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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Surrogate multi-fidelity data and model fusion for scientific discovery and uncertainty quantification in Earth System Models

This whitepaper addresses the Earth and Environmental Systems Sciences Division (EESSD)’s predictability challenges in modeling the integrated water cycle and data-model integration. Specifically, it focuses on reducing and characterizing the uncertainty in the representation of process models for unresolved physics, either due to model resolution or limited by the physical under standing or computational efficiency, and the use of observational data for in-situ process parameter optimization within ESM. The described methods may also be used to determine the nature of responses (e.g. strength and direction), and hence to identify critical processes that drive the overall ESM responses to perturbation in the forcing

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Direct mechanistic connection between acoustic signals and melt pool morphology during laser powder bed fusion

Various nondestructive diagnostic techniques have been proposed for in situ process monitoring of laser powder bed fusion (LPBF), including melt pool pyrometry, whole-layer optical imaging, acoustic emission, atomic emission spectroscopy, high speed melt pool imaging, and thermionic emission. Correlations between these in situ monitoring signals and defect formation have been demonstrated with acoustic signals having been shown to predict pore formation with especially high confidence in recent machine learning studies. Here, in this work, time-resolved acoustic data are collected in both the conduction and keyhole welding regimes of LPBF-processed Ti-6Al-4V alloy. A non-dimensionalized Strouhal number analysis, used in whistle aeroacoustics, is applied to demonstrate that the acoustic signals recorded in the keyhole regimes can be directly associated with the vapor depression morphology. This mechanistic understanding developed from whistle aeroacoustics shows that acoustic monitoring during the LPBF process can provide a direct probe into the vapor depression dynamics and defect occurrence, especially in the keyhole regimes relevant to printing and defect formation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Demonstrating the viability of Lagrangian in situ reduction on supercomputers

Performing exploratory analysis and visualization of large-scale time-varying computational science applications is challenging due to inaccuracies that arise from under-resolved data. In recent years, Lagrangian representations of the vector field computed using in situ processing are being increasingly researched and have emerged as a potential solution to enable exploration. However, prior works have offered limited estimates of the encumbrance on the simulation code as they consider “theoretical” in situ environments. Further, the effectiveness of this approach varies based on the nature of the vector field, benefitting from an in-depth investigation for each application area. With this study, an extended version of Sane et al. (2021), we contribute an evaluation of Lagrangian analysis viability and efficacy for simulation codes executing at scale on a supercomputer. We investigated previously unexplored cosmology and seismology applications as well as conducted a performance benchmarking study by using a hydrodynamics mini-application targeting exascale computing. Here, to inform encumbrance, we integrated in situ infrastructure with simulation codes, and evaluated Lagrangian in situ reduction in representative homogeneous and heterogeneous HPC environments. To inform post hoc accuracy, we conducted a statistical analysis across a range of spatiotemporal configurations as well as a qualitative evaluation. Additionally, our study contributes cost estimates for distributed-memory post hoc reconstruction. In all, we demonstrate viability for each application — data reduction to less than 1% of the total data via Lagrangian representations, while maintaining accurate reconstruction and requiring under 10% of total execution time in over 90% of our experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Diagnostic and predictive capabilities of the TCR digital platform

The Transformational Challenge Reactor program is leveraging additive manufacturing technologies to fabricate the nuclear components required to assemble a microreactor core. Compared with traditional manufacturing processes, additive manufacturing allows for direct observation of the interior of the component during manufacturing. This unique capability promises significant possibilities for creating a new paradigm for nuclear component qualification by leveraging in-situ process data. This report describes FY21 efforts to predict material tensile properties based on data collected during the laser powder bed fusion printing process. The primary focus of this report is the test campaign designed to generate the large quantities of training data required to implement artificial intelligence algorithms that can predict these material properties. Preliminary prediction results and a demonstration of the overall data collection, analysis, and visualization pipeline are also provided.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

ThermoPore: Predicting part porosity based on thermal images using deep learning

Part qualification is often a critical and labor-intensive process in additive manufacturing, particularly in the detection of defects such as porosity, which stands to benefit significantly from advancements in machine learning. We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a R 2 score of 0.57 and our model for porosity localization produced an average Intersection over Union (IoU) score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity “Digital Twins” based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

Deep learning↗
DOE OSTI2024

Achieving environmental stability in an atomically thin quantum spin Hall insulator via graphene intercalation

Atomic monolayers on semiconductor surfaces represent an emerging class of functional quantum materials in the two-dimensional limit — ranging from superconductors and Mott insulators to ferroelectrics and quantum spin Hall insulators. Indenene, a triangular monolayer of indium with a gap of ~ 120 meV is a quantum spin Hall insulator whose micron-scale epitaxial growth on SiC(0001) makes it technologically relevant. However, its suitability for room-temperature spintronics is challenged by the instability of its topological character in air. It is imperative to develop a strategy to protect the topological nature of indenene during ex situ processing and device fabrication. Here we show that intercalation of indenene into epitaxial graphene provides effective protection from the oxidising environment, while preserving an intact topological character. Our approach opens a rich realm of ex situ experimental opportunities, priming monolayer quantum spin Hall insulators for realistic device fabrication and access to topologically protected edge channels.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Facilitating Staging-based Unstructured Mesh Processing to Support Hybrid In-Situ Workflows

In-situ and in-transit processing alleviate the gap between the computing and I/O capabilities by scheduling data analytics close to the data source. Hybrid in-situ processing splits data analytics into two stages: the data processing that runs in-situ aims to extract regions of interest, which are then transferred to staging services for further in-transit analytics. To facilitate this type of hybrid in-situ processing, the data staging service needs to support complex intermediate data representations generated/consumed by the in-situ tasks. Unstructured (or irregular) mesh is one such derived data representation that is typically used and bridges simulation data and analytics. However, how staging services efficiently support unstructured mesh transfer and processing remains to be explored. This paper investigates design options for transferring and processing unstructured mesh data using staging services. Using polygonal mesh data as an example, we show that hybrid in-situ workflows with staging-based unstructured mesh processing can effectively support hybrid in-situ workflows, and can significantly decrease data movement overheads.

data-driven↗
DOE OSTI2025

Nickel Sulfide-Nanowire-Filled Carbon Nanotubes as an Efficient Overall Water Splitting Electrocatalyst

Pursuing stable, efficient, and cost-effective nanostructured bifunctional electrocatalysts is crucial for advancing the electrochemical water splitting process and enabling clean hydrogen energy production. In recent years, considerable efforts have focused on developing highly efficient and durable commercial electrocatalysts for the oxygen evolution reaction (OER) and overall water splitting (OWS). This research introduces an OWS electrocatalyst-nickel sulfide-filled carbon nanotubes grown on a carbon cloth substrate (Ni 3 S 2 @CNTs/CC), synthesized via a one-step in-situ process. The synergistic integration of metal sulfide (Ni 3 S 2 ) and carbon nanotubes provides abundant active sites for catalytic reactions, ensuring a robust composite nanostructure with enhanced durability. Furthermore, the electrocatalytic performance for OER and OWS has been significantly improved by a simple acid treatment to the electrocatalysts, which introduces physical and chemical defects, particularly oxygen functional groups (the acid-treated sample is termed as Ni 3 S 2 @CNTs/CC-AT). As OER electrocatalysts, Ni 3 S 2 @CNTs/CC and Ni 3 S 2 @CNTs/CC-AT present overpotentials of 304 and 200 mV, respectively, for achieving a current density of 10 mA/cm 2 in the OER process. Furthermore, for complete water splitting in 1.0 M KOH electrolyte, Ni 3 S 2 @CNTs/CC and Ni 3 S 2 @CNTs/CC-AT exhibit potentials of 1.63 and 1.44 V, respectively, to achieve a current density of 10 mA/cm 2 when employed as both anode and cathode. Moreover, Ni 3 S 2 @CNTs/CC and Ni 3 S 2 @CNTs/CC-AT demonstrate durable nature for 22 and 20 h durability in the OER and OWS processes, respectively, offering a promising alternative to ruthenium- and iridium-based electrocatalysts for electrochemical hydrogen production through water splitting. In conclusion, the in-situ synthesis method and acid treatment strategy described in this research are promising approaches to fabricating high-performance encapsulated carbon-nanotube-based electrocatalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effect of threshold parameters on infrared segmentation methods for porosity detection in electron beam powder bed fusion

In-situ process monitoring has seen significant interest in additive manufacturing to address qualification and certification goals. This is especially prevalent in metal powder bed fusion processes such as electron beam powder bed fusion (PBF-EB), with layer-wise infrared imaging being commonly used to detect defects. Here, this work compares two different segmentation methods (static thresholding and statistical thresholding) used for detecting porosity from in-situ infrared imaging data for PBF-EB. Samples were manufactured at a variety of focus offset values to induce porosity. Then, the segmented infrared images were compared to ex-situ X-ray computed tomography scans, which served as a ground-truth reference for objective evaluation. Through this analysis framework, the influential parameters, static threshold and N-value (number of standard deviations above the mean pixel value), respectively, for both image segmentation methods were analyzed and compared for their effects on porosity detection. With optimal parameter settings, the two methods had similar porosity detection performance, but the statistical method performed better under a larger variety of parameter settings.

Infrared imaging↗
DOE OSTI2023

Autonomous x-ray scattering

Autonomous experimentation (AE) is an emerging paradigm that seeks to automate the entire workflow of an experiment, including—crucially—the decision-making step. Beyond mere automation and efficiency, AE aims to liberate scientists to tackle more challenging and complex problems. We describe our recent progress in the application of this concept at synchrotron x-ray scattering beamlines. We automate the measurement instrument, data analysis, and decision-making, and couple them into an autonomous loop. We exploit Gaussian process modeling to compute a surrogate model and associated uncertainty for the experimental problem, and define an objective function exploiting these. We provide example applications of AE to x-ray scattering, including imaging of samples, exploration of physical spaces through combinatorial methods, and coupling to in situ processing platforms These uses demonstrate how autonomous x-ray scattering can enhance efficiency, and discover new materials.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

DTLMod: A simulation framework for in situ workflow optimization

In situ processing workflows have become essential for coping with the explosion in data volume and velocity in large-scale scientific computing, providing domain scientists with early insights at runtime. Multiple frameworks implement this paradigm through a data transport layer (DTL), offering different data access modes and deployment schemes, but researchers currently lack the appropriate tools to assess design and deployment options before committing to costly real experiments. We introduce DTLMod, an open-source simulated DTL that enables performance evaluation of in situ workflow configurations at scale. Built on SimGrid, it links into any SimGrid-based simulator and is available in C++ and Python. We evaluate DTLMod along four axes: scalability (tens of thousands of simulated processes across interconnected clusters in seconds, with linear memory scaling), versatility (three implementation variants trading fidelity for speed), accuracy (simulated times faithfully reflecting real behavior), and practical utility (two use cases demonstrating evidence-based workflow design decisions).

Suter, Fred [ORNL] (ORCID:0000000319021955)↗
DOE OSTI2023

ON PROCESS STABILITY IN WAAM-CMT OF ALUMINUM ALLOYS

Wire-arc additive manufacturing (WAAM) has become a cost-efficient metal additive manufacturing process. However, depositing aluminum with WAAM is challenging due to its sensitivity to heat input (linear energy density), which can cause undesirable surface topology waviness if not controlled. Thus, a process window is needed that can produce stable geometry and deposition conditions while minimizing production times. In this study, 5183 aluminum alloy wire is used to deposit 10-layer walls with varying wire feed speeds (WFS) and traverse speeds (TS) (at a constant WFS/TS ratio) and varying interpass temperature (IPT). In-situ process data consisting of optical contact-tip-workpiece-distance (CTWD) and current/voltage measurements are collected to determine process condition stability throughout the build. Part geometry is measured using a 3D scanner and build porosity is characterized via digital X-ray. A process window is identified that produces stable surface topology and process conditions at a minimal production time.

aluminum alloy↗
DOE OSTI2023

Methods for rapid identification of anomalous layers in laser powder bed fusion

In situ process monitoring is a key requirement for increased industry acceptance of powder bed Additive Manufacturing. As sensing technologies increase in maturity, attention must also be given to effective data exploration techniques. These data are often high-resolution and multi-modal, with each build consisting of thousands of layers. Here, in this paper, the authors propose two methods enabling users to rapidly identify layers of interest within a build. Both methods leverage results from deep learning based segmentations of in situ powder bed images. The first method is an unsupervised “reverse layer search” algorithm while the second method uses supervised machine learning.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Online task-space motion control for positioner-coordinated multi-robot manufacturing systems

Incorporating multiple robotic manipulators into large-scale manufacturing systems enhances production efficiency and expands manufacturing capabilities beyond those of single-robot systems. Workpiece positioners in robotic manufacturing have demonstrated significant benefits for process optimization, but coordination strategies for multi-robot systems with shared positioners have received limited attention. This work presents a task-space coordinated trajectory-tracking control framework for multi-robot manufacturing systems, in which robots coordinate their motions within a shared, dynamic workpiece positioning frame. A workpiece positioner actively adjusts the pose of the manufactured component to enable greater operational concurrency and improve overall production efficiency. The proposed motion-coordination scheme employs a distributed and scalable architecture, supporting coordination across heterogeneous multi-robot systems. Two optimization methodologies are introduced to manage kinematic redundancies and maintain continuous, near-optimal operation throughout the manufacturing process. The first strategy exploits a task-space dimensionality reduction to achieve locally optimal configurations by leveraging symmetry-axis rotations of the tool. The second strategy utilizes the workpiece positioner to drive the coordinated robots toward stable and kinematically favorable configurations. For both optimization strategies, multiple objectives are defined to improve key performance metrics, including manipulability, configuration consistency, proximity to mechanical limits, and motion efficiency. Addressing a key limitation of existing coordination approaches, the framework is designed around online setpoint modification, allowing coordinated robots to respond effectively to in-situ process feedback. The proposed control framework is validated using the Robot Operating System (ROS) middleware on a combination of physical and simulated multi-robot system hardware.

Arbogast, Alex [ORNL] (ORCID:0000000154740723)↗
DOE OSTI2021

Skin factor and potential formation damage from chemical and mechanical processes in a naturally fractured carbonate aquifer with implications to CO 2 sequestration

Here, in this study, we investigate formation damage due to acidization and water injection tests into the naturally fractured carbonate Middle Duperow Formation at Kevin Dome, Montana, potentially diminishing the chance of a future successful Geological Carbon Sequestration (GCS) project. Multiple well-test analytical models, correlated with core description and lithology data, are used to determine flow behavior and communication between the water injection interval and surrounding formations. An improved three-dimensional (3D) geologic model with dual-continuum matrix and fracture properties is constructed based on most recent seismic, core, and water sample measurements. Brine injection is simulated to verify the interpretation from the analytical models, followed by CO 2 injection simulation. Geochemical calculations are performed to understand the in-situ processes that led to formation damage. Our findings suggest: (1) there are two possible scenarios that could lead to a positive total effective skin factor and permeability decline: partial penetration and formation damage; (2) analytical models indicate a positive total skin factor, contradicting results of a previous study suggesting that the well was mildly stimulated; (3) numerical simulation supports the formation damage hypothesis by matching the pressure buildup observed during the latter two brine injection tests; (4) several mechanical and chemical processes may have occurred during injection to clog the matrix/fracture system: anhydrite fines migration and/or calcite precipitation. We then make preventative suggestions for future GCS projects into carbonate reservoirs and remediation recommendations for GCS operation at the Kevin Dome.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Predicting melt pool depth and grain length using multiple signatures from in-situ single camera two-wavelength imaging pyrometry for laser powder bed fusion

In laser powder bed fusion (LPBF), the in-situ process signatures are known to have a direct correlation with the microstructural properties of the solidified melt pool (MP). It is known that the MP cooling and heating rates, and laser processing parameters can critically determine the grain structure and thereby affect the part properties. The objective of this work is to study the feasibility of using in-process, high-speed imaging pyrometry for evaluating the solidified MP properties “below” the surface, such as depth and microstructural properties. To accomplish this, we employ an in-house single camera-based two-wavelength imaging pyrometry (STWIP) system for monitoring the printing of single-scan tracks with Inconel 718 on a commercial LPBF printer (EOS M290). Further, the lab designed STWIP system is a coaxial high-speed (>10,000 fps) imaging system capable of monitoring MP temperature, morphology, and intensity profiles. The temperature measurements from STWIP are emissivity independent. The STWIP measured MP signatures of the printed tracks are correlated with the ex-situ microscopy characterized MP depth and the average grain lengths. From the data analysis, using support vector machine (SVM)-based regression models, we found that the MP temperature signatures are crucial for an accurate prediction of MP depth and the grain length, thus validating the novelty and necessity of the developed in-situ monitoring methods and analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Synthesis, Processing, and Use of Isotopically Enriched Epitaxial Oxide Thin Films

Isotopic engineering has emerged as a key approach to study the nucleation, diffusion, phase transitions, and reactions of materials at an atomic level. It aims to uncover mass transport pathways, kinetics, and operational and failure mechanisms of functional materials and devices. Understanding these phenomena leads to deeper insights into important physical processes, such as the transport of ions in energy conversion and storage devices and the role of active sites and supports during heterogeneous catalytic reactions. Likewise, isotopic engineering is being pursued as a means of modifying functionality to enable future technological applications. In this Account, we summarize our recent work employing isotope labeling (e.g., 18 O 2 and 57 Fe) during thin film synthesis and postgrowth processing to reveal growth mechanisms, defect chemistry, and elemental diffusion under working and extreme conditions. Isotope-resolved analysis techniques with nanometer-scale spatial resolution, such as time-of-flight secondary ion mass spectrometry and atom probe tomography, facilitate the accurate quantification of isotopic placement and concentration in our well-defined heterostructures with precisely positioned, isotope-enriched layers. By measuring the nanometer-scale redistribution between natural abundance and isotopically enriched oxygen layers during the deposition of Fe 2 O 3 and Cr 2 O 3 by molecular beam epitaxy, we identified intermixing processes driven by surface adatoms occurring both at the film growth surface and within the first few layers below the surface. Further insights into synthesis mechanisms were gained by studying the tungsten oxide thin films grown by evaporating WO 3 powder in the presence of background 18 O 2 , revealing minimal incorporation of background oxygen during the film formation process. Thermal and radiation-enhanced diffusion in epitaxial Fe and Cr oxides were precisely tracked using 18 O and 57 Fe tracer layers incorporated into model epitaxial oxide thin films. This approach has allowed us to access thermal diffusion behavior at lower temperatures than previously measured, revealing a potential changeover in diffusion mechanism. Understanding radiation-enhanced diffusion in model oxides that represent the surface layers on the structural components of nuclear reactors informs our understanding of their corrosion behavior under irradiation. Isotopic labeling can also provide unique insights into the surface exchange reactions and defect chemistry of electrocatalysts. For instance, tracking the change in 18 O concentration at the surface of an epitaxial LaNiO 3 thin film after the electrocatalytic oxygen evolution reaction revealed the participation of lattice oxygen, confirming a hypothesis that had been proposed previously. Lastly, we highlight a new direction wherein we perform in situ processing studies utilizing isotopic tracers in conjunction with model epitaxial thin films within the atom probe tomography instrument. Additionally, this Account illustrates the great potential of isotopic engineering to enable fundamental mechanistic insights into physical processes and engineer functional properties in epitaxial films, heterostructures, and superlattices.

36 MATERIALS SCIENCE↗