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- Genre: Masters Thesis
Description
This thesis intends to cover the experimental investigation of the propagation of laser-generated optoacoustic waves in structural materials and how they can be utilized for damage detection. Firstly, a system for scanning a rectangular patch on the sample is designed. This is achieved with the help of xy stages which are connected to the laser head and allow it to move on a plane. Next, a parametric study was designed to determine the optimum testing parameters of the laser. The parameters so selected were then used in a series of tests which helped in discerning how the Ultrasound Waves behave when damage is induced in the sample (in the form of addition of masses). The first test was of increasing the mases in the sample. The second test was a scan of a rectangular area of the sample with and without damage to find the effect of the added masses. Finally, the data collected in such a manner is processed with the help of the Hilbert-Huang transform to determine the time of arrival. The major benefits from this study are the fact that this is a Non-Destructive imaging technique and thus can be used as a new method for detection of defects and is fairly cheap as well.
ContributorsRavi Narayanan, Venkateshwaran (Author) / Liu, Yongming (Thesis advisor) / Zhuang, Houlong (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2019
Description
Corrosion fatigue has been of prime concern in railways, aerospace, construction industries and so on. Even in the case of many medical equipment, corrosion fatigue is considered to be a major challenge. The fact that even high strength materials have lower resistance to corrosion fatigue makes it an interesting area for research. The analysis of propagation of fatigue crack growth under environmental interaction and the life prediction is significant to reduce the maintenance costs and assure structural integrity. Without proper investigation of the crack extension under corrosion fatigue, the scenario can lead to catastrophic disasters due to premature failure of a structure. An attempt has been made in this study to predict the corrosion fatigue crack growth with reasonable accuracy. Models that have been developed so far predict the crack propagation for constant amplitude loading (CAL). However, most of the industrial applications encounter random loading. Hence there is a need to develop models based on time scale. An existing time scale model that can predict the fatigue crack growth for constant and variable amplitude loading (VAL) in the Paris region is initially modified to extend the prediction to near threshold and unstable crack growth region. Extensive data collection was carried out to calibrate the model for corrosion fatigue crack growth (CFCG) based on the experimental data. The time scale model is improved to incorporate the effect of corrosive environments such as NaCl and dry hydrogen in the fatigue crack growth (FCG) by investigation of the trend in change of the crack growth. The time scale model gives the advantage of coupling the time phenomenon stress corrosion cracking which is suggested as a future work in this paper.
ContributorsKurian, Bianca (Author) / Liu, Yongming (Thesis advisor) / Nian, Qiong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2019
Description
This research seeks to answer the question if there is a singular relationship between stishovite nucleation and the atomistic structure of the preshocked amorphous SiO$_2$. To do this a stishovite manufacturing method is developed in which 1,152 samples were produced. The majority of these samples did crystallize. The method was produced through two rounds of experiments and fine-tuning with the pressure damp, temperature damp, shock pressure using an NPHug fix, and sample origin. A new random atomic insertion method was used to generate a new and different SiO$_2$ amorphous structure not before seen within the research literature. The optimal values for shock were found to be 60~GPa for randomly atom insertion samples and 55~GPa for quartz origin samples. Temperature damp appeared to have a slight effect optimizing at 0.05~ps and the pressure damp had no visible effect, testing was done with temperature damp from 0.05 to 0.5~ps and pressure damp from 0.1 to 10.0~ps. There appeared to be significant randomness in crystallization behavior. The preshocked and postnucleated samples were transformed into Gaussian fields of crystal, mass, and charge. These fields were divided and classified using a cut-off method taking the number of crystals produced in portions of each simulation and classifying each potion as nucleated or non-nucleated. Data in which some nucleation but not a critical amount was present was removed constituting 2.6\% to 20.3\% of data in all tests. A max method was also used which takes only the maximum portions of each simulation to classify as nucleating. There are three other variables tested within this work, a sample size of 18,000 or 72,728~atoms, Gaussian variance of 1 or 4~\AA, and Convolutional neural network (CNN) architecture of a garden verity or all convolution along with the portioning classification method, sample origination, and Gaussian field type. In total 64 tests were performed to try every combination of variable. No significant classifications were made by the CNNs to nucleation or non-nucleation portions. The results clearly confirmed that the data was not abstracting to atomistic structure and was random by all classifications of the CNNs. The all convolution CNN testing did show smoother outcomes in training with less fluctuations. 59\% of all validation accuracy was held at 0.5 for a random state and 84\% was within $\pm0.02$ of 0.5. It is conclusive that prenucleation structure is not the sole predictor of nucleation behavior. It is not conclusive if prenucleation structure is a partial or non-factor within nucleation of stishovite from amorphous SiO$_2$.
ContributorsChristen, Jonathan Scorr (Author) / Oswald, Jay (Thesis advisor) / Muhich, Christopher (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Over a population of flexible sheet metal assemblies of the same design, such as anautomobile door or hood, there typically are geometric variations that can become
apparent as a changing gap between the door/hood and the auto body. The gap
variations stem from allowable tolerances for the material (e.g., thickness, strength),
spring-back of components post-stamping, and residual stress distortions following
clamping and spot welding of the assemblies. To control the geometric effect from
these input variations, multi-stage nonlinear finite element analyses (FEA) are used
to relate the free-state nodal-point geometry to the inputs. Since the analyses are
time-consuming, a sparse grid of input values (the design space) is used.
Given the sprung-back nodal data from FEA, the objectives of this research are
to construct algorithms that generate geometric performance parameters (e.g. component
or assembly twist) and to explore the application of machine learning (ML)
for predicting these parameters in flexible assemblies at a greater resolution over
the design space than is feasible with FEA. To achieve this goal, three datasets of
FEA simulations were examined. Geometric parameters were extracted from two of
these. The first was for stamped hat-section sheet metal components and for straight
assemblies spot-welded from them, some with a flat unformed sheet as one component.
Subsequently, one of the assembly parameters, a minimum-zone magnitude,
was used to showcase the suitability of geometric parameters to be used for training a
ML algorithm, specifically a fully connected Multi-Layer Perceptron Neural Network.
The second dataset consisted of the nodal coordinates from FEA simulations of a
Honda-suggested T-joint assembly. Here, algorithms were produced for computing
17 geometric performance parameters. The third dataset comprised multiview images
of automobile hood structures. Three deep learning models were compared for
accuracy of feature extraction from the images and for directly estimating maximum
stress without time-consuming FEA.
A last important contribution is the validation of the new iterative virtual-workand-
robotics method for fitting nodal points that deviate spatially from a straight line
or circular arc. This allows geometric performance parameters for curved components
to be included in future flexible assembly datasets.
ContributorsKumar, Prakash (Author) / Davidson, Joseph (Thesis advisor) / Zhuang, Houlong (Thesis advisor) / Shah, Jami (Committee member) / Arizona State University (Publisher)
Created2024
Description
Magnetic textures, like skyrmions, merons, and domain walls are predicted to be future for the next-generation data-storage and information-transfer technologies due to their ultrafast spin-switching capabilities. However, most of these textures exist at low temperatures which is an issue for these applications. This thesis studies room temperature topological magnetic materials using Chromium telluride (CrTe2) and iron gallium telluride (Fe3GaTe2) as a model system via a combination of single-crystal synthesis, experimental structural, and magnetic characterization. The scientific knowledge gained by this work will be useful in designing unique topological textures beyond traditional Skyrmions to merons, bi-skyrmions, etc. which would be useful in improving energy-efficient storage solutions and advancing computational technologies for the future.
ContributorsMujumdar, Kshitish Raghavendra (Author) / Susarla, Sandhya (Thesis advisor) / Zhuang, Houlong (Thesis advisor) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2024
Description
Accurate knowledge and understanding of thermal conductivity is very important in awide variety of applications both at microscopic and macroscopic scales. Estimation,however varies widely with respect to scale and application. At a lattice level, calcu-lation of thermal conductivity of any particular alloy require very heavy computationeven for a relatively small number of atoms. This thesis aims to run conventionalmolecular dynamic simulations for a particular supercell and then employ a machinelearning based approach and compare the two in hopes of developing a method togreatly reduce computational costs as well as increase the scale and time frame ofthese systems. Conventional simulations were run using interatomic potentials basedon density function theory-basedab initiocalculations. Then deep learning neuralnetwork based interatomic potentials were used run similar simulations to comparethe two approaches.
ContributorsDabir, Anirudh (Author) / Zhuang, Houlong (Thesis advisor) / Nian, Qiong (Committee member) / Jiao, Yang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Alloying in semiconductors has enabled many civilian technologies in optoelectronic, photonic fields and more. While the phenomenon of alloying is well established in traditional bulk semiconductors, owing to vastly available ternary phase diagrams, the ability to alloy in 2D systems are less clear. Recently anisotropic materials such as ReS2 and TiS3 have been extensively studied due to their direct-gap semiconductor and high mobility behaviors. This work is a report on alloys of ReS2 & ReSe2 and TiS3 &TiSe3.
Alloying selenium into ReS2 in the creation of ReS2xSe2-x, tunes the band gap and changes its vibrational spectrum. Depositing this alloy using bottom up approach has resulted in the loss of crystallinity. This loss of crystallinity was evidenced by grain boundaries and point defect shown by TEM images.
Also, in the creation of TiS3xSe3-x, by alloying Se into TiS3, a fixed ratio of 8% selenium deposit into TiS3 host matrix is observed. This is despite the vastly differing precursor amounts and growth temperatures, as evinced by detailed TEM, EDAX, TEM diffraction, and Raman spectroscopy measurements. This unusual behavior contrasts with other well-known layered material systems such as MoSSe, WMoS2 where continuous alloying can be attained. Cluster expansion theory calculations suggest that only limited composition (x) can be achieved. Considering the fact that TiSe3 vdW crystals have not been synthesized in the past, these alloying rejections can be attributed to energetic instability in the ternary phase diagrams estimated by calculations performed. Overall findings highlight potential means and challenges in achieving stable alloying in promising direct gap and high carrier mobility TiS3 materials.
Alloying selenium into ReS2 in the creation of ReS2xSe2-x, tunes the band gap and changes its vibrational spectrum. Depositing this alloy using bottom up approach has resulted in the loss of crystallinity. This loss of crystallinity was evidenced by grain boundaries and point defect shown by TEM images.
Also, in the creation of TiS3xSe3-x, by alloying Se into TiS3, a fixed ratio of 8% selenium deposit into TiS3 host matrix is observed. This is despite the vastly differing precursor amounts and growth temperatures, as evinced by detailed TEM, EDAX, TEM diffraction, and Raman spectroscopy measurements. This unusual behavior contrasts with other well-known layered material systems such as MoSSe, WMoS2 where continuous alloying can be attained. Cluster expansion theory calculations suggest that only limited composition (x) can be achieved. Considering the fact that TiSe3 vdW crystals have not been synthesized in the past, these alloying rejections can be attributed to energetic instability in the ternary phase diagrams estimated by calculations performed. Overall findings highlight potential means and challenges in achieving stable alloying in promising direct gap and high carrier mobility TiS3 materials.
ContributorsAgarwal, Ashutosh (Author) / Tongay, Sefaattin (Thesis advisor) / Green, Matthew (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2018
Quantum Mechanical Study of the Electronic Structure and Thermoelectric Properties of Heusler Alloys
Description
Heusler alloys were discovered in 1903, and materials with half-metallic characteristics have drawn more attention from researchers since the advances in semiconductor industry [1]. Heusler alloys have found application as spin-filters, tunnel junctions or giant magnetoresistance (GMR) devices in technological applications [1]. In this work, the electronic structures, phonon dispersion, thermal properties, and electrical conductivities of PdMnSn and six novel alloys (AuCrSn, AuMnGe, Au2MnSn, Cu2NiGe, Pd2NiGe and Pt2CoSn) along with their magnetic moments are studied using ab initio calculations to understand the roots of half-metallicity in these alloys of Heusler family. From the phonon dispersion, the thermodynamic stability of the alloys in their respective phases is assessed. Phonon modes were also used to further understand the electrical transport in the crystals of these seven alloys. This study evaluates the relationship between materials' electrical conductivity and minority-spin bandgap in the band structure, and it provides suggestions for selecting constituent elements when designing new half-metallic Heusler alloys of C1b and L21 structures.
ContributorsPatel, Deep (Author) / Zhuang, Houlong (Thesis advisor) / Solanki, Kiran (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2023
Description
Computing the fluid phase interfaces in multiphase flow is a challenging area of research in fluids. The Volume of Fluid andLevel Set methods are a few algorithms that have been developed for reconstructing the multiphase fluid flow interfaces.
The thesis work focuses on exploring the ability of neural networks to reconstruct the multiphase fluid flow interfaces using
a data-driven approach.
The neural network model has liquid volume fraction stencils as an input, and it predicts the radius of the circle as an
output of the network which represents a phase interface separating two immiscible fluids inside a fluid domain. The liquid
volume fraction stencils are generated for randomly varying circle radii within a 1x1 domain using an open-source VOFI
library. These datasets are used to train the neural network. Once the model is trained, the predicted circular phase
interface from the neural network output is used to generate back the predicted liquid volume fraction stencils.
Error norms values are calculated to assess the error in the neural network model’s predicted liquid volume fraction
stencils with the actual liquid volume fraction stencils from the VOFI library. The neural network parameters are optimized
by testing them for different hyper-parameters to reduce the error norms. So as to minimize the difference between the
predicted and the actual liquid volume fraction stencils and errors in reconstructing the fluid phase interface geometry.
ContributorsPawar, Pranav Rajesh (Author) / Herrmann, Marcus (Thesis advisor) / Zhuang, Houlong (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2023
Description
The study aims to develop and evaluate failure prediction models that accurately predict crack initiation sites, fatigue life in additively manufactured Ti-6Al-4V, and burst pressure in relevant applications.The first part proposes a classification model to identify crack initiation sites in AM-built Ti-6Al-4V alloy. The model utilizes surface and pore-related parameters and achieves high accuracy (0.97) and robustness (F1 score of 0.98). Leveraging CT images for characterization and data extraction from the CT-images built STL files, the model effectively detects crack initiation sites while minimizing false positives and negatives. Data augmentation techniques, including SMOTE+Tomek Links, are employed to address imbalanced data distributions and improve model performance.
This study proposes the Probabilistic Physics-guided Neural Network 2.0 (PPgNN) for probabilistic fatigue life estimation. The presented approach overcomes the limitations of classical regression machine models commonly used to analyze fatigue data. One key advantage of the proposed method is incorporating known physics constraints, resulting in accurate and physically consistent predictions.
The efficacy of the model is demonstrated by training the model with multiple fatigue S-N curve data sets from open literature with relevant morphological data and tested using the data extracted from CT-built STL files. The results illustrate that PPgNN 2.0 is a flexible and robust model for predicting fatigue life and quantifying uncertainties by estimating the mean and standard deviation of the fatigue life. The loss function that trains the proposed model can capture the underlying distribution and reduce the prediction error.
A comparison study between the performance of neural network models highlights the benefits of physics-guided learning for fatigue data analysis. The proposed model demonstrates satisfactory learning capacity and generalization, providing accurate fatigue life predictions to unseen examples.
An elastic-plastic Finite Element Model (FEM) is developed in the second part to assess pipeline integrity, focusing on burst pressure estimation in high-pressure gas pipelines with interactive corrosion defects. The FEM accurately predicts burst pressure and evaluates the remaining useful life by considering the interaction between corrosion defects and neighboring pits. The FEM outperforms the well-known ASME-B31G method in handling interactive corrosion threats.
ContributorsBalamurugan, Rakesh (Author) / Liu, Yongming (Thesis advisor) / Zhuang, Houlong (Committee member) / Bhate, Dhruv (Committee member) / Arizona State University (Publisher)
Created2023