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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Africa's Megafans and Their Tectonic Setting

Megafans are a really extensive continental sediment bodies, fluvially derived, and fan-shaped in planform. Only those >80 km long were included in this study. Africa's megafans were mapped for purposes of both comprehensive geomorphic description and as a method of mapping by remote sensing large probable fluvial sediment bodies (we exclude sediment bodies deposited in well defined, modern floodplains and coastal deltas). Our criteria included a length dimension of >80 km and maximum width >40 km, partial cone morphology, and a radial drainage pattern. Visible and especially IR imagery were used to identify the features, combined with topographic SRTM data. We identified 99 megafans most of which are unstudied thus far. Their feeder rivers responsible for depositing megafan sediments rise on, and are consequent drainages oriented down the slopes of the swells that have dominated African landscapes since approximately 34 Ma (the high points in Africa's so-called basin-and-swell topography [1]). Most megafans (66%) have developed along these consequent rivers relatively near the swell cores, oriented radially away from the swells. The vast basins between the swells provide accommodation for megafan sediment wedges. Although clearly visible remotely, most megafans are inactive as a result of incision by the feeder river (which then no longer operates on the fan surface). Two tectonic settings control the location of Africa's megafans, 66% on swell flanks, and 33% related to rifts. (i) Swell flanks Most megafans are apexed relatively near the core of the parent swell, and are often clustered in groups: e.g., six on the west and north flanks of the Hoggar Swell (Algeria), seven on the north and south flanks of the Tibesti Swell (Libya-Chad borderlands), twelve on the west flank of the Ethiopian Swell, four on the east flank of the East African Swell (Kenya), Africa's largest, and eight around Angola's Bié Swell (western Zambia, northern Namibia). A cluster of possible fans lies on the western margin of the Congo Basin (Mayombe Swell), and on the coastal slopes of the Namibia Swell. Sheer size may have militated aginst the recognition of many megafans: the largest in the Sahara are the Teghahart (378 km, Hoggar Swell, Algeria), and the Wadi Albalata (340 km, Uweinat Swell, Egypt). In southern Africa the largest are the Cubango (320 km, Bié Swell, Angola/ Namibia), and the Limpopo (230 km, Mozambique). (ii) Rift zones (a) Steer's horns basins-wide depressions centered on rifts. The largest contiguous group (n=14) developed in a steer's-horns basin occupies the wide Muglad depression (200-350 km, South Sudan). Four rift-related megafans lie SE of Lake Chad (Chad). Nine megafans occupy the complex Anza Rift in Kenya/South Somalia. The Salamat megafan (Chad), is unusual because it oriented parallel with the linked Salamat, Doseo and Doba rift axes, and is consequently one of the longest in Africa (465 km). (b) Rift depressions sensu stricto. Most rifts are too narrow to provide a transverse dimension large enough to accommodate megafans. Although well-known, the Okavango Rift (NW Botswana, NE Namibia) is unique in Africa in hosting three megafans within identifiable faulted margins. The Nile megafan is Africa's largest (476 km) and comprises the vast Sudd wetland (South Sudan). An explanation for its remarkable size may be its location in a depression at the junction of two conducive tectonic zones, the East African Swell margin and the Muglad steer's-horns depression. Discharge of the River Nile, the largest in the region, has allowed the Nile megafan to outcompete neighboring megafans for space.

Wilkinson, M. J.↗
DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗