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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

Artificial intelligence (AI) embedded within digital models of manufacturing processes can be used to improve process productivity and product quality significantly. The application of such advanced capabilities particularly to highly digitalized processes such as metal additive manufacturing (AM) is likely to make those processes commercially more attractive. AI capabilities will reside within Digital Twins (DTs) which are living virtual replicas of the physical processes. DTs will be empowered to operate autonomously in a diagnostic control capacity to supervise processes and can be interrogated by the practitioner to inform the optimal processing route for any given product. The utility of the information gained from the DTs would depend on the quality of the digital models and, more importantly, their faster-solving surrogates which dwell within DTs for consultation during rapid decision-making. In this article, we point out the exceptional value of DTs in AM and focus on the need to create high-fidelity multiscale-multiphysics models for AM processes to feed the AI capabilities. We identify technical hurdles for their development, including those arising from the multiscale and multiphysics characteristics of the models, the difficulties in linking models of the subprocesses across scales and physics, and the scarcity of experimental data. We discuss the need for creating surrogate models using machine learning approaches for real-time problem-solving. We further identify non-technical barriers, such as the need for standardization and difficulties in collaborating across different types of institutions. We offer potential solutions for all these challenges, after reflecting on and researching discussions held at an international symposium on the subject in 2019. Here, we argue that a collaborative approach can not only help accelerate their development compared with disparate efforts, but also enhance the quality of the models by allowing modular development and linkages that account for interactions between the various sub-processes in AM. A high-level roadmap is suggested for starting such a collaboration.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effect of edge plasma density on hot spot in LHCD plasma in EAST

Hot spots are a serious challenge limiting long pulse operation with lower hybrid current drive (LHCD) in tokamaks. Here, in order to mitigate hot spots in the guard limiter and improve LHCD capability, the effect of edge plasma density on inducing hot spots and current drive has been studied in EAST. The temperature in the guard limiter of the LH antenna, inducing hot spot directly, increases with edge density and LH power. Studies show that the hot spot is mainly ascribed to the heat flux in front of LH antenna. Further simulation indicates that such spots correspond to the peak position of edge density due to local LH electric field. In addition, due to the stronger parametric instability (PI) behavior in the case of higher edge density, the current drive capability decreases with edge density. Strike-point splitting behaviour appears as density increase, in agreement with current profiles in the edge region and the reduction of total driven current, suggesting that more power is deposited in the edge region, which then contributes more to hot spot formation. These studies offer one possible idea to optimize the edge density so as to satisfy the coupling, mitigate the heat flux in the guard limiter, and improve current drive capability in fusion devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Projecting Permafrost Thaw of Sub-Arctic Tundra With a Thermodynamic Model Calibrated to Site Measurements

Northern circumpolar permafrost thaw affects global carbon cycling, as large amounts of stored soil carbon becomes accessible to microbial breakdown under a warming climate. The magnitude of carbon release is linked to the extent of permafrost thaw, which is locally variable and controlled by soil thermodynamics. Soil thermodynamic properties, such as thermal diffusivity, govern the reactivity of the soil-atmosphere thermal gradient, and are controlled by soil composition and drainage. In order to project permafrost thaw for an Alaskan tundra experimental site, we used seven years of site data to calibrate a soil thermodynamic model using a data assimilation technique. The model reproduced seasonal and interannual temperature dynamics for shallow (5–40 cm) and deep soil layers (2–4 m), and simulations of seasonal thaw depth closely matched observed data. The model was then used to project permafrost thaw at the site to the year 2100 using climate forcing data for three future climate scenarios (RCP 4.5, 6.0, and 8.5). Minimal permafrost thawing occurred until mean annual air temperatures rose above the freezing point, after which we measured over a 1 m increase in thaw depth for every 1 °C rise in mean annual air temperature. Under no projected warming scenario was permafrost remaining in the upper 3 m of soil by 2100. We demonstrated an effective data assimilation method that optimizes parameterization of a soil thermodynamic model. The sensitivity of local permafrost to climate warming illustrates the vulnerability of sub-Arctic tundra ecosystems to significant and rapid soil thawing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantum machine learning with differential privacy

Abstract Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of quantum operations on a classical computer. Many datasets used in machine learning are crowd sourced or contain some private information, but to the best of our knowledge, no current QML models are equipped with privacy-preserving features. This raises concerns as it is paramount that models do not expose sensitive information. Thus, privacy-preserving algorithms need to be implemented with QML. One solution is to make the machine learning algorithm differentially private, meaning the effect of a single data point on the training dataset is minimized. Differentially private machine learning models have been investigated, but differential privacy has not been thoroughly studied in the context of QML. In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm. This marks the first proof-of-principle demonstration of privacy-preserving QML. The experiments demonstrate that differentially private QML can protect user-sensitive information without signficiantly diminishing model accuracy. Although the quantum model is simulated and tested on a classical computer, it demonstrates potential to be efficiently implemented on near-term quantum devices [noisy intermediate-scale quantum (NISQ)]. The approach’s success is illustrated via the classification of spatially classed two-dimensional datasets and a binary MNIST classification. This implementation of privacy-preserving QML will ensure confidentiality and accurate learning on NISQ technology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

21 cm Power Spectrum Analysis of North Celestial Pole Observations with the Tianlai Dish Pathfinder Array

The Tianlai Dish Pathfinder Array (TDPA) is a radio interferometer designed to test techniques for 21 cm intensity mapping in the post-reionization Universe as a means of measuring large-scale cosmic structure. Using nine nights of observations targeting the North Celestial Pole field, totaling approximately 107 hr of integration time, we analyze data in the frequency range 700–800 MHz (corresponding to redshift z ∼ 0.9). We do the data format conversion, radio frequency interference flagging, calibration, imaging and point source subtraction, and foreground removal via Singular Value Decomposition. The spherically averaged power spectrum Δ 2 (k) is obtained. Furthermore, this work successfully establishes and validates a comprehensive data analysis framework for the TDPA. We identify key improvements including sky model refinement, increased integration time, and pipeline optimization that will enable future detection of the 21 cm signal through auto-correlation and cross-correlation with optical galaxy surveys.

cosmology: large-scale structure of universe↗
DOE OSTI2020

Fully Arbitrary Control of Frequency-Bin Qubits

Accurate control of two-level systems is a longstanding problem in quantum mechanics. One such quantum system is the frequency-bin qubit: a single photon existing in superposition of two discrete frequency modes. In this Letter, we demonstrate fully arbitrary control of frequency-bin qubits in a quantum frequency processor for the first time. Furthermore, we numerically establish optimal settings for multiple configurations of electro-optic phase modulators and pulse shapers, experimentally confirming near-unity mode-transformation fidelity for all fundamental rotations. Performance at the single-photon level is validated through the rotation of a single frequency-bin qubit to 41 points spread over the entire Bloch sphere, as well as tracking of the state path followed by the output of a tunable frequency beam splitter, with Bayesian tomography confirming state fidelities Fρ>0.98 for all cases. Overall, such high-fidelity transformations expand the practical potential of frequency encoding in quantum communications, offering exceptional precision and low noise in general qubit manipulation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
DOE OSTI2024

Networked Microgrids Optimization

This project is mainly about the operation optimization of three networked microgrids (MG), including centralized optimization and distributed optimization. The alternating direction method of multipliers (ADMM) algorithm is used for distributed optimization. In the distribution network considered here, there is a Distribution Management system (DMS) as the system coordinator and several networked microgrids. In grid-connected mode, power could be imported or exported at the distribution substation bus according to the utility rate, and the exchanged power at point of common coupling (PCC) of any microgrid has a limitation. In islanded mode, the power imports/exports at the distribution substation are zero. In both grid-connected and islanded mode, the distribution substation is taken as a slack bus with fixed voltage magnitude.

Chen, Yang [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2021

Experimental studies of plasma-antenna coupling with the JET Alfven Eigenmode Active Diagnostic

This paper presents a dedicated study of plasma-antenna (PA) coupling with the Alfven Eigenmode Active Diagnostic (AEAD) in JET. Stable AEs and their resonant frequencies f, damping rates gamma < 0, and toroidal mode numbers n are measured for various PA separations and limiter versus X-point magnetic configurations. Two stable AEs are observed to be resonantly excited at distinct low and high frequencies in limiter plasmas. The values of f and n do not vary with PA separation. However, |gamma| increases with PA separation for the low-f, but not high-f, mode, yet this may be due to slightly different edge conditions. The high-f AE is detected throughout the transition from limiter to X-point configuration, though its damping rate increases; the low-f mode, on the other hand, becomes unidentifiable. The linear resistive MHD code CASTOR is used to simulate the frequency scan of an AEAD-like external antenna. For the limiter pulses, the high-f mode is determined to be an n = 0 GAE, while the low-f mode is likely an n = 2 TAE. During the transition from limiter to X-point configuration, CASTOR indicates that n = 1 and 2 EAEs are excited in the edge gap. These results extend previous experimental studies in JET and Alcator C-Mod; validate the computational work performed by Dvornova et al 2020 Phys. Plasmas 27 012507; and provide guidance for the optimization of PA coupling in upcoming JET energetic particle experiments, for which the AEAD will aim to identify the contribution of alpha particles to AE drive during the DT campaign.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Influence of AA6061 surface preparation on the resistance to surface degradation of thermally grown boehmite films

Understanding substrate–coating interactions is crucial for designing durable, corrosion-resistant systems. This study investigates the effects of surface treatments— polishing, acid etching, and alkaline etching—on AA6061 aluminum alloy and its thermally grown boehmite coatings. Surface treatments were found to significantly alter boehmite film properties by modifying the alloy’s surface composition. X-ray photoelectron spectroscopy revealed a 10% alumina drop after acid etching alongside chemisorbed species formation in wet treatments. Structural analysis, including grazing incidence X-ray diffraction and TEM, showed α-Al 2 O 3 formation on polished surfaces, improving wear resistance but inducing cathodic E corr shifts, pointing to higher corrosion susceptibility. In contrast, acid and alkaline etching produced anodic E corr shifts with stable, pit-free films observed via potentiodynamic scans. Electrochemical impedance spectroscopy highlighted reduced oxide resistance with extended boehmite growth. The findings emphasize the role of surface pre-treatments and boehmite optimization in balancing durability and corrosion resistance for AA6061 substrates.

AA6061↗
DOE OSTI2021

Experimental study of the influence of gas puff locations on H-mode boundary plasmas with argon seeding on EAST

To investigate the optimal scenario of impurity seeding to obtain divertor plasma detachment for target protection, experiments with Ar&D 2 seeding from two different poloidal locations, the upper outer (UO) divertor target and lower outer (LO) target, were carried out on EAST. Partial energy detachment (the electron temperature near the strike point T e,spt ≤ 10 eV) were obtained with Ar&D 2 mixture puffing from the UO target and, for the first time, from the LO target into H-mode plasmas in the upper single null (USN) configuration. The peak heat flux q t on the UO target was significantly reduced (by ~80%). The rollover of ion flux density j s did not appear probably due to insufficient momentum loss, which is independent of the puff locations. The poloidal asymmetries of particle and heat fluxes on the targets have also been investigated. The UO-dominant asymmetry of particle flux was reversed, while the UO-dominant asymmetry of heat flux was mitigated but not reversed. The plasma confinement dropped by only 14% and 8.2% in the LO-puff case and UO-puff case, respectively, during detachment. The high level of C may contributed greatly to the higher radiation in the bulk plasma region and the greater decline in W MHD in the LO-puff case.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Nematic Correlation Length in Iron-Based Superconductors Probed by Inelastic X-Ray Scattering

Nematicity is ubiquitous in electronic phases of high-T c superconductors, particularly in the Fe-based systems. We used inelastic x-ray scattering to extract the temperature-dependent nematic correlation length ξ from the anomalous softening of acoustic phonon modes in FeSe, underdoped Ba(Fe 0.97 Co 0.03 ) 2 As 2 , and optimally doped Ba(Fe 0.94 Co 0.06 ) 2 As 2 . In all cases, we find that. is well described by a power law (T - T 0 ) -1/2 extending over a wide temperature range. Combined with the previously reported Curie-Weiss behavior of the nematic susceptibility, these results point to the mean-field character of the nematic transition, which we attribute to a sizable nematoelastic coupling that is likely detrimental to superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Pseudo-gray-scale halftone gratings for shearing and Hartmann wavefront sensors

Now in use on x-ray beamlines worldwide, shearing interferometry and Hartmann wavefront sensing provide effective feedback for measuring and optimizing high-quality beams. Conventionally, both approaches spatially modulate the beam properties (amplitude or phase) using two-tone, binary patterns, leading to deleterious diffraction effects that must be mitigated. In shearing, the presence of multiple diffraction orders affects measurement near boundaries. In Hartmann, diffraction limits the measurement point density. We demonstrate that the use of pseudo-gray-scale halftone patterns in the diffracting elements can improve the performance of both techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning

Wave energy converters (WECs) harness the untapped power of ocean waves to generate renewable energy, offering a promising solution to sustainable energy. An optimal WEC control strategy is essential to maximize power capture that dynamically adjusts system parameters in response to rapidly changing sea states. This study presents a novel control approach that leverages neural networks to estimate sea states from onboard WEC measurements such as position, velocity, and force. Using a point absorber WEC device as a test platform, our proposed approach estimates sea states in real-time and subsequently adjusts PID controller gains to maximize energy extraction. Simulation results across diverse sea conditions demonstrate that our strategy eliminates the need for external wave monitoring equipment while maintaining power capture efficiency. The results show that our neural network-based control technique can improve power capture by 25.6% while significantly reducing system complexity. This approach offers a practical alternative for WEC deployments where direct wave measurements are either infeasible or cost prohibitive.

PIDcontrol↗
DOE OSTI2024

Accelerating the Discovery of New, Single Phase High Entropy Ceramics via Active Learning

High-entropy ceramics have garnered interest due to their remarkable hardness, compressive strength, thermal stability, and fracture toughness; yet the discovery of new high-entropy ceramics (out of a tremendous number of possible elemental permutations) still largely requires costly, inefficient, trial-and-error experimental and computational approaches. The entropy forming ability (EFA) factor was recently proposed as a computational descriptor that positively correlates with the likelihood that a 5-metal high-entropy carbide (HECs) will form the desired single phase, homogeneous solid solution; however, discovery of new compositions is computationally expensive. If you consider 8 candidate metals, the HEC EFA approach uses 49 optimizations for each of the 56 unique 5-metal carbides, requiring a total of 2744 costly density functional theory calculations. Here, we describe an orders-of-magnitude more efficient active learning (AL) approach for identifying novel HECs. To begin, we compared numerous methods for generating composition-based feature vectors (e.g., magpie and mat2vec), deployed an ensemble of machine learning (ML) models to generate an average and distribution of predictions, and then utilized the distribution as an uncertainty. Here we then deployed an AL approach to extract new training data points where the ensemble of ML models predicted a high EFA value or was uncertain of the prediction. Our approach has the combined benefit of decreasing the amount of training data required to reach acceptable prediction qualities and biases the predictions toward identifying HECs with the desired high EFA values, which are tentatively correlated with the formation of single phase HECs. Using this approach, we increased the number of 5-metal carbides screened from 56 to 15,504, revealing 4 compositions with record-high EFA values that were previously unreported in the literature. Our AL framework is also generalizable and could be modified to rationally predict optimized candidate materials/combinations with a wide range of desired properties (e.g., mechanical stability, thermal conductivity).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Optimizing an Integrated Renewable-Electrolysis System

Hydrogen is a versatile energy carrier that is used in a wide variety of chemical and industrial processes. Producing hydrogen using electrolysis can enable integration of multiple sectors including electricity, heating, and industrial sectors; however, the cost of producing hydrogen from electrolysis remains a challenge for encouraging greater adoption. With growing amounts of renewable generation on the California grid, there is downward pressure on wholesale electricity prices, particularly during the afternoon from photovoltaics (PV). These lower, or even potentially negative prices, challenge the business cases for new and existing PV plants. In addition, as the grid transitions to less flexible generation, there is greater need for system flexibility. To help improve the economics for both solar PV and hydrogen production using electrolyzers, we explore the benefit of combining PV and electrolysis systems. The optimal breakeven hydrogen production cost for six unique market participation configurations is calculated at six candidate locations where PV is already installed. The six market configurations include islanded, separated, retail, net energy metering (NEM), hybrid retail/wholesale and wholesale. Using the Revenue Operation and Device Optimization Model (RODeO) model, the optimal breakeven hydrogen price over the lifetime of the equipment is calculated. The cost includes production, storage, and compression in preparation for gaseous delivery trucks. Revenue streams include the sale of hydrogen, low carbon fuel standard (LCFS) credits, renewable electricity sold to the grid, and Renewable Energy Credits (REC). The costs included are the electricity costs, capital and fixed operation and maintenance cost (FOM) for the electrolyzer, PV, and storage and compression systems as well as taxes and financing costs. In addition, cost reductions are achieved through retail and wholesale rate optimization, by which electricity is purchased at the lowest price and sold, if possible, at the highest price. For all locations, the breakeven hydrogen production cost results show that, in the order of decreasing cost, the system configurations are islanded (highest), separated, NEM, retail, hybrid retail/wholesale, and wholesale (lowest). The resulting system design balances between the capital and maintenance cost components, the operation costs (i.e., electricity costs) and the additional market revenues. The integration of solar PV and electrolysis is shown to provide a mutually beneficial relationship. For PV, integration with electrolysis offers the potential to hedge against wholesale market price volatility, and integration with electrolysis may offer the potential to defer or avoid transmission investment to deliver power to the point-of-use and instead use it on-site. When compared with SMR without considering any renewable hydrogen premiums, this study finds that PV + Electrolysis systems with current costs are likely not competitive; however, with cost reductions for electrolysis equipment consistent with DOE projections, it was found that systems with wholesale market access would be competitive, largely on account of both low capital costs and low-cost electricity. The electrolysis units can provide greater flexibility than is required based on retail rate optimization, so there is an opportunity for a utility or CAISO to increase system flexibility with PV + Electrolysis systems in return for commensurate compensation. In this way, there are potentially several solutions that fall between the hybrid configuration and the wholesale configuration that could provide sufficient compensation for a PV + Electrolysis unit to compete with SMR while also providing greater flexibility to the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Self‐Consistent Convolutional Density Functional Approximations: Application to Adsorption at Metal Surfaces

The exchange-correlation (XC) functional in density functional theory is used to approximate multi-electron interactions. A plethora of different functionals are available, but nearly all are based on the hierarchy of inputs commonly referred to as “Jacob's ladder.” This paper introduces an approach to construct XC functionals with inputs from convolutions of arbitrary kernels with the electron density, providing a route to move beyond Jacob's ladder. We derive the variational derivative of these functionals, showing consistency with the generalized gradient approximation (GGA), and provide equations for variational derivatives based on multipole features from convolutional kernels. A proof-of-concept functional, PBEq, which generalizes the PBEα framework with mathematical equation being a spatially-resolved function of the monopole of the electron density, is presented and implemented. It allows a single functional to use different GGAs at different spatial points in a system, while obeying PBE constraints. Analysis of the results underlines the importance of error cancellation and the XC potential in data-driven functional design. After testing on small molecules, bulk metals, and surface catalysts, the results indicate that this approach is a promising route to simultaneously optimize multiple properties of interest.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Meshfree Process Modeling and Experimental Validation of Friction Riveting of Aluminum 5052 to Aluminum 6061

Friction riveting (Fric-riveting) is an innovative, fast, and energy-efficient process for spot-joining metal-metal structures. Although fric-riveting has been studied experimentally in recent years, its process modeling is rarely found in the literature primarily because of the associated large material deformation, extreme thermomechanical conditions, and complex contact conditions. In this work, a mesh-free smoothed particle hydrodynamics (SPH) framework that can well handle the abovementioned numerical challenges is used to simulate the fric-riveting of AA5052 to AA6061. Predicted material morphology, multi-point temperatures, and plunge force are thoroughly validated by experimental observations. The material severe plastic deformation zone in the vicinity of the riveting zone is further predicted by the SPH model, which indicates the material mixing and potential grain refinement zone. Based on the validated model, process parameters can be optimized which yields better performance over the baseline case.

Friction riveting (fric-riveting), Smoothed partic↗