykes, J. H.↗
NASA NTRS2017

Framework for Processing Citizens Science Data for Applications to NASA Earth Science Missions

Citizen science (or crowdsourcing) has drawn much high-level recent and ongoing interest and support. It is poised to be applied, beyond the by-now fairly familiar use of, e.g., Twitter for natural hazards monitoring, to science research, such as augmenting the validation of NASA earth science mission data. This interest and support is seen in the 2014 National Plan for Civil Earth Observations, the 2015 White House forum on citizen science and crowdsourcing, the ongoing Senate Bill 2013 (Crowdsourcing and Citizen Science Act of 2015), the recent (August 2016) Open Geospatial Consortium (OGC) call for public participation in its newly-established Citizen Science Domain Working Group, and NASA's initiation of a new Citizen Science for Earth Systems Program (along with its first citizen science-focused solicitation for proposals). Over the past several years, we have been exploring the feasibility of extracting from the Twitter data stream useful information for application to NASA precipitation research, with both "passive" and "active" participation by the twitterers. The Twitter database, which recently passed its tenth anniversary, is potentially a rich source of real-time and historical global information for science applications. The time-varying set of "precipitation" tweets can be thought of as an organic network of rain gauges, potentially providing a widespread view of precipitation occurrence. The validation of satellite precipitation estimates is challenging, because many regions lack data or access to data, especially outside of the U.S. and in remote and developing areas. Mining the Twitter stream could augment these validation programs and, potentially, help tune existing algorithms. Our ongoing work, though exploratory, has resulted in key components for processing and managing tweets, including the capabilities to filter the Twitter stream in real time, to extract location information, to filter for exact phrases, and to plot tweet distributions. The key step is to process the "precipitation" tweets to be compatible with satellite-retrieved precipitation data. These key components for processing and managing "precipitation" tweets (and additional ones to be developed) are not limited to precipitation, nor are they limited to the Twitter social medium. Indeed, to maximize the value of our work for NASA earth science programs, these components should be generalized and be part of an overall framework for processing citizen science data for science research. In this paper, we outline such a framework.

earth science satellite data↗
NASA NTRS1993

Primary display latency criteria based on flying qualities and performance data

With a pilots' increasing use of visual cue augmentation, much requiring extensive pre-processing, there is a need to establish criteria for new avionics/display design. The timeliness and synchronization of the augmented cues is vital to ensure the performance quality required for precision mission task elements (MTEs) where augmented cues are the primary source of information to the pilot. Processing delays incurred while transforming sensor-supplied flight information into visual cues are unavoidable. Relationships between maximum control system delays and associated flying qualities levels are documented in MIL-F-83300 and MIL-F-8785. While cues representing aircraft status may be just as vital to the pilot as prompt control response for operations in instrument meteorological conditions, presently, there are no specification requirements on avionics system latency. To produce data relating avionics system latency to degradations in flying qualities, the Navy conducted two simulation investigations. During the investigations, flying qualities and performance data were recorded as simulated avionics system latency was varied. Correlated results of the investigation indicates that there is a detrimental impact of latency on flying qualities. Analysis of these results and consideration of key factors influencing their application indicate that: (1) Task performance degrades and pilot workload increases as latency is increased. Inconsistency in task performance increases as latency increases. (2) Latency reduces the probability of achieving Level 1 handling qualities with avionics system latency as low as 70 ms. (3) The data suggest that the achievement of desired performance will be ensured only at display latency values below 120 ms. (4) These data also suggest that avoidance of inadequate performance will be ensured only at display latency values below 150 ms.

Funk, John D., Jr.↗
NASA NTRS1974

A predictive pilot model for STOL aircraft landing

An optimal control approach has been used to model pilot performance during STOL flare and landing. The model is used to predict pilot landing performance for three STOL configurations, each having a different level of automatic control augmentation. Model predictions are compared with flight simulator data. It is concluded that the model can be effective design tool for studying analytically the effects of display modifications, different stability augmentation systems, and proposed changes in the landing area geometry.

Kleinman, D. L.↗
NASA NTRS2017

Board Level Proton Testing Book of Knowledge for NASA Electronic Parts and Packaging Program

This book of knowledge (BoK) provides a critical review of the benefits and difficulties associated with using proton irradiation as a means of exploring the radiation hardness of commercial-off-the-shelf (COTS) systems. This work was developed for the NASA Electronic Parts and Packaging (NEPP) Board Level Testing for the COTS task. The fundamental findings of this BoK are the following. The board-level test method can reduce the worst case estimate for a board's single-event effect (SEE) sensitivity compared to the case of no test data, but only by a factor of ten. The estimated worst case rate of failure for untested boards is about 0.1 SEE/board-day. By employing the use of protons with energies near or above 200 MeV, this rate can be safely reduced to 0.01 SEE/board-day, with only those SEEs with deep charge collection mechanisms rising this high. For general SEEs, such as static random-access memory (SRAM) upsets, single-event transients (SETs), single-event gate ruptures (SEGRs), and similar cases where the relevant charge collection depth is less than 10 μm, the worst case rate for SEE is below 0.001 SEE/board-day. Note that these bounds assume that no SEEs are observed during testing. When SEEs are observed during testing, the board-level test method can establish a reliable event rate in some orbits, though all established rates will be at or above 0.001 SEE/board-day. The board-level test approach we explore has picked up support as a radiation hardness assurance technique over the last twenty years. The approach originally was used to provide a very limited verification of the suitability of low cost assemblies to be used in the very benign environment of the International Space Station (ISS), in limited reliability applications. Recently the method has been gaining popularity as a way to establish a minimum level of SEE performance of systems that require somewhat higher reliability performance than previous applications. This sort of application of the method suggests a critical analysis of the method is in order. This is also of current consideration because the primary facility used for this type of work, the Indiana University Cyclotron Facility (IUCF) (also known as the Integrated Science and Technology (ISAT) hall), has closed permanently, and the future selection of alternate test facilities is critically important. This document reviews the main theoretical work on proton testing of assemblies over the last twenty years. It augments this with review of reported data generated from the method and other data that applies to the limitations of the proton board-level test approach. When protons are incident on a system for test they can produce spallation reactions. From these reactions, secondary particles with linear energy transfers (LETs) significantly higher than the incident protons can be produced. These secondary particles, together with the protons, can simulate a subset of the space environment for particles capable of inducing single event effects (SEEs). The proton board-level test approach has been used to bound SEE rates, establishing a maximum possible SEE rate that a test article may exhibit in space. This bound is not particularly useful in many cases because the bound is quite loose. We discuss the established limit that the proton board-level test approach leaves us with. The remaining possible SEE rates may be as high as one per ten years for most devices. The situation is actually more problematic for many SEE types with deep charge collection. In cases with these SEEs, the limits set by the proton board-level test can be on the order of one per 100 days. Because of the limited nature of the bounds established by proton testing alone, it is possible that tested devices will have actual SEE sensitivity that is very low (e.g., fewer than one event in 1 × 10(exp 4) years), but the test method will only be able to establish the limits indicated above. This BoK further examines other benefits of proton board-level testing besides hardness assurance. The primary alternate use is the injection of errors. Error injection, or fault injection, is something that is often done in a simulation environment. But the proton beam has the benefit of injecting the majority of actual SEEs without risk of something being missed, and without the risk of simulation artifacts misleading the SEE investigation.

Guertin, Steven M.↗
NASA NTRS1992

Use of an approximate similarity principle for the thermal scaling of a full-scale thrust augmenting ejector

Full temperature ejector model simulations are expensive, and difficult to implement experimentally. If an approximate similarity principle could be established, properly chosen performance parameters should be similar for both hot and cold flow tests if the initial Mach number and total pressures of the flow field are held constant. Existing ejector data is used to explore the utility of one particular similarity principle; the Munk and Prim similarity principle for isentropic flows. Static performance test data for a full-scale thrust augmenting ejector are analyzed for primary flow temperatures up to 1560 R. At different primary temperatures, exit pressure contours are compared for similarity. A nondimensional flow parameter is then used to eliminate primary nozzle temperature dependence and verify similarity between the hot and cold flow experiments.

Barankiewicz, Wendy S.↗
NASA NTRS1992

Use of an approximate similarity principle for the thermal scaling of a full-scale thrust augmenting ejector

Full temperature ejector model simulations are expensive, and difficult to implement experimentally. If an approximate similarity principle could be established, properly chosen performance parameters should be similar for both hot and cold flow tests if the initial Mach number and total pressures of the flow field are held constant. Existing ejector data is used to explore the utility of one particular similarity principle; the Munk and Prim similarity principle for isentropic flows. Static performance test data for a full-scale thrust augmenting ejector are analyzed for primary flow temperatures up to 1560 R. At different primary temperatures, exit pressure contours are compared for similarity. A nondimensional flow paramenter is then used to eliminate primary nozzle temperature dependence and verify similarity between the hot and cold flow experiments.

Barankiewicz, Wendy↗