I had a flight last week using a TeleMega and I spent a bunch of time over the weekend pouring through the log file, graphing results, etc.
On a whim tonight I decided to just give the raw file to ChatGPT and ask it to do some analysis and make some graphs. It did an excellent job on the graphs and just ok with the analysis of the data. It used the pyro charge events for max acceleration (not wrong, but...) and it could figure out when the main chute deployed, but I am still extremely impressed, since all I gave it was the raw data file and a short prompt. It actually had to figure out what all the columns meant and make sense of it all. I also intentionally threw it off with the altitude estimate in my prompt. My follow-up question regarding motor burn-out really stretched it (hilarious to watch it think out loud), but it nailed the answer.
We are not far from having it truly do all the analysis for us, but not yet. Also, where is the fun in that? Here is short transcript and the output.
PROMPT:
The attached file contains log data from an amateur rocket launch using a TeleMega flight computer. The rocket launched to about 12,000 feet, a drogue parachute deployed at apogee, and then the main parachute deployed around 1000 feet for a soft landing. The altitude is in meters, but I would like to see it in feet. Can you analyze this log file and provide me with a short summary of the flight and provide me with four graphs including altitude-time, acceleration-time, velocity-time, and tilt-time?
RESPONSE:
It seems that there is no 'velocity' column in the provided data. I will review the columns again and identify the available data for plotting. Let's proceed with the available data for the analysis and plots.
First, let's list all the columns to understand the available data and then proceed accordingly.
The available columns do not include 'velocity' but do include 'speed', which can be used instead. I will proceed with creating the summary and the requested plots using the available data.
Let's create the summary and generate the plots for altitude-time, acceleration-time, speed-time, and tilt-time.
Here is the summary of the rocket flight:
Follow-up PROMPT:
Can you determine when motor burn-out occurred using the accelerometer data?
RESPONSE:
To accurately determine the motor burn-out time using the accelerometer data, we need to look for the point where the acceleration drops significantly and does not rise back to the boost phase levels, indicating the end of thrust. I will analyze the acceleration data more carefully to find this point.
From the plot, it seems that the initial burn-out estimation at 0.63 seconds is indeed too early. We need to identify the point where the acceleration drops sharply and remains low, indicating the end of the motor burn.
Let's reanalyze the data by focusing on the period after the initial peak in acceleration to find a more accurate point for motor burn-out. We'll look for the point where the acceleration drops and stabilizes.
The identified motor burn-out time at 158.8 seconds is likely incorrect given typical rocket flight profiles. Instead of using an arbitrary threshold, I'll refine the analysis to identify a significant and sustained drop in acceleration more accurately.
Let's focus on the immediate drop following the peak acceleration, which typically marks the end of the boost phase.
It seems that using the current method, we're not able to identify the correct burn-out time. Let's re-evaluate the method to find the point where the acceleration drops significantly and does not return to high values, indicating motor burn-out.
I'll perform a more detailed step-by-step check through the acceleration data post the initial peak to find a more accurate burn-out point.
It seems that the data analysis is still identifying an implausibly late burn-out time. This suggests that there might be an issue with the method or the data itself.
Let's adjust our approach by focusing on the transition from the boost to the coast phase, which is a more reliable indicator of motor burn-out. This will involve looking at the transition in the 'state_name' from 'boost' to 'coast' and the corresponding time in the dataset.
The motor burn-out occurred at approximately 13.32 seconds into the flight, based on the transition from the 'boost' phase to the 'coast' phase. This is a more plausible time for motor burn-out in a typical rocket flight profile. If you need further details or analysis, please let me know!
Follow-up PROMPT:
what was the average descent speed in feet per second while under drogue, before the main chute was deployed and slowed the descent down further?
RESPONSE:
The average descent speed while under the drogue chute, before the main chute deployment, was approximately 90.34 feet per second.
If you need any further analysis or details, please let me know!
On a whim tonight I decided to just give the raw file to ChatGPT and ask it to do some analysis and make some graphs. It did an excellent job on the graphs and just ok with the analysis of the data. It used the pyro charge events for max acceleration (not wrong, but...) and it could figure out when the main chute deployed, but I am still extremely impressed, since all I gave it was the raw data file and a short prompt. It actually had to figure out what all the columns meant and make sense of it all. I also intentionally threw it off with the altitude estimate in my prompt. My follow-up question regarding motor burn-out really stretched it (hilarious to watch it think out loud), but it nailed the answer.
We are not far from having it truly do all the analysis for us, but not yet. Also, where is the fun in that? Here is short transcript and the output.
PROMPT:
The attached file contains log data from an amateur rocket launch using a TeleMega flight computer. The rocket launched to about 12,000 feet, a drogue parachute deployed at apogee, and then the main parachute deployed around 1000 feet for a soft landing. The altitude is in meters, but I would like to see it in feet. Can you analyze this log file and provide me with a short summary of the flight and provide me with four graphs including altitude-time, acceleration-time, velocity-time, and tilt-time?
RESPONSE:
It seems that there is no 'velocity' column in the provided data. I will review the columns again and identify the available data for plotting. Let's proceed with the available data for the analysis and plots.
First, let's list all the columns to understand the available data and then proceed accordingly.
The available columns do not include 'velocity' but do include 'speed', which can be used instead. I will proceed with creating the summary and the requested plots using the available data.
Let's create the summary and generate the plots for altitude-time, acceleration-time, speed-time, and tilt-time.
Here is the summary of the rocket flight:
- Max Altitude: 14,175 feet
- Max Speed: 215.05 m/s
- Max Acceleration: 60.8 g
- Apogee Time: 33.6 seconds
- Main Chute Deployment Time: -0.3 seconds (it appears there might be a negative time value due to initial conditions or logging start time)
Follow-up PROMPT:
Can you determine when motor burn-out occurred using the accelerometer data?
RESPONSE:
To accurately determine the motor burn-out time using the accelerometer data, we need to look for the point where the acceleration drops significantly and does not rise back to the boost phase levels, indicating the end of thrust. I will analyze the acceleration data more carefully to find this point.
From the plot, it seems that the initial burn-out estimation at 0.63 seconds is indeed too early. We need to identify the point where the acceleration drops sharply and remains low, indicating the end of the motor burn.
Let's reanalyze the data by focusing on the period after the initial peak in acceleration to find a more accurate point for motor burn-out. We'll look for the point where the acceleration drops and stabilizes.
The identified motor burn-out time at 158.8 seconds is likely incorrect given typical rocket flight profiles. Instead of using an arbitrary threshold, I'll refine the analysis to identify a significant and sustained drop in acceleration more accurately.
Let's focus on the immediate drop following the peak acceleration, which typically marks the end of the boost phase.
It seems that using the current method, we're not able to identify the correct burn-out time. Let's re-evaluate the method to find the point where the acceleration drops significantly and does not return to high values, indicating motor burn-out.
I'll perform a more detailed step-by-step check through the acceleration data post the initial peak to find a more accurate burn-out point.
It seems that the data analysis is still identifying an implausibly late burn-out time. This suggests that there might be an issue with the method or the data itself.
Let's adjust our approach by focusing on the transition from the boost to the coast phase, which is a more reliable indicator of motor burn-out. This will involve looking at the transition in the 'state_name' from 'boost' to 'coast' and the corresponding time in the dataset.
The motor burn-out occurred at approximately 13.32 seconds into the flight, based on the transition from the 'boost' phase to the 'coast' phase. This is a more plausible time for motor burn-out in a typical rocket flight profile. If you need further details or analysis, please let me know!
Follow-up PROMPT:
what was the average descent speed in feet per second while under drogue, before the main chute was deployed and slowed the descent down further?
RESPONSE:
The average descent speed while under the drogue chute, before the main chute deployment, was approximately 90.34 feet per second.
If you need any further analysis or details, please let me know!
