Engineering projects real-time supervision mechanism based on grid management

Abstract

The ocean contains a substantial amount of energy, and the efficient harvesting of this energy holds significant importance. Drawing inspiration from the biomimicry of octopus tentacles, this study introduces a synergistic mechanism designed to optimize energy harvesting through flowinduced motions, integrating boundary layer modulation via passive turbulence control (PTC) with dynamic system stiffness adjustments via time-varying stiffness (SIN & Trapezoidal patterns). The implementation of PTC facilitates global stability by managing the local instabilities caused by variable stiffness, culminating in a highly effective energy harvesting capability. Our investigations summarize the requisite conditions for peak energy harvesting efficacy, notably within the SIN/80° PTC and Trapezoid/60° PTC arrangements, which have been demonstrated to double the efficiency of energy harvesting with up to 57%, alongside a reduction in initial harvesting frequency and an enhancement in both instantaneous power output and vibration amplitude. Furthermore, an energy transfer characteristic map has been compiled to illustrate the mechanism coupled between boundary layer modulation and time-varying stiffness. This research not only introduces novel perspectives but also stands as a significant stride in the realm of wide band and efficient energy harvesting in the ocean.

Radar Error Correction Method Based on Improved Sparrow Search Algorithm

“…Obviously, Equation ( 11) is a nonlinear system of equations. In existing studies, no matter the GLS algorithm [22] or KF algorithm [21], Equation (11) needs to be transformed into a linear equation by using first-order Taylor expansion and ignoring higher-order terms, and then be solved. This approximation process introduces new deviations, making the estimation results inaccurate.…”

“…The radar error correction method based on the ISSA is simulated and verified. The estimation effect of the KF algorithm [21], GLS [22], SSA [32], and ISSA on the systematic error is analyzed and compared under the same conditions. With reference to the simulation model parameters in references [22,41], the simulation conditions set in this paper are shown in Table 1.…”

“…Considering that the error calibration of the three-coordinate radar needs to include three-dimensional position information, Wu proposed a real-time radar error correction algorithm based on ADS-B data, derived the error observation model, and used Kalman filtering (KF) algorithm to solve it. It is more stable than the algorithm that uses the mean error as the systematic error to complete the calibration [21]. Academician He's team derived a combined system error model, taking the ADS-B system delay into account, and solved it using generalized least squares (GLS) [22], both of which require the linearization of the nonlinear model to be solved.…”