An Inrush Current Limiting Strategy for Virtual-Oscillator-Controlled Grid-Forming Inverters
Not provided.
Energy & Fuels↗
SEARCH · Search NASA
Results for “control”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Fuzzy Controllers in the Adaptive Control System of a CNC Lathe
Traditional approaches to improving the technological system of CNC metal-cutting machines include increasing the rigidity, periodic maintenance and repair of its components, and operating at lower speeds. In practice, the most economical approach is stabilization of the inputs to the system. The development of adaptive control systems with stabilization of the cutting forces improves the precision and quality of machining in CNC metal-cutting machines, the productivity, and tool life. However, classical PID controllers are only effective in stabilizing the cutting forces in such systems if continuous real-time parameter adjustment is possible. That sharply increases the complexity of the controller. A new mathematical apparatus based on artificial intelligence (including fuzzy logic) permits the solution of adaptive control problems that previously could hardly even be formulated. The present work addresses the use of fuzzy logic in automatic stabilization of the cutting force for CNC lathes. Simulation of a fuzzy controller shows that this approach to automatic stabilization of the cutting force increases the machining efficiency on existing equipment with indeterminacy in the characteristics of the cutting system and the working environment.
42 ENGINEERING↗
Experimental Investigation of Temperature Feedback Control Systems Applicable to Turbojet-engine Control
Temperature - fuel-flow and temperature-area feedback control systems were investigated as means of controlling tailpipe gas temperature of a turbojet engine during transient operation in the high-speed region. Proportional-plus-integral control was used in both systems, but in the temperature-area control system it was necessary to add nonlinear components to the basic proportional-plus-integral control to provide satisfactory transient response to a desired step increase in temperature. Time integral of temperature-error functions were used as criteria for determining optimum transient response. A description of engine dynamics was obtained from frequency-response data.
ENGINES, TURBOJET↗
Task Demand Variation in Air Traffic Control: Implications for Controller Workload, Fatigue, and Performance
In air traffic control, task demand and workload have important implications for the safety and efficiency of air traffic, and remain dominant considerations. Within air traffic control, task demand is dynamic. However, research on demand transitions and subsequent controller perception and performance is limited. This research uses an air traffic control simulation to investigate the effect of task demand transitions, and the direction of those transitions, on workload and fatigue and one efficiency performance measure. Results indicate that a change in task demand appears to affect both workload and fatigue ratings, although not necessarily performance. In addition, participants workload and fatigue ratings in equivalent task demand periods appear to change depending on the demand period preceding the time of the current ratings. Further research is needed to enhance understanding of demand transition and workload history effects on operator experience and performance, in both air traffic control and other safety-critical domains.
workload history↗
Field Testing of LIDAR-Assisted Feedforward Control Algorithms for Improved Speed Control and Fatigue Load Reduction on a 600-kW Wind Turbine: Preprint
A severe challenge in controlling wind turbines is ensuring controller performance in the presence of a stochastic and unknown wind field, relying on the response of the turbine to generate control actions. Recent technologies such as LIDAR, allow sensing of the wind field before it reaches the rotor. In this work a field-testing campaign to test LIDAR Assisted Control (LAC) has been undertaken on a 600-kW turbine using a fixed, five-beam LIDAR system. The campaign compared the performance of a baseline controller to four LACs with progressively lower levels of feedback using 35 hours of collected data.
17 WIND ENERGY↗
Comparison of Probabilistic Forecasts for Predictive Voltage Control
This paper explores predictive cooperative voltage control in distribution systems with highly variable sources such as photovoltaics (PV). The goal is to maintain the voltage profile within the limits despite the fluctuations due to sudden changes in solar power generation. The predictive voltage control method relies on probabilistic solar power and load forecasts to select the optimum Voltage Regulator (VR) taps appropriately. VR taps are selected to minimize the risk of voltage violation. A modified version of the IEEE 123 system is used as the case study. A 100% penetration of solar power is assumed for the distribution system with profiles for solar generation and loads added to the system. Three different probabilistic forecast models (Quantile Regression (QR), Gaussian distribution and volatility forecasting using Generalized Autoregressive Conditional Heteroskedasticity (GARCH)) are explored in this study. The results for the VR taps and Voltage Deviation Index (VDI) are compared to find the most effective forecast model.
Panamtash, Hossein↗
Geometric Analysis Based Double Closed-loop Iterative Learning Control of Output PDF Shaping of Fiber Length Distribution in Refining Process
In order to improve the pulp quality and to reduce the energy consumption, the fiber length distribution (FLD) is generally employed as one of the important technological indexes in the refining process. Considering that the traditional mean and variance of fiber length are unable to adequately characterize the non-Gaussian distribution properties, this paper proposes a novel geometric analysis based double closed-loop iterative learning control (ILC) method for probability density function (PDF) shaping of output FLD in the refining process. Primarily, a RBF neural network (NN) with Gaussian-type is utilized to approximate the square root PDF in the inner loop, where the RBF basis function parameters (center and width) are tuned between any two adjacent batches by using an ILC law, and the subspace identification method can be applied to establish the state-space model of weight vector. Then, for the sake of accelerating the convergence rate of the closed-loop sysyem, a geometric analysis based ILC method is adopted in the outer loop. Lastly, both simulation and experiments demonstrate the effectiveness and practicability of the proposed approach.
42 ENGINEERING↗
Controlled Generation of Singlet Oxygen in Living Cells with Tunable Ratios of the Photochromic Switch in Metal–Organic Frameworks
Abstract Development of a photosensitizing system that can reversibly control the generation of singlet oxygen ( 1 O 2 ) is of great interest for photodynamic therapy (PDT). Recently several photosensitizer–photochromic‐switch dyads were reported as a potential means of the 1 O 2 control in PDT. However, the delivery of such a homogeneous molecular dyad as designed (e.g., optimal molar ratio) is extremely challenging in living systems. Herein we show a Zr‐MOF nanoplatform, demonstrating energy transfer‐based 1 O 2 controlled PDT. Our strategy allows for tuning the ratios between photosensitizer and the switch molecule, enabling maximum control of 1 O 2 generation. Meanwhile, the MOF provides proximal placement of the functional entities for efficient intermolecular energy transfer. As a result, the MOF nanoparticle formulation showed enhanced PDT efficacy with superior 1 O 2 control compared to that of homogeneous molecular analogues.
Park, Jihye↗
Nationwide HVAC Energy-Saving Potential Quantification for Office Buildings with Occupant-Centric Controls in Various Climates
The occupant-centric control (OCC) is receiving an increasing attention due to its ability to reduce building heating ventilation and air-conditioning (HVAC) system energy consumptions while not affecting the occupant thermal comfort. This paper aims to investigate and quantify the nationwide energy-saving potential of implementing the occupant-centric HVAC controls in typical office buildings using a whole building simulation software EnergyPlus. First, the medium office and large office from the Department of Energy (DOE) Commercial Prototype Building Models (CBPM) were enhanced to have detailed layouts and dynamic occupancy schedules. Then, a comprehensive simulation plan was created by incorporating the multiple zone-level and system-level occupant-centric building HVAC controls recommended by the updated ASHRAE Standard 90.1 – 2019 and ASHRAE Guideline 36 – 2018. Three control scenarios with different occupancy sensing methods were identified in this simulation plan. A nation-wide parametric analysis which includes two building types, three occupancy sensing scenarios, two building code versions, and 16 U.S. climate zones was carried out. The simulation results of the key control variables and HVAC energy consumption suggest that generally, both the occupancy presence sensor and occupant counting sensor could achieve energy savings for the office buildings in majority of the scenarios. However, compared with the occupancy presence sensor, which could support both the temperature setpoint reset and operational breathing zone airflow rate reset for the unoccupied zones, the occupant counting sensor only brings a marginal benefit. Besides, a higher HVAC energy-saving ratio could be achieved in the heating-dominated zone, since the energy reduction brought with the minimum outdoor airflow rate reset is stronger in the heating mode.
Pang, Zhihong↗
The QICK (Quantum Instrumentation Control Kit): Readout and control for qubits and detectors
We introduce a Xilinx RF System-on-Chip (RFSoC)-based qubit controller (called the Quantum Instrumentation Control Kit, or QICK for short), which supports the direct synthesis of control pulses with carrier frequencies of up to 6 GHz. The QICK can control multiple qubits or other quantum devices. The QICK consists of a digital board hosting an RFSoC field-programmable gate array, custom firmware, and software and an optional companion custom-designed analog front-end board. We characterize the analog performance of the system as well as its digital latency, important for quantum error correction and feedback protocols. We benchmark the controller by performing standard characterizations of a transmon qubit. We achieve an average gate fidelity of ℱ avg =99.93%. All of the schematics, firmware, and software are open-source.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Wind plant wake losses: Disconnect between turbine actuation and control of plant wakes with engineering wake models
Wake losses from neighboring plants may become a major factor in wind plant design and control as additional plants are constructed in areas with high wind resource availability. Because plant wakes span a large range of physical scales, from turbine rotor diameter to tens of kilometers, it is unclear whether conventional wake models or turbine control strategies are effective at the plant scale. Wake steering and axial induction control are evaluated in the current work as means of reducing the impact of neighboring wind plants on power and levelized cost of electricity. FLOw Redirection and Induction in Steady State (FLORIS) simulations were performed with the Gauss–Curl Hybrid and TurbOPark wake models as well as two operation and maintenance models to investigate control setpoint sensitivity to wake representation and economic factors. Both wake models estimate losses across a range of atmospheric conditions, although the wake loss magnitude is dependent on the wake model. Annual energy production and levelized cost of electricity are driven by wind direction frequency, with frequently aligned plants experiencing the greatest losses. However, both wake steering and axial induction are unable to mitigate the impact of upstream plants. Wake steering is constrained by plant geometry, since wake displacement is much less than the plant wake width, while axial induction requires curtailing the majority of turbines in upstream plants. Individual turbine strategies are limited by their effective scale and model representation. New wake models that include plant-scale physics are needed to facilitate the design of effective plant wake control strategies.
Scott, Ryan (ORCID:0000000328107574)↗
Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach
In this paper, we develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees. DPC is a form of approximate model predictive control (MPC), wherein the control policy is a neural network that learns a receding horizon, optimal control law. The proposed approach exploits a new form of sampled-data barrier function to enforce safety, while only interrupting the neural network-based controller near the boundary of the safe set. The effectiveness of the proposed approach is demonstrated in simulation.
Shaw Cortez, Wenceslao E.↗
A High-Speed, Real-Time Visualization and State Estimation Platform for Monitoring and Control of Electric Distribution Systems: Implementation and Field Results
Continued deployment of renewable and distributed energy resources is fundamentally changing the way that electric distribution systems are controlled and operated; more sophisticated active system control and greater situational awareness are needed. Real-time measurements and distribution system state estimation (DSSE) techniques enable more sophisticated system control and, when combined with visualization applications, greater situational awareness. This paper presents a novel demonstration of a high-speed, real-time DSSE platform and related control and visualization functionalities, implemented using existing open-source software and distribution system monitoring hardware. Live scrolling strip charts of meter data and intuitive annotated map visualizations of the entire state (obtained via DSSE) of a real-world distribution circuit are shown. The DSSE implementation is validated to demonstrate provision of accurate voltage data. This platform allows for enhanced control and situational awareness using only a minimum quantity of distribution system measurement units and modest data and software infrastructure.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Knock Intensity Distribution and a Stochastic Control Framework for Knock Control
One of the main factors limiting the efficiency of spark-ignited (SI) engines is the occurrence of engine knock. In high temperature and high pressure in-cylinder conditions, the fuel–air mixture auto-ignites creating pressure shock waves in the cylinder. Knock can significantly damage the engine and hinder its performance; as such, conservative knock control strategies are generally implemented which avoid such operating conditions at the cost of lower thermal efficiencies. Significant improvements in the performance of conventional knock controllers are possible if the properties of the knock process are better characterized and exploited in knock controller designs. One of the methods undertaken to better characterize knocking instances is to employ a probabilistic approach, in which the likelihood of knock is derived from the statistical distribution of knock intensity (KI). In this paper, it is shown that KI values at a fixed operating point for single fuel and dual fuel engines are accurately described using a mixed lognormal distribution. The fitting accuracy is compared against those for a randomly generated mixed-lognormally distributed dataset, and shown to exceed a 95% accuracy threshold for almost all of the operating points tested. Additionally, this paper discusses a stochastic knock control approach that leverages the mixed lognormal distribution to adjust spark timing based on KI measurements. Overall, this more informed knock control strategy would allow for improvements in engine performance and fuel efficiency by minimizing knock occurrences.
42 ENGINEERING↗
An Artificial-Intelligence and Machine-Learning-Based Methodology to Conduct Seemingly Strain-Controlled Fatigue Test in a Pressurized-Water-Reactor-Test-Loop-Autoclave, While Not Controlling the Strain
In general, low cycle fatigue analysis of pressurized water reactor (PWR) components, requires strain-controlled fatigue test data such as using strain versus life (ε–N) curves. Conducting strain-controlled fatigue tests under in-air conditions is not an issue. However, controlling strain in a PWR-test-loop-autoclave is a challenge, since an extensometer cannot be placed in a narrow autoclave (typically used in a high-temperature-pressure PWR-test-loop). This is due to lack of space inside an autoclave that houses the test specimen. In addition, installing a contact-type extensometer in the path of a high-pressure flow can be a challenge. These difficulties of using an extensometer inside an autoclave led us to use an outside-autoclave displacement sensor which measures the displacement of pull-rod-specimen assembly. However, in our study (based on in-air fatigue test data), we found that a pull-rod-controlled based fatigue test can lead to substantial cyclic hardening/softening resulting in substantially different cyclic strain amplitudes and their rates compared to the desired cyclic strain amplitudes and its rates. In this paper, we propose an Artificial-Intelligence and Machine-Learning based technique such as using k-means clustering technique to improve the pull-rod-control based fatigue test method, such that the gage-area strain amplitude and rates can reasonably be achieved. In support of this, we present the fatigue test results for both 316 SS base and 81/182 dissimilar-metal-weld specimens.
42 ENGINEERING↗
EPICS Controlled Collimator for Controlling Beam Sizes in HIPPO
Controlling the beam spot size and shape in a diffraction experiment determines the probed sample volume. The HIPPO - High-Pressure-Preferred Orientation– neutron time-offlight diffractometer is located at the Lujan Neutron Scattering Center in Los Alamos National Laboratories. HIPPO characterizes microstructural parameters, such as phase composition, strains, grain size, or texture, of bulk (cm-sized) samples. In the current setup, the beam spot has a 10 mm diameter. Using a collimator, consisting of two pairs of neutron absorbing boron-nitride slabs, horizontal and vertical dimensions of a rectangular beam spot can be defined. Using the HIPPO robotic sample changer for sample motion, the collimator would enable scanning of e.g. cylindrical samples along the cylinder axis by probing slices of such samples. The project presented here describes implementation of such a collimator, in particular the motion control software. We utilized the EPICS (Experimental Physics Interface and Control System) software interface to integrate the collimator control into the HIPPO instrument control system. Using EPICS, commands are sent to commercial stepper motors that move the beam windows.
43 PARTICLE ACCELERATORS↗