예제 #1
0
        private static double CalcCompliance(int WX, int WY, int penal, KXMSolvers kx, double[] Rd1, double[] U)
        {
            var    ke = kx.KE;
            double c  = 0.0;

            for (int elx = 0; elx < WX; elx++)
            {
                for (int ely = 0; ely < WY; ely++)
                {
                    int      n1   = (WY + 1) * elx + ely;
                    int      n2   = (WY + 1) * (elx + 1) + ely;
                    var      edof = np.array(2 * n1, 2 * n1 + 1, 2 * n2, 2 * n2 + 1, 2 * n2 + 2, 2 * n2 + 3, 2 * n1 + 2, 2 * n1 + 3);
                    double[] Ue   = new double[8];
                    for (int i = 0; i < edof.size; i++)
                    {
                        Ue[i] = U[edof[i]];
                    }
                    double xp = Math.Pow(Rd1[ely + elx * WY], penal);

                    double[] KEUe  = MatrixMath.MatVecProd(ke, Ue);
                    double   UEKUE = 0;
                    for (int i = 0; i < KEUe.Length; i++)
                    {
                        UEKUE += Ue[i] * KEUe[i];
                    }

                    c += xp * UEKUE;
                }
            }

            return(c);
        }
예제 #2
0
        private static double CalcTree(int WX, int WY, double volfraq, double[,] retval, int penal)
        {
            KXMSolvers kx  = new KXMSolvers();
            var        Rd1 = MatrixMath.To1D(retval);
            var        U   = kx.FE(WX, WY, Rd1, penal);

            /*   double[,] V;
             * var lambdas = kx.Modal(WX, WY, Rd1, penal, 1, out V);
             *
             * List<double> womega = new List<double>();
             * int iwReal = 0;
             * for (int i = 0; i < lambdas.Length; i++)
             * {
             *     double freq = Math.Sqrt(lambdas[i]) / (2 * Math.PI);
             *     womega.Add(freq);
             *     if (freq <= 1.0 || double.IsNaN(freq))
             *     {
             *         iwReal++;
             *     }
             * }
             * if (iwReal >= womega.Count && womega.Count > 0)
             * {
             *     iwReal = womega.Count - 1;
             * }
             * else if (womega.Count == 0)
             * {
             *     womega.Add(10000);
             *     iwReal = 0;
             * }
             *
             * double sumV = Rd1.Sum();
             * sumV = sumV - (double)(WX * WY) * volfraq;
             * double F = 0;
             * double F1 = 50.0;
             * double F2 = 50.05;
             * double F3 = 75.382;
             * if (womega.Count < iwReal + 3)
             * {
             *     F = 1e6;
             * }
             * else
             * {
             *     double d1 = womega[iwReal] - F1;
             *     double d2 = womega[iwReal + 1] - F2;
             *     double d3 = womega[iwReal + 2] - F3;
             *     F = (d1 * d1 + d2 * d2 + d3 * d3) * 1000.0;
             * }
             * /**/

            double c    = CalcCompliance(WX, WY, penal, kx, Rd1, U);
            double sumV = Rd1.Sum();

            sumV = sumV - (double)(WX * WY) * volfraq;
            double F = c * c + sumV * sumV;

            return(F);
        }
예제 #3
0
        public static double[] Test_eval_weave_Modal(Genome <int> g, int WX, int WY, double volfraq, double F1, double F2, double F3)
        {
            double[] retval = new double[1];
            int      penal  = 3;

            var        ge  = convertWeaverToField(g, WX, WY);
            KXMSolvers skx = new KXMSolvers();

            double[,] U;
            var           lambdas = skx.Modal(WX, WY, ge, penal, penal, out U);
            List <double> womega  = new List <double>();
            int           iwReal  = 0;

            for (int i = 0; i < lambdas.Length; i++)
            {
                double freq = Math.Sqrt(lambdas[i]) / (2 * Math.PI);
                womega.Add(freq);
                if (freq <= 1.0 || double.IsNaN(freq))
                {
                    iwReal++;
                }
            }
            if (iwReal >= womega.Count && womega.Count > 0)
            {
                iwReal = womega.Count - 1;
            }
            else if (womega.Count == 0)
            {
                womega.Add(10000);
                iwReal = 0;
            }
            if (womega.Count > iwReal + 3)
            {
                g.Addata = "";
                for (int i = iwReal; i < iwReal + 3; i++)
                {
                    g.Addata += "\t" + womega[i];
                }

                g.defs = U;
            }
            double sumV = ge.Sum();

            sumV = sumV - (double)(WX * WY) * volfraq;
            if (womega.Count < iwReal + 3)
            {
                retval[0] = sumV * sumV + Math.Pow(womega[iwReal] - 28.16, 2) * 1000.0;
            }
            else
            {
                double d1 = womega[iwReal] - F1;
                double d2 = womega[iwReal + 1] - F2;
                double d3 = womega[iwReal + 2] - F3;
                retval[0] = (d1 * d1 + d2 * d2 + d3 * d3) * 1000.0;
            }
            return(retval);
        }
예제 #4
0
        public static double[] Harmonic_eval_simp(Genome <double> g, int WX, int WY, double volfraq, double f1, double f2, double f3)
        {
            double[]   retval = new double[1];
            int        penal  = 3;
            int        qenal  = 1;
            KXMSolvers skx    = new KXMSolvers();
            Dictionary <int, double> SelectedUs = new Dictionary <int, double>();
            List <double>            AFC        = new List <double>();

            for (double w = f1; w <= f2; w += f3)
            {
                var    U            = skx.Harmonic(WX, WY, g.Genom, penal, qenal, w);
                double multW        = Math.Pow(2 * Math.PI * w, 2);
                double CornerDeform = Math.Sqrt(U[2 * (WY + 1) * (WX + 1) - 1] * U[2 * (WY + 1) * (WX + 1) - 1] + U[2 * (WY + 1) * (WX + 1) - 2] * U[2 * (WY + 1) * (WX + 1) - 2]);
                AFC.Add(CornerDeform);
                for (int elx = 0; elx <= WX; elx++)
                {
                    for (int ely = 0; ely <= WY; ely++)
                    {
                        if (ely == WY / 2)
                        {
                            double Mass = g.Genom[Math.Min(ely, WY - 1) + Math.Min(elx, WX - 1) * WY];
                            int    ind  = 2 * ((WY + 1) * elx + ely);
                            if (SelectedUs.ContainsKey(ind))
                            {
                                SelectedUs[ind] += multW * U[2 * ((WY + 1) * elx + ely)] * Mass;
                            }
                            else
                            {
                                SelectedUs.Add(ind, multW * U[2 * ((WY + 1) * elx + ely)] * Mass);
                            }

                            if (SelectedUs.ContainsKey(ind + 1))
                            {
                                SelectedUs[ind] += multW * U[2 * ((WY + 1) * elx + ely) + 1] * Mass;
                            }
                            else
                            {
                                SelectedUs.Add(ind + 1, multW * U[2 * ((WY + 1) * elx + ely) + 1] * Mass);
                            }
                        }
                    }
                }
            }
            foreach (var q in SelectedUs.Keys.ToList())
            {
                SelectedUs[q] = SelectedUs[q] / ((f2 - f1) / f3);
            }/**/
예제 #5
0
            }/**/

            for (int inner = 0; inner < 1; inner++)
            {
                var      Ukx = skx.FE_inded(WX, WY, g.Genom, SelectedUs, penal);
                var      ke  = skx.KE;
                double   c   = 0.0;
                double[] dc  = new double[WX * WY];
                for (int elx = 0; elx < WX; elx++)
                {
                    for (int ely = 0; ely < WY; ely++)
                    {
                        int      n1   = (WY + 1) * elx + ely;
                        int      n2   = (WY + 1) * (elx + 1) + ely;
                        var      edof = np.array(2 * n1, 2 * n1 + 1, 2 * n2, 2 * n2 + 1, 2 * n2 + 2, 2 * n2 + 3, 2 * n1 + 2, 2 * n1 + 3);
                        double[] Ue   = new double[8];
                        for (int i = 0; i < edof.size; i++)
                        {
                            Ue[i] = Ukx[edof[i]];
                        }
                        double   xp    = Math.Pow(g.Genom[ely + elx * WY], penal);
                        double[] KEUe  = MatrixMath.MatVecProd(ke, Ue);
                        double   UEKUE = 0;
                        for (int i = 0; i < KEUe.Length; i++)
                        {
                            UEKUE += Ue[i] * KEUe[i];
                        }
                        c += xp * UEKUE;
                        dc[ely + elx * WY] = -penal *Math.Pow(g.Genom[ely + elx * WY], penal - 1) * UEKUE;
                    }
                }

                dc      = skx.Check(WX, WY, 1.5, g.Genom, dc);
                g.Genom = skx.OC(WX, WY, g.Genom, volfraq, dc);
            }

            return(AFC.ToArray());
        }

        public static double[] Test_eval_simp(Genome <double> g, int WX, int WY, double volfraq, double f1, double f2, double f3)
        {
            double[] retval = new double[1];
            int      penal  = 3;


            KXMSolvers skx = new KXMSolvers();
            var        U   = skx.FE(WX, WY, g.Genom, penal);
            var        ke  = skx.KE;
            double     c   = 0.0;

            double[] dc = new double[WX * WY];
            for (int elx = 0; elx < WX; elx++)
            {
                for (int ely = 0; ely < WY; ely++)
                {
                    int      n1   = (WY + 1) * elx + ely;
                    int      n2   = (WY + 1) * (elx + 1) + ely;
                    var      edof = np.array(2 * n1, 2 * n1 + 1, 2 * n2, 2 * n2 + 1, 2 * n2 + 2, 2 * n2 + 3, 2 * n1 + 2, 2 * n1 + 3);
                    double[] Ue   = new double[8];
                    for (int i = 0; i < edof.size; i++)
                    {
                        Ue[i] = U[edof[i]];
                    }
                    double   xp    = Math.Pow(g.Genom[ely + elx * WY], penal);
                    double[] KEUe  = MatrixMath.MatVecProd(ke, Ue);
                    double   UEKUE = 0;
                    for (int i = 0; i < KEUe.Length; i++)
                    {
                        UEKUE += Ue[i] * KEUe[i];
                    }
                    c += xp * UEKUE;
                    dc[ely + elx * WY] = -penal *Math.Pow(g.Genom[ely + elx *WY], penal - 1) * UEKUE;
                }
            }
            dc      = skx.Check(WX, WY, 1.5, g.Genom, dc);
            g.Genom = skx.OC(WX, WY, g.Genom, volfraq, dc);
            double sumV = 0;

            foreach (var gg in g.Genom)
            {
                sumV += (double)gg;
            }
            sumV      = sumV - WX * WY * volfraq;
            retval[0] = c;
            return(retval);
        }

        public static double Test_eval(Genome <double> g, int WX, int WY, double volfraq)
        {
            double retval = 0.0f;
            int    penal  = 3;


            KXMSolvers skx = new KXMSolvers();
            var        U   = skx.FE(WX, WY, g.Genom, penal);

            var    ke = skx.KE;
            double c  = 0.0;

            for (int elx = 0; elx < WX; elx++)
            {
                for (int ely = 0; ely < WY; ely++)
                {
                    int      n1   = (WY + 1) * elx + ely;
                    int      n2   = (WY + 1) * (elx + 1) + ely;
                    var      edof = np.array(2 * n1, 2 * n1 + 1, 2 * n2, 2 * n2 + 1, 2 * n2 + 2, 2 * n2 + 3, 2 * n1 + 2, 2 * n1 + 3);
                    double[] Ue   = new double[8];
                    for (int i = 0; i < edof.size; i++)
                    {
                        Ue[i] = U[edof[i]];
                    }
                    double   xp    = Math.Pow(g.Genom[ely + elx * WY], penal);
                    double[] KEUe  = MatrixMath.MatVecProd(ke, Ue);
                    double   UEKUE = 0;
                    for (int i = 0; i < KEUe.Length; i++)
                    {
                        UEKUE += Ue[i] * KEUe[i];
                    }
                    c += xp * UEKUE;
                }
            }
            //  g.Genom = skx.Check(WX, WY, 1.5, g.Genom);
            double sumV = 0;

            foreach (var gg in g.Genom)
            {
                sumV += (double)gg;
            }
            sumV   = sumV - WX * WY * volfraq;
            retval = c * c + sumV * sumV * 2.0;
            return(retval);
        }

        /**/
        public static double Test_eval_weave(Genome <int> g, int WX, int WY, double volfraq)
        {
            double retval = 0;
            int    penal  = 3;

            var        ge  = convertWeaverToField(g, WX, WY);
            KXMSolvers skx = new KXMSolvers();
            var        U   = skx.FE(WX, WY, ge, penal);

            var    ke = skx.KE;
            double c  = 0.0;

            for (int elx = 0; elx < WX; elx++)
            {
                for (int ely = 0; ely < WY; ely++)
                {
                    int      n1   = (WY + 1) * elx + ely;
                    int      n2   = (WY + 1) * (elx + 1) + ely;
                    var      edof = np.array(2 * n1, 2 * n1 + 1, 2 * n2, 2 * n2 + 1, 2 * n2 + 2, 2 * n2 + 3, 2 * n1 + 2, 2 * n1 + 3);
                    double[] Ue   = new double[8];
                    for (int i = 0; i < edof.size; i++)
                    {
                        Ue[i] = U[edof[i]];
                    }
                    double xp = Math.Pow(ge[ely + elx * WY], penal);

                    double[] KEUe  = MatrixMath.MatVecProd(ke, Ue);
                    double   UEKUE = 0;
                    for (int i = 0; i < KEUe.Length; i++)
                    {
                        UEKUE += Ue[i] * KEUe[i];
                    }

                    c += xp * UEKUE;
                }
            }
            double sumV = ge.Sum();

            sumV   = sumV - (double)(WX * WY) * volfraq;
            retval = c * c * 1.0 + sumV * sumV * 0.0;
            return(retval);
        }
예제 #6
0
        public static double[,] GoSearch(IProgress <Image> progress, int WX, int WY, double volfraq, IProgress <double> prog)
        {
            Random r = new Random();

            double[,] retval = new double[WX, WY];
            QuadTree <double> SearchTree = new QuadTree <double>();

            SearchTree.Root.BB        = new System.Windows.Rect(0, 0, WX, WY);
            SearchTree.Root.NodeValue = 1.0;
            List <double> Graph     = new List <double>();
            float         MultySize = 20.0f;
            Image         MyImage   = new Bitmap((int)((WX * 4) * MultySize), (int)((WY * 4) * MultySize));
            Graphics      g         = Graphics.FromImage(MyImage);

            for (int h = 0; h < 10000; h++)
            {
                var           SearchRootList = SearchTree.getLeafs();
                double        step           = 0.3 * r.NextDouble() - 0.1;
                List <double> Gradient       = new List <double>();
                retval = convertTree2Mesh(SearchTree, WX, WY);
                int    penal = 3;
                double Fzero = CalcTree(WX, WY, volfraq, retval, penal);
                Graph.Insert(0, Fzero);


                foreach (var q in SearchRootList)
                {
                    var oldVal = q.NodeValue;
                    q.NodeValue = Math.Min(1.0, Math.Max(0.001, q.NodeValue + step));
                    retval      = convertTree2Mesh(SearchTree, WX, WY);

                    double F   = CalcTree(WX, WY, volfraq, retval, penal);
                    double Sum = MatrixMath.To1D(retval).Sum();

                    Gradient.Add((F - Fzero) / step);
                    q.NodeValue = oldVal;
                    ///

                    for (int ix = 0; ix < WX; ix++)
                    {
                        for (int iy = 0; iy < WY; iy++)
                        {
                            double H = 1.0 - (float)retval[ix, iy];
                            H = Math.Max(0, Math.Min(1.0, H));
                            if (double.IsNaN(H))
                            {
                                H = 0;
                            }

                            H = H * 255;
                            Color      customColor = Color.FromArgb(255, (int)H, (int)H, (int)H);
                            SolidBrush shadowBrush = new SolidBrush(customColor);
                            g.FillRectangle(shadowBrush, ix * MultySize, iy * MultySize, MultySize, MultySize);
                        }
                    }

                    for (int ix = 0; ix < WX; ix++)
                    {
                        for (int iy = 0; iy < WY; iy++)
                        {
                            double H = 1.0 - (float)retval[ix, iy];
                            H = Math.Max(0, Math.Min(1.0, H));
                            if (double.IsNaN(H))
                            {
                                H = 0;
                            }

                            H = H * 255;
                            Color      customColor = Color.FromArgb(255, (int)H, (int)H, (int)H);
                            SolidBrush shadowBrush = new SolidBrush(customColor);
                            g.FillRectangle(shadowBrush, ix * MultySize + MultySize * WX + 5, iy * MultySize, MultySize, MultySize);
                        }
                    }
                    progress.Report(MyImage);
                }
                float dY = 500;
                g.FillRectangle(new SolidBrush(Color.White), 0, dY - 150, 500, dY + 150);
                g.DrawLine(new Pen(Color.Black, 2), 0, dY, 500, dY);
                double data    = Graph[Graph.Count - 1];
                double olddata = Graph[Graph.Count - 1];
                float  MaxG    = (float)(Graph.Max() + 0.00001f);
                Debug.WriteLine(Graph.First());
                for (int i = 0; i < Math.Min(500, Graph.Count); i++)
                {
                    if (!double.IsNaN(Graph[i]))
                    {
                        data = Graph[i];
                        g.DrawLine(new Pen(Color.Blue, 2), Math.Min(500, Graph.Count) - i, dY - 150.0f * (float)olddata / MaxG, Math.Min(500, Graph.Count) - i - 1.0f, dY - 150.0f * (float)data / MaxG);
                        olddata = data;
                        progress.Report(MyImage);
                    }
                }
                SearchRootList = SearchRootList.OrderBy(d => (Gradient[SearchRootList.IndexOf(d)])).ToList();
                KXMSolvers kx   = new KXMSolvers();
                var        xnew = kx.OC(Gradient.Count, 1, SearchTree.GetLeafsValues().ToArray(), 0.5, Gradient.ToArray());
                double     gMx  = Gradient.Max();
                double     gMn  = Gradient.Min();
                foreach (var q in SearchRootList)
                {
                    double delta = -Gradient[SearchRootList.IndexOf(q)];
                    if (!q.IsLast)
                    {
                        //q.NodeValue = Math.Min(1.0, Math.Max(0.001, xnew[SearchRootList.IndexOf(q)]));
                        //q.NodeValue = Math.Min(1.0, Math.Max(0.001, q.NodeValue*Math.Sqrt(delta)));
                        q.NodeValue = Math.Min(1.0, Math.Max(0.001, q.NodeValue + step));
                        if (Math.Abs(q.NodeValue - 1.0) < 0.1)
                        {
                            q.SubDivide(q.NodeValue / 2);
                        }
                        break;
                    }
                    else
                    {
                        q.NodeValue = Math.Min(1.0, Math.Max(0.001, q.NodeValue + step));
                    }
                }
            }
            g.Dispose();
            retval = convertTree2Mesh(SearchTree, WX, WY);
            return(retval);
        }