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Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes/issues, but are citable by Digital Object Identifier (DOI).
Metal Water-Triple-Point Automatic Reproduction Control System for In-Situ Online Calibration of Temperature Sensors
Qiao Zhigang, Gao Dexin, Zhang Muzi, Zhao Shanshan, Wu Jiali, S Juu, Chen Shenggong, Jing Chao, Liu Hailing, Yang Bo, Wu Chi
, Available online  , doi: 10.3788/IRLA20240096
[Abstract](19) [FullText HTML] (7) [PDF 599KB](5)
  Objective  The triple point of water refers to the state where water, ice, and vapor coexist simultaneously, with an equilibrium temperature of 273.16 K (0.01 ℃). In the International Temperature Scale, the triple point of water serves as the sole reference point for defining the thermodynamic temperature unit Kelvin, and it is one of the most important fixed points in ITS-90 [1-2]. The thermodynamic temperature reproduction of water's triple point is crucial for practical temperature measurements [3].The reproduction of water's triple point is achieved by freezing an ice mantle inside a triple point of water cell. Widely used in the ITS-90 guidelines are triple point of water cells with borosilicate glass or fused silica shells. Traditional reproduction methods include the ice-salt mixture cooling method, dry ice cooling method, and liquid nitrogen cooling method. These methods all require the cooling of the triple point of water cell using dry ice, liquid nitrogen, or other cryogenic media, followed by freezing the high-purity water inside the cell and then storing it in an ice bath. While these traditional methods offer high reproduction accuracy and good results, they are complex, operationally difficult, and demand high standards for operators and the environment, making them inconvenient for on-site calibration and integrated applications [2-3]. Addressing the limitations of traditional triple point of water cells and reproduction methods for in-situ applications, such as the on-site calibration of temperature sensors in the deep sea, this paper investigates a miniaturized triple point reproduction control system suitable for the automatic calibration of temperature sensors, based on a self-developed miniature metal water triple point cell.  Methods  This control system utilizes the principle of spontaneous phase transition of high-purity water in a metal water triple point container, combined with a thermoelectric cooler (TEC) based on the semiconductor Peltier effect and a temperature control circuit, to achieve the automatic reproduction and maintenance of the water triple point. Temperature phase transition monitoring is achieved through the use of thermistors and temperature detection circuits. By employing a dual thermistor setup and TEC in a closed-loop control, the system adjusts the driving power of the TEC based on the temperature difference detected by the feedback resistors, thereby realizing the automatic reproduction and maintenance of the water triple point.  Results and Discussions  Figures 1(a) and (b) respectively illustrate the control schematic of the automatic reproduction system for the metal water triple point bottle and a photograph of the actual metal water triple point bottle. The research employed a miniaturized metal water triple point bottle, utilizing the principle of spontaneous phase transition of high-purity water, along with a thermoelectric cooler (TEC) based on the semiconductor Peltier effect and a temperature control circuit, to achieve the reproduction and maintenance of the water triple point. High sensitivity thermistors combined with a temperature detection circuit were used for monitoring the phase transition of high-purity water. A closed-loop control consisting of dual thermistors and the TEC was utilized. Based on the temperature difference detected by the feedback resistors, the study investigated the cooling demand of the high-purity water phase transition and established a thermodynamic model for the triple point bottle cooling system. By appropriately adjusting the TEC's driving power, the state of the water triple point was reproduced and maintained for an extended period. The measurement results in Figure 2 indicate that, significant supercooling of the high-purity water inside the metal water triple point bottle was observed. It remained unfrozen at the liquid-solid phase equilibrium temperature (0 ℃) and suddenly underwent a phase transition when the temperature reached the transition temperature (approximately −7.3 ℃), causing a rapid increase in the internal trap temperature, which then stabilized, with a stability duration of 20 minutes and a temperature fluctuation of ±1mK. The analysis of the experiment demonstrates that the miniaturized triple point temperature automatic reproduction control system based on the metal water triple point bottle can achieve spontaneous phase transition of high-purity water and maintain a stable temperature plateau for a certain period, facilitating high-precision in-situ temperature calibration of temperature sensors.  Conclusions  This study indicates that combining the metal water triple point bottle with properly arranged temperature monitoring sensors, a TEC cooling system, and a refrigeration control circuit and algorithm can automatically reproduce and maintain the high-purity water triple point state for 20 minutes, with a temperature fluctuation of ±1mK. This provides an accurate, stable, and sustainable environment for in-situ calibration of temperature sensors, serving high-precision in-situ temperature calibration in deep-sea and deep-space environments.
Verification of demodulation method for differential optical doppler velocimetry data
Zhang Zhijun, Song Ran, Jiang Lili, Zhang Xinyu, Li Bingbing, Chen Shenggong, Su Juan, Wu Chi
, Available online  , doi: 10.3788/IRLA20240094
[Abstract](15) [FullText HTML] (6) [PDF 720KB](2)
  Objective  In the field of physical oceanographic research, seawater flow velocity is one of the key parameters, primarily measured using acoustic Doppler velocimeters. In recent years, laser Doppler technology has made significant advancement in seawater flow velocity measurement. Laser Doppler velocimetry, with its simple and integrable structure, is expected to be a complementary technique with acoustic Doppler velocimeters in marine applications.Compared to acoustic velocity measurement techniques, laser Doppler velocimeters offer several advantages: their shorter wavelength (in the micron range) allows for the study of smaller-scale water features, and they can resist noise interference generated by underwater vehicles when used with unmanned underwater vehicles. However, due to seawater absorption and scattering, the detected signal is extremely weak and buried in strong noise, posing challenges for Doppler signal demodulation. Moreover, limited by the sampling frequency, there exists an error between the peak position of the obtained data spectrum and the true frequency. Therefore, effectively removing noise interference and improving measurement accuracy are crucial for laser Doppler velocimeters. In this paper, an adaptive filtering algorithm is employed to denoise the collected signal, followed by fast Fourier transform to enhance the signal-to-noise ratio. Three peak-finding algorithms are compared, and the Gaussian-LM algorithm is selected to process the power spectrum of the signal, bringing the peak position closer to the real peak value and thereby improving the demodulation accuracy of the Doppler signal and significantly reducing the error caused by noise.  Methods  The principle of laser Doppler velocimetry is illustrated in Figure 1(a). A laser beam is split into two equal beams by an optical fiber splitter after passing through a single-mode optical fiber. These two beams are then collimated into parallel beams by a collimator and directed onto a plano-convex lens at the end, which focuses the parallel beams onto a specific point outside the instrument, generating interference fringes at this focal point. When particles in the water pass through these interference fringes, they scatter light, which is collected by the plano-convex lens and converted into parallel light. This scattered light is then collected by an avalanche photodetector and converted into an electrical signal, which is acquired by an oscilloscope. The acquired signal undergoes algorithm processing to demodulate the flow velocity .Figure 1(b) is a field photo of the optical system prototype being tested in the Marine environment off Qingdao.The key to signal processing is accurately extracting the Doppler frequency shift from a large amount of noise, and the noise in the Doppler signal is non-stationary. Therefore, the least mean square error algorithm can be utilized to effectively denoise the Doppler signal. Fast Fourier transform shifts the focus of the research from the time domain to the frequency domain, where it is easier to analyze the regularity of the Doppler frequency. Further, the Gaussian-LM algorithm is employed to perform peak finding on the Doppler signal, obtaining accurate frequency information.  Results and Discussions  Through simulation, the optimal peak finding algorithm was selected. The Monte Carlo algorithm, Gaussian fitting algorithm, and Gaussian-LM algorithm were employed to perform peak finding on Gaussian signals with added noise, and their measurement accuracies were compared, as shown in Figure 2(a). Peak finding calculations were conducted on multiple datasets, and their standard deviations are illustrated in Figure 2(b). The results indicate that the Monte Carlo algorithm exhibited the lowest peak finding accuracy, while the Gaussian-LM algorithm demonstrated the highest accuracy. Moreover, the Gaussian-LM algorithm exhibited smaller standard deviation compared to other algorithms, with a lower fluctuation range, indicating greater stability. Therefore, the Gaussian-LM algorithm was chosen for peak finding in the Doppler signal.A comparative experiment on seawater velocity was conducted at the Zhongyuan Tourist Dock in Qingdao, China, using a home-made optical Doppler velocimetry (LDV) and an acoustic Doppler velocimeter (ADV model: SonTek, Argonaut-ADV). Algorithmic research was carried out on the obtained seawater velocity measurement data. Considering the different sampling rates of the two instruments, the data were first averaged over 30 minutes. From Figure 3(a), it can be observed that the data before algorithm processing roughly align with the trend of velocity values measured by ADV, but there are still discrepancies. However, the data after algorithm processing showed a higher degree of fitting with the data measured by ADV. Figure 3(b) illustrates the errors obtained by ADV for the data before and after processing, and calculates the average error. Through error analysis, it showed that the average error between the pre-processed LDV and ADV velocity measurements was 0.2905 cm/s, while the average error between the post-processed LDV and ADV velocity measurements was 0.2163 cm/s, indicating a reduction in error of 25.5%.  Conclusions  The signal of light scattering from suspended particles in seawater is extremely weak. Extracting signals submerged in noise and demodulating them to obtain velocity information poses a challenge for accurate measurements with laser Doppler velocimeters. In this paper, demodulation algorithms based on velocity data obtained from experiments in the near-shore of Qingdao was studied. Initially, through simulation and optimization, the Gaussian-LM algorithm was selected as the peak finding algorithm. Subsequently, signal denoising was performed based on the Least Mean Square (LMS) algorithm on the actual velocity data obtained during sea trials, combined with the Gaussian-LM algorithm for peak finding, achieving high-precision demodulation.Comparative experiments between home-made laser Doppler velocimeter and a well-known commercial acoustic Doppler velocimeter indicate that the post-processed velocity measurement error based on this algorithm is 0.21 cm/s, representing a 25.5% error reduction compared to pre-processing result.