Advanced Energy Storage Technologies for NASA’s Robotic Exploration in Extreme Environments
No abstract provided
SEARCH · Search NASA
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.
No abstract provided
In recent years, unsupervised deep learning ap-proaches have received a significant attention to estimate depthand visual odometry (VO) from unlabelled monocular imagesequences. However, their performance is limited in challengingenvironments due to perceptual degradation, occlusions andrapid motions. Moreover, the existing unsupervised methodssuffer from the lack of scale-consistency constraints acrossframes, which causes that the VO estimators fail to providepersistent trajectories over long sequences. In this study, wepropose a unsupervised monocular deep VO framework thatpredicts 6 degrees-of-freedom pose camera motion and depthmap of the scene from unlabelled RGB image sequences.We provide detailed quantitative and qualitative evaluationsof the proposed framework on a) a challenging dataset col-lected during the DARPA Subterranean challenge1; and b)the benchmark KITTI and Cityscapes datasets. The proposedapproach outperforms both traditional and state-of-the-artunsupervised deep VO methods providing better results for bothpose estimation and depth recovery. The presented approach ispart of the solution used by the COSTAR team participatingat the DARPA Subterranean Challenge
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Evaluate risk factors, biomarkers, and countermeasures for adaptation and resilience in ICE environments. Identify how meaningful work influences the relationship between risk factors, the valence and social process domains, and operational and performance outcomes. Develop an operationally acceptable and valid measure of meaningful work in ICE. Examine meaningful work as a countermeasure.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Effective thermal management is crucial for maintaining spacecraft and onboard systems at optimal temperatures, ensuring their longevity in space missions. Passive thermal management is particularly promising due to its lower power consumption compared to active systems. This study focuses on developing materials with low solar absorptance and large thermal emittance to facilitate self-cooling under the harsh thermal conditions of space. We employ electrospinning, a nano/micro-manufacturing technique, to create a lightweight, fibrous thermal control material from silica (SiO 2 ). Using scanning electron microscopy (SEM), we examine the nanoporous structure of the electrospun material. Its optical properties, including reflectance, transmittance, and absorptance across ultraviolet, visible, and infrared wavelengths, are assessed using spectrometers interfaced with integrating spheres. To test durability in space, we conduct high temperature endurance and ultraviolet resistance tests to observe changes in optical properties of the materials and evaluate their performance in low Earth orbit (LEO). We also compare the material's solar reflectance and thermal emittance to existing spacecraft materials. The findings suggest that electrospun silica nanofibers presents a new paradigm for passive thermal control in space applications.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Experimentation at irradiation test facilities are essential for reducing the innovation time of developmental fuels, fuel cladding, and structural materials employed in next-generation nuclear reactors. However, due to the harsh conditions generated in such reactors and limited instrumentation space, the evaluation of a material’s mechanical properties is often limited to characterization after the materials have been removed from reactor conditions, or post-irradiation examination. These experiments are costly, time-consuming, and fail to capture the critical time-evolving phenomena that occur during the irradiation experiments. Advanced manufactured digital image correlation patterns and strain sensor devices serve as two promising technologies that can be deployed in the confined and challenging orientations of these irradiation experiments while also providing key insight on salient materials phenomena (i.e., mechanical properties). To guide the development of the printed strain sensors prior to their deployment in critical experiments, the adhesion strength between the substrate and printed film interface is measured via tensile testing and a non-contact laser-induced spallation technique. The establishment of these process control steps helped guide the successful fabrication and testing of direct-write strain sensing devices discussed in this work. The fabrication process controls are necessary for enabling the sustained operation of these strain sensing device through experimentation and minimize the potential for premature failure.
This slide deck presents the research results in embedding fiber optic sensors in structural materials. Fiber optic sensors were embedded in stainless steel and nickel via electric field assisted sintering. The embedded sensors were evaluated in terms of fiber integrity, fiber-matrix bonding, fiber functionality, mechanical properties, and machinability.