Optimization Of Turbine Blades Research Articles

Multi-Objective Optimization and Intelligent Algorithm Study for 1.5mw Doubly Fed Wind Turbine Blades

Background: The demand for renewable energy has catapulted wind power to the forefront of sustainable energy options. Designing wind turbine blades is a multi-objective optimization problem with conflicting objectives. Optimization methods such as the weighted sum or the goal programming method fail in this context of conflicting objectives: minimum weight, cost, and strength. The study discusses the integration of artificial intelligence algorithms with conventional design approaches for optimal blades of a 1.5MW DFIG wind turbine. Advanced computational methods are used to integrate aerodynamic efficiency, structural durability, and economic feasibility. Methods: This paper uses a broad meta-analytical approach with the integration of finite element analysis (FEA)and artificial intelligence optimization algorithms. This paper will consider three critical optimization techniques: genetic algorithms (GA), particle swarm optimization (PSO), and gray wolf optimization (GWO). The blade performance will be considered for various operating conditions, including aerodynamic efficiency, structural integrity, and cost. This analysis will be performed based on IEC 61400 standards and new frontiers of innovative design optimization techniques. Results: Blade design optimization showed considerable improvement in using AI-driven approaches. For the GWO algorithm, better convergence is around 20% faster than the one obtained with traditional methods. This optimized design reduces weight by 8% and improves structural durability by an increase of 25% in fatigue life. For combined blade design, the maximum value of the power coefficient is 0.27, thus showing considerable improvement compared to the conventional designs. Besides, innovative material selection and design optimization could reduce the cost index by 17.6%. Conclusion: These results highlight that incorporating AI algorithms with traditional methods has significantly enhanced wind turbine blade optimization. This methodology was developed in such a way that it achieved a balanced solution for multi-objective designs, including computational efficiency. This study stresses industrial feasibility by using advanced optimization techniques for real applications, thus demonstrating their impact on the future of wind energy technological development.

Parametric modeling and optimal design of wind turbine blade structure

The lightweight design of the wind turbine blades plays an essential role in the stable operation of wind turbines, and the structural layout and layup design have a profound influence on the total weight of the blade. However, the blade structure partition is complex, and the parametric modeling and the interaction difficulties lead to increased computational cost, which makes it impossible to quantify the complete parametric modeling of the main structural components of the blade. To solve this problem, this paper proposes an optimization method based on structural parametric modeling for the wind turbine blade optimization framework. The method firstly adopts a structural parameterization method based on a two-dimensional matrix representation and layup parameter model to achieve the complete parametric modeling of the structural components of a particular 1.5 MW blade. Then, the strength and deformation calculation of the parametric model of the blade was carried out using the thin-walled structural mechanics direct stress theory. Finally, the thin-walled structural mechanics direct stress theory is combined with a genetic algorithm to carry out the structural optimization of the blade with the minimum total mass of the blade as the optimization objective, the blade tip deflection, and the maximum stress failure criterion in the fiber direction as the constraint conditions. The optimization results show that under the premise of satisfying the strength and stiffness of the blade, the total mass of the blade after optimization using the optimization method decreases by about 6%, which verifies the effectiveness of the optimization method in this paper.

Gradient-based structural optimization of a wind turbine blade root section including high-cycle fatigue constraints

Under the modern design paradigm of wind turbines, the continuous drive towards lower costs through longer blades is raising serious challenges in design, and structural optimization is key to solving the problems effectively. This article focuses on the challenging structural optimization of wind turbine blade root sections, which is scarcely treated in the literature owing to the complex and computationally expensive critical criteria, i.e. buckling, static failure and fatigue damage. The root is parametrized with 1894 thickness design variables and, to solve such an optimization problem efficiently, an adjoint gradient-based framework is proposed, which includes all the criteria mentioned for twelve load cases. Fatigue is notoriously difficult owing to its highly nonlinear behaviour, and its inclusion in the adjoint optimization framework for wind turbine blade optimization is a novelty. The resulting optimization behaviour, laminate layup and structural response are all illustrated, showing many active constraints at convergence, and a significant reduction in mass.

Optimizing of horizontal axis wind turbine blades using MATLAB-based blade element momentum theory with validation in QBlade and CFD software

This study presents the optimization of a small horizontal axis wind turbine blade at a low wind speed of 6 m/s. A MATLAB code employing Blade Element Momentum Theory (BEMT) was developed to optimize the chord length and twist angle. The optimized design was further analyzed and validated using QBlade software and ANSYS® 2022R1. The study considered two airfoils SG6041 and NACA4711 under Reynolds numbers of 1,000,000 and 100,000, respectively. The blades SG6041 and NACA4711 were divided into 10 and 20 segments for detailed analysis, resulting in power coefficients of 0.455 for SG6041 and 0.521 for NACA4711. The consistent results across MATLAB, QBlade, and ANSYS confirm the accuracy of the BEMT-based optimization process. The rotor achieved optimal performance at Tip speed ratios of 8.5 for SG6041 and 7.5 for NACA4711. Results demonstrated a high correlation between the power coefficients for QBlade and CFD simulations, with only a 1% variation for NACA4711 and 1.78% for SG6041. This study highlights a systematic approach for small wind turbine blade optimization, particularly at low wind speeds. Hence, it can be deemed that this approach is among the most crucial techniques for constructing and optimizing the blades and rotors of micro-wind turbines.

CFD simulation and aerodynamic optimization of two-stage axial high-pressure turbine blades

CFD simulation and aerodynamic optimization of two-stage axial high-pressure turbine blades

Optimization of Low-Pressure Turbine Blade by Means of Fine Inspection of Loss Production Mechanisms

Abstract In this study, Proper Orthogonal Decomposition (POD) was applied to Large Eddy Simulations (LES) of two high-loaded low-pressure turbine cascades under unsteady inflow conditions to investigate entropy production within different parts of the blade passage. The terms for Turbulent Kinetic Energy (TKE) production, diffusion, and dissipation from the stagnation pressure transport equation were integrated across the computational domain. The POD-based method enables the decomposition of contributions from various coherent flow dynamics to TKE production and dissipation, such as the migration, bowing, tilting, and reorientation of incoming wake filaments, as well as the breakdown of streaky structures in the blade boundary layer and the formation of Von Karman vortices in the blade wake. This approach helps designers identify the dominant POD modes (i.e., turbulent flow structures) responsible for loss generation, understand their dynamics and locate where they primarily act. Using this optimization strategy, a new low-pressure turbine profile was designed. LES calculations on the optimized geometry showed that it is possible to design a higher-loaded profile with a lower global loss coefficient. The new loading distribution, characterized by stronger acceleration in the early part of the blade passage, results in reduced upstream wake migration losses and lower TKE production in the trailing edge wake zone due to early suction side boundary layer transition.

A novel fatigue life model for MCrAlY coated superalloys considering interfacial microstructure evolution

MCrAlY coatings, extensively utilized for safeguarding turbine blades against oxidation and erosion, encounter impediments due to inter-diffusion between the coating and substrate, thereby exacerbating fatigue life degradation at elevated temperatures. In this study, we introduce a novel approach involving the modification of critical depth in interfacial strain energy density to elucidate the impact of interfacial microstructure evolution on mechanical properties. Building upon this concept, we propose a fatigue life prediction model, which incorporates the dynamic evolution of interfacial structure and mechanical characteristics. Validation against empirical data underscores the commendable precision of the model. Our inquiry not only advances the comprehension of mechanical-chemical coupled behaviors but also yields significant insights for the optimization and maintenance of turbine blades.

The effect of secondary dendrite orientation on thickness debit effect of nickel-based single-crystal superalloy with tubular samples

Secondary dendrite orientation and wall thickness considerably affect the stress rupture life of thin-walled samples. However, the effect of the secondary dendrite orientation on the thickness debit effect of nickel-based single-crystal superalloys has not been thoroughly investigated until now. Owing to geometrical constraints, typical sheet samples cannot reveal the mechanism responsible for the thickness debit effect in turbine blades. This study examined the effect of secondary dendrite orientation on the thickness debit effect of nickel-based single-crystal superalloys at 1100 °C/137 MPa in tubular samples. As the wall thickness decreased from 1.5 mm to 0.3 mm, the stress rupture life decreased from approximately 170 h to 64 h, demonstrating a noticeable thickness debit effect. Among the different secondary dendrite orientation areas, the variation in plastic deformation difference increased from 7 % (1.5 mm) to 45 % (0.5 mm) and subsequently decreased to 4 % (0.3 mm). In thinner samples, the thickness contraction and microstructure evolution were more pronounced in the [100] areas than that in the [110] and [210] areas. The theoretical calculation quantitatively indicated that for the effective stress increased, the contribution of plastic deformation (45 %) was slightly lower than that of oxidation (55 %) in 0.3 mm samples; nevertheless, plastic deformation played a prominent role in 0.5, 0.8, 1, and 1.5 mm samples and increased from 61 % (0.5 mm samples) to 85 % (1.5 mm samples). In thinner samples, the larger plastic deformation in the secondary dendrite orientation of the [100] areas and oxidation increased the effective stress, resulting in a shorter rupture life. These findings are conducive to the structural optimization and performance improvement of turbine blades.

Multidisciplinary Optimization of Axial Turbine Blade Based on CFD Modelling and FEA Analysis

Multidisciplinary Optimization of Axial Turbine Blade Based on CFD Modelling and FEA Analysis

Degradation performance rapid prediction and multi-objective operation optimization of gas turbine blades

The performance degradation of high-temperature blades will affect the safety and efficiency of operation. In response to issues such as ignoring strength characteristics, slow degradation performance prediction, insufficient applicability and accuracy of traditional proxy models, and long operation optimization cycle, a degradation performance prediction model of turbine blades based on the back propagation neural network is established in this research. Furthermore, the efficiency-stress multi-objective operation optimization method based on genetic algorithm is proposed. The results show that the R-square value of the degradation performance neural network prediction model is greater than 0.9 for predicting characteristic parameters. The prediction speed is improved by 104 orders of magnitude compared to the traditional computational fluid dynamics method. The influence mechanisms of turbine outlet flow rate, cooling gas inlet total temperature, and cooling gas flow rate on the degradation performance are revealed. Under fouling extreme conditions and corrosion/wear extreme conditions, the operating parameters of turbine blades are optimized. The maximum values of the efficiency improvement, power increase and maximum von Mises stress reduction are 0.74 %, 2.13 MW and 43 MPa, respectively. This study can provide an effective and novel method for real-time degradation performance prediction and rapid operating condition optimization of turbine blades.

Advancing Wind Energy Efficiency: A Systematic Review of Aerodynamic Optimization in Wind Turbine Blade Design

Amid rising global demand for sustainable energy, wind energy emerges as a crucial renewable resource, with the aerodynamic optimization of wind turbine blades playing a key role in enhancing energy efficiency. This systematic review scrutinizes recent advancements in blade aerodynamics, focusing on the integration of cutting-edge aerodynamic profiles, variable pitch and twist technologies, and innovative materials. It extensively explores the impact of Computational Fluid Dynamics (CFD) and Artificial Intelligence (AI) on blade design enhancements, illustrating their significant contributions to aerodynamic efficiency improvements. By reviewing research from the last decade, this paper provides a comprehensive overview of current trends, addresses ongoing challenges, and suggests potential future developments in wind turbine blade optimization. Aimed at researchers, engineers, and policymakers, this review serves as a crucial resource, guiding further innovations and aligning with global renewable energy objectives. Ultimately, this work seeks to facilitate technological advancements that enhance the efficiency and viability of wind energy solutions.

Multidisciplinary Design Optimization of Cooling Turbine Blade: An Integrated Approach with R/ICSM

This paper presents an efficient integrated multidisciplinary design optimization method for shaping a high-pressure cooling turbine blade in aero engines. This approach utilizes a novel regression/interpolation combination surrogate model (R/ICSM), facilitating comprehensive design optimization through collaborative coupling feature parameterization modeling and numerical simulation analysis across various disciplines. The optimized blade adjusts the load distribution on its surface, effectively eliminating flow separation at the tip and trailing edge. Notably, the optimized blade achieves a 0.69% increase in isentropic efficiency while satisfying aerodynamic, strength, and structural constraints. This highlights the effectiveness and progressiveness of the multidisciplinary design optimization method for a cooling turbine blade based on the R/ICSM in enhancing overall performance. It offers a novel and feasible approach for turbine blade design optimization and provides valuable insights for future research and applications.

Tidal turbine blade design optimization based on coupled deep learning and blade element momentum theory

The practical design optimization of blade structures is crucial for enhancing the power capture capability of tidal turbines. However, the significant computational costs required for directly optimizing turbine blades through numerical simulations limit the practical application of blade structure optimization. This paper proposes a framework for tidal turbine blade design optimization based on deep learning (DL) and blade element momentum (BEM). This framework employs control points to parameterize the three-dimensional geometric shape of the blades, uses convolutional neural networks to predict the hydrodynamic performance of each hydrofoil section, and couples BEM to forecast the performance of tidal turbine blades. The multi-objective non-dominated sorting genetic algorithm II is employed to optimize the geometric parameters of turbine blades to maximize the power coefficient and minimize the thrust coefficient, aiming to obtain the optimal trade-off solution. The results indicate that the prediction of the DL-BEM model agrees well with experimental data, significantly improving optimization efficiency. The optimized tidal turbine blades exhibit excellent power coefficients and reduced thrust coefficients, achieving a more balanced structural solution. The proposed optimization framework based on DL accurately and rapidly predicts the performance of tidal turbines, facilitating the design optimization of high-performance tidal turbine blades.

Knowledge transfer accelerated turbine blade optimization via an sample-weighted variational autoencoder

The aerodynamic shape optimization (ASO) of turbine blades is a typical expensive black-box problem. With the ever increasingly strict design requirements, high-fidelity simulations are demanded, and the related simulation cost increases sharply, which means fewer simulation calls are allowed within a design optimization cycle. Then, the problem as “how to achieve better solution with fewer simulations” becomes rather crucial. Inspired by the concept of transfer learning, we propose to leverage the samples of related completed tasks (also known as source samples) to address the above problem. Specifically, an sample-weighted variational autoencoder (VAE) is proposed, which includes the source samples as part of the training dataset to boost the proportion of high-scoring solution candidates in the learnt design space of target problem, and thus increasing the probability of attaining substantially better solutions. The including of source samples in VAE training also helps to increase the accuracy of reparameterizing the source samples in target design space. Furthermore, a multi-fidelity surrogate is used to guide the optimization search, which extracts information from the source samples to help the algorithm to more quickly arrive at the small neighborhood of the true optimal of the target problem. With the above, a knowledge transfer accelerated aerodynamic shape optimization procedure is proposed for the turbine blade design, labeled as KT-ASO. Through test on a highly-loaded low-pressure turbine blade and compared against the ASO procedures without knowledge transfer, the proposed KT-ASO is shown to reduce at least 50% function calls when achieving similar optimal solutions. In the meantime, under the same sample budget, our proposed KT-ASO is shown to attain much better solutions than the compared ASO procedures.

DLFSI: A deep learning static fluid-structure interaction model for hydrodynamic-structural optimization of composite tidal turbine blade

Horizontal axis tidal turbines (HATT) conversion of ocean tidal waves into electricity represents a promising source of clean and sustainable energy. However, the widespread adoption of these turbines has been hindered by persistent challenges, primarily stemming from the high costs associated with their construction and maintenance; and efficient conversion of tidal energy. Addressing these challenges is paramount for propelling tidal turbine technology and ensuring its economic viability. This study focuses on the efficient conversion of tidal energy into electricity by optimization of composite material turbine blades which is a complex problem that spans multiple physical domains, including hydrodynamics (the study of water flow) and structural mechanics (the study of material behavior under loads). To tackle these multifaceted challenges, we introduce an innovative DLFSI (Deep learning fluid-structure interaction) model which represents a groundbreaking approach to predict and optimize the hydrodynamic and structural performance of tidal turbine blades. DLFSI leverages the power of convolutional neural networks (CNN) to recognize intricate geometric features of turbine blades rapidly and accurately. By seamlessly integrating the blade element momentum (BEM) theory and finite element method (FEM), the DLFSI model facilitates comprehensive predictions of how composite blades will perform in real-world conditions. With this approach, we have achieved substantial improvements in critical performance metrics such as the power coefficient (a measure of energy conversion efficiency) and the maximum equivalent stress (a key indicator of structural integrity). The innovative DLFSI model presented in this study holds the potential for practical application within the realm of tidal turbine design and is poised to catalyze the sustainable progression of renewable energy technologies.

Structural Design Optimization of Tidal Current Turbine Blades Based on Structural Safety Factors

Structural Design Optimization of Tidal Current Turbine Blades Based on Structural Safety Factors

A cooled turbine blade design and optimization method considering the cooling structure influence

This study introduces a multidisciplinary design methodology tailored for enhancing the performance of cooled turbine blades by amalgamating thermal and aerodynamic calculation modules. The approach is unique in terms of its integration of a multi-objective optimization platform, aimed at refining aerodynamic performance and gauging the heat transfer capabilities during the preliminary aerodynamic design phase. To accomplish this objective, a one-dimensional pipe-network calculation tool was incorporated into the thermal module to quickly evaluate the heat transfer performance of the blades under different conditions. This tool also provides more realistic film hole inlet boundary conditions essential for three-dimensional aerodynamic calculations. Implementing this platform in optimizing a high-pressure turbine blade revealed a Pareto-optimal front, comprising −η1 and η2 (representing optimization objectives for aerodynamic and heat transfer performance, respectively), showcasing a constrained relationship. Upon scrutinizing three optimization cases against the prototype, optimization case 1 demonstrates the most significant enhancements in aerodynamic performance, showing a 0.2015% improvement in aerodynamic efficiency relative to the prototype. Conversely, optimization case 3 displays a comparatively modest augmentation in aerodynamic performance but excels notably in heat transfer performance, showcasing a 7.61% reduction in the maximum temperature of the blade surface compared to the prototype. Through adept optimization strategies and meticulous variable selection, we maintained a relatively stable mainstream mass flow across the optimization cases (less than 0.05% variation). These findings underscore the efficacy of our multidisciplinary design approach for cooled turbine blades, promising efficiency improvements in current design practices and potential reductions in project duration.

Enhancing the Efficiency of Horizontal Axis Wind Turbines Through Optimization of Blade Parameters

In this research, we delve into the promising potential of horizontal axis wind turbines to effectively meet the electricity needs of developing countries. By addressing the challenges posed by low Reynolds number airflow characteristics, we focus on specific airfoils—E471, S2055, and RG15—tailored for horizontal axis wind turbine blade optimization. Utilizing QBLADE software, we evaluate the lift coefficient, stall angle of attack, and lift‐to‐drag ratio coefficient efficiency of these airfoils across various thickness‐to‐camber ratios. The aerodynamic efficiency of the altered airfoils is assessed in terms of lift coefficient, drag coefficient, lift‐to‐drag ratio coefficient, and stall angle of attack at Reynolds number ranging from 50,000 to 500,000. The findings reveal that the optimized thickness‐to‐camber ratio results in peak lift‐to‐drag ratio coefficient values surpassing the reference airfoils across the Re range. These lift‐to‐drag ratio coefficient enhancements vary among airfoils and Re values. Furthermore, modifications to the E471, S2055, and RG15 airfoils increase the peak lift coefficient and stall angle of attack values across all Re ranges examined. The validation of results is achieved through comparison with experimental testing, solidifying the reliability of QBLADE software in predicting aerodynamic performance.

Optimization of turbine blade trailing edge cooling using self-organized geometries and multi-objective approaches

Gas turbines are widely utilized as essential power machinery across diverse energy-related industries. Efficient and durable cooling of gas turbine blades is crucial for the performance and life of gas turbine. However, achieving effective cooling for turbine blade trailing edges remained challenging due to the narrow, thin and structurally limited spaces. To address the aforementioned challenge, this study proposed a generative design method by employing self-organization equations to parameterize the topology of wedge-shaped channels within the trailing edge region as a substitute for the conventional pin-fin structures. Additionally, a multi-objective Bayesian approach was utilized to optimize the topological parameters, with comprehensive emphases on improving five design objectives. A comparative analysis of the performance was conducted between self-organized structures and pin-fin structures. Notably, the selected self-organized structure achieved an impressive 43 % increase in the total Nusselt number and a substantial 37 % reduction in local Nusselt number variance under the same pressure loss. The results demonstrated that self-organized structures exhibited superior heat transfer performance with lower pressure drop and improved uniformity. The performance improvement could be attributed to the strategic generation of solids by the proposed generative design algorithm, distributing the flow more reasonable.

A Design Optimization of Organic Rankine Cycle Turbine Blades with Radial Basis Neural Network

In the present study, a 100 kW organic Rankine cycle is suggested to recover heat energy from commercial ships. A radial-type turbine is employed with R1233zd(E) and back-to-back layout. To improve the performance of an organic Rankine power system, the efficiency of the turbine is significant. With the conventional approach, the optimization of a turbine requires a considerable amount of time and involves substantial costs. By combining design of experiments, an artificial neural network, and Latin hypercube sampling, it becomes possible to reduce costs and achieve rapid optimization. A radial basis neural network with machine learning technique, known for its advantages of being fast and easily applicable, has been implemented. Using such an approach, an increase in efficiency greater than 1% was achieved with minimal design changes at the first and second turbines.