Co-Simulation Signal Prediction for Stable Modular Simulation
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Solution Overview
Problem
Co-simulation in mechatronic systems faces inefficiencies due to numerical artefacts, instabilities, and increased processing time caused by incompatible software platforms and discrete communication of subunits, leading to reduced accuracy and flexibility.
Innovation Solution
The method involves disaggregating systems into subunits, predicting numerical data using a data-prediction model, delaying communication, and adjusting time steps to ensure smooth and accurate simulations, allowing for flexible application across various systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If co-simulation is used to simulate modular mechatronic systems with incompatible platforms, then the system can be simulated with flexible architecture and separate validation of subunits, but numerical artefacts and instabilities occur due to discrete communication between subunits
Solution Approach 1:
The patent introduces a coordinator as an intermediary component that manages communication between subunits. The coordinator collects output data from sending subunits, applies interpolation to reconstruct continuous signals, and distributes this reconstructed data to receiving subunits. This intermediary approach transforms discrete point-to-point communication into a coordinated continuous signal exchange, eliminating numerical artefacts while preserving modular architecture benefits.
Solution Approach 2:
The patent applies preliminary action by performing interpolation of output data before it is used by receiving subunits. Instead of directly using discrete output values from sending subunits, the coordinator pre-processes this data through interpolation to create continuous reconstructed signals. This preliminary reconstruction prevents numerical instabilities from occurring in the first place, rather than correcting them afterward.
2Measurement precision
If communication frequency between subunits is increased to improve accuracy, then numerical artefacts are reduced, but processing time increases
Solution Approach 1:
The patent implements continuity of useful action through interpolation that reconstructs continuous signals between discrete communication points. Instead of requiring frequent discrete communication updates, the interpolation method continuously reconstructs signal values between sampling points, maintaining signal continuity and accuracy without increasing communication frequency. This allows subunits to communicate less frequently while still achieving high accuracy.
Solution Approach 2:
The patent uses interpolation to create reconstructed copies of the original continuous signals from discrete sampled data. The coordinator generates interpolated copies of output signals that represent the continuous behavior between sampling points. These reconstructed signal copies allow receiving subunits to operate with high-fidelity continuous signals without requiring increased communication frequency between subunits.
3Productivity
If discrete communication is used between subunits, then parallel simulation is efficient, but discontinuities cause untimely restarting of internal solvers
Solution Approach 1:
The coordinator acts as an intermediary that smooths the interface between parallel subunit execution and solver operations. By interpolating output data and providing continuous reconstructed signals to receiving subunits, the coordinator eliminates the discontinuities that would otherwise trigger solver restarts. This allows parallel simulation efficiency to be maintained while ensuring solver stability through continuous signal input.
4Adaptability or versatility
If different software platforms are used for different subunits, then system modularity and separate validation are enabled, but incompatible platforms cause communication issues
Solution Approach 1:
The coordinator serves as a universal communication management component that works with any subunit regardless of its software platform. It provides a standardized interface for collecting output data, performing interpolation, and distributing reconstructed signals to receiving subunits. This universal coordinator handles platform incompatibilities centrally, allowing diverse subunits from different software platforms to communicate through a common standardized mechanism without increasing individual subunit complexity.
Data Source
AI summary
Numerical modular simulation of a system includes: (a) disaggregating said system into at least two subunit simulation subsystems, and (b) simulating the respective subunits stepwise repeatedly generating subsystem-step-output from subsystem-step-input during a respective subsystem-time-step (SMP). To improve accuracy and performance, said method includes the additional steps: (c) transmitting subsystem-step-inputs to a receiving subsystem and simulating this subsystem over a delay-time before its subsystem-step-outputs are generated, (d) receiving connection interface variables from a sending subsystem including at least one of: numerical data, at least parameters of a data-prediction-model of said numerical data, or a data-prediction-model assigned to said numerical data, (e) predicting said numerical data by a data-prediction-model over said delay-time to obtain predicted numerical data of said interface variables provided by said sending subsystem, and (f) starting the next simulation step of said receiving subsystem generating the next subsystem-step-output from subsystem-step-input, wherein said subsystem-step-input includes said predicted numerical data.


