How to automate tests on a physical model without real data

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SUMMARY

This discussion focuses on automating tests for physical models of aircraft or cars without real-world data. It concludes that while automated validation against real data is not feasible, a rough sanity check can be performed by calculating rates, accelerations, and "jerk" to ensure outputs remain within reasonable limits. The conversation emphasizes the importance of understanding the physical parameters of the models and suggests using object-oriented programming (OOP) languages or Python with numerical analysis libraries for model development.

PREREQUISITES
  • Understanding of physical modeling concepts for vehicles
  • Familiarity with object-oriented programming (OOP) languages
  • Knowledge of Python and numerical analysis libraries
  • Basic principles of physics, including rates, accelerations, and gravity
NEXT STEPS
  • Research methods for validating physical models without real data
  • Learn about Python numerical analysis libraries such as NumPy and SciPy
  • Explore object-oriented programming best practices for modeling
  • Investigate techniques for performing sanity checks on model outputs
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Engineers, software developers, and researchers involved in modeling physical systems, particularly those focused on vehicle dynamics and simulation accuracy.

DML
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I want to code up a (physical) model of an aircraft or car, and I want to create an automated integrated test to check that the code produces reasonable and realistic results. For example, I want to check that the path taken by the aircraft or car is physical and reasonable.

However, I don't have real, physical data of an aircraft or car to test my model against.

Is it possible to check the results from my code in an automated way without having real-life data to compare it against? If not, is the only viable alternative to test hundreds of permutations of the inputs to the code and manually check the output to see if the results are reasonable? If so, what is the best way to minimise time spent on checking the errors?

If it's not possible to check the model in an automated way then how to people/companies check that their physical models are as correct as possible? What methods do they use to ensure the greatest accuracy possible?
 
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DML said:
Is it possible to check the results from my code in an automated way without having real-life data to compare it against?
No.

DML said:
If not, is the only viable alternative to test hundreds of permutations of the inputs to the code and manually check the output to see if the results are reasonable?
I don't see that that will give you any assurance at all: all it will tell you is that your model produces outputs which you think are reasonable, not that it produces outputs that bear any relation to a real aircraft or car.
 
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For a very rough sanity check, you can calculate the rates, accelerations, and "jerk"s (the third derivative of position) and see if they stay within reasonable limits. Airplanes of certain types will have limits, depending on the types of maneuvers that they are performing. Also, remember that gravity and its direction play important roles in the limits. Anything more accurate would require more knowledge of the airplane parameters.
 
DML said:
I want to code up a (physical) model of an aircraft or car
Well if you model your own car, you should be able to validate it pretty well, no?
 
Depends what language to use, in general I would advice you to use OOP(object oriented) languages (since you will mostly work with object like models), or if you rather have complex or/and various mathematical formulas and this kind of stuff then you might try Python with some numerical analysis libraries... Maybe even matlab
 

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