High-temperature processes can have a direct impact on the state and physicochemical properties of materials, making high-temperature measurements important in scientific research, materials processing, and equipment evaluation. A temperature measurement method of color CCD based on matrix search is reported in this paper. In this method, the traditional temperature reverse calculation process is transformed into the forward matrix search process, and the process parameters such as medium absorption gamma correction are incorporated, which saves the calculation resources and is closer to the actual temperature measurement conditions. A temperature measurement process and a temperature visualization procedure are designed for this high-temperature measurement method, the flame temperature measurement experiments are carried out, and the error results are obtained. By comparing the solution results and chrominance deviation distance between the perturbation emissivity model and the non-perturbation emissivity model, the robustness of the solution method is discussed.

We present an optimization algorithm to obtain low-uncertainty dynamic pressure measurements from a force-transducer-based device. In this paper, the advantages and disadvantages of the methods that are commonly used to measure the propellant powder gas pressure, the applicable scope of dynamic pressure calibration devices, and the shortcomings of the traditional comparison calibration method based on the drop-weight device are firstly analysed in detail. Then, a dynamic calibration method for measuring pressure using a force sensor based on a drop-weight device is introduced. This method can effectively save time when many pressure sensors are calibrated simultaneously and extend the life of expensive reference sensors. However, the force sensor is installed between the drop-weight and the hammerhead by transition pieces through the connection mode of bolt fastening, which causes adverse effects such as additional pretightening and inertia forces. To solve these effects, the influence mechanisms of the pretightening force, the inertia force and other influence factors on the force measurement are theoretically analysed. Then a measurement correction method for the force measurement is proposed based on an artificial neural network optimized by a genetic algorithm. The training and testing data sets are obtained from calibration tests, and the selection criteria for the key parameters of the correction model is discussed. The evaluation results for the test data show that the correction model can effectively improve the force measurement accuracy of the force sensor. Compared with the traditional high-accuracy comparison calibration method, the percentage difference of the impact-force-based measurement is less than 0.6% and the relative uncertainty of the corrected force value is 1.95%, which can meet the requirements of engineering applications.

In this paper, a strain-gauge-type force sensor which can be directly mounted on the existing drop-weight device and used to calibrate the piezoelectric high-pressure sensor is designed. The absolute quasistatic pressure calibration principle and the measurement principle of the strain-gauge-type force sensor are described. Based on the mathematical relation model between force and pressure, the influence factors of pressure calibration accuracy are discussed, and the empirical formula for correcting the peak pressure is obtained. To analyze the mechanical strength of the force sensor, the modal parameters of the force sensor in the impact direction, and the pressure loss in the transmission process, a finite element simulation based on ANSYS software is performed. In addition, the static characteristic of the force sensor and the dynamic characteristic of the new drop-weight system composed of the force sensor, the mounting frame, and weight stacks are analyzed. To verify the accuracy of the empirical formula, the absolute quasistatic pressure calibration experiment is performed based on the calibration device. The final experimental results show that the accuracy of pressure calibration can be effectively improved by means of empirical formula correction. Taking the peak pressure measured by the reference pressure sensors as the standard value, the maximum calculation difference can be reduced from 3.42% (obtained by the direct calculation method) to 0.70% by the empirical formula correction method.

This paper applies back propagation neural network (BPNN) optimized by genetic algorithm (GA) for the prediction of pressure generated by a drop-weight device and the quasi-static calibration of piezoelectric high-pressure sensors for the measurement of propellant powder gas pressure. The method can effectively overcome the slow convergence and local minimum problems of BPNN. Based on test data of quasi-static comparison calibration method, a mathematical model between each parameter of drop-weight device and peak pressure and pulse width was established, through which the practical quasi-static calibration without continuously using expensive reference sensors could be realized. Compared with multiple linear regression method, the GA-BPNN model has higher prediction accuracy and stability. The percentages of prediction error of peak pressure and pulse width are less than 0.7% and 0.3%, respectively.