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## Study on Mine Ventilation Resistance Coefficient Inversion Based on Genetic Algorithm

### Abstract

The frictional resistance coefficient of ventilation of a roadway in a coal mine is a very important technical parameter in the design and renovation of mine ventilation. Calculations based on empirical formulae and field tests to calculate the resistance coefficient have limitations. An inversion method to calculate the mine ventilation resistance coefficient by using a few representative data of air flows and node pressures is proposed in this study. The mathematical model of the inversion method is developed based on the principle of least squares. The measured pressure and the calculated pressure deviation along with the measured flow and the calculated flow deviation are considered while defining the objective function, which also includes the node pressure, the air flow, and the ventilation resistance coefficient range constraints. The ventilation resistance coefficient inversion problem was converted to a nonlinear optimisation problem through the development of the model. A genetic algorithm (GA) was adopted to solve the ventilation resistance coefficient inversion problem. The GA was improved to enhance the global and the local search abilities of the algorithm for the ventilation resistance coefficient inversion problem.
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## Metamodel-based optimization of the labyrinth seal

### Abstract

The presented paper concerns CFD optimization of the straight-through labyrinth seal with a smooth land. The aim of the process was to reduce the leakage flow through a labyrinth seal with two fins. Due to the complexity of the problem and for the sake of the computation time, a decision was made to modify the standard evolutionary optimization algorithm by adding an approach based on a metamodel. Five basic geometrical parameters of the labyrinth seal were taken into account: the angles of the seal’s two fins, and the fin width, height and pitch. Other parameters were constrained, including the clearance over the fins. The CFD calculations were carried out using the ANSYS-CFX commercial code. The in-house optimization algorithm was prepared in the Matlab environment. The presented metamodel was built using a Multi-Layer Perceptron Neural Network which was trained using the Levenberg-Marquardt algorithm. The Neural Network training and validation were carried out based on the data from the CFD analysis performed for different geometrical configurations of the labyrinth seal. The initial response surface was built based on the design of the experiment (DOE). The novelty of the proposed methodology is the steady improvement in the response surface goodness of fit. The accuracy of the response surface is increased by CFD calculations of the labyrinth seal additional geometrical configurations. These configurations are created based on the evolutionary algorithm operators such as selection, crossover and mutation. The created metamodel makes it possible to run a fast optimization process using a previously prepared response surface. The metamodel solution is validated against CFD calculations. It then complements the next generation of the evolutionary algorithm.
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## Genetic minimisation of peak-to-peak level of a complex multi-tone signal

### Abstract

This paper presents results of evolutionary minimisation of peak-to-peak value of a multi-tone signal. The signal is the sum of multiple tones (channels) with constant amplitudes and frequencies combined with variable phases. An exemplary application is emergency broadcasting using widely used analogue broadcasting techniques: citizens band (CB) or VHF FM commercial broadcasting. The work presented illustrates a relatively simple problem, which, however, is characterised by large combinatorial complexity, so direct (exhaustive) search becomes completely impractical. The process of minimisation is based on genetic algorithm (GA), which proves its usability for given problem. The final result is a significant reduction of peak-to-peak level of given multi-tone signal, demonstrated by three real-life examples.
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## An identification procedure of electromagnetic parameters for an induction motor equivalent circuit including rotor deep bar effect

### Abstract

The paper presents an identification procedure of electromagnetic parameters for an induction motor equivalent circuit including rotor deep bar effect. The presented proce- dure employs information obtained from measurement realised under the load curve test, described in the standard PN-EN 60034-28: 2013. In the article, the selected impedance frequency characteristics of the tested induction machines derived from measurement have been compared with the corresponding characteristics calculated with the use of the adopted equivalent circuit with electromagnetic parameters determined according to the presented procedure. Furthermore, the characteristics computed on the basis of the classical machine T-type equivalent circuit, whose electromagnetic parameters had been identified in line with the chosen methodologies reported in the standards PN-EN 60034-28: 2013 and IEEE Std 112TM-2004, have been included in the comparative analysis as well. Additional verification of correctness of identified electromagnetic parameters has been realised through comparison of the steady-state power factor-slip and torque-slip characteristics determined experimentally and through the machine operation simulations carried out with the use of the considered equivalent circuits. The studies concerning induction motors with two types of rotor construction – a conventional single cage rotor and a solid rotor manufactured from magnetic material – have been presented in the paper.
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