Thermal systems often feature composite regions that are mechanically mated. In general, there exists a significant temperature drop across the interface between such regions which may be composed of similar or different materials. The parameter characterizing this temperature drop is the thermal contact resistance, which is defined as the ratio of the temperature drop to the heat flux normal to the interface. The thermal contact resistance is due to roughness effects between mating surfaces which cause certain regions of the mating surfaces to loose contact thereby creating gaps. In these gap regions, the principal modes of heat transfer are conduction across the contacting regions of the interface, conduction or natural convection in the fluid filling the gap regions of the interface, and radiation across the gap surfaces. Moreover, the contact resistance is a function of contact pressure as this can significantly alter the topology of the contact region. The thermal contact resistance is a phenomenologically complex function and can significantly alter prediction of thermal models of complex multi-component structures. Accurate estimates of thermal contact resistances are important in engineering calculations and find application in thermal analysis ranging from relatively simple layered and composite materials to more complex biomaterials. There have been many studies devoted to the theoretical predictions of thermal contact resistance and although general theories have been somewhat successful in predicting thermal contact resistances, most reliable results have been obtained experimentally. This is due to the fact that the nature of thermal contact resistance is quite complex and depends on many parameters including types of mating materials, surface characteristics of the interfacial region such as roughness and hardness, and contact pressure distribution. In experiments, temperatures are measured at a certain number of locations, usually close to the contact surface, and these measurements are used as inputs to a parameter estimation procedure to arrive at the sought-after thermal contact resistance. Most studies seek a single value for the contact resistance, while the resistance may in fact also vary spatially. In this thesis, an inverse problem (IP) is formulated to estimate the spatial variation of the thermal contact resistance along an interface in a two-dimensional configuration. Temperatures measured at discrete locations using embedded sensors appropriately placed in proximity to the interface provide the additional information required to solve the inverse problem. A superposition method serves to determine sensitivity coefficients and provides guidance in the location of the measuring points. Temperature measurements are then used to define a regularized quadratic functional that is minimized to yield the contact resistance between the two mating surfaces. A boundary element method analysis (BEM) provides the temperature field under current estimates of the contact resistance in the solution of the inverse problem when the geometry of interest is not regular, while an analytical solution can be used for regular geometries. Minimization of the IP functional is carried out by the Levenberg-Marquadt method or by a Genetic Algorithm depending on the problem under consideration. The L-curve method of Hansen is used to choose the optimal regularization parameter. A series of numerical examples are provided to demonstrate and validate the approach.

No / This paper presents a two-stage hybrid optimization algorithm based on a modified genetic algorithm. In the first stage, a global search is carried out over the design search space using a modified GA. The proposed modifications on the basic GA includes dynamically changing the population size throughout the GA process and the use of different forms of the penalty function in constraint handling. In the second stage, a local search based on the genetic algorithm solution is executed using a discretized form of Hooke and Jeeves method. The hybrid algorithm and the modifications to the basic genetic algorithm are examined on the design optimization of reinforced concrete flat slab buildings. The objective function is the total cost of the structure including the cost of concrete, formwork, reinforcement and foundation excavation. The constraints are defined according to the British Standard BS8110 for reinforced concrete structures. Comparative studies are presented to study the effect of different parameters of handling genetic algorithm on the optimized flat slab building. It has been shown that the proposed hybrid algorithm can improve genetic algorithm solutions at the expense of more function evaluations.

k-means is a simple and flexible clustering algorithm that has remained in common use for 50+ years. In this thesis, we discuss the algorithm in general, its advantages, weaknesses and how its ability to locate clusters can be enhanced with a suitable initialization method. We formulate appropriate requirements for the (batched) UnifRandom, k-means++ and Kaufman initialization methods and compare their performance on real and generated data through simulations. We find that all three methods (followed by the k-means procedure) are able to accurately locate at least up to nine well-separated clusters, but the appropriately batched UnifRandom and the Kaufman methods are both significantly more computationally expensive than the k-means++ method already for K = 5 clusters in a dataset of N = 1000 points.

A packet buffer for the protocol processor is a large memory space that holds incoming data packets for an application. Data packets for each application are stored in the form of FIFO queues in the packet buffer. Packets are dropped when the buffer is full. An efficient buffer management algorithm is required to manage the buffer space among the different FIFO queues and to avoid heavy packet loss. This thesis develops a simulation model for the packet buffer and studies the performance of conventional buffer management algorithms when applied to packet buffer. This thesis proposes a new buffer management algorithm, Dynamic Algorithm with Different Thresholds (DADT) to improve the packet loss ratio. This algorithm takes advantage of the different packet sizes for each application and proportionally allocates buffer space for each queue. The performance of the DADT algorithm is dependent upon the packet size distribution in a network traffic load. Three different network traffic loads are considered for our simulations. For the average network traffic load, the DADT algorithm shows an improvement of 6.7 % in packet loss ratio over the conventional dynamic buffer management algorithm. For the high and actual network traffic loads, the DADT algorithm shows an improvement of 5.45 % and 3.6 % in packet loss ratio respectively. Based on the simulation results, the DADT algorithm outperforms the conventional buffer management algorithms for various network traffic loads.

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