Tiantian Huang

A 70 MPa class hydrogen solenoid valve needs a metal seal tube with a high strength margin at the specified safety factor (proof pressure 175 MPa). A lightweight scheme is therefore proposed in which the thin-walled tube is reinforced by carbon-fiber hoop winding. S31603 austenitic stainless steel is ordinarily selected as the primary material for this pressure-bearing component owing to its excellent corrosion resistance and favorable hydrogen-embrittlement resistance. However, its low yield strength means that satisfying the strength requirement only by increasing the wall thickness would raise the structural mass, widen the non-magnetic gap and reduce the electromagnetic efficiency. To avoid this, a thin-walled S31603 tube is used as the metallic liner, and a mechanical model of the metal–composite structure is established on the basis of composite netting theory and thick-walled cylinder elasticity. The winding-layer thickness and the process parameters are then determined, and the design is verified by finite element analysis and pressure testing. The results show that, at the 175 MPa proof pressure, a 3 mm thick all-metal seal tube has an elastic limit of only about 65.4 MPa, far below the requirement. After carbon-fiber wet winding, the composite seal tube reaches the required strength, with a total wall thickness of only about 3 mm and a much lower mass. Because the carbon-fiber layer is non-magnetic, the structure improves the pressure-bearing capacity while introducing almost no additional reluctance. It effectively decouples the contradiction between the structural strength and the magnetic-circuit efficiency and offers a new route for the lightweight design of hydrogen fluid-control components.

Dynamic errors in measurement systems remain a topic lacking consensus in both its definition and evaluation. Addressing the issues that traditional definitions of dynamic errors fail to effectively separate static errors and that conventional evaluation parameters are overly complex for assessing dynamic performance, this article proposes a dynamic performance evaluation method based on a dynamic tracking parameter. First, the fundamental composition of measurement systems is introduced. Furthermore, based on the definitions of error and ideal measurement system, a total error model is established, which separates dynamic and static errors from the fundamental sources of error generation, thereby providing a rigorous definition of dynamic errors. Considering the time-varying nature of dynamic errors, the dynamic tracking parameter is defined from a power perspective, forming the basis of the evaluation framework. To comprehensively demonstrate the advantage of the proposed evaluation parameter, a correlation analysis with traditional dynamic performance evaluation parameters is conducted across multiple dimensions. Finally, an accelerometer is used as an example for validation. Simulation and experimental results confirm that the proposed evaluation parameter and method can accurately assess the dynamic performance of measurement systems.

The problem of dynamic performance in accelerometer is a topic that has not reached a consensus in terms of definition, standards, experiment and evaluation. In view of the problem that traditional methods of evaluating the dynamic performance of accelerometer involve single indicators and that time and frequency indicators are not related, thus being ineffective in evaluating accelerometer, a method based on multiple indices in the time and frequency domain for evaluating the dynamic performance of accelerometer is proposed. Firstly, the accelerometer system structure and mathematical model are introduced, and the dynamic response test and analysis of the accelerometer are performed. Performance indices are defined separately in the time and frequency domains. Secondly, the accelerometer’s deterministic and stochastic performance is analyzed based on the defined indices. Finally, a method for evaluating the dynamic performance of the accelerometer is proposed, and a test platform is set up to validate the proposed method of evaluating the dynamic performance of the accelerometer. Simulated and experimental results demonstrate that the proposed method, taking into account the time and frequency domain performance and the accuracy of the sensors and their application systems, can effectively evaluate the dynamic performance of the accelerometer.

It is very important for non-stationary signal processing, signal measurement, and signal enhancement to improve signal-to-noise ratio (SNR) and suppress noise. For complex non-stationary signals measurement, there is still a lack of effective methods to improve the signal-to-noise ratio without damaging original signal. Therefore, it is vital to develop signal processing method to improve SNR of non-stationary signal in many actual industrial measurement. This paper proposes a new method to enhance the SNR for non-stationary signal processing. First, a self-adapting mathematic model is presented to describe the short-time impulse with board spectrum, judge the non-stationary part of the signal, and determine the length of time window based on itself. Then, for the non-stationary signals in this short time window, the improved self-adaptive modulation is used to remove the non-stationary components and retain the stationary components. Third, the stationary component of the short-time window and the normal part of the original signal are connected via cubic spline interpolation algorithm. Finally, this paper analyzes the range of SNR improvement and measurement accuracy of the new method. Based on the results of experiment, the proposed method can effectively improve the signal-to-noise ratio of non-stationary signals and suppress noise, which is of great significance for measurement.

Cables or slip-rings are often used to power loads on a rotating unit in the rotation modulated inertial navigation system (RMINS). However, these power supply methods have the disadvantages of cable winding and slip ring friction and wear, which reduces the reliability and life of the RMINS. Therefore, this paper applies magnetic coupling resonant wireless power transfer (MCRWPT) technology to the RMINS to avoid the shortcomings of the above power supply methods. Furthermore, according to the structure and working characteristics of the RMINS, a simple design method of the MCRWPT system without any feedback control is proposed. Based on the ANSYS simulation, the magnetic shielding structure is designed to reduce magnetic leakage, and the efficiency of the MCRWPT system is optimized by designing the excitation frequency. Experiments verify the effectiveness of the proposed method. The experimental results show that the designed MCRWPT system can achieve an efficiency of 74.6% with an output power of 10 W and has been successfully applied to the uniaxial rotation module inertial navigation system. Finally, the design method of the MCRWPT system is simple, and it has guiding significance for the design of the wireless power transfer system in the RMINS.

Calibration and compensation techniques are essential to improve the accuracy of the strap-down inertial navigation system. Especially for the new uniaxial rotation module inertial navigation system (URMINS), replacing faulty uniaxial rotation modules introduces installation errors between modules and reduces navigation accuracy. Therefore, it is necessary to calibrate these systems effectively and compensate for the installation error between modules. This paper proposes a new self-calibration and compensation method for installation errors without additional information and equipment. Using the attitude, velocity, and position differences between the two sets of navigation information output from URMINS as measurements, a Kalman filter is constructed and the installation error is estimated. After URMINS is compensated for the installation error, the average of the demodulated redundant information is taken to calculate the carrier’s navigation information. The simulation results show that the proposed method can effectively assess the installation error between modules with an estimation accuracy better than 5”. Experimental results for static navigation show that the accuracy of heading angle and positioning can be improved by 73.12% and 81.19% after the URMINS has compensated for the estimated installation errors. Simulation and experimental results further validate the effectiveness of the proposed self-calibration and compensation method.

The traditional temperature modeling method is based on the full heating of the accelerometer to achieve thermal balance, which is not suitable for the cold start-up phase of the micro-accelerometer. For decreasing the complex temperature drift of the cold start-up phase, a new temperature compensation method based on a high-order Fourier transform combined model is proposed. The system structure and repeatability test of the micro digital quartz flexible accelerometer are provided at first. Additionally, we analyzed where the complex temperature drift of the cold start-up phase comes from based on the system structure and repeatability test. Secondly, a high-order temperature compensation model combined with K-means clustering and the symbiotic organisms search (SOS) algorithm is established with repeatability test data as training data. To verify the proposed temperature compensation model, a test platform was built to transmit the measured values before and after compensation with the proposed Fourier-related model and the other time-related model, which is also a model aiming at temperature compensation in the cold start-up phase. The experimental results indicate that the proposed method achieves better compensation accuracy compared with the traditional temperature compensation methods and the time-related compensation model. Furthermore, the compensation for the cold start-up phase has no effect on the original accuracy over the whole temperature range. The stability of the accelerometer can be significantly improved to about 30 μg in the start-up phase of different temperatures after compensation.

When strapdown gravimeter measures the underwater gravity field, the drift of inertial sensors, which is caused by thermal effects, degrades the performance. To solve this problem, a dual-sensor system for underwater gravity measurement is used. It is equipped with electrostatic suspended accelerometers (high-drift but high-resolution) and rotary modulation inertial measurement units (low-drift but low-resolution). For the dual-sensor gravimeter, we propose a new structure of the data processing algorithm. This Kalman-filter-based algorithm combines the advantage of two kinds of sensors to output low-drift and accurate gravity values. The data-fusion algorithm includes the drift estimation step and the Markov prediction step. The detailed structure and key issues are also described. Further, the performance of the algorithm is analyzed by simulation and real-world experiments. The result of real-world experiments shows that the accuracy of the gravity measurement in static conditions reaches 0.72mGal and the dynamic accuracy reaches about 1.96mGal.