Changeset 16428 for branches/2845_EnhancedProgress/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/RocketFuelFlow.cs
- Timestamp:
- 12/21/18 10:31:49 (5 years ago)
- Location:
- branches/2845_EnhancedProgress
- Files:
-
- 3 edited
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branches/2845_EnhancedProgress
- Property svn:mergeinfo changed
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branches/2845_EnhancedProgress/HeuristicLab.Problems.Instances.DataAnalysis
- Property svn:mergeinfo changed
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branches/2845_EnhancedProgress/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Physics/RocketFuelFlow.cs
r16264 r16428 27 27 namespace HeuristicLab.Problems.Instances.DataAnalysis { 28 28 class RocketFuelFlow : ArtificialRegressionDataDescriptor { 29 public override string Name { get { return "Rocket Fuel Flow f(X) = 4000*x1*x2/sqrt(x3)"; } }29 public override string Name { get { return "Rocket Fuel Flow m_dot = p0 A / sqrt(T0) * sqrt(γ/R (2/(γ+1))^((γ+1) / (γ-1)))"; } } 30 30 31 31 public override string Description { 32 32 get { 33 return "A full description of this problem instance is given in the paper: A multilevel block building algorithm for fast modeling generalized separable systems. " + Environment.NewLine + 34 "Authors: Chen Chen, Changtong Luo, Zonglin Jiang" + Environment.NewLine + 35 "Function: f(X) = 4000*x1*x2/sqrt(x3)" + Environment.NewLine + 36 "with x1 in [4,6], x2 in [0.5, 1.5], x3 in [250,260]"; 33 return "A full description of this problem instance is given in: " + Environment.NewLine + 34 "Chen Chen, Changtong Luo, Zonglin Jiang, \"A multilevel block building algorithm for fast " + 35 "modeling generalized separable systems\", Expert Systems with Applications, Volume 109, 2018, " + 36 "Pages 25-34 https://doi.org/10.1016/j.eswa.2018.05.021. " + Environment.NewLine + 37 "Function: m_dot = p0 A / sqrt(T0) * sqrt(γ/R (2/(γ+1))^((γ+1) / (γ-1)))" + Environment.NewLine + 38 "with p0 ∈ [4e5 Pa, 6e5 Pa]," + Environment.NewLine + 39 "A ∈ [0.5m², 1.5m²]," + Environment.NewLine + 40 "T0 ∈ [250°K, 260°K]," + Environment.NewLine + 41 "γ=1.4 and R=287 J/(kg*K)" + Environment.NewLine + 42 "The factor sqrt(γ/R (2/(γ+1))^((γ+1) / (γ-1))) is constant as γ and R are constants."; 37 43 } 38 44 } 39 45 40 protected override string TargetVariable { get { return " f(X)"; } }41 protected override string[] VariableNames { get { return new string[] { " x1", "x2", "x3", "f(X)" }; } }42 protected override string[] AllowedInputVariables { get { return new string[] { " x1", "x2", "x3" }; } }46 protected override string TargetVariable { get { return "m_dot"; } } 47 protected override string[] VariableNames { get { return new string[] { "p0", "A", "T0", "m_dot" }; } } 48 protected override string[] AllowedInputVariables { get { return new string[] { "p0", "A", "T0" }; } } 43 49 protected override int TrainingPartitionStart { get { return 0; } } 44 50 protected override int TrainingPartitionEnd { get { return 100; } } … … 58 64 59 65 List<List<double>> data = new List<List<double>>(); 60 var x1 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 4.0, 6.0).ToList();61 var x2= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList();62 var x3= ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 250.0, 260.0).ToList();66 var p0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 4.0e5, 6.0e5).ToList(); 67 var A = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList(); 68 var T0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 250.0, 260.0).ToList(); 63 69 64 List<double> fx = new List<double>(); 65 data.Add(x1); 66 data.Add(x2); 67 data.Add(x3); 68 data.Add(fx); 69 70 for (int i = 0; i < x1.Count; i++) { 71 double fxi = 4000 * x1[i] * x2[i] / Math.Sqrt(x3[i]); 72 fx.Add(fxi); 70 List<double> m_dot = new List<double>(); 71 data.Add(p0); 72 data.Add(A); 73 data.Add(T0); 74 data.Add(m_dot); 75 double R = 287.0; 76 double γ = 1.4; 77 var c = Math.Sqrt(γ / R * Math.Pow(2 / (γ + 1), (γ + 1) / (γ - 1))); 78 for (int i = 0; i < p0.Count; i++) { 79 double m_dot_i = p0[i] * A[i] / Math.Sqrt(T0[i]) * c; 80 m_dot.Add(m_dot_i); 73 81 } 74 82
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