PhD, University of Cincinnati, 2022, Engineering and Applied Science: Mechanical Engineering

Combined plant and control design (control co-design, or CCD) methods are generally used to address the synergistic coupling between the plant and control parts of a dynamic system. Recently, reliability-based design optimization (RBDO) principles have been used within CCD to address the design of stochastic dynamic systems. However, since the new reliability-based CCD (RBCCD) algorithms use all-at-once (AAO) formulations of CCD, only most-probable-point (MPP) methods can be used as a reliability analysis technique. This is a limitation as the use of such methods for highly-nonlinear RBCCD problems introduces solution error that could lead to system failure. A multidisciplinary feasible (MDF) formulation for RBCCD problems would eliminate this issue as the dynamic equality constraints would be satisfied through forward simulation. Since the RBCCD problem structure would be similar to traditional RBDO problems, any accurate reliability analysis method could be used. Therefore, in this work, a novel reliability-based MDF formulation of multidisciplinary dynamic system design optimization (RB-MDF-MDSDO) has been proposed for RBCCD. To quantify the uncertainty propagation, an accurate reliability analysis method using generalized polynomial chaos (gPC) expansions has been proposed. The effectiveness of the RB-MDF-MDSDO formulation and the proposed reliability analysis method are established via two test problems. The performance of the gPC method relative to the current state of the art, MPP methods, is relatively unknown for RBCCD applications. Specifically, the only known information pertains to RBDO applications, where the gPC expansion method is generally known to be more accurate, but also computationally more expensive than the MPP methods. Therefore, to benchmark the performance of the gPC expansion method against MPP methods, the first-ever double-loop and single-loop MPP-based formulations of RB-MDF-MDSDO are developed, and their solution accuracy and e (open full item for complete abstract)

Committee: Michael Alexander-Ramos Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); Sam Anand Ph.D. (Committee Member) Subjects: Engineering

Doctor of Philosophy (Ph.D.), University of Dayton, 2019, Electrical and Computer Engineering

A method is presented to incorporate physics-based controllability assessment in an aircraft Multi-disciplinary Analysis and Design Optimization environment with a target fidelity representing the traditional preliminary aircraft design phase. This method was designed with specific intended application to innovative vehicle concepts such as the Efficient Supersonic Air Vehicle, a tailless fighter-type aircraft which requires the use of innovative control effectors to achieve yaw control requirements. Typically, the layout of an aircraft is determined primarily through empirical design methods with minimal physical evaluation influencing the shape. As a result, the evaluation of new technologies such as these innovative control effectors in the past has been limited to placement and testing of them within existing free real estate on an otherwise complete vehicle design. The hypothesis of this dissertation is that inclusion of such technology in earliest stages of the design process has a greater chance of leading to optimal benefit and potentially a closed design for a tailless fighter-type aircraft. However, incorporation of technology that does not have a strong statistical basis through prior work requires some form of physical analysis to be performed in the design iteration. An aerodynamic study was performed to determine the optimal combination of fidelity and computation time for analyzing these types of configurations for the controls analysis in the MADO environment, resulting in the use of a multi-fidelity approach to aerodynamic analysis. This approach in turn requires a multi-fidelity, parameterized geometric model of the aircraft with automated generation of analysis mesh. In traditional aircraft design, the disciplines involved are isolated from each other in a linear manner such that one finishes prior to another beginning. Multidisciplinary approaches attempt to merge these. However, in open literature the fidelity level of vario (open full item for complete abstract)

Committee: Raúl Ordóñez Ph.D. (Advisor); Raymond Kolonay Ph.D. (Committee Member); Eric Balster Ph.D. (Committee Member); Keigo Hirakawa Ph.D. (Committee Member) Subjects: Aerospace Engineering; Engineering

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Mechanical Engineering

Optimization of dynamic engineering systems requires an integrated approach that accounts for the coupling between embodiment design and control system design, simultaneously. Generally known as combined design and control optimization (co-design), these methods offer superior system performance and reduced costs. Despite the widespread use of co-design techniques in the literature, extremely limited research has been done to address the issue of uncertainty in co-design problem formulations. This is problematic as all engineering models contain some level of uncertainty that might negatively affect the system's performance, if overlooked. Accounting for these uncertainties transforms the deterministic problem into a stochastic one, requiring the use of appropriate stochastic optimization approaches. Therefore, this dissertation serves as the starting point for research on stochastic co-design problems when the uncertainty is propagated into the system from random design decision variables and/or problem parameters. Specifically, a simultaneous co-design formulation within multidisciplinary dynamic system design optimization (MDSDO), along with a special class of direct methods, known as direct transcription (DT), are consistently used throughout this research as the basis for uncertainty considerations. Using techniques from robust design optimization (RDO), we develop a novel stochastic co-design formulation within MDSDO, known as robust MDSDO (R-MDSDO). This formulation enables a protective measure against uncertainties by minimizing the sensitivity of the objective function to variations in design decision variables and fixed problem parameters. The R-MDSDO formulation is applied to two case studies to assess its effectiveness and implementation challenges. A more rigorous evaluation of probabilistic constraints is required to ensure reliability. In this dissertation, we develop a novel stochastic co-design approach based on the principles of RBDO. We im (open full item for complete abstract)

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); Kelly Cohen Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2021, Engineering and Applied Science: Mechanical Engineering

Design and optimization of a dynamic system must account for synergy between plant and the associated controller to obtain a system-level optimum solution. If an active dynamic system is designed without considering the synergy between the plant design variables and the controller input based on the states, the optimizer might give sub-optimal results. This synergy between the plant and control is considered in combined plant and control design optimization which is also known as control co-design optimization. Currently control co-design has been applied to active dynamic systems such as hybrid electric vehicles and active suspension systems. Broadly, there are two classes of optimization algorithms, gradient-based and derivative-free methods, that can be used to solve any co-design problem. Most co-design problems are currently solved using derivative-based methods. These methods are best at giving an optimum solution when the problem is convex. However, multiple local optimum solutions are possible when the problem is non-convex. This might obscure the chances of achieving a global optimum solution. Derivative-based methods can be used to solve such problems by using a multi-start approach. This approach could be cumbersome though since it requires the initialization of plant and control design variables which spans the entire design space and thereby would be an arduous task. A more practical approach at doing a global search would be to use a derivative-free method to solve such problems. Although many derivative-free methods exist, Mesh Adaptive Direct Search (MADS) has got proven convergence proofs and has a computational expense that makes the solution to non-convex codesign problems tractable while maintaining sufficient global search capability. This work examines the capability of MADS at performing a global search by way of an autonomous electric vehicle powertrain control co-design study. The results from solving the control co (open full item for complete abstract)

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); David Thompson (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Engineering

Master of Science, The Ohio State University, 2020, Mechanical Engineering

Body structure design of automobiles is critical to achieve light weight and crash worthiness based on engineers' experience. To enhance experiential design, three computational methods are being developed: DOE based sampling, topology optimization and machine learning. This thesis produced data sets that can be used in DOE and machine learning. It also looked at different ways of using topology optimization for comparative studies. For all these tasks we need to extract feature attributes and patterns in association with performance parameters objectively. In the case of automotive hood models, the linear static analyses performance of hood lift and twist deflection, von Mises stress and geometry mass are the main performance factors of the comparative study. In this research, several pre-requisites, like geometry idealization, feature identification and parametrization, boundary condition standardization, were first investigated so that a standard way of Finite Element Modeling can be established. Then, an approach of generating large amount hood performance data, using ANSYS Workbench, was introduced to figure out the main effects parameter sets based on the design of experiments. By importing idealized model and design table, the hood performance data of 100 idealized models are generated in ANSYS DOE tool, accordingly. The hood performance data of idealized hood models generated in DOE are then exported to a statistic analysis software – MiniTab, to obtain the reduced set of parameters and parameter interactions through sensitivity study, based on factorial design analysis. Meanwhile, the topology optimization method for deriving the optimal structure of pocket feature pattern that can minimize deflection and improve the hood performance, was developed as a comparative study of structural optimization methods for automotive hood frames. Idealized solid model was created, with common imprinted surfaces, as well as boundary conditions with hood lift and hood twis (open full item for complete abstract)

Committee: Jami Shah (Advisor); Sandra Metzler (Committee Member) Subjects: Mechanical Engineering

PhD, University of Cincinnati, 2024, Engineering and Applied Science: Aerospace Engineering

The move towards renewable energy has highlighted the need for large-scale, environmentally friendly energy storage solutions. Among these, the Supercritical Carbon Dioxide (sCO2) power cycle is emerging as a promising technology for advanced energy conversion. The effectiveness of such systems depends heavily on the compressor's performance. Using optimization-based methods, a multistage axial compressor has been designed, and its first stage has been built and tested experimentally. Through a series of detailed design iterations and optimization strategies, 3D CFD analyses, the compressor's geometric and operational parameters were fine-tuned to address the unique challenges posed by operation using CO2. Key findings highlight the successful implementation of design optimization that significantly reduces aerodynamic losses and improves the overall efficiency of the compressor stages. The optimized compressor demonstrates robust performance across a range of operating conditions, particularly focusing on improved stall margin, which emphasizes the potential of sCO2 technology in contributing to efficient and sustainable energy systems. Further detailed studies using CFD to analyze cavity effects in shrouded configurations, tip clearance effects, real gas effects, and Reynolds number effects were performed. Experimental validations, conducted at the University of Notre Dame Turbomachinery Laboratory, confirm the CFD predictions and showcase the practical viability of the compressor design and the approach used. This work not only advances the state-of-the-art in turbomachinery design for supercritical fluids but also lays a foundation for future research into the integration of sCO2 and real gas based compressors in renewable energy systems and industrial applications. The insights gained from this study underscore the critical importance of tailored design and optimization strategies in overcoming the thermophysical challenges associated (open full item for complete abstract)

Committee: Mark Turner Sc.D. (Committee Chair); Daniel Cuppoletti Ph.D. (Committee Member); Kelly Cohen Ph.D. (Committee Member); Jeong-Seek Kang Ph.D. (Committee Member); Shaaban Abdallah Ph.D. (Committee Member) Subjects: Aerospace Engineering

MS, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

The development of dynamic systems (both physical plant and control systems) in a sequential manner often results in sub-optimal solutions. However, solutions obtained using combined physical and control system design methodologies have been observed to yield optimal solutions. The overarching interest in obtaining closed-loop solutions with decent computational cost requirements brings about the topic of interest - a comparison of two of the most popular methods employed to cater for this: Model Predictive Control and Dynamic Programming. If the primary requirement is real-time control with a need to handle constraints dynamically, Model Predictive Control (MPC) is the more practical choice. If the problem allows for offline computation and requires globally optimal solutions, and the state and action spaces are not extremely large, Dynamic Programming (DP) may be more practical. This work studies both methods with respect to accuracy, type of closed-loop feedback solutions, and computational efficiency. Both methods are incorporated within a nested control co-design formulation. To validate the accuracy of both techniques, their practical application is demonstrated through case studies involving a single link manipulator, a single pendulum-type crane, and a quarter car suspension system. Each case study includes a model description, problem formulation, and results obtained using both MPC and DP techniques. The findings highlight the effectiveness of nested formulations with feedback methods in achieving optimal control co-design, with comprehensive assessments of each approach.

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2024, Chemical Engineering

The ill effects of climate change are unfolding in real time, as species and ecosystems face irreversible destruction. Climate action is needed now more than ever, as ambitious targets set by the Paris Agreement seem far-reaching in the wake of global average temperatures above 1.5C over their pre-industrial levels recorded over a continuous 12 month period for the first time. Countries, organizations, and companies alike have pledged to limit their net greenhouse gas (GHG) emissions to the environment to zero, via nationally determined contributions and corporate net-zero commitments. Such commitments remain unattainable in the absence of guidance like convergent carbon accounting methods, systems models, and roadmapping frameworks. This dissertation seeks to bridge this gap for the chemicals and materials industry (CMI). The chemical industry generates the “hardest to abate” emissions among the industrial sector due to the fixed carbon content of its products. However, as chemical energy carriers such as hydrogen and methanol gain prominence as solutions to the intermittency issues of renewable energy, the net-zero transition of chemicals becomes tied to the net-zero goals of more expansive and ubiquitous industries such as the power sector. The decarbonization of chemicals to this end, requires estimation of material and carbon flows, and baseline emissions of its current global operations. The frameworks in literature lack appropriate structure and comprehensiveness for such analysis, and relevant process and price data are inaccessible and cost prohibitive. We therefore develop an inventory of first principle based, mass balance compliant, publicly available process and cost data for CMI processes, sourced from the public domain. We devise a regression framework capable of handling conflict ridden data, and an algorithm to map resource, intermediate, product, and emission flows of any chemical system with known product capacities. The resulting Global (open full item for complete abstract)

Committee: Bhavik Bakshi (Advisor); Joel Paulson (Committee Member); Lisa Hall (Committee Member) Subjects: Chemical Engineering; Climate Change; Energy; Engineering; Environmental Engineering; Technology

Master of Science, The Ohio State University, 2022, Materials Science and Engineering

Recent increases in computational power have led to growing enthusiasm about the volume of data that can be collected and analyzed for many applications. However, the amount of data some physical/virtual systems generate is so great that an increased reliance on mathematical, statistical, and algorithmic based approaches to analyze and make decisions from the data is required. Application of these computational tools can lead to sharper decision making and vast amounts of knowledge discovered. The abstraction of the scientific decision-making process has led many researchers to consider observing systems with more tunable experimental parameters. This makes traditional experimentation, which is based on human researchers conducting the experiment and using their intuition to drive the next set of experiments, intractable for these applications. Autonomous experimentation (AE) systems, which are also a byproduct of the computational explosion, are able to address this issue and have found use across the fields of biology, chemistry, and materials science. AE systems are typically capable of conducting certain types of experiments with lower and more reliable turnaround times as opposed to their human counterparts. The automated execution of experiments naturally leads one to think about how those experiments can be parallelized and otherwise completed faster due to the lack of human presence in the experimentation environment. Therefore, AE systems are considered when designing many high-throughput experimentation (HTE) efforts. This thesis presents an overview of the current state-of-the-art for AE systems in Chapter 1, a framework developed to increase the independence of AE systems from human assistance in Chapter 2, and a machine-learning (ML) data processing pipeline that automates the image post-processing phase of the analysis of backscattered-electron scanning electron microscope images in Chapter 3.

Committee: Stephen Niezgoda (Advisor); Joerg Jinschek (Advisor); Sriram Vijayan (Other); Gopal Viswanathan (Committee Member); Oksana Chkrebtii (Committee Member) Subjects: Business Administration; Computer Science; Engineering; Experiments; Industrial Engineering; Information Science; Information Systems; Information Technology; Metallurgy; Operations Research; Robotics; Statistics

Doctor of Philosophy, University of Akron, 2021, Electrical Engineering

In this thesis, a numerical model is developed for estimating the reliability of a Switched Reluctance Machine (SRM), and is then used in its design optimization. SRM design optimization is performed using the multi-objective surrogate optimization method. The multi-objective surrogate optimization method is chosen because of its ability to find a global solution within a given number of function evaluations. In this study, optimization considers reliability as one of the objectives or constraints to obtain an optimal design with high reliability. The optimum design should operate for the target lifetime (20000 hours) before maintenance or complete replacement. Since optimization using 2D FEA is slow, a modified analytical model is developed to more quickly obtain a suitable design. This model predicts flux linkage and torque for a given phase current and rotor position with minimum error. Tapering in the stator and rotor poles is introduced in addition to traditional rectangular poles to reduce the error in predicting flux linkage and torque at unaligned and partially aligned positions. An improved BH curve model with a knee adjustment factor is used to predict the magnetic characteristics of steel used in the machine. Assumptions and detailed derivation of models to estimate flux linkage, co-energy, and torque are included. Reliability is defined as the probability of a component or system operating for a given time. The probability of a component to operate is calculated based on its failure rate. The failure rate of the machine is obtained as the combination of the machine's base failure rate and the failure rate of the windings, rotor, and shaft. The reliability of the machine is obtained using state transition diagrams associated with the Markov model under normal and fault conditions. Along with the machine's reliability, Mean Time to Failure (MTTF) is also predicted for all operating conditions. Using the developed analytical and reliability models f (open full item for complete abstract)

Committee: Yilmaz Sozer PhD (Advisor); Jose Alexis De Abreu-Garcia PhD (Committee Member); Malik Elbuluk PhD (Committee Member); Nao Mimoto PhD (Committee Member); Dane Quinn PhD (Committee Member) Subjects: Electrical Engineering; Electromagnetics

MS, University of Cincinnati, 2020, Engineering and Applied Science: Mechanical Engineering

A key challenge in the design of hybrid-electric propulsion systems (HEPS) for aircraft is the complexity involved in handling efficient sizing of the components as well as control strategy between the multiple power sources. In order to handle this challenge effectively, combined optimal design and control (co-design) methods that enable the integration of energy management optimization along with the vehicle sizing are required. Even though some studies have explored such methods, they have done so in a computationally-intensive nested formulation with limited depth on the design and control modeling aspect. This thesis addresses these issues by posing the system design problem using a simultaneous formulation of the Multidisciplinary Dynamic System Design Optimization (MDSDO) co-design strategy. While the simultaneous formulation, generally, facilitates superior computational performance, the MDSDO method solves co-design problems from a more balanced perspective between design- and control-related variables. We apply this method to hybrid-electric aircraft propulsion system design with an objective to determine the optimal propulsion component designs and supervisory control strategies for a fixed mission profile such that the total energy consumption is minimized. The hybrid configuration is compared to the conventional reference aircraft, MQ-9 Reaper, on the basis of system efficiency. The individual powertrain components are mathematically modeled using physically-meaningful design variables and the mission analysis is done using mathematical simulations. In addition to that, a parametric study on the battery energy density is presented to explore the near-term viability of HEPS for aircraft

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); Mark Turner Sc.D. (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2019, Mechanical Engineering

Thin-walled frames are common in the automotive body structures due to their high strength to weight ratio to meet the requirements for different types of loadings and crash scenarios. Automotive structural engineers are exploring different ways of lightweigthing the Body In White (BIW) and one of them being topology optimization to improve fuel efficiency and lower CO2 emissions. Although topology optimization provides an optimal solution, it outputs monolithic solid cross-section structures that cannot be produced from the mainstream manufacturing processes. The research presented here aims to bridge the gap between the topology optimized automotive body structures and the traditional manufacturing processes by automatically post-processing the results to create hollow cross-sections for the body components inside the design space. The inner and outer styling surfaces control the design space boundaries that constrain the structural configurations and the size of parts. Topology optimization was performed on the voxelized version of the design space, and the results were typically meshed/triangulated surfaces stored as .stl files. The starting point of the post –processing methodology was the curve skeleton, which was the 1D representation of the load paths extracted from the topology optimized structure. Then, planes normal to the curve skeleton were created to intersect the design space and the topology optimized structure to obtain the 2D wireframe and section properties. Predefined parametrized cross-section libraries were then used to map to reside inside the design space boundaries. A surface model was generated by lofting these cross-sections for preliminary verification through finite element analysis and for designing joints connecting different components. Joints design was not part of this thesis.

Committee: Jami Shah (Advisor); Sandra Metzler (Committee Member) Subjects: Mechanical Engineering

MS, University of Cincinnati, 2018, Engineering and Applied Science: Mechanical Engineering

Dynamic systems incorporating physical plant and control systems should be designed in an integrated way to yield desirable and feasible solutions. Conventionally, these systems are designed in a sequential manner which often fails to produce system-level optimal solutions. However, combined physical and control system design (co-design) methods are able to manage the interactions between the physical artifact and the control part and consequently yield superior optimal solutions. Small-scale to moderate-scale dynamic systems can be addressed by using existing co-design methods effectively; nonetheless, these methods can be impractical and sometimes impossible to apply to large-scale dynamic systems which may hinder us from determining the optimal solution. This work addresses this issue by developing a new algorithm that combines decomposition-based optimization with a co-design method to optimize large-scale dynamic systems. Specifically, the new formulation applies a decomposition-based optimization strategy known as Analytical Target Cascading (ATC) to a co-design method known as Multidisciplinary Dynamic System Design Optimization (MDSDO) for the co-design of a representative large-scale dynamic system consisting of a plug-in hybrid-electric vehicle (PHEV) powertrain. Moreover, since many of dynamic systems may consist of several time-dependent linking variables among their subsystems, a new consistency measure for the management of such variables has also been proposed. To validate the accuracy of the presented method, the PHEV powertrain co-design problem has been studied with both simultaneous and ATC methods; results from the case studies indicate the new optimization formulation's ability in finding the system-level optimal solution.

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); Sam Anand Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Mechanical Engineering

Master of Arts, The Ohio State University, 2018, Psychology

Efficient data collection is one of the most important goals to be pursued in cognitive neuroimaging studies because of the exceptionally high cost of data acquisition. Design optimization methods have been developed in cognitive science to resolve this problem, but most of them lack generalizability because their functionality tends to rely on a specific type of cognitive models (e.g., psychometric functions) or research paradigm (e.g., task-to-region mapping). In addition, traditional optimal design methods fail to exploit neural and behavioral data simultaneously, which is essential for providing an integrative explanation of human cognition. As one of the possible solutions, we propose an implementation of Adaptive Design Optimization (ADO; Cavagnaro, Myung, Pitt, & Kujala, 2010) in model-based functional MRI (fMRI) experiments using a Joint Modeling Framework (B. M. Turner, Forstmann, et al., 2013). First, we introduce a general architecture of fMRI-based ADO and discuss practical considerations in real-world applications. Second, three simulation studies show that fMRI-based ADO estimates parameters more accurately and precisely than conventional, randomized experimental designs. Third, a real-time fMRI experiment validates the performance of fMRI-based ADO in the real-world setting. The result suggests that ADO performs better than randomized designs in terms of accuracy, but the unbalanced designs proposed by ADO may inflate the variability of trial-wise estimates of neural activation and therefore model parameters. Lastly, We discuss the limitations, further developments, and applications of fMRI-based ADO.

Committee: Brandon Turner (Advisor); Jay Myung (Committee Member); Zhong-Lin Lu (Committee Member) Subjects: Psychology

Doctor of Philosophy, University of Toledo, 2017, Mechanical Engineering

This dissertation proposes a multi-level optimization method for slender structures such as blades or towers of wind turbine structures. This method is suited performing structural optimizations of slender structures with a large number of design variables (DVs). The proposed method uses a two-level optimization process: a high-level for a global optimization of a structure and a low-level for optimizations of sectioned computational stations of the structure. The high-level optimization uses approximating functions to define target structural properties along the length of a structure, such as stiffness. The approximating functions are functions of the distance from the root of the structure that are defined using basis functions such as polynomials or exponential functions. The high-level DVs are the coefficients of the functions. Thus, the number of the high-level DVs is independent of the number of sections. Moreover, selecting smooth approximating functions help to obtain alternative designs with smooth shapes. The low-level optimization finds an optimum parametric design, such as laminate layups, that matches with the target structural properties defined at the high-level optimization. At the low-level optimization, the proposed method uses an optimizer in each section. Each optimizer is independent of the optimizers in the other sections, thereby decomposing a large optimization problem into several small ones. This approach reduces the number of DVs per optimizer at the low-level optimization which reduces the design space of each section and eliminates the design space of coupling between sections. Once optimum designs are found from all sections at the low-level, the high-level solvers evaluate them for the entire structure. The advantage of the proposed method is that it reduces the number of iterations of the high-level optimization because it considers a small number of high-level DVs. Computational efficiency increases because the computationally e (open full item for complete abstract)

Committee: Efstratios Nikolaidis Ph.D. (Committee Chair); Vijay Devabhaktuni Ph.D. (Committee Co-Chair); Abdollah Afjeh Ph.D. (Committee Co-Chair); Sorin Cioc Ph.D. (Committee Member); Douglas Nims Ph.D. (Committee Member); Larry Viterna Ph.D. (Committee Member) Subjects: Aerospace Engineering; Energy; Engineering; Environmental Economics; Environmental Engineering; Mechanical Engineering; Operations Research

MS, University of Cincinnati, 2016, Engineering and Applied Science: Mechanical Engineering

The design of large-scale, complex systems such as plug-in hybrid electric vehicles (PHEVs) motivates the use of formal optimization methods from both multidisciplinary design optimization (MDO) and optimal control theory. Traditionally, MDO methods have been used to address the integrated design of engineering systems comprised of multiple, interacting components and/or disciplines for superior static system performance. Optimal control theory, on the other hand, is often used to select the best operation strategy of a given dynamic system for superior dynamic system performance. Although many times in practice the optimal design and control of such dynamic systems are addressed almost independently, this approach generally yields sub-optimal overall design solutions. This is because the system architecture, or physical design, is inherently coupled with its operation strategy, or control design. Combined optimal design and control techniques, also known as co-design, can address this issue by using an integrated approach to enable superior design solutions for dynamic systems. This thesis focuses on the co-design of large-scale systems, specifically PHEVs based on simultaneous multidisciplinary dynamic system design optimization (MDSDO) methods using direct transcription (DT). In order to enable a simultaneous approach for optimizing the design and control of the PHEV, a toolbox was developed to design all the critical component of a PHEV powertrain including: electric motor, generator, engine, transmission, and high voltage battery. This toolbox takes the size related design variables as inputs and by using the embedded analytical equations, generates the output performance characteristics of each component. The MDSDO problem formulation is then solved using GPOPS-II,a DT-based MATLAB software for solving multiple-phase optimal control problems. DT-based simultaneous problem formulations in MDSDO has already been successfully used in moderate scale problems, howe (open full item for complete abstract)

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering; Mechanics

Master of Science (M.S.), University of Dayton, 2014, Aerospace Engineering

Surrogate models are widely used as approximations to exact functions that are computationally expensive to evaluate. The choice of model training information and the estimation of the accuracy of surrogate models are major research avenues. In this work, a unified dynamic framework for surrogate model training point selection and error estimation is proposed. Building auxiliary local surrogate models over sub-domains of the global surrogate model forms the basis of the framework. A discrepancy function, defined as the absolute difference between response predictions from global and local surrogate models for randomly chosen test candidates, drives the framework. The framework preferably evaluates the expensive exact function at locations, where the value of the discrepancy function is high and when a distance-constraint to previously existing training points are satisfied. As a result, the surrogate model is continually refined in regions of higher uncertainty in prediction, and a better spread of training points is also achieved. Unlike most training point selection approaches, the framework addresses surrogate training from two disparate contexts, as training in the presence and absence of derivative information. The local surrogate models use the derivative information when available and affect the framework via the discrepancy function, and helps determine the locations that require derivative information. The benefits of the dynamic training approach are demonstrated with analytical test functions and the construction of a two-dimensional aerodynamic database. The results show that the proposed method improves the convergence monotonicity and produces more accurate surrogate models, when compared to random and quasi-random training point selection strategies. The newly introduced discrepancy function is proposed as an approximation to the actual error in the prediction of the surrogate model leading to the quantities: root mean square discrepancy (RMSD) and (open full item for complete abstract)

Committee: Markus Rumpfkeil Ph.D (Committee Chair); Raymond Kolonay Ph.D (Committee Member); Aaron Altman Ph.D (Committee Member) Subjects: Aerospace Engineering; Civil Engineering; Mathematics; Mechanical Engineering; Physics

Doctor of Philosophy (PhD), Wright State University, 2008, Engineering PhD

The work performed investigates the system design and optimization of an undersea vehicle. The system design is driven by the available components, the missions the vehicle is required to perform, and the performance the system configuration yields. The system design consists of three design modules: path planning, component selection and sizing, and structural analysis. The path planning module uses a novel application of the Particle Swarm Optimization algorithm named Path Planning by Additive Freedom. Additionally, the unknown aspects of the mission space through which the path propagates are dealt with using an uncertainty quantification method known as Evidence Theory. Component selection and sizing are performed using the naval design tool SNARC. This program uses a branch and bound technique called the A* algorithm to choose the components that should be used in the system and what size they should be according to the mission profiles provided by the path. The structural analysis is performed using the ABAQUS finite element program to calculate the structural reliability of the system. This module uses the structure sizing data, as well as the hydrodynamic and hydrostatic forces from the mission profile, to calculate the system's reliability with respect to a buckling failure, the most common structural failure in undersea vehicles.

Committee: Ramana V. Grandhi PhD (Advisor); Haibo Dong PhD (Committee Member); Jay H. Kim PhD (Committee Member); Ravi C. Penmetsa PhD (Committee Member); Gregory W. Reich PhD (Committee Member) Subjects: Mechanical Engineering

Master of Science in Engineering (MSEgr), Wright State University, 2008, Mechanical Engineering

The traditional Risk Based Design (RBD) process involves designing a structure based on risk estimates obtained during several iterations of an optimization routine. This approach is computationally expensive for large-scale aircraft structural systems. The main objective of this research is to establish a RBD algorithm and produce RBD plots for stiffened plates. Basic steps to check functionality will be done by first analyzing a flat plate for which closed formed equations are available and then moving to more complex geometries like stiffened plates. Therefore, the concept of RBD plots that can be used for both structural sizing and risk assessment are introduced. RBD plots serve as a tool for failure probability assessment given geometry and applied load and can also be used to determine geometric constraints to be used in sizing given allowable failure probability. This approach transforms a reliability-based optimization problem into a deterministic optimization problem with geometric constraints.

Committee: Ravi C. Penmetsa PhD (Advisor); Ravi C. Penmetsa PhD (Committee Member); Eric Tuegel PhD (Committee Member); Nathan W. Klingbeil PhD (Committee Member); George P. G. Huang PhD (Other); Joseph F. Thomas, Jr. PhD (Other) Subjects: Aerospace Materials; Engineering; Mechanical Engineering

PhD, University of Cincinnati, 2008, Engineering : Electrical Engineering

In today's world, with ever increasing design complexity and constantly shrinking device sizes, the microelectronics industry faces the need to develop an entire system on a single chip (SoC). This need gives rise to the responsibility of developing mature Computer-Aided-Design (CAD) tools to tackle such complexities. Unlike digital CAD tools, automated synthesis tools for the irreplaceable analog sections are still immature. Circuit-level analog synthesis comprises of two steps – Topology formation and Sizing of the topology. Topology selection and topology generation are two approaches to topology formation. Research in topology selection has almost been discontinued owing to heavy designer dependency. But with the advent of evolutionary techniques like Genetic Algorithm (GA) and Genetic Programming (GP), topology generation gained popularity. Topology generation is the art of generating device level circuit schematics satisfying user specifications. This thesis makes a genuine endeavor to develop topology generation tools individually for both passive analog circuits involving R, L, and C components and active circuits that involve additional MOS devices. For passive circuits, we present a GA-based synthesis framework, where the component values for the first set of circuits are set through a deterministic computational technique. Further, the crossover technique for breeding off-springs from parent solutions obeys certain constraints to ensure the formation of structurally correct circuits. The work has been further extended with the introduction of novel selection and crossover strategies. The above techniques have been successful in synthesizing various low-pass and high-pass filter designs. In the pursuit of developing an active circuit topology generator, we have developed a self-learning optimization algorithm involving multiple design variables. To measure the effectiveness of this technique, we applied it first to a relatively easier domain viz. MPLS c (open full item for complete abstract)

Committee: Ranga Vemuri (Committee Chair); Wen-ben Jone (Committee Member); Harold Carter (Committee Member); Dharma Agrawal (Committee Member); Jintai Ding (Committee Member) Subjects: Computer Science; Electrical Engineering; Engineering; Systems Design