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Results for “solidification”

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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Giant enhancement of the magnetocaloric response in Ni–Co–Mn–Ti by rapid solidification

Magnetocaloric refrigeration is a solid-state cooling approach that assures high energy efficiency and low environmental impact. It remains uncompetitive with conventional vapor-compression technologies due to lack of high-performing materials that exhibit large magnetocaloric effects in low magnetic fields. In this work we report a game-changing enhancement of the magnetocaloric response in a transition-metal-based Ni–Co–Mn–Ti. Mechanically and chemically stable rapidly solidified ribbons exhibit magnetic entropy changes as high as ~27J·kg –1 K –1 for a moderate field change of 2T, comparable to or larger than the best known materials for near-room temperature applications. The ribbons can be easily manufactured in large quantities and the transition temperature can be adjusted by varying Co concentration.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Location specific solidification microstructure control in electron beam melting of Ti-6Al-4V

Relationships between prior beta grain size in solidified Ti-6Al-4V and melting process parameters in the Electron Beam Melting (EBM) process are investigated. Samples are built by varying a machine-dependent proprietary speed function to cover the process space. Optical microscopy is used to measure prior beta grain widths and assess the number of prior beta grains present in a melt pool in the raster region of the build. Despite the complicated evolution of beta grain sizes, the beta grain width scales with melt pool width. The resulting understanding of the relationship between primary machine variables and prior beta grain widths is a key step toward enabling the location specific control of as-built microstructure in the EBM process. Control of grain width in separate specimens and within a single specimen is demonstrated.

42 ENGINEERING↗
DOE OSTI2018

The anomalous solidification of concrete grindings from acid treatment

We report that concrete grinding from highway maintenance operations produces large amounts of a highly basic liquid residue, posing a significant challenge to proper disposal. To discard this residue with little risk to human health or the environment, it should be solidified and its pH should be neutralized. Herein we present the serendipitous discovery that both goals can be met by treatment with citric acid. Through the use of 13 C and 29 Si solid-state NMR we determined that such treatment removes the calcium from the concrete structure, thus allowing the solubilization of its one-dimensional silica chains. These silica chains then cross-link in solution forming a highly porous silica material that is able to absorb the water from the residue. Finally, this new waste treatment process, which may utilize other acids with low water content, promises to facilitate a significantly improved methodology for the disposal of concrete grinding residue in a safe and environmentally friendly manner.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Automated path planning for functionally graded materials considering phase stability and solidification behavior: Application to the Mo-Nb-Ta-Ti system

Functionally graded materials have the potential to improve upon monolithic parts by locally tailoring compositions to surrounding environmental conditions. Difficulties arise when designing composition gradients as incompatible materials can result in detrimental phase formation and failure of the gradient joint. As many alloys are multi-component, designing a composition gradient free of detrimental phases is difficult due to the large composition space available to explore. A framework was developed that improves the path planning algorithm and surrogate models with adaptive sampling schemes specific to their problem definition. A cost function was created to minimize a property (such as cracking susceptibility) along a path. This framework was applied to the Mo-Nb-Ta-Ti system as a case study to showcase the efficiency in building the surrogate models and in iterating different optimal compositionally graded paths.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep operator network surrogate for phase-field modeling of metal grain growth during solidification

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the the Allen-Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400° C at an average rate of 30° C / min , traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. Further, these relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.

36 MATERIALS SCIENCE↗