51 unsplitFaces_(candidateFaces.
size()),
63 scalar squareError =
sqr(cellError)().
average().value();
64 scalar deviation =
sqrt(squareError -
sqr(avgError));
66 Info<<
"avgError:" << avgError
67 <<
" squareError:" << squareError
68 <<
" deviation:" << deviation
71 scalar ref = avgError + deviation;
72 scalar unref = avgError - deviation;
74 Info<<
"evaluateError : refinement criterion : " << ref << endl
75 <<
" unrefinement criterion : " << unref <<
endl;
86 label unsplitFaceI = 0;
89 forAll(candidateFaces, candidateFaceI)
91 label faceI = candidateFaces[candidateFaceI];
93 if (markedFace[faceI])
95 Info<<
"evaluateError : protected candidate face:" << faceI
101 if (unsplitFaceI < (candidateFaces.
size()/2 + 1))
103 unsplitFaces_[unsplitFaceI++] = faceI;
110 unsplitFaces_.setSize(unsplitFaceI);
139 if ((cellError[cellI] > ref) && !markedCells[cellI])
145 refCells_.setSize(refCellI);
151 if ((cellError[cellI] > ref) && !markedCells[cellI])
153 refCells_[refCellI++] =
refineCell(cellI, gradTheta[cellI]);
157 Info<<
"evaluateError : selected " << unsplitFaces_.size()
158 <<
" faces out of " << candidateFaces.
size() <<
" for removal" <<
endl;
159 Info<<
"evaluateError : selected " << refCells_.size()
160 <<
" cells out of " << cellError.
size() <<
" for refinement" <<
endl;