Temperature Distribution Measurement Using the Gaussian Process Regression Method

Huaiping Mu,Xueyao Wang,Zhihong Li,Shi Liu

doi:10.1155/2017/2147935

Abstract

The temperature distribution in real-world industrial environments is often in a three-dimensional space, and developing a reliable method to predict such volumetric information is beneficial for the combustion diagnosis, the understandings of the complicated physical and chemical mechanisms behind the combustion process, the increase of the system efficiency, and the reduction of the pollutant emission. In accordance with the machine learning theory, in this paper, a new methodology is proposed to predict three-dimensional temperature distribution from the limited number of the scattered measurement data. The proposed prediction method includes two key phases. In the first phase, traditional technologies are employed to measure the scattered temperature data in a large-scale three-dimensional area. In the second phase, the Gaussian process regression method, with obvious superiorities, including satisfactory generalization ability, high robustness, and low computational complexity, is developed to predict three-dimensional temperature distributions. Numerical simulations and experimental results from a real-world three-dimensional combustion process indicate that the proposed prediction method is effective and robust, holds a good adaptability to cope with complicated, nonlinear, and high-dimensional problems, and can accurately predict three-dimensional temperature distributions under a relatively low sampling ratio. As a result, a practicable and effective method is introduced for three-dimensional temperature distribution.

Highlights

Three-dimensional (3D) temperature distribution plays an important role in combustion diagnosis tasks
We find that the predicted temperature distribution is consistent with experimental observations
As the diesel-liquid is vertically injected into the combustion area, the high temperature region of the combustion is close to the walls

Summary

Introduction

Three-dimensional (3D) temperature distribution plays an important role in combustion diagnosis tasks. The acquisition of the rich and accurate combustion temperature distribution details is of a great significance to the combustion adjustment and control. Owing to the challenges, such as high computational cost and complexity, the inaccurate properties of initial conditions, boundary conditions, geometrical conditions, and physical property parameters, it is hard for the former to achieve combustion diagnosis tasks in real-world applications. With the development of modern measurement technologies, the latter has attracted more and more attentions. Conventional flame monitoring systems are only used to judge whether there is a flame in a combustion space, and it is difficult to realize the quantitative measurement of the combustion process parameters. A lot of technologies based on the radiation image and radiation energy signal processing had been developed for 3D temperature distribution measurements [1–6]

Methods

Results

Conclusion