Digital twins are virtual renditions of the actual world that include real-world assets, connections, activities, and processes. Recent developments in technologies play a key role in advancing the digital twin concept in urban planning, designing, and monitoring. Moreover, the latest developments in remote sensing technology have resulted in accurate city-scale light detection and ranging (LiDAR) data, which can be used to represent urban objects (buildings, vegetation, roads, and utilities), enabling the creation of digital twin of urban landscapes. This study aims to build a digital twin of the University of North Texas (UNT) using LiDAR and GIS data. In this research, LiDAR point clouds are used to create 3D building and vegetation modeling along with other GIS data (bicycle racks and parking areas) in creating a digital twin model. 3D Basemap solutions of ArcGIS Pro and ArcGIS Online Scene Viewer, respectively, are used to create an initial 3D urban model and build the ultimate digital twin of UNT. The emergency management floorplans of UNT buildings are incorporated into the digital twin to increase emergency management efficiency. Moreover, solar power potential for individual buildings at UNT has been estimated using the Digital Surface Model (DSM) and integrated into the digital twin model to identify the buildings with the highest solar energy capacity. This study indicates that implementing a digital twin in a university enhances campus efficiency, safety, and sustainability, serving as a central system for a smart campus and contributing to intelligent urban growth.

Bats possess remarkably sophisticated biosonar systems that seamlessly integrate the physical encoding of information through intricate ear motions with the neural extraction and processsing of sensory information. While previous studies have endeavored to mimic the pinna (outer ear) dynamics of bats using fixed deformation patterns in biomimetic soft-robotic sonar heads, such physical approaches are inherently limited in their ability to comprehensively explore the vast actuation pattern space that may enable bats to adaptively sense across diverse environments and tasks.To overcome these limitations, this thesis presents the development of deep regression neural networks capable of predicting the beampattern (acoustic radiation pattern) of a soft-robotic pinna as function of its actuator states. The pinna model geometry is derived from a tomographic scan of the right ear of the greater horseshoe bat (textit{Rhinolophus ferrumequinum}. Three virtual actuators are incorporated into this model to simulate a range of shape deformations. For each unique actuation pattern producing a distinct pinna shape conformation, the corresponding ultrasonic beampattern is numerically estimated using a frequency-domain boundary element method (BEM) simulation, providing ground truth data. Two neural networks architectures, a multilayer perceptron (MLP) and a radial basis function network (RBFN) based on von Mises functions were evaluated for their ability to accurately reproduce these numerical beampattern estimates as a function of spherical coordinates azimuth and elevation. Both networks demonstrate comparably low errors in replicating the beampattern data. However, the MLP exhibits significantly higher computational efficiency, reducing training time by 7.4 seconds and inference time by 0.7 seconds compared to the RBFN. The superior computational performance of deep neural network models in inferring biomimetic pinna beampatterns from actuator states enables an extensive exploration of the vast actuation pattern space to identify pinna actuation patterns optimally suited for specific biosonar sensing tasks. This simulation-based approach provides a powerful framework for elucidating the functional principles underlying the dynamic shape adaptations observed in bat biosonar systems. / Master of Science / The aim is to understand how bats can dynamically change the shape of their outer ears (pinnae) to optimally detect sounds in different environments and for different tasks. Previous studies tried to mimic bat ear motions using fixed deformation patterns in robotic ear models, but this approach is limited. Instead this thesis uses deep learning neural networks to predict how changing the shape of a robotic bat pinna model affects its acoustic beampattern (how it radiates and receives sound). The pinna geometry is based on a 3D scan of a greater horseshoe bat ear, with three virtual "actuators" to deform the shape. For many different actuator patterns deforming the pinna, the resulting beampattern is calculated using computer simulations. Neural networks ( multilayer perceptron and radial basis function network) are trained on this data to accurately predict the beampattern from the actuator states. The multilayer perceptron network is found to be significantly more computationally efficient for this task. This neural network based approach allows rapidly exploring the vast range of possible pinna actuations to identify optimal shapes for specific biosonar sensing tasks, shedding light on principles of dynamic ear shape control in bats.

<p dir="ltr">McAlester Army Ammunition Plant (MCAAP) was established in 1943 as the U.S Naval Ammunition Depot. In World War II, MCAAP played a crucial part in supplying ammunition for the war efforts. Today, MCAAP is home to nearly 45,000 acres of land, producing almost all the bombs for the Army, Air Force, and Navy.</p><p dir="ltr">In November of 2023, the Army launched their 15-year modernization plan for their Organic Industrial Base (OIB). The plan aims to modernize facilities, processes, and the workforce to bring the OIB into the 21^st century. The Army’s OIB consists of 17 arsenals, depots, and ammunition plants, including MCAAP.</p><p dir="ltr">This thesis optimizes the operational variables of the U.S. Air Force’s Mark-84 production process at MCAAP. Using software (AnyLogic) to construct a Digital Twin of the existing process provides insights into the current operational dynamics, enabling a deep understanding of the system’s inefficiencies. Then, utilizing this understanding and the capabilities of the Digital Twin, we offer targeted recommendations for process improvement. This study aims not only to enhance the Mark-84 production process, but also to demonstrate the transformative potential of Digital Twins in optimizing manufacturing operations.</p>

<p>Human body models (HBMs) have been present in the automotive industry for simulating automotive related injury since the turn of the century and have in recent years found a place in assessment of soldier and sports related injury prediction and assessment. This issue is the lack of models that lie outside of the 50th percentile. By a simple application of physics, it is evident that acceleration or force will affect people of varying weights differently. To this end, having the ability to scale a 50th percentile HBM to targets for weight and stature would allow for better characterization on how an impact or acceleration event will affect people of differing size, especially when ~90% of males can fall outside the 50th percentile for weight and stature and HBMs models from vendors exist in only a few variations outside the 50th percentile [1]. Using Corvid Technologies’ 50th percentile model CAVEMAN (capable of being repositioned) as a base, scaled model from the 5th to 95th percentiles of stature and weight were generated based on ANSURII metrics, using a combination of 1D and 3D scaling transformations. These models met their stature and weight metrics when standing and weight metrics when positioned. </p> <p>After creation of a framework to scale the CAVEMAN HMB, creation of a digital twin to the HIRRT Lab helmet testing model commenced. With the HIRRT Lab’s history of experimental testing of football helmets, a natural turn of events was to bring helmet performance testing into the computational space. This digital twin was a natural evolution and addition to the HIRRT Lab’s helmet testing as it would enable manipulation of helmets that would be infeasible experimentally. After calibration of the barehead using experimental data, helmeted simulation began. Angle of impact, while it was found to effect peak translational acceleration, was found to profoundly effect peak rotational acceleration. With this in mind, various angles of impact were simulated to produce curves similar to experimental results. Helmeted simulations were qualitatively dissimilar to experimental data, prompting a modification of the padding material used by the models. Following various modifications of the padding material model, these inconsistencies between simulated helmets and experimentally tested helmets persisted. These inconsistencies highlight a need for better characterization of material, such as foam, and more thorough validation of simulated helmet models. The results of the helmeted simulations are difficult to quantify, as the evaluation criteria used for the BioCore model did not include rotational acceleration, indicating a need for further research and simulation is necessary. </p>

Glycosphingolipids such as α- and β-glucosylceramides (GlcCers) and α- and β- galactosylceramides (GalCers) are stereoisomers differentially synthesized by gut bacteria and their mammalian hosts in response to environmental insult. Thus, lipidomic assessment of α- and β-GlcCers and α- and β-GalCers is crucial for inferring biological functions and biomarker discovery. However, simultaneous quantification of these stereoisomeric lipids is difficult due to their virtually identical structures. Differential mobility mass spectrometry (DMS), as an orthogonal separation to high performance liquid chromatography used in electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS), can be used to separate stereoisomeric lipids. Generating LC-ESI-DMS-MS/MS methods for lipidomic analyses is exceedingly difficult demanding intensive manual optimization of DMS parameters that depend on the availability of synthetic lipid standards. Where synthetic standards do not exist, method development is not possible. To address this challenge, I developed a supervised in silico machine learning approach to accelerate method development for ion mobility-based quantification of lipid stereoisomers. I hypothesized that supervised neural network models could be used to learn the relationships between lipid structural characteristics and optimal DMS machine parameter values thereby reducing the total number of empirical experiments required to develop a DMS method and enabling users to “predict” DMS parameters for analytes that lack synthetic standards. Specifically, this thesis describes a supervised learning approach that learns the relationship between two DMS machine parameter values (separation voltage and compensation voltage) and two lipid structural features (N-Acyl chain length and degree of unsaturation). I describe here, iDMS, an algorithm that was trained on 17 lipid species, and can further simulate results of DMS manual method development and suggest optimal parameter values for 47 lipid species. This approach promises to greatly accelerate the development of assays for the detection of lipid stereoisomers in biological samples.

Maintenance management is a relevant issue in modern technical systems due to its financial, safety, and environmental implications. The need to rely on physical assets makes maintenance a necessary evil, which, on the other hand, allows achieving a high quality of end products, or services, and a safety level that is adequate for the regulatory requirements. The advent of the fourth industrial revolution offers meaningful opportunities to improve maintenance management; technologies such as Cyber-Physical Systems, the Internet of Things, and cloud computing enable realizing modern infrastructure to support decisions with advanced analytics. In this thesis, the optimization of maintenance policies is tackled in this renewed technological context. The research methods employed in this thesis include interviewing of subject experts, literature research, and numerical experiments. Mathematical modelling is used to model network effects in complex technical systems, and simulations are used to validate the proposed models and methodologies. The problem of maintenance policies comparison is addressed in one of the publications; using the proposed bi-objective analysis, an effective maintenance policy was identified. Maintenance of complex systems organized in a networked fashion is studied in another project, where maintenance costs and system performances are considered. The proposed model allowed to identify a set of non-dominated (in the Pareto sense) maintenance policies, and an efficient resolution procedure was developed. The possibility to use a digital twin to replicate a Cyber-Physical System for maintenance policies optimization is addressed in another publication. The main hurdles in realizing such a complex infrastructure are analyzed, and managerial implications are presented. Finally, following a qualitative research approach, the opportunities offered by additive manufacturing are identified and presented in a book chapter. The opportunities for both maintenance efficiency gains and new business models are identified and discussed.

Artificial intelligence (AI) has progressed significantly during the last several decades, along with the rapid advancements in computational power. This advanced technology is currently being employed in various engineering fields, not just in computer science. In aerospace engineering, AI and machine learning (ML), a major branch of AI, are now playing an important role in various applications, such as automated systems, unmanned aerial vehicles, aerospace optimum design structure, etc. This dissertation mainly focuses on structural engineering to employ AI to develop lighter and safer aircraft structures as well as challenges involving structural optimization and analysis. Therefore, various ML applications are studied in this research to provide novel frameworks for structural optimization, analysis, and design. First, the application of a deep-learning-based (DL) convolutional neural network (CNN) was studied to develop a surrogate model for providing optimum structural topology. Typically, conventional structural topology optimization requires a large number of computations due to the iterative finite element analyses (FEAs) needed to obtain optimal structural layouts under given load and boundary conditions. A proposed surrogate model in this study predicts the material density layout inputting the static analysis results using the initial geometry but without performing iterative FEAs. The developed surrogate models were validated with various example cases. Using the proposed method, the total calculation time was reduced by 98 % as compared to conventional topology optimization once the CNN had been trained. The predicted results have equal structural performance levels compared to the optimum structures derived by conventional topology optimization considered ``ground truths". Secondly, reinforcement learning (RL) is studied to create a stand-alone AI system that can design the structure from trial-and-error experiences. RL application is one of the major ML branches that mimic human behavior, specifically how human beings solve problems based on their experience. The main RL algorithm assumes that the human problem-solving process can be improved by earning positive and negative rewards from good and bad experiences, respectively. Therefore, this algorithm can be applied to solve structural design problems whereby engineers can improve the structural design by finding the weaknesses and enhancing them using a trial and error approach. To prove this concept, an AI system with the RL algorithm was implemented to drive the optimum truss structure using continuous and discrete cross-section choices under a set of given constraints. This study also proposed a unique reward function system to examine the constraints in structural design problems. As a result, the independent AI system can be developed from the experience-based training process, and this system can design the structure by itself without significant human intervention. Finally, this dissertation proposes an ML-based classification tool to categorize the vibrational mode shapes of tires. In general, tire vibration significantly affects driving quality, such as stability, ride comfort, noise performance, etc. Therefore, a comprehensive study for identifying the vibrational features is necessary to design the high-performance tire by considering the geometry, material, and operation conditions. Typically, the vibrational characteristics can be obtained from the modal test or numerical analysis. These identified modal characteristics can be used to categorize the tire mode shapes to determine the specific mode cause poorer driving performances. This study suggests a method to develop an ML-based classification tool that can efficiently categorize the mode shape using advanced feature recognition and classification algorithms. The best-performed classification tool can accurately predict the tire category without manual effort. Therefore, the proposed classification tool can be used to categorize the tire mode shapes for subsequent tire performance and improve the design process by reducing the time and resources for expensive calculations or experiments. / Doctor of Philosophy / Artificial intelligence (AI) has significantly progressed during the last several decades with the rapid advancement of computational capabilities. This advanced technology is currently employed to problems in various engineering fields, not just problems in computer science. Machine learning (ML), a major branch of AI, is actively applied to mechanical/structural problems since an ML model can replace a physical system with a surrogate model, which can be used to predict, control, and optimize its behavior. This dissertation provides a new framework to design and analyze structures using ML-based techniques. In particular, the latest ML technologies, such as convolutional neural networks, widely used for image processing and feature recognition, are applied to replace numerical calculations in structural optimization and analysis with the ML-based system. Also, this dissertation suggests how to develop a smart system that can design the structure by itself using reinforcement learning, which is utilized for autonomous driving systems and robot walking algorithms. Finally, this dissertation suggests an ML-based classification approach to categorize complex vibration modes of a structure.

Doctor of Philosophy / The long-term goal of this dissertation is to develop quality assurance methodologies for parts made using metal additive manufacturing (AM). Additive manufacturing is becoming a prominent manufacturing process due to its ability to generate complex structures that would otherwise be impossible to produce using traditional machining. This freedom of complexity enables engineers to make more efficient components and reduce part counts in assemblies. However, the AM process tends to generate random flaws that require manufacturers to perform extensive testing on all manufactured samples to ensure part quality. Due to this extensive testing, manufacturers have been slow to adopt the AM process. Thus, the goal of this dissertation is to understand, monitor, and predict the quality of metal AM parts as they are being printed to remove the need for post-manufacturing testing – hence the phrase Born Qualified. To enable Born Qualified manufacturing with AM, the objective of this dissertation was to use sensors installed on AM machines to monitor part quality during the process. With this objective, this dissertation focused on: (1) using acoustic signal monitoring to determine the onset of process instabilities that would generate flaws; (2) monitoring the process with multiple sensors to determine the specific type of flaws formed; (3) developing novel methods to monitor the sub-surface effects; and (4) combining multiple streams of sensor data with thermal simulations to detect flaw formation along with mechanical and material properties of the manufactured parts.

<p>The modern world of manufacturing is in the middle of an industrial revolution with the digital and physical worlds integrating through cyber-physical systems.  Through a virtual model that is able to communicate with its physical system known as the Digital Twin, catered decisions can be made based on the current state of the system.  The digital twin presents immense opportunities and challenges as there is a greater need to understand how these new technologies work together. </p> <p><br></p> <p>This thesis is an experimental investigation of the characteristics of the essential components of the Digital Twin.  A Digital Twin Framework is developed to explore the impacts of model accuracy and update frequency on the system’s performance measure. A simple inventory management system and a more complex manufacturing plant is modeled through the framework providing a method to study the interactions of the physical and digital systems with empirical data.</p> <p><br></p> <p><br></p> <p>As the decision policies are affected by the state changes in the system, designing the Digital Twin must account for the direct and indirect impact of its components. </p> <p>Furthermore, we show the importance of communication and information exchange between the Digital Twin and its physical system.  A key characteristic for developing and applying a digital twin is to monitor the update frequency and its impact on performance.  Through the study there are implications of optimal combinations of the digital twin components and how the physical system responds.  There are also limits to how effective the Digital Twin can be in certain instances and is an area of research that needs further investigation.  </p> <p><br></p> <p>The goal of this work is to help practitioners and researchers implement and use the Digital Twin more effectively.  Better understanding the interactions of the model components will help guide designing Digital Twins to be more effective as they become an integral part of the future of manufacturing.</p>

This paper will give an overview about the technological change in Industrial Hydraulics and the impact of the Digital Twin on the related new engineering processes and methods in order to overcome the challenges coming out of that technology change. Simulation models will more and more become a decisive factor for the engineering process. The Digital Twin will be a window of opportunity for innovations and a technology push for the engineering process and the products in the Industrial Hydraulics.