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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 NTRS2021

Capabilities and Performance of Juno’s Radio Science Instrumentation

The Juno Gravity Science Instrument is a radio science instrument onboard the Juno spacecraft, which entered orbit around Jupiter in 2016. The prime objective of the radio science investigation is to estimate the gravitational field of Jupiter from the Doppler shift on the radio link between the spacecraft and the Earth-based observing antennas of NASA’s Deep Space Network (DSN). The instrument is composed of a ground component at the DSN’s DSS-25 antenna, equipped with simultaneous dual X- and Ka-band transmitters and receivers, and a spacecraft component, which includes X- and Ka-band transponders to relay the transmitted signal back to Earth. The frequencies of these signals are measured using sensitive open-loop and closed-loop receivers of the DSN. Using the unique geometry of Juno’s orbit around Jupiter and the exquisite precision of the radio science instrumentation (~5-10 microns/sec one-way), the gravity field of Jupiter has been probed to unprecedented precision, allowing for discoveries of Jupiter’s core size and depth of the zonal winds. This precision is thanks to a detailed data processing and calibration techniques. An Advanced Water Vapor Radiometer measures the tropospheric delay and a linear combination X- and Ka-band links calibrates for Earth ionosphere, solar plasma, and Jovian plasma. Recent measurements probed the electron content inside Jupiter’s Io Plasma Torus, a doughnut-shaped ring of charged particles caught in Jupiter’s magnetosphere. Results from these measurements not only contributes to the scientific literature but also informs the performance of the instrument itself and can be used in future planning.

Oudrhiri, Kamal↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS2022

Juno Gravity Science: Five Years of Radio Science Operations with Ka-band Uplink

Since entering orbit on July 4, 2016, the Juno spacecraft has executed 34 closest approach passes of Jupiter, completing the prime mission. During each closest approach, called perijove, the spacecraft comes within 4,000 km of the cloud tops and the motion of the spacecraft becomes perturbed by the gravitational field of Jupiter. These small changes in the motion of the spacecraft are detected using the Juno Gravity Science Instrument by measuring the Doppler shift of the radio link between the Juno spacecraft and NASA’s Deep Space Network (DSN). During a majority of these closest approach passes, the 34-meter DSS-25 antenna transmits simultaneous X-band and Ka-band uplink to the spacecraft. Juno’s onboard X-band transponder and Ka-band translator phase-coherently return the signals back to Earth for reception at the same DSS-25 antenna. The precise frequency of these signals is measured by processing open-loop recordings of the signal. These measurements, characterized by ~5-10 micron/sec accuracies (after calibration of charged particle noise and Earth troposphere), have probed the gravity field of Jupiter to unprecedented precision, allowing for discoveries of Jupiter’s core size and depth of the zonal winds. Successful operations of the instrument during perijoves requires careful planning and coordination between DSN engineers, the Juno project, and the Juno science team. This work discusses the operations of the Juno Gravity Science Instrument after five years of prime mission operations. Lessons learned are documented to be applied to future missions and the Juno extended mission. Although the Juno extended mission formally started on August 1, 2021, on June 7, 2021, the trajectory was modified with a flyby of Ganymede, the third Galilean moon of Jupiter. Gravity and radio science investigations of Jupiter and its moons will continue to play a key role in Juno’s objectives during the extended mission.

Oudrhiri, Kamal↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of trained machine-learning models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a Windows app that has been created to deploy trained machine-learning models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of machine-learning application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). Current version of the app focuses on the performance prediction of conventional turbofans. The app gets user input for a turbofan design, preprocesses the input data, and deploys trained machine-learning models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The machine-learning predictive models were built by employing supervised deep-learning algorithm to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these machine-learning models using the app shows that Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage.

machine learning↗
NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS1980

Lunar core 15010/11 - Grain size, petrology, and implications for regolith dynamics

Petrographic observations of six samples from various depths show that core 15010/11 is relatively homogeneous and consists mostly of basalt fragments and minerals derived from basalts. However, it also contains an appreciable highland component nearly equally distributed among the fine-grained fraction, dark matrix breccia and identifiable highland grains, and agglutinates. Grain size data and agglutinates show that this core is not mature and does not contain a well developed reworking profile. The unusual properties of the core are thought to be a direct result of its close proximity to the rim of Hadley Rille. This location favors relatively rapid regolith erosion into the rille, produces a thin regolith, and provides an abundant supply of bedrock-derived ejecta.

Mckay, D. S.↗
NASA NTRS1980

The Apollo 17 drill core - Chemistry of size fractions and the nature of the fused soil component

It is shown that the Apollo 17 drill core 70009-70001 is heterogeneous with depth, containing five stratigraphic units, and has a bulk soil chemistry governed by the coarse fractions because of their greater weight proportions. The four components (1) KREEP, (2) anorthositic gabbro, (3) mare basalt, and (4) orange glass are used to model the compositions of the coarse and fine fractions of the entire drill core. It is found that the chemistry of the fused soil component in the five stratigraphic units is more similar to the chemistry of the fine, less than 20-micron fractions than the coarse fraction, suggesting that agglutinates may prefferentially meld and replicate the chemistry of the finer size fractions. The sources of Zn are the orange/black glasses, and the Zn profile is anticorrelated with the maturity index of Morris et al (1979), indicating the liberation of Zn during soil maturation.

Laul, J. C.↗
NASA NTRS1978

Core 74001/2 - Grain size and petrology as a key to the rate of in-situ reworking and lateral transport on the lunar surface

A suite of samples from the double drive tube 74001/2 has been studied. Material from this core mainly consists of orange and black droplets interpreted to be volcanic pyroclastic ejecta. Grain size analysis indicates that this material is very homogeneous in its grain size properties. It is also the finest and best sorted suite of soil samples in the lunar collection having a mean grain size of 40 microns and a mean standard deviation of 1.75 phi. The upper 5.5 cm of this core has apparently undergone in situ reworking by meteorites over a period of about 10 million years. This reworked zone contains 'exotic' grains including basalt, mineral fragments, vitric breccias, and agglutinates. One type of agglutinate is unique and has been made primarily from orange and black glass droplets melted and welded together by micrometeorite impacts. Other agglutinates are made mainly from basaltic fragments and minerals. The amount of 'exotic' material added to the core combined with an estimate of the location of the source areas for the 'exotic' material allows us to estimate that a maximum of about 0.36 gm per sq cm of regolith surface is added from a radius of about 1 meter in 10 million years. Furthermore, no more than about 0.01 gm per sq cm of regolith surface is added from a radius of about 100 meters in 10 million years.

Mckay, D. S.↗
NASA NTRS1979

The Apollo 17 drill core - Chemical systematics of grain size fractions

Data for 35 major, minor, and trace elements in 40 bulk and size fractions of core 70005-70003 (140-250 cm) are presented. The core is heterogeneous with depth. Moreover, the 1000 to 90 micron coarse fractions are nearly identical but quite different from the less than 20 micron fine fraction. The bulk soil chemistry is governed by the coarse fractions, because of their greater weight proportion in the sample. The 1000-90 micron fraction contains more ilmenite basalt and less orange glass components than the 90-20 micron fraction. The less than 20 micron fraction is consistently enriched in highland material at all depths in the drill core.

Laul, J. C.↗
NASA NTRS1972

Seismic investigations - The Viking Mars Lander.

A lightweight three-component short period seismometer has been developed for preliminary seismic investigations of Mars. Because of weight and data-rate constraints the Viking seismic experiment is far from optimal but it should, at a minimum, provide information about the microseismic level and an upper bound on the seismicity of the planet. If Mars is tectonically active a start can be made on the problem of the internal structure, dynamics, and composition of the planet. A good distribution of modest sized Marsquakes will make it possible to determine if Mars has a core. The size of the core is related to the conditions of planetary formation.

Anderson, D. L.↗
NASA NTRS1994

A C(18)O survey of dense cores in the Taurus molecular cloud: Signatures of evolution and protostellar collapse

We have mapped 11 dense cores in the Taurus molecular cloud in the C(18)O J = 2 goes to 1 line at a linear resolution of 0.02 pc. The core masses derived from C(18)O range from 0.06 to 5 solar mass. Five of them have embedded infrared sources, and six do not. Dense cores without infrared sources show multiple emission peaks. In contrast, dense cores with infrared sources have a single peak and smaller sizes. The cores with infrared sources have line widths that are 2-3 times the value expected from correlations found in previous surveys. This enhancement may be accounted for by models of gravitational collapse. The data are consistent with the idea that dense cores evolve first toward smaller sizes and smaller line width along the line width-size relation, and then toward larger line width and constant or smaller sizes as an infrared source becomes observable. A good collapse candidate, L1527, is identified based on the shapes of C(18)O and H2CO lines.

Zhou, Shudong↗
NASA NTRS2004

Single-event upset in highly scaled commercial silicon-on-insulator PowerPc microprocessors

Single event upset effects from heavy ions are measured for Motorola and IBM silicon-on-insulator (SOI) microprocessors with different feature sizes, and core voltages. The results are compared with results for similar devices with build substrates. The cross sections of the SOI processors are lower than their bulk counterparts, but the threshold is about the same, even though the charge collections depth is more than an order of magnitude smaller in the SOI devices. The scaling of the cross section with reduction of feature size and core voltage dependence for SOI microprocessors discussed.

single event upset (SEU)↗
NASA NTRS1971

Sizing an external-fueled in-core thermionic reactor.

Parametric studies on sizing of external-fueled in-core thermionic reactors are presented. Reactor physics results obtained for a variety of fuel element designs are used as a basis for nuclear criticality, power distribution, and control worth design. Thermionic performance results for a single fuel element for several sets of operating conditions are presented. An algorithm combining the electrical and reactor physics results in a form amenable to preliminary systems analysis is presented.

Nakashima, A. M.↗
NASA NTRS1984

The size of the lunar core

A 0.2 arc second phase shift in the 18.6 year forced precession of the lunar figure was inferred from analysis of lunar laser ranging data. The source of the phase shift is either viscous friction at a lunar core mantle boundary or solid friction caused by tidal flexing of the Moon by the Earth. Core mantle viscous coupling will explain the signature if the core radius R sub c approximately equals 330 km. On the other hand, solid friction can account for the signature only if the lunar solid friction 0 approximately equals 30 is abnormally small compared with that observed for, say, Mars (approximately equals 100 - 200). Although the inferred core radius is certainly within the limits imposed by the Apollo seismic experiment, it is significantly smaller than estimates of order 400-500 km from electromagnetic sounding. How accurate is the estimate of the R sub c derived from the phase shift? The effect of uncertainties in the frictional coupling mechanism, core density and ellipticity on the inferred core radius are discussed. The excitation of lunar free librations by core turbulence vis-a-vis other mechanisms (e.g., cometary or asteroidal impacts) and the influence of changes in lunar precession in the past on lunar dynamo generation are also examined.

Yoder, C. F.↗
NASA NTRS1991

An unbiased survey for dense cores in the Lynds 1630 molecular cloud

An unbiased, systematic survey for dense cores within the L1630 (Orion B) molecular cloud has been completed. This survey provides the first complete census of dense (n greater tha 10,000/cu cm) cores within a molecular cloud. To identify the dense gas, 3.6 square degrees of the L1630 cloud were surveyed in the J = 2-1 transition of CS. CS emission was detected over 10 percent of the area surveyed, and this emission is not uniformly distributed throughout the cloud but is confined to 42 dense cores. The size, shape, velocity dispersion, and mass of these cores are examined. Comparison of the mass contained within dense cores with the total gas mass within the surveyed region, estimated from CO emission, reveals that the dense cores constitute only a small fraction (not greater than 19 percent) of the total cloud mass.

Lada, Elizabeth A.↗
NASA NTRS1973

The induced magnetosphere of the moon. 1: Theory

An analytic solution for the magnetic field in the space defined by a spherical moon and its downstream cylindrical cavity formed by the solar wind is derived for interplanetary magnetic fields both parallel and perpendicular to the cavity axis. By superposition, the solution is obtained for arbitrary orientations of the interplanetary field. The theory is quasi-static and is formulated in terms of a scalar magnetic potential. Thus the moon model consists of a core of arbitrary size and infinite electrical conductivity surrounded by a nonconducting shell; the cavity volume is assumed to be nonconducting. The variation of the magnetic field on the lunar surface, both on the sunlit and on the dark side hemispheres, and on the cavity boundary is presented for various values of core radius. The solution yields the distribution of currents on the lunar sunlit surface and the surface of the cavity. Theoretical transfer functions are presented and their variations with position on the lunar surface and with core size are discussed.

Schubert, G.↗