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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Optimizing Grid Regulation With Gravity Storage Systems: A Comparative Analysis With Different Motor Inertias

The integration of renewable energy sources into power grids necessitates solutions for grid support and stability during fluctuations in electricity generation and demand. Gravity energy storage systems (GESS) are emerging as a promising technology for managing the balance between energy supply and demand. However, their capacity to optimize energy flow and offer voltage and frequency regulation amid imbalances in generation and demand is less reported. This paper investigates the control of GESS for optimizing energy flow during voltage and frequency regulation. The study evaluates the regulation capabilities of GESS with different motor inertias during a Texas grid event: one with a high-speed, low-inertia motor and another with a low-speed, high-inertia motor. Results indicate that both GESS scenarios provide fast frequency response by converting potential energy into kinetic energy and vice versa, with a response time of 1.5 s from the frequency variation. This aligns with grid requirements for primary frequency response from traditional synchronous generators and motors with large inertia. Furthermore, a GESS based on a high-inertia motor may be able to operate over a broader range of frequency variations, whereas a low-inertia system may be limited by thermal constraints of the motor.

frequency regulation↗
DOE OSTI2024

Optimizing Grid Regulation With Gravity Storage Systems: A Comparative Analysis With Different Motor Inertias: Preprint

The integration of renewable energy sources into power grids necessitates solutions for grid support and stability during fluctuations in electricity generation and demand. Gravity energy storage systems (GESS) are emerging as a promising technology for managing the balance between the energy supply and demand. However, their capacity to optimize energy flow and offer voltage and frequency regulation amid generation-demand imbalances is less reported. This paper investigates the control of GESS for optimizing energy flow during voltage and frequency regulation. The study evaluates the regulation capabilities of GESS with different motor inertias during a Texas grid event: one with a high-speed, low-inertia motor and another with a low-speed, high-inertia motor. Results indicate that both GESS systems provide fast frequency response by converting potential energy into kinetic energy and vice versa, with a response time of 1.5 seconds from the frequency variation. This aligns with grid requirements for primary frequency response from traditional synchronous generators and motors with large inertia. Furthermore, a GESS with a high-inertia motor may be able operate over a broader range of frequency variations whereas a low-inertia system may be limited by thermal constraints of the motor.

frequency regulation↗
DOE OSTI2024

Optimizing Grid Regulation with Gravity Energy Storage Systems: A Comparative Analysis with Different Motor Inertias

The integration of renewable energy sources into power grids necessitates solutions for grid support and stability during fluctuations in electricity generation and demand. Gravity energy storage systems (GESS) are emerging as a promising technology for managing the balance between energy supply and demand. However, their capacity to optimize energy flow and offer voltage and frequency regulation amid imbalances in generation and demand is less reported. This paper investigates the control of GESS for optimizing energy flow during voltage and frequency regulation. The study evaluates the regulation capabilities of GESS with different motor inertias during a Texas grid event: one with a high-speed, low-inertia motor and another with a low-speed, high-inertia motor. Results indicate that both GESS scenarios provide fast frequency response by converting potential energy into kinetic energy and vice versa, with a response time of 1.5 s from the frequency variation. This aligns with grid requirements for primary frequency response from traditional synchronous generators and motors with large inertia. Furthermore, a GESS based on a highinertia motor may be able to operate over a broader range of frequency variations, whereas a low-inertia system may be limited by thermal constraints of the motor.

ENERGY STORAGE,POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Deep Reinforcement Learning Based Control of Wind Turbines for Fast Frequency Response

In order to fulfill vital auxiliary grid services, such as load regulation, spin and non-spin reserve provision, and frequency support during emergencies, there is often a requirement for certain wind farms to operate in de-loaded modes. Leveraging the swift response capabilities of wind farms, this study demonstrates that reserving power in de-loaded modes can significantly enhance power grid stability and reliability during system contingencies. Controlling wind farms optimally for frequency support is intricate due to the nonlinearity of models and controllers and the complexity of wind farm interactions with power systems. Here, to address this challenge, this paper introduces a novel approach that integrates wind turbines into reinforcement learning-based solutions for frequency response. This innovative methodology utilizes the state-of-the-art reinforcement learning algorithm known as the surrogate-gradient-based evolutionary strategy. The proposed learning-based algorithm provides continuous control of wind farm output to rapidly stabilize system frequency and prevent unnecessary trips of under-frequency load shedding relays. To facilitate efficient training, parallel computing techniques are employed. The proposed methodology is evaluated on a modified IEEE-39 bus system, and simulation results reveal its efficacy in reliably supporting power system frequency and preventing the need for unnecessary load shedding.

Gao, Wei [Argonne National Laboratory (ANL), Argon↗
DOE OSTI2025

Influence of Water, Vacuum, and Temperature on Surface Conditions of a Zeolite‐based Molecular Sieve

Molecular sieves such as zeolite-based materials are ubiquitous in industrial separation processes. However, there is a significant gap in understanding the surface properties and adsorption mechanisms for commercial zeolites, as most research focuses on pure zeolite powders rather than industrially relevant forms. Here, this work addresses this gap in understanding by employing advanced characterization techniques, including positron annihilation spectroscopy, X-ray diffraction, scanning electron microscopy, X-ray fluorescence spectroscopy, X-ray photoelectron spectroscopy, liquid nitrogen sorption, and Fourier-transform infrared spectroscopy, to investigate the adsorption and desorption behavior of water in commercial zeolite 13X. Our research reveals insights into the pore-filling mechanisms, the impact of material binders on adsorption properties, and the dynamics of hydration and drying processes for zeolites. Monitoring changes on a minute scale allowed the distinction between fast and slow processes leading to sample drying. The identification of positronium bound to Na + ions indicated that water molecules remain in the vicinity of Na + ions after air-drying zeolite 13X. These findings highlight the importance of various environmental conditions in restoring zeolite properties to baseline after hydration, with significant implications for optimizing industrial processes. This work sets the direction for further research aimed at developing more efficient and robust separation techniques.

Beads Binder↗
DOE OSTI2022

Prevalence and scalable control of localized networks

The ability to control network dynamics is essential for ensuring desirable functionality of many technological, biological, and social systems. Such systems often consist of a large number of network elements, and controlling large-scale networks remains challenging because the computation and communication requirements increase prohibitively fast with network size. Here, we introduce a notion of network locality that can be exploited to make the control of networks scalable, even when the dynamics are nonlinear. We show that network locality is captured by an information metric and is almost universally observed across real and model networks. In localized networks, the optimal control actions and system responses are both shown to be necessarily concentrated in small neighborhoods induced by the information metric. This allows us to develop localized algorithms for determining network controllability and optimizing the placement of driver nodes. This also allows us to develop a localized algorithm for designing local feedback controllers that approach the performance of the corresponding best global controllers, while incurring a computational cost orders-of-magnitude lower. Here, we validate the locality, performance, and efficiency of the algorithms in Kuramoto oscillator networks, as well as three large empirical networks: synchronization dynamics in the Eastern US power grid, epidemic spreading mediated by the global air-transportation network, and Alzheimer’s disease dynamics in a human brain network. Taken together, our results establish that large networks can be controlled with computation and communication costs comparable to those for small networks.

42 ENGINEERING↗
DOE OSTI2023

Adaptive Time Step Control for Multirate Infinitesimal Methods

Multirate methods have been used for decades to temporally evolve initial-value problems in which different components evolve on distinct time scales, and thus use of different step sizes for these components can result in increased computational efficiency. Generally, such methods select these different step sizes based on experimentation or stability considerations. For problems that evolve on a single time scale, adaptivity approaches that strive to control local temporal error are widely used to achieve numerical results of a desired accuracy with minimal computational effort, while alleviating the need for manual experimentation with different time step sizes. However, there is a notable gap in the publication record on the development of adaptive time step controllers for multirate methods. In this paper, we extend the single-rate controller work of Gustafsson [ACM Trans. Math. Software, 20 (1994), pp. 496-517] to the multirate method setting. Specifically, we develop controllers based on polynomial approximations to the principal error functions for both the "fast" and "slow" time scales within multirate infinitesimal (MRI) methods. We additionally investigate a variety of approaches for estimating the errors arising from each time scale within MRI methods. We then numerically evaluate the proposed multirate controllers and error estimation strategies on a range of multirate test problems, comparing their performance against an estimated optimal performance. Through this work, we combine the most performant of these approaches to arrive at a set of multirate adaptive time step controllers that robustly achieve desired solution accuracy with minimal computational effort.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Impact of Higher Fidelity Design Iterations on Critical System Criteria

Nuclear criticality experiments are effective at informing the performance of nuclear data libraries across many applications. This work explores the implications of refining critical experiment MCNP models from their low fidelity optimization phase to penultimate neutronic models. Specifically, this work is focused on two series of plutonium fueled experiments funded through internal programs at Los Alamos National Laboratory building off previous efforts under the EUCLID (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) collaboration. Thales, the first of the two collaborations, is a fast spectrum Ta-reflected plutonium experiment to support operations at PF-4. The second experiment are twin configurations designed to target the intermediate energy cross sections in 239 Pu. Motivation for this experiment stems from the PARallel Approach of Differential and InteGral Measurements (PARADIGM) collaboration which hopes to achieve a significant reduction in 239 Pu cross section uncertainties in the intermediate region.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Strength stability at high temperatures for additively manufactured alumina forming austenitic alloy

Several fast-spectrum nuclear reactors designed to generate high power (~450 MWe) rely on forced convection of media such as supercritical CO 2 , sodium, or liquid lead to cool the nuclear core, operating at temperatures up to 600 °C. Cost-effective, high-strength Fe-based alumina forming austenitic (AFA) alloys are a promising candidate for the fabrication of critical nuclear components. This study investigated laser powder bed fusion (LPBF) processing of an AFA alloy composition optimized for improved creep resistance. Electron microscopy revealed an elongated grain structure along the build direction with a fine sub-grain cellular structure decorated with (Cr,Fe,Nb) 23 C 6 carbide precipitates at the intercellular boundaries. Finally, at temperatures of 20–900 °C, the LPBF alloy's superior tensile properties compared to its arc-melted counterpart and other advanced steels (e.g., SS316) were attributed to the distribution of nano-sized carbide precipitates, whereas the high ductility was attributed to the LPBF alloy's elongated grain structure.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Heliostat optical error inspection with polarimetric imaging drone

On a Concentrated Solar Power (CSP) field, optical errors have significant impacts on the collection efficiency of heliostats. Fast, cost-effective, labor-efficient, and non-intrusive autonomous field inspection remains a challenge. Approaches using imaging drone, i.e., Unmanned Aerial Vehicle (UAV) system integrated with high resolution visible imaging sensors, have been developed to address these challenges; however, these approaches are often limited by insufficient imaging contrast. Here, in this study, we report a polarimetry-based method with a polarization imaging system integrated on UAV to enhance imaging contrast for in-situ detection of heliostat mirrors without interrupting field operation. We developed an optical model for skylight polarization pattern to simulate the polarization images of heliostat mirrors and obtained optimized waypoints for polarimetric imaging drone flight path to capture images with enhanced contrast. The polarimetric imaging-based method improved the success rate of edge detections in scenarios which were challenging for mirror edge detection with conventional imaging sensors. We have performed field tests to achieve significantly enhanced heliostat edge detection success rate and investigate the feasibility of integrating polarimetric imaging method with existing imaging-based heliostat inspection methods, i.e., Polarimetric Imaging Heliostat Inspection Method (PIHIM). Our preliminary field test results suggest that the PIHIM hold the promise to enable sufficient imaging contrast for real-time autonomous imaging and detection of heliostat field, thus suitable for non-interruptive fast CSP field inspection during its operation.

CSP Field↗
DOE OSTI2023

Robust stellarator optimization via flat mirror magnetic fields

Stellarator magnetic configurations need to be optimized in order to meet all the required properties of a fusion reactor. In this work, it is shown that a flat-mirror quasi-isodynamic (QI) configuration (i.e. a QI configuration with sufficiently small radial variation of the mirror term) can achieve small radial transport of energy and good confinement of bulk and fast ions even if it is not very close to perfect omnigeneity, and for a wide range of plasma scenarios, including low $β$ and small radial electric field. This opens the door to constructing better stellarator reactors. On the one hand, they would be easier to design, as they would be robust against error fields. On the other hand, they would be easier to operate since, both during startup and steady-state operation, they would require less auxiliary power, and the heat loads on plasma-facing components caused by fast ion losses would be reduced to acceptable levels.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Simulation of the impact of particle recycling on the plasma in MPS-LD device based on the BOUT++ LPD module

A linear plasma device (LPD) module has been developed under the BOUT++ framework to simulate plasma transport in the MPS-LD. However, previously, the LPD module used a simplistic neutral particle model that only includes particle density and velocity, which prevents the full understanding of the plasma-neutrals interactions. In this work, we further optimize the neutral model by using a more complete neutral fluid model containing the continuity equation, momentum equation, and energy equation. The reactions such as charge exchange, excitation, and radiation collisions are included. Since the neutral particle source is mainly provided by particle recycling from the target, a particle recycling model is employed, which includes both fast reflection and slow thermal release. The upgraded LPD module is applied to simulate the argon (Ar) discharge experiment of MPS-LD, and the benchmark against experiment measurement and SOLPS-ITER simulation results are presented. Good agreements are obtained, showing the validation of the upgraded module. After that, the impact of particle recycling on Ar plasma is investigated. It is found that a higher recycling coefficient (R) promotes the achievement of high-density plasma at the target. In conclusion, the recycled Ar atoms change target plasma pressure as well as plasma-neutral collisions, which both contribute to plasma momentum loss, thus promoting the rollover of ion flux to the target.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Trigger Detection for the sPHENIX Experiment via Bipartite Graph Networks with Set Transformer

Trigger (interesting events) detection is crucial to high-energy and nuclear physics experiments because it improves data acquisition efficiency. It also plays a vital role in facilitating the downstream offline data analysis process. The sPHENIX detector, located at the Relativistic Heavy Ion Collider in Brookhaven National Laboratory, is one of the largest nuclear physics experiments on a world scale and is optimized to detect physics processes involving charm and beauty quarks. Furthermore, these particles are produced in collisions involving two proton beams, two gold nuclei beams, or a combination of the two and give critical insights into the formation of the early universe. This paper presents a model architecture for trigger detection with geometric information from two fast silicon detectors. Transverse momentum is introduced as an intermediate feature from physics heuristics. We also prove its importance through our training experiments. Each event consists of tracks and can be viewed as a graph. A bipartite graph neural network is integrated with the attention mechanism to design a binary classification model. Compared with the state-of-the-art algorithm for trigger detection, our model is parsimonious and increases the accuracy and the AUC score by more than 15%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The high-power helicon program at DIII-D: gearing up for first experiments

Helicon current drive, also called fast wave current drive in the lower hybrid range of frequencies, has long been regarded as a promising current drive tool for reactor grade plasmas. A newly installed MW-level system at DIII-D will be the first test of this technology in reactor-relevant plasmas, in the sense that full single-pass absorption is expected. A 30-module traveling wave antenna has been installed and optimized in-vessel in early 2020. The linear electromagnetic characteristics of the unloaded module array have been extensively tested both on the bench and in the vessel at instrumentation power levels. Excellent performance has been achieved, ~2% reflected power and ~1.5% dissipated power per module in air, in a 10 MHz band around 476 MHz. Stripline feeds on both ends of the antenna allow either co or counter current drive. The installation of a 1.2 MW klystron and associated high-power electronics was completed in Fall 2020. Commissioning of the antenna is ongoing. An important goal of this experiment is to validate the helicon current drive physics basis using an extensive set of new and upgraded diagnostics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Optimization of irradiation configuration using spherical t-designs for laser-direct-drive inertial confinement fusion

Abstract A new class of beam configurations is proposed for symmetric-direct-drive inertial confinement fusion laser systems. These configurations are based on spherical t -designs that are studied in spherical design theory in mathematics (Delsarte et al 1977 Geom. Dedicata 6 363). Employing t -design configurations offers elimination of spherical-harmonic intensity modulations for modes ℓ ⩽ t . Additionally, these configurations provide fast decay of intensity nonuniformities with increasing number of beams and symmetric intensity patterns on the surface of the target. Methods developed in spherical design theory offer a convenient, systematic way of obtaining beam configurations for an arbitrary number of beams.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

3D printed carbon fiber reinforced carbon as an energy efficient alternative to graphite for EFAS tooling

As Electric Field Assisted Sintering (EFAS) gains more industrial acceptance and use, it becomes more important to develop more efficient means to implement this technology. To this aim, 3D printed continuous carbon fiber reinforced carbon (CCC) was manufactured and fabricated into tooling for EFAS systems as an alternative to traditional graphite tooling. The impact of fiber orientation on the thermal and electrical properties of the CCC was characterized. Sample material was sintered in Tokai G535 graphite tooling, under common processing conditions and compared with CCC tooling. There was nearly 50 % energy savings compared to graphite while maintaining equivalent sample density and microstructure plus keeping ram temperatures 39 % cooler. This is due to spatial control of generated heat and thermal diffusivity within the molds, by means of fiber orientation anisotropy. Finite element modeling of the tooling design supported the experimental results as well as displays the effect of optimization of this 3D printed CCC material.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Shear-aligned large-area organic semiconductor crystals through extended π–π interaction

Small molecule-based organic semiconductors are of broad interest in organic field-effect transistors (OFETs) due to their potential for high crystallinity and electrical performance. The 2D molecule, TIPS-peri-pentacenopentacene (TIPS-PPP), which is the vertical extension of the 1D TIPS-pentacene (TIPS-PEN) molecule, offers a lower bandgap, higher aromaticity, and an enhanced π–π interaction with neighboring molecules in the solid state when compared to TIPS-PEN. However, an in-depth understanding of the relationship between the molecule structure, solid-state molecular packing, and the electronic properties has not been reported due to poor control over the TIPS-PPP crystallite size. In this work, we successfully engineered highly oriented large-area TIPS-PPP crystals through the solution shear coating technique. Additionally, compared with narrow ribbon-like TIPS-PEN crystals, TIPS-PPP crystals can grow centimeters long and over 500 μm wide. TIPS-PPP molecules are less susceptible to forming metastable polymorphs than TIPS-PEN molecules upon fast evaporation. The crystal structure of TIPS-PPP is also thermally stable at 250 °C. Notably, the anisotropic charge carrier mobility of TIPS-PPP crystals is resolved through fabricating bottom-gate top-contact devices, with a hole mobility of 3.1 cm 2 V -1 s -1 along the preferred packing direction. Further device optimization using top-gate bottom-contact devices improved the mobility up to 6.5 cm 2 V -1 s -1 , which is among the highest for pentacene-derivative-based organic semiconductors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗