suboptimumg.loganalysis.irl_car

IrlCar — real-world car wrapper combining Car + vehicle_setup parameters.

class suboptimumg.loganalysis.irl_car.IrlCar(car, setup)

Bases: object

Car + real-world setup parameters for log analysis.

Wraps a Car instance (loaded from car.yaml) with the extra session-specific parameters from vehicle_setup.yaml. Provides derived computation methods that combine both sources.

Parameters:

• car (Car) – Simulation car object (carries mass, wheelbase, track widths, CG height, etc. via car.params).

• setup (dict) – Processed vehicle setup dict (from load_vehicle_setup()). Curve entries should already be FittedCurve objects.

aero_force_from_pots(front_pot_delta_mm, rear_pot_delta_mm, ax_g=0.0)

Estimate front/rear aero downforce from shock pot deltas.

Parameters:

• front_pot_delta_mm (float | ndarray) – Change in average axle pot displacement from a reference (zero-aero) state, in mm. Positive = compression.

• rear_pot_delta_mm (float | ndarray) – Change in average axle pot displacement from a reference (zero-aero) state, in mm. Positive = compression.

• ax_g (float | ndarray) – Longitudinal acceleration in g’s (positive = forward accel, negative = braking). Used to subtract weight transfer through the springs (accounting for anti-dive/squat).

Returns:

Estimated aero downforce at each axle in Newtons.

Return type:

(front_aero_N, rear_aero_N)

classmethod from_yaml(car_yaml='parameters/car.yaml', setup_yaml='parameters/vehicle_setup.yaml')

Load both YAML files and construct an IrlCar.

Parameters:

• car_yaml (str)

• setup_yaml (str)

Return type:

IrlCar

heave_to_force(heave_m, axle)

Heave displacement (m) -> vertical spring force (N).

F = heave_stiffness * heave_m

Parameters:

• heave_m (float | ndarray)

• axle (str)

Return type:

float | ndarray

lateral_weight_transfer(ay_g)

Lateral weight transfer per axle.

Combines elastic (roll stiffness distribution) and geometric (direct load path through CG) components.

Parameters:

ay_g (float or array) – Lateral acceleration in g’s (positive = turning left).

Returns:

Weight transfer at the front and rear axles in Newtons. Positive values indicate load transfer to the outside wheel.

Return type:

(front_wt_N, rear_wt_N)

left_tire_angle(sw_deg)

Left tire steer angle via ackermann symmetry: -f(-x).

Parameters:

sw_deg (float | ndarray)

Return type:

ndarray

longitudinal_weight_transfer(ax_g)

Longitudinal weight transfer on the front axle.

Parameters:

ax_g (float or array) – Longitudinal acceleration in g’s (positive = accelerating forward, negative = braking).

Returns:

Change in front axle normal force (N). Negative under acceleration (rear loads up), positive under braking (front loads up). Anti-dive/squat percentages reduce the geometric weight transfer through the springs.

Return type:

float or np.ndarray

pot_to_force(pot_mm, axle)

Shock pot displacement (mm) -> vertical spring force (N).

Combines pot_to_heave() and heave_to_force().

Parameters:

• pot_mm (float | ndarray)

• axle (str)

Return type:

float | ndarray

pot_to_heave(pot_mm, axle)

Shock pot displacement (mm) -> chassis heave displacement (m).

heave_m = pot_mm / 1000 * MR

Parameters:

• pot_mm (float | ndarray)

• axle (str)

Return type:

float | ndarray

right_tire_angle(sw_deg)

Right tire steer angle from the ackermann curve.

Parameters:

sw_deg (float or array) – Steering wheel angle in degrees. Positive = right turn (right tire is inner, steers more); negative = left turn (right tire is outer, steers less).

Return type:

ndarray

roll_stiffness_distribution()

Front roll stiffness as a fraction of total (0-1).

Return type:

float

roll_stiffness_total()

Total roll stiffness (front + rear) in Nm/rad.

Return type:

float

tire_angles(sw_deg)

Return (left_deg, right_deg) for a given steering input.

Parameters:

sw_deg (float | ndarray)

Return type:

tuple[ndarray, ndarray]