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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Intrinsic use control for system and use controlled component security

An Initialization Unit (IU) initiates an initial secure connection with an Intrinsic Use Control (IUC) Chip based on very large random numbers (VLRNs). The IUC Chip in turn initiates a secondary secure connection between it and one or more Use Controlled Components (UCCs). Polling by the IU allows confirmation of an ongoing secure connection, and also allows the IUC Chip to confirm the secondary secure connection to the UCCs. Removal or improper polling response from one of the UCCs results in a response from the IUC Chip that may include notification of tampering, or temporary or permanent discontinued operation of the offending UCC. Permanent discontinued operation may include destruction of the offending UCC, and cascaded discontinued operation of all other UCCs secured by the IUC Chip. A UCC may in turn be another nested layer of IUC Chips, controlling a corresponding layer of UCCs, ad infinitum.

Hart, Mark Miles↗
DOE OSTI2022

Method for controlling fiber growth in a laser heated pedestal growth system by controlling a laser power output, a pedestal feedstock rate of motion, and a draw rate

One or more embodiments relate to a method for controlling fiber growth and fiber diameter in a laser heated pedestal growth (LHPG) system so as to provide long, continuous single-crystal optical fibers of uniform diameter. The method generally provides three independent parameter feedback controls to control the molten zone height, laser power, and fiber drawing rates simultaneously in order to reduce the mismatch between instantaneous diameter changes and current diameter. The method permits the growth of fibers with non-uniform diameters along the fiber's length. The method also provides the capability to stop the LHPG system, remove the exhausted pedestal feedstock with a second pedestal feedstock, and restart the LHPG system to provide a continuous fiber.

Buric, Michael P.↗
DOE OSTI2022

Development of a nearly autonomous management and control system for advanced reactors

This presentation discusses the development of a Nearly Autonomous Management and Control (NAMAC) system for advanced reactors. This presentation first introduces the NAMAC-enabled plant control. Next, this presentation introduces major technologies of implementing NAMAC, including operational workflow, digital twin, and advanced machine learning algorithms. To demonstrate the capability of the NAMAC system, NAMAC is connected with a simulator of the Experimental Breeder Reactor II, where NAMAC is activated to provide recommendations during a single loss of flow accident.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Deep Reinforcement Learning Based HVAC Control for Reducing Carbon Footprint of Buildings

In this paper, we present our work on deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) with the goal of reducing carbon emission. We performed this task using 1) Marginal Operating Emission Rates (MOER), where the objective was to shift the demand to the low emission period of the day and 2) Time-Of-Use (TOU) demand-response price where the objective was to shift the demand to low price period of the day. This was achieved by learning an optimal pre-cooing strategy. We found the carbon emission reduction in the range of 6%-16% depending on the opportunity presented by the MOER signal. Similarly, we observed the carbon emission reduction in the range of 23%-29% during the peak price period when TOU price was used. The results clearly demonstrated the applicability of our approach in reducing the carbon footprint of the building.

carbon emission↗
DOE OSTI2023

Active Suspension Parameters Identification: An Algebraic Approach and its Application to Suspension Travel Control

The parameters of an active suspension need to be identified online, such that the suspension control system can be adapted to mechanical wear and load change. Recursive least squares and observer-based methods are frequently utilized to fulfill this purpose. However, they can yield slow parameter identification due to their asymptotic nature. We propose an algebraic identifier to estimate the parameters of an active suspension online, which does not maintain an asymptotic convergence phase. Simulation results demonstrate the effectiveness of the proposed algebraic approach.

Wang, Zejiang↗