Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.
This paper presents a method for optimizing blade designs in smart rotors; the objective is to maximize power regardless of wind conditions. An extensive analysis of what is known as “smart blades,” from aeronautical solutions and helicopter rotors is provided. Moreover, trends in computational and experimental research are analyzed, an assessment and categorization of the options available for aerodynamic control surfaces are made. The study and analysis of its main components such as sensors, mechanisms of actuation, and materials are included. Advance research in this technology is presented as a potential solution for more efficient blade designs, and methods for reducing aerodynamic loads are discussed.
Wind energy technology is facing new challenges due to the increment in rotor diameter. Nowadays, several studies focus on the development of new flow control methods for load alleviation, in order to increase the lifetime of the blades. This paper describes a shape morphing-based method for smart blades. The study includes an aerodynamic model with a computational search algorithm to find the optimal Cp. A section with shape morphing technology was developed to prove the performance of the method. The smart blade prototype section incorporates a novel structure with a flexible skin and a compliant mechanism. This deformable structure achieves the required displacements for different NACA profiles through camber morphing. In this way, the efficiency and the load variations are improved. The compliant mechanism has to be as light as possible and it has to be competitive in cost. In order to achieve these limitations, different actuating mechanisms were evaluated. Among different possibilities, servo actuators presented higher load/weight capabilities and the required displacement ratios to cover the entire deformable range. The airfoil is modified according to the wind condition and the wind speed is the input variable for controlling the actuators displacement. The control algorithm has a very high frequency response; in this way, the blade profile can be modified in a shorter time and it can respond to high wind velocity variations. Therefore, a deformable section improves the overall performance of wind turbines since it increases power and extends the lifetime of the blades.
This article presents the analysis of the performance of a flexible wind turbine blade. The simulation analysis is based on a 3 m span blade prototype. The blade has a flexible surface and a cam mechanism that modifies the aerodynamic profile and adapts the surface to different configurations. The blade surface was built with a flexible fiberglass composite, and the internal mechanism consists of a flexible structure actuated with an eccentric cam. The cam mechanism deforms five sections of the blade, and the airfoil geometry for each section was measured from zero cam displacement to full cam displacement. The measured data were interpolated to obtain the aerodynamic profiles of the five sections to model the flexible blade in the simulation process. The simulation analysis consisted of determining the different aerodynamic coefficients for different deformed surfaces and a range of wind speeds. The aerodynamic coefficients were calculated with the BEM method (QBlade®); as a result, the data performance of the flexible blade was compared for the different deformation configurations. Finally, a decrease of up to approximately 6% in the mean bending moment suggests that the flexible turbine rotor presented in this article can be used to reduce extreme and fatigue loads on wind turbines.
Nowadays, the growth in the diameter of the rotors and the power capacity of the machinery require the application of constant monitoring to predict failures and reduce maintenance activities. These activities prevent emergency stops and failures that require the replacement of components; however, these operations involve a high cost. The system presented in this paper integrates a fast data processing method that can analyze monitoring signals in real-time. The overall system includes a set of sensors for the measurement of acceleration, angular velocity, temperature, voltage, electrical current, and oil quality. The system presented is able to produce a frequency spectrum with a length of 8192 points. The FFT module was implemented in a FPGA (Field Programmable Gate Array) and it generates in real time, the frequency spectrum. This system adjusts the dominant frequencies to the wind turbine velocity.
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