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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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At least 631 records · Page 35

Angular navigation capabilities of short baseline phase delay interferometry

Short baseline phase delay interferometry can provide angular navigation capabilities which are competitive with current accuracies for group delay Very Long Baseline Interferometry (VLBI) on intercontinental baselines. In addition, a number of operational considerations make short baseline interferometry attractive, including enhanced mutual visibility, ability to observe at higher elevation angles, and the possibility of processing data in realtime. To make use of the higher precision phase delay datatype, however, the cycle ambiguity associated with the observed interferometric phase must be resolved. Results are presented for two experiments, on baselines of 5.9 and 253 km, for which RF phase connection appears to have been reliably achieved for several groups of angularly close radio sources. The dependence of the resulting phase delay residuals on elevation suggests that temporal fluctuations in the line-of-sight troposphere delay are a dominant error source. The observed RMS phase delay residuals of 10 psec or less suggest that baselines of order 100 km could provide high-efficiency angular navigation with accuracies well below 50 nrad. Plans for further work at the Goldstone Deep Space Communication Complex are reviewed.

Edwards, C. D.↗

Ground-to-orbit laser propulsion: Advanced applications

Laser propulsion uses a large fixed laser to supply energy to heat an inert propellant in a rocket thruster. Such a system has two potential advantages: extreme simplicity of the thruster, and potentially high performance, particularly high exhaust velocity. By taking advantage of the simplicity of the thruster, it should be possible to launch small (10 to 1000 kg) payloads to orbit using roughly 1 MW of average laser power per kg of payload. The incremental cost of such launches would be of an order of $200/kg for the smallest systems, decreasing to essentially the cost of electricity to run the laser (a few times $10/kg) for larger systems. Although the individual payload size would be smaller, a laser launch system would be inherently high-volume, with the capacity to launch tens of thousands of payloads per year. Also, with high exhaust velocity, a laser launch system could launch payloads to high velocities - geosynchronous transfer, Earth escape, or beyond - at a relatively small premium over launches to LEO. The status of pulsed laser propulsion is briefly reviewed including proposals for advanced vehicles. Several applications appropriate to the early part of the next century and perhaps valuable well into the next millennium are discussed qualitatively: space habitat supply, deep space mission supply, nuclear waste disposal, and manned vehicle launching.

Kare, Jordin T.↗

How to get unlimited observing time on a 4 metre telescope

As the astronomical community moves ever more towards fewer and larger telescopes it is not just desirable but essential to make the maximum possible use of these new facilities. The Hitchhiker parallel CCD camera has been specially designed to increase the scientific output of a large telescope by imaging the off-axis field of view. The large data set collected by this instrument consists of deep CCD multicolor data of the distant Universe and is extremely well suited to the study of faint galaxies and their evolution, as well as other areas. The instrument's design and some of its projects are briefly discussed below and demonstrates the scientific value such instruments could have if incorporated on all new telescopes.

Driver, S. P.↗

Wind dynamics in SMC X-1. 1: Hydrodynamic simulation

We present a three-dimensional hydrodynamic simulation of the disrupted stellar wind in the high-mass X-ray binary system SMC X-1. The three dominant processes that determine the geometry of the wind in high X-ray luminosity systems such as SMC X-1 are the X-ray suppression of the stellar wind from the X-ray irradiated face of the primary star, the focusing of the radiatively driven wind in the X-ray shadow by the effects of stellar rotation, and the rapid X-ray heating of gas in the vicinity of the X-ray source, including the X-ray illuminated surface of the primary star. The resulting distribution of circumstellar gas provides a successful explanation for the asymmetric, extended eclipse transitions and the intensity of the deep eclipse X-ray emission in SMC X-1, as well as a possible explanation for the X-ray dips seen near superior conjunction of the X-ray source in Cyg X-1.

Blondin, John M.↗

Ultra High Voltage Propellant Isolators and Insulators for JIMO Ion Thrusters

Within NASA's Project Prometheus, high specific impulse ion thrusters for electric propulsion of spacecraft for the proposed Jupiter Icy Moon Orbiter (JIMO) mission to three of Jupiter's moons: Callisto, Ganymede and Europa will require high voltage operation to meet mission propulsion. The anticipated approx.6,500 volt net ion energy will require electrical insulation and propellant isolation which must exceed that used successfully by the NASA Solar Electric Propulsion Technology Readiness (NSTAR) Deep Space 1 mission thruster by a factor of approx.6. Xenon propellant isolator prototypes that operate at near one atmosphere and prototypes that operate at low pressures (<100 Torr) have been designed and are being tested for suitability to the JIMO mission requirements. Propellant isolators must be durable to Paschen breakdown, sputter contamination, high temperature, and high voltage while operating for factors longer duration than for the Deep Space 1 Mission. Insulators used to mount the thrusters as well as those needed to support the ion optics have also been designed and are under evaluation. Isolator and insulator concepts, design issues, design guidelines, fabrication considerations and performance issues are presented. The objective of the investigation was to identify candidate isolators and insulators that are sufficiently robust to perform durably and reliably during the proposed JIMO mission.

Banks, Bruce A.↗

Uplink Array Calibration via Far-Field Power Maximization

Uplink antenna arrays have the potential to greatly increase the Deep Space Network s high-data-rate uplink capabilities as well as useful range, and to provide additional uplink signal power during critical spacecraft emergencies. While techniques for calibrating an array of receive antennas have been addressed previously, proven concepts for uplink array calibration have yet to be demonstrated. This article describes a method of utilizing the Moon as a natural far-field reflector for calibrating a phased array of uplink antennas. Using this calibration technique, the radio frequency carriers transmitted by each antenna of the array are optimally phased to ensure that the uplink power received by the spacecraft is maximized.

Vilnrotter, V.↗

Response to MRO's end-to-end data accountability challenges

(MRO) on August 12, 2005. It carries six science instruments and three engineering payloads. Because MRO will produce an unprecedented number of science products, it will transmit a much higher data volume via high data rate than any other deep space mission to date. Keeping track of MRO products as well as relay products would be a daunting, expensive task without a well-planned data-product tracking strategy. To respond to this challenge, the MRO project developed the End-to- End Data Accountability System by utilizing existing information available from both ground and flight elements. Therefore, a capability to perform first-order problem diagnosis is essential in order for MRO to answer the questions, where is my data? and when will my data be available? This paper details the approaches taken, design and implementation of the tools, procedures and teams that track data products from the time they are predicted until they arrive in the hands of the end users.

data delivery status tracking↗

BioSensor Users' Guide

The BioSensor is a fully autonomous 3-color LED-based spectrophotometer paired with a fluidics system that supports microbes in liquid culture. Originally developed for the BioSentinel CubeSat mission to study the response of a wild type and mutant strain of yeast to the deep space environment, the BioSensor consists of a series of 16-well, independently plumbed fluidics cards. The cards utilize filters to allow the fluidic system to administer reagents while constraining the microbes in their wells. Heaters on each card incubate to the appropriate growth temperature once the experiment begins. During the active experiment, the LED/detector system measures the transmission of light through each well at three specific wavelengths, similar to a standard benchtop spectrophotometer. The transmission/absorbance kinetics curves for each well are telemetered back to Earth, along with temperature data, for analysis on the ground. The BioSensor is being upgraded from its original CubeSat free-flyer interface to be a secondary payload on lunar landers, Gateway, and other BLEO applications, while maintaining the same functionality and science utility for future experiments.

Matthew Lera↗

BioSensor Users' Guide

The BioSensor is a fully autonomous 3-color LED-based spectrophotometer paired with a fluidics system that supports microbes in liquid culture. Originally developed for the BioSentinel CubeSat mission to study the response of a wild type and mutant strain of yeast to the deep space environment, the BioSensor consists of a series of 16-well, independently plumbed fluidics cards. The cards utilize filters to allow the fluidic system to administer reagents while constraining the microbes in their wells. Heaters on each card incubate to the appropriate growth temperature once the experiment begins. During the active experiment, the LED/detector system measures the transmission of light through each well at three specific wavelengths, similar to a standard benchtop spectrophotometer. The transmission/ absorbance kinetics curves for each well are telemetered back to Earth, along with temperature data, for analysis on the ground. The BioSensor is being upgraded from its original CubeSat free-flyer interface to be a secondary payload on lunar landers, Gateway, and other BLEO applications, while maintaining the same functionality and science utility for future experiments.

Matthew Lera↗

Future Lunar Geophysical Mission Opportunities Including the Lunar Geophysical Network and CLPS

In the next few years, several opportunities are underway to take new geophysical observations of the Moon including geodetic and seismic. NASA’s novel Commercial Lunar Payload Services (CLPS) program seeks to acquire delivery services from 14 US companies. Nine funded task orders have been selected with payloads from multiple disciplines. Here we review the upcoming geophysical CLPS payloads and their measurement objectives then we provide a review of the Lunar Geophysical Network mission in development for New Frontiers 5. The Lunar Geophysical Network (LGN) mission is proposed to land on the Moon in the early 2030’s and deploy packages at four locations to enable continuous geophysical measurements for a minimum of 6 and a goal of 10 years. Returning to the lunar surface with a long-lived geophysical network is a key next step to advance lunar and planetary science. LGN will greatly expand our primarily Apollo-based knowledge of the deep lunar interior by identifying and characterizing mantle melt layers, as well as core size and state. To meet the mission objectives, the instrument suite provides complementary seismic, geodetic, heat flow, and electromagnetic (EM) observations. We discuss the network landing site requirements and provide example sites that meet these requirements. Landing sites include the P-5 region within the Procellarum KREEP Terrane (PKT; (lat:15˚; lon:-35˚), Schickard basin (lat:-44.3˚; lon:-55.1˚), Crisium basin (lat:18.5˚; lon:61.8˚), and the farside Korolev basin (lat:-2.4˚; lon:-159.3˚) (Figure 1). Network optimization considers the best locations to observe seismic core phases, e.g., ScS and PKP. Ray path density and proximity to young fault scarps are also analyzed to provide increased opportunities for seismic observations. Geodetic constraints from laser ranging require the LGN to have at least three nearside stations at maximum limb distances. Heat flow and EM measurements should be obtained away from terrane boundaries and from magnetic anomalies at locations representative of global trends. In our recent paper, an in-depth case study is provided for Mare Crisium. We also discuss the consequences for scientific return of less-than-optimal locations or number of stations.

Moons↗

Future Lunar Geophysical Mission Opportunities Including the Lunar Geophysical Network, Artemis and CLPS

In the next few years, several opportunities are underway to take new geophysical observations of the Moon including geodetic and seismic. NASA’s novel Commercial Lunar Payload Services (CLPS) program seeks to acquire delivery services from 14 US companies. Nine funded task orders have been selected with payloads from multiple disciplines. Here we review the upcoming geophysical CLPS payloads and their measurement objectives then we provide a review of the Lunar Geophysical Network mission in development for New Frontiers 5. The Lunar Geophysical Network (LGN) mission is proposed to land on the Moon in the early 2030’s and deploy packages at four locations to enable continuous geophysical measurements for a minimum of 6 and a goal of 10 years. Returning to the lunar surface with a long-lived geophysical network is a key next step to advance lunar and planetary science. LGN will greatly expand our primarily Apollo-based knowledge of the deep lunar interior by identifying and characterizing mantle melt layers, as well as core size and state. To meet the mission objectives, the instrument suite provides complementary seismic, geodetic, heat flow, and electromagnetic (EM) observations. We discuss the network landing site requirements and provide example sites that meet these requirements. Landing sites include the P-5 region within the Procellarum KREEP Terrane (PKT; (lat:15˚; lon:-35˚), Schickard basin (lat:-44.3˚; lon:-55.1˚), Crisium basin (lat:18.5˚; lon:61.8˚), and the farside Korolev basin (lat:-2.4˚; lon:-159.3˚) (Figure 1). Network optimization considers the best locations to observe seismic core phases, e.g., ScS and PKP. Ray path density and proximity to young fault scarps are also analyzed to provide increased opportunities for seismic observations. Geodetic constraints from laser ranging require the LGN to have at least three nearside stations at maximum limb distances. Heat flow and EM measurements should be obtained away from terrane boundaries and from magnetic anomalies at locations representative of global trends. In our recent paper, an in-depth case study is provided for Mare Crisium. We also discuss the consequences for scientific return of less-than-optimal locations or number of stations.

Moons↗

Standards for AI/ML and Emerging Technologies

Standards development activities require a keen and deep understanding of the problem being solved by the standard as well as the technologies being deployed in any reference implementation of the solution. It is important to understand the mechanisms and limits of the fundamental, underlying science of implementation and verification technologies used to realize and assure systems. We need to understand the limits of what current process and metrics can provide with respect to new technologies. US leadership is important in this endeavor, and it is vital that we have a measured approach that yields sound results. We wish to start with simple, well-defined, non-safety critical applications and then progress to functions which have (1) clearly defined requirements, (2) means of checking the answer/output, and (3) means of intervention and mitigation of incorrect answers/outputs.

Standards Development↗

Assurance Issues in Developing AI/ML Components (and their Standards) for Civil Aviation

Standards development activities require a keen and deep understanding of the problem being solved by the standard as well as the technologies being deployed in any reference implementation of the solution. It is important to understand the mechanisms and limits of the fundamental, underlying science of implementation and verification technologies used to realize and assure systems. We need to understand the limits of what current process and metrics can provide with respect to new technologies. US leadership is important in this endeavor, and it is vital that we have a measured approach that yields sound results. We wish to start with simple, well-defined, non-safety critical applications and then progress to functions which have (1) clearly defined requirements, (2) means of checking the answer/output, and (3) means of intervention and mitigation of incorrect answers/outputs.

Aviation Safety↗

Signal discrimination for neutron-antineutron oscillation sensitivity study at DUNE

The Deep Underground Neutrino Experiment (DUNE) aims to measure neutrino oscillations as well as search for beyond the standard model physics such as baryon number violating (BNV) processes. DUNE will use a 70 kt Liquid Argon Time Projection Chamber (LArTPC) located more than 1 km underground. A promising BNV process is neutron-antineutron oscillation $\left(n\rightarrow\bar{n}\right)$ which, if discovered, would offer unique insight into the baryon asymmetry of the universe. We are developing a classification algorithm that separates $n\rightarrow\bar{n}$ events from major background atmospheric neutrino interactions using DUNE far detector simulations. We will perform the classification of signals and backgrounds by analyzing key features such as the multiplicity, isotropy, and kinematics of the reconstructed events. In the future, this algorithm can be used to obtain the sensitivity of the DUNE detectors to the neutron-antineutron oscillation lifetime.

Wheeler, Justin↗

Techno-Economic Analysis of Geologically Connected Seawater Air Conditioning (GeoSWAC) Concept for District Cooling at the University of Puerto Rico at Rio Piedras: Preprint

At the University of Puerto Rico at Rio Piedras, a central chilled water plant supplies cooling to several campus buildings, contributing significantly to electricity demand during daytime peak hours, particularly in the summer months. These operational challenges are exacerbated by Puerto Rico's tropical rainforest climate and a power grid vulnerable to frequent disruptions caused by hurricanes and tropical storms. This study presents a techno-economic analysis of the existing chilled water plant serving four representative campus buildings and introduces a conceptual alternative: the Geologically connected Seawater Air Conditioning (GeoSWAC) system. GeoSWAC leverages stable low temperatures of deep ocean water (~1 km depth), hydraulically connected to an inland well, to deliver cooling without the use of vapor-compression refrigeration. Using modeled annual cooling loads and chiller performance data, capital costs, energy consumption, and levelized cost of cooling (LCOC) were evaluated for both systems. While GeoSWAC showed higher capital costs than the chiller-based scenario, operational costs were significantly lower at $26k-$53k annually, resulting in a lower LCOC between $2.3/MWh and $8.2/MWh compared to $30.2/MWh-$33.6/MWh for the chiller scenario. These results suggest that the GeoSWAC system offers a promising, low-energy, and climate-resilient alternative for large-scale cooling in tropical coastal environments, with significant potential to reduce peak electricity demand and improve long-term system reliability.

15 GEOTHERMAL ENERGY↗

Biochemical Detection and Identification False Alarm Rate Dependence on Wavelength Using Laser Induced Fluorescence

Most organic and many inorganic materials absorb strongly in specific wavelength ranges in the deep UV between about 220nm and 300nm. Excitation within these absorption bands results in native fluorescence emission. Each compound or composite material, such as a bacterial spore, has a unique excitation-emission fingerprint that can be used to provide information about the material. The sensitivity and specificity with which these materials can be detected and identified depends on the excitation wavelength and the number and location of observation wavelengths.We will present data on our deep ultraviolet Targeted Ultraviolet Chemical Sensors that demonstrate the sensitivity and specificity of the sensors. In particular, we will demonstrate the ability to quantitatively differentiate a wide range of biochemical agent targets against a wide range of background materials. We will describe the relationship between spectral resolution and specificity in target identification, as well as simple, fast, algorithms to identify materials.Hand-held, battery operated instruments using a deep UV laser and multi-band detection have been developed and deployed on missions to the Antarctic, the Arctic, and the deep ocean with the capability of detecting a single bacterial spore and to differentiate a wide range of organic and biological compounds.

native flourescence↗

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗

Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation

We evaluate the performance of the Karhunen-Loève Deep Neural Network (KL-DNN) framework for surrogate modeling and approximate Bayesian parameter estimation in partial differential equation models. In the surrogate model, the Karhunen-Loève (KL) expansions are used for the dimensionality reduction of the number of unknown parameters and variables, and a deep neural network is employed to relate the reduced space of parameters to that of the state variables. The KL-DNN surrogate model is used to formulate a maximum-a-posteriori-like least-squares problem, which is randomized to draw samples of the posterior distribution of the parameters. We test the proposed framework for a hypothetical unconfined aquifer via comparison with the forward MODFLOW and inverse PEST++ iterative ensemble smoother (IES) solutions as well as the state-of-the-art Fourier neural operator (FNO) and deep operator networks (DeepONets) operator learning surrogate models. Our results show that the KL-DNN surrogate model outperforms FNO and DeepONet for forward predictions. For solving inverse problems, the randomized algorithm provides the same or more accurate Bayesian predictions of the parameters than IES as evidenced by the higher log-predictive probability of both the estimated parameter field and the forecast hydraulic head. The posterior mean obtained from the randomized algorithm is closer to the reference parameter field than that obtained with FNO as the maximum a posteriori estimate.

Approximate Bayesian inference↗