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I am currently attempting to code a basic 2D simulation of an airplane's flight computing acceleration, speed and position at each timestep. I have finally achieved a satisfactory simulation but now I face a new problem : After a period of time my computations start to become instable and quickly diverge towards infinity.

Example of instability. I have done some experiments and it seems that the higher the theta angle between the ground and the axis of the plane the later the instability comes or does not even come. I think this might be due to the approximations made by Python regarding small values. I have tried to use Decimals but the problem was the same. Does anyone have an idea on how I can suppress or at least greatly reduce instability making it happen after a much longer number of timesteps?

The problem may come from a wrong definition of the dynamics, if you have ideas on how to improve it please share :. Without any code it is not possible to find out exactly what is wrong. It is likely that you are using trigonometric functions like "tan" with inut values that yield values close to infinity - in this case even the smallest error in the way computers deal with numbers not a Python peculiarity will multiply enormously. What you have to do is to identify in your formula where is that you are getting these runaway values, and solve it symbolically, getting to an equivalent formula that won't need to calculate numeric results at these regions.

Learn more. I'm coding an airplane simulation, why do my computations become unstable? Ask Question. Asked 7 months ago. Active 7 months ago. Viewed 37 times. The problem may come from a wrong definition of the dynamics, if you have ideas on how to improve it please share : Thank you.

YannBerthelot YannBerthelot 11 1 1 bronze badge. Active Oldest Votes. It is likely that you are using trigonometric functions like "tan" with inut values that yield values close to infinity - in this case even the smallest error in the way computers deal with numbers not a Python peculiarity will multiply enormously In [15]: math.Lower academic grades are denoted by C.

Complicated, lower-level programming languages are named C. However, the C that scared me most on a fine winter morning was on my boarding pass — Southwest boarding group C. Group A boards first, then B and finally C. Every person is given a number in the queue for example, A or B and enters the airplane according to the number in the respective group. You can fathom my frustration when I was assigned the number C for my flight. I was thus one of the last few to board the flight.

The time that I had on hand while boarding groups A and B were being let in I spent in sad and quiet contemplation. I had never really given much thought to how airlines chose to board their airplanes but that day I did. I wondered about the possibility of analyzing the boarding procedure and comparing the performance of various methods.

If you were given an airplane and a few hundred volunteers and were asked to test the performance of a boarding method, what would you do? Assign them seats, line them up, fill the airplane and measure the time it takes for the process to complete. The main idea can be broken down into the following five points:.

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Now that we have defined the raw idea, let us try to translate this to an algorithm and implement it in code. Before we start writing the code, we need to define a few things based on heursitics like passenger speeds and movement rules.

Let us assume that the time required for the average person to move from one seat to the other in the aisle is 1. Be aware that we are not defining any units here like s or min.

You can consider this to be non-dimensional time. If we assign a time of 1 to all passengers, the model will be less applicable to real-life since everyone has their own different speeds. To make it realistic, we can sample the time of aisle movement from a Gaussian distribution or bell-curve with a mean of 1 and a standard deviation of 0.

Therefore, we can account for variability in speeds of passengers.

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When a passenger arrives at their designated row, they will first stow their luggage in the overhead bin and then move on to their seat. This takes up some time and causes the passengers behind to come to a stop. Let us assume that this time is equal to 2 twice the time it takes to move. Regarding rules of movement from aisle to seat, there are 6 possible scenarios:. The time it takes to move from the aisle to the respective seat depends on which of these scenarios is applicable. For a window seat, it is faster to move in if the adjoining seats are vacant.

Otherwise the people in the adjoining seats have to first come out in the aisle, let the passenger in and then go back to their own positions. All this can take up a significant chunk of time in the boarding process. We define the multipliers as follows adding 2 to every multiplier to account for stowing luggage :. We are now ready to begin with the algorithm and code.

We will go through the implementation of the above ideas in a Jupyter notebook. Snippets of code are posted below and the link to the whole Jupyter notebook is given at the end.

We will only need the scipy module for the simulation so we start by importing that. Note: Additional features like visualization will require plotting tools like matplotlib but that is not covered in this demonstration. Since Southwest Airlines usually uses a Boeingwe shall include the specifications of that in our model.

It has 23 rows and 6 columns.Some text editors e. So, if you edit an airline xxx. Python tools for manipulating X-Plane's apt. ControllerBuddy allows the mapping of game controller axes and buttons to virtual joystick, keyboard and mouse commands. Specialized actions and modes shift-states allow the creation of complex input profiles, which can suit a wide variety of target applications running either on the local or a remote PC.

Cross-platform cross-simulator pilot client for virtual air traffic networks. Solar radiation model for flight dynamics.

A work in progress to update a custom Microsoft Flight Simulator weather preset with the current conditions of a given airport.

Beard and Timothy W. Anyway, seasonal support can be improved, particularly snowy winter, by producing new versions of textures with snow incorporated, e. A fully-featured flight simulator, capable of real-time lifting-line aerodynamic modelling.

Control canards is a project to control the roll moment of the rocket during flight. A web-based program to find routes for flight simulators via filtering. Add a description, image, and links to the flight-simulation topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the flight-simulation topic, visit your repo's landing page and select "manage topics. Learn more. We use optional third-party analytics cookies to understand how you use GitHub.

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Open Empty draw section throws errors and breaks canvas. Open Testing: t. Open XML validation. This is a dynamic simulation for quadrotor UAV. Updated Sep 8, Jupyter Notebook. An online app to view your X-Plane flight.

Updated Jul 17, TypeScript. Updated Oct 27, Python.

Flight mechanics utils.AeroSandbox is a Python package for aircraft design optimization that leverages modern tools for reverse-mode automatic differentiation and large-scale design optimization. At its heart, AeroSandbox is a collection of end-to-end automatic-differentiable models and analysis tools for aircraft design applications. This property of automatic-differentiability dramatically improves performance on large problems; design problems with thousands or tens of thousands of decision variables solve in seconds on a laptop.

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AeroSandbox has powerful aerodynamics solvers written from the ground up, and AeroSandbox can also be used as a standalone aerodynamics solver if desired. Like other modules, these solvers are differentiable. Therefore, in half a second, you can calculate not only the aerodynamic performance of an airplane, but also the sensitivity of aerodynamic performance with respect to an arbitary number of design variables. Runtime of 0. Note the strong three-dimensionality of the flow near the tip.

Install with pip install AeroSandbox.

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Requires Python 3. To get examples as well, clone from master on GitHub. Nightly builds available on develop branch.

AeroSandbox is designed to have extremely intuitive, high-level, and human-readable code. You yes, you! The best part is that by adding just a few more lines of code, you can not only get the performance at a specified design point, but also the derivatives of any performance variable with respect to any design variable.

Thanks to reverse-mode automatic differentiation, this process only requires the time of one additional flow solution, regardless of the number of design variables. The primary purpose for this repository is to explore existing methods for aerodynamic analysis and develop new methods within a unified code base. If successful, this could be orders of magnitude faster than volume-mesh-based CFD while retaining high accuracy XFoil is a 2D example of this.

This code is made open-source in hopes that the aerospace community can benefit from this work. If you like this software, please consider donating to support development via PayPal at paypal. Please note that, while the entirety of the codebase should be cross-platform compatible, AeroSandbox has only been tested on Windows 10 in Python 3.I also recommend the 32bit version for increased compatibility with libraries even on a 64bit machine. We'll be using pyserial to communicate with the Arduino.

Python will control the flight simulator by emulating the PC's mouse. To do that we need the pywin32 library for Python. There are several components to this project: 1. Python moves the PC's Mouse 3.

The full code is shown in the Next Step. For now let's see the main ideas that make the code work. At the start of the code we need to establish what readings from the Accelerometer correspond to level.

We need to do this so we'll know when the device has been tilted away from level. We need to communicate the State of the Pushbutton Switch to Python so it can takeover mouse control when pressed and relinquish it when pressed again.

### A simulation framework to analyze airplane boarding methods

We'll be using Python3. The full code is shown in the Next Ste p. To do that we try each one in turn. If it fails we deal with the exception and keep looking. Actually we don't want to know the switch state.

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We want to know when the button has been pressed. We can do this by keeping track of the Previous value and subtracting the present value from it.

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We read the Serial data being sent by the Arduino using ser. Next we calculate the x,y position for the cursor based on the "centre" position i.

## flight-simulation

We also give some control over the sensitivity i. SetCursorPos int x ,int y We're Done. Close the Serial Port ser. Why not use the ideas from this Instructable to do something else?

I've had some questions on how to add more buttons. Here's what you need to do: Add them on the board and wire them up, as with the one in the project. Connect them to some digital pins e.

See where swpin and swState are used in the Arduino code? Send that as a Serial. In Python; again make a new switch state e. Good luck with it.

Question 2 years ago. Google Earth also have the advantage of adding personalized landmarks that retail avionics softwares don't all do and at a great cost.

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Reply 4 years ago. The Python program has not been able to read from a device on any of the 15 COM ports it tried.

Then it gives up with the "NameError". You should see stuff being written from Arduino. I love the combination of Python and the Arduino. So I have created a collection about it. Amazing,I will try this soon.Released: Oct 24, View statistics for this project via Libraries. This library allows Python scripts to read and set variables within MSFS and trigger events within the simulation.

It also includes, as an example, "Cockpit Companion", a flask mini http server which runs locally. It provides a web UI with a moving map and simulation variables. Event IDs. Simulation Variables. Oct 24, Oct 16, Oct 10, Oct 4, Oct 1, Sep 26, Sep 21, Sep 20, Sep 19, Sep 18, Sep 16, Sep 15, Sep 13, Sep 11, Sep 10, From Icarus burning his wings to the Wright brothers soaring through the sky, it took mankind thousands of years to learn how to fly, but how long will it take an AI to do the same?

Hello everyone! In this series of articles, I am going to write about my journey into using artificial intelligence to make a plane fly. Reinforcement Learning is the branch of machine L e arning making algorithms learn how to do things rather than telling them how to do it that deals with the training of an artificial intelligence through an action-and-reward process.

The agent will interact with the environmentselecting actions, and observing their results new states and rewards while trying to optimize its return the sum of its rewards. We will basically be creating a basic airplane simulation. There are four forces that affects a plane. The two horizontal forces are:. The two vertical forces are:.

I have chosen to simulate an Airbus A because of its wide use across the world as well as the number of data available for this aircraft The illustrations will still be made using my wooden toy plane but the figures will be based on the actual plane, such figures can be found in [1].

We will be modeling:. The aim of the plane model is to compute the acceleration, speed, and position which I will be calling dynamics in the rest of the article of the plane for any given timestep based on conditions of the previous timestep. Throughout this study, we will be using the ground frame of reference.

To compute the dynamic of the plane at a given time step in the frame of reference of the ground we will use the following relations:. We now have a way to calculate velocity and position based on acceleration. In other words, the sum of the forces applied to an object is equal to its mass times its acceleration. As presented before we have weight, thrust, drag, and lift. This one is a bit more complicated.

The turbojet basically sucks the air coming in front of the plane and expels it at greater speed thus pushing the plane forward. As the altitude increases, the efficiency of the turbojets decreases due to the rarefaction of the air leading for less air being sucked in and accelerated. We will be using the following relation to account for this reduction which I have approximated from graphs found online[2]. We will neglect the impact of temperature variations on the turbofans efficiency as we suppose air density variation already does account for this.

Lift is the force created by the difference between speeds above and beneath the wing allowing to compensate for the weight and therefore gain altitude.

### AI learns to fly (Part 1) | Airplane simulation and Reinforcement Learning

Even though their impact on flight is totally different, they are both aerodynamic forces and are obtained through a similar formula:. As we have seen when studying thrust, air density decreases with altitude. We thus have:. Before going any further, we need to establish the difference between drag and lift. Lift and drag coefficients, which are used to describe how easily the flow goes around the shape, are proportional to the angle of attack.