Gao Peng, Zhuo Yue, Sun Guodong
Peng Gao, School of Mechanical and Intelligent Manufacturing, Jiujiang University, Jiujiang, 332005, China.
Technology Department of Library, Yue Zhuo, Jiujiang University, Jiujiang, 332005, China.
Sci Rep. 2025 Sep 1;15(1):32081. doi: 10.1038/s41598-025-15694-2.
While existing mathematical formulas have provided empirical references, significant discrepancies were observed in the calculated heat transfer coefficient (HTC) values when these formulations were applied to high-pressure water descaling processes. Consequently, the analysis of the descaling temperature field was rendered inaccurate. This study undertook a systematic investigation of the impacts of various operational variables, including water flow rate, nozzle-to-billet standoff distance, nozzle geometric parameters, and installation configuration, which were subsequently converted into water flux. Following a parametric analysis of the effects of water flux and surface temperature, comprehensive computational fluid dynamics (CFD) simulations were performed to quantitatively assess the HTC in high-pressure water descaling processes. To improve practical applicability, a mathematical model for the HTC in high-pressure water descaling was developed through nonlinear regression analysis, thereby establishing a predictive relationship between process parameters and heat transfer characteristics. The HTC under operational conditions was quantitatively assessed by employing a regression-based mathematical model formulated in this study, which delineated explicit relationships between process variables and thermal transport mechanisms in high-pressure water descaling systems. The HTC values obtained were subsequently utilized in finite element analysis (FEA) to perform numerical simulations of thermal profiles during the descaling process. To validate the thermal behavior predicted by the FEA, real-time infrared thermography measurements of billet surface temperature were conducted during industrial-scale descaling operations, thereby establishing a comparative validation framework between numerical simulations and field-acquired thermal data. The results indicated that the maximum discrepancy between the simulated and measured temperatures was 28 °C, while the minimum discrepancy was recorded at 1 °C. A quantitative correlation was established between the numerical predictions and experimental measurements, thereby affirming the predictive accuracy of the regression-derived HTC model developed in this study.
虽然现有的数学公式提供了经验参考，但将这些公式应用于高压水除鳞过程时，计算得到的传热系数（HTC）值存在显著差异。因此，除鳞温度场的分析变得不准确。本研究系统地调查了各种操作变量的影响，包括水流量、喷嘴与坯料的间距、喷嘴几何参数和安装配置，随后将这些变量转换为水通量。在对水通量和表面温度的影响进行参数分析之后，进行了全面的计算流体动力学（CFD）模拟，以定量评估高压水除鳞过程中的HTC。为了提高实际适用性，通过非线性回归分析建立了高压水除鳞中HTC的数学模型，从而建立了工艺参数与传热特性之间的预测关系。本研究制定的基于回归的数学模型对操作条件下的HTC进行了定量评估，该模型描绘了高压水除鳞系统中工艺变量与热传输机制之间的明确关系。随后将获得的HTC值用于有限元分析（FEA），以对除鳞过程中的热分布进行数值模拟。为了验证FEA预测的热行为，在工业规模的除鳞操作期间对坯料表面温度进行了实时红外热成像测量，从而建立了数值模拟与现场获取的热数据之间的比较验证框架。结果表明，模拟温度与测量温度之间的最大差异为28°C，最小差异为1°C。在数值预测和实验测量之间建立了定量相关性，从而证实了本研究中开发的基于回归的HTC模型的预测准确性。
