Junmin Jing

Multi-scale vortex structures constitute the intrinsic skeleton of turbulent flows and govern the energy cascade process in oceanic turbulence. Elucidating their evolutionary dynamics is crucial for understanding turbulent mixing and transport. In this study, we develop an innovative Energy Cascade Multilayer Directed Network (ECMDN) framework grounded in complex network theory to directly characterize nonlinear energy coupling pathways and directional transfers among multi-scale vortices in real marine environments. By integrating multi-parameter fusion node definitions, multi-scale interaction detection, and energy transfer direction identification, the ECMDN reconstructs the nonlinear turbulent system into a topologically interpretable structure. The emergent network properties enable quantitative characterization of intermittency and inhomogeneity in the energy cascade, offering new insights into vortex interactions and cross-scale energy transfer mechanisms. Compared with conventional cascade diagnostics including spectral flux, third-order velocity structure functions, multifractal analysis and shell models that require homogeneity and local equilibrium assumptions and only output global averaged energy flux, the proposed ECMDN multilayer network retains point-wise depth coordinates of each vortex interaction, separates directed forward/inverse energy edges, and quantifies intermittency via topological metrics. Analysis of the single Shenhu thermocline shear segment demonstrates these differentiated analytical capabilities of the proposed framework. Application to shear measurements from the Shenhu Sea reveals the simultaneous occurrence of forward and inverse energy cascades, manifesting a synchronous dual-energy-cascade pattern. This indicates that vortices at a given scale can concurrently transfer energy to larger- or smaller-scale structures and receive energy from larger- or smaller-scale counterparts during the cascade process. Our findings observe a typical synchronous dual-energy-cascade pattern in the strong thermocline of the Shenhu Sea, providing a novel theoretical and methodological framework for investigating the spatiotemporal evolution of stratified ocean turbulent mixing and advancing our understanding of geophysical fluid dynamics.

A miniaturized (70 × 7.7 cm) multi-parameter synchronous observation system was developed for in situ ocean turbulence measurement, integrating micro-electromechanical system (MEMS)-based two-dimensional (2D) turbulence, pressure, temperature, conductivity, and attitude sensors. Field tests conducted at a depth of 1800 m in the northern South China Sea validated the system’s accuracy through comparisons with standard CTD (Conductivity, Temperature, and Depth) sensors, dual-probe consistency analysis, and Nasmyth spectrum fitting. The system precisely captured thermoclines, internal waves, and turbulent shear fluctuations at a depth of approximately 125 m, revealing enhanced turbulence near the thermocline due to intensified shear effects. With high spatiotemporal synchronization and reliability, the system provides an effective solution for studying multiscale ocean turbulence and associated dynamic processes.

This study introduces a dual-resonator quartz pressure sensor employing a novel stress redistribution mechanism with orthogonal force beams. Utilizing the anisotropy of AT-cut quartz, the design integrates X- and Z-resonators with force paths parallel and perpendicular to the X-axis, respectively. This configuration induces opposite pressure sensitivities, yielding a high-gain differential output. Finite element analysis guided the geometric optimization for maximum sensitivity and robustness. The fabricated device achieves a record differential sensitivity of 378.72 Hz/MPa at 25°C, increasing to 413.84 Hz/MPa at 80°C over 0–10 MPa. This positive temperature coefficient of sensitivity, combined with excellent linearity, low hysteresis (<0.18% FS), and high thermal stability, underscores its potential for demanding applications like downhole monitoring, advancing resonant sensor design through strategic mechanical engineering.

Pressure measurement is of great importance due to its wide range of applications in many fields. AT-cut quartz, with its exceptional precision and durability, stands out as an excellent pressure transducer due to its superior accuracy and stable performance over time. However, its intrinsic temperature dependence significantly hinders its potential application in varying temperature environments. Herein, three different learning algorithms (i.e., multivariate polynomial regression, multilayer perceptron networks, and support vector regression) are elaborated in detail and applied to establish the prediction models for compensating the temperature effect of the resonant pressure sensor, respectively. The AC-cut quartz, which is sensitive to temperature variations, is paired with the AT-cut quartz, providing the essential temperature information. The output frequencies derived from the AT-cut and AC-cut quartzes are selected as input data for these learning algorithms. Through experimental validation, all three methods are effective, and a remarkable improvement in accuracy can be achieved. Among the three methods, the MPR model has exceptionally high accuracy in predicting pressure. The calculated residual error over the temperature range of −10–40 °C is less than 0.008% of 40 MPa full scale (FS). An intelligent automatic compensation and real-time processing system for the resonant pressure sensor is developed as well, which may contribute to improving the efficiency in online calibration and large-scale industrialization. This paper paves a promising way for the temperature compensation of resonant pressure sensors.

This work proposes a design for a direct-reading conductivity sensor with a parallel symmetrical four-electrode structure, which integrates a silicon-based platinum thin-film strip electrode and a serpentine temperature compensation electrode. The optimal structural parameters of the electrode were determined by finite element simulations performed via COMSOL Multiphysics. Next, the designed conductivity sensor chip was fabricated using MEMS technology, and subsequently, the conductivity measurement circuit was designed to test the fabricated sensor’s performance. In laboratory tests, the optimal AC excitation frequency was observed to be 1.067 kHz, while the maximum measurement range was 0–107.41 mS/cm and the measurement precision in low concentration range (0–76.422 mS/cm) was ±0.1 mS/cm. Furthermore, the maximum measurement error of the sensor evaluated using the National Center of Ocean Standards and Metrology was ±0.073 mS/cm. The designed sensor possesses the characteristics of high accuracy, high range, and miniaturization, and enables real-time reading of conductivity value and temperature compensation, which is of great significance for the on-site observation of the physical parameters of marine environment.

Microelectromechanical system (MEMS) pressure sensors have a wide range of applications based on the advantages of mature technology and easy integration. Among them, piezoresistive sensors have attracted great attention with the advantage of simple back-end processing circuits. However, less research has been reported on the performance of piezoresistive pressure sensors in dynamic environments, especially considering the vibrations and shocks frequently encountered during the application of the sensors. To address these issues, this paper proposes a design method for a MEMS piezoresistive pressure sensor, and the fabricated sensor is evaluated in a series of systematic dynamic environmental adaptability tests. After testing, the output sensitivity of the sensor chip was 9.21 mV∙bar⁻¹, while the nonlinearity was 0.069% FSS. The sensor overreacts to rapidly changing pressure environments and can withstand acceleration shocks of up to 20× g. In addition, the sensor is capable of providing normal output over the vibration frequency range of 0–5000 Hz with a temperature coefficient sensitivity of −0.30% FSS °C⁻¹ over the temperature range of 0–80 °C. Our proposed sensor can play a key role in applications with wide pressure ranges, high-frequency vibrations, and high acceleration shocks, as well as guide MEMS-based pressure sensors in high pressure ranges and complex environmental adaptability in their design.