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(1 - 3 of 3)
- Title
- THERMODYNAMIC PROPERTIES AND PHASE EQUILIBRIA OF SELECTED HEUSLER COMPOUNDS
- Creator
- Yin, Ming
- Date
- 2015, 2015-12
- Description
-
Heusler compounds are ternary intermetallics with many promising properties such as spin polarization and magnetic shape memory effect. A...
Show moreHeusler compounds are ternary intermetallics with many promising properties such as spin polarization and magnetic shape memory effect. A better understanding of their thermodynamic properties facilitates future design and development. Therefore, standard enthalpies of formation and heat capacities from room temperature to 1500 K of selected Heusler compounds X2YZ (X = Co, Fe, Ni, Pd, Rh, Ru; Y = Co, Cu, Fe, Hf, Mn, Ni, Ti, V, Zr; Z = Al, Ga, In, Si, Ge, Sn) and half-Heusler compounds XYSn (X = Au, Co, Fe, Ir, Ni, Pd, Pt, Rh; Y = Hf, Mn, Ti, Zr) were measured using high temperature direct reaction calorimetry. The measured standard enthalpies of formation were compared with those predicted from ab initio calculations and the extended semiempirical Miedema's model. Trends in standard enthalpy of formation with respect to the periodic classification of elements were discussed. The effect of a fourth element (Co, Cu, Fe, Pd; Ti, V; Al, Ga, In, Si, Ge) on the standard enthalpy of formation of Ni2MnSn was also investigated. Lattice parameters of the compounds with an L21 structure were determined using X-ray powder diffraction analysis. Differential scanning calorimetry was used to determine melting points and phase transformation temperatures. Phase relationships were investigated using scanning electron microscopy with an energy dispersive spectrometer. The isothermal section of the Fe-Sn-Ti ternary system at 873 K was established using equilibrated alloys. Three ternary compounds including the Heusler compound Fe2SnTi were observed. A new ternary compound Fe5Sn9Ti6 was reported and the crystal structure of FeSnTi2 was determined for the first time.
Ph.D. in Mechanical, Materials and Aerospace Engineering
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- Title
- Inviscid Shock Propagation within a Variable-Geometry Scramjet Inlet
- Creator
- Grybko, Maciej
- Date
- 2021
- Description
-
The study concerns the propagation of shockwaves within an inlet of a scramjet engine and effect of inlet geometry variation on performance. A...
Show moreThe study concerns the propagation of shockwaves within an inlet of a scramjet engine and effect of inlet geometry variation on performance. A Python code was developed to simulate and visualize a flowfield within a scramjet inlet, based on inviscid oblique shock theory. The program was validated against NASA Shock software, and the results differed only by round-off error (0.05%). Subsequently a geometric sensitivity study was conducted, showing that throughout acceleration from Mach 5 to Mach 20 parameters like inlet height could be varied to ensure constant number of shocks within an inlet (preventing discontinuous changes of flowfield), whereas lower wedge angle could control compression required for optimal combustion. Correspondingly, a trajectory was determined with a constraint on static pressure entering combustion chamber (100 kPa). For an arbitrary baseline inlet geometry, it was established that beyond Mach 10 the scramjet would exceed structural load limit, despite delivering sufficient conditions for rapid combustion. Nevertheless, below Mach 10 it would operate efficiently, proving that hydrocarbon-fueled scramjets can have a fixed geometry. For higher speeds, a variable geometry is a necessity.
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- Title
- Machine Learning for NDE Imaging Applications
- Creator
- Zhang, Xin
- Date
- 2023
- Description
-
Infrared Thermography and Ultrasonic Imaging of materials are promising non-destructive evaluation (NDE) methods but signals face challenges...
Show moreInfrared Thermography and Ultrasonic Imaging of materials are promising non-destructive evaluation (NDE) methods but signals face challenges to be analyzed and characterized due to the nature of complex signal patterns and poor signal-to-noise ratios (SNR). Industries such as nuclear energy, are constructed with components produced using high-strength superalloys. These metallic components face challenges for wide deployment because material defects and mechanical conditions need to be non-destructively evaluated to identify potential danger before they enter service. Low NDE performance and lack of automation, particularly considering the complex environment in the in-situation NDE and nuclear power plant, present a major challenge to implement conventional NDE. This study solves the problems of using the advantages of machine learning as signal processing methods for Infrared Thermography and Ultrasonic NDE imaging applications. In Pulsed Infrared Thermography (PIT), for quality control of metal additive manufacturing, we proposed an intelligent PIT NDE system and developed innovative unsupervised learning models and thermal tomography 3D imaging algorithms to detect calibrated internal defects (pores) of various sizes and depths for different nuclear-grade metallic structures. Unsupervised learning aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Difficulties to detect defects by using PIT are thermal imaging noise patterns; uneven heating of the specimen; defects of micron-level size with overly weak temperature signals and so on. The unsupervised learning methods overcome these barriers and achieve the high defect detection accuracies (F-score) of 0.96 to detect large defects and 0.89 to detect microscopic defects, and can successfully detect defects with diameter of only 0.101-mm. In addition, we researched and developed innovative unsupervised learning models to compress high-resolution PIT imaging data and achieve the average high compression ratio >30 and a highest compression of 46 with reconstruction accuracy peak signal-to-noise ratio (PSNR) >73dB while preserving weak thermal features corresponding to microscopic defects. In ultrasonic NDE imaging, for structural health monitoring of materials, we built a high-performance ultrasonic computational system to inspect the integrity of high-strength metallic materials which are used in high-temperature corrosive environments of nuclear reactors. For system automation, we have been developing neural networks with various architectures for grain size estimation by characterizing the ultrasonic backscattered signals with high accuracy and data-efficiency. In addition, we introduce a response-based teacher-student knowledge distillation training framework to train neural networks and achieve 99.27% characterization accuracy with a high image processed throughput of 192 images/second on testing. Furthermore, we introduce a reinforcement learning based neural architecture search framework to automatically model the optimal neural networks design for ultrasonic flaws detection. At last, we comprehensively researched the performance of using unsupervised learning methods to compress 3D ultrasonic data and achieve high compression performance using only 4.25% of the acquired experimental data.
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