Browsing by Author "Iroz, Igor"

Now showing 1 - 4 of 4

Open Access
Interpolation‐based parametric model order reduction of automotive brake systems for frequency‐domain analyses
(2023) Matter, Fabian; Iroz, Igor; Eberhard, Peter
Brake squeal describes noise with different frequencies that can be emitted during the braking process. Typically, the frequencies are in the range of 1 to 16 kHz. Although the noise has virtually no effect on braking performance, strong attempts are made to identify and eliminate the noise as it can be very unpleasant and annoying. In the field of numerical simulation, the brake is typically modeled using the Finite Element method, and this results in a high‐dimensional equation of motion. For the analysis of brake squeal, gyroscopic and circulatory effects, as well as damping and friction, must be considered correctly. For the subsequent analysis, the high‐dimensional damped nonlinear equation system is linearized. This results in terms that are non‐symmetric and dependent on the rotational frequency of the brake rotor. Many parameter points to be evaluated implies many evaluations to determine the relevant parameters of the unstable system. In order to increase the efficiency of the process, the system is typically reduced with a truncated modal transformation. However, with this method the damping and the velocity‐dependent terms, which have a significant influence on the system, are neglected for the calculation of the eigenmodes, and this can lead to inaccurate reduced models. In this paper, we present results of other methods of model order reduction applied on an industrial high‐dimensional brake model. Using moment matching methods combined with parametric model order reduction, both the damping and the various parameter‐dependent terms of the brake model can be taken into account in the reduction step. Thus, better results in the frequency domain can be obtained. On the one hand, as usual in brake analysis, the complex eigenvalues are evaluated, but on the other hand also the transfer behavior in terms of the frequency response. In each case, the classical and the new reduction method are compared with each other.
Open Access
Inverse fuzzy arithmetic for the quality assessment of substructured models
(2015) Iroz, Igor; Carvajal, Sergio; Hanss, Michael; Eberhard, Peter
The dynamical analysis of complex structures often suffers from large computational efforts, so that the application of substructuring methods has gained increasing importance in the last years. Substructuring enables dividing large finite element models and reducing the resulting multiple bodies, yielding a reduction of, in this case, complex eigenvalue calculation time. This method is used to predict the appearance of friction-induced vibrations such as squeal in brake systems. Since the method is very sensitive to changes in parameter values, uncertainties influencing the results are included and identified. As uncertain parameters, standard coupling elements are considered and modeled by so-called fuzzy numbers, which are particularly well suited to represent epis- temic uncertainties of modeled physical phenomena. The influence of these uncertainties is transferred to undamped and damped eigenfrequencies of a substructured model by means of direct fuzzy analyses. An inverse fuzzy arithmetical approach is applied to identify the uncertain parameters that optimally cover the undamped reference eigenfrequencies of a non-substructured, full model. If a validity criteria is defined, a positive decision in favor of the most adequate model can be performed.
Open Access
Methods of model order reduction for coupled systems applied to a brake disc‐wheel composite
(2023) Matter, Fabian; Iroz, Igor; Eberhard, Peter
In this contribution, investigations on model order reduction for coupled systems composed from components of a passenger car are shown. In today's development processes, the simulation of mechanical components is indispensable and large Finite Element models are often used for this purpose. For the calculation of time‐domain or frequency‐domain analyses, for example, a lot of computing power is required. However, with the application of model order reduction methods, this effort can be reduced, but this results in a trade‐off between the reduction error and the computational time. Since the computation of reduction bases for complete systems can be computationally expensive, it is of interest to be able to reduce components individually and then assemble them into a reduced overall model. This can result in both, a saving of computational effort when creating the bases, as well as a saving of the required memory space. Furthermore, there are many possible combinations of components in the modular systems of today's automotive industry, which emphasizes the model order reduction by parts and not by assemblies. In this work, methods of model order reduction for coupled systems are presented and will be tested on components in the chassis of a sports car. Therefore, an assembly consisting of a brake disc and wheel rim together with the wheel hub are investigated. For this purpose, the eigenmodes and transfer functions of the overall model, the reduced overall model and the assembly built from individual reduced bodies are compared.
Open Access
Uncertainties in road vehicle suspensions
(2015) Schiehlen, Werner; Iroz, Igor
Road vehicles are subject to random excitation by the unevenness of the road. For a dynamical analysis, vehicle models of the vertical vibrations as well as guideway models of the road unevenness are required. The fundamental dynamics of vehicle suspensions can be already modeled by a quarter car featuring the decoupling of the car body motion and the wheel motion. This suspension model is characterized by five design parameters where two of them, the shock absorber and the tire spring, are highly uncertain due to wear and poor maintenance. For the assessment of the vehicles performance three criteria have to be used: ride comfort, driving safety and suspension travel. These criteria depend on all the five design parameters resulting in a conflict or a pareto-optimal problem, respectively. In this paper, the uncertainties of the parameters are projected into a criteria space in order to support the decision to be made on the basis of a pareto-optimal problem. Simulations with uncertainties support the robust suspension design. It is shown that controlled suspension parameters remain uncertain due to the unpredictable decisions made by the driver.