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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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76 records · Page 5
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DOE OSTI2024

Impedance sources (Z sources) with inherent fault protection for resilient and fire-free electricity grids

Modern societies would not survive without electricity and at the same time electrical faults could cause and have caused many catastrophes—mainly deadly fires—to our societies. There are two types of electricity sources: the voltage source such as generators, charged batteries and capacitors, and the current source such as charged inductors, current-regulated rectifiers, and superconducting magnetic energy storage. An “ideal” voltage source—that is often-sought-or-intentionally engineered—generates a constant voltage irrespective of its load current, and an “ideal” current source injects a constant current irrespective of its load voltage. However, two problems exist: (1) voltage or current sources do not represent many emerging natural/renewable energy sources such as wind turbine generators, photovoltaic cells, and fuel cells, whose output voltage and current are strongly dependent on each other, and (2) a short-circuit fault to an artificially-made and controlled “ideal” voltage source or an open-circuit fault to an “ideal” current source can cause catastrophic failures of the source itself and its surrounding circuits due to large (theoretically infinite) short-circuit current or open-circuit voltage. Here we introduce an impedance source concept to represent, characterize, and model those electricity sources whose output voltage and current are strongly dependent on each other. First, we found that many electric sources with no feedback (or active) control of their output voltage and/or current are a natural impedance source with inherent fault protection at short-circuit or open-circuit faults. Second, any electrical source can be artificially controlled to mimic a natural impedance source. Finally, we show how to apply natural impedance sources and nature-mimicking artificially-controlled sources to the electricity grid—the most complex machine ever made by human beings—to realize electricity grids that are naturally stable, self-protected against electrical faults, and resilient to natural and human-made events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Multiport high-pressure synchrotron x-ray microscopy cell (CRADA Final Report)

The LBNL team developed a multiport cell allowing for fluid flow, with electrical contacts for sample bias, and with reference electrodes. The cell is currently installed and operating in beam-line 11.0.2 of the ALS, the Berkeley Synchrotron. The cell is closed by a membrane consisting of several stacked layers of graphene (1 to 3). The graphene membrane is supported on a 100 nm thick Si3N4 membrane covered with a 20 nm thin gold film for improved adhesion of the graphene and to ensure electrical conductivity. The Si3N4 film is perforated with a periodic array of holes 1 to 2 micrometers diameter. The cell can withhold an internal pressure (gas or liquid) of up to 10 atmospheres for in situ/operando studies using X-ray Photoelectron emission (XPS), and X-ray Absorption Spectroscopy (XAS). It is also ideal for tip-enhanced nano-Infrared Spectroscopy studies of molecules at the interface between the graphene and the liquid or gas filling the cell. Materials studied in the LBNL laboratory are in the form of nanoparticles, or thin films (few nm) deposited on the side of the graphene membrane facing the solution or gas inside the cell.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Demonstration of Decentralized Physics-Driven Learning

In typical artificial neural networks, neurons adjust according to global calculations of a central processor, but in the brain, neurons and synapses self-adjust based on local information. Contrastive learning algorithms have recently been proposed to train physical systems, such as fluidic, mechanical, or electrical networks, to perform machine-learning tasks from local evolution rules. However, to date, such systems have only been implemented in silico due to the engineering challenge of creating elements that autonomously evolve based on their own response to two sets of global boundary conditions. Here, we introduce and implement a physics-driven contrastive learning scheme for a network of variable resistors, using circuitry to locally compare the response of two identical networks subjected to the two different sets of boundary conditions. Using this method, our system effectively trains itself, optimizing its resistance values without the use of a central processor or external information storage. Once the system is trained for a specified allostery, regression, or classification task, the task is subsequently performed rapidly and automatically by the physical imperative to minimize power dissipation in response to the given voltage inputs. Here, we demonstrate that, unlike typical computers, such learning systems are robust to extreme damage (and thus manufacturing defects) due to their decentralized learning. Our twin-network approach is therefore readily scalable to extremely large or nonlinear networks, where its distributed nature will be an enormous advantage; a laboratory network of only 500 edges will already outpace its in silico counterpart.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗