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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Using an operator training simulator in the undergraduate chemical engineering curriculim

An operator training simulator (OTS) is to the chemical engineer what a flight simulator is to the aerospace engineer. The basis of an OTS is a high-fidelity dynamic model of a chemical process that allows an engineer to simulate start-up, shut-down, and normal operation. It can also be used to test the skill and ability of an engineer or operator to respond and control some unforeseen situation(s) through the use of programmed malfunctions. West Virginia University (WVU) is a member of the National Energy Technology Laboratory’s Regional University Alliance (NETL-RUA). Working through the NETL-RUA, the authors have spent the last four years collaborating on the development of a high-fidelity OTS for an Integrated Gasification Combined Cycle (IGCC) power plant with CO{sub 2} capture that is the cornerstone of the AVESTARTM (Advanced Virtual Energy Simulation Training And Research) Center with sister facilities at NETL and WVU in Morgantown, WV. This OTS is capable of real-time dynamic simulation of IGCC plant operation, including start-up, shut-down, and power demand load following. The dynamic simulator and its human machine interfaces (HMIs) are based on the DYNSIM and InTouch software, respectively, from Invensys Operations Management. The purpose of this presentation is to discuss the authors’ experiences in using this sophisticated dynamic simulation-based OTS as a hands-on teaching tool in the undergraduate chemical engineering curriculum. At present, the OTS has been used in two separate courses: a new process simulation course and a traditional process control course. In the process simulation course, concepts of steady-state and dynamic simulations were covered prior to exposing the students to the OTS. Moreover, digital logic and the concept of equipment requiring one or more permissive states to be enabled prior to successful operation were also covered. Students were briefed about start-up procedures and the importance of following a predetermined sequence of actions in order to start-up the plant successfully. Student experience with the dynamic simulator consisted of a six-hour training session in which the Claus sulfur capture unit of the IGCC plant was started up. The students were able to operate the simulator through the InTouch-based HMI displays and study and understand the underlying dynamic modeling approach used in the DYNSIM-based simulator. The concepts learned during the training sessions were further reinforced when students developed their own DYNSIM models for a chemical process and wrote a detailed start-up procedure. In the process control course, students learned how the plant responds dynamically to changes in the manipulated inputs, as well as how the control system impacts plant performance, stability, robustness and disturbance rejection characteristics. The OTS provided the opportunity to study the dynamics of complicated, “real-life” process plants consisting of hundreds of pieces of equipment. Students implemented ideal forcing functions, tracked the time-delay through the entire plant, studied the response of open-loop unstable systems, and learned “good practices” in control system design by taking into account the real-world events where significant deviations from the “ideal” or “expected” response can occur. The theory of closed-loop stability was reinforced by implementing limiting proportional gain for stability limits of real plants. Finally, students were divided into several groups where each group was tasked to control a section of the plant within a set of operating limits in the face of disturbances and simulated process faults. At the end of this test, they suggested ways to improve the control system performance based on the theory they learned in class and the hands-on experience they earned while working on the OTS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Optimisation of the Kaplan hydropower system via PID 2 and digital twin

Here, this paper proposes a proportional–integral-double–derivative (PID 2 ) optimisation method for the Kaplan hydropower system by building a digital twin. The study first uses one multilayer perceptron (MLP) to model the hydroturbine dynamic and then adopts three connected MLPs to model the generator dynamic, both in an open-loop fashion. Inspired by stochastic distribution control (SDC) theory, we regard the training of the turbine's neural network model as a process control problem, and we propose minimising entropy loss to update the network parameters. The next step is to build the digital twin by connecting the neural network models with a PID 2 controller and a lead-lag exciter and run the whole model in a closed-loop fashion. After that, a binary search approach is applied to optimise the PID 2 parameters based on the obtained digital twin model. The simulation results show that the proposed method can reduce the mean square tracking error by more than 90%. Furthermore, the method is extended to jointly optimise the PID 2 controller and excitation system gains through multiobjective optimisation, leveraging Pareto frontier analysis to balance active power and voltage tracking performance. Simulation results confirm the effectiveness of the proposed method, achieving a 83.46% reduction in relative mean square error of active power, a 47.13% reduction in terminal voltage tracking error, and an 82.78% improvement in the overall scalarized objective.

Hydropower system↗
DOE OSTI2026

Regional surrogates for predictive control of digital twins

Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.

Data-driven model↗
DOE OSTI2016

Modeling and Compensation Design for a Power Hardware-in-the-Loop Simulation of an AC Distribution System

Power hardware-in-the-loop (PHIL) simulation, where actual hardware under text is coupled with a real-time digital model in closed loop, is a powerful tool for analyzing new methods of control for emerging distributed power systems. However, without careful design and compensation of the interface between the simulated and actual systems, PHIL simulations may exhibit instability and modeling inaccuracies. This paper addresses issues that arise in the PHIL simulation of a hardware battery inverter interfaced with a simulated distribution feeder. Both the stability and accuracy issues are modeled and characterized, and a methodology for design of PHIL interface compensation to ensure stability and accuracy is presented. The stability and accuracy of the resulting compensated PHIL simulation is then shown by experiment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Coastal Ohio Wind Project for Reduced Barriers to Deployment of Offshore Wind Energy

The Coastal Ohio Wind Project was created to establish the viability of wind turbines on the coastal and offshore regions of Northern Ohio. The project’s main goal was to improve operational unit strategies used for environmental impact assessment of offshore turbines on lake wildlife by optimizing and fusing data from the multi-instrument surveillance system and providing an engineering analysis of potential design/operational alternatives for offshore wind turbines. The project also developed a general economic model for offshore WTG deployment to quantify potential revenue losses due to wind turbine shutdown related to ice and avian issues. In a previous phase of this project (Award Number: DE-FG36-06GO86096), we developed a surveillance system that was used to collect different parameters such as passage rates, flight paths, flight directions, and flight altitudes of nocturnal migrating species, movements of birds and bats, and bird calls for assessing patterns and peak passage rates during migration. To derive such parameters we used thermal IR imaging cameras, acoustic recorders, and marine radar Furuno (XANK250), which was coupled with a XIR3000B digitizing card from Russell Technologies and open source radR processing software. The integration yielded a development of different computational techniques and methods, which we further developed and optimized as a combined surveillance system. To accomplish this task we implemented marine radar calibration, optimization of processing parameters, and fusion of the multi-sensor data in order to make inferences about the potential avian targets. The main goal of the data fusion from the multi-sensor environment was aimed at reduction of uncertainties while providing acceptable confidence levels with detailed information about the migration patterns. Another component comprised of an assessment of wind resources in a near lake environment and an investigation of the effectiveness of ice coating materials to mitigate adverse effects of ice formation on wind turbine structures. Firstly, a Zephir LiDAR system was acquired and installed at Woodlands School in Huron, Ohio, which is located near Lake Erie. Wind resource data were obtained at ten measurement heights, 200m, 150m, 100m, 80m, 60m, 40m, 38m, 30m, 20m, and 10m. The Woodlands School’s wind turbine anemometer also measured the wind speed at the hub height. These data were collected for approximately one year. The hub anemometer data correlated well with the LiDAR wind speed measurements at the same height. The data also showed that on several days different power levels were recorded by the turbine at the same wind speed as indicated by the hub anemometer. The corresponding LiDAR data showed that this difference can be attributed to variability in the wind over the turbine rotor swept area, which the hub anemometer could not detect. The observation suggests that single point hub wind velocity measurements are inadequate to accurately estimate the power generated by a turbine at all times since the hub wind speed is not a good indicator of the wind speed over the turbine rotor swept area when winds are changing rapidly. To assess the effectiveness of ice coatings to mitigate the impact of ice on turbine structures, a closed-loop icing research tunnel (IRT) was designed and constructed. By controlling the temperature, air speed, water content and liquid droplet size, the tunnel enabled consistent and repeatable ice accretion under a variety of conditions with temperatures between approximately 0°C and -20°C and wind speeds up to 40 miles per hour in the tunnel’s test section. The tunnel’s cooling unit maintained the tunnel temperature within ±0.2°C. The coatings evaluated in the study were Boyd Coatings Research Company’s CRC6040R3, MicroPhase Coatings Inc.’s PhaseBreak TP, ESL and Flex coatings. Similar overall performance was observed in all coatings tested in that water droplets form on the test articles beginning at the stagnation region and spreading in the downstream direction in time. When comparing ice accumulation characteristics for the four coatings tested, for ice thickness during accumulation the CRC6040R3 had the least, followed by the ESL, Flex, and TP. However, when comparing the coatings’ ability to reduce ice adhesion, the Flex showed the highest adhesion reduction, followed by the ESL, TP and CRC 6040R3 coatings. The ice accumulated on the Flex coated surface shed under gravity when rotated 90 degrees following the tests while the other coatings required application of varying degrees of force to remove the ice. In conclusion, the ice coatings tested were not sufficient in preventing ice accumulation on all surfaces. However, Flex coating shows promise in mitigating ice on the rotor blades under the gravitational and centrifugal forces. Only the effect of gravity in shedding the ice was considered in this study. Further research will be needed to evaluate this coating on rotating blades in the icing tunnel to characterize its effectiveness. Lastly, the development of economic feasibility models used existing approaches adapted for offshore deployment in marine settings to one more suitable for Lake Erie deployment. Two different wind turbine models were tested and dynamic return on investment (ROI) model scenarios were generated. For the purpose of estimating power generation three bladed wind turbines of 3 MW capacity were selected including Model1- Leitwind LTW101-3.000-kW and Model2-Vostro V90-3.0 MW. The analysis were based on the revenue aspect of decision making of deploying wind turbines in the Ohio coastal region. The installation cost, maintenance and operational aspects were disregarded due to unavailability of data. The adjusted varying price (residential and industrial sector) and projected future price of electricity in different years suggested that the Leitwind model could generate $\$ $32.4 million of revenue in 25 years if the supply electricity is in the residential sector, while it would be $\$ $14.7million if the supply is in the industrial sector. For the Vostro model these figures are $\$ $28.6 million for residential sector and $\$ $12.9 million for industrial sector for 25 years.

17 WIND ENERGY↗
DOE OSTI2017

Modeling and Compensation Design for a Power Hardware-in-the-Loop Simulation of an AC Distribution System: Preprint

Power hardware-in-the-loop (PHIL) simulation, where actual hardware under text is coupled with a real-time digital model in closed loop, is a powerful tool for analyzing new methods of control for emerging distributed power systems. However, without careful design and compensation of the interface between the simulated and actual systems, PHIL simulations may exhibit instability and modeling inaccuracies. This paper addresses issues that arise in the PHIL simulation of a hardware battery inverter interfaced with a simulated distribution feeder. Both the stability and accuracy issues are modeled and characterized, and a methodology for design of PHIL interface compensation to ensure stability and accuracy is presented. The stability and accuracy of the resulting compensated PHIL simulation is then shown by experiment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Real-time avoidance of the L-mode and H-mode density limit via machine-learned stability metrics

Reliable operation of burning plasma tokamaks will require robust control strategies to avoid macroscopic instability limits such as the L-mode and H-mode density limits (LDL, HDL). In this work, we explore closed-loop avoidance of these phenomena at DIII-D using machine-learned risk metrics. Feedback control is implemented via the ‘DL Supervisor’ scheme, which regulates the chosen risk metric by reducing the density target or increasing NBI heating in real-time. Using the LDL 25 risk metric, the LDL is reproducibly suppressed. We also introduce an HDL risk metric in this study, HDL 25 , which reduces the False Positive Rate by 2x compared to the Greenwald fraction. Applying this scaling to a plasma current ramp-down, we successfully avoid an HDL-driven H/L back-transition. These experiments constitute the first demonstration of real-time DL avoidance using machine-learned risk metrics. These instability metrics outline a path to safer high-density operation, more reliable ramp-down scenarios, and improved off-normal control for next-step devices such as ITER and SPARC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees

We present differentiable predictive control (DPC), a method for offline learning of constrained neural control policies for nonlinear dynamical systems with performance guarantees. We show that the sensitivities of the parametric optimal control problem can be used to obtain direct policy gradients. Specifically, we employ automatic differentiation (AD) to efficiently compute the sensitivities of the model predictive control (MPC) objective function and constraints penalties. To guarantee safety upon deployment, we derive probabilistic guarantees on closed-loop stability and constraint satisfaction based on indicator functions and Hoeffding’s inequality. We empirically demonstrate that the proposed method can learn neural control policies for various parametric optimal control tasks. In particular, we show that the proposed DPC method can stabilize systems with unstable dynamics, track time-varying references, and satisfy nonlinear state and input constraints. Our DPC method has practical time savings compared to alternative approaches for fast and memory-efficient controller design. Specifically, DPC does not depend on a supervisory controller as opposed to approximate MPC based on imitation learning. We demonstrate that, without losing performance, DPC is scalable with greatly reduced demands on memory and computation compared to implicit and explicit MPC while being more sample efficient than model-free reinforcement learning (RL) algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Active control of resistive wall mode via modification of external tearing index

Modification of the external tearing index, Δ ext ′ , by magnetic feedback is analytically investigated for the purpose of controlling the resistive plasma resistive wall mode (RP-RWM). The matching method is pursued by deriving expressions for the close-loop Δ ext ′ and by linking it to the counterpart from the inner layer. Various feedback coil configurations are found to generally reduce Δ ext ′ and stabilize the RWM, with either proportional or derivative control. Feedback modification of Δ ext ′ is found to be generally independent of the inner layer resistive interchange index D R , confirming that feedback action primarily modifies the solution in the outer ideal region for the RP-RWM. Exception occurs when either the inner layer favorable curvature effect becomes sufficiently large or the feedback action is sufficiently strong to introduce a rotating RP-RWM in the static plasma, leading to complex-valued close-loop Δ ext ′ . The perturbed magnetic energy dissipation in the outer region, associated with the eddy current in the resistive wall, is identified as the key physics reason for feedback induced complex Δ ext ′ . Similar results are also obtained for active control of the external kink instability, whose open-loop growth rate is significantly reduced by inclusion of the plasma resistivity. Within the single poloidal harmonic approximation, which is most suitable for the matching approach, external active coils combined with poloidal sensors are often found to be more efficient for feedback stabilization of the mode at large proportional gain values. This counter-intuitive result is explained as the lack of (non-resonant) poloidal harmonics for proper description of the feedback coil geometry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Model-based Cyber-attack Detection for Voltage Source Converters in Island Microgrids

With the upgrading of communication and control in microgrids, cyber threats on the power converters are increasing. Here, in this paper, a novel model-based cyber-attack detection methodology is proposed for each voltage source converter in microgrids. The Harmonics State Space Matrix (H-Matrix) is used to build the closed-loop transfer function, providing model-based estimation with the grid voltage and control reference. Then, the space phase model (SPM) is used to calculate residual to detect cyber-attacks in voltage source converter (VSC). As controller and converter parameters are included in the H-matrix, the proposed method also could detect cyber-attacks when voltage fluctuation occurs in the grid. To verify the feasibility of the proposed method, several attacks targeting VSC in an islanded microgrid are simulated in MATLAB. Simulation and detection results are introduced to demonstrate the resilience and feasibility of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Identification and control of plasma vertical position using neural network in Damavand tokamak

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Composite Robust $H_\infty$ Control for Uncertain Stochastic Nonlinear Systems with State Delay via Disturbance Observer

Here in this note, the robust stochastic stabilization and robust H_infinity control problems are investigated for uncertain stochastic time-delay systems with nonlinearity and multiple disturbances. By estimating the disturbance, which can be described by an exogenous model, a composite hierarchical control scheme is proposed that integrates the output of the disturbance observer with state feedback control law. Sufficient conditions for the existence of the disturbance observer and composite hierarchical controller are established in terms of linear matrix inequalities, which ensure the mean-square asymptotic stability of the resulting closed-loop system and the disturbance attenuation. It has been shown that the disturbance rejection performance can also be achieved. A numerical example is provided to show the potential of the proposed techniques and encouraging results have been obtained.

42 ENGINEERING↗
DOE OSTI2024

A tutorial review of machine learning-based model predictive control methods

Abstract This tutorial review provides a comprehensive overview of machine learning (ML)-based model predictive control (MPC) methods, covering both theoretical and practical aspects. It provides a theoretical analysis of closed-loop stability based on the generalization error of ML models and addresses practical challenges such as data scarcity, data quality, the curse of dimensionality, model uncertainty, computational efficiency, and safety from both modeling and control perspectives. The application of these methods is demonstrated using a nonlinear chemical process example, with open-source code available on GitHub. The paper concludes with a discussion on future research directions in ML-based MPC.

Wu, Zhe [Department of Chemical and Biomolecular E↗
DOE OSTI2015

Compliance and control characteristics of an additive manufactured-flexure stage

This paper presents a compliance and positioning control characteristics of additive manufactured-nanopositioning system consisted of the flexure mechanism and voice coil motor (VCM). The double compound notch type flexure stage was designed to utilize the elastic deformation of two symmetrical four-bar mechanisms to provide a millimeter-level working range. Additive manufacturing (AM) process, stereolithography, was used to fabricate the flexure stage. The AM stage was inspected by using 3D X-ray computerized tomography scanner: air-voids and shape irregularity. The compliance, open-loop resonance peak, and damping ratio of the AM stage were measured 0.317 mm/N, 80 Hz, and 0.19, respectively. The AM stage was proportional-integral-derivative positioning feedback-controlled and the capacitive type sensor was used to measure the displacement. As a result, the AM flexure mechanism was successfully 25 nm positioning controlled within 500 μm range. The resonance peak was found approximately at 280 Hz in closed-loop. This research showed that the AM flexure mechanism and the VCM can provide millimeter range with high precision and can be a good alternative to an expensive metal-based flexure mechanism and piezoelectric transducer.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Robust Optimal Control of Inverter-Based Resources Under Grid-Forming Operation

In this paper, we propose and solve a robust control problem for inverter-based resources under grid-forming operation to regulate the voltage and frequency. One major challenge is to mitigate the effect of unmeasurable load current disturbance, grid and load parametric uncertainties. Moreover, strong coupling between the state variables on both the AC and DC sides, as well as between the modulating control input and the frequency impose additional challenges. To address these challenges, first, a robust control problem is solved at the high level via transformation into an equivalent, but more tractable, optimal control problem. Then, in the middle layer a voltage control law is designed on the one side, and a frequency control law on the other side. Finally, an inverter filter current controller is designed to complete the controller design. Theoretical results are derived to provide stability guarantees for the resulting closed-loop system. Specifically, we show that the inverter current injection error is dissipative, the frequency error is semi-globally asymptotically stable, and the inverter terminal voltage error is globally asymptotically stable, all with provided sufficient conditions. Here, numerical simulation experiments are used to validate the theoretical claims. Furthermore, the developed controller is compared with existing work in literature to show the efficacy of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

On Stability of PQ-Controlled Grid-Following and Droop-Control Grid-Forming Inverters

In this paper, the nonlinearity impacts of PQ-control schemes and LCL filter parameter variations on the stability of grid-interactive inverters are studied under weak grid conditions. This work demonstrates that the control nonlinearity and parameter variations can cause unexpected instability at low power levels. This instability issue in the nominal operating range may occur under specific control parameters due to the shift of the low-frequency eigenvalues of PQ-controlled inverters. A full-order small-signal model for PQ-controlled inverters is applied to analyze this instability issue. Furthermore, the impacts of the droop coefficients on the stability of the droop-controlled grid-forming inverter are also investigated using the closed-loop inverter model. The findings are experimentally verified using a small-scale two-level 208 V, 5 kVA inverter feeding a 12 kW NHR 9410 power grid emulator and a hardware setup of a microgrid fed by two 5 kVA 208V inverters.

Hossen, Tareq↗
DOE OSTI2019

Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network based Equivalent Model

The dynamics of powertrain control systems are complicated and involve both nonlinear plant model and control functionalities, albeit they are well defined and formulated using first principle approaches. This constitutes difficulties in exploring implementable optimal tuning rules for some selected control parameters using vehicle-to-vehicle (V2V) communications. This paper presents a way to use neural networks (NN) to represent the problem of parameter tuning for optimizing fuel consumption. For this purpose, physical modelling and validation have been firstly performed for the closed loop powertrain system of the concerned vehicle for some given driving cycles. This is then followed by the sensitivity analysis that selects most influential control parameters to optimize. Using the data generated from the obtained physical models, an equivalent NN formulation has finally been obtained that gives simple yet unified objectives and constraints ready to be used to solve the optimization problem that produces optimal tuning rules for the selected control parameters to minimize fuel consumption.

Parameter optimization, V2X, Powertrain modeling↗