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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Abradable Sealing Materials for Emerging IGCC-Based Turbine Systems (Final Report)

Reducing the gap between rotating and stationary parts in gas turbine engines, and mitigating gas leakage via these paths, can significantly increase the performance and attendant efficiency. One approach to reducing such gaps is to deploy abradable coatings on the stationary shroud components as seals, to reduce the blade-tip clearances under operational conditions. Abradable coatings must be able to withstand high temperature oxidation, thermal cycling, and erosion, while providing optimal controlled abrasion and associated shape retention. Existing abradable seals utilized in power generation turbines have generally been optimized for use with natural gas-fired systems; however, preliminary testing of syngas and high-hydrogen-content (HHC) fired turbines has shown that the stability of hot-section materials (and presumably abradable sealing materials) may be substantially altered due to characteristic changes in the combustion by-products (partial pressures of water vapor, etc.) as well as characteristic impurities and particulate matter entrained in the fuel. The primary focus of this research program has been to develop a mechanistic understanding of how the altered combustion environments modify the thermo-mechanical stability and performance of the sealing materials, including the requisite abradable sealing behavior. A key component of the research program carried out has been an exploration of mechanisms underpinning observed degradation processes, and an assessment of connections to the combustion environments and characteristic non-combustible constituents. The ultimate goal of the project has been to advance the goals of the Advanced Turbine Program by developing materials design protocols leading to turbine hot-section components with improved resistance to service lifetime degradation under advanced fuels exposures. This research program has evaluated the performance and degradation of ceramic abradable seals used in the high-temperature turbine sections of gas turbine engines. The focus has been two-fold: (1) to understand performance of such materials in relation to the turbines operating on coal-derived syngas and high hydrogen content (HHC) fuels, ultimately seeking an improved understanding of factors that control performance and durability of abradable seal materials for these combustion environments (and play into materials design protocols); and (2) to evaluate the potential of alternative materials as abradable seals for the higher temperatures expected with IGCC and HHC fueled turbine systems. In particular, we carried out an initial assessment of multi-layered systems of existing abradable seal materials (YSZ) and other ceramics. The research program also investigated several classes of abradable coatings under simulated exposures to syngas-based combustion environments, evaluating the relevant wear behavior, hardness, stability under cyclic oxidation, and general thermo-mechanical behavior. The research also attempted to develop alternatives to carrying out full-scale combustion-based abradability test rig exposures to evaluate materials system performance. The research has been focused on correlating the measured thermo-mechanical behavior, and controlled abrasive wear, with the intrinsic properties of the multilayer coatings and processing-controlled microstructural features that have shown attractive machinability characteristics based on weak interfacial bonding between the constituent phases at elevate temperatures (while maintaining thermochemical stability). This program has resulted in an improved mechanistic understanding of factors governing performance of high temperature abradable seals, and degradation mechanisms unique to IGCC-based power generation turbine systems and the realization of coal-derived syngas and HHC-based combustion environments – ultimately with the goal of developing a knowledge base upon which the design of coatings that retain optimal sealing characteristics and are more resistant to the observed wear/attack mechanisms, important aspects of advancing the goals of the DOE Advanced Turbine Program.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Resilient Operation of Networked Community Microgrids with High Solar Penetration

This project, funded by the US Department of Energy’s Solar Energy Technologies Office (SETO), focused on the operation of microgrids as a coordinated network. The primary objective, which was successfully achieved, was to develop both control strategies and hardware solutions to support the resilient and efficient operation of networked microgrids with high solar penetration. The work was structured around the following four main tasks: • Development of distributed and scalable optimization algorithms for AC-coupled networked microgrids. • Design and implementation of a novel DC interconnection hardware to enable precise power exchange between microgrids. • Laboratory operational validation of the developed technologies using 480 V testbeds and commercially available hardware. • Field operational validation of the complete solution in Adjuntas, Puerto Rico, interconnecting two kW-scale, split-phase microgrids of Casa Pueblo’s microgrids. This project addressed multiple technical challenges across the domains of optimization, control, hardware interconnection, and protection. One of its key contributions was delivering tangible, real-world solutions for networking microgrids. In contrast to purely theoretical or simulation-based work, this project included full-scale hardware operational validation both in the lab and in the field. The work conducted as part of this project—in collaboration with the University of Puerto Rico; the University of Tennessee, Knoxville; the University of Central Florida; and Casa Pueblo—has advanced the state of the art in networked microgrids. Key contributions include the development of distributed control strategies, practical solutions for real-world implementation challenges, and the introduction of a novel DC interlink approach for microgrid interconnection. The project featured both laboratory and field validation using commercial off-the-shelf components. The field deployment successfully validated that a group of microgrids can operate in a coordinated manner, enabling precise power flow between systems and mutual support during extreme events. This project resulted in 15 journal publications and 15 conference papers; 5 graduate students and 15 undergraduate students were supported. The codes of distributed optimization and forecasting were made open-source through OSTI.gov for distributed optimization and forecasting. All the publications are available in the ORNL-hosted project landing page. The DC interlink with state-of-charge balancing control was operationally validated in Adjuntas by interconnecting two real-world, 240 V split-phase microgrids. To the best knowledge of the team, this represents the first operational validation of AC microgrids interconnected via DC-interlinks. As a culmination of this project, a follow-on grant was awarded to support the technology transfer of the distributed optimization framework to a commercial microgrid controller, Stellar Edge, developed by the California-based company New Sun Road.

14 SOLAR ENERGY↗
DOE OSTI2017

Model Predictive Control of parametric excited pitch-surge modes in wave energy converters

For a heave-pitch-surge three-degrees-of-freedom wave energy converter, the heave mode is usually decoupled from the pitch-surge modes for small motions. The pitch-surge modes are usually coupled and are parametrically excited by the heave mode, depending on the buoy geometry. In this paper, a Model Predictive Control is applied to the parametric excited pitch-surge motion, while the heave motion is optimized independently. The optimality conditions are derived, and a gradient-based numerical optimization algorithm is used to search for the optimal control. Numerical tests are conducted for regular and Bretschneider waves. The results demonstrate that the proposed control can be implemented to harvest more than three times the energy that can be harvested using a heave-only wave energy converter. As a result, the energy harvested using a parametrically excited model is higher than that is harvested when using a linear model.

16 TIDAL AND WAVE POWER↗
DOE OSTI2014

Optimal charging profiles for mechanically constrained lithium-ion batteries

The cost and safety related issues of lithium-ion batteries require intelligent charging profiles that can efficiently utilize the battery. This paper illustrates the application of dynamic optimization in obtaining the optimal current profile for charging a lithium-ion battery using a single-particle model while incorporating intercalation-induced stress generation. In this paper, we focus on the problem of maximizing the charge stored in a given time while restricting the development of stresses inside the particle. Conventional charging profiles for lithium-ion batteries (e.g., constant current followed by constant voltage) were not derived by considering capacity fade mechanisms. These charging profiles are not only inefficient in terms of lifetime usage of the batteries but are also slower since they do not exploit the changing dynamics of the system. Dynamic optimization based approaches have been used to derive optimal charging and discharging profiles with different objective functions. The progress made in understanding the capacity fade mechanisms has paved the way for inclusion of that knowledge in deriving optimal controls. While past efforts included thermal constraints, this paper for the first time presents strategies for optimally charging batteries by guaranteeing minimal mechanical damage to the electrode particles during intercalation. In addition, an executable form of the code has been developed and provided. This code can be used to identify optimal charging profiles for any material and design parameters.

Suthar, B↗
DOE OSTI2024

Metric Learning to Accelerate Convergence of Operator Splitting Methods

Recent developments in machine learning have led to promising advances in accelerating the solution of constrained optimization problems. Increasing demand for real-time decision-making capabilities in applications such as artificial intelligence and optimal control has led to a variety of proposed strategies for learning to produce fast solutions to optimization problems. For example, recent works have shown that it is possible to accelerate the convergence of optimization algorithms by learning to select their parameters, such as gradient descent stepsizes. This work proposes a new approach, in which the underlying metric spaces of proximal operator splitting algorithms are learned to maximize convergence rate. While prior works in optimization theory have derived optimal metrics in simple cases, no such result exists for many practical problem forms including general Quadratic Programming (QP). This paper shows how differentiable optimization can enable the end-to-end learning of proximal metrics, enhancing the convergence of proximal algorithms for QP problems beyond what is possible based on known theory. Additionally, the results illustrate a strong connection between the learned proximal metrics and active constraints at the optima, leading to an interpretation in which the predicted proximal metrics can be viewed as a form of active set prediction.

King, Ethan [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2026

Digital Twin-Enabled Adaptive Control for Hydroelectric Systems: Turbine Governor and Voltage Regulation

This paper presents a comprehensive digital twin (DT) framework for hydroelectric systems that enables adaptive control of turbine governors and excitation systems without requiring detailed manufacturer specifications. The proposed framework integrates neural network-based system identification with stabilizing adaptive control laws for the installed turbine controller and middle-branch adaptive tuning for the installed voltage regulator. Using real operational data from Unit C-8 at Rocky Reach Dam (1,349 MW capacity), highfidelity neural network models are developed to capture turbine and generator dynamics without requiring detailed manufacturer specifications. The DT enables safe controller synthesis and validation in simulation before deployment. For turbine control, the proposed method achieves a 79.9% mean square error (MSE) reduction compared with that of an optimal controller. For voltage regulation, the adaptive excitation controller achieves approximately 42.6% MSE reduction while preserving installed protection logic. The results demonstrate that DT technology provides a practical pathway for modernizing hydropower control systems with minimal operational disruption.

Gui, Yonghao [ORNL] (ORCID:0000000250435534)↗
DOE OSTI2020

Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids

This paper presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While unconstrained reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method considers AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore, we have developed a distributed consensus-based optimization approach to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Lastly, numerical experiments have been devised to verify the performance of the proposed method.

, policy gradient↗
DOE OSTI2016

Building-Wide, Adaptive Energy Management Systems for High-Performance Buildings: Final CRADA Report

Development and field demonstration of the minimum ratio policy for occupancy-driven, predictive control of outdoor air ventilation. Technology transfer of Argonne’s methods for occupancy estimation and forecasting and for M&V to BuildingIQ for their deployment. Selection of CO2 sensing as the currently best-available technology for occupancy-driven controls. Accelerated restart capability for the commercial BuildingIQ system using horizon shifting strategies applied to receding horizon optimal control problems. Empirical-based evidence of 30% chilled water energy savings and 22% total HVAC energy savings achievable with the BuildingIQ system operating in the APS Office Building on-site at Argonne.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Optimization problems governed by systems of PDEs with uncertainties

This paper reviews current theoretical and numerical approaches to optimization problems governed by partial differential equations (PDEs) that depend on random variables or random fields. Such problems arise in many engineering, science, economics and societal decision-making tasks. This paper focuses on problems in which the governing PDEs are parametrized by the random variables/fields, and the decisions are made at the beginning and are not revised once uncertainty is revealed. Examples of such problems are presented to motivate the topic of this paper, and to illustrate the impact of different ways to model uncertainty in the formulations of the optimization problem and their impact on the solution. A linear–quadratic elliptic optimal control problem is used to provide a detailed discussion of the set-up for the risk-neutral optimization problem formulation, study the existence and characterization of its solution, and survey numerical methods for computing it. Different ways to model uncertainty in the PDE-constrained optimization problem are surveyed in an abstract setting, including risk measures, distributionally robust optimization formulations, probabilistic functions and chance constraints, and stochastic orders. Furthermore, approximation-based optimization approaches and stochastic methods for the solution of the large-scale PDE-constrained optimization problems under uncertainty are described. Some possible future research directions are outlined.

Heinkenschloss, Matthias [Rice Univ., Houston, TX ↗
DOE OSTI2018

Three-dimensional alignment of asymmetric-top molecules induced by polarization-shaped optimal laser pulses

Optimal control simulation is applied to numerically design nonresonant laser pulses that maximize the degrees of three-dimensional (3D) alignment of SO 2 using the lowest-order induced-dipole interaction. In our trials, combinations of more than two mutually orthogonal, linearly polarized subpulses are always obtained as the optimal solutions. Each subpulse in the optimal pulses impulsively excites the rotational wave packet. The optimal pulses effectively cooperate with the rotational dynamics up to only a few partial revival timings owing to the rotational dephasing that determines the effective control periods. The control mechanisms are interpreted in terms of the time derivatives of the expectation values of the squares of the direction cosines, that of the rotational energy, and the interplay between them. We find a special and important role of the last subpulses as they align the molecular axes using the interaction through the two smallest polarizability components, while the other subpulses excite the rotational wave packet mainly through the largest polarizability component. Lastly, the control pulses composed of the specified number of subpulses are also numerically optimized by actively utilizing the instantaneous penalty to systematically show the superiority of the use of more than two subpulses over that of two subpulses, the latter of which leads to the saturation of the degree of 3D alignment as a function of total fluence.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Computation-Efficient Algorithm for Distributed Feedback Optimization of Distribution Grids

Feedback-based optimization algorithms use real-time measurements to update the optimal control for the underlying system which may not be fully identified. Recently, we have developed a distributed feedback-based algorithm [1] that avoids the requirement of fast communication between central computing and local actuator/sensor agents. This paper extends the work by greatly reducing the number of copies of variables involved in the distributed feedback-based algorithm, which results in faster convergence and lower communication requirement. The main idea is to leverage the specific structural properties of the admittance matrix for distribution systems with tree network topology. We also show the effectiveness of the proposed algorithm in simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Electricity Pricing aware Deep Reinforcement Learning based Intelligent HVAC Control

Recently, deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) has gained a lot of attention due to DRL's ability to optimally control HVAC for minimizing operational cost while maintaining resident's comfort. The success of such DRL-based techniques largely depends on the articulation of the problem in terms of states, actions, and reward function. Inclusion of the electricity pricing information in the problem formulation can play an important role in saving the cost of HVAC operation. However, less attention has been given in the literature on formulating well-crafted state features based on electricity pricing. In this work, we propose an approach for training the DRL model with a specific focus on feature engineering based on electricity pricing. During training, we generate random but sufficiently realistic electricity price signals so that the pre-trained DRL model is robust and adaptive to the dynamic and variable electricity prices. The validation results are encouraging and show the potential of ≈12%-15% savings in the one day cost of HVAC operation, proving the usefulness of including electricity pricing related features as state features.

Kurte, Kuldeep↗
DOE OSTI2024

A Transfer Learning Strategy for Improving the Data Efficiency of Deep Reinforcement Learning Control in Smart Buildings

Reinforcement learning (RL) is a powerful tool that has shown promising results in many domains such as robotics and game-playing. Because RL algorithms learn optimal control policies by continuously interacting with their environments, these algorithms require a lot of data to learn, which limits their application to a wide range of domains. For this reason, there is an immense need for improving the training and data efficiency of RL. Towards addressing this research gap, this paper proposes a transfer learning (TL) approach to improve the efficiency of the RL algorithms by reducing data need and, thus, reducing training time. To demonstrate the proposed approach, a knowledge transfer from a set of buildings to another building was conducted. The results show that the proposed TL approach is a promising method that can efficiently harness the information from similar RL tasks and reduce the data needs of RL algorithms.

Amasyali, Kadir↗
DOE OSTI2018

Gas Reliability Analysis Integrated Library (GRAIL)

The Gas Reliability Analysis Integrated Library (GRAIL) is a prototype computational tool for solving a class of intra-day model-predictive control problems for a simplified network model of midstream natural gas pipeline systems. The model accurately resolves dynamic gas flows through long pipes using a non-ideal gas equation of state. It can be used to provide optimal intra-day flow schedules as well as spatiotemporal economic values of natural gas while ensuring that pipeline hydraulic limitations, compressor station constraints, operational factors, and pre-existing shipping contracts are satisfied. The inputs to the code are a model of the pipeline system as well as time-series data that specify boundary conditions on the network and time-dependent constraints on the optimal control problem. The outputs are time-series data that provide an approximately optimal solution of flows, pressures, compressor settings, and sensitivities to the objective function.

Zlotnik, Anatoly↗
DOE OSTI2017

Mathematical Modeling in Systems for Operational Evaluation of the Stress-Strain State of the Arch-Gravity Dam at the Sayano-Shushenskaya Hydroelectric Power Plant

Current studies of the stress-strain state of the dam at the Sayano-Shushenskaya Hydroelectric Power Plant at VNIIG based on mathematical modeling including full scale and experimental data are described. Applications and programs intended for automatic operational evaluation of the stress-strain state of the dam for optimizing control of the upper race level in the course of the annual filling-drawdown cycle and during seismic events are examined. Improvements in systems for monitoring the stress-strain state of concrete dams are proposed.

42 ENGINEERING↗
DOE OSTI2021

A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks

This paper presents a secure reinforcement learning (RL) based control method for unknown linear time-invariant cyber-physical systems (CPSs) that are subjected to compositional attacks such as eavesdropping and covert attack. We consider the attack scenario where the attacker learns about the dynamic model during the exploration phase of the learning conducted by the designer to learn a linear quadratic regulator (LQR), and thereafter, use such information to conduct a covert attack on the dynamic system, which we refer to as doubly learning-based control and attack (DLCA) framework. We propose a dynamic camouflaging based attack-resilient reinforcement learning (ARRL) algorithm which can learn the desired optimal controller for the dynamic system, and at the same time, can inject sufficient misinformation in the estimation of system dynamics by the attacker. The algorithm is accompanied by theoretical guarantees and extensive numerical experiments on a consensus multi-agent system and on a benchmark power grid model.

Mukherjee, Sayak↗
DOE OSTI2018

Optimal subhourly electricity resource dispatch under multiple price signals with high renewable generation availability

This article proposes a system-wide optimal resource dispatch strategy that enables a shift from a primarily energy cost-based approach, to a strategy using simultaneous price signals for energy, power and ramping behavior. A formal method to compute the optimal sub-hourly power trajectory is derived for a system when the price of energy and ramping are both significant. Optimal control functions are obtained in both time and frequency domains, and a discrete-time solution suitable for periodic feedback control systems is presented. The method is applied to North America Western Interconnection for the planning year 2024, and it is shown that an optimal dispatch strategy that simultaneously considers both the cost of energy and the cost of ramping leads to significant cost savings in systems with high levels of renewable generation: the savings exceed 25% of the total system operating cost for a 50% renewables scenario.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Harnessing Quantum Information Science for Enhancing Sensors in Harsh Fossil Energy Environments

The main goals of this project are to utilize real-time quantum dynamics simulations and quantum optimal control algorithms to (1) harness near-surface nitrogen vacancy (NV) centers to detect chemical analytes in harsh fossil energy environments, and (2) design optimally constructed electromagnetic fields for initializing these near-surface NV center spins for efficient sensor performance and detectivity.

20 FOSSIL-FUELED POWER PLANTS↗