University Physics I · unit UPI-12
Static Equilibrium & Elasticity
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.
Extra challenging questions
A selection from this pathway. Open a topic below for its complete practice and challenge set.
Translational & Rotational Equilibrium: infer an input with uncertainty
A rigid body is in equilibrium when the external forces on it add to zero and their torques about a point add to zero, so neither its centre of mass nor its rotation accelerates; static equilibrium also requires it to start at rest. Use the supplied topic model Y_out = fa+fb-600, where Y_out is NET FORCE ΣF_y (N). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. fa is Lift F₁ at end A, measured in N. Keep fb (Lift F₂ at end B) = 300 N; p (Axis position p from A) = 1 m fixed. For this exercise use only 189.75 N ≤ fa ≤ 410.25 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 = 24.255 N, with a bounded reading uncertainty ±12.899 N. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown fa by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.02205 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.
NET FORCE ΣF_y against fa
Predictions from the supplied Translational & Rotational Equilibrium 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 FORCE ΣF_y / N
Horizontal axis (x): fa: Lift F₁ at end A / N
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| fa: Lift F₁ at end A / N | NET FORCE ΣF_y / N |
|---|---|
| 189.75 | -110.25 |
| 244.875 | -55.125 |
| 300 | 0 |
| 355.125 | 55.125 |
| 410.25 | 110.25 |
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(fa) = 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(189.75) = -110.25 N and Y_out(410.25) = 110.25 N. The target lies between them. On the selected branch the response is strictly increasing.
- Endpoint interpolation gives fa_line = 189.75 + (24.255 − -110.25)(410.25 − 189.75)/(110.25 − -110.25) = 324.255 N. 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 fa = 324.255 N; substituting back gives Y_out = 24.255 N.
- The interpolation error in the input is |fa_line − fa| = 0 N. This is within the requested numerical tolerance.
- Invert both limits Y_out = 11.356 N and 37.154 N. Sort the roots even if the response decreases: 311.356 N ≤ fa ≤ 337.154 N.
- The lower and upper input deviations are 12.899 N and 12.899 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 fa ≈ 324.255 N; compatible interval [311.356, 337.154] N. Endpoint interpolation gives 324.255 N.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.
Center of Mass & Center of Gravity: infer an input with uncertainty
The center of gravity is the point where a body's entire weight can be taken to act when computing torques; in a uniform gravitational field it coincides with the center of mass, the mass-weighted average position r_cm = Σ mᵢrᵢ / M. Use the supplied topic model Y_out = deg(atan(0.3*(20+load)/(15+load*shelf))), where Y_out is TIPPING ANGLE θ_c (°). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; deg converts radians to degrees; unmarked trigonometric functions use radians. load is Load mass m, measured in kg. Keep tilt (Tilt about the edge θ) = 15 °; shelf (Height of the load's center) = 1.2 m fixed. For this exercise use only 6.375 kg ≤ load ≤ 53.625 kg 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 = 16.135698 °, with a bounded reading uncertainty ±0.08713 °. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown load by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.004725 kg, 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.
TIPPING ANGLE θ_c against load
Predictions from the supplied Center of Mass & Center of Gravity 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): TIPPING ANGLE θ_c / °
Horizontal axis (x): load: Load mass m / kg
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| load: Load mass m / kg | TIPPING ANGLE θ_c / ° |
|---|---|
| 6.375 | 19.25623592318662 |
| 18.1875 | 17.28090729149899 |
| 29.999999999999996 | 16.389540334034788 |
| 41.81249999999999 | 15.882242083630004 |
| 53.62499999999999 | 15.554830345290936 |
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(load) = 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(6.375) = 19.2562 ° and Y_out(53.625) = 15.5548 °. The target lies between them. On the selected branch the response is strictly decreasing.
- Endpoint interpolation gives load_line = 6.375 + (16.1357 − 19.2562)(53.625 − 6.375)/(15.5548 − 19.2562) = 46.21 kg. 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 load = 35.1975 kg; substituting back gives Y_out = 16.135698 °.
- The interpolation error in the input is |load_line − load| = 11.0125 kg. This is larger than the requested numerical tolerance.
- Invert both limits Y_out = 16.048568 ° and 16.222828 °. Sort the roots even if the response decreases: 33.2775 kg ≤ load ≤ 37.2836 kg.
- The lower and upper input deviations are 1.91996 kg and 2.08615 kg. 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 load ≈ 35.1975 kg; compatible interval [33.2775, 37.2836] kg. Endpoint interpolation gives 46.21 kg.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.
Moments & Couples: infer an input with uncertainty
A couple is a pair of equal and opposite forces whose lines of action are a perpendicular distance d apart; it exerts no net force, and its moment C = F d is the same about every point. Use the supplied topic model Y_out = f*(d/2+p)+f*(d/2-p), where Y_out is NET M₁ + M₂ (N m). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. f is Force magnitude F, measured in N. Keep d (Separation of the lines d) = 0.5 m; p (Position of P from the midpoint) = -0.8 m fixed. For this exercise use only 13.1875 N ≤ f ≤ 36.8125 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 = 13.799375 N m, with a bounded reading uncertainty ±0.69103 N m. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown f by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.0023625 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.
NET M₁ + M₂ against f
Predictions from the supplied Moments & Couples 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 M₁ + M₂ / N m
Horizontal axis (x): f: Force magnitude F / N
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| f: Force magnitude F / N | NET M₁ + M₂ / N m |
|---|---|
| 13.1875 | 6.59375 |
| 19.09375 | 9.546875000000002 |
| 25 | 12.499999999999998 |
| 30.90625 | 15.453125 |
| 36.8125 | 18.40625 |
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(f) = 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(13.1875) = 6.59375 N m and Y_out(36.8125) = 18.4063 N m. The target lies between them. On the selected branch the response is strictly increasing.
- Endpoint interpolation gives f_line = 13.1875 + (13.7994 − 6.59375)(36.8125 − 13.1875)/(18.4063 − 6.59375) = 27.5987 N. 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 f = 27.5987 N; substituting back gives Y_out = 13.799375 N m.
- The interpolation error in the input is |f_line − f| = 3.55271e-15 N. This is within the requested numerical tolerance.
- Invert both limits Y_out = 13.108345 N m and 14.490405 N m. Sort the roots even if the response decreases: 26.2167 N ≤ f ≤ 28.9808 N.
- The lower and upper input deviations are 1.38206 N and 1.38206 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 f ≈ 27.5987 N; compatible interval [26.2167, 28.9808] N. Endpoint interpolation gives 27.5987 N.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.
Beams, Supports & Reactions: infer an input with uncertainty
A support reaction is the force or couple a support exerts on a structure, with one component for each motion the support prevents: one for a roller or a cable, two for a pin, three for a fixed end. Use the supplied topic model Y_out = 6*x*sqrt(d*d+h*h)/(d*h), where Y_out is CABLE TENSION T (kN). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793; sqrt(x) means √x. h is Anchor height h of D above A, measured in m. Keep d (Cable attachment E at distance d) = 4 m; x (Load position x from A) = 2.75 m fixed. For this exercise use only 1.2125 m ≤ h ≤ 2.7875 m 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 = 8.6405371 kN, with a bounded reading uncertainty ±0.21386 kN. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown h by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.0001575 m, 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.
CABLE TENSION T against h
Predictions from the supplied Beams, Supports & Reactions 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): CABLE TENSION T / kN
Horizontal axis (x): h: Anchor height h of D above A / m
Axes show the supplied calibration interval; the origin may lie outside the plot.
Read the exact data as a table
| h: Anchor height h of D above A / m | CABLE TENSION T / kN |
|---|---|
| 1.2125 | 14.219705444097219 |
| 1.6062499999999997 | 11.069655964070389 |
| 1.9999999999999998 | 9.223780407186634 |
| 2.39375 | 8.032956498181582 |
| 2.7874999999999996 | 7.2148132649102985 |
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(h) = 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(1.2125) = 14.2197 kN and Y_out(2.7875) = 7.21481 kN. The target lies between them. On the selected branch the response is strictly decreasing.
- Endpoint interpolation gives h_line = 1.2125 + (8.64054 − 14.2197)(2.7875 − 1.2125)/(7.21481 − 14.2197) = 2.46694 m. 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 h = 2.17325 m; substituting back gives Y_out = 8.6405371 kN.
- The interpolation error in the input is |h_line − h| = 0.293686 m. This is larger than the requested numerical tolerance.
- Invert both limits Y_out = 8.4266771 kN and 8.8543971 kN. Sort the roots even if the response decreases: 2.10598 m ≤ h ≤ 2.24551 m.
- The lower and upper input deviations are 0.0672722 m and 0.0722557 m. 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 h ≈ 2.17325 m; compatible interval [2.10598, 2.24551] m. Endpoint interpolation gives 2.46694 m.
Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.