Deep encoder–decoder hierarchical convolutional neural networks for conjugate heat transfer surrogate modeling
Conjugate heat transfer (CHT) analyses are vital for the design of many energy systems.
However, high-fidelity CHT numerical simulations are computationally intensive, which limits …
However, high-fidelity CHT numerical simulations are computationally intensive, which limits …
Control policy transfer of deep reinforcement learning based intelligent forced heat convection control
Deep reinforcement learning (DRL) has gradually emerged as a novel and effective method
for intelligent control of conjugate heat transfer. Through proper training, DRL agent usually …
for intelligent control of conjugate heat transfer. Through proper training, DRL agent usually …
Reconstruction of temperature field in nanofluid-filled annular receiver with fins using deep hybrid transformer-convolutional neural network
This study proposes a deep Hybrid Transformer-Convolutional Neural Network (HTCNN) to
reconstruct the temperature field in the nanofluid-filled Parabolic Trough Collector receivers …
reconstruct the temperature field in the nanofluid-filled Parabolic Trough Collector receivers …
[HTML][HTML] The Temperature Field Prediction and Estimation of Ti-Al Alloy Twin-Wire Plasma Arc Additive Manufacturing Using a One-Dimensional Convolution Neural …
N Pan, X Ye, P Xia, G Zhang - Applied Sciences, 2024 - mdpi.com
Plasma arc deposition as an additive manufacturing technology has unique advantages for
producing parts with complex shapes through layer-by-layer deposition. It is critical to predict …
producing parts with complex shapes through layer-by-layer deposition. It is critical to predict …