Newton's Second Law: the Momentum Principle: Difference between revisions
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Integrating both sides yields <math>\int d\vec{p} = \int \vec{F}_{net}dt</math> | Integrating both sides yields <math>\int d\vec{p} = \int \vec{F}_{net}dt</math> | ||
which simplifies to <math>\ | which simplifies to <math>\delta vec{p} = \int \vec{F}_{net}dt</math> | ||
===A Computational Model=== | ===A Computational Model=== |
Revision as of 15:17, 17 May 2019
This page describes Newton's second law, also known as the momentum principle, which relates net force to the change in linear momentum. This principle is used to predict the effects of forces on the motion of objects.
The Main Idea
Newton's second law, also known as the momentum principle, states that [math]\displaystyle{ \vec{F}_{net} = \frac{d\vec{p}_{system}}{dt} }[/math] where [math]\displaystyle{ \vec{p} }[/math] is the linear momentum of the system and [math]\displaystyle{ \vec{F}_{net} }[/math] is the net external force acting on the system from its surroundings. Often, the system in question consists of a single particle whose motion we want to predict. Note that both force and momentum are vector quantities, and that the change in momentum as a result of a force will always be in the direction of that force.
The momentum principle has no derivation, as it is considered the definition of force.
A Mathematical Model
The momentum principle states that [math]\displaystyle{ \vec{F}_{net} = \frac{d\vec{p}}{dt} }[/math]. Recall that [math]\displaystyle{ \vec{p} = m\vec{v} }[/math]. Therefore, by product rule, [math]\displaystyle{ \frac{d\vec{p}}{dt} = m\frac{d\vec{v}}{dt} + \vec{v}\frac{dm}{dt} }[/math]. Under the assumption that mass is constant, the second term becomes 0, and the momentum principle becomes [math]\displaystyle{ \vec{F}_{net} = m\frac{d\vec{v}}{dt} }[/math], or [math]\displaystyle{ \vec{F}_{net} = m\vec{a} }[/math], which may be a more familiar form of Newton's second law. The form [math]\displaystyle{ \vec{F}_{net} = \frac{d\vec{p}}{dt} }[/math] is preferred for several reasons:
- When a particle accumulates mass that was initially at rest, such as a snowball rolling downhill, the term [math]\displaystyle{ \vec{v}\frac{dm}{dt} }[/math] is not 0, and [math]\displaystyle{ \vec{F}_{net} = m\vec{a} }[/math] is no longer accurate, while [math]\displaystyle{ \vec{F}_{net} = \frac{d\vec{p}}{dt} }[/math] is;
- As a particle approaches the speed of light, [math]\displaystyle{ \vec{F}_{net} = m\vec{a} }[/math] is no longer accurate, while [math]\displaystyle{ \vec{F}_{net} = \frac{d\vec{p}}{dt} }[/math] is, as long as relativistic momentum is used; and
- [math]\displaystyle{ \vec{F}_{net} = \frac{d\vec{p}}{dt} }[/math] has a more direct rotational analogue (that is, it will be easier to accurately learn rotational physics if you learn linear physics using this form.)
The momentum principle is responsible for the relationship between impulse and momentum:
[math]\displaystyle{ \vec{F}_{net} = \frac{d\vec{p}}{dt} }[/math]
can be arranged to [math]\displaystyle{ d\vec{p} = \vec{F}_{net}dt }[/math].
Integrating both sides yields [math]\displaystyle{ \int d\vec{p} = \int \vec{F}_{net}dt }[/math]
which simplifies to [math]\displaystyle{ \delta vec{p} = \int \vec{F}_{net}dt }[/math]
A Computational Model
Often in computational simulations of particles, a momentum variable is assigned to each particle. Such simulations usually occur in "time steps," or iterations of a loop representing a time interval. In each time step, the particles' momenta are updated according to the momentum principle, and their velocities are calculated by dividing each particle's momentum by its mass. The velocities are used to update the positions of the particles.
The following is an example of a line of vPython responsible for updating the momentum of a particle according to the momentum principle: p = p + fnet*deltat
This simulation is an example of a program that uses the momentum principle. It simulates the motion of a cart, represented by a rectangle, being blown by a gust of wind. https://trinket.io/glowscript/ce43925647
For more information, see iterative prediction
Examples
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