Due to the continuously increasing demand in Computer Aided Engineering (CAE), it is essential for high efficiency and transparency to automate and standardize processes. In many cases, Simulation Data Management (SDM) software is used for this purpose. To achieve all mechanical target values of a product, there are several standard disciplines in the field of CAE, such as Crash, Noise Vibration Harshness (NVH) or Fatigue. Assembly, solving and postprocessing for these disciplines can differ greatly from one another. For this reason, it is best practice in many companies to carry out the optimization of a model in each discipline separately and to compare the results and structural adjustments with other disciplines at regular intervals.
In recent years, crash and safety simulations have reached a very high level of accuracy in the prediction of the crashworthiness of the vehicle and the probability of injury for occupants and pedestrians under a multitude of loading scenarios. Among several factors, this achievement is also attributed to the fine resolution of finite element models that enables the precise representation of even the smallest parts and geometric features affecting the simulation results and the increase in the number of simulated loadcases. However, accuracy does come with a cost: Model size and variability have considerably increased, together with the number of loading scenarios that need to be simulated on each model variant.
In recent years, the demand for faster product development has been increasing year after year. In addition, as requirements and their levels become more sophisticated, data-driven development that makes use of past data is attracting attention. Compared to experiments, simulations are characterized by the ease of retaining data that can be used for analysis, but this creates the problem of handling huge amounts of result data. In order to overcome this challenge, we propose a method to detect similar behaviors based on the distance in the modal space obtained from the animation results of past calculations with a reduced dimension reduction technique.
From an industrial or productive standpoint, the scale of simulation models, the number of involved simulation model components, and the complexity of the utilized processes with a vast amount of data are at a level that is challenging to manage manually. The introduction of virtual testing adds to the complexity of the development process and the quantity of data to be handled. Consequently, the use of a Simulation Data Management (SDM) system for this purpose can be advantageous or even indispensable. The introduction of virtual testing can be accomplished in several steps. The initial step is the automation of data preparation, encompassing both input data and produced result data for both the OEM and the testing authority. Subsequent steps involve the implementation of individual processes and security mechanisms against data manipulation. This paper primarily addresses the initial step and outlines a methodology for achieving the objective of safeguarding against data manipulation and intellectual property (IP) infringement by OEMs.
Manage multi-disciplinary load cases in SDM: Model setup and evaluation of results