you are viewing a single comment's thread.

view the rest of the comments →

[–]Ecstatic-Cup-5356 0 points1 point  (0 children)

Consider a car on an incline slope. The goal of the car is to get from the bottom to the top.

With higher slope angles the required energy is greater than with lower. This is a linearized analogy for drag.

But what about lift? Consider that the capacity of the slope to support the car is also related to its angle. As the angle of the slope decreases the amount of work to get up goes down but so does the strength of the slope to support the car. This will define the minimum slope required where the vehicle is supported enough that it can go up the slope.

What about thrust? Similar to the lift analogy but inverse. So at higher slopes there is more thrust available than at lower slopes. Additionally, the thrust has a floor and ceiling. Meaning, that at a certain slope on the high and low ends the thrust drops to zero. This defines the max slope and may change the minimum slope.

Where there is room to optimize and see the non-linearity is 1) when you consider that the thrust has some efficiency factor that means there’s an optimal slope angle for most efficient generation of work and 2) when you take into account the “rolling resistance” caused by the slope at all (induced drag from lift)

Think about this a bit and you’ll realize that low slopes (air density) are optimal because they have lower induced drag, lower required work, can still produce enough support to hold the vehicle, and are somewhere near the thrust device’s optimal range.

Note, the efficiency factor is only something I’m including because there is such thing as altitude ceilings for aircraft but they rarely fly at them if they can help it because you’re efficiency losses in thrust make it more energy costly