Structural dynamics breakthrough: 10 powerful Siemens contributions driving a better future at IMAC 2026

Abstract:
Modern engineering faces unprecedented challenges in vibration testing as structures become increasingly complex. Traditional uniaxial testing often fails to replicate real-world damage mechanisms. This presentation introduces a novel multiaxial fatigue damage formulation, extending standard fatigue analysis to multi-axis tests. By incorporating cross-correlations and a multi-DOF severity metric, the approach enables more accurate mission synthesis and test tailoring, ensuring that multi-axis platforms are fully leveraged for durability validation.

Abstract:
Impact testing is a cornerstone of experimental structural dynamics, but efficiency and quality are often limited by manual processes. This session highlights the Smart Hit Selection (SHS) method, which automates optimal impact selection for Frequency Response Function (FRF) measurements. Additionally, a Hybrid AI approach combines machine learning and rule-based expertise to automate modal parameter estimation, delivering results with expert-level accuracy and minimal human intervention.

Abstract:
Dynamic testing of spacecraft and components is vital to assess their  integrity under operational loads. This presentation introduces a virtual shaker testing approach, where a real-time digital twin of the test setup is connected to the vibration controller. This allows engineers to safely test an emulation of the entire system and predict the outcome of the physical test before its execution, improving setup, control parameters, and without the risk of damaging the device under test and the shaker.

Abstract:
This study presents a comprehensive approach to fuel cell stack testing, combining experimental modal analysis, finite element model updating, and fixture decoupling. The results demonstrate the importance of accurate testing and model correlation for predicting structural integrity and optimizing design in real-world operational conditions.

Abstract:
This work introduces a novel convolutional neural network (CNN) approach for extracting and magnifying Operational Deflection Shapes (ODS) from high-speed video. The method delivers clean, high-quality ODS animations  enabling non-contact, full-field observation of structural dynamics.

Abstract:
This presentation explores the use of Video Motion Magnification (VMM) for pre-test analysis in vibration control tests. The goal is to visualize the full-field structural dynamics in a rapid manner to identify the most critical responses, helping test engineers to de-risk the test campaign. By integrating synthetic image generation in Blender, the feasibility of camera-based displacement retrieval can be validated before physical testing, helping to optimize test setups.

Abstract:
Understanding fluid-structure interaction is vital for modern lightweight structures. This session presents a Kalman-based virtual sensing framework that uses a multiphysics approach to estimate full-field strain, displacement, and pressure fields from limited sensor data, reducing the need for extensive instrumentation.

Abstract:
This work validates and automates a signal processing algorithm for detecting damage in wind turbine blades. Developed by the Mechanical and Aerospace Engineering Department at University of Rome  “Sapienza”, the approach combines Principal Component Analysis (PCA) and Independent Component Analysis (ICA), to support efficient, real-time monitoring and early damage detection. The entire process has been integrated within an automated monitoring workflow using Simcenter Testlab Workflow Automation (TWA), enabling automatic calculation and tracking of the damage indicator across multiple measurement sessions.

Abstract:
This presentation demonstrates a phase-based video motion magnification technique for full-field Operational Modal Analysis  (OMA). The approach enables extraction of modal parameters and mode shapes using only a single camera, validated on both laboratory and full-scale aircraft tests.

Abstract:
Integrating battery packs into electric vehicles poses unique NVH (Noise, Vibration, Harshness) challenges. This session discusses experimental Frequency-Based Substructuring (FBS) for predicting and optimizing NVH performance. Modal modeling is hereby considered for increasing data consistency. A novel modal model identification approach is proposed, and its targeted use in battery-to-body integration is evaluated.