Engineering topics
Hu, Shanshan
Publications and source records attributed to Hu, Shanshan.
Machine learning and high-throughput computational guided development of high temperature oxidation-resisting Ni-Co-Cr-Al-Fe based high-entropy alloys
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Enhancing the accuracy and generality of the Debye–Grüneisen Model: Optimizing the volume dependence for accurate predictions across varied compositions
In this work, we have introduced an optimized Debye-Grüneisen model that revolutionizes the determination of the Debye temperature and Grüneisen parameters. Unlike conventional methods, our model requires only the 0 K energy volume data for a material as input, eliminating the need to determine the bulk modulus and its pressure derivative, which often pose challenges due to numerical uncertainties. This unique feature sets our model apart from existing approaches and streamlines the process, enabling accurate predictions of thermal expansion behavior across various materials. To demonstrate its effectiveness, we showcase its excellent agreement with measured coefficients of thermal expansion (CTE) for the nickel-cobalt-chromium-aluminum-yttrium (Ni-Co-Cr-Al-Y) bond-coating system. Additionally, we apply our approach by conducting a high-throughput search for potential bond-coating materials among 90,000 compositions within the aluminum-cobalt-chromium-iron-nickel (Al-Co-Cr-Fe-Ni) system. From this extensive search, four compositions are synthesized, and the measured CTE values agree very well with theoretical predictions, hence validating our approach. In conclusion, the current optimized Debye-Grüneisen model combined with Density Functional Theory (DFT)-based thermodynamic database enables reliable and efficient high-throughput calculations of CTE of of a material without expensive phonon calculations.
Computational design of high entropy alloy coating for hydrogen turbine applications
This project aims to develop novel high entropy alloy (HEA)-based coatings to protect critical components in hydrogen-fueled turbine power system. The HEA-coatings will demonstrate superior performance in hydrogen combustion environment to commercial NiCoCrAlY coating in current natural gas turbine system. The HEA coating facilitates the formation of a protective scale of alpha-alumina to slow down the inward diffusion of oxidizing species and the outward diffusion of metal elements, and possesses ultrahigh corrosion and spallation resistance to prolong the service lifetime of critical components in hydrogen turbine power system. Aimed to accelerate the discovery of novel HEA coating compositions, high throughput computational modeling including CALPHAD and density functional theory and machine learning are performed to predict phase stability, oxygen permeability, oxidation rate constant, coefficient of thermal expansion, and mechanical properties. Based on the modeling and machine learning prediction, experimental validation is performed. Preliminary results will be presented and approaches to minimize oxidation will be discussed.
Analysis of dislocation configurations in SiC crystals through X-ray topography aided by ray tracing simulations
Silicon carbide as a wide bandgap semiconductor is of great research interest for its widespread deployment in a range of electronic and optoelectronic devices, particularly in power electronics. However, defects in silicon carbide crystals are still major concerns that is hampering the development of high-performance devices. X-ray topography, particularly using the synchrotron beam has been instrumental in characterizing and analyzing defect configurations in silicon carbide crystals to optimize crystal growth as well as understand the effect of defects on device performance. Here, in recent years, the use of ray-tracing simulation technique based on the orientation contrast mechanism to simulate contrast of defects observed on actual X-ray topographs has proven to be an effective approach to investigate the nature of crystallographic defects in various semiconductors. This review discusses the principle of ray-tracing simulation and its application and modifications to incorporate the effects of surface relaxation and photoelectric absorption to better simulate different dislocations observed in 4H–SiC as well as 6H–SiC crystals of various orientations. The adaptation to weak beam topography and plane wave topography is also discussed. The application of ray-tracing simulation in dislocation characterization of silicon carbide of different polytypes is systematically reviewed including different types of dislocations observed in both off-axis wafers and axial-sliced samples through synchrotron X-ray topography under various beam conditions, recording geometries and reflections. The result of ray-tracing simulation is further utilized in other studies including the investigation of effective penetration depth of all types of dislocations lying on the basal plane on grazing-incidence X-ray topography.
Investigation of defect formation at the early stage of PVT-grown 4H-SiC crystals
Here, several 4° off-axis 4H-SiC wafers with several hundred microns of initial-stage growth by PVT method are investigated by Synchrotron Monochromatic Beam X-ray Topography (SMBXT). Defect behavior across the seed/newly grown layer interface are demonstrated. Comparison of early stage grown layers to seed sample indicates generation of threading edge dislocation (TED) and threading screw dislocation (TSD)/ threading mixed dislocation (TMD) pairs at the interface while most basal plane dislocations (BPDs) are deflected into TEDs. The (0001) facet of the crystal is already formed at early stage growth at the edge of the wafer and high nitrogen incorporation in facet leads to conditions favorable for nucleation and glide of Shockley/double Shockley faults with layers of 3C-SiC deposited on facet acting as nuclei. Unique-shaped dislocations are observed at early stage growth, which are caused by deflection of TSDs/TMDs and TEDs by macrosteps near the periphery of the sample and the subsequent glide of the a components. The effect of the quality of the seed surface before growth is manifested as randomly oriented arrays of pairs of TEDs and TSDs/TMDs on the as grown surface resulting from residual surface damage from scratches.
Hot Corrosion Monitoring in Boilers Using a Nonlinear Estimator and Electrochemical Noise-Based Corrosion Sensors
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Novel refractory high-entropy metal-ceramic composites with superior mechanical properties
A new concept of refractory high-entropy metal-ceramic composites (HEMCC) has been proposed that combines the outstanding physical properties of both high-entropy alloy (HEA) and high-entropy ceramic (HEC). As the first HEMCC system, to the best of our knowledge, TiTaNbZr-(TiTaNbZr)C, has been developed by a powder metallurgy process. The HEA and HEC phases exhibit body-centered cubic (BCC) and rock-salt B1 crystal structures, respectively, and both phases have non-equimolar chemical compositions. Further, with the increase of the HEC phase in HEMCC, the hardness is enhanced while the density and fracture toughness are decreased. HEA50C sintered from 50vol% HEA and 50vol% HEC precursor powders shows a favorable combination of flexural strength (541±48MPa) and fracture toughness (6.93±0.27 MPa·m 1/2 ) at room temperature and a high compressive strength at 1300ºC (275MPa). The optimized mechanical performance of HEMCC might be attributed to the combination of the ductile HEA and strong HEC phases, smaller grain size, and crack arrest at HEC/HEA interfaces.
High Temperature Electrochemical Sensors for In-Situ Corrosion Monitoring in Coal-Based Power Generation Boilers
In this project, we successfully achieved the goals we proposed: 1. optimization and development of electrochemical sensor, 2. sensor construction and package, 3. sensor testing @Longview Power Plant and data analysis, 4. lab-scale sensor optimization and corrosion database development, 5. electrochemical and corrosion monitoring validation and 6. TEA.