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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

SMART: Simplified Melting And Rotation-joint Technology

Oil based parabolic trough solar power plants are the most commercially mature CSP technology. However, the upper limit of about 400°C of the current organic heat transfer fluid(HTF)significantly limits the future potential of the technology. Advances in parabolic trough receiver and collector technology have enabled higher operating temperatures of potentially 500°C or above. The search for an improved higher temperature HTF has identified inorganic molten salts, specifically the mix referred to as Solar Salt, a 60:40 mix of sodium nitrate and potassium nitrate salt. However, Solar Salt starts to freeze at about 240°C. This poses a significant challenge for large parabolic trough plants that could have many kilometers of header piping and hundreds of kilometers of receiver piping all filled with molten salt. Plants using molten salt need to be designed to minimize the risk of freezing and to be able to recover from freeze events. Studies and field experiments have shown that this appears to be feasible and the approach appears to have strong economic advantages over conventional trough plants. However, some technical challenges remain related to the use of molten salt in trough solar fields, the cost of the freeze recovery system is significant, and many still question whether the risk of using molten salt is worth the economic upside. In our view, the potential economic upside justifies the continued look at molten salt HTF in parabolic trough plants. The objective of this project was to address the key technical issue remaining, look for opportunities to reduce the cost of the freeze recovery system, and improve the general information and tools available for assessing the design, performance and economics of trough plants using molten salt HTF.

14 SOLAR ENERGY↗
DOE OSTI2022

Smart Microgrids

The Nation’s electrical power depends on one bulk power grid to support security and economic prosperity. According to the Department of Homeland Security’s Homeland Threat Assessment of 2020, the largest cyber threat to homeland security is potential disruption to critical infrastructure, including power grids. Critical infrastructure includes the physical and cyber systems which generate, transmit, and distribute electricity with an impact on economic security, public health, or safety. The surety of the Nation’s power grid is vital for providing essential services and would put the population at risk if disrupted. Power outages can have catastrophic consequences for critical organizations such as hospitals and military installations. Additionally, the current fossil-fuel dependent power grid is extremely fragile and vulnerable to overloads, storms that destroy power lines, and cyber-attacks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Smart Pixels: towards on-sensor inference of charged particle track parameters and uncertainties

The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained to determine angular information of the charged particle passing through from pixel-cluster properties. This detector technology immediately improves the situation for offline tracking, but any major improvements in physics reach are unrealized since they are dominated by lowest-level hardware trigger acceptance. We will demonstrate track angle and hit position prediction, including errors, using a mixture density network within a single layer of silicon as well as the progress towards and status of implementing the neural network in hardware on both FPGAs and ASICs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Smart pixel sensors Towards on-sensor filtering of pixel clusters with deep learning

High granularity silicon pixel sensors are at the heart of energy frontier particle physics collider experiments. At an collision rate of 40\,MHz, these detectors create massive amounts of data. Signal processing that handles data incoming at those rate and intelligently reduces the data within the pixelated region of the detector \textit{at rate} will enhance physics performance and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first work, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector's data volume by 54.4-75.4\%. The network is designed and simulated as a custom readout integrated circuit with 28\,nm CMOS technology and is expected to operate at less than 300\,$\mu W$ with an area of less than 0.2\,mm$^2$.

43 PARTICLE ACCELERATORS↗