Initial Parameters Research Articles

Adaptive rock-paper-scissors game enhances eco-evolutionary performance at cost of dynamic stability

The rock-paper-scissors (RPS) game is a classic model for exploring the performance of how multiple strategies interact and evolve over time. The classic RPS game assumes a fixed benefit and cost for each strategy against another one when two players meet, while its evolutionary game considers the frequency dynamics of the three strategies with each's fitness influenced by its net payoff. This may not reflect the complexity of real-world scenarios as strategies can co-adapt with each other. We introduce an adaptive RPS game that captures the dynamics of strategy densities with trait-mediated payoffs, and the adaptive dynamics of coevolving traits via incremental mutations leading to adaptively evolving payoffs. Results show that the adaptive RPS game approaches a steady state of strategy density, if any, faster than the evolutionary RPS game. The stable coexistence of all strategies in the evolutionary game can be easily destabilized in the adaptive game. Strategies that are allowed to adaptively change their traits also performed better and achieved greater strategy densities than those fixed strategies in the adaptive game. The coevolving strategies in the adaptive RPS game exhibit complex and diverse attractors in the trait space, sensitive to both initial conditions and model parameters, but exhibiting positive payoffs with greater benefits than costs. These findings highlight how adaptive games enhance strategy performance by sacrificing eco-evolutionary dynamic stability.

Can the Initial Parameters of Functional Scales Predict Recovery in Patients with Complete Spinal Cord Injury? A Retrospective Cohort Study.

Regaining greater independence in performing daily activities constitutes a priority for people with tetraplegia following spinal cord injury (SCI). The highest expectations are connected with the improvement of hand function. Therefore, it is so important for the clinician to identify reliable and commonly applicable prognostic factors for functional improvement. The aim of this study was to conduct an analysis to assess the impact of initial functional factors on the clinical improvement in patients during early neurological rehabilitation (ENR). This study assessed 38 patients with complete SCI aged 17-78 who underwent ENR in 2012-2022. The analysis included the motor score from the AIS (MS), the Barthel Index (BI) and the SCIM scale values at the beginning of the ENR program and after its completion. During ENR, patients achieved a statistically significant improvement in MS, BI and SCIM. The initial MS and the level of neurological injury constituted the predictors of functional improvement during ENR. Significant statistical relationships were observed primarily in the correlations between the initial MS and BI, and the increase in the analyzed functional scales of SCI patients. Higher initial MS may increase the chances of a greater and faster functional improvement during ENR.

Imitation learning enabled fast and adaptive task scheduling in cloud

Studies of resource provision in cloud computing have drawn extensive attention, since effective task scheduling solutions promise an energy-efficient way of utilizing resources while meeting diverse requirements of users. Deep reinforcement learning (DRL) has demonstrated its outstanding capability in tackling this issue with the ability of online self-learning, however, it is still prevented by the low sampling efficiency, poor sample validity, and slow convergence speed especially for deadline constrained applications. To address these challenges, an Imitation Learning Enabled Fast and Adaptive Task Scheduling (ILETS) framework based on DRL is proposed in this paper. First, we introduce behavior cloning to provide a well-behaved and robust model through Offline Initial Network Parameters Training (OINPT) so as to guarantee the initial decision-making quality of DRL. Next, we design a novel Online Asynchronous Imitation Learning (OAIL)-based method to assist the DRL agent to re-optimize its policy and to against the oscillations caused by the high dynamic of the cloud, which promises DRL agent moving towards the optimal policy with a fast and stable process. Extensive experiments on the real-world dataset have demonstrated that the proposed ILETS can consistently produce shorter response time, lower energy consumption and higher success rate than the baselines and other state-of-the-art methods at the accelerated convergence speed.

Vibration reliability of steel beams prestressed by drawing

Introduction. Vibrations of building structures and mechanism parts were analyzed. The need to investigate the oscillation processes and vibration reliability of steel beams prestressed by web drawing was formulated. The subject of study is structural steel. The object of study is bimetallic steel beam prestressed without rods.Materials and methods. The scientific inquiry is based on the basics of structural mechanics of buildings and structures: superposition principle, differential equation of deflection curve of a bar, energy method, and methods of determination of stress-strain state of prestressed steel bars.Results. A comparative analysis of vibration reliability of non-prestressed beams and prestressed structures of equal bearing capacity was performed. Rotations of the beam supporting nodes loaded by prestressing forces and external impacts were determined by integration of differential equation of deflection curve of a split bar. The support moments in rigid supporting nodes of structures were determined on the basis of superposition principle. The developed methods of stressed condition of prestressed bars are the basis for determination of normal stresses in the sections of beams under study. The resultant stresses were obtained by algebraic addition of prestresses and stresses from external loads. Dynamic parameters of bearing capacity of beams were determined on the basis of works by I.M. Rabinovich and V.A. Kiselev. The oscillation circular frequency of conventional and prestressed beams was established, analytical expressions for determination of angular velocity of prestressed bending elements were formulated, and the dynamic deflections and factors of structures were determined. It is found that the circular frequency of prestressed beams hinged in supporting nodes compared to the circular frequency of conventional beams decreases by a factor of 1.4 and by a factor of 5.6 in beams with rigid supports. Angular velocity decreases by a factor of 1.4 (hinge supports) and 6.8 (rigid supports), respectively. The deflections of prestressed beams are reduced by a factor of 1,87: 11,9. There is a significant reduction in the stressed condition of prestressed structures.Conclusions. A hinged traditional beam under external and vibration loads in limit state is in the material yield zone and does not meet the first and second limit state conditions. These structures have the lowest vibration reliability. Prestressed structures are more reliable. With rigid supporting nodes, the moments of prestressing forces coincide with the supporting moments and produce hogging with the vector opposite to the external load deflection vector. In the limit state, total deflections are less than the external load deflections. Stresses in the structure decrease. Since load moments and beam deflections are initial parameters for dealing with dynamic strength tasks, we may state that prestressed beams with rigid supporting nodes have an increased vibration reliability.

Application of model updating to a large‐scale hybrid simulation

AbstractModel updating can enhance hybrid simulation by utilizing the experimental data from the physically tested substructure to update the parameters of like‐components in the numerical substructure throughout the test, improving the overall accuracy and reducing the extent of the experimental setup. Identifying and updating parameters can be challenging, especially when coupling between degrees of freedom (DOF) must be considered or the specimen experiences loading scenarios which result in newly observed behavior. To explore the performance of model updating under these challenging conditions, a large‐scale hybrid simulation was conducted using a model of a major toll bridge with seismic isolation lead rubber bearings (LRB). One LRB is physically tested considering axial, shear, and rotational loading, while the remainder of the bearings are simulated and updated with a phenomenological model within the numerical substructure. A weighted adaptive constrained unscented Kalman filter is applied as the online model updating algorithm. The study explored the effect of learning over different loading patterns, the selection of initial model parameters, and the selection of the physically tested substructure. The improvement of numerical model hysteresis performance accuracy of the force prediction demonstrates the benefits of model updating in large‐scale hybrid simulation.

Toward Chemical Accuracy with Shallow Quantum Circuits: A Clifford-Based Hamiltonian Engineering Approach.

Achieving chemical accuracy with shallow quantum circuits is a significant challenge in quantum computational chemistry, particularly for near-term quantum devices. In this work, we present a Clifford-based Hamiltonian engineering algorithm, namely CHEM, that addresses the trade-off between circuit depth and accuracy. Based on a variational quantum eigensolver and hardware-efficient ansatz, our method designs the Clifford-based Hamiltonian transformation that (1) ensures a set of initial circuit parameters corresponding to the Hartree-Fock energy can be generated, (2) effectively maximizes the initial energy gradient with respect to circuit parameters, (3) imposes negligible overhead for classical processing and does not require additional quantum resources, and (4) is compatible with any circuit topology. We demonstrate the efficacy of our approach using a quantum hardware emulator, achieving chemical accuracy for systems as large as 12 qubits with fewer than 30 two-qubit gates. Our Clifford-based Hamiltonian engineering approach offers a promising avenue for practical quantum computational chemistry on near-term quantum devices.

Interpretable Prediction Modeling for Froth Flotation via Stacked Graph Convolutional Network

Real-time prediction of key quality variables based on data-driven soft sensor modeling is an important way to monitor flotation status and product quality in the froth flotation process. However, the existing data-driven methods have limitations in terms of nonlinear feature extraction and interpretability. In addition, the prevalent correlations between process variables can help improve the model interpretability and feature extraction of the model, but there are still challenges in exploring the potential correlations between process variables due to the complexity of the industrial process mechanism and the presence of high noise in process data. These relationships can be abstracted as edges between nodes in a graph representation. To fully explore the deep process-related structural features of data, a novel stacked graph convolutional network (S-GCN) is proposed. First, S-GCN aggregates the features of neighboring nodes by an adaptive adjacency matrix to achieve the extraction of structural features among process variables. Then, the labeled quality variable data detected by laboratory assay analysis are utilized to pre-train the network layer by layer to obtain better initial parameters and adjacency matrices for quality prediction. Finally, the prediction model is constructed based on the learned parameters to explore the interpretability of model and perform prediction tasks. Experimental results on real industrial potassium chloride froth flotation process data show a significant improvement in the prediction accuracy and interpretability of the proposed method.

Advancements in base isolation for seismic mitigation: Perspectives on elastomeric and lead rubber bearings

Base isolation represents an environmentally sustainable and highly effective technique for mitigating structural responses to strong seismic forces generated by plate boundary tectonic activities. Its primary benefits include preventing structural collapse, minimizing property damage, and safeguarding human lives during severe earthquakes. This passive, energy-efficient approach is often complemented by supplementary dampers to further reduce seismic impact. This study provides a comprehensive overview of elastomeric and lead rubber isolators, covering their theoretical, experimental, and numerical aspects. The paper examines the seismic response of structures equipped with elastomeric and lead rubber bearings, including a discussion of their pros and cons. This paper presents the comparison of fixed base and rubber isolated base in SAP 2000 to assess the effectiveness of base isolation. Additionally, it presents findings from shaking table tests, relevant building codes, and practical applications, considering the impact of events beyond initial design parameters. The review delves into the historical evolution of elastomeric and lead rubber-bearing systems, offering valuable insights into their contemporary understanding. Furthermore, the paper introduces three-dimensional isolators designed to attenuate ground motion responses in both horizontal and vertical directions. It investigates the effects of soil-structure interaction and evaluates isolator responses under blast and impact aircraft loading conditions. Notably, specific types of bearings exhibit exceptional energy dissipation capabilities during catastrophic seismic events, leading to reduced floor acceleration and inter-storey sway at critical levels. In conclusion, the study offers future recommendations and identifies potential constraints in this field

Learn to Adapt for Self-Supervised Monocular Depth Estimation.

Monocular depth estimation is one of the fundamental tasks in environmental perception and has achieved tremendous progress by virtue of deep learning. However, the performance of trained models tends to degrade or deteriorate when employed on other new datasets due to the gap between different datasets. Though some methods utilize domain adaptation technologies to jointly train different domains and narrow the gap between them, the trained models cannot generalize to new domains that are not involved in training. To boost the transferability of self-supervised monocular depth estimation models and mitigate the issue of meta-overfitting, we train the model in the pipeline of meta-learning and propose an adversarial depth estimation task. We adopt model-agnostic meta-learning (MAML) to obtain universal initial parameters for further adaptation and train the network in an adversarial manner to extract domain-invariant representations for easing meta-overfitting. In addition, we propose a constraint to impose upon cross-task depth consistency to compel the depth estimation to be identical in different adversarial tasks, which improves the performance of our method and smoothens the training process. Experiments on four new datasets demonstrate that our method adapts quite fast to new domains. Our method trained after 0.5 epoch achieves comparable results with the state-of-the-art methods trained at least 20 epochs.

Diagnostics of the airless wheel by the combined method

For construction and road machinery, tubeless tires made on the basis of elastic polyurethanes are being used more often. The diagnosis of airless tires made of elastic polymer materials in order to determine the conditions of their safe operation is becoming more and more relevant. The use of roller stands does not give accurate results, because the round support surface of the rollers introduces distortion. A method of obtaining basic parameters and characteristics on simple stands is proposed. Use their values for tire research and diagnostics in software environments such as Ansys, Solidworks, AutoCad. For the designed and manufactured sample of an airless wheel on the stands, the dependences of the change in the static radius of the wheel depending on the load and the change in the deflection of the wheel depending on the lateral force at different loads on the wheel were obtained. They became the basis for the initial parameters and characteristics of the wheel modeled in the Solidworks environment. Its properties were simulated for three cases of wheel rolling: “driven wheel”, “driving wheel” and “free wheel”. The obtained results show that the most difficult mode is the movement of the wheel on the deformable support base. In this mode, the opposite sides of the spokes work in tension and compression when blowing the spokes to a vertical state. The values of stress do not exceed the critical allowable ones during rupture or during compression for polyurethane. Therefore, it can be diagnosed that the wheel is in satisfactory condition.

Stabilization of roughness parameters of rolls of temper mill during idle running in stand

The main results of improving in the condition of the surface layer of the working rolls of the training mill with their joint rotation pressed against each other in a state without a trained strip, are considered in order to further minimize changes in the roughness parameters of the trained strip from roll to roll. The analysis of changes in the roughness parameters of the working rolls during their running-in as a result of the elastic-plastic interaction of the microsteps of their surfaces is carried out. Mathematical dependences are obtained for calculating of the roughness parameters of the working rolls during their idle running in the stand, taking into account the ergodicity function of the process. It is shown that the permissible degree of reduction of the height parameters of the rough layer of the working rolls before stabilization during their running is determined by contact loads, physical and mechanical properties of the material of the rolls, the initial parameters of the microgeometry of their surfaces and friction conditions, and the time to stabilize of the roughness parameters depends mainly on the contact load and the speed of running-in, decreasing with decreasing load and increasing speed. It is determined that the maximum possible reduction in the initial roughness of the roll treated with shot after idle running them in a stand with a coefficient of friction of 0.3 is on average 57 %. A further change in roughness is associated with the effect of wear of the rough layer during the training of a cold-rolled strip. The obtained mathematical dependencies allow you to assign processing parameters, such as the compression force of the rolls, the speed or at a given speed — the running-in time.

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.

Accurate classification techniques are essential for the early diagnosis and treatment of patients with diabetic retinopathy (DR). However, the limited amount of annotated DR data poses a challenge for existing deep-learning models. This article proposes a difficulty-aware and task-augmentation method based on meta-learning (DaTa-ML) model for few-shot DR classification with fundus images. The difficulty-aware (Da) method operates by dynamically modifying the cross-entropy loss function applied to learning tasks. This methodology has the ability to intelligently down-weight simpler tasks, while simultaneously prioritizing more challenging tasks. These adjustments occur automatically and aim to optimize the learning process. Additionally, the task-augmentation (Ta) method is used to enhance the meta-training process by augmenting the number of tasks through image rotation and improving the feature-extraction capability. To implement the expansion of the meta-training tasks, various task instances can be sampled during the meta-training stage. Ultimately, the proposed Ta method was introduced to optimize the initialization parameters and enhance the meta-generalization performance of the model. The DaTa-ML model showed promising results by effectively addressing the challenges associated with few-shot DR classification. The Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 blindness detection data set was used to evaluate the DaTa-ML model. The results showed that with only 1% of the training data (5-way, 20-shot) and a single update step (training time reduced by 90%), the DaTa-ML model had an accuracy rate of 89.6% on the test data, which is a 1.7% improvement over the transfer-learning method [i.e., residual neural network (ResNet)50 pre-trained on ImageNet], and a 16.8% improvement over scratch-built models (i.e., ResNet50 without pre-trained weights), despite having fewer trainable parameters (the parameters used by the DaTa-ML model are only 0.47% of the ResNet50 parameters). The DaTa-ML model provides a more efficient DR classification solution with little annotated data and has significant advantages over state-of-the-art methods. Thus, it could be used to guide and assist ophthalmologists to determine the severity of DR.

Evaluation of the effects of oxygen enrichment on combustion stability of biodiesel through a PSO-EMD-RBF model: An experimental study

<abstract> <p>In this study, we processed the flame images of biodiesel combustion in industrial furnaces, classified and evaluated flame states using digital image processing techniques, and proposed a combustion stability index (CSI) using the particle swarm optimization (PSO) algorithm. In order to more accurately predict the combustion stability under different oxygen concentrations, we proposed a method that combines the Multi-Input Radial basis function neural network (RBF-NN) with empirical mode decomposition (EMD). Initially, the EMD method was employed to decompose the original time series of CSI. Subsequently, a decomposition model incorporating initial parameters and CSI was established using the radial basis function. The results of the computations indicate that the EMD-RBF-NN model significantly outperforms existing models in enhancing the accuracy of CSI.</p> </abstract>

OPTICAL-GRAPHIC STUDIES OF HYDROGEN ADDITIVES’ EFFECTS ON DIESEL FUEL ATOMIZATION PARAMETERS

This paper considers a promising method of enhancing the effectiveness of diesel engines. This method uses the addition of hydrogen in a small amount (up to 2% by mass). The hydrogen additive is added to the high-pressure fuel line before the injector. Based on the experimental findings, a reduction in the engine’s specific fuel consumption of up to 3% was achieved in comparison to the baseline configuration. A research study was conducted at the Admiral Makarov National University of Shipbuilding using a newly established experimental setup to assess the impact of hydrogen additives on primary fuel delivery, spray characteristics, and overall engine performance. Among the experiments conducted, one investigated fuel atomization parameters, focusing on how the presence of hydrogen in the fuel influenced the fuel jet’s characteristics. A high-speed camera with a high resolution was used to record the optical-graphic study to isolate and extract individual shots of the torch’s expansion, thus obtaining images devoid of ignition and flickering. After conducting image processing and constructing jet models, along with subsequent analysis, it becomes apparent that the addition of hydrogen to the primary fuel results in an enhancement of spray quality. The torch volume expanded by approximately 10% to 15%, while the jet length diminished by approximately 8% to 10%. Consequently, the average diameter of the atomized fuel droplets decreases by up to 10%, with the extent of reduction contingent upon the initial parameters and configurations.

Control of Dynamic Biped Walking Robot

The objective of this article is to present control algorithms of biped dynamic walking robot. A mathematical model of the walking robot is compiled. The construction of the robot is based on minimalist design principles - absolutely solid body with two legs. Analytical dependencies between six coordinates of the body, coordinates of the left and right legs and absolute coordinates of the feet are defined. The block diagram of control system is presented. Differential equations of walking are compiled. The dynamic stability of biped walking robot near the required trajectory is provided by discrete control of the parameters: «when and where to put foot». To define these parameters, it is proposed to use an inverted mathematical pendulum as an ideal mechanism. Its linearized differential equations allow present the feet coordinates according to the measured initial and final parameters (at the begging and at the end of the step). The article paid particular attention to results of such control method with various walking modes. The coordinates of the robot body, its velocities and accelerations, angles of rotation were investigated. Calculations confirmed the small deviations of the proposed model of walking from the ideal one. The developed methodology allows improve the construction and control system of biped dynamic walking robots at the design stage. Some of the tasks, it can be used: design machines simulating human walking, with high speed and maneuverability, with point feet and synchronized with natural uncontrolled movements.

Pediatrik hasta popülasyonunda COVİD-19 sebebiyle hidroksiklorokin ve azitromisin kullanımın EKG parametreleri üzerine etkileri

Aim: Due to COVID-19 infection, the use of two drugs, hydroxychloroquine and azithromycin, with a high potential for arrhythmia, came to the fore in the pediatric patient group at the beginning of 2020, during the search for treatment. The aim is to reveal the synergistic arrhythmic effects of these two drugs in prolonging the QT interval on the ECG. Materials and Methods: First of all, patients taking hydroxychloroquine were identified. Demographic data of these patients were recorded. In addition to hydroxychloroquine, azithromycin and other treatments they used were also recorded. Those with ECG data were selected. Transmyocardial repolarization parameters calculated by ECG were calculated retrospectively (QT, QTc, Tpe, Tpe/QT, Tpe/QTc). Then, laboratory findings and radiological imaging of these patients were recorded. Results: Twenty-three pediatric patients who met the study criteria were identified. All of the patients were asymptomatic or mild disease. When initial and post-drug ECG parameters were compared; It was observed that the drugs did not have a significant arrhythmogenic effect on ECG parameters, especially QT interval and QTc. Conclusion: Unlike the literature showing arrhythmic effects of these drugs in adult COVID-19 disease, hydroxychloroquine and azithromycin did not show such an effect in the pediatric population.

Novel Hybrid SOR- and AOR-Based Multi-User Detection for Uplink M-MIMO B5G Systems

The Internet of Things (IoT) is one of the most important wireless sensor network (WSN) applications in 5G systems and requires a large amount of wireless data transmission. Therefore, massive multiple-input multiple-output (M-MIMO) has become a crucial type of technology and trend in the future of beyond fifth-generation (B5G) wireless network communication systems. However, as the number of antennas increases, this also causes a significant increase in complexity at the receiving end. This is a challenge that must be overcome. To reduce the BER, confine the computational complexity, and produce a form of detection suitable for 4G and B5G environments simultaneously, we propose a novel multi-user detection (MUD) scheme for the uplink of M-MIMO orthogonal frequency division multiplexing (OFDM) and universal filtered multi-carrier (UFMC) systems that combines the merits of successive over-relaxation (SOR) and accelerated over-relaxation (AOR) named mixed over-relaxation (MOR). Herein, we divide MOR into the initial and collaboration stages. The former will produce the appropriate initial parameters to improve feasibility and divergence risk. Then, the latter achieves rapid convergence and refinement performance through alternating iterations. The conducted simulations show that our proposed form of detection, compared with the BER performance of traditional SOR and AOR, can achieve 99.999% and 99.998% improvement, respectively, and keep the complexity at O(N2). It balances BER performance and complexity with fewer iterations.

A Hybrid Algorithms Based on the Aizawa Attractor and Rabbit-Lightweight Cipher for Image Encryption

Social media and networks rely heavily on images. Those images should be distributed in a private manner. Image encryption is therefore one of the most crucial components of cyber security. In the present study, an effective image encryption technique is developed that combines the Rabbit Algorithm, a simple algorithm, with the Attractor of Aizawa, a chaotic map. The lightweight encryption algorithm (Rabbit Algorithm), which is a 3D dynamic system, is made more secure by the Attractor of Aizawa. The process separates color images into blocks by first dividing them into bands of red, green, and blue (RGB). The presented approach generates multiple keys, or sequences, based on the initial parameters and conditions, which are then utilized for encrypting each one of the blocks. The peak signal-to-noise ratio (PSNR), Mean Squared Error (MSE), and structural similarity index measure (SSIM) tests are among performance tests that are used to assess the effectiveness of the approach. With the help of National Institute of Standards and Technology (NIST), the produced keys have been checked for randomness. Additionally, research is being done into how long decryption and encryption take. The encryption approach is particularly secure because of the chaotic attractor's unpredictability. The correlation coefficient, Shannon entropy, and a histogram are used to analyse the encryption technique's performance. The findings demonstrate that the suggested encryption approach dependably operates since it makes use of a mechanism to strengthen security against the attack of known or selected plain-text. The outcomes also demonstrated that both decoding and encoding using the suggested encoding approach require less time. For instance, images 3's decoding time is 0.002000, whereas images 3's encoding time is 0.001200.

Analysis of the Sum Minimization Possibilities of Heat Exchanger Core Masses in Internal Combustion Engine Cooling Systems

Rational designs for cooling systems (CS) of internal combustion engines are formulated in the form of a series of circulation circuits. The engine is integrated into a circuit with the highest and unregulated flow of internal circuit coolant (ICC). All heat exchangers are placed in circulation circuits with relatively reduced and adjustable ICC flow rates. While the circuits are usually interconnected, they can also be designed to operate independently. This CS scheme enables the achievement of the minimum possible value of the sum of masses of exchanger cores, denoted as MΣ. The reduction in MΣ is achieved through the regulation of ICC flow in a closed circulation loop involving two heat exchangers. The variations in MΣ based on the circuit parameters have been thoroughly investigated. The reduction in MΣ can also be applicable to more intricate systems. A decrease in MΣ, under identical initial CS parameters, may occur in different magnitudes, depending on the specific features of the CS scheme and the operating conditions of the heat exchangers within it. Cooling systems, constructed with the same initial parameters and comprising multiple circulation circuits that meet all criteria for rational design, may exhibit diverse configurations. Examples of such systems are explored, and the minimum values of MΣ are calculated for each. It has been determined that the disparity in the minimum values of MΣ for such systems, while maintaining equal efficiency, can exceed 30%. The selection of the optimal CS scheme is contingent not only on achieving the minimum possible value of MΣ but also on various other factors.

MODELING AND JUSTIFICATION OF THE DESIGN OF WIND TURN BLADES UNDER HIGH WIND SPEED CONDITIONS

One of the main problems that need to be addressed in the study is the use of renewable energy by justifying the design of the blades to reduce fuel consumption to reduce power by creating mechanical power using two or three-blade ordinary wind turbines. Each blade length was 0.22 meters and the pitch angle of the blade was 2 degrees to reach the maximum power from the wind as shown in the simulation depending on the Beta limit. Solidwork is used to design the turbine and to find its moment of inertia and initial parameters. Matlab was used to simulate the system equations to find the turbine power coefficient depending on the ideal pitch angle (Beta) and the tip speed ratio (Lambda), and also to find the required mechanical power to drive different small mechanical devices with power assumed 1400 watts. As a result, the wind turbine gives optimal power required at wind speed near 31.5 m /s (113.4 Km/h), and for less wind speed the device could work with less efficiency. Otherwise, high speed produces the best efficiency. Keywords: modeling, design, blades, power, wind turbine, renewable energy, efficiency.