To do that we need to know the acceleration of the jumper over time Al though

To do that we need to know the acceleration of the

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To do that, we need to know the acceleration of the jumper over time. Al- though we compute acceleration in the slope function, it is not included in the results. It might be tempting to record the acceleration each time the slope function runs, but we should not do that. The ODE solver calls the slope function many times with different values of state and t . Because of the way the solver works, not all of the states and times are actually part of the solution. So recording acceleration while the solver is running would not work. Instead, we can use the computed velocities to estimate acceleration as a func- tion of time. The modsim library provides gradient , which uses NumPy to estimate the derivative of a TimeSeries . Here’s how it works: a = gradient(results.v) In the notebook for this chapter, chap21.ipynb , you can finish this problem by finding the combination of L and k that allows the jumper to complete the bungee dunk while minimizing the acceleration they experience. For instruc- tions on downloading and running the code, see Section 0.4.
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184 Chapter 21 Air resistance
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Chapter 22 Projectiles in 2-D In the previous chapter we modeled objects moving in one dimension, with and without drag. Now let’s move on to two dimensions, and baseball! In this chapter we model the flight of a baseball including the effect of air resistance. In the next chapter we use this model to solve an optimization problem. 22.1 Baseball To model the flight of a baseball, we have to make some modeling decisions. To get started, we ignore any spin that might be on the ball, and the resulting Magnus force (see ). Under this assumption, the ball travels in a vertical plane, so we’ll run simulations in two dimensions, rather than three. Air resistance has a substantial effect on most projectiles in air, so we will include a drag force. To model air resistance, we’ll need the mass, frontal area, and drag coefficient of a baseball. Mass and diameter are easy to find (see baseball ). Drag coefficient is only a little harder; according to The Physics
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186 Chapter 22 Projectiles in 2-D of Baseball 1 , the drag coefficient of a baseball is approximately 0.33 (with no units). However, this value does depend on velocity. At low velocities it might be as high as 0.5, and at high velocities as low as 0.28. Furthermore, the transition between these regimes typically happens exactly in the range of velocities we are interested in, between 20 m/s and 40 m/s. Nevertheless, we’ll start with a simple model where the drag coefficient does not depend on velocity; as an exercise at the end of this chapter, you will have a chance to implement a more detailed model and see what effect is has on the results. But first we need a new computational tool, the Vector object. 22.2 Vectors Now that we are working in two dimensions, we will find it useful to work with vector quantities , that is, quantities that represent both a magnitude and a direction. We will use vectors to represent positions, velocities, accelerations, and forces in two and three dimensions.
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