Numerical Simulation of Single‐Jet Impingement Cooling of a Seamless Steel Tube

Rui, Zhang; Zhenlei, L; Chen, Dong; Yuan, Guo

doi:10.1002/srin.202100708

Cited by 2 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But before going into detail, let's quickly come back to this concept called neural networks [21][22][23] A neural network extracts nonlinear patterns from data. it is therefore a set of interconnected formal neurons allowing the resolution of complex problems such as pattern recognition or natural language processing, thanks to the adjustment of the weighting coefficients in a learning phase [24][25][26][27][28].A neural network is inspired by the functioning of biological neurons and takes shape in a computer in the form of an algorithm. The neural network can modify itself according to the results of its actions, which allows learning and problem solving without an algorithm, therefore without classical programming [29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Amroun,

Hafid,

Ammi

2022

IJICS

View full text Add to dashboard Cite

One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate the representation of a particular phenomenon. Indeed, the computational cost generated by some fluid mechanics models pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. This paper proposes a new framework called OPTI-ENS: a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. An approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e-09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Amroun,

Hafid,

Ammi

2022

IJICS

View full text Add to dashboard Cite

show abstract

Numerical simulation of single-jet impact cooling and double-jet impact cooling of hot-rolled L-shaped steel based on multiphase flow model

Li,

Zhao,

Zhang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

In this paper, numerical simulations of single-jet impingement cooling and double-jet impingement cooling processes of heated L-shaped steel are carried out using the VOF model. The SIMPLEC pressure–velocity coupling algorithm and realizable k-ε model are used for the solution. The effects of jet position, water flow, and jet distance in the single-jet condition are analyzed in the simulations. The distributions of impact pressure, turbulence kinetic energy, and Nusselt number were obtained, as well as the variation of the peak values of these three factors with the jet position, water flow, and jet distance. The water flow rate is 3–11 L/min, and the jet distance is 5–25 cm. The effect of the distance between the two nozzles on the jet cooling uniformity under the dual jet condition was also analyzed. The distance between the two nozzles was 15–45 mm. The results showed that the variation of water flow rate had a greater effect on the ability of jet cooling compared with the jet position and jet distance, and the heat transfer efficiency also increased gradually with the increase of water flow, but the increased rate of heat transfer efficiency decreased gradually. When the flow rate increased from 3 to 11 L/min, the maximum instantaneous cooling rates at 1/4 of the thickness of the short side upper side, long side upper side, short side lower side, and long side lower side positions increased by 38.9%, 48.5%, 48.2%, and 32.9%, respectively. To ensure that the jet does not shift, the jet distance should be less than or equal to 10 cm. In the case of the double jet, the nozzle distance is 1.5 cm, and the cooling uniformity of the cooling area between the two nozzles is better. The peak Nusselt number in the cooling area of each part under the double jet cooling condition increased by 5%, 9.4%, 10.2%, and 13.3%, respectively, compared with the single jet.

show abstract

Numerical Simulation of Single‐Jet Impingement Cooling of a Seamless Steel Tube

Cited by 2 publications

References 26 publications

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Numerical simulation of single-jet impact cooling and double-jet impact cooling of hot-rolled L-shaped steel based on multiphase flow model

Contact Info

Product

Resources

About