Characterisation of Nonlinear Structural Dynamic Systems in Conceptual Design
Doctoral thesis, 2018

The engine and driveline systems of passenger cars generates and distributes the necessary driving power and are major contributors to vehicle emissions, noise and vibrations, etc. More environmental friendly technologies under development are expected to intensify and add new comfort related problems, since most of them affect vibration sources or system damping. A successful balancing of fundamental system qualities requires a better use of simulation in early design phase. This work focus on virtual tools for analysis of low-frequency structural dynamic vibrations. In conceptual driveline design, many possible system solutions are studied in parallel and their often nonlinear behaviour requires robustness evaluation across full operating and design parameter ranges. This situation calls for virtual methods that are generally valid and meet the demand for rapid prototyping. Thus, models need to be as simple as possible and as accurate as required for capturing phenomena that occur in real drivelines. Further, analysis tools must efficiently process data sets from extensive parameter variations and extract fundamental system characteristics that can be used to reliably rate competing proposals. For this, a complementing design analysis methodology is proposed that improves current automotive development tools and workflow. A general and over-parameterised multi-body system model is constructed from detailed linear structural and schematic nonlinear parts. State-space reduction methods are then applied to modal components to balance prediction accuracy and evaluation speed of resulting conceptual design models. Parameter variations in fully known system models are simulated under ideal periodic loading and low noise conditions. A feature based frequency analysis approach is used to extract precise system characteristics and sort responses into qualitative classes. To efficiently process large amounts of generated data, statistical learning methods are used to automate the response classification.

Structural dynamics

Model order reduction

Nonlinear characterisation

Multi-body dynamics

Frequency response functions

Multisines

Response classification

Driveline systems

Concept design analysis

Stepped-sine

Support vector machine

SB-316, Sven Hultins Gata 6
Opponent: Prof. Gaëtan Kerschen, Department of Aerospace and Mechanical Engineering, University of Liège, Belgium

Author

Niclas S Andersson

Chalmers, Mechanics and Maritime Sciences

Driveline model calibration and validation in an automotive 4-cylinder Diesel application

International Conference on Noise and Vibration Engineering 2012, ,; Vol. 5(2012)p. 3841-3855

Paper in proceedings

Efficient Component Reductions in a Large-Scale Flexible Multibody Model

SAE International Journal of Vehicle Dynamics, Stability, and NVH,; Vol. 2(2018)p. 5-26

Journal article

Feature-Based Response Classification in Nonlinear Structural Design Simulations

SAE International Journal of Vehicle Dynamics, Stability, and NVH,; Vol. 2(2018)p. 185-202

Journal article

N. Andersson, T. Abrahamsson, Comparision of stimuli for nonlinear system response classification

Driving Forces

Sustainable development

Areas of Advance

Transport

Subject Categories

Vehicle Engineering

Embedded Systems

Other Electrical Engineering, Electronic Engineering, Information Engineering

ISBN

978-91-7597-782-9

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4463

Publisher

Chalmers University of Technology

SB-316, Sven Hultins Gata 6

Opponent: Prof. Gaëtan Kerschen, Department of Aerospace and Mechanical Engineering, University of Liège, Belgium

More information

Latest update

8/15/2018