Master of Science (MS), Ohio University, 2020, Industrial and Systems Engineering (Engineering and Technology)

Process improvements in hospitals usually focus on a single department (eg. emergency department, operating theater, specialty clinic, etc). However, actions taken in one department inevitably affect the performance of other departments. Therefore, higher efficiency improvements can be obtained by considering the patient care process as one synergetic activity involving several departments and various sets of resources. In this research we propose an integrated approach for modeling the patient lifecycle for multiple departments. First we describe a patient flow from his/her entry into the hospital through a progressive care unit until the patient has fully recovered. We use process mapping methods to address value added activities and other necessary activities in the patient lifecycle. Then, a simulation model is developed in Simio using customized objects created in previous works. Those customized objects carry their own logic and behavior. For example, the Bed object includes logic for a patient recovering while using several hospital resources (nurses, therapist) in his/her hospital stay. Those objects were used to build several configurations of an integrated model with multiple departments. Data about patient arrival patterns, their health acuity, and procedure needs were obtained from a real hospital in order to test our approach. The procedures duration data (which were different for different levels of patient acuity and for different surgical and other procedures) were used to obtain service distribution using statistical analysis methods. Modular simulation objects and data distributions from real hospitals allowed us to build an integrated simulation model with several configurations of the process flow. Simulation experiments were performed on these models and performance recorded. The recommendation for implementations in the hospitals is also reported.

Committee: Dusan Sormaz (Advisor); Gursel Suer (Committee Member); Diana Schwerha (Committee Member); Vic Matta (Committee Member) Subjects: Engineering; Health Care; Industrial Engineering

Doctor of Philosophy, The Ohio State University, 2022, Welding Engineering

The automotive industry wants to advance integrated computational materials engineering (ICME) approaches that combine models of joining processes and microstructural evolution for prediction of material property gradients and ultimately the mechanical performance of multi-sheet lap joints. Despite the increasing demand for computational optimization within vehicle structures and the increased use of low-density materials, modern integrated modeling frameworks of automotive lap joining are often limited to the resistance spot welding (RSW) of conventional steels. Moreover, important phenomena in steel weldments, like decomposition of austenite on-cooling, tempering of martensite, and microstructure-dependent flow stress and damage properties are too material-specific for universal application. In this research, generalized metallurgical and mechanical modeling strategies are investigated for increased applicability to a wider range of steels and joining processes. The study evaluates: the reliability of heat transfer predictions within state-of-the-art numerical models of RSW, the accuracy of existing austenite decomposition models, the readiness of steel time-temperature-transformation (TTT) diagram tools containing CALPHAD-calculated parameters, the generality of a recently developed martensite tempering model, and the determination of RSW fusion and heat-affected zone flow stress and fracture behavior. Results show that state-of-the art finite element models of RSW that are validated using experimental weld nugget dimensions have a propensity to underpredict cooling rates. A JMAK and additivity rule approach calibrated with experimental TTT diagram data exhibited the greatest accuracy when predicting AHSS austenite decomposition; however, calibrations using calculated TTT diagrams better facilitated material optimization. Generalized parameters within a JMAK-type model of martensite tempering successfully predicted HAZ softening within martensitic and dual-phase (open full item for complete abstract)

Committee: Antonio Ramirez (Advisor); Avraham Benatar (Committee Member); Boian Alexandrov (Committee Member) Subjects: Materials Science

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

Modern radio frequency (RF) microsystems are challenged to deliver improved performance across an ever-changing landscape of applications and requirements. As the demand for high-power and high-frequency systems grows, heterogeneous integration of new, high-power compound semiconductor (CS) technologies with existing silicon-based circuitry may lead to a paradigm shift in the field of RF electronics. Intimate integration of high-power technologies leads to increased power densities which forces thermal management considerations to the forefront of design. At the moment, the availability thermal analysis tools for heterogeneously integrated technologies are limited, and thermal considerations are often relegated to the back end of the design cycle. In this work, a multiscale finite element approach is developed for thermal management of heterogeneous integrated circuits. The proposed method is capable of simulating heat flow at multiple length scales using submodeling techniques which incorporate high spatial resolution near the active region while including realistic approximations of global boundary conditions. Thermal simulations are presented here for a device implemented using DARPA's Diverse Accessible Heterogeneous Integration (DAHI). The device's thermal behavior is explored for a variety of possible configurations and operating conditions. To better inform future circuit designers, the device's primary thermal bottlenecks are identified and quantified in terms of their influence on temperatures within the device.

Committee: Rebecca Dupaix (Advisor); Waleed Khalil (Committee Member) Subjects: Electrical Engineering; Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2014, Mechanical Engineering

In this thesis, a mathematical model of an automotive powertrain featuring a wet dual clutch transmission is developed. The overall model is comprised of models that describe the dynamic behavior of the engine, the transmission mechanical components, the hydraulic actuation components, and the vehicle and driveline. A lumped-parameter model, that incorporates fluid film dynamics and a simplified thermal model, is used to describe wet clutch friction. The model of the hydraulic actuation system includes detailed models of the clutch and synchronizer actuation subsystems. A simulation of the dynamic powertrain model is built using AMEsim and MATLAB/Simulink. The powertrain simulator is used to demonstrate how changes in transmission parameters affect the quality of clutch-to-clutch shifts and the overall dynamic response of the powertrain. Based on this model, measurements of clutch pressure and the rotational speeds and estimated accelerations at the input and output sides of the clutch are used in the design of a friction parameter estimation scheme that can be implemented offline using past simulation data or online using current simulation signals. For both offline and online cases, simulation results demonstrate that friction parameters are estimated with reasonable accuracy. An integrated powertrain controller is developed with a model-based feedforward controller and multiple feedback loops. The feedforward controller, which generates a pressure command to either clutch, is developed by inverting a simplified model of the powertrain, and using a static friction model to relate clutch pressure to friction torque. The inputs to the feedforward controller are speeds and estimated accelerations of the engine and clutches. The feedforward controller adapts to changes in friction characteristics by updating the friction parameters used in the static friction model using the values generated by the estimation scheme. The feedback controller contains loops tha (open full item for complete abstract)

Committee: Krishnaswamy Srinivasan (Advisor); Shawn Midlam-Mohler (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

Doctor of Philosophy (PhD), Wright State University, 2009, Engineering PhD

A simulation framework that integrates process-driven and event-driven approaches offers a powerful combination of tools to the modeler. In process-driven simulation models, the system can be represented by block diagrams or system networks through which entities flow to mimic real life system objects. In event-driven models, the system can be represented by event graphs, which focus on the abstraction of the event rather than on observable physical entities. In this research, a simulation environment is proposed that integrates both the approaches, i.e. process and event. One of the main outcomes of working in such an environment is that modelers can manage the complexity of large models based on real-world systems through process orientation, while retaining the control over the attributes, variables and the logic through event orientation. Discrete event simulation is often taught to students at either the event level or the process level. A simulation tool that effectively preserves both levels would be helpful in more effectively educating future simulation modelers.

Committee: Frank Ciarallo PhD (Advisor); Raymond Hill PhD (Committee Member); Sundaram Narayanan PhD (Committee Member); Thomas Hartrum PhD (Committee Member); Marcus Perry PhD (Committee Member) Subjects: Computer Science; Industrial Engineering

Doctor of Philosophy, University of Akron, 2006, Mechanical Engineering

In structural engineering problems, uncertainty is inherent in the load, strength and material property. The resulting stochastic problem can be solved numerically using the computationally intensive Monte Carlo technique. The stochastic finite element method is an alternative approach. This method is based on the perturbation technique. Uncertainties are considered as random variables with a relatively modest fluctuation about the mean. The present study develops the perturbation formulation as the primary stochastic analysis tool. The formulation is analytically elegant and numerically inexpensive. The stochastic analyzer is integrated next into the design optimization testbed CometBoards of NASA Glenn Research Center. The design tool in the stochastic domain was also extended to obtain a robust formulation that can minimize the variation of the objective function. The stochastic analysis utilizes both force and displacement formulations. The force formulation in the literature is referred to as the integrated force method (IFM). Its dual or the dual integrated force method (IFMD) became the stiffness formulation. The first- and second-order perturbation techniques were applied to the governing formulae of force and displacement methods to obtain closed form expressions for the mean and standard deviation of response parameters consisting of internal force, displacement and member stress. Stochastic sensitivity analysis was formulated for selected response variables. The analytical methods also included Neumann expansion with Monte Carlo simulation as well as a variational energy formulation and simplification and reduction on stochastic calculations. Formulas of the stochastic analysis were programmed in Maple V software as well as in the FORTRAN language. Solutions were obtained for a set of examples, which were verified via Monte Carlo simulations. The IFM/IFMD perturbation methods yield response very efficiently for modest fluctuation in random variables. The (open full item for complete abstract)

Committee: Dr. Michelle Hoo Fatt (Advisor); Dr. Surya Patnaik (Advisor); Dr. Graham Kelley (Other); Dr. Craig Menzemer (Other); Dr. Dmitry Golavaty (Other) Subjects: