Prediction of the working conditions for the pulse tube cooler based on artificial neural network model

Thermal response analysis and parameter prediction of additively manufactured polymers

“…ANNs can predict hidden functional relationships between the effective processing parameters and polymer phase change or thermal behavior of the extrusion system as inputs and output of networks, respectively [74]. ANNs were developed to mimic the biological nervous system [75,76]. The nodes or neurons, weights and connections (dendrites and synapses) are the main components of ANNs.…”

Study on Phase-Shift Mechanism and Kriging-Based Global Optimization of the Active Displacer Pulse Tube Refrigerators

“…However, the current optimization method applied to the ADPTR is based on the linear variation of the optimization target with each operating parameter, which may not be so ideal in practice and presents a more complex variation pattern making the optimization more difficult. Therefore, a more scientifically rigorous optimization algorithm of the combination of artificial intelligence optimization algorithms and efficient agent modeling has be applied to PTRs, such as the combination of Response Surface Methodology (RSM) and Non-Sorted Genetic Algorithm II to optimize ITPTR [22], artificial neural network and particle swarm optimization to optimize orifice PTR [23], and artificial neural network (ANN) model to predict and optimize compressor operating condition of ITPTR [24]. The abovementioned main application object of the optimization design research is still focused on PTR with a traditional phase shifter, where there are few studies related to the optimization of design parameters for the ADPTR, and we will perform a further in-depth analysis of the ADPTR with more advanced optimization algorithms in the future.…”