Seals are basic mechanical devices commonly used in machinery to avoid undesired flow losses of working fluids. To understand the working of these seals specifically those placed between relatively moving parts is still one of the major engineering challenges for the scientific community. Particularly Annular seals are one of the most widely used in rotating machinery comprising turbines, compressors and pumps. They are mounted on the shaft that rotates within a stationary case. These seal designs make an impact on (i) machinery energy conversion efficiency and (ii) rotor dynamic stability due to the interaction between rotor and stator through fluid flow leakage. Among all annular seals straight through rectangular labyrinth seals are the most commonly used ones. Their designs have not changed much a lot since its inception by C.J. Parsons [1] back in 1901. These seals provide resistance to the fluid flow through tortuous path comprising of series of cavities and clearances. The sharp tooth converts the pressure energy to the kinetic which is dissipated through turbulence viscosity interaction in the cavity. To understand the accurate amount of leakage the flow is modeled using the discharge coefficient and for each tooth and the kinetic energy carry over coefficients. This research work is aimed at understanding the fluid flow though labyrinth seals with tooth mounted on the rotor. A matrix of fluid flow simulations has been carried out using commercially available CFD software Fluent® where all parameters effecting the flow field has been studied to understand their effect on the coefficients defining the seal losses. Also the rotor surface speed has been used varied in a step by step manner to understand the fluid flow behavior in high speed turbo-machinery. The carry over coefficient is found to be the function of all the geometric elements defining the labyrinth tooth configuration. A relation between the flow parameters and the carry over coefficient has also been established. The discharge coefficient of the first tooth has been found to be lower and varying in a different manner as compared to a tooth from a multiple cavity seal. Its dependence upon flow parameters and dimensionless geometric constants has been established. The discharge coefficient of the first teeth is found to be increasing with increasing tooth width. Further the compressibility factor has been defined to incorporate the deviation of the performance of seals with compressible fluid to that with the incompressible flow. Its dependence upon pressure ratio and shaft speed has also been established. Using all the above the mentioned relations it would be easy decide upon the tooth configuration for a given rotating machinery or understand the behavior of the seal currently in use.

Nearly twenty years ago the discovery of side channel attacks has warned the world that security is more than just a mathematical problem. Serious considerations need to be placed on the implementation and its physical media. Nowadays the ever-growing ubiquitous computing calls for in-pace development of security solutions. Although the physical security has attracted increasing public attention, side channel security remains as a problem that is far from being completely solved. An important problem is how much expertise is required by a side channel adversary. The essential interest is to explore whether detailed knowledge about implementation and leakage model are indispensable for a successful side channel attack. If such knowledge is not a prerequisite, attacks can be mounted by even inexperienced adversaries. Hence the threat from physical observables may be underestimated. Another urgent problem is how to secure a cryptographic system in the exposure of unavoidable leakage. Although many countermeasures have been developed, their effectiveness pends empirical verification and the side channel security needs to be evaluated systematically. The research in this dissertation focuses on two topics, leakage-model independent side channel analysis and security evaluation, which are described from three perspectives: leakage detection, exploitation and quantification. To free side channel analysis from the complicated procedure of leakage modeling, an observation to observation comparison approach is proposed. Several attacks presented in this work follow this approach. They exhibit efficient leakage detection and exploitation under various leakage models and implementations. More importantly, this achievement no longer relies on or even requires precise leakage modeling. For the security evaluation, a weak maximum likelihood approach is proposed. It provides a quantification of the loss of full key security due to the presence of side channel leakage. A constructive algorithm is developed following this approach. The algorithm can be used by security lab to measure the leakage resilience. It can also be used by a side channel adversary to determine whether limited side channel information suffices the full key recovery at affordable expense.

Leakage current and process variations are two primary hurdles in modern VLSI design. It depends exponentially on process and environmental parameters and hence small variations in these result in a large spread in leakage current of manufactured dies. Traditionally, Exponential Quadratic(EQ) models have been used to model leakage current as a function of process parameters which can model limited non-linearity and hence become inaccurate for large process variations. Artificial Neural Networks (ANN) have shown great promise in modeling circuit parameters for CAD applications. We model leakage with ANN models which perform better than the EQ models for increased process variations. However, the complex nature of the ANN model, with the standard sigmoidal activation functions, does not allow analytical expressions for its mean and variance for the case of Gaussian process variations. We propose the use of a new activation function that allows us to derive an analytical expression for the mean and a semi-analytical expression for the variance of the ANN based leakage model. To the best of our knowledge this is the first result in this direction. All existing SLA frameworks are closely tied to the EQ leakage model and hence fail to work with sophisticated ANN models. We therefore set up an SLA framework that can efficiently work with these ANN models. Results show that the CDF of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo based simulations, being less than 1\% and 2\% respectively across a range of voltage and temperature values. The complexity of our framework is similar to existing SLA frameworks yet more accurate over a larger range of variations. Ignoring the thermal profile of the chip leads to a gross error of nearly 50\% in the prediction of leakage yield. Our neural network model also includes the voltage and temperature as input parameters, thereby enabling voltage and temperature aware statistical leakage analysis (SLA). Similarly leakage CDF can be predicted across a range of supply and body voltages since they are both part of the model. Our framework used analytical techniques to account for local variations and Monte Carlo techniques for global variations and hence it can also be used for Non-Gaussian global variations.