You can get this data using the Fast-F1 python package. It's the same data behind sites like the awesome f1-tempo
https://github.com/theOehrly/Fast-F1
The script I use looks something like the one below, which will grab a bunch of data points from the driver(s) and session you pick and dump it out to a CSV. From there it's just a simple relationship of tyre rolling radius and overall gear ratio to engine rpm; play with numbers to get a good match to the session data.
Looking forward to running the same job next week after the test sessions. I don't expect we'll see any changes compared to last year.
# Import the required packages
import os
import pandas as pd
import fastf1
from fastf1.core import Laps
# Enable caching to avoid unnecessary strain on the API
# Replace "fastf1cache" with your cache location
fastf1.Cache.enable_cache('fastf1cache')
# Use variables year, wknd, ses, and driver to specify the race, session and driver
year = 2023
wknd = 1
ses = 'R'
driver = 'VER'
# Obtain and load session data
session = fastf1.get_session(year, wknd, ses)
session.load()
# Select fastest lap for specified driver
lap = session.laps.pick_driver(driver).pick_fastest()
# Write telemetry data for lap to tel variable
tel = lap.get_telemetry()
# Export telemetry to current working directory as .csv file
cwd = os.getcwd()
tel.to_csv(cwd + "\\VER_23_race.csv")