Comprehensive analyses of transport phenomena and thermodynamics of complex multiphysics systems are laborious and computationally intensive. Yet, such analyses are often required during the design of thermal and process equipment. As a remedy, this paper puts forward a novel approach to the prediction of transport behaviours of multiphysics systems, offering significant reductions in the computational time and cost. This is based on machine learning techniques that utilize the data generated by computational fluid dynamics for training purposes. The physical system under investigation includes a stagnation-point flow of a hybrid nanofluid (Cu−Al₂O₃/Water) over a blunt object embedded in porous media. The problem further involves mixed convection, entropy generation, local thermal non-equilibrium and non-linear thermal radiation within the porous medium. The SVR (Support Machine Vector) model is employed to approximate velocity, temperature, Nusselt number and shear-stress as well as entropy generation and Bejan number functions. Further, PSO meta-heuristic algorithm is applied to propose correlations for Nusselt number and shear stress. The effects of Nusselt number, temperature fields and shear stress on the surface of the blunt-body as well as thermal and frictional entropy generation are analysed over a wide range of parameters. Further, it is shown that the generated correlations allow a quantitative evaluation of the contribution of a large number of variables to Nusselt number and shear stress. This makes the combined computational and artificial intelligence (AI) approach most suitable for design purposes.

复杂多物理场系统的传输现象和热力学的综合分析是费力且计算量大的。然而，在热和工艺设备的设计过程中经常需要这样的分析。作为补救措施，本文提出了一种预测多物理场系统传输行为的新方法，显着减少了计算时间和成本。这是基于机器学习技术，该技术利用计算流体动力学生成的数据进行训练。正在研究的物理系统包括混合纳米流体（Cu-Al ₂ O ₃/水）在嵌入多孔介质中的钝物体上。该问题进一步涉及多孔介质内的混合对流、熵产生、局部热非平衡和非线性热辐射。SVR（支持机器向量）模型用于近似速度、温度、努塞尔特数和剪应力以及熵生成和 Bejan 数函数。此外，PSO 元启发式算法被应用于提出 Nusselt 数和剪应力的相关性。Nusselt 数、温度场和剪切应力对钝体表面的影响以及热和摩擦熵的产生在很宽的参数范围内进行了分析。更多，结果表明，生成的相关性允许对大量变量对 Nusselt 数和剪切应力的贡献进行定量评估。这使得计算和人工智能 (AI) 相结合的方法最适合设计目的。