This research contributes to the development of a structural optimization software system designed to support design optimization. The focus of this thesis work is on formulating strategies to obtain accurate solutions and enhance the efficiency of the optimization process, particularly when dealing with large and complex finite element (FE) models, utilizing statistical concepts. A potential avenue explored in this study is the adaptive response surface optimization process. The adaptive response surface optimization method involves the adaptive control of samples selected through the design of experiments and empirical models constructed via the response surface methodology, with the sampling of the design space and empirical model terms dynamically adjusted throughout the optimization progression. The empirical models are constructed with statistically significant terms to maximize the utilization of information from each sample generated using the design of experiments. If the available information is fully utilized by the empirical model and the adaptive response surface optimization process needs to progress further until an optimal solution is identified, additional samples are generated. The methodology is applied to a benchmark bell crank problem, optimizing the bell crank for maximum operational value by simultaneously increasing fatigue life and reducing the overall component cost. This demonstration showcases the structural optimization software's capability to handle both design and manufacturing aspects seamlessly. The approach to solving the structural optimization problem involves constructing a constrained parametric bell crank part in Abaqus/CAE as it facilitates easy manipulation of the geometry. The entire process of geometry generation, meshing, simulation, and output extraction was supported by developing Python scripts. Response surface model building and other statistical analyses are conducted using the JMP statistical software. Nonlinear constrained optimization is executed through the sequential quadratic programming (SLSQP solver) from the SciPy library, allowing optimization on the response surfaces representing the objective function and constraints to identify the optimal solution. The optimal solution is obtained utilizing a small composite design with individual response surface models for the objective function and each constraint, is compared with results from the Abaqus finite element model, and the percentage difference was 0.9% at the optimal design variable values. / Master of Science / Optimization processes, in general, require multiple iterations to converge to the optimal solution. Structural optimization, dealing with large and complex computationally intensive models are typically very time-consuming. To address this challenge, approximations of the actual design space, called response surfaces, are created using the statistical concept known as response surface methodology. Response surfaces are developed by selecting specific regions within the design space and studying them using complex computational models. The results obtained from these computational models are combined with statistical tools to build a response surface that approximately represents the actual design objective function and the associated constraints of the design within the specified design space. In this research, an adaptive approach called adaptive response surface optimization is implemented. In this approach, the regions studied and the response surfaces are dynamically adjusted based on the progression of the optimization process. Such adaptability significantly accelerates the structural optimization process and yields successful results. To illustrate this method, a benchmark problem was solved using the finite element solver Abaqus, the statistical software JMP, and the optimization toolbox from the Scipy library.

The success of any racing car hinges on three key factors: its speed, handling, and reliability. In a highly competitive environment where lap times are extremely tight, even slight variations in components can significantly affect performance and, consequently, lap times. At the heart of a race car's performance lies the upright—a critical component of its suspension system. The upright serves to link the suspension arms to the wheels, effectively transmitting steering and braking forces to the suspension setup. Achieving optimal performance requires finding the right balance between lightweight design and ample stiffness, crucial for maintaining precise steering geometry and overall vehicle dynamics, especially under intense loads. Furthermore, there is a need to explore the system of structural optimization and seamlessly integrate Finite Element (FE) Models into the mathematical optimization process. This thesis explores a technique for parametric structural optimization utilizing finite element analysis and response surfaces to minimize the weight of the upright. Various constraints such as frequency, stress, displacement, and fatigue are taken into consideration during this optimization process. A parametric finite element model of the upright was designed, along with the mathematical formulation of the optimization problem as a nonlinear programming problem, based on the design objectives and suspension geometry. By conducting parameter sensitivity analysis, three design variables were chosen from a pool of five, and response surfaces were constructed to represent the constraints and objective function to be used to solve the optimization problem using Sequential Quadratic Programming (SQP). To streamline the process of parameter sensitivity analysis and response surface development, a Python scripting procedure was employed to automate the finite element job analysis and results extraction. The optimized upright design resulted in overall weight reduction of 25.3% from the maximum weight design of the parameterized upright. / Master of Science / The success of any racing car depends on three key factors: its speed, handling and reliability. In a highly competitive environment where lap times are extremely tight, even slight variations in components can significantly affect performance and consequently, lap times. At the heart of a race car's performance lies the upright—a critical component of its suspension system. The upright serves to link the suspension arms to the wheels, effectively transmitting steering and braking forces to the suspension setup. To achieve the best performance, upright must be as light as possible but it needs to be strong enough to ensure that the car is predictable when turning in a corner or while braking. Additionally, there is a need to explore methods of structural optimization and integrate finite element analysis seamlessly into the optimization process. Finite element analysis (FEA) is the use of part models, simulations, and calculations to predict and understand how an object might behave under certain physical conditions. This thesis examines a technique for optimizing the upright by designing it with numerous adjustable features for testing and then utilizing response surfaces to minimize its weight. Throughout this process, factors such as vibration, stress, deformation, and fatigue are carefully considered. A detailed parametric finite element model of the upright was developed, alongside the formulation of the optimization problem as a nonlinear programming problem, based on the objectives of the design and the geometry of the suspension. Through rigorous testing of parameters for optimization potential, design variables are selected for optimization. Response surfaces were then constructed to represent the constraints and objective function necessary to solve the optimization problem using Sequential Quadratic Programming (SQP). To enhance the efficiency of this process, a Python script was created to handle specific tasks within the finite element solver. This automation streamlined the analysis of the finite element model and the extraction of results. Ultimately, the optimized design of the upright yielded a 25.3% reduction in weight compared to its maximum weight configuration.

A numerical analysis of the loop tack test is presented to study the behavior of the strip and the influence of several factors, and the results are compared with experimental ones. The numerical results can be applied to model the performance of a pressure sensitive adhesive (PSA). Since the simulation of the loop tack test includes geometrical and material nonlinearities, it is solved numerically by the finite element method. The finite element program ABAQUS is used throughout the research. As the teardrop shaped loop is pushed down onto the adhesive and then pulled up, the variation of the loop behavior is investigated using two-dimensional (2D) and three-dimensional (3D) models. A bilinear elastic-plastic constitutive law is used for the strip. The deformation of the pressure sensitive adhesive is approximated as uniaxial extension of independent adhesive strands. A Winkler-type nonlinear elastic foundation and a viscoelastic foundation are used to model the PSA. A nonlinear elastic spring function is used, which is composed of a compression region for the bonding phase and a tension region for the debonding phase. A debonding failure criterion is assumed, in which an adhesive strand will debond when it reaches a certain length. During the bonding phase, it is assumed that the loop is perfectly bonded, and the contact time is not included. Curves of the pulling force versus the top displacement (i.e., tack curves) are obtained throughout the simulation. A parametric study is made with respect to the nonlinear spring function parameters, experimental uncertainties, and strip thickness. Anticlastic bending behavior is shown in the 3D analysis, and the contact patterns are presented. The effects of the elasticity modulus of the PSA for the elastic foundation and the displacement rate for the viscoelastic model are investigated. / Ph. D.

Linear and nonlinear finite element analyses are used for the investigation of rectangular anchorage zones with the presence of a support reaction. The investigation is conducted based on four load configurations consisting of concentric, inclined concentric, eccentric, and inclined eccentric loads. The method of model construction is illustrated thoroughly. The influence of several parameters, including anchorage ratio, inclination of prestressing load, eccentricity, magnitude of the reaction force, bearing plate ratio, and the location of the reaction force, is studied. Both graphical and numerical presentations of the results from each load configuration are given. Improved equations, which are modified from the equations presented in the AASHTO Standard Specifications (2002), are proposed. The results from the equations are compared to those from the finite element method. Nonlinear finite element analysis is used to verify the applicability of the equations and to study a new bursting steel arrangement. Linear and nonlinear finite element analyses are also used for the study of non-rectangular anchorage zones. Four basic load configurations, including concentric, eccentric, inclined concentric, and inclined eccentric loads, are investigated. The shell element is selected for the construction of the finite element models. Several parameters, consisting of anchorage ratio, inclination of prestressing load, eccentricity, web thickness, ratio of web thickness to flange thickness, and flange width, are chosen for parametric studies. The results from the studies are presented graphically and numerically. Equations to calculate the bursting force and location of the force are developed from the Strut-and-Tie Model approach. The verification of the formulations and the investigation of bursting steel arrangement are conducted using nonlinear finite element analysis. / Ph. D.

Noise levels in modern industrial and military environments are constantly increasing, requiring the improvement of current hearing protection devices. The improvement of passive hearing protection devices lies in examining the performance of major contributors to reduction of noise attenuation. The finite element method can be used to fully explore single hearing protection (SHP) and double hearing protection (DHP) systems, and the major performance mechanisms can be observed numerically as well as visually in modern postprocessing software. This thesis focuses on developing and evaluating double hearing protection finite element models, and exploring the behavior mechanisms responsible for reduced noise attenuation. The double hearing protection model studied consists of an earmuff preloaded to a barrier covered to simulate human flesh, and a foam earplug installed inside a rigid cylinder designed to simulate the human ear canal. Pressure readings are taken at the bottom of the simulated ear canal assembly. Advanced finite element models are used to reconcile differences between the experimental and finite element results, and to investigate the behavior of the modeled system. The foam earplug material properties for the finite element model are required in the same shear state of stress and boundary condition configuration as the experimental DHP setup, therefore a novel material extraction method is used to obtain this data. The effects of radial compression preload on the earplugs are considered, and the resulting foam earplug shear material properties are input into the finite element DHP model where the effects of the updated foam material properties are observed. / Master of Science

Abstract Biomechanics are especially important when it comes to the lower third of the face which is composed of a single load-bearing structure, the mandible. Implementation of recent developments in image processing, material sciences and computational technology allows the verification of these principles defining the appropriate practice. The studies listed in the thesis, benchmark from the simple to the more complicated mandibular surgical procedures. The aims were to build patient specific, custom made, composite reconstructions using newly learned techniques. Cross-sectional imaging with Cone Beam Computer Tomography was used to build bone models. The mandible at various ages, undergoing minor oral surgery, partial cross-section reduction with or without reinforcements and complete transection were simulated under biting conditions. Industry standard free form modelling, reverse engineering techniques and Finite Element Analysis were used. Internal and external validations of certain modelling elements were introduced. The mandible became stiffer with increasing age. Minimization of the reduction of the main load-bearing structures was noted to be crucial. The External Oblique Ridge was one such a structure. Partial thickness defects were best spanned by Dynamic Compression Plates. If the remaining amount of bone was insufficient or the bone quality was poor then Locking Compression Plates were preferred. Rounding or the use of a stop-hole was recommended to reduce the risk of fracture development especially without additional Prophylactic Internal Fixation. Fixation using a single reconstruction plate with three screws on either side in the four most common segmental defects was sufficient. Locking monocortical screw fixation was superior over non-locking systems. The suitability of CBCT in bone scanning was demonstrated, highlighting the positional dependent differences within the scanned volume. It should be noted that the relevance and validity of such simulations depends on the quality and the setup. In the future, biomechanically customized fixation can complement tissue engineering procedures and regenerative techniques by providing the precise physical dimensions and biomechanical requirements of the planned reconstructions. / Abstrakti Biomekaniikan ymmärtäminen on tärkeää kovakudoskirurgiassa. Periaatteet ovat erityisen tärkeitä, kun kyseessä on kasvojen alin kolmannes, joka muodostuu yhdestä kantavasta rakenteesta eli alaleuasta. Viime aikojen kehitys kuvankäsittelyssä, materiaalitieteessä ja tietokoneteknologiassa ovat mahdollistaneet näiden periaatteiden tarkistamisen käytännössä. Tämän opinnäytetyön osatöissä tutkittiin biomekaniikkaa erityyppisissä leikkauksissa. Tavoitteena on rakentaa tulevaisuudessa potilaille mittatilaustyönä erilaisista materiaaleista korjausosia käyttäen hyväksi uusinta tietoa ja tekniikkaa. Leikekuvantamista kuten Multi Detectoria ja viime aikoina kartiokeilatietokonetomografiaa (KKTT) käytettiin luumallien valmistamisessa. Eri-ikäisten alaleukoja, joihin tehtiin pieniä suukirurgisia toimenpiteitä, osaosteomioita vahvistuksen kanssa tai ilman vahvistusta ja täydellisiä alaleuan katkaisuja, simuloitiin kuormitusolosuhteissa. Teollisuudessa standardoitua vapaamuotoista mallinnusta ja käänteistä tekniikkaa sekä Finite Element Analysis-menetemää käytettiin. Mallinnuksessa käytettiin lisäksi sisäistä ja ulkoista validointia. Alaleuka koveni iän myötä. Leuan kestävyyden kannalta oli ratkaisevaa että tärkeisiin kantaviin rakenteisiin puututtiin mahdollisimman vähän. Oblique Ridge oli yksi tällainen rakenne. Osaosteotomioissa paras ratkaisu oli dynaaminen kompressiolevy. Jos jäljelle jäävän luun määrä tai laatu oli heikko niin sitten lukittuvat puristuskompressiolevyt toimivat parhaiten. Luun pyöristäminen tai pysäytysreiän käyttö oli suositeltavaa vähentämään murtumariskiä varsinkin ilman profylaktista kiskotusta. Neljän yleisimmän segmentaalisen defektin kiinnitys yhdellä levyllä ja kolmella ruuvilla levyn molemmin puolin oli riittävä. Lukittuva monokortikaalinen ruuvikiinnitys oli ylivoimainen verrattuna ei-lukittuvaan systeemiin. KKTT osoittautui parhaaksi menetelmäksi alaleuan kuvantamisessa. Kaikki havainnot voivat toimia yleisohjeena kun harjoitellaan edellä mainittuja toimenpiteitä. On huomattava, että tällaisen simulaation merkitys ja todenmukaisuus riippuu sen laadusta ja asennuksesta. Tulevaisuudessa biomekaanisesti tarkkojen mittausten perusteella suunniteltu luun kiinnitys voi palvella kudosteknologian avulla tehtäviä rekonstruktioita antamalla toimenpiteessä tarvittavat tarkat fysikaaliset mitat ja kuormitusarvot.

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Master of Science / Department of Civil Engineering / Hayder A. Rasheed / Damage detection and structural health monitoring are topics that have been receiving increased attention from researchers around the world. A structure can accumulate damage during its service life, which in turn can impair the structure’s safety. Currently, visual inspection is performed by experienced personnel in order to evaluate damage in structures. This approach is affected by the constraints of time and availability of qualified personnel. This study aims to facilitate damage evaluation and detection in concrete bridge girders without the need for visual inspection while minimizing field measurements. Simply-supported beams with different geometric, material and cracking parameters (cracks’ depth, width and location) were modeled in three phases using Abaqus finite element analysis software in order to obtain stiffness values at specified nodes. In the first two phases, beams were modeled using beam elements. Phase I included beams with a single crack, while phase II included beams with up to two cracks. For phase III, beams with a single crack were modeled using plane stress elements. The resulting damage databases from the three phases were then used to train two types of Artificial Neural Networks (ANNs). The first network type (ANNf) solves the forward problem of providing a health index parameter based on the predicted stiffness values. The second network type (ANNi) solves the inverse problem of predicting the most probable cracking pattern, where a unique analytical solution is not attainable. In phase I, beams with 3, 5, 7 and 9 stiffness nodes and a single crack were modeled. For the forward problem, ANNIf had the geometric, material and cracking parameters as inputs and stiffness values as outputs. This network provided excellent prediction accuracy measures (R2 > 99%). For the inverse problem, ANNIi had the geometric and material parameters as well as stiffness values as inputs and the cracking parameters as outputs. Better prediction accuracy measures were achieved when more stiffness nodes were utilized in the ANN modeling process. It was also observed that decreasing the number of required outputs immensely improved the quality of predictions provided by the ANN. This network provided less accurate predictions (R2 = 68%) compared to ANNIf, however, ANNIi still provided reasonable results, considering the non-uniqueness of this problem’s solution. In phase II, beams with 9 stiffness nodes and two cracks were modeled following the same procedure. ANNIIf provided excellent results (R2 > 99%) while ANNIIi had less accurate (R2 = 65%) but still reasonable predictions. Finally, in phase III, simple span beams with 3, 5, 7 and 9 stiffness nodes and a single crack were modeled using plane stress elements. ANNIIIf (R2 > 99%) provided excellent results while ANNIIIi had less accurate (R2 = 65%) but still reasonable predictions. Predictions in this phase were very accurate for the crack depth and location parameters (R2 = 97% and 99%, respectively). Further inspection showed that ANNIIIi provided more accurate predictions when compared with ANNIi. Overall, the obtained results were reasonable and showed good agreement with the actual values. This indicates that using ANNs is an excellent approach to damage evaluation, and a viable approach to obtain the, analytically unattainable, solution of the inverse damage detection problem.

Doctor of Philosophy / Department of Mechanical and Nuclear Engineering / Youqi Wang / Textile fabrics and textile composite materials demonstrate exceptional mechanical properties, including high stiffness, high strength to weight ratio, damage tolerance, chemical resistance, high temperature tolerance and low thermal expansion. Recent advances in weaving techniques have caused various textile fabrics to gain applications in high performance products, such as aircrafts frames, aircrafts engine blades, ballistic panels, helmets, aerospace components, racing car bodies, net-shape joints and blood vessels. Fabric mechanical properties are determined by fabric internal architectures and fabric micro-geometries are determined by the textile manufacturing process. As the need for high performance textile materials increases, textile preforms with improved thickness and more complex structures are designed and manufactured. Therefore, the study of textile fabrics requires a reliable and efficient CAD/CAM tool that models fabric micro-geometry through computer simulation and links the manufacturing process with fabric micro-geometry, mechanical properties and weavability. Dynamic Weaving Process Simulation is developed to simulate the entire textile process. It employs the digital element approach to simulate weaving actions, reed motion, boundary tension and fiber-to-fiber contact and friction. Dynamic Weaving Process Simulation models a Jacquard loom machine, in which the weaving process primarily consists of four steps: weft insertion, beating up, weaving and taking up. Dynamic Weaving Process Simulation simulates these steps according to the underlying loom kinematics and kinetics. First, a weft yarn moves to the fell position under displacement constraints, followed by a beating-up action performed by reed elements. Warp yarns then change positions according to the yarn interlacing pattern defined by a weaving matrix, and taking-up action is simulated to collect woven fabric for continuous weaving process simulation. A Jacquard loom machine individually controls each warp yarn for maximum flexibility of warp motion, managed by the weaving matrix in simulation. Constant boundary tension is implemented to simulate the spring at each warp end. In addition, process simulation adopts re-mesh function to store woven fabric and add new weft yarns for continuous weaving simulation. Dynamic Weaving Process Simulation fully models loom kinetics and kinematics involved in the weaving process. However, the step-by-step simulation of the 3D weaving process requires additional calculation time and computer resource. In order to promote simulation efficiency, enable finer yarn discretization and improve accuracy of fabric micro geometry, parallel computing is implemented in this research and efficiency promotion is presented in this dissertation. The Dynamic Weaving Process Simulation model links fabric micro-geometry with the manufacturing process, allowing determination of weavability of specific weaving pattern and process design. Effects of various weaving process parameters on fabric micro-geometry, fabric mechanical properties and weavability can be investigated with the simulation method.