Multispectral Radiation Thermometry Research Articles

Experimental study on surface temperature and emissivity of rotating turbine blades of a micro turbojet engine

The temperature of the rotating turbine blades of a turbojet engine must be known to assess the combustion state and improve turbine blade quality. Obtaining online temperature measurements of high-speed rotating turbine blades in high-temperature gas flow remains challenging. A multispectral radiation thermometry method without emissivity assumption is proposed to measure the temperature of high-speed rotating turbine blades of a micro turbojet engine. The relative radiative intensity is calculated by accounting for smooth changes in the wavelength using the moving narrowband method to determine the influence of emissivity on multispectral radiation thermometry results. The method’s performance in measuring the surface temperature of high-speed rotating objects is tested. The wavelength range of 970 nm to 1700 nm is selected because the radiation from gases is weaker in comparison to the radiation emitted by solid surfaces within this band. The spectral radiation of the turbine blade surface is measured at three rotational speeds: 38,000 r/min, 48,000 r/min, and 58,000 r/min. The results show that, unlike thermocouples that measure the temperature of the hot gas flow near the blade, multispectral radiation thermometry provides the average surface temperature with high consistency (with a deviation smaller than 50 K). Multispectral radiation thermometry can be applied to measure the surface temperature and emissivity of high-speed rotating objects, such as the blades of turbojet engines.

Response to “Comment on ‘Fast data processing method for multispectral radiation thermometry based on Euclidean distance optimization’”

Response to “Comment on ‘Fast data processing method for multispectral radiation thermometry based on Euclidean distance optimization’”

Multi-spectral radiation thermometry of space point targets based on spectral image pixel binning

The temperature characteristics of space point targets are essential indicators of their operational status and performance. To address the issue of significant temperature measurement errors in space point targets caused by low temperatures and a low imaging signal-to-noise ratio (SNR), we propose a mathematical model for multi-spectral radiation thermometry, derived from the principles of dual-band radiation thermometry. Furthermore, a multi-spectral image pixel binning method is introduced to enhance the SNR and minimize measurement errors. The experimental results indicate that the proposed multi-spectral radiation thermometry outperforms dual-band radiation thermometry. After merging 2 to 20 pixels, multi-spectral radiation thermometry in the 3.75–4.1 and 4.3–4.62 µm bands demonstrates an enhanced SNR and reduced temperature measurement errors. For a 378.15 K blackbody, the relative errors decrease from 1.52% and 2.19% to 0.26% and 0.74%, respectively, after merging six and eight pixels in the two different bands, compared to unmerged images. This method provides a valuable reference for developing techniques to enhance the SNR and improve temperature measurement accuracy for space point targets.

Multi-Spectral Radiation Temperature Measurement: A High-Precision Method Based on Inversion Using an Enhanced Particle Swarm Optimization Algorithm with Multiple Strategies.

Multi-spectral temperature measurement technology has been found to have extensive applications in engineering practice. Addressing the challenges posed by unknown emissivity in multi-spectral temperature measurement data processing, this paper adds emissivity constraints to the objective function. It proposes a multi-spectral radiation temperature measurement data processing model realized through a particle swarm optimization algorithm improved based on multiple strategies. This paper simulates six material models with distinct emissivity trends. The simulation results indicate that the algorithm calculates an average relative temperature error of less than 0.3%, with an average computation time of merely 0.24 s. When applied to the temperature testing of silicon carbide and tungsten, experimental data further confirmed its accuracy: the absolute temperature error for silicon carbide (tungsten) is less than 4 K (7 K), and the average relative error is below 0.4% (0.3%), while two materials maintain an average computation time of 0.33 s. In summary, the improved particle swarm optimization algorithm demonstrates strong performance and high accuracy in multi-spectral radiation thermometry, making it a feasible solution for addressing multi-spectral temperature measurement challenges in practical engineering applications. Additionally, it can be extended to other multi-spectral systems.

Comment on "Fast data processing method for multispectral radiation thermometry based on Euclidean distance optimization"

Comment on "Fast data processing method for multispectral radiation thermometry based on Euclidean distance optimization"

Multispectral radiation thermometry platform based on wireless data transmission.

In metal smelting, precise temperature control is of vital importance for reaction rates, efficiency, and product quality. Traditional methods such as thermocouples have inherent limitations, but multispectral radiation thermometry (MRT) offers high resolution and reliability. This paper proposes a multispectral radiation thermometry platform featuring wireless data transmission, which enables remote data transfer and precise temperature measurements. The platform was meticulously calibrated, and six common emissivity models were inverted with high accuracy. The results of temperature measurements conducted at a copper smelting site demonstrated an excellent degree of agreement with those obtained using disposable thermocouples. The platform has the potential to be applied in harsh environments, offering, to our knowledge, a novel approach to temperature measurement in metal smelting processes.

Multi-spectral radiation thermometry based on the reconstructed spectral emissivity model

With the aim of solving the problem of detecting the true temperature of objects using multi-spectral radiation thermometry, this paper proposes a multi-spectral radiation thermometry method based on the reconstructed spectral emissivity model. Compared with traditional hypothetical spectral emissivity models, this model is a reconstruction emissivity model based on actual measured brightness and known wavelength quantities, which can improve the characterization of the real emissivity state of the measured object and not be affected by any hypothetical conditions or artificial preset parameters. In order to verify the effectiveness of the proposed method for detecting the temperature of real problems, a total of eight typical spectral emissivity models, spectral data of Si3N4 ceramics and ZrO2 ceramics, and measurement data of rocket motor casings are selected for experiments. The experimental results indicate that the true temperature calculated by the proposed method has a relative error of less than 1 %, and the calculation time is kept at the millisecond level, which meets the real-time multi-spectral temperature measurement requirements.

Fast data processing method for multispectral radiation thermometry based on Euclidean distance optimization.

This study presents a fast and accurate data processing method for multispectral radiation thermometry that can accurately measure the true temperature of steel materials without requiring a priori emissivity model. The method generates a temperature matrix by inputting emissivity values at different wavelengths and selects a reference vector from the matrix. Then, it rearranges the temperature matrices at other wavelengths and calculates the Euclidean distance between each column element of the rearranged matrix and the reference vector. The method uses an unconstrained optimization technique to minimize the Euclidean distance and obtain the true temperature and emissivity of the object simultaneously. We evaluate the performance of the method by simulation and experiment in the response band of 1.4 ∼ 2.5 µm and temperature range of 873 ∼ 1173 K. The simulation results indicate that the relative error of the inverted temperature is within 0.229%, and the average computation time is less than 112.301 ms. The experimental results show that the maximum temperature error during the measurement process is 0.813%. Our method provides a feasible and efficient solution for real-time temperature measurement of steel materials.

Data processing for simultaneous inversion of emissivity and temperature using improved CABCSMA and target-to-best DE algorithms in multispectral radiation thermometry (MRT).

In this paper, what we believe to be, a new combined algorithm of artificial bee colony and slime mould algorithm (CABCSMA) and a differential evolution (DE) algorithm using target-to-best variation strategy are proposed to process the data based on Planck's radiation law and the mathematical model of reference temperature. The material model with 6 different emissivity trends is simulated. Simulation results show that the average relative error of CABCSMA algorithm is less than 0.68%, and the average calculation time is 0.44s. The average relative error of DE algorithm is less than 0.43%, and the average calculation time is only 0.06s. The two algorithms were applied to the temperature test of silicon carbide sample, tungsten material and rocket engine nozzle. The experimental results show that the relative error of silicon carbide experimental temperature is less than 0.41% and 0.28%, and the relative error of tungsten material experimental temperature is less than 0.31% and 0.3%. The relative errors of rocket engine nozzle temperature experiments are within 0.68% and 0.52%, respectively. The results show that these two algorithms are expected to be applied in practical measurement scenarios.

Optimizing MRT data processing via comparative analysis of SA and PSO algorithms: a simulation and numerical study.

The unknown emissivity still poses a significant challenge in the data processing of Multispectral radiation thermometry (MRT). In order to achieve global optimal solution with fast convergence speed and strong robustness, a systematic comparative analysis of particle swarm optimization (PSO) and simulated annealing (SA) algorithms in the application in MRT is presented in this paper. The simulations of six hypothetical emissivity models were compared, and the results indicate that the PSO algorithm is superior to the SA algorithm in accuracy, efficiency and stability. The measured data of the surface temperature of rocket motor nozzle is simulated by the PSO algorithm, the maximum absolute error and the maximum relative error are 16.27 K and 0.65%, and the calculation time is less than 0.3 s. The superior performance of the PSO algorithm indicates that it can be well used in data processing for accurate temperature measurement in MRT, and the method proposed in this paper can be extended to other multispectral systems and applied to various industrial processes under high temperature conditions.

The development of a multispectral pyrometer for achievable true temperature field measurements of the explosion flame

The temperature measurement of explosion flames is a key challenge in the field of weapons research. Many pyrometers have been developed to determine the accurate temperature of the explosion flame. However, these pyrometers can only measure the true temperature curve and the brightness temperature field of explosion flames, not their true temperature field. A multispectral pyrometer that measures the true temperature field of the explosion flame is developed in this paper. The multispectral pyrometer acquires brightness temperature field images at four different wavelengths of the explosion flame at the same moment using synchronous spectral-splitting technology. The pixels of the obtained four brightness temperature field images are then aligned using an edge feature-based image matching algorithm. The true temperature value is finally calculated for each pixel using multispectral radiation thermometry to construct the true temperature field of the explosion flame. Based on the experimental results, the true temperature field of the explosion flame can be measured with the proposed multispectral pyrometer.

Development of A Multi-Spectral Pyrometry Sensor for High-Speed Transient Surface-Temperature Measurements in Combustion-Relevant Harsh Environments.

Accurate and high-speed transient surface-temperature measurements of combustion devices including internal combustion (IC) engines, gas turbines, etc., provide validation targets and boundary conditions for computational fluid dynamics models, and are broadly relevant to technology advancements such as performance improvement and emissions reduction. Development and demonstration of a multi-infrared-channel pyrometry-based optical instrument for high-speed surface-temperature measurement is described. The measurement principle is based on multi-spectral radiation thermometry (MRT) and uses surface thermal radiation at four discrete spectral regions and a corresponding emissivity model to obtain surface temperature via non-linear least squares (NLLS) optimization. Rules of thumb for specifying the spectral regions and considerations to avoid interference with common combustion products are developed; the impact of these along with linear and non-linear MRT analysis are assessed as a function of temperature and signal-to-noise ratio. A multi-start method to determine the MRT-solution global optimum is described and demonstrated. The resulting multi-channel transient pyrometry instrument is described along with practical considerations including optical-alignment drift, matching intra-channel transient response, and solution-confidence indicators. The instrument demonstrated excellent >97% accuracy and >99% 2-sigma precision over the 400−800 °C range, with ~20 µs (50 kHz, equivalent to 0.2 cad at 2000 RPM IC-engine operation) transient response in the bench validation.

Multi-spectral radiation thermometry based on mixed kernel support vector regression

With the development of measurement science and technology, multi-spectral radiation thermometry has been widely used in various fields. In the study of its data processing methods, machine learning technology has gathered wide concern due to the advantage of not affected by the unknown object emissivity. However, the existing machine learning thermometry models need generous data samples to support. It has become a key problem hindering the application of machine learning technology in multi-spectral radiation thermometry. In order to reduce the dependence on training samples while ensuring the high measurement accuracy, this study proposes a multi-spectral radiation thermometry based on mixed kernel support vector regression. In this method, a new method of spectral feature selection is proposed for building appropriate training data set. A mixed kernel function is constructed for support vector regression (SVR) thermometry model to make the nonlinear approximating function between the target spectral radiance and temperature has strong stability. Bayesian optimization is used to adjust the super parameters of the thermometry model, so as to ensure the best performance of model. The temperature measurement experiment results of aviation aluminum alloy show that the performance of proposed thermometry is stable in the measurement range under the small sample training data, and the absolute error is not more than 5 K.

Multi-spectral radiation thermometry based on an Alpha spectrum-LM algorithm under the background of high temperature and intense reflection.

In order to meet the needs of multi-spectral radiation temperature measurement under high temperature background, this paper studies the problems of reflected radiation interference and spectral emissivity difficult to obtain in high temperature and intense reflection environment. First, using discrete triangular surface elements and radiation angle coefficients, an analysis model of high temperature background reflected radiation is constructed to describe the variation characteristics of high temperature background reflected radiation. Secondly, the least squares support vector machine (LSSVM) is optimized by particle swarm optimization (PSO) algorithm, and an emissivity model identification algorithm based on Alpha spectrum-Levenberg Marquarelt (LM) algorithm is proposed, which has stronger applicability and accuracy than existing emissivity model identification methods. Finally, the high temperature background radiation and the emissivity model are combined to construct and solve the multi-spectral target equation, so as to realize the reflected radiation error correction and radiation temperature measurement under the high temperature and intense reflection background. The simulation and experimental comparison with the existing methods show that the temperature measurement error of the radiation temperature measurement method proposed in this paper is below 9.5K, which can effectively correct the reflected radiation error and further improve the temperature measurement accuracy.

A data processing algorithm for multispectral radiation thermometry based on multi-segment linear model and secondary inversion

Multispectral radiation thermometry is a branch of infrared temperature measurement technology with wide application in many industries. Although this technology has been developed for decades, there is still a problem in its data processing, that is, how to accurately solve the radiation equations without the information of object’s emissivity. The traditional fixed emissivity model method cannot adapt to all objects. Therefore, this study proposes a data processing algorithm for multispectral radiation thermometry based on multi-segment linear model and secondary inversion. The algorithm can automatically select the multi-segment linear model suitable for the measured object, and realize the accurate calculation of temperature and emissivity through the secondary inversion. The efficiency and accuracy of the algorithm are verified through simulations. The experimental results of temperature measurement of aviation alloy prove the effectiveness of this algorithm in practical application.

Cutting temperature measurement in turning using fiber-optic multi-spectral radiation thermometry and its application in tool wear status recognition

The cutting temperature is essential for phenomena understanding and quality improvement in metal cutting while its in-situ online measurement is still a challenge. This paper presents a near-infrared fiber-optic multi-spectral method for in-situ online cutting temperature measurement. Using thermal radiation spectrum for temperature measurement, the method optimizes the lower limit of temperature measurement to 150 °C while improving accuracy. The calibration shows that in the range of above 250 °C, the average relative error of temperature measurement is stable below 0.5%. The titanium alloy cutting experiments are carried out. In-situ online measurement of tool temperatures in dry/wet cuttings are realized using the self-developed system. The influence of cutting parameters on cutting temperature is studied, and the real-time response of the temperature measurement system to the cutting state is verified. As for industrial application, the capability of the system in heavy-duty turning is proved by railway wheelsets turning experiments. Tool wear experiments are conducted, and a positive correlation between the cutting temperature and tool wear is revealed. Tool wear status recognition is realized based on cutting temperature by sparse autoencoder and k-means clustering, and a recognition accuracy of 97.3% is achieved. These results indicate promising prospects in cutting mechanism research, machining status monitoring and industrial applications, empowering the advancement of intelligent manufacturing and industry 4.0.

Light-field multi-spectral radiation thermometry.

This Letter proposes light-field multi-spectral radiation thermometry based on an unfocused light-field camera, which can simultaneously record directions and intensities of incident rays. In this method, the direction information of rays is substituted by radiation spectrums via placing an array of filters in front of camera main lens, such that the image sensor can simultaneously acquire spectrums and intensities of rays. By decoupling a raw multi-spectral light-field (MSLF) image and utilizing traditional multi-wavelength pyrometer algorithms, the scalar field of surface temperature distribution can be achieved. To verify the method, measurement errors of different temperature levels on several typical areas of MSLF images are analyzed. In addition, the validation experiment demonstrates that accurate surface temperature measurement can be achieved with a single lens, single monochromatic image sensor, and just one snapshot in the proposed method.

Inversion of target radiant temperature and distance via spectrum separation.

Accurate measurement of temperature and distance is essential for applications in multiple fields such as air defense, antimissile systems, laser damage, and infrared remote sensing. However, ranging currently relies on either laser or infrared radiation characteristics, neither of which fully utilize the substantial information contained in the spectrum. In this study, we realized the simultaneous inversion of target radiant temperature and distance via spectrum separation based on the distance information from the atmospheric spectrum as well as the temperature information from multispectral radiation thermometry (MRT), by measuring the thermal radiation spectrum of distant high-temperature objects. The study initially involved conducting a theoretical analysis of long-distance MRT in the atmosphere by utilizing the least squares and Newton's iteration methods. Once the thermal radiation spectrum and the long-distance atmospheric spectral transmittance were acquired from nonlinear models, highly precise and accurate radiant temperature and distance information was derived via simultaneous inversion. Subsequently, experimental verifications were performed to measure an explosion spectrum at a temperature of approximately 2000 K and a distance of 9 km. The spectral band was 400-900 nm, and the temperature and distance of the explosion calculated by the inversion was 2004.4 K and 9.9 km, respectively. The inversion distance error was 10%, indicating that the simultaneous inversion of radiant temperature and distance was successful and that the proposed method was effective. The method proposed in this study can be applied to the measurement of radiant temperature and distance of long-distance objects.

Detection method of combustion oscillation characteristics under strong noise background

After the lean fuel premixed combustion technology is applied to aero engines, severe combustion oscillations will be cased and led to hidden safety hazards such as engine vibration, further energy waste and other problems. Therefore, it is increasingly important to actively control combustion oscillations. In this paper, a multispectral radiation thermometry (MRT) is used to analyze the hydroxyl group, which is a measurable research object in the combustion chamber of an aero engine, and to fit the functional relationship between the radiation intensity ratio and the temperature in different bands. The theoretical value of the error is <2%. At the same time, in order to solve the problem of weak detection signal and excessive interference signal, an improved frequency domain filtering method based on fast Fourier transform is designed. Besides, the FPGA platform is used to ensure the real-time performance of the temperature measurement system, and simulations and experiments are performed. An oscillating signal with an oscillation frequency of 315 Hz is obtained on the established test platform, and the error is only 1.42%.

EMISSIVITY MODEL OF STEEL 316L AT 800–1100 K DURING OXIDE LAYER GROWTH ON THE SPECIMEN SURFACE

This work strived to study the variation in the spectral emissivity of steel 316L specimens with wavelength at a certain temperature. The spectral emissivity and radiances from the specimen surface were measured by multispectral radiation thermometry at wavelengths of 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, and 2.1 μm and at temperatures from 800 to 1100 K in increments of 20 K. The specimen was heated and kept at a certain temperature in air for 6 h. The temperature of the specimen surface was measured by two thermocouples, which were symmetrically welded onto the front surface of the specimens near the measuring area for accurate monitoring of the surface temperature. The average of their readings was regarded as the real temperature. Variation in the spectral emissivity with wavelength was studied at different temperatures and different heating times. Effect of surface oxidation on the fitting accuracy of the emissivity models was evaluated. Two favorite models were determined. With the radiant values measured by thermometry and in combination with the least-squares fitting technique, two favorite models were examined. On the whole, both the five-parameter LLWE and LWE models could yield the best overall temperature prediction and could farthest relieve the effect of surface oxidation on the accuracy of temperature prediction over the present wavelength and temperature ranges.