There is an impulsion to introduce machine-learning algorithms into the thermo-fluids areas.
Machine learning modeling, especially in high-precision regression problems, relies on …

Surrogate models of temperature field calculation based on deep learning have gained
popularity in recent years because it does not need to establish complex mathematical …

Deep neural networks (DNNs) are promising alternatives to simulate physical problems.
These networks are capable of eliminating the requirement of numerical iterations. The …

Temperature field prediction is of great importance in the thermal design of systems
engineering, and building a surrogate model is an effective method for the task. Ensuring a …

Predicting the performance of thermoelectric generators (TEGs) is an essential part of
designing high‐performance TEGs. However, due to the complexity of the TEG system, the …

Calculation of transient temperature fields is widely used in engineering application and is
also very crucial. Nevertheless, the existing methods for the prediction of complex transient …

With several advantages and as an alternative to predict physics field, machine learning
methods can be classified into two distinct types: data-driven relying on training data and …

Failure to blow ash on the heated surface of the boiler will cause a drop in heat transfer rate
and even industrial safety accidents. Nowadays, the shortcomings of the fixed soot blowing …

Flow through complex thermodynamic machinery is intricate, incorporating turbulence,
compressibility effects, combustion, and solid–fluid interactions, posing a challenge to …

In the present study, an advanced type of artificial intelligence, a deep neural network, is
employed to learn the physic of conduction heat transfer in 2D geometries. A dataset …