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.
This paper presents the current stage of the development of EA-MOSGWA – a tool for identifying causal genes in Genome Wide Association Studies (GWAS). The main goal of GWAS is to identify chromosomal regions which are associated with a particular disease (e.g. diabetes, cancer) or with some quantitative trait (e.g height or blood pressure). To this end hundreds of thousands of Single Nucleotide Polymorphisms (SNP) are genotyped. One is then interested to identify as many SNPs as possible which are associated with the trait in question, while at the same time minimizing the number of false detections.
The software package MOSGWA allows to detect SNPs via variable selection using the criterion mBIC2, a modified version of the Schwarz Bayesian Information Criterion. MOSGWA tries to minimize mBIC2 using some stepwise selection methods, whereas EA-MOSGWA applies some advanced evolutionary algorithms to achieve the same goal. We present results from an extensive simulation study where we compare the performance of EA-MOSGWA when using different parameter settings. We also consider using a clustering procedure to relax the multiple testing correction in mBIC2. Finally we compare results from EA-MOSGWA with the original stepwise search from MOSGWA, and show that the newly proposed algorithm has good properties in terms of minimizing the mBIC2 criterion, as well as in minimizing the misclassification rate of detected SNPs.
This paper presents a method of selection of regulator parameters in a control system using evolutionary algorithm. The control system has one PI controller and one hysteresis controller. The value of the proportional band and the value of the Integral time were defined by evolutionary algorithms. The object of control was a Brown Boveri GS10A motor. The task functions were the step change of rotational speed and step change of the motor's torque. The control system with the parameters selected by means of the evolutionary method was verified by using MATLAB/Simulink environment.
A numerical method is developed for estimation of temperature distributions inside tissues heated by external RF hyperthermia with external circular coil. The computational method relies on a solution of electromagnetic field problem in sinusoidal steadyt state. The heat transfer problem is treated in three dimensions with axis symmetry model. Than the bioheat diffusion equation under a steady-state condition is solved to determine the temperature distributions inside tumour and surrounding tissues. The heat removal due to the blood circulation is also taken into account. Numerical results are presented for heat generated by ferromagnetic nanoparticles in order to minimize negative effects of radiofrequency radiation.
In the paper an application of evolutionary algorithm to design and optimization of combinational digital circuits with respect to transistor count is presented. Multiple layer chromosomes increasing the algorithm efficiency are introduced. Four combinational circuits with truth tables chosen from literature are designed using proposed method. Obtained results are in many cases better than those obtained using other methods.
Resonance assignment remains one of the hardest stages in RNA tertiary structure determination with the use of Nuclear Magnetic Resonance spectroscopy. We propose an evolutionary algorithm being a tool for an automatization of the procedure. NOE pathway, which determines the assignments, is constructed during an analysis of possible connections between resonances within aromatic and anomeric region of 2D-NOESY spectra resulting from appropriate NMR experiments. Computational tests demonstrate the performance of the evolutionary algorithm as compared with the exact branch-and-cut procedure applied for the experimental and simulated spectral data for RNA molecules.