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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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DOE OSTI2024

Composable optimization and control toolkit for scientific applications

Applications of Artificial Intelligence (AI) and Machine Learning (ML) can improve the computational efficiency and scientific research output. In order to improve interoperability and reuse of AI/ML software, a composable approach is required. This talk presents a composable approach for scientific workflow development that allows seamless integration of various modules developed by independent researchers. These practices will reduce redundant software development by allowing re-use of workflow modules across projects, teams, departments and facilities. We will present three use cases that follow the composable approach namely, Scientific Optimization and Control Toolkit (SOCT), SciDAC QuantOm workflow, and JLab Nuclear Physics experimental workflows. This talk will dive deeper into SOCT and present the details of the composable code development for optimization and control algorithms using reinforcement learning.

Rajput, Kishansingh↗
DOE OSTI2024

A safe reinforcement learning algorithm for supervisory control of power plants

Traditional control theory-based methods require tailored engineering for each system and constant fine-tuning. In power plant control, one often needs to obtain a precise representation of the system dynamics and carefully design the control scheme accordingly. Model-free Reinforcement learning (RL) has emerged as a promising solution for control tasks due to its ability to learn from trial-and-error interactions with the environment. It eliminates the need for explicitly modeling the environment’s dynamics, which is potentially inaccurate. However, the direct imposition of state constraints in power plant control raises challenges for standard RL methods. To address this, we propose a chance-constrained RL algorithm based on Proximal Policy Optimization for supervisory control. Our method employs Lagrangian relaxation to convert the constrained optimization problem into an unconstrained objective, where trainable Lagrange multipliers enforce the state constraints. In conclusion, our approach achieves the smallest distance of violation and violation rate in a load-follow maneuver for an advanced Nuclear Power Plant design.

constrained optimization↗
DOE OSTI2011

Fast-kick-off monotonically convergent algorithm for searching optimal control fields

This Rapid Communication presents a fast-kick-off search algorithm for quickly finding optimal control fields in the state-to-state transition probability control problems, especially those with poorly chosen initial control fields. The algorithm is based on a recently formulated monotonically convergent scheme [T.-S. Ho and H. Rabitz, Phys. Rev. E 82, 026703 (2010)]. Specifically, the local temporal refinement of the control field at each iteration is weighted by a fractional inverse power of the instantaneous overlap of the backward-propagating wave function, associated with the target state and the control field from the previous iteration, and the forward-propagating wave function, associated with the initial state and the concurrently refining control field. Extensive numerical simulations for controls of vibrational transitions and ultrafast electron tunneling show that the new algorithm not only greatly improves the search efficiency but also is able to attain good monotonic convergence quality when further frequency constraints are required. The algorithm is particularly effective when the corresponding control dynamics involves a large number of energy levels or ultrashort control pulses.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Energy-Efficient and Comfortable Buildings through Multivariate Integrated Control (ECoMIC)

This project aims to develop an integrated control solution for enhanced energy efficiency and user comfort in commercial buildings. The developed technology is a zone-based control framework that minimizes energy usage while maintaining occupants’ visual and thermal comfort through control of electric lights, motorized venetian blinds and thermostats. The control framework is designed following a modular, scalable and flexible architecture to facilitate easy integration with exiting building management systems. The control framework contains two key algorithms: 1) the lighting load balancing algorithm and 2) the thermostat control algorithm. The lighting load balancing algorithm adopts a model-based closed-loop control approach to determine the optimal electric light and venetian blind settings. It is formulated into an optimization problem with minimizing lighting-related energy consumptions as the objective and delivering adequate task light and preventing daylight glare as the constraints. The thermostat control algorithm is based on a well-established thermal comfort model and formulated as a root-finding problem to dynamically determine the optimal thermostat setpoint for both energy savings and improved thermal comfort. To address building-wide scalability, a system architecture was developed for the zone-based control technology. Three levels of services are defined in the architecture: external services, facility level services and zone level services. The zone-level service includes the control algorithms described above as well as the corresponding interfaces, profiles, sensors and actuators to realize the zone controller. The facility level services connect to the zones through a backbone network, handle supervisory level information and controls, and thus facilitate building-wide scalability. The external services provide communication capability to entities outside of the building for grid interaction and remote access. Various aspects of the developed control technology were evaluated and verified through both simulations and testbed implementations. Simulations coupling a DOE medium office reference building in EnergyPlus building simulation software and a prototype controller in Matlab were performed. During summer time in a mixed-humid climate zone, the simulations revealed reductions of 27% and 42% in electric lighting load and cooling load, respectively, when compared to an advanced base case with daylight dimming and blinds automatically tilted to block direct sun. Two single-room testbeds were established. The testbed at Philips Lighting business building (Rosemont, IL) was designed for quantifying energy performance of integrated controls. This particular implementation achieved 40% and 79% savings on lighting and HVAC energy, respectively, compared to a relatively simple base case operated on predefined schedules. While the resulting energy savings was very encouraging, it should be noted that there may be several caveats associated with it. 1) The test was run during late spring and early summer, and the savings numbers might not be directly used to extrapolate the annual energy savings. 2) Due to the needs for separate control and metering of the small-scale demonstrator within a large building, the HVAC system, hence the corresponding savings, did not represent a typical energy code-compliant design. 3) The light level in the control case was regulated at a particular setpoint, which was lower than then the full-on light level in the base case, and the savings resulted from tuning down the light level to the setpoint was not attributable to the contribution of the developed technology. The testbed at the Lawrence Berkeley National Laboratory (Berkeley, CA) specifically focused on glare control integration, and has demonstrated the feasibility and capability of the glare detection and prevention technique. While the short one-month test in this testbed provided a functional indication of the developed technology, and it would require at least a full solstice-to-solstice cycle to ruinously quantify the performance, which was not possible within the project timeframe. There are certain limitations inherited from the operational assumptions, which could potentially affect the effectiveness and applicability of the developed control technologies. The system takes a typical ceiling-mounting approach for the photosensor locations, and therefore, the control performance relies on proper commissioning or the built-in intelligence of the photosensor for pertinent task light level estimations. For spaces where daylight penetration diminishes significantly deeper into the zone, certain modification to the control algorithms is required to accommodate multiple lighting control subzones and the corresponding sensors for providing a more uniform light level across the entire zone. Integrated control of visual and thermal comfort requires the lighting control zone and thermal control zone to coincide with each other. In other words, the area illuminated by a lighting circuit needs to be the same area served by the thermostat. Thus, the original zoning will potentially constrain the applicability of this technology in retrofitting projects. This project demonstrated the technical feasibility of a zone-based integrated control technology. From the simulation results and testbed implementations, up to 60% lighting energy savings in daylit areas relative to a “no-controls” case can easily be achieved. A 20% reduction of whole building energy consumption is also attainable. In the aspect of occupant comfort, the testbed demonstrated the ability to maintain specified light level on the workplane while promptly mitigate daylight glare 90% of the time. The control system also managed to maintain the thermal environment at a comfortable level 90% of the time. The aspect of system scalability was guaranteed by the system architecture design, based on which the testbeds were instantiated. Analysis on the aspect of economic benefit has yielded an about 6-year payback time for a medium-sized building, including the installation of all hardware and software, such as motorized blinds and LED luminaires. The payback time can be significantly reduced if part of the hardware is already in place for retrofitting projects. It needs to be noted that since the payback analysis was partly based on the testbed performance results, it is constrained by the caveats associated with the testbed implementations. The main uncertainty lies in the contribution from the space conditioning energy savings as it was non-trivial to realistically configure a room-size HVAC system for directly extrapolating whole-building HVAC energy savings. It is recommended to further evaluate the developed technology at a larger scale, where the lighting and HVAC energy consumption can be realistically measured at the building level, to more rigorously quantify the performance potentials.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Data-Enabled Predictive Control for Building HVAC Systems

Model predictive control is widely used as a control technology for the computation of optimal control inputs of building heating, ventilating, and air conditioning (HVAC) systems. However, both the benefits and widespread adoption of model predictive control (MPC) are hindered by the effort of model creation, calibration, and accuracy of the predictions. In this paper, we apply the data-enabled predictive control (DeePC) algorithm for designing controls for building HVAC systems. The algorithm solely depends on input/output data from the system to predict future state trajectories without the need for system identification. The algorithm relies on the idea that a vector space of all input–output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given the input signal is persistently exciting. Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated building modeled in EnergyPlus is a modified commercial large office prototype building served by an air handling unit-variable air volume HVAC system. Temperature setpoints of zones are used as control variables to minimize the HVAC energy cost of the building considering a time-of-use electricity rate structure. Furthermore, sensitivity analysis is conducted to gain insights into the effect of parameter tuning on DeePC performance. Simulation results are used to illustrate the performance of the algorithm and compare the algorithm with model-based MPC and occupancy-based setpoint controller. Overall, DeePC achieves similar performance compared to MPC for lower engineering effort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Implementation and test of an automated control hunting fault correction algorithm in a fault detection and diagnostics tool

Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2011

Image-Based Verification Algorithms for Arms Control

PNNL is developing and evaluating radiographic image analysis techniques (active and passive) for verifying sensitive objects in a material control or warhead counting regime in which sensitive information may be acquired and processed behind an information barrier. Since sensitive image information cannot be present outside the information barrier, these techniques are necessary to extract features from the full images and reduce them to relevant parameters (attributes) of the inspected items. This evaluation can be done behind the information barrier, allowing for reporting and storage of non-sensitive attributes only. Several advances have been made to radiographic object verification algorithms, in the areas of spectral imaging for passive detectors and estimation of material density in active radiographic images. Both of these advances are pertinent in an arms control context. While passive radiographic images produced by previous work may be evaluated for the presence of emissive objects, approaches which leverage the spectroscopic potential of the detectors allow a much greater discrimination of SNM from background and other sources. Spectral passive imaging approaches to warhead discrimination and counting include specific materials and geometric arrangement localization, as well as “spectral difference” metrics which group regions with similar spectra together. These approaches may improve resolution for discrimination between materials in addition to locating SNM within surrounding shielding and/or structural elements. Previous work by our group has developed the capability to discern material density and composition in radiographic images by examining the edge transition characteristics of objects. The material construction of an object can be investigated in this way. In a weapons counting or discrimination context, unknown occultation of objects of interest, as well as additional elements of warhead construction, construction materials of varying geometry and makeup and various angles of radiograph are expected to impact algorithm performance. Advances in material discrimination algorithms are presented as a mechanism to help make these approaches robust to these sorts of variations.

Robinson, Sean M.↗
DOE OSTI2014

A high-speed, high-efficiency phase controller for coherent beam combining based on SPGD algorithm

A phase controller for coherent beam combining (CBC) of fibre lasers has been designed and manufactured based on a stochastic parallel gradient descent (SPGD) algorithm and a field programmable gate array (FPGA). The theoretical analysis shows that the iteration rate is higher than 1.9 MHz, and the average compensation bandwidth of CBC for 5 or 20 channels is 50 kHz or 12.5 kHz, respectively. The tests show that the phase controller ensures reliable phase locking of lasers: When the phases of five lasers are locked by the improved control strategy with a variable gain, the energy encircled in the target is increased by 23 times than that in the single output, the phase control accuracy is better than λ/20, and the combining efficiency is 92%. (control of laser radiation parameters)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Development of autonomous multirotor platform for exploration missions

This paper outlines development process of unmanned multirotor aerial vehicle HF-4X, which consists of design and manufacturing semi-autonomous UAV dedicated for indoor flight, which would be capable of stable and controllable mission flight. A micro air vehicle was designed to participate in the International Micro Air Vehicle Conference and Flight Competition. In this paper much attention was paid to the structure of flight control system, stabilization algorithms, analysis of IMU sensors, fusion algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Stochastic Model Predictive Control With Gaussian Wind Direction Preview for Wake Steering

This article addresses the problem of wake steering control for wind farms that explicitly consider the tradeoff between farm-level power generation and yaw duty cycle under variable and uncertain wind conditions. A novel stochastic model predictive control (MPC) algorithm is presented, which utilizes a stochastic model of the freestream wind field components in a receding horizon framework to compute optimal yaw set points that maximize the expected value of the farm power while constraining the yaw actuation. Different configurations of the algorithm are evaluated using a steady-state wind farm simulator. The proposed stochastic MPC algorithm can plan control actions over a future prediction horizon based on probabilistic estimates of the incoming wind magnitude and direction.

17 WIND ENERGY↗
DOE OSTI2020

Optimization-Based Fast-Frequency Estimation and Control of Low-Inertia Microgrids

The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

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↗
DOE OSTI2025

Low-latency quantum control using AI algorithms on cryogenic microelectronics platforms

Superconducting quantum computers are a particular technology that involve the realization of qubits by means of LC circuits with a Josephson junction. As shown in Fig. 1, this latter addition has the effect of introducing a nonlinearity (anharmonicity) that makes the frequency spacing between each energy level non-uniform. This is a desired effect for separating the two lowest levels, used for computation, from the higher ones, that are theoretically infinite and which are not desired to be used.

97 MATHEMATICS AND COMPUTING↗