Saber for Mechatronic Systems

Simulation and Analysis

Highly successful product design requires exceptional quality, reliability, and performance. The design process must be effective to ensure these qualities and to reduce the design cycle and costs. Getting to market early, and avoiding product defects later are critical factors for maximizing revenue over the product life cycle.

Engineering teams achieve these goals by applying systematic modeling, design, simulation, and analysis methods made possible by an integrated flow of proven tools and capabilities.

System Simulation

Effective system simulation requires a powerful simulator (mathematical engine that solves the network of equations represented by models and their interconnections in a circuit or system). Comprehensive simulation requires the support of various model abstraction levels, signal types, and physical domains (discussed above).

The Saber simulator provides the reliability and flexibility needed by designers to analyze complete systems including electrical, mechanical, hydraulic, software-controlled systems, and other technologies as well as PCB designs.

Grid Computing / Distributed Iterative Analysis (DIA)

Statistical analysis plays a crucial role in the objective to maximize design reliability. Obtaining a significant sample of measured test data and design variants often requires hundreds or thousands of run-cycles. Physical prototyping is just not practical to achieve the numbers of tests needed. Moving the design to a virtual world via simulation meets these objectives on an appropriate time-scale.

A significant challenge to effective simulation-based statistical analysis is that it can be extremely compute intensive. Applying Saber's "Distributed Iterative Analysis" capabilities across a computer grid provides dramatic improvements to these run-time requirements, resulting in astonishingly fast and far-reaching statistical analyses.

SAE Technical Paper: 
Designing Automotive Subsystems Using Virtual Manufacturing and Distributed Computing (General Motors)

Analyses for a Robust Design Methodology

Adopting Robust Design principles to improve reliability means making system performance immune to variations in design technologies, component parameters, manufacturing processes, and environmental conditions. This systematic approach includes the application of a series of analyses in steps. These are nominal design, sensitivity analysis, parametric analysis, statistical analysis, stress analysis, and failure modes analysis.

Saber advances a Robust Design methodology in part by supporting these analyses.

Technical Paper: 
Improving System Reliability Using the Saber Simulator in a Robust Design Flow.

Post Processing / Results Viewing

When simulation is complete, designers need an effective way to view the results, make calculations, and ask new questions.

Saber's graphical waveform analyzer and post-processing solution provides an easy-to-use way to view signals and parameters deep within the hierarchy of a system or model. Additional detail can be extracted without re-running the simulation which saves considerable time in the design process.

Saber Advantages

  • Increase design portability through model language standards VHDL-AMS and MAST
  • Protect intellectual property with model encryption
  • Apply full system simulation with mixed-signal, multi-level, and multi-domain model support
  • Save time and ensure accuracy by accessing over 30,000 characterized models
  • Create new models quickly and accurately with model characterization tools and utilities
  • Implement a Robust Design methodology with advanced analysis capabilities (stress, sensitivity, statistical, etc.)
  • Perform extremely compute intensive statistical analysis faster with grid computing