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We discuss the history of X-ray observations of the 11 Car system, concentrating on the periodic variability discovered in the 1990s. We discuss the interpretation of these variations, concentrating on a model of the system as a "collidingwind" binary. This interpretation allows the physical and orbital parameters of eta Car and its companion star to be constrained.
Game changing advances come about by the introduction of new technologies at a time when societal needs create the opportunity for new market solutions. A unique opportunity exists for NASA to bring about such a mobility revolution in General Aviation, extendable to other aviation markets, to maintain leadership in aviation by the United States. This report outlines the research carried out so far under NASA's leadership towards developing a new mobility choice, called Zip Aviation1,2,3. The feasibility, technology and system gaps that need to be addressed, and pathways for successful implementation have been investigated to guide future investment. The past decade indicates exciting trends in transportation technologies, which are quickly evolving. Automobiles are embracing automation to ease driver tasks as well as to completely control the vehicle with added safety (Figure 1). Electric propulsion is providing zero tail-pipe emission vehicles with dramatically lower energy and maintenance costs. These technologies have not yet been applied to aviation, yet offer compelling potential benefits across all aviation markets, and in particular to General Aviation (GA) as an early adopter market. The benefits of such an adoption are applicable in the following areas: Safety: The GA market experiences accident rates that are substantially higher than automobiles or commercial airlines, with 7.5 fatal accidents per 100 million vehicle miles compared to 1.3 for automobiles and.068 for airlines. Approximately 80% of these accidents are caused by some form of pilot error, with another 13% caused by single point propulsion system failure. Emissions: Environmental constraints are pushing for the elimination of 100Low Lead (LL) fuel used in most GA aircraft, with aviation fuel the #1 source of lead emissions into the environment. Aircraft also have no emission control systems (i.e. no catalytic converters etc.), so they are gross hydrocarbon polluters compared to automobiles. Community Noise: Hub and smaller GA airports are facing increasing noise restrictions, and while commercial airliners have dramatically decreased their community noise footprint over the past 30 years, GA aircraft noise has essentially remained same, and moreover, is located in closer proximity to neighborhoods and businesses. Operating Costs: GA operating costs have risen dramatically due to average fuel costs of over $6 per gallon, which has constrained the market over the past decade and resulted in more than 50% lower sales and 35% less yearly operations. Infusion of autonomy and electric propulsion technologies can accomplish not only a transformation of the GA market, but also provide a technology enablement bridge for both larger aircraft and the emerging civil Unmanned Aerial Systems (UAS) markets. The NASA Advanced General Aviation Transport Experiments (AGATE) project successfully used a similar approach to enable the introduction of primary composite structures and flat panel displays in the 1990s, establishing both the technology and certification standardization to permit quick adoption through partnerships with industry, academia, and the Federal Aviation Administration (FAA). Regional and airliner markets are experiencing constant pressure to achieve decreasing levels of community emissions and noise, while lowering operating costs and improving safety. But to what degree can these new technology frontiers impact aircraft safety, the environment, operations, cost, and performance? Are the benefits transformational enough to fundamentally alter aircraft competiveness and productivity to permit much greater aviation use for high speed and On-Demand Mobility (ODM)? These questions were asked in a Zip aviation system study named after the Zip Car, an emerging car-sharing business model. Zip Aviation investigates the potential to enable new emergent markets for aviation that offer "more flexibility than the existing transportation solutions." These studies indicate that autonomy and electric propulsion technology infusions offer a unique opportunity to provide breakthrough capabilities for new high speed, on-demand travel alternatives that can leapfrog the need for future expensive ground-based infrastructure investment. At the same time, such investments offer a method of laying the foundation for these technologies to be incubated for commercial aviation at lower cost, and with lower initial certification thresholds due to the relatively poor capabilities of GA aircraft to permit early adoption and private market capitalization by rapid technology accelerations, as depicted in Figure 2.
Eta Carinae (Eta Car), its interacting winds and historical ejecta provide an unique astrophysical laboratory that permits addressing a multitude of questions ranging from stellar evolution, colliding winds, chemical enrichment, nebular excitation to the formation of molecules and dust. Every 5.54 years, Eta Car changes from high excitation to several-months-long low excitation caused by modulation of the massive interacting winds due to a very eccentric binary orbit. The surrounding Homunculus (Figure 1) and Little Homunculus, thrown out in the 1840s Great Eruption and the 1890s Lesser Eruption, respond to the changing flux, providing clues to many physical phenomena of great interest to astrophysicists.
Eta Carinae is an ideal astrophysical laboratory for studying massive binary interactions and evolution, and stellar wind-wind collisions. Recent three-dimensional (3D) simulations set the stage for understanding the highly complex 3D flows in eta Car. Observations of different broad high- and low-ionization forbidden emission lines provide an excellent tool to constrain the orientation of the system, the primary's mass-loss rate, and the ionizing flux of the hot secondary. In this work we present the first steps towards generating synthetic observations to compare with available and future HST/STIS data. We present initial results from full 3D radiative transfer simulations of the interacting winds in eta Car.We use the SimpleX algorithm to post-process the output from 3D SPH simulations and obtain the ionization fractions of hydrogen and helium assuming three different mass-loss rates for the primary star. The resultant ionization maps of both species constrain the regions where the observed forbidden emission lines can form. Including collisional ionization is necessary to achieve a better description of the ionization states, especially in the areas shielded from the secondary's radiation. We find that reducing the primary's mass-loss rate increases the volume of ionized gas, creating larger areas where the forbidden emission lines can form.We conclude that post processing 3D SPH data with SimpleX is a viable tool to create ionization maps for eta Car.
In this paper we describe measurements of the bidirectional reflectance-distribution function (BRDF) acquired over a 30-year period (1984-2014) by the National Aeronautics and Space Administration's (NASA's) Cloud Absorption Radiometer (CAR). Our BRDF database encompasses various natural surfaces that are representative of many land cover or ecosystem types found throughout the world. CAR's unique measurement geometry allows a comparison of measurements acquired from different satellite instruments with various geometrical configurations, none of which are capable of obtaining such a complete and nearly instantaneous BRDF. This database is therefore of great value in validating many satellite sensors and assessing corrections of reflectances for angular effects. These data can also be used to evaluate the ability of analytical models to reproduce the observed directional signatures, to develop BRDF models that are suitable for sub-kilometer-scale satellite observations over both homogeneous and heterogeneous landscape types, and to test future spaceborne sensors. All of these BRDF data are publicly available and accessible in hierarchical data format (http:car.gsfc.nasa.gov/).
The primary objective this summer is "evaluating standards for wireless architecture for the internet of things". The Internet of Things is the network of physical objects or "things" embedded with electronics, software, sensors and network connectivity which enables these objects to collect and exchange data and make decisions based on said data. This was accomplished by creating a semi-autonomous vehicle that takes advantage of multiple sensors, cameras, and onboard computers and combined them with a mesh network which enabled communication across large distances with little to no interruption. The mesh network took advantage of what is known as DTN - Disruption Tolerant Networking which according to NASA is the new communications protocol that is "the first step towards interplanetary internet." The use of DTN comes from the fact that it will store information if an interruption in communications is detected and even forward that information via other relays within range so that the data is not lost. This translates well into the project because as the car moves further away from whatever is sending it commands (in this case a joystick), the information can still be forwarded to the car with little to no loss of information thanks to the mesh nodes around the driving area.
We report on Swift X-ray Telescope observations of Eta Carinae ( Car), an extremely massive, long-period, highly eccentric binary obtained during the 2014.6 X-ray minimumperiastron passage. These observations show that Car may have been particularly bright in X-rays going into the X-ray minimum state, while the duration of the 2014 X-ray minimum was intermediate between the extended minima seen in 1998.0 and 2003.5 by Rossi X-Ray Timing Explorer (RXTE), and the shorter minimum in 2009.0. The hardness ratios derived from the Swift observations showed a relatively smooth increase to a peak value occurring 40.5 days after the start of the X-ray minimum, though these observations cannot reliably measure the X-ray hardness during the deepest part of the X-ray minimum when contamination by the central constant emission component is significant. By comparing the timings of the RXTE and Swift observations near the X-ray minima, we derive an updated X-ray period of P X equals 2023.7 +/- 0.7 days, in good agreement with periods derived from observations at other wavelengths, and we compare the X-ray changes with variations in the He ii lambda 4686 emission. The middle of the Deep Minimum interval, as defined by the Swift column density variations, is in good agreement with the time of periastron passage derived from the He ii 4686 line variations.
A search for the progenitor of SN 2010jl, an unusually luminous core-collapse supernova of Type IIn, using pre-explosion Hubble/WFPC2 and Spitzer/IRAC images of the region, yielded upper limits on the UV and near infrared (IR) fluxes from any candidate star. These upper limits constrain the luminosity and effective temperature of the progenitor, the mass of any pre-existing dust in its surrounding circumstellar medium (CSM), and dust proximity to the star. A lower limit on the CSM dust mass is required to hide a luminous progenitor from detection by Hubble. Upper limits on the CSM dust mass and constraints on its proximity to the star are set by requiring that the absorbed and reradiated IR emission not exceed the IRAC upper limits. Using the combined extinction-IR emission constraints, we present viable M(sub d)-R(sub 1) combinations, where M(sub d) and R(sub 1) are the CSM dust mass and its inner radius. These depend on the CSM outer radius, dust composition and grain size, and the properties of the progenitor. The results constrain the pre-supernova evolution of the progenitor, and the nature and origin of the observed post-explosion IR emission from SN 2010jl. In particular, an eta Car-type progenitor will require at least 4 mag of visual extinction to avoid detection by Hubble. This can be achieved with dust masses greater than approximately equal to 10(exp -3) solar mass (less than the estimated 0.2-0.5 solar mass around eta Car), which must be located at distances of greater than approximately equal to 10(exp 16) cm from the star to avoid detection by Spitzer.
The quantification of uncertainty in satellite-derived global surface albedo products is a critical aspect in producing complete, physically consistent, and decadal land property data records for studying ecosystem change. A challenge in validating albedo measurements acquired from space is the ability to overcome the spatial scaling errors that can produce disagreements between satellite and field-measured values. Here, we present the results from an accuracy assessment of MODIS and Landsat-TM albedo retrievals, based on collocated comparisons with tower and airborne Cloud Absorption Radiometer (CAR) measurements collected during the 2007 Cloud and Land Surface Interaction Campaign (CLASIC). The initial focus was on evaluating intersensor consistency through comparisons of intrinsic bidirectional reflectance estimates. Local and regional assessments were then performed to obtain estimates of the resulting scaling uncertainties, and to establish the accuracy of albedo reconstructions during extended periods of precipitation. In general, the satellite derived estimates met the accuracy requirements established for the high-quality MODIS operational albedos at 500 m (the greater of 0.02 units or +/-10% of surface measured values). However, results reveal a high degree of variability in the root-mean-square error (RMSE) and bias of MODIS visible (0.3-0.7 μm) and Landsat-TM shortwave (0.3-5.0 μm) albedos; where, in some cases, retrieval uncertainties were found to be in excess of 15%. Results suggest that an overall improvement in MODIS shortwave albedo retrieval accuracy of 7.8%, based on comparisons between MODIS and CAR albedos, resulted from the removal of sub-grid scale mismatch errors when directly scaling-up the tower measurements to the MODIS satellite footprint.
Emerging applications in artificial intelligence, such as driverless cars and autonomous aircraft promise to be more efficient, cheaper to operate, and always available. However, ensuring the safety of these systems remains a major challenge to their certification and adoption. These autonomous systems are expected to routinely make safety-critical decisions where failures can have serious consequences including loss of life and property. Testing and validation techniques aim to identify and diagnose potential failures before the system is deployed. However, finding failure scenarios in autonomous systems can be very challenging due to high-dimensional and continuous state spaces, interaction with large environments over many time steps, and the rarity of failures. This talk presents Adaptive Stress Testing (AST), a simulation-based testing framework for finding the most likely path to a failure event of a safety-critical system. The key idea of AST is that stress testing can be formulated as a Partially Observable Markov Decision Process (POMDP), which enables reinforcement learning techniques to be used for finding failure events. Reinforcement learning algorithms can efficiently explore the search space and have been shown to scale to very large systems. We present applications of AST to find failures in various safety-critical systems including the aircraft collision avoidance systems, autonomous cars, and small unmanned aerial vehicles.
A model and software for design and analysis of a Proton Exchange Membrane fuel cell (PEMFC) system are developed for hydrogen eVTOL aircraft. Examples are provided of stacks designed for 80 kWe and 500 kWe net electrical power. Examples are provided of eVTOL designed for 250 and 400 lb payload. The trade-offs included stack characteristics, hydrogen storage characteristics, and aircraft payload and range. The objectives were to identify the key technology drivers of a hydrogen rotorcraft, establish technology targets for a viable aircraft, and recommend research to address the fundamental pre-competitive barriers. The current U.S. infrastructure on hydrogen informed the targets and recommendations. The key conclusion is that the advances made in cell electrochemical power density over the last decade might allow a PEMFC system to meet, or even beat, piston engine powered light-utility rotorcraft. The development must focus on the key drivers of a hydrogen eVTOL. The drivers are ultra-light stack cooling and short-term hydrogen storage. A stack system of net electrical power 100−150 kWe with specific power 1.1 kWe/kg including air, cooling, and electrical subsystems, and a tank storage of 15% weight fraction hydrogen are the minimum targets to meet the performance of a modern piston-engine rotorcraft, with a range of 180 nautical miles, payload of 400 lb, and gross weight of about 1400 lb. This is defined as the objective aircraft. If only one target is met, an aircraft of half the range could be produced, with a 30% greater gross take-off weight. This is defined as an intermediate aircraft. Because definitive conclusions are premature without weights and loads data on a flight-worthy stack system, it is recommended that a fuel cell powered hydrogen eVTOL demonstrator be built and flown. The existing hydrogen infrastructure for cars provide ample opportunity to create a pilot program. About 12 metric tons of retail hydrogen are available for cars every day in California, which is less than 0.05% of the yearly hydrogen production in the U.S.. A hypothetical fleet of 100 aircraft, operating 3 flights a day, would increase the demand by 2.25 tons per day and require 140 MWh of renewable electricity for green hydrogen.
Presentation offers methodology and approaches for increasing autonomy in aviation and uses car industry (self-driving cars) as comparison.
Following a series of crashes in which semiautonomous cars collided with high-visibility emergency vehicles, this article looks at the human factors challenges of partially automated driving. I review recent research that investigates the capabilities of driver assist systems, the limits of human attention and vigilance, the impact of automation use on driver fatigue, and driver understanding of all of the above. I review the efforts being made to align driver understanding with system reality: instructions and warnings, point-of-sale driver training, automation training standards, and vehicle design. I conclude that several important safety concepts, revealed by research, have yet to be effectively communicated to drivers who will need them to operate these cars safely.
The bidirectional reflectance factor, BRF, retrieved by the Multiangle Imaging SpectroRadiometer, MISR, is compared with those coincidently measured from aircraft, by the Cloud Absorption Radiometer, CAR, and MISR airborne simulator, AirMISR, and on the ground, by the Portable Apparatus for Rabid Acquisition of Bidirectional Observations of Land and Atmosphere, PARABOLA Ill. The intercomparisons are made for six types of surfaces: bright desert, salt pans, grassland, forests, snow and the Dismal Swamp. The results show that MISR BRF values are mostly within +/- 10% in agreement with the corresponding airborne and ground measurements, independent of the surface types This study is part of an effort to evaluate and validate MISR surface products.
The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wild fire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.
Supergiant alpha Car UV spectrophotometry from Gemini 11, comparing with model atmosphere
Pocket sized transmitter, frequency modulated by crystal derived tones, with integral loop antenna provides police with easy operating alert signal communicator which uses patrol car radio to relay signal. Communication channels are time shared by several patrol units.
Nickel-zinc batteries for use in hybrid heat engine/electric systems of low pollutant passenger cars, increasing service life by aerospace technology