Development and validation of the multicopter modelling code: a physics-based tool for multicopter analysis

被引:1
|
作者
Oppong, David Kofi [1 ]
Obu, Evans [2 ]
Asare, Timothy [3 ]
Aidam, God'sable Sitsofe Koku [1 ]
机构
[1] Kwame Nkrumah Univ Sci & Technol, Dept Mech Engn, Kumasi, Ghana
[2] Christian Serv Univ Coll, Dept Comp Sci & Informat Technol, Kumasi, Ghana
[3] Ashesi Univ, Dept Engn, Accra, Ghana
来源
AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY | 2022年 / 94卷 / 10期
关键词
Multicopter; Model; MATLAB; Physics;
D O I
10.1108/AEAT-01-2022-0011
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Purpose This study aims to present details of the development and validation of the multicopter modelling code (MMC), a tool for the analysis of small-scale multicopters based on flight physics. Design/methodology/approach The development effort involved the study of aircraft dynamics and translating the equations of motion into MATLAB code. The authors also developed several auxiliary functions, so that the tool could trim the aircraft about a steady state, linearize the dynamic equations to produce a model that could be used for control systems design and carry out flight simulation. Findings MMC proved to be of good accuracy, producing results similar to those of other software such as AcuSolve, Overflow and the Rensselaer Multicopter Analysis Code (RMAC), which served as the motivation for this study. Originality/value The tool presented here provides an alternative to the aforementioned software, which are not freely available, programmed in MATLAB, a language well known to engineers and scientists.
引用
收藏
页码:1684 / 1691
页数:8
相关论文
共 50 条
  • [31] PHYSICS-BASED MODELLING OF THE LIFE CYCLE OF ENERGY IN THE SOLAR SYSTEM
    Lapenta, G.
    ECLA: EUROPEAN CONFERENCE ON LABORATORY ASTROPHYSICS, 2013, 58 : 83 - 90
  • [32] A physics-based neural network for flight dynamics modelling and simulation
    Terrin Stachiw
    Alexander Crain
    Joseph Ricciardi
    Advanced Modeling and Simulation in Engineering Sciences, 9
  • [33] Physics-Based Modelling of a Piezoelectric Actuator Using Genetic Algorithm
    Miri, Narges
    Mohammadzaheri, Morteza
    Chen, Lei
    Grainger, Steven
    Bazghaleh, Mohsen
    2013 IEEE SYMPOSIUM ON INDUSTRIAL ELECTRONICS & APPLICATIONS (ISIEA 2013), 2013, : 16 - 20
  • [34] Development and integration of a tool for physics-based shape and topology optimization in the MOOSE multiphysics simulation framework
    Altahhan, Muhammad Ramzy
    Herring, Nicholas
    Schunert, Sebastian
    Azmy, Yousry
    PROGRESS IN NUCLEAR ENERGY, 2025, 181
  • [35] Physics-based modelling of the ECRH system for MHD control applications
    Tsironis, Christos
    Vasileiadou, Soultana
    Giannopoulos, Iordanis K.
    FUSION ENGINEERING AND DESIGN, 2017, 123 : 504 - 507
  • [36] Physics-based modeling tool development for spectral-sensing measurements under atmospheric attenuation
    Xiao, Xifeng
    Voelz, David G.
    Montoya, Joseph
    Salinas, Miguel
    AUTOMATIC TARGET RECOGNITION XXVII, 2017, 10202
  • [37] LOCAL TOOL WEAR PREDICTION USING PHYSICS-BASED MODELS
    Olortegui-Yume, Jorge A.
    Park, Kyung-Hee
    Kwon, Patrick Y.
    PROCEEDINGS OF THE STLE/ASME INTERNATIONAL JOINT TRIBOLOGY CONFERENCE 2008, 2009, : 739 - 741
  • [38] A physics-based neural network for flight dynamics modelling and simulation
    Stachiw, Terrin
    Crain, Alexander
    Ricciardi, Joseph
    ADVANCED MODELING AND SIMULATION IN ENGINEERING SCIENCES, 2022, 9 (01)
  • [39] PHYSICS-BASED PROXY MODELLING OF SOLVENT TRANSPORT IN VAPEX PROCESS
    Shi, Jindong
    Leung, Juliana Y.
    CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 2014, 92 (08): : 1467 - 1480
  • [40] Development of Hydrogen Fuel Cell-Battery Hybrid Multicopter System Thermal Management and Power Management System Based on AMESim
    Choi, Jihyun
    Park, Hyun-Jong
    Han, Jaeyoung
    ENERGIES, 2025, 18 (02)