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University Physics I · topic UPI-03.08

Multidimensional motion problem studio

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

Multidimensional motion problem studio: predict a finite change

A multidimensional motion problem states conditions a motion must meet — a point to pass through, a body to meet, a surface to follow — and asks for the launch or timing that meets them, with every component on one shared clock. Use the supplied topic model Y_out = 30*(1+tand(th)^2)*(1-g*30*tand(th)/v^2)*pi/180, where Y_out is AIM SENSITIVITY dy/dθ (m per degree). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sind/cosd/tand use degrees; g = 9.81 m s⁻². th is Launch angle θ, measured in °. Keep v (Launch speed v₀) = 20 m s⁻¹; h (Target height H) = 6 m fixed. For this exercise use only 16.9063 ° ≤ th ≤ 68.0938 ° 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 th = 28.1675 ° and 35.3337 °. Find the signed output change ΔY_out and the average sensitivity ΔY_out/Δth across this interval. State whether the response increases or decreases; distinguish this interval average from a derivative at one point.

Explore the question data

AIM SENSITIVITY dy/dθ against th

Predictions from the supplied Multidimensional motion problem studio 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): AIM SENSITIVITY dy/dθ / m per degree

AIM SENSITIVITY dy/dθ against thPredictions from the supplied Multidimensional motion problem studio 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: th: Launch angle θ / °. Vertical axis: AIM SENSITIVITY dy/dθ / m per degree. Exact values are available in the data table below.-3.263-2.301-1.338-0.37590.586714.8628.6842.556.3270.14xy

Horizontal axis (x): th: Launch angle θ / °

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

Reading 1th: Launch angle θ / °: 16.91AIM SENSITIVITY dy/dθ / m per degree: 0.4441
Read the exact data as a table
AIM SENSITIVITY dy/dθ against th — supplied values
th: Launch angle θ / °AIM SENSITIVITY dy/dθ / m per degree
16.906250.4440623474720346
29.7031250.4027113760358464
42.50.31383428173665506
55.296875-0.10085585628770602
68.09375-3.120853020804925

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

Show a hint

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

Reveal the worked solution
  1. At th = 28.1675 °, substitute into Y_out = 30*(1+tand(th)^2)*(1-g*30*tand(th)/v^2)*pi/180: Y₀ = 0.4083 m per degree.
  2. At th = 35.3337 °, the same fixed-parameter model gives Y₁ = 0.376386 m per degree.
  3. Signed change: ΔY_out = Y₁ − Y₀ = -0.0319133 m per degree; the input change is Δth = 7.16625 °.
  4. Average sensitivity is ΔY_out/Δth = -0.00445328 m per degree per °. Its negative sign means that this response decreases 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 = 0.4083 m per degree, Y₁ = 0.376386 m per degree; ΔY_out = -0.0319133 m per degree; average sensitivity = -0.00445328 m per degree per °.

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

Multidimensional motion problem studio: infer an input with uncertainty

A multidimensional motion problem states conditions a motion must meet — a point to pass through, a body to meet, a surface to follow — and asks for the launch or timing that meets them, with every component on one shared clock. Use the supplied topic model Y_out = 30*(1+tand(th)^2)*(1-g*30*tand(th)/v^2)*pi/180, where Y_out is AIM SENSITIVITY dy/dθ (m per degree). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sind/cosd/tand use degrees; g = 9.81 m s⁻². th is Launch angle θ, measured in °. Keep v (Launch speed v₀) = 20 m s⁻¹; h (Target height H) = 6 m fixed. For this exercise use only 16.9063 ° ≤ th ≤ 68.0938 ° 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 = 0.21052481 m per degree, with a bounded reading uncertainty ±0.035031 m per degree. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown th by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.00511875 °, 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

AIM SENSITIVITY dy/dθ against th

Predictions from the supplied Multidimensional motion problem studio 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): AIM SENSITIVITY dy/dθ / m per degree

AIM SENSITIVITY dy/dθ against thPredictions from the supplied Multidimensional motion problem studio 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: th: Launch angle θ / °. Vertical axis: AIM SENSITIVITY dy/dθ / m per degree. Exact values are available in the data table below.-3.263-2.301-1.338-0.37590.586714.8628.6842.556.3270.14xy

Horizontal axis (x): th: Launch angle θ / °

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

Reading 1th: Launch angle θ / °: 16.91AIM SENSITIVITY dy/dθ / m per degree: 0.4441
Read the exact data as a table
AIM SENSITIVITY dy/dθ against th — supplied values
th: Launch angle θ / °AIM SENSITIVITY dy/dθ / m per degree
16.906250.4440623474720346
29.7031250.4027113760358464
42.50.31383428173665506
55.296875-0.10085585628770602
68.09375-3.120853020804925

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(th) = 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(16.9063) = 0.444062 m per degree and Y_out(68.0938) = -3.12085 m per degree. The target lies between them. On the selected branch the response is strictly decreasing.
  2. Endpoint interpolation gives th_line = 16.9063 + (0.210525 − 0.444062)(68.0938 − 16.9063)/(-3.12085 − 0.444062) = 20.2595 °. 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 th = 48.1306 °; substituting back gives Y_out = 0.21052481 m per degree.
  4. The interpolation error in the input is |th_line − th| = 27.8711 °. This is larger than the requested numerical tolerance.
  5. Invert both limits Y_out = 0.17549381 m per degree and 0.24555581 m per degree. Sort the roots even if the response decreases: 46.6298 ° ≤ th ≤ 49.3853 °.
  6. The lower and upper input deviations are 1.50084 ° and 1.25468 °. 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 th ≈ 48.1306 °; compatible interval [46.6298, 49.3853] °. Endpoint interpolation gives 20.2595 °.

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