Multi-trailer articulated heavy vehicles (MTAHVs) exhibit unstable motion modes at high speeds, including jack-knifing, trailer swing, and roll-over. These unstable motion modes may lead to fatal accidents. On the other hand, these vehicle combinations have poor maneuverability at low speeds. Of all contradictory design criteria of MTAHVs, the trade-off relationship between the maneuverability at low speeds and the lateral stability at high speeds is the most important and fundamental. This trade-off relationship has not been adequately addressed. The goal of this research is to address this trade-off relationship through the design optimization of MTAHVs with active safety systems. A parallel design optimization (PDO) method is developed and applied to the design of MTAHVs with integrated active safety systems, which involve active trailer steering (ATS) control, anti-roll (AR) control, differential braking (BD) control, and a variety of combinations of these three control strategies. To derive model-based controllers, a single-trailer articulated heavy vehicle (STAHV) model with 5 degrees of freedom (DOF) and a MTAHV model with 7 DOF are generated. The vehicle models are validated with those derived using a commercial software package, TruckSim, in order to examine their applicability for the design optimization of MTAHVs with active safety systems. The PDO method is implemented to perform the concurrent design of the plant (vehicle model) and controllers. To simulate the closed-loop testing maneuvers, a driver model is developed and it is used to drive the virtual vehicle following the prescribed path. Case studies indicate that the PDO method is effective for identifying desired design variables and predicting performance envelopes in the early design stages of MTAHVs with active safety systems. / UOIT

The demand for energy and cost efficient buildings has made architects and contractors more aware of the resources consumed by the built environment. While the actual economic and environmental costs of future construction can never be completely predicted, energy simulations and cost modeling have become accepted ways to guide the design and construction process by comparing possible outcomes. These tools are now commonplace in the construction industry, and researchers are continuing to develop new and innovative strategies to optimize building design and construction. Previous research has proven that genetic algorithms are effective methods to evaluate and optimize building design in situations that contain a large number of possible solutions. The technique makes a computationally difficult multi-optimization process possible but is still a reactive and time consuming process that focuses on evaluation rather than solution generation. This research presented in this paper builds upon established multi-objective optimization techniques that use an energy simulator to estimate a conceptual building’s energy use as well as construction cost. The study compares simulations of a simplified model of a 3-story inpatient hospital located in Atlanta, Georgia using a defined set of variables. A combined global minimum of annual energy consumption and total construction is sought after using a method that utilizes a genetic algorithm. The second phase of this research uses a modified approach that combines the traditional genetic algorithm with a seeding method that utilizes previous results. A new set of simulations were established that duplicates the initial trials using a slightly modified set of design variables. The simulation was altered, and the phase one trials were utilized as the first generation of simulated solutions. The objective of this thesis is to explore one method of making energy use and cost estimating more accessible to the construction industry by combining simulation optimization and indexing. The results indicate that this study’s proposed augmented approach has potential benefits to building design optimization, although more research is required to validate this hypothesis in its entirety. This study concludes that the proposed approach can potentially reduce the time needed for individual optimization exercises by creating a cumulative, robust catalog of previous computations that will inform and seed future analyses. The research was conducted in five general stages. The first part defines the research problem and scope of research to be conducted. In the second part, the concepts of genetic algorithms and energy simulation are explored in a comprehensive literature review. The remaining parts explain the trial simulations performed in this study. Part three explains the experiment’s methodology, and part four describes the simulation results. The fifth and final part looks at what the possible conclusions that can be made from analyzing the study’s results.

The optimum values for the section variables like channel side slope,bottom width,depth and radius for triangular,rectangular, trapezoidal and circular channels are computed by minimizing the cost of the channel section.Manning &rsquo / s uniform flow formula is treated as a constraint for the optimization model.The cost function is arranged to include the cost of lining,cost of earthwork and the increment in the cost of earthwork with the depth below the ground surface.The optimum values of section variables are expressed as simple functions of unit cost terms.Unique values of optimum section variables are obtained for the case of minimum area or minimum wetted perimeter problems.

In this study an automated multidisciplinary design optimization code is developed for the minimum weight design of a composite wing box. The multidisciplinary static strength, aeroelastic stability, and manufacturing requirements are simultaneously addressed in a global optimization environment through a genetic search algorithm. The static strength requirements include obtaining positive margins of safety for all the structural parts. The modified engineering bending theory together with the coarse finite element model methodology is utilized to determine the stress distribution. The nonlinear effects, stemming from load redistribution in the structure after buckling occurs, are also taken into account. The buckling analysis is based on the Rayleigh-Ritz method and the Gerard method is used for the crippling analysis. The aeroelastic stability requirements include obtaining a flutter/divergence free wing box with a prescribed damping level. The root locus method is used for aeroelastic stability analysis. The unsteady aerodynamic loads in the Laplace domain are obtained from their counterparts in the frequency domain by using Rogers rational function approximations. The outer geometry of the wing is assumed fixed and the design variables included physical properties like thicknesses, cross sectional dimensions, the number of plies and their corresponding orientation angles. The developed code, which utilizes MSC/NASTRAN&reg / as a finite element solver, is used to design a single cell, wing box with internal metallic substructure and composite skins.

Robotic automation has been the major driving force in modern industrial developments. High speed pick-and-place operations find their place in many manufacturing applications. The goal of this project is to develop a class of high speed robots that has a planar workspace. The presented robots are intended for pick-and-place applications that have a relatively large workspace. In order to achieve this goal, the robots must be both stiff and light. The design strategies adapted in this study were expanded from the research work by Prof Khajepour and Dr. Behzadipour. The fundamental principles are to utilize a parallel mechanism to enhance robot stiffness and cable construction to reduce moving inertia. A required condition for using cable construction is the ability to hold all cables under tension. This can only be achieved under certain conditions. The design phase of the study includes a static analysis on the robot manipulator that ensures certain mechanical components are always held under tension. This idea is extended to address dynamic situations where the manipulator velocity and acceleration are bounded. Two concept robot configurations, 2D-Deltabot, and 2D-Betabot are presented. Through a series of analyses from the robot inverse kinematic model, the dynamic properties of a robot can be computed in an effective manner. It was determined that the presented robots can achieve 4g acceleration and 4m/s maximum speed within their 700mm by 100mm workspace with a pair of 890W rotary actuators controlling two degrees of freedom. The 2D-Deltabot was chosen for prototype development. A kinematics calibration algorithm was developed to enhance the robot accuracy. Experimental test results had shown that the 2D-Deltabot was capable of running at 81 cycles per minute on a 730mm long pick-and-place path. Further experiments showed that the robot had a position accuracy of 0. 62mm and a position repeatability of 0. 15mm, despite a few manufacturing errors from the prototype fabrication.

Robotic automation has been the major driving force in modern industrial developments. High speed pick-and-place operations find their place in many manufacturing applications. The goal of this project is to develop a class of high speed robots that has a planar workspace. The presented robots are intended for pick-and-place applications that have a relatively large workspace. In order to achieve this goal, the robots must be both stiff and light. The design strategies adapted in this study were expanded from the research work by Prof Khajepour and Dr. Behzadipour. The fundamental principles are to utilize a parallel mechanism to enhance robot stiffness and cable construction to reduce moving inertia. A required condition for using cable construction is the ability to hold all cables under tension. This can only be achieved under certain conditions. The design phase of the study includes a static analysis on the robot manipulator that ensures certain mechanical components are always held under tension. This idea is extended to address dynamic situations where the manipulator velocity and acceleration are bounded. Two concept robot configurations, 2D-Deltabot, and 2D-Betabot are presented. Through a series of analyses from the robot inverse kinematic model, the dynamic properties of a robot can be computed in an effective manner. It was determined that the presented robots can achieve 4g acceleration and 4m/s maximum speed within their 700mm by 100mm workspace with a pair of 890W rotary actuators controlling two degrees of freedom. The 2D-Deltabot was chosen for prototype development. A kinematics calibration algorithm was developed to enhance the robot accuracy. Experimental test results had shown that the 2D-Deltabot was capable of running at 81 cycles per minute on a 730mm long pick-and-place path. Further experiments showed that the robot had a position accuracy of 0. 62mm and a position repeatability of 0. 15mm, despite a few manufacturing errors from the prototype fabrication.

Multidisciplinary design optimization of aerospace vehicles has attracted interest of many researchers. Well known aerospace companies are developing tools for the mutlidisciplinary design optimization. However, the multiobjective optimization of the design is a new and important area investigated very little by the researchers. This thesis will examine the approaches to the multiobjective and mutlidisciplinary design optimization of rockets and missiles. In the study, multiobjective optimization method called MC-MOSA will be used.

Design process of a solid rocket motor with the objective of meeting certain mission requirements can be specified as a search for a best set of design parameters within the overall design constraints. In order to ensure that the best possible design amongst all achievable designs is being achieved, optimization is required during the design process. In this thesis, an optimization tool for internal ballistic design of solid rocket motors was developed. A direct search method Complex algorithm is used in this study. The optimization algorithm changes the grain geometric parameters and nozzle throat diameter within the specified bounds, finally achieving the optimum results. Optimization tool developed in this study involves geometric modeling of the propellant grain, burnback analysis, a 0-dimensional ballistic performance prediction analysis of rocket motor and the mathematical optimization algorithm. The code developed is verified against pretested rocket motor performance.

Radar antenna structures especially array antennas which are integrated onto structures of aerial vehicles are subject to dynamic structural and aerodynamic loads. Due to occurrences of these dynamic loads there will be certain dynamic deformations which affect the antenna&rsquo / s performance in an adverse manner. The influence of deformations and vibrations are important on array antenna structures, since they cause a change in orientation of elements of the phased array antenna which affects the gain of the antenna negatively. In this study, vibration characteristics of a particular radar antenna structure are optimized using topology and stiffener design optimization methods such that negative effects of mechanical vibrations on functional performance of radar antenna are minimized. Topology and stiffener design optimization techniques are performed separately by the use of ANSYS Finite Element (FE) software in order to modify the design of the radar antenna structure such that its critical natural frequencies in the range of 0-500 Hz are shifted out of the dominant peak sinusoid frequency range of the air platform. As a result of this, it will be possible to minimize the vibration response of the phased array elements in the frequency range of 0-500 Hz / hence better antenna performance can be achieved. In addition to this, it will also be possible to minimize the broadband random vibration response of base excitation coming from air platform.

Recent developments in technology are changing the trend both in satellite design and application of that technology. As the number of small satellites built by experts from academia and private companies increases, more effective ways of inserting those satellites into orbit is needed. Among the various studies that focus on the launch of such small satellites, research on design of Launch Vehicle tailored for nano-satellites attracts special attention. In this thesis, Multiple Cooling Multi Objective Simulated Annealing algorithm is applied for the conceptual design of Launch vehicle for nano-satellites. A set of fitness functions are cooled individually, and acceptance is based on the maximum value of the acceptance probabilities calculated. Angle of attack and propulsion characteristics are employed as optimization parameters. Algorithm finds the optimum trajectory as well as the design parameters that satisfies user defined constraints. In this study burnout velocity, and payload mass are defined as objectives. The methodolgy is applied for different design scenarios including multistage, air and ground launch vehicles.