System models and model classification in tribological system development

Abstract

Current requirements for the reduction of CO2 emissions, as well as for the improvement of durability and reliability of sociotechnical systems such as passenger cars, lead to an increase in development effort in order to increase efficiency and system lifetime. Tribological systems play an essential role in the development of sociotechnical systems, but have proved to be particularly complex. The development of tribological systems, as part of the overall system under development, is an interdisciplinary effort. Involvement of solid mechanics, fluid mechanics, rheology, and many more scientific disciplines is essential to cope with the high number of nonlinear relationships, which often cause unpredictable system behavior. This paper contributes to the scientific field of tribology by introducing concepts of model‐based systems engineering for the specific case of elastohydrodynamic lubrication states. The elastohydrodynamic lubrication state of tribological system has been chosen as example to show how system models can be used to better describe the behavior of a system by connecting several specific models. In order to gain an overview of the models used in tribological system development, a system cube was used to structure the models. The system cube enabled gaps and overlapping model zones to be identified. Finally, the role of system models in development and the benefit of using system models to solve problems that cannot be solved by a single technical discipline but only in an interdisciplinary effort are discussed. An approach to connect models and methods to describe a system in an elastohydrodynamic lubrication state is presented.

A Classification Method for the Systematic Identification of Models and Workflows in MBSE

“…Jockisch and Rosendahl describe models by their characteristics on a high level by the four groups graphical, technical, semantic and semantic-scientific [23]. Faustmann et al provide a link between the system model and domain specific models using a three-dimensional cube consisting of discipline, technical domain and level [24]. Jacobs et al [4] adapt the three main characteristics of Stachowiak for domain models in MBSE context by providing the three axes of model classification: scope (mapping property), purpose (pragmatism) and fidelity (reduction property).…”

Optimization of Unsupervised Neural Machine Translation Based on Syntactic Knowledge Improvement

“…As a research direction that has been developing rapidly in recent years, the main purpose of UNMT is to provide the best solution to the problem in the field of unsupervised translation, and to provide the best solution to the problem. Its main purpose is to carry out automatic translation without parallel corpus, so as to improve the speed and accuracy of machine translation [3][4]. Currently, the traditional UNMT model is still facing a series of challenges, for example, in the environment without parallel corpus, UNMT is often difficult to obtain effective linguistic correspondences, thus affecting the quality and accuracy of translation [5].…”