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This thesis deals with the application of optimisation based Validation and Verification (V&V) analysis on aerospace vehicles in order to determine their worst case performance metrics. To this end, three aerospace models relating to satellite and launcher vehicles provided by European Space Agency (ESA) on various projects are utilised. As a means to quicken the process of optimisation based V&V analysis, surrogate models are developed using polynomial chaos method. Surro- gate models provide a quick way to ascertain the worst case directions as computation time required for evaluating them is very small. A sin- gle evaluation of a surrogate model takes less than a second. Another contribution of this thesis is the evaluation of operational safety margin metric with the help of surrogate models. Operational safety margin is a metric defined in the uncertain parameter space and is related to the distance between the nominal parameter value and the first instance of performance criteria violation. This metric can help to gauge the robustness of the controller but requires the evaluation of the model in the constraint function and hence could be computationally intensive. As surrogate models are computationally very cheap, they are utilised to rapidly compute the operational safety margin metric. But this metric focuses only on finding a safe region around the nominal parameter value and the possibility of other disjoint safe regions are not explored. In order to find other safe or failure regions in the param- eter space, the method of Bernstein expansion method is utilised on surrogate polynomial models to help characterise the uncertain param- eter space into safe and failure regions. Furthermore, Binomial failure analysis is used to assign failure probabilities to failure regions which might help the designer to determine if a re-design of the controller is required or not. The methodologies of optimisation based V&V, surrogate modelling, operational safety margin, Bernstein expansion method and risk assessment have been combined together to form the WCAT-II MATLAB toolbox.

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Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This thesis evaluates the application of these methods to the problem domain of skin permeability. ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. Historically, refining mathematical models used to predict percutaneous drug absorption has been thought of as a key factor in this field. Quantitative Structure-Activity Relationships (QSARs) models are used extensively for this purpose. However, advanced ML methods successfully outperform the traditional linear QSAR models. In this thesis, the application of ML methods to percutaneous absorption are investigated and evaluated. The major approach used in this thesis is Gaussian process (GP) regression method. This research seeks to enhance the prediction performance by using local non-linear models obtained from applying clustering algorithms. In addition, to increase the model's quality, a kernel is generated based on both numerical chemical variables and categorical experimental descriptors. Monte Carlo algorithm is also employed to generate reliable models from variable data which is inevitable in biological experiments. The datasets used for this study are small and it may raise the over-fitting/under-fitting problem. In this research I attempt to find optimal values of skin permeability using GP optimisation algorithms within small datasets. Although these methods are applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.

Zum kraft- und formschlüssigen Fügen von dünnwandigen, filigranen Betonfertigteilen eignen sich besonders kontinuierliche, flächige Verbindungen, die durch Verkleben erzielt werden können. Im Rahmen dieses Vorhabens sollten möglichst einfach zu handhabende Klebverbindungen für Platten- und Scheibenbauteile konstruiert und optimiert werden. Die Verbindungen sollten so ausgelegt werden, dass sie sowohl Beanspruchungen aus einzelnen Schnittgrößen als auch aus Schnittgrößenkombinationen übertragen können. Die einzelnen Parameter, die dabei untersucht werden sollten, sind im Wesentlichen die Klebefugengeometrie sowie die Oberflächenbeschaffenheit und Bewehrung der zu verklebenden Bauteile. Als Klebstoff sollte in erster Linie ein zementgebundener Hochleistungsmörtel, eingesetzt werden. Das Optimieren der Klebefugengeometrie und der Bewehrung in der Nähe der Fuge erfolgte mit Hilfe eines mathematisch fundierten Shape- und Topologie-Optimierungsverfahrens, wobei die Fugengeometrie sowie das Interface zwischen Beton und Bewehrung mit Level-Set-Funktionen beschrieben wurden. Dieses Verfahren nutzt den topologischen Gradienten als Maß für die Sensitivität der Zielfunktion bzgl. der Größe einer Pore oder eines Risses in einem gegebenen Punkt im Bauteil [1]. Auf der Grundlage von Kleinteilversuchen wurden Bruchkriterien entwickelt, die die Festigkeit der Klebefuge sowohl für eine kombinierte Schub-Druck- als auch für eine Schub-Zugbeanspruchung beschreiben. Basierend auf den gewonnenen Erkenntnissen wurden weiterhin Entscheidungshilfen zur Fugengestaltung sowie ein ingenieurmäßiger Bemessungsansatz erarbeitet. [Aus: Kurzzusammenfassung] / Continuous two-dimensional connections, which are achieved by gluing are particularly suitable for the force-locking and form-fit joining of thin-walled, filigree precast concrete elements. Within the framework of this project, easy-to-handle adhesive joints for plate and plane components can be designed and optimized. The connections should be designed in such a way that they can withstand both stresses from individual internal forces as well as from combinations of internal forces. In essence, the individual parameters that should be investigated are the geometry of the bonding joint as well as the surface condition and reinforcement of the components to be bonded. A cementitious high-performance mortar should primarily be used as adhesive. The optimization of the adhesive joint geometry and the reinforcement close to the joint was carried out by means of a mathematically founded shape and topology optimization method, whereby the joint geometry as well as the interface between concrete and reinforcement were described with level set functions. This method uses the topological gradient as a measure of the sensitivity of the objective function to the size of a pore or crack at a given point in the structural member [1]. On the basis of small part tests, fracture criteria were developed which describe the strength of the adhesive joint both for a combined shear-compressive and a shear-tensile load. Based on the knowledge gained, decision supports for joint design and an engineering design approach were also developed. [Off: Summary]

No / This paper proposes a hybrid optimization method for optimal allocation of wind turbines (WTs) that combines a fast and elitist multiobjective genetic algorithm (MO-GA) and the market-based optimal power flow (OPF) to jointly minimize the total energy losses and maximize the net present value associated with the WT investment over a planning horizon. The method is conceived for distributed-generator-owning distribution network operators to find the optimal numbers and sizes of WTs among different potential combinations. MO-GA is used to select, among all the candidate buses, the optimal sites and sizes of WTs. A nondominated sorting GA II procedure is used for finding multiple Pareto-optimal solutions in a multiobjective optimization problem, while market-based OPF is used to simulate an electricity market session. The effectiveness of the method is demonstrated with an 84-bus 11.4-kV radial distribution system.