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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Status of MQXFB Quadrupole Magnets for HL-LHC

The MQXFB magnets are superconducting quadrupoles with nominal peak field on the conductor of 11.3 T. With their magnetic length of 7.2 m, they stand as the longest Nb3Sn accelerator magnets designed and manufactured up to now. Together with the companion MQXFA 4.2 m long units, built by the US Accelerator Research Program, they are at the heart of HL-LHC, as they shall replace the inner triplet quadrupoles at either side of the ATLAS and CMS interaction regions of the LHC. This technology has benefited from many years of development, and this specific design was validated with successful short models (MQXFS, 1.2 m long). More recently, several MQXFA magnets were shown to satisfy HL-LHC requirements. In this paper, we report on the cold test results of four MQXFB magnets, focusing on performance, training, behavior after thermal and powering cycles, and field quality. We then provide an update of the overall status, including ongoing verifications of design changes at the level of the coil fabrication.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Performance Monitoring of African Micro-Grids: Good Practices and Operational Data

This report investigates the current understanding and use of performance monitoring in the micro-grid sector by developers. It highlights the importance of performance monitoring by providing key performance monitoring indicators and suggesting performance monitoring activities that improve projects and programs. This report also presents the analysis of the performance data of 36 micro-grids currently operating in Africa, in one of the largest such assessments conducted to date within the sector and concludes by recommending potential next steps towards integrating performance monitoring into the micro-grid sector.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Instabilities driven by the fundamental crabbing mode

The Electron Ion Collider (EIC) requires crab cavities to correct for crossing angle effects. These cavities produce a time varying horizontal kick that makes the collision head on in the center of mass frame of the bunches. This results in a luminosity that is very close to the the luminosity obtained without a crossing angle. Producing the kick requires a transverse RF field. This field requires a superconducting resonator and reasonable power levels require loaded quality factors of order a million. This results is a very large transverse impedance for the crabbing mode. The horizontal beta function is large and this, coupled with the large transverse impedance of the resonator can lead to strong instabilities. In the first section of the paper a simple model is used to describe the situation without feedback. There are large ranges of tune leading to strong instabilities in both the electron and hadron storage rings of the EIC. These estimates are confirmed with simulations. In the second section, RF feedback is used to reduce the apparent impedance of the crabbing mode. Simulating the actual feedback would require both a significant rewrite of the tracking code and a significant reduction in computational speed. Instead, the apparent impedance is fitted using one pole filters, which allows use of the existing simulation code. In the third section a transverse damping system is outlined.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Powering the Woods Hole X-Spar Buoy with Ocean Wave Energy—A Control Co-Design Feasibility Study

Despite its success in measuring air–sea exchange, the Woods Hole Oceanographic Institution’s (WHOI) X-Spar Buoy faces operational limitations due to energy constraints, motivating the integration of an energy harvesting apparatus to improve its deployment duration and capabilities. This work explores the feasibility of an augmented, self-powered system in two parts. Part 1 presents the collaborative design between X-Spar developers and wave energy researchers translating user needs into specific functional requirements. Based on requirements like desired power levels, deployability, survivability, and minimal interference with environmental data collection, unsuitable concepts are pre-eliminated from further feasibility study consideration. In part 2, we focus on one of the promising concepts: an internal rigid body wave energy converter. We apply control co-design methods to consider commercial of the shelf hardware components in the dynamic models and investigate the concept’s power conversion capabilities using linear 2-port wave-to-wire models with concurrently optimized control algorithms that are distinct for every considered hardware configuration. During this feasibility study we utilize two different control algorithms, the numerically optimal (but acausal) benchmark and the optimized damping feedback. We assess the sensitivity of average power to variations in drive-train friction, a parameter with high uncertainty, and analyze stroke limitations to ensure operational constraints are met. Our results indicate that a well-designed power take-off (PTO) system could significantly extend the WEC-Spar’s mission by providing additional electrical power without compromising data quality.

autonomous systems↗
DOE OSTI2023

A combination interferometric and morphological image processing approach to rapid quality assessment of additively manufactured cellular truss core components

Advanced manufacturing (AM) processes such as laser powder bed fusion (LPBF) are increasingly capable of fabricating components with useful and unprecedented mechanical properties by incorporating complex internal bracing structures. From the standpoint of quality control and assessment, however, internally complex assemblies present significant build-verification challenges. Here we propose a hybrid approach to the inspection involving the application of computer-aided speckle interferometry (CASI) and morphological image processing as a rapid, inexpensive, and facile method for AM quality control. The described methodology has low capital equipment costs, is full-field and non-contact, can be used in an industrial setting, and has very low requirements in terms of operator training and expertise. Consisting primarily of the combination of image processing software with a simple optical system of variable sensitivity, the method is shown to be effective for inspection of a titanium honeycomb component subjected to differential pressure. Results are compared to those achieved with computed tomography (CT), immersion ultrasound testing (UT), and optical holographic interferometry. Here we propose several possible processing strategies for automated quality assessment based on this powerful hybrid approach.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing

A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. Finally, the validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.

42 ENGINEERING↗
DOE OSTI2021

National direct-drive program on OMEGA and the National Ignition Facility

A major advantage of the laser direct-drive (DD) approach to ignition is the increased fraction of laser drive energy coupled to the hot spot and relaxed hot-spot requirements for the peak pressure and convergence ratios relative to the indirect-drive approach at equivalent laser energy. With the goal of a successful ignition demonstration using DD, the recently established national strategy has several elements and involves multiple national and international institutions. These elements include the experimental demonstration on OMEGA cryogenic implosions of hot-spot conditions relevant for ignition at MJ-scale energies available at the National Ignition Facility (NIF) and developing an understanding of laser-plasma interactions and laser coupling using DD experiments on the NIF. DD designs require reaching central stagnation pressures in excess of 100 Gbar. The current experiments on OMEGA have achieved inferred peak pressures of 56 Gbar (Regan et al 2016 Phys. Rev. Lett. 117 025001). Extensive analysis of the cryogenic target experiments and two- and three-dimensional simulations suggest that power balance, target offset, and target quality are the main limiting factors in target performance. In addition, cross-beam energy transfer (CBET) has been identified as the main mechanism reducing laser coupling. Reaching the goal of demonstrating hydrodynamic equivalence on OMEGA includes improving laser power balance, target position, and target quality at shot time. CBET must also be significantly reduced and several strategies have been identified to address this issue. (2016-01-01)

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Ultralow-threshold thin-film lithium niobate optical parametric oscillator

Materials with strong second-order ( χ <#comment/> ( 2 ) ) optical nonlinearity, especially lithium niobate, play a critical role in building optical parametric oscillators (OPOs). However, chip-scale integration of low-loss χ <#comment/> ( 2 ) materials remains challenging and limits the threshold power of on-chip χ <#comment/> ( 2 ) OPO. Here we report an on-chip lithium niobate optical parametric oscillator at the telecom wavelengths using a quasi-phase-matched, high-quality microring resonator, whose threshold power ( ∼ <#comment/> 30 µ <#comment/> W ) is 400 times lower than that in previous χ <#comment/> ( 2 ) integrated photonics platforms. An on-chip power conversion efficiency of 11% is obtained from pump to signal and idler fields at a pump power of 93 µW. The OPO wavelength tuning is achieved by varying the pump frequency and chip temperature. With the lowest power threshold among all on-chip OPOs demonstrated so far, as well as advantages including high conversion efficiency, flexibility in quasi-phase-matching, and device scalability, the thin-film lithium niobate OPO opens new opportunities for chip-based tunable classical and quantum light sources and provides a potential platform for realizing photonic neural networks.

Lu, Juanjuan (ORCID:0000000319389119)↗
DOE OSTI2024

Melt Pool characteristics on surface roughness and printability of 316L stainless steel in laser powder bed fusion

Purpose Surface quality and porosity significantly influence the structural and functional properties of the final product. This study aims to establish and explain the underlying relationships among processing parameters, top surface roughness and porosity level in additively manufactured 316L stainless steel. Design/methodology/approach A systematic variation of printing process parameters was conducted to print cubic samples based on laser power, speed and their combinations of energy density. Melt pool morphologies and dimensions, surface roughness quantified by arithmetic mean height (Sa) and porosity levels were characterized via optical confocal microscopy. Findings The study reveals that the laser power required to achieve optimal top surface quality increases with the volumetric energy density (VED) levels. A smooth top surface (Sa < 15 µm) or a rough surface with humps at high VEDs (VED > 133.3 J/mm 3 ) can serve as indicators for fully dense bulk samples, while rough top surfaces resulting from melt pool discontinuity correlate with high porosity levels. Under insufficient VED, melt pool discontinuity dominates the top surface. At high VEDs, surface quality improves with increased power as mitigation of melt pool discontinuity, followed by the deterioration with hump formation. Originality/value This study reveals and summarizes the formation mechanism of dominant features on top surface features and offers a potential method to predict the porosity by observing the top surface features with consideration of processing conditions.

Engineering↗
DOE OSTI2024

Plasma electron acceleration driven by a long-wave-infrared laser

Laser-driven plasma accelerators provide tabletop sources of relativistic electron bunches and femtosecond x-ray pulses, but usually require petawatt-class solid-state-laser pulses of wavelength λ L ~ 1 μm. Longer-λ L lasers can potentially accelerate higher-quality bunches, since they require less power to drive larger wakes in less dense plasma. Here, we report on a self-injecting plasma accelerator driven by a long-wave-infrared laser: a chirped-pulse-amplified CO 2 laser (λ L ≈ 10 μm). Through optical scattering experiments, we observed wakes that 4-ps CO 2 pulses with < 1/2 terawatt (TW) peak power drove in hydrogen plasma of electron density down to 4 × 10 17 cm –3 (1/100 atmospheric density) via a self-modulation (SM) instability. Shorter, more powerful CO 2 pulses drove wakes in plasma down to 3 × 10 16 cm –3 that captured and accelerated plasma electrons to relativistic energy. Collimated quasi-monoenergetic features in the electron output marked the onset of a transition from SM to bubble-regime acceleration, portending future higher-quality accelerators driven by yet shorter, more powerful pulses.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation

In this work we address the problem of accelerating complex power-grid simulation through machine learning ( ML). Specifically, we develop a framework, Smart-PGSim,which generates multitask-learning (MTL) neural network (NN)models to predict the initial values of variables critical to the problem convergence. MTL models allow information sharing when predicting multiple dependent variables while including customized layers to predict individual variables. We show that,to achieve the required accuracy, it is paramount to embed domain-specific constraints derived from the specific power-grid components in the MTL model. Smart-PGSim then employs the predicted initial values as a high-quality initial condition for the power-grid numerical solver (warm start), resulting in both higher performance compared to state-of-the-art solutions while maintaining the required accuracy. Smart-PGSim brings 2.60×speedup on average (up to 3.28×) computed over 10,000 problems, without losing solution optimality.

machine learning, neural networks↗
DOE OSTI2020

Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services

Flexible loads have great potential to improve the electric grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. This paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environmental impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Indicator-directed Dynamic Power Management for Iterative Workloads on GPU-Accelerated Systems

Modern high-performance and warehouse computing centers show strong interest in minimizing system power consumption while satisfying customers’ quality of service (QoS). Dynamic voltage and frequency scaling (DVFS) is effective for achieving this goal. Nevertheless, automating the process online and making it transparent to users must address three major challenges: (1) Complexity — today’s hardware components (e.g., CPUs, GPUs, memory, network, etc.) can be configured in several or dozens of frequency/voltage states for satisfying divergent system demands. Given their combination and the emergence of heterogeneity, searching the optimal configuration in the design space online can be timing consuming. (2) QoS guarantee — user-defined objectives such as power constraint and performance target must be monitored, predicted and ensured at the best effort. (3) Adaptability — various known and unknown workloads run on systems. Workloads characteristics should be quickly determined and configurations dynamically adjusted in accord with workloads and QoS. In this work, we focus on applications exhibiting an interesting feature – iterative or periodic, which is common among conventional HPC and emerging machine learning workloads. We propose an online dynamic power-performance (ODPP) management framework to dynamically adjust GPU DVFS configurations to meet performance and power objectives and constraints, without any code annotation or intrusion. Particularly, ODPP extracts the performance and power indicators for applications from their resources utilization profiles in a short episode. It further automatically constructs an accurate model that infers from the indicators how the application's performance and power vary with GPU core and memory frequencies. Aided with the model, for both seen and unseen applications, ODPP can quickly determine the most appropriate DVFS configuration for their execution. We evaluate ODPP on an NVIDIA GPU using multiple exascale computing (ECP) and deep learning applications.

Zou, Pengfei↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2022

Using Synchrophasor Status Word as Data Quality Indicator: What to Expect in the Field?

Data quality plays a crucial role in successful applications of synchrophasor data in power system operation and control. This paper presents the results of a data quality analysis of a multi-year field-recorded synchrophasor dataset. The analysis has identified several typical data quality issues encountered in the field data. An examination of the PMU status words included with the dataset has revealed several inconsistent implementations and the lack of correlation between the PMU data quality and the status word, which impacts the usefulness of such information. Our investigation has concluded that the status word alone as found in the recorded field dataset could not be used as a reliable indicator of data quality for field-recorded data. Several recommendations are proposed to improve the usefulness of the PMU status word.

Cheng, Zheyuan↗
DOE OSTI2025

Enhancing Time Synchronization in Smart Grid With White Rabbit: Theory, Architecture, and Challenges

The smart grid aims to provide economically efficient and sustainable power to consumers with high quality and security. However, the increasing integration of distributed renewable energy sources presents challenges for smart grid protection and control systems. Here, to enhance smart grid operations, this article introduces White Rabbit (WR) as a more precise and accurate time synchronization technique. To explore White Rabbit’s potential applications in the smart grid, this study first explains existing time synchronization techniques and their limitations, followed by an analysis of the role and importance of time synchronization in smart grids. A comprehensive survey is then conducted, covering the theories, principles, implementations, performances, and existing application cases of White Rabbit. The findings suggest that White Rabbit is a promising technique for smart grid deployment. However, several challenges remain on the path to large-scale implementation. These challenges are analyzed in detail, highlighting key areas for future research.

Liu, Yu [University of Tennessee, Knoxville, TN (U↗
DOE OSTI2024

A Deep Learning Approach for In-Network Synchrophasor Missing Data Recovery Using Programmable Network Switches

Phasor measurement unit (PMU) networks deliver accurate and timely measurements, which is essential for managing today’s electric power systems. To ensure data quality and enhance the cyber-resilience of PMU networks against malicious attacks and data errors, this study presents an online PMU missing data recovery scheme by leveraging P4 programmable switches. The data plane incorporates a customized PMU protocol parser that abstracts the necessary payload data for recovery. Recovery processes are executed in the control plane using a pre-trained machine learning model. Both traditional and advanced ML models, such as transformer and TimeGPT, are explicitly employed for data prediction. This approach ensures rapid and precise data recovery. Performance evaluations focus on recovery speed and accuracy, using a real dataset from a campus microgrid. With 20% missing PMU data, the mean absolute percentage error for voltage magnitude is 0.0384%, and the phase angle error discrepancy is approximately 0.4064%.

Phasor Measurement Unit, Machine Learning, Program↗