Research

ONGOING Projects

Aerodynamic and Aeroacoustic Optimization of Chevron Nozzles in Transonic Flight Conditions

Exploration of Reinforcement Learning Based Jet Actuation Optimization and Sparse Sensor Placement to Reduce Drag in Airfoils

Design and Geometry Optimization of Turbines Using Deep Generative Learning Methods

Reduced Order 3D Turbulence Modelling Through Super Resolution and Autoencoder Based GANs

COMPLETED Projects

Quantum-Based Salp Swarm Algorithm Driven Design Optimization of Savonius Wind Turbine-Cylindrical Deflector System

Paras Singh, Vishal Jaiswal, Subhrajit Roy, Aryan Tyagi, Gaurav Kumar and Raj Kumar Singh

Savonius turbines, prominent in small-scale wind turbine applications operating under low-speed conditions, encounter limitations due to opposing torque on the returning blade, impeding high efficiency. A viable solution involves mitigating this retarding torque by directing incoming airflow through a cylindrical deflector. However, such flow control is highly contingent upon the location and size of the cylindrical deflector, and its angular velocity. This study introduces a novel design optimization framework tailored for enhancing the turbine-deflector system’s performance. Leveraging surrogate models for computational efficiency, six different models were assessed, with Kriging selected for subsequent analysis based on its superior performance at approximately the relation between design parameters and objective function. The training data for the surrogate model and the flow field data around the system were obtained through Unsteady Reynolds-Averaged Navier Stokes (URANS) simulations using a sliding mesh technique. An in-house code for the Quantum-based Salp Swarm Optimization (QSSO) algorithm was then employed to obtain design parameters corresponding to the peak power coefficient (Cp) for the stationary deflector-turbine system. Additionally, the QSSO algorithm was quantitatively compared with nine other competing algorithms. The optimized stationary deflector-turbine system showed an improvement of 26.94% in Cp at a Tip Speed Ratio (TSR) of 0.9 compared to the baseline case. Further investigation into the effect of deflector rotational velocity (ω) revealed significant improvements: 40.98% and 11.33% enhancement at ω = 3 rad/s, and 51.23% and 19.42% at ω = 40 rad/s, compared to configurations without a deflector and with the optimized stationary deflector, respectively at a TSR of 0.9. In conclusion, this study introduces a robust optimization framework that not only improves the performance of Savonius turbines but also underscores the potential of surrogate modeling and advanced optimization algorithms in addressing aerodynamic challenges within wind turbine design. Importantly, the framework’s applicability extends beyond this study, offering opportunities for multi-parameter optimization of components across the energy sector.

Maximizing Savonius Turbine Performance using Kriging Surrogate Model and Grey Wolf-Driven Cylindrical Deflector Optimization

Paras Singh, Vishal Jaiswal, Subhrajit Roy and Raj Kumar Singh

10th International and 50th National Conference on Fluid Mechanics and Fluid Power

With the growing power demand and the imperative for renewable energy sources, wind power stands as a vital component of the energy transition. To optimize energy production, researchers have focused on design optimization of Savonius-type vertical axis wind turbines (VAWTs). The current study utilizes Unsteady Reynolds-Averaged Navier Stokes (URANS) simulations using the sliding mesh technique to obtain flow field data and power coefficients. A Kriging Surrogate model is trained on the numerical data of randomly initialized data points to construct a response surface model. The Grey Wolf Optimization (GWO) algorithm is then utilized to achieve the global maxima on this surface, using the turbine’s power coefficient as the objective function. A comparative analysis is carried out between simulation and experimental data from prior studies to validate the accuracy of the numerical model. The optimized turbine-deflector configuration shows an improvement of 34.24% in power coefficient. Additionally, the GWO algorithm's effectiveness is compared to Particle Swarm Optimization (PSO) and is found to be better in most cases, converging towards the global maxima faster. This study explores a relatively unexplored realm of metaheuristic optimization of wind turbines using deflectors, for efficient energy harvesting, presenting promising prospects for enhancing renewable sources.

Efficient Model Reduction for Transonic Airflow Simulations using POD-RBF

Anubhav Dhyani, Aryan Tyagi and Raj Kumar Singh

14th Asian Computational Fluid Dynamics Conference

Computational Fluid Dynamics (CFD) simulations for each angle of attack are expensive when it comes to transonic airflow. To overcome this issue, model reduction techniques have been developed over the years. This paper introduces a modified approach called Proper Orthogonal Decomposition-Radial Basis Functions (POD-RBF) for model reduction in transonic airflow over an RAE 2822 wing. The proposed POD-RBF method effectively captures essential flow dynamics while significantly reducing computational complexity. The study contributes to the understanding of model reduction techniques for transonic airflow simulations, facilitating the development of efficient computational tools for aerodynamic analysis, airfoil design optimization, and accelerating research intransonic flows. The trained model showed a deviation of less than 5% from the target value.

COPD-FlowNet : Elevating Non-Invasive COPD Diagnosis with Physics Simulations

Aryan Tyagi, Aryaman Rao, Shubhanshu Rao and Raj Kumar Singh

Chronic Obstructive Pulmonary Disorder (COPD) is a prevalent respiratory disease that significantly impacts the quality of life of affected individuals. This paper presents COPD-FlowNet, a novel deep-learning framework that leverages a custom Generative Adversarial Network (GAN) to generate synthetic Computational Fluid Dynamics (CFD) velocity field images specific to the trachea of COPD patients. These synthetic images serve as a valuable resource for data augmentation and model training. Additionally, COPD-FlowNet incorporates a custom Convolutional Neural Network (CNN) architecture to predict the location of the obstruction site.

Optimization of Inverted Double-Element Airfoil in Ground Effect using Improved HHO and Kriging Surrogate Model

Paras Singh, Arun Ravindranath, Aryan Tyagi, Aryaman Rao and Raj Kumar Singh

14th Asian Computational Fluid Dynamics Conference

In the automotive industry, multi-element wings have been used to improve the aerodynamics of race cars. Multi-element wings can enhance a vehicle's handling and stability by reducing drag and increasing downforce, allowing it to corner more effectively and achieve higher speeds. Performance gains by utilizing the ground effect are highly sensitive to the wing setup. This study focuses on identifying the optimum design parameters for the airfoil to achieve the desired downforce and drag performance. The design parameters chosen are ride height, flap overlap, flap angle, and flap gap (the spacing between the flap and the main airfoil). These parameters are optimized for three different use cases: high downforce, low drag, and a setup with the highest airfoil efficiency. The force coefficient and flow field data were gathered using two-dimensional (2D) Reynolds Averaged Navier Stokes (RANS) simulations, with the turbulent flow modeled using the k-ω Shear Stress Transport (SST) turbulence model. The Improved Harris Hawks Optimization (HHO) algorithm was used to obtain the optimal configuration of the double-element and the resulting designs showed a significant improvement in downforce and drag performance compared to the baseline designs. Improved HHO was further compared with other state-of-the-art algorithms for assessing the algorithm's performance for a problem with highly non-linear behavior, where it was able to demonstrate its ability to obtain the optimal solutions more efficiently.

A Novel Framework for Optimizing Gurney Flaps using RBF Neural Network and Cuckoo Search Algorithm

Aryan Tyagi, Paras Singh, Aryaman Rao, Gaurav Kumar and Raj Kumar Singh

Acta Mechanica

Enhancing aerodynamic efficiency is vital for optimizing aircraft performance and operational effectiveness. It enables greater speeds and reduced fuel consumption, leading to lower operating costs. Hence, the implementation of Gurney flaps represents a promising avenue for improving airfoil aerodynamics. The optimization of Gurney flaps holds considerable ramifications for improving the lift and stall characteristics of airfoils in aircraft and wind turbine blade designs. The efficacy of implementing Gurney flaps hinges significantly on its design parameters, namely, flap height and mounting angle. This study attempts to optimize these parameters using a design optimization framework, which incorporates training a Radial Basis Function surrogate model based on CFD data from two-dimensional (2D) Reynolds-Averaged Navier-Stokes (RANS) simulations. The Cuckoo Search algorithm is then employed to obtain the optimal design parameters and compared with other competing optimization algorithms. The optimized Gurney flap configuration shows a notable improvement of 10.28% in Cl/Cd, with a flap height of 1.9%c and a flap mounting angle of −58°. The study highlights the effectiveness of the proposed design optimization framework and furnishes valuable insights into optimizing Gurney flap parameters. The comparison of metaheuristic algorithms serves to enhance the study’s contribution to Gurney flap design optimization.

Comparative Analysis of Different Profiles of Riblets on an Airfoil Using Large-Eddy Simulations

Aryan Tyagi, Aakash Ghosh and Raj Kumar Singh

2023 IEEE Aerospace Conference

Nature has adapted throughout its existence, from humans to fishes, and amongst these adaptations, several nature-driven optimizations have made species better suited to the current environment. These optimizations can be found in Mako Sharks, which have developed geometric surface patterns that allow them to move faster. These patterns have been named riblets, and it is observed that they induce skew-induced secondary flows, which improve the overall flow behavior around the sharks. Secondary flows create a pocket of low-velocity air in the ridges, resulting in the high-speed flow being concentrated on the tips of the riblets, further resulting in reduced shear stress. Moreover, the vortical motion of the secondary flows allows a more significant transfer of energy in the boundary layer, which aids in delaying the boundary layer separation. This study develops an LES framework to simulate the flow around a NACA-0012 airfoil. CFD simulations were performed using four shapes of riblets: sawtooth, scalloped, blade, and inverted U-shape. The drag coefficient at various angles of attack was compared against the drag coefficient values of the bare airfoil. The scalloped riblets showed the maximum reduction in drag coefficient (8% lesser than the bare airfoil), followed by sawtooth, inverted U-shape, and blade-shape. It was observed that the wall shear stress was concentrated on the riblet tips instead of the whole upper surface of the airfoil.

A Synergistic Framework Leveraging Autoencoders and Generative Adversarial Networks for the Synthesis of Computational Fluid Dynamics Results in Aerofoil Aerodynamics

Tanishq Nandal, Vaibhav Fulara and Raj Kumar Singh

International Journal of Mechanical and Production Engineering

In the realm of computational fluid dynamics (CFD), accurate prediction of aerodynamic behaviour plays a pivotal role in aerofoil design and optimization. This study proposes a novel approach that synergistically combines autoencoders and Generative Adversarial Networks (GANs) for the purpose of generating CFD results. Our innovative framework harnesses the intrinsic capabilities of autoencoders to encode aerofoil geometries into a compressed and informative 20-length vector representation. Subsequently, a conditional GAN network adeptly translates this vector into precise pressure-distribution plots, accounting for fixed wind velocity, angle of attack, and turbulence level specifications. The training process utilizes a meticulously curated dataset acquired from JavaFoil software, encompassing a comprehensive range of aerofoil geometries. The proposed approach exhibits profound potential in reducing the time and costs associated with aerodynamic prediction, enabling efficient evaluation of aerofoil performance. The findings contribute to the advancement of computational techniques in fluid dynamics and pave the way for enhanced design and optimization processes in aerodynamics.

Generation and Parameterization of Forced Isotropic Turbulent Flow Using Autoencoders and Generative Adversarial Networks

Kanishk, Tanishk Nandal, Prince Tyagi and Raj Kumar Singh

Autoencoders and generative neural network models have recently gained popularity in fluid mechanics due to their spontaneity and low processing time instead of high fidelity CFD simulations. Auto encoders are used as model order reduction tools in applications of fluid mechanics by compressing input high-dimensional data using an encoder to map the input space into a lower-dimensional latent space. Whereas, generative models such as Variational Auto-encoders (VAEs) and Generative Adversarial Networks (GANs) are proving to be effective in generating solutions to chaotic models with high 'randomness' such as turbulent flows. In this study, forced isotropic turbulence flow is generated by parameterizing into some basic statistical characteristics. The models trained on pre-simulated data from dependencies on these characteristics and the flow generation is then affected by varying these parameters. The latent vectors pushed along the generator models like the decoders and generators contain independent entries which can be used to create different outputs with similar properties. The use of neural network-based architecture removes the need for dependency on the classical mesh-based Navier-Stoke equation estimation which is prominent in many CFD software.

A Comparative Study of Various Deep Learning Techniques for Spatio-temporal Super-resolution Reconstruction of Forced Isotropic Turbulent Flows

T.S.Sachin Venkatesh, Rajat Srivastava Pratyush Bhatt, Prince Tyagi and Raj Kumar Singh

ASME 2021 International Mechanical Engineering Congress and Exposition

Super-resolution is an innovative technique that upscales the resolution of an image or a video and thus enables us to reconstruct high-fidelity images from low-resolution data. This study performs super-resolution analysis on turbulent flow fields spatially and temporally using various state-of-the-art machine learning techniques like ESPCN, ESRGAN and TecoGAN to reconstruct high-resolution flow fields from low-resolution flow field data, especially keeping in mind the need for low resource consumption and rapid results production/verification. The dataset used for this study is extracted from the ‘isotropic 1024 coarse’ dataset which is a part of Johns Hopkins Turbulence Databases (JHTDB). We have utilized pre-trained models and fine tuned them to our needs, so as to minimize the computational resources and the time required for the implementation of the super-resolution models. The advantages presented by this method far exceed the expectations and the outcomes of regular single structure models. The results obtained through these models are then compared using MSE, PSNR, SAM, VIF and SCC metrics in order to evaluate the upscaled results, find the balance between computational power and output quality, and then identify the most accurate and efficient model for spatial and temporal super-resolution of turbulent flow fields.

Metaheuristic Optimization Framework for Drag Reduction Using Bioinspired Surface Riblets

Aakash Ghosh, Priyam Gupta, Jayant and Raj Kumar Singh

Emulating natural mechanisms in technology has become a very efficient technique to optimize and improve the current machinery. Riblets are such kinds of bio-inspired surface patterns which are seen on Sharks. Their geometric properties induce secondary flows resulting in lower drag, allowing Sharks to achieve such high speeds. Secondary flows are broadly subcategorized into Prandtl’s first and second kind; the secondary flows induced by riblets belong primarily to Prandtl’s second kind. They develop due to both the turbulent and geometric characteristics at the fluid-surface interface. These secondary flows have been proven to delay the boundary layer separation by reducing momentum losses. This paper aims to optimize the use of these bio-inspired patterns on an airfoil. By applying the ribbed surface on an airfoil, the boundary layer separation can be delayed, thus leading to a reduction in drag and increasing the critical angle of attack. Specific geometric properties such as the height and the wavelength of the riblets are varied to find the most optimal design. The design framework couples the optimization algorithm, computational fluid dynamics, and post-processing analysis. A genetic algorithm is used as a means of optimizing the riblet parameters. Specifically, the Invasive Weed Model is implemented for optimization due to its commendable performance in converging close to global optima. The optimization process is initialized by generating a population of riblet profiles with randomly distributed geometry parameters. These profiles are then evolved over generations with the objective to minimize the drag, which is computed through Computational Fluid Dynamics simulations conducted on OpenFOAM. The Coefficient of Drag computed at a constant angle of attack is taken as the fitness value. The evolution process involves the competitive selection of fit candidates and mutation of geometry parameters in order to best scrutinize the design space. New profiles are generated by randomly sampling from the normal distribution around the existing profiles. The standard deviation of the distribution decreases over generations in order to converge to a solution. For this study, the ribbed surface is applied onto a NACA-0012 airfoil which is meshed using SnappyHexMesh, allowing for the effortless pairing of the genetic algorithm. The SIMPLE solver is employed for the OpenFOAM simulations. The optimized geometries show a significant reduction in drag as compared to bare airfoils. Lastly, a flow field analysis of the optimized geometry established using the genetic algorithm is done to understand the riblets-induced enhancement in the aerodynamic efficiency of the airfoil.

Analysis of Generative Adversarial Networks for Data Driven Inverse Airfoil Design

Priyam Gupta, Prince Tyagi, Raj Kumar Singh

Data-Driven Methods have led to new approaches in the field of Aerodynamic Design and have recently found success in Inverse Design. The conventional Inverse Design methods are analytically complex and mathematically demanding to formulate. This research attempts to perform Inverse Airfoil Design using Generative Adversarial Networks(GANs) with the objective to generate airfoil shapes that produce desired Pressure Distribution at given flow conditions. The Convolutional Neural Network-based Generator extracts features from the pressure coefficient profiles and predicts the corresponding airfoil shape coordinates. These deep ConvNet structures eliminate the problems posed by shape parameterization in classical methods and extract patterns from the data with finer details. The Discriminator then attempts to differentiate between the actual and the predicted airfoils. The training procedure of a GAN is mathematically formulated as a zero-sum game wherein the Discriminator minimizes the error between actual and generated samples. At the same time, the Generator maximizes the probability of the Discriminator to differentiate incorrectly. This makes them robust to deal with multi-modal data and highly effective for inverse design problems. However, GANs are generally unstable to train and suffer from the problem of mode collapse and overtraining. Therefore, this work examines the performance of three Generative Adversarial Network architectures to obtain a model that is stable, computationally efficient, and has competitive prediction accuracy. The candidate GANs include Wasserstein GAN, Boundary Seeking GAN, and Bidirectional GAN. The networks are trained on a database of airfoil shapes and pressure coefficient distribution, which is obtained for Reynolds number of 100,000 and an angle of attack of 3°. It is shown that Boundary Seeking Generative Adversarial Network produces highly accurate results and is computationally least expensive to train.

Evaluation of Pressure Fields From Particle Image Velocimetry Data Using Machine Learning

Sushrut, Manan Basu, Priyam Gupta and Raj Kumar Singh

This research attempts to develop an optimization scheme by integrating Genetic Algorithm and Artificial Neural Network (ANN) - surrogate model which was successfully implemented to optimize Leading Edge Slat shape and configuration. The optimization model used Invasive Weed Optimization. Bezier curves were used as an aerodynamic shape parameterization method to ensure the generation of smooth-contoured slat profiles. Geometry, Overhang, Depth and Deflection were taken as the defining parameters for each individual. These parameters were varied within a range which is 3.4% to 15% of slat chord for Overhang, -5% to 4% of slat chord for Depth, -5% to 5% of initial deflection for Deflection and the ordinate of shape control points had a variation of y+3 to y-3. The standard deviation decreases non linearly from 6 to 0.005 with a modulation index of 3. Multiple Computational Fluid Dynamics simulations were run for each individual under various operating conditions to evaluate their fitness (lift to drag ratio). The data generated from this process was used as training and test sets for the ANN. Shape control points, angle of attack, Reynolds number and operating conditions were taken as input parameters for the neural network to predict lift to drag ratio. The developed technique showed approximately 85% improvement in the time taken and allowed algorithm to better explore design space.

Metaheuristic Optimization of Dual-element Vertical Axis Wind Turbine Using Genetic Algorithm

Sushrut, Priyam Gupta and Raj Kumar Singh

ASME 2019 Gas Turbine India Conference

This work presents a framework for the optimization of Dual-Element Vertical Axis Wind Turbine (VAWT) Blade configurations for improvement in power generation. Multi-element nature of the turbine was specifically chosen as this configuration offers better-attached flow over a conventional single element H-type turbine. The framework was based on a genetic evolutionary algorithm which is a metaheuristic optimization technique based on the principle of survival of the fittest. The class of genetic algorithm used was Invasive Weed Optimization. The geometry of the turbine consists of a rotor with three sets of dual-element airfoil oriented symmetrically. Effective chord length and relative chord angle were taken as modifying parameters for generating new configurations. The fitness of each individual was evaluated by performing two-dimensional Computational Fluid Dynamics Simulations. OpenFOAM was used for performing numerical simulations. The model was found ideal for optimizing multi-element VAWT configuration.

A Natural Evolution Based Numerical Optimisation Framework to Develop and Enhance Airfoil - Slat Arrangement

Sushrut, Priyam Gupta and Raj Kumar Singh

ASME 2019 International Mechanical Engineering Congress and Exposition

Leading Edge Slats are popularly being put into practice due to their capability to provide a significant increase in the lift generated by the wing airfoil and decrease in the stall. Consequently, their optimum design is critical for increased fuel efficiency and minimized environmental impact. This work attempts to develop and optimize the Leading-Edge Slat geometry and its orientation with respect to airfoil using Genetic Algorithm. The class of Genetic Algorithm implemented was Invasive Weed Optimization as it showed significant potential in converging design to an optimal solution. For the study, Clark Y was taken as test airfoil.

Parametric Numerical Study of Attenuating Turbulence in Flows Past Honeycombs Using Large Eddy Simulation

Sushrut and Raj Kumar Singh

The work studies the characteristics of a fully turbulent flow of water through a conduit by use of corrugated structures. Methodologies including rough-ribbed walls and particle injection have been utilized for turbulence attenuation in the past. Screens and corrugations are yet another effective tools for reducing turbulence. The proposed investigation focuses on the application of honeycombs which are introduced in the flow for turbulence attenuation inside rectangular conduits. Large Eddy Simulations in three dimensions were performed with OpenFOAM using a pressure-implicit solver and the standard Smagorinsky subgrid-scale model. Dampening of the spanwise velocity component and a relative increase in streamwise velocity component downstream of the corrugation was observed. The power spectral densities (PSD) of the flow upstream and downstream of the corrugation were examined and compared. A significant decrease in turbulent flow power density was observed.

Design and Development of a Smartphone-based Particle Image Velocimetry System

Sushrut, Vishesh Kashyap, Mrudang Mathur, Nehal Amit Jajal and Raj Kumar Singh

The components in a typical PIV system are required to be precise and accurately calibrated. However, due to local unavailability of components and the high cost of import, very few institutions in developing countries such as India possess a PIV system. This project focuses on constructing a smartphone-based particle image velocimetry system for educational use. Using smartphone cameras and low-intensity laser diodes, established open-channel flow is visualised around a given test-body. Hydrogen bubbles obtained through the electrolysis of metal electrodes at an experimentally-obtained optimum voltage are utilised as seeding particles. An open-source MATLAB toolbox is used to track particle motion across a number of frames and obtain measurements of velocity reliably.

Computational Study of Modified Savonius Turbine Using Dynamic Meshing and Its Installation at the Rithala Sewage Plant, Delhi Jal Board

Rachit Gupta, Ravikant Rajan and Raj Kumar Singh

Numerous efforts have been made by various researchers to improve the efficiency of hydrokinetic turbines. In the Rithala Sewage Plant, Delhi Jal Board, the effluent channel from the aeration tank was blocked by 60% on the upstream and 40% on the downstream using GI (Galvanized Iron) deflector plates which gave it a closed domain like a guide box. The turbine was placed between the two plates. The design of the turbine is one of many effects, which is the result of the analysis of CFD simulation in ANSYS. These results have been used in modeling a vertical axis Savonius turbine with a technique of dynamic meshing and 6 degrees of freedom in ANSYS Fluent to simulate the impact of the flow of water on the modified S-shaped blades in the channel. The power generated can be provided back to the sewage plant to run their equivalent instruments.

NUMERICAL INVESTIGATION OF CONJUGATE HEAT TRANSFER IN CAROTID ARTERY BIFURCATION

Mrudang Mathur, Nehal Amit Jajal, Raj Kumar Singh

The Carotid Artery is the primary supplier of oxygenated blood to the human brain. It originates at the aortic arch and moves upwards on either side of the neck as Common Carotid Artery (CCA), before bifurcating into the Internal Carotid Artery (ICA) - supplying blood to the brainstem, and the External Carotid Artery (ECA) which supplies blood to the face. The point of separation of CCA into ICA and ECA is known as the Carotid Bifurcation. The velocity profile in carotid artery can develop certain sites where wall shear stress oscillates; this initialises the localisation of atherosclerotic diseases. This study aims to establish a mathematical relationship between the degree of stenosis in the carotid artery and its Bio-Heat transfer characteristics. Numerical simulations were conducted on ANSYS Fluent model the temperature profile of the tissue surrounding the artery. A three-dimensional conjugate heat transfer study was performed on idealised versions of the carotid artery bifurcation geometry, encapsulated in a tissue model resembling the human neck.

SHAPE OPTIMIZATION OF A VERTICAL AXIS WIND TURBINE USING INVASIVE WEED OPTIMIZATION

Joe Joseph, Pranshu Pant, Raghav Sood, Ayushi Mehrotra, Raj Kumar Singh

Wind energy is amongst the most abundant forms of renewable energy sources available, however, the problem with harnessing wind energy is its unpredictable and intermittent nature. To circumvent this problem, this project was conceptualised to develop a blade profile for vertical axis wind turbine with the aim of maximum efficiency at varying operating conditions using novel optimisation methods like genetic algorithm. By using Genetic Algorithms, the Darwinian process of natural selection in weed colonies (Invasive Weed Optimisation) was mimicked. The design cycle was begun design cycle by randomly generating an initial population of various profiles (phenotypes) and spread it over a problem space at random positions. To compare designs, a fitness function was evaluated using CFD to calculate the power produced about a defined origin. Subsequently, an automated Python code was developed to carry out this Invasive Weed Optimization. Furthermore, physical testing of the wind turbine design was carried out to validate the blade design.

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