Turbine Parts Research Articles

Fault detection of key parts of wind turbine based on BP neural network combination prediction model

A BP neural network incorporated regression forecast technique based upon fragment swarm optimization (PSO) is proposed to design the state of crucial components of wind turbine so regarding realize mistake identification and detection. Firstly, specification recognition is carried out on the collection and tracking information of the system, and parameters connected to fault detection are extracted. Then, the residual optimization issue is made use of to establish the forecast model of nonlinear state evaluation and semantic network combination, and the gearbox temperature level or generator bearing are input as criteria right into the semantic network combination model and single model specifically, and the precision of the design is mirrored by the examination index. Lastly, BP design and PSO-BP combined forecast model are developed respectively by using the actual operation data of wind ranch SCADA, and the mistake state is evaluated according to whether the anticipated residual exceeds the set threshold, so regarding keep an eye on the temperature level of wind turbine transmission and generator bearing. By contrasting the data videotaped prior to and after the failing and making the information prediction analysis, the speculative results show that the forecast model established in this paper is viable for the device element fault detection.

A Digital Engineering Analysis of an Additively-Manufactured Turbine Vane

Abstract Additive manufacturing (AM) has transformed the ability to accelerate gas turbine component research and development at a fraction of the cost and time associated with conventional manufacturing. However, whereas prior works have assessed manufacturing variability in cast turbine airfoils, limited data are available to understand the impact of as-built deviations in AM turbine parts. As metal additive airfoils are becoming more prevalent in research turbine architectures, it is increasingly important to understand the effects of potential hardware deviations specific to additively-manufactured parts. With this goal in mind, the current study utilizes a digital engineering approach to evaluate the aerodynamic impact of surface deviations on a high-pressure turbine vane design created for research purposes. Reynolds-averaged Navier–Stokes-based computational fluid dynamics studies derived from structured light scans of as-built turbine vanes are used to quantify performance relative to design-intent geometries. Further computational analyses compare results from individual serialized parts with an average vane doublet geometry serving as a surrogate for the entire wheel. Particular emphasis in the study focuses on external surface defects caused by internal cooling features that are inherent through additive manufacturing and how these features can impact the vane performance. Ultimately, this study identifies specific regions of the vane that are subject to increased sensitivity, which benefits future designers intending to use AM as a tool for turbine research and development.

Cracking mechanism of CA6NM hydro turbine steel in cavitation erosion

Cavitation erosion is a main reason for the damage of hydro turbines. It is important to understand the cracking mechanism of hydro turbine steel in cavitation erosion. In this study, the crack initiation in CA6NM steel after cavitation erosion was examined through electron backscatter diffraction analyses. The crack propagation was investigated adopting focused ion beam milling and transmission electron microscopy. The reversed austenite transformed to strain-induced martensite in cavitation erosion. The lattice parameter of the strain-induced martensite was greater than that of the thermal martensite, which led to stress concentration between the two martensites and caused crack formation and propagation in the region. Finally, approaches aiming to improve the cavitation erosion resistance of hydro turbine parts are suggested.

Scaling Heat Transfer and Pressure Losses of Novel Additively Manufactured Rib Designs

Abstract Rib turbulators are a key cooling feature that enables increased heat transfer on the interior of gas turbine components. As metal additive manufacturing becomes available for developing turbine parts, it is becoming increasingly important to understand how the surface roughness intrinsic to this manufacturing method impacts the performance of rib turbulators. To explore what impact roughness has on rib turbulator performance, several relevant scale test coupons were manufactured on two different additive machines out of IN718. Additionally, several coupons were built at large scales to effectively reduce the relative roughness size impacts and determine scale effects. A range of different wavy broken rib designs, varying both rib wavelength and orientation within a channel were evaluated. Coupon geometry and surface roughness were characterized using both computed tomography scans and optical profilometry. Variations between the print methods were found to have limited impact on the surface roughness, but significant impact on the accuracy to meet the design intent with rib heights varying by 30%. Following characterization, coupons were flow tested and it was found that the ribs that more regularly disturbed the flow as a function of their geometry most significantly enhanced heat transfer and pressure drop. The performance index of the broken wavy ribs was similar to other advanced rib designs, such as broken or V shaped ribs, but augmentations to heat transfer and pressure drop were generally lower. The rib performance was compared as a function of relative roughness, and it was found that increases in relative roughness resulted in increased friction factor and heat transfer, especially at higher Reynolds numbers.

Large eddy simulation of in-hole blockage effects on cooling performance of fan-shaped holes

Film-cooling and thermal barrier coating approaches are commonly used in gas turbines to protect the turbine parts from the high-temperature flow of gases. However, one drawback of this method is that the coatings can partially obstruct the cooling hole exit on the turbine blade surfaces, resulting in reduced cooling performance. Therefore, this study aimed to investigate the effects of in-hole blockage thickness on cooling effectiveness and flow characteristics of fan-shaped holes using large eddy simulations (LES). A laidback fan-shaped hole located on a flat plate was the reference cooling hole. Numerical simulations were conducted for two different blocked cooling hole configurations with various blockage thicknesses (t/D = 0.5 and 0.9) at three different blowing ratios (0.5, 1.0, and 2.0). The cooling effectiveness computed by the LES method was validated for both unblocked and blocked cooling holes, and the results were compared to the experiments at various blowing ratios. It was revealed that an increase in blockage thickness had a significant impact on the area-averaged cooling effectiveness, particularly at high blowing ratios. The overall area-averaged cooling effectiveness for the t/D = 0.9 case was approximately 75 % lower than that of the unblocked cooling hole. Moreover, assessment of the instantaneous velocity field inside the hole over time revealed that larger blockage thicknesses resulted in greater velocity fluctuations and a more unsteady flow.

Study on dynamic stress variation of head cover bolt during transient operation of pump turbine

Abstract The head cover bolt is an important connecting part of pump turbine, if it is damaged, it will affect the operation safety of the unit and the power station. During the transient process, such as start-up and load rejection in extreme case, the head cover bolt is subjected to complex load action, and its stress also changes, and extreme cases may occur. The dynamic stress level of the bolt will have a certain influence on its fatigue life, therefore, in order to further study the dynamic stress level and variation law of the bolt, this paper relies on a pumped-storage station, the dynamic stress of the head cover bolt during the start-up and load rejection is measured, and the pressure pulsation between the head cover and the runner and the bladeless zone are collected synchronously. Through the data analysis, the bolt dynamic stress level and variation law and the correlation between stress and pressure fluctuation under the power generation and pumping conditions are summarized.

Analyses of non-clean surface compressor blade cascades’ aerodynamic performance deterioration

Abstract The efficiency and dependability of the engine are directly impacted by the compressor’s performance, which is a crucial part of gas turbines. The blade cascade is significantly influenced by surface roughness in terms of flow characteristics and aerodynamic performance. In this study, the NACA65-410 airfoil is selected as the research object to investigate the variations in blade cascade performance and flow characteristics under different inflow and roughness conditions. The results show that the smooth surface blade cascade performs optimally when the Reynolds number increases from 120,000 to 200,000. As the angle of attack increases from -5° to 8°, the blade cascade performance deteriorates, and the degree of performance degradation increases with the increase in angle of attack. For the roughened blade cascade surface, the performance initially improves and then decreases as the equivalent sand grain density increases, and there exists a critical equivalent sand grain density that maximizes the blade cascade performance. When the Reynolds number is 120,000, the critical equivalent sand grain density is 225 μm, and when the Reynolds number is 160,000, the critical equivalent sand grain density is 111 μm. Based on the findings of this study, the degradation pattern of blade cascade performance can be predicted, and it can be utilized to enhance the overall performance of the compressor.

RATIONALE FOR THE TECHNOLOGY OF PROTECTING THE METAL SURFACE IN THE FLOW PATH OF A HYDRAULIC TURBINE TO REDUCE CAVITATION WEAR

The relevance of the problem is due to the significant scale of cavitation destruction of hydraulic turbines of many operating hydroelectric power stations and the high economic costs of major repairs of hydraulic units. Methods for restoring surfaces damaged by cavitation in the flow path of hydraulic turbines at hydroelectric power stations are considered. Based on a review and comparison of existing technologies, recommendations are made that significantly increase the periods between major overhauls of hydraulic turbine parts. It has been shown that material damage due to cavitation can be minimized by using cavitation-resistant materials. A feasibility study for a combined method of repairing a hydraulic unit is presented.

Взаимосвязь окружной скорости и потерь энергии в проточной части центростремительной турбины с частичным облопачиванием рабочего колеса

Low-consumption turbomachines are devices that play an important role in the drive of various units in the field of shipbuilding, aircraft engineering and other branches of heavy engineering. They have some advantages over a high-average power turbine. The largest number of low-consumption turbomachines are made partial, i.e. with partial intake. The principle of a turbine with partial flapping of the impeller is considered as one of the types of partial turbomachines. The influence of the main velocity characteristic of the turbine stage on the loss of kinetic energy in the stage is investigated. The simulation of gas dynamic processes occurring in the turbine stage was carried out using the ANSYS Workbench software package. With the help of this complex, a three-dimensional geometric model of the turbine stage with varying degrees of impeller damping was created. By applying the finite element method, a computational grid, a computational model are generated, and boundary conditions of a numerical experiment are set. The result of the numerical experiment is graphs of the dependence of kinetic energy loss on the circumferential velocity (speed characteristics of the turbine stage). This dependence can be represented not only graphically, but also with the help of mathematical apparatus. An example of such an apparatus is the polynomial dependence. The considered mathematical design can be used in order to optimize mathematical models of gas flow in the flow part of a low-flow turbine. Cubic two-parameter polynomials of kinetic energy losses in the flow part of the nozzle and impeller are obtained, and an assessment of its applicability in the current mathematical model is given.

Preparation, Characterization, and High-Temperature Anti-Seizing Application of CrAlN-Based Gradient Multilayer Coatings

High-temperature fasteners are metal parts of gas turbines and steam turbines, which work at high temperatures and under stress for a long time. However, the frequent seizures of fasteners bring great trouble to the normal maintenance of power plants. In this paper, three kinds of dense and controllable CrAlN-based gradient multilayer coatings were prepared on the samples and screws by arc ion plating (AIP) technology. The morphology, composition, structure, nano hardness, adhesion, residual stress, and room temperature tribological performance of the coating were investigated. To evaluate the high-temperature, anti-seizing performance, coated screws were heated to 700 °C for 140 h with a torque of 20 N·m. The results indicate that the CrN/CrAlN multilayer coating shows better comprehensive properties. The characterization of coated screws proved that the coating structures obtained on the screws were similar to the flat samples. However, the as-prepared coating on the screws showed different thickness variation rules, which was related to the clamping method, deposition distance, and screw shape. After a simulation service, the thread of the screw remained intact with similar structure and thinner thickness. The above results indicate that the high-temperature seize prevention of fasteners can be successfully achieved by preparing a CrAlN-based multilayer coating, which is suitable for fasteners with service temperatures below 700 °C.

An investigation into the erosion and wear mechanisms observed in abradable ytterbium disilicate environmental barrier coatings

Abradable environmental barrier coatings (EBCs) are used to reduce clearances between blades and casings in the hottest parts of aero gas turbines, increasing the overall efficiency of the turbine, however, research into the mechanical performance of such coatings is limited. The aim of this work was to better understand the relationship between microstructure and erosion and wear performance in abradable EBCs, since publicly available literature regarding such coatings is scarce and the potential efficiency gains in gas turbines are significant. In this study, ytterbium disilicate abradable EBCs containing three different porosity levels (determined by fugitive polyester phase addition) were deposited using atmospheric plasma spraying with porosity levels of 8, 15 and 21.5% by area. These coatings were then characterised in numerous ways; the porosity was quantified, the thermal conductivity was measured, the superficial hardness and erosion resistance were measured, and finally, the coatings were subjected to a rig test designed to simulate in-service cutting mechanisms against a tipped turbine blade. The results show that increasing the level of porosity via increasing the amount of pore forming phase in the feedstock, led to reduced erosion resistance and improved cutting by a turbine blade.

Structural Analysis & Effect of Impingement Cooling Flow in the Internal Surface on Temperature Distribution of a Vane in Gas Turbine Using Taguchi Technique

Abstract: Gas turbine always consider one of the most important systems in the modern engineering applications, because it has continuous ability to generate electric power. In gas turbines the major portion of performance dependency lies upon turbine blade design ,the blades are considered one of the important and expensive parts in the gas turbines , where the blades of first stage from failure .The blades of the gas turbine suffer from tensile stresses due to centrifugal forces resulting from the high rotational speed and because of the loading of densegasses at a high temperature and speed ,as the centrifugal force is one of the problems that thedesigner of the turbine blades faces , as the designer aims to reduce stresses within the permissible limit. CFD study was carried out for evaluating the performance of a utility Steam Turbine. The flow in a turbine blade passage is complex and involves understanding of energy conversion in three dimensional geometries. The performance of turbine depends on efficient energy conversion and analyzing the flow path behavior in the various componentsof Steam Turbine. This study seeks the optimization of an axial turbine from a small gas turbine engine developed by ITA using Computational Fluid Dynamics (CFD) and Multi- Objective Optimization techniques focused on geometry changes to maximize the turbine performance. The simulation process will be done through the use of the commercial software SolidWorks.

Vertical Axis Wind Turbine

The key components of a vertical-axis wind turbine (VAWT) are located at the base of the turbine, with the primary rotor shaft aligned transversely to the wind. Because of this arrangement, maintenance and repairs are made easier because the generator and gearbox are located close to the ground. The parts of a vertical axis wind turbine include the blade, shaft, bearing, frame, and blade support. In short, the rotor rotates because of the dynamic pressure exerted by the wind on the blades. Concurrently, the opposing side of the blades encounters aerodynamic hindrance, occasionally referred to as "drag." We experience something similar when we run or cycle: airflow is constantly working against us. Key Words: Turbine, Dynamic pressure, Rotar resistance, Shaft.

Ziegler-Nichols Based Controller Optimization for DFIG Wind Turbines

Wind power conversion systems are becoming the most preferable alternate renewable energy source. Doubly Fed Induction Generator’s utilization rotating parts in wind turbines is increasing with the wind power penetration to the electric grid. The observation becomes more noticeable when there are abnormal conditions within the electrical power system. In this paper, MPPT Controller is incorporated with DFIG through the MATLAB Simulink program is simulated. A conventional PI controller is tuned with Ziegler-Nichol’s (Z-N) method and was implemented to the DFIG Control Block. The rotor-side converter (RSC) is utilized to regulate the power generated by the doubly-fed induction generator (DFIG), while the direct current (DC) bus voltage is controlled by the grid-side converter (GSC). The DFIG's angular and comparative angular speeds are used to generate the reference torque value in the MPPT controller. This improved the time response, output power, electromotive torque, and other current and voltage characteristics. A comparison was made between the output evaluation of the inbuilt controller and the output evaluation of the proposed controller. The MPPT with tuned PI Controller's results show that the suggested system, which uses DFIG coupled to a rotor, is accurate, dependable, and feasible. DOI: https://doi.org/10.52783/tjjpt.v45.i02.6966

Characterization and Prediction of Wind Turbine Blade Damage Based on Fiber Grating Sensor

INTRODUCTION: As a renewable and clean use of energy, wind power generation has a very important role in the new energy generation industry. For the many parts of various wind turbines, the safety and reliability of wind turbine blades are very important. OBJECTIVES: The energy spectrum simulation algorithm included in the wavelet analysis method is used to simulate and analyzewind turbine blade damage, to verify the correctness and validity of wind turbine blade damage analysis. METHODS: Matlab simulation is used to introduce the experiments related to the static and dynamic detection of fiber grating sensors, analyze the signal characteristics of the wind turbine blade when it is damaged by the impact, and provide a basis for the analysis of the external damage of large wind turbine blade. RESULTS: The main results obtained in this paper are the following. By analyzing the decomposition of wavelet packets, the gradient change of wavelet impact energy spectrum before and after the wavelet damage was obtained and compared with the histogram, and the impact energy spectrum of each three-dimensional wavelet energy packet in the image was compared and analyzed, which can well realize the recognition of wavelet damage gradient for solid composite materials. CONCLUSION: With the help of Matlab simulation to collect the impact response signal, using the wavelet packet energy spectrum method to analyze the signal, can derive the characteristics of wind turbine blade damage.

Exploring the REEs Energy Footprint: Interlocking AI/ML with an Empirical Approach for Analysis of Energy Consumption in REEs Production

Rare earth elements (REEs including Sc, Y) are critical minerals for developing sustainable energy sources. The gradual transition adopted in developed and developing countries to meet energy targets has propelled the need for REEs in addition to critical metals (CMs). The rise in demand which has propelled REEs into the spotlight is driven by the crucial role these REEs play in technologies that aim to reduce our carbon footprint in the atmosphere. Regarding decarbonized technologies in the energy sector, REEs are widely applied for use in NdFeB permanent magnets, which are crucial parts of wind turbines and motors of electric vehicles. The underlying motive behind exploring the energy and carbon footprint caused by REEs production is to provide a more complete context and rationale for REEs usage that is more holistic. Incorporating artificial intelligence (AI)/machine learning (ML) models with empirical approaches aids in flowsheet validation, and thus, it presents a vivid holistic picture. The energy needed for REEs production is linked with the source of REEs. The availability of REEs varies widely across the globe. REEs are either produced from ores with associated gangue or impurities. In contrast, in other scenarios, REEs can be produced from the waste of other mineral deposits or discarded REEs-based products. These variations in the source of feed materials, and the associated grade and mineral associations, vary the process flowsheet for each type of production. Thus, the ability to figure out energy outcomes from various scenarios, and a knowledge of energy requirements for the production and commercialization of multiple opportunities, is needed. However, this type of information concerning REEs production is not readily available as a standardized value for a particular material, according to its source and processing method. The related approach for deciding the energy and carbon footprint for different processing approaches and sources relies on the following three sub-processes: mining, beneficiation, and refining. Some sources require incorporating all three, whereas others need two or one, depending on resource availability. The available resources in the literature tend to focus on the life cycle assessment of REEs, using various sources, and they focus little on the energy footprint. For example, a few researchers have focused on the cumulative energy needed for REE production without making assessments of viability. Thus, this article aims to discuss the energy needs for each process, rather than on a specific flowsheet, to define process viability more effectively regarding energy need, availability, and the related carbon footprint.

Bearing Fault Diagnosis Based on VMD-WPT-ViT for Wind Turbine

As an important part of wind turbine, the fault diagnosis of rolling bearings is helpful to maintain the stable operation of the fan. However, most of the existing fault diagnosis methods focus on one-dimensional time series data, which is not only difficult to extract multi-level feature information, but also fails to consider the noise problem in the signal. Therefore, to solve the above problems, a noise reduction algorithm combining variational mode decomposition (VMD) and wavelet packet threshold noise reduction (WPT) is proposed, and Vision-Transformer (ViT) model is combined to realize the fault diagnosis of rolling bearings of wind turbines. VMD is used to decompose the original signal and find out the noisy component. WPT is used to de-noise the component, and the de-noised component and other components are reconstructed to form a pure signal and converted into a two-dimensional image. Finally, a diagnostic model is built based on Vision-transformer. The experimental results show that the proposed method can effectively improve the diagnostic accuracy by extracting the advanced global features of the signal.

Feature Extraction of Flow Sediment Content of Hydropower Unit Based on Voiceprint Signal

The hydropower turbine parts running in the sand-bearing flow will experience surface wear, leading to a decline in the hydropower unit’s stability, mechanical performance, and efficiency. A voiceprint signal-based method is proposed for extracting the flow sediment content feature of the hydropower unit. Firstly, the operating voiceprint information of the hydropower unit is obtained, and the signal is decomposed by the Ensemble Empirical Mode Decomposition (EEMD) algorithm, and a series of intrinsic mode functions (IMFs) are obtained. Combined with correlation analysis, more sensitive IMF components are extracted and input into a convolutional neural network (CNN) for training, and the multi-dimensional output of the fully connected layer of CNN is used as the feature vector. The k-means clustering algorithm is used to calculate the eigenvector clustering center of the hydropower unit with a clean flow state and a high sediment content state, and the characteristic index of the hydropower unit sediment content is constructed based on the Euclidean distance method. We define this characteristic index as SI, and the change in the SI value can reflect the degree of sediment content in the flow of the unit. A higher SI value indicates a lower sediment content, while a lower SI value suggests a higher sediment content. Combined with the sediment voiceprint data of the test bench, when the water flow changed from clear water to high sediment flow (1.492 × 105 mg/L), the SI value decreased from 1 to 0.06, and when the water flow with high sediment content returned to clear water, the SI value returned to 1. The experiment proves the effectiveness of the method. The extracted feature index can be used to detect the flow sediment content of the hydropower unit and give early warning in time, so as to improve the maintenance level of the hydropower unit.

Model development of bond graph based wind turbine

<span>Just recently, wind energy continues to be one of most important renewable energy resources, by cause of its production is ecologically friendly; for that reason, the technology built for the renewable energy production by way of wind turbines takes excessive challenges in the study. Due to several physical domains prevailing in wind turbine, namely aerodynamical, mechanical and electrical, the modeling of wind turbine is problematic; thus, modeling based on physical techniques has a superior reliability in these circumstances. One of these approaches is bond graph modeling that model system evolved from conservation law of both of mass and energy comprising in the structures. This study presents modeling the parts of bond graph-based wind turbine. Then, sub models are connected together to attain the entire model of wind turbine for simulation based on 20-Sim software. The proposed wind turbine is 2.5 kW of variable velocity wind turbine with three blades, gearbox, tower, and doubly-fed induction generator type. The effectiveness of bond graph modeling system on wind turbine has been proven in simulation results.</span>

A review of recent advances in fault diagnosis based on deep neural networks

Bearings are essential components in mechanical systems, supporting the rotation of various machine parts in motors, wind turbines, vehicles, and industrial robots, making their health critical for system performance and reliability. Traditional diagnosis methods, such as vibration and acoustic analysis, along with temperature monitoring, often demand expertise and may struggle to detect early faults. However, the introduction of deep learning technology has created new opportunities for more effective bearing fault diagnosis. The application of deep learning-based bearing fault diagnosis in the industrial sector has gained significant attention and multiple types of deep learning networks have already been successfully implemented. This paper aims to provide a clear review of bearing fault diagnosis based on deep learning algorithms. This essay focuses on two of the most popular deep learning networks, Autoencoder and Convolutional Neural Networks. Their mechanism and applications are analyzed based on essays and research paper related to the field of bearing fault diagnosis. Finally, conclusions are presented to summarize the current development and point out faced challenges and future trends of these deep learning networks. It is also expected that this narrative not only serves as a cogent overview of the contemporary fault diagnosis technologies but also provides convenience and inspiration for further study in this field.