Multispectral radiation thermometry approach based on feasible region constraints-divide and conquer optimization.

Area of Science:

• Thermophysics
• Metrology
• Optimization Algorithms

Background:

• Multispectral radiation thermometry is crucial for non-contact temperature measurement, especially in extreme conditions.
• Accurate temperature retrieval is hindered by the challenge of unknown object emissivity.
• Existing methods often struggle with precision in complex environments.

Purpose of the Study:

• To develop a robust method for accurate temperature measurement in multispectral radiation thermometry.
• To overcome the limitations posed by unknown emissivity in non-contact temperature sensing.
• To enhance the precision and efficiency of temperature inversion algorithms.

Main Methods:

• A collaborative optimization approach combining feasible region constraints (PCR-PSO) and divide and conquer optimization (multi-BFGS).
• Utilizing feasible domain constraints to refine temperature estimations.
• Employing a multi-BFGS algorithm within a divide and conquer framework for iterative refinement.

Main Results:

• Simulation results demonstrated a reduction in temperature inversion error from 0.59% to 0.19% compared to the traditional BFGS algorithm.
• Experimental validation with stainless steel samples showed an average error of less than 0.2%.
• Achieved an average processing time of 0.2 seconds, indicating suitability for real-time applications.

Conclusions:

• The proposed collaborative optimization approach significantly enhances the accuracy of multispectral radiation thermometry.
• The method effectively addresses the challenge of unknown emissivity, leading to more reliable temperature measurements.
• The approach shows strong potential for real-time, high-precision temperature monitoring in various engineering applications.