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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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The prelimary design of the propulsion subsystem for the CRAF and Cassini missions is described. It is noted that the propulsion module subsystem must provide impulse for a number of functions required by these missions. These functions include the acquisition of celestial references and trajectory correction maneuvers. A schedule for the CRAF/Cassini Project is provided.
Vibration Pattern Imager (VPI) system and software designed to control and acquire data from laser vibrometer sensors. Based on personal computer, includes digital signal-processing (DSP) board and analog input/output (I/O) circuit board to control sensors and process data. Originally developed for use with Ometron VPI Sensor, adapted to any commercially available sensor that provides analog output signal and requires analog inputs for control of mirror position. VPI system includes graphical user interface providing for interactive control through keyboard and mouse-selected menu options. Main menu controls all functions for setup, acquisition of data, display, file operations, and leaving program. Two types of data acquired with system: single point or "full field". Written in C language and Texas Instrument' TMS320C30 assembly language.
Flying in poor visibility conditions, such as rain, snow, fog or haze, is inherently dangerous. However these conditions can occur at nearly any location, so inevitably pilots must successfully navigate through them. At NASA Langley Research Center (LaRC), under support of the Aviation Safety and Security Program Office and the Systems Engineering Directorate, we are developing an Enhanced Vision System (EVS) that combines image enhancement and synthetic vision elements to assist pilots flying through adverse weather conditions. This system uses a combination of forward-looking infrared and visible sensors for data acquisition. A core function of the system is to enhance and fuse the sensor data in order to increase the information content and quality of the captured imagery. These operations must be performed in real-time for the pilot to use while flying. For image enhancement, we are using the LaRC patented Retinex algorithm since it performs exceptionally well for improving low-contrast range imagery typically seen during poor visibility conditions. In general, real-time operation of the Retinex requires specialized hardware. To date, we have successfully implemented a single-sensor real-time version of the Retinex on several different Digital Signal Processor (DSP) platforms. In this paper we give an overview of the EVS and its performance requirements for real-time enhancement and fusion and we discuss our current real-time Retinex implementations on DSPs.
The Short-Baseline Neutrino (SBN) program at Fermilab is a cutting-edge project in experimental neutrino physics. One of its main goals is to systematically investigate the possible existence of eV-scale sterile neutrinos. This phenomenon has been hypothesized to explain some anomalies found in short-range experiments and, if confirmed, would imply a substantial extension of the Standard Model. SBN also offers an important opportunity to deepen the understanding of neutrino-nucleus interactions in the GeV energy range, through the use of Liquid Argon Time Projection Chambers (LArTPC) detectors, a fundamental technology also for the future DUNE experiment. The SBN experimental infrastructure consists of three detectors aligned along the Booster Neutrino Beamline at Fermilab. Among them, the detector located closest to the neutrino source, SBND (Short-Baseline Near Detector), positioned approximately 110 meters from the target, plays a key role in directly characterizing the initial neutrino flux. This allows for a direct comparison with the measurements from the far detector, ICARUS, located about 600 meters from the source, in order to search for potential signs of anomalous neutrino oscillations. My master's thesis focuses on the commissioning and characterization activities of the SBND detector, with particular reference to the Cosmic Ray Tagger (CRT). The CRT is a subsystem for identifying and rejecting events produced by cosmic rays, which constitute the main source of background for surface experiments like SBND. The activity began with the commissioning of the final components of the detector, as well as their validation to verify their correct functioning and signal acquisition. A central part of my work involved studying the veto efficiency of the CRT system, analyzing the rate of cosmic ray-induced events to quantify any loss of neutrino-induced events caused by cosmic background. This allowed for a more precise evaluation of the systematic impact of the CRT on the useful physics sample. A further phase of my analysis involved an in-depth study of the temporal correlation between the CRT signals and those acquired by the LArTPC's internal photodetector system, consisting of photomultiplier tubes and X-ARAPUCA devices. The objective is to explore the possibility of using combined temporal information as an additional criterion for discriminating between cosmic signals and signals genuinely due to neutrino interaction. Preliminary results indicate the presence of characteristic temporal signatures that could be exploited to improve event selection and increase the purity of the neutrino-induced sample. These methodologies will certainly contribute to the optimization of SBND analysis strategies and, more generally, to a better understanding of background mechanisms in next-generation LArTPC experiments.
This manual describes the use of the primary ultrasonics task (PUT) and the transducer characterization system (XC) for the collection, processing, and recording of data received from a pulse-echo ultrasonic system. Both PUT and XC include five primary functions common to many real-time data acquisition systems. Some of these functions are implemented using the same code in both systems. The solicitation and acceptance of operator control input is emphasized. Those operations not under user control are explained.
Spatial acquisition and precision beam pointing functions are critical to spaceborne laser communication systems. In the present invention, a single high bandwidth CCD detector is used to perform both spatial acquisition and tracking functions. Compared to previous lasercom hardware design, the array tracking concept offers reduced system complexity by reducing the number of optical elements in the design. Specifically, the design requires only one detector and one beam steering mechanism. It also provides the means to optically close the point-ahead control loop. The technology required for high bandwidth array tracking was examined and shown to be consistent with current state of the art. The single detector design can lead to a significantly reduced system complexity and a lower system cost.
Spatial acquisition and precision beam pointing functions are critical to spaceborne laser communication systems. In the present invention a single high bandwidth CCD detector is used to perform both spatial acquisition and tracking functions. Compared to previous lasercom hardware design, the array tracking concept offers reduced system complexity by reducing the number of optical elements in the design. Specifically, the design requires only one detector and one beam steering mechanism. It also provides means to optically close the point-ahead control loop. The technology required for high bandwidth array tracking was examined and shown to be consistent with current state of the art. The single detector design can lead to a significantly reduced system complexity and a lower system cost.
The Wide-Field Infrared Survey Telescope (WFIRST) will have the largest near-IR focal plane ever flown by NASA, a total of 18 4K x 4K devices. The project has adopted a system-level approach to detector control and data acquisition where 1) control and processing intelligence is pushed into components closer to the detector to maximize signal integrity, 2) functions are performed at the highest allowable temperatures, and 3) the electronics are designed to ensure that the intrinsic detector noise is the limiting factor for system performance. For WFIRST, the detector arrays operate at 90 to 100 K, the detector control and data acquisition functions are performed by a custom ASIC at 150 to 180 K, and the main data processing electronics are at the ambient temperature of the spacecraft, notionally approx.300 K. The new ASIC is the main interface between the cryogenic detectors and the warm instrument electronics. Its single-chip design provides basic clocking for most types of hybrid detectors with CMOS ROICs. It includes a flexible but simple-to-program sequencer, with the option of microprocessor control for more elaborate readout schemes that may be data-dependent. All analog biases, digital clocks, and analog-to-digital conversion functions are incorporated and are connected to the nearby detectors with a short cable that can provide thermal isolation. The interface to the warm electronics is simple and robust through multiple LVDS channels. It also includes features that support parallel operation of multiple ASICs to control detectors that may have more capability or requirements than can be supported by a single chip.
Spatial acquisition and precision beam pointing functions are critical to spaceborne laser communication systems. A recent system study indicated that a single high bandwidth CCD detector can be used to perform both spatial acquisition and tracking functions. Compared to previous lasercom hardware design, the arrray tracking cocept offers reduced system complexity by reducing the number of optical elements in the design.
The time-temperature relationship of magnetization is a subject of much interest and debate by paleomagnetists, rock magnetists, and magnetic anomaly modellers. We have investigated this relationship by studying the viscous remanent magnetization (VRM) of coarse-grained multidomain (MD) magnetite. Our experiments covered the temperature range from 22 to 400 C, times from minutes to months, and included both Australian granulite samples and multidomain magnetite samples synthesized by the glass ceramic method. VRM acquisition was found to generally increase with temperature but not always at the rate predicted from classical thermal fluctuation theories. Thermal cycling between room temperature (at which the measurements were made) and the VRM acquisition temperature sharply decreased the temperature dependence of the VRM. Room temperature VRM acquisition accelerates with time when plotted on a semilog plot, whereas at elevated temperature the curves are quasilinear against log(time) for both the natural and synthetic samples. This change in behavior may suggest a variation in the VRM acquisition mechanism as a function of temperature for MD magnetite. The granulites have a nearly linear increase in VRM acquisition rate with temperature whereas the glass ceramics display little change in the acquisition rate between 22 and 200 C, but increase by nearly a factor of 3 by 400 C. The increase in VRM of the glass ceramics between 200 and 400 C is in general qualitative agreement with thermal fluctuation theory. There was no systematic change in the rate of VRM acquisition with grain size for the multidomain magnetites used in this study. Elevated temperature (e.g., 400 C) VRM acquisition by the deep crustal granulites, if extrapolated over the Brunhes chron, would produce a magnetization of several A/m which, if true, is of the order required by models for the source of long-wavelength magnetic anomalies.
Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.
The optimum acquisition sweep strategy was determined for a PN code despreader when the a priori probability density function was not uniform. A psuedo noise spread spectrum system was considered which could be utilized in the DSN to combat radio frequency interference. In a sample case, when the a priori probability density function was Gaussian, the acquisition time was reduced by about 41% compared to a uniform sweep approach.
Two data systems were developed for use in airborne research. Both have distributed intelligence and are programmed for interactive support among computers and with human operators. The C-141 system (ADAMS) performs flight planning and telescope control functions in addition to its primary role of data acquisition; the CV-990 system (ADDAS) performs data management functions in support of many research experiments operating concurrently. Each system is arranged for maximum reliability in the first priority function, precision data acquisition.
The organic form of nitrogen (N) is a critical intermediate in mutualistic and competitive root-microbial interactions, mediated by extracellular enzymes. Visualization of the hotspots of organic N and proteolytic activity might be valuable for revealing root functional specificity in N acquisition and transformation at the level of individual roots and compartments. For the first time, we used time-lapse amino-mapping and zymography to co-localize and map the spatial distribution of amino-N and leucine aminopeptidase (LAP) activity in the soil and different root parts of maize (Zea mays L.). Amino-N distribution was mainly associated with seminal roots and root tips, where it overlapped with LAP activity hotspots. In the lateral roots and bulk soil, however, LAP activity was decoupled from amino-N. Distinct functional traits revealed themselves as the highest amino-N content and LAP activity in seminal root tips and as the largest relative extent of the rhizosphere in lateral root tips. Co-localized amino-N and LAP activities highlighted different nutrient acquisition strategies mediated by root-microbe interactions, depending on the root compartment. Seminal roots and their tips appeared to adopt mutualistic strategies, potentially attracting root-associated microorganisms through releasing oligo- and polypeptides. In contrast, lateral roots, with amino-N detected only at their tips, demonstrated stronger N competition, relying on the enzyme activity of the rhizosphere microbial community for N acquisition. These insights emphasized the role of root functional specialization in shaping plant-microbe interactions, offering pathways to enhance nutrient use efficiency.
Tree species that are successful in tropical lowlands have different acquisition strategies to overcome soil phosphorus (P) limitations. Some of these strategies belowground include adjustments in fine-root traits, such as morphology, architecture, association with arbuscular mycorrhizal fungi, and phosphatase activity. Trade-offs among P-acquisition strategies are expected because of their respective carbon cost. However, empirical evidence remains scarce which hinders our understanding of soil P-acquisition processes in tropical forests. Here, we measured seven fine-root functional traits related to P acquisition of five common tree species in three sites of the Luquillo Experimental Forest in Puerto Rico. We then described species-specific P-acquisition strategies and explored the changes in fine-root trait expression from 6 months before to 6 months after two consecutive hurricanes, Irma and María, passed over the island. We found that variations in root trait expression were driven mainly by the large interspecific differences across the three selected sites. In addition, we revealed a trade-off between highly colonized fine roots with high phosphatase activity and fine roots that have a high degree of branching. Furthermore, the former strategy was adopted by pioneer species ( Spathodea campanulata and Cecropia schreberiana ), whereas the latter was adopted by non-pioneer species (mostly Dacryodes excelsa and Prestoea montana ). Additionally, we found that root trait expression did not change comparing 6 months before and after the hurricanes, with the exception of root phosphatase activity. Altogether, our results suggest a combination of structural and physiological root traits for soil P acquisition in P-poor tropical soils by common tropical tree species, and show stability on most of the root trait expression after hurricane disturbances.
Abstract The lack of high-quality datasets in materials science hinders artificial intelligence (AI)-driven alloy design. To address this challenge, wire arc additive manufacturing (WAAM) was employed to fabricate graded alloys, generating extensive data for machine learning (ML)-assisted property prediction. ML models were developed using high-throughput experiments, computational models, and genetic algorithm to optimize feature selection, successfully predicting hardness and porosity. The ML model demonstrated its efficacy by designing a gradient alloy with enhanced properties. However, scaling up revealed uncertainties in tensile property and porosity due to differences in size and thermal conditions between the designed alloy build and the gradient print used to construct the ML model. This underscores the need for uncertainty quantification and process optimization in WAAM-driven alloy design. Our work advances AI-integrated additive manufacturing, offering a rapid approach to exploring process–structure–property relationships and accelerating materials development.
The performance of a star tracker equipped with a focal plane detector was evaluated. The CID board is an array of 256 x 256 pixels which are 20 x 20 micrometers in dimension. The tracker used for test was a breadboard tracker system developed by BASD. Unique acquisition and tracking algorithms are employed to enhance performance. A pattern recognition process is used to test for proper image spread function and to avoid false acquisition on noise. A very linear, high gain, interpixel transfer function is derived for interpolating star position. The lens used in the tracker has an EFL of 100 mm. The tracker has an FOV of 2.93 degrees resulting in a pixel angular subtense of 41.253 arc sec in each axis. The test procedure used for the program presented a star to the tracker in a circular pattern of positions; the pattern was formed by projecting a simulated star through a rotatable deviation wedge. Further tests determined readout noise, Noise Equivalent Displacement during track, and spatial noise during acquisition by taking related data and reducing it.
TListSpectrum is a C++ class developed inside the CERN high-energy physics analysis C++ framework ROOT. This class structure was developed to assist in the processing, visualization, and analysis of list-mode or time-stamped radiation spectroscopy data. The class structure currently contains parsing and functionality to synthesize list-mode data from CAEN and Mirion Lynx radiation spectroscopy digital acquisition systems along with feature functionality to post-process data sets and build coincident data sets from the instrument.