Engineering topics

Yang, Shizhong

Publications and source records attributed to Yang, Shizhong.

13 records

DOE OSTI2024

Study of Helium Irradiation Effect on Al6061 Alloy Fabricated by Additive Friction Stir Deposition

Additive friction stir deposition (AFS-D) is considered a productive method of additive manufacturing (AM) due to its ability to produce dense mechanical parts at a faster deposition rate compared to other AM methods. Al6061 alloy finds extensive application in aerospace and nuclear engineering; nevertheless, exposure to radiation or high-energy particles over time tends to deteriorate their mechanical performance. However, the effect of radiation on the components manufactured using the AFS-D method is still unexamined. In this work, samples from the as-fabricated Al6061 alloy, by AFS-D, and the Al6061 feedstock rod were irradiated with He+ ions to 10 dpa at ambient temperature. The microstructural and mechanical changes induced by irradiation of He+ were examined using a scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM), and nanoindentation. This study demonstrates that, at 10 dpa of irradiation damage, the feedstock Al6061 produced a bigger size of He bubbles than the AFS-D Al6061. Nanoindentation analysis revealed that both the feedstock Al6061 and AFS-D Al6061 samples have experienced radiation-induced hardening. These studies provide a valuable understanding of the microstructural and mechanical performance of AFS-D materials in radiation environments, offering essential data for the selection of materials and processing methods for potential application in aerospace and nuclear engineering.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Large-Scale Visualization of 3D Unstructured Groundwater Model Using Cave Automated Virtual Environment

The immersive three-dimensional (3D) virtual reality (VR) visualization of groundwater models allows us to deepen our understanding of aquifer systems and provide better solutions to present groundwater-related problems, such as groundwater recharge, water quality, and sustainability. Visualization assists in accurately developing groundwater models and revealing important subsurface features, including faulting, folding, and unconformity. However, assessing model accuracy poses challenges due to the complexity of geology and groundwater systems. This research demonstrates a workflow to visualize and analyze raw 3D unstructured groundwater model data using an immersive Cave Automated Virtual Environment (CAVE). To visualize the unstructured groundwater model data, the raw dataset is converted into interactive CAVE-compatible formats utilizing a set of tools: ParaView, Blender, and Unity. This enables researchers to immerse themselves in the data, identifying influential patterns and relationships. e resulting insights can inform the development of sophisticated machine-learning models for groundwater level prediction. The CAVE’s immersive capabilities allow intuitive exploration from various perspectives, providing a more holistic understanding of the factors affecting groundwater levels. These insights are crucial to improve predictive models. The CAVE results also facilitate collaborative analysis and have potential applications in training and education. is research demonstrates the value of immersive VR tools such as the CAVE for unraveling intricacies within high-dimensional scientific data to drive real-world forecasting and modeling applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Molecular dynamics simulation studies of 1,3-dimethyl imidazolium nitrate ionic liquid with water

The fundamental understanding of intermolecular interactions of ionic liquids (ILs) with water is essential in predicting IL–water thermodynamic properties. In this study, intermolecular or noncovalent interactions were studied for 1,3-dimethyl imidazolium [DMIM]+ cation and nitrate [NO 3 ] – anion with water, employing quantum mechanics and molecular dynamics simulations. Molecular dynamics simulations were performed using a revised multipolar polarizable force field. The effect of water on ionic liquids was evaluated in terms of thermodynamic and dynamic properties. Thermodynamic properties included liquid densities ρ, excess molar volumes ΔV E , and liquid structures g(r)⁠. Dynamic properties included self-diffusion coefficients D of mixture constituents as a function of water concentration. The density of ionic liquid–water mixtures monotonically decrease with increasing concentration of water. A negative excess volume was obtained for low and high water concentrations, demonstrating strong intermolecular interactions of water with ionic liquid components. Liquid structures of ionic liquid–water mixtures revealed a tendency for anions to interact with cations at shorter intermolecular distances when the water concentration is increased. Diffusion rates were found to increase for all mixture components with increase in the fraction of water. A significant change in the diffusion rate was found at ~0.3 weight fraction of water. However, the water self-diffusion coefficient was dominant at all concentrations. Furthermore, the ratio of water/anion and anion/cation self-diffusion coefficients was found to decrease linearly with increasing concentration of water molecules.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Machine learning prediction of the mechanical properties of refractory multicomponent alloys based on a dataset of phase and first principles simulation

In this work, a dataset including structural and mechanical properties of refractory multicomponent alloys was developed by fusing computations of phase diagram (CALPHAD) and density functional theory (DFT). The refractory multicomponent alloys, also named refractory complex concentrated alloys (CCAs) which contain 2–5 types of refractory elements were constructed based on Special Quasi-random Structure (SQS). The phase of alloys was predicted using CALPHAD and the mechanical property of alloys with stable and single body-centered cubic (BCC) at high temperature (over 1,500°C) was investigated using DFT-based simulation. As a result, a dataset with 393 refractory alloys and 12 features, including volume, melting temperature, density, energy, elastic constants, mechanical moduli, and hardness, were produced. To test the capability of the dataset on supporting machine learning (ML) study to investigate the property of CCAs, CALPHAD, and DFT calculations were compared with principal components analysis (PCA) technique and rule of mixture (ROM), respectively. It is demonstrated that the CALPHAD and DFT results are more in line with experimental observations for the alloy phase, structural and mechanical properties. Furthermore, the data were utilized to train a verity of ML models to predict the performance of certain CCAs with advanced mechanical properties, highlighting the usefulness of the dataset for ML technique on CCA property prediction.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Thermal Pressure in the Thermal Equation of State for Solid and a Proposed Substitute

Abstract The thermal equation of state (TEOS) for solids is a mathematic model among pressure, temperature and density, and is essential for geophysical, geochemical, and other high pressure–temperature (high P–T) researches. However, in the last few decades, there has been a growing concern about the accuracy of the pressure scales of the calibrants, and efforts have been made to improve it by either introducing a reference standard or building new thermal pressure models. The existing thermal equation of state, P ( V , T ) = P ( V , T 0 ) + P th ( V , T ), consists of an isothermal compression and an isochoric heating, while the thermal pressure is the pressure change in the isochoric heating. In this paper, we demonstrate that, for solids in a soft pressure medium in a diamond anvil cell, the thermal pressure can neither be determined from a single heating process, nor from the thermal pressure of its calibrant. To avoid the thermal pressure, we propose to replace the thermal pressure with a well-known thermal expansion model, and integrate it with the isothermal compression model to yields a Birch–Murnaghan-expansion TEOS model, called VPT TEOS. The predicted pressure of MgO and Au at ambient pressure from Birch–Murnaghan-expansion VPT TEOS model matches the experimental pressure of zero (0) GPa very well, while the pressure prediction from the approximated Anderson PVT TEOS exhibit a big deviation and a wrong trend.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Predicting Elastic Constants of Refractory Complex Concentrated Alloys Using Machine Learning Approach

Refractory complex concentrated alloys (RCCAs) have drawn increasing attention recently owing to their balanced mechanical properties, including excellent creep resistance, ductility, and oxidation resistance. The mechanical and thermal properties of RCCAs are directly linked with the elastic constants. However, it is time consuming and expensive to obtain the elastic constants of RCCAs with conventional trial-and-error experiments. The elastic constants of RCCAs are predicted using a combination of density functional theory simulation data and machine learning (ML) algorithms in this study. The elastic constants of several RCCAs are predicted using the random forest regressor, gradient boosting regressor (GBR), and XGBoost regression models. Based on performance metrics R-squared, mean average error and root mean square error, the GBR model was found to be most promising in predicting the elastic constant of RCCAs among the three ML models. Additionally, GBR model accuracy was verified using the other four RHEAs dataset which was never seen by the GBR model, and reasonable agreements between ML prediction and available results were found. The present findings show that the GBR model can be used to predict the elastic constant of new RHEAs more accurately without performing any expensive computational and experimental work.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Pressure-Dependent Thermal Expansion Coefficient by a Diamond Anvil Cell

A thermal expansion coefficient (αp) is an essential thermophysical quantity for high-pressure research. The thermal expansion coefficient is the volume change over the temperature in an isobaric heating process. Although isobaric heating processes for diamond anvil cell (DAC) have been conducted by keeping the pressure constant during heating, pressure determination at high-temperature high pressure is debatable for decades long. In this paper, a revertible heating/cooling approach is presented, while its pressure determination at high-temperature high pressure is not required. We report a pressure-dependent thermal expansion coefficient of MgO at 9.5 GPa by reversible heating/cooling was determined by a DAC and its result matches the one collected by a large volume press, whose isobaric heating was conducted by manually adjusting its load/pressure.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Computational exploration of biomedical HfNbTaTiZr and Hf 0.5 Nb 0.5 Ta 0.5 Ti 1.5 Zr refractory high-entropy alloys

Refractory high entropy alloys (RHEAs) have been proven to be a potential candidate in the biomedical field due to their balanced mechanical properties and biocompatible composition. Recent experimental findings show that RHEAs like HfNbTaTiZr and Hf 0.5 Nb 0.5 Ta 0.5 Ti 1.5 Zr have good mechanical properties such as high polarization and wear resistance than others which establish them as potential materials for biomedical application. In this work, we performed first-principles density functional theory calculations on the mechanical and thermal properties of HfNbTaTiZr and Hf 0.5 Nb 0.5 Ta 0.5 Ti 1.5 Zr. The predicted lattice constant, density, Young's modulus, and Vickers hardness are consistent with the available experimental report, which verifies the accuracy of the applied model. The thermal coefficient of linear expansion of both RHEAs has been investigated by utilizing the Debye theory. The present methods could be applied to study other future RHEAs on exploration of their physical properties.

36 MATERIALS SCIENCE↗

DOE OSTI2021

In situ study on the compression deformation of MoNbTaVW high-entropy alloy

The excellent mechanical properties of high-entropy alloys (HEAs) make them promising materials for advances in science and technology. However, the underlying mechanism of plastic deformation is not well understood. In situ experiments are urgently required to provide a fundamental understanding of the plastic deformation under high pressure. We performed in situ synchrotron X-ray diffraction (XRD) experiments to study compression deformation behavior of the HEA MoNbTaVW in a radial diamond anvil cell (rDAC). Our results show that the strength and ratio of the stress-to-shear modulus values are ~1.5 and 3 times that of pure tungsten (W), respectively. MoNbTaVW showed plastic deformation above 5 GPa and displayed a much stronger texture. In this work, we found that the active dislocation behavior is mainly responsible for the high strength in MoNbTaVW under compression. This unique technique opens a new avenue to investigate the in situ mechanical properties and their mechanism in other types of HEAs.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Yield strength prediction of high-entropy alloys using machine learning

Yield strength at high temperature is an important parameter in the design and application of high entropy alloys (HEAs). However, the experimental measurement of yield strength at high temperature is quite costly, complicated, and time-consuming. Therefore, it is essential to identify and apply a robust method for the accurate prediction of yield strength at high temperature from the available experimental and simulation data. In this study, for the first time, a machine learning (ML) method based on the regression technique of random forest (RF) regressor is used to predict the yield strength of HEAs at the desired temperature. Further, the yield strengths of MoNbTaTiW and HfMoNbTaTiZr at 800 °C and 1200 °C, are predicted using the RF regressor model. We find that the results are consistent with the experimental reports, showing that the RF regressor model predicts the yield strength of HEAs at the desired temperatures with high accuracy.

36 MATERIALS SCIENCE↗

DOE OSTI2020

First-principles study on the mechanical and thermodynamic properties of MoNbTaTiW

Refractory high-entropy alloys (RHEAs) are emerging as new materials for high temperature structural applications because of their stable mechanical and thermal properties at temperatures higher than 2273 K. In this study, the mechanical properties of MoNbTaTiW REDEA are examined by applying calculations based on first-principles density functional theory (DFT) and using a large unit cell with 100 randomized atoms. The phase calculation of MoNbTaTiW with CALPHAD method shows the existence of a stable body-centered cubic structure at a high temperature and a hexagonal closely packed phase at a low temperature. The predicted phase, shear modulus, Young’s modulus, Poisson’s ratio, and hardness values are consistent with available experimental results. The linear thermal expansion coefficient, vibrational entropy, and vibrational heat capacity of MoNbTaTiW RHEA are investigated in accordance with Debye-Grüneisen theory. These results may provide a basis for future research related to the application of RHEAs.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Carbide Formation in Refractory Mo 15 Nb 20 Re 15 Ta 30 W 20 Alloy under a Combined High-Pressure and High-Temperature Condition

In this work, the formation of carbide with the concertation of carbon at 0.1 at.% in refractory high-entropy alloy (RHEA) Mo 15 Nb 20 Re 15 Ta 30 W 20 was studied under both ambient and high-pressure high-temperature conditions. The x-ray diffraction of dilute carbon (C)-doped RHEA under ambient pressure showed that the phases and lattice constant of RHEA were not influenced by the addition of 0.1 at.% C. In contrast, C-doped RHEA showed unexpected phase formation and transformation under combined high-pressure and high-temperature conditions by resistively employing the heated diamond anvil cell (DAC) technique. The new FCC_L1 2 phase appeared at 6 GPa and 809 °C and preserved the ambient temperature and pressure. High-pressure and high-temperature promoted the formation of carbides Ta 3 C and Nb 3 C, which are stable and may further improve the mechanical performance of the dilute C-doped alloy Mo 15 Nb 20 Re 15 Ta 30 W 20 .

36 MATERIALS SCIENCE↗

DOE OSTI2020

Mechanical and Thermal Properties of Low-Density Al 20+x Cr 20-x Mo 20-y Ti 20 V 20+y Alloys

Refractory high-entropy alloys (RHEAs) Al 20+x Cr 20-x Mo 20-y Ti 20 V 20+y ((x, y) = (0, 0), (0, 10), and (10, 15)) were computationally studied to obtain a low density and a better mechanical property. The density functional theory (DFT) method was employed to compute the structural and mechanical properties of the alloys, based on a large unit cell model of randomly distributed elements. Debye–Grüneisen theory was used to study the thermal properties of Al 20+x Cr 20-x Mo 20-y Ti 20 V 20+y . The phase diagram calculation shows that all three RHEAs have a single body-centered cubic (BCC) structure at high temperatures ranging from 1000 K to 2000 K. The RHEA Al 30 Cr 10 Mo 5 Ti 20 V 35 has shown a low density of 5.16 g/cm 3 and a hardness of 5.56 GPa. The studied RHEAs could be potential candidates for high-temperature application materials where high hardness, ductility, and low density are required.

36 MATERIALS SCIENCE↗