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Using the simplest possible power parameterisation

Create a random trajectory

import numpy as np
import pandas as pd

np.random.seed(12345)

N_points = 100

trajectory = pd.DataFrame(dict(
    u_ship_og=12 + 0.1 * np.random.normal(size=(N_points, )),
    v_ship_og=3 + 0.1 * np.random.normal(size=(N_points, )),
    u_current=2.0 * np.random.normal(size=(N_points, )),
    v_current=2.0 * np.random.normal(size=(N_points, )),
))

display(trajectory)

	u_ship_og	v_ship_og	u_current	v_current
0	11.979529	2.843434	2.254962	3.494467
1	12.047894	2.943746	-1.136727	-2.820492
2	11.948056	2.996734	0.618724	-0.756483
3	11.944427	2.907099	-1.154771	-0.691641
4	12.196578	2.951743	-2.337268	0.760125
...	...	...	...	...
95	12.079525	3.091098	-0.451048	0.262203
96	12.011811	2.897910	2.699452	-1.395228
97	11.925147	2.858658	2.700599	2.671299
98	12.058497	3.129661	-0.773307	-0.302078
99	12.015268	3.025228	1.731979	0.885876

100 rows × 4 columns

Estimate power needed to maintain speed over ground

from rasmus_fuel.simplest import power_maintain_sog, power_to_fuel_burning_rate

coefficient = 1.0

trajectory["power"] = power_maintain_sog(**trajectory, coeff=coefficient)

trajectory["fuel_rate"] = power_to_fuel_burning_rate(trajectory["power"])

display(trajectory)

	u_ship_og	v_ship_og	u_current	v_current	power	fuel_rate
0	11.979529	2.843434	2.254962	3.494467	1117.364353	0.000053
1	12.047894	2.943746	-1.136727	-2.820492	2529.913503	0.000120
2	11.948056	2.996734	0.618724	-0.756483	1749.777352	0.000083
3	11.944427	2.907099	-1.154771	-0.691641	2267.591153	0.000108
4	12.196578	2.951743	-2.337268	0.760125	2700.526265	0.000129
...	...	...	...	...	...	...
95	12.079525	3.091098	-0.451048	0.262203	2056.733168	0.000098
96	12.011811	2.897910	2.699452	-1.395228	1274.605855	0.000061
97	11.925147	2.858658	2.700599	2.671299	1019.888809	0.000049
98	12.058497	3.129661	-0.773307	-0.302078	2197.932609	0.000105
99	12.015268	3.025228	1.731979	0.885876	1365.750152	0.000065

100 rows × 6 columns

trajectory.plot(subplots=True);