Hybrid state-space coupling of experimental models and parametric reduced order models for enhanced real-time simulations
Hybrid state-space coupling of experimental models and parametric reduced order models for enhanced real-time simulations
Virtual prototyping has emerged as a key enabler in modern engineering design, allowing extensive virtual testing during the early development phases and reducing reliance on costly and time-consuming physical prototypes. In the NVH field, virtual prototyping techniques are essential for accurately predicting vibro-acoustic behavior, which is critical to achieving optimal vehicle performance and passenger comfort. This paper presents an innovative hybrid state-space coupling framework that integrates parametric reduced order models with systems characterized experimentally. The proposed approach enables the real-time computation of target responses derived from blocked force time histories. By leveraging the advantages of state-space representation and parametric model order reduction technology, the framework offers several key benefits. It supports fast design optimization by eliminating the computational overhead of recomputing transfer functions, a task traditionally required during iterative simulation processes. Additionally, the flexible architecture of the framework enhances its adaptability to various interactive applications. A critical aspect of the workflow proposed is the system identification process, which should respect certain physical properties of the systems to be coupled. This adherence to physical constraints is essential to guarantee the stability of the coupled structure and achieving accurate simulation result. The virtual coupling is evaluated both in terms of frequency response functions and time-domain responses, demonstrating results that align with state-of-the-art approaches. Computation analyses show the advantage of the approach proposed over frequency-based techniques. The findings underscore the framework’s potential as a powerful tool for NVH analysts and design engineers, enabling faster and more reliable decision-making during early development stages.
Dynamic Substructuring, Noise, Vibration and Harshness (NVH), Parametric Model Order Reduction, Real-time simulation, State-Space Substructuring, System Identification, Virtual Prototyping
Salamone, Nicolò
df65e865-0e3e-4050-8345-886da566cf72
Bianciardi, Fabio
f163bf18-51d6-4f1d-9f3b-e825be303cd3
Elkafafy, Mahmoud
b244f38a-82fc-4a18-9ed8-a39263d0e989
Staiger, Julian
5074c130-37df-409b-a299-3d262723fa23
De Gregoriis, Daniel
cb8130fa-4a12-47a7-8f85-15411cf6bf02
Van Ophem, Sjoerd
bb3fb37e-577b-4152-86bc-2248943f882d
Desmet, Wim
deeaf534-7d83-4644-89cb-aa5fcfb5c73a
15 January 2026
Salamone, Nicolò
df65e865-0e3e-4050-8345-886da566cf72
Bianciardi, Fabio
f163bf18-51d6-4f1d-9f3b-e825be303cd3
Elkafafy, Mahmoud
b244f38a-82fc-4a18-9ed8-a39263d0e989
Staiger, Julian
5074c130-37df-409b-a299-3d262723fa23
De Gregoriis, Daniel
cb8130fa-4a12-47a7-8f85-15411cf6bf02
Van Ophem, Sjoerd
bb3fb37e-577b-4152-86bc-2248943f882d
Desmet, Wim
deeaf534-7d83-4644-89cb-aa5fcfb5c73a
Salamone, Nicolò, Bianciardi, Fabio, Elkafafy, Mahmoud, Staiger, Julian, De Gregoriis, Daniel, Van Ophem, Sjoerd and Desmet, Wim
(2026)
Hybrid state-space coupling of experimental models and parametric reduced order models for enhanced real-time simulations.
Mechanical Systems and Signal Processing, 243, [113685].
(doi:10.1016/j.ymssp.2025.113685).
Abstract
Virtual prototyping has emerged as a key enabler in modern engineering design, allowing extensive virtual testing during the early development phases and reducing reliance on costly and time-consuming physical prototypes. In the NVH field, virtual prototyping techniques are essential for accurately predicting vibro-acoustic behavior, which is critical to achieving optimal vehicle performance and passenger comfort. This paper presents an innovative hybrid state-space coupling framework that integrates parametric reduced order models with systems characterized experimentally. The proposed approach enables the real-time computation of target responses derived from blocked force time histories. By leveraging the advantages of state-space representation and parametric model order reduction technology, the framework offers several key benefits. It supports fast design optimization by eliminating the computational overhead of recomputing transfer functions, a task traditionally required during iterative simulation processes. Additionally, the flexible architecture of the framework enhances its adaptability to various interactive applications. A critical aspect of the workflow proposed is the system identification process, which should respect certain physical properties of the systems to be coupled. This adherence to physical constraints is essential to guarantee the stability of the coupled structure and achieving accurate simulation result. The virtual coupling is evaluated both in terms of frequency response functions and time-domain responses, demonstrating results that align with state-of-the-art approaches. Computation analyses show the advantage of the approach proposed over frequency-based techniques. The findings underscore the framework’s potential as a powerful tool for NVH analysts and design engineers, enabling faster and more reliable decision-making during early development stages.
Text
HybridStateSpaceCoupling_rev6
- Accepted Manuscript
Restricted to Repository staff only until 6 December 2026.
Request a copy
More information
Accepted/In Press date: 22 November 2025
e-pub ahead of print date: 6 December 2025
Published date: 15 January 2026
Keywords:
Dynamic Substructuring, Noise, Vibration and Harshness (NVH), Parametric Model Order Reduction, Real-time simulation, State-Space Substructuring, System Identification, Virtual Prototyping
Identifiers
Local EPrints ID: 511290
URI: http://eprints.soton.ac.uk/id/eprint/511290
ISSN: 0888-3270
PURE UUID: 474a6677-b4de-48e4-b1cb-5a0b4193ae95
Catalogue record
Date deposited: 11 May 2026 16:46
Last modified: 12 May 2026 02:14
Export record
Altmetrics
Contributors
Author:
Nicolò Salamone
Author:
Fabio Bianciardi
Author:
Mahmoud Elkafafy
Author:
Julian Staiger
Author:
Daniel De Gregoriis
Author:
Sjoerd Van Ophem
Author:
Wim Desmet
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics