JOURNAL ARTICLE
Torque combiner and rigid shaft simulation using Simscape.
Published In: International Journal of Modeling, Simulation & Scientific Computing, 2025, v. 16, n. 5. P. 1 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Suryawan, Fajar; Ulinuha, Agus; Dwi Anggono, Agus; Nazrin Md Isa, Mohd 3 of 3
Abstract
Torque combiners play a pivotal role in mechanical systems where multiple torque sources act in concert to drive a common load, such as in hybrid vehicles and multi-motor industrial equipment. This study proposes a simulation framework using Simscape, incorporating a distributed rotational mass-spring-damper shaft model that permits real-time inclusion of variable torque sources, inertial loads, and disturbances. Validation scenarios demonstrate the model's ability to accurately handle system identification, proportional-integral (PI) control, and dynamic load sharing. Key results show that torque is effectively distributed across multiple motors, with steady-state torque loads of 45 N ⋅ m divided equally among one, two, and three motors — each drawing 9.2, 4.7, and 3.2 A, respectively — leading to improved rebalancing times and peak efficiency of 90.9% in the two-motor scenario. The model maintains performance under fault conditions and dynamically engages spare actuators in adaptive actuation scenarios. These features affirm the framework's robustness and its potential utility in the design and analysis of fault-tolerant, multi-source torque systems in mechanical engineering. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Modeling, Simulation & Scientific Computing. 2025/10, Vol. 16, Issue 5, p1
- Document Type:Article
- Subject Area:Physics
- Publication Date:2025
- ISSN:17939623
- DOI:10.1142/S1793962325500588
- Accession Number:189187851
- Copyright Statement:Copyright of International Journal of Modeling, Simulation & Scientific Computing is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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