Parametric And Optimization Study: OpenFOAM And Dakota - Cineca

1m ago
1 Views
0 Downloads
823.53 KB
22 Pages
Last View : 1m ago
Last Download : n/a
Upload by : Konnor Frawley
Transcription

Workshop "HPC enabling of OpenFOAM for CFD applications"CINECA, Casalecchio di Reno, Bologna, 26 November 2012Parametric and Optimization study:OpenFOAM and DakotaIvan Spisso, i.spisso@cineca.it, CFD Numerical AnalystSuperComputing Applications and Innovation (SCAI) DepartmentCINECA

Outline of the presentation DAKOTA in a nutshell (1) The loosely coupled loop of DAKOTA (1) Key DAKOTA Capabilities (4) Parallelism in Dakota (6) DAKOTA on PLX: job submission, input file and loosely coupled loop (3) Advanced Simulation Code Interfaces: OpenFOAM (2) Simulation Control and quality check (2)

Dakota in a NutshellDesign and Analysis toolKit for Optimization and Terascale Applicationsincludes a wide array of algorithm capabilities to support engineeringtransformation through advanced modeling and simulation.Adds to simulation-based answering fundamental science and engineeringquestions: What are the crucial factors/parameters and how do they affectmetrics? (sensitivity) How safe, reliable, robust, or variable is my system?(quantification of margins and uncertainty: QMU, UQ) What is the best performing design or control? (optimization) What models and parameters best match experimental data?(calibration) All rely on iterative analysis with a computational model for thephenomenon of interest

Automated Iterative AnalysisAutomate typical “parameter variation” studies with ageneric interface to simulations and advanced methodsDAKOTAOptimization, sensitivity analysisparameter estimation, uncertain quantificationParameters(design, state)The loosely-coupled loopResponsemetricsComputational Model (simulation)Black box: any code: mechanics, circuits,high energy physics, biology, chemistry Semi-intrusive: Matlab, Python, multi-physics,OpenFOAM Can support experimental testing: examine many accidentconditions with computer models, then physically test a fewworst-case conditions.

Key DAKOTA Capabilities Generic interface to simulations Time-tested and advanced algorithms to address nonsmooth,discontinuous, multimodal, expensive, mixed variable, failure-prone Strategies to combine methods for advanced studies or improveefficiency with surrogates (meta-models) Mixed deterministic / probabilistic analysis Supports scalable parallel computations on clusters !! Object-oriented code; modern software quality practices JAGUAR 2.0, new graphical user interface in Java, based on EclipseIDE/Workbench. Windows, Mac, Linux support. Additional details: http://www.cs.sandia.gov/dakota Software downloads: stable releases and nightly builds (freely availableworldwide via GNU LGPL) Installed on PLX (module load profile/advanced autoload dakota) likeSandia National Lab

Optimization GOAL: Vary parameters to extremize objectives, while satisfyingconstraints to find (or tune) the best design, estimate best parameters,analyze worst-case surety, e.g., determine:– delivery network that maximizes profit while minimizing environmentalimpact– case geometry that minimizes drag and weight, or maximize thepressure force, yet is sufficiently strong and safe– material atomic configuration of minimum energy

DAKOTA Optimization MethodsDakota includes Gradient and non-gradient-basedDerivative-free methods methods. Several numerical package areavailable: commercial, developedinternally to Sandia and freesoftware from open-sourcecommunity. Gradient-based methods (DAKOTA will compute finite differencegradients and FD/quasi-Hessians ifnecessary) COLINY (PS, APPS, SolisWets,COBYLA2, EAs, DIRECT)JEGA (single/multi-obj GeneticAlgorithms)EGO (efficient global opt viaGaussian Process models)DIRECT (Gablonsky, Sandiadeveloped)OPT (parallel direct search)DOT (various constrained)CONMIN (CONstrained MINinization) Library:FRCG (Fletcher-Reeves Conjugate Gradient),MFD.NLPQL (SQP, Sequential quadraticprogramming) NLPQL (SQP) OPT (CG, Newton)Calibration (least-squares) NL2SOL (GN QH) NLSSOL (SQP) OPT (Gaussian-Newton)

Considerations when Choosing anOptimization MethodKey considerations: Local and global sensitivity study data; trend and smoothness Simulation expense Constraint types present Goal: local optimization (improvement) or global optimization (best possible)Unconstrained or bound-constrained problems: Smooth and cheap: nearly any method; gradient-based methods will be fastest Smooth and expensive: gradient-based methods Nonsmooth and cheap: non-gradient methods such as pattern search (local opt), genetic algorithms(global opt), DIRECT (global opt), or surrogate-based optimization (quasi local/global opt)Nonsmooth and expensive: surrogate-based optimization (SBO)*Non-linearly-constrained problems: Smooth and cheap: gradient-based methods Smooth and expensive: gradient-based methods Nonsmooth and cheap: non-gradient methods w/ penalty functions, SBO Nonsmooth and expensive: SBO

Scalable ParallelismNested parallel models support large-scale applications and architectures.

User’s Manual:Application Parallelism Use CasesThe parallel computing capabilities provided by DAKOTA are extensive and can bedaunting at first Single-level parallel computing models use: asyncrhronus local, message passing, andhybrid approaches. This method can be combined to build multiple level of parallelism.

Dakota Parallelism, Case 3: Dakota Serial, Tile NProcessor JobsGiven an allocation of M S*N processors, schedule S simultaneous jobsExample: 42 nodes reserved nodes (PLX 1 node 12 procs) , S 21 simultaneousjobs, N 24 processor application runs 1 nodes to run dakota in serialHow would you achieve this?Running dakota in serialasynchronous evaluation concurrency 21Launching application:mpirun -np 24 machinefile simpleFoam -paralleljob scheduler PBS with machinefile listTotal time: residual control on OF, wall time limit for the job

Case 3 Mechanics: Machine File Managementbased When job starts, parse available resource list (e.g., SLURM NODELIST or PBS NODEFILE) into a single list Divide the resources into S files (applicNodeFile.*), each containing N resources For each evaluation, lock a nodefile, run the application using the nodefile, free thenodefile Many variations possible, including specializations where the application size N eitherdivides the number of processors per node or is a multiple of

Standard Dakota Paralellism Standard Dakota implementation, need to wait the completion of a slot ofevaluation concurrency to restart Unused booked computational time, no maximize of the computationalresourcesTImeR22R23R24R36R42End of lastsimulationR1R2R3. . . . . . . . .R15. . . . . . . . .R21Computational resources

Improved Dakota ParalellismImproved Dakota implementation: when an application run completes,need to schedule another job on the freed block of processors,implemented by CINECA's staff Best exploitation of the computational resources. Computational “relay” R26TImeR24R25R23End of lastSimulation, firstconcurrencyR22R2R1R15. . . . . . . . .R23. . . . . . . . .R8Computational resources

Example of Job Submission for Parameter Study42 nodes reserved nodes 1 nodes to run dakota in serial

Example of Input File for Parameter StudyThere are six specification blocks that may appear in DAKOTA input files.

Loosely-coupled loop for DAKOTA in PLXDAKOTAOptimization (example: parametric study,gradient-based method)PBS Job SchedulerCluster of SMP'sDakotaParameters FileDataPre-processingSimulationInput Fileexample.glfDakotaResults FileParametric mesh readable onal Model (simulation)OpenFOAM run-plx.shmpirun -np 48 simpleFoam -parallelSimulationOutput File

Advanced Simulation Code Interfaces: OpenFOAMData pre- and post-processingExample modify the geometry and/or boundary conditions, to optimize acfd quantity Use dprepro to as a parser to modify your input

Advanced Simulation Code Interfaces: OpenFOAM1. Create a template simulation input file by identifying the fields in the giveninput file that correspond to the input in DAKOTA. Example file 0/U,0/U.template2. Use dprepro as parser to reflect names of the DAKOTA parameters filesUz in x13. Insert the change in the loosely-coupled loop4. Change the post-processing section to reflect the revised extractionprocess. Extract your quantity from the output file, with grep command ormore sophisticated extraction tools. Example, extract forces of pressure.outputx1

Simulation Control and quality checkGuidelinesStart with a parametric study to check the influence of the Design of Experimentsvariable Estimate your computational budget Check the single simulation, dakot.out Check the residual of OpenFOAMFor study involving geometrical change, a robust and good quality mesh ismandatory. Use visualization

Check the Quality: Residuals Run Time Visualization of residual implemented by CINECA staff Go to working dir Click one time: Total Residual Click again: Residual on pressure

Bonus SlidesFrequently Asked QuestionsWhy are you releasing DAKOTA as open source?To foster collaborations and streamline the licensing process. Of particular note is the fact thatan export control classification of "publicly available" allows us to work effectively with universities. How is it that Sandia can release government software as open source?Sandia is a government-owned, contractor-operated (GOCO) national laboratory operated forthe U.S. Department of Energy (DOE) by Lockheed Martin Corporation. The authority to releaseopen source software resides with the DOE, and DAKOTA has gone through a series of copyrightassertion and classification approvals to allow release to the general public, (under LGPL).Important proponents for the open source release of Sandia software are the DOE's AcceleratedStrategic Computing (ASC) Program Office and the DOE's Office of Science. Personal noteReminder: Open Source and GPL does not imply zero priceComputer time is still expensive – but cost is unavoidable Software support, help with running and customization is still required Engineers running the code are the most costly part: better!

Optimization (example: parametric study, gradient-based method) Computational Model (simulation) OpenFOAM run-plx.sh mpirun -np 48 simpleFoam -parallel example.glf Parametric mesh readable in PointWise Dakota Parameters File Simulation Input File createPatch Simulation Output File Dakota Results File Data Pre-processing Data Post-processing PBS .

Related Documents:

INTRODUCTION TO OPENFOAM open Field Operation And Manipulation C libraries Name. INTRODUCTION TO OPENFOAM open Field Operation And Manipulation C libraries Rita F. Carvalho, MARE, Department of Civil Engineering, University of Coimbra, Portugal OpenFOAM Equations Solvers How to use/code Examples Conclusions 3 25 26 33 46 49 50. SOLVE PARTIAL DIFFERENTIAL EQUATIONS (PDE .

Implementing Fast Parallel Linear System Solvers In OpenFOAM based on CUDA Daniel P. Combest and Dr. P.A. Ramachandran and Dr. M.P. Dudukovic Optimization, HPC, and Pre- and Post-Processing I Session. 6th OpenFOAM Workshop Penn State University. June 15th 2011 Chemical Reaction Engineering Laboratory (CREL)

parametric models of the system in terms of their input- output transformational properties. Furthermore, the non-parametric model may suggest specific modifications in the structure of the respective parametric model. This combined utility of parametric and non-parametric modeling methods is presented in the companion paper (part II).

Surface is partitioned into parametric patches: Watt Figure 6.25 Same ideas as parametric splines! Parametric Patches Each patch is defined by blending control points Same ideas as parametric curves! FvDFH Figure 11.44 Parametric Patches Point Q(u,v) on the patch is the tensor product of parametric curves defined by the control points

1. CFD Tools This chapter will give an overview over the main features of OpenFOAM and COMSOL, as well as their di erences. 1.1. OpenFOAM OpenFOAM (Open Field Operation and Manipulation) [1,2,3] is a free and open source computational uid dynamics (CFD) toolbox. It is developed by OpenCFD Ltd. and distributed

MODELING AND CHARACTER STRUCTURAL INTEGRITY AND RELIABILITY OpenFOAM Group 69 people MULTIPHYSISCS AND MULTISCALE SIMULATION . OpenFOAM 2nd Iberian Meeting 28 & 29 May 2018 Santiago de Compostela - Spain . Lean Cloud App aiming at zero-defect manufacturing (i.e. scotch, flash, weld lines, air-trapped) while minimising injection time. .

by ESI group, a French-based CAE software company. OpenFOAM is a set of solvers and utilities primarily for CFD with capabilities of standard tasks of CFD working ow from pre-processing, solving and post-processing. OpenFOAM solves wide-rage of problem from in

OpenFOAM training sessions and OpenFOAM workshops. We gratefully acknowledge the following OpenFOAM users for their consent to use their material: Hrvoje Jasak. Wikki Ltd. Hakan Nilsson. Department of Applied Mechanics, Chalmers University of Technology. Eric Paterson. Applied Research Laboratory Professor of Mechanical

OPENFOAM GUIDE FOR BEGINNERS List of Figures 1.1 Overview of OpenFOAM structure, extracted from [1] . . . . . . . .1 2.1 Viscous incompressible ow between two plane-parallel plates with

On the use of OpenFOAM to model Oscillating wave surge converters Schmitt, P., & Elsaesser, B. (2015). On the use of OpenFOAM to model Oscillating wave surge converters. Ocean Engineering, 108, 98-104. DOI: 10.1016/j.oceaneng.2015.07.055 Published in: Ocean Engineering Document

that the parametric methods are superior to the semi-parametric approaches. In particular, the likelihood and Two-Step estimators are preferred as they are found to be more robust and consistent for practical application. Keywords Extreme rainfall·Extreme value index·Semi-parametric and parametric estimators·Generalized Pareto Distribution

Learning Goals Parametric Surfaces Tangent Planes Surface Area Review Parametric Curves and Parametric Surfaces Parametric Curve A parametric curve in R3 is given by r(t) x(t)i y(t)j z(t)k where a t b There is one parameter, because a curve is a one-dimensional object There are three component functions, because the curve lives in three .

parametric and non-parametric EWS suggest that monetary expansions, which may reflect rapid increases in credit growth, are expected to increase crisis incidence. Finally, government instability plays is significant in the parametric EWS, but does not play an important role not in the non-parametric EWS.

In general, the semi-parametric and non-parametric methods are found to outperform parametric methods (see Bastos [2010], Loterman et al. [2012], Qi and Zhao [2011], Altman and Kalotay [2014], Hartmann-Wendels, Miller, and Tows [2014], and Tobback et al. [2014]). The papers comparing various parametric methods in the literature, however, are

the design process. Parametric modeling is accomplished by identifying and creating the key features of the design with the aid of computer software. The design variables, described in the sketches and features, can be used to quickly modify/update the design. In Creo Parametric, the parametric part modeling process involves the following steps: 1.

Keywords—parametric design model; digital design; parametric art; urban art . I. INTRODUCTION There has been a trend to adapt parametric design in the fields of architecture and urban design recently. Indeed, parametric design in architecture can date back to the hanging chain model created by Gaudi [1]. Although

use a non-parametric method such as kernel smoothing in order to suggest a parametric form for each component in the model. Here, we explore issues that arise in the use of kernel smoothing and semi-parametric approaches in estimating separable point process models for wildfire incidence in a particular region.

Creo Parametric 2.0 Tutorial Creo Parametric 1.0 Tutorial and MultiMedia DVD was written for Creo Parametric1.0. PTC released Creo Parametric 2.0 in the Spring of 2012. This book is fully compatible with Creo Parametric 2.0 except for the changes shown in this insert. Chapter 1 1-23 The Reori

parametric regression models. A serious disadvantage of parametric modeling is that a parametric model may be too ctive assumption difficulty in the parametric regression, non-parametric regression has gained popular attention in the literature. ere are many nonparametric and smoothing

pengantar anatomi dan fisiologi ami rachmi 15 juli 2011 doc.ami.prodi tw.2011. peraturan 1. toleransi waktu 10 menit 2. hp vibrasi 3. tidak makan dan minum 4. pakaian rapih, sopan, tidak memakai sandal 5. bila tidak hadir memberitahu langsung dosen, surat doc.ami.prodi tw.2011. anatomi berasal dari bahasa latin yaitu, * ana : bagian, memisahkan * tomi (tomie) : iris/ potong anatomi adalah ilmu .