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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bilinear dynamic mode decomposition for quantum control

Abstract Data-driven methods for establishing quantum optimal control (QOC) using time-dependent control pulses tailored to specific quantum dynamical systems and desired control objectives are critical for many emerging quantum technologies. We develop a data-driven regression procedure, bilinear dynamic mode decomposition (biDMD), that leverages time-series measurements to establish quantum system identification for QOC. The biDMD optimization framework is a physics-informed regression that makes use of the known underlying Hamiltonian structure. Further, the biDMD can be modified to model both fast and slow sampling of control signals, the latter by way of stroboscopic sampling strategies. The biDMD method provides a flexible, interpretable, and adaptive regression framework for real-time, online implementation in quantum systems. Further, the method has strong theoretical connections to Koopman theory, which approximates nonlinear dynamics with linear operators. In comparison with many machine learning paradigms minimal data is needed to construct a biDMD model, and the model is easily updated as new data is collected. We demonstrate the efficacy and performance of the approach on a number of representative quantum systems, showing that it also matches experimental results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Spatiotemporal control of structure and dynamics in a polar active fluid

We apply optimal control theory to drive a polar active fluid into new behaviors: relocating asters, reorienting waves, and on-demand switching between states. This study reveals general principles to program active matter for useful functions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Multi-scale, Multi-disciplinary, and Multi-agent Explainable AI with Koopman-Undergirded Learning, Prediction, and Analysis (M3EA KULPA) (Project Closeout Report)

The goal of this project was to develop and use domain-aware machine learning formulations, based on the Koopman Operator (KO), for modelling multi-scale, multi-disciplinary (e.g., multi-physics), and/or multi-agent systems. The project developed these formulations for the following cases: • Systems with dynamics at two separate time scales, • Systems with a bi-level hierarchical control structure, • Systems with bi-level hierarchical control and dynamics at two separate time scales (the lower level controls operating at the faster time scale), and • Systems with n separate but interacting agents/disciplines (with/without control, respectively); the controls for each agent could include bi-level hierarchical control and dynamics at two separate time scales as described above. The project then defined a set of dynamical systems consisting of different nonlinear oscillators that could be used to test these different formulations and then subsequently learned the KO models for those systems. With the KO models, we were able to do the following: • Quantify system stability, including both long-term and transient behavior, • Quantify the effects of feedbacks between the different time scales and agents/disciplines in terms of those feedbacks’ effects on system stability, • Replace a standard Proportional-Integral (PI) control in the hierarchical control structure with a KO-based Linear-Quadratic Regular (LQR), a form of optimal control, • Calculate optimal supervisory control policies a) with and without time scale separated dynamics at the lower level control levels and b) with both PI and KO-based LQR lower level control policies, and • Calculate dynamic Nash equilibria for multi-agent systems where each agent makes its own control decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A field programmable gate array-based timing and control system for the dynamic compression sector

A field programmable gate array (FPGA)-based timing and trigger control system has been developed for the Dynamic Compression Sector (DCS) user facility located at the Advanced Photon Source (APS) at Argonne National Laboratory. The DCS is a first-of-its-kind capability dedicated to dynamic compression science. All components of the DCS laser shock station—x-ray choppers, single-shot shutter, internal laser triggers, and shot diagnostics—must be synchronized with respect to the arrival of x rays in the hutch. An FPGA synchronized to the APS storage ring radio frequency clock (352 MHz) generates trigger signals for each stage of the laser and x-ray shutter system with low jitter. The developed FPGA-based control system was the first system used to control the laser and the shutter system since its commissioning, and it has been developing since then to improve the timing jitter. The system is composed of a Zynq FPGA, a debug card, line drivers, and a power supply. The delay and offsets of the trigger signals can be adjusted by using a user-friendly graphical user interface with high precision. Here, the details of the system architecture, timing requirements, firmware, and software implementation along with the performance evaluation are presented in this paper. The system offers low timing jitter (15.5 ps rms) with respect to the APS 352 MHz clock, suitable for the 100 ps (FWHM) x-ray bunch duration at the APS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A New Distributed Model-Free Control Strategy to Diminish Distribution System Voltage Violations

This paper proposes a new distributed model-free control (MFC) strategy for dynamic voltage control to diminish distribution systems' voltage violations. The objective is to maintain all critical load bus voltages within the acceptable ANSI Range A (+/- 5% of nominal). The distributed MFC strategy, which only requires local voltage measurements from designated load buses, controls online the reactive power generation of available synchronous generator (SG)-based and photovoltaic (PV)-based distributed generators (DGs). The distributed MFC strategy is computationally efficient and does not require modelling of the different system components and disturbances. Time-domain dynamic simulations are conducted for the 21-bus test distribution system fed by multiple DGs to verify the performance of the proposed MFC strategy, and the results are compared against the conventional model-based microgrid voltage stabilizer (MGVS) control strategy. The simulation results show that the distributed MFC strategy provides minimal voltage violations and achieves the dynamic voltage stability of the system under diverse disturbances.

Hatipoglu, Kenan↗
DOE OSTI2024

Dynamic Simulation Modeling and Control of a Desiccant Assisted Direct-expansion Air Handling Unit

Desirable built environments demand simultaneous regulation of thermal comfort and indoor air quality (IAQ) with energy-efficient operation of heating, ventilation and air conditioning (HV AC) systems, which involves controls of temperature, humidity and airborne contaminants simultaneously. This paper presents the efforts of dynamic modeling and initial development control strategy for a desiccant-assisted multi-functional air handling unit (AHU) coupled with a direct-expansion rooftop unit (RTU) system, which aims to achieve multiple functions for indoor environment conditioning with energy efficient control. The RTU-AHU system includes a desiccant wheel for dehumidification and a conceptual direct air capture (DAC) filtering device for CO2 regulation. A Modelica-based dynamic model is developed for this conceptual system, and a simple decentralized control strategy is designed, which combines a differential-enthalpy based AHU return-air ratio control, a demand-controlled ventilation, and supply-air temperature humidity control via the RTU and DW controls. The proposed control method is evaluated with the Modelica simulation model for a selected set of scenarios

Pan, Chao↗
DOE OSTI2022

Accelerated Wind-Turbine Wake Recovery Through Actuation of the Tip-Vortex Instability

Advances in wind-plant control have often focused on more effectively balancing power between neighboring turbines. Wake steering is one such method that provides control-based improvements in a quasi-static way, but this does little to fundamentally change the wake recovery process, and thus, it has limited potential. This study investigates use of another control paradigm known as dynamic wake control (DWC) to excite the mutual inductance instability between adjacent tip-vortex structures, thereby accelerating the breakdown of the structures. The current work carries this approach beyond the hypothetical by applying the excitation via turbine control vectors that already exist on all modern wind turbines: blade pitch and rotor speed control. The investigation leverages a free-vortex wake method (FVWM) that allows a thorough exploration of relevant frequencies and amplitudes of harmonic forcing for each control vector (as well as the phase difference between the vectors for a tandem configuration) while still capturing the essential tip-vortex dynamics. The FVWM output feeds into a Fourier stability analysis working to pinpoint candidate DWC strategies suggesting fastest wake recovery. Near-wake length reductions of >80% are demonstrated, although without considering inflow turbulence. Analysis is provided to interpret these predictions considering the presence of turbulence in a real atmospheric inflow.

17 WIND ENERGY↗
DOE OSTI2021

Power increases using wind direction spatial filtering for wind farm control: Evaluation using FLORIS, modified for dynamic settings

As wind energy plays a growing role in the energy sector, new methods for controlling wind turbines and wind farms to maximize performance are garnering industry interest. A developing body of research treats the entire wind farm as a control system, with individual turbines acting as agents in a network, allowing farm-level objectives to be considered. Two promising developments in this research are wake steering control, which seeks to increase the power generated at a wind farm by directing the wakes of upstream turbines away from downstream ones, and communication-based spatial filtering, which seeks to improve the quality of information used by turbine- and farm-level controllers by combining measurements of the wind field collected at the individual turbines. The latter method has been shown to improve the estimates of wind direction at the turbines; however, the resulting potential for increased power capture warrants further investigation. With this paper, we begin to address this gap by combining wake steering with wind direction spatial filtering. To do so, we present a preliminary method for assessing the power capture of dynamic controllers using wind farm codes designed for time-averaged simulation. This allows us to generate results much more rapidly than would be possible using high-fidelity wind farm simulators and may be useful in many wind farm control design applications.

17 WIND ENERGY↗
DOE OSTI2022

Pathway to Zero Energy Windows: Advancing Technologies and Market Adoption

Improving the performance of windows has the potential to reduce U.S. annual energy use by 1.7% and CO2 emissions by 1.9% in 2050. Beyond static windows, dynamic technologies could reduce U.S. annual energy use by 1.6 quads and CO2 emissions by 68 million metric tons. In addition, the widespread adoption of static and dynamic technologies would substantially reduce peak electricity demand from buildings. Beyond energy use and CO2 emissions reductions, improving window performance will increase occupant comfort and well-being. In a new report, Pathway to Zero Energy Windows: Advancing Technologies and Market Adoption, the U.S. Department of Energy (DOE) outlines multiple avenues for technology development, deployment, and adoption to increase the impact that windows can have on decarbonizing America's buildings.

building energy efficiency↗
DOE OSTI2020

Servo-aero-gravo-elastic (SAGE) scaling and its application to a 13-MW downwind turbine

Reduced scale wind turbines can be extremely cost-effective to test new rotor concepts since prototype costs are heavily dependent on the rotor diameter. Ideally, the scaled model would have the same non-dimensional deflections, dynamics, and control behavior as the full-scale model. This would provide a high-fidelity demonstration of the full-scale performance, which is ideal if the full-scale turbine has significant aeroelastic interactions. To this end, servo-aero-gravo-elastic (SAGE) scaling is developed and applied to a 13-MW turbine that is scaled to a 20% scale model. The scaling preserves the tip-speed ratio, the rotor speed normalized by the flapping frequency, and the tip deflections normalized by the blade length. In addition, the controller employs the same control structure (gain-scheduled pitch control and variable speed torque control) and is scaled dynamically (e.g., matching non-dimensional time constant of the pitch angle, etc.). Furthermore, the thrust, gravity, and centrifugal moments are scaled such that the load angles are preserved as a function of a non-dimensional wind speed. However, the environmental scaling must consider differences in Reynolds number (since this parameter cannot be held constant) and subsequent changes in the axial induction factor. While the presented results showcase these differences during operational conditions, the non-dimensional tip deflections remain comparable through all wind speed ranges, indicating the viability of the SAGE scaling method in matching full-scale aeroelastic responses.

Kaminski, Meghan (ORCID:0000000322334784)↗
DOE OSTI2023

Deep-Learning-Based Koopman Modeling for Online Control Synthesis of Nonlinear Power System Transient Dynamics

Power system stability and control have become more challenging due to the increasing uncertainty associated with renewable generation. Here, the performance of conventional control is highly driven by the physics-based offline-developed dynamic models that can deviate from the actual system characteristics under different operating conditions and/or configurations. Data-driven approaches based on online measurements can be a better solution to addressing these issues by capturing real-time operation conditions. This article describes a novel fully data-driven probabilistic framework to derive a linear representation of postcontingency grid dynamics and online prescribe control based on the derived model to enhance transient stability. The complex nonlinear power system dynamics is approximated by a linear model by using multiple neural network modules that infer distributions of the observations and introducing a Koopman layer to sample possible Koopman linear models from the inferred distributions. The trained model features linearity that can be easily incorporated into the existing linear control design paradigm and ease the controller design process. The effectiveness of Koopman-based control designs is validated through comparative case studies, which demonstrate increased prediction accuracy and control performance when applied to a power system with heterogeneous generator dynamics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Control of the Folding Dynamics of Self-Reconfiguring Magnetic Microbots Using Liquid Crystallinity

Reconfigurable microdevices are being explored in a range of contexts where their life-like abilities to move and change shape are important. While much work has been done to control the motion of these microdevices by engineering their geometry and composition, little is known about their dynamics in complex fluid environments with non-Newtonian rheology. Here in this paper, it is shown how the actuation dynamics of reconfigurable microdevices made by assembly of patchy magnetic microcubes, which are referred to as “microbots,” can be modulated by their interactions with the anisotropic viscoelastic environment of a liquid crystal (LC). The free energy arising from the elastic strain of LC and formation of topological defects around the microbots influences their folding dynamics, which can be tuned by tailoring both the far-field orientation of the LC and the local ordering of the LC at the microbot surfaces. These findings represent a first step toward establishing a general set of design rules to control the dynamics of microbots using complex anisotropic fluids.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Lab-Scale, Closed-Loop Experimental Characterization, Model Refinement, and Validation of a Hydrokinetic Energy-Harvesting Ocean Kite

This paper presents a study wherein we experimentally characterize the dynamics and control system of a lab-scale ocean kite, and then refine, validate, and extrapolate this model for use in a full-scale system. Ocean kite systems, which harvest tidal and ocean current resources through high-efficiency cross-current motion, enable energy extraction with an order of magnitude less material (and cost) than stationary systems with the same rated power output. However, an ocean kite represents a nascent technology that is characterized by relatively complex dynamics and requires sophisticated control algorithms. In order to characterize the dynamics and control of ocean kite systems rapidly, at a relatively low cost, the authors have developed a lab-scale, closed-loop prototyping environment for characterizing tethered systems, whereby 3D printed systems are tethered and flown in a water channel environment. While this system has been shown to be capable of yielding similar dynamic characteristics to some full-scale systems, there are also fundamental limitations to the geometric scales and flow speeds within the water channel environment, making many other real-world scenarios impossible to replicate from the standpoint of dynamic similarity. Here, to address these scenarios, we show how the lab-scale framework is used to refine and validate a scalable dynamic model of a tethered system, which can then be extrapolated to full-scale operation. In this work, we present an extensive case study of this model refinement, validation, and extrapolation on an ocean kite system intended for operation in the Gulf Stream or similar current environments.

42 ENGINEERING↗
DOE OSTI2020

The Heuristic Dynamic Programming Approach in Boost Converters

In this study, a heuristic dynamic programming controller is proposed to control a boost converter. Conventional controllers such as proportional-integral-derivative (PID) or proportional-integral (PI) are designed based on the linearized small-signal model near the operating point. Therefore, the performance of the controller during the start-up, the load change, or the input voltage variation is not optimal since the system model changes by varying the operating point. The heuristic dynamic programming controller optimally controls the boost converter by following the approximate dynamic programming. The advantage of the HDP is that the neural network-based characteristic of the proposed controller enables boost converters to easily cope with large disturbances. An HDP with a well-trained critic and action networks can perform as an optimal controller for the boost converter. To compare the effectiveness of the traditional PI-based and the HDP boost converter, the simulation results are provided.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Comments on Control of Wave Energy Converters

The main objective of this letter is to consolidate the knowledge about the dynamics and control of oscillating-body wave energy converters (WECs). A number of studies have shown that control systems strongly affect power absorption; however, there remains a need for a concise and integrated explanation of the theoretical and practical implications that control can have on both performance and the broader WEC design process. This short letter attempts to fill this gap by presenting a discussion on the key practical aspects concerning the dynamics and control of oscillating-body WEC. In particular, the focus is on the choice of control models and a simple causal control scheme suitable for real-time implementation. To conclude, consideration is given to the effect of the power takeoff (PTO) on the maximization of electrical power, thus leading to the derivation of useful conditions for the control co-design of the PTO system.

42 ENGINEERING↗
DOE OSTI2020

Peak load reduction and load shaping in HVAC and refrigeration systems in commercial buildings by using a novel lightweight dynamic priority-based control strategy

Reducing peak power demand in a building can reduce electricity expenses for the building owner and contribute to the efficiency and reliability of the electrical power grid. For the building owner, reduced expenses come from the reduction or elimination of peak power charges on electricity bills. For the power system operator, reducing peak power demand leads to a more predictable load profile and reduces stress on the electric grid system. Herein we present a computationally inexpensive, dynamic, and retrofit-deployable control strategy to effect peak load reduction and load shaping. The effectiveness of the control strategy is examined in a simulation with 80 air-conditioning units and 40 refrigeration units. The results show that a peak demand reduction of 60 kW can be achieved relative to peak demand in a typical set point–based approach. The proposed strategy was deployed in a gymnasium building with four rooftop HVAC units, where it showed over 15% peak demand (kW) reduction savings while maintaining or lowering energy consumption (in kilowatt-hours) relative to the set point–based thermostat controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Manipulating and Probing Ultrafast Atomic and Molecular Dynamics (Final Report)

This program has focused on the exploration and control of dynamics in atoms, small molecules, clusters, and micro- or nano-structures following ultrafast excitation and/or driven by strong oscillating fields. In our principal experiments, gas phase atoms and molecules were exposed to short pulses of electromagnetic radiation with frequencies ranging from THz into the XUV spectral regions, and with durations from picoseconds to attoseconds. The pulses coherently induce and/or probe dynamics in the electronic, vibrational, and rotational degrees of freedom of the targets. The overarching goal of the various projects has been to exploit strong-field processes and/or implement novel ultrafast techniques for manipulating and probing coherent electronic and nuclear motion. Typically, the relevant system evolution occurs over attosecond to picosecond timescales. In doing so, we have gained a more complete picture of correlated multi-particle dynamics in atoms, molecules, and other complex systems and expanded the toolbox of useful techniques for manipulating and probing those dynamics. Grant funds were primarily used to: (i) pay salaries and fringe benefits for the PI and postdocs and stipends, tuition, and health care costs for graduate students; (ii) cover travel expenses for the PI, postdocs, and students to attend and participate in professional meetings, conferences, and workshops; and (iii) purchase research materials, supplies, and equipment required for experiments. The research resulted in the publication of 24 peer reviewed articles (including 1 in Nature Physics, 2 in Nature Communications, 7 in Physical Review Letters, and 12 in Physical Review A), and 4 PhD dissertations. The specific results from different funding periods are summarized separately in the main report.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Comparison of Optimal Energy Management Strategies Using Dynamic Programming, Model Predictive Control, and Constant Velocity Prediction

Due to the recent advancements in autonomous vehicle technology, future vehicle velocity predictions are becoming more robust which allows fuel economy (FE) improvements in hybrid electric vehicles through optimal energy management strategies (EMS). A real- world highway drive cycle (DC) and a controls-oriented 2017 Toyota Prius Prime model are used to study potential FE improvements. We proposed three important metrics for comparison: (1) perfect full drive cycle prediction using dynamic programming, (2) 10-second prediction horizon model predictive control (MPC), and (3) 10-second constant velocity prediction. These different velocity predictions are put into an optimal EMS derivation algorithm to derive optimal engine torque and engine speed. The results show that the constant velocity prediction algorithm outperformed the baseline control strategy but underperformed the MPC strategy with an average 1.58% and 2.45% of FE improvement with highway and city-highway DC. Also, using a 10-second prediction window MPC strategy provided FE improvement results close to the full drive cycle prediction case. MPC has the potential to achieve 60%-65% and 70% - 80% of global FE improvement over highway and city-highway DC respectively.

Patel, Amol Arvind↗