Phase-field (PF) models are one of the most powerful tools to simulate microstructural evolution in metallic materials, polymers, and ceramics. However, existing PF approaches rely on rigorous mathematical model development, sophisticated numerical schemes, and high-performance computing for accuracy. Although recently developed surrogate microstructure models employ deep-learning techniques and reconstruction of microstructures from lower-dimensional data, their accuracy is fairly limited as spatio-temporal information is lost in the pursuit of dimensional reduction. Given these limitations, a novel data-driven emulator (DDE) for extrapolation prediction of microstructural evolution is presented, which combines an image-based convolutional and recurrent neural network (CRNN) with tensor decomposition, while leveraging previously obtained PF datasets for training. To assess the robustness of DDE, the emulation sequence and the scaling behavior with phase-field simulations for several noisy initial states are compared. In conclusion, the effectiveness of the microstructure emulation technique is explored in the context of accelerating runtime, along with an emphasis on its trade-off with accuracy.Meanwhile, an interpolation DDE has also been tested, which is based on obtaining a low-dimensional representation of the microstructures via tensor decomposition and subsequently predicting the microstructure evolution in the low-dimensional space using Gaussian process regression (GPR). Once the microstructure predictions are obtained in the low-dimensional space, a hybrid input-output phase retrieval algorithm will be employed to reconstruct the microstructures. As proof of concept, the results on microstructure prediction for spinodal decomposition are presented, although the method itself is agnostic of the material parameters. Results show that GPR-based DDE model are able to predict microstructure evolution sequences that closely resemble the true microstructures (average normalized mean square of 6.78 × 10−7) at time scales half of that employed in obtaining training data. This data-driven microstructure emulator opens new avenues to predict the microstructural evolution by leveraging phase-field simulations and physical experimentation where the time resolution is often quite large due to limited resources and physical constraints, such as the phase coarsening experiments previously performed in microgravity. Future work will also be discussed and demonstrate the intended utilization of these two approaches for 3D microstructure prediction through their combined application.

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.

For the past two centuries, coal has played a vital role as the primary carbon source, fueling industries and enabling the production of essential carbon-rich materials, including carbon nanotubes, graphite, and diamond. However, the global transition towards sustainable energy production has resulted in a decline in coal usage for energy purposes, with the United States alone witnessing a substantial 50% reduction over the past decade. This shift aligns with the UN’s 2030 sustainability goals, which emphasize the reduction of greenhouse gas emissions and the promotion of cleaner energy sources. Despite the decreased use in energy production, the abundance of coal has sparked interest in exploring its potential for other sustainable and valuable applications.In this context, Direct Ink Writing (DIW) has emerged as a promising additive manufacturing technique that employs liquid or gel-like resins to construct three-dimensional structures. DIW offers a unique advantage by allowing the incorporation of particulate reinforcements, which enhance the properties and functionalities of the materials. This study focuses on evaluating the viability of coal as a sustainable and cost-effective substitute for other carbon-based reinforcements, such as graphite or carbon nanotubes. The research utilizes a thermosetting resin based on phenol-formaldehyde (commercially known as Bakelite) as the matrix, while pulverized coal (250 µm) and carbon black (CB) function as the reinforcements. The DIW ink is meticulously formulated to exhibit shear-thinning behavior, facilitating uniform and continuous printing of structures. Mechanical property testing of the printed structures was conducted following ASTM standards. Interestingly, the study reveals that incorporating a 2 wt% concentration of coal in the resin yields the most significant improvements in tensile modulus and flexural strength, with enhancements of 35% and 12.5% respectively. These findings underscore the promising potential of coal as a sustainable and environmentally friendly reinforcement material in additive manufacturing applications. By harnessing the unique properties of coal, this research opens new avenues for its utilization in the pursuit of greener and more efficient manufacturing processes.

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.

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.

With increasing advance complexity in the structure to be 3D printed, the use of post processing removal of support structures has become more complicated thing due to the need of newer tool case to remove supports in such scenarios. Attempts have been made to study, research and experiment the dissolvable and recyclable photo-initiated polymeric resin that can be used to build support structure. Vat photo-polymerization method of manufacturing was selected due to wide range of materials that can be selected and researched which can have the potential to be selected further for large scale manufacturing. Deep understanding of the recyclable polymer was done by performing chemical and mechanical property test. Varying light intensities are used to study the curing properties and respective dissolving properties. In this thesis document, recyclable and dissolvable polymeric resin have been selected to print the support structures which can be later dissolved and recycled.The resin was exposed to varying light projections using grayscales of 255, 200 and 150 showing different dissolving time of each structure. Dissolving time of the printed parts were studied by varying the surface to volume ratios of the part. Higher the surface to volume ratios of the printed part resulted in lower time it takes to dissolve the part in the dissolving solution. The mechanical strengths of the recycled part were found to be pretty solid as compared to the freshly prepared resin, good sign of using it for multiple times without degrading its strength. Cactus shaped model was printed using commercial red resin and supports with the recyclable solution to deeply understand the working and dissolving properties of recyclable resin. Without any external efforts, the supports were easily dissolved in the solution, leaving the cactus intact. Further work is carried on printing Meta shaped gyroid lattice structure in effort to lower the dissolving time of the supports while maintaining enough mechanical stress. Future efforts will be made to conduct the rheology test and further lower the dissolving time as much it can to be ready for the commercial large scale applications.

National Airspace Systems (NAS) are complex cyber-physical systems that require swift air traffic management (ATM) to ensure flight safety and efficiency. With the surging demand for air travel and the increasing intricacy of aviation systems, the need for advanced technologies to support air traffic management and air traffic control (ATC) service has become more crucial than ever. Data-driven models or artificial intelligence (AI) have been conceptually investigated by various parties and shown immense potential, especially when provided with a vast volume of real-world data. These data include traffic information, weather contours, operational reports, terrain information, flight procedures, and aviation regulations. Data-driven models learn from historical experiences and observations and provide expeditious recommendations and decision support for various operation tasks, directly contributing to the digital transformation in aviation. This dissertation reports several research studies covering different aspects of air traffic management and ATC service utilizing data-driven modeling, which are validated using real-world big data (flight tracks, flight events, convective weather, workload probes). These studies encompass a range of topics, including trajectory recommendations, weather studies, landing operations, and aviation human factors. Specifically, the topics explored are (i) trajectory recommendations under weather conditions, which examine the impact of convective weather on last on-file flight plans and provide calibrated trajectories based on convective weather; (ii) multi-aircraft trajectory predictions, which study the intention of multiple mid-air aircraft in the near-terminal airspace and provide trajectory predictions; (iii) flight scheduling operations, which involve probabilistic machine learning-enhanced optimization algorithms for robust and efficient aircraft landing sequencing; (iv) aviation human factors, which predict air traffic controller workload level from flight traffic data with conformalized graph neural network. The uncertainties associated with these studies are given special attention and addressed through Bayesian/probabilistic machine learning. Finally, discussions on high-level AI-enabled ATM research directions are provided, hoping to extend the proposed studies in the future. This dissertation demonstrates that data-driven modeling has great potential for aviation digital twins, revolutionizing the aviation decision-making process and enhancing the safety and efficiency of ATM. Moreover, these research directions are not merely add-ons to existing aviation practices but also contribute to the future of transportation, particularly in the development of autonomous systems.

Progressive miniaturization in electronics demands advanced materials with excellent energy conversion and transport properties. Opportunities exist in novel material morphologies such as hierarchical structures, multi-functional composites and nanoscale architectures which may offer mechanical, thermal and electronic properties tailored to a wide range of applications (e.g., aerospace, robotics, biomedical etc.). However, the manufacturing capabilities have always posed a grand challenge in realizing the advanced material morphologies. Furthermore, the multi-scale modeling of complex material architectures has been extremely challenging owing to the limitations in computation methodologies and lack of understanding in nano-/micro-meter scale physics. To address these challenges, this work considers the morphology effect on carbon nanotube (CNT)-based composites, CNT fibers and thermoelectric (TE) materials. First, this work reports additively manufacturable TE morphologies and analyzes the thermo-electric transport behavior. This research introduces innovative honeycomb TE architectures that showed ~26% efficiency increase and ~25% density reduction compared to conventional rectangular TE architectures. Moreover, this work presents 3D printable compositionally segmented TE architecture which provides record-high efficiencies (up to 8.7%) over wide temperature ranges if the composition and aspect ratio of multiple TE materials are optimized within a single TE device. Next, this research proposes computationally efficient two-dimensional (2D) finite element model (FEM) to study the electrical and thermal properties in CNT based composites by simultaneously considering the stochastic CNT distributions, CNT fractions (upto 80%) and interfacial resistances. The FEM allows to estimate the theoretical maximum possible conductivities with corresponding interfacial resistances if the CNT morphologies are carefully controlled, along with appreciable insight into the energy transport physics. Then, this work proposes a data-driven surrogate model based on convolutional neural networks to rapidly approximate the composite conductivities in a second with accuracy > 98%, compared to FEM taking >100 minutes per simulation. Finally, this research presents a pseudo 2D FEM to approximate the electrical and thermal properties in CNT fibers at various CNT aspect ratios (up to 10,000) by simultaneously considering CNT-CNT interfacial effects along with the stochastic distribution of inter-bundle voids.

High-entropy alloys (HEAs) is a new class of materials which have been studied heavily due to their special mechanical properties. HEAs refers to alloys with multiple equimolar or nearly equimolar elements. HEAs show exceptional and attractive properties currently absent from conventional alloys, which make them the center of intense investigation. HEAs obtain their properties from four core effects that they exhibit and most of the work on them have been dedicated to study their mechanical properties. In contrast, little or no research have gone into studying the functional or even thermal properties of HEAs. Some HEAs have also shown exceptional or very high melting points. According to the definition of HEAs, Si-Ge-Sn alloys with equal or comparable concentrations of the three group IV elements belong to the category of HEAs. Thus, the equimolar components of Si-Ge-Sn alloys probably allow their atomic structures to display the same fundamental effects of metallic HEAs. The experimental fabrication of such alloys has been proven to be very difficult, which is mainly due to differences between the properties of their constituent elements, as indicated from their binary phase diagrams. However, previous computational studies have shown that SiGeSn HEAs have some very interesting properties, such as high electrical conductivity, low thermal conductivity and semiconducting properties. In this work, going for a complete characterization of the SiGeSn HEA properties, the melting point of this alloy is studied using classical molecular dynamics (MD) simulations and density functional theory (DFT) calculations. The aim is to investigate the effects of high Sn content in this alloy on the melting point compared with the traditional SiGe alloys. Classical MD simulations results strongly indicates that none of the available empirical potentials is able to predict accurate or reasonable melting points for SiGeSn HEAs and most of its subsystems. DFT calculations results show that SiGeSn HEA have a melting point which represent the mean value of its constituent elements and that no special deviations are found. This work contributes to the study of SiGeSn HEA properties, which can serve as guidance before the successful experimental fabrication of this alloy.