This study introduces a new method to locate cracks in wind turbine blades using the support vector machine algorithm and the tangential vibration signal measured at the root blade in static conditions. The method was implemented in hardware and experimentally validated on 200 W wind turbine blades. The blade conditions were healthy, and transverse cracked at the root, midsection, and tip. The experimental procedure is easy, and only one low-cost piezoelectric accelerometer is needed, which is affordable and straightforward to install. The machine learning technique used requires a small dataset and low computing power. The results show exceptional performance, achieving an accuracy of 99.37% and a precision of 98.77%. This approach enhances the reliability of wind turbine blade monitoring. It provides a robust early detection and maintenance solution, improving operational efficiency and safety in wind energy production. K-nearest neighbors and decision trees are also used for comparison purposes.

Wind turbine blades in excessive wind conditions present extreme deflection problems. For this reason, an analysis of the structural response of composite reinforced box beams is developed. For this purpose, reinforced box beams were fabricated to improve the bending strength in the flapwise direction of the wind turbine blades. The box beams were analyzed with three-dimensional models using the Finite Element Method (FEM) and validated with bending tests at four-points and two-points. The box beam meets the characteristics of lightness and mechanical strength. Experimental four-point bending results showed that reinforced cross-sections decrease displacements by 30.09% and increase their stiffness to 43.41% for a box beam without structural reinforcement. In addition, the two-point bending results showed a difference of 18.98% between the displacements of the beams with structural reinforcements. In the FEM analysis, a maximum error of 11.24% was obtained when correlating the maximum displacement value with the experimental results of the beams.

The growing energy demand has increased the number of wind turbines, raising the need to monitor blade health. Since blades are prone to damage that can cause severe failures, early detection is crucial. Machine learning-based monitoring systems can identify and locate cracks without interrupting energy production, enabling timely maintenance. This study provides a comparative analysis and approach to the application and effectiveness of different vibration-based machine learning algorithms to detect the presence of cracks, identify the cracked blade, and locate the zone where the crack occurs in rotating blades of a small wind turbine. The datasets comprise root vibration signals, derived from healthy and cracked blades of a wind turbine in operational conditions. In this study, the blades are not considered identical. The sampling set dimension and the number of features were variables considered during the development and assessment of different models based on decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), and multilayer perceptron algorithms (MLP). Overall, the KNN models are the clear winners in terms of training efficiency, even as the sample size increases. DT is the most efficient algorithm in terms of test speed, followed by SVM, MLP, and KNN.

A firewood stove’s combustion chamber can withstand temperatures of 1500 °C. To prevent the deterioration of a firewood stove due to excessive heat, it is necessary to use thermal insulation materials that stop heat transfer to the walls. These materials must be economical and durable. This work examines the materials used in the construction of combustion chambers of firewood stoves in southern Mexico and Central America. This field study collects information and samples of materials used in the manufacture of firewood stoves. Heat transfer experiments are conducted, and the thermal properties of each material are analyzed. As a result, methodology and information is provided for the manufacture of future plancha-type firewood stoves used in the study area, such as pine wood (pinus chiapensis) which is mainly used as casing for firewood stoves in coniferous forest areas; in addition, the use of wood ash as thermal insulation material is proposed since it does not present direct costs and has a thermal conductivity between 0.10 and 0.20 W/m°C and a melting point greater than 1500 °C. The next layer proposed is hollow brick, a high-temperature-resistant material that can be used as support due to its mechanical strength and low thermal conductivity of 0.6 W/m°C. Finally, the use of calcium hydroxide as a coating material is proposed, applied in the form of a paste or paint to detail the imperfections of the combustion chamber construction as it resists temperatures above 1000 °C.

Approximately 2.3 billion people still cook over open fires or on basic stoves using polluting fuels, generating indoor air pollution responsible for 3.7 million premature deaths annually. Progress toward real-world health impact has been constrained by a persistent disconnect between computational fluid dynamics (CFD) modeling, standardized experimental evaluation, and sociocultural adoption research. This scoping review maps the current state of evidence across these three domains, analyzing 143 peer-reviewed studies published between 2007 and 2025 using predefined inclusion criteria and bibliometric analysis with VOSviewer v.1.6.20. Thirteen cookstove technologies were characterized by compiling heterogeneous evidence from Water Boiling Tests (WBTs), CFD simulations with k-ε turbulence closure, and CO and PM_2.5 emission protocols. Direct combustion stoves achieve thermal efficiencies of 10–21% under real-world conditions, while TLUD gasifiers and forced-draft systems with densified fuels reach 30–47%. Bibliometric analysis reveals that engineering, epidemiology, and social sciences operate as isolated research communities. None of the technology reviewed simultaneously integrated computational validation, field emissions assessment, and clinical impact evaluation; this a gap remains the central barrier to translating laboratory performance into measurable public health outcomes. These findings point toward integrated research designs connecting fluid dynamic optimization with exposure modeling, clinical follow-up, and the sociocultural needs of communities.

This work presents conditioned and normalized vibration signal datasets acquired from the spanwise axis of the three blades of a rotating bladed system operating at a constant rotational speed of 240 rpm. The conditioned dataset was obtained using piezoelectric accelerometers mounted at the blade roots. The accelerometer output signals were conditioned and recorded by a dedicated data acquisition system. The signals were acquired under both healthy and damaged operating conditions. Baseline vibration signals were first recorded with all three blades in a healthy condition. Subsequently, cracks were deliberately introduced at three different locations along the blade span—the root, middle, and tip zones. Each crack location was independently evaluated on each of the three blades, resulting in a comprehensive dataset that includes healthy operation and all combinations of blade–damage locations. The datasets enable analysis of the system’s vibratory response and of dynamic information propagation toward the blade root, depending on the crack zone. Their main contribution is to provide reliable experimental data for the development, validation, and benchmarking of vibration-based diagnostic and structural health monitoring techniques. Furthermore, the datasets serve as valuable resources for advancing early crack detection strategies and enhancing the reliability of rotating industrial equipment with blades, such as fans, compressors, turbines, and aerogenerators.