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ispartof: pages:1659-1671 ispartof: PROCEEDINGS OF INTERNATIONAL CONFERENCE ON NOISE AND VIBRATION ENGINEERING (ISMA2020) / INTERNATIONAL CONFERENCE ON UNCERTAINTY IN STRUCTURAL DYNAMICS (USD2020) pages:1659-1671 ispartof: International Conference on Noise and Vibration Engineering (ISMA) / International Conference on Uncertainty in Structural Dynamics (USD) location:BELGIUM, Leuven date:7 Sep - 9 Sep 2020 status: published

Over the years, concept models have found increased use in the design of mechanical systems, such as automotive applications. However, these models can also be exploited as low-cost counterparts of high-fidelity models for use in online applications such as virtual sensors and control. In this work the use of structural models for input force estimation is discussed. Two different approaches are compared to model a twistbeam rear-suspension for estimating the vertical input forces. the first model is an equivalent trailing arm description and the second is a modal reduced model. An experimental setup in which the forces are measured through a Kistler cell is used to estimate the vertical input force from an optical tracking system. The equivalent trailing arm model is shown to be incapable to produce accurate results due to the strong coupling of the vertical motion with the non-vertical loads, whereas the modal reduced model accurately reproduces the vertical forces. ispartof: pages:1639-1651 ispartof: ISMA proceedings pages:1639-1651 ispartof: . International Conference on Noise and Vibration Engineering location:Leuven, Belgium date:15 Sep - 17 Sep 2014 status: published

ispartof: Multibody System Dynamics status: submitted

Over the past decades models of structural systems have moved from single-query applications in uniform domain towards a design process where multiple queries of a multi-domain model are required for different design parameters. In order to limit the computational burden associated with these multi-query applications, parametric model order reduction (pMOR) becomes an indispensable tool. These techniques allow a low(er) cost evaluation of the original model (e.g. a finite element model) for a range of design parameters. This paper investigates the possibility of applying a greedy algorithm to generate a reduced order basis (ROB) from sampled eigenvectors for structural dynamics applications. The resulting ROB can be used in projection-based pMOR techniques and in the numerical example it is used to reduce an affine representation of a finite element (FE). ispartof: pages:3563-3571 ispartof: Proceedings of the 27th international conference on noise and vibration engineering. pages:3563-3571 ispartof: ISMA 2016 location:Leuven date:19 Sep - 21 Sep 2016 status: published

Modern mechatronic powertrains are characterized by an ever increasing power density and high-dynamic operation. In addition, high demands are put on the energy efficiency and the behaviour in terms of noise and vibrations. This is true for powertrains in industrial machines, wind turbines and vehicles. The increasingly strict requirements, together with a constant pressure on shortening development times, have led to a growing use of models in the design phase of mechatronic systems. State-of-the-art modelling techniques allow to simulate the complex vibration behaviour of mechanical components and systems in detail. However, a purely model-based approach often fails to answer decisively how the machine will behave in actual operating conditions. The remaining uncertainty is usually due to poorly known (time-varying) model parameters or an unknown excitation to which the operational system is subjected. The subsequent discrepancy between model and reality may lead to unwanted vibrations and noise emission, a lack of performance because of sub-optimal control or - in extreme cases - the failure of a component or the entire system. In principle, such uncertainty can be greatly reduced by instrumenting the powertrain and analysing the measured data. The most relevant dynamic quantities such as forces and moments can only be measured in highly intrusive manners, though. As a result, such measurements are almost never feasible to obtain on operational machines. This work has aimed to progress towards more insight in the operational behaviour of mechatronic powertrains by combining model and sensor information. It makes use of existing techniques concerning model-based estimation of states and inputs. The a–priori unknown input here corresponds to the torque with which the powertrain is excited by an unknown load. The choice for focusing on the torque estimation follows from the fact that this quantity is of prime importance to the powertrain operation but can be measured directly only in rare cases. The here composed model combines knowledge driveline that provides the connection to its load. The multiphysical nature of the model allows to combine also measurements from the electrical and mechanical domains. The resulting virtual torque sensor has been validated experimentally on a here developed and purpose-built test setup. The design of this setup is such that the mechatronic powertrain dynamics are decoupled from those of its surrounding frame, as well as from the environment. Thus, the powertrain model can be identified and employed without precise knowledge of the frame dynamics. The focus of this work is on the high-dynamic character of the unknown torsional excitation. It has been shown that an electrical actuator model is in most cases insufficient for identifying the load torque accurately in the structural excitation bandwidth of 0 Hz to 200 Hz. On the other hand, an electro-mechanical powertrain model does allow to reach this goal. Next to the necessity of the mechanical model, this work has clarified the influence of available instrumentation on the achievable bandwidth of the virtual torque sensor. Classical criteria for making a suitable selection of sensors do not provide a useful conclusion in this respect. By considering the linearized closed-loop bandwidth of the input estimation, the added value of rotational acceleration sensors becomes visible. Through the use of these sensors, the useful bandwidth of the virtual torque sensor is increased significantly. 1. Introduction 2. Modelling of mechatronic systems 3. State estimation 4. Methodology overview 5. Development of a test setup for experimental validation 6. Multi-physical powertrain model 7. Sensor selection 8. Identification & Calibration 9. Experimental results 10. Conclusion status: published