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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Segmented annular combustion system

An annular combustion system includes a fuel nozzle, a panel fuel injector including a fuel plenum and at least one premixing channel therein, an inner liner including an inner band radially spaced from an outer band and an inner flow annulus formed therebetween, and an outer liner radially spaced from the inner liner and comprising an inner band radially spaced from an outer band and an outer flow annulus formed therebetween. The fuel nozzle is positioned radially between the inner liner and the outer liner, and the panel fuel injector extends radially between the inner liner and the outer liner. At least one of the inner flow passage and the outer flow passage is in fluid communication with at least one of a premix air plenum, a cooling air cavity and a cooling air plenum defined within the panel fuel injector.

42 ENGINEERING↗
DOE OSTI2021

Segmented annular combustion system

An annular combustion system includes a fuel nozzle, a panel fuel injector including a fuel plenum and at least one premixing channel therein, an inner liner having a hot side surface and a cool side surface and an outer liner having a hot side surface and a cool side surface. The inner liner, the outer liner and the panel fuel injector partially define a hot gas path downstream from the fuel nozzle. The inner liner and/or the outer liner define a plurality of micro-channel cooling passages which is disposed between the hot side surface and cool side surface of the inner liner. Each micro-channel cooling passage of the first plurality of micro-channel cooling passages is in fluid communication with an inlet hole defined along the cool side surface of the respective liner and an outlet hole.

Berry, Jonathan Dwight↗
DOE OSTI2022

Increased material flow throughput in large scale additive manufacturing through movable segmented build platform

An assembly for additive manufacturing includes a build housing including a base, a print head, and a print head support connected to the base for supporting the print head above the base. A driver system is provided for moving the print head and the base relative to one another. A build platform comprising a build support and at least one platen. The build support can be detachably engageable to the housing. The platens can be detachably engageable to the build support. The build support can include registration structure for registering the position of the build support relative to the build housing. A method of additive manufacturing is also disclosed.

Kunc, Vlastimil↗
DOE OSTI2024

Distribution Transformer Demand: Understanding Demand Segmentation, Drivers, and Management Through 2050

The National Renewable Energy Laboratory (NREL) has been working closely with the U.S. Department of Energy's Office of Electricity (OE) to understand the critical drivers and potential means of managing distribution transformer demand through 2050. This effort has consulted with utility representative organizations and transformer manufacturers to understand the problem, characterized the in-service assets, and modeled future demand. Distribution transformers, or service transformers, range from 10 to 5,000 kilovolt-amperes (kVA), have a high-side voltage of less than 34.5 kilovolts, and have step-down power delivery for customer end use. This research will help the manufacturing sector understand production requirements and better inform utility strategies for managing their demand.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Heterogeneous Point Set Transformers for Segmentation of Multiple View Particle Detectors

NOvA is a long-baseline neutrino oscillation experiment that detects neutrino particles from the NuMI beam at Fermilab. Before data from this experiment can be used in analyses, raw hits in the detector must be matched to their source particles, and the type of each particle must be identified. This task has commonly been done using a mix of traditional clustering approaches and convolutional neural networks (CNNs). Due to the construction of the detector, the data is presented as two sparse 2D images: an XZ and a YZ view of the detector, rather than a 3D representation. We propose a point set neural network that operates on the sparse matrices with an operation that mixes information from both views. Our model uses less than 10% of the memory required using previous methods while achieving a 96.8% AUC score, a higher score than obtained when both views are processed independently (85.4%).

Robles, Edgar E. [UC, Irvine (main)]↗
DOE OSTI2025

SHF: Symmetrical Hierarchical Forest with Pretrained Vision Transformer Encoder for High-Resolution Medical Segmentation

This paper presents a novel approach to addressing the long-sequence problem in high-resolution medical images for Vision Transformers (ViTs). Using smaller patches as tokens can enhance ViT performance, but quadratically increases computation and memory requirements. Therefore, the common practice for applying ViTs to high-resolution images is either to: (a) employ complex sub-quadratic attention schemes or (b) use large to medium-sized patches and rely on additional mechanisms within the model to capture the spatial hierarchy of details. We propose Symmetrical Hierarchical Forest (SHF), a lightweight approach that adaptively patches the input image to increase token information density and encode hierarchical spatial structures into the input embedding. We then apply a reverse depatching scheme to the output embeddings of the transformer encoder, eliminating the need for convolution-based decoders. Unlike previous methods that modify attention mechanisms or use a complex hierarchy of interacting models, SHF can be retrofitted to any ViT model to allow it to learn the hierarchical structure of details in high-resolution images without requiring architectural changes. Experimental results demonstrate significant gains in computational efficiency and performance: on the PAIP WSI dataset, we achieved a 3∼32×speedup or a 2.95%∼7.03% increase in accuracy (measured by Dice score) at a 64K2 resolution with the same computational budget, compared to state-of-the-art production models. On the 3D medical datasets BTCV and KiTS, training was 6×faster, with accuracy gains of 6.93% and 5.9%, respectively, compared to models without SHF.

Zhang, Enzhi [Hokkaido University, Japan]↗