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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 595 records · Page 33

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DOE OSTI2012

Control system design for maintaining CO{sub 2} capture in IGCC power plants while loading-following

Load-following requirements for future integrated gasification combined cycle (IGCC) power plants with precombustion CO{sub 2} capture are expected to be far more challenging as electricity produced by renewable energy is connected to the grid and strict environmental limits become mandatory requirements. In this work, loadfollowing studies are performed using a comprehensive dynamic model of an IGCC plant with pre-combustion CO{sub 2} capture developed in Aspen Engineering Suite (AES). Considering multiple single-loop controllers for power demand load following, the preferred IGCC control strategy from the perspective of a power producer is gas turbine (GT) lead with gasifier follow. In this strategy, the GT controls the load by manipulating its firing rate while the slurry feed flow to the gasifier is manipulated to control the syngas pressure at the GT inlet. The syngas pressure control is an integrating process with significant time delay mainly because of the large piping and equipment volumes between the gasifier and the GT inlet. A modified proportional–integral–derivative (PID) control is considered for IGCC syngas pressure control. The desired CO{sub 2} capture rate must be maintained while the IGCC plant follows the load. For maintaining the desired CO{sub 2} capture rate, the control performance of PID control is compared with linear model predictive control (LMPC). The results show that the LMPC outperforms PID control for maintaining CO{sub 2} capture rates in an IGCC power plant while load following.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

Validation and Demonstration of Control System Functional Capabilities within the IES Plug-and-Play Simulation Environment

The concept of an integrated energy system (IES) is meant to combine different energy technologies in synergistic ways to achieve a more secure and economical energy supply. The RAVEN-based HYBRID framework is used to find the optimal installed capacity and the optimal economical dispatch of each component of the IES. The new RAVEN plugin for grid and capacity optimization (HERON) only addresses the limits that affect the production variables and the corresponding rates of variation (explicit constraints). However, other variables are subject to constraints, and the associated limits should be accounted for (implicit constraints). In particular, for the power dispatch problem, the optimization algorithm takes into account the limits on the electrical power output and the corresponding hourly power variations but does not consider other constraints on process variables whose response affects the service life of the IES. This report describes a scheme that allows accounting for implicit constraints without increasing the size of the optimization problem. To obtain a more accurate approximation of the nonlinear dynamic behavior, a parametric version of the dynamic mode decomposition with control (DMDc) algorithm was developed to derive the state-space representation matrices of the IES components at different scheduling parameter. Thanks to this approach, a more accurate approximation of the system response can be obtained, the limits imposed by thermal mechanical implicit constraints can be translated into power dispatch limits, and the feedbacks to HERON power dispatcher can be provided. To assess the developed methodology, a power dispatching test case composed of three power generating and storage units (Balance of Plant, Secondary Energy Source, Thermal Energy Storage) was developed. The power output of each one of the three units was optimized to meet the imposed time-dependent load demand trajectory and to maximize the IES profitability by meeting both the explicit and implicit constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

A tyrosine phosphoregulatory system controls exopolysaccharide biosynthesis and biofilm formation in Vibrio cholerae

Production of an extracellular matrix is essential for biofilm formation, as this matrix both secures and protects the cells it encases. Mechanisms underlying production and assembly of matrices are poorly understood. Vibrio cholerae, relies heavily on biofilm formation for survival, infectivity, and transmission. Biofilm formation requires Vibrio polysaccharide (VPS), which is produced by vps gene-products, yet the function of these products remains unknown. Here, we demonstrate that the vps gene-products vpsO and vpsU encode respectively for a tyrosine kinase and a cognate tyrosine phosphatase. Collectively, VpsO and VpsU act as a tyrosine phosphoregulatory system to modulate VPS production. We present structures of VpsU and the kinase domain of VpsO, and we report observed autocatalytic tyrosine phosphorylation of the VpsO C-terminal tail. The position and amount of tyrosine phosphorylation in the VpsO C-terminal tail represses VPS production and biofilm formation through a mechanism involving the modulation of VpsO oligomerization. We found that tyrosine phosphorylation enhances stability of VpsO. Regulation of VpsO phosphorylation by the phosphatase VpsU is vital for maintaining native VPS levels. This study provides new insights into the mechanism and regulation of VPS production and establishes general principles of biofilm matrix production and its inhibition.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Toward Common Weakness Enumerations in Industrial Control Systems

Here, the storyline of MITRE’s common weakness enumeration framework illustrates how the security and privacy technical community can collaborate/cooperate with policy makers to advance policy, giving it specifics and filling gaps of technical knowledge to improve security and resilience of critical infrastructure.

42 ENGINEERING↗

DOE OSTI2022

Advancing Platooning with ADAS (Advanced Driver-Assistance Systems) Control Integration and Assessment

Application of Cooperative Adaptive Cruise Control (CACC) to heavy duty trucks known as truck platooning has shown fuel economy improvements on the test track under ideal driving conditions. However, limited test data is available to assess the truck platooning under real-world driving conditions. Under this Cummins-led project that was funded by the U.S. Department of Energy, truck platooning with CACC has been tested on a real-world interstate highway and the results of the project are reviewed in this report. At first, the real-world driving conditions were characterized using National Renewable Energy Laboratory (NREL) Fleet DNA database to define test factors, including route, terrain, and highway traffic. Afterward, both test track and on-highway testing guided by SAE J1321 procedures were conducted to assess truck platooning under controlled and real-world driving conditions. On-highway testing was done on a highway route in Indiana, consisting of low, medium, and high road grade segments. The highway test results of 2-truck and 3-truck platooning showed considerably reduced fuel savings compared to the controlled test track data, which mainly stems from the traffic or high-grade portions of the route. However, integration of Cummins powertrain and vehicle eco-driving features such as predictive cruise control and neutral coasting called ADEPT™ on the lead truck showed an improvement of fuel saving for the trucks in CACC operation. Furthermore, the importance of tire connectivity in efficient and safe operation of the trucks in platooning is characterized.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Extremum Seeking-Based Control System for Particle Accelerator Beam Loss Minimization

Particle accelerators throughout the world vary widely in terms of age and availability of advanced noninvasive diagnostics that provide varying levels of detail about the accelerated beams. Additionally, beam loss measurements and current monitors are ubiquitous in the accelerator community, they are noninvasive, and they are some of the most important metrics in terms of preventing damage or irradiation of beam pipes and equipment for high-energy machines. However, beam loss and current measurements are difficult to use for feedback tuning because of a lack of a known analytic relationship between scalar loss measurements throughout an accelerator and the hundreds of thousands of parameters that influence the beam and time variation of the beam source itself. In this work, we present a model-independent extremum seeking (ES) controller, which has been implemented for automated tuning and optimization of the Los Alamos Neutron Science Center (LANSCE) linear ion accelerator based only on scalar quantities, such as beam loss or flux measurements. We demonstrate the approach on various accelerator subsystems, including groups of radio frequency (RF) accelerating cavities, quadrupole magnets, bending magnets, and steering magnets. This tool is now available as a use graphical user interface (GUI) in the LANSCE control room, allowing beam physicists to choose arbitrary groups of parameters and beam loss or current monitors for adaptive tuning.

43 PARTICLE ACCELERATORS↗

DOE OSTI2013

INL Control System Situational Awareness Technology Final Report 2013

The Situational Awareness project is a comprehensive undertaking of Idaho National Laboratory (INL) in an effort to produce technologies capable of defending the country’s energy sector infrastructure from cyber-attack. INL has addressed this challenge through research and development of an interoperable suite of tools that safeguard critical energy sector infrastructure. The technologies in this project include the Sophia Tool, Mesh Mapper (MM) Tool, Intelligent Cyber Sensor (ICS) Tool, and Data Fusion Tool (DFT). Each is designed to function effectively on its own, or they can be integrated in a variety of customized configurations based on the end user’s risk profile and security needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020