Blade Twist Angle Research Articles

CFD Analysis of Drone Blade

Drone technology is seen to be rapidly advancing in various fields and applications including photography, military, transportation, sports, and many more. Therefore, each drone design requires different aerodynamic requirements, which includes different types of propeller designs. By revolving and generating airflow, the propellers give drones or unmanned aerial vehicles (UAV) a lift force or thrust. This paper presents a novel integrated study of the aerodynamic performance and acoustic signature of different propellers with a specific focus on the blade twist angle effect. Designed using CATIA V5 and computational fluid dynamic (CFD) simulations were utilized to examine and compare the aerodynamic performance, Drag and Lift between different shapes of the drone propellers. Therefore, this work falls on the study of the aerodynamic effect of different drone blades

Maximisation the autonomous flight performance of unmanned helicopter using BSO algorithm

Abstract The usage areas of rotary or fixed wing unmanned aerial vehicles (UAV) have become very widespread with technological developments. For this reason, UAV designs differ in terms of aerodynamic design, flight performance and endurance depending on the intended use. In this study, maximising of the autonomous flight performance of the unmanned helicopter produced at Erciyes University using an optimisation algorithm is discussed. For this purpose, the input parameters of the dynamic model are chosen as blade length, blade mass density, blade chord width and blade twist angle of the unmanned helicopter and the proportional, integral, derivative gain coefficients of the lateral axis of the autopilot. The output parameters of the dynamic model are selected as settling time, rise time and maximum overshoot, which are autonomous performance parameters. The dynamic model consisting of helicopter and autopilot parameters is integrated into the back-tracking search optimisation (BSO) algorithm as an objective function. In the optimization process, where mean squared error (MSE) is used as the performance criterion, optimum input and output values were determined. Thus, helicopter and autopilot parameters, which are among the factors affecting autonomous performance, are taken into account with equal importance and simultaneously. Simulations show that the obtained values are satisfactory. With this approach based on the optimisation method, complex and time-consuming dynamic model calculations are reduced, time and cost are saved, and practicality is achieved in applications. Therefore, this approach can be an innovative and alternative method to improve UAV designs and increase flight performance compared to classical methods.

CFD-PBM coupled modeling of the liquid-liquid dispersion characteristics and structure optimization for Kenics static mixer

Kenics static mixers (KSM) are extensively used in industrial mixing–reaction processes by virtue of high mixing efficiency, low power homogenization and easy continuous production. Resolving liquid droplet size and its distribution and thus revealing the dispersion characteristics are of great significance for structural optimization and process intensification in the KSM. In this work, a computational fluid dynamics–population balance model (CFD–PBM) coupled method is employed to systematically investigate the effects of operating conditions and structural parameters of KSM on droplet size and its distribution, to further reveal the liquid–liquid dispersion characteristics. Results indicate that higher Reynolds numbers or higher dispersed phase volume fractions increase energy dissipation, reducing Sauter mean diameter (SMD) of dispersed phase droplets and with a shift in droplet size distribution (DSD) towards smaller size. Smaller aspect ratios, greater blade twist and assembly angles amplify shear rate, leading to smaller droplet size and a narrower DSD in the smaller range. The degree of impact exerted by the aspect ratio is notably greater. Notably, mixing elements with different spin enhance shear and stretching efficiency. Compared to the same spin, SMD becomes 3.7–5.8 times smaller in the smaller size range with a significantly narrower distribution. Taking into account the pressure drop and efficiency in a comprehensive manner, optimized structural parameters for the mixing element encompass an aspect ratio of 1–1.5, a blade twist angle of 180°, an assembly angle of 90°, and interlaced assembly of adjacent elements with different spin. This work provides vital theoretical underpinning and future reference for enhancing KSM performance.

Numerical Investigation of Lucid Spherical Cross-Axis Flow Turbine with Asymmetric Airfoil Sections and the Effect of Different Parameters of Blades on Its Performance

The numerical investigation has been performed on the cross-axis-flow lucid spherical turbine. This type of cross-axis flow turbine generates moments through the forces acting on its blade cross-sections. To evaluate its power and performance, a three-dimensional simulation procedure was performed. The experimental results of Bachant and Wosnik have been used to verify the numerical predictions. The spherical lucid model turbine which they examined had 4 blades with NACA 0020 section and 16cm chord length. Drag and power coefficients were used to compare the data for the water inlet velocity 1m/s and different non-dimensional tip-speed-ratio (inlet velocity / linear rotating velocity of the blade). This paper has selected two airfoil sections, NACA 2412 and NACA 64(3)418, to design the turbine blades. The influence of four effective blade parameters, inclusive of profile section type, chord length, number of blades, and blade twist angles, on turbine performance over a wide range of tip speed ratios, is investigated. It can deduce that the power coefficient has increased up to 22% for NACA 2412 compared to the experimental test. Also, the three-bladed turbine possesses the best results among all models. For this model, the power coefficient increased by 12% and 71% for NACA 2412 and NACA 64(3)418 sections, respectively. The twist of the blades increases the power coefficient by 19% and 31% for NACA 2412 and NACA 64(3)418 sections inside the channel respectively. Increasing the blade chord length causes to increase in power coefficient of up to 12% for NACA 2412 section compared to the experimental test.

Investigation on aeroelastic characteristics of mistuned low-speed axial compressor rotor: Numerical methodology and optimization

In this paper, the aerodynamic and aeroelastic characteristics of a low-speed axial compressor were explored by using a multidisciplinary optimization approach. The numerical methodology utilized the parametric benefits of the free-form deformation and Gaussian process-based Kriging in predicting and optimizing the aerodynamic performance (total pressure ratio and isentropic efficiency) and statistical results of forced response (mean μ and standard deviation σ of amplitude magnification) with frequency mistuning. Based on this point, an optimization framework was proposed, coupled with in-house codes for calculating the aerodynamic damping associated with flutter and vibration amplitude of the forced response. Since only the rotor blade was optimized, the exit flow angle at the rotor outlet combined with blade equivalent von Mises stress and aerodynamic damping were chosen as constraints. The results indicated that the total pressure ratio and isentropic efficiency experienced increases by 0.4% and 1.7%, respectively at the point near peak efficiency, while simultaneously achieving a statistical reduction in amplitude magnification. Specifically, aerodynamic analysis revealed that blade twist, backward sweep and forward lean can contribute to the improvement of aerodynamic performance. However, it should be noted that the forward lean of the blade tip led to an increase of equivalent von Mises stress on the blade pressure surface. From aeroelastic analysis, data mining results implied a negative correlation between blade twist and aerodynamic damping, indicating that, an increase in the blade twist angle led to a reduction in aerodynamic damping. These findings highlighted one of the significant conflicts between aerodynamic and aeroelastic performance. Additionally, a strong linear correlation was observed between the mean and standard deviation of forced response, which indicated that a larger blade amplitude magnification increased its sensitivity to frequency mistuning.

Large-eddy simulations of turbulent flows in arrays of helical- and straight-bladed vertical-axis wind turbines

Effects of helical-shaped blades on the flow characteristics and power production of finite-length wind farms composed of vertical-axis wind turbines (VAWTs) are studied numerically using large-eddy simulation (LES). Two helical-bladed VAWTs (with opposite blade twist angles) are studied against one straight-bladed VAWT in different array configurations with coarse, intermediate, and tight spacings. Statistical analysis of the LES data shows that the helical-bladed VAWTs can improve the mean power production in the fully developed region of the array by about 4.94%–7.33% compared with the corresponding straight-bladed VAWT cases. The helical-bladed VAWTs also cover the azimuth angle more smoothly during the rotation, resulting in about 47.6%–60.1% reduction in the temporal fluctuation of the VAWT power output. Using the helical-bladed VAWTs also reduces the fatigue load on the structure by significantly reducing the spanwise bending moment (relative to the bottom base), which may improve the longevity of the VAWT system to reduce the long-term maintenance cost.

Performance evaluation of the savonius hydrokinetic turbine using soft computing techniques

Savonius hydrokinetic turbine is a vertical axis turbine suitable for generating electrical power used in rivers and canals. The performance of the turbine is described in terms of power coefficient. The power coefficient (CP) depends on input variables such as aspect ratio, overlap ratio, blockage ratio, number of blades, blade arc angle, blade shape factor, twist angle, Reynolds number, and Tip Speed Ratio (TSR). In this study, different soft computing methods, namely CatBoost, Artificial Neural Network (ANN), Random Forests (RF), Multivariate Adaptive Regression Splines (MARS), Adaptive Neuro-Fuzzy Inference System (ANFIS), ANFIS-Genetic algorithm (ANFIS-GA), ANFIS-Slime Mold Algorithm (ANFIS-SMA), ANFIS-Marine Predators Algorithm (ANFIS-MPA) along with Linear Regression (LR) were used to predict the power coefficient of Savonius hydrokinetic turbines for the first time. In order to obtain appropriate data, experimental tests were conducted in an open water channel for different design configurations of Savonius turbines. Further, more data were collected from different references. These data were used for the training and testing process of the models. The precision of the methods was evaluated using multiple statistical indices. Three different training-testing scenarios were prepared to provide a reliable predictive model, and in all scenarios, the CatBoost method was superior. Sensitive analysis showed that aspect ratio is the most important design parameter among all effective parameters. As a result, the CatBoost is recommended to predict a Savonius hydrokinetic turbine's performance considering multi-input variables.

Numerical Simulation and Optimization of the Airflow Field of a Forage Drum Mower

The repeated cutting of forage can cause grass breakage and affect the performance of the forage drum mower in harvesting forage. Also, it is worth paying attention to the effect of airflow around the cutter on the cutting and feeding processes. To explore the characteristics of the airflow field around the cutter and optimize the key parameters of the airflow, an analysis of the airflow field of the forage drum mower equipped with twisted blades, tilting discs, and guide plates was conducted through numerical simulation. Furthermore, an orthogonal experiment was carried out by using the numerical simulation model. According to the experimental results, the optimal velocity of the airflow for gathering, lifting, and feeding was reached when the disc speed was 2000 r/min, the blade twist angle was 8°, the disc tilt angle was 4°, and the number of guide plates was 2. On this basis, a prediction model of airflow parameters was constructed, and the parameters of airflow around the cutter were measured on a test bench. According to the measurement results, the results of prediction by the model were consistent with the simulation results. Also, compared with the data of airflow measurement, the average error of the model prediction value was −5.83% for the velocity of the gathering airflow, 2.37% for the velocity of the lifting airflow, and 4.20% for the velocity of the feeding airflow, which demonstrates the reliability of the simulation results and prediction model. The results of this research provide a practical reference for the optimal design of the forage drum mower.

High-Fidelity Modeling and Investigation on Blade Shape and Twist Angle Effects on the Efficiency of Small-Scale Wind Turbines

A high-fidelity analysis is carried out in order to evaluate the effects of blade shape, airfoil cross-section. as well as twist angle distribution on the yielded torque and generated power of a horizontal axis Small-Scale Wind Turbine (SSWT). A computational modeling and an effective design for a small turbine with a blade length of 25 cm subject to a 4 m/s freestream velocity are presented, in which a segregated RANS solver is utilized. Four airfoil profiles are assessed, namely NACA0012, NACA0015, NACA4412, and NACA4415, and two blade shape configurations, rectangular and tapered, are evaluated. The flow around the rotating turbines is investigated along with blade stresses and performance output for each configuration. Subsequently, the impact of various linear and nonlinear twist distributions on SSWT efficiency is also examined. Results show that for the studied operating conditions corresponding to low-speed flows, the rectangular blade configuration outperforms the tapered blade shape from the generated torque and power perspectives, while the tapered shape configuration represents an attractive design choice from the yielded stresses point of view. Additionally, while the nonlinear twist configuration results in the best performance among the configurations studied, an SSWT blade design implementing a linear twist distribution can be highly competitive provided that a good slope is carefully selected.

Blade Twist Effects on Aerodynamic Performance and Noise Reduction in a Multirotor Propeller

This paper presents a novel integrated study of the aerodynamic performance and acoustic signature of multirotor propellers with a specific focus on the blade twist angle effect. Experimental measurements and computational fluid dynamic (CFD) simulations were utilized to examine and compare the aerodynamic performance and noise reduction between twisted and untwisted blades. A 2D phase-locked particle image velocimetry (PIV) was employed to visualize flow structures at specific blade locations in terms of tip vortices and trailing edge vortices. Good consistency between the simulations and measurements was observed in aerodynamic and acoustic performance. It is verified that the propellers with twisted blades enable a maximum increase of 9.3% in the figure of merit compared to untwisted blades while achieving the same thrust production and are further capable to reduce overall sound pressure level by a maximum of 4.3 dB. CFD results reveal that the twisted propeller remarkedly reduces far-field loading noise by suppressing trailing-edge vortices, hence mitigating kinetic energy fluctuation at the blade tip, while having minimal impact on thickness noise. This study points to the crucial role of blade twists in altering the aeroacoustic characteristics, indicating that optimal designs could lead to significant improvements in both aerodynamic and acoustic performance.

Optimal design of horizontal axis tidal current turbine blade

As the tidal current energy has become a significant replacement for conventional energy, the efficiency of the tidal current turbine is highly stressed. An optimal design of the turbine blades can greatly boost the energy conversion efficiency of the turbine. This study focused on the key technologies of hydraulic turbine geometric modeling and reconstruction. The blade element momentum theory was adopted to obtain the distribution of the twist angle and chord of blade, based on which a parameterized model of the original rotor was established. Further, the artificial neural network and the Genetic Algorithm (GA) were utilized to optimize the performance of the original blades. Meanwhile, the approximate model trained by Artificial Neural Network (ANN) was used as the objective function of GA optimization, which greatly improved the iterative speed. Then, an error reactive mechanism was established to ensure the accuracy of the performance prediction. According to numerical simulations based on Computational Fluid Dynamics (CFD), the energy conversion efficiency of the optimized rotor was higher than that of the original rotor under the blade-tip speed ratios considered. The efficiency was improved by 8.5% at most. And the thrust coefficient increased significantly in the optimized model.

Combining Deep Neural Network with Genetic Algorithm for Axial Flow Fan Design and Development

Axial flow fans are commonly used for a system or machinery cooling process. It also used for ventilating warehouses, factories, and garages. In the fan manufacturing industry, the demand for varying fan operating points makes design parameters complicated because many design parameters affect the fan performance. This study combines the deep neural network (DNN) with a genetic algorithm (GA) for axial flow design and development. The characteristic fan curve (P-Q Curve) can be generated when the relevant fan parameters are imported into this system. The system parameters can be adjusted to achieve the required characteristic curve. After the wind tunnel test is performed for verification, the data are integrated and corrected to reduce manufacturing costs and design time. This study discusses a small axial flow fan NACA and analyzes fan features, such as the blade root chord length, blade tip chord length, pitch angle, twist angle, fan diameter, and blade number. Afterwards, the wind tunnel performance test was performed and the fan performance curve obtained. The feature and performance test data were discussed using deep learning. The Python programming language was used for programming and the data were trained repeatedly. The greater the number of parameter data, the more accurate the prediction. Whether the performance condition is met could be learnt from the training result. All parameters were calculated using a genetic algorithm. The optimized fan features and performance were screened out to implement the intelligent fan design. This method can solve many fan suppliers’ fan design problems.

Wind turbine model-test method for achieving similarity of both model- and full-scale thrusts and torques

For model tests of a floating offshore wind turbine (FOWT) system, a great challenge is how to model the interaction between the wind turbine and floating platform with correctly-scaled aerodynamic and hydrodynamic loads because of a well-known contradiction between Reynolds and Froude scaling. Several approaches have been proposed in the literature to tackle the challenge but none of them can correctly and simultaneously model the scaled thrust and torque, and so the interaction between turbine and platform. This paper will present a new model-test method for achieving similarity of both thrust and torque. This is achieved by redesigning the model blades with keeping the blade twist angle same as that of the full-scale turbine and by adjusting the pitch angle of blades and rotational speed of wind turbine in model tests. Numerical simulations and wind tunnel model tests are carried out to validate the present method. Both numerical results and experimental data show that the present method can realize the similarity of thrust and torque simultaneously, and thus, making it possible to study full interaction between turbine and floating platform by physical testing in wind-wave basins.

Blade tip characteristics of turbine disks with cracks

Crack detection of turbine disks is of great significance in aero-engine health monitoring. The traditional non-destructive testing (NDT) methods are not applicable for in-situ or online monitoring of crack propagation. In order to monitor the crack propagation via measured signals, it is necessary to investigate the dynamic characteristics of cracked turbine disks first. In this paper, finite element analysis (FEA) is carried out to study the blade tip characteristics of turbine disks with different types and size of cracks. Three observation parameters, i.e., blade tip clearance change, blade spacing and blade twist angle, are proposed as crack indicators. The effects of different crack types (e.g., circumferential crack, radial crack, crack at the center hole and crack at the bottom of the mortise) on the observation parameters are discussed qualitatively. Then the circumferential crack is selected as an example for the quantitative analysis. The circumferential cracks with different length, width, location and number are studied and compared systematically. The qualitative analysis results show that the three observation parameters can be used to detect different crack types except the crack at the center hole. The quantitative analysis results show that blade spacing is most sensitive to size parameters without crack width. The conclusions can provide proofs for the blade tip timing (BTT) of cracked turbine disks.

Structural and Aerodynamic Characteristics Analysis of a Small V-shaped Vertical Axis Wind Turbine Rotor

ABSTRACT A detailed comprehensive study of a proposed V-shaped turbine rotor is performed in order to examine its structural and aerodynamic characteristics. The structural analysis section of this work determined the most structurally sound blade geometry from 3 different blade geometries. Using the chosen blade profile, 8 rotor models are developed and are tested numerically. These rotor models are of different configurations, focusing on an inclination angle of 45 and 60 degrees, blade twist angle of 0 and 5 degrees and number of blades. Four rotor models are taken from the initial tests for further study, 3B45D which is a three-bladed rotor with a 45 degree inclination angle, 5B45D which is a five-bladed rotor with a 45 degree inclination angle, 5B45D (5deg) which is a five-bladed rotor with a 45 degree inclination angle and a 5 degree blade twist angle, and 5B60D which is a five-bladed rotor with a 60 degree inclination angle. From the structural study, blade design 2 held the best structural characteristics. The design showed an excellent Von-Mises at 8.8 MPa for static structural and 22.2 MPa for fatigue stress response which is well below the yield strength of ABS Plastic. Both experimental and numerical data have relatively good agreement that is within 15% difference. Overall, maximum power coefficient achieved in this study is about 0.259 numerically and 0.213 experimentally. The best performance is seen by the 3B45D rotor in both static and dynamic study considering experimental and numerical corroboration of results.

Static Analysis of the Last Stage Low Pressure Steam Turbine Blade to Improve Blade twist Angle

Demand for electricity is growing continuously and the production is still based on limited sources of energy, mainly the turbomachinery, which include basically the steam and gas turbines. Turbomachinery is responsible for about 80% of the electricity generated worldwide. A large variety of turbine blade geometries are in use today, however, there is no specific attempt made to compare them based on performance.This paper presents a study on the effect of the twist angle of the steam turbine blade on different choice blade materials. For the study, the aerodynamic and static loading of the 19th stage low pressure turbine blade of Egbin thermal power station was evaluated. Static analysis based on computational fluid dynamics (CFD) was used to simulate the conditions of the turbine blade during normal operating conditions. Alternative materials (such as stainless steel 316, Titanium alloy) and varying twist angles were observed to be showing different forms of results in the simulation on ANSYS. From these, an optimized twist angle was generated using Response Surface Optimization on ANSYS RSO, and the consequent results were discussed.

Performance Characterization of a Kenics Static Mixer Using Slotted Elements

The pharmaceutical, biomedical, bioenergy, and consumer products industries rely heavily on the mixing of fluids. The bulk of the processes that are undergone in these industries are laminar flowing fluids with the attendant need for low-pressure losses which the Kenics static mixers are better suited for. The mixing of fluids is achieved through the splitting and shearing at successive element junctions and stretching and folding along the length of the elements. The performance of Kenics static mixers can be enhanced by optimizing the total blade twist angle, however, the blade pitch has little or no impactful effect. The device geometry optimization is a criterion for its performance enhancement. This study investigates the output mixture strength of the Kenics static mixer using slotted mixer elements solved numerically with Finite Element Method code; COMSOL Multiphysics. The Kenics static mixer performance was optimized with the use of slotted mixer elements as it was observed that the slot width size and a varying number of slots on the mixer elements had an impact on its performance.

Seismic assessment of wind turbines: How crucial is rotor-nacelle-assembly numerical modeling?

The cross-section and the structural twist angle of a typical wind turbine blade vary along its span. This complicates its realistic modeling in nonlinear dynamic analysis of wind turbines when seismic performance estimates are sought. As a result, the lumped mass approach is most commonly used to model the rotor-nacelle-assembly (RNA). The RNA is eccentric to the tower top, and the blades tend to induce rotary inertia on the tower. The exclusion of this rotary inertia and the rotor eccentricity can impact the structural response of the wind turbines as the RNA contributes significantly to the total mass of the system. Moreover, the blades are long, slender structural components that can vibrate and deform independently under seismic excitation. The lumped mass approach intrinsically considers the rigid-body inertia for the RNA, which inevitably acts as a part of the tower top. This can affect the seismic vulnerability estimation of the offshore wind turbines (OWT) at a degree that has not yet been properly quantified. To explore this issue, the present study discusses the effects of the three key RNA parameters, i.e., (i) rotary inertia of the blades, (ii) rotor eccentricity, and (iii) blades’ flexibility, on the seismic failure and fragility of OWT under shallow crustal earthquakes. Results show that the rotary inertia affects the higher modes, which in turn influence the height of the tower failure zones. It is also shown that different levels of RNA modeling refinement affect the predicted failure probabilities, particularly under pulse-like ground motions, while the same estimates are overestimated if the conventional rigid body lumped mass rotary inertia is used. Even worse, they can be underestimated (thus less safe) when the rotary inertia is completely ignored, compared with the refined modeling of flexible turbine blades. These results are revealing as they highlight that seismic hazard can indeed pose a significant design issue for OWTs in some regions.

Horizontal axis wind turbine modelling and data analysis by multilinear regression

Abstract. The modelling of each horizontal axis wind turbine (HAWT) differs due to variation in operating conditions, dynamic parameters, and components. Thus, the choice of profiles also varies for specific applications. So for the better choice of profiles, the wind turbine performance is analysed for different parameters and working conditions. The efficiency of HAWTs mainly depends on the blade, which in turn is related to the profile of the blade, blade orientation, and tip size. Hence, the main aim of the present work is to evaluate the performance of HAWTs for three different blade tip sizes and six different blade twist angles for three major NACA (National Advisory Committee for Aeronautics) airfoils. A statistical analysis is also carried out to find the influence of different performance parameters such as drag, lift, vorticity, and normal force. The static design parameters are considered based on the available literature. A three-bladed offshore HAWT is adopted as the research object in the study. Data visualization using star glyphs and sunray plots is performed, along with multilinear regression analysis. From the multilinear regression analysis and reliable empirical correlations, it is known that drag coefficient and lift coefficient parameters have less significance in contrast to the other parameters which have more significance in the regression model. The different results obtained in terms of parametric coefficients provide an effective way to generate appropriate airfoil profiles for given HAWTs. Thus, the study helps to achieve better turbine performance, and it serves as a benchmark for future studies on HAWTs.

A reinforcement learning based blade twist angle distribution searching method for optimizing wind turbine energy power

The twist angle distribution (TAD) of a wind turbine blade determines its efficiency in terms of electricity production. As the blade is normally deployed in dynamic wind environments where wind speed varies widely, it is crucial to search the optimal TAD for different wind speeds in the design process. Due to the need to call a large number of complex simulators, traditional methods for optimal TAD searching are inefficient and time-consuming, which hinders the blade design process. Hence, this work presents a reinforcement learning-based method for searching optimal TAD efficiently, which is named RL-TAD. The fundamental idea of RL-TAD is to learn the TAD searching policy by an agent and reuse this agent to search optimal TAD under different wind speeds. This idea divided RL-TAD from the commonly used genetic algorithm-based methods, which only output the TAD and ignore reusing the searching policy. The RL-TAD includes the offline stage and online stage. In the offline stage, the RL-TAD learns the policy by reinforcement learning, in which the environment is constructed by a surrogate model, and a new reward policy is developed by integrating design experiences. Then in the online stage, the trained agent can be deployed to search TAD for different wind speeds. To verify the proposed RL-TAD, a case study is detailed. The empirical results show that the RL-TAD can converge to the optimal TAD at a speed of 3–5 times faster than the genetic algorithm-based method in the offline stage and also a better wind energy power coefficient is obtained. Besides, the response time is less than 0.1 s when using the trained agent to search the TAD, which proofs its potential to be used in rapid optimal TAD searching. Further, the rapid optimal TAD searching ability can support the real-time control of the wind turbine blade.