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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 OSTI2024

Direct Power Control of Back to Back Modular Multilevel Converter with Advanced Grid Support Functions for Grid Forming Application

Possibility of using back-to-back modular multilevel converter system with a direct power control architecture has been investigated in this paper. The rectifier side is controlled to be in grid following mode whereas the inverter side is controlled in grid forming mode. Both the sides’ local controllers use Lyapunov energy function based architecture to accomplish the objective of active and reactive power control as well as maintaining a fixed user defined voltage over the dc bus on the rectifier side. Therefore, the proposed direct power control architecture has embedded dc bus control architecture to maintain fixed user defined value of the capacitors. Similar architecture is implemented on the inverter side to accomplish either voltage control during grid forming or power control during grid following. Advanced grid support functionalities based on IEEE-1547-2018 is implemented and utilized to generate the reference values for either the rectifier or the inverter sides. The overall system is modeled based on MATLAB/Simulink and PLECS domain and various important case studies to verify the efficacy of the overall system has been presented.

direct power control (DPC)↗
DOE OSTI2022

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Abstract Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Spin qubit properties of the boron-vacancy/carbon defect in the two-dimensional hexagonal boron nitride

Spin qubit defects in two-dimensional materials have a number of advantages over those in three-dimensional hosts including simpler technologies for defect creation and control, as well as qubit accessibility. In this work, we select the V B C B defect in the hexagonal boron nitride (hBN) as a possible optically controllable spin qubit and explain its triplet ground state and neutrality. In this defect a boron vacancy is combined with a carbon dopant substituting the closest boron atom to the vacancy. Our density-functional-theory calculations confirmed that the system has dynamically stable spin triplet and singlet ground states. As revealed from our linear response GW calculations, the spin-sensitive electronic states are localized around the three undercoordinated N atoms and make local peaks in the density of electronic states within the bandgap. Using the triplet and singlet ground state energies, as well as the energies of the optically excited states, obtained from solution to the Bethe–Salpeter equation, we construct the spin-polarization cycle, which is found to be favorable for the spin qubit initialization. The calculated zero-field splitting parameters ensure that the splitting energy between the spin projections in the triplet ground state is comparable to that of the known spin qubits. We thus propose the V B C B defect in hBN as a promising spin qubit.

2D BN↗
DOE OSTI2020

Assessments of data centers for provision of frequency regulation

There are numerous opportunities for data centers to participate in demand response programs considering their large energy capacities, flexible working environments and workloads, redundant design and operation, etc. As a type of demand response, frequency regulation requires fast response, and its potential is not fully explored by data centers yet. This paper proposes a synergistic control strategy for data center frequency regulation which uses both IT and cooling systems. It combines power management techniques at the server level with control of the chilled water supply temperature to track the regulation signal from the electrical market. A frequency regulation flexibility factor is also proposed to increase the IT capacity for frequency regulation. The performance of the control strategy is studied through numerical simulations using an equation based object-oriented Modelica platform designed for data centers. Simulation results show that with well-tuned control parameters, data centers can provide frequency regulation service in both regulation up and down. The performance of data centers in providing frequency regulation service is largely influenced by the regulation capacity bid, frequency regulation flexibility factor, workload condition, and cooling mode of the cooling system, and not significantly influenced by the time constant of chillers. In addition, compared with a server-only control strategy, the proposed synergistic control strategy can provide an extra regulation capacity of 3% of the design power when chillers are activated. Here, when chillers are deactivated, both strategies have a similar regulation capacity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Nuclear microreactor transient and load-following control with deep reinforcement learning

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional–integral–derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID by one half to one third. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL’s strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance---an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with at least 10% and upwards of 150% less control effort. These findings illustrate RL's potential for autonomous nuclear reactor control, laying the groundwork for future integration into high-fidelity simulations and experimental validation efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Modeling and control of cascaded bridgeless multilevel rectifier under unbalanced load conditions

The goal of this project is to model and control a novel unidirectional cascaded multilevel bridgeless rectifier as an active front end in medium and high voltage applications. This topology has many advantages over a conventional cascaded H-bridge rectifier, such as lower implementation cost, higher reliability, and greater flexibility with similar power quality. The complete design process of the proposed converter is developed step by step in order to meet all the desired objectives. The steady-state mathematical model is used to develop a method for the voltage balancing of dc cells. Power factor analysis is discussed to mathematically derive requirements for the number of partially controlled and fully controlled H-bridges in the proposed H-bridge converter. Power loss, efficiency, and cost comparison studies between the traditional cascaded H-Bridge converter and the proposed bridgeless converter demonstrate the advantages. After exploring various well-established control methods, a novel control strategy is proposed to achieve dc voltage balancing, fast and robust grid synchronization, power factor correction, and elimination of zero crossing current distortion under both balanced and unbalanced load conditions. The converter can also be used for reactive power compensation in a grid tied power system if a sufficient number of fully controlled H-bridge modules are included. Processor-In-the-Loop (PIL) simulation has been the utilized to validate the performance of discrete control structure. Simulation and experimental results validate the models and control method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Smarter building start – A distributed solution

A significant focus of research and new technologies for reducing energy use in buildings is on operating the systems more efficiently during the times when the systems are active. However, there is a large potential for energy savings in determining the periods when the systems should or should not be active. This scheduling aspect of operation is often overlooked even though relatively simple solutions can unlock substantial energy savings. In this paper we describe a smart building start (SBS) algorithm that considers multiple zones in a building to determine individual schedules for room controllers as well as the central systems based on solving a simple optimization problem. Application of the SBS algorithm to multiple interconnected systems enables a staggered start-up that minimizes peak loads and also ensures comfort is within a target range with minimal system run time. The SBS algorithm extends the capability of traditional optimal start and is designed to be simple to deploy and robust. Simulation results as well as results from tests in a real building with a VAV system are presented. The presented algorithm is applicable to any type of building with a zonal or multi-zone HVAC system. To function, it needs to be able to change setpoints in rooms and monitor room temperatures, as well as, if desired, turn the central system on or off.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Safe Reinforcement Learning for Emergency Load Shedding of Power Systems

The paradigm shift in the electric power grid necessitates a revisit of existing control methods to ensure the grid’s security and resilience. In particular, the increased uncertainties and rapidly changing operational conditions in power systems have revealed outstanding issues in terms of either speed, adaptiveness, or scalability of the existing control methods for power systems. On the other hand, the availability of massive real-time data can provide a clearer picture of what is happening in the grid. Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach leveraging massive data for fast and adaptive grid control. However, like most existing machine learning (ML)- based control techniques, RL control usually cannot guarantee the safety of the power systems. In this paper, we introduce a novel method for safe RL-based load shedding of power systems that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Numerical simulation on the IEEE 39-bus testcase is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.

reinforcement learning, emergency control, power g↗
DOE OSTI2020

A review of select human-building interfaces and their relationship to human behavior, energy use and occupant comfort

In recent years, research has emerged to quantitatively and qualitatively understand occupants' interactions with buildings. However, there has been surprisingly little research on building interfaces and how their design, context (e.g., location), and underlying logic impact their usability and occupants’ perceived control, as well as the resulting comfort and energy performance. This means that research is needed to better understand how occupants interact with building interfaces in both commercial and residential applications; both applications are important to address as there are many differences in interface types, level of control and understanding, and even expectations of engagement. Herein, this paper provides a cursory review and discussion of select common building interfaces: windows, window shades/blinds, thermostats, and lighting controls. The goal of this paper is to review literature related to these human-building interfaces to explore interface characteristics, current design and use challenges, and relationships between building interfaces and occupants. Human-building interface interactions are complex, more research is needed to understand design, use, and characteristics. Common themes emerged throughout the literature review to explain occupant interactions (or lack of interactions) with building interfaces, which included thermal and visual comfort, ease and access of control, interface/control placement, poor interface/control design, lack of understanding, and social-behavioral dynamics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Seasonal performance evaluation of zero-superheat active refrigerant charge control for variable-speed heat pumps

Heat pumps account for a significant portion of electricity consumption in buildings, making their energy efficiency a critical area of research. Control optimization is recognized as an effective approach for enhancing the efficiency of building HVAC systems. Specifically, since the efficiency of heat pumps increases with reduced superheat and the optimal refrigerant charge level varies with operating conditions, zero-superheat active charge control is a promising strategy to maximize the system’s COP. However, due to its complexity, this control strategy remains in the research phase and has only been tested under limited operating conditions. This paper proposes a methodology to evaluate the seasonal performance improvement of zero-superheat active charge control across a full range of heating conditions in buildings. The methodology includes the development of an optimal controller that utilizes an accumulator to store excess refrigerant and manages the active charge level at zero superheat through subcooling control, as well as the creation of a detailed physics-based heat pump model. Targeted a 3-ton variable-speed heat pump with R410A refrigerant, we selected ten U.S. cities representing different climate regions for seasonal performance evaluation. Our simulations indicate that, depending on the climate conditions, the optimal control strategy could achieve energy savings ranging from 5.8 % to 8.0 % over the entire heating season. These results suggest that zero-superheat active charge control, combined with coordinated compressor and fan speeds, could substantially improve the energy efficiency of heat pump systems while maintaining the required heating capacity.

Guo, Fangzhou↗
DOE OSTI2020

Raw leaf gas exchange data in the Amazon basin, 2014-2016

This data package contains raw leaf gas exchange data used to support the publication "Leaf isoprene and monoterpene emission distribution across hyperdominant tree genera in the Amazon basin," Phytochemistry, in-press. Data was collected from 162 individuals, distributed among 25 botanical families and 113 species, from four sites in the Amazon. The data was collected using a modified portable photosynthesis system for environmental control coupled to a thermal desorption tube for volatile isoprenoid collections. Collected TD tube samples were analysed in Manaus, Brazil, using thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). The dataset includes both photosynthesis and isoprene/monoterpene emission rates from leaves under standard environmental conditions as well as controlled light response curves. For detailed methods and raw data, refer to the publication and related material cited in Dataset References.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Nonlinear model predictive control for mode‐switching operation of reversible solid oxide cell systems

Abstract Solid oxide cells (SOCs) are a promising dual‐mode technology for the production of hydrogen through high‐temperature water electrolysis, and the generation of power through a fuel cell reaction that consumes hydrogen. Switching between these two modes as the price of electricity fluctuates requires reversible SOC operation and accurate tracking of hydrogen and power production set points. Moreover, a well‐functioning control system is important to avoid cell degradation during mode‐switching operation. In this article, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional‐integral (PI) control strategies, while switching between the modes of hydrogen and power production. While both control methods provide similar performance across various metrics during mode switching, NMPC demonstrates a significant advantage in reducing cell thermal gradients and curvatures (mixed spatial‐temporal partial derivatives), thereby helping to mitigate long‐term degradation.

08 HYDROGEN↗
DOE OSTI2025

Non-invasive Pipe Pressure Monitoring for Safeguards

Our project explores a new solution to complement current safeguards toolbox by independently, and noninvasively, determining the pressure inside uranium hexafluoride (UF 6 )-carrying Gas Centrifuge Enrichment Plant (GCEP) pipes. Proof-of-principle, non-invasive pipe pressure monitoring tests were conducted through measurements of hoop and axial strain using an optical-interference dilatometry technique based on fiber Bragg gratings (FBGs) inscribed in the core of telecommunication-type 125 µm diameter optical fibers. The fibers were affixed to commercial stainless steel and aluminum tubes and pipes of the type expected to be found at GCEP’s, each measuring four inches in diameter. When the internal air pressure in the pipes changes their external diameter and length follow, and the pitch in the FBG shrinks and/or expands accordingly. In turn, these changes result in a shift of the light wavelength reflected by the FBGs. Several FBGs physically attached to the pipe, as well as control FBGs not attached, were interrogated during the process using state-of-the-art commercial interrogators that operate on swept-wavelength laser technology. Strains measured were contrasted against calculations based on the known elastic and mechanical properties of the pipes. Good agreement is found, and strategies are proposed for further improvement of the experimental resolution as well as future implementation of this novel approach for GCEP safeguards.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Challenges and transformative opportunities in superconductor vortex physics

In superconductors, the motion of vortices introduces unwanted dissipation that is disruptive to applications. Fortunately, material defects can immobilize vortices, acting as vortex pinning centers, which engenders dramatic improvements in superconductor material properties and device operation. This has motivated decades of research into developing methods of tailoring the disorder landscape in superconductors to increase the strength of vortex pinning. Yet, efficacious materials engineering still eludes us. The electromagnetic properties of real (disordered) superconducting materials cannot yet be reliably predicted, such that designing superconductors for applications remains a largely inefficient process of trial and error. This is ultimately due to large gaps in our knowledge of vortex dynamics: the field is challenged by the extremely complex interplay between vortex elasticity, vortex-vortex interactions, and material disorder. Here, we review obstacles and recent successes in understanding and controlling vortex dynamics in superconducting materials and devices. We further identify major open questions and discuss opportunities for transformative research in the field. This includes improving our understanding of vortex creep, determining and reaching the ceiling for the critical current, advanced microscopy to garner accurate structure-property relationships, frontiers in predictive simulations and the benefits of artificial intelligence, as well as controlling and exploiting vortices in quantum information applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Electrochemical generation of liquid and solid sulfur on two-dimensional layered materials with distinct areal capacities

It has recently been shown that sulfur, a solid material in its elementary form S8, can stay in a supercooled state as liquid sulfur in an electrochemical cell. We establish that this newly discovered state could have implications for lithium–sulfur batteries. Here, through in situ studies of electrochemical sulfur generation, we show that liquid (supercooled) and solid elementary sulfur possess very different areal capacities over the same charging period. To control the physical state of sulfur, we studied its growth on two-dimensional layered materials. We found that on the basal plane, only liquid sulfur accumulates; by contrast, at the edge sites, liquid sulfur accumulates if the thickness of the two-dimensional material is small, whereas solid sulfur nucleates if the thickness is large (tens of nanometres). Correlating the sulfur states with their respective areal capacities, as well as controlling the growth of sulfur on two-dimensional materials, could provide insights for the design of future lithium–sulfur batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Macroscale and Nanoscale Photoelectrochemical Behavior of p-Type Si(111) Covered by a Single Layer of Graphene or Hexagonal Boron Nitride

Two-dimensional (2D) materials may enable a general approach to the introduction of a dipole at a semiconductor surface as well as control over other properties of the double layer at a semiconductor/liquid interface. Vastly different properties can be found in the 2D materials currently studied due in part to the range of the distribution of density-of-states. In this work, the open-circuit voltage (V oc ) of p-Si–H, p-Si/Gr (graphene), and p-Si/h-BN (hexagonal boron nitride) in contact with a series of one-electron outer-sphere redox couples was investigated by macroscale measurements as well as by scanning electrochemical cell microscopy (SECCM). The band gaps of Gr and h-BN (0–5.97 eV) encompass the wide range of band gaps for 2D materials, so these interfaces (p-Si/Gr and p-Si/h-BN) serve as useful references to understand the behavior of 2D materials more generally. The value of Voc shifted with respect to the effective potential of the contacting solution, with slopes (ΔV oc /ΔE Eff ) of -0.27 and -0.38 for p-Si/Gr and p-Si/h-BN, respectively, indicating that band bending at the p-Si/h-BN and p-Si/Gr interfaces responds at least partially to changes in the electrochemical potential of the contacting liquid electrolyte. Additionally, SECCM is shown to be an effective method to interrogate the nanoscale photoelectrochemical behavior of an interface, showing little spatial variance over scales exceeding the grain size of the CVD-grown 2D materials in this work. The measurements demonstrated that the polycrystalline nature of the 2D materials had little effect on the results and confirmed that the macroscale measurements reflected the junction behavior at the nanoscale.

2D material↗
DOE OSTI2022

A Semiconducting Two‐Dimensional Polymer as an Organic Electrochemical Transistor Active Layer

Abstract Organic electrochemical transistors (OECTs) are devices with broad potential in bioelectronic sensing, circuits, and neuromorphic hardware. Their unique properties arise from the use of organic mixed ionic/electronic conductors (OMIECs) as the active channel. Typical OMIECs are linear polymers, where defined and controlled microstructure/morphology, and reliable characterization of transport and charging can be elusive. Semiconducting two‐dimensional polymers (2DPs) present a new avenue in OMIEC materials development, enabling electronic transport along with precise control of well‐defined channels ideal for ion transport/intercalation. To this end, a recently reported 2DP, TIIP, is synthesized and patterned at 10 µm resolution as the channel of a transistor. The TIIP films demonstrate textured microstructure and show semiconducting properties with accessible oxidation states. Operating in an aqueous electrolyte, the 2DP‐OECT exhibits a device‐scale hole mobility of 0.05 cm 2 V –1 s –1 and a µ C * figure of merit of 1.75 F cm –1 V –1 s –1 . 2DP OMIECs thus offer new synthetic degrees of freedom to control OECT performance and may enable additional opportunities such as ion selectivity or improved stability through reduced morphological modulation during device operation.

2D-polymer↗
DOE OSTI2023

The wave energy converter control competition (WECCCOMP): Wave energy control algorithms compared in both simulation and tank testing

The wave energy control competition established a benchmark problem which was offered as an open challenge to the wave energy system control community. The competition had two stages: In the first stage, competitors used a standard wave energy simulation platform (WEC-Sim) to evaluate their controllers while, in the second stage, competitors were invited to test their controllers in a real-time implementation on a prototype system in a wave tank. The performance function used was based on converted energy across a range of standard sea states, but also included aspects related to economic performance, such as peak/average power, peak force, etc. This paper compares simulated and experimental results and, in particular, examines if the results obtained in a linear system simulation are borne out in reality. Overall, within the scope of the device tested, the range of sea states employed, and the performance metric used, the conclusion is that high-performance WEC controllers work well in practice, with good carry-over from simulation to experimentation. However, the availability of a good WEC mathematical model is deemed to be crucial.

16 TIDAL AND WAVE POWER↗