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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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In this paper, we present a space invariant topology to enable the Independent Component Analysis (ICA) to solve chemical detection from two unknown mixing chemical sources.
A method of recovering unknown aberrations in an optical system includes collecting intensity data produced by the optical system, generating an initial estimate of a phase of the optical system, iteratively performing a phase retrieval on the intensity data to generate a phase estimate using an initial diversity function corresponding to the intensity data, generating a phase map from the phase retrieval phase estimate, decomposing the phase map to generate a decomposition vector, generating an updated diversity function by combining the initial diversity function with the decomposition vector, generating an updated estimate of the phase of the optical system by removing the initial diversity function from the phase map. The method may further include repeating the process beginning with iteratively performing a phase retrieval on the intensity data using the updated estimate of the phase of the optical system in place of the initial estimate of the phase of the optical system, and using the updated diversity function in place of the initial diversity function, until a predetermined convergence is achieved.
During flight-testing of the National Aeronautics and Space Administration (NASA) Gulfstream III (G-III) airplane (Gulfstream Aerospace Corporation, Savannah, Georgia) SubsoniC Research Aircraft Testbed (SCRAT) between March 2013 and April 2015 it became evident that the sensor array used for stagnation point detection was not functioning as expected. The stagnation point detection system is a self calibrating hot-film array; the calibration was unknown and varied between flights, however, the channel with the lowest power consumption was expected to correspond with the point of least surface shear. While individual channels showed the expected behavior for the hot-film sensors, more often than not the lowest power consumption occurred at a single sensor (despite in-flight maneuvering) in the array located far from the expected stagnation point. An algorithm was developed to process the available system output and determine the stagnation point location. After multiple updates and refinements, the final algorithm was not sensitive to the failure of a single sensor in the array, but adjacent failures beneath the stagnation point crippled the algorithm.
This presentation presents the results of an algorithm to find the leading edge stagnation point using a sensor array with unknown calibration.
On September 8, 2016, the third NASA New Frontiers mission launched on an Atlas V 411. The Origins, Spectral Interpretation, Resource Identification, Security-Regolith Explorer (OSIRIS-REx) will rendezvous with asteroid Bennu in 2018, collect a sample in 2020, and return that sample to Earth in September 2023. The development team has overcome a number of challenges in order to design and build a system that will make contact with an unexplored, airless, low-gravity body. This paper will provide an overview of the mission, then focus in on the system-level challenges and some of the key system-level processes. Some of the lessons here are unique to the type of mission, like discussion of operating at a largely-unknown, low-gravity object. Other lessons, particularly from the build phase, have broad implications. The OSIRIS-REx risk management process was particularly effective in achieving an on-time and under-budget development effort. The systematic requirements management and verification and the system validation also helped identify numerous potential problems. The final assessment of the OSIRIS-REx performance will need to wait until the sample is returned in 2023, but this post-launch assessment will capture some of the key systems-engineering lessons from the development team.
We present the first results of an all-sky search for continuous gravitational waves from unknown spinning neutron stars in binary systems using LIGO and Virgo data. Using a specially developed analysis program, the TwoSpect algorithm, the search was carried out on data from the sixth LIGO science run and the second and third Virgo science runs. The search covers a range of frequencies from 20 Hz to 520 Hz, a range of orbital periods from 2 to ∼2,254 h and a frequency- and period-dependent range of frequency modulation depths from 0.277 to 100 mHz. This corresponds to a range of projected semimajor axes of the orbit from ∼0.6 × 10(exp −3) ls to ∼6,500 ls assuming the orbit of the binary is circular. While no plausible candidate gravitational wave events survive the pipeline, upper limits are set on the analyzed data. The most sensitive 95% confidence upper limit obtained on gravitational wave strain is 2.3 × 10(exp −24) at 217 Hz, assuming the source waves are circularly polarized. Although this search has been optimized for circular binary orbits, the upper limits obtained remain valid for orbital eccentricities as large as 0.9. In addition, upper limits are placed on continuous gravitational wave emission from the low-mass x-ray binary Scorpius X-1 between 20 Hz and 57.25 Hz.
We present an advanced Pole-from-Silhouette (PFS) algorithm, which is robust to illumination conditions and non-zero sun phase. PFS is an important step in the optical navigation pipeline for an autonomous small spacecraft to approach an unknown small body. The algo- rithm estimates the rotation pole and 3D shape (visual hull) of the small body using only the lit pixels within the silhouette of the small body, the body’s rotation rate, the spacecraft attitude, and the spacecraft-target relative distance, which is estimated from orbit determination. We present detailed numerical simulations and multiple sensitivity analyses to demonstrate the effectiveness of our proposed PFS algorithm in different scenarios and target bodies.
A reliable method for pose estimation of an unknown and uncooperative space target using monocular vision remains an open problem. Vision-based pose determination can be challenging in case of unfavorable illumination, time-varying conditions due to rotational motion and relative orbit, and scale ambiguity resolution. To address these challenges, we propose a novel collaborative pose determination algorithm called Multi- Spacecraft Simultaneous Estimation of Pose and Shape algorithm or M-SEPS.Within M-SEPS, a team of chaser spacecraft, each equipped with a monocular camera, exchange information over a local network to jointly estimate the relative kinematic state of the target and its sparse shape landmarks. In this approach, each spacecraft processes its own images and observes particular target landmarks in parallel and in a distributed fashion. Then, the local network is exploited by the spacecraft to share their consensus proposals and aggregate them to achieve the joint estimate. We validate our algorithm using simulations of relative orbits and observations, captured by each chaser spacecraft. To the best of the authors’ knowledge, this is the first cooperative, vision-based algorithm for estimating the pose and shape of a space object for an arbitrary number of spacecraft.
In real-world applications, the consideration of state and parameter uncertainties is an important part of any navigation and control system. Uncertainties can arise because of the instruments, communication systems, or external disturbances. Parameter uncertainty can be due to uncertainties in the data or the calibration process used. The objective of this research is to develop a rigid body pose estimation scheme on nonlinear manifolds of rigid body motion groups for unknown mass properties moments of inertia with application in space launch. An unscented Kalman filter is developed on special Euclidean space and their tangent bundle to address uncertainties in the states and inertia properties. This estimator accounts for uncertainties, while considering rotational-translational coupling, and avoids singularity or non-uniqueness issues. Since this estimator is based on the nonlinear manifolds on which rigid body motion evolves, it is reliable and is expected to result in high level of accuracy. The estimators are validated on a generic model of Logistic Module (LM) and a cuboid spacecraft model. The algorithms can then be tested on Dragon XL, HTV-X, and possibly other spacecraft.
A method of rapid, genome and proteome based identification of unknown pathogenic or non-pathogenic organisms in a complex sample. The entire sample is analyzed by creating millions of emulsion encapsulated microdroplets, each containing a single pathogenic or non-pathogenic organism sized particle and appropriate reagents for amplification. Following amplification, the amplified product is analyzed.
The polynomial chaos expansion (PCE) technique is used to build surrogate models of the dependences of gamma-ray fluxes and neutron multiplication to unknown physical parameters in radiological source/shield systems. These surrogate models are used with the DiffeRential Evolution Adaptive Metropolis (DREAM) method to solve and quantify uncertainty in inverse transport problems. Measured data in the inverse problems includes both passive gamma rays and neutron multiplication. The PCE approach is shown to increase the speed of DREAM by factors of greater than 80 while not degrading the accuracy of the solution.
Apparatus and methods are disclosed for coordination of a population of Thermostatically Controlled Loads (TCLs) with unknown parameters to achieve group objectives including bidding and market clearing strategies designed to motivate self-interested users to realize efficient energy allocation subject to a peak power constraint. In one examples of the disclosed technology, a method of operating a load includes estimating a set of values for unmeasured parameters of the load's thermal environment based on output measurements of the thermal environment, determining an energy response based on the estimated set of values for the unmeasured parameters, and transmitting a bid for power for a finite time period based on the determined energy response to the coordinator. A clearing price is received from the coordinator responsive to the transmitted bid and power is sent to the load responsive to the received clearing price.
The polynomial chaos expansion technique is used to build surrogate models of the dependences of gamma-ray fluxes and neutron multiplication to unknown physical parameters in radiological source/shield systems. These surrogate models are used with the Differential Evolution Adaptive Metropolis (DREAM), a method to solve and quantify uncertainty in inverse transport problems. Measured data in the inverse problems includes both passive gamma rays and neutron multiplication. The polynomial chaos expansion approach is shown to increase the speed of DREAM by factors of greater than 60 while not degrading the accuracy of the solution. (authors)