[16264] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17181] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[16264] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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[17174] | 25 | using HeuristicLab.Common;
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[16264] | 26 | using HeuristicLab.Random;
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| 27 |
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| 28 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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[16495] | 29 | public class AircraftLift : ArtificialRegressionDataDescriptor {
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[17174] | 30 | public override string Name { get { return "Aircraft Lift Coefficient C_L = C_Lα (α - α0) + C_Lδ_e δ_e S_HT / S_ref"; } }
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[16264] | 31 |
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| 32 | public override string Description {
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| 33 | get {
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[16495] | 34 | return "A full description of this problem instance is given in: " + Environment.NewLine +
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| 35 | "Chen Chen, Changtong Luo, Zonglin Jiang, \"A multilevel block building algorithm for fast " +
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| 36 | "modeling generalized separable systems\", Expert Systems with Applications, Volume 109, 2018, " +
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| 37 | "Pages 25-34 https://doi.org/10.1016/j.eswa.2018.05.021. " + Environment.NewLine +
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[17174] | 38 | "Function: C_L = C_Lα (α - α0) + C_Lδ_e δ_e S_HT / S_ref" + Environment.NewLine +
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| 39 | "the lift coefficient of the main airfoil C_Lα ∈ [0.4, 0.8]," + Environment.NewLine +
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| 40 | "tha angle of attack α ∈ [5°, 10°]," + Environment.NewLine +
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| 41 | "the lift coefficient of the horizontal tail C_Lδ_e ∈ [0.4, 0.8]," + Environment.NewLine +
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| 42 | "δ_e ∈ [5°, 10°]," + Environment.NewLine +
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[16495] | 43 | "S_HT ∈ [1m², 1.5m²]," + Environment.NewLine +
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| 44 | "S_ref ∈ [5m², 7m²]," + Environment.NewLine +
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[17174] | 45 | "the zero-lift angle of attack α0 is set to -2°";
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[16264] | 46 | }
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| 47 | }
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| 48 |
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[16495] | 49 | protected override string TargetVariable { get { return "C_L"; } }
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[17174] | 50 | protected override string[] VariableNames { get { return new string[] { "C_Lα", "α", "C_Lδ_e", "δ_e", "S_HT", "S_ref", "C_L", "C_L_noise" }; } }
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| 51 | protected override string[] AllowedInputVariables { get { return new string[] { "C_Lα", "α", "C_Lδ_e", "δ_e", "S_HT", "S_ref" }; } }
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[16264] | 52 | protected override int TrainingPartitionStart { get { return 0; } }
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| 53 | protected override int TrainingPartitionEnd { get { return 100; } }
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| 54 | protected override int TestPartitionStart { get { return 100; } }
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| 55 | protected override int TestPartitionEnd { get { return 200; } }
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| 56 |
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| 57 | public int Seed { get; private set; }
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| 58 |
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| 59 | public AircraftLift() : this((int)System.DateTime.Now.Ticks) { }
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| 60 |
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| 61 | public AircraftLift(int seed) {
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| 62 | Seed = seed;
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| 63 | }
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| 64 |
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| 65 | protected override List<List<double>> GenerateValues() {
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| 66 | var rand = new MersenneTwister((uint)Seed);
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| 67 |
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| 68 | List<List<double>> data = new List<List<double>>();
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[16495] | 69 | var C_La = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.4, 0.8).ToList();
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| 70 | var a = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 10.0).ToList();
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| 71 | var C_Ld_e = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.4, 0.8).ToList();
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| 72 | var d_e = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 10.0).ToList();
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| 73 | var S_HT = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();
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| 74 | var S_ref = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 7.0).ToList();
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[16264] | 75 |
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[17174] | 76 | var C_L = new List<double>();
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| 77 | var C_L_noise = new List<double>();
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[16495] | 78 | data.Add(C_La);
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| 79 | data.Add(a);
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| 80 | data.Add(C_Ld_e);
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| 81 | data.Add(d_e);
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| 82 | data.Add(S_HT);
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| 83 | data.Add(S_ref);
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| 84 | data.Add(C_L);
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[17174] | 85 | data.Add(C_L_noise);
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[16264] | 86 |
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[16495] | 87 | double a0 = -2.0;
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| 88 |
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| 89 | for (int i = 0; i < C_La.Count; i++) {
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| 90 | double C_Li = C_La[i] * (a[i] - a0) + C_Ld_e[i] * d_e[i] * S_HT[i] / S_ref[i];
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| 91 | C_L.Add(C_Li);
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[16264] | 92 | }
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| 93 |
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[17174] | 94 |
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| 95 | var sigma_noise = 0.05 * C_L.StandardDeviationPop();
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| 96 | C_L_noise.AddRange(C_L.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
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| 97 |
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[16264] | 98 | return data;
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| 99 | }
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| 100 | }
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| 101 | }
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