Scholarlink

<p>In the past decade, piezoelectric wind energy harvesters have proven to be an effective method for generating electrical energy from fluid flow in small-scale applications. However, their performance is often limited by low power output, which necessitates the exploration of bluff body shape designs. The bluff body with angles and semicircles was adapted from previous research on opening angle designs to enhance aerodynamic forces, thereby increasing energy collection efficiency. This study was conducted to develop a piezoelectric energy harvester (PEH) using various bluff body configurations, evaluate its performance through experimental methods, and validate the results using an Artificial Neural Network (ANN). The experiment was conducted using a wind tunnel structure equipped with a wind source and integrating piezoelectric elements with beams to optimize power output, resulting in a 2-DOF indirectly driven (PEH) with high reliability and wide bandwidth. Experimental research was conducted on eight bluff body geometries, namely (100°, 120°, 140°, 160°, R5, R6, R7, and R8) with varying cutting angles and semicircular profiles across wind speeds from 1 to 10&#xa0;m/s. Among the tested configurations, the R8 geometry achieved the highest mean power output of 74.4&#xa0;mW, corresponding to an observed increase of approximately 25.9% relative to the 160° configuration. Prediction using an Artificial Neural Network (ANN) was carried out with a Radial Basis Function Network (RBFN), which was trained on the experimental dataset to predict the output for the new 110° and R5.5 bluff body shapes. The RFBN results show an R<sup>2</sup> value of 0.9675 for the 110° bluff shape and 0.9974. The results from the predictions were validated with experimental data, and it was found that the new R5.5 and 110° bluff body configurations showed a strong correlation for the proposed prediction model. This finding represents a significant advancement in piezoelectric systems to enable self-powered wireless sensor nodes for structural health monitoring, the Internet of Things (IoT), and smart agriculture.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Wind-Induced Piezoelectric Energy Harvester Optimization and ANN-Based Performance Prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月16日

查看全文
  • Muhammad Mohamed Salleh,
  • Izzuddin Zaman,
  • Khairul Rijal Wagiman,
  • Mohd Shukri Mokhtar,
  • Anis Nabihah Ibrahim,
  • Mohamad Zaky Chumari,
  • Mathan Sambu,
  • Muhammad Izzat Badrin

摘要

In the past decade, piezoelectric wind energy harvesters have proven to be an effective method for generating electrical energy from fluid flow in small-scale applications. However, their performance is often limited by low power output, which necessitates the exploration of bluff body shape designs. The bluff body with angles and semicircles was adapted from previous research on opening angle designs to enhance aerodynamic forces, thereby increasing energy collection efficiency. This study was conducted to develop a piezoelectric energy harvester (PEH) using various bluff body configurations, evaluate its performance through experimental methods, and validate the results using an Artificial Neural Network (ANN). The experiment was conducted using a wind tunnel structure equipped with a wind source and integrating piezoelectric elements with beams to optimize power output, resulting in a 2-DOF indirectly driven (PEH) with high reliability and wide bandwidth. Experimental research was conducted on eight bluff body geometries, namely (100°, 120°, 140°, 160°, R5, R6, R7, and R8) with varying cutting angles and semicircular profiles across wind speeds from 1 to 10 m/s. Among the tested configurations, the R8 geometry achieved the highest mean power output of 74.4 mW, corresponding to an observed increase of approximately 25.9% relative to the 160° configuration. Prediction using an Artificial Neural Network (ANN) was carried out with a Radial Basis Function Network (RBFN), which was trained on the experimental dataset to predict the output for the new 110° and R5.5 bluff body shapes. The RFBN results show an R2 value of 0.9675 for the 110° bluff shape and 0.9974. The results from the predictions were validated with experimental data, and it was found that the new R5.5 and 110° bluff body configurations showed a strong correlation for the proposed prediction model. This finding represents a significant advancement in piezoelectric systems to enable self-powered wireless sensor nodes for structural health monitoring, the Internet of Things (IoT), and smart agriculture.