Simulation Guide
Knowledge Guides /What inputs does electromechanical–thermal coupled simulation require, and how do you avoid isolated calculations?
Last updated: · New Horizon Digital Outsourcing Team
Electromechanical–thermal coupling connects mechanical loads, electrical losses, and temperature feedback. Placing screenshots from three software tools side by side does not constitute coupling. Exchange variables, units, update frequencies, and directions of energy transfer must be defined.
Prepare inputs along the energy path
- Start with operating conditions and organize speed, loads, power-supply conditions, control methods, and heat-dissipation boundaries. State sources and applicable temperature ranges for geometry and material parameters, and measurement or fitting conditions for motor efficiency maps or loss models. If information is missing, perform parameter-sensitivity analysis first rather than implicitly assuming ideal cooling.
How to choose the level of coupling
- Lumped-parameter models can support option screening, while local hot spots or complex flows may require higher fidelity. Specify which states provide bidirectional feedback and which are one-way inputs. Greater complexity does not necessarily make a model better suited to the task; first assess whether target metrics are sensitive to local details.
How to check coupled solving
- First verify each single-domain model under steady-state or simple operating conditions, then introduce coupling gradually. Check sign conventions and units for power and heat flow, and compare peaks and response times at different exchange step sizes. If oscillations occur, check solver settings, delays, and interface definitions instead of merely adjusting displayed curves.
How to organize acceptance evidence
- Recommended deliverables include an operating-condition matrix, variable mappings, coupling descriptions, convergence comparisons, and limits of results. Explain error sources separately for peak temperature, energy consumption, and response time. Compare simulation conclusions with available experimental data; where experiments are absent, clearly identify predictions as predictions rather than measured proof.