Saied Taheri

Virginia Tech

Papers

2

Total Citations

7

H-Index

2

About

Dr. Saied Taheri is a leading researcher in structural dynamics and system identification, with a focus on advanced modeling techniques for complex mechanical systems. His work centers on developing reduced-order models and modular approaches for predicting dynamic responses, particularly through receptance coupling and frequency-based substructuring (FBS) methods. Taheri’s major contributions include pioneering the Generalized Receptance Coupling and Frequency-Based Substructuring (GRCFBS) method, which enables efficient analysis of reconfigurable systems by breaking them into manageable subsystems. His 2024 paper on reduced-order modeling for dynamic system identification (4 citations) demonstrates a powerful technique for lumped and distributed parameter systems, while his work on modular half-vehicle modeling (3 citations) showcases practical applications in automotive dynamics. Though early in citation accumulation, these works are gaining traction for their potential to streamline vibration analysis in industries like automotive and aerospace. Taheri’s research is notable for bridging theoretical substructuring with real-world system reconfigurability, offering engineers a scalable toolkit for dynamic design and diagnostics.

Research Focus

Key Achievements

2
H-Index
2
Papers
7
Total Citations
4
Avg Citations/Paper
🏆 Most Cited Paper
Reduced-Order Modeling for Dynamic System Identification with Lumped and Distributed Parameters via Receptance Coupling Using Frequency-Based Substructuring (FBS)
4 citations · 2024
📈 Most Prolific Year: 2024 (2 Papers)
🤝 Key Collaborators: 1
🏛 Institutions: Virginia Tech

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago