Spherical Optimization and beyond

In summary, the conversation revolves around finding the most cost-efficient dimensions for a tent in the shape of a spherical cap with a given volume. The goal is to derive a formula for maximum cost efficiency while maintaining the original volume and shape. The equations used are for the volume and surface area of a dome, with the volume being a known constant. The conversation discusses using these equations to solve for the surface area in terms of a single variable and then minimizing it to find the optimal dimensions. The conversation also briefly touches on finding the minimum volume, which is a constant.
  • #1
Muon12
34
0
I have a semi-project due tommorrow that basically asks the following question: If you are designing a tent in the shape of a spherical cap (a sphere with the lower portion sliced away by a plane) and the material used for the roof costs 2.5 times more per square foot than the material used for the floor, what should the dimensions of the tent be to minimize the cost of materials used? Additional info: the volume of the tent will be 150 cu ft.
Goal: To derive a formula for maximum cost efficiency while retaining the original volume and shape of the tent.

The most difficult part of this problem is simply knowing where to start. I realize that when optimizing, I have to keep the minimums and maximum values of my equation in mind. In this case, I want to use as little ceiling material as possible for the tent while making sure that it remains a dome-shaped tent. Formulas used in this problem are the "volume of a dome" equation which I believe is V=((pi*h)/6)(3r^2+h^2) and the surface area formula of a dome: S=pi(h^2+r^2).
I wish I knew where to go from this beginning point, because short of taking the derivatives of these functions (with respect to what though?), I don't know to do.
 
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  • #2
All right. You have two equations in three variables (note: I haven't checked your equations, I'll just assume they're right).

[tex]V = \frac{\pi h}{6}(3r^2 + h^2)[/tex]
and
[tex]S = \pi(h^2 + r^2)[/tex]

Notice that these are two equations in three variables--not four--because V is constant and known! V = 150 ft^3.

So why don't you use these two equations to solve for S in terms of a single variable (it doesn't matter which one), and then minimize that equation?

cookiemonster
 
  • #3
there is still one more thing...

I've solved for r now and have a formula for the surface area of the dome in terms of h only. Now I need to minimize the "ceiling area" of this 150 cu.ft. tent. With an equation: S=pi(h^2+[(300/pi*h)-(h^2/3)]) I can now differenciate the surface area equation. So, do I simply differenciate the equation and look for the min. value, or is there more?
 
  • #4
It's as simple as you think it is. Just differentiate and find the minimum.

As for your other post, you mentioned (perhaps a typo) that you can solve for either the minimum volume or the minimum surface area from that equation. Just a reminder: the volume is constant. It can't be minimized any more than it is.

cookiemonster
 
  • #5
:smile: [?] [zz)] :frown: :wink: [b(]

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Related to Spherical Optimization and beyond

1. What is spherical optimization?

Spherical optimization is a mathematical process used to find the optimal solution for a problem that involves a sphere or spherical shape. It considers multiple variables and constraints to determine the best possible outcome within the given parameters.

2. How is spherical optimization different from other optimization techniques?

Spherical optimization differs from other optimization techniques because it specifically focuses on finding the best solution for problems involving spherical shapes, rather than general optimization for any shape or form. It takes into account the unique properties and constraints of spheres, such as their surface area and volume.

3. What are some real-world applications of spherical optimization?

Spherical optimization has many practical applications, including in physics, astronomy, engineering, and computer graphics. It can be used to optimize the shape and size of satellite dishes, design efficient spherical buildings, and determine the trajectory of planets and satellites in space.

4. What are some challenges in using spherical optimization?

One of the main challenges in using spherical optimization is the complexity of the mathematical equations involved. It often requires advanced knowledge of calculus and linear algebra to accurately solve for the optimal solution. Additionally, the optimization process can be time-consuming and computationally intensive.

5. Are there any limitations to spherical optimization?

As with any optimization technique, there are limitations to what spherical optimization can achieve. It may not always find the global optimum solution and can be sensitive to initial conditions. It also assumes a perfect spherical shape, which may not be the case in real-world scenarios.

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