The regulators for thermal energy transfer, performances of heat exchangers, turbine blades subject to cooling structure, and energy storage procedures claim the use of a heated fluid with partially heated circular obstructions rooted in confined domains. Owing to such importance we consider a partially heated double forward-facing step (DFFS). To be more specific, from the inlet of DFFS, the viscous stream flows in parabolic form and the Neumann condition is implemented at the outlet. At each wall, no slip is incorporated. The mathematical formulation is constructed to narrate the flow field. The translation of the centers of mounted heated obstructions is considered in three separate situations. For every event, the strength of the Nusselt number is debated numerically. For all cases, the drag coefficient for partially heated obstruction is found a decreasing function of the Reynolds number. Besides this, for better estimation of Drag Coefficient (DC) and Lift Coefficient (LC), an artificial neural network (ANN) model with multilayer perceptron (MLP) is developed. MoD values shows that the error rates of the ANN model are very low. The findings show that the constructed ANN model can accurately predict DC and LC values with very low error rates.

用于热能传递的调节器、热交换器的性能、经受冷却结构的涡轮叶片和能量存储程序声称使用具有部分加热的圆形障碍物的加热流体，该障碍物植根于受限区域。由于如此重要​​，我们考虑部分加热的双前向台阶 (DFFS)。更具体地说，从 DFFS 的入口开始，粘性流以抛物线形式流动，并且在出口处执行诺伊曼条件。在每面墙上，没有滑动。构造数学公式来叙述流场。在三种不同的情况下考虑安装的加热障碍物的中心的平移。对于每个事件，努塞尔数的强度都在数字上进行辩论。对于所有情况，部分加热障碍物的阻力系数是雷诺数的递减函数。除此之外，为了更好地估计阻力系数（DC）和升力系数（LC），开发了一种具有多层感知器（MLP）的人工神经网络（ANN）模型。MoD 值表明 ANN 模型的错误率非常低。研究结果表明，构建的人工神经网络模型可以准确地预测 DC 和 LC 值，并且错误率非常低。