Direct acting solenoid on–off valves are key fluid power components whose efficiency is dependent upon the state of the spool’s axial motion. By sensing the trajectory of the valve spool, more efficient control schemes can be implemented. Therefore, the goal of this study is to derive an analytical model for spool displacement sensing based on coil inductance. First, a mathematical model of the coil inductance as a function of air gap width and lumped magnetic reluctance is derived. Second, to solve the inductance from coil current, an optimization to obtain an initial value based on physical constraints is proposed. Furthermore, an experiment using a laser triangulation sensor is designed to correlate the magnetic reluctance to the air gap. Lastly, using the obtained empirical reluctance model to eliminate unknowns from the proposed air gap-inductance model, the model in atmosphere or hydraulic oil environments was tested. Initial results showed that the proposed model is capable of calculating the spool displacement based on the coil current, and the estimation errors compared to the laser measurement are within ±7% in air environment.

Based on the conceptual framework of digital diaspora, this article explores the role social media plays in the development of migrant cultural identity and its political potential among digital forms. Using in-depth interviews and online observations, it analyzes the motives, purposes, strategies and outcomes of the use of WeChat, a leading Chinese social media platform, by Chinese female immigrants in Switzerland. Through thematic analysis, the article distills three patterns of WeChat usage by these women. Firstly, it notes how they make use of the technological affordances of WeChat to remain connected with their native culture. Secondly, it explains how, after acquisition of economic capital, they are able to forge a steady intersectional identity in their new host country. And finally, it problematizes their strategy to strive for social equality.

Precision modeling of the hydraulic gear pump pressure dynamics depends on the accurate prediction of volumetric displacement in the inter-tooth spaces of the gear. By accurate reconstruction of the gear profile, detailed transient volumetric information can be determined. Therefore, this paper reports a non-contact gear measurement device using two opposing laser triangulation sensors, and the key geometrical models to reconstruct the profile with geometrical error compensation. An optimization-based key parameter calculation method is also proposed to find the unknown orientation of the sensor. Finally, an experimental setup is established, the performance of the device is tested and the geometric model is validated. Initial results showed that the method is able to reconstruct the target tooth profile and compensated results can reduce the geometrical error by up to 98% compared to the uncalibrated ones.

Efficiently salvaging shipwrecks is of the utmost importance for safeguarding shipping safety and preserving the marine ecosystem. However, traditional methods find it difficult to salvage shipwrecks in deep water. This article presents a novel salvage technology that involves multiple hydraulic claws for directly catching and lifting a 2500-ton shipwreck at 600 m depth. To ensure lifting stability, a semi-active heave compensation (SAHC) system was employed for each lifter to mitigate the effects of sea waves. However, the response delays arising from the hydraulic, control, and filtering systems resist the heave compensation performance. Predicting the barge motion to mitigate measuring and filtering delays and achieve leading compensation is necessary for the salvage. Therefore, a multivariate long short-term memory (LSTM) based neural network was trained to forecast the barge’s heave and pitch motions, exhibiting satisfactory results for the next 5 s. According to the results of numerical simulations, the proposed LSTM-based motion predictive SAHC system demonstrates remarkable effectiveness in compensating for shipwreck motion.

The bulk modulus of hydraulic fluid is dependent on the quantity of entrained gas in the fluid. In this paper, an effective fluid bulk modulus model that captures dynamic gas absorption during pressure transients is derived from the overall mass transfer theory. Optical measurement of a microgas bubble volume is used to determine the interfacial mass transport. Compared to traditional models, the proposed model is able to capture the 10% gap in the pressure profile between the first and second cycles, when simulating multiple compression cycles of an oil sample with 0.65% entrained gas by volume at 8 MPa.