In this article, a design methodology for complex composite aircraft structures is presented. The developed approach combines a multi-objective optimization method and a parameterized simulation model using a design concept database. Due to the combination of discrete and continuous design variables describing the structures, evolutionary algorithms are used within the presented optimization approach. The approach requires an evaluation of the design alternatives that is performed by parameterized simulation models. The variability of these models is achieved using a design concept database that contains different layouts for each implemented structural part. Due to the complexity of the generated aircraft structures, the finite element method is applied for the calculation of the structural behaviour. The applicability of the developed design approach will be demonstrated by optimizing two composite aircraft fuselage examples. The obtained results show that the developed methodology is useful and reliable for designing complex aircraft structures.

This dissertation presents an investigation and design optimization of energy absorbent protective systems that protect the brain. Specifically, the energy absorption characteristics of the bighorn sheep skull-horn system were quantified and used to inform a topology optimization performed on a football helmet facemask leading to reduced values of brain injury indicators. The horn keratin of a bighorn sheep was experimentally characterized in different stress states, strain rates, and moisture contents. Horn keratin demonstrated a clear strain rate dependence in both tension and compression. As the strain rate increased, the flow stress increased. Also, increased moisture content decreased the strength and increased ductility. The hydrated horn keratin energy absorption increased at high strain rates when compared to quasi-static data. The keratin experimental data was then used to inform constitutive models employed in the simulation of bighorn sheep head impacts at 5.5 m/s. Accelerations values as high as 607 G’s were observed in finite element simulations for rams butting their heads, which is an order of magnitude higher than predicted brain injury threshold values. In the most extreme case, maximum tensile pressure and maximum shear strains in the ram brain were 245 kPa and 0.28, respectively. These values could serve as true injury metrics for human head impacts. Finally, a helmeted human head Finite Element (FE) model is created, validated, and used to recreate impacts from a linear impactor. The results from these simulations are used to train a surrogate model, which is in turn utilized in multi-objective design optimization. Brain injury indicators were significantly reduced by performing multi-objective design optimization on a football helmet facemask. In particular, the tensile pressure and maximum shear strain in the brain decreased 7.5 % and 39.5 %, respectively when comparing the optimal designs to the baseline design. While the maximum tensile pressure and maximum shear strain values in the brain for helmeted head impacts (30.2 kPa and 0.011) were far less than the ram impacts (245 kPa and 0.28), helmet impacts up to 12.3 m/s have been recorded, and could easily surpass these thresholds.

Modeling and simulation have become fully ingrained into the set of design and development tools that are broadly used in the field of power electronics. To state simply, they represent the fastest and safest way to study a circuit or system, thus aiding in the research, design, diagnosis, and debugging phases of power converter development. Advances in computing technologies have also enabled the ability to conduct reliability and production yield analyses to ensure that the system performance can meet given requirements despite the presence of inevitable manufacturing variability and variations in the operating conditions. However, the trustworthiness of all the model-based design techniques depends entirely on the accuracy of the simulation models used, which, thus far, has not yet been fully considered. Prior to this research, heuristic safety factors were used to compensate for deviation of real system performance from the predictions made using modeling and simulation. This approach resulted invariably in a more conservative design process. In this research, a modeling and design approach with parametric and model-form uncertainty quantification is formulated to bridge the modeling and simulation accuracy and reliance gaps that have hindered the full exploitation of model-based design techniques. Prior to this research, a few design approaches were developed to account for variability in the design process; these approaches have not shown the capability to be applicable to complex systems. This research, however, demonstrates that the implementation of the proposed modeling approach is able to handle complex power converters and systems. A systematic study for developing a simplified test bed for uncertainty quantification analysis is introduced accordingly. For illustrative purposes, the proposed modeling approach is applied to the switching model of a modular multilevel converter to improve the existing modeling practice and validate the model used in the design of this large-scale power converter. The proposed modeling and design methodology is also extended to design optimization, where a robust multi-objective design and optimization approach with parametric and model form uncertainty quantification is proposed. A sensitivity index is defined accordingly as a quantitative measure of system design robustness, with regards to manufacturing variability and modeling inaccuracies in the design of systems with multiple performance functions. The optimum design solution is realized by exploring the Pareto Front of the enhanced performance space, where the model-form error associated with each design is used to modify the estimated performance measures. The parametric sensitivity of each design point is also considered to discern between cases and help identify the most parametrically-robust of the Pareto-optimal design solutions. To demonstrate the benefits of incorporating uncertainty quantification analysis into the design optimization from a more practical standpoint, a Vienna-type rectifier is used as a case study to compare the theoretical analysis with a comprehensive experimental validation. This research shows that the model-form error and sensitivity of each design point can potentially change the performance space and the resultant Pareto Front. As a result, ignoring these main sources of uncertainty in the design will result in incorrect decision-making and the choice of a design that is not an optimum design solution in practice. / Ph. D. / Modeling and simulation have become fully ingrained into the set of design and development tools that are broadly used in the field of power electronics. To state simply, they represent the fastest and safest way to study a circuit or system, thus aiding in the research, design, diagnosis, and debugging phases of power converter development. Advances in computing technologies have also enabled the ability to conduct reliability and production yield analyses to ensure that the system performance can meet given requirements despite the presence of inevitable manufacturing variability and variations in the operating conditions. However, the trustworthiness of all the model-based design techniques depends entirely on the accuracy of the simulation models used, which has not yet been fully considered. In this research, a modeling and design approach with parametric and model-form uncertainty quantification is formulated to bridge the modeling and simulation accuracy and reliance gaps that have hindered the full exploitation of model-based design techniques. The proposed modeling and design methodology is also extended to design optimization, where a robust multi-objective design and optimization approach with parametric and model-form uncertainty quantification is proposed. A sensitivity index is defined accordingly as a quantitative measure of system design robustness, with regards to manufacturing variability and modeling inaccuracy in the design of systems with multiple performance functions. This research shows that the model-form error and sensitivity of each design point can potentially change the performance space and resultant Pareto Front. As a result, ignoring these main sources of uncertainty in the design will result in incorrect decision making and the choice of a design that is not an optimum design solution in practice.