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ÖgeThermodynamic stability of binary compounds: A comprehensive computational and machine learning approach(Graduate School, 2024-06-06)Exploration and exhaustive comprehension of novel materials are the main objectives of materials science. Laboratory evaluations have been the primary method by which substantial advancements have been achieved throughout the development of this scientific field. The contributions of density functional theory (DFT) algorithms have significantly altered the field of materials science over the past twenty years. These algorithms balance accuracy and efficiency. Supercomputers have enabled substantial breakthroughs in predicting electrical properties of crystal formations, facilitating a fundamental transition in the discipline. Developments of robust algorithms and lower computing costs have made data-driven approaches in materials research more widely adopted. Researchers can now analyze enormous datasets to guide experiments and uncover novel materials. Although databases are frequently used in contemporary materials science, there are some gaps regarding phonon calculations and the thermal properties of compounds. To address this deficiency, this thesis calculates the phonon stability, heat capacities at 298.15 K, formation enthalpies, formation entropies, and Gibbs free energies of binary structures. A total of 879 binary structures were examined, and the results of these calculations were compiled into a data set. In a recent study by my research team, the formation enthalpies and mechanical strengths of binary structures at absolute zero were investigated. This thesis contributes to this work by providing detailed analyses of the dynamic stability and thermodynamic properties of the same binary structures, supporting the findings of my team's prior research. In the initial phase of this thesis, the thermodynamic properties and phonon stabilities of the compounds were calculated. Subsequently, inspired by the PN-PN table model proposed and utilized in our team's recent work, this data set was mapped and visualized on a PN-PN table according to the periodic numbers (PN) assigned to the elements in the structures. This approach enabled the integrated visualization of phonon stability and other thermodynamic properties. Consequently, the chemical similarities between structures were more easily comprehended through the groups in the map, and the so-called forbidden regions were highlighted. Forbidden regions are regions in which specific pairings of elements are unable to form stable phases, which provides critical information on stability based on the PN numbers of the elements. The basic principle of the periodic numbering approach is as follows: First, periodic numbers (PN) are assigned to the elements with respect to their electronegativity, principal quantum number, and valence shell configuration, and then this numbering is extended to binary systems. This makes it easier to understand the chemical trends in the compounds formed by the elements and to predict phase formation. Although there are some exceptions in this mapping, it clearly shows the structures where phase formation is not expected. In our team's previous work, the PN-PN table significantly facilitated the identification of critical regions in different chemical systems and allowed for the analysis of trends in the chemical properties of equiatomic binary phases. Based on this, density functional theory-based thermodynamic calculations were performed in this thesis, providing thermodynamic data supporting the inferences of formation enthalpy and crystal structure stability calculated in our team's previous studies. A total of 879 structures' phonon stabilities were determined, and heat contribution values were calculated. Thus, the phonon stability and heat contribution data obtained from this thesis can be integrated with the mechanical strength properties of the structures from our team's previous findings. This allows for a more detailed interpretation of the relationship between phonon and mechanical stability. Additionally, using the elemental and structural properties of the compounds, machine learning techniques were applied to the current data set. Random Forest, Support Vector Machines (SVM), Gradient Boosting, and Decision Trees were assessed for their capacity to predict phonon stability. The Decision Tree model exhibited the highest performance, with an accuracy rate of 80\%. These models' accuracy was significantly enhanced by elemental descriptors such as band center, mean covalent radius, and mean electronegativity. The band center indicates the effect of the position in the electronic band structure on phonon stability, the mean covalent radius reflects the bonding properties of atoms, and the mean electronegativity determines the atoms' tendencies to attract electrons, thus affecting phonon stability. For predicting Gibbs free energy, Random Forest Regression, K-Nearest Neighbors (KNN) Regression, Support Vector Regression (SVR), and Linear Regression models were used. The performance of these models was evaluated using a 5-fold cross-validation method. The Random Forest Regression model exhibited the highest performance with an average score of 0.846. This result indicates that Random Forest Regression is the most effective model for predicting Gibbs free energy. These findings may encourage the broader application of machine learning techniques in future research. This significant step in understanding and modeling thermodynamic properties plays a critical role in optimizing material structures. In the future, it is expected that the methods of this study will be adapted and developed more specifically for certain material classes or other academic applications. This approach also serves as an efficient example of the discovery and design planning processes in materials science.
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ÖgeOptimization of mixing efficiency of air and hydrogen in scramjet combustor(Graduate School, 2025-01-22)This thesis investigates the optimization of a scramjet combustor to enhance mixing efficiency and total pressure recovery (TPR) through a combination of computational fluid dynamics (CFD) simulations and Bayesian optimization (BO). Scramjet engines, operating at hypersonic speeds, rely on efficient fuel-air mixing in a very short timeframe to achieve effective combustion. Improving mixing efficiency is critical for ensuring stable combustion and maximizing thrust, while maintaining TPR is essential to harness the energy of the flow without incurring excessive pressure losses. A two-dimensional CFD model of a scramjet combustor, developed using OpenFOAM's reactingFoam solver with chemical reactions disabled, forms the core of this investigation. The model incorporates a k–ω SST turbulence model to accurately capture complex flow phenomena, including shock-wave/boundary-layer interactions and turbulent shear layers. The combustor geometry is based on a DLR configuration and is systematically varied by changing key geometric parameters: wedge angle, distance between injectors, and injection angle. These parameters influence the flow structure, jet penetration, and turbulence intensity, ultimately affecting both mixing efficiency and TPR. Bayesian optimization is employed to identify the optimal combination of parameters. A Gaussian Process (GP) surrogate model approximates the objective function, defined as a weighted sum of mixing efficiency and TPR. The optimization process begins with an initial set of samples selected systematically (without employing previously assumed sampling methods), ensuring a broad exploration of the parameter space. The GP surrogate is iteratively updated as new CFD evaluations are performed, guiding the search toward promising regions that balance exploration and exploitation. Results demonstrate that carefully chosen parameters can significantly improve mixing efficiency and achieve a favorable compromise with TPR. The optimal configuration identified through this process enhances fuel-air interaction, resulting in more uniform distribution of the hydrogen mass fraction at downstream locations. Ultimately, this study provides valuable insights into the complex interplay between geometric design and aerodynamic performance in scramjet combustors, offering a robust methodology to guide future hypersonic propulsion system development.
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ÖgeExploiting optimal supports in enhanced multivariance products representation for lossy compression of hyperspectral images(Graduate School, 2024-01-31)Data serves as an irreplacable foundation of modern society as it is the core element of numerous fields such as technological innovations, scientific advancements, and economic decisions. It enables insights into domains of knowledge and experience, assistance with decision-making tasks, and predictions for future outcomes. It has progressed since the very beginning of time from being knowledge and information kept in physical formats like carvings on cave walls and events conserved as inscriptions, to evolving into such mathematical structures that can be obtained by any interaction made through current technological devices like interactions on social media and observations acquired through the use of advanced tools owing to technological advancements. Data transforming into more detailed and complex structures poses efficiency challenges that entails computational methods which can handle the processing and handling of such structures. For the data handling needs, many methods have been proposed and have been in use thus far. Each has its advantages as well as some drawbacks in the form of problems in either certain limitations or computational complexity issues. Some alternative workarounds have been suggested for these kinds of issues such as embracing an iterative approach rather than employing direct solutions and techniques have been customized to fit specific workflows. Moreover, some innovative approaches operate on representations of data that have undergone compression and transformation, rendering them into more easily processable structures. An important aspect of these practices is the preservation of the data's characteristic features. Compression methods execute this procedure in unique ways like exploiting the eigenvalues and eigenvectors or utilizing singular values. These techniques not only streamline the processing of data but also contribute to the efficiency and accuracy of analyses by retaining characteristic features throughout the compression process. In the field of data processing, an understanding of these diverse methodologies proves convenience in selecting the most effective solutions for the application under consideration. Hyperspectral imaging is an area that requires such computational techniques to process the collected data due to its high dimensional workflow. It outputs 3-dimensional mathematical structures where the first two dimensions correspond to the spatial attributes of the captured area while the third dimension captures the spectral information with respect to the obtaining device's capacity of retrieving bands. As a result, the fibers in the data's third dimension relate to spectral signatures that empower the identification of objects and materials. The ability to analyze these spectral data opens doors to multiple useful applications in numerous areas like remote sensing, agriculture, medical imaging, archaeology, and urban planning. Recent studies in computational sciences for high-dimensional structures have adopted new methods that improve the overall processing performance and make more in-depth analyses possible. Considering the relational design in its third dimension, the High Dimensional Model Representation (HDMR) is a technique that hyperspectral imaging can benefit deeply thanks to its decorrelation properties. The aim of HDMR is to represent multivariate functions in terms of lower dimensional ones. But thanks to the way it was defined, this technique is also applicable on tensors, hence, it can be used to decompose a given tensor in terms of less dimensional entities where each element refers to the attitude of a certain combination of dimensions. This ability of HDMR addresses the decorrelation of each dimension of the given data. The decorrelation procedure enables reducing the noise and removing artifacts while preserving the high-frequency components. Hence, it can be said that HDMR is a suitable compression technique for high-dimensional data with strong relations on individual axes such as hyperspectral images. HDMR employs a set of weights and support vectors to represent data, consequently, necessitating calculation steps. These entities are either assigned certain values or arranged using techniques like Averaged Directional Supports (ADS) but the process of calculating the optimal entities can also be optimized by employing iterative methods such as the Alternating Direction Method of Multipliers (ADMM) where the entailments of HDMR could be used as constraints of ADMM. A sub-method of HDMR which is called the Enhanced Multivariance Products Representation (EMPR) specializes in optimizing the representation by focusing on the support vectors. The weights are assumed to be constant values or scalars and the support vectors are managed by the previously mentioned calculation techniques. As these methods employ the main data for the calculation of the support vectors, they introduce a more robust method EMPR compared to HDMR. Iterative approaches like ADMM can assist in properties of these support vectors such as enforcing sparsity for better representions and improving denoising capabilities. This thesis work explores the hyperspectral imaging area and proposes a new perspective on the decomposition methods by bringing a tensor-based iterative approach to EMPR through the use of ADMM. The study compares the proposed method's performance and efficiency with some other well-known tensor decomposition techniques, namely CANDECOMP/PARAFAC Alternating Least Squares (CP-ALS) and Tucker Decomposition (TD), while also comparing the results to EMPR's regular application by ADS. Multiple tests are performed on hyperspectral datasets which are 3-dimensional and as a result, the proposed technique is arranged to be applicable on any 3-dimensional tensor especially data that can benefit the decorrelation properties of EMPR. As a result of EMPR, the relations in each dimension and the combinations of these dimensions are acquired through the support vectors. Results from multiple metrics prove that the proposed method performs similarly to the mentioned tensor decomposition methods for specified ranks and the decorrelated dimensions are successfully represented by the 1-dimensional EMPR components. Tests also employ the 2-dimensional components to reveal the effect on final representations with comparisons to CP-ALS and TD aiming for multiple rank options. The key point of this proposed technique lies in EMPR's superior decorrelation ability. Not only does it demonstrate the capability of reconstructing high-dimensional data with similar accuracy but it also highlights its potential to reduce noise and artefacts in the process. These results are particularly promising for any lossy compression task including Cartesian geometry utilizing tensor decomposition techniques where accurate and efficient data processing is paramount. Furthermore, this performance advantage paves the way for advancements in lossy compression techniques, enabling researchers and practitioners to gain more precise insights from data.
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ÖgeVisualization based analysis of gene networks using high dimensional model representation(Graduate School, 2024-07-01)Genetic studies have revolutionized our understanding of the biological mechanisms underlying health and disease. By exploring the intricate details of the human genome, researchers can identify genetic variations that contribute to various phenotypic outcomes. One of the key advancements in this field is gene network analysis, which examines the complex interactions between genes and how they regulate cellular processes. This approach provides a comprehensive view of the biological systems and uncovers the pathways involved in disease mechanisms. Genome-Wide Association Studies (GWAS) play a pivotal role among the methodologies utilized in gene network analysis. GWAS involves scanning the genome for slight variations, known as single nucleotide polymorphisms (SNPs), that occur more frequently in individuals with a particular disease or trait than in those without. By identifying these associations, GWAS helps pinpoint genetic factors contributing to disease susceptibility and progression, paving the way for personalized medicine and targeted therapeutic strategies. By integrating various variant analysis techniques, researchers can develop a deeper understanding of the genetic architecture of diseases, leading to significant advancements in diagnostics, treatment, and prevention. Gene network and pathway analyses are essential components of genetic studies, offering insights into genes' complex interactions and functions within a biological systems. However, both face significant computational challenges, mainly when dealing with high-dimensional genomic data. Analyzing vast datasets containing gene expression profiles and genetic variations demands sophisticated computational methods capable of handling their scale and complexity. Conventional statistical methods frequently require assistance to become effective, demanding complex computational approaches like data visualization, network modeling, and machine learning algorithms. In addition, the complexity of biological networks and pathways makes analysis even more complicated, necessitating the use of powerful computational tools to interpret regulatory mechanisms and simulate complex biological processes correctly. Overcoming these challenges is crucial for gaining deeper insights into gene networks and pathways, thereby advancing our understanding of their roles in health and disease. In pathway analysis, scientists employ data collected from many sources, such as Genome-Wide Association Studies (GWAS), to identify target genes and connect them to known pathways using Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. However, pathway analysis presents major computing challenges, especially when large, high-dimensional genomic datasets are involved. Researchers have developed innovative methods such as High Dimensional Model Representation (HDMR), Chaos Game Representation (CGR), and visual analysis of DNA sequences based on a variant logic construction method called VARCH to overcome these challenges. By mapping genetic sequences into visual representations, these innovative approaches can help identify potential genetic markers and better understand biological processes. These computational methods must be included in gene network and pathway investigations to fully understand the complex architecture of genetic interactions and how they affect health and diseases. In this thesis, we harnessed three sophisticated computational methodologies: Chaos Game Representation, visual analysis of DNA sequences based on variant logic construction called VARCH, and High Dimensional Model Representation, each offering unique contributions to the variant analysis, respectively CGR, a prevalent technique in bioinformatics, translates genetic sequences into visually interpretable diagrams, clarifying complex structures and patterns in the sequences. On the other hand, VARCH converts sequences into a feature space, successfully capturing each aspect of their complexity and uncertainty. These techniques are effective instruments in our search for potential genetic markers that might help us distinguish between the patient and control groups in our investigation. Furthermore, we utilized HDMR for dimension reduction, an essential technique for simplifying the complex structure in high-dimensional genomic data. By condensing data dimensions, HDMR facilitated more efficient and accurate classification, enabling us to uncover sensitive genetic relationships and patterns that might have remained hidden otherwise. Integrating these computational techniques provided robust solutions for analyzing genetic data from the mTOR pathway, enriching our comprehension of the genetic mechanisms supporting various phenotypic outcomes. In our study, we begin on a mission to deepen our comprehension of the intricate genetic patterns intertwined with diverse phenotypic outcomes. Focusing on genetic data sourced from the mTOR pathway, we leveraged state-of-the-art computational methodologies to unravel hidden insights. Our primary objective was to assess the efficacy of CGR, VARCH, and HDMR in gene network analyses. As we analyzed the data, the results were quite compelling. Both CGR and VARCH methods demonstrated notable accuracy in genetic classification, with VARCH exhibiting a significant edge over CGR in terms of accuracy and sensitivity metrics. This superiority was underscored by VARCH's ability to considerably minimize binary cross-entropy (BCE) loss values, demonstrating the ability to reduce errors in predictions. However, we examined the computing overheads associated with each methodology in detail, providing insight into the challenging trade-off between computational complexity and accuracy. Despite the more significant parameters, VARCH's computational requirements were apparent, although its performance was better than CGR's. Our study demonstrates the potential of computational tools for unraveling gene complexities while also acting as an essential reminder of how crucial it is to overcome the complex environment of computational constraints carefully, helping researchers search for the best possible method selection and optimization.
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ÖgeAugmented superpixel based anomaly detection in hyperspectral imagery(Graduate School, 2024-07-01)The detection of anomalies in hyperspectral images depends on several factors. Here, the spatial proximity of anomalies and confusion in the background image can create a bottleneck at the point of anomaly detection. Hyperspectral images are tensor data, in which each pixel contains both spatial and spectral information. These complex data structures pose significant challenges for traditional anomaly detection methods, which often struggle to account for the intricate relationships between the different spectral bands. In this thesis, a method called "Augmented Superpixel (Hyperpixel) Based Anomaly Detection in Hyperspectral Imagery" is proposed. This method aims to enhance the anomaly detection by leveraging advanced dimensionality reduction and segmentation techniques. Our approach begins by reducing the three-dimensional HSI data using methods such as high-dimensional model representation and Principal Component Analysis. This step simplifies the data while preserving critical spectral and spatial information. By capturing the most significant components of the data, these techniques help eliminate noise and irrelevant details, thereby making the subsequent analysis more focused and effective. We then applied segmentation methods such as Simple Linear Iterative Clustering and Linear Spectral Clustering to divide the image into distinct regions known as superpixels. Each superpixel is augmented with its first-order neighbors to form hyperpixels, which provide a richer context for anomaly detection. The augmentation process ensures that the local context is considered, thereby enhancing the ability to detect subtle anomalies that may be missed when examining individual superpixels in isolation. This neighborhood information is crucial for accurately identifying the boundaries of anomalies and distinguishing them from normal variations in the data. Finally, we applied the Local Outlier Factor algorithm to these hyperpixels to identify the outlier points that signify anomalies. The capability of the Local Outlier Factor to evaluate local density deviations enables it to accurately identify anomalies, even in densely populated or intricate backgrounds. The combination of these techniques ensures comprehensive and precise analysis that can handle the diverse characteristics of hyperspectral datasets. The proposed algorithm was tested using various hyperspectral image datasets and demonstrated good performance in detecting anomalies. By integrating dimensionality reduction, segmentation, and anomaly detection techniques, this method effectively manages the complexity of the hyperspectral data. This comprehensive approach allows for accurate identification of anomalies, even in challenging conditions where anomalies are closely packed or the background is complex. Through rigorous experimentation, the algorithm demonstrated robustness and reliability, making it a promising tool for hyperspectral image analyses. Its versatility and high accuracy across different datasets underline its potential for broad application in fields such as remote sensing, environmental monitoring, and urban planning. The ability to adapt to various anomaly characteristics and dataset structures makes this method a valuable addition to the toolkit for hyperspectral image-analysis techniques.