University Physics I · topic UPI-12.06
Ladders, hinges, and friction
Connect the ideas, make your working visible, and test the result. Use the hints when you need a starting point; open the full solution after your attempt.
Further practice
Work through a fresh case before tackling the extension.
Ladders, Hinges & Friction: predict a finite change
A multiple-contact equilibrium problem finds every support force on a rigid body — normal forces, friction and hinge components — from ΣF = 0 and Στ = 0, then checks that each friction stays within μₛN and each normal force and tension stays positive. Use the supplied topic model Y_out = 100*(7.5+20*d)/(95*mu*tand(th)), where Y_out is GRIP IN USE f/μₛN (%). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sind/cosd/tand use degrees. mu is Floor friction coefficient μₛ, dimensionless. Keep th (Ladder angle θ to the floor) = 65 °; d (Climber distance up the 4.0 m ladder) = 2 m fixed. For this exercise use only 0.26375 ≤ mu ≤ 0.73625 on this continuous branch. A grid of test settings is an input restriction for this exercise, not by itself a claim of physical quantisation. This calculation retains the topic model's stated assumptions; it does not extend them to a different physical setup. Calculate Y_out at mu = 0.3677 and 0.43385. Find the signed output change ΔY_out and the average sensitivity ΔY_out/Δmu across this interval. State whether the response increases or decreases; distinguish this interval average from a derivative at one point.
GRIP IN USE f/μₛN against mu
Predictions from the supplied Ladders, Hinges & Friction model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law.
Vertical axis (y): GRIP IN USE f/μₛN / %
Horizontal axis (x): mu: Floor friction coefficient μₛ
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| mu: Floor friction coefficient μₛ | GRIP IN USE f/μₛN / % |
|---|---|
| 0.26375000000000004 | 88.39955604834095 |
| 0.3818750000000001 | 61.05501252438606 |
| 0.5 | 46.630765815499856 |
| 0.618125 | 37.71952745439827 |
| 0.7362500000000001 | 31.66775267606102 |
These are the supplied values, not a worked answer. No fitted trend is drawn.
Show a hint
Substitute each value of mu separately while keeping every other input fixed, then subtract in the same order.
Reveal the worked solution
- At mu = 0.3677, substitute into Y_out = 100*(7.5+20*d)/(95*mu*tand(th)): Y₀ = 63.4087 %.
- At mu = 0.43385, the same fixed-parameter model gives Y₁ = 53.7407 %.
- Signed change: ΔY_out = Y₁ − Y₀ = -9.66806 %; the input change is Δmu = 0.06615.
- Average sensitivity is ΔY_out/Δmu = -146.154 % per unit input. Its negative sign means that this response decreases over the specified interval.
- This is a secant slope between two settings. It equals a point derivative only for a locally linear relation or in a justified small-interval limit.
Answer Y₀ = 63.4087 %, Y₁ = 53.7407 %; ΔY_out = -9.66806 %; average sensitivity = -146.154 % per unit input.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.
Extra challenging questions
Work through the linked stages and explain what each result means.
Ladders, Hinges & Friction: infer an input with uncertainty
A multiple-contact equilibrium problem finds every support force on a rigid body — normal forces, friction and hinge components — from ΣF = 0 and Στ = 0, then checks that each friction stays within μₛN and each normal force and tension stays positive. Use the supplied topic model Y_out = 100*(7.5+20*d)/(95*mu*tand(th)), where Y_out is GRIP IN USE f/μₛN (%). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sind/cosd/tand use degrees. mu is Floor friction coefficient μₛ, dimensionless. Keep th (Ladder angle θ to the floor) = 65 °; d (Climber distance up the 4.0 m ladder) = 2 m fixed. For this exercise use only 0.26375 ≤ mu ≤ 0.73625 on this continuous branch. A grid of test settings is an input restriction for this exercise, not by itself a claim of physical quantisation. This calculation retains the topic model's stated assumptions; it does not extend them to a different physical setup. A reading gives Y_out = 42.239926 %, with a bounded reading uncertainty ±1.5858 %. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown mu by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.00004725, using a numerical bracket if required. Find the full input interval consistent with the bounded reading uncertainty and compare it with a symmetric input-error assumption. Do not assume that the supplied calibration points prove monotonicity outside this branch.
GRIP IN USE f/μₛN against mu
Predictions from the supplied Ladders, Hinges & Friction model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law.
Vertical axis (y): GRIP IN USE f/μₛN / %
Horizontal axis (x): mu: Floor friction coefficient μₛ
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| mu: Floor friction coefficient μₛ | GRIP IN USE f/μₛN / % |
|---|---|
| 0.26375000000000004 | 88.39955604834095 |
| 0.3818750000000001 | 61.05501252438606 |
| 0.5 | 46.630765815499856 |
| 0.618125 | 37.71952745439827 |
| 0.7362500000000001 | 31.66775267606102 |
These are the supplied values, not a worked answer. No fitted trend is drawn.
Show a hint
The model's endpoint predictions bracket the reading. Solve Y_out(mu) = Y_measured and then solve again at Y_measured − u and Y_measured + u; a nonlinear inverse can give unequal input deviations.
Reveal the worked solution
- Endpoint predictions are Y_out(0.26375) = 88.3996 % and Y_out(0.73625) = 31.6678 %. The target lies between them. On the selected branch the response is strictly decreasing.
- Endpoint interpolation gives mu_line = 0.26375 + (42.2399 − 88.3996)(0.73625 − 0.26375)/(31.6678 − 88.3996) = 0.648198. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
- Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives mu = 0.551975; substituting back gives Y_out = 42.239926 %.
- The interpolation error in the input is |mu_line − mu| = 0.096223. This is larger than the requested numerical tolerance.
- Invert both limits Y_out = 40.654126 % and 43.825726 %. Sort the roots even if the response decreases: 0.532002 ≤ mu ≤ 0.573506.
- The lower and upper input deviations are 0.0199728 and 0.0215309. They differ because the inverse response is nonlinear; a symmetric error bar would only be a local approximation. This interval includes reading uncertainty only, not model error or uncertainty in fixed inputs.
Answer mu ≈ 0.551975; compatible interval [0.532002, 0.573506]. Endpoint interpolation gives 0.648198.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.