This article presents the theoretical and experimental performance of a Solar Cavity Receiver (SCR) which operates under controlled conditions of radiation and flow regulation using an Artificial Neural Network (ANN); the objective of this application of neural networks is to obtain the maximum temperature of a fluid in an open hydraulic circuit that presents variations in pressure and temperature of the inlet fluid. The SCR is evaluated for two cases: In the first case, inlet fluid presents a constant temperature and pressure variations of 62–96 kPa, and in the second case, the system presents variations in pressure 55–89 kPa and temperature 29 °C to 31 °C; it was found that an ANN presents a response with a varied range of 2–14%, with average temperature variations lower than 6% respect to the set-point, with the advantage of being self-regulated through ANN training.

本文介绍了使用人工神经网络 (ANN) 在受控辐射和流量调节条件下运行的太阳腔接收器 (SCR) 的理论和实验性能；神经网络应用的目的是获得开放液压回路中流体的最高温度，该回路呈现入口流体的压力和温度变化。SCR 在两种情况下进行评估：在第一种情况下，入口流体呈现恒定的温度和 62-96 kPa 的压力变化，在第二种情况下，系统的压力变化在 55-89 kPa 和温度 29 °C 到31℃；发现人工神经网络的响应变化范围为 2-14%，平均温度变化低于设定点的 6%，