A stiffened DKT shell element is formulated for the linear static analysis of stiffened plates and shells. Three-noded triangular shell elements and two-noded beam elements with 18 and 12 degrees of freedom are used respectively in the formulation. The stiffeners follow the nodal lines of the shell element. Eccentricity of the stiffener is taken into account. The dynamic and stability characteristic of the element is also investigated. With the developed computer program, the results obtained by the proposed element agrees fairly well with the existing literature.

Stiffeners attached to composite panels and shells may significantly increase the overall buckling load of the resultant stiffened structure. Initially, an extensive literature review was conducted over the past ten years of published work wherein research was conducted on grid stiffened composite structures and stiffened panels, due to their applications in weight sensitive structures. Failure modes identified in the literature had been addressed and divided into a few categories including: buckling of the skin between stiffeners, stiffener crippling and overall buckling. Different methods have been used to predict those failures. These different methods can be divided into two main categories, the smeared stiffener method and the discrete stiffener method. Both of these methods were used and compared in this thesis. First, a buckling analysis was conducted for the case of a grid stiffened composite pressure vessel. Second, a buckling analysis was conducted under the compressive load on the composite stiffened panels for the case of one, two and three longitudinal stiffeners and then, using different parameters, stiffened panels under combined compressive and shear load for the case of one longitudinal centric stiffener and one longitudinal eccentric stiffener, two stiffeners and three stiffeners. / Master of Science

Finite Element (FE) analysis is a powerful numerical technique widely utilized to simulate the real-world response of complex engineering structures. With the advancements in adaptive optimization frameworks, multi-fidelity (coupled shell-solid) FE models are increasingly sought during the early design stages where a large design space is being explored. This is because multi-fidelity models have the potential to provide accurate solutions at a much lower computational cost. However, the time and effort required to create accurate and optimal multi-fidelity models with acceptable meshes for highly complex structures is still significant and is a major bottleneck in the FE modeling process. Additionally, there is a significant level of subjectivity involved in the decision-making about the multi-fidelity element topology due to a high dependence on the analyst's experience and expertise, which often leads to disagreements between analysts regarding the optimal modeling approach and heavy losses due to schedule delays. Moreover, this analyst-to-analyst variability can also result in significantly different final engineering designs. Thus, there is a greater need to accelerate the FE modeling process by automating the development of robust and adaptable multi-fidelity models as well as eliminating the subjectivity and art involved in the development of multi-fidelity models. This dissertation presents techniques and frameworks for accelerating the finite element modeling process of multi-fidelity models. A framework for the automated development of multi-fidelity models with adaptable 2-D/3-D topology using the parameterized full-fidelity and structural fidelity models is presented. Additionally, issues related to the automated meshing of highly complex assemblies is discussed and a strategic volume decomposition technique blueprint is proposed for achieving robust hexahedral meshes in complicated assembly models. A comparison of the full-solid, full-shell, and different multi-fidelity models of a highly complex stiffened thin-walled pressure vessel under external and internal tank pressure is presented. Results reveal that automation of multi-fidelity model generation in an integrated fashion including the geometry creation, meshing and post-processing can result in considerable reduction in cost and efforts. Secondly, the issue of analyst-to-analyst variability is addressed using a Decision Tree (DT) based Fuzzy Inference System (FIS) for recommending optimal 2D-3D element topology for a multi-fidelity model. Specifically, the FIS takes the structural geometry and desired accuracy as inputs (for a range of load cases) and infers the optimal 2D-3D topology distribution. Once developed, the FIS can provide real-time optimal choices along with interpretability that provides confidence to the analyst regarding the modeling choices. The proposed techniques and frameworks can be generalized to more complex problems including non-linear finite element models and as well as adaptable mesh generation schemes. / Doctor of Philosophy / Structural analysis is the process of determining the response (mainly, deformation and stresses) of a structure under specified loads and external conditions. This is often performed using computational modeling of the structure to approximate its response in real-life conditions. The Finite Element Method (FEM) is a powerful and widely used numerical technique utilized in engineering applications to evaluate the physical performance of structures in several engineering disciplines, including aerospace and ocean engineering. As optimum designs are increasing sought in industries, the need to develop computationally efficient models becomes necessary to explore a large design space. As such, optimal multi-fidelity models are preferred that utilize higher fidelity computational domain in the critical areas and a lower fidelity domain in less critical areas to provide an optimal trade-off between accuracy and efficiency. However, the development of such optimal models involves a high level of expertise in making a-priori and a-posteriori optimal modeling decisions. Such experience based variability between analysts is often a major cause of schedule delays and considerable differences in final engineering designs. A combination of automated model development and optimization along with an expert system that relieves the analyst of the need for experience and expertise in making software and theoretical assumptions for the model can result in a powerful and cost-effective computational modeling process that accelerates technological advancements. This dissertation proposes techniques for automating robust development of complex multi-fidelity models. Along with these techniques, a data-driven expert system framework is proposed that makes optimal multi-fidelity modeling choices based on the structural configuration and desired accuracy level.