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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 19 records

Demonstration of tokamak vertical stability control based on non-inductive Faraday-effect polarimetry measurements

Long-pulse or steady-state fusion reactors are envisioned to control vertical stability based on non-inductive measurements, i.e. that do not rely on temporal change of magnetic field. For the first time, vertical stability control using non-inductive Faraday-effect polarimetry measurements has been demonstrated. The Radial Interferometer-Polarimeter system on DIII-D is capable of microsecond resolution and was used to absolutely determine the vertical position of the plasma magnetic axis Z 0 . A vertical stability controller was developed to robustly stabilize diverted plasmas using Faraday-based measurements. The system was able to stabilize against vertical displacement events with growth rates up to 350 s -1 in elongated and elliptical plasma shapes, and instabilities with even higher growth rates are likely to be controllable with further improvements to controller tuning. Tests show that the Faraday-based controller remains effective and is capable of recovering from loss of control even when the plasma vertical position is far from the region where the linear model used to calculate Z 0 is most valid. Faraday control has also been activated during plasma ramp-up, demonstrating the robustness of the technique to larger systematic diagnostic uncertainty at low electron density.

Faraday-effect polarimetry↗

Special Issue on the 20th Workshop on MHD Stability Control

The 20th workshop on magnetohydrodynamic (MHD) stability control took place November 22–24, 2015, in Princeton Plasma Physics Laboratory (PPPL), following the American Physical Society—Division of Plasma Physics annual meeting on November 16–20 in Savannah, GA. The purpose of this workshop is to stimulate in depth discussion and motivate future research in the areas of MHD stability physics and control of magnetically confined plasmas. Furthermore, the workshop was organized jointly by Auburn University, Columbia University, General Atomics, Princeton Plasma Physics Laboratory, University of Wisconsin-Madison, and the Los Alamos National Laboratory, and under the auspices of the US/Japan Collaboration.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Special issue on the 24th workshop on MHD stability control

We report the 24th workshop on magnetohydrodynamic (MHD) stability control was held on the Columbia University campus 28–30th October, 2019. The purpose of this annual workshop is to discuss cutting edge results and form future plans for active control of MHD instabilities in magnetic confinement fusion reactor concepts. The program was organized by an international committee of representatives from Auburn University (USA), Columbia University (USA), Massachusetts Institute of Technology (USA), Princeton Plasma Physics Laboratory (USA), Princeton University (USA), Tottori University (Japan), Kyoto Institute of Technology (Japan), National Institute of Radiological Sciences (Japan), National Institute for Fusion Science (Japan), Institutes of Physical Science—Chinese Academy of Sciences (People's Republic of China), and Consiglio Nazionale delle Ricerche (Italian Republic). Dr Gerald Navratil and Columbia University, in particular, were responsible for organizing and hosting the meeting on their beautiful Morningside Heights campus.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Technology readiness assessment of magnetohydrodynamic stability control

The technology readiness level scale is revised with clear definitions for magnetohydrodynamic stability control technologies for magnetic confinement fusion. The definitions are in the form of a sentence with three dimensions, which are the system complexity, the test environment, and the cost. A clear and simple definition is sought after to make an assessment based on the evidence which is agreeable to the fusion energy development community. A sample technology readiness assessment (TRA) for the electron cyclotron current drive-based control of neoclassical tearing mode for ST Advanced Reactor, using the proposed scale, reveals that the risk of a TRA being misinterpreted is high, which can only be avoided with an understanding of the procedure described in this paper.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Dynamic Distribution Voltage Stability Control Using Distributed Energy Resources for Improved Resiliency

The main goal of this paper is to present the development and simulation of a new dynamic voltage stability control technique, with a focus on effective control and coordination of all available reactive power generation resources in a microgrid setting. Both power electronics and synchronous generator based distributed energy resources (DERs), and grid-supporting functions, are considered to accommodate a high penetration of diverse DERs and energy storage by using an enhanced microgrid voltage stabilizer. A systematic controller parameter synthesizing method is employed, and the overall dynamic voltage stability of the system is studied and evaluated with a time-domain dynamic simulation during emergency, hazard and disaster events. The results with the new approach show significant improvements and enhancements on the dynamic voltage profile of the distribution system, leading towards a more resilient grid system with high penetration of diverse DERs during emergency, hazard and disaster events.

Hatipoglu, Kenan↗

Simulation-based assessment of ASHRAE Guideline 36, considering energy performance, indoor air quality, and control stability

This study assesses American Society of Heating, Refrigerating and Air-Conditioning Engineers Guideline 36 (G36) with a typical medium office building. Specifically, this study employed a Modelica model of a variable air volume (VAV) system that serves this building, which includes components for representing indoor virus transmission and filtration. It then implemented the G36 control sequences for both water-side and air-side equipment in Python. After that, this study conducted the assessment by co-simulating G36 and the Modelica model using the Building Operations Testing Framework. Unlike existing works, this work has three unique features: (1) It considers the interactions between control sequences for water-side and air-side equipment of the studied VAV system. (2) It assesses the performance of G36 from the perspective of IAQ. (3) It examines the short-term behaviors of the studied building under G36 to understand the control stability. This assessment confirms significant energy savings from G36, largely because of the interaction between supply air temperature and hot water controls. It also reveals a trade-off between the ability to slow the spread of virus and the energy performance via demand controlled ventilation. Lastly, it emphasizes the necessity of tuning local feedback control when implementing G36.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A hierarchical gray-box dynamic modeling methodology for direct-expansion cooling systems to support control stability analysis; Méthodologie de modélisation dynamique hiérarchique de type boîte grise pour les systèmes de refroidissement à détente directe afin de soutenir l'analyse de stabilité de la commande

In this paper, a gray-box dynamic modeling approach for direct-expansion cooling systems is presented. The overall approach incorporates a multi-stage training procedure that consists of 1) identification of component sub-models from quasi-steady-state performance data, 2) system model integration with estimation of refrigerant charge and 3) fine tuning of thermal capacitances of the evaporator and condenser to capture the system dynamic responses. Compared to traditional physics-based models, the proposed modeling approach has advantages including reduced engineering efforts in the model development phase, improved computational efficiency and enhanced prediction accuracy. The modeling method was validated using a 3-ton variable-speed heat pump and proved to be capable of accurately predicting the system transient behaviors over a wide range of operating conditions. The established dynamic model was then applied for control stability analysis, with a specific goal of determining a proper control execution time step. The case study results showed that the stable control execution time step could change significantly, from 3 sec to 19 sec, as the operating conditions and control settings vary, and a proper selection of the execution time step is critical to ensure stable and reliable operations.

42 ENGINEERING↗

Editorial for the 28th Workshop on Magnetohydrodynamic (MHD) Stability Control

This special issue contains four papers which were presented at the 28th Workshop on Magnetohydrodymanic (MHD) Stability Control. The workshop was held at Auburn University and on Zoom from 3–5 October 2024. Furthermore, this annual workshop covers the active and passive control of MHD instabilities such as kink, tearing and edge-localized modes in tokamaks, reversed field pinches, spherical tori or other magnetic confinement devices.

Yang, J. [Princeton Plasma Physics Laboratory (PPP↗

A data-driven approach to real-time vertical position estimation for NSTX-U vertical stability control

In this paper, a database of 77 996 plasma equilibrium reconstructions from 727 discharges during the initial operation of the NSTX-U spherical tokamak is analyzed to develop a statistically robust model of the plasma vertical position for real-time control. A variety of regression models are developed and tested, ranging in complexity from linear models to deep neural networks, and including input signals ranging from the four pairs of flux loops used historically on NSTX-U up to the full set of 389 real-time signals available to the plasma control system. A linear model based on 140 real-time magnetics signals is found to offer excellent accuracy, with a coefficient of determination R 2 = 0.906. The robustness of this model to limited training data, new operating scenarios, and signal errors is tested, and a procedure is demonstrated to tune the model parameters to optimize its robustness. A time-dependent plasma equilibrium solver, TokaMaker, is used to simulate vertical stability control in NSTX-U, demonstrating that it should be possible to iteratively tune the parameters of a linear vertical position model to stabilize both positive and negative triangularity plasmas in future experiments.

magnetic diagnostics↗

Safe Reinforcement Learning-Based Transient Stability Control for Islanded Microgrids With Topology Reconfiguration

This paper proposes a safe reinforcement learning (RL)-based transient stability emergency control (TSEC) method for islanded microgrids. RL requires extensive interaction with the environment to learn control strategies, hence, a data-driven approach is used as a substitute for time-consuming time-domain simulation calculations. Deep sigma point processes (DSPP), which is a Gaussian process model, is utilized to predict the normal distribution of transient stability of microgrids and to construct a transient stability chance constraint. Reward-constrained policy optimization (RCPO) can simultaneously achieve objective prediction, policy learning, and constraint cost coefficient update across multiple timescales. RCPO interacts with the DSPP-based microgrid environment through a multi-process parallel manner, greatly increasing the training speed. Case studies on a real islanded microgrid demonstrate that the proposed method can efficiently and quickly obtain the optimal emergency control strategy while adhering to all hard constraints.

14 SOLAR ENERGY↗

Software Control Program For Transportable Microgrid State-of-charge Balancing And Frequency Stability Controls

A deterministic state-of-charge (SOC) balancing approach software control code is introduced as an integral secondary management to primary control layer of an islanded small microgrid or nanogrid system made up of multiple grid-forming inverter/battery/solar combination systems, where each set of batteries with each inverter are on independent DC buses (i.e. non-paralleled on the DC sides). A DERMS-level control approach, algorithm and automation controller program was developed to improve coordination and enable microgrid asset compliance and SOC balancing, enabling provision of a system-level power stability support architecture, load support, and asset scalability. The architecture is configured to treat each unit or micro/nano-grid as a node in a microgrid network, allowing for autonomous DERMS control regarding load and SOC balancing and power stability. As the network grows with the addition of units, greater coordination efforts may be required. The ideal small network microgrid ranges from 2-10 inverter/battery units before additional control parameters must be considered in the existing architecture. The control approach focuses on a deterministic state-of-charge analysis as the primary level control process followed by a secondary control loop using a forced frequency-watt droop strategy to conform off-the-shelf components into behaving under a leader-follower configuration. Adopting this control scheme has been shown to allow for a balanced, unit-coordinated microgrid network, enabling stable power flow. The deterministic state-of-charge approach is introduced as an integral primary control layer of an islanded small network microgrid. A standard strategy for SOC balancing is implementing a battery management system (BMS) to control SOC on the DC side. An alternative approach is to determine how to coordinate sending and receiving power on the AC side with multiple units. The latter approach assesses all the integrated units in the microgrid network. Once the individual units are identified, further system data is required to calculate each unit's total kWh, provided information about its capability to supply or consume kWh and availability. The secondary control layer in the multi-layered small network microgrid methodology uses the primary layer’s decision to initiate frequency setpoint changes, initializing the SOC balancing. The secondary control layer considers numerous system-dependent variables to enable a charging and discharging profile based on adjustable frequency setpoints. The combined architecture will result in stable, coordinated power flow enhancing an AC microgrid's functionalities.

Myers, KurtS [Idaho National Laboratory (INL), Ida↗

A Riccati Solution for the Ideal MHD Plasma Response with Applications to Real-time Stability Control

To effectuate near real-time feedback control of ideal MHD instabilities in a tokamak geometry, a rapid solution for stability analysis is a prerequisite. Toward this end, we reformulate the δW stability method with a Hamilton-Jacobi theory, elucidating analytical and numerical features of the generic tokamak ideal MHD stability problem. The plasma response matrix is demonstrated to be the solution of an ideal MHD matrix Riccati differential equation (MRDE). Since Riccati equations are prevalent in the control theory literature, such a shift in perspective brings to bear a range of numerical methods that are well-suited to the robust, fast solution of control problems. We discuss the usefulness of Riccati techniques in solving the stiff ODEs often encountered in ideal MHD stability analyses-—for example, in tokamak edge and stellarator physics. We then demonstrate the applicability of such methods to an existing 2D ideal MHD stability code—DCON—enabling its parallel operation in near real-time. Output is shown to match with high accuracy, and with wall-clock time ≪ 1s. Such speed may help enable active feedback ideal MHD stability control, especially in tokamak plasmas whose ideal MHD equilibria evolve with inductive timescale τ > 1s-—as in ITER.

Parallel↗

Metal Adsorption Controls Stability of Layered Manganese Oxides

Hexagonal birnessite, a typical layered Mn oxide (LMO), can adsorb and oxidize Mn(II) and thereby transform to Mn(III)-rich hexagonal birnessite, triclinic birnessite, and/or tunneled Mn oxides (TMOs), remarkably changing the environmental behavior of Mn oxides. We have determined the effects of co-existing cations on the transformation by incubating Mn(II)-bearing δ-MnO 2 at pH 8 under anoxic conditions for 25 d (dissolved Mn < 11 μM). In the Li + , Na + or K + chloride solution, the Mn(II)-bearing δ-MnO 2 first transforms to Mn(III)-rich δ-MnO 2 and/or triclinic birnessite (T-bir) due to the Mn(II)-Mn(IV) comproportionation, most of which eventually transform to a 4 × 4 TMO. In contrast, Mn(III)-rich δ-MnO 2 and T-bir form and persist in the Mg 2+ or Ca 2+ chloride solution. Yet, in the presence of surface adsorbed Cu(II), Mn(II)-bearing δ-MnO 2 turns into Mn(III)-rich δ-MnO 2 without forming T-bir or TMOs. The stabilizing power of the cations on the δ-MnO 2 structure positively correlates with their binding strength to δ-MnO 2 (Li + , Na + or K + < Mg 2+ or Ca 2+ < Cu(II)). Since metal adsorption decreases the surface energy of minerals, our result suggests that the surface energy largely controls the thermodynamic stability of LMOs. Our study implies that the adsorption of divalent metal cations, particularly transition metals, can be a prime cause of the high abundance of LMOs, rather than the more stable TMO phases, in the environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Reinforcement Learning of Structured Stabilizing Control for Linear Systems With Unknown State Matrix

This paper delves into designing feedback control gains for a continuous-time linear quadratic regulator (LQR) problem that is constrained to certain predefined structure with unknown state matrix. We bring forth the ideas from reinforcement learning (RL) in conjunction with sufficient stability and performance guarantees in order to design these structured gains using the trajectory measurements of states and controls. Here we first formulate a model-based framework using dynamic programming (DP) to embed the structural constraint to the LQR gain computation in the continuous-time setting, and then subsequently, formulate a policy iteration RL algorithm that can alleviate the requirement of known state matrix in conjunction with maintaining the feedback gain structure. The design enables a distributed learning control design which is necessary for many large-scale cyber-physical systems. Theoretical guarantees are provided for stability and convergence of the structured reinforcement learning (SRL) algorithm. We validate our theoretical results with numerical simulations on a multi-agent networked linear time-invariant (LTI) dynamic system.

42 ENGINEERING↗

Protein–Protein Complex Stability Controls Substrate Scope in a β‐Ketoacyl‐ACP Reductase Specific for Medium Chains

Assembly-line enzymes carry out multistep synthesis of important metabolites by using acyl carrier proteins (ACPs) to shuttle intermediates along defined sequences of active sites. Despite longstanding interest in reprogramming these systems for metabolic engineering and biosynthetic chemistry, the mechanisms underlying their reaction order remain poorly understood and difficult to control. In this work, we describe a β-ketoacyl-ACP reductase from Pseudomonas putida (PpFabG4) with an unusual selectivity for medium chains and use it to explore the molecular basis of substrate specificity in enzymes that pull intermediates from fatty acid synthesis, a common route to specialized products. X-ray crystallography shows no obvious barriers to short-chain binding. Molecular simulations and supporting mutational analyses indicate that substrate preference arises instead from a weak enzyme–ACP interaction that is stabilized by medium acyl chains but not by short chains. Indeed, mutations that strengthen this interaction for PpFabG4 or weaken it for EcFabG, an Escherichia coli β-ketoacyl-ACP reductase with a broad substrate specificity, can enhance or reduce activity on short-chain substrates by over 100-fold. Our findings show how the stability of enzyme-ACP interactions can control substrate scope in promiscuous enzymes and guide the exchange of intermediates between (and within) assembly-line systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗