├── .gitignore ├── .gitlab-ci.yml ├── .vscode ├── c_cpp_properties.json ├── settings.json └── tasks.json ├── LICENSE ├── Make ├── files └── options ├── README.md ├── convergenceDetection.C ├── convergenceDetection.H └── convergenceDetectionDict /.gitignore: -------------------------------------------------------------------------------- 1 | InInclde* 2 | Make/linux64GccDPInt32Opt/* 3 | -------------------------------------------------------------------------------- /.gitlab-ci.yml: -------------------------------------------------------------------------------- 1 | image: "google/cloud-sdk:alpine" 2 | 3 | stages: 4 | - upload 5 | 6 | before_script: 7 | - echo "$GOOGLE_SERVICE_ACCOUNT" >> "/root/google.json" 8 | 9 | upload_zip: 10 | stage: upload 11 | script: 12 | - git archive -o ${CI_PROJECT_NAME}-${CI_COMMIT_REF_NAME}.zip -9 HEAD 13 | - gcloud auth activate-service-account --project=halogen-chemist-162620 --key-file=/root/google.json 14 | - gsutil -h Cache-Control:private cp ./${CI_PROJECT_NAME}-${CI_COMMIT_REF_NAME}.zip gs://airshaper-releases 15 | except: 16 | - /^feature\/.*$/ 17 | tags: 18 | - feature 19 | -------------------------------------------------------------------------------- /.vscode/c_cpp_properties.json: -------------------------------------------------------------------------------- 1 | { 2 | "configurations": [ 3 | { 4 | "name": "Linux", 5 | "includePath": [ 6 | "${workspaceFolder}/**", 7 | "/usr/lib/openfoam/openfoam2206/src/**" 8 | ], 9 | "defines": [], 10 | "compilerPath": "/usr/bin/gcc", 11 | "cStandard": "gnu11", 12 | "cppStandard": "gnu++14", 13 | "intelliSenseMode": "gcc-x86" 14 | } 15 | ], 16 | "version": 4 17 | } -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "files.associations": { 3 | "*Dict": "OpenFOAM", 4 | "*Properties": "OpenFOAM", 5 | "fvSchemes": "OpenFOAM", 6 | "fvSolution": "OpenFOAM", 7 | "**/constant/g": "OpenFOAM", 8 | "**/0/*": "OpenFOAM", 9 | "averaging": "OpenFOAM", 10 | "convergenceDetection.C": "cpp", 11 | "convergenceDetection.H": "cpp" 12 | } 13 | } -------------------------------------------------------------------------------- /.vscode/tasks.json: -------------------------------------------------------------------------------- 1 | { 2 | "version": "2.0.0", 3 | "tasks": [ 4 | { 5 | "type": "shell", 6 | "label": "wmake-build", 7 | "command": "wmake -j", 8 | "problemMatcher": [ 9 | "$gcc" 10 | ], 11 | "group": { 12 | "kind": "build", 13 | "isDefault": true 14 | } 15 | } 16 | ] 17 | } -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | convergence-detection 635 | Copyright (C) 2022 airshaper 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 2022 airshaper 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /Make/files: -------------------------------------------------------------------------------- 1 | convergenceDetection.C 2 | 3 | LIB = $(FOAM_USER_LIBBIN)/libconvergenceDetection 4 | -------------------------------------------------------------------------------- /Make/options: -------------------------------------------------------------------------------- 1 | EXE_INC = \ 2 | -I$(LIB_SRC)/finiteVolume/lnInclude \ 3 | -I$(LIB_SRC)/meshTools/lnInclude \ 4 | -I$(LIB_SRC)/functionObjects/forces/lnInclude \ 5 | -I$(LIB_SRC)/functionObjects/field/lnInclude \ 6 | -I$(LIB_SRC)/surfMesh/lnInclude \ 7 | -I$(LIB_SRC)/sampling/lnInclude 8 | 9 | LIB_LIBS = \ 10 | -lfiniteVolume \ 11 | -lmeshTools \ 12 | -lforces \ 13 | -lfieldFunctionObjects \ 14 | -lsurfMesh \ 15 | -lsampling 16 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Openfoam.com convergence detection 2 | 3 | ## Motivation 4 | 5 | Why we think this is better when runTimeControl in openfoam and residuals 6 | 7 | ## How it works 8 | 9 | This functionObject runs every iteration, it pulls force data and it stores the total force which is later used for the gradient calculation. 10 | 11 | ## Parameters explanation 12 | 13 | ### windowNormalization 0.5; 14 | 15 | ### windowGradient 0.5; 16 | 17 | ### windowGradientAveraging 0.99; 18 | 19 | ### windowEvaluation 0.333; 20 | 21 | ### windowEvaluationAveraging 0.333; 22 | 23 | ### thresholdConvergence 0.075; 24 | 25 | ### thresholdAveraging 0.025; 26 | 27 | ### iterationMaxConvergence 4000; 28 | 29 | If simulation does not converge start forced averging at 4000 iterations 30 | 31 | ### iterationMaxAveraging 2000; 32 | 33 | If simulation does not converge within 4000 iterations run 2000 averaging iterations so in total simulation will have 6000 iterations 34 | 35 | ### iterationMinConvergence 200; 36 | 37 | Minimum number of iterations in case simulation converges before 200 iterations 38 | 39 | ### iterationMinAveraging 200; 40 | 41 | Minimum number of averaging iterations in case simulation converges at iterationMinConvergence 42 | 43 | ### forceStabilityFactor 2; 44 | 45 | In case forces explode this factor is used to check if forces are 2 times higher or lower when the converged value and in case of that it stops the simulation. If this happens it means your mesh is not good enough. 46 | 47 | ## Credits 48 | 49 | Original Python implementation by João Miranda 50 | 51 | OpenFoam C++ Implementation by Nikola Majksner 52 | -------------------------------------------------------------------------------- /convergenceDetection.C: -------------------------------------------------------------------------------- 1 | /*---------------------------------------------------------------------------*\ 2 | ========= | 3 | \\ / F ield | OpenFOAM: The Open Source CFD Toolbox 4 | \\ / O peration | 5 | \\ / A nd | www.openfoam.com 6 | \\/ M anipulation | 7 | ------------------------------------------------------------------------------- 8 | Copyright (C) 2022 Nikola Majksner, Wouter Remmerie, AirShaper 9 | ------------------------------------------------------------------------------- 10 | License 11 | This file is part of OpenFOAM. 12 | 13 | OpenFOAM is free software: you can redistribute it and/or modify it 14 | under the terms of the GNU General Public License as published by 15 | the Free Software Foundation, either version 3 of the License, or 16 | (at your option) any later version. 17 | 18 | OpenFOAM is distributed in the hope that it will be useful, but WITHOUT 19 | ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or 20 | FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License 21 | for more details. 22 | 23 | You should have received a copy of the GNU General Public License 24 | along with OpenFOAM. If not, see . 25 | 26 | \*---------------------------------------------------------------------------*/ 27 | 28 | #include "convergenceDetection.H" 29 | #include "Time.H" 30 | #include "addToRunTimeSelectionTable.H" 31 | #include "cartesianCS.H" 32 | #include 33 | #include 34 | #include 35 | 36 | using namespace Foam::functionObjects::fieldValues; 37 | 38 | // * * * * * * * * * * * * * * Static Data Members * * * * * * * * * * * * * // 39 | 40 | namespace Foam 41 | { 42 | namespace functionObjects 43 | { 44 | defineTypeNameAndDebug(convergenceDetection, 0); 45 | addToRunTimeSelectionTable(functionObject, convergenceDetection, dictionary); 46 | } 47 | } 48 | 49 | // * * * * * * * * * * * * * * * * Constructors * * * * * * * * * * * * * * // 50 | 51 | Foam::functionObjects::convergenceDetection::convergenceDetection( 52 | const word &name, 53 | const Time &runTime, 54 | const dictionary &dict) 55 | : forces(name, runTime, dict, false), 56 | windowNormalization_(0.5), 57 | windowGradient_(0.5), 58 | windowGradientAveraging_(0.99), 59 | windowEvaluation_(0.333), 60 | windowEvaluationAveraging_(0.333), 61 | thresholdConvergence_(0.075), 62 | thresholdAveraging_(0.025), 63 | iterationMaxConvergence_(4000), 64 | iterationMaxAveraging_(2000), 65 | convergenceStabilityFactor_(2), 66 | iterationMinConvergence_(200), 67 | iterationMinAveraging_(200), 68 | convergenceFound_(false), 69 | simulationFinished_(false), 70 | forcedConvergence_(false), 71 | currentIteration_(0), 72 | averagingStartedAt_(0), 73 | patches_(wordRes()), 74 | rho_("rhoInf"), 75 | rhoInf_(1.225), 76 | CofR_(Zero), 77 | normalizedDataMeanConverged_(), 78 | porousObjectsNames_(), 79 | rotatingObjectsNames_(), 80 | porousNames_(), 81 | rotatingNames_(), 82 | porousObjects_(), 83 | rotatingObjects_(), 84 | coordSysPtrC_(nullptr), 85 | convergenceData_(), 86 | gradData_(), 87 | gradDataAveraging_(), 88 | convergenceNormalized_(), 89 | dataNormalized_(), 90 | convergenceNormalizedAveraging_(), 91 | totalForceFilePtr_(nullptr), 92 | gradientFilePtr_(nullptr), 93 | gradientAveragedFilePtr_(nullptr) 94 | 95 | { 96 | read(dict); 97 | 98 | setCoordinateSystem(dict); 99 | 100 | if (dict.found("porous")) 101 | { 102 | porousObjectsNames_ = dict.subDict("porous").toc(); 103 | for (const word &name : porousObjectsNames_) 104 | { 105 | porousObjects_.push_back(new surfaceFieldValue(name, runTime, dict.subDict("porous").subDict(name))); 106 | const dictionary &porousSubDict = dict.subDict("porous").subDict(name); 107 | word porousName = porousSubDict.lookupOrDefault("name", name); 108 | porousNames_.push_back(porousName); 109 | } 110 | } 111 | 112 | if (dict.found("rotation_forces")) 113 | { 114 | rotatingObjectsNames_ = dict.subDict("rotation_forces").toc(); 115 | for (const word &name : rotatingObjectsNames_) 116 | { 117 | rotatingObjects_.push_back(new forces(name, runTime, dict.subDict("rotation_forces").subDict(name))); 118 | coordSysPtrC_.clear(); 119 | coordSysPtrC_.reset(new coordSystem::cartesian(dict.subDict("rotation_forces").subDict(name))); 120 | const dictionary &rotatingSubDict = dict.subDict("rotation_forces").subDict(name); 121 | word rotatingName = rotatingSubDict.lookupOrDefault("name", name); 122 | rotatingNames_.push_back(rotatingName); 123 | } 124 | } 125 | } 126 | 127 | // * * * * * * * * * * * * * * * Member Functions * * * * * * * * * * * * * // 128 | 129 | bool Foam::functionObjects::convergenceDetection::read(const dictionary &dict) 130 | { 131 | forces::read(dict); 132 | 133 | dict.readIfPresent("windowNormalization", windowNormalization_); 134 | dict.readIfPresent("windowGradient", windowGradient_); 135 | dict.readIfPresent("windowGradientAveraging", windowGradientAveraging_); 136 | dict.readIfPresent("windowEvaluation", windowEvaluation_); 137 | dict.readIfPresent("windowEvaluationAveraging", windowEvaluationAveraging_); 138 | dict.readIfPresent("thresholdConvergence", thresholdConvergence_); 139 | dict.readIfPresent("thresholdAveraging", thresholdAveraging_); 140 | dict.readIfPresent("iterationMaxConvergence", iterationMaxConvergence_); 141 | dict.readIfPresent("iterationMaxAveraging", iterationMaxAveraging_); 142 | dict.readIfPresent("convergenceStabilityFactor_", convergenceStabilityFactor_); 143 | dict.readIfPresent("iterationMinConvergence", iterationMinConvergence_); 144 | dict.readIfPresent("iterationMinAveraging", iterationMinAveraging_); 145 | 146 | if (!exists(time().globalPath() + "/system/averaging")) 147 | { 148 | FatalError 149 | << "Averaging file does not exists, please create averaging file in system/averaging and include it in controlDict (#include \"averaging\")" << nl 150 | << "https://www.openfoam.com/documentation/guides/latest/doc/guide-fos-field-fieldAverage.html" << nl 151 | << abort(FatalError); 152 | 153 | return false; 154 | } 155 | 156 | return true; 157 | } 158 | 159 | bool Foam::functionObjects::convergenceDetection::execute() 160 | { 161 | forces::execute(); 162 | 163 | if (!porousObjects_.empty()) 164 | { 165 | unsigned porousCount = 0; 166 | for (surfaceFieldValue *sfv : porousObjects_) 167 | { 168 | sfv->write(); 169 | scalar area = sfv->getResult("areaNormalIntegrate(" + porousNames_[porousCount] + ",U)"); 170 | 171 | convergenceData_[porousNames_[porousCount]].push_back(area); 172 | porousCount++; 173 | } 174 | } 175 | 176 | if (!rotatingObjects_.empty()) 177 | { 178 | unsigned roCount = 0; 179 | for (forces *f : rotatingObjects_) 180 | { 181 | f->calcForcesMoments(); 182 | const vector forces = f->forceEff(); 183 | 184 | const double thrust = coordSysPtrC_->localVector(forces)[0]; 185 | 186 | convergenceData_[rotatingNames_[roCount]].push_back(thrust); 187 | roCount++; 188 | } 189 | } 190 | 191 | const vector sumForce = forces::forceEff(); 192 | 193 | const double totalForce = sqrt(pow(sumForce[0], 2) + pow(sumForce[1], 2) + pow(sumForce[2], 2)); 194 | 195 | convergenceData_["forces"].push_back(totalForce); 196 | 197 | currentIteration_ = time().value(); 198 | 199 | // wait for a few iterations before starting gradient calculation 200 | if (currentIteration_ >= 5) 201 | { 202 | 203 | std::map caclculatedGradient = calculateGradient(); 204 | for (const auto &cd : caclculatedGradient) 205 | { 206 | gradData_[cd.first].push_back(cd.second); 207 | } 208 | } 209 | 210 | // need few iterations before starting calculation 211 | if (currentIteration_ >= iterationMinConvergence_) 212 | { 213 | if (!convergenceFound_) 214 | { 215 | convergenceEvaluation(); 216 | } 217 | // Start to check at iterationMinAveraging to avoid calculate each iteration 218 | if (currentIteration_ >= iterationMinAveraging_ && 219 | !simulationFinished_ && 220 | convergenceFound_) 221 | { 222 | std::map cmg(convergenceMaxGradient()); 223 | 224 | bool shouldBeKickedOut = std::any_of(cmg.begin(), cmg.end(), [this](const auto &cmg) 225 | { return cmg.second >= thresholdConvergence_; }); 226 | 227 | if ((shouldBeKickedOut && !forcedConvergence_) && 228 | (currentIteration_ < iterationMaxConvergence_)) 229 | { 230 | stopAveraging(); 231 | } 232 | std::map caclculatedGradientAveraging = calculateGradientAveraging(); 233 | for (const auto &c : caclculatedGradientAveraging) 234 | { 235 | gradDataAveraging_[c.first].push_back(c.second); 236 | } 237 | isExploded(); 238 | isFinished(); 239 | } 240 | } 241 | 242 | return true; 243 | } 244 | 245 | bool Foam::functionObjects::convergenceDetection::end() 246 | { 247 | return true; 248 | } 249 | 250 | bool Foam::functionObjects::convergenceDetection::write() 251 | { 252 | if (writeToFile()) 253 | { 254 | 255 | createDataFile(); 256 | writeDataFile(totalForceFilePtr_(), convergenceData_["forces"].back()); 257 | if (currentIteration_ >= 5) 258 | { 259 | writeDataFile(gradientFilePtr_(), gradData_["forces"].back()); 260 | } 261 | if (convergenceFound_) 262 | { 263 | writeDataFile(gradientAveragedFilePtr_(), gradDataAveraging_["forces"].back()); 264 | } 265 | forces::write(); 266 | } 267 | return true; 268 | } 269 | 270 | // * * * * * * * * * * * * * * * Protected Functions * * * * * * * * * * * * * // 271 | 272 | void Foam::functionObjects::convergenceDetection::createDataFile() 273 | { 274 | if (!totalForceFilePtr_.valid()) 275 | { 276 | totalForceFilePtr_ = createFile("totalForce"); 277 | writeDataFileHeader("totalForce", "total (x,y,z)", totalForceFilePtr_()); 278 | } 279 | 280 | if (!gradientFilePtr_.valid()) 281 | { 282 | gradientFilePtr_ = createFile("gradient"); 283 | writeDataFileHeader("Gradient", "Gradient", gradientFilePtr_()); 284 | } 285 | 286 | if (!gradientAveragedFilePtr_.valid()) 287 | { 288 | gradientAveragedFilePtr_ = createFile("gradientAveraged"); 289 | writeDataFileHeader("GradientAveraged", "GradientAveraged", gradientAveragedFilePtr_()); 290 | } 291 | } 292 | 293 | void Foam::functionObjects::convergenceDetection::writeDataFileHeader( 294 | const word &header, 295 | const word &tabbedName, 296 | OFstream &os) const 297 | { 298 | writeHeader(os, header); 299 | writeHeader(os, ""); 300 | writeCommented(os, "Time"); 301 | writeTabbed(os, tabbedName); 302 | 303 | os << endl; 304 | } 305 | 306 | void Foam::functionObjects::convergenceDetection::writeDataFile( 307 | OFstream &os, double &value) const 308 | { 309 | writeCurrentTime(os); 310 | 311 | writeValue(os, value); 312 | 313 | os << endl; 314 | } 315 | 316 | bool Foam::functionObjects::convergenceDetection::reachedMaxIterations() 317 | { 318 | return currentIteration_ >= (iterationMaxConvergence_ + iterationMaxAveraging_); 319 | } 320 | 321 | bool Foam::functionObjects::convergenceDetection::minIterationsForAveraging() 322 | { 323 | return currentIteration_ >= static_cast(1.1 * averagingStartedAt_) && 324 | currentIteration_ >= static_cast(iterationMinAveraging_ + averagingStartedAt_); 325 | } 326 | 327 | bool Foam::functionObjects::convergenceDetection::isAveraged() 328 | { 329 | std::map amg = averagingMaxGradient(); 330 | 331 | bool allDataAveragedEnough = std::all_of(amg.begin(), amg.end(), [this](const auto &amg) 332 | { return amg.second < thresholdAveraging_; }); 333 | 334 | bool averagingMaxGradientGtZero = std::all_of(amg.begin(), amg.end(), [](const auto &amg) 335 | { return amg.second > 0.0; }); 336 | return allDataAveragedEnough && 337 | averagingMaxGradientGtZero && 338 | currentIteration_ > static_cast(1.25 * averagingStartedAt_) && 339 | !simulationFinished_; 340 | } 341 | 342 | void Foam::functionObjects::convergenceDetection::isFinished() 343 | { 344 | if ((isAveraged() || 345 | reachedMaxIterations()) && 346 | minIterationsForAveraging()) 347 | { 348 | std::map amg = averagingMaxGradient(); 349 | Info << "###########" << endl; 350 | Info << "Simulation should stop!!!!" << endl; 351 | for (auto const &a : amg) 352 | { 353 | Info << "Gradient of " << a.first << ": " << a.second << endl; 354 | } 355 | Info << "Condition for averaging: " << thresholdAveraging_ << endl; 356 | Info << "###########" << endl; 357 | 358 | time().stopAt(Time::saWriteNow); 359 | toggleAveraging(false); 360 | simulationFinished_ = true; 361 | } 362 | } 363 | 364 | void Foam::functionObjects::convergenceDetection::stopAveraging() 365 | { 366 | convergenceFound_ = false; 367 | toggleAveraging(false); 368 | gradDataAveraging_.clear(); 369 | if (exists(time().globalPath() + "/averaging")) 370 | { 371 | rm(time().globalPath() + "/averaging"); 372 | } 373 | Info << "## Getting out of averaging ###" << endl; 374 | } 375 | 376 | void Foam::functionObjects::convergenceDetection::isExploded() 377 | { 378 | bool anyNormalizedMeanDataGtZero = std::any_of(normalizedDataMeanConverged_.begin(), normalizedDataMeanConverged_.end(), [](const auto &ndmc) 379 | { return ndmc.second > 0; }); 380 | 381 | if (anyNormalizedMeanDataGtZero && convergenceFound_) 382 | { 383 | double test1 = normalizedDataMeanConverged_["forces"] / convergenceStabilityFactor_; 384 | double test2 = normalizedDataMeanConverged_["forces"] * convergenceStabilityFactor_; 385 | double lastIterationForces = convergenceData_["forces"].back(); 386 | 387 | if (lastIterationForces < test1 or lastIterationForces > test2) 388 | { 389 | if (Pstream::master()) 390 | { 391 | FatalErrorInFunction 392 | << "Forces Exploded, mean force value converged: " << lastIterationForces << nl 393 | << exit(FatalError); 394 | } 395 | } 396 | } 397 | } 398 | 399 | void Foam::functionObjects::convergenceDetection::convergenceEvaluation() 400 | { 401 | std::map cmg(convergenceMaxGradient()); 402 | 403 | bool allDataConverged = std::all_of(cmg.begin(), cmg.end(), [this](const auto &cmg) 404 | { return cmg.second < thresholdConvergence_; }); 405 | 406 | if ((allDataConverged && 407 | currentIteration_ >= iterationMinConvergence_ && 408 | !convergenceFound_) || 409 | (currentIteration_ >= iterationMaxConvergence_ && 410 | !convergenceFound_)) 411 | { 412 | convergenceFound_ = true; 413 | if (currentIteration_ >= iterationMaxConvergence_) 414 | { 415 | forcedConvergence_ = true; 416 | } 417 | word forcedConvergence = forcedConvergence_ ? "Forced " : ""; 418 | Info << "#####################################" << endl; 419 | Info << forcedConvergence << "Convergence Found" << endl; 420 | Info << "#####################################" << endl; 421 | for (auto const &c : cmg) 422 | { 423 | Info << "Gradient of " << c.first << ": " << c.second << endl; 424 | } 425 | Info << "Condition for convergence: " << thresholdConvergence_ << endl; 426 | for (auto const &c : cmg) 427 | { 428 | Info << "Condition for " << c.first << "is : " << (c.second < thresholdConvergence_) << endl; 429 | } 430 | 431 | averagingStartedAt_ = currentIteration_; 432 | 433 | int windowDataNormalization = static_cast(currentIteration_ * windowNormalization_); 434 | 435 | std::map> normalizedData; 436 | 437 | for (auto const &d : convergenceData_) 438 | { 439 | normalizedData[d.first].insert(normalizedData[d.first].end(), d.second.end() - windowDataNormalization, d.second.end()); 440 | } 441 | 442 | normalizedDataMeanConverged_ = meanValue(normalizedData); 443 | 444 | OFstream os(time().globalPath() + "/averaging"); 445 | os << averagingStartedAt_ << endl; 446 | 447 | toggleAveraging(true); 448 | } 449 | } 450 | 451 | void Foam::functionObjects::convergenceDetection::toggleAveraging(bool toggle) 452 | { 453 | IOdictionary averaging(IOobject( 454 | "averaging", 455 | time().caseSystem(), 456 | obr_, 457 | IOobject::MUST_READ_IF_MODIFIED, 458 | IOobject::AUTO_WRITE)); 459 | averaging.subDict("fieldAverage1").set("timeStart", toggle ? currentIteration_ : 0); 460 | averaging.subDict("fieldAverage1").set("enabled", toggle); 461 | averaging.regIOobject::write(); 462 | } 463 | 464 | std::map> Foam::functionObjects::convergenceDetection::divideData(std::map dataMean) 465 | { 466 | std::map> dividedData; 467 | 468 | for (const auto &cd : convergenceData_) 469 | { 470 | for (auto itr : cd.second) 471 | dividedData[cd.first].push_back(itr / dataMean[cd.first]); 472 | } 473 | 474 | return dividedData; 475 | } 476 | 477 | std::map Foam::functionObjects::convergenceDetection::meanValue(std::map> normalizedData) 478 | { 479 | std::map meanData; 480 | 481 | for (const auto &nd : normalizedData) 482 | { 483 | double sum = 0.0; 484 | for (auto itr : nd.second) 485 | sum += itr; 486 | meanData[nd.first] = sum / nd.second.size(); 487 | } 488 | 489 | return meanData; // for transient change this 490 | } 491 | 492 | std::map Foam::functionObjects::convergenceDetection::calculateGradientAveraging() 493 | { 494 | int averagingSize = currentIteration_ - averagingStartedAt_; 495 | std::map gradient; 496 | 497 | // allow some data to come in 498 | if (averagingSize >= 5) 499 | { 500 | double maxIterationNormalized = static_cast(currentIteration_) / static_cast(averagingStartedAt_); 501 | 502 | convergenceNormalizedAveraging_ = divideData(normalizedDataMeanConverged_); 503 | 504 | double start = 1 + (maxIterationNormalized / averagingSize); 505 | double end = maxIterationNormalized + maxIterationNormalized / averagingSize; 506 | double step = (maxIterationNormalized - 1) / averagingSize; 507 | 508 | std::vector xAxisNormalized = arange(start, end, step); 509 | 510 | std::map> windowDataForGradient; 511 | 512 | int windowDataNormalization = static_cast(xAxisNormalized.size() * windowGradientAveraging_); 513 | 514 | for (auto const &c : convergenceNormalizedAveraging_) 515 | { 516 | windowDataForGradient[c.first].insert(windowDataForGradient[c.first].end(), c.second.end() - windowDataNormalization, c.second.end()); 517 | } 518 | 519 | std::vector windowXAxisForGradient(xAxisNormalized.end() - windowDataNormalization, xAxisNormalized.end()); 520 | 521 | if (windowDataForGradient["forces"].size() < 5) 522 | { 523 | for (const auto &d : windowDataForGradient) 524 | { 525 | gradient[d.first] = 0.0; 526 | } 527 | return gradient; 528 | } 529 | 530 | for (const auto &d : windowDataForGradient) 531 | { 532 | gradient[d.first] = polyfit(windowXAxisForGradient, d.second, 1, {0}); 533 | } 534 | 535 | return gradient; 536 | } 537 | for (const auto &n : normalizedDataMeanConverged_) 538 | { 539 | gradient[n.first] = 0.0; 540 | } 541 | return gradient; 542 | } 543 | 544 | std::map Foam::functionObjects::convergenceDetection::calculateGradient() 545 | { 546 | int windowDataNormalization = static_cast(currentIteration_ * windowNormalization_); 547 | int windowDataGradient = static_cast(currentIteration_ * windowGradient_); 548 | 549 | std::map> normalizedData; 550 | 551 | for (auto const &d : convergenceData_) 552 | { 553 | normalizedData[d.first].insert(normalizedData[d.first].end(), d.second.end() - windowDataNormalization, d.second.end()); 554 | } 555 | 556 | std::map dataMean = meanValue(normalizedData); 557 | 558 | dataNormalized_ = divideData(dataMean); 559 | 560 | // take this into account for transient 561 | double start = 1.0f / currentIteration_; 562 | double end = 1.0f + 1.0f / currentIteration_; 563 | double step = 1.0f / currentIteration_; 564 | 565 | std::vector xAxisNormalized = arange(start, end, step); 566 | 567 | std::map> windowDataForGradient; 568 | 569 | for (auto const &d : dataNormalized_) 570 | { 571 | windowDataForGradient[d.first].insert(windowDataForGradient[d.first].end(), d.second.end() - windowDataGradient, d.second.end()); 572 | } 573 | 574 | std::vector windowXAxisForGradient(xAxisNormalized.end() - windowDataGradient, xAxisNormalized.end()); 575 | 576 | std::map gradient; 577 | 578 | for (const auto &data : windowDataForGradient) 579 | { 580 | gradient[data.first] = polyfit(windowXAxisForGradient, data.second, 1, {0}); 581 | } 582 | 583 | return gradient; 584 | } 585 | 586 | // DRY 587 | std::map Foam::functionObjects::convergenceDetection::convergenceMaxGradient() 588 | { 589 | int evaluationWindow = static_cast(windowEvaluation_ * gradData_["forces"].size()); 590 | std::map> dataForEvaluation; 591 | std::map maxGradient; 592 | std::map defaultGradient; 593 | 594 | for (const auto &gd : gradData_) 595 | { 596 | dataForEvaluation[gd.first].insert(dataForEvaluation[gd.first].end(), gd.second.end() - evaluationWindow, gd.second.end()); 597 | } 598 | 599 | if (dataForEvaluation["forces"].size() > 0) 600 | { 601 | for (const auto &afe : dataForEvaluation) 602 | { 603 | double maxValue = *std::max_element(afe.second.begin(), afe.second.end()); 604 | double minValue = *std::min_element(afe.second.begin(), afe.second.end()); 605 | 606 | maxGradient[afe.first] = max(maxValue, fabs(minValue)); 607 | } 608 | 609 | return maxGradient; 610 | } 611 | for (const auto &gd : gradData_) 612 | { 613 | defaultGradient[gd.first] = 1.0; 614 | } 615 | 616 | return defaultGradient; 617 | } 618 | 619 | // DRY 620 | std::map Foam::functionObjects::convergenceDetection::averagingMaxGradient() 621 | { 622 | int evaluationWindow = static_cast( 623 | windowEvaluationAveraging_ * gradDataAveraging_["forces"].size()); 624 | 625 | std::map> dataForEvaluation; 626 | std::map maxGradient; 627 | std::map defaultGradient; 628 | 629 | for (const auto &gd : gradDataAveraging_) 630 | { 631 | dataForEvaluation[gd.first].insert(dataForEvaluation[gd.first].end(), gd.second.end() - evaluationWindow, gd.second.end()); 632 | } 633 | 634 | if (dataForEvaluation["forces"].size() > 0) 635 | { 636 | for (const auto &afe : dataForEvaluation) 637 | { 638 | double maxValue = *std::max_element(afe.second.begin(), afe.second.end()); 639 | double minValue = *std::min_element(afe.second.begin(), afe.second.end()); 640 | 641 | maxGradient[afe.first] = max(maxValue, fabs(minValue)); 642 | } 643 | 644 | return maxGradient; 645 | } 646 | for (const auto &gd : gradDataAveraging_) 647 | { 648 | defaultGradient[gd.first] = 1.0; 649 | } 650 | 651 | return defaultGradient; 652 | } 653 | 654 | std::vector Foam::functionObjects::convergenceDetection::arange(double start, double stop, double step) 655 | { 656 | std::vector values; 657 | for (double value = start; value < stop; value += step) 658 | values.push_back(value); 659 | return values; 660 | } 661 | // https://gist.github.com/chrisengelsma/108f7ab0a746323beaaf7d6634cf4add 662 | double Foam::functionObjects::convergenceDetection::polyfit( 663 | const std::vector x, 664 | const std::vector y, 665 | const int order, 666 | std::vector coeffs) 667 | { 668 | // The size of xValues and yValues should be same 669 | if (x.size() != y.size()) 670 | { 671 | throw std::runtime_error("The size of x & y arrays are different"); 672 | return {}; 673 | } 674 | // The size of xValues and yValues cannot be 0, should not happen 675 | if (x.size() == 0 || y.size() == 0) 676 | { 677 | throw std::runtime_error("The size of x or y arrays is 0"); 678 | return {}; 679 | } 680 | 681 | size_t N = x.size(); 682 | int n = order; 683 | int np1 = n + 1; 684 | int np2 = n + 2; 685 | int tnp1 = 2 * n + 1; 686 | double tmp; 687 | 688 | // X = vector that stores values of sigma(xi^2n) 689 | std::vector X(tnp1); 690 | for (int i = 0; i < tnp1; ++i) 691 | { 692 | X[i] = 0; 693 | for (size_t j = 0; j < N; ++j) 694 | X[i] += static_cast(pow(x[j], i)); 695 | } 696 | 697 | // a = vector to store final coefficients. 698 | std::vector a(np1); 699 | 700 | // B = normal augmented matrix that stores the equations. 701 | std::vector> B(np1, std::vector(np2, 0)); 702 | 703 | for (int i = 0; i <= n; ++i) 704 | for (int j = 0; j <= n; ++j) 705 | B[i][j] = X[i + j]; 706 | 707 | // Y = vector to store values of sigma(xi^n * yi) 708 | std::vector Y(np1); 709 | for (int i = 0; i < np1; ++i) 710 | { 711 | Y[i] = static_cast(0); 712 | for (size_t j = 0; j < N; ++j) 713 | { 714 | Y[i] += static_cast(pow(x[j], i) * y[j]); 715 | } 716 | } 717 | 718 | // Load values of Y as last column of B 719 | for (int i = 0; i <= n; ++i) 720 | B[i][np1] = Y[i]; 721 | 722 | n += 1; 723 | int nm1 = n - 1; 724 | 725 | // Pivotisation of the B matrix. 726 | for (int i = 0; i < n; ++i) 727 | for (int k = i + 1; k < n; ++k) 728 | if (B[i][i] < B[k][i]) 729 | for (int j = 0; j <= n; ++j) 730 | { 731 | tmp = B[i][j]; 732 | B[i][j] = B[k][j]; 733 | B[k][j] = tmp; 734 | } 735 | 736 | // Performs the Gaussian elimination. 737 | // (1) Make all elements below the pivot equals to zero 738 | // or eliminate the variable. 739 | for (int i = 0; i < nm1; ++i) 740 | for (int k = i + 1; k < n; ++k) 741 | { 742 | double t = B[k][i] / B[i][i]; 743 | for (int j = 0; j <= n; ++j) 744 | B[k][j] -= t * B[i][j]; // (1) 745 | } 746 | 747 | // Back substitution. 748 | // (1) Set the variable as the rhs of last equation 749 | // (2) Subtract all lhs values except the target coefficient. 750 | // (3) Divide rhs by coefficient of variable being calculated. 751 | for (int i = nm1; i >= 0; --i) 752 | { 753 | a[i] = B[i][n]; // (1) 754 | for (int j = 0; j < n; ++j) 755 | if (j != i) 756 | a[i] -= B[i][j] * a[j]; // (2) 757 | a[i] /= B[i][i]; // (3) 758 | } 759 | 760 | coeffs.resize(a.size()); 761 | for (size_t i = 0; i < a.size(); ++i) 762 | coeffs[i] = a[i]; 763 | 764 | return static_cast(coeffs[1]); 765 | } 766 | 767 | // ************************************************************************* // -------------------------------------------------------------------------------- /convergenceDetection.H: -------------------------------------------------------------------------------- 1 | /*---------------------------------------------------------------------------*\ 2 | ========= | 3 | \\ / F ield | OpenFOAM: The Open Source CFD Toolbox 4 | \\ / O peration | 5 | \\ / A nd | www.openfoam.com 6 | \\/ M anipulation | 7 | ------------------------------------------------------------------------------- 8 | Copyright (C) 2022 Nikola Majksner, Wouter Remmerie, AirShaper 9 | ------------------------------------------------------------------------------- 10 | License 11 | This file is part of OpenFOAM. 12 | 13 | OpenFOAM is free software: you can redistribute it and/or modify it 14 | under the terms of the GNU General Public License as published by 15 | the Free Software Foundation, either version 3 of the License, or 16 | (at your option) any later version. 17 | 18 | OpenFOAM is distributed in the hope that it will be useful, but WITHOUT 19 | ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or 20 | FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License 21 | for more details. 22 | 23 | You should have received a copy of the GNU General Public License 24 | along with OpenFOAM. If not, see . 25 | 26 | Class 27 | Foam::functionObjects::convergenceDetection 28 | 29 | Group 30 | grpFieldFunctionObjects 31 | 32 | Description 33 | 34 | 35 | 36 | \f[ 37 | x = x_{ref}^x + \rho \omega 38 | \f] 39 | 40 | 41 | where 42 | \vartable 43 | \rho | [units, e.g. kg/m3] 44 | \omega | \f$ \nabla \cdot \vec U \f$ 45 | ... | ... 46 | \endvartable 47 | 48 | 49 | where \f$ x_k \f$ is ... 50 | 51 | 52 | \table 53 | Operand | Type | Location 54 | input | {vol,surface}\Field(s) |$FOAM_CASE/\/\s 56 | output file | dat | $FOAM_CASE/postProcessing/\/\/\ 58 | output field | volScalarField | $FOAM_CASE/\/\ 59 | \endtable 60 | 61 | Usage 62 | Minimal example by using \c system/controlDict.functions: 63 | \verbatim 64 | convergenceDetection1 65 | { 66 | // Mandatory entries (unmodifiable) 67 | type convergenceDetection; 68 | libs (convergenceDetectionFunctionObject); 69 | 70 | // Mandatory entries (runtime modifiable) 71 | ... 72 | 73 | // Mandatory (inherited) entries (unmodifiable) 74 | ... 75 | 76 | // Mandatory (inherited) entries (runtime unmodifiable) 77 | ... 78 | 79 | // Optional entries (unmodifiable) 80 | ... 81 | 82 | // Optional entries (runtime modifiable) 83 | boolData ; 84 | labelData