Simulation-driven optimization of direct solar dryers for household use: A combined CFD and ANN-GA approach

IF 5.4 3区工程技术 Q2 ENERGY & FUELS

Thermal Science and Engineering Progress Pub Date : 2025-09-18 DOI:10.1016/j.tsep.2025.104112

Kittipos Loksupapaiboon , Panit Kamma , Juthanee Phromjan , Siwakorn Phakdee , Machimontorn Promtong , Jetsadaporn Priyadumkol , Chakrit Suvanjumrat

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引用次数: 0

Abstract

This study introduces a novel, integrated optimization framework for domestic solar dryers that uniquely combines computational fluid dynamics (CFD), artificial neural networks (ANN), and genetic algorithms (GA) to achieve superior thermal uniformity and enhanced drying performance. Unlike conventional trial-and-error or replication-based designs—which often result in non-uniform temperature fields and inefficient energy usage—this research systematically addresses heat distribution challenges through a data-driven and simulation-validated approach. CFD simulations, conducted using OpenFOAM and validated via no-load experimental testing, revealed non-uniform drying patterns during initial trials with pineapple slices. These findings informed the development of a machine learning model, where a validated CFD dataset (error <7.33 %) was used to train an ANN-GA system. This hybrid model achieved high predictive accuracy (R² = 0.98) with an average error of only 3.87 %, enabling precise prediction and optimization of dryer performance. The optimized configuration delivered an exceptionally uniform temperature distribution (mean 46.15 °C, SD = 0.07 °C), making a significant advancement over conventional designs. The integration of CFD-based physical modeling with AI-driven optimization constitutes a key innovation of this study, offering a replicable and scalable method for the development of high-efficiency domestic solar drying systems.

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家用直接太阳能干燥机的仿真驱动优化：CFD和ANN-GA相结合的方法

本研究介绍了一种新型的集成优化框架，该框架独特地结合了计算流体动力学（CFD）、人工神经网络（ANN）和遗传算法（GA），以实现优越的热均匀性和增强的干燥性能。与传统的试错或基于复制的设计不同，这些设计通常会导致温度场不均匀和能源使用效率低下，该研究通过数据驱动和模拟验证的方法系统地解决了热量分布的挑战。使用OpenFOAM进行CFD模拟，并通过空载实验测试进行验证，结果显示菠萝片在初始试验期间的干燥模式不均匀。这些发现为机器学习模型的开发提供了信息，其中使用经过验证的CFD数据集（误差<； 7.33%）来训练ANN-GA系统。该混合模型具有较高的预测精度（R2 = 0.98），平均误差仅为3.87%，实现了对干燥机性能的精确预测和优化。优化后的配置提供了异常均匀的温度分布（平均46.15°C, SD = 0.07°C），与传统设计相比有了显著的进步。将基于cfd的物理建模与人工智能驱动的优化相结合是本研究的关键创新，为高效家用太阳能干燥系统的开发提供了可复制和可扩展的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Thermal Science and Engineering Progress Chemical Engineering-Fluid Flow and Transfer Processes

CiteScore

7.20

自引率

10.40%

发文量

327

审稿时长

41 days

期刊介绍： Thermal Science and Engineering Progress (TSEP) publishes original, high-quality research articles that span activities ranging from fundamental scientific research and discussion of the more controversial thermodynamic theories, to developments in thermal engineering that are in many instances examples of the way scientists and engineers are addressing the challenges facing a growing population – smart cities and global warming – maximising thermodynamic efficiencies and minimising all heat losses. It is intended that these will be of current relevance and interest to industry, academia and other practitioners. It is evident that many specialised journals in thermal and, to some extent, in fluid disciplines tend to focus on topics that can be classified as fundamental in nature, or are ‘applied’ and near-market. Thermal Science and Engineering Progress will bridge the gap between these two areas, allowing authors to make an easy choice, should they or a journal editor feel that their papers are ‘out of scope’ when considering other journals. The range of topics covered by Thermal Science and Engineering Progress addresses the rapid rate of development being made in thermal transfer processes as they affect traditional fields, and important growth in the topical research areas of aerospace, thermal biological and medical systems, electronics and nano-technologies, renewable energy systems, food production (including agriculture), and the need to minimise man-made thermal impacts on climate change. Review articles on appropriate topics for TSEP are encouraged, although until TSEP is fully established, these will be limited in number. Before submitting such articles, please contact one of the Editors, or a member of the Editorial Advisory Board with an outline of your proposal and your expertise in the area of your review.