Tianwen-1 Mars entry vehicle trajectory and atmosphere reconstruction preliminary analysis

The Tianwen-1 Mars entry vehicle successfully landed on the surface of Mars in southern Utopia Planitia on May 15, 2021, at 7:18 (UTC+8). To acquire valuable Martian flight data, a scientific instrumentation package consisting of a flush air data system and a multilayer temperature-sensing system was installed aboard the entry vehicle. A combined approach was applied in the entry, descent, and landing trajectory reconstruction using all available data obtained by the inertial measurement unit and the flush air data system. An aerodynamic database covering the entire flight regime was generated using computational fluid dynamics methods to assist in the reconstruction process. A preliminary analysis of the trajectory reconstruction result, along with the atmosphere reconstruction and aerodynamic performance, was conducted. The results show that the trajectory agrees closely with the nominal trajectory and the wind-relative attitude. Suspected wind occurred at the end of the trajectory.

Deployment dynamics analysis of CALLISTO’s approach and landing system

Prior to landing of reusable space transportation systems, the vehicle’s landing legs needs to be fully deployed to enable a safe landing and further re-use of the space vehicle. During that phase the deployment system has to overcome harsh and challenging environmental conditions. In this study, a numerical simulator is developed in order to investigate these influences on the landing leg deployment dynamics. By means of an extensive aerodynamic database and a broad approach flight domain, the influence of aerodynamics, exhaust plume, and vehicle’s attitude on the deployment dynamics is analyzed. This study shows on the example of the first stage demonstrator CALLISTO (Cooperative Action Leading to Launcher Innovation in Stage Toss back Operations), that thrust level, vehicle attitude, and the deployment system parameters affect the deployment performance.

Performance Prediction Program for Wind-Assisted Cargo Ships

Wind-Assisted Propulsion Systems (WAPS) can play a key role in achieving the IMO 2050 targets on reducing the total annual GHG emissions from international shipping by at least 50%. The present project deals with the development of a six degree of freedom (DoF) Performance Prediction Program (PPP) for wind-assisted cargo ships aimed at contributing knowledge on WAPS performance. It is a fast and easy tool, able to predict the performance of any commercial ship with three possible different WAPS installed: rotor sails, rigid wing sails and DynaRigs; with only the ship main particulars and general dimensions as input data. The tool is based on semi-empirical methods and a WAPS aerodynamic database created from published data on lift and drag coefficients, which can be interpolated with the aim to scale to different sizes and configurations. A model validation is carried out to evaluate its reliability. The results are compared with the real sailing data of a Long Range 2 (LR2) class wind-assisted tanker vessel, the Maersk Pelican. The study indicates that the PPP shows good agreement with the technology suppliers’ own modelling tool and reasonable agreement with the trends of the real sailing measurements. However, for downwind sailing conditions, the predictions are more conservative than the measured values. Lastly, results showing and comparing power savings for the three different WAPS are presented. Rotor Sails are found to be the most efficient WAPS studied with a much higher potential of driving force generation per square meter of projected sail area.

Modeling, Simulation, and Cruise Characteristics of Wingtip-Jointed Composite Aircraft

In this paper, multibody dynamic modeling and a simulation method for the wingtip-jointing process of a new-concept composite aircraft system are investigated. When the wingtips of two aircraft are jointed, the resultant wingtip-jointed aircraft is regarded as variable-geometry multiple rigid bodies, and a seven-degree-of-freedom non-linear dynamic model is established by mathematical derivation. The slip-meshing method is adopted to analyze the unsteady aerodynamic influence. We also present specific aerodynamic database acquisition methods under the quasi-steady assumption. Based on this, the simulation results indicate that the longitudinal and lateral movements are highly jointed and complex. A new composite aircraft system is investigated, in order to meet the balance requirement. With the lift–drag ratio (K) considered, the piecewise cubic Hermite interpolation (PCHIP) method, with a sufficient sample size, was utilized to help the cruise strategy optimization analysis under fixed altitude and speed conditions. Meanwhile, distribution of cruise characteristics with different sampling values of composite flight characteristic parameters were also analyzed. The research results can be used as a reference for new-concept composite aircraft model establishment, simulation, and multibody dynamic characteristic investigation.

Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

A quadcopter is a rotorcraft with a simple mechanical construction. It has the same hovering capability similar to the traditional helicopter, but it is easier to maintain. The quadcopter is hard to control due to its unstable system with highly coupled and non-linear dynamics. In order to design a robust control algorithm, it is crucial to obtain a precise quadrotor flight dynamics through system identification approach. System identification is a method of finding the mathematical model of the dynamics system using the input-output data measurement. Neural network (NN) based system identification is excellent alternative modeling because it reduces development costs and time by avoiding governing equations and large aerodynamic database. NN based system identification has successfully identified the quadcopter dynamics with good accuracy. This paper gives an overview of the characteristic of the quadcopter and presents a comprehensive survey of the modeling techniques used to determine the flight dynamics of a quadrotor with a particular focus on NN based system identification method. The presented research works have been classified into different categories such as the first principle modeling, system identification and implementation of NN based system identification in quadcopter platform. Finally, the paper highlights challenges that need to be addressed in developing efficient NN based system identification model for unmanned quadcopter system.

Neural Network-Based System Identification for Quadcopter Dynamic Modeling: A Review

A quadcopter is a rotorcraft with a simple mechanical construction. It has the same hovering capability similar to the traditional helicopter, but it is easier to maintain. The quadcopter is hard to control due to its unstable system with highly coupled and non-linear dynamics. In order to design a robust control algorithm, it is crucial to obtain a precise quadrotor flight dynamics through system identification approach. System identification is a method of finding the mathematical model of the dynamics system using the input-output data measurement. Neural network (NN) based system identification is excellent alternative modeling because it reduces development costs and time by avoiding governing equations and large aerodynamic database. NN based system identification has successfully identified the quadcopter dynamics with good accuracy. This paper gives an overview of the characteristic of the quadcopter and presents a comprehensive survey of the modeling techniques used to determine the flight dynamics of a quadrotor with a particular focus on NN based system identification method. The presented research works have been classified into different categories such as the first principle modeling, system identification and implementation of NN based system identification in quadcopter platform. Finally, the paper highlights challenges that need to be addressed in developing efficient NN based system identification model for unmanned quadcopter system.