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University Physics I · topic UPI-04.05

Free-body diagrams and system boundaries

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.

Further practice · 01

Free-Body Diagrams & System Boundaries: predict a finite change

A free-body diagram represents one chosen system as a dot carrying one labelled arrow for every external force on it, a contact force wherever something touches the boundary plus gravity, and nothing else. Use the supplied topic model Y_out = (1+s+max(0,s-2))*(p-r)/5, where Y_out is NET EXTERNAL FORCE ΣF_x (N). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; max(a,b) selects the larger value. p is Forward push P on cart A, measured in N. Keep s (System: 1 = A, 2 = B, 3 = A + B) = 1; r (Backward push R on cart B) = 15 N fixed. For this exercise use only 22.125 N ≤ p ≤ 37.875 N 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 p = 25.59 N and 27.795 N. Find the signed output change ΔY_out and the average sensitivity ΔY_out/Δp across this interval. State whether the response increases or decreases; distinguish this interval average from a derivative at one point.

Explore the question data

NET EXTERNAL FORCE ΣF_x against p

Predictions from the supplied Free-Body Diagrams & System Boundaries 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): NET EXTERNAL FORCE ΣF_x / N

NET EXTERNAL FORCE ΣF_x against pPredictions from the supplied Free-Body Diagrams & System Boundaries 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. Horizontal axis: p: Forward push P on cart A / N. Vertical axis: NET EXTERNAL FORCE ΣF_x / N. Exact values are available in the data table below.2.5984.29967.7019.40221.525.753034.2538.51xy

Horizontal axis (x): p: Forward push P on cart A / N

Axes show the supplied calibration interval; the origin may lie outside the plot.

Reading 1p: Forward push P on cart A / N: 22.13NET EXTERNAL FORCE ΣF_x / N: 2.85
Read the exact data as a table
NET EXTERNAL FORCE ΣF_x against p — supplied values
p: Forward push P on cart A / NNET EXTERNAL FORCE ΣF_x / N
22.1252.85
26.06254.425
306
33.93757.575
37.8759.15

These are the supplied values, not a worked answer. No fitted trend is drawn.

Show a hint

Substitute each value of p separately while keeping every other input fixed, then subtract in the same order.

Reveal the worked solution
  1. At p = 25.59 N, substitute into Y_out = (1+s+max(0,s-2))*(p-r)/5: Y₀ = 4.236 N.
  2. At p = 27.795 N, the same fixed-parameter model gives Y₁ = 5.118 N.
  3. Signed change: ΔY_out = Y₁ − Y₀ = 0.882 N; the input change is Δp = 2.205 N.
  4. Average sensitivity is ΔY_out/Δp = 0.4 N per N. Its positive sign means that this response increases over the specified interval.
  5. 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 = 4.236 N, Y₁ = 5.118 N; ΔY_out = 0.882 N; average sensitivity = 0.4 N per N.

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.

Extra challenging · 01

Free-Body Diagrams & System Boundaries: infer an input with uncertainty

A free-body diagram represents one chosen system as a dot carrying one labelled arrow for every external force on it, a contact force wherever something touches the boundary plus gravity, and nothing else. Use the supplied topic model Y_out = (1+s+max(0,s-2))*(p-r)/5, where Y_out is NET EXTERNAL FORCE ΣF_x (N). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; max(a,b) selects the larger value. p is Forward push P on cart A, measured in N. Keep s (System: 1 = A, 2 = B, 3 = A + B) = 1; r (Backward push R on cart B) = 15 N fixed. For this exercise use only 22.125 N ≤ p ≤ 37.875 N 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 = 6.693 N, with a bounded reading uncertainty ±0.36855 N. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown p by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.001575 N, 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.

Explore the question data

NET EXTERNAL FORCE ΣF_x against p

Predictions from the supplied Free-Body Diagrams & System Boundaries 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): NET EXTERNAL FORCE ΣF_x / N

NET EXTERNAL FORCE ΣF_x against pPredictions from the supplied Free-Body Diagrams & System Boundaries 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. Horizontal axis: p: Forward push P on cart A / N. Vertical axis: NET EXTERNAL FORCE ΣF_x / N. Exact values are available in the data table below.2.5984.29967.7019.40221.525.753034.2538.51xy

Horizontal axis (x): p: Forward push P on cart A / N

Axes show the supplied calibration interval; the origin may lie outside the plot.

Reading 1p: Forward push P on cart A / N: 22.13NET EXTERNAL FORCE ΣF_x / N: 2.85
Read the exact data as a table
NET EXTERNAL FORCE ΣF_x against p — supplied values
p: Forward push P on cart A / NNET EXTERNAL FORCE ΣF_x / N
22.1252.85
26.06254.425
306
33.93757.575
37.8759.15

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(p) = 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
  1. Endpoint predictions are Y_out(22.125) = 2.85 N and Y_out(37.875) = 9.15 N. The target lies between them. On the selected branch the response is strictly increasing.
  2. Endpoint interpolation gives p_line = 22.125 + (6.693 − 2.85)(37.875 − 22.125)/(9.15 − 2.85) = 31.7325 N. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
  3. Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives p = 31.7325 N; substituting back gives Y_out = 6.693 N.
  4. The interpolation error in the input is |p_line − p| = 3.55271e-15 N. This is within the requested numerical tolerance.
  5. Invert both limits Y_out = 6.32445 N and 7.06155 N. Sort the roots even if the response decreases: 30.8111 N ≤ p ≤ 32.6539 N.
  6. The lower and upper input deviations are 0.921375 N and 0.921375 N. They agree on this branch; this does not establish linearity for changes in other inputs or outside the interval. This interval includes reading uncertainty only, not model error or uncertainty in fixed inputs.

Answer p ≈ 31.7325 N; compatible interval [30.8111, 32.6539] N. Endpoint interpolation gives 31.7325 N.

Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.