Containerless Processing Studies in the MSFC Electrostatic Levitator

The goal of this work was to investigate intra-alloy relationships, as they pertain to rapid solidification, which can be applied to computational materials modeling. Those relationships can be utilized to improve the accuracy of predictive modeling by leveraging previous experimental results. With that in mind, FeeCo samples were prepared with 30e50 at.% cobalt and they were processed via electrostatic levitation (ESL) or electromagnetic levitation (EML). The samples were levitated, melted, and allowed to cool and solidify in a vacuum for ESL testing and under He gas for EML testing. If sufficient undercooling was achieved, the sample solidified via double recalescence. In that event, the metastable dephase would grow into the undercooled liquid, and then the stable gephase would grow into a combination of the metastable phase and remaining undercooled liquid, or mushy zone. The velocities of the solid phases growing into undercooled liquid were analyzed with current dendrite growth theories. The purpose of the growth velocity analyses was twofold: 1) Assess the validity of current dendrite theory as it applies to the FeeCo system. 2) Evaluate the kinetic growth coefficient, m, assuming a constant kinetic rate parameter, V o. The results of the analyses indicate that it is reasonable to assume that the kinetic rate parameter, V o , is constant for a given phase within an alloy system if DH f =T 2 m does not vary significantly within the system, or within the composition range of interest. The average growth velocities of the stable phase into the mushy zone, V gd , for the Fee30, 40, and 50 at.% Co compositions are 1.6, 2.4, and 4.9 m/s, respectively, which scale with the thermal driving forces of the transformations, DT gd , which are 10 K, 24 K, and 40 K, respectively.

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“…X o is the initial solute atomic fraction, and V d is the atomic diffusive speed at the solid-liquid interface, shown in Equation (11).…”

Section: Discussionmentioning

confidence: 99%

“…The sample was then melted, allowed to cool radiatively, and then solidify. An article by Rogers and SanSoucie [11] gives a full description of the experimental setup. The solidification was monitored with a Phantom V7.1 high-speed camera, which was operating at 30,000e40,000 frames per second at 128 Â 128 image resolution.…”

Section: Experimental Methodsmentioning

confidence: 99%

Solidification velocity of undercooled Fe–Co alloys

Rodriguez¹,

Kreischer²,

Volkmann³

et al. 2017

Acta Materialia

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“…Analysis was conducted on the datasets taken from the archives from undercooling experiments at the NASA Marshall Space Flight Center Electrostatic Levitation (ESL) Facility [16] on a Ti37-Zr42-Ni12 and Ti39.5-Zr39.5-Ni21 (at%) quasicrystal-forming alloy.…”

Section: Dataset From Ti-zr-ni Alloysmentioning

confidence: 99%

“…High speed infra-red imaging systems can be developed in order to extend the temperature range downward to allow imaging of low-temperature systems, but the technology is not yet currently advanced enough to support ongoing aluminum-based, deep eutectic, or metallic glass-forming alloy characterization. Containerless processing techniques on spherical samples are often used to conduct phase selection and growth kinetics investigations [6][7][8][9][10][11][12][13][14][15][16], and statistical methods have been used with a number of studies based on a dataset developed from repeated experiment cycles, which focused on prediction of undercooling temperature distributions [17][18][19][20][21], and estimation of nucleation rates [17,20].…”

Section: Introductionmentioning

confidence: 99%

Statistical learning for evaluation of crystal growth in low-melting alloy droplets with application to quasicrystal-forming Ti–Zr–Ni alloys

Xiao¹,

Rodriguez²,

Kelton³

et al. 2020

Modelling Simul. Mater. Sci. Eng.

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The evaluation of the crystal growth in undercooled alloy melts is essential for investigations of their solidification behavior. Low-melting alloy systems usually exhibit little self-illumination and cinematography is not often feasible. A time-temperature data based statistical learning approach is presented to evaluate the crystal growth in an undercooled droplet, where a spatial geometry-informed, errors-in-variables probabilistic model is developed based on the prediction of the dendrite growth paths. The preferable growth mechanism and growth velocities can be evaluated from statistical model selection and parameter estimation. Based only on the pyrometry data of two Ti–Zr–Ni alloys, a bulk growth mechanism is justified and the growth velocities in both the stable C14 phase and metastable icosahedral phase are able to be measured and statistically interpreted.

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“…The processing atmosphere of the chamber is high vacuum, typically in the 10 −6 torr range. Although the sample is levitated, a laser heats the samples and the temperature is monitored by optical pyrometers [10,11]. The emissivity value is determined through the melt plateau (due to the latent heat of fusion).…”

Section: Introductionmentioning

confidence: 99%

Experiments Using a Ground-Based Electrostatic Levitator and Numerical Modeling of Melt Convection for the Iron-Cobalt System in Support of Space Experiments

Lee¹,

SanSoucie

2017

JOM

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Materials research is being conducted using an electromagnetic levitator installed in the International Space Station. Various metallic alloys were tested to elucidate unknown links among the structures, processes, and properties. To accomplish the mission of these space experiments, several ground-based activities have been carried out. This paper presents some of our ground-based supporting experiments and numerical modeling efforts. Mass evaporation of Fe50Co50, one of the flight compositions, was predicted numerically and validated by the tests using an electrostatic levitator (ESL). The density of various compositions within the Fe-Co system was measured with ESL. These results serve as reference data for the space experiments. The convection inside a electromagneticallylevitated droplet was also modeled to predict the flow status, shear rate, and convection velocity under various process parameters, which is essential information for designing and analyzing the space experiments of some flight compositions influenced by convection. INTRODUCTIONSince 2015, several materials experiments have been performed using the materials science laboratory electromagnetic levitator (MSL-EML) aboard the International Space Station (ISS). More than 50 principal investigators from 13 different countries have been participating in this space program. Thermophysical properties, transport phenomena, and glass/quasicrystal formation of various alloys are being studied using MSL-EML. To increase the chance of success in the space experiments, the team has meticulously planned and prepared for their science experiments. The safety aspects, mass evaporation [1] and thermophysical properties [2-4] of each sample have been measured as part of the ground support program.The measurement of mass evaporation is important. Evaporation often causes composition shifts of tested alloys resulting from preferential evaporation of volatile species. Such composition shifts can be reduced by limiting superheating and time for thermal hold during property measurements. On the other hand, evaporated materials may end up as aerosols, which can be hazardous to the health of the astronauts. Otherwise the evaporated materials will deposit on the optics, obscuring measurements, or on the coils. Excessive coil deposition can cause flaking of deposited materials, cross-contamination, short circuiting, and arcing. Each batch has 18 samples, and the amount of mass evaporation of each sample is different depending on the composition and thermal history. After discussion among scientists, the thermal cycles of each samples are adjusted such that the total amount of mass evaporation does not exceed a designated value.For flight samples whose internal convection is of interest, numerical models were developed [5] and used [6]. The developed numerical models have been used to predict the convection status under an anticipated combination of process parameters. The status of convection affects phase selection during solidification of metal alloys which exhibit ...

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Containerless Processing Studies in the MSFC Electrostatic Levitator

Cited by 8 publications

References 10 publications

Solidification velocity of undercooled Fe–Co alloys

Solidification velocity of undercooled Fe–Co alloys

Statistical learning for evaluation of crystal growth in low-melting alloy droplets with application to quasicrystal-forming Ti–Zr–Ni alloys

Experiments Using a Ground-Based Electrostatic Levitator and Numerical Modeling of Melt Convection for the Iron-Cobalt System in Support of Space Experiments

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