Multidisciplinary Optimization applied to the Oil&Gas Industry. Rodrigo Ferraz Nicolas Spogis ESSS

Transcription

1 Multidisciplinary Optimization applied to the Oil&Gas Industry Rodrigo Ferraz Nicolas Spogis ESSS

2 Agenda modefrontier Example: Impeller Optimization using CFD and modefrontier

3 Why using optimization software? Not straight to obtain optimized results in: Problems involving large number of parameters; Constrained problems; Multi-objective studies Ex.: Increase power and reduce consumption at the same time. Multi-disciplinary analyses; Optimization software also helps in: Reducing man time to create models Help understanding the role of the different parameters Combining CAE tools and optimization eliminates trial and error technique 3

4 Defining modefrontier modefrontier is a multi-objective optimization and design environment, written to allow easy coupling to almost any computer aided engineering (CAE) tool, whether commercial or in-house.

5 modefrontier Around the World Icon UK Local Distributor EMT-R Local Distributor Mindware Local Distributor bluecape Local Distributor ESTECO GmbH CDAJ Local Distributor ESTECO NA North America SIREHNA Local Distributor Albert Turtscher Local Distributor ESSS Local Distributor Engin Soft Trading Local Distributor ESTECO s.r.l. Headquarter/R&D ESTECO Local Resellers

6 CUSTOMERS: over 150 accounts

7 modefrontier modefrontier: Multi Objective Design Environment: Process Integration and Optimization Software; Allows both single and multi-objective optimization; Allows for Robust Design and Six-Sigma studies; Easily wrapped around almost any CAE software; Several post-processing tools; WorksonUnix,LinuxandWindows;

8 The Concept behind modefrontier Input Variables: Entities that define the design space. The Black Box: Generates the outputs accordingly to the inputs Output Variables: Measures from the system

9 The Black Box The black boxcan be: A set of solvers that models and solves in a numerical manner the design problem (e.g. CAD/CAE tools) A set of experiments that produces some data Multi-disciplinary Scenario CFD (Fluent, CFX, 1D tools, etc) CAD (CATIA, UGS, PROE) Others (In-House codes, MATLAB, Excel) FEM (Ansys,Nastran,Ma dymo, etc)

10 Process Integration and Optimization

11 Example: Impeller Optimization using CFX and modefrontier Study conducted by: Nicolas Spogis, PhD. José Roberto Nunhez, PhD

12 Study Objectives Design of a high efficiently hydrofoil: Pure Axial Flow (minimum Radial Flow) Promote solid dispersion Torque Reduction Saving Costs Cortesy of Unicamp: Prof. José Roberto Nunhez Through the use of: Computational Fluid Dynamics Multi-Objective Design Optimization

13 Impeller optimization steps CAD and Mesh Generation CFD Solver Icem Scripts CFX Scripts Impeller Batch Scripts Multidisciplinary Optimization Post Process

14 Input Variables Variable Minimum Value Maximum Value Discrete / Continuum Impeller diameter Continuum Root chord Continuum Tip chord Continuum Root chord angle 20 degrees 70 degrees Continuum (related to rotation axis) (related to rotation axis) Tip chord angle 30 degrees (related to rotation axis) 95 degrees (related to rotation axis) Continuum Root profile DAE11, S1223, E387, FX different profiles Tip profile DAE11, S1223, E387, FX different profiles Discrete Discrete

15 Blade Profiles: Low Reynolds Hydrofoil

16 Objective Functions Vessel solid concentration variance Heavy solid suspension Pure Axial Flow Lack of Radial Flow s 2 = 1 n 1 n ( C i C) i= 1 2 Pumping effectiveness Torque Reduction Saving Costs P efect = q p

17 Computational requirements One evaluation takes approximately 5.2 hours of computing time; Optimization requires several evaluations to converge to an optimum; Strategy: Combine CFD with Response Surface Methodology (metamodels);

18 Using metamodels Initial DOE Filtered designs (constraints, Information) Response Surfaces (RSM) Genetic algorithms use the RSM information to find the optimum point

19 Pareto Frontier Non Dominated Points Dominated Points

20 CFD Results Original Design: Average axial Flow (presence of radial flow) High Np Torque High power consumption Low solid suspension Optimum Design: Pure axial Flow (minimum radial flow) Low Np Torque Low power consumption High solid suspension Solid supension velocity

21 Experimental results: Cortesy of Unicamp: Prof. José Roberto Nunhez Solid Suspension N=290 RPM ->P = 6.09 W N=172 RPM ->P = 6.06 W N=390 RPM ->P = W N=232 RPM ->P = W

22 Solid Suspension Experimental results: Cortesy of Unicamp (Prof. José Roberto Nunhez)

23 Final remarks We would like to thank Professor José Roberto Nunhez and Unicamp for providing the laboratory and helping with planning and execution of the experimental analyses presented in this work; This study is part of the work conducted by Nicolas Spogis at his doctorate at Unicamp University, under Professor s José Roberto Nunhez orientation.

24 END Obrigado Rodrigo Ferraz +55 (48)