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University Physics II · topic UPII-E3.03

Isothermal, isobaric, isochoric, and adiabatic processes

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

Isothermal, isobaric, isochoric and adiabatic processes: predict a finite change

A named gas process is a quasi-static path on which one thing is held fixed — the volume, the pressure, the temperature, or the heat flow at zero — and that single constraint fixes both the shape of the p-V curve and the split of energy between heat and work. Use the supplied topic model Y_out = 1/(v2^gamma), where Y_out is ADIABAT p₂/p₁ (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. v2 is Final volume V₂ / V₁, measured in × V₁. Keep gamma (Adiabatic index γ = C_p/C_V) = 1.4 fixed. For this exercise use only 1.87187 × V₁ ≤ v2 ≤ 4.62812 × V₁ 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 v2 = 2.47825 × V₁ and 2.86412 × V₁. Find the signed output change ΔY_out and the average sensitivity ΔY_out/Δv2 across this interval. State whether the response increases or decreases; distinguish this interval average from a derivative at one point.

Explore the question data

ADIABAT p₂/p₁ against v2

Predictions from the supplied Isothermal, isobaric, isochoric and adiabatic processes 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): ADIABAT p₂/p₁

ADIABAT p₂/p₁ against v2Predictions from the supplied Isothermal, isobaric, isochoric and adiabatic processes 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: v2: Final volume V₂ / V₁ / × V₁. Vertical axis: ADIABAT p₂/p₁. Exact values are available in the data table below.0.10510.18580.26640.3470.42771.7622.5063.253.9944.738xy

Horizontal axis (x): v2: Final volume V₂ / V₁ / × V₁

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

Reading 1v2: Final volume V₂ / V₁ / × V₁: 1.872ADIABAT p₂/p₁: 0.4157
Read the exact data as a table
ADIABAT p₂/p₁ against v2 — supplied values
v2: Final volume V₂ / V₁ / × V₁ADIABAT p₂/p₁
1.8718750.415731008000429
2.56093749999999960.2680658114927191
3.250.1920274410793859
3.93906249999999950.14670671060248142
4.6281250.11706656833371051

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

Show a hint

Substitute each value of v2 separately while keeping every other input fixed, then subtract in the same order.

Reveal the worked solution
  1. At v2 = 2.47825 × V₁, substitute into Y_out = 1/(v2^gamma): Y₀ = 0.280671.
  2. At v2 = 2.86412 × V₁, the same fixed-parameter model gives Y₁ = 0.229198.
  3. Signed change: ΔY_out = Y₁ − Y₀ = -0.0514723; the input change is Δv2 = 0.385875 × V₁.
  4. Average sensitivity is ΔY_out/Δv2 = -0.133391 1 per × V₁. 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 = 0.280671, Y₁ = 0.229198; ΔY_out = -0.0514723; average sensitivity = -0.133391 1 per × V₁.

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

Isothermal, isobaric, isochoric and adiabatic processes: infer an input with uncertainty

A named gas process is a quasi-static path on which one thing is held fixed — the volume, the pressure, the temperature, or the heat flow at zero — and that single constraint fixes both the shape of the p-V curve and the split of energy between heat and work. Use the supplied topic model Y_out = 1/(v2^gamma), where Y_out is ADIABAT p₂/p₁ (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. v2 is Final volume V₂ / V₁, measured in × V₁. Keep gamma (Adiabatic index γ = C_p/C_V) = 1.4 fixed. For this exercise use only 1.87187 × V₁ ≤ v2 ≤ 4.62812 × V₁ 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 = 0.16948631, with a bounded reading uncertainty ±0.007863. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown v2 by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.000275625 × V₁, 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

ADIABAT p₂/p₁ against v2

Predictions from the supplied Isothermal, isobaric, isochoric and adiabatic processes 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): ADIABAT p₂/p₁

ADIABAT p₂/p₁ against v2Predictions from the supplied Isothermal, isobaric, isochoric and adiabatic processes 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: v2: Final volume V₂ / V₁ / × V₁. Vertical axis: ADIABAT p₂/p₁. Exact values are available in the data table below.0.10510.18580.26640.3470.42771.7622.5063.253.9944.738xy

Horizontal axis (x): v2: Final volume V₂ / V₁ / × V₁

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

Reading 1v2: Final volume V₂ / V₁ / × V₁: 1.872ADIABAT p₂/p₁: 0.4157
Read the exact data as a table
ADIABAT p₂/p₁ against v2 — supplied values
v2: Final volume V₂ / V₁ / × V₁ADIABAT p₂/p₁
1.8718750.415731008000429
2.56093749999999960.2680658114927191
3.250.1920274410793859
3.93906249999999950.14670671060248142
4.6281250.11706656833371051

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(v2) = 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(1.87187) = 0.415731 and Y_out(4.62812) = 0.117067. The target lies between them. On the selected branch the response is strictly decreasing.
  2. Endpoint interpolation gives v2_line = 1.87187 + (0.169486 − 0.415731)(4.62812 − 1.87187)/(0.117067 − 0.415731) = 4.14436 × V₁. 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 v2 = 3.55319 × V₁; substituting back gives Y_out = 0.16948631.
  4. The interpolation error in the input is |v2_line − v2| = 0.591177 × V₁. This is larger than the requested numerical tolerance.
  5. Invert both limits Y_out = 0.16162331 and 0.17734931. Sort the roots even if the response decreases: 3.43994 × V₁ ≤ v2 ≤ 3.67582 × V₁.
  6. The lower and upper input deviations are 0.113252 × V₁ and 0.122633 × V₁. 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 v2 ≈ 3.55319 × V₁; compatible interval [3.43994, 3.67582] × V₁. Endpoint interpolation gives 4.14436 × V₁.

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