Optimal Blade Research Articles

An experimental study to investigate the effect of pitch angle and wave characteristics on the performance of a horizontal axis tidal turbine

This study investigates the performance characteristics of a scaled-down three-bladed tidal turbine in a towing tank. The effects of pitch angle, electrical load, wave characteristics, and current velocity on turbine performance are examined. The experimental model, a 1:6 scale horizontal-axis tidal turbine with a rotor diameter of 1.15 m, is tested in the Strait of Khoran in Iran, selected based on tidal potential evaluation. After calibration and uncertainty analysis, experimental tests are conducted using the Taguchi method to optimize parameters and explore turbine performance in different conditions: current-only and wave-current interactions. Results demonstrate the direct relationship between power, momentum, thrust, rotor speed, and current velocity. Notably, power and momentum coefficients exhibit nonlinear (polynomial) variations at different tip speed ratios (TSRs). The presence of waves consistently enhances turbine performance compared to the no-wave mode, albeit with a lesser impact than other variable parameters. A pitch angle of 18.77° in the presence of waves yields a significant increase of up to 31.1% in power coefficient and up to 23.6% in momentum coefficient, compared to the no-wave mode. The optimal blade angle generally falls within the range of 30–35 mm, with 35 mm (18.77°) and 30 mm (22.72°) as the respective optimum pitch angles for wave-current interaction and the no-wave state. Optimal electrical load is achieved by setting Rheostat equal to 20 Ω, resulting in a remarkable 11% to 40% increase in system efficiency. In the upstream flow, the maximum power and momentum coefficients are observed as 0.219 and 0.033, respectively, at a TSR of 6.62 in the presence of waves with a dimensionless wave number of 3.43. Similarly, in the no-wave mode, the optimal power and momentum coefficients are obtained as 0.205 and 0.0286, respectively, at a TSR of 7.18. These findings highlight the significance of waves with a dimensionless wave number of 3.43.

CFD Validation of Moment Balancing Method on Drag-Dominant Tidal Turbines (DDTTs)

Current performance analysis processes for drag-dominant tidal turbines are unsuitable as disk actuator theory lacks support for varying swept blockage area, bypass flow downstream interaction, and parasitic rotor drag, whereas blade element momentum theory is computably effective for three-blade lift-dominated aerofoil. This study proposes a novel technique to calculate the optimal turbine tip speed ratio (TSR) with a cost-effective and user-friendly moment balancing algorithm. A reliable dynamic TSR matrix was developed with varying rotational speeds and fluid velocities, unlike previous works simulated at a fixed fluid velocity. Thrust and idle moments are introduced as functions of inlet fluid velocity and rotational speed, respectively. The quadratic relationships are verified through regression analysis, and net moment equations are established. Rotational speed was a reliable predictor for Pinwheel’s idle moment, while inlet velocity was a reliable predictor for thrust moment for both models. The optimal (Cp, TSR) values for Pinwheel and Savonius turbines were (0.223, 2.37) and (0.63, 0.29), respectively, within an acceptable error range for experimental validation. This study aims to improve prevailing industry practices by enhancing an engineer’s understanding of optimal blade design by adjusting the rotor speed to suit the inlet flow case compared to ‘trial and error’ with cost-intensive simulations.

Performance investigation on blade arc angle and blade shape factor of a Savonius hydrokinetic turbine using artificial neural network

ABSTRACT Hydrokinetic turbines can generate electricity in remote rural locations using the kinetic energy of nearby rivers or canals. The Savonius hydrokinetic turbine (SHKT) is the easiest to design and manufacture. Optimization of blade shape factor (p/q) and blade arc angle (Φ) can contribute significantly to enhancing the efficiency of a turbine. The present work proposes a time-saving and reliable method to design an optimized SHKT by using a blend of experimentally validated 3D computational fluid dynamics (CFD) simulations and Artificial Neural Network (ANN). To optimize the turbine blade, the turbine performance needs to be analyzed for a number of values of blade parameters selected at very small intervals. Performing so many CFD simulations is a costly task. Application of an ANN tool trained using a smaller number of CFD results should significantly curtail the costs while maintaining reliability as well. The power coefficient (Cp) of SHKT was obtained using CFD simulations for some selected sets of Φ and p/q. These results were used to train the ANN by creating a parametric map between the input parameters viz. Φ, p/q, and the output parameter Cp. The trained ANN tool was further used to predict the turbine’s performance for sets of input parameters varying at very small intervals. The blade with p/q of 0.2 and Φ of 148° provides a maximum Cp of 0.209 at a TSR of 0.8. This optimal blade was 10.5% more efficient than the standard semicircular blade.

Structure optimization of rotor cage blades for turbo air classifier based on entropy production analysis

Entropy production theory based on the second law of thermodynamics was introduced for evaluating the flow field inside the turbo air classifier. The three new types of rotor cage with the wedge blades, the inverted wedge blades and the spindle blades were designed, and the flow field and the classification performance of the classifiers were investigated. The results show that, compared to the rectangular blades, the productions of total entropy, turbulent entropy and wall entropy of the wedge blades are reduced by 17.3%, 25.86% and 3.34%, respectively. The corresponding effective airflow area increases by 7.5%, and the residence time of 5 μm particle is shorten by 16%. The classifier with the wedge blades has smaller cut size and higher classifying sharpness. The results validate that the turbulent entropy generation can be an indicator for monitoring the overall flow field and the classifiers’ performance.

Design optimization of composite fan blade in aircraft engine subjected to bird strike loading

Bird strike has been a perennial problem for all airline companies in the world. It is the most important design criteria for the fan blades of an aircraft engine. As it is not possible to manufacture and test aircraft engines again and again for small design changes, through the simulation analysis, it is possible to study the ways to reduce the impact of the bird on a jet engine by using appropriate design and manufacturing methods for the blade. This research suggests using two fibers (hybrid) in place of the single fiber composite blade which is currently in use to reduce the delamination issues. In the first stage of this research, representative composite coupon models for combinations of hybrid fiber joint positions were created and linear static analysis was performed. For the validation of simulation methodology, a few coupons were manufactured and tested in the laboratory. Further, dynamic bird strike analysis on sub-element level models was carried out in the second stage with various joint location combinations. Next, the plate-level representative blade model was designed with the original dimensions of the aircraft engine fan blade, and bird strike analysis was performed. The behavior of the representative plate with hybrid interface was studied, and the levels of inter-laminar shear strain were checked, by varying the joint location of the two composites. Some of the shortlisted cases do show significant promise of being damage tolerant under bird strike loading.

Aero‐servo‐elastic co‐optimization of large wind turbine blades with distributed aerodynamic control devices

AbstractThis work introduces automated wind turbine optimization techniques based on full aero‐servo‐elastic models and investigates the potential of trailing edge flaps to reduce the levelized cost of energy (LCOE) of wind turbines. The Wind Energy with Integrated Servo‐control (WEIS) framework is improved to conduct the presented research. Novel methods for the generic implementation and tuning of trailing edge flap devices and their controller are also introduced. Primary flap and controller parameters are optimized to demonstrate potential maximum blade tip deflection reductions of 21%. Concurrent design optimization (i.e., co‐design) of a novel segmented wind turbine blade with trailing edge flaps and its controller is then conducted to demonstrate blade cost savings of 5%. Additionally, rotor diameter co‐design optimization is demonstrated to reduce the LCOE by 1.3% without significant load increases to the tower. These results demonstrate the efficacy of control co‐design optimization using trailing edge flaps, and the entirety of this work provides a foundation for numerous control co‐design‐oriented studies for distributed aerodynamic control devices.

Excitation force on a pump-jet propeller: The effect of the blade number

This study investigates the influence of blade number on the propulsion performance and excitation force of pump-jet propellers (PJPs) using computational fluid dynamics. The primary objective is to determine the optimal blade number arrangement for reducing the excitation force of PJPs while operating behind a submarine. The numerical simulation method used is validated, employing the Reynolds-averaged Navier–Stokes method based on the Shear Stress Transport turbulence model to solve the turbulent flow problem, and the equation is discretized using the finite volume method. By comparing and analyzing the spectrum, it was found that the blade number would affect the unsteady force of single blades in six aspects, and when it impacts the Key Frequency,the amplitude of the excitation force varies dramatically. Through harmonic analysis and strip analysis, this study uncovers the influential mechanism of the blade number on excitation force. Additionally, a unique wake mechanism arising from the combined effect of stator and rudder is discovered by examining various number of stator blade and rudder. Ultimately, the optimal blade number of stator-rotor configurations is found to be 15 and 10, respectively. This study has implications for the design of PJPs with low excitation.

Turbine blade optimization considering smoothness of the Mach number using deep reinforcement learning

The Mach number distribution is an important factor in turbine design. Smooth Mach number distribution is an important feature of blades with small aerodynamic loss. However, existing gradient-based methods, such as adjoint methods, are problematic when the Mach number distribution's smoothness is considered an objective function. In the present study, we propose an optimization method based on deep reinforcement learning (DRL) to maximize the smoothness of the Mach number distribution. In addition, the task was assigned repeatedly with slightly different conditions rather than viewing the turbine design task as a single optimization task. A situation such as this requires each optimization problem to be solved in a short time. For this purpose, we propose an optimization method based on DRL. The computation time for training the model is long; however, once trained, the model can be reused to solve different problems, and the computation time to solve each optimization problem is short. The numerical examples show that the trained agent successfully improves the smoothness, and the model can be reused for various tasks under different conditions.

Parameter Optimization of Centrifugal Pump Splitter Blades with Artificial Fish Swarm Algorithm

Low specific speed centrifugal pumps typically suffer from low efficiency and severe backflow; adding optimally structured splitter blades can play a role. In this paper, the distribution of pressure and velocity in the flow channel is analyzed using CFD simulation for a low specific speed centrifugal pump. The geometric parameters of the splitter blade are optimized using an orthogonal test and an artificial fish swarm algorithm; then the optimal splitter blade structure is obtained. Results showed that the splitter blade not only effectively solves the backflow of the flow channel and compresses the range of the trailing vortex, but it also alleviates the cavitation at the inlet of the main blade. When considering the best head, the order of influence of each factor is: Splitter blade thickness > Splitter blade inlet diameter > Splitter blade inlet width. At this time, the thickness of the splitter blade is 4.5 mm, splitter blade inlet diameter is 155 mm (0.775) and Splitter blade inlet width is 23 mm. Through the closed pump experimental system, it is confirmed that hydraulic performance has been improved.

Machine learning based on computational fluid dynamics enables geometric design optimisation of the NeoVAD blades

The NeoVAD is a proposed paediatric axial-flow Left Ventricular Assist Device (LVAD), small enough to be implanted in infants. The design of the impeller and diffuser blades is important for hydrodynamic performance and haemocompatibility of the pump. This study aimed to optimise the blades for pump efficiency using Computational Fluid Dynamics (CFD), machine learning and global optimisation. Meshing of each design typically included 6 million hexahedral elements and a Shear Stress Transport turbulence model was used to close the Reynolds Averaged Navier–Stokes equations. CFD models of 32 base geometries, operating at 8 flow rates between 0.5 and 4 L/min, were created to match experimental studies. These were validated by comparison of the pressure-flow and efficiency-flow curves with those experimentally measured for all base prototype pumps. A surrogate model was required to allow the optimisation routine to conduct an efficient search; a multi-linear regression, Gaussian Process Regression and a Bayesian Regularised Artificial Neural Network predicted the optimisation objective at design points not explicitly simulated. A Genetic Algorithm was used to search for an optimal design. The optimised design offered a 5.51% increase in efficiency at design point (a 20.9% performance increase) as compared to the best performing pump from the 32 base designs. An optimisation method for the blade design of LVADs has been shown to work for a single objective function and future work will consider multi-objective optimisation.

The influence of the impeller design features on the combined operating process of the torque-flow pump

Purpose. Evaluation of the effect of changing the width of the impeller blades on the characteristics of a torque-flow pump. Searching for the optimal blade extension into the free chamber of the pump. A torque-flow pump of the “Turo” type SVN 500/32 was chosen as the subject of the research work. Methodology. A number of numerical experiments were conducted to determine the flow structure in the flowing part of a torque-flow pump. The width of the impeller blade was chosen as a variable. Numerical experiments were carried out using the ANSYS CFX software package. The integral parameters of the researched pump were determined in order to build the integral characteristics. Findings. The structure of the general flow and toroidal vortex was studied and analyzed in the torque-flow pump. A flow model was built in a torque-flow pumps with basic and modernized design. A relationship between the parameters of the pump and the change in the impeller blade width was found. The width of the impeller blade was changed in the range from min = -20 to max = +100 mm. Originality. The paper researched the effect of additional hydraulic losses caused by the mismatch between the center of the toroidal vortex and the edges of the impeller blades on the integral characteristics of the torque-flow pump. Practical value. A significant increase in the operating parameters of the “Turo” type torque-flow pump was achieved with the help of modernization of the impeller design. This allows expanding the range of the pump’s operation. At the same time, it is not required to replace such expensive elements as the pump casing.

Decomposition-Based Multi-Classifier-Assisted Evolutionary Algorithm for Bi-Objective Optimal Wind Farm Energy Capture

With the wake effect between different wind turbines, a wind farm generally aims to achieve the maximum energy capture by implementing the optimal pitch angle and blade tip speed ratio under different wind speeds. During this process, the balance of fatigue load distribution is easily neglected because it is difficult to be considered, and, thus, a high maintenance cost results. Herein, a novel bi-objective optimal wind farm energy capture (OWFEC) is constructed via simultaneously taking the maximum power output and the balance of fatigue load distribution into account. To rapidly acquire the high-quality Pareto optimal solutions, the decomposition-based multi-classifier-assisted evolutionary algorithm is designed for the presented bi-objective OWFEC. In order to evaluate the effectiveness and performance of the proposed technique, the simulations are carried out with three different scales of wind farms, while five familiar Pareto-based meta-heuristic algorithms are introduced for performance comparison.

Development and Performance Test of a Simulation-Based Tractor-Attachable Wind-Blast-Type Onion Stem Cutting Machine

Stem cutting process of many steps in onion farming involves the least supply of machines. Machines developed in foreign countries are not suitable owing to the geographic characteristics of Korea; it is necessary to develop customized machines. Therefore, in this study, a tractor-attachable blast-type onion stem cutting machine was developed to address these issues. To determine the installation angle of a detachable blade with a 300-mm length, 115-mm width, 5-mm thickness, and a cutting angle of 45° at the tip, we compared the simulation results with the analysis results obtained by fabricating a simple experimental device. Despite small differences in values, the R2 between the two results was found to be 0.99; if using a regression equation, they could be adequate for use in estimation. Moreover, by measuring the motor power consumption, wind speed, and high-speed camera shooting, the stem was raised, regardless of its water content, when the installation angle was greater than 30°. Motor power consumption could also be reduced when the installation angle was 30° rather than 40°. Therefore, the optimal cutter blade installation angle for the tractor-attachable blast-type onion stem cutting machine was confirmed to be 30°. The developed machine was tested at various rotating speeds. The best performance was obtained when the test was repeated thrice at a driving speed of 0.4 m/s. The remaining onion stem length averaged 9.98 cm, the standard deviation was 4.72 cm and the stem cutting rate was 96.8%.

Multi-Objective Structural Optimization of a Composite Wind Turbine Blade Considering Natural Frequencies of Vibration and Global Stability

Aspects concerning resonance and global stability of a wind turbine blade must be carefully considered in its optimal design. In this paper, a composite wind turbine blade with an external geometry based on the NREL 5 MW model was subjected to multi-objective structural optimization considering these aspects. Four multi-objective structural optimization problems are formulated considering the blade mass, the maximum blade tip displacement, the natural frequencies of vibration, and the critical load factor as objective functions. The design variables are the number of plies, material, and fiber orientation. The design constraints are the materials’ margin of safety, the blade’s allowable tip displacement, and the minimum load factor. The blade model is submitted to the loads determined by the actuator lines theory and discretized in a finite element parameterized model using the Femap software according to geometric design variables. Among many multi-objective evolutionary algorithms available in the literature concerning evolutionary computation, the NSGA-II is the adopted evolutionary algorithm to solve the multi-objective optimization problems. Pareto fronts are obtained and performance indicators are used to evaluate the distribution of the non-dominated solutions. Multi-criteria decision-making is used to extract the solutions from the Pareto fronts according to the decision-maker’s preferences. The values of the objective functions, design variables, and constraints are presented for each extracted solution. The proposed study is expected to contribute to the multi-objective optimization and the structural design of wind turbine blades.

Underwater propeller turbine blade redesign based on developed inverse design method for energy performance improvement and cavitation suppression

This paper conducts a systematic study to optimize the high-speed propeller turbine blade with high energy losses and severe cavitation. The trubine blade takes the role to provide the sustainable power for the underwater vehicle, whose structure is complex and crucial. To redesign the blade effectively, a developed inverse design method to generate various design schemes has been adopted with the union of the derived constrained zone and the one-dimensional theory. Established on the design method, a design procedure comprised of the CFD simulation, the LHS sampling, the multi-source constrained screening, the Kriging model, and the MOPSO algorithm was proposed. By setting energy performances (thrust, consuming power) and the cavity size as the objectives, the adaptive optimization of the runner blade installed on the underwater vehicle has been implemented, and two optimized models were finally designed out. Via further comparative analysis of the original and the redesigned blades, the maximum thrust increasement is 6.6%, the maximum power reduction is 3.1%, while the maximum cavity size reduction can be as much as 2.67%. Moreover, there exists a large structural difference for the runner blade before and after optimization, which could provide multiple options for further application. Finally, the specific inner flow analysis consisted of the performance calculation at different cruising speeds, and 3D analysis of key flows fields has been applied to reveal the reason to improve the performance and suppress the cavitation. Further, the energy balance theory was adopted to extract the specific losses at different hydraulic components, which demonstrates that the optimized propeller turbine blades can improve the energy performance. The study is expected to provide a technical reference for the energy performance improvement and cavitation suppression of the high-speed turbine machinery.

Airfoil Shape Optimization of a Horizontal Axis Wind Turbine Blade using a Discrete Adjoint Solver

In this study, airfoil shape optimization of a wind turbine blade is performed using the ANSYS Fluent Adjoint Solver. The aim of this optimization process is to increase the wind turbine output power, and the objective function is to maximize the airfoil lift to drag ratio (Cl/CD ). This study is applied to the NREL phase VI wind turbine, therefore, the S809 airfoil is used as a reference profile. First, for the validation of the applied numerical model, steady-state simulations are carried out for the S809 airfoil at various angles of attack. Then, the optimization is performed with the airfoil set at a fixed angle of attack, , considering three Reynolds numbers, Re =3 105,4.8 105 and 106. Next, computations are performed for the fluid flow around the optimized airfoils at angles of attack AOA= 6.1° ranging from 0° to 20°. The results show that (i) the lift to drag ratios of the optimized airfoils are significantly improved compared to the baseline S809 airfoil, (ii) this improvement is sensitive to the Reynolds number, and (iii) the Cl/CD ratios are also improved for another angle of attack values. Thereafter, the optimized airfoils are used for the design of the NREL Phase VI blade and the aerodynamic performances of this new wind turbine are assessed using the open-source code QBlade. These latter results indicate that when the blades are designed with the optimized airfoils, the wind turbine aerodynamic performances increase significantly. Indeed, at a wind speed of 10 m/s, the power output of the wind turbine is improved by about 38% compared to that of the original turbine.

Wind turbine optimization through optimal blade shape design for low wind speeds

Current electricity needs continue to rely on depleting fossil fuels such as fossil fuel and coal. The current government effort is to find non-fossil fuel alternative energy sources. Wind energy is one of the efforts to use ecologically friendly renewable alternative energy. Studies on the development and use of new and renewable energy are now undertaken. One of them is a series of research on the utilization of wind in numerous places of Indonesia through the construction of wind turbines. The blade that explicitly makes contact with the wind is one of the most critical sections of a wind turbine. The shape of the airfoil determines whether or not the blade is used. The focus of this research was to determine the optimal type of airfoil by comparing the coefficient of power (Cp), maximum power, and lowest power produced by various NACA (National Advisory Committee for Aeronautics) airfoils. NACA 4410, 4412, and 4415 airfoils were employed in this study.

Evaluation of energy loss in a low-head axial flow turbine under different blade numbers using entropy production method

The internal flow loss characteristics of axial flow turbines (AFT), which are often utilized to generate hydropower, have not yet been completely explored. The flow loss characteristics of the turbine under varied blade numbers (z = 2, z = 3, and z = 4) are examined in this work using a low head AFT as the research object using a combination of entropy production theory and numerical simulation. According to a thorough investigation of entropy generation, the majority of energy losses occur downstream in the wake zone, at the flow channel's edge, and close to the blade tip. The major source of entropy production in the turbine is turbulent dissipation. The findings showed that as the number of blades increased, energy loss decreased. At the leading edge and flow cascade when z = 2 at the design flow rate, peak turbulence dissipation of 581 W/m3 and 39.11 W/m-3 were measured. On the other hand, z = 4 had the lowest levels of dissipation. The optimal blade number, according to the case studies, is z = 4, since it has the lowest energy loss and most efficiency.

Multi-fidelity optimization of a quiet propeller based on deep deterministic policy gradient and transfer learning

In a propeller blade optimization, both aerodynamic and aeroacoustic performance were considered simultaneously. A multi-fidelity sampling scheme was adopted by Transfer Learning (TL) to improve the overall optimization efficiency. A Deep Neural Network (DNN) was selected to map the non-linear relationship between the blade parameters and the aerodynamic/aeroacoustic performance, with the optimization being achieved by implementing a deep reinforcement learning algorithm, namely, Deep Deterministic Policy Gradient (DDPG), upon which a Multi-fidelity DNN based surrogate model (TL-MFDNN) was introduced with Transfer Learning between pre-trained and retrained processes. It was found that, by comparing the TL-MFDNN surrogate model based optimization with DDPG optimization using direct CFD simulation, the overall computing cost can be saved up to 77.3% and the optimized propeller has maximum noise reduction of up to 1.69 dB, with a negligible penalty on propulsive performance.

Aerodynamic design and optimization of a small-scale wind turbine blade using a novel artificial bee colony algorithm based on blade element momentum (ABC-BEM) theory

In this study, a novel optimization technique called the Artificial Bee Colony Algorithm based on Blade Element Momentum Theory (ABC-BEM) was developed and applied for the first time to design a small-scale wind turbine blade. Using the ABC-BEM, the aerodynamic geometry of a 1 kW small-scale wind turbine blade was optimized in terms of optimal chord length and twist angle distributions. The objective function used in the optimization study was the power coefficient, and the design parameters were the tip speed ratio, nominal wind speed, and the diameter of the rotor. Both experimental and numerical methods were used to investigate the aerodynamic performance of the optimized blades. Aerodynamic lift and drag coefficients were obtained experimentally for different low Reynolds numbers and angles of attack in a wind tunnel, while numerical simulations were conducted using Computational Fluid Dynamics (CFD) with the Transition k-kL-ω transition model. A flow visualization technique known as a smoke-wire test was carried out to analyze the flow patterns on the blades. The study resulted that the new turbine designs were more successful than the reference turbine in terms of power output. The reference turbine produced 1 kW at a wind speed of 11 m/s, while the new designs produced the same power at 9 m/s. Additionally, the reference turbine reached a maximum power of 1.2 kW at 13 m/s, while the new designs reached a maximum power of 1.46 kW at 10 m/s. These results indicated the success of the Novel ABC-BEM algorithm in optimizing the aerodynamic performance of small-scale turbine blades. This study provided an innovative approach to wind turbine design optimization and a new insight into wind turbine design.