You need to enable JavaScript to run this app.
优惠活动
大模型
产品
解决方案
定价
更多

如何证明两类热力学微分关系?含焓与内能的等式推导

热力学微分关系的证明方法与具体推导

嘿,这两个热力学关系的证明核心都是全微分的充要条件和热力学基本方程的应用,咱们一步步来拆解清楚:

1. 如何证明两个微分之间的关系?

常用的思路有这几个:

  • 利用全微分的充要条件:如果状态函数$Z(x,y)$的全微分是$dZ = Mdx + Ndy$,则必然满足二阶混合偏导数相等:$\left(\frac{\partial M}{\partial y}\right)_x = \left(\frac{\partial N}{\partial x}\right)_y$——这也是麦克斯韦关系的本质来源。
  • 借助热力学基本方程(比如$dU=TdS-pdV$、$dH=TdS+Vdp$等),把目标变量转化为已知的状态函数微分形式,再对比系数。
  • 利用偏导数链式法则和循环关系(比如$\left(\frac{\partial A}{\partial B}\right)_C \left(\frac{\partial B}{\partial C}\right)_A \left(\frac{\partial C}{\partial A}\right)_B = -1$)来转换偏导数形式。

2. 具体热力学关系的证明

2.1 证明 $\left(\frac{\partial V}{\partial S}\right)_p = \left(\frac{\partial T}{\partial p}\right)_S$

咱们从焓的定义和基本方程入手:

  1. 已知焓的定义:$H = U + pV$,两边取全微分:
    $$dH = dU + pdV + Vdp$$
  2. 代入热力学基本方程$dU = TdS - pdV$,替换后化简:
    $$dH = (TdS - pdV) + pdV + Vdp = TdS + Vdp$$
  3. 把$H$看作状态变量$S$和$p$的函数,即$H(S,p)$,它的全微分形式为:
    $$dH = \left(\frac{\partial H}{\partial S}\right)_p dS + \left(\frac{\partial H}{\partial p}\right)_S dp$$
  4. 对比上面两个$dH$的表达式,对应系数相等,得到:
    $$\left(\frac{\partial H}{\partial S}\right)_p = T, \quad \left(\frac{\partial H}{\partial p}\right)_S = V$$
  5. 根据全微分的充要条件,二阶混合偏导数相等:
    $$\frac{\partial}{\partial p}\left(\frac{\partial H}{\partial S}\right)_p = \frac{\partial}{\partial S}\left(\frac{\partial H}{\partial p}\right)_S$$
    展开后就是:
    $$\left(\frac{\partial T}{\partial p}\right)_S = \left(\frac{\partial V}{\partial S}\right)_p$$
    这就完成了第一个等式的证明~

2.2 证明 $\left(\frac{\partial S}{\partial V}\right)_p \left(\frac{\partial T}{\partial p}\right)_V - \left(\frac{\partial S}{\partial p}\right)_V \left(\frac{\partial T}{\partial V}\right)_p = 1$

按照题目要求,把$U$看作$p$和$V$的函数来推导:

  1. 首先,$U(p,V)$的全微分形式为:
    $$dU = \left(\frac{\partial U}{\partial p}\right)_V dp + \left(\frac{\partial U}{\partial V}\right)_p dV$$
  2. 结合热力学基本方程$dU = TdS - pdV$,把$TdS$移到左边:
    $$TdS = dU + pdV = \left(\frac{\partial U}{\partial p}\right)_V dp + \left[\left(\frac{\partial U}{\partial V}\right)_p + p\right] dV$$
  3. 两边除以$T$,得到$S(p,V)$的全微分形式:
    $$dS = \frac{1}{T}\left(\frac{\partial U}{\partial p}\right)_V dp + \frac{1}{T}\left[\left(\frac{\partial U}{\partial V}\right)_p + p\right] dV$$
    对比$dS = \left(\frac{\partial S}{\partial p}\right)_V dp + \left(\frac{\partial S}{\partial V}\right)_p dV$,可得系数关系:
    $$\left(\frac{\partial S}{\partial p}\right)_V = \frac{1}{T}\left(\frac{\partial U}{\partial p}\right)_V, \quad \left(\frac{\partial S}{\partial V}\right)_p = \frac{1}{T}\left[\left(\frac{\partial U}{\partial V}\right)_p + p\right]$$
  4. 利用状态函数$U(p,V)$的二阶混合偏导数相等:
    $$\frac{\partial}{\partial V}\left(\frac{\partial U}{\partial p}\right)_V = \frac{\partial}{\partial p}\left(\frac{\partial U}{\partial V}\right)_p$$
  5. 把左边用$\left(\frac{\partial S}{\partial p}\right)_V$替换,右边用$\left(\frac{\partial S}{\partial V}\right)_p$替换:
    • 左边:$\frac{\partial}{\partial V}\left[T\left(\frac{\partial S}{\partial p}\right)_V\right]_p = \left(\frac{\partial T}{\partial V}\right)_p \left(\frac{\partial S}{\partial p}\right)_V + T \cdot \frac{\partial^2 S}{\partial V \partial p}$
    • 右边:$\frac{\partial}{\partial p}\left[T\left(\frac{\partial S}{\partial V}\right)_p - p\right]_V = \left(\frac{\partial T}{\partial p}\right)_V \left(\frac{\partial S}{\partial V}\right)_p + T \cdot \frac{\partial^2 S}{\partial p \partial V} - 1$
  6. 因为$S(p,V)$的二阶混合偏导数相等($\frac{\partial^2 S}{\partial V \partial p} = \frac{\partial^2 S}{\partial p \partial V}$),两边的$T$乘二阶偏导项可以抵消,剩下:
    $$\left(\frac{\partial T}{\partial V}\right)_p \left(\frac{\partial S}{\partial p}\right)_V = \left(\frac{\partial T}{\partial p}\right)_V \left(\frac{\partial S}{\partial V}\right)_p - 1$$
  7. 移项整理后就得到目标等式:
    $$\left(\frac{\partial S}{\partial V}\right)_p \left(\frac{\partial T}{\partial p}\right)_V - \left(\frac{\partial S}{\partial p}\right)_V \left(\frac{\partial T}{\partial V}\right)_p = 1$$

内容的提问来源于stack exchange,提问作者oldselflearner1959

相关产品推荐
方舟 Agent Plan

超全模态模型 × Harness 升级,最新支持 Deepseek-V4.1-Flash、GLM-5.3 系列、Doubao-Seedream-5.0-pro、Kimi-K3 (部分), 限时 9.9 元起

最近更新时间:2026.05.19 09:10:56