本文主要内容如下:
- 1. 变形梯度
- 2. 变形梯度的逆
- 3. 相对变形梯度
- 4. 两点张量
1. 变形梯度
a. 运动变形前,参考构型中某代表性物质点 A 邻域内的线元:
 
     
      
       
        
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        d\vec{X}=dX^A\vec{G}_A=dx^i\vec{c}_i
       
      
     dX=dXAGA=dxici
 b. 运动变形后,线元 
    
     
      
       
        d
       
       
        
         X
        
        
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       d\vec{X}
      
     
    dX 映射为当前构型中的线元 
    
     
      
       
        d
       
       
        
         x
        
        
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       d\vec{x}
      
     
    dx:
 
     
      
       
        
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        d\vec{x}=dx^i\vec{g}_i=dX^A\vec{C}_A
       
      
     dx=dxigi=dXACA
 如下图所示:
 
根据映射关系:
 
     
      
       
        
         
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        \vec{x}=\vec{x}(X^1,X^2,X^3,t)
       
      
     x=x(X1,X2,X3,t)
 有:
 
     
      
       
        
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        d\vec{x}=\dfrac{\partial \vec{x}}{\partial X^A}dX^A=\left(\dfrac{\partial \vec{x}}{\partial X^A}\otimes\vec{G}^A\right)\cdot d\vec{X}\triangleq \bold F\cdot d\vec{X}
       
      
     dx=∂XA∂xdXA=(∂XA∂x⊗GA)⋅dX≜F⋅dX
 将 
    
     
      
       
        F
       
      
      
       \bold F
      
     
    F 称作 变形梯度 。可见,变形梯度(仿射量)实现了A点邻域内变形前线元到变形后线元的线性映射。
根据变形梯度的定义与不同坐标系间基的关系,有
 
     
      
       
        
         
          
           
          
         
         
          
           
            
            
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        \begin{aligned} &\bold{F}\triangleq\dfrac{\partial \vec{x}}{\partial X^A}\otimes\vec{G}^A\triangleq\vec{x}\triangledown_0\\\\ &\ \ \ =\vec{C}_A\otimes\vec{G}^A\\\\ &\ \ \ =F^B_{\bullet A}\vec{G}_B\otimes\vec{G}^A\\\\ &\ \ \ =x^i_{,A}\vec{g}_i\otimes\vec{G}^A=\vec{g}_i\otimes\vec{c}\ ^i\\\\ &\ \ \ =F^j_{\bullet i}\vec{c}_j\otimes\vec{c}\ ^i \end{aligned}
       
      
     F≜∂XA∂x⊗GA≜x▽0   =CA⊗GA   =F∙ABGB⊗GA   =x,Aigi⊗GA=gi⊗c i   =F∙ijcj⊗c i
 由上面的关系可知:
- 变形梯度可写作随体坐标系与固定坐标系基矢的张量积;
- 变形梯度在物质坐标系下的混合分量 F ∙ A B F^B_{\bullet A} F∙AB 也即为物质坐标系与随体坐标系 { X A , t } \{X^A,t\} {XA,t} 间的坐标转换系数 F ∙ A B F^B_{\bullet A} F∙AB;
- 变形梯度在空间坐标系下的混合分量 F ∙ i j F^j_{\bullet i} F∙ij 也即为空间坐标系与随体坐标系 { x i , t 0 } \{x^i,t_0\} {xi,t0} 间的坐标转换系数 F ∙ i j F^j_{\bullet i} F∙ij。
另外,变形梯度张量也可由位移在物质坐标系下的右梯度进行表示,由于:
 
     
      
       
        
         
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        \vec{X}+\vec{u}=\vec{x}+\vec{b}
       
      
     X+u=x+b
 式中, 
    
     
      
       
        
         b
        
        
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       \vec{b}
      
     
    b 为参考坐标系与空间坐标系原点的位矢差,是常矢。则
 
     
      
       
        
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        \bold F =\dfrac{\partial \vec{x}}{\partial X^A}\otimes\vec{G}^A =\dfrac{\partial }{\partial X^A}(\vec{X}+\vec{u})\otimes\vec{G}^A =\bold I+\dfrac{\partial \vec{u}}{\partial X^A}\otimes\vec{G}^A=\bold I +\vec{u}\triangledown_0
       
      
     F=∂XA∂x⊗GA=∂XA∂(X+u)⊗GA=I+∂XA∂u⊗GA=I+u▽0
变形梯度的行列式:
 
     
      
       
        
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        \mathscr{J}\triangleq det(\bold F)=det([F^B_{\bullet A}])=det([g^B_{i}][x^i_{,A}])=det([x^i_{,A}]) \begin{vmatrix} \vec{G}^1\cdot\vec{g}_1 & \vec{G}^1\cdot\vec{g}_2 & \vec{G}^1\cdot\vec{g}_3\\\\ \vec{G}^2\cdot\vec{g}_1 & \vec{G}^2\cdot\vec{g}_2 & \vec{G}^2\cdot\vec{g}_3\\\\ \vec{G}^3\cdot\vec{g}_1 & \vec{G}^3\cdot\vec{g}_2 & \vec{G}^3\cdot\vec{g}_3 \end{vmatrix}\\\ \\ =det([x^i_{,A}])[\vec{G}^1\cdot(\vec{G}^2\times\vec{G}^3)][\vec{g}_1\cdot(\vec{g}_2\times\vec{g}_3)]=det([x^i_{,A}])\sqrt{\dfrac{g}{G}}\ne0
       
      
     J≜det(F)=det([F∙AB])=det([giB][x,Ai])=det([x,Ai])
              G1⋅g1G2⋅g1G3⋅g1G1⋅g2G2⋅g2G3⋅g2G1⋅g3G2⋅g3G3⋅g3
               =det([x,Ai])[G1⋅(G2×G3)][g1⋅(g2×g3)]=det([x,Ai])Gg=0
 其中,
 
     
      
       
        
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        G=\vec{G}^1\cdot(\vec{G}^2\times\vec{G}^3);g=\vec{g}_1\cdot(\vec{g}_2\times\vec{g}_3)
       
      
     G=G1⋅(G2×G3);g=g1⋅(g2×g3)
 或者
 
     
      
       
        
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        C=det([{C}_{AB}])=det([F^M_{\bullet A}]^T[G_{MN}][F^N_{\bullet B}])=det^2(\bold F)G
       
      
     C=det([CAB])=det([F∙AM]T[GMN][F∙BN])=det2(F)G
 则
 
     
      
       
        
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        det^2(\bold F)=\dfrac{C}{G}\ne0
       
      
     det2(F)=GC=0
 变形梯度的行列式不为零,说明变形梯度是正则仿射量。
2. 变形梯度的逆
根据映射关系:
 
     
      
       
        
         
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        \vec{X}=\vec{X}(x^1,x^2,x^3,t)
       
      
     X=X(x1,x2,x3,t)
 得:
 
     
      
       
        
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          x
         
         
          ⃗
         
        
       
       
        d\vec{X}=\dfrac{\partial \vec{X}}{\partial x^i}d{x}^i=\left(\dfrac{\partial \vec{X}}{\partial x^i}\otimes\vec{g}\ ^i\right)\cdot d\vec{x}\triangleq \overset{-1}{\bold F}\cdot d\vec{x}
       
      
     dX=∂xi∂Xdxi=(∂xi∂X⊗g i)⋅dx≜F−1⋅dx
 根据定义:
 
     
      
       
        
         
          
           
          
         
         
          
           
            
            
             
              
               
                F
               
              
              
               
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                1
               
              
             
            
            
             ≜
            
            
             
              
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                X
               
               
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        \begin{aligned} &\overset{-1}{\bold F}\triangleq \dfrac{\partial \vec{X}}{\partial x^i}\otimes\vec{g}\ ^i\triangleq\vec{X}\triangledown\\\\ &\ \ \ =\vec c_i\otimes\vec{g}^i\\\\ &\ \ \ =\overset{-1}{F}\ ^{j}_{,i}\vec{g}_j\otimes\vec{g}^i\\\\ &\ \ \ =X^A_{,i}\vec{G}_A\otimes\vec{g}^i=\vec{G}_A\otimes\vec{C}^A\\\\ &\ \ \ =\overset{-1}{F}\ ^{B}_{,A}\vec{C}_B\otimes\vec{C}^A \\\\ &\ \ \ =\dfrac{\partial }{\partial x^i}(\vec{x}-\vec{u})\otimes\vec{g}^i \\\\ &\ \ \ =\bold I-\dfrac{\partial \vec{u}}{\partial x^i}\otimes\vec{g}^i =\bold I-\vec{u}\triangledown \end{aligned}
       
      
     F−1≜∂xi∂X⊗g i≜X▽   =ci⊗gi   =F−1 ,ijgj⊗gi   =X,iAGA⊗gi=GA⊗CA   =F−1 ,ABCB⊗CA   =∂xi∂(x−u)⊗gi   =I−∂xi∂u⊗gi=I−u▽
 又因为:
 
     
      
       
        
         
          
           
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        \overset{-1}{\bold F}\cdot\bold{F}=(\vec c_i\otimes\vec{g}^i)\cdot(\vec{g}_j\otimes\vec{c}\ ^j)=\delta^i_j\vec c_i\otimes\vec{c}\ ^j=\bold I
       
      
     F−1⋅F=(ci⊗gi)⋅(gj⊗c j)=δjici⊗c j=I
 因此,
     
      
       
        
         
          
           
            F
           
          
          
           
            −
           
           
            1
           
          
         
        
       
       
        \overset{-1}{\bold F}
       
      
     F−1 为变形梯度仿射量的逆。
3. 相对变形梯度
4. 两点张量
定义:若某张量的分量或张量基涉及两个不互相独立的坐标系,便称之为 两点张量。
比如,变形梯度或其逆为两点张量的实例:
 
     
      
       
        
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         =
        
        
         
          F
         
         
          
           ∙
          
          
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          B
         
        
        
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           G
          
          
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          A
         
        
       
       
        \bold F=F^B_{\bullet A}\vec{G}_B\otimes\vec{G}^A
       
      
     F=F∙ABGB⊗GA
 上述形式上似乎只与物质坐标系相关,但注意到:
 
     
      
       
        
         
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        F^B_{\bullet A} =X^A_{,i}g^i_B =\dfrac{\partial X^A(\vec{x},t)}{\partial x^i}\dfrac{\partial x^i(\vec{X},t)}{\partial X^B}
       
      
     F∙AB=X,iAgBi=∂xi∂XA(x,t)∂XB∂xi(X,t)
 说明其分量涉及物质坐标系与空间坐标系。
最后尤其指出:两点张量关于坐标的导数应当是全导数。具体来说,若张量 
    
     
      
       
        Ψ
       
      
      
       \bold \Psi
      
     
    Ψ 是建立在坐标系 
    
     
      
       
        {
       
       
        
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        }
       
      
      
       \{\vec{X}\}
      
     
    {X} 与 
    
     
      
       
        {
       
       
        
         x
        
        
         ⃗
        
       
       
        }
       
      
      
       \{\vec{x}\}
      
     
    {x} 上的两点张量,则
 
     
      
       
        
         
          
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            A
           
          
         
        
       
       
        \dfrac{d\bold\Psi}{d X^A} =\dfrac{\partial\bold\Psi}{\partial X^A}+\dfrac{\partial\bold\Psi}{\partial x^i}\dfrac{\partial x^i}{\partial X^A}
       
      
     dXAdΨ=∂XA∂Ψ+∂xi∂Ψ∂XA∂xi



















