Why Lap Time Is Not Enough: The Physics Behind a Professional Vehicle Model

In simulation, it is relatively straightforward to produce a car that appears fast.

Increase power. Reduce mass. Add aerodynamic downforce. Adjust grip coefficients. With the right combination of parameters, almost any model can be made to generate a convincing lap time trace.

To a casual observer, the speed profile may resemble that of a Formula 1 car. The peak lateral accelerations might look plausible. Sector times may fall within expected ranges.

Yet behaviour is not defined by outputs alone.

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As Barney Hassell, Head of Vehicle Dynamics at Virtex, puts it:

“One could easily modify a road-car model to produce a lap time and speed profile that looks like that of an F1 car by updating mass, power, grip, drag and downforce. However, it would not behave as an F1 car, and it would not respond to changes in the same way. A driver would immediately note that it wasn’t right, and an engineer would spot it as soon as they analysed the data.”

For professional teams, that distinction is fundamental. Simulation is not intended to entertain or approximate. It must replicate behaviour with sufficient fidelity that engineering conclusions remain valid.

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Behaviour over appearance

Two vehicles can produce similar lap times while relying on entirely different underlying mechanisms. A car that reaches a corner entry speed through excessive global grip scaling will not exhibit the same load sensitivity, transient response or balance characteristics as a car derived from correct physical relationships.

If suspension compliance is simplified, aero sensitivity decoupled from ride height, or tyre load response approximated rather than modelled correctly, the system may still “look” fast. However, it will not respond correctly to setup changes. Damper adjustments may produce unrealistic balance shifts. Ride height changes may not generate the expected aerodynamic consequences. Brake bias tuning may lack sensitivity in the appropriate range.

For an engineer attempting to use simulation as a decision-making tool, this becomes counter-productive.

Capturing the correct physics is therefore not optional. It is the foundation.

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Building the correct system topology

Although certain aspects of vehicle dynamics are common across all cars, the structure of the system itself varies considerably.

“Some basic vehicle dynamics physics is common to all cars,” Barney explains, “but we need to create the correct physical system models for the specific car we are simulating. Is it front-wheel drive with a combustion engine, or rear-wheel drive and electric? Does it use a MacPherson strut or a multi-link suspension? We then connect all the system models together using the correct physics to create the proper topology, so that each component interacts with the others in the right way.”

Topology, in this context, refers to how subsystems are physically arranged and coupled. Suspension geometry defines load paths. Powertrain layout determines torque distribution. Aero platform sensitivity links ride height to downforce and balance. Each subsystem must not only be individually accurate, but also connected correctly.

There are, in theory, countless possible combinations. In practice, technical regulations constrain the range of architectures seen in professional motorsport. It is not uncommon for cars across different series to share broad topological similarities, although their parameter values and detailed behaviours may differ substantially. Conversely, different architectures can compete within the same category depending on regulation allowances.

Regardless of configuration, the modelling objective remains consistent: assemble the correct physical structure first, then define it precisely.

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Parameterisation: where models become unique

Once topology is defined, parameterisation begins.

“Parameterisation is about defining, in detail, the characteristics of every component in the model,” Barney says. “Every car model is truly unique and bespoke.”

Some parameters are directly available from design data: mass distribution, geometry definitions, inertia tensors. Others may be obtained from rig testing, such as damper force-velocity curves, bump-stop characteristics or engine dynamometer maps.

However, not every parameter is explicitly measurable. In those cases, specialised track data analysis techniques are applied. Simulator driving feedback is also used to refine behaviour iteratively. This process of inference and correlation is essential to closing the gap between theoretical representation and real-world behaviour.

The goal is not simply to produce a plausible car. It is to create a digital twin whose responses to setup changes, environmental variation and driver inputs match the real vehicle within an acceptable engineering tolerance.

Controlled complexity as an engineering tool

One of the less obvious advantages of high-fidelity modelling lies in the ability to control complexity.

“In our simulations,” Barney notes, “we can increase or reduce the complexity of the physical effects captured in the model. By varying that complexity, we can control factors in the simulator that we cannot control in the real world, making it a powerful tool for repeatable evaluations and investigations.”

In real-world testing, environmental variables, tyre evolution and track conditions introduce noise into data. In a simulator environment built on physically correct models, it becomes possible to isolate specific effects. An engineer can evaluate the influence of a single parameter while holding others constant. Sensitivity studies can be performed with high repeatability. Hypotheses can be tested without the logistical and financial constraints of track time.

This capability transforms simulation from a training environment into an experimental platform.

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Beyond entertainment

Consumer-oriented simulation platforms often prioritise visual realism and subjective feel. For professional engineering work, those qualities are insufficient on their own.

A professional simulator must support correlation exercises, setup optimisation, development direction studies and driver preparation with confidence that the underlying physics are robust. It must behave correctly not only in steady-state cornering, but in transient response, combined slip, aero platform migration and load transfer.

Lap time is a consequence of correct physics, not the objective in itself.

For teams operating at the highest level, the value of simulation lies in its ability to support decisions before the car turns a wheel on track. That requires models built on accurate topology, rigorous parameterisation and validated physical relationships.

Anything less may look convincing. It will not, however, withstand engineering scrutiny.

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