The application of digital interferometric techniques in the measurement of forced convection in compact channels is examined. Michelson Interferometry and Mach–Zehnder Interferometry have been compared and contrasted, in terms of the suitability for temperature measurements. A wedge-fringe setting parallel to the heated surface has been utilized as the basis of the fringe analysis, for effective measurement in small dimension channels. Michelson interferometry was found to provide a larger number of data points in the visualization field, thus proving to be a better method of measurement in compact passages. Heat transfer parameters, useful in heat sink design, were also calculated.

Industrial Wireless Sensor Networks (IWSNs) play a vital role in today’s Industrial Internet of Things (IoT) systems. The existing Enhanced Energy Optimization Model has made progress in improving energy use; however, it still faces issues with adapting to changing network traffic, slow clustering convergence, and limited optimization capabilities. To address these problems, this paper presents a hybrid intelligent framework that improves energy savings, clustering convergence, and adaptive network optimization in IWSNs. The new model combines Enhanced Aphid–Ant Mutualism Optimization (EAAM) for efficient clustering, an Improved Artificial Bee Colony (I-ABC) algorithm for ongoing energy parameter adjustments, and a Graph Neural Network (GNN)-based predictor for adapting to changing topologies and traffic conditions. EAAM provides fair cluster-head selection and minimizes control overhead, while I-ABC adjusts transmission power and duty cycles to reduce energy loss. The GNN module captures spatial correlations between sensor nodes to provide proactive energy prediction and adaptive reconfiguration, with lower training overhead than deep reinforcement learning. Experimental results on benchmark IWSN datasets verify that the proposed EAAM–I-ABC–GNN framework exhibits 18%–25% better overall energy efficiency, a 22% increase in network lifetime, and 15% improved clustering stability compared to the baseline EEOM and traditional hybrid methods. These findings validate that the proposed hybrid model substantially improves scalability, flexibility, and sustainability, providing an efficient and intelligent solution for future Industrial IoT networks.