Cooling of the hot gas path components plays a key role in modern gas turbines. It allows, due to efficiency reasons, to operate the machines with temperature exceeding components' melting point. The cooling system however brings about some disadvantages as well. If so, we need to enforce the positive effects of cooling and diminish the drawbacks, which influence the reliability of components and the whole machine. To solve such a task we have to perform an optimization which makes it possible to reach the desired goal. The task is approached in the 3D configuration. The search process is performed by means of the evolutionary approach with floatingpoint representation of design variables. Each cooling structure candidate is evaluated on the basis of thermo-mechanical FEM computations done with Ansys via automatically generated script file. These computations are parallelized. The results are compared with the reference case which is the C3X airfoil and they show a potential stored in the cooling system. Appropriate passage distribution makes it possible to improve the operation condition for highly loaded components. Application of evolutionary approach, although most suitable for such problems, is time consuming, so more advanced approach (Conjugate Heat Transfer) requires huge computational power. The analysis is based on original procedure which involves optimization of size and location of internal cooling passages of cylindrical shape within the airfoil. All the channels can freely move within the airfoil cross section and also their number can change. Such a procedure is original.

The paper presents an analogue circuit testing method that engages the analysis of the time response to a non-periodic stimulus specialized for the verification of selected specifications. The decision about the current circuit diagnostic state depends on an amplitude spectrum decomposition of the time response measured during the test. A shape of the test excitation spectrum is optimized with the use of a differential evolution algorithm and it allows for achieving maximum fault coverage and the optimal conditions for fault isolation. Genotypes of the evolutionary system encode the amplitude spectrum of candidates for testing stimuli by means of rectangle frequency windows with amplitudes determined evolutionarily.

In this paper, an algorithm will be presented that enables solving the two-phase inverse Stefan problem, where the additional information consists of temperature measurements in selected points of the solid phase. The problem consists in the reconstruction of the function describing the heat transfer coefficient, so that the temperature in the given points of the solid phase would differ as little as possible from the predefined values. The featured examples of calculations show a very good approximation of the exact solution and stability of the algorithm.

Comparison of Evolutionary Algorithm and Heuristics for Flow Optimization in P2P Systems Nowadays, many Internet users make use of Peer-to-Peer (P2P) systems to download electronic content including music, movies, software, etc. Growing popularity in P2P based protocol implementations for file sharing purposes caused that the P2P traffic exceeds Web traffic and in accordance with to many statistics, P2P systems produce a more than 50% of the whole Internet traffic. Therefore, P2P systems provide remarkable income for Internet Service Providers (ISP). However, at the same time P2P systems generates many problems related to traffic engineering, optimization, network congestion. In this paper we focus on the problem of flow optimization in P2P file sharing systems. Corresponding to BitTorrent-based systems behaviour, the optimization of P2P flows is very complex and in this work we consider different heuristic strategies for content distribution and moreover we propose a new evolutionary algorithm (EA) for this problem. We compare results of the algorithms against optimal results yielded by CPLEX solver for networks including 10 peers and relation to random algorithm for 100-node systems. According to numerical experiments, the EA provides solutions close to optimal for small instances and all of the heuristics exhibit a superior performance over random search.