Aerodynamic Performance Prediction Research Articles

Aerodynamic Performance Prediction for Wide-Incidence Turbines Using Graph Neural Network Models Driven by Small-Scale Experimental Data

Abstract To rapidly and accurately predict turbine rotor blade losses within a wide range of incidences (−50–30 deg), graph neural networks (GNNs) are utilized to predict the aerodynamic parameters of two-dimensional turbine blades based on a small-scale experimental dataset. By comparing the backpropagation neural network (BPnn) model and computational fluid dynamics (CFD) results, it is demonstrated that GNNs with appropriately designed graph structures can accurately and quickly predict high-fidelity aerodynamic parameters based on limited experimental data. Unlike traditional data-driven modeling approaches, two innovative methods for improving blade profiles into graph structures are proposed. Relatively few input features are used to comprehensively and effectively represent the turbine blade profile by applying blade profile features in the GNN. The research findings indicate that due to the graph structure, which divides the turbine blade profile into five nodes based on five key points, coupled with high-fidelity experimental data and the unique weight updating mechanism of the graph attention network (GAT) model, the GAT-5 model exhibits the best performance among the studied models. Additionally, when assessing unknown validation blade profiles, the GAT-5 model maintains an absolute error below 6% at an incidence angle of 30 deg compared to the experimental results.

General framework for unsteady aerodynamic prediction of airfoils based on deep transfer learning

Analyzing the unsteady aerodynamic performance of airfoils under dynamic stall using computational fluid dynamics (CFD) is computationally intensive. Although deep learning models can quickly predict aerodynamic parameters, their generalization capability on a small-scale dataset is often poor. This paper presents a novel deep transfer learning (TL) framework that combines model-based and instance-based transfer methods, termed synergistic instance-model TL. This framework facilitates rapid predictions of unsteady aerodynamic performance for various airfoils and pitch oscillations from the small-scale dataset. The framework integrates the accelerated training speed of model-based methods with the dynamic dataset expansion benefits of instance-based approaches. Initially, a pre-trained Wasserstein-deep convolutional generative adversarial network (W-DCGAN) is developed, combining a convolutional neural network with a generative adversarial network to predict aerodynamic hysteresis loops for the SC1095 airfoil in the source domain. The framework then fine-tunes the pre-trained model and incorporates weighted source domain dataset into the small-scale target domain dataset, producing the transferred model W-DCGAN-TL. This approach significantly reduces prediction inaccuracies compared to model-based and non-TL methods when applied to the small-scale dataset. The framework's flexibility allows the use of pre-trained models and datasets from related aerodynamic problems to address issues with insufficient data. Consequently, it is expected to reduce the dependency on extensive datasets, enhance design efficiency, and minimize resource requirements.

Aerodynamic and aeroacoustic design of electric ducted fans

This paper describes the aerodynamic and aeroacoustic design of an electric ducted fan (EDF). A prototype EDF was designed and built with three different rotor-stator sets to operate at design flow coefficients ϕd=[0.60,0.75,0.90]. Computational Fluid Dynamics (CFD) simulations show that changing the design flow coefficient changes the proportions of endwall and blade profile loss, with the ϕd=0.75 design providing the optimum balance. CFD predictions of aerodynamic performance are compared with experimental measurements and show good agreement. Data collected from full annulus Unsteady Reynolds-Averaged Navier Stokes (URANS) CFD simulations is used to predict tonal noise radiation using a numerical solution to Farassat's Formulation 1A, and compare favourably to acoustic measurements in an anechoic chamber. All three EDF iterations show similar levels of broadband noise radiation, although tonal noise radiation decreases with increasing design flow coefficient and results in more favourable Sound Quality Metrics performance. Design flow coefficient is shown to have a first-order impact on both the aerodynamic and aeroacoustic behaviour of the EDF, and it is proposed that an optimal design, encompassing both aerodynamic performance and reduced acoustic impact, can be achieved at design flow coefficients between ϕd=0.75 and ϕd=0.90.

Applying Machine Learning Techniques: Uncertainty Quantification in Nonlinear Dynamics Characters Predictions via Gated Recurrent Unit-Based Reduced-Order Models

The development of reduced-order models has been a pivotal advancement in the computational analysis of fluid dynamics, substantially simplifying the complexity and boosting the efficiency of simulations. The accuracy and practicality of these models largely depend on the reduction techniques applied and the inherent characteristics of the fluid dynamics systems they represent. In this paper, we introduce an innovative machine-learning framework for assessing model uncertainty in computationally intensive reduced-order models. By combining subspace construction methods with advanced Bayesian inference techniques, our approach effectively captures the posterior distribution of model parameters, thereby providing an accurate representation of uncertainty in aerodynamic performance predictions. We employ the NACA0012 airfoil as a case study to validate our method’s ability to enhance the efficiency of reduced-order models and precisely measure the uncertainty inherent in predictions made by recurrent neural networks. It is important to note that our approach is influenced by specific constraints and variables that significantly impact the mean and variability of the predicted final lift coefficient distribution. Our findings indicate that setting the goodness of fit (R2) threshold above 0.985 markedly improves the correlation between Computational Fluid Dynamics (CFD) outcomes and model predictions, increasing from 72.2% to 97.9% as the interval amplification factor adjusts from 1.5 to 3. However, this adjustment causes a considerable expansion of the confidence interval, from 0.0737 to 0.1282, an increase of over 70%. Despite these challenges, our machine learning-based methodology provides essential insights into the further development of reduced-order modeling and uncertainty quantification in fluid dynamics. This highlights the need for ongoing research into the model parameters, especially in applications concerning aircraft control systems, to meet design specifications and ensure system reliability.

A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models

With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of artificial intelligence and airfoil aerodynamic optimization. In this paper, many critical aerodynamic optimization steps where data-driven advanced models are employed are reviewed. These steps include geometric parameterization, aerodynamic solving and performance evaluation, and model optimization. In this way, the improvements in the airfoil aerodynamic optimization area led by data-driven advanced models are introduced. These improvements involve more accurate global description of airfoil, faster prediction of aerodynamic performance, and more intelligent optimization modeling. Finally, the challenges and prospect of applying data-driven advanced models to aerodynamic optimization are discussed.

Optimization of a high through-flow design turbine using response surface method

Enhancing the through-flow capability of the turbine facilitates the potential to decrease the external dimension of the engine, leading to a reduction in weight and an increase in the thrust-to-weight ratio. The optimization of blade profile in terms of aerodynamic design is an essential strategy to improve the overall performance of the high through-flow design turbine. This study aims to develop an automated optimization technique, utilizing the response surface method (RSM), to enable reliable prediction and rapid optimization of aerodynamic performance. A parametric modeling method is devised to generate blade profiles with continuous curvature throughout the automated optimization process. The Box–Behnken experimental design, in combination with Reynolds-averaged Navier–Stokes numerical calculation, is employed to construct a second-order polynomial RSM approximation model. The optimization process comprises two levels: improving the through-flow capability and reducing blade profile loss. The optimized profile, Opt1, achieves a substantial 13.19% improvement in through-flow capability at the cost of a 12.27% increase in blade profile loss. Thus, further optimization is performed to minimize blade profile loss based on the Opt1 scheme. Geometric constraints are applied to the most influential parameters affecting through-flow capability to mitigate their impacts. Compared to the Opt1, the final optimized profile, Opt2, achieves a substantial 31.83% decrease in blade profile loss with a negligible sacrifice of 0.23% in through-flow capability.

Prediction and comparison of aerodynamic performance and flow control for smooth and wavy tube banks in crossflow at subcritical Reynolds numbers using a 3D RANS approach

This paper investigates the effects of introducing wavy cylinders of appropriate wavelength into tube bundles for the passive control of flow-induced vibration. The Reynolds-averaged Navier–Stokes approach RANS was used to study the turbulent flow characteristics within staggered tube bundles having transverse and longitudinal pitch-to-diameter ratios of 3.8 and 2.1, respectively. Two wavy models are tested with different wavelength-to-mean diameter ratios, namely: Model-A with [Formula: see text] and Model-B with [Formula: see text], and their performances are compared with those of a smooth circular tube bundle Model-C ([Formula: see text]), in a range of subcritical Reynolds numbers [Formula: see text]. Mean velocity and pressure fields as well as aerodynamic coefficients are presented and compared. According to the obtained results, the wavy tube bundle with [Formula: see text] exhibits the best performance, with slightly smaller [Formula: see text] values than the smooth tube bundle. However, results show that the wavy model with [Formula: see text] is clearly the worst with an increase of [Formula: see text] up to 40%. Numerical outcomes also reveal that the interaction of neighboring rows and columns affects the fluctuation of [Formula: see text] values relative to each tube position. Moreover, [Formula: see text] evolutions depend significantly on the variation of Re as well, where drag reduction is more substantial by increasing Re. Thus, wavy-shaped tubes with optimum parameters could be more suitable for flow control by enhancing the secondary flow, turbulence and fluid mixing.

Airfoil aerodynamic performance prediction using machine learning and surrogate modeling

In recent times, machine learning algorithms have gained significant traction in addressing aerodynamic challenges. These algorithms prove invaluable for predicting the aerodynamic performance, specifically the Lift-to-Drag ratio of airfoil datasets, when the dataset is sufficiently large and diverse. In this paper, we delve into an exploration of five machine learning algorithms: Random Forest, Gradient Boosting Regression, Decision Tree Regressor, AdaBoost Algorithm, and Linear Regression. These algorithms are scrutinized within the context of various train/test ratios to predict a crucial aerodynamic performance metric—the lift-to-drag ratio—for different angle of attack values. Our evaluation encompasses an array of metrics including R2, Mean Square Error, Training time, and Evaluation time. Upon analysis, the Random Forest Method, with a train/test ratio of 0.2, emerges as the frontrunner, showcasing superior predictive performance when compared to its counterparts. Conversely, the Linear Regression algorithm distinguishes itself by excelling in training and evaluation times among the algorithms under scrutiny.

Plane Cascade Aerodynamic Performance Prediction Based on Metric Learning for Multi-Output Gaussian Process Regression

Multi-output Gaussian process regression measures the similarity between samples based on Euclidean distance and assigns the same weight to each feature. However, there are significant differences in the aerodynamic performance of plane cascades composed of symmetric and asymmetric blade shapes, and there are also significant differences between the geometry of the plane cascades formed by different blade shapes and the experimental working conditions. There are large differences in geometric and working condition parameters in the features, which makes it difficult to accurately measure the similarity between different samples when there are fewer samples. For this problem, a metric learning for the multi-output Gaussian process regression method (ML_MOGPR) for aerodynamic performance prediction of the plane cascade is proposed. It shares parameters between multiple output Gaussian distributions during training and measures the similarity between input samples in a new embedding space to reduce bias and improve overall prediction accuracy. For the analysis of ML_MOGPR prediction results, the overall prediction accuracy is significantly improved compared with multi-output Gaussian process regression (MOGPR), backpropagation neural network (BPNN), and multi-task learning neural network (MTLNN). The experimental results show that ML_MOGPR is effective in predicting the performance of the plane cascade, and it can quickly and accurately make a preliminary estimate of the aerodynamic performance and meet the performance parameter estimation accuracy requirements in the early stage.

Prediction of aerodynamic and thermal performances for gas foil journal bearing with an axial cooling throughflow by machine learning method

Prediction of aerodynamic and thermal performances for gas foil journal bearing with an axial cooling throughflow by machine learning method

Numerical Investigation on Aerodynamics of a Rectangular Blade Rotor under Mars Air Conditions Using Large Eddy Simulation

The study of the aerodynamics of a flight vehicle under Martian air conditions is experimentally difficult due to its characteristics such as low air density and temperature, making the vehicle operate at an ultra-low Reynolds number, which in turn introduces a complex flow field. In this paper, to find a proper computational fluid dynamics (CFD) method with which to better understand the aerodynamics of rotor-type aircraft under Martian air conditions, the aerodynamic performance of a rotor with rectangular blades under ultra-low density Martian air conditions is studied. A simulation scheme using a large eddy simulation solver and sliding meshing technology is established, and the method is verified based on experimental results from a Mars Air Simulator (MAS). In addition, the influence of the test bench and chamber is investigated through flow field analysis. The results show that the established method can predict lift in a very accurate manner, but that the torque prediction is not so promising. The study also determines that the fixture and the chamber wall of an MAS has little influence on the prediction of aerodynamic performance due to the quickly decreasing of flow speed and dissipation of vortexes. The test bench has about 5% influence on lift prediction, possibly due to the ground effect of the bench. In addition, simulation under actual Martian air conditions shows that the results agree well with the MAS experiment’s results, indicating that the temperature difference has little influence on the lift performance, and therefore that the MAS is a good tool for the lift prediction of Martian helicopters.

The Study of the Lift Coefficient of NACA-0012 Airfoil Subjected to an Incompressible Media by Using the Cartesian Cut Cell Method

In order to shorten product design time, engineers need to create high quality meshes within a few days or hours. Newer automated techniques have been published to tackle this need, the Cartesian Cut Cell is one of them. This study is focused on the effect of different Cut Cell meshing strategies on the accuracy of aerodynamic performance predictions. The method can be described as a methodology in which Cut Cells are applied to the geometry utilizing a process involving rectangular/hexagonal cells on a regular lattice cutting through the geometry. The Cut Cell meshing is a general purpose designed for ANSYS FLUENT, making use of Workbench to construct the airfoil shape and the mesh. The results obtained for NACA-0012 are computed using two models available in ANSYS FLUENT, namely the Eq. 2 k-ω SST and the Eq. 3 k-kl-ω models. The three-dimensional numerical simulations were created for steady incompressible flow around NACA-0012 shape. Lift coefficient, Boundary layer thickness, mesh expansion ratio, and mesh density variation parameters were investigated. For this application both models produce good lift results. k-kl-ω produce better lift and the results are close to the measured data. The Cut Cell method showed a very good agreement between Computational Fluid Dynamics results and experimental data. This work illustrated that the Cartesian Cut Cell method has the ability to generate high quality mesh, which captures the details of the viscous boundary layer easily. The future work is to use more sophisticated turbulence models and mesh refinement for air craft wing with flap.

Prediction of aerodynamic performance of NREL offshore 5‐MW baseline wind turbine considering power loss at varying wind speeds

AbstractIn this study, a computational fluid dynamics (CFD) model was developed to simulate the aerodynamic performance of the National Renewable Energy Laboratory (NREL) offshore 5‐MW baseline wind turbine with single rotor and full wind turbine. Using statistical methods, the relation between pitch angle and performance when the speed is above the rated wind speed was analyzed; furthermore, other published data were compiled to establish the functional equations of power, thrust with various inflow wind speeds, and pitch angles. In addition, according to shape optimization based on parametric modeling, the two‐dimensional cross‐section of the wind turbine blade can be defined through a metasurface approach, and the three‐dimensional surface modeling of the wind turbine blade, nacelle, and tower is completed using the nonuniform rational B‐splines (NURBS) interpolator. In terms of aerodynamic simulation, the unstructured polygon mesh was used herein to discretize the space and simulate the highly curved and twisted surfaces of the blade. In this study, the compact and accurate mesh form obtained through a grid independence test will be used to analyze the distribution of the pressure coefficient, shear stress coefficient, and limited streamline on the blade surface at various inflow wind speeds and pitch angles to understand the differences between different turbulence models and the causes of power and thrust attenuation at high inflow wind speeds. In addition, the phenomena of blade‐tip vortices, dynamic stall, blade loading, and the interaction between nacelle and tower were collectively explored.

Aerodynamic and structural multidisciplinary optimization design method of fan rotors based on blade curvature constraints

The optimization design of the actual fan/compressor rotors is a multidisciplinary problem. The aerodynamics and strength multidisciplinary optimization design of the fan/compressor rotors can improve the aerodynamic performance of rotors as much as possible under the condition of satisfying the structural strength requirements. However, the conventional multidisciplinary optimization of aerodynamics and structures will significantly increase the amount of computation cost and time period. Thus, it is difficult for conventional multidisciplinary optimization methods to be used in real-world engineering practices. In order to reduce the computational burden and time cost of the multidisciplinary optimization design, this paper firstly proposes a multidisciplinary optimization design method for rotors based on blade curvature constraints. In the optimization, the self-organizing map (SOM) method is applied to analyze and extract the constraint value of the blade curvature. And the blade curvature constraint value is used as a penalty function instead of the time-consuming high-fidelity FEM, which greatly reduces the computational cost and runtime of the multidisciplinary optimization. Besides, the Free-form deformation (FFD) method is adopted to realize the flexible deformation of the three-dimensional blade with the smallest number of design variables and polynomial chaos Kriging is used as a surrogate model for aerodynamic performance prediction in the optimization process. The method verification and mechanism analysis are carried out by studying a fan rotor. Data mining of FEM samples shows that, in the optimization design space, the maximum spanwise curvature of the blade is monotonically related to the maximum stress of the blade. And the maximum streamwise curvature, maximum spanwise slope, maximum streamwise slope, and maximum thickness of the blade have no related relationship with the maximum stress of the blade. Compared with the conventional multidisciplinary optimization design method based on the surrogate models of high-fidelity computational fluid dynamics (CFD) and finite element method (FEM), the time cost of the multidisciplinary optimization design method based on curvature constraints is reduced by 10 times, and the time cost of the multidisciplinary optimization is significantly reduced. The results show that, in terms of structural performance, the maximum stress of the aerodynamic optimization method without blade curvature constraints is 426 MPa, which exceeds the yield limit of aluminum alloy material (420 MPa), and the maximum stress of the multidisciplinary optimization design with blade curvature constraints proposed in this paper is 344 MPa. In terms of aerodynamic performance, the isentropic efficiency of the aerodynamic optimization method without blade curvature constraints and the method proposed in this paper are increased by 2.1% and 1.8%, respectively. Therefore, the proposed method reduces the maximum stress of the blade by 19.5% with little influence on the aerodynamic performance.

Modification and Validation of 1D Loss Models for the Off-Design Performance Prediction of Centrifugal Compressors with Splitter Blades

One-dimensional (1D) aerodynamic performance predictions are very often conducted by researchers and designers during the preliminary design of centrifugal compressors. This paper focuses on a 1D prediction method for centrifugal compressors with splitter blades, which is rarely seen in the open literature. One-dimensional prediction of aerodynamic overall performance is made for centrifugal compressors with different technical design specifications. However, the aerodynamic overall prediction accuracy relies on the accuracy of the 1D-loss-models used. Therefore, an optimum combination of loss models is proposed by summarizing a variety of loss models presented in the public literature. In addition, an optimization method is utilized to optimize some coefficients involved in loss models in order to improve the generality of the combined model. The modified models obtained in this study are proved to have good predictive accuracy.

물리 기반 자유후류 모델링을 통한 소형 무인기용 동축 로터 블레이드의 최적 설계

Coaxial rotors can generate high thrust with a small aircraft volume, which makes them an attractive configuration for small unmanned aerial vehicles (UAV), particularly for hovering and low-speed flights. However, the aerodynamic interference between the upper and lower rotors makes it more challenging to predict design trends for aerodynamic performance. Three-dimensional compressible unsteady Reynolds-averaged Navier-Stokes (3D URANS) solver can be a suitable choice, however, it requires burdensome computational cost for design optimization. One alternative that can alleviate the computational burden of design optimization with little loss of accuracy is lifting-line theory coupled with a free-wake method. Although the free-wake method provides a high degree of freedom in modeling the wake effects, a well-adjusted wake model can provide an accurate prediction of aerodynamic performance and design trends. In this study, the free-wake model has been adjusted to correlate the aerodynamic environment, such as sectional thrust, induced velocity, the effective angle of attack, and wake geometry, predicted by the 3D URANS simulation for the baseline coaxial rotor. By adjusting the physical model parameters of free-wake, such as core type, growth rate, and its initial strength, the free-wake model is adjusted based on the physical relationship, not the simple linear scaling factor generally used to inflow factor. The optimal coaxial rotor was derived based on the performance prediction of the adjusted free-wake model. The 3D URANS simulation was also conducted for the optimal coaxial rotor and compared the aerodynamic environments predicted by the adjusted freewake model. The performance index and aerodynamic environments predicted by both solvers also showed similar results for the optimal coaxial rotor. The present physics-based freewake adjustment can be an efficient design approach with consideration of the complex aerodynamic phenomena of a coaxial rotor system.

A review of Stall Delay Models and their Application on Hybrid Methods

This paper is a review on the stall delay phenomenon that Horizontal Axis Wind Turbines (HAWT) encounter under typical flow conditions and its numerical modelling. Aerodynamic performance predictions of HAWT have been often carried out through Computational Fluid Dynamics method with the combination of the concept of actuator disk i.e. hybrid method. For this purpose, the hybrid method is presented in details together with the numerical modelling of such stall delay phenomenon. Despite modern wind turbines are equipped with sophisticated control systems for avoiding stall, nevertheless, stall is still inevitable in the near root region of the rotor blade. This paper focuses on recent research development materials which have been undertaken on the stall delay phenomenon where the engineering models (stall delay models) of the literature being presented and criticized based on the predictions obtained from the NREL Phase VI wind turbine experiments.

Prediction and optimization of airfoil aerodynamic performance using deep neural network coupled Bayesian method

In this paper, we proposed an innovative Bayesian optimization (BO) coupled with deep learning for rapid airfoil shape optimization to maximize aerodynamic performance of airfoils. The proposed aerodynamic coefficient prediction model (ACPM) consists of a convolutional path and a fully connected path, which enables the reconstruction of the end-to-end mapping between the Hicks–Henne (H–H) parameterized geometry and the aerodynamic coefficients of an airfoil. The computational fluid dynamics (CFD) model is first validated with the data in the literature, and the numerically simulated lift and drag coefficients were set as the ground truth to guide the model training and validate the network model based ACPM. The average accuracy of lift and drag coefficient predictions are both about 99%, and the determination coefficient R2 are more than 0.9970 and 0.9539, respectively. Coupled with the proposed ACPM, instead of the conventional expensive CFD simulator, the Bayesian method improved the ratio of lift and drag coefficients by more than 43%, where the optimized shape parameters of the airfoil coincide well with the results by the CFD. Furthermore, the whole optimization time is less than 2 min, two orders faster than the traditional BO-CFD framework. The obtained results demonstrate the great potential of the BO-ACPM framework in fast and accurate airfoil shape optimization and design.

Novel Machine-Learning-Based Stall Delay Correction Model for Improving Blade Element Momentum Analysis in Wind Turbine Performance Prediction

Wind turbine blades experience excessive load due to inaccuracies in the prediction of aerodynamic loads by conventional methods during design, leading to structural failure. The blade element momentum (BEM) method is possibly the oldest and best-known design tool for evaluating the aerodynamic performance of wind turbine blades due to its simplicity and short processing time. As the turbine rotates, the aerofoil lift coefficient enhances, notably in the rotor’s inboard section, relative to the value predicted by 2D experimentation or computational fluid dynamics (CFD) for the identical angle of attack; this is induced by centrifugal pumping action and the Coriolis force, thus delaying the occurrence of stall. This rotational effect is regarded as having a significant influence on the rotor blade’s aerodynamic performance, which the BEM method does not capture, as it depends on 2D aerofoil characteristics. Correction models derived from the traditional hard computing mathematical method are used in the BEM predictions to take into account stall delay. Unfortunately, it has been observed from the earlier literature that these models either utterly fail or inaccurately predict the enhancement in lift coefficient due to stall delay. Consequently, this paper proposes a novel stall delay correction model based on the soft computing technique known as symbolic regression for high-level precise aerodynamic performance prediction by the BEM process. In complement to the correction model for the lift coefficient, a preliminary correction model for the drag coefficient is also suggested. The model is engendered from the disparity in 3D and 2D aerofoil coefficients over the blade length for different wind speeds for the NREL Phase VI turbine. The proposed model’s accuracy is evaluated by validating the 3D aerofoil coefficients computed from the experimental results of a second wind turbine known as the MEXICO rotor.

Optimal Design and Aerodynamic Performance Prediction of a Horizontal Axis Small-Scale Wind Turbine

The improved aerodynamic design of a horizontal axis small-scale wind turbine blade is crucial to increasing the efficiency and annual energy production of the turbine. One of the vital stages in aerodynamic design is the selection of the airfoil. Using the existing airfoils for a blade design which results in higher turbine characteristics is tedious. Consequently, this paper provides an optimal design strategy for a horizontal axis small-scale wind turbine blade through the multiobjective optimization of the airfoil using the Nondominated Sorting Genetic Algorithm II (NSGA-II). The latter outperforms the other commonly used genetic algorithms (GAs), as well as the Computational Fluid Dynamics (CFD) investigation of the different airfoil types and the wind turbine rotors on the steady or unsteady state aerodynamic performance. An NACA4412 airfoil with higher aerodynamic efficiency is considered as a baseline for the optimization in order to increase the lift coefficient and lift to drag ratio while avoiding excessive variations in the maximum relative thickness and area. The optimized airfoil (NACA4412-OPT) is used as the cross-sectional profile in the design procedure for a novel 1.15 m diameter three-bladed wind turbine rotor at a wind speed of 11.5 m/s, tip speed ratio of 4.65, and pitch angle of 0.2° by the Wilson design method. The two-dimensional analysis demonstrates that the optimized airfoil outperforms the other airfoils yielding the highest lift coefficient and lift to drag ratio, as well as a larger pitch range. The three-dimensional analysis shows that the time-averaged power coefficient value (0.33) of the new wind turbine is almost 26% higher and more stable than that of the original wind turbine while avoiding a high increase of the axial thrust.