Search NASA⌕ Search

SEARCH · Search NASA

Results for “Nuclear Science & Technology”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

1,380 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Stability Measurements of the Radio Science System at the 34-m High-Efficiency Antennas

From 1991 to 1993 the fractional frequency stability of the operational Radio Science System was measured at DSS's 15, 45, and 65. These stations are designed to have the most stable uplink and downlink equipment in the Deep Space Network (DSN). Some measurements were performed when the antenna was moving and the frequency was ramped. The stability, including contributions of all elements in the station except for the antenna and the hydrogen maser, was measured to be 0.3 to 1.3 x 10 -15 when the frequency was fixed, and 0.6 to 6.0 x 10 -15 when the frequency was ramped (sample interval, 1000 sec). Only one measurement out of fifteen exceeded specification. In all other cases, when previous measurements on the antenna and the hydrogen maser were added, a total system stability requirement of 5.0 x 10 -15 as met. In addition, ambient temperature was found to cause phase variation in the measurements at a rate of 5.5 deg of phase per deg C.

T T Pham↗
NASA NTRS1966

Research Achievements Review, Volume II

Materials research - diffusion bonding methods, stress corrosion tests, nondestructive testing, material designs for electronic equipment, and nonmetallic material development.

Materials Science↗
NASA NTRS2004

Lunar and Planetary Science XXXV: Icy Worlds: Moving and Grooving

Reports from the conference session entitled Icy Worlds: Moving and Grooving, include:Mass Anomalies on Ganymede; Europan Chaos and Lenticulae: A Synthesis of Size, Spacing, and Areal Density Analyses; Thermal and Topographic Tests of Europa Chaos Formation Models; Flexure of Europa s Lithosphere Due to Ridge-Loading; Ridges on Europa: Origin by Incremental Ice-Wedging ; Convergent Boundaries on Europa: a Numerical Approach to Euler Pole Analysis and Its' Implications for Plate Reconstruction; Numerical Simulations of Subsolidus Convection in the Ice Shell of Europa: Implications for the Thermal Evolution and Present State; Effects of Plasticity on Convection in an Ice Shell: Implications for Europa; Non-Newtonian Convection and Compositional Buoyancy: Advances in Modeling Convection and Dome Formation on Europa; Convective Instability in Ice I: Application to Callisto and Ganymede; Crater Size Distributions on Callisto: A Galileo SSI Summary; Neutron Diffraction Studies of Planetary Ices; and H2O2 Synthesis Induced by Irradiation of H2O with Energetic H+ and Ar+ Ions at Various Temperatures.

Source record↗
NASA NTRSDate not supplied

Thermostructural Testing of PICA-D for NASA Planetary Science Missions

Phenolic Impregnated Carbon Ablator–Domestic (PICA‑D) has been selected as the heatshield thermal protection system (TPS) material for two upcoming NASA planetary science missions: the Dragonfly mission to Titan and the Mars Sample Retrieval Lander (SRL). Early testing revealed differences between PICA‑D and heritage PICA, particularly in in‑plane (IP) tensile stiffness and thermal expansion. Thermostructural analyses using Finite Element Method (FEM) tools subsequently predicted the potential for IP compressive failure in the near‑surface layers of PICA‑D under both Dragonfly and SRL flight environments. Over the past three years, the Dragonfly and SRL teams have carried out an extensive thermostructural qualification campaign to address these concerns and validate PICA‑D for flight. This effort began with a comprehensive mechanical property characterization program at Kratos test laboratories, which significantly improved understanding of PICA‑D mechanical behavior and increased the fidelity of FEM predictions. The teams also conducted six large‑scale test entries at the National Solar Thermal Test Facility (NSTTF) solar tower, exposing PICA‑D articles—including gap fillers and representative design features or flaws—to combined thermal and mechanical loads. The talk will summarize key findings from these mechanical and thermostructural test campaigns and present the current status of PICA‑D qualification for NASA’s planetary science missions.

TPS↗
DOE OSTI2026

Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation

Abstract Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation The open-source is publicly available at: https://github.com/datax-lab/BioExPL.

Baek, Beomsu [Department of Computer Science, Univ↗