A Multibody Mathematical Model to Simulate the Dynamic Behavior of Aerial Work Platforms Using Python

Understanding and knowing the extent of dynamic loads (forces and moments) acting on critical points of structures from the early stages of new product design allows companies to reduce product time-to-market and cut costs associated with physical validation tests. This is made possible through the use of commercial software that enables the simulation of the dynamic behavior of a wide range of mechanical systems, and beyond. However, the use of such software is not straightforward and often requires a specialist within the company to have in-depth knowledge of both simulations and the software itself. Consequently, many companies give up the use of these tools, compromising the whole product development process, falling into the design and testing iterative loop. The multibody mathematical model developed in this study, simulated using python programming language, allows easy retrieval of all the necessary information without requiring the user to be a specialist in multibody dynamics simulations. This model is intended as a tool specifically designed for the type of product (aerial work platform) that serves, during the conceptual and preliminary design phases, to predict dynamic loads through calculations. The balance between the simplicity of such a tool and the accuracy of the results is the key point for the success of this work.

Authors

Institutions

Publication Details

Published
2025-03-06
DOI
https://doi.org/10.3390/engproc2025085036
Citations
1
Primary Topic
Dynamics and Control of Mechanical Systems
Type
article
Field-Weighted Citation Impact
1.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Multibody Mathematical Model to Simulate the Dynamic Behavior of Aerial Work Platforms Using Python

Massimiliano Palmieri, Filippo Cianetti, Giacomo Cangi, Alessandro Angeletti
1 citations
Dynamics and Control of Mechanical Systems
1.21
article

A Multibody Mathematical Model to Simulate the Dynamic Behavior of Aerial Work Platforms Using Python

Massimiliano Palmieri, Filippo Cianetti, Giacomo Cangi, Alessandro Angeletti
article en
1 citations

Abstract

Understanding and knowing the extent of dynamic loads (forces and moments) acting on critical points of structures from the early stages of new product design allows companies to reduce product time-to-market and cut costs associated with physical validation tests. This is made possible through the use of commercial software that enables the simulation of the dynamic behavior of a wide range of mechanical systems, and beyond. However, the use of such software is not straightforward and often requires a specialist within the company to have in-depth knowledge of both simulations and the software itself. Consequently, many companies give up the use of these tools, compromising the whole product development process, falling into the design and testing iterative loop. The multibody mathematical model developed in this study, simulated using python programming language, allows easy retrieval of all the necessary information without requiring the user to be a specialist in multibody dynamics simulations. This model is intended as a tool specifically designed for the type of product (aerial work platform) that serves, during the conceptual and preliminary design phases, to predict dynamic loads through calculations. The balance between the simplicity of such a tool and the accuracy of the results is the key point for the success of this work.

University of Perugia (IT)
Openalex Percentile: Top 24%
Dynamics and Control of Mechanical Systems
1.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.