VTechWorks Repository :: Browsing by Author "Acar, Pinar"

Browsing by Author "Acar, Pinar"

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3-D Printing, Characterizing and Evaluating the Mechanical Properties of 316L Stainless Steel Materials with Gradient Microstructure
Stephen, Juanita Peche (Virginia Tech, 2021-03-24)
Making gradient in the microstructure of metals is proven to be a superior method for improving their mechanical properties. In this research, we 3D print, characterize and evaluate the mechanical properties of 316L Stainless Steel with a gradient in their microstructure. During 3D printing, the gradient in the microstructure is created by tailoring the processing parameters (hatch spacing, scanning speed, and laser power and scanning speed) of the Selective Laser Melting (SLM). The Materials with Graded Microstructure (MGMs) are characterized by optical and scanning electron microscopy (SEM). Image processing framework is utilized to reveal the distribution of cells and melt pools shapes and sizes in the volume of the material when the processing parameters change. It is shown that the laser power, scanning speed and the hatch spacing have a more significant effect on the size and shape of cells and melt pools compared to the speed. Multiple Dog bones are 3D printed with a microstructure that has smaller features (cells and melt polls) at the edges of the structure compared to the center. Tensile and fatigue tests are performed and compared for samples with constant and graded microstructures.
Adaptive Communication Interfaces for Human-Robot Collaboration
Christie, Benjamin Alexander (Virginia Tech, 2024-05-07)
Robots can use a collection of auditory, visual, or haptic interfaces to convey information to human collaborators. The way these interfaces select signals typically depends on the task that the human is trying to complete: for instance, a haptic wristband may vibrate when the human is moving quickly and stop when the user is stationary. But people interpret the same signals in different ways, so what one user finds intuitive another user may not understand. In the absence of task knowledge, conveying signals is even more difficult: without knowing what the human wants to do, how should the robot select signals that helps them accomplish their task? When paired with the seemingly infinite ways that humans can interpret signals, designing an optimal interface for all users seems impossible. This thesis presents an information-theoretic approach to communication in task-agnostic settings: a unified algorithmic formalism for learning co-adaptive interfaces from scratch without task knowledge. The resulting approach is user-specific and not tied to any interface modality. This method is further improved by introducing symmetrical properties using priors on communication. Although we cannot anticipate how a human will interpret signals, we can anticipate interface properties that humans may like. By integrating these functional priors in the aforementioned learning scheme, we achieve performance far better than baselines that have access to task knowledge. The results presented here indicate that users subjectively prefer interfaces generated from the presented learning scheme while enabling better performance and more efficient interactions.
Biomineralized Composites: Material Design Strategies at Building-Block and Composite Levels
Deng, Zhifei (Virginia Tech, 2023-01-12)
Biomineral composites, consisting of intercrystalline organics and biogenic minerals, have evolved unique structural designs to fulfill mechanical and other biological functionalities. Aside from the intricate architectures at the composite level and 3D assemblies of the biomineral building blocks, the individual mineral blocks enclose intracrystalline structural features that contribute to the strengthening and toughening at the intrinsic material level. Therefore, the design strategies of biomineralized composites can be categorized into two structural levels, the individual building block level and the composite level, respectively. This dissertation aims at revealing the material design strategies at both levels for the bioinspired designs of advanced structural ceramics. At the building block level, there is a lack of comparative quantification of the mechanical properties between geological and biogenic minerals. Correspondingly, I first benchmark the mechanical property difference between biogenic and geological calcite through nanoindentation techniques. The selected biogenic calcite includes Atrina rigida prisms and Placuna placenta laths, corresponding to calcite {0001}, and {101 ̅8} planes. The natural cleavage plane {101 ̅4} of geological calcite was added to the comparative study. Under indentation load, geological calcite deforms plastically via twinning and slips under low loads, and shifts to cleavage fracture under high loads. In comparison, the P. placenta composites, composed of micro-sized single-crystal laths and extensive intercrystalline organic interfaces, exhibit better crack resistance. In contrast, the single-crystal A. rigida prisms show brittle fracture with no obvious plastic deformation. Secondly, how the internal microstructures and loading types affect the mechanical properties of individual building blocks is investigated. The prismatic building blocks are obtained from the bivalves A. rigida and Sinanodonta woodiana, where the former consists of single-crystal calcite and the latter consists of polycrystalline aragonite. The comparative investigation under different loading conditions is conducted through micro-bending and nanoindentation. The continuous mineral matrix in A. rigida prisms leads to comparable modulus under tensile and compressive loadings in the elastic regime, while the high-density intracrystalline nanoinclusions contribute to the conchoidal fracture behaviors (instead of brittle cleavage). In comparison, the interlocking grain boundaries in S. woodiana prisms correlate with easier tensile deformation (smaller tensile modulus) than compression, as well as the intergranular fracture morphologies. The third topic in the biomineral-level investigation focuses on how biomineral utilizes residual stress at the macroscopic scale. The selected model system is the spine from the sea urchin Heterocentrotus mamillatus, which has a bicontinuous porous structure and mesocrystalline texture. It is confirmed that the spine has a macroscopic stress field with residual tension in the central medulla and compression in the radiating layers. The multimodal characterizations on the spine conclude that the structural origins are not associated with the gradient distribution of the intracrystalline defects, including Mg substitution in the calcite matrix, intracrystalline organics, and amorphous calcium carbonates (ACC). It is hypothesized that the residual stress is generated due to the volume expansion during ACC crystallization at the compacted growth front. At the composite level, even though enhanced crack resistance is expected in biomineralized composites due to their hierarchical structures, the correlation between their 3D composite structures and damage/crack evolution is quite limited in the literature. I developed in-situ testing devices integrated with synchrotron-based X-ray tomography to capture the crack propagation in the materials, including the four-point bending and compression/indentation configurations. Two representative models are chosen to demonstrate the deformation of biomineralized composites under bending and compression, respectively, including the calcium carbonate-based gastropod shell (Melo diadema) and the hydroxyapatite-based fish teeth (Pogonias cromis). Also, the two composites are designed to achieve different functional requirements, i.e., enhanced fracture toughness vs. wear resistance. The comprehensive characterizations of these two composites revealed how biological structural composites are designed accordingly to their functional needs. For the crossed-lamellar M. diadema shell, directional dependence of the shell property was revealed, where the transversal direction (perpendicular to the growth line) represents both the stronger and tougher direction, but the longitudinal direction is more resistant to notches and defects. For the P. cromis teeth, the enhanced wear resistance of the near-surface enameloid originates from the intricate designs at the microscale, with c-axes of hydroxyapatite crystals and micro-sized enameloid rods coaligned with biting direction and F and Zn doping. In addition, the fracture morphologies of the fish teeth correlate with the microstructures; the enameloid exhibits corrugated fracture paths due to the interwoven fibrous building blocks, and the dentin exhibits clean planar fracture surfaces.
Boat-shaped Buoy Optimization of an Ocean Wave Energy Converter Using Neural Networks and Genetic Algorithms
Lin, Weihan (Virginia Tech, 2023-01-19)
The point absorber is one of the most popular types of ocean wave energy converter (WEC) that harvests energy from the ocean. Often such a WEC is deployed in an ocean location with tidal currents or ocean streams, or serves as a mobile platform to power the blue economy. The shape of the floating body, or buoy, of the point absorber type WEC is important for the wave energy capture ratio and for the current drag force. In this work, a new approach to optimize the shape of the point absorber buoy is developed to reduce the ocean current drag force on the buoy while capturing more energy from ocean waves. A specific parametric modeling is constructed to define the shape of the buoy with 12 parameters. The implementation of neural networks significantly reduces the computational time compared to solving hydrodynamics equations for each iteration. And the optimal shape of the buoy is solved using a genetic algorithm with multiple self-defined functions. The final optimal shape of the buoy in a case study reduces 68.7% of current drag force compared to a cylinder-shaped buoy, while maintaining the same level of energy capture ratio from ocean waves. The method presented in this work has the capability to define and optimize a complex buoy shape, and solve for a multi-objective optimization problem.
Computational and Machine Learning-Reinforced Modeling and Design of Materials under Uncertainty
Hasan, Md Mahmudul (Virginia Tech, 2023-07-05)
The component-level performance of materials is fundamentally determined by the underlying microstructural features. Therefore, designing high-performance materials using multi-scale models plays a significant role to improve the predictability, reliability, proper functioning, and longevity of components for a wide range of applications in the fields of aerospace, electronics, energy, and structural engineering. This thesis aims to develop new methodologies to design microstructures under inherent material uncertainty by incorporating machine learning techniques. To achieve this objective, the study addresses gradient-based and machine learning-driven design optimization methods to enhance homogenized linear and non-linear properties of polycrystalline microstructures. However, variations arising from the thermo-mechanical processing of materials affect microstructural features and properties by propagating over multiple length scales. To quantify this inherent microstructural uncertainty, this study introduces a linear programming-based analytical method. When this analytical uncertainty quantification formulation is not applicable (e.g., uncertainty propagation on non-linear properties), a machine learning-based inverse design approach is presented to quantify the microstructural uncertainty. Example design problems are discussed for different polycrystalline systems (e.g., Titanium, Aluminium, and Galfenol). Though conventional machine learning performs well when used for designing microstructures or modeling material properties, its predictions may still fail to satisfy design constraints associated with the physics of the system. Therefore, the physics-informed neural network (PINN) is developed to incorporate problem physics in the machine learning formulation. In this study, a PINN model is built and integrated into materials design to study the deformation processes of Copper and a Titanium-Aluminum alloy.
Computational Design of 2D-Mechanical Metamaterials
McMillan, Kiara Lia (Virginia Tech, 2022-06-22)
Mechanical metamaterials are novel materials that display unique properties from their underlying microstructure topology rather than the constituent material they are made from. Their effective properties displayed at macroscale depend on the design of their microstructural topology. In this work, two classes of mechanical metamaterials are studied within the 2D-space. The first class is made of trusses, referred to as truss-based mechanical metamaterials. These materials are studied through their application to a beam component, where finite element analysis is performed to determine how truss-based microstructures affect the displacement behavior of the beam. This analysis is further subsidized with the development of a graphical user interface, where users can design a beam made of truss-based microstructures to see how their design affects the beam's behavior. The second class of mechanical metamaterial investigated is made of self-assembled structures, called spinodoids. Their smooth topology makes them less prone to high stress concentrations present in truss-based mechanical metamaterials. A large database of spinodoids is generated in this study. Through data-driven modeling the geometry of the spinodoids is coupled with their Young's modulus value to approach inverse design under uncertainty. To see mechanical metamaterials applied to industry they need to be better understood and thoroughly characterized. Furthermore, more tools that specifically help push the ease in the design of these metamaterials are needed. This work aims to improve the understanding of mechanical metamaterials and develop efficient computational design strategies catered solely for them.
Computational Reconstruction and Quantification of Aerospace Materials
Long, Matthew Thomas (Virginia Tech, 2024-05-14)
Microstructure reconstruction is a necessary tool for use in multi-scale modeling, as it allows for the analysis of the microstructure of a material without the cost of measuring all of the required data for the analysis. For microstructure reconstruction to be effective, the synthetic microstructure needs to predict what a small sample of measured data would look like on a larger domain. The Markov Random Field (MRF) algorithm is a method of generating statistically similar microstructures for this process. In this work, two key factors of the MRF algorithm are analyzed. The first factor explored is how the base features of the microstructure related to orientation and grain/phase topology information influence the selection of the MRF parameters to perform the reconstruction. The second focus is on the analysis of the numerical uncertainty (epistemic uncertainty) that arises from the use of the MRF algorithm. This is done by first removing the material uncertainty (aleatoric uncertainty), which is the noise that is inherent in the original image representing the experimental data. The epistemic uncertainty that arises from the MRF algorithm is analyzed through the study of the percentage of isolated pixels and the difference in average grain sizes between the initial image and the reconstructed image. This research mainly focuses on two different microstructures, B4C-TiB2 and Ti-7Al, which are a ceramic composite and a metallic alloy, respectively. Both of them are candidate materials for many aerospace systems owing to their desirable mechanical performance under large thermo-mechanical stresses.
Continuum Analytical Shape Sensitivity Analysis of 1-D Elastic Bar
Nayak, Soumya Sambit (Virginia Tech, 2021-01-06)
In this thesis, a continuum sensitivity analysis method is presented for calculation of shape sensitivities of an elastic bar. The governing differential equations and boundary conditions for the elastic bar are differentiated with respect to the shape design parameter to derive the continuum sensitivity equations. The continuum sensitivity equations are linear ordinary differential equations in terms of local or material shape design derivatives, otherwise known as shape sensitivities. One of the novelties of this work is the derivation of three variational formulations for obtaining shape sensitivities, one in terms of the local sensitivity and two in terms of the material sensitivity. These derivations involve evaluating (a) the variational form of the continuum sensitivity equations, or (b) the sensitivity of the variational form of the analysis equations. We demonstrate their implementation for various combinations of design velocity and global basis functions. These variational formulations are further solved using finite element analysis. The order of convergence of each variational formulation is determined by comparing the sensitivity solutions with the exact solutions for analytical test cases. This research focusses on 1-D structural equations. In future work, the three variational formulations can be derived for 2-D and 3-D structural and fluid domains.
Cooperative Prediction and Planning Under Uncertainty for Autonomous Robots
Nayak, Anshul Abhijit (Virginia Tech, 2024-10-11)
Autonomous robots are set to become ubiquitous in the future, with applications ranging from autonomous cars to assistive household robots. These systems must operate in close proximity of dynamic and static objects, including humans and other non-autonomous systems, adding complexity to their decision-making processes. The behaviour of such objects is often stochastic and hard to predict. Making robust decisions under such uncertain scenarios can be challenging for these autonomous robots. In the past, researchers have used deterministic approach to predict the motion of surrounding objects. However, these approaches can be over-confident and do not capture the stochastic behaviour of surrounding objects necessary for safe decision-making. In this dissertation, we show the importance of probabilistic prediction of surrounding dynamic objects and their incorporation into planning for safety-critical decision making. We utilise Bayesian inference models such as Monte Carlo dropout and deep ensemble to probabilistically predict the motion of surrounding objects. Our probabilistic trajectory forecasting model showed improvement over standard deterministic approaches and could handle adverse scenarios such as sensor noise and occlusion during prediction. The uncertainty-inclusive prediction of surrounding objects has been incorporated into planning. The inclusion of predicted states of surrounding objects with associated uncertainty enables the robot make proactive decisions while avoiding collisions.
Deep Reinforcement Learning for Multi-Phase Microstructure Design
Yang, Jiongzhi; Harish, Srivatsa; Li, Candy; Zhao, Hengduo; Antous, Brittney; Acar, Pinar (2021-03-22)
This paper presents a de-novo computational design method driven by deep reinforcement learning to achieve reliable predictions and optimum properties for periodic microstructures. With recent developments in 3-D printing, microstructures can have complex geometries and material phases fabricated to achieve targeted mechanical performance. These material property enhancements are promising in improving the mechanical, thermal, and dynamic performance in multiple engineering systems, ranging from energy harvesting applications to spacecraft components. The study investigates a novel and efficient computational framework that integrates deep reinforcement learning algorithms into finite element-based material simulations to quantitatively model and design 3-D printed periodic microstructures. These algorithms focus on improving the mechanical and thermal performance of engineering components by optimizing a microstructural architecture to meet different design requirements. Additionally, the machine learning solutions demonstrated equivalent results to the physics-based simulations while significantly improving the computational time efficiency. The outcomes of the project show promise to the automation of the design and manufacturing of microstructures to enable their fabrication in large quantities with the utilization of the 3-D printing technology.
Design, Fabrication, and Characterization of Metals Reinforced with Two-Dimensional (2D) Materials
Charleston, Jonathan (Virginia Tech, 2023-07-05)
The development of metals that can overcome the strength-ductility-weight trade-off has been an ongoing challenge in engineering for many decades. A promising option for making such materials are Metal matrix composites (MMCs). MMCs contain dispersions of reinforcement in the form of fibers, particles, or platelets that significantly improve their thermal, electrical, or mechanical performance. This dissertation focuses on reinforcement with two-dimensional (2D) materials due to their unprecedented mechanical properties. For instance, compared to steel, the most well-studied 2D material, graphene, is nearly forty times stronger (130 GPa) and five times stiffer (1 TPa). Examples of reinforcement by graphene have achieved increases in strength of 60% due to load transfer at the metal/graphene interface and dislocation blocking by the graphene. However, the superior mechanical properties of graphene are not fully transferred to the matrix in conventional MMCs, a phenomenon known as the "valley of death." In an effort to develop key insight into how the relationships between composite design, processing, structure, properties, and mechanics can be used to more effectively transfer the intrinsic mechanical properties of reinforcements to bulk composite materials, nanolayered composite systems made of Ni, Cu, and NiTi reinforced with graphene or 2D hexagonal boron nitride h-BN is studied using experimental techniques and molecular dynamics (MD) simulations.
Exploring the Stochastic Performance of Metallic Microstructures With Multi-Scale Models
Senthilnathan, Arulmurugan (Virginia Tech, 2023-06-01)
Titanium-7%wt-Aluminum (Ti-7Al) has been of interest to the aerospace industry owing to its good structural and thermal properties. However, extensive research is still needed to study the structural behavior and determine the material properties of Ti-7Al. The homogenized macro-scale material properties are directly related to the crystallographic structure at the micro-scale. Furthermore, microstructural uncertainties arising from experiments and computational methods propagate on the material properties used for designing aircraft components. Therefore, multi-scale modeling is employed to characterize the microstructural features of Ti-7Al and computationally predict the macro-scale material properties such as Young's modulus and yield strength using machine learning techniques. Investigation of microstructural features across large domains through experiments requires rigorous and tedious sample preparation procedures that often lead to material waste. Therefore, computational microstructure reconstruction methods that predict the large-scale evolution of microstructural topology given the small-scale experimental information are developed to minimize experimental cost and time. However, it is important to verify the synthetic microstructures with respect to the experimental data by characterizing microstructural features such as grain size and grain shape. While the relationship between homogenized material properties and grain sizes of microstructures is well-studied through the Hall-Petch effect, the influences of grain shapes, especially in complex additively manufactured microstructure topologies, are yet to be explored. Therefore, this work addresses the gap in the mathematical quantification of microstructural topology by developing measures for the computational characterization of microstructures. Moreover, the synthesized microstructures are modeled through crystal plasticity simulations to determine the material properties. However, such crystal plasticity simulations require significant computing times. In addition, the inherent uncertainty of experimental data is propagated on the material properties through the synthetic microstructure representations. Therefore, the aforementioned problems are addressed in this work by explicitly quantifying the microstructural topology and predicting the material properties and their variations through the development of surrogate models. Next, this work extends the proposed multi-scale models of microstructure-property relationships to magnetic materials to investigate the ferromagnetic-paramagnetic phase transition. Here, the same Ising model-based multi-scale approach used for microstructure reconstruction is implemented for investigating the ferromagnetic-paramagnetic phase transition of magnetic materials. The previous research on the magnetic phase transition problem neglects the effects of the long-range interactions between magnetic spins and external magnetic fields. Therefore, this study aims to build a multi-scale modeling environment that can quantify the large-scale interactions between magnetic spins and external fields.
Investigating the Process-Structure-Property Relationships in Vat Photopolymerization to Enable Fabrication of Performance Polymers
Meenakshisundaram, Viswanath (Virginia Tech, 2021-01-07)
Vat photopolymerization's (VP) use in large-scale industrial manufacturing is limited due to poor scalability, and limited catalogue of engineering polymers. The challenges in scalability stem from an inherent process paradox: the feature resolution, part size, and manufacturing throughput cannot be maximized simultaneously in standard VP platforms. In addition, VP's inability to process viscous and high-molecular weight engineering polymers limits the VP materials catalogue. To address these limitations, the research presented in this work was conducted in two stages: (1) Development and modeling of new VP platforms to address the scalability and viscosity challenges, and (2) Investigating the influence of using the new processes on the cured polymer network structure and mechanical properties. First, a scanning mask projection vat photopolymerization (S-MPVP) system was developed to address the scalability limitations in VP systems. The process paradox was resolved by scanning the mask projection device across the resin surface while simultaneously projecting the layer as a movie. Using actual projected pixel irradiance distribution, a process model was developed to capture the interaction between projected pixels and the resin, and predict the resulting cure profile with an error of 2.9%. The S-MPVP model was then extended for processing heterogeneous UV scattering resins (i.e. UV curable polymer colloids). Using computer vision, the scattering of incident UV radiation on the resin surface was successfully captured and used to predict scattering-compensated printing parameters (bitmap pattern, exposure time , scanning speed). The developed reverse-curing model was used to successfully fabricate complex features using photocurable SBR latex with XY errors < 1.3%. To address the low manufacturing throughput of VP systems, a recoat-less, volumetric curing VP system that fabricates parts by continuously irradiating the resin surface with a movie composed of different gray-scaled bitmap images ( Free-surface movie mask projection (FreeMMaP)) was developed. The effect of cumulative exposure on the cure profile (X,Y,Z dimensions) was investigated and used to develop an iterative gray-scaling algorithm that generated a combination of gray-scaled bitmap images and exposure times that result in accurate volumetric curing (errors in XY plane and Z axis < 5% and 3% respectively). Results of this work demonstrate that the elimination of the recoating process increased manufacturing speed by 8.05 times and enabled high-resolution fabrication with highly viscous resins or soft gels. Then, highly viscous resins were made processible in VP systems by using elevated processing temperatures to lower resin viscosity. New characterization techniques were developed to determine the threshold printing temperature and time that prevented the onset of thermally-induced polymerization. The effect of printing temperature on curing, cured polymer structure, cured polymer mechanical properties, and printable aspect ratio was also investigated using diacrylate and dimethacrylate resins. Results of this investigation revealed increasing printing temperature resulted in improvements in crosslink density, tensile strength, and printability. However, presence of hydroxl groups on the resin backbone caused deterioration of crosslink density, mechanical properties, and curing properties at elevated printing temperatures. Finally, the lack of a systematic, constraint based approach to resin design was bridged by using the results of earlier process-structure-property explorations to create an intuitive framework for resin screening and design. Key screening parameters (such as UV absorptivity, plateau storage modulus) and design parameters (such as photoinitiator concentration, polymer concentration, UV blocker concentration) were identified and the methods to optimize them to meet the desired printability metrics were demonstrated using case studies. Most work in vat photopolymerization either deal with materials development or process development and modeling. This dissertation is placed at the intersection of process development and materials development, thus giving it an unique perspective for exploring the inter-dependency of machine and material. The process models, machines and techniques used in this work to make a material printable will serve as a guide for chemists and engineers working on the next generation of vat photopolymerization machines and materials.
Investigation of Chloride-induced Stress Corrosion Cracking for Long-Term Storage of Spent Nuclear Fuel in Dry Storage Systems
Shakhatreh, Abdulsalam Ismail (Virginia Tech, 2022-09-14)
Chloride-induced stress corrosion cracking (CISCC) has been identified as the main degradation mechanism for spent nuclear fuel dry storage canisters. This type of induced cracking is complex and depends on several factors, such as material composition, exposure temperature, relative humidity, applied tensile stress, and atmospheric salt concentration. An accelerated experiment was designed to simulate marine environments in a controlled fogging chamber to examine 304 and 304L stainless steel U-bend and welded U-bend samples. The samples were exposed to chloride rich and humid fogging in a corrosion chamber at 35℃ continuously for 4 weeks, 8 weeks, and 12 weeks. The same experiment was repeated at 50℃ for 4 weeks, 8 weeks, and 14 weeks to study the sensitivity of CISCC to temperature changes. A qualitative evaluation of optical micrographs from a 3D Surface Profiler was performed for 16 corroded samples and compared with 2 reference samples. Cracking was observed on 12 out of 16 samples exposed to 35℃ and 50℃ for durations ranging from 8 to 14 weeks. Likely cracking observations were noted on 4 out of 16 samples. A quantitative statistical analysis was also performed using surface profile depth (valley) data from corroded and reference samples. The quantitative analysis examined the effect of temperature, welding, exposure duration, and material composition. The quantitative results were compared with the qualitative results and literature published in CISCC.
Machine Learning based Methods to Improve Power System Operation under High Renewable Pennetration
Bhavsar, Sujal Pradipkumar (Virginia Tech, 2022-09-19)
In an attempt to thwart global warming in a concerted way, more than 130 countries have committed to becoming carbon neutral around 2050. In the United States, the Biden ad- ministration has called for 100% clean energy by 2035. It is estimated that in order to meet that target, the energy production from solar and wind should increase to 50-70% from the current 11% share. Under higher penetration of solar and wind, the intermittency of the energy source poses critical problems in forecasting, uncertainty quantification, reserve man- agement, unit commitment, and economic dispatch, and presents unique challenges to the distribution system, including predicting solar adoption by the user as well as forecasting end-use load profiles. While these problems are complex, advances in machine learning and artificial intelligence provide opportunities for novel paradigms for addressing the challenges. The overall aim of the dissertation is to harness data-driven and model-based techniques and develop computationally efficient tools for improved power systems operation under high re- newables penetration in the next-generation electric grid. Some of the salient contributions of this work are the reduction in the number of uncertain scenarios by 99%; dramatic reduc- tion in the computational overhead to simulate stochastic unit commitment and economic dispatch on a single-node electric-grid system to merely 10 seconds from 24 hours; reduc- tion in the total monthly operating cost of two-stage stochastic economic dispatch by an average of 5%, and reduction in average overall reserve due to intermittency in renewables by 50%; and improvement in the existing end-use load prediction and rooftop PV adopter identification tools by a considerable margin.
Mathematical Strategies for Design Optimization of Multiphase Materials
Catania, Rick; Diraz, Abdalla; Maier, Dominic; Tagle, Armani; Acar, Pinar (Hindawi Publishing Corp, 2019-03-12)
This work addresses various mathematical solution strategies adapted for design optimization of multiphase materials. The goal is to improve the structural performance by optimizing the distribution of multiple phases that constitute the material. Examples include the optimization of multiphase materials and composites with spatially varying fiber paths using a finite element analysis scheme. In the first application, the phase distribution of a two-phase material is optimized to improve the structural performance. A radial basis function (RBF) based machine learning algorithm is utilized to perform a computationally efficient design optimization and it is found to provide equivalent results with the physical model. The second application concentrates on the optimization of spatially varying fiber paths of a composite material. The fiber paths are described by the Non-Uniform Rational Bezier (B)-Spline Surface (NURBS) using a bidirectional control point representation including 25 parameters. The optimum fiber path is obtained for various loading configurations by optimizing the NURBS parameters that control the overall distribution of fibers. Next, a direct sensitivity analysis is conducted to choose the critical set of parameters from the design point to improve the computational time efficiency. The optimized fiber path obtained with the reduced number of NURBS parameters is found to provide similar structural properties compared to the optimized fiber path that is modeled with a full NURBS representation with 25 parameters.
Model Reduction of Power Networks
Safaee, Bita (Virginia Tech, 2022-06-08)
A power grid network is an interconnected network of coupled devices that generate, transmit and distribute power to consumers. These complex and usually large-scale systems have high dimensional models that are computationally expensive to simulate especially in real time applications, stability analysis, and control design. Model order reduction (MOR) tackles this issue by approximating these high dimensional models with reduced high-fidelity representations. When the internal description of the models is not available, the reduced representations are constructed by data. In this dissertation, we investigate four problems regarding the MOR and data-driven modeling of the power networks model, particularly the swing equations. We first develop a parametric MOR approach for linearized parametric swing equations that preserves the physically-meaningful second-order structure of the swing equations dynamics. Parameters in the model correspond to variations in operating conditions. We employ a global basis approach to develop the parametric reduced model. We obtain these local bases by $mathcal{H}_2$-based interpolatory model reduction and then concatenate them to form a global basis. We develop a framework to enrich this global basis based on a residue analysis to ensure bounded $mathcal{H}_2$ and $mathcal{H}_infty$ errors over the entire parameter domain. Then, we focus on nonlinear power grid networks and develop a structure-preserving system-theoretic model reduction framework. First, to perform an intermediate model reduction step, we convert the original nonlinear system to an equivalent quadratic nonlinear model via a lifting transformation. Then, we employ the $mathcal{H}_2$-based model reduction approach, Quadratic Iterative Rational Krylov Algorithm (Q-IRKA). Using a special subspace structure of the model reduction bases resulting from Q-IRKA and the structure of the underlying power network model, we form our final reduction basis that yields a reduced model of the same second-order structure as the original model. Next, we focus on a data-driven modeling framework for power network dynamics by applying the Lift and Learn approach. Once again, with the help of the lifting transformation, we lift the snapshot data resulting from the simulation of the original nonlinear swing equations such that the resulting lifted-data corresponds to a quadratic nonlinearity. We then, project the lifted data onto a lower dimensional basis via a singular value decomposition. By employing a least-squares measure, we fit the reduced quadratic matrices to this reduced lifted data. Moreover, we investigate various regularization approaches. Finally, inspired by the second-order sparse identification of nonlinear dynamics (SINDY) method, we propose a structure-preserving data-driven system identification method for the nonlinear swing equations. Using the special structure on the right-hand-side of power systems dynamics, we choose functions in the SINDY library of terms, and enforce sparsity in the SINDY output of coefficients. Throughout the dissertation, we use various power network models to illustrate the effectiveness of our approaches.
Multi-scale computational modeling of lightweight aluminum-lithium alloys
Acar, Pinar (Elsevier Ltd, 2019-03-07)
The present study addresses the multi-scale computational modeling of a lightweight Aluminum-Lithium (Al-Li) 2070 alloy. The Al-Li alloys display significant anisotropy in material properties because of their strong crystallographic texture. To understand the relationships between processing, microstructural textures at different material points and tailored material properties, a multi-scale simulation is performed by controlling the texture evolution during deformation. To achieve the multi-scale framework, a crystal plasticity model based on a one-point probability descriptor, Orientation Distribution Function (ODF), is implemented to study the texture evolution. Next, a two-way coupled multi-scale model is developed, where the deformation gradient at the macro-scale integration points is passed to the micro-scale ODF model and the homogenized stress tensor at the micro-scale is passed back to the macro-scale model. A gradient-based optimization scheme which incorporates the multi-scale continuum sensitivity method is utilized to calibrate the slip system parameters of the alloy using the available experimental data. Next, the multi-scale simulations are performed for compression and tension using the calibrated crystal plasticity model, and the texture data is compared to the experiments. With the presented multi-scale modeling scheme, we achieve the location-specific texture predictions for a new generation Al-Li alloy for different deformation processes.
Multiscale Modeling of Fatigue and Fracture in Polycrystalline Metals, 3D Printed Metals, and Bio-inspired Materials
Ghodratighalati, Mohamad (Virginia Tech, 2020-03-16)
The goal of this research is developing a computational framework to study mechanical fatigue and fracture at different length scales for a broad range of materials. The developed multiscale framework is utilized to study the details of fracture and fatigue for the rolling contact in rails, additively manufactured alloys, and bio-inspired hierarchical materials. Rolling contact fatigue (RCF) is a major source of failure and a dominant cause of maintenance and replacements in many railways around the world. The highly-localized stress in a relatively small contact area at the wheel-rail interface promotes micro-crack initiation and propagation near the surface of the rail. 2D and 3D microstructural-based computational frameworks are developed for studying the rolling contact fatigue in rail materials. The method can predict RCF life and simulate crack initiation sites under various conditions. The results obtained from studying RCF behavior in different conditions will help better maintenance of the railways and increase the safety of trains. The developed framework is employed to study the fracture and fatigue behavior in 3D printed metallic alloys fabricated by selective laser melting (SLM) method. SLM method as a part of metal additive manufacturing (AM) technologies is revolutionizing the manufacturing sector and is being utilized across a diverse array of industries, including biomedical, automotive, aerospace, energy, consumer goods, and many others. Since experiments on 3D printed alloys are considerably time-consuming and expensive, computational analysis is a proper alternative to reduce cost and time. In this research, a computational framework is developed to study fracture and fatigue in different scales in 3D printed alloys fabricated by the SLM method. Our method for studying the fatigue at the microstructural level of 3D printed alloys is pioneering with no similar work being available in the literature. Our studies can be used as a first step toward establishing comprehensive numerical frameworks to investigate fracture and fatigue behavior of 3D metallic devices with complex geometries, fabricated by 3D printing. Composite materials are fabricated by combining the attractive mechanical properties of materials into one system. A combination of materials with different mechanical properties, size, geometry, and order of different phases can lead to fabricating a new material with a wide range of properties. A fundamental problem in engineering is how to find the design that exhibits the best combination of these properties. Biological composites like bone, nacre, and teeth attracted much attention among the researchers. These materials are constructed from simple building blocks and show an uncommon combination of high strength and toughness. By inspiring from simple building blocks in bio-inspired materials, we have simulated fracture behavior of a pre-designed composite material consisting of soft and stiff building blocks. The results show a better performance of bio-inspired composites compared to their building blocks. Furthermore, an optimization methodology is implemented into the designing the bio-inspired composites for the first time, which enables us to perform the bio-inspired material design with the target of finding the most efficient geometries that can resist defects in their structure. This study can be used as an effective reference for creating damage-tolerant structures with improved mechanical behavior.
Multiscale Structures and Mechanics of Biomineralized Lattices in Hexactinellid sponges and Echinoderms
Chen, Hongshun (Virginia Tech, 2023-06-30)
Biomineralized lattice materials with have high mineral contents (~ 99 wt%), usually "conceal" multiscale structural arrangements for unique mechanical or functional performance, such as the remarkable damage tolerance despite of the brittle nature of the constituents (e.g., biogenic silica and calcite). However, the quantitative explorations of the structure-mechanics relationships in multiscale of biomineralized lattices remain insufficient and hence hinder the leverage of the functional benefits to design architected cellular materials. In this dissertation, I selected two groups of marine animals (i.e., Hexactinellid sponges and Echinoderms) for systematic structural-mechanical study. Their biomineralized lattice skeletons exhibit three representative types of multiscale structures: 1) multiscale hierarchical structure: skeleton of Hexactinellid sponge such as Euplectella aspergillum; 2) multiscale functionally graded structure: spine of sea urchin Heterocentrotus mammillatus; and 3) dual-scale (atomic and microlattice scales) periodic structure: ossicle of starfish Protoreaster nodosus. This dissertation develops quantitatively the structural-mechanical/functional correlations in biomineralized cellular materials for bio-inspired material design. Four different species of Hexactinellid sponges have been studied with particular focus on the species E. aspergillum. As an example of the multiscale hierarchical biomineralized lattice, the extremely lightweight skeleton (~99% porosity) of E. aspergillum exhibits 1) amorphous nanoparticular biogenic silica; 2) micron-sized fibrous spicule with cylindrically laminated silica layers separated by organic interfaces; 3) spicule bundles where the individual spicules merged by secondary silica deposition; 4) a centimeter-sized Voronoi-like cellular dome known as sieve plate; and 5) a centimeter-sized cylindrically arranged rectangular lattice with double-diagonal reinforcement and external helical ridge. Here, we discovered a series of mechanical or functional properties or formation process of structures in different length scales: 1) for the biogenic silica in three different species of Hexactinellid sponge, consistent modulus and hardness of the biogenic silica throughout the cross section of the spicule are found via substantial correlation between the measured values and locations; 2) for the sieve plate, the Voronoi-like cellular dome constructed by porous branch with increased height achieves balance between improved mechanical stiffness and large pore opening for sponge's current pumping mechanism; 3) via microstructural study, the formation process of the sieve plate is proposed; and 4) for the cylindrical skeletal body, the double-diagonal configuration and the ridge structure are found to provide tendency to optimize torsional rigidity, and enhanced radial stiffening and improved permeability, respectively. The cellular structure in the spine of the H. mammillatus (i.e., stereom) made of ~99wt% of single-crystalline calcite shows a multiscale functionally graded structure. We developed and optimized a cellular network analysis workflow on the large-volume 3D lattice structure obtained from the synchrotron-based micro-Computed Tomography scan. The analysis provides quantitative descriptions of the branch, ring structure, and septum which reveals a functionally graded structure in multiscale from the center region to the edge region of the spine: 1) in microscale, the branch thickness and length increases, resulting in a significantly decreased porosity; and 2) in macroscale, the center region of the spine with galleried stereom of highly aligned branches transits to the edge region with laminar stereom of radially arranged septa and interconnecting branches. The multiscale structural variations lead to the mechanical variations the increased elastic modulus and mechanical isotropy from the center to the edge of the spine. This provides a biological pathway for designing the lightweight, strong, and tough beam with multiscale structural gradient. In previous work, we discovered that ossicle in starfish P. nodosus possesses a unique dual-scale periodic lattice structure, which means periodic single crystal calcite in nanoscale and diamond triply periodic minimal surface (diamond-TPMS) lattice in microscale. It has three unique structural features: 1) microlattice dislocations in ossicles similar to those found in crystals with diamond cubic lattice; 2) a diamond-TPMS microlattice with ca. 50% relative density; and 3) dual-scale crystallographic coalignment between c-axis of the single-crystalline constituent calcite and the [111] direction of the diamond-TPMS microlattice. Based on this work, this dissertation mainly reveals: 1) unique type and core structures of the dislocations in the ossicle for stiffness, strength, and toughness; 2) the 3D property compensation of dual-scale crystallographic coalignment for improved mechanical isotropy; and 3) mechanical benefits (improved mechanical isotropy and effective fragment jamming) and morphological benefits (minimal surface and highest surface area to volume ratio) for 50% relative density.

Browsing by Author "Acar, Pinar"

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