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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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731 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

The New CCSDS Standard for Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression

This paper describes the emerging Issue 2 of the CCSDS-123.0-B standard for low-complexity compression of multispectral and hyperspectral imagery, focusing on its new features and capabilities. Most significantly, this new issue incorporates a closed-loop quantization scheme to provide near-lossless compression capability while still supporting lossless compression, and introduces a new entropy coding option that provides better compression of low-entropy data.

Kiely, Aaron B.↗
NASA NTRS2004

An Efficient End-To-End Model for the Kepler Photometer

The Kepler Mission is designed to characterize the frequency of Earth-sized planets in the habitable zones of solar-like stars in the solar galactic neighborhood by observing >100,000 main-sequence stars in a >100 square degree field of view (FOV) and seeking evidence of transiting planets. As part of the system engineering effort, we have developed an End-To-End Model (ETEM) of the photometer to better characterize the expected performance of the instrument and to guide us in making design trades. This model incorporates engineering information such as the point spread function, time histories of pointing offsets, operating temperature, quantization noise, the effects of shutterless readout, and read noise. Astrophysical parameters, such as a realistic distribution of stars vs. magnitude for the chosen FOV, zodiacal light, and cosmic ray events are also included. For a given set of design and operating parameters, ETEM generates pixel time series for all pixels of interest for a single CCD channel of the photometer. These time series are then processed to form light curves for the target stars and the impact of various noise sources on the combined differential photometric precision can be determined. This model is of particular value when investigating the effects of noise sources that cannot be easily subjected to direct analysis, such as residual pointing offsets, thermal drift or cosmic ray effects. This version of ETEM features extremely efficient computation times relative to the previous version while maintaining a high degree of fidelity with respect to the realism of the relevant phenomena.

performance modeling↗
NASA NTRS2015

Measuring and Modeling Shared Visual Attention

Multi-person teams are sometimes responsible for critical tasks, such as flying an airliner. Here we present a method using gaze tracking data to assess shared visual attention, a term we use to describe the situation where team members are attending to a common set of elements in the environment. Gaze data are quantized with respect to a set of N areas of interest (AOIs); these are then used to construct a time series of N dimensional vectors, with each vector component representing one of the AOIs, all set to 0 except for the component corresponding to the currently fixated AOI, which is set to 1. The resulting sequence of vectors can be averaged in time, with the result that each vector component represents the proportion of time that the corresponding AOI was fixated within the given time interval.We present two methods for comparing sequences of this sort, one based on computing the time varying correlation of the averaged vectors, and another based on a chi-square test testing the hypothesis that the observed gaze proportions are drawn from identical probability distributions.We have evaluated the method using synthetic data sets, in which the behavior was modeled as a series of activities, each of which was modeled as a first-order Markov process. By tabulating distributions for pairs of identical and disparate activities, we are able to perform a receiver operating characteristic (ROC) analysis, allowing us to choose appropriate criteria and estimate error rates. Using these criteria, we have applied the methods to data from airline crews, collected in a high-fidelity flight simulator (Gontar Hoermann, 2014). We conclude by considering the problem of automatic (blind) discovery of activities, using methods developed for text analysis.

Mulligan, Jeffrey B.↗
NASA NTRS2017

Thermal Expansion from Stochastic Nonlinear Acoustic Fields

The methods of stochastic mechanics are applied to nonlinear acoustic fields generated by random acoustic radiation sources associated with oscillating lattice sites in nonlinear lattices. The assumption of stochastically independent zero-point and temperature-dependent acoustic fields leads to expressions of the thermodynamic internal and Helmholtz free energies per unit mass in terms of modal energies per unit mass that account for the nonlinearity of the propagation modes. The thermodynamic state functions canonically transform to the familiar results of a system of quantized, simple harmonic oscillators in the linear field limit. The thermal expansion coefficient, derived from the Helmholtz free energy per unit mass, is obtained as a sum of the zero-point modal nonlinearity parameters, weighted by temperature-dependent, modal heat capacities. The relationship is fully anharmonic, in contrast to commonly used quasi-harmonic models, and predicts with excellent agreement the experimentally observed null thermal expansion of vitreous silica at the ‘cross-over’ temperature corresponding to the balance between long wavelength modes that contribute negative nonlinearity parameters to the modal sum and short wavelength modes that contribute positive nonlinearity parameters.

Cantrell, John H., Jr.↗
NASA NTRS2021

NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis

Using state-of-the-art artificial intelligence (AI)frameworks onboard spacecraft is challenging because common spacecraft processors cannot provide comparable performance to datacenters with server-grade CPUs and GPUs available for terrestrial applications and advanced deep-learning networks. This limitation makes small, lo w-p o we r AI microchip architectures, such as the Google Coral Edge Tensor Processing Unit (TPU), attractive for space missions where the application-specific design enables both high-performance and power-efficient computing for AI applications. To address these challenging considerations for space deployment, this research introduces the design and capabilities of a CubeSat-sized Edge TPU-based co-processor card, known as the SpaceCube Low-power Ed g e Artificial Intelligence Resilient Node (SC-LEARN). This design conforms to NASA’s CubeSat Card Specification (CS2) for integration into next-generation SmallSat and CubeSat systems. This paper describes the overarching architecture and design of the SC-LEARN, as well as, the supporting test card designed for rapid prototyping and evaluation. The SC-LEARN was developed with three operational modes: (1) a high-performance parallel-processing mode,(2)a fault-tolerant mode for onboard resilience, and (3) a power-saving mode with cold spares. Importantly, this research also elaborates on both training and quantization of Tensor Flow models for the SC-LEARN for use onboard with representative, open-source datasets. Lastly, we describe future research plans, including radiation-beam testing and flight demonstration.

Advanced avionics↗
NASA NTRSDate not supplied

Where Lies the Quantumness Behind Detecting Electromagnetic Waves for Frequencies from Infrared and Up?

The synthesis of Newtonian concept of corpuscles during emission with Huygens’ concept of secondary-wavelets during propagation implies that all EM radiations from quantized atoms and molecules are released as discrete amount of energies. However, they propagate out as time-finite Maxwellian light pulses. Huygens also underscored that his secondary wavelets keep propagating as independent pulses in the absence of any interacting medium, or until intercepted by an interface with a medium or a detector. Then we use the Superposition Principle and the coherence theory to derive Einstein’s photoelectric equation by summing innumerable random time-finite pulses. This process driven approach should yield the characteristic statistical variations of photoelectron current pulses, as generated by photodetectors for different kinds of light sources. Lamb & Scully originally proposed this semiclassical approach without assuming that light actually consists of time finite pulses. The quantumness remains confined within the excitation and de-excitations processes in the material particles.

Electromagnetic Waves↗
NASA NTRS2023

Testing theories of Gravitation with the Interstellar Probe Radio Experiment

General Relativity (GR) will soon celebrate its 110th birthday, holding up against all experimental enquiry. Nonetheless, unification theories attempting to quantize gravity, such as string theory, are gaining footing. These hypothesize additional scalar, vector, and tensor long-range fields that couple to matter (Will, 2014), introducing violations to GR. Although such violations have never been detected, it is likely that GR will not be the ultimate theory of gravity. What is certain is that gravity tests are alive and well, pushing the validity of GR to new scales and accuracies, or -potentially- suggesting alternative routes for new physics. Building upon the legacy of Voyager and Pioneer missions, which demonstrated the capability to survive in the outer reaches of the solar system, the Interstellar Probe mission concept (McNutt et al., 2022) aims to characterise our heliosphere through state-of-the-art instrumentation, opening new frontiers also for GR testing. In this work, we investigate the possibility of constraining the Nordtvedt parameter η and the mass of the graviton via the Compton wavelength λc, by simulating the processing of 10 years of radiometric data from the Interstellar Probe. Station calibration and clock synchronisation, as well as limiting spacecraft precession manoeuvres are highlighted as key strategies for obtaining high-quality estimates. In the most favourable scenario, η can be constrained to less than 1.5 · 10 -5 , reducing the uncertainty obtained via Lunar Laser Ranging (Hofmann and Müller, 2018), and a lower bound of 1.4 ‧ 10 14 km is set for λc, improving the estimates obtained from planetary ephemerides (Bernus et al., 2020) and gravitational wave detection (Abbott et al., Jun 2021). Extending ranging measurement acquisition to 20 years improves the results tenfold. This experiment interrogates fundamental physics from a unique dynamical setting, investigating possible violations of the Equivalence Principle (EP) underlying GR.

general relativity↗
NASA NTRSDate not supplied

Discrete Rotational Energy for Polyatomic Molecules in Direct Simulation Monte Carlo

Accurate prediction of aerothermodynamic loads in thermal non-equilibrium flows requires precise modeling of internal energy exchange. While previous direct simulation Monte Carlo frameworks have successfully implemented discrete rotational energy models for diatomic species, the treatment of polyatomic molecules has traditionally relied on continuous energy assumptions that break down at low temperatures and neglect critical high-temperature corrections. This study extends the discrete rotational energy models of Boyd and Gimelshein to fully encompass polyatomic molecules. The proposed framework implements quantized rotational energy level sampling for linear, spherical, and symmetric/asymmetric top rotors. Crucially, the model incorporates centrifugal distortion to address the limitations of the rigid-rotor assumption at hypersonic temperatures, and accounts for nuclear spin parity, which dictates the permissible rotational states and macroscopic specific heats at low temperatures. The model is verified through equilibrium sampling procedures, demonstrating agreement with theoretical quantum Boltzmann distributions and accurately reproducing thermophysical properties across a wide range of temperatures.

DSMC↗
NASA NTRS2023

Neural-Based Compression Scheme for Solar Image Data

Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make. Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy which could affect scientific analysis of the data. On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression. In this work, we propose a neural network-based lossy compression method to be used in NASA’s data-intensive imagery missions. We chose NASA’s Solar Dynamics Observatory (SDO) mission which transmits 1.4 terabytes of data each day as a proof of concept for the proposed algorithm. In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs. The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective. We also studied how optimizing perceptual losses could help our neural compressor to preserve high-frequency details of the data in the reconstructed compressed image. Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs such as JPEG and JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet (EUV) data. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images, and generated consistent segmentations, even at a compression rate of ∼ 0.1 bits per pixel (compared to 8 bits per pixel on the original data) using EUV data from SDO.

Image coding↗