Sporadic E responds to the 2022 Tonga volcano eruptions recorded by the Meridian Project

In this study,ionosonde observations over Fuke(19.5°N,109.1°E),Wuhan(30.5°N,114.4°E),and Mohe(53.5°N,122.3°E)were analyzed to demonstrate the responses of the sporadic E()to the severe atmospheric disturbances caused by the Tonga volcanic eruptions on January 15,2022.The most prominent signature was the disappearance of the layer after~10:00 UT over Wuhan and Fuke,which was attributed to the vertical drift caused by the eruptions.The occurred intermittently after 13:00 UT following the arrival of the tropospheric Lamb wave.To examine the causal mechanism for the intermittence,we also included data of horizontal winds in the mesosphere and lower thermosphere region recorded by the meteor radars at Wuhan and Mohe in this study.The wind disturbances with periods of~20 hours contributed to the formation of the layer in the nighttime on January 15.

Biomechanical Response of the Root System in Tomato Seedlings under Wind Disturbance

Wind disturbance as a green method can effectively prevent the overgrowth of tomato seedlings,and its mechanism may be related to root system mechanics.This study characterized the biophysical mechanical properties of taproot and lateral roots of tomato seedlings at five seedling ages and seedling substrates with three different moisture content.The corresponding root system-substrate finite element(FE)model was then developed and validated.The study showed that seedling age significantly affected the biomechanical properties of the taproot and lateral roots of the seedlings and that moisture content significantly affected the biomechanical properties of the seedling substrate(p<0.05).The established FE model was sensitive to wind speed,substrate moisture content,strong seedling index,and seedling age and was robust.The multiple linear regression equations obtained could predict the maximum stress and strain of the root system of tomato seedlings in the wind field.The strong seedling index had the greatest impact on the biomechanical response of the seedling root system during wind disturbance,followed by wind speed.In contrast,seedling age had no significant effect on the biomechanical response of the root system during wind disturbance.In the simulation,no mechanical damage was observed on the tissue of the seedling root system,but there were some strain behaviors.Based on the plant stress resistance,wind disturbance may affect the growth and development of the root system in the later growth stage.In this study,finite element and statistical analysis methods were combined to provide an effective approach for indepth analysis of the biomechanical mechanisms of wind disturbances that inhibit tomato seedlings’growth from the root system’s perspective.

Wind disturbance rejection control based on the dynamic parameters estimation of quadrotors UAV

The turbulence or gust in quadrotor flight environment causes drastic changes in the unmanned aerial vehicle(UAV)aerodynamic parameters.Especially,rotor thrust coefficient and reaction torque coefficient of the UAV encounter uncertainty fluctuation,which may undermine the control performance.A real-time estimation strategy for these aerodynamic parameters is proposed to improve the identification on the disturbance.First,the unscented Kalman filter(UKF)algorithm is used to estimate the UAV states and aerodynamic parameters.Then,a double-loop structure,consisting of position and attitude,is designed for the trajectory tracking control.In the outer loop,a proportional-derivative controller is adopted to carry out position tracking and provide Euler angle references for the inner loop,called attitude controller.Moreover,the attitude controller is designed using an inverse dynamic technique.The main contribution of this study is to propose a joint estimation on the aerodynamic parameters with wind disturbance as well as the UAV states.This strategy plays an important role in refining time-varying parameters of wind disturbance.A number of simulations are executed to verify the effectiveness of the proposed method.

Two-stage Multi-objective Optimization and Decision-making Method for Integrated Energy System Under Wind Generation Disturbances

Although integrated energy systems(IES)are currently modest in size,their scheduling faces strong challenges,stemming from both wind generation disturbances and the system’s complexity,including intrinsic heterogeneity and pronounced non-linearity.For this reason,a two-stage algorithm called the Multi-Objective Group Search Optimizer with Pre-Exploration(MOGSOPE)is proposed to efficiently achieve the optimal solution under wind generation disturbances.The optimizer has an embedded trainable surrogate model,Deep Neural Networks(DNNs),to explore the common features of the multiscenario search space in advance,guiding the population toward a more efficient search in each scenario.Furthermore,a multiscenario Multi-Attribute Decision Making(MADM)approach is proposed to make the final decision from all alternatives in different wind scenarios.It reflects not only the decisionmaker’s(DM)interests in other indicators of IES but also their risk preference for wind generation disturbances.A case study conducted in Barry Island shows the superior convergence and diversity of MOGSOPE in comparison to other optimization algorithms.With respect to numerical performance metrics HV,IGD,and SI,the proposed optimizer exhibits improvements of 3.1036%,4.8740%,and 4.2443%over MOGSO,and 4.2435%,6.2479%,and 52.9230%over NSGAII,respectively.What’s more,the effectiveness of the multi-scenario MADM in making final decisions under uncertainty is demonstrated,particularly in optimal scheduling of IES under wind generation disturbances.

Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight path.To cope with various wind conditions,this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the inner-loop.Since the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint,a scaling mechanism is introduced to tune the proportions of the compensation and nominal components.Moreover,an optimization problem is formulated to pursue a maximum wind compensation in strong winds,which can be solved analytically to yield two scaling factors.A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector field.High-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises,achieves promising accuracy in normal winds,and mitigates the deviation from a desired path in wild winds.

A quantum inspired genetic algorithm for multimodal optimization of wind disturbance alleviation flight control system

This paper develops a Quantum-inspired Genetic Algorithm(QGA) to find the sets of optimal parameters for the wind disturbance alleviation Flight Control System(FCS). To search the problem domain more evenly and uniformly, the lattice rule based stratification method is used to create new chromosomes. The chromosomes are coded and updated according to quantuminspired strategies. A niching method is used to ensure every chromosome can converge to its corresponding local minimum in the optimization process. A parallel archive system is adopted to monitor the chromosomes on-line and save all potential feasible solutions in the optimization process. An adaptive search strategy is used to gradually adjust the search domain of each niche to finally approach the local minima. The solutions found by the QGA are compared with some other Multimodal Optimization(MO) algorithms and are tested on the FCS of the Boeing 747 to demonstrate the effectiveness of the proposed algorithm.

Vector Field Based Sliding Mode Control of Curved Path Following for Miniature Unmanned Aerial Vehicles in Winds

In this paper, a curved path following control algorithm for miniature unmanned aerial vehicles（UAVs） in winds with constant speed and altitude is developed. Different to the widely considered line or orbit following, the curved path to be followed is defined in terms of the arc-length parameter, which can be straight lines, orbits, B-splines or any other curves provided that they are smooth. The proposed path following control algorithm, named by VF-SMC, is combining the vector field（VF） strategy with the sliding mode control（SMC） method. It is proven that the designed algorithm guarantees the tracking errors to be a bounded ball in the presence of winds, with the aid of the Lyapunov method and the BIBO stability. The algorithm is validated both in Matlab-based simulations and high-fidelity semi-physical simulations. In Matlab-based simulations, the proposed algorithm is verified for straight lines, orbits and B-splines to show its wide usage in different curves.The high-fidelity semi-physical simulation system is composed of actual autopilot controller, ground station and X-Plane flight simulator in-loop. In semi-physical simulations, the proposed algorithm is verified for B-spline path following under various gain parameters and wind conditions thoroughly.All experiments show the accuracy in curved path following and the excellent robustness to wind disturbances of the proposed algorithm.