The aim of the dissertation is to develop a new numerical optimisation technique for the diffuser geometry of a typical turbocharger compressor, using a non-parametric optimisation method (adjoint). This leads to an increase in power and thermal efficiency in real-world drive cycles for passenger car engines.
The geometry and experimental data correspond to the TD025-05T4 compressor from the 1.2-liter Renault Megane passenger car supplied by MTEE. In this study, a set of numerical simulations were conducted along two turbocharger compressor speed lines at 150,000 rpm and 80,000 rpm to analyse and validate the results against experimental data. Three points on each speed line are selected: one point each in regions close to surge and choke and a point in the stable zone of the compressor map.
In addition, this study optimises the diffuser geometry in a passenger vehicle turbocharger compressor using a gradient-based solution approach employing a non-parametrical adjoint shaping optimisation for ideal gas turbulent compressible flow applications. The adjoint solver is a gradient-based optimisation that can automatically generate a series of iterations of a design so that the mesh gradually changes shape to meet a single goal, like the efficiency of the compressor in this case.
The study considers a total of six operating cases on the compressor map to optimise the full and partial load compressor operations, leading to a real-world drive cycle. These cases are the three cases (closer to surge, stable midpoint, and closer to the choke point) on each of the speed lines. A typical result for mid-stable operation on a 150,000 (rpm) speed line shows a gradual increase in efficiency up to a maximum of 2.6% improvement.
While, for choke and surge optimisation, the geometry variation of the optimised diffuser is different, in the stable central area for both speed lines, the geometry change is consistent. Therefore, the diffuser can be made to work best for both half and full load engine operation.
As a result, the optimum diffuser geometry impacts engine efficiency and the overall performance of a typical passenger car for real drive cycles, increasing power and slightly improving thermal efficiency. When a typical car engine is running at full and half-load in real-world operation, the improved compressor efficiency is expected to make a small difference. This will make the engine more powerful and more efficient by about 0.1%.
Table of Contents
Declaration of authorship
abstract
Acknowledgements
List of Publications
Nomenclature
Table of Contents
List of Figures
List of Tables
Chapter 1 Introduction
1.1 Rationale
1.2 Aim and Objectives
1.3 Research Methodology and Impact
1.4 Thesis Structure
Chapter 2 Literature Review.
2.1 Research Background
2.1.1 Drive Cycles
2.1.2 Types of Drive Cycles
2.1.3 Emission Test Cycles
2.1.4 Turbocharger Development History
2.1.5 Motivation of this Research
2.2 Turbocharger Compressor
2.2.1 Turbocharger Compressor Cycle
2.2.2 Types of Turbocharger Compressors
2.2.3 Compressor Performance Characteristics
2.2.4 Compressor Flow Phenomenon
2.2.5 Turbocharger Compressor Numerical Simulation
2.3 Turbocharger Compressor Diffuser
2.3.1 Diffuser Performance
2.3.2 Diffuser Geometry
2.4 Centrifugal Compressor Losses
2.5 Optimisation Methods
2.6 Optimisation Tools for CFD
2.6.1 Manual Optimisation and Scripting (MOS)
2.6.2 Design of Experiments (DoE)
2.6.3 Response Surfaces Results (RSR)
2.6.4 Goal Driven Optimisation
2.6.5 RBF-Morph
2.6.6 Adjoint Solution
2.7 Adjoint Shape Optimisation
2.8 Adjoint Method Theory
2.9 Adjoint Solver Discrete Versus Continuous
2.10 High-Fidelity CFD-Based Shape Optimisation
2.10.1 Shape Optimisation with the ANSYS Adjoint Solver
2.10.2 High-fidelity Gradient-Based Aerodynamic Design Optimisation
2.11 OFF-Design Performance Prediction
Chapter 3 Research Methods and Strategy
3.1 Overall Strategy
3.2 Reynolds Averaged Navier-Stokes equations
3.2.1 Continuity Equation
3.2.2 Momentum Equation:
3.2.3 Energy Equation
3.3 Ideal gas equation
3.4 Turbulence Models in Turbomachinery
3.4.1 k-ε Turbulence Model
3.4.2 K-Omega Turbulence Model
3.4.3 SST K-Omega turbulence model
3.4.4 Eddy-Viscosity Models
3.4.5 Large Eddy Simulations Navier-Stokes Equations
3.5 General Adjoint Solver Assumptions
3.6 Adjoint Method Equations
3.7 Combustion Engine Performance Model
3.7.1 Engine Geometry
3.7.2 Ideal Four-Stroke Process
3.7.3 Exhaust Stroke
3.7.4 Intake Stroke
3.7.5 Four-Stroke Otto Gas Cycle Analysis
3.8 CFD Uncertainty Analysis
3.8.1 Input Uncertainty
3.8.2 Physical Model Uncertainty
3.9 Engine Uncertainty Analysis
3.9.1 Measurement Uncertainties
3.9.2 Model Uncertainties
3.9.3 Uncertainty Analysis Inputs
3.9.4 Crank Angle and RPM uncertainty
Chapter 4 Numerical Setup and Validation
4.1 Geometry Preparation
4.2 Meshing Quality
4.3 Numerical Setting
4.4 Numerical Model Validation
Chapter 5 Numerical Analysis
5.1 Mesh Refinement
5.2 Boundary Conditions and Numerical Results
5.3 Predicted Result and Discussion
Chapter 6 Adjoint Method Optimisation
6.1 Baseline Geometry Optimisation
6.2 Mesh Refinement Cases Point 24
6.3 Baseline Settings and Results Mesh Independency Discussion
6.4 Adjoint Solver Settings
6.5 Adjoint and Baseline Results Discussion
6.6 Post-Processing Analysis
6.6.1 Compressor and Diffuser Point 8
6.6.2 Compressor and Diffuser Point 10
6.6.3 Compressor and Diffuser Point 13
6.6.4 Compressor and Diffuser Point 23
6.6.5 Compressor and Diffuser Point 24
6.6.6 Compressor and Diffuser Point 27
6.7 Optimised Diffuser Proposal for Real-World Cycle
6.8 Engine Performance Impact
Chapter 7 Conclusion
7.1 Summary and Conclusion
7.2 Contribution to Knowledge
7.3 Recommendations
References
APPENDIX
APPENDIX A - NUMERICAL ANALYSIS
APPENDIX B - INITIAL OPTIMISATION PROCESS
APPENDIX C – INITIAL BASELINE SETTING AND RESULTS
APPENDIX D – INITIAL ADJOINT SETTINGS
APPENDIX E – INITIAL ADJOINT RESULTS
APPENDIX F – INITIAL ENGINE IMPACT RESULTS
APPENDIX G – BASICS ADJOINT APPLICATION
Declaration of authorship
It should be noted that the copyright of this research work lies with the author and the research submitted for the Doctor of Philosophy follows my own original work that no one wrote in my place. I have not copied the work of others and I have used my own words to document that all sources, such as ideas, equations, figures and tables, were included in the reference list. I also declare that I have personally consulted all the sources of the working report prepared at Anglia Ruskin University, Faculty of Science and Engineering, England. In addition, I declare that I have not yet presented this document to other institutions in order to obtain diplomas, academic titles, certificates, etc., or that I have already published all or part of it. “Copyright of this work report stays with the writer. Any extract from this report should be published with the written authorisation of the author and the information received from it should be acknowledged “.
Supervisors:
Dr Ahad Ramezanpour
Dr. Aaron Costall (External All Rights Reserved)
Signed: Kristaq Hazizi
Date: 31/08/2021
ANGLIA RUSKIN UNIVERSITY
abstract
FACULTY OF SCIENCE & ENGINEERING
DOCTOR OF PHILOSOPHY
Aerodynamic Optimisation of Turbocharger Compressor Diffuser Geometry for Real-World Drive Cycles
KRISTAQ HAZIZI
August 2021
The aim of the dissertation is to develop a new numerical optimisation technique for the diffuser geometry of a typical turbocharger compressor, using a non-parametric optimisation method (adjoint). This leads to an increase in power and thermal efficiency in real-world drive cycles for passenger car engines.
The geometry and experimental data correspond to the TD025-05T4 compressor from the 1.2-liter Renault Megane passenger car supplied by MTEE. In this study, a set of numerical simulations were conducted along two turbocharger compressor speed lines at 150,000 rpm and 80,000 rpm to analyse and validate the results against experimental data. Three points on each speed line are selected: one point each in regions close to surge and choke and a point in the stable zone of the compressor map. The domain includes the full compressor stage, comprising intake, impeller as a multiple reference frame, diffuser and outlet.
The k-omega SST turbulence model is used to solve the compressible flow using ANSYS Fluent software. The simulations predict compressor performance in terms of the total-to-total pressure ratio and total-to-total efficiency. Results reveal the predicted pressure ratio error is in the range of 1-6%. Instead, the predicted efficiency error was overpredicted by up to 20% in the region close to the choke.
In addition, this study optimises the diffuser geometry in a passenger vehicle turbocharger compressor using a gradient-based solution approach employing a non-parametrical adjoint shaping optimisation for ideal gas turbulent compressible flow applications. The adjoint solver is a gradient-based optimisation that can automatically generate a series of iterations of a design so that the mesh gradually changes shape to meet a single goal, like the efficiency of the compressor in this case.
The study considers a total of six operating cases on the compressor map to optimise the full and partial load compressor operations, leading to a real-world drive cycle. These cases are the three cases (closer to surge, stable midpoint, and closer to the choke point) on each of the speed lines. A typical result for mid-stable operation on a 150,000 (rpm) speed line shows a gradual increase in efficiency up to a maximum of 2.6% improvement.
While, for choke and surge optimisation, the geometry variation of the optimised diffuser is different, in the stable central area for both speed lines, the geometry change is consistent. Therefore, the diffuser can be made to work best for both half and full load engine operation.
As a result, the optimum diffuser geometry impacts engine efficiency and the overall performance of a typical passenger car for real drive cycles, increasing power and slightly improving thermal efficiency. When a typical car engine is running at full and half-load in real-world operation, the improved compressor efficiency is expected to make a small difference. This will make the engine more powerful and more efficient by about 0.1%.
Key words: Turbocharger compressor, CFD, k-omega SST turbulence model, Compressor performance, Efficiency, pressure-ratio, Optimisation, Adjoint solver, Power output engine, Thermal efficiency.
Acknowledgements
Studying part-time and working full-time was a great challenge and this work is the result of many long and constructive discussions. First, I want to express my first supervisor, my gratitude and appreciation to Dr. Ahad Ramezanpur for sharing his valuable experiences, guidance and persistent support in this research process.
I want to say to my supervisors, Dr. Aaron Costall and Dr. Mehrdad Asadi, thank you very much for your helpful suggestions and valuable discussions about this working guide throughout my research. Thanks to the industry collaboration, Mitsubishi Turbocharger and Engine Europe (MTEE) for providing the initial compressor geometry and the compressor test data. Sincere gratitude to Prof. Hassan Shirvani for his ideas, conversations and interest in this research work. I would like to express my special thanks and my sincere appreciation and gratitude to Professor Keith Jones for his assistance and advice in my first publishing process. In addition, I am also thankful to Samuel Wilson for his encouragement and continuous support whenever I was discouraged at the most difficult times.
I also take this opportunity to express my special thanks to my family. My beloved wife, for her never-ending love, patience, encouragement, care and support and my only son. Without their support, reaching this point and completing this research would not have been possible. I can never fully express my gratitude using words. You both made my PhD journey worthwhile. This thesis could be the best gift.
Most of all, I thank Almighty God very much for his grace and patience in completing this doctorate and for everything I have achieved so far in my life, because none of them would have been possible without his plan for my life.
Finally, I thank the academics and administration of the University of Anglia Ruskin for their advice and assistance in my academic journey.
List of Publications
1. Hazizi, K. et al. (2019) ‘Numerical analysis of a turbocharger compressor’, E3S Web of Conferences. Edited by A. Mohamad et al., 128, p. 06012. doi: 10.1051/e3sconf/201912806012.
2. Hazizi, K., Ramezanpour, A. and Costall, A. (2021) ‘Numerical Optimisation of The Diffuser In a Typical Turbocharger Compressor Using The Adjoint Method’, Automotive and Engine Technology, p. 22. doi: 10.1007/s41104-022-00108-6.
Nomenclature
This thesis describes the terminology used here. Some of the symbols listed here have more than one meaning. The context should reveal the precise meaning of the symbol in the document.
Roman Symbols-Upper Case
Abbildung in dieser Leseprobe nicht enthalten
Roman Symbols-Lower Case
Abbildung in dieser Leseprobe nicht enthalten
Greek Symbols
Abbildung in dieser Leseprobe nicht enthalten
Subscripts
Abbildung in dieser Leseprobe nicht enthalten
Abbreviations-Lower/Upper Case
Abbildung in dieser Leseprobe nicht enthalten
List of Figures
Figure 1 Typical turbocharger cross-section assembly (Chakour, 2019)
Figure 2 Schematic representation of the chassis dynamometer emission test facility (Alves et al., 2015)
Figure 3 Typical Compressor Map (Barrera-Medrano et al., 2017)
Figure 4 Generic distance-based drive cycles speed curve (Akner, 2019)
Figure 5 The drive cycle WLTP used to test passenger car (Akner, 2019)
Figure 6 ECE 15 Cycle (DieselNet, 2022)
Figure 7 EUDC Cycle (DieselNet, 2022)
Figure 8 EUDC Cycle, Real-world drive cycle (DieselNet, 2022)
Figure 9 EUDC Cycle for Low Power Vehicles (DieselNet, 2022)
Figure 10 Turbochargers technology trend (Turbocharger, 2013)
Figure 11 Sector World CO2 emissions in 2011 (Padzillah, 2015)
Figure 12 Average CO2 Emissions for New Passenger Cars (Gong, 2016)
Figure 13 CO2 Reduction by Downsizing for 1470 kg (Gong, 2016)
Figure 14 UK domestic transport GHG emissions 2007 (Padzillah, 2015)
Figure 15 European passenger car tailpipe CO2 trajectories (Padzillah, 2015)
Figure 16 Energy losses of a typical light vehicle (%) (Padzillah, 2015)
Figure 17 Turbocharger Compressor Cycle (Honeywell, 2018)
Figure 18 Turbo Explanation (© MITSUBISHI TURBOCHARGER AND ENGINE EUROPE B.V., 2018)
Figure 19 Waste-gate Turbocharger (Gong, 2016)
Figure 20 Improving VGT Low Speed Torque (Gong, 2016)
Figure 21 Variable Geometry Turbochargers Working Principles (VGT) (Gong, 2016)
Figure 22 A two-stage turbocharger (Gong, 2016)
Figure 23 An electrically assisted turbocharger (Gong, 2016)
Figure 24 Turbocharger variable flow (VFT) (Gong, 2016)
Figure 25 Configuration of a Single Stage Centrifugal Compressor (Kangsoo Im, 2012)
Figure 26 Turbocharger Variations Compressor Stage of Pressure Ratio Total-to-Total (Achilleos et al., 2014)
Figure 27 h-s Diagram for a Typical Compressor (Watson and Janota, 1982)
Figure 28 Efficiency Deviation (Achilleos et al., 2014)
Figure 29 Performance characteristic of a compressor (Wu, 2014)
Figure 30 Velocity Triangle General Compressor Flow streamline (Wu, 2014)
Figure 31 Turbocharger working principle (Wu, 2014)
Figure 32 Different types of impeller blading (Wu, 2014)
Figure 33 Typical Compressor Map (Watson and Janota, 1982)
Figure 34 Compressor Surging and Choking Diagram (BEST MECHANICAL ENGINEERING, 2019)
Figure 35 Horseshoe vortex pressure and suction-side legs (Wu, 2014)
Figure 36 Laminar separation bubble formation process (Wu, 2014)
Figure 37 Flow over the tip gap or an unshrouded blade (Denton, 1993)
Figure 38 Two different types of jet wakes at the impeller exit (Wu, 2014)
Figure 39 Secondary Flow in Cross-Section Normal to Streamwise Direction (Wu, 2014)
Figure 40 Mollier diagram for a diffuser flow (Dixon and Hall, 2014)
Figure 41 h-s diffuser diagram (Dixon and Hall, 2014)
Figure 42 Turbocharger Compressor Stage Section Cut (Achilleos and Paris, 2014)
Figure 43 Diffuser Geometry System (Achilleos et al., 2014)
Figure 44 Centrifugal Compressor Losses (Erickson, 2008)
Figure 45 Real Experiment left and CFD Simulation right (Rumsey and Beutner, 2006)
Figure 46 Finite Difference, Finite Volume, Finite Element (Barber, 2000)
Figure 47 Finite Difference Method (Sharma, Mukhopadhyay and Agarwal, 1986)
Figure 48 Finite Volume Cell (Ramezani and Stipcich, 2016)
Figure 49 1 Cell-Centred, Nod-Centred (Ramezani and Stipcich, 2016)
Figure 50 View of a Conventional Flow Solver (ANSYS, 2019e)
Figure 51 Connection Observables with Inputs (ANSYS, 2019e)
Figure 52 Adjoint solver design workflow, one full cycle (Canonsburg, 2012)
Figure 53 Method Description (ANSYS, 2019e)
Figure 54 Engine Slider-Crank Geometry (Ferguson and Kirkpatrick, 2016)
Figure 55 Four-stroke inlet and exhaust flow; 𝑃i = inlet pressure; 𝑃e = exhaust pressure, (Ferguson and Kirkpatrick, 2016)
Figure 56 The exhaust stroke (4 to 5 to 6) illustrating residual mass, (Ferguson and Kirkpatrick, 2016)
Figure 57 The error distribution of the VE model compared to reference VE (Hoops, 2010)
Figure 58 Body Compressor CAD
Figure 59 Impeller 3D View CAD
Figure 60 The Assembly of Volute and Impeller in Section View
Figure 61 Fluid Domain
Figure 62 Fluid domain name selection
Figure 63 Flow Domain Mesh Cut-Section 1.3 Million mesh elements
Figure 64 Monitor Residual Simulation Results Validation Design Point 24
Figure 65 2 (Million) Case Mesh Elements Refinement
Figure 66 3 (Million) Case Mesh Elements Refinement
Figure 67 5 (Million) Case Mesh Elements Refinement
Figure 68 Monitor Residual Plot Point 24, 2 (Million)
Figure 69 Monitor Residual Plot Point 27, 2 (Million)
Figure 70 Monitor Residual Plot Point 23, 2 (Million)
Figure 71 Monitor Residual Plot Point 8, 2 (Million)
Figure 72 Monitor Residual Plot Point 10, 2 (Million)
Figure 73 Monitor Residual Plot Point 13, 2 (Million)
Figure 74 Compressor Map, TD025-05T4
Figure 75 Predicticted Pressure Ratio Points 23, 24 and 27, 150,000 (rpm)
Figure 76 Predicted Pressure Ratio Results Points 8, 10 and 13, 80,000 (rpm)
Figure 77 Predicted Efficiency Results Points 23, 24 and 27, 150,000 (rpm)
Figure 78 Predicted Efficiency Results Points 8, 10 and 13, 80,000 (rpm)
Figure 79 Predicted Pressure Ratio, % Error Points 23, 24 and 27, 150,000 (rpm)
Figure 80 Predicted Presure Ratio, % Error Points 8, 10 and 13, 80,000 (rpm)
Figure 81 Predicted Efficiency % Error Results Points 23, 24 and 27, 150,000 (rpm)
Figure 82 Predicted Efficiency % Error Results Points 8, 10 and 13, 80,000 (rpm)
Figure 83 3D Geometry View for Optimisation Design
Figure 84 Baseline diffuser geometry dimensions
Figure 85 Layers of the Near Wall Region (Johansson, 2016)
Figure 86 Cut-Section View Case 1.6 million of Mesh Elements
Figure 87 Cut-Section View Case 2 million of Mesh Elements
Figure 88 Cut-Section View Case 2.5 million of Mesh Elements
Figure 89 Cut-Section View Case 3 million of Mesh Elements
Figure 90 Residual Plot Point 24 Case 1.6 M, Continuity Equation Tolerance 0.001
Figure 91 Residual Plot Point 24 Case 2 M, Continuity Equation Tolerance 0.001
Figure 92 Residual Plot Point 24 Case 2.5 M, Continuity Equation Tolerance 0.001
Figure 93 Residual Plot Point 24 Case 3 M, Continuity Equation Tolerance 0.001
Figure 94 Residual Plot Point 24 Case 3 M, Continuity Equation Tolerance 1e-05
Figure 95 Residual Plot Point 24 Case 3.6 M, y plus = 0.00005
Figure 96 Residual Plot Point 24 Case 3.6 M, y plus = 0.00001
Figure 97 Residual Monitors Plot Point 8, Case 3 M
Figure 98 Residual Monitors Plot Point 10, Case 3 M
Figure 99 Residual Monitors Plot Point 13, Case 3 M
Figure 100 Residual Monitors Plot Point 23, Case 3 M
Figure 101 Residual Monitors Plot Point 24, Case 3 M
Figure 102 Residual Monitors Plot Point 27, Case 3 M
Figure 103 Residual Monitors Plot Baseline and Adjoint design iterations (Left and Right), All operating areas, Surge, Central and Choke from top to bottom, Speed lines 80,000 rpm
Figure 104 Residual Monitors Plot Baseline and Adjoint design iterations (Left and Right), All operating areas, Surge, Central and Choke from top to bottom, Speed line 150,000 rpm
Figure 105 Compressor Measuring Location Planes for Post-Processing, Baseline and Optimised Geometry
Figure 106 Diffuser Geometry Measuring Location Planes for Post-Processing, Baseline and Optimised Geometry
Figure 107 Contours Compressor Total Pressure Cut-Section View; Point 8, Baseline (Top), Optimised (Bottom)
Figure 108 Auto Range Contours Diffuser Total Pressure Cut-Section View, Point 8 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 109 Manual Range Contours Diffuser Total Pressure Cut-Section View, Point 8; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 110 Contours Compressor Total Temperature Cut-Section View; Point 8, Baseline (Top), Optimised (Bottom)
Figure 111 Auto Range Contours Diffuser Total Temperature Cut-Section View, Point 8; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 112 Manual Range Contours Diffuser Total Temperature Cut-Section View, Point 8; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 113 Contours Compressor Velocity Magnitude Cut-Section View; Point 8, Baseline (Top), Optimised (Bottom)
Figure 114 Auto Range Contours Diffuser Velocity Magnitude Cut-Section View, Point 8; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 115 Manual Range Contours Diffuser Velocity Magnitude Cut-Section View, Point 8; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 116 Contours Compressor Total Pressure Cut-Section View; Point 10, Baseline (Top), Optimised (Bottom)
Figure 117 Auto Range Contours Diffuser Total Pressure Cut-Section View, Point 10; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 118 Manual Range Contours Diffuser Total Pressure Cut-Section View, Point 10; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 119 Contours Compressor Total Temperature Cut-Section View; Point 10, Baseline (Top), Optimised (Bottom)
Figure 120 Auto Range Contours Diffuser Total Temperature Cut-Section View, Point 10; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column) C
Figure 121 Manual Range Contours Diffuser Total Temperature Cut-Section View, Point 10; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 122 Contours Compressor Velocity Magnitude Cut-Section View; Point 10, Baseline (Top), Optimised (Bottom)
Figure 123 Auto Range Contours Diffuser Velocity Magnitude Cut-Section View, Point 10 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 124 Manual Range Contours Diffuser Velocity Magnitude Cut-Section View, Point 10; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 125 Contours Compressor Total Pressure Cut-Section View; Point 13, Baseline (Top), Optimised (Bottom)
Figure 126 Auto Range Contours Diffuser Total Pressure Cut-Section View, Point 13 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 127 Manual Range Contours Diffuser Total Pressure Cut-Section View, Point 13; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 128 Contours Compressor Total Temperature Cut-Section View; Point 13, Baseline (Top), Optimised (Bottom)
Figure 129 Auto Range Contours Diffuser Total Temperature Cut-Section View, Point 13 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 130 Manual Range Contours Diffuser Total Temperature Cut-Section View, Point 13; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 131 Contours Compressor Velocity Magnitude Cut-Section View; Point 13, Baseline (Top), Optimised (Bottom)
Figure 132 Auto Range Contours Diffuser Velocity Magnitude Cut-Section View, Point 13 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 133 Manual Range Contours Diffuser Velocity Magnitude Cut-Section View, Point 13; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 134 Contours Compressor Total Pressure Cut-Section View; Point 23, Baseline (Top), Optimised (Bottom)
Figure 135 Auto Range Contours Diffuser Total Pressure Cut-Section View, Point 23 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 136 Manual Range Contours Diffuser Total Pressure Cut-Section View, Point 23; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 137 Contours Compressor Total Temperature Cut-Section View; Point 23, Baseline (Top), Optimised (Bottom)
Figure 138 Auto Range Contours Diffuser Total Temperature Cut-Section View, Point 23 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 139 Manual Range Contours Diffuser Total Temperature Cut-Section View, Point 23; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 140 Contours Compressor Velocity Magnitude Cut-Section View; Point 23, Baseline (Top), Optimised (Bottom)
Figure 141 Auto Range Contours Diffuser Velocity Magnitude Cut-Section View, Point 23 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 142 Manual Range Contours Diffuser Velocity Magnitude Cut-Section View, Point 23; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 143 Contours Compressor Total Pressure Cut-Section View; Point 24, Baseline (Top), Optimised (Bottom)
Figure 144 Auto Range Contours Diffuser Total Pressure Cut-Section View, Point 24; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 145 Manual Range Contours Diffuser Total Pressure Cut-Section View, Point 24; x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 146 Contours Compressor Total Temperature Cut-Section View; Point 24, Baseline (Top), Optimised (Bottom)
Figure 147 Auto Range Contours Diffuser Total Temperature Cut-Section View; Point 24, x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 148 Manual Range Contours Diffuser Total Temperature Cut-Section View; Point 24, x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 149 Contours Compressor Velocity Magnitude Cut-Section View; Point 24, Baseline (Top), Optimised (Bottom)
Figure 150 Auto Range Contours Diffuser Velocity Magnitude Cut-Section View; Point 24, x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 151 Manual Range Contours Diffuser Velocity Magnitude Cut-Section View; Point 24, x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 152 Contours Compressor Total Pressure Cut-Section View; Point 27, Baseline (Top), Optimised (Bottom)
Figure 153 Auto Range Contours Diffuser Total Pressure Cut-Section View, Point 27 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 154 Manual Range Contours Diffuser Total Pressure Cut-Section View, Point 27 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 155 Contours Compressor Total Temperature Cut-Section View; Point 27, Baseline (Top), Optimised (Bottom)
Figure 156 Auto Range Contours Diffuser Total Temperature Cut-Section View, Point 27 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 157 Manual Range Contours Diffuser Total Temperature Cut-Section View, Point 27 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 158 Contours Compressor Velocity Magnitude Cut-Section View; Point 27, Baseline (Top), Optimised (Bottom)
Figure 159 Auto Range Contours Diffuser Velocity Magnitude Cut-Section View, Point 27 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 160 Manual Range Contours Diffuser Velocity Magnitude Cut-Section View, Point 27 (Top); x =25% (Top), x = 50% (Middle) and x =75% (Bottom), Baseline (Left Column), Optimised (Right Column)
Figure 161 Diffuser Geometry Changes, Surge, Central, and Choke area (Top, 8, Middle, 10 and Bottom, 13), Speedline 80,000 rpm
Figure 162 Diffuser Geometry Changes, Surge, Central, and Choke area (Top, 23 Middle, 24 and Bottom, 27), Speedline 150,000 rpm
Figure 163 Diffuser Geometry Changes Operating Central area, Speedline 150,000rpm, 3D front view left side and 3D back view right side
Figure 164 New Diffuser Geometry
Figure 165 Monitor Residual Plot Point 8 3M
Figure 166 Monitor Residual Plot Point 10 3M
Figure 167 Monitor Residual Plot Point 13 3M
Figure 168 Monitor Residual Plot Point 23 3M
Figure 169 Monitor Residual Plot Point 27 3M
Figure 170 Monitor Residual Plot Point 24 3M
Figure 171 Monitor Residual Plot Point 8 5M
Figure 172 Monitor Residual Plot Point 10 5M
Figure 173 Monitor Residual Plot Point 13 5M
Figure 174 Monitor Residual Plot Point 23 5M
Figure 175 Monitor Residual Plot Point 24 5M
Figure 176 Monitor Residual Plot Point 27 5M
Figure 177 Initial Baseline Compressor Fluid Domain Geometry
Figure 178 Initial Compressor Baseline Cut-Section View Case 1.6M
Figure 179 Residual Monitors Plot Initial Case 1.6M Point 8
Figure 180 Residual Monitors Plot Initial Case 1.6M Point 10
Figure 181 Residual Monitors Plot Initial Case 1.6M Point 13
Figure 182 Residual Monitors Plot Initial Case 1.6M Point 23
Figure 183 Residual Monitors Plot Initial Case 1.6M Point 24
Figure 184 Residual Monitors Plot Initial Case 1.6M Point 27
Figure 185 1 Residual Plot Equations (Continuity, 103) Case 1.6M Point 24
Figure 186 Residual Plot Equations (Continuity, 10-5) Case 1.6 Point 24
Figure 187 Adjoint Optimisation Result point 24 of Efficiency Improvement
Figure 188 Compressor Absolute Pressure Case 1.6M Point 24
Figure 189 Compressor Total Temperature Case 1.6M Point 24
Figure 190 Compressor Velocity Magnitude Case 1.6M Point 24
Figure 191 Diffuser Absolute Pressure x = 25% Case 1.6M Point 24
Figure 192 Diffuser Absolute Pressure x = 50% Case 1.6M Point 24
Figure 193 Diffuser Absolute Pressure x = 75% Case 1.6M Point 24
Figure 194 Diffuser Total Temperature x = 25% Case 1.6M Point 24
Figure 195 Diffuser Total Temperature x = 50% Case 1.6M Point 24
Figure 196 Diffuser Total Temperature x = 75% Case 1.6M Point 24
Figure 197 Diffuser Velocity Magnitude x = 25% Case 1.6M Point 24
Figure 198 Diffuser Velocity Magnitude x = 50% Case 1.6M Point 24
Figure 199 Diffuser Velocity Magnitude x = 75% Case 1.6M Point 24
Figure 200 Diffuser 3D Shape Sensitivity View Case 1.6M Point 24
Figure 201 Diffuser Path lines 3D view Case 1.6M Point 24
Figure 202 Diffuser Geometry Variation design points 8, 10,13, 23, 24, 27 at Front and Back (Hub and Shroud Wall)
Figure 203 Workflow
List of Tables
Table 1 Experimental data from Mitsubishi Turbocharger and Engine Europe (MTEE)
Table 2 Experimental data from MTEE and Engine speed calculated
Table 3 Summary of selected cycles parameters
Table 4 Comparison Adjoint method versus parametric design
Table 5 Extension Capabilities (ANSYS, 2019e)
Table 6 Multi-Objective Adjoint Optimisation Point 24
Table 7 Parameter-Based and Parameter-Free Optimisation
Table 8 Continuous Versus Discrete Comparison List
Table 9 Basic Input Data for the Renault Magane 2012H5Ft (1.2 TCe) Petrol
Table 10 Skewness value
Table 11 Orthogonal Quality mesh metrics spectrum
Table 12 Name selection
Table 13 Ansys Meshing Size Setting
Table 14 Ansys Fluent Launcher
Table 15 Ansys General Task Page
Table 16 Materials and Fluid Properties Selection
Table 17 Cell Zone Conditions
Table 18 Solution Method Setting
Table 19 Solution Controll Setting
Table 20 Inputs of Flow Parameters and Properties
Table 21 Ansys Models Selection
Table 22 Boundary Conditions
Table 23 Residual Monitor Setting Equation Tolerance Criteria
Table 24 Experimental Data Design Point 24
Table 25 Boundary Conditions Design Point 24
Table 26 Comparison Pressure Ratio and Efficiency in %
Table 27 Inflation Mesh Setting
Table 28 Ansys Meshing Size Setting 2 (Million)
Table 29 Boundary Conditions Points 8, 10, 23 and 24
Table 30 Boundary Conditions Points 13 and 27
Table 31 Pressure Ratio and Efficiency Simulation Results of all Points and Cases
Table 32 Predicted Pressure Ratio Results Points 23, 24 and 27, 150,000 (rpm)
Table 33 Predicted Pressure Ratio Results Points 8, 10 and 13, 80,000 (rpm)
Table 34 Predicted Efficiency Results Points 23, 24 and 27, 150,000 (rpm)
Table 35 Predicted Efficiency Results Points 8, 10 and 13, 80,000 (rpm)
Table 36 Predicted Pressure Ratio, % Error Points 23, 24 and 27, 150,000 (rpm)
Table 37 Predicted Pressure Ratio, % Error Points 8, 10 and 13, 80,000 (rpm)
Table 38 Predicted Efficiency % Error Results Points 23, 24 and 27 150,000 (rpm)
Table 39 Predicted Efficiency % Error Results Points 8, 10 and 13, 80,000 (rpm)
Table 40 Input Parameters Calculation First Cell High
Table 41 Output Parameters Calculation First Cell High
Table 42 Ansys Sizing Mesh Settings
Table 43 Ansys Inflation Mesh Settings
Table 44 Mesh Elements Number Case 1.6 M
Table 45 Mesh Elements Number Case 2M
Table 46 Mesh Elements Number Case 2.5 M
Table 47 Mesh Elements Number Case 3M
Table 48 Boundary Conditions Point 24 Baseline Optimisation process
Table 49 Baseline Point 24 Mesh Study
Table 50 Y Plus Values Comparison
Table 51 Further Investigation Baseline Point 24 Mesh Study
Table 52 Plus Values Comparison
Table 53 Baseline Efficiency Results for all Operating Points
Table 54 Adjoint Solution Methods
Table 55 Adjoint Solution Controls
Table 56 Adjoint Residual Monitors Equations
Table 57 Region Geometry Settings
Table 58 Adjoint versus Baseline Results for all Operating Points
Table 59 Diffuser Baseline/Optimised Total Pressure Variation, Point
Table 60 Diffuser Baseline/Optimised Total Temperature Variation, Point 8
Table 61 Diffuser Baseline/Optimised Velocity Magnitude Variation, Point 8
Table 62 Diffuser Baseline/Optimised Total Pressure Variation, Point 10
Table 63 Diffuser Baseline/Optimised Total Temperature Variation, Point 10
Table 64 Diffuser Baseline/Optimised Velocity Magnitude Variation, Point 10
Table 65 Diffuser Baseline/Optimised Total Pressure Variation, Point 13
Table 66 Diffuser Baseline/Optimised Total Temperature Variation, Point 13
Table 67 Diffuser Baseline/Optimised Velocity Magnitude Variation, Point 13
Table 68 Diffuser Baseline/Optimised Total Pressure Variation, Point 23
Table 69 Diffuser Baseline/Optimised Total Temperature Variation, Point 23
Table 70 Diffuser Baseline/Optimised Velocity Magnitude Variation, Point 23
Table 71 Diffuser Baseline/Optimised Total Pressure Variation, Point 24
Table 72 Diffuser Baseline/Optimised Total Temperature Variation, Point 24
Table 73 Diffuser Baseline/Optimised Velocity Magnitude Variation, Point 24
Table 74 Diffuser Baseline/Optimised Total Pressure Variation, Point 27
Table 75 Diffuser Baseline/Optimised Total Temperature Variation, Point 27
Table 76 Diffuser Baseline/Optimised Velocity Magnitude Variation, Point 27
Table 77 Diffuser Geometry Change for all Operating Points
Table 78 Comparison Efficiency Improvement Baseline versus Reverse
Table 79 General Information of a Typical Passenger Car
Table 80 Engine Geometry (Cylinder dimensions)
Table 81 Other Input Data Operating Point 24
Table 82 Compressor optimisation results point 24 Case 3M
Table 83 Calculation Engine Performance Model, Point 24, Baseline
Table 84 Calculation Engine Performance Model, Point 24, Optimised
Table 85 Engine Power and Thermal Efficiency Results Renault H5F 85kw
Table 86 Improvement Power and Thermal Efficiency Results Renault H5F 85kw
Table 87 Mesh Elements Number
Table 88 Boundary Conditions Initial Baseline Geometry
Table 89 Residual Equation Convergence Criteria
Table 90 Report Definition Fluid Properties Case 1.6 Point 24
Table 91 Initial Case 1.6M Adjoint Solution Methods
Table 92 Initial Case 1.6M Adjoint Solution Controls
Table 93 Initial Case 1.6M Adjoint Residual Monitors Equations
Table 94 Initial Case 1.6M Region Geometry Settings
Table 95 Initial Case 1.6M Adjoint Solver Results Point 24 Efficiency Observation.
Table 96 Adjoint Results for all Operating Points
Table 97 Diffuser Geometry Change for all Operating Points
Table 98 Geometry Change Variation Compressor Diffuser Geometry for all Operating Points
Table 99 Compressor optimisation results point 24 Case 1.6M
Table 100 Engine Power and Thermal Efficiency Results Audi S3
Table 101 Improvement Power and Thermal Efficiency Results Audi S3
Chapter 1 Introduction
1.1 Rationale
Over the last two decades, EU emission targets have influenced significant changes in automotive drivetrain technology. OEMs have invested in a variety of technologies, including turbocharging and engine downsizing, to comply with increasingly stringent directives. Even greater efficiency gains will be required in the coming years, and it remains to be seen which technologies will prove effective over time. Turbocharging has been a key technology for improving fuel efficiency and lowering emissions. At the moment, there is an ongoing trend toward engine downsizing and the incorporation of turbochargers to increase power (International Quality and Productivity Center, 2022).
Currently, 99.8% of world transportation relies on internal combustion engines (ICE) and 95% of transportation energy is derived from petroleum liquid fuels. It is therefore important that ICEs performance are enhanced to reduce the local and global effect of transport on the environment (Leach et al., 2020). Recent data from the European Commission shows that passenger cars account for around 12 percent of the total CO2 (carbon dioxide) emitted in Europe (European Commission, 2017). As a by-product of fossil fuel combustion, carbon dioxide is the most significant greenhouse gas that contributes to global warming. For new cars, the European Commission has set mandatory targets for CO2 emissions reduction due to the importance of their environmental impact and energy demand.
According to EU regulations, new cars sold in the European Union after 2015 must emit no more than 130 g CO2/km - approximately 5.6 l/100 km of gasoline or 4.9 l/100 km of diesel. Currently, the 2021 target dictates that new vehicles registered in the EU do not exceed an average CO2 emission of 95 grams per kilometre. The future next target The European Union redefined the roadmap for 2050 at the Paris climate conference in 2015 (Commission, 2020), with the aim of developing a more climate-friendly and less energy-consuming economy. There are a few technologies that manufacturers are using to meet emission standards like EGR and selective catalytic reduction, as well as an increasing trend of downsizing engines and fitting them with turbochargers to increase power.
For nearly a century, turbocharging has been the most effective technique and nowadays, to achieve these targets, we apply different technologies (Watson and Janota, 1982). The performance of an internal combustion engine is determined by the amount of fuel that is burned in each cylinder per cycle and the speed at which it can work. So, the more oxygen during the compression phase of the engine, the greater the amount of fuel ignited and thus the higher the engine power developed (Vinet and Zhedanov, 2011).
In addition to the increased focus on climate change and energy savings, the automotive industry is considering the efficiency of internal combustion engines. One source of inefficiency is the significant amount of fuel energy lost in the exhaust process. Turbochargers recover some of this energy to raise intake pressure, improving the power density and efficiency of the internal combustion engine. This permits downsized, smaller volume engines to have comparable energy outputs as larger, non-turbocharged engines (Keller et al., 2011). Engine downsizing along with boosting technology is now the most effective emission control technology (Alshammari, Alshammari and Pesyridis, 2019). The key benefits of downsizing technologies include a substantial improvement in engine power and torque without increasing engine size (Watson and Janota, 1982).
Internal Combustion (IC) Engine Downsizing is a method that offers different benefits, including the purpose of providing more power, reducing fuel consumption and gas emissions (Patil, Varade and Wadkar, 2017). Smaller engines are less efficient than larger ones, so their maximum power is reduced as a result of downsizing (Dickmann et al., 2005). There are different methods of increase air density and pressure in a turbocharger, improving the performance of the turbocharger compressor.
The European Commission regulation on CO2 emissions requires the development of technological solutions to limit the emissions of pollutants while reducing engine fuel consumption (EPRS, European Parliamentary Research Service and Erbach, 2020). Increased demand for new technologies and systems to improve fuel economy and energy efficiency while reducing CO2 emissions is a result of the ambitious goal of reducing CO2 (Stephenson and Powertrain, 2009).
The automotive industry demands high performance and power from small engines, and similarly, legislation requires lower emissions from internal combustion engines (Tesfa et al., 2014). The turbocharger compressors are highly effective and enable achieving a high level of boost (Zhang, 2010). Figure 1 shows a standard turbocharger cross-section, including the centrifugal compressor and its parts, as well as the radial inflow turbine. The stationary assembly around the impeller which contains the volute, the diffuser and the impeller shield is called the compressor case (Chakour, 2019).
In a centrifugal compressor, the flow increases significantly in the radius direction through the impeller. This results in significantly more pressure per stage but a much more complex flow with greater aerodynamic losses (lower adiabatic efficiency). The flow is then decelerated by using a diffuser to minimise the circumferential velocity component, converting its kinetic energy into pressure increase (diffusion). The high flow rate (changes the flow angle within a component), affects aerodynamic losses in the diffuser significantly and increases the tendency towards reversal of flow in the compressor (Chakour, 2019).
Abbildung in dieser Leseprobe nicht enthalten
Figure 1 Typical turbocharger cross-section assembly (Chakour, 2019)
The diffuser is simple in design and cheaper, with minimum flow losses should transform the kinetic energy into pressure energy (Anish and Sitaram, 2009). Due to its complicated geometry shape, it has low efficiency and high turbulence (Jiao et al., 2009).
However, diffuser optimisation provides prominent prospects with less effort. The need to further optimise the turbocharger forces the turbocharger engineers to analyse the geometrical properties that can improve the performance of an internal combustion engine. The diffuser section is an element that can not only provide an increase in efficiency but can also provide more stable operating states for turbocharger compressors. Nevertheless, a loss in the examination of the geometrical composition of the turbocharger diffusers for engines in passenger cars has been found (Abdelfatah et al., 2016).
The numerical design optimisation process has been completed against dimensionless numbers for different geometrical parameters of the diffuser, aiming at innovative approaches for optimising the diffuser geometry compared to previous similar attempts (Jaatinen et al., 2013; Abdelmadjid, Mohamed and Boussad, 2013; and Ahmed et al., 2015). The optimal design of the axial flow stage turbocharger compressor remains an area of research thrust. This is due to the presence of many parameters that require the realisation in conflict of efficient algorithms.
To optimise conflicting objective functions, the conventional optimisation techniques presented are not very suitable. In addition, due to manufacturing errors, erosion (environment variables) or damage, geometric uncertainty may be present in the compressor diffuser shape (Kumar, 2006). The literature review shows that there are not many reports of multi-target optimisation of the axial flow compressor in the output stage. From the point of view of optimum design, it is very important to optimise the output stage to develop high overall pressure.
On-board diagnostics (OBD) standards have been implemented due to changes in international vehicle emissions legislation. Although OBD was designed to aid in vehicle inspection and maintenance, it is now used to measure vehicle operating parameters such as engine speed and load during normal (real-world) vehicle use, as well as exhaust emission tests.
A chassis dynamometer was used to conduct the exhaust emission tests. The chassis dynamometer platform is a large roller that is positioned beneath the vehicle's tyres. When conducting a chassis dynamometer test, the vehicle being tested is driven onto the dynamometer platform using a predefined driving cycle (Alves et al., 2015). The dynamometer platform serves as a substitute for road resistance. During a test, the chassis dynamometer accurately measures the engine's power, speed, torque, and exhaust temperature, among other parameters.
Two dilution tunnels, one for diesel and the other for gasoline, are used to mix the exhausts of the vehicles being tested with filtered ambient air. This is known as a Constant Volume Sampler (CVS). The dilution tunnel is used to collect data and perform sampling. So that the emission factors can be calculated, servo-controlled sampling units and a chassis dynamometer are used for this purpose. A schematic representation of the dynamometer facility is given in Figure 2.
Abbildung in dieser Leseprobe nicht enthalten
Figure 2 Schematic representation of the chassis dynamometer emission test facility (Alves et al., 2015)
Furthermore, this study focused on the extent of such improvement after analysing the basic principles affecting engine performance and exhaust emission control technology, by optimising diffuser geometry for actual driving cycles. A driving cycle commonly represents a set of vehicle speed lines and points to reflect a real-world driving cycle. There is not enough research to optimise the diffuser using non-parametric optimisation methods across the real-world drive cycle (speed lines and operating points close to surge and choke as well as stable compressor operation). To evaluate these restrictions, in particular in the exit area of the diffuser, a numerical simulation was performed (Achilleos et al., 2014). Mitsubishi Turbocharger and Engine Europe (MTEE) used a benchmark passenger car drive cycle research procedure of a 1.2-liter petrol engine 85 KW, Renault Megane 2012, accurately reflecting the real-world driving pattern. A vehicle's emissions, fuel consumption, and powertrain performance can all be evaluated using drive cycles.
[...]
- Quote paper
- Dr Kristaq Hazizi (Author), 2022, Aerodynamic Optimisation of Turbocharger Compressor Diffuser Geometry for Real-World Drive Cycles, Munich, GRIN Verlag, https://www.grin.com/document/1290023
-
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X. -
Upload your own papers! Earn money and win an iPhone X.