private double[] NormalizedGradientByFlux(SinglePhaseField field, int jCell, NodeSet nodeSet)
{
if (this.gradientX == null)
{
// Evaluate gradient
gradientX = new SinglePhaseField(field.Basis, "gradientX");
gradientY = new SinglePhaseField(field.Basis, "gradientY");
gradientX.DerivativeByFlux(1.0, field, d: 0);
gradientY.DerivativeByFlux(1.0, field, d: 1);
if (this.patchRecoveryGradient)
{
gradientX = ShockFindingExtensions.PatchRecovery(gradientX);
gradientY = ShockFindingExtensions.PatchRecovery(gradientY);
}
}
if (this.resultGradX == null)
{
resultGradX = MultidimensionalArray.Create(1, 1);
resultGradY = MultidimensionalArray.Create(1, 1);
}
gradientX.Evaluate(jCell, 1, nodeSet, resultGradX);
gradientY.Evaluate(jCell, 1, nodeSet, resultGradY);
double[] gradient = new double[] { resultGradX[0, 0], resultGradY[0, 0] };
gradient.Normalize();
return(gradient);
}
/// <summary>
/// Create clustering based on the density
/// </summary>
/// <param name="numOfClusters">Needed by <see cref="Kmeans"/></param>
/// <param name="initialMeans">Needed by <see cref="Kmeans"/></param>
/// <returns>
/// Clustering as <see cref="MultidimensionalArray"/>
/// [0]: x, [1]: y, [2]: data, [3]: cellToCluster (e.g. cell 0 is in cluster 1), [4]: local cell index
/// </returns>
public MultidimensionalArray CreateClustering_Density(int numOfClusters, double[] initialMeans)
{
Console.WriteLine("CreateClustering_Density: START");
double[] data = ShockFindingExtensions.GetFinalFunctionValues(input, inputExtended.ExtractSubArrayShallow(-1, 0));
Kmeans kmeans = new Kmeans(data, numOfClusters, initialMeans);
int[] cellToCluster = kmeans.Cluster();
MultidimensionalArray clustering = MultidimensionalArray.Create(data.Length, 5);
for (int i = 0; i < input.Lengths[0]; i++)
{
clustering[i, 0] = input[i, (int)inputExtended[i, 0] - 1, 0]; // x
clustering[i, 1] = input[i, (int)inputExtended[i, 0] - 1, 1]; // y
clustering[i, 2] = data[i]; // data value
clustering[i, 3] = cellToCluster[i]; // cellToCluster (e.g. cell 0 is in cluster 1)
clustering[i, 4] = inputExtended[i, 2]; // local cell index
}
_clusterings.Add(clustering);
Console.WriteLine("CreateClustering_Density: END");
return(clustering);
}
/// <summary>
/// Reconstructs a level set <see cref="SinglePhaseField"/> from a given set of points
/// </summary>
/// <param name="field">The DG field to work with</param>
/// <param name="clustering"> as <see cref="MultidimensionalArray"/>
/// Lenghts --> [0]: numOfPoints, [1]: 5
/// [1] --> [0]: x, [1]: y, [2]: data, [3]: cellToCluster (e.g. cell 0 is in cluster 1), [4]: local cell index
/// </param>
/// <param name="patchRecovery">Use <see cref="ShockFindingExtensions.PatchRecovery(SinglePhaseField)"/></param>
/// <param name="continuous">Use <see cref="ShockFindingExtensions.ContinuousLevelSet(SinglePhaseField, MultidimensionalArray)"/></param>
/// <returns>Returns the reconstructed level set as <see cref="SinglePhaseField"/></returns>
public SinglePhaseField ReconstructLevelSet(SinglePhaseField field = null, MultidimensionalArray clustering = null, bool patchRecovery = true, bool continuous = true)
{
Console.WriteLine("ReconstructLevelSet: START");
if (field == null)
{
field = this.densityField;
}
Console.WriteLine(string.Format("ReconstructLevelSet based on field {0}", field.Identification));
if (clustering == null)
{
clustering = _clusterings.Last();
Console.WriteLine(string.Format("ReconstructLevelSet based on clustering {0}", _clusterings.Count() - 1));
}
else
{
Console.WriteLine("ReconstructLevelSet based on user-defined clustering");
}
// Extract points (x-coordinates, y-coordinates) for reconstruction from clustering
MultidimensionalArray points = clustering.ExtractSubArrayShallow(new int[] { 0, 0 }, new int[] { clustering.Lengths[0] - 1, 1 });
_levelSetFields.Add(geometryLevelSetField);
SinglePhaseField levelSetField = ShockFindingExtensions.ReconstructLevelSetField(field, points);
_levelSetFields.Add(levelSetField);
if (patchRecovery)
{
levelSetField = ShockFindingExtensions.PatchRecovery(levelSetField);
_levelSetFields.Add(levelSetField);
}
if (continuous)
{
levelSetField = ShockFindingExtensions.ContinuousLevelSet(levelSetField, clustering.ExtractSubArrayShallow(-1, 4).To1DArray());
_levelSetFields.Add(levelSetField);
}
Console.WriteLine("ReconstructLevelSet: END");
return(levelSetField);
}
/// <summary>
/// Algorithm to find an inflection point along a curve in the direction of the gradient
/// </summary>
/// <param name="gridData">The corresponding grid</param>
/// <param name="field">The DG field, which shall be evalauted</param>
/// <param name="results">
/// The points along the curve (first point has to be user defined)
/// Lenghts --> [0]: maxIterations + 1, [1]: 5
/// [1]: x | y | function values | second derivatives | step sizes
/// </param>
/// <param name="iterations">The amount of iterations needed in order to find the inflection point</param>
/// <param name="converged">Has an inflection point been found?</param>
/// <param name = "jLocal" >Local cell index of the inflection point</ param >
/// <param name="byFlux">Option for the calculation of the first and second order derivatives, default: true</param>
private void WalkOnCurve(GridData gridData, SinglePhaseField field, MultidimensionalArray results, out int iterations, out bool converged, out int jLocal, bool byFlux = true)
{
// Init
converged = false;
// Current (global) point
double[] currentPoint = new double[] { results[0, 0], results[0, 1] };
// Compute global cell index of current point
gridData.LocatePoint(currentPoint, out long GlobalId, out long GlobalIndex, out bool IsInside, out bool OnThisProcess);
// Compute local node set
NodeSet nodeSet = ShockFindingExtensions.GetLocalNodeSet(gridData, currentPoint, (int)GlobalIndex);
// Get local cell index of current point
int j0Grd = gridData.CellPartitioning.i0;
jLocal = (int)(GlobalIndex - j0Grd);
// Evaluate the second derivative
try {
if (byFlux)
{
results[0, 3] = SecondDerivativeByFlux(field, jLocal, nodeSet);
}
else
{
results[0, 3] = ShockFindingExtensions.SecondDerivative(field, jLocal, nodeSet);
}
} catch (NotSupportedException) {
iterations = 0;
return;
}
// Evaluate the function
MultidimensionalArray f = MultidimensionalArray.Create(1, 1);
field.Evaluate(jLocal, 1, nodeSet, f);
results[0, 2] = f[0, 0];
// Set initial step size to 0.5 * h_minGlobal
results[0, 4] = 0.5 * gridData.Cells.h_minGlobal;
int n = 1;
while (n < results.Lengths[0])
{
// Evaluate the gradient of the current point
double[] gradient;
if (byFlux)
{
gradient = NormalizedGradientByFlux(field, jLocal, nodeSet);
}
else
{
gradient = ShockFindingExtensions.NormalizedGradient(field, jLocal, nodeSet);
}
// Compute new point along curve
currentPoint[0] = currentPoint[0] + gradient[0] * results[n - 1, 4];
currentPoint[1] = currentPoint[1] + gradient[1] * results[n - 1, 4];
// New point has been calculated --> old node set is invalid
nodeSet = null;
// Check if new point is still in the same cell or has moved to one of its neighbours
if (!gridData.Cells.IsInCell(currentPoint, jLocal))
{
// Get indices of cell neighbours
gridData.GetCellNeighbours(jLocal, GetCellNeighbours_Mode.ViaVertices, out int[] cellNeighbours, out int[] connectingEntities);
double[] newLocalCoord = new double[currentPoint.Length];
bool found = false;
// Find neighbour
foreach (int neighbour in cellNeighbours)
{
if (gridData.Cells.IsInCell(currentPoint, neighbour, newLocalCoord))
{
// If neighbour has been found, update
jLocal = neighbour + j0Grd;
nodeSet = ShockFindingExtensions.GetLocalNodeSet(gridData, currentPoint, jLocal);
found = true;
break;
}
}
if (found == false)
{
iterations = n;
return;
}
}
else
{
// New point is still in the same cell --> update only the coordiantes (local node set)
nodeSet = ShockFindingExtensions.GetLocalNodeSet(gridData, currentPoint, jLocal + j0Grd);
}
// Update output
results[n, 0] = currentPoint[0];
results[n, 1] = currentPoint[1];
// Evaluate the function
f.Clear();
field.Evaluate(jLocal, 1, nodeSet, f);
results[n, 2] = f[0, 0];
// Evaluate the second derivative of the new point
try {
if (byFlux)
{
results[n, 3] = SecondDerivativeByFlux(field, jLocal, nodeSet);
}
else
{
results[n, 3] = ShockFindingExtensions.SecondDerivative(field, jLocal, nodeSet);
}
} catch (NotSupportedException) {
break;
}
// Check if sign of second derivative has changed
// Halve the step size and change direction
if (Math.Sign(results[n, 3]) != Math.Sign(results[n - 1, 3]))
{
results[n, 4] = -results[n - 1, 4] / 2;
}
else
{
results[n, 4] = results[n - 1, 4];
}
n++;
// Termination criterion
// Remark: n has already been incremented!
double[] diff = new double[currentPoint.Length];
diff[0] = results[n - 1, 0] - results[n - 2, 0];
diff[1] = results[n - 1, 1] - results[n - 2, 1];
if (diff.L2Norm() < 1e-12)
{
converged = true;
break;
}
}
iterations = n;
return;
}
private double SecondDerivativeByFlux(SinglePhaseField field, int jCell, NodeSet nodeSet)
{
if (this.gradientX == null)
{
// Evaluate gradient
gradientX = new SinglePhaseField(field.Basis, "gradientX");
gradientY = new SinglePhaseField(field.Basis, "gradientY");
gradientX.DerivativeByFlux(1.0, field, d: 0);
gradientY.DerivativeByFlux(1.0, field, d: 1);
if (this.patchRecoveryGradient)
{
gradientX = ShockFindingExtensions.PatchRecovery(gradientX);
gradientY = ShockFindingExtensions.PatchRecovery(gradientY);
}
}
if (this.hessianXX == null)
{
// Evaluate Hessian matrix
hessianXX = new SinglePhaseField(field.Basis, "hessianXX");
hessianXY = new SinglePhaseField(field.Basis, "hessianXY");
hessianYX = new SinglePhaseField(field.Basis, "hessianYX");
hessianYY = new SinglePhaseField(field.Basis, "hessianYY");
hessianXX.DerivativeByFlux(1.0, gradientX, d: 0);
hessianXY.DerivativeByFlux(1.0, gradientX, d: 1);
hessianYX.DerivativeByFlux(1.0, gradientY, d: 0);
hessianYY.DerivativeByFlux(1.0, gradientY, d: 1);
if (this.patchRecoveryHessian)
{
hessianXX = ShockFindingExtensions.PatchRecovery(hessianXX);
hessianXY = ShockFindingExtensions.PatchRecovery(hessianXY);
hessianYX = ShockFindingExtensions.PatchRecovery(hessianYX);
hessianYY = ShockFindingExtensions.PatchRecovery(hessianYY);
}
}
if (this.resultGradX == null)
{
resultGradX = MultidimensionalArray.Create(1, 1);
resultGradY = MultidimensionalArray.Create(1, 1);
}
if (this.resultHessXX == null)
{
resultHessXX = MultidimensionalArray.Create(1, 1);
resultHessXY = MultidimensionalArray.Create(1, 1);
resultHessYX = MultidimensionalArray.Create(1, 1);
resultHessYY = MultidimensionalArray.Create(1, 1);
}
gradientX.Evaluate(jCell, 1, nodeSet, resultGradX);
gradientY.Evaluate(jCell, 1, nodeSet, resultGradY);
hessianXX.Evaluate(jCell, 1, nodeSet, resultHessXX);
hessianXY.Evaluate(jCell, 1, nodeSet, resultHessXY);
hessianYX.Evaluate(jCell, 1, nodeSet, resultHessYX);
hessianYY.Evaluate(jCell, 1, nodeSet, resultHessYY);
// Compute second derivative along curve
double g_alpha_alpha = 2 * ((resultHessXX[0, 0] * resultGradX[0, 0] + resultHessXY[0, 0] * resultGradY[0, 0]) * resultGradX[0, 0]
+ (resultHessYX[0, 0] * resultGradX[0, 0] + resultHessYY[0, 0] * resultGradY[0, 0]) * resultGradY[0, 0]);
if (g_alpha_alpha == 0.0 || g_alpha_alpha.IsNaN())
{
throw new NotSupportedException("Second derivative is zero");
}
return(g_alpha_alpha);
}
/// <summary>
/// Main method in order to find inflection points of a DG field
/// </summary>
/// <param name="seeding">Setup for seeding, <see cref="SeedingSetup"/>></param>
/// <param name="patchRecoveryGradient">Enable patch recovery for the gradient of the DG field</param>
/// <param name="patchRecoveryHessian">Enable patch recovery for the second derivatives of the DG field</param>
/// <param name="eliminateNonConverged"> Eliminate non-converged points entirely</param>
/// <param name="maxNumOfIterations">Maximum number of iterations when searching for the inflection point</param>
/// <param name="eps">Threshold (inflection point is reached)</param>
/// <returns></returns>
public MultidimensionalArray FindPoints(SeedingSetup seeding = SeedingSetup.av, bool patchRecoveryGradient = true, bool patchRecoveryHessian = true, bool eliminateNonConverged = false, int maxNumOfIterations = 100, double eps = 1e-12)
{
this.patchRecoveryGradient = patchRecoveryGradient;
this.patchRecoveryHessian = patchRecoveryHessian;
#region Create seedings points based on artificial viscosity
int numOfPoints;
switch (seeding)
{
case SeedingSetup.av:
MultidimensionalArray avValues = ShockFindingExtensions.GetAVMeanValues(gridData, avField);
numOfPoints = avValues.Lengths[0];
Results = MultidimensionalArray.Create(numOfPoints, maxNumOfIterations + 1, 5);
// Seed points
for (int i = 0; i < numOfPoints; i++)
{
int jLocal = (int)avValues[i, 0];
double[] cellCenter = gridData.Cells.GetCenter(jLocal);
Results[i, 0, 0] = cellCenter[0];
Results[i, 0, 1] = cellCenter[1];
}
break;
case SeedingSetup.av3x3:
MultidimensionalArray avValues3x3 = ShockFindingExtensions.GetAVMeanValues(gridData, avField);
int numOfCells = avValues3x3.Lengths[0];
int numOfPointsPerCell = 9;
numOfPoints = numOfCells * numOfPointsPerCell;
Results = MultidimensionalArray.Create(numOfPoints, maxNumOfIterations + 1, 5);
// Seed points
for (int i = 0; i < numOfCells; i++)
{
int jLocal = (int)avValues3x3[i, 0];
MultidimensionalArray grid = ShockFindingExtensions.Get3x3Grid(gridData, jLocal);
for (int j = 0; j < numOfPointsPerCell; j++)
{
Results[i * numOfPointsPerCell + j, 0, 0] = grid[j, 0];
Results[i * numOfPointsPerCell + j, 0, 1] = grid[j, 1];
}
}
break;
case SeedingSetup.everywhere:
numOfPoints = gridData.Cells.Count;
Results = MultidimensionalArray.Create(numOfPoints, maxNumOfIterations + 1, 5);
// Seed points
for (int i = 0; i < numOfPoints; i++)
{
double[] cellCenter = gridData.Cells.GetCenter(i);
Results[i, 0, 0] = cellCenter[0];
Results[i, 0, 1] = cellCenter[1];
}
break;
default:
throw new NotSupportedException("This setting does not exist.");
}
ResultsExtended = MultidimensionalArray.Create(numOfPoints, 3);
//IterationsNeeded = new int[numOfPoints];
//Converged = new bool[numOfPoints];
//jCell = new int[numOfPoints];
Console.WriteLine("Total number of seeding points: " + Results.Lengths[0]);
#endregion
#region Find inflection point for every seeding point
Console.WriteLine("WALKING ON CURVES: START");
Console.WriteLine("(Counting starts with 0)");
for (int i = 0; i < Results.Lengths[0]; i++)
{
WalkOnCurve(gridData, densityField, Results.ExtractSubArrayShallow(i, -1, -1), out int iter, out bool pointFound, out int jLocal);
// Save more stuff
ResultsExtended[i, 0] = iter;
ResultsExtended[i, 1] = pointFound == true ? 1 : -1;
ResultsExtended[i, 2] = jLocal;
if (i == 0)
{
Console.WriteLine("Point " + i + " (first)");
}
else if (i == Results.Lengths[0] - 1)
{
Console.WriteLine("Point " + i + " (last)");
}
else if (i % 100 == 0)
{
Console.WriteLine("Point " + i);
}
#if DEBUG
if (!pointFound)
{
Console.WriteLine(String.Format("Point {0}: not converged", i));
}
#endif
}
if (eliminateNonConverged)
{
ShockFindingExtensions.EliminateNonConvergedPoints(Results, ResultsExtended, out MultidimensionalArray results_SO, out MultidimensionalArray resultsExtended_SO);
//Results_SO = results_SO;
//ResultsExtended_SO = resultsExtended_SO;
Results = results_SO;
ResultsExtended = resultsExtended_SO;
}
Console.WriteLine("WALKING ON CURVES: END");
#endregion
return(Results);
}
