Recent developments in technology are changing the trend both in satellite design and application of that technology. As the number of small satellites built by experts from academia and private companies increases, more effective ways of inserting those satellites into orbit is needed. Among the various studies that focus on the launch of such small satellites, research on design of Launch Vehicle tailored for nano-satellites attracts special attention. In this thesis, Multiple Cooling Multi Objective Simulated Annealing algorithm is applied for the conceptual design of Launch vehicle for nano-satellites. A set of fitness functions are cooled individually, and acceptance is based on the maximum value of the acceptance probabilities calculated. Angle of attack and propulsion characteristics are employed as optimization parameters. Algorithm finds the optimum trajectory as well as the design parameters that satisfies user defined constraints. In this study burnout velocity, and payload mass are defined as objectives. The methodolgy is applied for different design scenarios including multistage, air and ground launch vehicles.

The goal of "greener" aviation is one of the main challenges in aircraft design. The target of Europeans "Flightpath 2050'' and IATA is to reduced the net aviation CO2 emission by 75% relative to 2000 and 50% relative to 2005, respectively. Novel unconventional aircraft claim to increase the efficiency and reduce the environmental impact. Designs differing from the conventional tube-low-wing concept are investigated regarding their performance benefit. The employment of a high aspect ratio wing is an effective way to increase the aerodynamic efficiency. However, the long and slender wing structure is more flexible and thus more prone to aeroelastic effects. Critical phenomena, such as flutter and limit-cycle oscillation are more likely to drive the design. Therefore it is important to assess the interdependence of aerodynamic and structural forces. The effects of the wings flexibility can affect the design and off-design performance, possibly jeopardizing the intended efficiency benefit. To evaluate the different disciplines involved in aircraft design, a multi-disciplinary design optimization environment offers the required tools. While computationally demanding, the obtained solution is more efficient if the disciplines are assessed simultaneously. Equipped with low- and high-fidelity assessments, aircraft performance can be predicted at the preliminary design stage, while mitigating some computational expenses. To further reduce the computational burden, adaptive surrogate modelling approaches can be employed, requiring less computational evaluations while efficiently guiding the optimization process towards improved designs. Considering surrogate models for expensive physics based objective and constraint functions bears the disadvantage of more uncertainty in the models. Thus, a new technique is proposed to utilizing the probability of feasibility for the constraints in combination with a transformed normalized objective function to address the uncertainty consideration. The approach is assessed via mathematical test functions and an engineering application and compared against established methods. The results suggests an applicability of the method, with further improvements to be examined. Limitations are revealed regarding local optima and convergence. Further, the degree of maturity does not yet suffice for industrial applications. In a multi-disciplinary design optimization of a high aspect ratio wing aircraft and a strut braced wing aircraft a more classical EGO approach was therefore the choice of approach. The configurations were optimized towards a multi-objective, blending manufacturing and operational costs. Towards cost efficient evaluations, investigations were performed to incorporate high-fidelity assessments, yet limiting their number by reducing active constraints. Driven by aero-structural and aeroelastic constraints, the novel designs could improve the performance satisfactory. / Graduate