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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Surrogate Modelling of 3rd Integer Resonant Extraction at Fermilab Delivery Ring

We present an ongoing work in which a surrogate model is being developed to reproduce the response dynamics of the third-integer resonant extraction process in the Delivery Ring (DR) at Fermilab. This effort is in pursuit of smoothly extracting circulating beam to the Mu2e Experiment s production target, wherein the goal is to extract a uniform slice of the circulating $1e12$ protons in the DR over 25,000 turns (43~ms). The DR contains 3 harmonic sextupoles which excite a third-integer resonance as well as three fast, tune-ramping quadrupole magnets which drive the horizontal tune towards the $29/3$ resonance. In our initial work the surrogate model trains on a semi-analytical simulation provided in the same format as live data. Using Reinforcement Learning (and other potential ML methods), the trained surrogate acts as the environment in which a simple ML control agent could learn to dynamically adjust the quadrupole ramp at 430 break points within the 43 microsecond spill window. The control agent will be hosted on a dedicated Arria 10 FPGA, introducing its own requirements on control agent architecture. In this work we report the accuracy and fidelity of surrogate models in comparison to the response dynamics of the physics simulator.

Narayanan, Aakaash [Fermilab] (ORCID:0000000157944↗
DOE OSTI2023

Dynamic Thermal Performance Analysis of PCM Products Used for Energy Efficiency and Internal Climate Control in Buildings

PCMs are attractive for the future generation of buildings, where energy efficiency targets and thermal comfort expectations are increasingly prioritized. Experimental analysis of local thermal processes in these dynamic components and whole-building energy consumption predictions are essential for the proper implementation of PCMs in buildings. This paper discusses the experimental analysis of the thermophysical characteristics of both a latent heat storage material (PCM) and a product containing this PCM. The prototype product under investigation is a panelized PCM technology containing inorganic, salt-hydrate-based PCM. The thermal analysis includes studies of melting and freezing temperatures, enthalpy changes during phase change processes, nucleation intensity, sub-cooling effects, and PCM stability. The PCM’s stability is also investigated, as is the ability of PCM products to control local temperatures and peak load transmission times. Two inorganic PCM formulations based on calcium chloride hexahydrate (CaCl 2 .6H 2 O) were prepared and tested in laboratory conditions. Material-scale testing results were compared with outcomes from the system-scale analysis, using both laboratory test methods as well as field exposure in test huts. This work demonstrates that PCM technologies used in buildings can effectively control both the magnitude of thermal storage capacity as well as the time of the peak thermal load. It was found that commonly used material-scale testing methods may not always be beneficial in assessing the dynamic thermal performance characteristics of building technologies containing PCMs.

42 ENGINEERING↗
DOE OSTI2021

Automated Cyber Security Testing Platform for Industrial Control Systems

Nuclear Power Plants (NPPs) are a complex system of coupled physics controlled by a network of Programmable Logic Controllers (PLCs). These PLCs communicate process data across the network to coordinate control actions with each other and inform the operators of process variables and control decisions. Networking the PLCs allows more effective process control and provides the operator more information which results in more efficient plant operation. This interconnectivity creates new security issues, as operators have more access to the plant controls, so will bad actors. As plant networks become more digitized and encompass more sophisticated controllers, the network surface exposed to cyber interference grows. Understanding the dynamics of these coupled systems of physics, control logic, and network communications is critical to their protection. The research into the cybersecurity of the Operational Technologies of NPPs is developing and requires a platform that can allow high fidelity physics simulations to interact with digital networks of controllers. This will require three main components: a network simulation environment, a physics simulator, and virtual PLCs (vPLC) that represent typical industry hardware. A platform that incorporates these three components to provide the most accurate representation of actual NPP networks and controllers is developed in this paper.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Cyber-Physical Secure Observer-Based Corrective Control under Compromised Sensor Measurements

In this paper we introduce the objectives and design principles of corrective control under cyber-physical attacks. We propose two types of observer-based corrective control for both the open-loop stable and the open-loop unstable LTI systems. The basic idea of our corrective control design is to use the observer as the ground-truth during the attack, making the plant dynamics follow the observer behavior. This is the opposite to the no-attack-detected period in which the observer is designed to follow the plant dynamics. We show stability of the proposed control under compromised sensor measurements, and quantify the effects of the discrepancy between the observer and the plant. Numerical examples, with illustrations using microgrid energy dynamics, are presented to show benefits of the proposed corrective control.

Wu, Dan↗
DOE OSTI2023

Inverse reinforcement learning control for building energy management

Reinforcement learning (RL) based control is widely considered a promising approach in building automation and control as it has demonstrated the potential to deal with complex objectives in adjacent domains like robotics, autonomous vehicles, gaming applications, and advertisement recommendations. When applied to any environment, model-free RL learns to improve its control performance over time without requiring a control model, by receiving and then analyzing feedback from the building environment after each control action. Operational objectives are becoming increasingly complex through the simultaneous consideration of thermal comfort, carbon emissions, grid services, and indoor air quality. In this context, conventional rule-based control approaches are proving sub-optimal, mostly heuristic, and inadequate. The model-free and self learning nature of RL appears promising and attractive as it may address the scalability issues associated with advanced control approaches. However, it suffers from long training times and unstable control behavior during the early stages of its learning process, which makes it unsuitable to be applied directly to buildings. This paper addresses these issues using an inverse reinforcement learning approach (IRL), a technique utilized to learn the objective of a controller agent which is considered an expert in its respective domain. Here, we consider a rule-based control as the expert demonstrator. IRL is different from a direct imitation (i.e., direct mapping of states to actions) of control actions as it tries to find the underlying intent of an expert's policy, providing the controller with a better-generalized policy for unseen states or environments with slightly different dynamics. This approach propels the RL controller's policy to levels similar to or better than that of a rule-based policy before it starts learning by interacting with the building. This makes RL for building energy management applications more practical as it prevents the erratic and exploratory behavior in the initial training period, simultaneously speeding up the learning process when compared to applying an untrained RL agent directly to a building environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Dynamic Modelling and Performance Evaluation of a Chemisorption Heat Pump for Cold Climate

Space heating is a significant contributor to the energy consumption in residential buildings, especially in cold climates where electric-driven heat pumps lose the advantage of high efficiency and conventional gas-combustion furnaces have limited efficiency. Chemical adsorption (chemisorption) heat pumps (CSHP) have the potential for producing heating with high energy efficiency under cold operating conditions. A CSHP operates intermittently between desorption and adsorption modes, and their performance is affected by system design and transient system control. This study developed a dynamic model and used it to evaluate the daily system performance under different designs and controls, particularly the buffer tank size and the controlled minimum heating power. The simulation results revealed the effects of these parameters on the system performance and helped identify the optimal design and control for the highest coefficient of performance and heating capacity.

Yang, Zhiyao↗
DOE OSTI2023

Model-based predictive control of multi-stage air-source heat pumps integrated with phase change material-embedded ceilings

This paper presents a model-based predictive control strategy to optimize the operations of phase change material (PCM) ceiling panels coupled with a multi-stage air-source heat pump. A three-stage prototype heat pump unit has been built and tested in the laboratory, with the low and medium stages designed for space heating/cooling and the high compression stage dedicated to charging of the PCM energy storage. To facilitate optimal control of the integrated heat pump system, a mixed-integer linear programming formulation is derived through linearization of the heat pump model and a mixed-integer reformulation of the PCM dynamic governing equations. A predictive control strategy is synthesized based on the resultant control formulation and implemented in a receding horizon scheme that optimizes the PCM charging and the zone temperature schedules simultaneously to leverage both the passive (associated with building construction materials) and active (PCM) storage capacities of a building. The control strategy has been tested along with three benchmarking control scenarios using a co-simulation platform for a prototypical detached house in Atlanta, GA. In this study, test results showed that application of the proposed control strategy to the PCM-integrated heat pump could provide 27.1% electricity cost savings while a fine tuned rule-based control strategy could achieve cost savings of 20.4%, compared to a baseline case without PCM storage, under a time-of-use rate tariff.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Command of active and responsive elastomers by topological defects and patterns

The project resulted in the development of stimuli-responsive elastomer coatings formed by photopolymerized liquid crystal molecules. The molecular orientation of the liquid crystal elastomers is coupled to rubber-like elasticity. The project developed an approach to produce complex patterns of molecular orientation in elastomers by employing photopatterning technique based on plasmonic metamasks that convert unpolarized incoming light into a transmitted light beam with spatially-varying linear polarization. The polarization-modulated light beam aligns the film of azodye molecules, which serves as the substrate for liquid crystal elastomer coating. The alignment of the azodye molecules follows the polarization pattern with a phase shift of the alignment direction by 90 degrees. The azodye substrate imposes orientation of molecules in the adjacent liquid crystal layer which is preserved after the monomers are polymerized into a liquid crystal elastomer. By using differently aligned substrates one could produce various patterns of molecular orientation in the elastomer, which dictate the mechanical properties of the elastomers and their response to external stimuli such as temperature, humidity, and ultraviolet irradiation. In particular, the predesigned molecular orientation produces deterministically defined topography response of the liquid crystal coatings, in which their free surface changes from flat to either locally elevated or locally compressed, depending on whether the pre-inscribed molecular orientation is predesigned with a splay or bend deformation. The research established that the mechanism of this deterministic relation between the dynamic topography and pre-inscribed molecular orientation is rooted in the change of the tensorial order parameter that characterizes the degree of orientational order, and in the contraction/expansion of polymer molecules in response to the order parameter changes. In particular, lowering the order causes contraction of polymer globules. The work demonstrated that the dynamic topography of elastomer coatings with three-dimensional pattern of orientational order includes up and down motion of the material (along the normal to the coating) and also shifts in lateral directions. We demonstrate that the deterministic relationship between the orientational patterns and variations of coatings’ profile is caused by forces that are mathematically equivalent to active forces in the system of “pullers” or “pushers” in active matter, demonstrating universality of the out-of-equilibrium behavior. The project resulted in theoretical models that predictively describe the dynamic response of coatings. The topography of liquid crystal elastomers is sensitive to stimuli such as temperature, ultraviolet irradiation, and humidity. The applicability of the patterned approach to produce dynamic coatings was expanded from nematic elastomers to their smectic counterparts. The project combined a battery of imaging techniques to unveil the dynamic properties of the coatings, ranging from the state-of-the-art dynamic holographic microscopy to fluorescent confocal polarizing microscopy and polscope microscopy. The research uncovered potential applications of dynamic elastomer coatings, such as control placement of colloidal particles, engineering of living tissues with alignment patterns of cells that follow the orientational order of elastomer coatings. The approach developed in the project can be used to design programmable dynamic coatings with functionalities that mimic biological tissues such as skin.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dynamic Microgrids in Resilient Distribution Systems With Reconfigurable Cyber-Physical Networks

Modern distribution systems energized by inverter-interfaced distributed generators (DGs) operate as coupled cyber-physical networks (C/P-networks), where the controllable components in the physical network (P-network) are coordinated through the cyber network (C-network). The concept of dynamic microgrids (MGs) operation has been adopted to enable distribution system autonomous operation with varying electric boundaries. To further enhance system operation resiliency and flexibility, dynamic MGs operation with reconfigurable C/P-networks is discussed in this work. An evaluation framework is proposed to assess the operational feasibility of distribution feeders with multiple inverter-based dynamic MGs and come out with possible restoration solutions in the context of cross-layer C/P-network reconfiguration. Furthermore, distributed controllers are developed for components with different operating characteristics to realize seamless system topology variations and provide coordinated secondary regulation in various operation modes. Furthermore, the proposed evaluation framework along with the developed distributed controller has been validated using a Hardware-in-the-Loop (HIL) real-time CPS testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Quantum optimal control of superconducting qubits based on machine-learning characterization

Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations in detailed numerical simulations of a superconducting transmon qubit. Furthermore, we demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of ∼99.99%.

Artificial neural networks↗
DOE OSTI2021

Grey-box modeling and application for building energy simulations - A critical review

Grey-box modeling, as one of the three fundamental modeling techniques for building energy models, has many advantages compared with black-box modeling and white-box modeling. Additionally, it has been widely applied to solve problems of building technologies, such as building load estimation, control and optimization, and building-grid integration. However, a thorough review of grey-box modeling is not available. This review study systematically investigated various aspects of grey-box modeling for buildings. First, the fundamental aspects of grey-box modeling are presented, including the theoretical background, modeling of building elements, modeling order, modeling diagram, and order reduction. Second, the detailed modeling approaches are discussed. Third, multiple applications of grey-box modeling are investigated for building energy domain, which are categorized into the following groups: heat dynamics analysis, thermal load estimation, building control and optimization, district/urban scale energy modeling, and building-grid integration. Finally, the available software packages for grey-box modeling are compared. Overall, the challenges of using grey-box modeling can be summarized as follows: (1) the theoretical limitations and assumptions of grey-box modeling are unclear; (2) grey-box model naming convention and structure are confusing; (3) grey-box model creation is vague; (4) suitable applications of grey-box models are unknown; and (5) grey-box models lack unified software solutions for wider adoption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Associative polymers with controlled sticker placement: How reversible bond distribution and density govern polymer dynamics

Associative polymers with precisely arranged stickers offer opportunities to program material properties with molecular precision. Yet, it remains unclear how the placement and fraction of stickers dictate structure, dynamics, and macroscopic properties. By developing a model unentangled polymer system with hydrogen-bonding stickers, we show that randomly distributed stickers neither form clusters nor change flow properties, whereas stickers placed at chain ends drive nanocluster formation even at low concentrations. Adding more end stickers produces a rubbery plateau spanning eight decades in frequency with two distinct relaxation timescales, in contrast to the single plateau predicted by the classic sticky Rouse model. These results demonstrate that sticker distribution dictates whether associative polymers undergo nanocluster formation or microphase separation, while substantial alterations in dynamics and viscoelasticity require both sticker aggregation and thermomechanical stability of associated domains. Our findings resolve a longstanding debate on associative polymer dynamics and provide molecular design rules for programmable soft materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Space and Time Dynamics of Transpiration in the East River Watershed: Biotic and Abiotic Controls

Alpine forests have important impacts on water resources by affecting how much of the precipitation that falls on a watershed is routed to streamflow or returned to the atmosphere as transpiration – so-called “green water”. In many of the high-altitude watersheds that supply critical water resources to the western US, it remains a challenge to predict how much water forests will utilize limiting the ability to predict both short and long term information on downstream water resources. The uncertainty emerges because transpiration varies between tree species, across landscapes and over time in ways that cannot readily be predicted from physically-based models for evaporation. In this project, we utilized a technique called sap flux to measure the rate of water use for three common species of fir, spruce and aspen distributed across a hillslope in the East River Watershed from 2019-2021. We also made ~weekly measurements of the concentration of stable isotopic tracers in transpiration to understand not only how much water was being used but whether it originated from summer rain or snowmelt. Firstly, our results show that the species tend to use similar amounts of total water but they achieve this similar cumulative water flux both by using water at different times of the year and by using different water sources. This shows the importance of including species-level information into models used to make seasonal streamflow predictions. Secondly, our results show that trees lower on the hillslope use older waters all through the summer whereas trees higher on the hillslope rely more heavily on recent summer rain. This means trees lower on the hillslope are less sensitive to year-to-year changes in summer climate. Lastly, we found that sites with dense forest stands displayed the largest amounts of year-to-year changes in transpiration and thus these high localized spots in the watershed are the primary drivers of changes in “green water” use. The results provide an unprecedented spatial and temporal picture of forest water use that confirms a number of fundamental hypotheses on hillslope ecohydrology that had not previously been tested against observations. Predictions of streamflow that will benefit from this work are highly valuable for agriculture management, flood control and ecological restoration efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Hybrid nanocapsules for in situ TEM imaging of gas evolution reactions in confined liquids

Liquid cell transmission electron microscopy (TEM) enables the direct observation of dynamic physical and chemical processes in liquids at the nanoscale. Quantitative investigations into reactions with fast kinetics and/or multiple reagents will benefit from further advances in liquid cell design that facilitate rapid in situ mixing and precise control over reagent volumes and concentrations. This work reports the development of inorganic–organic nanocapsules for high-resolution TEM imaging of nanoscale reactions in liquids with well-defined zeptoliter volumes. These hybrid nanocapsules, with 48 nm average diameter, consist of a thin layer of gold coating a lipid vesicle. As a model reaction, the nucleation, growth, and diffusion of nanobubbles generated by the radiolysis of water is investigated inside the nanocapsules. When the nanobubbles are sufficiently small (10–25 nm diameter), they are mobile in the nanocapsules, but their movement deviates from Brownian motion, which may result from geometric confinement by the nanocapsules. Gases and fluids can be transported between two nanocapsules when they fuse, demonstrating in situ mixing without using complex microfluidic schemes. The ability to synthesize nanocapsules with controlled sizes and to monitor dynamics simultaneously inside multiple nanocapsules provides opportunities to investigate nanoscale processes such as single nanoparticle synthesis in confined volumes and biological processes such as biomineralization and membrane dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Interactions between molecular-scale biogeochemical processes and hyporheic exchange for understanding coupled Fe-S-C cycling in iron-rich riparian wetlands (Final Technical Report)

Riparian wetlands are dynamic interfaces that exert strong control over water quality, contaminant mobility, and greenhouse gas emissions. These systems are characterized by hyporheic exchange between oxic surface water and anoxic groundwater, which generates steep redox gradients and promotes spatially and temporally variable microbial activity. Despite growing recognition of tightly coupled iron (Fe), sulfur (S), and carbon (C) cycling in these environments, the mechanisms governing these interactions—particularly under low-sulfate freshwater conditions—remain poorly constrained. This project developed a mechanistic understanding of how hydrologic variability and microbial processes interact to control Fe–S–C cycling in iron-rich riparian wetlands. Using a multi-scale and multi-method approach integrating field observations, geochemical and spectroscopic analyses, metagenomics, and reactive transport modeling, we demonstrate that “cryptic” sulfur cycling—rapid sulfur transformations involving intermediate-valence species—plays a dominant and previously underrecognized role in freshwater wetlands. These processes persist despite low sulfate concentrations and significantly influence iron reduction, carbon mineralization, and methane dynamics. The results show that cryptic sulfur cycling enhances dissolved Fe 2+ production, regulates methane concentrations, and is strongly controlled by climate-driven hydrologic fluxes. By linking hydroclimate, subsurface flow, and biogeochemical reactions, this work provides a predictive framework for understanding how wetland systems respond to environmental change, with direct implications for water quality and carbon cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Controlled Formation of Conduction Channels in Memristive Devices Observed by X–ray Multimodal Imaging

Neuromorphic computing provides a means for achieving faster and more energy efficient computations than conventional digital computers for artificial intelligence (AI). However, its current accuracy is generally less than the dominant software-based AI. The key to improving accuracy is to reduce the intrinsic randomness of memristive devices, emulating synapses in the brain for neuromorphic computing. Here using a planar device as a model system, the controlled formation of conduction channels is achieved with high oxygen vacancy concentrations through the design of sharp protrusions in the electrode gap, as observed by X-ray multimodal imaging of both oxygen stoichiometry and crystallinity. Classical molecular dynamics simulations confirm that the controlled formation of conduction channels arises from confinement of the electric field, yielding a reproducible spatial distribution of oxygen vacancies across switching cycles. Furthermore, this work demonstrates an effective route to control the otherwise random electroforming process by electrode design, facilitating the development of more accurate memristive devices for neuromorphic computing.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Correlating Protein Dynamics and Catalytic Activity of a Model Hydrogenase Using Paramagnetic and Biological Nuclear Magnetic Resonance Spectroscopy

Rational catalyst design remains a significant challenge, with electronic structure, steric, and electrostatic effects known to contribute to activity. Recently, dynamics has been recognized as another factor that impacts catalysis, though identifying and predicting these effects has remained out of reach. Nickel-substituted rubredoxin (NiRd), a protein-based mimic of a hydrogenase enzyme, serves as a model catalytic system in which dynamics can be systematically investigated with respect to activity. While over 30 secondary-sphere mutants of NiRd have been shown to be catalytically active, no significant correlation was observed between the rates and catalytic overpotential or electronic structure, prompting questions about the protein-derived factors that modulate activity. Here, in this work, NMR spectroscopy was used to investigate the roles of substrate accessibility, protein dynamics, and protein stability in controlling catalysis. Significant paramagnetic effects from the nickel center (S = 1) isolate the methylene proton resonances of the metal-coordinating cysteine residues. The sensitivity of resonance positions and linewidths to local environment offers an opportunity to study dynamical molecular changes around the metal center with high resolution. Machine learning algorithms were employed to identify correlations between the catalytic activity and the paramagnetic NMR spectra. These analyses revealed spectroscopic features of specific cysteine protons that report on catalytic overpotential and increased turnover rates, which are further supported by the results obtained using high-field NMR techniques. Collectively, these studies indicate the potential for multifrequency NMR techniques to resolve key contributors to catalytic activity and highlight the importance of local and outer-sphere dynamics.

Protein Engineering↗
DOE OSTI2022

On Projection of Safe Operation for Grid-Following Inverters - Grid Parameter Estimation

This work investigates the projection of safe operation for grid-following inverters using a reference model. This work applies recursive-least square and model reference adaptive estimation techniques to estimate the unknown grid parameters. The estimated grid parameters are used in a dynamic reference model to project the safe operation of the inverter. This work also demonstrates that the controller nonlinearity and parameter variations can cause unsafe operations, such as unexpected instability at low power levels, namely hidden mode of instability. This instability issue in the nominal operating range may occur for given control parameters in a weak-grid condition. The dynamic reference model for PQ-controlled inverters is applied to analyze this instability issue using the estimated grid parameters. Both adaptive identification techniques can estimate the unknown parameters accurately, and the dynamic reference model can safeguard inverters from an unsafe operation, i.e., hidden instability, by examining the incoming new power setpoints before engaging them to the local controller. The findings are experimentally verified using a small-scale two-level 208 V, 5 kVA inverter feeding a 12 kW NHR 9410 power grid emulator.

Hossen, Tareq↗