But many of the levers that unlock efficiency – such as reduced oil capacity (used inside reduction gearboxes), gear geometry or bearing type – can erode robustness to an unacceptable level if they are not modelled and validated with care.
Simulation has become a central component to managing these complex trade-offs. With a robust simulation toolchain, selected for accuracy, speed and output value at every stage, programmes move faster, risks reduce and products deliver improved attributes with minimal compromise.
Balancing trade-offs
Higher efficiency requirements push components close to their operational limits; systems must be close to optimal to remain robust. Even small design changes can shift factors such as effectiveness of lubrication or NVH.
Without credible models of oil distribution, dynamic behaviour and structural performance, poor decisions can be made. This results in systems that fall short of performance attributes or durability targets.
Thermal management is equally unforgiving. Higher power density means it is key to producing a successful, efficient system. This necessitates thermal analysis, which drives design decisions.
Early architectural models are critical. These simple models must expose the trade-offs between packaging, efficiency and component life. By doing so early, engineers can identify potential conflicts between lubrication, thermal and mechanical constraints before they manifest as reliability or performance issues, averting costly iterations later in the programme.
No universal ‘correct’ tool
Selecting the right simulation software is critical to managing the efficiency, durability, NVH and performance trade-offs. There is no universal ‘correct’ tool for every scenario. The right choice depends on the challenge, the development phase and the desired outputs.
In architecture selection and concept phases, fast iterations and trend capture are priorities. They help to accelerate decision-making, experiment with factors using a ‘design of experiment’ approach, and generally understand the sensitivity of the system. In detailed design and virtual verification, confidence in the results is critical. It is imperative to understand what is not being simulated and the level of error inherent in the model.
While trend analysis in early concept studies enables the rapid exploration of trade-offs, accuracy is essential during the detailed design and verification stage. Models need to provide confidence that the correct hardware decisions have been made, and physical testing will be successful.
Output utility is equally important; engineers need decision-ready data, such as oil distribution, churning losses and thermal characteristics, which directly inform oil levels, oil jet angles and oil baffle geometry. Without outputs that are easy to interpret, even accurate simulations can lead to rework, inefficiency or compromised durability. Although it’s possible to achieve a high level of confidence in simulation, interpretation of the results is critical to the development of the system.
Choosing the right simulation software
Choosing the right simulation software starts with clear engineering goals. In a transmission lubrication and power loss study, that means avoiding under‑lubrication to prevent excess heat and damage, and avoiding over-lubrication, which can add cost, mass and increase churning losses.
When selecting software, assess candidate solvers side-by-side and against physical test data. The same applies when selecting structural, fatigue or electromagnetic simulation software. Anchor this on three tests: is the accuracy sufficient for the decision, does solver speed match programme cadence, and do the outputs inform design and test plans?
Designing a robust toolset means using fast, simple models to capture trends early, then increasing detail and accuracy when needed, while understanding the limitations of the simulation and its inputs. This allows engineers to tailor models so the speed matches the programme cadence. Outputs must be decision-ready to quickly guide the design development and provide confidence in the virtual validation.
Throughout this process, engineers must document their set-up learnings into rules, implement version control and build repeatable procedures across teams. This creates a reliable, reusable workflow that supports confident design decisions.
Staged workflow
So how does simulation software work in action? For an electric drive unit (EDU), a staged workflow can reduce risks, shorten iteration loops and build confidence.
Step 1 – Understand the fundamentals
Begin with simple learning steps. Where lubrication feeds exist, use a sub-model to understand the steady state behaviour. For dynamic behaviour, model gears in isolation and confirm that the solver produces useful outputs, such as oil reach and film presence, before scaling up.
Step 2 – Apply learnings
Apply those learnings to a full transmission model. Evaluate system‑level behaviour through distribution to each mesh and bearing, flow rates and cumulative losses. Understand the effect of variable operating parameters such as speed, incline angle, accelerations and temperature. Use the results to prioritise design changes with the highest benefits and develop theories to verify with physical testing.
Step 3 – Clear-case rig testing
Clear-case testing confirms lubrication performance by allowing direct visualisation of oil trajectories, pooling and splash patterns at a range of operating points. Tilt testing can be used to confirm what happens at different operating angles and accelerations while varying oil viscosity can represent thermal effects.
Ideas developed in simulation can be tested and verified while power loss is logged. When targets are not met, simulation can be refined and solutions developed virtually and physically in parallel.
Engineers can capture learnings by correlating trends and targeted measures, identifying where gaps remain. This helps build robust guidance and clarifies its shortcomings, empowering engineers to apply informed decisions in future programmes. The payoff is fewer tuning loops, fewer hardware versions and a growing library of robust set-up rules that inform simulation limitations.
Step 4 – Let the results guide you
Finally, drive hardware decisions with confidence. Lock variables such as oil level, jet placement, jet sizing and baffle design ahead of final verification and endurance testing. This sequencing prevents late surprises and keeps the programmes scheduled.
Closing the loop: reduce tests, raise confidence
Delivering more range without sacrificing durability, NVH or time to market demands a simulation-led workflow that suits the decision at hand. Engineers require quick, credible modelling to reserve physical tests for design verification and edge cases. Their expert judgement should define simulation limits, outline risks and target verification tests where they add most value.
By understanding the requirements of the analysis, engineers can select tools for the outputs that matter and evaluate them on accuracy, speed and usefulness. This allows teams to move from isolated studies to full-system models, validate early with clear‑case rigs and capture set‑up rules in a continually developing analysis guide.
This approach applies regardless of the analysis or the system: build the fundamentals and understand the system, produce analysis with useful outputs and drive development through targeted tests and correlated simulation models. Used systematically, this turns simulation into an operational advantage – improving efficiency and keeping programmes on schedule.
Want to build your simulations expertise? IMechE's Simulation and Modelling conference returns from 29-30 September, taking place at Austin Court in Birmingham. The event provides a forum for the technical community to share knowledge, discuss emerging challenges and exchange ideas, helping to ensure the UK remains at the forefront of simulation and modelling. Learn more and book now.
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