Master of Science, The Ohio State University, 2021, Mechanical Engineering

The EcoCAR Mobility Challenge is a student competition that tasks universities across North America with the hybridization and SAE Level 2 automation of a 2019 Chevy Blazer. In years 2 and 3 of the competition, the Ohio State EcoCAR team committed considerable effort to the development of an adaptive cruise control (ACC) feature. This paper provides a detailed discussion of what motivated the selection of a modified PID controller as the control method of choice for ACC. The state flow used by the team to achieve independent distance and velocity control is also reviewed. After designing the controller, the team performed particle swarm optimization to identify the ideal proportional, integral, and derivative gain values. In doing so, the team managed to greatly reduce maximum acceleration, RMS acceleration, and maximum jerk in simulation. While doing so, the efficiency of the vehicle was also improved by 8.45 percent. Then, in order to validate the real-world performance of the novel adaptive cruise controller, the team conducted a full range of anything-in-the-loop (XIL) testing. Across model, hardware, and vehicle closed-loop testing, Ohio State identified and resolved numerous potential issues in the controller and its implementation in the vehicle. Additionally, the safety and comfort of the ACC feature were verified across all testing environments, affirming the fidelity of the model and preparing the team for in-vehicle testing. Lastly, using a real target vehicle and live sensor data, Ohio State performed approach tests that demonstrate the functionality of its ACC in a real-world environment.

Committee: Shawn Midlam-Mohler (Advisor); Giorgio Rizzoni (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

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

The automotive industry is driving towards electrification. As the emission and fuel economy standards get more stringent, manufactures are electrifying their vehicle platforms by developing more hybrid electric vehicles. Although new technology boosts the fuel economy, it also brings new challenges. One of them is that customers often find discrepancies between the rated fuel economy number and the number they get during real world operation. Therefore, there is a need to investigate the issue and develop a new calibration process for optimizing the HEV fuel economy over both certification and real-world operation. In this research, a model-based calibration process is developed. The process uses meta-heuristic algorithms to optimize five look-up tables that are relevant to fuel economy of the HEV. Four different meta-heuristic algorithms, namely PSO, GA and two hybrids, are investigated and compared. It is found that PSO has reasonably good performance and can deliver its performance consistently under different conditions. Other algorithms may have better performance under certain scenarios, but they are sensitive to constraints in test problems and fail to get rational solutions in the real problem. The research also investigates methods to reduce number of parameters to optimize, the initialization of the optimization set and ways to generate representative drive cycles based on real-world driving data. The important thing is that these methods are not vehicle-specific and therefore can be migrated to calibration of other HEVs easily.

Committee: Giorgio Rizzoni (Advisor); Marcello Canova (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, University of Akron, 2017, Civil Engineering

The main objective of this thesis is to increase the performance of repair materials in concrete overlays. To meet this objective, a new material selection process is applied. Then, with a focus on volume change in concrete overlays new models for shrinkage and creep are developed, and the ACI model for shrinkage is modied. First, a straightforward repair material selection procedure which covers all criteria and technical requirements is developed. In this regard, a recently proposed MCDM method, namely comprehensive VIKOR is used. Then, the strategy is applied on ve dierent types of patch repair materials to validate the accuracy of the outcomes of the proposed procedure. Second, the improvement of ACI model for shrinkage of concrete containing three types of pozzolans including silica fume (SF) Fly ash (FA) and Slag (SL) is conducted base on a comprehensive database. Particle Swarm Optimization (PSO) method is used to modify time function of ACI model for each type of pozzolan. In addition, a new correction factor associated with compressive strength is generated to capture the eect of dosage and type of each pozzolans. The results of several indicators iii show better prediction performance the modied ACI model compared to it's original formula. Third, new empirical models are derived to predict the compressive strength of preformed foam cellular concrete using volumetric and weighted approaches. The proposed models are generated by utilizing a robust predictive tool known as genetic programming. A comprehensive database is collected from the literature to cover a wide range of mixture components (such as sand and pozzolans) and mix proportions. The models link the compressive strength to binder, water, and foam volume. Validation of the best model is carried out by using a portion of the data set that is not employed in the calibration process. A comparative study is conducted to evaluate the performance of the proposed model ver (open full item for complete abstract)

Committee: Robert Y Liang (Advisor); Alper Buldum (Committee Member); Junliang Tao (Committee Member); Zhe Luo (Committee Member); Chang Ye (Committee Member) Subjects: Civil Engineering

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

Organic Rankine Cycles are used for Waste Heat Recovery from low temperature heat sources. In an Internal Combustion Engine, roughly one-third of the fuel energy is sent out through the exhaust. ORC's were investigated for fuel efficiency improvements for heavy duty trucks in the 70's during the oil crisis. ORC's have once again gained interest with the current energy scenario and advances in technology. A recuperated ORC with R245fa as refrigerant is modeled in this thesis using the commercial 1-D simulation software GT-SUITE. The ORC is designed to extract energy from the exhaust of a gasoline powered light duty vehicle. Control inputs for the ORC are pump speed, expander speed and exhaust gas bypass valve position. The exhaust gas is not a steady source of heat, with varying temperature and mass flow rate depending on the operation of the vehicle. To maintain the ORC at a pre-determined operating state, feed-forward maps will be created. Exergy destruction is proposed to be used as a parameter that limits the control effort within reasonable limits. A second law analysis will be performed to identify points of exergy destruction and the ORC will be optimized using a Multi-Objective Particle Swarm Optimization algorithm to generate a Pareto front of net power output and exergy destruction in the system for a single exhaust condition that will allow the decision maker to choose a suitable state for the ORC. The Pareto front will be constructed for other off-design exhaust conditions and the trends will be observed between multiple exhaust gas conditions.

Committee: Marcello Canova PhD (Advisor); Shawn Midlam-Mohler PhD (Committee Member) Subjects: Automotive Engineering

Doctor of Philosophy (PhD), Wright State University, 2011, Engineering PhD

High speed trains are the most efficient means of public transportation. However the speed of the train needs to be increased (> 350 km/hr) to cover large distances in a short time to make it accessible to large population. With the increase in speed, number of issues related to efficiency, safety and comfort like the aerodynamic drag, structural strength, as well as the noise levels inside and outside of the train etc. need to be considered in the design of the high speed trains. Hence making it a multi disciplinary design problem. There are a large number of parameters from different disciplines that need to be tuned to identify the best design. The parameters need to be optimized to identify the best design configuration that meets the design requirements. This requires the use of robust and efficient optimization algorithms. Evolutionary algorithms have been used extensively in the engineering design optimization problems, but they suffer from a drawback of lack of robustness. One of the objectives of this research is to address the robustness issue of currently available optimization algorithms. A hybrid GA-PSO algorithm combining the benefits of both the original algorithms GA and PSO is proposed in this research. The hybrid GA-PSO algorithm was observed to be robust and accurate based upon the tests. The computer simulations required to complete the optimization of this problem are expensive both in terms of computational resources as well as time. To minimize the computational effort an adaptive surrogate model based on kriging was used during optimization. The accuracy of the surrogate model was checked during the optimization process using the parameter called expected improvement value (EIV) and is updated whenever found to be inadequate. The optimization algorithm combined with the adaptive surrogate modeling technique is tested on Branin function and is found to be robust and efficient. The optimization of a high speed train is an MDO problem. The MDO p (open full item for complete abstract)

Committee: George Huang PhD (Committee Chair); Ravi Penmetsa PhD (Committee Co-Chair); Haibo Dong PhD (Committee Member); Jonathan Black PhD (Committee Member); Norihiko Watanabe PhD (Committee Member) Subjects: Engineering; Mechanical Engineering

MS, University of Cincinnati, 2008, Engineering : Computer Engineering

Networks are ubiquitous in both engineered and natural systems, and can often become extremely complex. Designing such complex networks (e.g., communication or traffic networks) requires optimization with respect to many factors, and is computationally infeasible for large networks. It is important, therefore, to develop efficient heuristic methods for such design, but discovering such heuristics is often extremely difficult. This thesis presents a reverse-engineering approach to finding heuristics for designing near-optimal weighted networks embedded in 2-dimensional space. The weights represent link costs that are a supralinear function of link length. The goal is to design a fully connected network that minimizes both total cost and the average number of edges on the shortest paths between all node pairs. The approach begins by generating a set of reasonably, but not infeasibly, large optimal networks using particle swarm optimization (PSO). These are compared against a population of randomly generated networks with similar numbers of edges, yielding a set of features associated preferentially with the optimized networks and not with poor networks. These results are used to devise simple heuristics for network design, mainly using features based on local information as well as easily available global information. These heuristic decision functions are then used to configure much larger networks (which could not be optimized explicitly), and the quality of these networks is compared with randomly generated networks of equivalent complexity to validate the heuristics. These heuristics were able to generate optimal networks as compared to the random networks.

Committee: Ali Minai (Committee Chair); Kenneth Berman (Committee Member); Karen Davis (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2012, Biophysics

This thesis presents a novel multiple ligand simultaneous docking (MLSD) method for simulating protein-ligand molecular recognition and a novel protocol for fragment-based drug design by combining MLSD and drug repositioning. Different cancer molecular targets, namely GP130 and STAT3, in IL-6/GP130/STAT3 signaling pathway were used as use cases for the proposed MLSD and drug design protocol. Conventional docking methods simulate only one single ligand at a time during docking process. In reality, molecular recognition process always involves multiple molecular species. The first part of this research developed a MLSD simulation method which can simulate the orchestrated action of multiple ligands binding to the active site of protein. The methodology proves robust through systematic testing against several diverse model systems: E. coli PNP complex with two substrates, SHP2NSH2 complex with two peptides and cancer target Bcl-xL in complex with ABT-737 fragments. In ABT-737 and SHP2NSH2 cases, conventional single ligand docking failed to find correct binding modes due to energetic and dynamic coupling among ligands, whereas MLSD resulted in the correct binding modes. In PNP case, the MLSD simulations captured the binding dynamics, which is consistent with proposed enzymatic mechanism from the experiment. The work also compared two search strategies: Lamarckian Genetic Algorithm (LGA) and Particle Swarm Optimization (PSO), which had respective advantages depending on the specific systems. Molecular docking finds its major applications in drug design and discovery. Conventional high throughput screening (HTS) drug discovery approach identifies many hits, but few of them can be developed into drugs. The second part of this research applied MLSD to fragment-based drug design and proposed a novel drug discovery protocol by combining MLSD and drug repositioning. It proceeds as follows. 1. A small library of drug scaffolds is identified for the binding hot spots of target (open full item for complete abstract)

Committee: Chenglong Li (Advisor); Kun Huang (Committee Member); Michael Poirier (Committee Member); Guo-Liang Wang (Committee Member) Subjects: Bioinformatics; Biomedical Research; Biophysics; Molecular Biology; Molecular Chemistry; Pharmacology

Master of Sciences, Case Western Reserve University, 2011, EMC - Mechanical Engineering

Two-dimensional shape of a wind turbine blade was optimized by means of Particle Swarm Optimization. By following blade element theory, lift coefficient Cl and drag coefficient Cd were used as objective functions. In order to compute the objective functions, flow field around airfoils were calculated by Re-Normalization Group (RNG) k-ε model. Shapes of airfoils were defined by modified PARSEC method with 10 parameters.Two optimization cases were conducted with maximum thickness limited to 10% and 20% of the chord length respectively. In both cases, Reynolds number was set at 2.0×106, which is the design condition of S809 airfoil. S809 airfoil is a well known airfoil used in wind turbines and many experimental data are available. The angle of attack for the optimization was set at 5.13 deg., the mount angle of S809. Non-dominated solutions obtained by this research were compared with the performance of S809 at several angles of attack. The results of optimization showed that 1) there is a strong influence of maximum thickness of airfoil to its performance, 2) non-dominated solutions constitute a gradual relationship which implies that there are many airfoil shapes that could be considered as an optimum. The resulting shape along this Pareto front showed higher performance than the existing blade section (i.e. NREL S809) in certain conditions.

Committee: James S. T'ien PhD (Committee Chair); Meng-Sing Liou PhD (Committee Member); J. Iwan D. Alexander PhD (Committee Member) Subjects: Aerospace Materials; Ecology; Energy; Engineering; Fluid Dynamics; Mechanical Engineering