One of the biggest challenges of modern aviation is the development of technologies that reduce or eliminate emissions of harmful combustion components into the atmosphere. European authorities are imposing increasingly stringent emissions regulations. Therefore, new models of combustion chambers, new combustion methods, as well as new types of aviation fuels must be developed. This article presents the possibilities of using hydrogen propulsion in aviation. The reasons for conducting research on hydrogen propulsion are discussed, as well as the history of the introduction of hydrogen propulsion into aircraft engines. Problems that can be encountered in the production and storage of hydrogen are identified and explained. Proposals for the use of hydrogen combustion or the use of fuel cells to power turbine engines are also presented, and the economic aspect of this type of fuel is discussed.

The article presents the process of selecting and optimising artificial neural networks based on the example of determining the stress distribution in a disk-drum structure compressor stage of an aircraft turbine engine. The presented algorithm allows the determination of von Mises stress values which can be part of the penalty function for further mass optimization of the structure. A method of a parametric model description of a compressor stage is presented in order to prepare a reduced stress distribution for training artificial neural networks. A comparative analysis of selected neural network training algorithms combined with the optimisation of their structure is presented. A genetic algorithm was used to determine the optimal number of hidden layers and neurons in a layer. The objective function was to minimise the absolute value of the relative error and standard deviation of stresses determined by FEM and artificial neural networks. The results are presented in the form of the Pareto front due to the stochastic optimisation process.

The structural and strength analysis of the materials used to construct an important engine element such as the turbine is of great significance, at both the design stage and during tests and training relating to emergency situations. This paper presents the results of a study on the chemical composition, morphology, and phased structure of the metallic construction material used to produce the blades of the high- and low-pressure turbines of the RD-33 jet engine, which is the propulsion unit of the MiG-29 aircraft. On the basis of an analysis of the chemical composition and phased structure, the data obtained from tests of the blade material allowed the grade of the alloy used to construct the tested elements of the jet engine turbine to be determined. The structural stability of the material was found to be lower in comparison with the engine operating conditions, which was shown by a clear decrease in the resistance properties of the blade material. The results obtained may be used as a basis for analyzing the life span of an object or a selection of material replacements, which may enable the production of the analyzed engine element.

This paper presents a numerical analysis of the stability of the flow parameters along the intake duct of an aircraft jet turbine engine. This problem has been investigated by many research teams and was included in the literature analysis. The unstable operation of a turbojet intake system can be the consequence of many adverse factors, including an intake vortex. The investigated intake system, due to its low location to the plane of the airport, is highly susceptible to the formation of an intake vortex. The phenomenon of an intake vortex can, in the worst-case scenario, result in the surging of the turbojet, and even engine stalling. This paper presents a developed model of the forward section of an aircraft, complete with its intake duct, and the method of its discretization. The intake-system model and numerical analysis were performed in Ansys Fluent. The flow parameters adopted for numerical simulations, under specific boundary conditions, corresponded to the operating conditions of the engine cooperating with the investigated intake system. The numerical calculations were performed assuming an air-pressure rise in the end section of the engine-intake system, reflecting the reduction in the pitch angle of the inlet stator blades of the fan. As a result, the pressure distributions in a significant cross section in the intake system were obtained. The results were analyzed with the quantitative distribution of the pressure fields by applying a dimensionless potential-pressure ratio. The pressure ratio enabled a comparative analysis of the nonuniformity of the total-pressure distribution in selected cross sections of the intake system. The results were revealing in terms of growing unstable flows in the flow duct. A major conclusion drawn from the results, by testing the dimensionless potential-pressure ratio, was that, within certain limits, it was possible to improve the flow uniformity by increasing the throttling pressure.

This article presents a genetic algorithm modification inspired by events related to great extinctions. The main objective of the modification was to minimize the number of objective function solutions until the minimum for the function was established. It was assumed that, within each step, a population should be smaller than that recommended in the applicable literature, the number of iterations should be limited, the solution area should be variable, and a great extinction event should take place following several iterations. Calculations were performed for 10 individuals within a population, 10 iterations, two generations each, with a great extinction event happening once every three iterations. The developed algorithm was presented, capable of indicating the minimum number of Eggholder and Rastrigin functions, with a higher probability than the master algorithm (default “ga” in MATLAB) at the same number of objective function solutions. An algorithm was proposed focusing on minimizing the randomization of the objective function, which may be an alternative to the surrogate model. Typically, the emphasis is on achieving as much accuracy as possible. This article presents a method for minimizing the randomization of the objective function and obtaining the highest possible accuracy. A method is presented which minimizes the disadvantages of the largest computation time and the need to generate many samples for typical genetic algorithms (GAs). Optimization results for the classic GA, GEGA, WOA, SMA, and SSA algorithms for the Eggholder and Rastrigin functions were compared. A modification of the genetic algorithm was made to obtain a global extreme with satisfactory accuracy and a sufficiently high probability, while minimizing the number of samples calculated on the basis of the objective function. The developed methodology was used to fulfill the target function for the turbine disc.

The high demands placed on aircraft turbine engines necessitate the use of the latest engine compressors which must be increasingly efficient and more robust due to the increased loads. The key safety issue in this context is to ensure compressor stability over all engine speed ranges and...