Expansive soils have damage light structures due to movement of soil which was a common problem all around the world. Soils exhibiting expansive properties were common throughout Australia. The damage to light structures founded on expansive soils in Victoria occurred mainly in properties built on quaternary basaltic clays and Tertiary to Ordovician clays. A review of existing literature in the area of expansive soils showed a lack of a thorough scientific diagnostic of the damage to light structures founded on expansive soils. Very few studies had been performed on damage to light structures on expansive soils in Victoria. There were no models so far to predict damage condition to light structures. More over, most of the reports on damage to light structures on expansive soils in Victoria were poorly documented. The aim of this research project was to develop a model to predict the damage condition of light structure on expansive soils in Victoria. A hybrid Neural Network trained with Genetic Algorithm was adopted for the de-velopment of the Predictive Damage Condition model. The Neural Network and Ge-netic Algorithm toolboxes from MATLAB� version 7.1 were used. The development of a Predictive Damage Condition model was driven by the shortage of defined quanti-tative studies and methods of selecting the factors that influenced the damage to light structure on expansive soils. The data used was based on information extracted from the Building Housing Commission which was recorded by different engineering companies based only on the tenants complain and site investigation of the properties. A series of factors that were believed to be dominant in influencing damage to light structures were chosen including: structural type, foundation, the presence of vegetation, soil type, age, and climate change. The model showed that it was able to resolve the problems facing light structures on expansive soils. First and foremost, the Predictive Damage Condition model was able to predict the damage condition or damage class using different combinations of fac-tors. It was also possible to identify the factors contributing to the damage of the struc-ture and to assess their relative importance in causing damage to light structures on expansive soil. It was found that the construction footing and vegetation were the most important among all the other input parameters. Change in Thornthwaite Moisture In-dex or climate was ranked second. Construction wall and age, were ranked third and fourth respectively while both region and geology were ranked fifth. In addition, Change in Thornthwaite Moisture Index was noted to have the strongest correlation with other input parameters.

This bachelor’s thesis investigates the feasibility of designing a lightweight, aero-dynamic child bike trailer using composite materials for its main structural ele-ments. The project encompasses theoretical design, finite element analysis (FEA),and computational fluid dynamics (CFD) to optimize the structure for weight re-duction and improved aerodynamics while ensuring strength. Key considerationsinclude material selection, laminate stiffness, and manufacturing methods. Thestudy provides a plan for future prototype development. Findings suggest that acomposite-based design can significantly reduce weight and enhance aerodynamicperformance compared to traditional materials and structural design.

The interaction between fluids and structures, which is an interdisciplinary problem, has gained importance in a wide range of scientific and engineering applications. Thanks to new advances in computer technology, the numerical analysis of multiphysics phenomena has aroused growing interest. Fluid-structure interactions have been numerically and experimentally studied by many researchers and published by several books, papers, and review papers. Hou et al. (2012) [3] have also published a review paper entitled “Numerical methods for fluid-structure interaction”, which provides useful knowledge about different approaches for FSI analysis. The key challenge encountered in any numerical FSI analysis is the coupling between the two independent domains with clear distinctions. For example, a structure domain requires discretizing by a Lagrangian mesh where the mesh is fixed to the mass and follows the mass motion. In fact, the Lagrangian mesh is able to deform and follows an individual structural mass as it moves through space and time. Nonetheless, the fluid mesh remains intact within the space, where the fluid flows as time passes. The numerical approaches with regard to FSI phenomena can be divided into two main categories, namely the monolithic approach and the partitioned approach. In the former, a single system equation for the whole problem is solved simultaneously by a unified algorithm; however, in the latter, the fluid and the structure are discretized with their proper mesh and solved separately by different numerical algorithms. When a fluid flow interacts with a structure, the pressure load arising from the fluid flow is exerted on the structure, followed by deformations, stresses, and strains of the structure. Depending on the resulting deformation and the rate of the variations, a one-way or two-way coupling analysis can be conducted. Fluid-structure interaction (FSI) is characterized by the interaction of some movable or deformable structure with an internal or surrounding fluid flow. In a fluid-structure interaction (FSI), the laws that describe fluid dynamics and structural mechanics are coupled. There is also another classification for FSI problems on the basis of mesh methods: conforming methods and non-conforming methods. In the first method, the interface condition is regarded as a physical boundary (interface boundary) moving during the solution time, which imposes the mesh for the fluid domain to be updated in conformity with the new position for the interface. In contrast, the implementation of the second method eliminates a need for the fluid mesh update on the account of the fact that the interface requirement is enforced by constraints on the system equations instead of the physical boundary motion. In this work, we study numerically and experimentally the fluid-structure interaction comprising a flexible slender shaped structure, free surface flow and potentially interacting rigid structures, categorized in flood protection applications, whereas more emphasis is given to numerical analysis. Objectives of this study are defined in detail as follows: The initial aim is the numerical analysis of the behavior of a down-scale membrane loaded by hydrostatic pressures, where the numerical results have to be validated against available experimental data. A further case which has to be investigated is how the full scale flexible flood barrier behaves when approached and impacted by an accelerated massive flotsam. The numerical model has to be built so as to replicate the same physical phenomenon investigated experimentally. It enables a comparison between the numerical and experimental analyses to be drawn. A more complicated case where the flexible down-scale membrane interacts with a propagated water wave is a further target area to study. Moreover, an experimental investigation is required to validate the numerical results by way of comparison. The ultimate goal is to perform a similitude analysis upon which a correlation between the full-scale prototype and the down-scale model can be formed. The implementation of the similarity laws enables the behavior of the full scale prototype to be quantitatively assessed on the basis of the available data for the down-scale model. In addition, in order to validate the accuracy of the similitude analysis, numerical analyses have to be carried out.:Contents Zusammenfassung I ABSTRACT IV Nomenclature X 1 Introduction 1 1.1 Work overview 2 1.2 Literature review 3 1.2.1 The non-conforming methods 6 1.2.2 The conforming (partitioned) approaches 11 1.2.2.1 Interface data transfer 16 1.2.2.2 Accuracy, stability and efficiency 16 1.2.2.3 Modification of interface conditions: Robin transmission conditions 18 1.3 Concluding remarks 19 2 Methodology-numerical methods for fluid-structure interaction analysis (FSI) 20 2.1 Single FV framework 21 2.1.1 The prism layer mesher 24 2.1.2 Turbulence modeling 24 2.2 Preparation of the standalone Abaqus model 27 2.2.1 Damping by bulk viscosity 28 2.2.2 Coulomb friction damping 29 2.2.3 Rayleigh damping 29 2.2.4 Determination of the Rayleigh damping parameters based on the Chowdhury procedure 29 2.2.5 The frequency response function (FRF) measurement 30 2.2.6 The half-power bandwidth method 31 2.3 Explicit partitioned coupling 33 2.4 Implicit partitioned coupling 39 2.5 Overset mesh 40 2.6 Concluding remarks 42 3 Verification and validation of the structural model 44 3.1 Numerical model setup of the down-scale membrane 44 3.2 Comparing similarity between numerical and experimental results 46 3.2.1 Hypothesis test terminology 46 3.2.2 Curve fitting 47 3.2.3 Similarity measures between two curves 48 3.3 Results (down-scale membrane) 52 3.3.1 Similarity tests for the contact length 54 3.3.2 Similarity tests for the slope 58 3.3.3 Similarity tests for the displacement in Y direction 60 3.4 Concluding remarks 63 4 Numerical model setup of the original membrane for impact analysis 66 4.1 Structure domain 67 4.2 Fluid domain 72 4.2.1 Standard mesh and results 74 4.2.2 Overset mesh 80 4.3 Co-simulation model setup and results 88 4.4 Concluding remarks 96 5 Numerical wave generation 100 5.1 Theoretical estimation of the waves 107 5.2 Numerical wave tank setup 110 5.3 Results 114 5.4 Concluding remarks 119 6 Validity of the model with dynamic pressure 121 6.1 Wave tank 123 6.2 Structure domain 127 6.3 Fluid domain 130 6.4 Co-simulation model setup 136 6.5 Experimental approach 137 6.6 Results 141 6.6.1 Similarity tests for the displacement of the membrane in X direction 156 6.6.2 Similarity tests for the displacement of the membrane in Y direction 160 6.6.3 Similarity tests for the displacement of the membrane in Z direction 164 6.7 Concluding remarks 168 7 Similarity 171 7.1 Motivation 171 7.2 Governing equations 174 7.3 Buckingham Pi theorem 175 7.4 Dimensionless numbers 175 Similitude requirement 177 7.5 Simulation setup 178 7.6 Results 179 7.7 Concluding remarks 191 8 Summary, conclusions and outlook 192 List of figures 199 List of tables 209 References 210