Write , and . At crystal orientation , the conductivity tensor is . Because the comparison tensor is scalar, its resolvent rotates in the same way:
Uniform angular averaging uses and . It therefore sends any such rotated diagonal tensor to half its trace times . In particular,
Dividing these scalar coefficients in the stated Hashin-Shtrikman conductivity variational principle gives
This averaging calculation uses the uniform orientation model in the question; a nonuniform distribution need not give isotropic effective conductivity.
The lower Hashin-Shtrikman conductivity bounds use a comparison no larger than every local principal conductivity, so . The upper variational bound uses . These opposite directions come from the sign of the contrast in the polarization functional: positive contrast gives a maximizing lower principle, and negative contrast a minimizing upper principle. The endpoint values are understood as limits when a contrast eigenvalue vanishes. Since
the strongest lower and upper values in their admissible comparison ranges are, respectively,
Thus in the statistically isotropic polycrystal setting of these Hashin-Shtrikman conductivity bounds.
The self-consistent conductivity approximation chooses the comparison conductivity equal to the predicted conductivity, . Multiplication by the positive denominator cancels the terms and leaves . Only the positive root is an admissible conductivity:
For a direct proof of its ordering relative to the variational bounds, set . Then
Both quantities are nonnegative, proving , with equality throughout when . The self-consistent conductivity approximation is a closure calculation; no unrequested claim of exactness for every spatial arrangement follows just from this fixed-point computation.
A self-consistent conductivity approximation sets the homogeneous comparison effective conductivity equal to the predicted response. For a uniformly oriented two-dimensional polycrystal with principal conductivities , the comparison formula gives , so its positive solution is . This is an approximation defined by a fixed-point closure; exactness requires additional geometric or statistical hypotheses.