Beam target neutron production using laser ablated deuterated polyethylene on a 1 MA, 100 ns z-pinch device
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neutron source, activation, ions, beam target↗
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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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Overview of Past and Future EVA Suit Testing and Training in Chamber B at NASA JSC
The Crew and Thermal System Division’s Chamber B has received an increase in demand for next generation suit testing. Chamber B is the National Aeronautics and Space Administration (NASA) Johnson Space Center’s only human-rated thermal vacuum (TVAC) chamber. Historically it was used in Gemini, Apollo, Skylab, Shuttle, and the International Space Station (ISS) suit tests. Recently, the chamber has been returned to service with new capabilities for suit testing. In 2023, The Exploration Extravehicular Mobility Unit (xEMU) underwent a 5-day, extensive thermal vacuum test that included both a full-bodied Exploration Pressure Garment Suit (xPGS) as well as a high fidelity Short xEMU. SpaceX has also used the chamber for human-in-the-loop (HITL) qualification and acceptance testing on their flight Polaris Dawn suits for the first-ever commercial extravehicular activity (EVA). Current chamber Manlock B2 upgrades include the support of two test subjects at the same time with updated chamber interface and support systems for the next generation of suits. This paper will discuss the history of spacesuit testing in the chamber, the recent testing for commercial and NASA suits, and upgrades to accommodate new test requirements.
Polaris Dawn↗
Overview of Past and Future EVA Suit Testing and Training in Chamber B at NASA JSC
The Crew and Thermal System Division’s Chamber B has received an increase in demand for next generation suit testing. Chamber B is the National Aeronautics and Space Administration (NASA) Johnson Space Center’s only human-rated thermal vacuum (TVAC) chamber. Historically it was used in Gemini, Apollo, Skylab, Shuttle, and the International Space Station (ISS) suit tests. Recently, the chamber has been returned to service with new capabilities for suit testing. In 2023, The Exploration Extravehicular Mobility Unit (xEMU) underwent a 5-day, extensive thermal vacuum test that included both a full-bodied Exploration Pressure Garment Suit (xPGS) as well as a high fidelity Short xEMU. SpaceX has also used the chamber for human-in-the-loop (HITL) qualification and acceptance testing on their flight Polaris Dawn suits for the first-ever commercial extravehicular activity (EVA). Current chamber Manlock B2 upgrades include the support of two test subjects at the same time with updated chamber interface and support systems for the next generation of suits. This paper will discuss the history of spacesuit testing in the chamber, the recent testing for commercial and NASA suits, and upgrades to accommodate new test requirements.
Human-in-the-loop↗
The design, fabrication and delivery of a spacelab neutral buoyancy Instrument Pointing System (IPS) mockup
Underwater simulations of EVA contingency operations such as manual jettison, payload disconnect, and payload clamp actuation were used to define crew aid needs and mockup pecularities and characteristics to verify the validity of simulation using the trainer. A set of mockup instrument pointing system tests was conducted and minor modifications and refinements were made. Flight configuration struts were tested and verified to be operable by the flight crew. Tasks involved in developing the following end items are described: IPS gimbal system, payload, and payload clamp assembly; the igloos (volumetric); spacelab pallets, experiments, and hardware; experiment, and hardware; experiment 7; and EVA hand tools, support hardware (handrails and foot restraints). The test plan preparation and test support are also covered.
Vanvalkenburgh, C. N.↗
Active member vibration control for a 4 meter primary reflector support structure
The design and testing of a new low voltage piezoelectric active member with integrated load cell and displacement sensor is described. This active member is intended for micron level vibration and structural shape control of the Precision Segmented Reflector test-bed. The test-bed is an erectable 4 meter diameter backup support truss for a 2.4 meter focal length parabolic reflector. Active damping of the test-bed is then demonstrated using the newly developed active members. The control technique used is referred to as bridge feedback. With this technique the internal sensors are used in a local feedback loop to match the active member's input impedance to the structure's load impedance, which then maximizes vibrational energy dissipation. The active damping effectiveness is then evaluated from closed loop frequency responses.
Umland, J. W.↗
Viking mission support
The final operations tests prior to Mars orbit insertion for Viking 1 are discussed together with the real-time operations support for the two months preceding this event. Several special operational strategies and procedures designed to optimize the DSN support during the critical planetary phases of the mission are described. The final phases of Network implementation for Viking and the support afforded to the radio science experiments and Mars radar observations are also included.
Mudgway, D. J.↗
In-situ characterization of a background calibration line for east coast aerial measuring
Report outlines the methodology used to establish a background calibration line for AMS-East operations at Joint Base Andrews. The results provide a general isotopic characterization of the soil within the calibration line at Joint Base Andrews, and the expected terrestrial exposure rate which can be used to aerial measuring.
61 RADIATION PROTECTION AND DOSIMETRY↗
Regulatory Testing of RPP-WTP HLW Glasses to Support Delisting Compliance, VSL-04R4780-1, Rev. 0 (Sep 2004)
The primary goal of the testing described in this report was to collect data to demonstrate compliance of the immobilized high-level waste (IHLW) glasses with delisting requirements. The collected data will be used to support a petition to delist the IHLW glasses destined for the national disposal facility. The Delisting Data Quality Objectives (DQO) (Cook and Blumenkranz 2003) identified a list of (16) inorganic constituents of potential concern (COPCs) and their associated limits for delisting. These COPCs can be divided into three groups, Cases 1, 2, and 3, based on their Toxicity Characteristic Leaching Procedure (TCLP) responses versus their respective delisting limits. To briefly summarize, Case 1 COPCs are those that, when loaded at their highest expected concentration in Waste Treatment Plant (WTP) glasses, are not expected to leach at their respective delisting limits when the glasses are exposed to the TCLP. Case 2 COPCs may reach the delisting limits in TCLP leachates of WTP glasses if loaded to concentrations near their maximum expected concentrations in glass. Finally, Case 3 COPCs are components that are likely to be present in concentrations sufficient to exceed their respective delisting limits in TCLP leachates of some possible glasses. The test objective was to show that, for (i) the expected range of inorganic contents in the waste feed to the Hanford WTP high level waste (HLW) vitrification facility, (ii) the expected range of glass product compositions, and (iii) the Case 1 and Case 2 COPCs identified by the DQO, the IHLW glasses meet all the relevant requirements for delisting. For Case 3 COPCs (i.e., Cd), the testing was to demonstrate the relationship between glass composition and TCLP cadmium (Cd) release, and then employ the results to develop TCLP-composition response models. During WTP operations, TCLP-composition models can be used to predict, within the required statistical uncertainties, TCLP responses of IHLW production glasses that are within the compositional region used to develop the model.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Preliminary Analysis of the Cenozoic Section from Core Drilled in the Rock Valley Fault Zone, Nevada National Security Site
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58 GEOSCIENCES↗
Preliminary Observations from Corehole Drilling in an Active Fault Zone in Rock Valley, Nevada
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58 GEOSCIENCES↗
Water Across Synthetic Aperture Radar Data (WASARD): SAR Water Body Classification for the Open Data Cube
The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australia’s WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS. Sentinel-1, Open Data Cube, Earth Observations, Machine Learning, Water Detection 1. INTRODUCTION Water classification is an important function of Earth imaging satellites, as accurate remote classification of land and water can assist in land use analysis, flood prediction, climate change research, as well as a variety of agricultural applications [2]. The ability to identify bodies of water remotely via satellite is immensely cheaper than contracting surveys of the areas in question, meaning that an application that can accurately use satellite data towards this function can make valuable information available to nations which would not be able to afford it otherwise. Highly reliable applications for the remote detection of water currently exist for use with optical satellite data such as that provided by LANDSAT. One such application, Geoscience Australia’s Water Observations from Space (WOFS) has already been ported for use with the Open Data Cube [6]. However, water detection using optical data from Landsat is constrained by its relatively long revisit cycle of 16 days [5], and water detection using any optical data is constrained in that it lacks the ability to make accurate classifications through cloud cover [2]. The alternative solution which solves these problems is water detection using SAR data, which images the Earth using cloud-penetrating microwaves. Because of its advantages over optical data, much research has been done into water detection using SAR data. Traditionally, this has been done using the thresholding method, which involves picking a polarization band and labeling all pixels for which this band’s value is below a certain threshold as containing water. The thresholding method works since water tends to return a much lower backscatter value to the satellite than land [1]. However, this method can be flawed since estimating the proper threshold is often imprecise, complicated, and labor intensive for the end user. Thresholding also tends to use data from only one SAR polarization, when a combination of polarizations can provide insight into whether water is present. [2] In order to alleviate these problems, this paper presents an application for the Open Data Cube to detect water from SAR data using support vector machine (SVM) classification. 2. PLATFORM WASARD is an application for the Open Data Cube, a mechanism which provides a simple yet efficient means of ingesting, storing, and retrieving remote sensing data. Data can be ingested and made analysis ready according to whatever specifications the researcher chooses, and easily resampled to artificially alter a scene’s resolution. Currently WASARD supports water detection on scenes from ESA’s Sentinel-1 and JAXA’s ALOS. When testing WASARD, Sentinel-1 was most commonly used due to its relatively high spatial resolution and its rapid 6 day revisit cycle [5]. With minor alterations to the application's code, however, it could support data from other satellites. 3. METHODOLOGY Using supervised classification, WASARD compares SAR data to a dataset pre-classified by WOFS in order to train an SVM classifier. This classifier is then used to detect water in other SAR scenes outside the training set. Accuracy was measured according to the following metrics: Precision: a measure of what percentage of the points WASARD labels as water are truly water Recall: a measure of what percentage of the total water cover WASARD was able to identify. F1 Score: a harmonic average of the precision and recall scores Both precision and recall are calculated at the end of the training phase, when the trained classifier is compared to a testing dataset. Because the WOFS algorithm’s classifications are used as the truth values when training a WASARD classifier, when precision and recall are mentioned in this paper, they are always with respect to the values produced by WOFS on a similar scene of Landsat data, which themselves have a classification accuracy of 97% [6]. Visual representations of water identified by WASARD in this paper were produced using the function wasard_plot(), which is included in WASARD. 3.1 Algorithm Selection The machine learning model used by WASARD is the Linear Support Vector Machine (SVM). This model uses a supervised learning algorithm to develop a classifier, meaning it creates a vector which can be multiplied by the vector formed by the relevant data bands to determine whether a pixel in a SAR scene contains water. This classifier is trained by comparing data points from selected bands in a SAR scene to their respective labels, which in this case are “water” or “not water” as given by the WOFS algorithm. The SVM was selected over the Random Forest model, which outperformed the SVM in training speed, but had a greater classification time and lower accuracy, and the Multilayer Perceptron Artificial Neural Network, which had a slightly higher average accuracy than the SVM, but much greater training and classification times. Figure 1: Visual representation of the SVM Classifier. Each white point represents a pixel in a SAR scene. In Figure 1, the diagonal line separating pixels determined to be water from those determined not to be water represents the actual classification vector produced by the SVM. It is worth noting that once the model has been trained, classification of pixels is done in a similar manner as in the thresholding method. This is especially true if only one band was used to train the model. 3.1 Feature Selection Sentinel-1 collects data from two bands: the Vertical/Vertical polarization (VV) and the Vertical/Horizontal polarization (VH). When 100 SVM classifiers were created for each polarization individually, and for the combination of the two, the following results were achieved: Figure 2: Accuracy of classifiers trained using different polarization bands. Precision and Recall were measured with respect to the values produced by WOFS. Figure 2 demonstrates that using both the VV and VH bands trades slightly lower recall for significantly greater precision when compared with the VH band alone, and that using the VV band alone is inferior in both metrics. WASARD therefore defaults to using both the VV and VH bands, and includes the option to use solely the VH band. The VV polarization’s lower precision compared to the VH polarization is in contrast to results from previous research and may merit further analysis [4]. 3.2 Training a Classifier The steps in training a classifier with WASARD are 1. Selecting two scenes (one SAR, one optical) with the same spatial extents, and acquired close to each other in time, with a preference that the scenes are taken on the same day. 2. Using the WOFS algorithm to produce an array of the detected water in the scene of optical data, to be used as the labels during supervised learning 3. Data points from the selected bands from the SAR acquisition are bundled together into an array with the corresponding labels gathered from WOFS. A random sample with an equal number of points labeled “Water” and “Not Water” is selected to be partitioned into a training and a testing dataset 4. Using Scikit-Learn’s LinearSVC object, the training dataset is used to produce a classifier, which is then tested against the testing dataset to determine its precision and recall The result is a wasard_classifier object, which has the following attributes: 1. f1, recall, and precision: 3 metrics used to determine the classifier’s accuracy 2. Coefficient: Vector which the SVM uses to make its predictions. The classifier detects water when the dot product of the coefficient and the vector formed by the SAR bands is positive 3. Save(): allows a user to save a classifier to the disk in order to use it without retraining 4. wasard_classify(): Classifies an entire xarray of SAR data using the SVM classifier All of the above steps are performed automatically when the user creates a wasard_classifier object. 3.3 Classifying a Dataset Once the classifier has been created, it can be used to detect water in an xarray of SAR data using wasard_classify(). By taking the dot product of the classifier’s coefficients and the vector formed by the selected bands of SAR data, an array of predictions is constructed. A classifier can effectively be used on the same spatial extents as the ones where it was trained, or on any area with a similar landscape. While
Kreiser, Zachary↗
A RAM architecture for concurrent access and on-chip testing
A novel RAM architecture supporting concurrent memory access and on-chip testing (CMAT) is proposed. A large-capacity memory chip is decomposed into test neighborhoods (TNDs), each of which is tested independently. When there are data stored in a TND, the data are saved into a buffer before testing the TND, and the TND's contents are restored using buffered data after testing the TND. If an external request is not made to the TND, the request can be directed to the addressed memory cells. Otherwise, the buffered data can be loaded back into the TND, or the request is detoured to a corresponding buffer. By deriving an analytical model, the performance penalty and hardware overhead of the CMAT architecture are shown to be very small.
Liu, Jyh-Charn↗
Advanced Test Reactor Long-Term (20-Year) Operational Strategy
The Department of Energy Office of Nuclear Energy (DOE-NE) and the Naval Nuclear Propulsion Program (NNPP) have identified the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) as a critical need for continued thermal irradiation testing capability to support and advance U.S. commercial and naval nuclear power systems. The ATR is currently the only suitable test reactor operating in the world that can provide these capabilities at volume. ATR must, then, plan to operate for at least the next 20 years. As of the beginning of 2024, ATR has operated for 57 years, with many of the original reactor structures, systems, and components installed and operated for 60 years. Some ATR Complex and reactor support equipment that is currently in use was installed 75 years ago with the startup of the Materials Testing Reactor (MTR).
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Understanding (La,Sr)(Co,Fe)O$_{3-δ}$ Phase Instability within SOECs Using a Combined Experimental and Atomistic Modeling Approach
Understanding the onset of degradation in the air electrode within solid oxide electrolysis cells (SOECs), and the subsequent impact on cell performance, is a critical step in mitigating the performance losses and stability issues of SOECs. In an effort to identify early onset degradation phenomena, SOECs were characterized as fabricated and after testing potentiostatically at 1.3 V for 1000 h at 750 °C. SOEC air electrodes composed of a 1:1 composite of La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3–δ (6428-LSCF) and Gd 0.1 Ce 0.9 O 1.95 (GDC) were studied using synchrotron X-ray diffraction (XRD), scanning transmission electron microscopy coupled with energy dispersive X-ray spectroscopy (STEM-EDS), and X-ray absorption near-edge spectroscopy (XANES) to evaluate the changes in the air electrode structurally and chemically. These techniques show the migration of Sr species from the air electrode through pores in the GDC barrier layer, progressing to the electrolyte boundary, where it accumulates and reacts with (Zr 0.84 Y 0.16 )O 2–δ (YSZ) to form SrZrO 3 . Microscopy results are paired with atomistic simulations to better understand the relationship between the thermodynamic instability of 6428-LSCF and cell fabrication/testing conditions. First-principles calculations reveal that LSCF-6428 is not stable during cell manufacturing and testing conditions, which supports the experimental identification of secondary phases in both as-fabricated and tested cells. Together, these results demonstrate that the challenging environments encountered by SOECs during cell manufacturing and operation lead to instabilities of the target 6428-LSCF anode material and underscore the need for more durable, high-performing SOEC components.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Distributed fiber sensing systems for 3D combustion temperature field monitoring in coal-fired boilers using optically generated acoustic waves (Final Report)
In this project, we have developed and tested three kinds of fiber optic sensing systems for real time monitoring of temperature variations within an industrial scale boiler furnace. The fiber optic sensing systems target spatial and temporal distributions of high temperature profiles in a boiler furnace in fossil power plants. The reconstructed temperature profile will provide critical input for the control mechanisms to optimize the combustion process. This temperature profile will address the essential problem for fossil power plants in achieving higher efficiency and fewer pollutant emissions. Acoustic pyrometer systems have been used to reconstruct temperature field of power plant boilers based on measuring TOF (times-of-flight) of sound waves along some straight paths in a 2D cross-section of the boiler. In this project, optically generated acoustic signals from a fiber optic sensing system have replaced the acoustic signals generated from an electrical transducer. A 3D reconstruction algorithm replaced the previous 2D model. In this project, three kinds of fiber optic sensing systems have been developed and tested. They are fiber optic sensing system I, fiber optic sensing system II (Distributed Sensing System I) and fiber optic sensing system III (Distributed Sensing System II). For fiber optic sensing system I, the fiber optic ultrasound generator acts as a signal generator. A microphone, hydrophone or other electronic devices serve as a signal receiver. In this system, there are one generator and one receiver. Distance test, water temperature test, air temperature test, air temperature reconstruction, and GE ISBF pilot test were performed by Fiber optic sensing system I. The fiber optic sensing system I successfully detected temperature in all these tests. We got 2D temperature reconstruction results by using the fiber optic sensing system I and it matched the reference data. The fiber optic sensing system I successfully survived in GE ISBF boiler environment (480 °F). For fiber optic sensing system II (Distributed Sensing System I), it is an all optical ultrasound system. The fiber optic ultrasound generator acts as a signal generator. Fiber Bragg Grating (FBG) and Fabry-Perot (FP) sensor act as a signal receiver. In this system, there is one generator and one receiver. Aluminum plate temperature test, furnace high temperature test, and GE ISBF pilot test were performed by the fiber optic sensing system II. Fiber optic sensing system II successfully detected the temperature in all these tests. The fiber optic sensing system II successfully survived at up to 700 °C furnace environment and 320 °C GE ISBF boiler environment. For fiber optic sensing system III (Distributed Sensing System II), it is also an all optical ultrasound system. The fiber optic ultrasound generator acts as a signal generator. Multiple FP fiber sensors act as signal receivers. In this system, there is one generator and three receivers. Three GE ISBF pilot tests were performed by fiber optic sensing system III. The fiber optic sensing system III survived in the cold flow tests in GE’s ISBF pilot test facility. However, we didn’t get high temperature data by using this system since the nanosecond laser issues. During the period of the project, test trials, data simulation and algorithm optimization was performed successfully. For real time temperature field construction, the sampling rate must be fast enough to capture the field variations. The technology of Code-division multiple access (CDMA) is well studied which could allow parallel multiplexing, even if signals overlap in time or frequencies. Moreover, it has been known that extending the length of signal significantly improves SNR. For acoustic signals, these multiplexing techniques have also been widely used, mainly for sonar and acoustic communications. The CDMA modulation technique has been proposed and studied to guarantee high network throughput, low channel access delay and low energy consumption. We have studied the temperature field reconstruction using Gaussian Radial Basis Functions (GRBF)-based approximation approach. Reconstruction of 3D temperature field using Neural Networks with measured TOF and known propagation paths is feasible. 2D and 3D temperature field reconstruction simulation results are achieved. The milestone status is shown in Table 1. We finished milestone 1-8 and milestone 10. For milestone 9, we did three pilot tests by using the fiber optic sensing system III (Distributed Sensing System II) at GE Power. However, due to the failure of the ns laser, we did not get the temperature results. We conducted some additional tasks that were not originally proposed: 1) We fabricated a fiber optic sensing system I and did a pilot test based on this system. 2) In the proposal, we proposed two pilot tests at GE Power. In reality, we finished at least seven pilot tests at GE Power. GE Power has made a lot of efforts for supporting the pilot tests. 3) We got a simulation results based on CDMA. In summary, most of the tasks have been accomplished. The outcome of this project removed a few barriers that hinder the achievement of the final product of the distributed sensing systems. With the successful accomplishment of this project, a prototype of the fiber optic sensing system can be fabricated to attract more interests from companies and other funding agencies.
47 OTHER INSTRUMENTATION↗
Shuttle active thermal control system development testing. Volume 1: Overall summary
A summary is given of a series of thermal vacuum tests designed to support the development of the orbiter active thermal control system (ATCS) and included testing of a wide heat load range modular radiator system (MRS) configured to the March 1973 orbiter baseline system, a candidate weight reducing radiator/water cooling system, and a smaller radiator system with a high performance radiator coating. The tests verified the performance of the baseline system and obtained detailed design information for application of a wide heat load range modular radiator system to the orbiter. The two candidate ATCS weight reducing designs have undergone extensive concept verification testing and their system operating characteristics have been determined in sufficient detail for application to the orbiter. Design information has been obtained for an integrated radiator/water cooling system that provides for vehicle heat rejection as well as water management of the excess fuel cell water. Processing techniques have been developed and verified for the application of a high performance thermal control coating to large radiator areas subjected to a temperature range of -280 F to +160 F.
Howell, H. R.↗
Performance and modeling of the GSFC pointing acquisition and tracking system for laser communications
In this paper the NASA Goddard Space Flight Center (GSFC) laser communication pointing, acquisition, and tracking laboratory demonstration program is discussed. This program entails the construction of a flexible brass-board simulation tool, and a supporting computer simulation effort. The result of this program will be a viable test bed to support the design and specification of space-based laser communication terminals and, additionally, to address critical performance issues such as tracking robustness in the presence of background (e.g., stellar or earth) noise and/or internally generated thermal noise.
Fox, N. D.↗
SRB Environment Evaluation and Analysis. Volume 2: RSRB Joint Filling Test/Analysis Improvements
Following the Challenger accident a very comprehensive solid rocket booster (SRB) redesign program was initiated. One objective of the program was to develop expertise at NASA/MSFC in the techniques for analyzing the flow of hot gases in the SRB joints. Several test programs were undertaken to provide a data base of joint performance with manufactured defects in the joints to allow hot gases to fill the joints. This data base was used also to develop the analytical techniques. Some of the test programs were Joint Environment Simulator (JES), Nozzle Joint Environment Simulator (NJES), Transient Pressure Test Article (TPTA), and Seventy-Pound Charge (SPC). In 1988 the TPTA test hardware was moved from the Utah site to MSFC and several RSRM tests were scheduled, to be followed by tests for the ASRM program. REMTECH Inc. supported these activities with pretest estimates of the flow conditions in the test joints, and post-test analysis and evaluation of the measurements. During this support REMTECH identified deficiencies in the gas-measurement instrumentation that existed in the TPTA hardware, made recommendations for its replacement, and identified improvements to the analytical tools used in the test support. Only one test was completed under the TPTA RSRM test program, and those scheduled for the ASRM were rescheduled to a time after the expiration of this contract. The attention of this effort was directed toward improvements in the analytical techniques in preparation for when the ASRM program begins.
Knox, E. C.↗