De Souza, B. R. D. - Optimization Ethanol SI-Engine

7/29/2019 De Souza, B. R. D. - Optimization Ethanol SI-Engine

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Multidisciplinary optimization of an

ethanol SI-engine

Bernardo Reis Dreyer de Souza, M.Sc.


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Agenda

About Sygma Motors

Motivation and Objectives

Problem Definition

Multidisciplinary Workflow

Disciplinary Workflows

Preliminary Results


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To provide innovative

solutions, with high

technological content and

environmentally

responsible, to be used in

internal combustion

engines.

Mission


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VSE Vale Solues em Energia: Ethanol / VNG

Engines Project.

VSE - Vale Solues em Energia: Ethanol GenSet

Project.

Customers & Projects

FPT FIAT Powertrain Technologies: Engines

Characterization Services


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Performance Modeling

INFRA-STRUCTURE

LAB

DIGITALMODELLING,

SIMULATION &ANALYSIS

Rapid Prototyping

Mechanical Design

SCRE Single Cylinder Research

EnginePIV Particle Image Velocimetry

Digital Scanning

Valve train bench

MoTeC dSpace FLEX DI ECU

Engine Control Systems

Infra-Structure

Combustion Visualization

Systems

Spray Visualization System

CFD Computational Fluid Dynamics


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Motivation and Objectives

Motivation Engine design is a multidisciplinary process

Ethanol engines are receiving more attention

Lower carbon emissions

Higher knocking resistance compared to gasoline

Renewable

Objectives

Demonstrate an application of multidisciplinary design optimization ininternal combustion engines

Apply the process for a research engine

Models are validated

Show some techniques that aid in the integration of several disciplines inmodeFRONTIER


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Problem Definition

Optimize the engine efficiency Indicated Efficiency

Engine efficiency without taking friction into account

Volumetric Efficiency

Breathing capacity

Enables the engine to deliver more power

Design Variables

Piston bowl geometry

Keeping the compression ratio

Lift profiles for intake and exhaust valves

Ignition timing

Fuel mass per cycle Constraints

Valvetrain dynamic behavior

Avoid collision between valves and piston

Piston maximum temperature

Piston maximum stress


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Analysis and Design Process

ParameterizedGeometry

Dynamics

Optimization

Thermo-mechanical Analysis

Performance

Analysis

Parameterized

Kinematics


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Workflow Implementation

The worfkflow is divided into disciplinary subsystems

Data packges are exchanged between disciplines


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Typical disciplinary implementation in modeFRONTIER

Receive Data

Disciplinary Variables

Deliver Data

Process Execution

External Variables


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GG - Geometry Generation

Piston bowl shape parameterization Separate models for mesh generation and performance analysis

Full piston model for mesh generation

Piston crown model for performance analysis


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VKD - Valvetrain Kinematics Design

Kinematic Design and Analysis Valve lift profiles parameterization

Polynomial segments

Cam profile generation

Valve/Piston collision analysis


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PFA Performance Analysis

1-D thermodynamic simulation of the four-stroke cycle Calculates the efficiencies, heat flow and pressure

Runs a predictive combustion model

Pressure

Heat Flow


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Influence of piston bowl geometry on combustion

Predictive combustion model Interaction between flame front and combustion chambers walls

Example:Flame front area Fuel burn rate

p-V Diagram


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TMA Thermo-mechanical analysis

Piston finite element meshing Temperature analysis

Stress analysis

Pressure

Heat Flow


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VDD Valvetrain Dynamics Design

Checks if the valvelifts profiles generated by VKD satisfy dynamicconstraints

Enters a local optimization for each valve, if constraints are violated

Nested workflows

Check dynamics Optimize dynamics


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VDO Valvetrain Dynamics Optimization

Optimizes specific variables of the dynamic domain of the valvetrainsystem

The same dynamic model from VDD is used


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Results

Preliminary results using Surrogate models Radial Basis Functions interpolation

Optimization run with NSGA-II

Population of 50 individuals

100 generations


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Results

Parallel chart for Pareto frontier

High Indicated Efficiency Profile

High Volumetric Efficiency Profile

Opposite trend obtained from CFD!


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Correlation on Pareto frontier


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Conclusions

Multidisciplinary design optimization will help us design competitivetechnologies

More disciplines have to be included

Obtain turbulence parameters by CFD

swirl, tumble and squish

Models need to be improved

Emissions prediction


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Acknowledgements

Patrick CardosoClayton Zabeu

Giovanni Kotinda

Jean Rezende

Felipe Cardoso

Leandro VieiraOlindo Cruz

Oswaldo Franca Jr.

Ivan Trindade

Documents

De Souza, B. R. D. - Optimization Ethanol SI-Engine