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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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21 records · Page 2

Compute Element and Interface Box for the Hazard Detection System

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) program is building a sensor that enables a spacecraft to evaluate autonomously a potential landing area to generate a list of hazardous and safe landing sites. It will also provide navigation inputs relative to those safe sites. The Hazard Detection System Compute Element (HDS-CE) box combines a field-programmable gate array (FPGA) board for sensor integration and timing, with a multicore computer board for processing. The FPGA does system-level timing and data aggregation, and acts as a go-between, removing the real-time requirements from the processor and labeling events with a high resolution time. The processor manages the behavior of the system, controls the instruments connected to the HDS-CE, and services the "heavy lifting" computational requirements for analyzing the potential landing spots.

Villalpando, Carlos Y.↗

User's Manual and Final Report for Hot-SMAC GUI Development

A new software package called Higher Order Theory-Structural/Micro Analysis Code (HOT-SMAC) has been developed as an effective alternative to the finite element approach for Functionally Graded Material (FGM) modeling. HOT-SMAC is a self-contained package including pre- and post-processing through an intuitive graphical user interface, along with the well-established Higher Order Theory for Functionally Graded Materials (HOTFGM) thermomechanical analysis engine. This document represents a Getting Started/User's Manual for HOT-SMAC and a final report for its development. First, the features of the software are presented in a simple step-by-step example where a HOT-SMAC model representing a functionally graded material is created, mechanical and thermal boundary conditions are applied, the model is analyzed and results are reviewed. In a second step-by-step example, a HOT-SMAC model of an actively cooled metallic channel with ceramic thermal barrier coating is built and analyzed. HOT-SMAC results from this model are compared to recently published results (NASA/TM-2001-210702) for two grid densities. Finally, a prototype integration of HOTSMAC with the commercially available HyperSizer(R) structural analysis and sizing software is presented. In this integration, local strain results from HyperSizer's structural analysis are fed to a detailed HOT-SMAC model of the flange-to-facesheet bond region of a stiffened panel. HOT-SMAC is then used to determine the peak shear and peel (normal) stresses between the facesheet and bonded flange of the panel and determine the "free edge" effects.

Yarrington, Phil↗

Performance Assessment of LunaNet’s Augmented Forward Signal

LunaNet provides a common set of interoperable specifications for communication and position, navigation and time (PNT) services and interfaces soon to be implemented in lunar vicinity. The LunaNet Interoperability Specification (LNIS) provides the design for the GNSS-like Augmented Forward Signal (AFS), which enables orbiting and surface users in lunar space, such as Artemis, to estimate their position, velocity, and time. The specification of AFS defines two orthogonal signal components on a single carrier: the in-phase component (AFS-I), a lower-chip-rate data channel tailored for applications where low SWaP (Size, Weight, and Power) is critical (e.g., IoT devices or search and rescue), and the quadrature component (AFS-Q), a high-chip-rate data-less pilot signal for high-precision, robust lunar navigation and positioning applications. An initial description of AFS was provided in [1], with initial analysis results shown in [2] and [3] and the current signal in space description provided in [4]. As part of NASA's Lunar Communication Relay and Navigation Systems (LCRNS) project, this work expands upon the initial analysis results and proposes a new expanded set of AFS-Q spreading codes that exceed the cross-correlation and autocorrelation sidelobe performance of L1C and other GNSS signals, while providing additional expansion capabilities for future provider satellites. A set of 420 codes was selected from a Weil-based code derived from the prime number 10247, which is larger than the 10243 prime number used to derive Beidou’s B1C Weil sequences. Both the initial set of 210 codes and the expanded set of 420 codes are shown to provide the best cross-correlation of any 10230-chip satellite navigation codes. The performance is demonstrated for hierarchical sets of spreading codes optimized and organized in sets of 30 codes. The new codes were developed using an optimization approach and correlation methodology described in [5]. The work also compares LunaNet’s AFS to terrestrial GNSS signals in terms of acquisition, tracking, and data demodulation performance. Performance is evaluated for receivers that only track the 1.023 MCPS data channel spreading code for low SWaP IoT use cases, as well as for receivers that track both the 1.023 MCPS data channel and the 5.115 MCPS pilot channel spreading code for high-performance use cases. Performance is assessed in the presence of interference and thermal noise. The analysis is performed in terms of expected operating conditions on the lunar surface. Several unique flexibility aspects of the augmented forward signal are described, including the use of the Q channel’s secondary and tertiary codes to enable variable coherent integrations during acquisition. This is compared to GNSS signals such as L5/E5 and MBOC in terms of achievable processing gain for interference mitigation versus acquisition complexity. The work details acquisition and tracking techniques used to optimally acquire and track the primary, secondary, and tertiary codes on the Q channel, as well as acquisition of the I channel spreading code. Acquisition of the 8 ms Q channel spreading code is also compared to joint acquisition of the I and Q channel primary codes in noise and interference environments.

LCRNS↗