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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Experimental results and numerical modeling of solidification during aircraft high-g arcs

The question of how the Coriolis and gravity gradient forces during high-gravity maneuvers compare to that for common centrifuges is addressed. Microstructural examination of samples solidified during high-gravity arcs reveals no evidence of convection dampening. As a first approximation, the high-gravity arc is modeled as a centrifuge with a radius of 20,480 ft and angular speed of 0.318 rpm. Scaling analysis indicates that the Coriolis and gravity gradient expected on the aircraft high gravity arc are less than that for the centrifuges by a factor of 100. Detailed Navier-Stokes analysis of the fluid flow and thermal fields during solidification of aluminum and Cd-Te during KC-135 high gravity show that convective flows of about 1 mm/s are induced. The thermal field is only slightly modified by the convection. Coriolis and gravity gradient during solidification in KC-135 high-gravity arcs, even at accelerations that have been shown to produce significant convective flow dampening in the centrifuge systems, are found to have no significant influence on the melt thermal and flowfields.

Curreri, P. A.↗
NASA NTRSDate not supplied

Three-dimensional in Situ Observations of Polycrystalline Microstructure Evolution During Directional Solidification of Transparent Alloys Aboard the ISS and Quantitative Comparison with Numerical Modeling

Cellular/dendritic microstructures formed during solidification have a crucial influence on the mechanical properties of a wide range of structural alloys. By minimizing the amount of gravity-induced convection in the liquid, directional solidification experiments using transparent organic alloys conducted in the DECLIC-DSI onboard the International Space Station have provided unique 3D in situ observations of the spatiotemporal evolution of the solid-liquid interface during the formation of cellular and dendritic microstructures under purely diffusive growth conditions in polycrystalline samples containing several grains with a small misorientation with respect to the temperature gradient. Those observations have made it possible to perform benchmark quantitative comparisons with the predictions of state-of-the-art phase-field simulations of microstructure formation in 3D on experimentally relevant length and time scales. This talk will report quantitative comparisons between microgravity experiments and phase-field simulations in succinonitrile-camphor alloys of two different compositions that shed new light on the role of subgrain boundaries in the spatiotemporal evolution of the primary cellular spacing and the selection of dendritic array structures.

Kaihua Ji↗
DOE OSTI2020

Analysis of structure-property gradients in orthogonally machined chips and workpiece subsurface

Production of machined surfaces with optimized mechanical properties requires fundamental process-structure-property knowledge. In this paper, we focus on structure-property gradients in orthogonally cut OFHC Copper chips and the workpiece subsurface. We utilize orientation imaging microscopy and statistical spatial correlation metrics to quantify the microstructure. Constitutive elastic-plastic properties of the workpiece subsurface and chips are measured using spherical nanoindentation. Analysis of the data reveals significant structure and property gradients in both regions. Differences and similarities in structure and property gradients near the workpiece surface and tool-chip interface, and in the chip middle are discussed and explained.

42 ENGINEERING↗
DOE OSTI2022

In situ melt pool measurements for laser powder bed fusion using multi sensing and correlation analysis

Laser powder bed fusion is a promising technology for local deposition and microstructure control, but it suffers from defects such as delamination and porosity due to the lack of understanding of melt pool dynamics. To study the fundamental behavior of the melt pool, both geometric and thermal sensing with high spatial and temporal resolutions are necessary. This work applies and integrates three advanced sensing technologies: synchrotron X-ray imaging, high-speed IR camera, and high-spatial-resolution IR camera to characterize the evolution of the melt pool shape, keyhole, vapor plume, and thermal evolution in Ti–6Al–4V and 410 stainless steel spot melt cases. Aside from presenting the sensing capability, this paper develops an effective algorithm for high-speed X-ray imaging data to identify melt pool geometries accurately. Preprocessing methods are also implemented for the IR data to estimate the emissivity value and extrapolate the saturated pixels. Quantifications on boundary velocities, melt pool dimensions, thermal gradients, and cooling rates are performed, enabling future comprehensive melt pool dynamics and microstructure analysis. The study discovers a strong correlation between the thermal and X-ray data, demonstrating the feasibility of using relatively cheap IR cameras to predict features that currently can only be captured using costly synchrotron X-ray imaging. Such correlation can be used for future thermal-based melt pool control and model validation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Combining synchrotron X-ray diffraction, mechanistic modeling and machine learning for in situ subsurface temperature quantification during laser melting

Laser melting, such as that encountered during additive manufacturing, produces extreme gradients of temperature in both space and time, which in turn influence microstructural development in the material. Qualification and model validation of the process itself and the resulting material necessitate the ability to characterize these temperature fields. However, well established means to directly probe the material temperature below the surface of an alloy while it is being processed are limited. To address this gap in characterization capabilities, a novel means is presented to extract subsurface temperature-distribution metrics, with uncertainty, from in situ synchrotron X-ray diffraction measurements to provide quantitative temperature evolution data during laser melting. Temperature-distribution metrics are determined using Gaussian process regression supervised machine-learning surrogate models trained with a combination of mechanistic modeling (heat transfer and fluid flow) and X-ray diffraction simulation. The trained surrogate model uncertainties are found to range from 5 to 15% depending on the metric and current temperature. The surrogate models are then applied to experimental data to extract temperature metrics from an Inconel 625 nickel superalloy wall specimen during laser melting. The maximum temperatures of the solid phase in the diffraction volume through melting and cooling are found to reach the solidus temperature as expected, with the mean and minimum temperatures found to be several hundred degrees less. The extracted temperature metrics near melting are determined to be more accurate because of the lower relative levels of mechanical elastic strains. However, uncertainties for temperature metrics during cooling are increased due to the effects of thermomechanical stress.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Thermosolutal convection during cellular arrayed growth of Pb-Sn alloys

Thermosolutal convection caused by the solute build up ahead of the growing arrays of cells and dendrites results in macrosegregation along the length of the Pb-Sn alloy (10 to 58 wt pct Sn) specimens when they are directionally solidified in a positive thermal gradient (melt on top, solid below, and gravity pointing down). At a constant thermal gradient, the extent of macrosegregation increases with decreasing growth speed as the microstructure changes from dendritic, to cellular and to planar. An empirical parameter, effective partition coefficient, obtained from the dependence of the longitudinal macrosegregation on fraction distance solidified can be used to represent the extent of macrosegregation.

Tewari, S. N.↗
NASA NTRS2004

On Efficient Multigrid Methods for Materials Processing Flows with Small Particles

Multiscale modeling of materials requires simulations of multiple levels of structural hierarchy. The computational efficiency of numerical methods becomes a critical factor for simulating large physical systems with highly desperate length scales. Multigrid methods are known for their superior efficiency in representing/resolving different levels of physical details. The efficiency is achieved by employing interactively different discretizations on different scales (grids). To assist optimization of manufacturing conditions for materials processing with numerous particles (e.g., dispersion of particles, controlling flow viscosity and clusters), a new multigrid algorithm has been developed for a case of multiscale modeling of flows with small particles that have various length scales. The optimal efficiency of the algorithm is crucial for accurate predictions of the effect of processing conditions (e.g., pressure and velocity gradients) on the local flow fields that control the formation of various microstructures or clusters.

Thomas, James↗
DOE OSTI2025

Laser‐Powder Melt Pool Solidification Dynamics and Microstructural Engineering of Ti‐5553 Microlattices

The fine geometric and topological control afforded by additive manufacturing technologies has enabled the manufacture of architected materials across length scales, and enabling tunable mechanical performance as a function of local and global design. Progress has been made to tune the mechanical response of architected materials through geometry, but understanding how the geometry and processing conditions will inform the microstructure remains a challenge due to the rapid solidification in laser powder bed fusion. This study uses in situ X-ray imaging and electron backscatter diffraction microscopy to demonstrate that the melt pool size, microstructure morphology, and elastic strain distribution is influenced by a combination of lattice geometry and laser processing conditions. These results indicate that within larger melt pools the local thermal gradients are sufficient to enable a columnar-to-equiaxed transition across the melt pool. Furthermore, the solidification mechanisms producing these microstructures are examined across the first 5 ms of melting and solidification, described via in situ high-speed X-ray imaging and mirrored via multiphysics simulation.

additive manufacturing↗
DOE OSTI2022

Generalized thermo-mechanical framework for heterogeneous materials through asymptotic homogenization

Abstract A fundamental understanding of the interaction between microstructure and underlying physical mechanisms is essential, especially for developing more accurate multi-physics models for heterogeneous materials. Effects of microstructure on the material response at the macroscale are modeled by using the generalized thermomechanics. In this study, strain gradient theory is employed as a higher-order theory on the macroscale with thermodynamics modeled as a first-order theory on the microscale. Hence, energy depends only on the temperature such that we circumvent an extension of Fourier’s law and analyze the “simplest” thermo-mechanical model in strain gradient elasticity. Developing multiphysics models for heterogeneous materials is indeed a challenge and even this “simplest” model in generalized thermomechanics creates dozens of parameters to be determined. We develop a thermo-mechanical framework, in which microstructure is modeled as a periodic structure and through asymptotic homogenization approach, higher-order parameters at macroscopic scale are calculated. To illustrate the importance of higher-order parameters in overall thermo-mechanical response of a heterogeneous materials, finite element method (FEM) is employed with the aid of open-source codes (FEniCS). Verification example of a bulk system and several case studies of porous structures demonstrate how such numerical framework can be beneficial in the design of materials with tailored microstructures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Microstructural Characterization of the Second High Fluence Baffle-Former Bolt Retrieved from a Westinghouse Two-loop Downflow Type PWR

As one of the pressurized water reactor (PWR) internal components, baffle-former bolts (BFBs) are subjected to significant mechanical stress and neutron irradiation from the reactor core during the plant operation. Over the long operation period, these conditions lead to potential degradation and reduced load-carrying capacity of the bolts. In support of evaluating long-term operational performance of materials used in core internal components, the Oak Ridge National Laboratory (ORNL), through the Department of Energy (DOE), Light Water Reactor Sustainability (LWRS) Program, Materials Research Pathway (MRP) has harvested two high fluence BFBs from a commercial Westinghouse two-loop downflow type PWR. The two bolts of interest, i.e. bolts # 4412 and 4416, were withdrawn from service in 2011 as part of a preventative replacement plan. No identification of cracking or potential damage was found for these bolts during their removal in 2011. However, the bolts required a lower torque for removal from the baffle structure than the original torque specified during installation. Irradiation displacement damage levels in the bolts range from 15 to 41 displacements per atom. The goal of this project is to perform detailed microstructural and mechanical property characterization of BFBs following in-service exposures. The information from these bolts will be integral to the LWRS program initiatives in evaluating end of life microstructure and properties. Furthermore, valuable data will be obtained that can be incorporated into model predictions of long-term irradiation behavior and compared to results obtained in high flux experimental reactor conditions. In this report, we present our latest study in FY22 on microstructural characterizations of the second high fluence baffle-former bolt, i.e., bolt # 4412. Analytical electron microscopy and atom probe tomography characterization were performed. The radiation-induced defects in the material add to the large wealth of knowledge for neutron-induced defects in 304/316 grades of stainless steels, specifically for radiation-induced precipitation after high fluence commercial PWR irradiation. The main findings are summarized as follows: 1) The cavity size was considerably larger in the bolt thread section than in the bolt head, with the bolt thread section having a bimodal distribution of cavities greater than ~6 nm in diameter and less than ~3 nm in diameter. The bolt head only had the small-sized cavities. In addition, there was a denuded zone of large cavities near grain boundaries in the thread section of the bolt. 2) Radiation-induced precipitation in the BFB #4412 was highly complex, with the volume fraction, size, and number density of Ni/Si and Cu-rich precipitates depending strongly on the radiation temperature/dose. In many cases, co-precipitates of adjoined clusters were found with Ni/Si-rich precipitates sandwiched between Cu-rich clusters and Mo/Cr/P-rich clusters. 3) Solute segregation out of solution was highest for most solutes in the thread section of the bolt #4412 with the exception of Cu, which experienced more separation out of solution into Curich clusters in the bolt head section. This highlights the difference in the mechanisms for precipitation of Ni/Si clusters, which have the Ni 3 Si phase composition, and precipitation of Cu-rich clusters. 4) There appear to be multiple simultaneous influences that affect the microstructural variation along the length of the bolt that overcomes the ~2X difference in irradiation dose between the bolt head and the bolt thread. The irradiation temperature, thermal/fast neutron ratio variation, potential strain gradient, and exposure to PWR coolant water that each section of the bolt sees may have more influence on the microstructural evolution than the total irradiation dose. The bolt thread and shank, with higher temperature, higher relative fast neutron flux, higher strain, and exposure to coolant but lower dose, underwent more enhanced cavity formation, precipitation, and solute segregation than the bolt head section.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

High-resolution 3D strain and orientation mapping within a grain of a directed energy deposition laser additively manufactured superalloy

The industrialization of Laser Additive Manufacturing (LAM) is challenged by the undesirable microstructures and high residual stresses originating from the fast and complex solidification process. Non-destructive assessment of the mechanical performance controlling deformation patterning is therefore critical. Here, we use Dark Field X-ray Microscopy (DFXM) to map the 3D subsurface intragranular orientation and strain variations throughout a surface-breaking grain within a directed energy deposition nickel superalloy. DFXM results reveal a highly heterogenous 3D microstructure in terms of the local orientation and lattice strain. The grain comprises ≈ 5 μm-sized cells with alternating strain states, as high as 5 x10 -3 , and orientation differences <0.5°. The DFXM results are compared to Electron Backscatter Diffraction measurements of the same grain from its cut-off surface. Here, we discuss the microstructure developments during LAM, rationalising the development of the deformation patterning from the extreme thermal gradients during processing and the susceptibility for solute segregation.

36 MATERIALS SCIENCE↗
NASA NTRS2024

Finite Element Simulation of the Direct Energy Deposition using Comsol Multiphysics

Direct Energy Deposition (DED) is an emerging technology extensively employed in metal Additive Manufacturing (AM). Despite its widespread industrial application, mathematical modeling in this domain remains challenging. This complexity arises from the intricate nature of the modeling approach and the nonlinear behavior of material parameters across a broad temperature range. Consequently, experimentalists often resort to a trial-and-error method to achieve structures with desired properties. However, this approach can be time-consuming and may not always yield parts with the requisite characteristics, highlighting the necessity for mathematical modeling to enhance manufacturing success. This study focuses on thin-walled manufacturing with a single bead thickness, adjusting laser parameters to produce such structures. The laser cladding speed, typical for DED manufacturing, is considered to be on the order of centimeters per second. The various wall thicknesses and heights are explored to discern the general characteristics of walls manufactured via this DED approach. Our modeling approach diverges from the conventional DED modeling based on activation-deactivation of predefined mesh domains, commonly implemented in many finite element codes. Instead, a method is proposed that effectively models layer cladding and melt pool dynamics, enabling predictions of the microstructure in the resulting structures. The formation mechanisms of cellular, dendritic columnar, and stray (equiaxed) grains, which arise from the interplay between nucleation and growth from the surface is analyzed. The study also examines the feasibility of microstructure formation to demonstrate the various thermal characteristics inherent in wall manufacturing. Our results illustrate how different process parameters influence the temperature gradient and cooling rate of the molten pool, subsequently affecting the primary dendrite arm spacing (PDAS). This modeling technique allows for the investigation of diverse thermal conditions, facilitating the prediction of microstructure and residual stresses in the manufactured parts.

modeling↗
DOE OSTI2022

Machine learning in nuclear materials research

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. Herein, this review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Rethinking materials simulations: Blending direct numerical simulations with neural operators

Abstract Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of multiscale spatiotemporal interactions, and the need to reach long-time integration. We develop a method that blends direct numerical solvers with neural operators to accelerate such simulations. This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism to enable accurate extrapolation and efficient time-to-solution predictions of the dynamics. We demonstrate the effectiveness of this hybrid framework on simulations of microstructure evolution via the phase-field method. Such simulations exhibit high spatial gradients and the co-evolution of different material phases with simultaneous slow and fast materials dynamics. We establish accurate extrapolation of the coupled solver with large speed-up compared to DNS depending on the hybrid strategy utilized. This methodology is generalizable to a broad range of materials simulations, from solid mechanics to fluid dynamics, geophysics, climate, and more.

36 MATERIALS SCIENCE↗
NASA NTRS1988

Gravitational macrosegregation in unidirectionally solidified lead-tin alloy

Nine small samples of binary lead-tin alloys were solidifed unidirectionally upward, with both cooling rate and thermal gradient being closely controlled. Results presented include thermal measurements, chemical composition measurements, and microstructural characterization. In the six Pb-15 wt pct Sn ingots, normal macrosegregation was observed, with Sn content being highest at the top of the ingot. No significant macrosegretation was noted in the three ingots of nominal composition Sn-15 wt pct Sn.

Wang, L.↗
NASA NTRS1991

Joining of materials with engineered interlayers

A reliable method to join a variety of different materials is developed which uses engineered coatings to produce strong bonds by solid-state techniques at low temperatures (300-400 C). The strong bonds are capable of accommodating the strains created between Si and most substrates due to differences in thermal expansion coefficients. The approach encompasses two phases: noble metal compliant interlayers and functionally gradient interlayer bonding. Preliminary results are presented and the Ag interlayer bonds are microstructurally characterized confirming that the planar magnetron sputtered coatings/bonds are fully dense and metallurgically sound.

Challenger, Kenneth D.↗
DOE OSTI2026

Instabilities and phase transitions in architected metamaterials: a gradient-enhanced continuum approach

Architected metamaterials such as foams and lattices exhibit a wide range of properties governed by microstructural instabilities and emerging phase transitions. Their macroscopic response–including energy dissipation during impact, large recoverable deformations, morphing between configurations, and auxetic behavior–remains difficult to capture with conventional continuum models, which often rely on discrete approaches that limit scalability. In this work, we propose a nonlocal continuum formulation that captures both stable and unstable responses of elastic architected metamaterials. The framework extends anisotropic hyperelasticity by introducing nonlocal variables and internal length scales reflective of microstructural features. Local polyconvex free-energy models are systematically augmented with two families of non-(poly)convex energies, enabling both metastable and bistable responses. Implementation in a finite element framework enables solution using a hybrid monolithic–staggered strategy. Simulations capture densification fronts, forward and reverse transitions, hysteresis loops, imperfection sensitivity, and globally coordinated auxetic modes. Overall, this framework provides a robust foundation for accelerated modeling of instability-driven phenomena in architected metamaterials, while enabling extensions to anisotropic, dissipative, and active systems as well as integration with data-driven and machine learning approaches.

42 ENGINEERING↗
DOE OSTI2023

An operando synchrotron study on the effect of wire melting state on solidification microstructures of Inconel 718 in wire-laser directed energy deposition

Directed energy deposition (DED) with a coaxial wire-laser configuration has gained significant attention in recent years for the production of large-scale metallic components because of its low directional dependence, fast deposition rate, high feedstock efficiency, and low manufacturing costs. This work studies the coaxial wire-laser DED process of Inconel 718 alloy under a stable deposition condition with a relatively low input volumetric energy density (55.5J/mm 3 ). Post characterization reveals a cluster of refined grains at the center-bottom region of the as-printed track. Operando high-energy synchrotron X-ray experiments and multi-physics modeling are applied innovatively to study the fundamental mechanism responsible for the formation of this microstructure. The X-ray diffraction experiment provides direct evidence, which is supported by the simulation, that the feeding wire can reach the melt pool bottom and release solid particles (primarily carbides) near the mushy zone owing to insufficient melting. Consequently, these sub-micron sized particles suppress the growth of large columnar grains and cause the formation of unique microstructural heterogeneity. In conclusion, this discovery offers new opportunities for tailoring the solidification microstructure by controlling the melting state of the feedstock wire in DED process, in addition to commonly known factors such as the thermal gradient and solidification velocity.

42 ENGINEERING↗