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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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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

3d Kinematic Passive-steering Control with Roll Compensation for Rover Trajectory Following Over Rough Terrain

Some of the most appealing areas for future planetary surface exploration lie in rough, uneven terrains, such as craters and cold traps, which are currently inaccessible by state-of-the-art robotic systems [1]. To provide modularity for access and in-situ sampling within such extreme environments, Jet Propulsion Laboratory (JPL) and California Institute of Technology have collaborated to develop the DuAxel rover system, a modular robot composed of two Axel rappelling vehicles docked to a central module into a four-wheeled configuration, suited for driving long distances due to articulated passivesteering capabilities [2]. Inspired by Carnegie Mellon’s Zoë rover [3], a 3D kinematic control strategy, leveraging a novel, DuAxel-centric model, has been developed to enable precise trajectory following over rough, flat terrain with presence of obstacles.

McGarey, Patrick↗
NASA NTRSDate not supplied

A Machine Learning Framework for Error Compensation in Radiative Transfer Calculations

Radiative heat transfer influences the amount of heat flux transferred to the surface of the hypersonic vehicle, which is essential to evaluate the performance of thermal protection systems. The radiative heat flux is found to be computationally prohibitive while accounting for the variation in spatial, angular, and spectral domains. A new methodology has been recently developed to alleviate the cost of computation in the spectral domain by constructing flow-agnostic reduced-order models (ROMs). The developed spectral ROM databases provide grouping strategies that account for non-equilibrium absorption and emission as well as interaction between disparate species due to spectral overlap in associated radiative processes. However, the developed ROMs need to be optimized for a specific combination of interacting gas species and would need to re-calibrated in case individual species are added/omitted. In this work, we use various machine learning (ML) techniques to approximate the radiative intensities determined by a ROM optimized for a specific gas mixture. The ML model relies on the ROM databases developed for a single species which ignores any spectral overlap. Thus, radiation evaluation starts with a simple summation of radiative intensities predicted using these non-calibrated ROMs for the contributing species. The ML framework then provides a correction to account for the interplay in the frequency, i.e., emission of photons by one species and absorption by another, and yields mixture-specific radiation fields. Once trained on the individual ROM databases, the ML framework offers instantaneous corrections that serves as a time/cost effective alternative to the optimization of ROMs for a specific gas mixture. The ML framework is trained on both the high fidelity and ROM evaluated line of sight (LOS) data from Orion, Stardust, and FIRE II cases to obtain a general purpose correction model for earth re-entry scenarios when radiation contributions from both atomic nitrogen and atomic oxygen are considered. A geometric length scale parameter is used in the training process to account for errors introduced in the ROM databases as a consequence of high optical thickness. The efficacy of the ML framework is underscored through extensive analysis of train and test errors with respect to all the re-entry scenarios. The applicability of such an ML framework was further corroborated by embedding it in a state-of-the-art US3D - NERO system for determining the radiative heat flux transferred to the hypersonic vehicle surface.

Radiation↗
DOE OSTI2014

Crosstalk compensation in analysis of energy storage devices

Estimating impedance of energy storage devices includes generating input signals at various frequencies with a frequency step factor therebetween. An excitation time record (ETR) is generated to include a summation of the input signals and a deviation matrix of coefficients is generated relative to the excitation time record to determine crosstalk between the input signals. An energy storage device is stimulated with the ETR and simultaneously a response time record (RTR) is captured that is indicative of a response of the energy storage device to the ETR. The deviation matrix is applied to the RTR to determine an in-phase component and a quadrature component of an impedance of the energy storage device at each of the different frequencies with the crosstalk between the input signals substantially removed. This approach enables rapid impedance spectra measurements that can be completed within one period of the lowest frequency or less.

25 ENERGY STORAGE↗
DOE OSTI2014

Intrinsic feature-based pose measurement for imaging motion compensation

Systems and methods for generating motion corrected tomographic images are provided. A method includes obtaining first images of a region of interest (ROI) to be imaged and associated with a first time, where the first images are associated with different positions and orientations with respect to the ROI. The method also includes defining an active region in the each of the first images and selecting intrinsic features in each of the first images based on the active region. Second, identifying a portion of the intrinsic features temporally and spatially matching intrinsic features in corresponding ones of second images of the ROI associated with a second time prior to the first time and computing three-dimensional (3D) coordinates for the portion of the intrinsic features. Finally, the method includes computing a relative pose for the first images based on the 3D coordinates.

47 OTHER INSTRUMENTATION↗