Scholarlink

<p>Digital twin is a cutting-edge technology in the energy industry, capable of predicting real-time operation data for equipment performance monitoring and operational optimization. However, methods for calibrating and fusing digital twin prediction with limited in-situ measured data are still lacking, especially for equipment involving complicated multiphase flow and chemical reactions like coal-fired boilers. In this work, using coal-fired boiler water wall temperature monitoring as an example, we propose a digital twin approach that reconstructs the water wall temperature distribution with high spatial resolution in real time and calibrates the reconstruction using in-situ water wall temperature data. The digital twin is established using the gappy proper orthogonal decomposition (POD) reduced-order model by fusing CFD solutions and measured data. The reconstruction accuracy of the digital twin was initially validated. And then, the minimum number of measured data sampling points required for precise reconstruction was investigated. An improved uniform data collection method was subsequently developed. After that, the computational time required for the digital twin and the traditional CFD was compared. Finally, the reconstruction method was further validated by in-situ measured temperature from the in-service boiler. Results indicate that the established digital twin can precisely reconstruct the water wall temperature in real time. Thirty-nine sampling points are sufficient to reconstruct the temperature distribution with the original data collection method. The proposed uniform data collection method further reduces the mean relative errors to less than 0.4% across four test cases, and with the constrained technique, the errors decrease to 0.374% and 0.345% for Cases 1 and 3, which had poor reconstructions using the original sampling point arrangement. In addition, the reconstruction time of the digital twin is also considerably reduced compared to CFD. Engineering application indicates that the reconstructed temperatures are highly consistent with in-situ measured data. The established water wall temperature digital twin is beneficial for water wall tube overheating detection and operation optimization.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Self-Calibrating Digital Twin Approach by Integrating CFD and Sensor Data for Coal-Fired Boiler Water Wall Temperature

  • 期刊论文

  • 发表日期：

    2025年4月15日

查看全文
  • Tianyi Wang,
  • Wenqi Zhong,
  • Xi Chen,
  • Qilei Ma,
  • Yonghua Gu,
  • Wenli Dong,
  • Zhichao Pan

摘要

Digital twin is a cutting-edge technology in the energy industry, capable of predicting real-time operation data for equipment performance monitoring and operational optimization. However, methods for calibrating and fusing digital twin prediction with limited in-situ measured data are still lacking, especially for equipment involving complicated multiphase flow and chemical reactions like coal-fired boilers. In this work, using coal-fired boiler water wall temperature monitoring as an example, we propose a digital twin approach that reconstructs the water wall temperature distribution with high spatial resolution in real time and calibrates the reconstruction using in-situ water wall temperature data. The digital twin is established using the gappy proper orthogonal decomposition (POD) reduced-order model by fusing CFD solutions and measured data. The reconstruction accuracy of the digital twin was initially validated. And then, the minimum number of measured data sampling points required for precise reconstruction was investigated. An improved uniform data collection method was subsequently developed. After that, the computational time required for the digital twin and the traditional CFD was compared. Finally, the reconstruction method was further validated by in-situ measured temperature from the in-service boiler. Results indicate that the established digital twin can precisely reconstruct the water wall temperature in real time. Thirty-nine sampling points are sufficient to reconstruct the temperature distribution with the original data collection method. The proposed uniform data collection method further reduces the mean relative errors to less than 0.4% across four test cases, and with the constrained technique, the errors decrease to 0.374% and 0.345% for Cases 1 and 3, which had poor reconstructions using the original sampling point arrangement. In addition, the reconstruction time of the digital twin is also considerably reduced compared to CFD. Engineering application indicates that the reconstructed temperatures are highly consistent with in-situ measured data. The established water wall temperature digital twin is beneficial for water wall tube overheating detection and operation optimization.