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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Real‐Time Optical Process Monitoring for Structure and Property Control of Aerosol Jet Printed Functional Materials

Abstract Aerosol jet printing is a popular digital additive manufacturing method for flexible and hybrid electronics, but it lacks sophisticated real‐time process control schemes that would enable more widespread adoption in manufacturing environments. Here, an optical measurement system is introduced to track the aerosol density upstream of the printhead. The measured optical extinction, combined with the aerosol flow rate, is directly related to deposition rate and accurately predicts functional materials properties such as the electrical resistance of printed graphene films. This real‐time system offers a compelling solution for process drift and batch‐to‐batch variability, rendering it a valuable tool for both real‐time control of aerosol jet printing and fundamental studies of the underlying process science.

Tafoya, Rebecca R.↗
DOE OSTI2021

A digital holography ex situ measurement characterization of plasma-exposed surface erosion from an electrothermal arc source

Digital holography has been proposed to fulfill a need for an imaging diagnostic capable of in situ monitoring of surface erosion caused by plasma–material interaction in nuclear fusion devices. A digital holography diagnostic for 3D surface erosion measurement has been developed at Oak Ridge National Laboratory with the goal of deployment on a plasma device. A proof-of-concept in situ demonstration is planned which would involve measurement of plasma erosion on targets exposed to an electrothermal arc source. This work presents the results of an ex situ characterization of the capability and limitations of holographic imaging of targets exposed to the arc source. Targets were designed to provide a fiducial for comparison of deformed and unaffected areas. The results indicated that the average net erosion was ~150 nm/plasma exposure, which is expected to be within the diagnostic’s measurement capacity. Surface roughness averages determined by holographic image analysis showed good agreement with measurements taken with a profilometer. Finally, the limit of the holography diagnostic’s x–y spatial resolution was characterized by comparison with scanning electron microscope imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Object-space optimization of tomographic reconstructions for additive manufacturing

Volumetric 3D printing motivated by computed axial lithography enables rapid printing of homogeneous parts but requires a high dimensionality gradient-descent optimization to calculate image sets. Here we introduce a new, simpler approach to image-computation that algebraically optimizes a model of the printed object, significantly improving print accuracy of complex parts under imperfect material and optical precision by improving optical dose contrast between the target and surrounding regions. Quality metrics for volumetric printing are defined and shown to be significantly improved by the new algorithm. The approach is extended beyond binary printing to grayscale control of conversion to enable functionally graded materials. The flexibility of the technique is digitally demonstrated with realistic projector point spread functions, printing around occluding structures, printing with restricted angular range, and incorporation of materials chemistry such as inhibition. Finally, simulations show that the method facilitates new printing modalities such as printing into flat, rather than cylindrical packages to extend the applications of volumetric printing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improved digital construction binder solution utilizing calcium aluminate additive for Infrastructure Scale Additive Manufacturing

Oak Ridge National Laboratory (ORNL) worked with Kerneos Inc., a Division of Imerys USA, Inc. to develop an improved digital construction binder using calcium aluminate additives produced by Kerneos along with locally available concrete materials. When the two-stage (2K) binder formulation was added to commercially available Portland Limestone Cement and sand to form a digital construction mortar, its strength exceeded a number of commercially available digital construction binders both at early and later ages. At the same time, the developed formulation showed precise control of setting time after addition and showed very low shrinkage making it a strong, reliable, and easily customizable alternative to fixed proprietary digital construction binders.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Material Identification Using Dual Energy X-ray Absorptiometry

Two implementations of dual energy X-ray absorptiometry were studied to identify materials using X-ray attenuation data taken with the Digital Radiography and Computed Tomography (DRCT) systems that were developed for the Recovered Chemical Materiel Directorate (RCMD). Maitrejean et al.’s approach utilizes eigen effects through Principal Component Analysis, while Osipov et al.’s approach proposed a physics-based method. Both approaches approximate mass attenuation coefficients of materials as a linear combination of basis functions (eigen effects) or physics-based equations. A set of coefficients {a 1 , a 2 , a 3 } or {B, D} were found by parameter optimization in EXCEL Solver. The identification parameters, {$\frac{a_{2}}{a_{1}}$, $\frac{a_{3}}{a_{1}}$} or estimated effective atomic number $\hat{Z}$ from {B, D}, were calculated to identify material of an aluminum 8 step wedge and a steel 8 step wedge in X-ray radiography images taken by a DRCT system. Maitrejean et al.’s approach was unable to provide reliable $\frac{a_{3}}{a_{1}}$ ratio values for identification of materials. Osipov et al.’s approach was found to be more robust in identify materials with a semi-empirical formula derived from test results in this study.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Tunable analog thermal material

Naturally-occurring thermal materials usually possess specific thermal conductivity ($\textit{κ}$), forming a digital set of $\textit{κ}$ values. Emerging thermal metamaterials have been deployed to realize effective thermal conductivities unattainable in natural materials. However, the effective thermal conductivities of such mixing-based thermal metamaterials are still in digital fashion, i.e., the effective conductivity remains discrete and static. Here, we report an analog thermal material whose effective conductivity can be in-situ tuned from near-zero to near-infinity $\textit{κ}$. The proof-of-concept scheme consists of a spinning core made of uncured polydimethylsiloxane (PDMS) and fixed bilayer rings made of silicone grease and steel. Thanks to the spinning PDMS and its induced convective effects, we can mold the heat flow robustly with continuously changing and anisotropic $\textit{κ}$. Our work enables a single functional thermal material to meet the challenging demands of flexible thermal manipulation. It also provides platforms to investigate heat transfer in systems with moving components.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Digital image correlation and infrared thermography data for seven unique geometries of 304L stainless steel

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating and the virtual fields method, MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods—specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models—we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations. Commensurate extensometer strain data from tensile dog bones is provided as well for comparison. We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Comparative Study on Representativeness and Stochastic Efficacy of Miniature Tensile Specimen Testing

Here, in this article, a miniature dog bone tensile coupon design was tested against the existing ASTM standard specimen design. Specimens were prepared from commercially sourced austenitic stainless steel 304 alloy, and a defect-ridden additively manufactured 304L alloy was studied. By utilizing a tensile specimen design that is 1/230th volume of the smallest ASTM E8-04(2016), Standard Test Methods for Tension Testing of Metallic Materials, dog bone specimen, coupled to a digital image correlation (DIC) setup, case studies were performed to compare tensile property measurements and strain field evolution. Whereas yield strength measurements were observed to be similar, post-yield, the ultimate strength measurements and ductility measurements from the miniature specimens were observed to be higher than the ASTM specimen design. Although the strength measurements were comparable, the strain evolution was found to differ in the miniature specimens. Studies to assess effects of varying thickness and defect population were also pursued on the miniature tensile specimen. From the DIC strain field estimations, the peak local strain values at ultimate tensile strength were observed to be increasing with reducing specimen thickness. Testing of defect ridden stainless steel revealed the sensitivity to failure through strain localization and the influence of defect size was captured in the strength measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

SESAME: ASCII2 File Format

A new ASCII format for SESAME data is explicitly defined and dubbed ASCII2. This format fixes some of the onerous limitations of the legacy ASCII-styled format in addition to relaxing the FORTRAN style fixed-format layout of data tables. With the exception of the multi-tiered indexes of the legacy binary format, the new ASCII2 is more compatible with the binary in that it does not restrict the extent of various integer data (e.g., SESAME material and table numbers) to six digits nor does it restrict the extent or precision of the various floating point data. There will be very limited support of the legacy ASCII-styled formats in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Unified Software Architecture for Advanced Materials and Manufacturing Technologies Data Management and Processing: FY 2023 Multidimensional Data Correlation Platform

This report details the various digital manufacturing activities ongoing at ORNL as part of the Advanced Materials and Manufacturing Technologies (AMMT) program. The AMMT program is exploring a data-driven approach to demonstrate the use of AM for the fabrication of components for nuclear applications, with the goal of providing a greater understanding of manufacturing quality outcomes that would pave the way toward the development of standards for certification and qualification. The objective of this work package is to establish a digital manufacturing discipline common to all participants of the AMMT program to improve the performance, reliability, and lifetime of nuclear components. As part of this effort, we will develop a unified software architecture for AMMT data management and processing, deploy the digital platform across AMMT participants’ facilities, and generate pedigreed datasets in a common format across multiple labs and facilities. To this end, the MDDC work package has focused on three activities during FY23. First, the MDF Digital Tool was overhauled to better serve the needs of the AMMT program. Next, multiple laser powder bed fusion (L-PBF) systems at the MDF were upgraded to a common sensor package for collecting comparable in situ data across machines. Finally, various improvements relevant to the AMMT program were implemented in the ORNL-developed software tool, Peregrine. This report marks the completion of FY23 milestone M3CR-22OR0403051: Report Describing the Architecture of the Digital Platform to Support AMMT Activities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Experimental evidence of disordered crystalline premixing in sputter-deposited Ni(V)/Al multilayers

The sputter deposition of alternating layers of Ni(V) and Al forms a reactive multilayer known to undergo self-propagating formation reactions when ignited. The sequential deposition process leads to nanometer-scale premixing of reactants at each included interface, which ultimately affects multilayer exothermicity. This work performs the direct measurement of a disordered face-centered cubic (FCC) solid solution premixed phase at the interfaces of Ni(V)/Al multilayers via scanning transmission electron microscopy. The crystallinity of the observed phase differs from previously reported a priori predictions of an amorphous interlayer. The disordered FCC phase retains its symmetry after annealing for 16 h at 135 ± 5 °C, but the lattice parameter shifts consistently with an Al-rich composition. The existence of a crystalline premix in Ni(V)/Al is attributed to the electronic contribution to the entropy of crystallization. The importance of electronic entropy to the phase formation of energetic materials motivates its inclusion when constructing digital twins for atomistic kinetics and ignition sensitivity.

Crystal structure↗
DOE OSTI2024

Embedded Fluidic Sensing and Control with Soft Open‐Cell Foams

Abstract The synthesis of soft matter intelligence with circuit‐driven logic has enabled a new class of robots that perform complex tasks or conform to specialized form factors in unique ways that cannot be realized through conventional designs. Translating this hybrid approach to fluidic systems, the present work addresses the need for sheet‐based circuit materials by leveraging the innate porosity of foam—a soft material—to develop pneumatic components that support digital logic, mixed‐signal control, and analog force sensing in wearables and soft robots. Analytical tools and experimental techniques developed in this work serve to elucidate compressible gas flow through porous sheets, and to inform the design of centimeter‐sized foam resistors with fluidic resistances on the order of 10 9 Pa s m −3 . When embedded inside soft robots and wearables, these resistors facilitate diverse functionalities spanning both sensing and control domains, including digital logic using textile logic gates, digital‐to‐analog signal conversion using ladder networks, and analog sensing of forces up to 40 N via compression‐induced changes in resistance. By combining features of both circuit‐based and materials‐based approaches, foam‐enabled fluidic circuits serve as a useful paradigm for future hybrid robotic architectures that fully embody the sensing and computing capabilities of soft fluidic materials.

Rajappan, Anoop↗
DOE OSTI2025

Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators

Abstract Real-time monitoring is a foundation of nuclear digital twin technology, crucial for detecting material degradation and maintaining nuclear system integrity. Traditional physical sensor systems face limitations, particularly in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a transformative solution by complementing physical sensors in monitoring critical degradation indicators. This paper introduces the use of Deep Operator Networks (DeepONet) to predict key thermal-hydraulic parameters in the hot leg of pressurized water reactor. DeepONet acts as a virtual sensor, mapping operational inputs to spatially distributed system behaviors without requiring frequent retraining. Our results show that DeepONet achieves low mean squared and Relative L2 error, making predictions 1400 times faster than traditional CFD simulations . These characteristics enable DeepONet to function as a real-time virtual sensor, synchronizing with the physical system to track degradation conditions and provide insights within the digital twin framework for nuclear systems.

Hossain, Raisa↗
DOE OSTI2024

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)

Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prominent MLLMs against two critical tasks: (i) reaction diagram parsing and (ii) resolving cross-modality data interdependencies. The frontrunner MLLM achieved ≥96% accuracy in both tasks, with the strategic integration of single-shot visual prompts and image pre-processing techniques. We integrate this capability into a toolkit named MERMES (multimodal reaction mining pipeline for electrosynthesis). Our toolkit functions as an end-to-end MLLM-powered pipeline that integrates article retrieval, information extraction and multimodal analysis for streamlining and automating knowledge extraction. This work lays the groundwork for the increased utilization of MLLMs to accelerate the digitization of chemistry knowledge for data-driven research.

Leong, Shi Xuan↗
DOE OSTI2022

Deformation twinning and detwinning in extruded Mg-4Al: In-situ experiment and crystal plasticity simulation

Deformation twinning and detwinning in extruded Mg-4Al were investigated using in-situ SEM-DIC experiments and crystal plasticity finite element (CPFE) simulation. In this study, the in-situ SEM-DIC method was used to provide a unique set of data including twin/detwin characteristics and twin area fraction in addition to strain maps. A statistical analysis of the activation of twin variants and twin area fraction during both twinning and detwinning was conducted. A strong correlation was found between twin growth/shrinkage and the Schmid Factor (SF) for individual twin variants Higher twin SF during loading and unloading led to higher twin growth and shrinkage, respectively. However, after the applied compressive strain was removed, the pattern of the twin area fractions of the residual twin variants versus their nominal SFs did not follow the trend observed at the maximum compressive strain. Using a systematic methodology and an advanced twin/detwin model, the PRISMS-Plasticity CPFE simulation was calibrated using experimentally determined stress versus strain and twin area fraction versus strain information. A comprehensive evaluation of the CPFE model was conducted to determine its ability to capture the statistics of twin variants activation and twin area fraction. CPFE accurately captured the statistical aspects of both twinning and detwinning. Finally, it also predicted the first dominant twin variant for 47.5% of the grains and at least one of the two dominant twin variants for 80% of the grains at maximum compressive strain.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High temperature creep model development using in-situ 3-D DIC techniques during a simulated LOCA transient

In-situ strain measurements of fuel cladding can enable high-throughput data collection and validation to support accelerated qualification of cladding materials. Here, in this work, 3D digital image correlation was used to map strain for both Zircaloy-4 (Zry-4) and Cr-coated Zry-4 during two types of cladding rupture experiments: isobaric temperature ramp tests at 5 °C/s and isothermal pressure jump tests at 600 °C. Zry-4 strain data initially showed a temperature dependence expected for creep deformation, yet a shift to a new plastic deformation mechanism not reported in literature dominated during the finals seconds prior to rupture. Cr-coated Zry-4 did not show the change in deformation mechanism at the end of life and showed a delay in measurable creep deformation. Stress dependences were similar for Zry-4 and Cr/Zry-4 during pressure jump tests. Cr-coatings were found to decrease the strain rate during both testing scenarios. Creep parameters were calculated to support modelling efforts regarding design basis accidents.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A machining digital twin for hybrid manufacturing

Hybrid manufacturing consisting of metal additively manufactured preforms and computer numerical control (CNC) machining has been established to be an effective method for high material use rates. However, hybrid manufacturing introduces unique challenges. Near-net shape designs are typically selected, which result in a smaller margin for part placement within the stock and stringent requirements for work coordinate system identification. Additionally, less stock material reduces the preform stiffness, which limits the material removal rates during machining. This paper demonstrates a digital twin for CNC machining of a wire arc additively manufactured preform that implements: 1) structured light scanning for stock model identification and tool path generation; 2) a fused filament fabrication apparatus to attach temporary fiducials and scan targets to the preform that enable coordinate system definition for both the CAM and CNC machine; 3) preform and tool tip frequency response function measurements to enable stable milling parameter selection; and 4) post-manufacturing measurements of geometry, surface finish, and structural dynamics to confirm designer intent. These efforts define key components of the machining digital twin for hybrid manufacturing.

36 MATERIALS SCIENCE↗