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A literature review is presented of the work done in the numerical modeling of wind flows. Pertinent computational techniques are described, as well as the necessary assumptions used to simplify the governing equations. A steady state model is outlined, based on the data obtained at the Deep Space Communications complex at Goldstone, California.
The spatial distribution of the 158 microns CII fine structure line in the galactic sources W3, NGC 1977, and NGC 2023 is mapped. The emission arises from warm (100 to 300 K), dense photodissociation regions at the surface of molecular gas. In all three sources, the emission extends over parsec scales or greater. For W3 and NGC 1977, where the UV source/molecular cloud geometry presents an edge on view of the variation of CII intensity into the molecular gas, two dimensional models of the CII emission which include the effects of gas clumping and scattering by dust on the transport of UV photons are constructed. The observed CII distribution and intensity is well modeled by a clumpy or filamentary distribution to the molecular gas, with a clump/interclump gas density ratio of 100 or more, which allows deep penetration of carbon ionizing UV photons into the clouds. The penetration of UV into clumped molecular clouds may also explain the extended far IR continuum emission from these sources. The total luminosity of CII emission from a clumpy molecular cloud with adjacent or embedded octave band stars can be as much as an order of magnitude higher than the CII luminosity of a uniform cloud. In addition, the extended penetration of UV into molecular clouds will affect the abundances of atomic and molecular species and increase the fractional ionization of interclump gas and UV illuminated clump surfaces.
Al Umchaimin, in Arabic 'hiding place' or 'place of ambush', is located at latitude 32 degrees 35.5 N and longitude 39 degrees 25 E. The nearly circular depression averages 2.75 km in diameter and is 33-42 m deep. It is floored with fine-grained, clay-rich deposits, estimated to be 36 m thick, the surface of which shows well-developed desiccation fissures or mudcracks when dry. Because of its nearly circular planimetric shape and its apparent isolation from other surface and subsurface features, it has been considered by some to be a possible meteorite impact structure and by others to be a surface collapse feature that originated following removal of magma from the subsurface as the magma extruded elsewhere. Al Umchaimin was listed in the U.S. Geological Survey tabulation of 110 structures worldwide for which a meteorite impact origin had been suggested. It was placed in Category 6 Structures for which more data are required for classification. It is concluded that, on the basis of the studies that have been made of Al Umchaimin and on the basis of the brief site visit made, Al Umchaimin probably is not an impact structure but most likely resulted from the enlargement and coalescence of sink holes and eventual collapse of the roof material into the resulting cavity.
Rawinsonde data from tropical Pacific stations were examined for westward-propagating 3-6-day meridional wind oscillations in the troposphere and lower stratosphere, 1973-1992. Four types were identified from cross-spectrum and principal component analysis. (1) The dominant oscillation, near 250 mb, had a period slightly greater than 5 days, zonal wavenumber 4-6, and modified Rossby-gravity structure near the date line. (2) In the western Pacific lower troposphere there was broadband activity with short zonal scale, coupled to upper-tropospheric waves in NH summer. (3) In the central Pacific, during NH autumn, there was a well-defined approximately 4 1/2-day oscillation with maximum amplitude in the lower troposphere and baroclinic phase tilt above. The vertical structure suggested coupling to deep tropical convection; this interpretation was supported by correlation of meridional wind with antisymmetric outgoing longwave radiation. (4) In the stratosphere, Rossby-gravity waves had periods less than or equal to 4 days and zonal wavenumber 3-4. Unlike tropospheric waves, these disturbances were coherent in a shallow layer, largest in west phase of quasi-biennial oscillation (QBO) and annual cycle (NH winter-spring).
We present photometric and spectroscopic observations of a recently identified X-ray bright, magnetic cataclysmic variable, RJ 051542+0104.7. The X-ray source was discovered serendipitously by Walter et al. (1994; Walter & Zoonematkermani 1994), and their data imply the presence of a heated white dwarf compatible with a magnetic accretion column. Buckley (1994) has reported the presence of circular polarization. Our optical photometry shows that the system undergoes eclipses with a period of 7.9835 +/- 0.0002 hours. The eclipse is 1 magnitude deep and appears to be total. Our spectra were all obtained near eclipse and show a strong blue continuum with broad Balmer emission as well as He II (4686 and 5411 A), C III/N III (4650 A) and He I lines. The blue continuum and emission lines disappear during eclipse, leaving only the spectrum of a M0 star. Using the observed magnitude during eclipse and ignoring interstellar extinction, the distance is approximately 500 pc. For the secondary to fill its Roche lobe, it must be at least 20% larger than that of a normal dM0 star. This may indicate the red dwarf is far from local thermodynamic equilibrium (LTE) due to strong magnetic braking. We find evidence for cyclotron humps in the continuum spectrum which imply a magnetic field strength in the cyclotron emitting region of up to 55 MG. The unexpected existence of a long period AM Her system should provide constraints on models of polar evolution.
NASA s Vision for Space Exploration outlines a very ambitious program for the next several decades of the Space Agency endeavors. Ahead is the completion of the International Space Station (ISS); safely flight the shuttle (STS) until 2010; develop and fly the Crew Exploration Vehicle (Orion) by no later than 2014; return to the moon by no later than 2020; extend human presence across the solar system and beyond; implement a sustainable and affordable human and robotic program; develop supporting innovative technologies, knowledge and infrastructure; and promote international and commercial participation in exploration. To achieve these goals, a series of enabling technologies must be developed or matured in a timely manner. Some of these technologies are: spacecraft RF technology (e.g., high power sources and large antennas which using surface receive arrays can get up to 1 Gbps from Mars), uplink arraying (reduce reliance on large ground-based antennas and high operation costs; single point of failure; enable greater data-rates or greater effective distance; scalable, evolvable, flexible scheduling), software define radio (i.e., reconfigurable, flexible interoperability allows for in flight updates open architecture; reduces mass, power, volume), and optical communications (high capacity communications with low mass/power required; significantly increases data rates for deep space). This presentation will discuss some of the work being performed at the NASA Glenn Research Center, Cleveland, Ohio, in antenna technology as well as other on-going RF communications efforts.
NASA technology has made commercially available a new, inexpensive, conveniently-carried device for protection, of people exposed to potentially dangerous levels of microwave radiation. Microwaves are radio emissions of extremely high frequency. They can be hazardous but the degree of hazard is not yet well understood. Generally, it is believed that low intensity radiation of short duration is not harmful but that exposure to high levels can induce deep internal burns, affecting the circulatory and nervous systems, and particularly the eyes. The Department of Labor's Occupational Safety and Health Administration (OSHA) has established an allowable safe threshold of exposure. However, people working near high intensity sources of microwave energy-for example, radar antennas and television transmitters-may be unknowingly exposed to radiation levels beyond the safe limit. This poses not only a personal safety problem but also a problem for employers in terms of productivity loss, workman's compensation claims and possible liability litigation. Earlier-developed monitoring devices which warn personnel of dangerous radiation levels have their shortcomings. They can be cumbersome and awkward to use while working. They also require continual visual monitoring to determine if a person is in a dangerous area of radiation, and they are relatively expensive, another deterrent to their widespread adoption. In response to the need for a cheaper and more effective warning system, Jet Propulsion Laboratory developed, under NASA auspices, a new, battery-powered Microwave Radiation Hazard Detector. To bring the product to the commercial market, California Institute Research Foundation, the patent holder, granted an exclusive license to Cicoil Corporation, Chatsworth, California, an electronic components manufacturer.
Function: Monitor global change and thunderstorm processes through observations of Earth's high-latitude lightning. This instrument will combine long-lived sampling of individual thunderstorms with long-term observations of lightning at high latitudes: How is global change affecting thunderstorm patterns; How do high-latitude thunderstorms differ from low-latitude? Why is the Gateway the optimal facility for this instrument / research: Expected DSG (Deep Space Gateway) orbits will provide nearly continuous viewing of the Earth's high latitudes (50 degrees latitude and poleward); These regions are not well covered by existing lightning mappers (e.g., Lightning Imaging Sensor / LIS, or Geostationary Lightning Mapper / GLM); Polar, Molniya, Tundra, etc. Earth orbits have significant drawbacks related to continuous coverage and/or stable FOVs (Fields of View).
The Dawn spacecraft was launched on September 27th, 2007. Its mission is to rendezvous with and observe the two largest bodies in the main asteroid belt, Vesta and Ceres. It has completed over a year’s worth of direct observations of Vesta from early 2011 through late 2012. In the spring of 2015, the Dawn spacecraft entered orbit around the asteroid Ceres for the start of what is expected to be more than a year of science operations. The science data collected from this encounter consist of infrared (IR) images and spectra, visible images through a number of color filters, gamma ray detections and measurements of the Ceres gravity field. These data will be collected during several science phases: an Approach phase (1500000-4860 km from Ceres), a Survey orbit (4860 km radius), a High Altitude Mapping Orbit (HAMO) (1940 km radius) and a Low Altitude Mapping Orbit (LAMO) (855 km radius). The Approach phase included three Rotational Characterization (RC) opportunities. Designing each science orbit and successfully transferring into that orbit requires a sufficiently accurate estimate of Ceres physical parameters (body fixed frame, GM and harmonics). This paper focuses on work performed to estimate Ceres physical parameters using Deep Space Network (DSN) radiometric tracking data and optical measurements derived from science camera imagery. This paper describes planning for the data acquisition, as well as processing techniques and methodology. The trajectories predicted by the gravity field estimations are also compared with the actual as-flown trajectories. Observations of the gravity at high altitudes are found to be sufficient to design precision orbits at lower altitudes. Follow-up analysis after successfully reaching LAMO is included, as is a discussion of lessons learned.
To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.
To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.
In this paper and its companion, Differential Equation Approximation Using Gradient-Boosted Quantile Regression, Robison et al., we examine an approach to quantifying model uncertainty with the aim of increasing the trustworthiness of computational models in human-machine interactions. In Differential Equation Approximation Using Gradient-Boosted Quantile Regression, we focus on gradient-boosted decision trees, while in this one, we give more details about deep ensembles. Uncertainty quantification is crucial for building trustworthy autonomous decision-making agents in human-machine teams. There are two types of uncertainties: aleatoric and epistemic. The former is related to the inherent stochasticity (noise) of the process, whereas the latter is associated with the lack of knowledge or representation capability of models, such as neural networks. By lack of knowledge, we mean the model’s inability to accurately predict outputs for all possible inputs. The aleatory uncertainty can be estimated fairly easily with, for example, filters, whereas epistemic uncertainty is challenging to compute. This paper uses deep ensembles to quantify both aleatory and epistemic uncertainty. It can act as an uncertainty-aware surrogate transition model for decision-making frameworks. "Uncertainty-aware" means that the surrogate transition model should make predictions along with confidence in those predictions. In the context of decision-making, the transition models are ordinary differential equations (ODEs). Since ODEs can be simulated to make one-step or multi-step predictions, a good surrogate model for them should perform reasonably well in both modes. In a multi-step approach, the trajectory sampling method TS∞ was used to propagate uncertainty over multiple steps. The cartpole dynamical system was selected to demonstrate the ability of deep ensembles as good surrogate transition models for decision-making frameworks. The deep ensembles modeled the dynamics of cartpole ODEs and made uncertainty-aware predictions in single-step and multi-step transition modes.
Ice clouds and falling snow are ubiquitous globally and play important roles in the Earth’s radiation budget and precipitation processes. Ice particle microphysical properties (e.g., size, habit and orientation) are not only influenced by ambient environment’s dynamic and thermodynamic conditions, but also intimately connect to the cloud radiative effects and particle fall speeds, which therefore impact up to the future climate projection and down to the details of the surface precipitation (e.g., onset-time, location, type and strength). Our previous work revealed that high-frequency Polarimetric radiance Difference (PD) from passive microwave sensors is a good indicator of the bulk aspect ratio of horizontally oriented ice particles that are often occur inside anvil clouds and/or stratiform precipitations. In this current work, we further investigate the dynamic/thermodynamic mechanisms and cloud/precipitation structures associated with ice-phase microphysics corresponding to different PD signals. In order to do so, collocated CloudSat radar (W-band) and Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM-DPR, Ku/Ka bands) observations as well as European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere background profiles are grouped according to the magnitude of PD for only stratiform precipitation and/or anvil cloud scenes. We found that horizontally-oriented snow aggregates or large snow particles are likely the major contributor to the high-PD signals at 166 GHz, while low-PD magnitudes can be attributed to small cloud ice, randomly oriented snow aggregates, riming snow or super-cooled water. Further, high (low) PD scenes are found to be associated with stronger (weaker) wind shear and higher (lower) ambient humidity, both of which help promote (prohibit) the growth of frozen particles and the organization of convective systems. An ensemble of squall line cases is studied at the end to demonstrate that the PD asymmetry in the leading and trailing edges of the deep convection line is closely tied to the anvil cloud and stratiform precipitation layers respectively, suggesting the potential usefulness of PD as a proxy of stratiform/convective precipitation flag, as well as a proxy of convection life stage.
The air quality control equipment aboard the International Space Station (ISS) and future deep space exploration vehicles provide the vital function of maintaining a clean cabin environment for the crew and the hardware. This becomes a serious challenge in pressurized space compartments since no outside air ventilation is possible, and a larger particulate load is imposed on the filtration system due to lack of sedimentation. The ISS Environmental Control and Life Support (ECLS) system architecture in the U.S. Segment uses a distributed particulate filtration approach consisting of traditional High-Efficiency Particulate Air (HEPA) filters deployed at multiple locations in each U.S. Seg-ment module; these filters are referred to as Bacterial Filter Elements, or BFEs. In our previous work, we presented results of efficiency and pressure drop measurements for a sample set of two returned BFEs with a service life of 2.5 years. In this follow-on work, we present similar efficiency, pressure drop, and leak tests results for a larger sample set of six returned BFEs. The results of this work can aid the ISS Program in managing BFE logistics inventory through the stations planned lifetime as well as provide insight for managing filter element logistics for future exploration missions. These results also can provide meaningful guidance for particulate filter designs under consideration for future deep space exploration missions.
The air quality control equipment aboard the International Space Station (ISS) and future deep space exploration vehicles provide the vital function of maintaining a clean cabin environment for the crew and the hardware. This becomes a serious challenge in pressurized space compartments since no outside air ventilation is possible, and a larger particulate load is imposed on the filtration system due to lack of sedimentation. The ISS Environmental Control and Life Support (ECLS) system architecture in the U.S. Segment uses a distributed particulate filtration approach consisting of traditional High-Efficiency Particulate Air (HEPA) filters deployed at multiple locations in each U.S. Seg-ment module; these filters are referred to as Bacterial Filter Elements, or BFEs. In our previous work, we presented results of efficiency and pressure drop measurements for a sample set of two returned BFEs with a service life of 2.5 years. In this follow-on work, we present similar efficiency, pressure drop, and leak tests results for a larger sample set of six returned BFEs. The results of this work can aid the ISS Program in managing BFE logistics inventory through the stations planned lifetime as well as provide insight for managing filter element logistics for future exploration missions. These results also can provide meaningful guidance for particulate filter designs under consideration for future deep space exploration missions.
Monsoon rainfalls sustain the livelihood of more than half of the world's population. The interaction between natural/anthropogenic aerosols, clouds, and precipitation is a critical mechanism that drives the water cycle and fresh water distribution. Analyses of the longterm trend of July-August precipitation anomaly for the last 50 years in the 20" century depict that the largest regional precipitation deficit occurs over the Sahel, where the monsoon water cycle plays an important role. Thus, it is of paramount importance to study how dust aerosols, as well as air pollution and smoke, influence monsoon variability. The NASA African Monsoon Multidisciplinary Activities (NAMMA) was conducted during the international AMMA Special Observation Period (SOP-3) of September 2006 to better comprehend the key attributes of the Saharan Air Layer (SAL) and how they evolve from the source regions to the Atlantic Ocean. The SAL occurs during the late spring through early fall and originates as a result of low-level convergence induced by heat lows over the Sahara that lifts hot, dry, dust laden air aloft into a well mixed layer that extends up to 500mb. This is crucial for understanding the impact of SAL on the key atmospheric processes that determine precipitation over West Africa and tropical cyclogenesis. Results obtained from the synergy of satellite (Deep- Blue) and surface (SMART-COMMIT) observations will be presented and discussed how the physical, optical and radiative properties of the dust in the SAL evolve from the continental to the marine environment.
Our knowledge of the composition and physical properties of planetary atmospheres is important for the understanding of the origin and evolution of the planets themselves, as well as for providing a comparative basis for understanding the Earth's atmosphere.
NASA will launch the Nancy Grace Roman Space Telescope (hereafter Roman ) in the second half of this decade, which will allow for a generation-defining measurement of dark energy through multiple probes, including Type Ia supernovae (SNe Ia). To impro v e decisions on surv e y strate gy, we hav e created the first simulations of realistic Roman images that include artificial SNe Ia injected as point sources in the images. Our analysis combines work done on Roman simulations for weak gravitational lensing studies as well as catalogue-level simulations of SN Ia samples. We have created a time series of images o v er 2 yr containing ∼1050 SNe Ia, co v ering a 1 deg 2 subarea of a planned 5 deg 2 deep survey. We have released these images publicly for community use along with input catalogues of all injected sources. We create secondary products from these images by generating coadded images and demonstrating reco v ery of transient sources using image subtraction. We perform first-use analyses on these images in order to measure galaxy detection efficiency, point source detection efficiency, and host-galaxy association biases. The simulated images can be found here at https:// roman.ipac.caltech.edu/sims/ SN Survey Image sim.html .