Turbine Components Research Articles

Exploring mass and economic potentials of rare earth elements recycling from electric vehicles at end-of-life

AbstractRare earth elements (REEs) are fundamental for various modern technologies and industrial applications. One significant application of REEs is in the production of neodymium-iron-boron (NdFeB) magnets, which are key components in electric vehicles (EVs), wind turbines, and electronic devices. These applications play a crucial role in driving the ecological and digital transition, highlighting the significance of REEs as strategic materials. With the dominance of very few countries in the REEs global supply and the rising of EVs demand, several concerns regarding resource availability, supply chain security, and price volatility have heightened the importance of efficient NdFeB magnet recycling, especially in Europe. This study assessed the elemental recycling potential of REEs from EV components through collaboration with authorized treatment facilities and metal recyclers in Italy. The study focused on three representative electric vehicles: a compact car, a van, and a hybrid vehicle. NdFeB magnets were found in various components, including the electric drive motor, air conditioning system, electric power steering, alternator, and electric gear box. The content of NdFeB magnets and REEs inside these components has been determined and economic feasibility of their recycling has been estimated by considering the intrinsic value of the raw materials contained. Despite being preliminary results, the economic value of REEs and Cu recoverable attested a promising potential for recycling, while the direct dismantling of magnets from the engine proves economically unviable for the studied components. Therefore, the study emphasizes the need for the development of specific recycling processes such as demagnetization and mechanical processing of the motors. The study also analysed the dismantling times of the target components from the vehicle and their relative economic impact on the potential for recovery.

Experimental study on transpiration cooling through additively manufactured porous structures

Film cooling and thermal barrier coating are the most common external cooling techniques used for gas turbine components. In film cooling, coolant air is brought to the external surface via rows of discrete holes. This study investigates an alternative geometry, referred to as transpiration cooling. Here, the coolant air is brought to the external surface through an additively manufactured porous structure. An experimental study was performed to characterize the downstream cooling performance by obtaining film cooling effectiveness and heat transfer coefficient using thermochromic liquid crystals. The experimental setup was first validated using a cylindrical film cooling sample and the results were compared to results obtained from previously published film cooling studies. After validation, transpiration cooling samples were investigated. A total of three porous structure samples with different thicknesses were subjected to testing. The thickness of the porous structure had insignificant effect on both the heat transfer coefficient and the film cooling effectiveness. The samples were tested at blowing ratios ranging from 0.5 to 2.0. Results showed an increase in laterally averaged heat transfer coefficient with increasing blowing ratio and compared to cylindrical film cooling, transpiration cooling yielded significantly higher distributions. The spanwise averaged film cooling effectiveness displayed a similar trend with higher values for transpiration cooling, mainly due to a more even mixing in the lateral direction. Values of the heat flux ratio showed that the high heat transfer coefficient penalizes the porous cooling method and only for low blowing ratios may transpiration cooling be the beneficial choice. From visualizations with water and carbon dioxide, it could be concluded that the inclination angle is close to 90° which yields a chaotic flow field and, in turn, high values of the heat transfer coefficient. By changing the additive manufacturing's build direction, a more favorable inclination angle should be achievable and, thereby, an opportunity to utilize the porous structure's excellent mixing in the spanwise direction.

Anomaly detection of wind turbine based on norm-linear-ConvNeXt-TCN

The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer linear residual module and the improved temporal convolutional network (TCN) with a new norm-linear-ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average normalized root mean square error between the reconstructed data and actual data. The streaming peaks-over-threshold real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the uniform manifold approximation and projection dimensionality reduction technique and kernel density estimation.

Improved YOLOv5 Based on Multi-Strategy Integration for Multi-Category Wind Turbine Surface Defect Detection

Wind energy is a renewable resource with abundant reserves, and its sustainable development and utilization are crucial. The components of wind turbines, particularly the blades and various surfaces, require meticulous defect detection and maintenance due to their significance. The operational status of wind turbine generators directly impacts the efficiency and safe operation of wind farms. Traditional surface defect detection methods for wind turbines often involve manual operations, which suffer from issues such as high subjectivity, elevated risks, low accuracy, and inefficiency. The emergence of computer vision technologies based on deep learning has provided a novel approach to surface defect detection in wind turbines. However, existing datasets designed for wind turbine surface defects exhibit overall category scarcity and an imbalance in samples between categories. The algorithms designed face challenges, with low detection rates for small samples. Hence, this study first constructs a benchmark dataset for wind turbine surface defects comprising seven categories that encompass all common surface defects. Simultaneously, a wind turbine surface defect detection algorithm based on improved YOLOv5 is designed. Initially, a multi-scale copy-paste data augmentation method is proposed, introducing scale factors to randomly resize the bounding boxes before copy-pasting. This alleviates sample imbalances and significantly enhances the algorithm’s detection capabilities for targets of different sizes. Subsequently, a dynamic label assignment strategy based on the Hungarian algorithm is introduced that calculates the matching costs by weighing different losses, enhancing the network’s ability to learn positive and negative samples. To address overfitting and misrecognition resulting from strong data augmentation, a two-stage progressive training method is proposed, aiding the model’s natural convergence and improving generalization performance. Furthermore, a multi-scenario negative-sample-guided learning method is introduced that involves incorporating unlabeled background images from various scenarios into training, guiding the model to learn negative samples and reducing misrecognition. Finally, slicing-aided hyper inference is introduced, facilitating large-scale inference for wind turbine surface defects in actual industrial scenarios. The improved algorithm demonstrates a 3.1% increase in the mean average precision (mAP) on the custom dataset, achieving 95.7% accuracy in mAP_50 (the IoU threshold is half of the mAP). Notably, the mAPs for small, medium, and large targets increase by 18.6%, 16.4%, and 6.8%, respectively. The experimental results indicate that the enhanced algorithm exhibits high detection accuracy, providing a new and more efficient solution for the field of wind turbine surface defect detection.

Designing Large Segmented Flexible Conical Plain Bearings as Wind Turbine Main Bearings

Main bearings are failure-prone components in wind turbines, significantly increasing the LCOE of wind energy. Therefore, segmented plain bearings are being discussed as replacement for the currently used rolling bearings. Segmented plain bearings allow an up-tower replacement of faulty segments without the need to dismantle the WT drivetrain, as is required for rolling bearing replacements. One such bearing is the segmented, flexible, conical “FlexPad” plain bearing as alternative to existing double row tapered roller bearings. Designing the FlexPad bearing manually is very complex and thus time-consuming. Hence, during the research project a new holistic design method for large and highly loaded, conical plain bearings was developed. To prove the suitability of the method, two bearing prototypes were built and tested. The prototype, designed with the new method, outperformed the first, manually designed bearing, in terms of the design objectives. Most importantly, the friction losses were reduced by 32.1%. The project results show that the new method is able to efficiently design safely operating FlexPad bearing variants for wind turbines.

Impact of interfacial texture on failure behaviour of 8YSZ thermal barrier coatings under thermal cyclic loading

Thermal barrier coatings (TBCs) provide thermal protection for high-temperature components of aero-engines and steam turbines, but are susceptible to failure under thermal cyclic loads. The effective design of interfacial texture is one of the approaches to enhance the lifetime of ceramic TBCs. In this study, four types of interfacial textures were designed on the surface of substrates, and 8YSZ coatings of equal thickness were prepared using atmospheric plasma spraying, and thermal cycling tests were carried out. Meanwhile, the thermal cycle lifetime, microstructure evolution, residual stress, and energy release rate criterion were also studied. The optimal morphology and parameters of the texture are determined. It is found that the lifetime of the concave trapezoidal texture is the longest, with a deflection angle of 67°, diameter of 700 μm, depth of 500 μm, and spacing of 5 mm. This study can provide theoretical guidance for the interfacial texture design of ceramic TBCs.

Challenges on prognostics and health management for wind turbine components

This paper explores the applicability of prognostics and health management (PHM) for wind turbines (WTs), presenting the PHM approach along with challenges and opportunities in the context of WT components. First, the PHM framework is introduced, consisting of three blocks: observation, analysis, and action. Critical components and failure modes for WTs are identified, and data acquisition strategies using supervisory control and data adquisition (SCADA) and condition monitoring (CM) data are discussed. Prognostics, specifically remaining useful life (RUL) estimation, employs physics model-based, data-driven, and hybrid models. Finally, challenges and opportunities related to data, analysis and CM, and developing RUL prediction models have been found. Data challenges include data standardization, limited public datasets, and data quality issues. Analysis and CM challenges address new sensorless and non-intrusive techniques, as well as the fusion of data sources. Prognostics model challenges involve uncertainty management, interpretability issues, and the need for online updates. Addressing challenges requires incorporating physical knowledge, utilizing transfer learning, and improving online RUL prediction methods.

WR21 marine gas turbine thermodynamic simulator for ship propulsion studies

An original modular mathematical model, based on physical laws, for steady state and dynamics performance simulation of the WR21 gas turbine is presented in the paper. The model, developed in Matlab-Simulink language, is organized in modular form. Each gas turbine component (i.e. compressor, turbine, combustor, heat exchanger) is modelled in a specific simulator block, that simulates the performance of its pertinent component by means of steady state performance maps and/or correlations, time dependent momentum, energy and mass equations, nonlinear algebraic equations. The great application flexibility of the component simulator modules, allows to modelling any gas turbine layout (i.e. axial or radial flow compressor and turbine, rotating shafts number, heat exchangers). The WR21gas turbine, developed mainly for marine propulsion application, is a three shafts gas turbine with thermal regeneration. It is characterized by a high thermodynamic efficiency, which remains high up to 30% of the design power. The WR21 simulator results are validated by comparison with manufacturer or literature data. The WR21 gas turbine main performances are compared to those of the LM 2500 gas turbine, obtained by a previously author paper. The LM 2500 is the gas turbine currently most used in naval propulsion plants. In the article, a comparison between the steady state working data of the two gas turbines is reported in tabular and graphical form and commented.

Predicting mechanical failure of polycrystalline dual-phase nickel-based alloys by numerical homogenization using a phase field damage model

Brazing of nickel-based alloys plays a major role in the assembly of turbine components, e.g., abradable sealing systems. In a brazed joint of nickel-based alloys a composition of brittle and ductile phases can be formed if the brazing conditions are not ideal. This heterogeneous microstructure is a crucial challenge for predicting the damage behavior of a brazed joint. The initiation and evolution of microdamage inside of the brittle phase of a virtual dual-phase microstructure representing the material in a brazed joint is studied by means of numerical simulations. A phase field approach for brittle damage is employed on the microscale. The simulation approach is capable of depicting phenomena of microcracking like kinking and branching due to heterogeneous stress and strain fields on the microscale. No information regarding the initiation sites and pathways of microcracks is needed a priori. The reliability of calculating the effective critical energy quantities as a microstructure-based criterion for macroscopic damage is assessed. The effective critical strain energy density and the effective critical energy release rate are evaluated for single-phase microstructures, and the approach is transferred to dual-phase microstructures. The local critical strain energy density turns out to be better suited as a model input parameter on the microscale as well as for a microstructure-based prediction of macroscopic damage compared to a model employing the energy release rate. Regarding the uncertainty of the model prediction, using the effective critical energy release rate leads to a standard deviation which is five times larger than the standard deviation in the predicted effective critical strain energy density.

Analysis of Sediment Erosion in Pelton Nozzles and Needles Affected by Particle Size

The sediment erosion of Pelton turbine components is a major challenge in the operation and development of high-head water resources, especially in mountainous areas with high sediment yield. In this paper, a study using numerical simulation was conducted with different sediment particle sizes in the fine sand range. And the erosion mechanism of the Pelton turbine injector was analyzed. The Eulerian Lagrange method was adopted to simulate the gas–liquid–solid flow. The Mansouri’s model was applied to estimate the injector erosion. The predicted erosion results were in accord with field erosion photographs. In particular, the asymmetrical erosion distribution on the needle surface was physically reproduced. With the sediment particle size increasing from 0.05 mm, the needle erosion rate decreased, while the nozzle casing erosion rate increased dramatically. In order to clarify this tendency, the characteristics of the three-phase flow were analyzed. Interestingly, the results show that with the rise in particle size, the separation of particles and water streamlines became more serious in the contraction section of the nozzle mouth. Consequently, it caused the enhancement of erosion of the nozzle surfaces and weakened the erosion of the needle surfaces. Significant engineering insights may be provided for weakening Pelton injector erosion with needle guides in the current study.

Study on the Particle Deposition Characteristics of Transpiration Cooling Structures with Sintered Wire Mesh.

Transpiration cooling based on a porous structure has an ultra-high cooling efficiency, which is expected to be one solution to improve the cooling technology of aero-engine turbine blades. However, particulate impurities in the gas flow channel continue to deposit on the surface of turbine components, blocking cooling holes, which causes great harm to the cooling of turbine blades. In this study, a sintered metal mesh plate was selected as the transpiration cooling structure, and the evolution of particle deposition quality and deposition thickness on the transpiration cooling surface with time, as well as spatial distributions of particle deposition thickness at different times, were explored through experimental and simulation methods. The results showed that, with the increase in spray time, deposition quality and maximum deposition thickness of the transpiration cooling surface gradually increased. Along the main-stream direction, when spray time was short, deposition thickness was higher in a narrow range upstream of the experimental specimen. With the increase in spray time, deposition thickness gradually decreased along the direction of the transpiration cooling mainstream. In the spanwise direction, when spray time was very short, deposition thickness in the spanwise direction was more consistent and, after spray time increased further, the deposition thickness distribution began to tend to a "∩"-type distribution. It can be seen from the simulation results of the metal wire mesh particle deposition that particles were easily deposited on the windward side of the metal wire in the main-stream direction, which agreed with the experimental distribution characteristics of the metal wire mesh deposition. Moreover, the increase in blowing ratio reduced the deposition of particles on the wall of the metal wire mesh.

Effect of constraint removal on single-crystal blade dimensions during investment casting

Single-crystal turbine blades, manufactured via investment casting, are key components of high-performance aero engines and gas turbines. The final dimensions of the blades depend on the cumulative effect of heat shrinkage at different stages, including the superalloy, mold shell, and ceramic core, etc. To clarify the effect of these stages on the blade dimensions, we first use the heat transfer coefficient calculated from inverse heat-conduction analysis to simulate the directional solidification. By extracting and mapping the final physical field from the previous stage to the next stage, four successive constraint removal processes are performed at different time points. Subsequently, we establish a time-varying model for the entire process of directional solidification and constraint removal. The errors of the extraction/mapping are consistently less than 0.002 mm in all three directions, thus confirming the feasibility of data transfer. Secondly, we analyze the dimensional deviation patterns of the casting after sequentially removing the mold shell, process rib, grain selector, and ceramic core constraints, with emphasis on the dimensional accuracy of the airfoil while disregarding the dovetail generated from machining. The final average deviations of Section 1, Section 2, and Section 3 of the airfoil are 0.1896 mm, 0.1723 mm, and 0.1909 mm, and the maximum deviations occur consistently at the trailing edge near the tip. Finally, three section deviations are validated via measurements performed using a vacuum furnace and industrial computed tomography. The results show that the proposed method improves the dimensional accuracy of hollow turbine blades.

Performance analysis of an idealized Darrieus–Savonius combined vertical axis wind turbine

AbstractTo investigate the effect of force distributions of each turbine component on the power performance of the Darrieus–Savonius combined vertical axis wind turbine (hybrid VAWT), the hybrid VAWT is modeled as idealized turbine under various force distributions. The goal of idealization is to simplify the intricate interactions between the Savonius and Darrieus components. The simulation actuator surfaces with uniform force distributions lead to a cost‐effective way to identify the optimal force distribution of each turbine component. The numerical model was validated against momentum theory. The results demonstrated that the numerical and theoretical results yield similar predictions in the low‐thrust cases but show differences in the high‐thrust cases. The maximum power coefficient of an idealized hybrid VAWT with given thrust coefficient is lower than that of a single actuator. This is a consequence of the nonoptimal loading on the actuator. The results indicate that an idealized hybrid VAWT does not show a significant power increase compared with an optimal single Darrieus rotor. Therefore, the presence of a Savonius rotor inside a Darrieus rotor leads to a lower power output in any circumstance. The hybrid configuration is primarily advantageous for the start‐up performance of the combined rotor, which is not explored in this study.

A similarity method to correct cooling effectiveness from a scaled turbine blade experiment

The cooling effectiveness of a turbine blade is conventionally validated through scaled model experiments which involve the challenge of thermal similarity. To ensure accurate prediction of cooling performance in a real engine, it is necessary to investigate the dimension scaling effect and establish a reliable correction method for the cooling effectiveness obtained from the experiment. This paper presents a novel method derived from a one-dimensional heat transfer model to correct the scaling effects on thermal similarity. A group of experiments with a dummy blade is conducted to verify the feasibility and accuracy of the correction method. Results show the dissimilarity between scaled articles due to geometry dimension, material thermal properties, and alignment of the thermal barrier coating. A down-scaled model has higher cooling effectiveness than its full-length counterpart because of the enhanced internal convective heat transfer and reduced external surface heat transfer. In addition, dissimilarity due to mismatched heat conductivity of blade material and surface convective heat transfer coefficient can be corrected using Biot number replacement. Misalignment of thermal barrier coating can be replaced by a pseudo-blade metal of equivalent heat resistance and wall thickness including the metal and coating layer. Experimental result from a scaled model is corrected to improve the prediction accuracy of its full-length performance. Corrected cooling effectiveness matches its prototype within an uncertainty of 10 %. The present method is applicable to evaluate gas turbine components by means of scaled model experiments.

Low cycle fatigue behavior and life prediction of a directionally solidified alloy

Low cycle fatigue behavior and life prediction of a directionally solidified alloy

Study of erosion-induced leakage flow in guide vane of Francis turbine using erosion-coupled mesh deformation simulations

Erosion of the hydro-turbine components in sediment-laden flows results in a reduction in the efficiency and life of turbine components. The particles in the water affect the operation of both stationary and dynamic components. In the present study, the guide vanes and facing plates of a Francis turbine are numerically investigated regarding their susceptibility towards erosion using an erosion-induced geometry deformation approach. The effects of non-uniform changes in the clearance gap between the guide vanes and facing plates on the erosion and leakage flow are explored. A segment of the actual distributor is analysed for the design and part load operation of the turbine. The simulations were performed for a period equivalent to 2400 h and the comparisons are drawn with the geometry at the end of 240 and 1200 h, respectively. The results show an increase in thickness loss with operation time, however, the position of maximum thickness loss shifted towards the tailing edge for guide vanes and towards center for the face plate. The increase in clearance gap leads to higher cross-flow across the clearance gap and thus, the crossflow and the erosion rate feed off each other. The erosion of the guide vane and face plate was symmetric for BEP but asymmetric for PL. Further, the simulations were performed with three different initial clearance gaps ranging from 0.5 mm to 1.5 mm highlighting the changes in erosion rate for different clearances. The present work provides important engineering insights for the design and operation of the Francis turbine guide vanes to control their erosion and leakage flow.

Multi-objective optimization of transpiration cooling for high pressure turbine vane

Transpiration cooling is a promising cooling measure for high-temperature components in gas turbines. Although the flow and heat transfer characteristics of transpiration cooling have been investigated in several experimental and numerical researches, the design optimization of transpiration cooling is less studied until now. In this paper, a multi-objective optimization framework based on the ANN surrogate model and the NSGA-Ⅱ is built to optimize the transpiration cooling of C3X turbine vane. The transpiration cooled C3X vane consists of multiple porous zones whose porosities could be different and are treated as the design parameters. The optimization objectives are minimum area-averaged vane surface temperature and minimum aerodynamic loss. A total of 960 CFD simulations are conducted to prepare the sample dataset. After the validation of the surrogate-based optimization framework, the Pareto front is obtained as an output. Three representative Pareto-optimal points are chosen from the Pareto front, considering the best cooling performance, the lowest aerodynamic loss, and a balance between them. The porosity distributions and coolant allocations, the cooling performances, and the aerodynamic losses for the three Pareto points are discussed, and the involved flow and heat transfer characteristics are analyzed. The results reveal that to reach a balance between the cooling efficiency and the aerodynamic loss for an optimal transpiration cooled C3X vane, coolant should be appropriately distributed to the leading edge, as well as zones immediately downstream of the leading edge.

Optimal age replacement time for coherent systems under Geometric Point Process

In spatial context, the understanding of event dynamics and point distributions is a fundamental requirement. This study addresses a critical need by introducing the Geometric Point Process (GPP) and explaining its potential impact on optimizing the determination of optimal replacement ages for coherent systems. Our paper has three overarching aims: firstly, to bridge the gap by presenting the novel GPP model that effectively captures the geometric distribution of points, filling a void in point process theory. Secondly, we calculate the optimal replacement age for the coherent system based on the principles of GPP. This involves employing the Infinitesimal-Look Ahead stopping rule within the framework of Smooth Semi-Martingale (SSM) decomposition. A comprehensive comparative analysis is conducted to assess the distinct features and advantages of the GPP in relation to other point processes, offering valuable insights into its applicability and performance. Lastly, we extend the practical application of GPP by examining optimal replacement strategies for components in coherent systems, offering a bridge between theory and real-world practices, especially in the context of aircraft and wind turbine component deterioration. Additionally, we conduct reliability and sensitivity analyses, providing management suggestions, and present Monte Carlo simulations as an alternative method for validating optimal solutions. This research invites the industrial community to utilize the proposed replacement policy, paving the way for exploring geometric characteristics in stochastic processes. The timeliness and significance of this work are underscored by the relatively unexplored field of smooth semi-martingales, with the most recent publication dating back to 2015 by Bueno.

Data-driven framework for prediction and optimization of gas turbine blade film cooling

Film cooling is a crucial technique for protecting critical components of gas turbines from excessive temperatures. Multiparameter film cooling optimization is still relatively time-consuming owing to the substantial computational demands of computational fluid dynamics (CFD) methods. To reduce the computational cost, the present study develops a data-driven framework for predicting and optimizing the film cooling effectiveness of high-pressure turbines based on deep learning. Multiple rows of cooling holes located on the pressure surface of the turbine blade are optimized, with the coolant hole diameter, the incline angle, and the compound angle as design parameters. A conditional generative adversarial network model combining a gated recurrent unit and a convolutional neural network is designed to establish the complex nonlinear regression between the design parameters and the film cooling effectiveness. The surrogate model is trained and tested using independent CFD results. A sparrow search algorithm and the well-trained surrogate model are combined to acquire the optimal film cooling parameters. The proposed framework is found to improve multi-row film cooling effectiveness by 21.2% at an acceptable computational cost.

Impact of Fuel Quality on Gas Turbine Components

Most industrial gas turbines operate with various gaseous and liquid fuels. For effective and sustainable performance, it is essential to determine the best fuel suitable for the operation of the gas power system. The heating value, Joule Thompson coefficient, dew point, Wobbe index, and other physical characteristics of the suggested fuel must be ascertained. This research explains a means of providing consistent treatment to determine the listed physical properties. It also reviews the impact that specific fuel characteristics have on the combustion process, as well as the overall operation of a gas turbine and its components.