还原(数学)
导纳
流离失所(心理学)
计算机科学
材料科学
数学
心理学
工程类
几何学
电气工程
电阻抗
心理治疗师
作者
Domen Ocepek,Francesco Trainotti,Gregor Čepon,Daniel J. Rixen,Miha Boltežar
出处
期刊:River Publishers eBooks
[River Publishers]
日期:2024-10-21
卷期号:: 67-75
标识
DOI:10.1007/978-3-031-68897-3_8
摘要
When determining critical paths for transmission of sound and vibration in assembled products, transfer path analysis (TPA) is a reliable and effective tool. TPA represents a source with a set of forces that replicate the operational responses. The indirect determination of the forces at the interface is commonly performed using an inverse procedure; however, admittance-based TPA methods are often strongly influenced by imperfect measurements. Given that the condition number of the transfer path admittance is high, this can lead to severe error amplification in the equivalent (also known as blocked) forces. In order to overcome this problem, regularization techniques such as singular value truncation or Tikhonov regularization are usually suggested. These techniques generally improve the accuracy of the determined interface forces but provide little insight into what is the actual source of errors. In this chapter, we investigate the benefits of projecting measured displacements into various representative subspaces in the scope of admittance-based TPA methods. In particular, a comparative investigation of three established reduction bases using singular, physical, and interface deflection modes is conducted. The definition of the reduced subspace using different sets of modes assures only dynamic information, which is relevant and dominant for the measured configuration, is retained after the reduction. Hence, badly observed dynamic, commonly dominated by measurement errors, is effectively filtered out. The main point of interest of this study is the effect of projecting measured displacements to the reduced domain on the transferability of the equivalent forces. The feasibility of all approaches is supported by an experimental case study, which can guide the reader in selecting a suitable approach for their specific needs.
科研通智能强力驱动
Strongly Powered by AbleSci AI