One-dimensional computer-aided engineering (1D CAE), also referred to as mechatronic system simulation, is multi-domain systems simulation in combination with controls. It is an approach to modeling and analyzing multi-domain systems, and thus predicting their multidisciplinary performance, by connecting validated analytical modeling blocks of electrical, hydraulic, pneumatic and mechanical subsystems into a comprehensive and schematic full-system model.
1D CAE helps you create a concept design of complex mechatronic systems, analyze their transient and steady-state behavior, and front-load design decisions when integrating intelligent systems into your product.
1D CAE software uses validated libraries containing predefined components for different physical domains. These standard representations allow you to investigate different concepts at the very early stages of the design, even before any CAD (computer-aided design) geometry is available. Parameters can be refined and details can be added as they become available, making 1D CAE a perfect complement to detailed 3D CAE throughout the entire design cycle.
1D CAE calculations are very efficient. The components are analytically defined, and have input and output ports. Causality is created by connecting the inputs of a component to the output of another one (and vice-versa). The resulting mathematical system has a very limited number of degrees of freedom compared to 3D CAE. This solution speed, the openness of 1D CAE software to different types of software codes and the real-time capabilities allow you to streamline the system development process. 1D CAE offers you an open development approach, starting from functional requirements to physical modeling and simulation, enabling concurrent engineering of mechatronic systems in a collaborative design environment.
Optimize complex multi-domain mechatronic systems from the concept phase, even before detailed CAD is available
Study transient and steady state behavior
Balance product performance and regulation constraints according to brand-critical attributes
Achieve optimized design architectures before committing to expensive and time-consuming prototype testing
Quickly analyze a multitude of design options
Achieve optimal designs while reducing the number of physical prototypes