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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Parametric reduced-order modeling for component-oriented treatment and localized nonlinear feature inclusion

Abstract We propose coupling a physics-based reduction framework with a suited response decomposition technique to derive a component-oriented reduction (COR) approach, which is suitable for assembly systems featuring localized nonlinearities. Dependencies on influencing parameters are injected into the reduced-order model (ROM), thus ensuring robustness and validity over a domain of parametric inputs, while capturing nonlinear effects. The implemented approach employs individual component modes to capture localized features while additionally relying on reduced modes of a global nature to approximate the system’s dynamics accurately. The global modes are derived from a linear monolithic system, defined as a result of a coordinate separation scheme, which permits the proposed COR-ROM to naturally couple the response between linear and nonlinear subdomains. The derived low-order representation utilizes a proper orthogonal decomposition projection and is additionally reinforced with the inclusion of a hyper-reduction technique to capture the underlying high-fidelity model response while providing accelerated computations. The resulting approach is exemplified in the synthetic case studies of a four-story shear frame with multiple nonlinear regions driven by hysteresis and a large-scale kingpin connection featuring plasticity.

Vlachas, Konstantinos (ORCID:000000029124219X)↗
DOE OSTI2025

Optimizing inference of segmentation on high-resolution images in MLExchange

MLExchange is a machine learning (ML) operations platform providing web user-interfaces (UIs) for data visualization and analysis pipelines at synchrotron facilities. Among these UIs is the segmentation app which helps synchrotron users utilize ML algorithms to automatically segment high-resolution scientific images with minimal manual annotation effort. In this work, we share code optimizations that significantly speed up the segmentation inference workflow of large data in short time. By optimizing the sequence of CPU-GPU data transfers and introducing CPU parallelization to key operations, we improve the per-device, per-image frame computational efficiency and observe close to 3×$$\times$$ speedup over the original segmentation inference workflow run time when utilizing a single GPU. Further adaptations enabling multi-GPU inference yield more than 40×$$\times$$ speedup with 100 GPUs compared to the optimized single GPU inference workflow. This acceleration of the segmentation inference workflow will provide MLExchange users with easy access to segmentation results with little wait time.

Lu, Shizhao↗
DOE OSTI2020

Quality Control of Additively Manufactured Metallic Structures with Machine Learning of Thermography Images

Additive manufacturing (AM) of high-strength metals is currently based on the laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Quality control (QC) requires nondestructive evaluation of actual AM structures. Flash thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, the detection sensitivity of this method is limited by noise. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatial–temporal blind source separation and spatial–temporal sparse dictionary learning methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced by the LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. Finally, the ML methods with higher accuracy require a longer runtime. However, this runtime is sufficiently short to perform QC within a realistic time frame.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Ending the Energy-Poverty Nexus: An Ethical Imperative for Just Transitions

Arguments for a just transition are integral to debates about climate change and the drive to create a carbon-neutral economy. There are currently two broad approaches rooted in ethics and justice for framing just energy transitions. The first can be described as internal to the transition and emphasizes the anticipation, assessment, and redressing of harms created by the transition itself and the inclusion in transition governance of groups or communities potentially harmed by its disruptions. In this article, we propose a second approach to ethics and justice in an energy transition, which we describe as systemic or societal in scope. This approach complements attention to the proximate dynamics and impacts of the transition process with a focus on the distant societal and economic outcomes the transition brings into being and how they compare to conditions prior to the transition. It poses the question: do the transformative social, economic, and technological changes wrought by energy systems create more just societies and economies, or do they instead reinforce or recreate long-standing injustices and inequalities? We illustrate this approach with an assessment of one of the most significant existing forms of energy injustice: the energy-poverty nexus. We argue that the energy-poverty nexus reflects configurations of socio-energy systems that create complex, extractive feedbacks between energy insecurity and economic insecurity and, over time, reinforce or exacerbate poverty. We further argue that just energy transitions should work to disentangle these configurations and re-design them so as to create generative rather than extractive feedbacks, thus ending the energy-poverty nexus and creating long-term outcomes that are more just, equitable, and fair.

14 SOLAR ENERGY↗
DOE OSTI2022

Statistical Estimation of Strain Using Spatial Correlation Functions

Ex-situ estimation of strains from deformed micrographs is not possible as there are no persistent features which can be tracked. Two point spatial statistics enable the rigorous quantification of spatial patterns in heterogeneous media. In this paper, we propose a novel method for estimating strains directly from dissimilar micrographs using a continuum mechanics approach. Rather than operating directly on images from sequential frames, as is done in digital image correlation, we operate on different microstructure realizations. This is made possible by comparing the spatial autocorrelation maps of deformed and undeformed micrographs rather than direct comparison of images. Additionally, a Bayesian framework is proposed for quantifying uncertainty. We first illustrate the efficacy of this method on speckle pattern images from digital image correlation experiments. Then, we demonstrate that the method is capable of operating on dissimilar micrographs using deformed synthetic binary microstructures. Finally, we present a case study on polycrystalline additively manufactured 316L deformed via tension. The proposed method works well and we discuss implications and limitations of the presented work.

36 MATERIALS SCIENCE↗
DOE OSTI2024

L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration

Abstract Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm 2 and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Laser Speckle Pattern Stability in Hypersonic Regimes for Experimental Mechanics and Metrological Studies

Here, an empirical study assessing the suitability of laser speckle patterns for use in experimental mechanics research conducted at hypersonic conditions is presented. Speckle patterns generated via coherent optical scattering from a gold-coated graphite surface inserted into a Mach 10 air stream were collected and analyzed using Computer-Aided Speckle Interferometry using two methodologies: a typical spanwise approach involving increasing temporal span between image pairs, and a sequential approach that utilizes a combination of temporal re-referencing and spatial resampling. Results were characterized for spatial-temporal stability and variance. Our observations demonstrate that optical methods relying on stable and properly formed speckle patterns can be used in a hypersonic regime at various Reynolds numbers and common camera frame rates, but the technique is sensitive to the conditions of the airflow and the processing methodology chosen.

42 ENGINEERING↗
DOE OSTI2026

Impedance Based Fixture Neutralization (FINE) Method to Replicate Field Environments in Laboratory Settings

Test article vibration test responses will vary from one test laboratory to another due to differences in shaker and fixture dynamic characteristics. This is also seen between the field and laboratory due to the differing dynamic characteristics between field assembly loading and boundary conditions compared to the laboratory configuration. This work introduces a technique called Impedance Fixture Neutralization which customizes input forces to cause consistent responses for the Device Under Test across different vibration testing conditions. The customized force neutralizes the dynamic variations between configurations for the Device Under Test. The device responses can be replicated in several situations: when using a different fixture with the same attachment points, when the test article mounting location on the fixture changes, when the force location changes, or any combination of these situations. Impedance Fixture Neutralization uses the uncoupled dynamic characteristics of the test article and excitation fixtures to customize the input thereby causing the same test article responses between two mounting configurations (either field to laboratory or two different laboratory configurations). The application of the technique is shown using an analytical model of a two-beam system and analytically using experimental FRFs from plate and frame component hardware characterization tests. In both cases a device under test is attached to two test fixtures wherein the dynamic differences in the configurations are neutralized.

Laboratory↗
DOE OSTI2023

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Evaluating the intrinsic resistance to balling of alloys: A High-throughput physics-informed and data-enabled approach

To date, the vast majority of work on metal additive manufacturing (AM) has been framed in terms of the need to tune processing conditions for a particular AM technology in order to print conventional alloys, oftentimes developed for fabrication methods other than AM. This approach overlooks the fact that historically, many engineering alloy system has been designed with a particular processing route in mind, e.g., ingot metallurgy, powder metallurgy, rapid quenching, etc. There are thus significant opportunities to design alloys specifically for AM. A key challenge is that alloy design requires performance metrics that can be optimized by exploring the alloy chemistry space. Here, we present a study in which we examine how intrinsic thermophysical properties can be used to estimate performance metrics related to the behavior of a solidifying metal droplet under AM-relevant conditions. By identifying these intrinsic properties, it is possible to directly incorporate ‘intrinsic printability’, specifically ‘intrinsic resistance to balling’, into AM-focused alloy design.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Toward extreme high-temperature supercritical CO 2 power cycles: Leakage characterization of ceramic 3D-printed heat exchangers

We report future supercritical carbon dioxide (sCO 2 ) Brayton power cycles demand high-performance gas-to-gas heat exchangers (HXs) operating under extreme temperature and pressure conditions at which most existing superalloy materials fail to function safely. Ceramic HXs are deemed excellent candidates for advanced sCO 2 power plants as they can withstand high-temperature working environments. Particularly, ceramic 3D printing enables compact HX topologies employing complex and efficient heat transfer features. However, ceramic 3D-printed walls separating hot and cold flow streams are susceptible to a through-plane leakage inherent to a powder-based manufacturing process, including ceramic 3D printing. A potential leakage through ceramic separating walls poses a significant challenge in developing reliable ceramic 3D-printed HXs and could deteriorate thermal performance. In this study, various parameters, including feedstock slurry, 3D-printing direction, and post-processing conditions, are considered, for the first time, to characterize the argon gas leakage rate associated with alumina 3D-printed parts. Three 3D-printed ceramic structures of flat plates, curved tubes, and small-scale plate-and-frame HXs with various thicknesses are systematically studied to determine powder and ceramic 3D-printing conditions to eliminate the through-plane leakage. The results showed that an alumina 3D-printed plate with a thickness of 0.75 mm demonstrates a permeability of 6 x 10 -4 milli-darcy. An alumina 3D-printed tube with a wall thickness of 0.9 mm revealed a permeability of 9.6 x 10 -7 milli-darcy. Furthermore, leakage test results of functional 3D-printed modules showed a dependency on the 3D-printing direction. Particularly, alumina cell-scale HXs employing 1.5-mm-thick horizontal and vertical 3D-printed separating walls demonstrated impermeability and gas permeability of 7.2 x 10 -5 millidarcy, respectively. Insights gained from the present study facilitate the development of complex ceramic 3D-printed HXs and other balance of plant components for next-generation high-temperature high-pressure sCO 2 power cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Actinium–DOTA coordination in water from hybrid ML/MM: Structure, free energies, and water-exchange pathways

Quantitative simulation of trivalent ƒ-block chelates in water remains challenging because bonded and non-bonded force-field models make different approximations for coordination structure, exchange dynamics, and ion–ligand interactions in highly charged systems. Here, we develop a hybrid machine-learning/molecular-mechanics (ML/MM) framework for Ac 3+ –DOTA in explicit solvent by training an E(3)-equivariant neural network potential (MACELES) on mechanically embedded QM/MM data for Ac aquo and Ac–DOTA species and coupling it to NAMD 2.14 with particle-mesh Ewald electrostatics. Nanosecond ML/MM trajectories remain numerically stable and preserve chelate integrity, yielding a compact DOTA inner shell with an inner-sphere water coordination number of CN Ac,O w ≈ 1.7 arising from a dynamic equilibrium between one- and two-water states (37.5% and 59.9% of frames; three waters 2.5%). A 5 ns potential of mean force shows two low-lying basins at CN Ac,O w ≈ 1 and CN Ac,O w ≈ 2. DFT end-state free energies are consistent with the ML/MM profile, and DFT minimum-energy paths provide a qualitative electronic-structure reference for the observed basin connectivity. State-resolved kinetics reveal picosecond water-exchange pathways that couple hydration changes to transient DOTA arm fluctuations, and training-set comparisons show that temperature-matched Ac–DOTA data optimize energy/force accuracy while more diverse solvated data improve charge prediction. Overall, the present hybrid ML/MM model provides a practical description of Ac 3+ –DOTA hydration thermodynamics and short-time exchange behavior in explicit water at MD-like cost.

Actinium↗
DOE OSTI2021

Quantum field theory with the generalized uncertainty principle II: Quantum Electrodynamics

Highlights: • Relativistic Generalized Uncertainty Principle gives Frame independent minimum length. • Quantum Gravity modified Dirac equation from Modified Klein–Gordon Equations. • There exists a Quantum Gravity modified Lagrangian for a spinor field theory. • Feynman vertices contain 2 fermions & up to 5 gauge bosons are allowed. • Minimum length modifies the amplitude of Electrodynamic electron–muon scattering. Continuing our earlier work on the application of the Relativistic Generalized Uncertainty Principle (RGUP) to quantum field theories, in this paper we study Quantum Electrodynamics (QED) with minimum length. We obtain expressions for the Lagrangian, Feynman rules and scattering amplitudes of the theory, and discuss their consequences for current and future high energy physics experiments. We hope this will provide an improved window for testing Quantum Gravity effects in the laboratory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Fracton phases via exotic higher-form symmetry-breaking

We study p-string condensation mechanisms for fracton phases from the viewpoint of higher-form symmetry, focusing on the examples of the X-cube model and the rank-two symmetric-tensor scalar charge theory. This work is motivated by questions of the relationship between fracton phases and continuum quantum field theories, and also provides general principles to describe p-string condensation independent of specific lattice model constructions. We give a perspective on higher-form symmetry in lattice models in terms of cellular homology. Applying this perspective to the coupled-layer construction of the X-cube model, we identify a foliated 1-form symmetry that is broken in the X-cube phase, but preserved in the phase of decoupled toric code layers. Similar considerations for the scalar charge theory lead to a framed 1-form symmetry. These symmetries are distinct from standard 1-form symmetries that arise, for instance, in relativistic quantum field theory. Here, we also give a general discussion on interpreting p-string condensation, and related constructions involving gauging of symmetry, in terms of higher-form symmetry.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

General tensor structure for electron scattering in terms of invariant responses

The general structure of semi-inclusive polarized electron scattering from polarized spin-1/2 targets is developed for use at all energy scales, from modest-energy nuclear physics applications to use in very high energy particle physics. The leptonic and hadronic tensors that enter in the formalism are constructed in a general covariant way in terms of kinematic factors that are frame dependent but model independent and invariant response functions which contain all of the model-dependent dynamics. In the process of developing the general problem the relationships to the conventional responses expressed in terms of the helicity components of the exchanged virtual photon are presented. For semi-inclusive electron scattering with polarized electrons and polarized spin-1/2 targets one finds that 18 invariant response functions are required, each depending on four Lorentz scalar invariants. Additionally it is shown how the semi-inclusive cross sections are related via integrations over the momentum of the selected coincidence particle and sums over open channels.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Realizing efficient operations of Ni-cermet-based fuel cells on hydrocarbons via an in situ self-assembled metal/oxide nano-heterostructured catalyst

Operating Ni-cermet-based fuel cells on hydrocarbons is charming by largely hindered by poor coking tolerance and severe deterioration. Here, in this study, we report an effective metal/oxide nano-heterostructured catalyst with a nominal composition of Pr 0.95 Ru 0.05 O 2-δ (PRO), which is in situ self-assembled to a Pr 0.95 Ru 0.05-x O 2-δ oxide frame coated with Ru metallic nanoparticles (denoted as Ru/PRO) under the operation condition. When applied to the Ni-cermet (Ni-YSZ) anodes, the cells achieve decent peak power densities of 1.784 and 1.870 W cm -2 on CH 4 and C 3 H 8 with only 3% H 2 O at 750 °C, respectively. Moreover, the cells with Ru/PRO-coated anode demonstrate excellent durability when operated on CH 4 for ~ 220 h and C 3 H 8 for ~115 h. It is demonstrated that the Ru/PRO generates hydroxyl species that react with carbon species, followed by the formation of COH intermediates on Ni anode surfaces for removing the coking, as confirmed by experiments and computations.

30 DIRECT ENERGY CONVERSION↗