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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system

Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. Furthermore, there is still a lack of in-depth quantitative studies on their approximation levels to the true optimality, especially in the building energy domain. To fill in the gap, this paper provides a numerical framework that enables the evaluation of the optimality levels of different controllers for building energy systems. This framework is then used to comprehensively compare the optimal control performance of both MPC and DRL controllers with given computation budgets for a single zone fan coil unit system. Note the optimality is estimated based on a user-specific selection of trade-off weights among energy costs, thermal comfort and control slew rates. Compared with the best optimality we can find through expensive optimization simulations, the best DRL agent can maximally approximate the optimality by 96.54%, which outperforms the best MPC whose optimality level is 90.11%. However, due to the stochasticity, the DRL agent is only expected to approximate the optimality by 90.42%, which is almost equivalent to the best MPC. Except for Proximal Policy Optimization (PPO), all DRL agents can have a better approximation to the optimality than the best MPC, and are expected to have better approximation than the MPC with a prediction horizon of 32 steps (15 min per step). In terms of reducing energy cost and thermal discomfort, MPC can outperform the rule-based control (RBC) by 18.47%–25.44%. DRL can be expected to outperform RBC by 18.95%–25.65% ,and the best DRL control policy can outperform RBC by 20.29%–29.72%. Although the comparison of the optimality level is performed in a perfect setting, e.g., MPC assumes perfect models, and DRL assumes a perfect offline training process and online deployment process, this can shed insight on their capabilities of approximating to the original dynamic programming problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Scalable Control Co-design for Resilient-by-Design Cyber Physical Systems

Critical infrastructure networks, such as power and transportation networks, are often modelled as cyber-physical systems. With ever increasing complexity of these systems, there is a need for newer and more relevant metrics and design tools that will co-optimize the physical system components and control policies to guarantee resilience against cyber and natural threats. To this end, a simulation-based control co-design computational framework that will concurrently determine the system and control parameters of a cyber-physical system to meet pre-specified resilience, operational and economic objectives has been developed. The capabilities of the developed co-design engine are demonstrated by designing the physical components and control parameters of a microgrid system that will meet its resiliency objectives when subjected to various cyber and physical threats.

42 ENGINEERING↗
DOE OSTI2024

Towards Autonomous Experiments by Connecting High Performance Microscopy with High Performance Computing

The digitization of controls, data, and analysis in microscopy is bringing the idea of autonomous microscopes closer to reality than ever before. Automated transmission electron microscopy (TEM) is already fairly routine for some experiments the only require simple repetitive tasks such as imaging biological macromolecules for single particle cryoEM [1], tilt series for electron tomography [2], and movies for crystallography [3]. The vast majority of TEM experiments are conducted completely by human operators who choose the regions of interest, optimize experimental parameters, and make decisions about data quality visually during an experiment. The field is still a long way from having completely autonomous TEMs that can adapt to sample difficulties and tune experimental parameters based on data quality and desired experimental outcomes. Part of the issue is the lack of capability for feeding information learned from on-line, live data analysis back into the on-going experiment [4]. Furthermore, this presentation will discuss current capabilities for large scale data reduction and analysis using high performance computing (i.e. supercomputing) and progress towards developing a true feed-back loop that places data analysis and theory in the experimental loop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Model-Free Control of Grid-Interactive Efficient Buildings Under Communication Time Delays

Grid-interactive efficient buildings (GEBs) have recently been used to enhance the reliability and stability of the electric grid through demand response (DR) programs. However, most existing DR control strategies require accurate modeling of the various building thermostatically controlled loads (TCLs) and are computationally expensive. To address these challenges, a model-free control (MFC)-based strategy has recently been introduced for coordinating and controlling GEBs. MFC is a data-enabled control strategy that is computationally efficient and does not require the analytical models of the various building equipment. In this paper, we numerically investigate the impact of communication time delays on the performance of MFC in maintaining the TCLs' temperatures within the desired comfort levels while meeting the assigned power allocation constraint.

Telsang, Bhagyashri [University of Tennessee, Knox↗
DOE OSTI2020

Within-Host Phenotypic Evolution and the Population-Level Control of Chronic Viral Infections by Treatment and Prophylaxis

Chronic viral infections can persist for decades spanning thousands of viral generations, leading to a highly diverse population of viruses with its own complex evolutionary history. We propose an expandable mathematical framework for understanding how the emergence of genetic and phenotypic diversity affects the population-level control of those infections by both non-curative treatment and chemo-prophylactic measures. Our frameworks allows both neutral and phenotypic evolution, and we consider the specific evolution of contagiousness, resistance to therapy, and efficacy of prophylaxis. We compute both the controlled and uncontrolled, population-level basic reproduction number accounting for the within-host evolutionary process where new phenotypes emerge and are lost in infected persons, which we also extend to include both treatment and prophylactic control efforts. We used these results to discuss the conditions under which the relative efficacy of prophylactic versus therapeutic methods of control are superior. Finally, we give expressions for the endemic equilibrium of these models for certain constrained versions of the within-host evolutionary model providing a potential method for estimating within-host evolutionary parameters from population-level genetic sequence data.

sensitivity analysis↗
DOE OSTI2024

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Robust Output Feedback Control Design for Inertia Emulation by Wind Turbine Generators

Wind generation has gained widespread use as a renewable energy source. Most wind turbines and other renewables connected to the grid through converters result in a reduction in the natural inertial response to grid frequency changes. The doubly-fed induction generator (DFIG) can be controlled to compensate for this reduction and, in fact, provide faster response than traditional synchronous machines. This paper proposes to design observer based output feedback linear quadratic regulator (LQR) and H control laws to realize the inertia emulation function and deliver fast frequency support. Furthermore, the aim is to track the reference speed by a diesel synchronous generator (DSG) in order to reach the desired inertia. The control signal is computed based on a reduced order model using the balanced truncation technique. A comparison with selective modal analysis (SMA) and balanced truncation model reduction techniques is presented. Comprehensive results show the effective emulation of synthetic inertia by implementing the control laws on a nonlinear three- phase diesel-wind system. The proposed technique is analyzed for different short circuit ratio (SCR) scenarios.

17 WIND ENERGY↗
DOE OSTI2024

A Hierarchical OPF Algorithm with Improved Gradient Evaluation in Three-Phase Networks

Linear approximation commonly used in solving alternating-current optimal power flow (AC-OPF) simplifies the system models but incurs accumulated voltage errors in large power networks. Such errors will make the primal-dual type gradient algorithms converge to solutions with voltage violation. In this paper, we improve a recent hierarchical OPF algorithm that rested on primal-dual gradients evaluated with a linearized distribution power flow model. Specifically, we propose a more accurate gradient evaluation method based on an unbalanced three-phase nonlinear distribution power flow model to mitigate the errors arising from linearization. The resultant gradients feature a blocked structure that enables our development of an improved hierarchical primal-dual algorithm to solve the OPF problem. Numerical results on the IEEE 123-bus test feeder and a 4,518-node test feeder show that the proposed method can enhance voltage safety at comparable computational efficiency with the linearized algorithm.

approximation algorithms↗
DOE OSTI2020

Human-System Interface Design Review Guidelines

The U.S. Nuclear Regulatory Commission (NRC) staff reviews the human factors engineering (HFE) aspects of nuclear power plants in accordance with the Standard Review Plan (NUREG-0800, Standard Review Plan for the Review of Safety Analysis Reports for Nuclear Power Plants: LWR Edition). The Human Factors Engineering Program Review Model (NUREG-0711, Revision 3, issued November 2012) contains detailed design review procedures. As part of the review process, the interfaces between plant personnel and the plant's systems and components are evaluated for conformance with HFE guidelines. This document, Human-System Interface Design Review Guidelines (NUREG-0700, Revision 3), provides the guidelines necessary to perform this evaluation. The review guidelines address the physical and functional characteristics of human-system interfaces (HSIs). Because these guidelines only address the HFE aspects of design and not other related considerations, such as instrumentation and control and structural design, they are referred to as HFE guidelines. In addition to the review of actual HSIs, the NRC staff can use the NUREG-0700 guidelines to evaluate a design-specific HFE guidelines document or style guide. The HFE guidelines are organized into four basic parts, which are divided into sections. Part I contains guidelines for the basic HSI elements: information displays, user-interface interaction and management, and analog displays and controls. These elements are used as building blocks to develop HSI systems to serve specific functions. Part II contains the guidelines for reviewing the following HSI systems: alarm system, safety parameter display system, group-view display system, soft control system, computer-based procedure system, automation system, and communication system. Part III provides guidelines for the review of workstations and workplaces. Part IV provides guidelines for the review of HSI support (i.e., maintainability of digital systems and degraded HSI and instrumentation and control conditions).

42 ENGINEERING↗
DOE OSTI2022

Controlled precision QUBO-based algorithm to compute eigenvectors of symmetric matrices

We describe an algorithm to compute the extremal eigenvalues and corresponding eigenvectors of a symmetric matrix which is based on solving a sequence of Quadratic Binary Optimization problems. This algorithm is robust across many different classes of symmetric matrices; It can compute the eigenvector/eigenvalue pair to essentially any arbitrary precision, and with minor modifications, can also solve the generalized eigenvalue problem. Performance is analyzed on small random matrices and selected larger matrices from practical applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Skewering the silos: using Brick to enable portable analytics, modeling and controls in buildings

Nearly all large commercial buildings have heating, ventilation and air conditioning (HVAC) systems, lighting systems, safety and other systems controlled by a computer—a dedicated server with a building energy management system (BMS). However, these BMSs are proprietary with each building’s assets (that is, fans, valves, pumps, and their setpoints) named and coded uniquely by the BMS vendor or engineer; building analytics and control algorithms are written specific to the assets and the building. Thus, any control updates or analytics to improve building performance—especially critical to reduce greenhouse emissions or improve load flexibility—are labor intensive and costly. The Brick schema was developed so the same analysis or control algorithms can work on a variety of buildings if each is digitally represented in a Brick data model. The goal of this project was to further the development of Brick to extend it beyond an academic project with demonstrated success in a small field study, to a practical choice for industrial and commercial stakeholders seeking to realize value from building data. To do this, we executed four objectives: (1) expand the Brick schema including its modeling capabilities and vocabulary, (2) develop tools for integrating Brick with existing digital technologies and representations in buildings, (3) develop an open-source analytics platform to facilitate use of Brick in delivering data value, and (4) demonstrate Brick-driven analytics and controls in real settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Data-driven quantum approximate optimization algorithm for power systems

Abstract Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems. It is especially promising for Distributed Energy Resources (DERs) dominant systems that have been widely developed to promote energy sustainability. In those systems, knowing the maximum sections of power and data delivery is essential for monitoring, operation, and control. However, high computational effort is required. By leveraging quantum resources, Quantum Approximate Optimization Algorithm (QAOA) provides a means to search for these sections efficiently. However, QAOA performance relies heavily on critical parameters, especially for weighted graphs. Here we present a data-driven QAOA, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density. We verify the strategy with 39,774 expectation value calculations. Without parameter optimization, our data-driven QAOA is comparable with the Goemans-Williamson algorithm. This work advances QAOA and pilots its practical application to power systems in noisy intermediate-scale quantum devices.

Jing, Hang (ORCID:0000000256343489)↗
DOE OSTI2020

Innovations in optimization and control of accelerators using methods of differential geometry and genetic algorithms (Final Report)

Online tuning of particle accelerators is necessary in order to achieve optimal machine performance. However, it is also a major challenge due to the large parameter space which must be searched and the fact that many of the desir- able objectives compete with one another, and so will not reach their optimal values simultaneously. In order to mitigate these issues, we have explored using dimension-reduction techniques to reduce the size of the parameter space which must be searched and multi-objective genetic algorithms to obtain the sets of tuning parameters which provide pareto-optimal values for the objectives. These methods have enabled us to obtain improved values of the vertical emittance at the Cornell Electron Storage Ring (CESR), and to do so with greater control of orbit errors. Algorithmic tuning is a multidisciplinary endeavour, requiring expertise in beam dynamics, diagnostics, control systems and computer science, and thus a key practical problem is to formulate a common language in which experts with different specialties can communicate. We developed a solution to this problem in the form of a generic accelerator software interface that allows for rapid prototyping of optimization and control algorithms. Our interface is built on the Experimental Physics and Industrial Control Systems (EPICS) and makes possible testing control code in simulation before deployment on real accelerators, as well as deployment of third-party optimization code.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Relaxed Multibang Regularization for the Combinatorial Integral Approximation

Multibang regularization and combinatorial integral approximation decompositions are two actively researched techniques for integer optimal control. In this work, we consider a class of polyhedral functions that arise particularly as convex lower envelopes of multibang regularizers and show that they have beneficial properties with respect to regularization of relaxations of integer optimal control problems. We extend the algorithmic framework of the combinatorial integral approximation such that a subsequence of the computed discrete-valued controls converges to the infimum of the regularized integer control problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Achieving designed texture and flows in bulk active nematics using optimal control theory

Being intrinsically nonequilibrium, active materials can potentially perform functions that would be thermodynamically forbidden in passive materials. However, active systems have diverse local attractors that correspond to distinct dynamical states, many of which exhibit chaotic turbulent-like dynamics and thus cannot perform work or useful functions. Designing such a system to choose a specific dynamical state is a formidable challenge. Motivated by recent advances enabling optogenetic control of experimental active materials, we describe an optimal control theory framework that identifies a spatiotemporal sequence of light-generated activity that drives an active nematic system toward a prescribed dynamical steady state. Active nematics are unstable to spontaneous defect proliferation and chaotic streaming dynamics in the absence of control. We demonstrate that optimal control theory can compute activity fields that redirect the dynamics into a variety of alternative dynamical programs and functions. This includes dynamically reconfiguring between states, selecting and stabilizing emergent behaviors that do not correspond to attractors, and are hence unstable in the uncontrolled system. Furthermore, our results provide a roadmap to leverage optical control methods to rationally design structure, dynamics, and function in a wide variety of active materials.

Complex systems theory↗