The utilization of various techniques, both passive and active, to enhance heat transfer in fluids by scientists is continually advancing. One of the active methods being explored is the impact of a magnetic field (B) on flow to improve heat transfer. This study focuses on experimentally evaluating the effectiveness of B and the rotation of B on heat transfer of ferrofluid. The findings indicate that by increasing the volume fraction of nanoparticles, heat transfer can be enhanced by approximately 2.73–2.82 %. Furthermore, the use of B and intensifying its intensity leads to a 3.75–3.8 % enhancement in heat transfer. Rotating the B and increasing the rotational speed also contribute to improved heat transfer, with a 0.2 rad/s increase in rotation speed resulting in a 5%–12.43 % improvement in heat transfer. By utilizing available data and employing artificial intelligence methods such as Group Method of Data Handling (GMDH) and Long Short-Term Memory (LSTM), an estimation of the Nusselt number () has been achieved. The outcomes demonstrate that both models have displayed high accuracy in predicting , with GMDH showing superior accuracy compared to LSTM in estimating

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基于人工智能的旋转磁场下铁磁流体流动传热强化预测：实验研究

科学家们不断利用各种被动和主动技术来增强流体的传热。正在探索的积极方法之一是通过磁场 (B) 对流动的影响来改善传热。本研究的重点是通过实验评估 B 和 B 的旋转对铁磁流体传热的有效性。研究结果表明，通过增加纳米粒子的体积分数，传热可以增强约 2.73-2.82%。此外，B 的使用并增强其强度可使传热增强 3.75-3.8%。旋转 B 并提高转速也有助于改善传热，转速每增加 0.2 rad/s，传热就会改善 5%–12.43%。通过利用现有数据并采用分组数据处理方法（GMDH）和长短期记忆（LSTM）等人工智能方法，已经实现了努塞尔数（）的估计。结果表明，两种模型都显示出较高的预测准确性，其中 GMDH 在估计方面比 LSTM 表现出更高的准确性。