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Osteoporosis is a disease prevalent in older adults, characterized by increased bone porosity resulting in significant fracture risk. Orthopaedic implants are designed and validated against cadavers from the general ‘healthy’ population, but little is known about their response in osteoporotic bone. Orthopaedic implants can also be developed using synthetic bones, if they have been demonstrated to be representative of healthy bone, and offer a number of advantages. To date, no synthetic femur has been validated for the osteoporotic population. The purpose of this study was to assess novel synthetic femurs for representing this population. Custom jigs were manufactured to test two sets of ten synthetic femurs and five isolated cadaveric femurs in four-point bending, torsion, axial compression, axial failure, and screw pullout, using an Instron mechanical testing machine to record load-displacement data. Statistical significance was found in bending, torsion, and screw pullout between both synthetic sets and cadavers using one-way ANOVA with post-hoc Tukey analysis. In all instances, the synthetic femurs had lower coefficients of variation than natural specimens. Both synthetic and cadaveric femurs were CT scanned prior to testing. The data were used to measure key anatomical details and to develop a series of numerical models of the synthetic bones, using Materialize Mimics® and ABAQUS® software, evaluated using axial and bending data. The model was modified by reducing cortical thickness and modulus in an attempt to make the synthetic model better represent osteoporotic bone. Establishing synthetic femurs as suitable replacements for osteoporotic bone allows for improved orthopaedic implant development. The digital model constructed allows the synthetic to be further analyzed, improving expected response of the synthetic bones. These synthetic bones could provide a foundation for development of effective orthopaedics for this population. / Thesis / Master of Applied Science (MASc) / The considerations and parameters in the design of orthopaedic implants for osteoporotic bone are relatively unknown. Orthopaedic implants can be evaluated with synthetic bones, which offer a number of advantages to natural specimens, assuming they are sufficiently representative of natural bone. No physical synthetic model yet exists that represents an osteoporotic femur. In the present work, synthetic femurs were subjected to bending, torsion, axial compression, and screw pullout and compared to natural osteoporotic specimens. The synthetics were significantly different to natural specimens in bending, torsion, and screw pullout. A numerical model was created, evaluated, and tested in finite element software alongside modified models with reduced modulus and cortical thickness to assess stiffness. Recommendations were made to improve the accuracy of a future synthetic model. The synthetic femurs tested were not representative of osteoporotic femurs, but may be feasible alternatives with minor modifications and could be useful in future orthopaedics design.

The alkali-aggregate reaction was first reported in 1940 as a cause of severe cracking in some concrete structures. It is only in recent years that papers have been published dealing with the effects of AAR on the performance of structures. This thesis outlines a methodology for numerical simulation of the response of concrete subjected to continuing AAR. First a constitutive model is presented based on the framework proposed by Pietruszczak (1996). The formulation incorporates an assumption that the rate of expansion depends on the confining pressure, the age of concrete and the temperature. The progress in the reaction is coupled with the degradation of mechanical properties, in particular the elastic modulus and the compressive and tensile strengths. Subsequently, the procedures for generating finite element models are described, including geometric modeling, mesh generation techniques, graphical representation of the results and interfacing between pre- and post-processor and the finite element solvers. The numerical analysis, undertaken in this thesis, pertains to the Beauharnois Powerhouse, situated in Quebec, Canada. The powerhouse has been experiencing problems related to a continuing expansion of concrete due to AAR since the early 1960’s. The progressive formation of macrocracks and the volumetric expansion in concrete has caused operational problems, such as the reduction in clearance between turbine runner blades and throat rings. In this study, typical structural units of the Beauharnois Powerhouse were selected for the numerical analysis. The AAR constitutive model was applied in a finite element framework. Mechanical properties of concrete were carefully evaluated based on available experimental data. Simulations were focused on the deformation and the time history of progressive macro/microcracking due to continuing reaction. Structural responses under isothermal as well as non-isothermal conditions were simulated. The results of the numerical analyses were then compared with in-situ measurements. / Thesis / Master of Engineering (ME)

Indiana University-Purdue University Indianapolis (IUPUI) / Lithium-Ion Batteries (Li-ion batteries or LIBs) have been extensively used in a wide variety of industrial applications and consumer electronics. Additive Manufacturing (AM) or 3D printing (3DP) techniques have evolved to allow the fabrication of complex structures of various compositions in a wide range of applications. The objective of the thesis is to investigate the application of 3DP to fabricate a LIB, using a modified process from the literature [1]. The ultimate goal is to improve the electrochemical performances of LIBs while maintaining design flexibility with a 3D printed 3D architecture. In this research, both the cathode and anode in the form of specifically formulated slurry were extruded into filaments using a high-temperature pellet-based extruder. Specifically, filament composites made of graphite and Polylactic Acid (PLA) were fabricated and tested to produce anodes. Investigations on two other types of PLA-based filament composites respectively made of Lithium Manganese Oxide (LMO) and Lithium Nickel Manganese Cobalt Oxide (NMC) were also conducted to produce cathodes. Several filaments with various materials ratios were formulated in order to optimize printability and battery capacities. Finally, flat battery electrode disks similar to conventional electrodes were fabricated using the fused deposition modeling (FDM) process and assembled in half-cells and full cells. Finally, the electrochemical properties of half cells and full cells were characterized. Additionally, in parallel to the experiment, a 1-D finite element (FE) model was developed to understand the electrochemical performance of the anode half-cells made of graphite. Moreover, a simplified machine learning (ML) model through the Gaussian Process Regression was used to predict the voltage of a certain half-cell based on input parameters such as charge and discharge capacity. The results of this research showed that 3D printing technology is capable to fabricate LIBs. For the 3D printed LIB, cells have improved electrochemical properties by increasing the material content of active materials (i.e., graphite, LMO, and NMC) within the PLA matrix, along with incorporating a plasticizer material. The FE model of graphite anode showed a similar trend of discharge curve as the experiment. Finally, the ML model demonstrated a reasonably good prediction of charge and discharge voltages.

To address the challenges associated with urban expansion and environmental changes, innovative interlocking concrete block pavement (ICBP) is being researched for usage in urban areas. The ICBP is designed to have higher durability and better long-term performance compared to traditional asphalt pavement. Using recycled concrete aggregates (RCA) and supplementary cementing materials (SCMs) can provide many environmental benefits. The objective of this research is to investigate the mechanical properties of concrete with recycled materials. This also involves the assessment of deflection and stresses associated with ICBP using the finite element method. Four concrete mixtures with different RCA and SCMs contents were designed and cast. The RCA replacement levels were 20% and 40%, while slag and glass pozzolan were added to improve mechanical properties. The results showed that the use of RCA had adverse impacts on workability. The 28 days compressive strength of the Control Mix was 40 MPa. The compressive strength of Mix 3 was 40.5 MPa which was the highest strength among all mixtures. It demonstrated that a 40% RCA replacement level could have a non-negative effect on mechanical properties when the SCMs are added. A three-dimensional pavement model was established using ABAQUS software. The orthogonal experimental design was used to evaluate the effects of the length/width ratio of blocks, the block thickness, the elastic modulus, and the laying pattern of blocks on the deflection and von Mises stress of all ICBP models under the vertical load. Considering the deflection of the loading area, the length/width ratio had the greatest effect, then comes with thickness, elastic modulus, and laying pattern according to the Range Analysis. The bigger block size and higher elastic modulus of blocks could provide even better performance. Overall, the herringbone laying pattern is recommended as the optimum laying pattern with minimum deflection. It also contributes to better load spreading. / Thesis / Master of Applied Science (MASc)

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.