Underconstrained Cable-Driven Parallel Suspension System of Virtual Flight Test Model in Wind Tunnel

An underconstrained cable-driven parallel robot(CDPR)suspension system was designed for a virtual flight testing(VFT)model.This mechanism includes two identical upper and lower kinematic chains,each of which comprises a cylindrical pair,rotating pair,and cable parallelogram.The model is pulled via two cables at the top and bottom and fixed by a yaw turntable,which can realize free coupling and decoupling with three rotational degrees of freedom of the model.First,the underconstrained CDPR suspension system of the VFT model was designed according to the mechanics theory,the degrees of freedom were verified,and the support platform was optimized to realize the coincidence between the model’s center of mass and the rotation center of the mechanism during the motion to ensure the stability of the support system.Finally,kinematic and dynamical modeling of the underconstrained CDPR suspension system was conducted;the system stiffness and stability criteria were deduced.Thus,the modeling of an underconstrained,reconfigurable,passively driven CDPR was understood comprehensively.Furthermore,dynamic simulations and experiments were used to verify that the proposed system meets the support requirements of the wind tunnel-based VFT model.This study serves as the foundation for subsequent wind tunnel test research on identifying the aerodynamic parameters of aircraft models,and also provides new avenues for the development of novel support methods for thewind tunnel testmodel.

Observable-specific phase biases of Wuhan multi-GNSS experiment analysis center’s rapid satellite products

Precise Point Positioning(PPP)with Ambiguity Resolution(AR)is an important high-precision positioning technique that is gaining popularity in geodetic and geophysical applications.The implementation of PPP-AR requires precise products such as orbits,clocks,code,and phase biases.As one of the analysis centers of the International Global Navigation Satellite System(GNSS)Service(IGS),the Wuhan University Multi-GNSS experiment(WUM)Analysis Center(AC)has provided multi-GNSS Observable-Specific Bias(OSB)products with the associated orbit and clock products.In this article,we first introduce the models and generation strategies of WUM rapid phase clock/bias products and orbit-related products(with a latency of less than 16 h).Then,we assess the performance of these products by comparing them with those of other ACs and by testing the PPP-AR positioning precision,using data from Day of the Year(DOY)047 to DOY 078 in 2022.It is found that the peak-to-peak value of phase OSBs is within 2 ns,and their fluctuations are caused by the clock day boundary discontinuities.The associated Global Positioning System(GPS)orbits have the best consistency with European Space Agency(ESA)products,and those of other systems rank in the medium place.GLObal NAvigation Satellite System(GLONASS)clocks show slightly inconsistency with other ACs’due to the antenna thrust power adopted,while the phase clocks of other GNSSs show no distortion compared with legacy clocks.With well-estimated phase products for Precise Orbit Determination(POD),the intrinsic precision is improved by 14%,17%,and 24%for GPS,Galileo navigation satellite system(Galileo),and BeiDou-3 Navigation Satellite System(BDS-3),respectively.The root mean square of PPP-AR using our products in static mode with respect to IGS weekly solutions can reach 0.16 cm,0.16 cm,and 0.44 cm in the east,north,and up directions,respectively.The multi-GNSS wide-lane ambiguity fixing rates are all above 90%,while the narrow-lane fixing rates above 80%.In conclusion,the phase OSB products at WUM have good precision and performance,which will benefit multi-GNSS PPP-AR and POD.

Tilting stability analysis and experiment of the 3-DOF lifting platform for hilly orchards

An orchard-lifting platform is a type of mechanical equipment to assist growers in fruit picking,fruit tree pruning,flower thinning,and other operations.In its operational processes,the tilting stability directly affects the operational safety and adaptability under complex terrain conditions,while critical tilting angle is an important criterion to evaluate the tilting stability.Based on the structure and the operating characteristics of the three degree of freedom(3-DOF)lifting platform for hilly orchards,the tilting stability was analyzed in different parked states,and the theoretical expressions of critical tilting angle were obtained;in the theoretical expressions,the influencing factors on tilting stability were determined as the parked positionβ1,the manned worktable rotary positionβ2,the lifting height h,and the load m.Based on the multi-body dynamics principle,the tilting stability simulation was carried out.The relative error of tilting angles was approximately 4.6%between simulation and tilting verification experiment,which indicated that the results of tilting stability simulation were reliable.Therefore,the multi-body dynamics simulation was used for further clarifying the influencing factors on tilting stability.A virtual orthogonal test was designed,and the results showed that critical tilting angle ranged from 20°to 44°when the factors were at different values,which indicated that the 3-DOF lifting platform for hilly orchards had a high tilting stability performance and could adapt to the operating conditions of hills with slope angles from 5°to 20°.The results of the range analysis and ANOVA showed that the influence intensity of factors on tilting stability wasβ1>h>m>β2;at the same time,β1,h,and m exerted significant effect on tilting stability.The tilting stability first decreased and then either increased or decreased with the increasing lifting height;it gradually decreased with the increasing load.It also showed that the position of the manned worktable along the slope down always had the lowest tilting stability.This research can provide a theoretical basis and reference for the analysis of tilting stability of the lifting machinery for hilly orchards.

A novel pure data-selection framework for day-ahead wind power forecasting

Numerical weather prediction(NWP)data possess internal inaccuracies,such as low NWP wind speed corresponding to high actual wind power generation.This study is intended to reduce the negative effects of such inaccuracies by proposing a pure data-selection framework(PDF)to choose useful data prior to modeling,thus improving the accuracy of day-ahead wind power forecasting.Briefly,we convert an entire NWP training dataset into many small subsets and then select the best subset combination via a validation set to build a forecasting model.Although a small subset can increase selection flexibility,it can also produce billions of subset combinations,resulting in computational issues.To address this problem,we incorporated metamodeling and optimization steps into PDF.We then proposed a design and analysis of the computer experiments-based metamodeling algorithm and heuristic-exhaustive search optimization algorithm,respectively.Experimental results demonstrate that(1)it is necessary to select data before constructing a forecasting model;(2)using a smaller subset will likely increase selection flexibility,leading to a more accurate forecasting model;(3)PDF can generate a better training dataset than similarity-based data selection methods(e.g.,K-means and support vector classification);and(4)choosing data before building a forecasting model produces a more accurate forecasting model compared with using a machine learning method to construct a model directly.