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 in extreme conditions.
• Accurate temperature retrieval is hindered by unknown object emissivity.
• Existing methods face challenges in achieving high precision.

Purpose of the Study:

• To develop an advanced method for accurate temperature measurement in multispectral radiation thermometry.
• To address the challenge of unknown emissivity in temperature inversion.
• To improve the accuracy and efficiency of non-contact temperature sensing.

Main Methods:

• A collaborative optimization approach combining feasible region constraints (PCR-PSO) and divide and conquer optimization (multi-BFGS).
• Utilizing a feasible domain constraints method (PCR-PSO) for initial optimization.
• Employing a multi-BFGS algorithm within a divide and conquer framework for refined temperature inversion.

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 yielded an average error of less than 0.2%.
• The method 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 overcomes the challenge of unknown emissivity in temperature measurements.
• This technique shows strong potential for real-time, high-precision temperature monitoring in various engineering fields.