Turbine Design Research Articles

An Open Test Case for High-Speed Low-Pressure Turbines: The SPLEEN C1 Cascade

Aviation accounts for a significant share of global CO2 emissions, necessitating efficient propulsion technologies to achieve net-zero emissions by 2050. Geared turbofan architectures offer a promising solution by enabling higher bypass ratios and improved fuel efficiency. However, geared turbofans introduce significant aerodynamic and structural challenges, particularly in the low-pressure turbine. Current understanding of high-speed low-pressure turbine behavior under engine-representative conditions is limited, especially regarding unsteady wake interactions, secondary flows, and compressibility effects. To address these gaps, this work presents a novel test case of high-speed low-pressure turbines, the SPLEEN C1. The test case and experimental methodology are depicted. The study includes the commissioning and characterization of a transonic low-density linear cascade capable of testing quasi-3D flows. The rig’s operational stability, periodicity, and inlet flow characterization are assessed in terms of loss and turbulence quantities to ensure an accurate representation of engine conditions. These findings provide a validated experimental platform for studying complex flow interactions in high-speed low-pressure turbines, supporting future turbine design and efficiency advancements. This article is a revised and expanded version of a paper entitled “An Experimental Test Case for Transonic Low-Pressure Turbines-Part I: Rig Design, Instrumentation and Experimental Methodology” originally published in the proceedings of the ASME Turbo Expo 2022 held in Rotterdam on 13–17 June 2022.

Investigation on heat transfer mechanism simulation and structure optimization design of hydraulic turbine bearing semi-ring cooler

Investigation on heat transfer mechanism simulation and structure optimization design of hydraulic turbine bearing semi-ring cooler

Deep mining of megawatt large wind turbine actual operating data: exploration of Bayesian parameter fusion modeling

Abstract Real-time operation data analysis and condition monitoring of large wind turbines are crucial for efficient and safe operation of wind farms. In response to this, an accurate prediction model architecture based on the multivariate linear regression algorithm is proposed for a deeper understanding of the actual operation of large wind turbines. Comparing different prediction variables, we have confirmed that the average wind direction and average wind speed play a core role in predicting active power. The display has significantly better approximation than the traditional multiple linear regression (MLR), especially in the peak region of period-like variation. The quasi-periodic peak variation characteristics of the active power near the observed series 2000, 2500 and 3000 are realized, and the peak prediction results of B-MLR are significantly better than those of traditional MLR. Meanwhile, for RMSE and R-squared, B-MLR is significantly better than the traditional MLR algorithm in the active power prediction of wind turbines. However, the data complexity in actual applications poses a challenge to the effectiveness of the Bayesian fusion algorithm. Further optimization of the algorithm is needed to cope with the complex and variable real data environment. This study provides scientific evidence for the efficient operation, precise maintenance, and environmentally friendly design of wind turbines, promoting the continuous progress and development of wind power generation technology.

Triangle Velocity Analysis of the Pelton Turbine Design in Microhydro Power Plant

Triangle Velocity Analysis of the Pelton Turbine Design in Microhydro Power Plant

AI-Based Clustering of Numerical Flow Fields for Accelerating the Optimization of an Axial Turbine

The growing number of Renewable Energy Sources has increased the demand for innovative and high-performing turbine designs. Due to the increase in computing resources over recent years, numerical optimization using Evolutionary Algorithm (EAs) has become established. Nevertheless, EAs require many expensive Computational Fluid Dynamics (CFD) simulations, and more computational resources are needed with an increasing number of design parameters. In this work, an adapted optimization algorithm is introduced. By employing an Artificial Intelligence (AI)-based design assistant, turbines with a similar flow field are clustered into groups and provide a dataset to train AI models. These AI models can predict the flow field’s clustering before a CFD simulation is performed. The turbine’s efficiency and cavitation volume are predicted by analyzing the turbine’s properties inside the predicted clustering group. Turbines with properties below a certain threshold are not CFD-simulated but estimated by the design assistant. By this procedure, currently, more than 30% of the cfd

The Role of Advanced Materials in the Optimization of Wind Energy Systems: A Physics Based Approach

The transition towards renewable energy sources is essential for addressing climate change and reducing greenhouse gas emissions, positioning wind energy as a vital component of sustainable power generation. This paper investigates the pivotal role of advanced materials in optimizing the efficiency and reliability of wind energy systems through a physics-based approach. Recent advancements in material science including carbon fiber reinforced polymers (CFRPs), glass fiber reinforced polymers (GFRPs), and nanomaterials such as graphene and carbon nanotubes are evaluated for their potential to significantly enhance mechanical properties, reduce weight, and improve energy conversion efficiencies of wind turbines. A comprehensive review of the literature reveals the historical context of wind turbine materials and emphasizes the transition from traditional construction methods using steel and wood to innovative composite materials. The study introduces a novel methodology for the integration of advanced materials into turbine design, supported by numerical simulations and experimental validations. The impact of these materials on key operational performance metrics, including power output, structural integrity, and aerodynamic efficiency, is quantified. Moreover, the application of smart materials for real time structural health monitoring is explored, highlighting the potential for predictive maintenance that can prolong the lifespan of wind turbines. The findings suggest that although the initial costs of advanced materials may be higher, their superior performance characteristics offer significant long-term economic benefits and sustainability advantages. The discussion concludes with recommendations for future research directions, including the optimization of hybrid material systems, advancements in manufacturing techniques, and comprehensive long-term durability assessments. This study underscores the critical necessity for continued innovation in materials science to enhance the resilience and environmental efficiency of wind energy systems, thereby contributing positively to the global transition towards sustainable energy solutions.

Insights from the Last Decade in Computational Fluid Dynamics (CFD) Design and Performance Enhancement of Darrieus Wind Turbines

This review provides an analysis of advancements in the design and performance assessment of Darrieus wind turbines over the past decade, with a focus on the contributions of computational fluid dynamics (CFD) to this field. The primary objective is to present insights from studies conducted between 2014 and 2024, emphasizing the enhancement of Darrieus wind turbine performance through various technological innovations. The research methodology employed for this review includes a critical analysis of published articles related to Darrieus turbines. The focus on the period from 2014 to 2024 was considered to highlight recent parametric CFD studies on Darrieus turbines, avoiding overlap with previously published reviews and maintaining originality relative to existing review works in the literature. By synthesizing a collection of articles, the review discusses a wide range of recent investigations utilizing CFD modeling techniques, including both 2D and 3D simulations. These studies predominantly utilize the “Ansys-Fluent” V12.0 and “STAR CCM+” V9.02 solvers to evaluate the aerodynamic performance of Darrieus rotors. Technological advancements focus on modifying the geometry of Darrieus, including alterations to blade profiles, chord length, rotor diameter, number of blades, turbine height, rotor solidity, and the integration of multiple rotors in various configurations. Additionally, the incorporation of flow deflectors, the use of advanced blade shapes, such as V-shaped or twisted blades, and the application of an opening ratio on the blades are explored to enhance rotor efficiency. The review highlights the significant impact of these geometric modifications on key performance metrics, particularly the moment and power coefficients. A dedicated section presents CFD-derived visualizations, including vorticity fields, turbulence contours illustrated through the Q-criterion, velocity vectors, and dynamic pressure contours. These visualizations provide a description of the flow structures around the modified Darrieus rotors. Moreover, the review includes an analysis of the dynamic performance curves of Darrieus, which show improvements resulting from the modifications of the baseline design. This analysis covers the evolution of pressure coefficients, moment coefficients, and the increased power output of Darrieus.

Full-scale wind turbine performance assessment: a customised, sensor-augmented aeroelastic modelling approach

Abstract. Blade erosion of wind turbines causes significant performance degradation, impairs aerodynamic efficiency, and reduces power production. However, traditional monitoring systems based on supervisory control and data acquisition (SCADA) data, which rely on operational data from turbines, lack effectiveness at early detection and quantification of these losses. This research builds on an established turbine performance integral (TPI) method with a sensor-augmented aeroelastic modelling approach to enhance wind turbine performance assessment, focusing on blade erosion. Applying this approach to a distinct multi-megawatt turbine model, the study integrates multibody aeroelastic simulations and real-world operational data analysis. The study identified readily available sensors that were sensitive to blade surface roughness changes caused by erosion. Operational data analysis of offshore wind turbines validated the initial sensor selection and approach. Refined simulations using further virtual sensors quantified the effect size of these sensors' output under different turbulence levels and blade states, employing Cohen's d – a dimensionless metric measuring the standardised difference between two means. For the turbine investigated, findings indicate that sensors such as blade tip torsion, blade root flap moment, shaft moment, and tower moments, especially under lower turbulence intensities, are particularly sensitive to erosion. This confirms the need for turbine-specific, controller-informed sensor selection and emphasises the limitations of generic solutions. This research provides an approach for bridging simulation insights with operational data for turbine-specific performance assessment, contributing to the development of condition monitoring systems (CMSs), resilient turbine designs, and maintenance strategies tailored to specific operating conditions.

Challenges in detecting wind turbine power loss: the effects of blade erosion, turbulence, and time averaging

Abstract. Establishing a clear correlation between blade leading-edge erosion (LEE) and the performance of operational wind turbines is challenging due to the complex interaction of various factors. This study aims to improve the understanding and analysis of real wind turbine measurements by employing aeroelastic simulations to investigate the combined effects of LEE, turbulence intensity (TI), and time averaging as a data processing technique and to show how they obscure the effects of erosion. The study does not aim to investigate each contributing factor in detail but seeks to provide insights through selected examples, thereby illustrating how these conditions hinder the detection of blade erosion’s effects on power loss. An aeroelastic model provided by an offshore original-equipment manufacturer (OEM) was used to simulate various scenarios. Turbulence intensity was varied for a range of wind speeds, and the aerofoil characteristics for the blade were modified to simulate different degrees of erosion, represented by varying levels of roughness. For a given site, findings reveal that even mild simulated erosion can reduce the annual energy production (AEP) by 0.82 % at 6 % TI, while more severe erosion leads to a 1.46 % decrease. Furthermore, increasing TI exacerbates these losses, with 15 % TI causing up to 2.14 % AEP reduction for eroded blades, making it increasingly difficult to distinguish between the effects of blade erosion and turbulence intensity on turbine performance. These effects are most pronounced at sites with lower average wind speeds. Moreover, the interaction between TI levels and longer time-averaging periods, which vary with wind speed, can obscure the true magnitude of LEE's impact on short-term power fluctuations. This study suggests that 10 min time-averaging periods can mask performance and that analysing unsteady-rotor data with shorter time periods, such as 1 s periods, is preferable. The work emphasises the importance of considering the blade condition's impact in the context of various influencing factors for accurate AEP assessments, performance monitoring, and improved wind turbine design for operational wind turbines.

Evaluation of Wind Power Plants from the Aspect of Earthquake Design

Türkiye in recent years, consequently, their share in the total generation of energy within the country is also increasing every year. Considering the cost of wind power plants, together with the targeted amount of energy generation, earthquake planning for these systems becomes crucial. Earthquakes are one of the principal risks of wind energy investments, especially when considering the earthquake hazard level in a large portion of the country. Under the present circumstances, the companies producing wind turbines conduct the analysis and planning they deem necessary and forward these to the investors in our country. The extent to which manufacturers consider earthquake parameters and analysis methods in the design of wind turbines for earthquake resistance is not well-defined. The lack of clarity makes it difficult for investors to accurately evaluate the suitability of wind turbine tower and foundation designs for the specific conditions of the country. This study evaluates earthquake design methods by looking into the regulations used in the design of wind turbines around the world. The magnitude of ground motion considered in the earthquake design, the method of analysis used, the load combinations on which the designs are based, and the criteria for the designs have been inspected. Moreover, the design documents for a wind turbine that is being constructed has been examined in detail, with the earthquake design stage being especially scrutinized. Assessments and recommendations have been made regarding the design specifications, analysis methods and criteria used in the analysis and design of the wind turbines currently being built in our country. Subsequently, obtained recommendations have been taken into consideration to propose an earthquake design procedure for the earthquake design of wind turbines.

Sensitivity of Dynamic Stall Models to Dynamic Excitation on Large Flexible Wind Turbine Blades in Edgewise Vibrations

Present studies are specifically aimed at investigating the sensitivity of different dynamic stall models when exposed to various excitation frequencies. The investigations are targeted at blade edgewise vibrations. This is carried out on a modified version of the IEA 15 MW reference wind turbine employing a wind turbine design tool, DNV Bladed. State-of-the-art dynamic stall models for wind turbine applications, such as the ye model, Beddoes–Leishman (BL) model and the newly developed IAG model, are evaluated. The beginning of the research work starts by evaluating different dynamic stall models on rigid blade section forces against known airfoil datasets. Then, the blade flexibility is considered to enable systematic evaluations of the blade flexibility influences in comparison to the rigid blade cases. It is observed that the range of the angle of attack grows depending on the excitation frequency and the adopted dynamic stall model. The critical excitation frequency range and the effects of twist distribution are then identified from the studies, which can be useful as a rough guidance when designing wind turbine blades.

ANALYSIS OF LOAD ON PERMANENT MAGNET SYNCHRONOUS GENERATOR (PMSG) 12S8P WITH SPEED VARIATION

Abstract. This study investigates the impact of speed variation on the performance of a Permanent Magnet Synchronous Generator (PMSG) configured with 12 slots and 8 poles (12S8P) for wind energy conversion systems. PMSG has gained attention due to its high efficiency, simple design, and ability to operate effectively at low speeds, making it particularly suitable for renewable energy applications like wind turbines. Using MagNet simulation software, the performance of the PMSG was analyzed over a speed range of 1000–6000 rpm and load variations of 10–50 ohms. The results showed a maximum magnetic flux of 0.000927 Wb, while the no-load average voltage was 17.78 V at a speed of 1000 rpm. Furthermore, it was observed that voltage and current increased proportionally with speed. Optimal torque performance was achieved at a 30-ohm load at a speed of 5000 rpm, highlighting the importance of load optimization for effective energy conversion. Efficiency analysis revealed that the generator achieved its highest efficiency of 92.96% at a 10-ohm load and a speed of 5000 rpm. This study also emphasizes the influence of material properties, slot-pole design, and load conditions on generator performance. These findings provide critical insights for the design and optimization of wind turbines, particularly in regions with varying wind speeds. Moreover, this research contributes to advancing the utilization of renewable energy technologies by offering data-driven strategies for enhancing system efficiency and reliability..

Effects of Leading-edge Tubercles on the Aerodynamic Performance of Rectangular Blades for low-speed Wind Turbine Applications

The rapid depletion of conventional energy resources like fossil fuels has harmed the environment. Hence, there is an urgent need to seek alternative and sustainable energy sources. Wind energy is considered one of the efficient sources of energy that can be converted to a useful form of electrical energy. Though the field of wind engineering has developed in recent years there is still scope for improvement in the effective utilization of energy. The turbine blades' aerodynamics and the turbulent fluid flow characteristics largely determine the wind turbine's energy efficiency. Hence, in the present studies, we investigated the improvement of small wind turbine blade design by incorporating bioinspired tubercles into blades. One of the issues of small wind turbines is the low-capacity factor in power. The wind in such circumstances under buildings and other adjacent obstructions for small turbines is normally weak, unstable, and turbulent in wind speed and direction. Thus, the design of small turbines needs to be improved to capture low wind speeds and to respond quickly to turbulent wind resource areas. Biomimetics is a science that helps us adapt designs from nature to solve modern problems. The wing-like flipper of the humpback whale (Megaptera novaeangliae) has a morphology with potential for aerodynamic applications. The humpback whale flipper has several sinusoidal rounded bumps, called tubercles which modify the flow over the blade surface, creating vortices between the tubercles. Therefore, we conducted an XFOIL analysis of symmetrical NACA airfoils and we observed that NACA0012 could produce the best lift/drag ratio. The airfoil was then validated using Computational Fluid Dynamics (CFD) analysis in which the results were found comparable to that of the published data. Finally, CFD analysis was conducted for tubercles with a different pitch-to-amplitude ratio (p/A) and the tubercled airfoil with p/A of 6 provided the best result.

The Role of Advanced Materials in the Optimization of Wind Energy Systems: A Physics Based Approach

The transition towards renewable energy sources is essential for addressing climate change and reducing greenhouse gas emissions, positioning wind energy as a vital component of sustainable power generation. This paper investigates the pivotal role of advanced materials in optimizing the efficiency and reliability of wind energy systems through a physics-based approach. Recent advancements in material science including carbon fiber reinforced polymers (CFRPs), glass fiber reinforced polymers (GFRPs), and nanomaterial’s such as graphene and carbon nanotubes are evaluated for their potential to significantly enhance mechanical properties, reduce weight, and improve energy conversion efficiencies of wind turbines. A comprehensive review of the literature reveals the historical context of wind turbine materials and emphasizes the transition from traditional construction methods using steel and wood to innovative composite materials. The study introduces a novel methodology for the integration of advanced materials into turbine design, supported by numerical simulations and experimental validations. The impact of these materials on key operational performance metrics, including power output, structural integrity, and aerodynamic efficiency, is quantified. Moreover, the application of smart materials for real time structural health monitoring is explored, highlighting the potential for predictive maintenance that can prolong the lifespan of wind turbines. The findings suggest that although the initial costs of advanced materials may be higher, their superior performance characteristics offer significant long-term economic benefits and sustainability advantages. The discussion concludes with recommendations for future research directions, including the optimization of hybrid material systems, advancements in manufacturing techniques, and comprehensive long-term durability assessments. This study underscores the critical necessity for continued innovation in materials science to enhance the resilience and environmental efficiency of wind energy systems, thereby contributing positively to the global transition towards sustainable energy solutions.

Reusable Turbine Material GH4586: Stochastic Analysis and Reliability Assessment of Low‐Cycle Fatigue Life Parameters at Multiple Temperatures

ABSTRACTTurbines, crucial in reusable rocket engines, benefit significantly from precise fatigue life forecasting in their force‐thermal cyclic loading conditions. Further, low‐cycle fatigue life predictions can be random due to uncertainties in material properties. This study aims to analyze the probabilistic low‐cycle fatigue life of GH4586 under different temperature. Mechanical and thermal tests were carried out for the temperatures between 87 K and 1173 K. Random distributions of low‐cycle fatigue parameters were obtained using various methods. The cyclic life reliability of a reusable rocket engine turbine rotor blisk was analyzed using stochastic low‐cycle fatigue parameters. Mitchell's method had a higher prediction accuracy when the reliability was greater than 0.8. Sensitivity analysis shows the significant impact of GH4586 fatigue parameters on cyclic life reliability. Sensitivity coefficients of strength coefficient and ductility coefficient are 32.73% and 27.06%, respectively. These findings provide valuable insights that inform the design of turbines and enhance their reliability.

Materials Design and Structural Health Monitoring of Horizontal Axis Offshore Wind Turbines: A State-of-the-Art Review.

In recent decades, Offshore Wind Turbines (OWTs) have become crucial to the clean energy transition, yet they face significant safety challenges due to harsh marine conditions. Key issues include blade damage, material corrosion, and structural degradation, necessitating advanced materials and real-time monitoring systems for enhanced reliability. Carbon fiber has emerged as a preferred material for turbine blades due to its strength-to-weight ratio, although its high cost remains a barrier. Structural Health Monitoring Systems (SHMS) play a vital role in detecting potential faults through real-time data on structural responses and environmental conditions. Effective monitoring approaches include vibration analysis and acoustic emission detection, which facilitate early identification of anomalies. Additionally, robust data transmission technologies are essential for SHMS effectiveness. This paper reviews material design strategies, data acquisition methods, and safety assessment techniques for OWTs, addressing current challenges and future directions in the field.

A Wind Speed Forecasting Method Using Gaussian Process Regression Model Under Data Uncertainty

Abstract Wind speed forecasting plays a pivotal role in power prediction, daily operations, and optimal scheduling of wind farms. However, accurately predicting wind speed remains challenging due to data uncertainties and the inherent randomness of wind resources. This paper introduces a novel wind speed forecasting method by combining Bayesian discrete wavelet packet thresholding (BDWPT) into Gaussian Process Regression (GPR). The BDWPT method is first employed to adaptively remove noise from wind speed data, retaining the main trend characteristics of the time series while removing redundant information. The GPR model is then utilized to capture the remaining randomness and effectively predict future probabilistic trends in wind speed. Comparative studies using real-world wind farm data demonstrate the advantages of the proposed method in both one-step and multistep forecasting scenarios, showcasing its potential to enhance turbine design and power management under uncertain conditions.

Harnessing More Power of Wind with a Novel Wind Turbine Design

Harnessing More Power of Wind with a Novel Wind Turbine Design

OPTIMIZING CROSSFLOW TURBINE BLADES FOR POWER ENHANCEMENT AT PICO HYDRO POWER PLANT (PLTPH) USING ANSYS FLUENT

Renewable energy has become an important solution in dealing with the problem of climate change and resource scarcity. This study aims to analyze the effect of variations in the number of Crossflow turbine blades on output power at the Pico Hydro Power Plant (PLTPH). PLTPH is a water-based power plant with less than 5 kW of power, ideal for remote areas. Simulations were performed using SolidWorks software for turbine geometry design, Computational Fluid Dynamics (CFD), and ANSYS Fluent for fluid flow simulation. The simulation results show that the number of spoons and the speed of the water flow have a significant impact on the power produced. The optimal configuration is found on a 20-blade turbine that produces a maximum power of 4,695 Watts at a flow rate of 6 m/s. These findings can guide the efficient design of Crossflow turbines for PLTPH applications in remote areas.

Radially-azimuthally discretized blade-element momentum theory for skewed coaxial turbines

Radially-azimuthally discretized blade-element momentum theory for skewed coaxial turbines