White Paper: Designing Scalable, Deterministic XiL Simulation Environments

A mathematically correct model does not automatically produce a trustworthy test. The execution environment surrounding the model determines when inputs arrive, what order processes run in, how values reach hardware, and whether a test can be reproduced and investigated afterward.

Download this white paper to explore the architectural decisions that support deterministic, scalable, and repeatable X-in-the-loop simulation.

Why Simulation Architecture Matters

As XiL environments grow, engineering teams must coordinate models running at different rates, expanding I/O requirements, multiple modeling toolchains, test automation, visualization, and data logging.

If these components are not managed within a predictable execution architecture, timing variations, overruns, and hidden dependencies can undermine test validity—even when the models themselves are accurate.

Using SIMulation Workbench (SimWB) as a practical reference implementation, this white paper examines how the surrounding simulation architecture affects the reliability of engineering results.

What You Will Learn

• How cyclic execution and explicit dependencies create repeatable timing and execution order
• Why overrun detection is essential for identifying tests that fail to meet their configured timing
• How a shared Real-Time Database separates simulation models from hardware-specific I/OThe differences between multi-model, multirate, and multicore simulation
• How to scale models and I/O while managing performance headroom
• How Simulink, FMI/FMU, SIMPACK, and hand-written models can operate within one test environment
• How session configurations, logging, monitoring, and playback create reproducible test evidence
• How off-target development can reduce hardware contention without being mistaken for real-time validation

Evaluate XiL Platforms Against Your Workload

The paper also includes a practical framework for evaluating XiL platforms based on scheduling, model-to-I/O independence, multicore placement, integration requirements, automation, logging, and reproducibility.

Rather than relying on a vendor demonstration or isolated benchmark, these questions can help your team determine how a platform will perform with your models, I/O, logging requirements, and timing constraints.

Download the white paper

Learn how to build XiL tests that your engineering team can repeat, investigate, and defend.