Fatigue Analysis of the Microturbine Rotor Disc Made of 7075 Aluminium Alloy Using a New Hybrid Calculation Method

Zych, Paweł; Żywica, Grzegorz

doi:10.3390/ma15030834

Cited by 9 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of this, the blade should be checked in the next iteration of the stage design, which is the mechanical strength finite element simulation. Such analyses, along with stress optimization, are possible whether for pumps [63] or turbines [64,65].…”

Section: Comparison Of Geometrymentioning

confidence: 99%

Example of Using Particle Swarm Optimization Algorithm with Nelder–Mead Method for Flow Improvement in Axial Last Stage of Gas–Steam Turbine

Ziółkowski,

Witanowski,

Głuch

et al. 2024

Energies

View full text Add to dashboard Cite

This article focuses principally on the comparison baseline and the optimized flow efficiency of the final stage of an axial turbine operating on a gas–steam mixture by applying a hybrid Nelder–Mead and the particle swarm optimization method. Optimization algorithms are combined with CFD calculations to determine the flowpaths and thermodynamic parameters. The working fluid in this study is a mixture of steam and gas produced in a wet combustion chamber, therefore the new turbine type is currently undergoing theoretical research. The purpose of this work is to redesign and examine the last stage of the gas–steam turbine’s flow characteristics. Among the optimized variables, there are parameters characterizing the shape of the endwall contours within the rotor domain. The values of the maximized objective function, which is the isentropic efficiency of the turbine stage, are found from the 3D RANS computation of the flowpath geometry changing during the improvement scheme. The optimization process allows the stage efficiency to be increased by almost 4 percentage points. To achieve high-quality results, a mesh of over 20 million elements is used, where the percentage error in efficiency between the previous and current mesh sizes drops below 0.05%.

show abstract

Section: Comparison Of Geometrymentioning

confidence: 99%

Example of Using Particle Swarm Optimization Algorithm with Nelder–Mead Method for Flow Improvement in Axial Last Stage of Gas–Steam Turbine

Ziółkowski,

Witanowski,

Głuch

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…It is used to measure the surface roughness of various materials, including metals, plastics, ceramics, and composites. The technical details related to the Isoscope FMP30's capabilities for measuring paint and anodic layer thicknesses are presented below [43][44][45][46]:…”

Section: Measurement Of Paint Thickness Layersmentioning

confidence: 99%

Research on the Influence of Coating Technologies on Adhesion Anti-Corrosion Layers in the Case of Al7175 Aluminum Alloy

2023

View full text Add to dashboard Cite

A key element in ensuring the service life and strength of aluminum alloys in many industrial applications is the adhesion of anti-corrosion coatings. The aim of this study is to analyze how coating processes affect the adhesion of anti-corrosion coatings on aluminum alloy 7175. In other words, the influence of the nature of the elementary layers that form the anti-corrosion coating was studied for the following: the anodic layer, the primer, and the topcoat. To learn more about the different coating technologies and how they affect adhesion, a thorough literature review was carried out. In addition, a case study using electrocoating and thermal spraying was conducted to show the impact of coating processes on adhesion. The results showed that electrodeposition, as opposed to thermal spraying, improved the adhesion of anti-corrosion coatings. In the case of the aluminum alloy analyzed, there is a significant difference in terms of the adhesion strength of the anti-corrosion coatings. This resistance is influenced by both the anodic coating (BSA TSA SAA) and the type of primer and topcoat used (water-based or solvent-based). The correct choice of anode coat and primer and topcoat can lead to an increase (or decrease) in the adhesion strength of the paint coat by 20%. In conclusion, this study highlights how crucial it is to select the best coating process to maximize the adhesion and durability of aluminum alloys under corrosion conditions.

show abstract

“…[4]. The critical part is the high turbine section where all the above-mentioned loads are applied [5,6]. The extreme heat emitted by the combustion exhaust gases has a major impact on the turbine disc and blades stress and turbine life properties.…”

Section: Introductionmentioning

confidence: 99%

Jet Engine Turbine Mechanical Properties Prediction by Using Progressive Numerical Methods

Spodniak,

Hovanec,

Korba

2023

Aerospace

View full text Add to dashboard Cite

The propulsion system for an aircraft is one of its most crucial systems; therefore, its reliable work must be ensured during all operational conditions and regimes. Modern materials, techniques and methods are used to ensure this goal; however, there is still room for improvement of this complex system. The proposed manuscript describes a progressive approach for the mechanical properties prediction of the turbine section during jet engine operation using an artificial neural network, and it illustrates its application on a small experimental jet engine. The mechanical properties are predicted based on the measured temperature, pressure and rpm during the jet engine operation, and targets for the artificial neural network are finite element analyses results. The artificial neural network (ANN) is trained using training data from the experimental measurements (temperatures, pressure and rpm) and the results from finite element analyses of the small experimental engine turbine section proposed in the paper. The predicted mechanical stress by ANN achieved high accuracy in comparison to the finite element analyses results, with an error of 1.38% for predicted mechanical stress and correlation coefficients higher than 0.99. Mechanical stress and deformation prediction of the turbine section is a time-consuming process when the finite element method is employed; however, the method with artificial neural network application presented in this paper decreased the solving time significantly. Mechanical structural analyses performed in ANSYS software using finite element modeling take around 30–40 min for one load step. In contrast, the artificial neural network presented in this paper predicts the stress and deformation for one load step in less than 0.00000044 s.

show abstract

Fatigue Analysis of the Microturbine Rotor Disc Made of 7075 Aluminium Alloy Using a New Hybrid Calculation Method

Cited by 9 publications

References 40 publications

Example of Using Particle Swarm Optimization Algorithm with Nelder–Mead Method for Flow Improvement in Axial Last Stage of Gas–Steam Turbine

Example of Using Particle Swarm Optimization Algorithm with Nelder–Mead Method for Flow Improvement in Axial Last Stage of Gas–Steam Turbine

Research on the Influence of Coating Technologies on Adhesion Anti-Corrosion Layers in the Case of Al7175 Aluminum Alloy

Jet Engine Turbine Mechanical Properties Prediction by Using Progressive Numerical Methods

Contact Info

Product

Resources

About