Proefschrift
ter verkrijging van de graad van doctor
aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus prof. ir. K. C. A. M. Luyben,
voorzitter van het College voor Promoties
in het openbaar te verdedigen op maandag 4 maart 2013 om 15:00 uur
door
Wei SHI
Maritiem Ingenieur
geboren te Daxian, Sichuan, China
Dit proefschrift is goedgekeurd door de promotoren:
Prof. ir. D. Stapersma
Samenstelling promotiecommissie:
Rector Magnificus
voorzitter
Prof. ir. D. Stapersma
Technische Universiteit Delft, promotor
Prof. dr. J. Carlton
City University London, UK
Prof. dr. H. Nijmeijer
Technische Universiteit Eindhoven
Prof. dr. G.J. Witkamp
Technische Universiteit Delft
Prof. dr. ir. C. van Rhee
Technische Universiteit Delft
Prof. ir. J.J. Hopman
Technische Universiteit Delft
Dr. ir. R. G. van de Ketterij
MTI Holland B.V., advisor
Published by: VSSD
Website: http://www.vssd.nl/hlf E-mail: hlf@vssd.nl
ISBN
978-90-6562-315-7Cover design: Lili JIANG
Copyright © 2013 by W. SHI
All rights reserved. No part of the material protected by this copyright notice may be
reproduced or utilized in any form or by any means, electronic or mechanical, including
photocopying, recording or by any information storage and retrieval system, without the
prior permission of the author
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2.1. Introduction ... 8
2.1.1. Characteristics of a dredge cycle ... 8
2.1.2. Objectives of onboard measurements ... 8
2.2. Design of measurement ... 10
2.2.1. Selected energy systems ... 10
2.2.2. Measured operations ... 11
2.2.3. Selected measuring variables ... 11
2.3. Data post-processing ... 12
2.3.1. Signal synchronization ... 12
Synchronization of time shift ... 12
Synchronization of time delay... 13
2.3.2. Correction of sensor time lag ... 15
2.3.3. Filtering ... 16
2.3.4. Signal organization ... 17
2.3.5. Unit conversion of NOx emission ... 17
Simplified approximation ... 21
2.4. Results and discussions (constant engine speed) ... 22
2.4.1. Dynamic load to the energy system ... 22
Dynamic load to the propulsion system ... 22
Dynamic load to the dredging system ... 24
Dynamic load to the main diesel engine ... 25
2.4.2. Fuel consumption, air consumption and exhaust emissions ... 27
Steady state operation ... 28
Transient operation ... 30
Dynamic operation ... 35
2.5. Operation at variable engine speed ... 43
2.6. Conclusions ... 46
3 NON-LINEAR SIMULATION MODEL ... 48
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3.1.1. Introduction ... 48
3.1.2. Concept ... 48
3.1.3. Preparation of available information ... 49
Physical parameters ... 49
Measured variables ... 49
Theories ... 49
3.1.4. Requirements from the model ... 50
Objectives... 50
Time scale and time steps ... 50
Dynamics ... 51
Contents ... 51
3.1.5. Summary ... 52
3.2. Rotational speed calculation ... 53
3.3. Power supply system ... 53
3.3.1. Diesel engine model ... 54
Governor model ... 55
Fuel pump model ... 55
Engine core model ... 55
Exhaust emission model ... 59
3.3.2. Transmission system model ... 62
Gearbox model ... 62
Shaft model ... 62
3.4. Propulsion system ... 63
3.4.1. Ship speed calculation ... 63
3.4.2. Ship model ... 64
Calculation of longitudinal ship hull resistance ... 64
Consideration of external disturbance ... 64
3.4.3. Propeller performance model ... 65
3.5. Dredging system ... 67
3.5.1. Slurry flow calculation ... 67
3.5.2. Approximation of density distribution... 69
3.5.3. Centrifugal dredge pump model ... 69
Pumping water ... 70
Correction to slurry transportation ... 71
3.5.4. Pipeline system model ... 73
Pressure losses in pipe ... 73
Pressure loss in draghead ... 74
Kinetic pressure difference ... 74
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Matching of propeller performance model ... 86
4.2.3. Dredging system ... 88
Matching of centrifugal dredge pump model ... 88
Matching of pipeline system model ... 89
4.3. Validation ... 90
4.3.1. Theory of validation ... 90
Validation of steady state operations ... 90
Validation of dynamic operations ... 91
4.3.2. Validation of power supply system ... 91
Steady state operations ... 92
Dynamic operation ... 95
4.3.3. Validation of propulsion system ... 96
Steady state operations ... 97
Dynamic operations ... 97
Discussion... 98
4.3.4. Validation of dredging system ... 98
4.3.5. Validation of total energy system ... 101
4.4. Conclusions and remarks ... 104
5 LINEAR MODEL ... 106
5.1. Introduction ... 106
5.2. Basic principles ... 106
5.2.1. Normalization and linearization ... 106
Linear approximation of algebraic operation ... 106
Linear approximation of differential equation ... 107
5.2.2. Dynamics ... 107
5.3. Propulsion energy system ... 107
5.3.1. Shaft speed loop ... 108
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Calculation of gearbox shaft torque ... 109
Calculation of propeller shaft torque ... 109
5.3.2. Ship speed loop ... 110
Dynamic of ship speed loop ... 110
Calculation of propeller thrust ... 111
Calculation of ship resistance... 111
5.3.3. Combination of shaft speed loop and ship speed loop ... 113
5.3.4. Numerical example ... 113
5.4. Dredging energy system... 117
5.4.1. Shaft speed loop ... 117
Dynamics of shaft speed loop... 117
Calculation of gearbox shaft torque ... 118
Calculation of pump shaft torque ... 118
5.4.2. Slurry flow loop ... 121
Dynamic of slurry flow loop ... 121
Calculation of delivered pressure difference ... 121
Calculation of required pressure difference ... 121
5.4.3. Combination of shaft speed loop and slurry flow loop ... 123
Overall block diagram ... 123
5.4.4. Numerical example ... 124
5.5. Characteristics of linear model ... 126
5.6. Conclusion and recommendation ... 126
5.6.1. Conclusions ... 126
5.6.2. Recommendations ... 126
6 POTENTIAL OF LINEAR MODEL IN COMPARISON OF NON-LINEAR
MODEL ... 130
6.1. Introduction ... 130
6.1.1. Application ... 130
6.1.2. Model speed ... 130
6.1.3. Limitations ... 131
6.2. Linear model of propulsion energy system ... 131
6.2.1. Frequency band of interests ... 131
6.2.2. Impact of changing normalised derivatives for small variations at nominal point ... 132
Ship resistance related normalised derivatives ... 132
Propeller related normalised derivatives ... 135
Engine related normalised derivatives ... 138
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Summary ... 151
6.3.3. Impact of nominal normalised derivatives on large variations at nominal point ... 151
6.3.4. Impact of nominal normalised derivatives on operating at non-nominal point 153 6.3.5. Conclusions ... 155
7 EVALUATION, CONCLUSION AND RECOMMENDATION ... 158
7.1. Onboard measurement ... 158
7.1.1. Lessons learned ... 158
7.1.2. Conclusions ... 158
Constant engine speed ... 159
Variable engine speed ... 159
7.1.3. Recommendations ... 160
7.2. Modelling ... 160
7.2.1. Lessons learned ... 160 7.2.2. Conclusions ... 161 7.2.3. Recommendations ... 162 Non-linear model ... 162 Linear model ... 163Reference ... 165
Abbreviation ... 169
Symbol ... 171
Subscript ... 175
Superscript ... 179
A MEASUREMENT ... 182
A.1. Summary of measured variables ... 182
A.2. Cross-correlation ... 183
A.3. Correction of exhaust gas transport delay ... 184
A.3.1. Step1: transport delay inside the analysis equipment ... 185
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System F ... 186
A.3.2. Step2: transport delay from sample location to device inlet ... 186
A.3.3. Step3: transport delay from engine to the sample location ... 187
A.4. Calculation of NOx mass flow ... 188
A.4.1. Scope ... 188
A.4.2. Calculation from wet exhaust condition ... 191
Oxygen balance for λ approximation ... 191
Carbon balance for λ approximation ... 192
A.4.3. Calculation from dry exhaust condition ... 193
Oxygen balance for λ approximation ... 193
Carbon balance for λ approximation ... 193
B PRINCIPLE OF NORMALIZATION AND LINEARIZATION ... 196
B.1. Normalization ... 196
B.2. Linearization of algebraic operation ... 196
B.3. Linearization of differential equation ... 196
C NON-LINEAR MODEL STRUCTURE ... 200
C.1. Mean value first principle diesel engine model ... 200
C.1.1. Gas exchange ... 201
C.1.2. Closed Cylinder process ... 202
C.2. Propeller model ... 202
C.3. Fluid velocity calculation ... 203
C.3.1. Derivation of the Reynolds transport law ... 203
C.3.2. Mass balance analysis ... 205
C.3.3. Force balance analysis ... 206
C.3.4. Momentum balance ... 206
C.4. Approximation of density distribution ... 207
C.4.1. Local density approximation ... 208
C.4.2. Numerical approximation of average density ... 208
C.5. Dredge pump model ... 209
C.5.1. Flow determination ... 209
C.5.2. Pumping water ... 209
Calculation of the Euler pressure difference ... 209
Calculation of the theoretical pressure difference ... 212
Calculation of actual pressure difference ... 213
Calculation of pump shaft power ... 214
Calculation of pump efficiency ... 216
C.5.3. Correction to slurry transportation ... 216
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E.1. Validation of power supply system in steady state operations ... 226
E.2. Validation of propulsion system in steady state operations ... 233
F LINEAR MODEL IN FREQUENCY DOMAIN ... 236
F.1. Propulsion energy system ... 236
F.1.1. Transfer functions ... 236
Overall transfer function ... 237
F.1.2. Results of the numerical example ... 242
F.2. Dredging energy system ... 245
F.2.1. Transfer functions ... 245
Overall transfer function ... 246
F.2.2. Results of the numerical example ... 250
Summary ... 251
Samenvatting ... 253
Acknowledgement ... 255
1
CHAPTER 1
2
1 INTRODUCTION
1.1. Background and relevance
1.1.1. What price speed?
In (Gabrielli & von Karman, 1950), the authors stated:
“The history of technique and engineering testifies to the irresistible urge of humanity toward increasing the speed of locomotion. Means of locomotion on the ground, on the surface of, and within water, through the air, and perhaps through empty space, compete in an ever-growing effort toward higher velocities. At a certain speed, any particular type becomes so inefficient and
uneconomical that it is unable to compete with other more appropriate types.”
In their paper, the tractive force P/V required per gross weight W of the vehicle is plotted against the maximum speed V, as shown in Figure 1. 1. A virtual limit line is drawn. Apparently, for a certain transport mode, when approaching this limit for a further increase of speed more energy is required for transporting the same amount of weight. Gabrielli & von Karman argue that Froude number (represents a ratio between inertial force in the fluid and gravity), Reynolds number (represents friction resistance) or Mach number (represents compressibility effects) but more importantly structural effects (i.e. material properties) make the technical solution inefficient and uneconomical. For decades, research is focusing on reducing the slope and/or position of the limit line to achieve higher maximum speed with fewer penalties on the specific tractive force.
1.1.2. What price transportation?
Currently, due to economic and environmental pressure, high maximum speed is no longer the highest priority in design for most transportation modes as illustrated by the abandonment of commercial supersonic flight, suspension of some large High Speed Ferry services and slow steaming in general. Instead, more and more attention has been paid to the question: What price transportation? Price implies both economically and environmentally cost, which could have a strong financial and social effect in the future.
Looking at the shipping industry, ‘transport’ represents the prime objective of most ships, i.e. transporting a certain amount of ‘cargo’ over a certain distance with a certain speed (setting aside naval ships, tugs and other special ships whose prime mission is delivering some 'complex service’). Therefore, the price of transportation must no longer be the specific tractive force (P/V, which actually is energy per distance covered), but should be the amount of consumed fuel (or emissions in case of the environmental aspect) per mile. Also the weight should be replaced by payload as already recommended by Gabrielli & von Karman in their paper. In fact in 1950 they already argued:
“Of course the real measure of economy should be the work necessary to transport certain useful load, over a given distance. Therefore, our conclusions – in so far as economy is concerned – are strictly correct only if the ratio of useful load to gross weight remains constant. The authors hope that somebody will carry further the present analysis substituting the useful load for the gross weight.”
Because of its large cargo/passenger capacity and relatively low speed, for decades, shipping has been considered as the cheapest (financially) and the cleanest (environmentally) of all transport modes. But, in fact, in terms of total amount, the shipping industry consumes more energy than its competitors and has a very large environmental impact: according to the Second IMO GHG Study 2009 (IMO 2009), which is the most comprehensive and authoritative assessment of the level of GHG emitted by ships, international shipping was estimated to have emitted 870 million tonnes, or about 2.7% of the global man-made emissions of CO2 in 2007.
Meanwhile, because fuel costs can, depending of the type of ship, represent as much as 50-60% of total ship operating costs, the priority of the ship owners to reduce fuel consumption has been always high. In particular during the recent economic recession, ship speeds have been reduced remarkably to achieve more favourable fuel consumption per ton payload per mile. In addition,
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Figure 1. 1 Specific resistance of single vehicles (Gabrielli, 1950)1.1.3. What price dredging?
Producing dredging vessels and stationary dredging equipment makes up only a small fraction of the activity in the shipping industry as a whole seen world-wide, but for the Netherlands having the market leader within its boundaries, it is a different situation. Because of its unique working principle, working method and working environment, the dedicated question in this case should be: what price dredging?
- In principle, dredging vessels can be considered as ‘special’ transport vessels. Contrary to ‘normal’ transport vessels, a dredger normally transports its ‘cargo’ (dredged material) both vertically and horizontally and the latter for a relative short distance compared to normal ships. The vertical transport is done by hydraulic means (two phase flow) while the horizontal transport is a combination of sailing and hydraulic transport.
- In the dredging industry, the quantitative indicator for ‘traditional’ transport vessels: g/ton-mile for fuel consumption is no longer appropriate. Instead, g/m3 of dredge material is of the more
4
interesting parameter. Therefore, to achieve high efficiency, reducing fuel consumption and increasing production are of equal importance.
- Most of the dredging ships/equipment operate in port regions which often are close to highly populated areas, therefore, of the urgency of reducing air emissions is much higher than for ‘traditional’ transportation vessels and not seldom conditions are negotiated and agreed in the contract.
- Because of its unique working principle and the requirement of high production rates, the performance of the energy system tends to be very dynamical. It is influenced by the local environmental conditions (the weather, tide & current, the sea bottom profile, the characteristics of the material, the discharge site location etc.) and the excavation process (erosion, excavation and cutting)
1.1.4. Focus on TSHD
A Trailing Suction Hopper Dredger (TSHD) is different from stationary dredging equipment. This type of ship works in cycles:
- The empty ship sails to the dredge location,
- Then it excavates and (hydraulically) transports the material from the sea bottom to its hopper,
- Subsequently it sails to discharge location with a fully loaded hopper of dredge material, - Finally it discharges and then sails to the dredge location again.
Therefore, a TSHD essentially has two energy systems:
- The propulsion energy system: the drive-train for the propulsion provides thrust to overcome the resistance of the moving ship
- The dredging energy system: the drive-train for the dredge pump provides a pressure head to overcome the pressure losses of the hydraulic transport process.
Both the propulsion energy system and the dredging energy system always are dynamically loaded to get the best performance per working cycle, which, as described in 1.1.3, should in fact be defined as the total consumed fuel per produced material (g fuel/ m3 material). So, reducing fuel consumption and increasing production are two ways of achieving it. However, the latter concerns the excavation process and hydraulic transportation process while the first really is the topic of concern within the field of marine engineering. Therefore, this research will deal mainly with the fuel consumption and consequently the emissions.
In order to reduce both fuel consumption and exhaust emissions, the very first step is to understand and be able to predict the behaviour of the two energy systems and in particular during dynamic and transient operations. However, reliable data under real operational conditions is scarce and the understanding of the influence of dynamic load on the energy system behaviour and emissions are only fragmentary. This research is therefore conducted to answer the question: How does the dynamic load influence the energy system behaviour (i.e. the fuel consumption, the exhaust emissions and the response to disturbances) of TSHDs?
1.2. Research objectives
To answer the research question, the goal of this PhD research is set as:
To know, to capture, to understand and to be able to predict the behaviour of the energy system of TSHDs, in particular under dynamic loads
To obtain knowledge of the behaviour of an energy system, the straightforward way is to do measurements. As far as known, such real time measurements have not been carried out on board of TSHDs. Therefore, the first objective of this PhD research was to prepare and carry out real time measurements on board of TSHDs and analyse the measured data.
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is in the B category. In the same paper the authors argue that for simulations in the B category first principle time domain simulation models are required and these will be developed in this thesis.
Although a time domain simulation model is able to provide insight to the system, it needs a considerable amount of system details for development and matching. It is therefore not very effective in the system design stage, when prediction of trends and potential problems are required in the first place. Meanwhile, regarding the preparation and post-processing of the onboard measurements and the execution of the time domain simulation model, one of the important issues is to determine the response of the system to the ‘disturbances’. Analysis in a frequency domain could then be productive. In addition, analysis in the frequency domain is also important to preliminarily determine the necessity of a control system and exam the control strategies of an existing or new energy system. Therefore, linear models in the frequency domain of the energy systems are derived and verified here.
The objectives and the related research questions are summarized as:
- Design and execute on board real time measurements of hopper dredgers. o What operational variables need to be measured?
o How to produce an organized dataset from on board real time measurements? o What can be observed and concluded from the measurements?
- Develop a time domain simulation model that is able to give insight to the energy system and good prediction under dynamic load:
o What should the architecture of such a complex model look like?
o What models are available for the development of the simulation model and what models need to be developed?
o What is expected from the simulation model in terms of output data?
o What is the validity of the total model with respect to the power supply system variables, the propulsion system variables and the dredging system variables?
- Derive a frequency domain model that is able to show the response of the energy system to external disturbances and control commands:
o How to transform a complex non-linear system to a linear model?
o How do the external disturbances and control commands propagate through the drive-train?
o What are the key parameters and can they be easily estimated? o What are the applications, advantages and limits of a linear model?
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1.3. Outline
This thesis is structured into 7 chapters and 5 appendices.
Chapter 1 gives the research background, the objectives and the outline.
Chapter 2 presents all the features related to onboard measurements, from the selection of the considered energy systems to the selection of measuring variables, from the data post processing to the presentation of the results and a discussion of those results. This is the very first time that, during real dredge cycles, measurements on energy system behaviour have been executed real time onboard of TSHDs, therefore it can be seen as the most important contribution of this work.
Chapter 3 and Chapter 4 deal with the development of the simulation models in the time domain, the so-called ‘non-linear’ models. In Chapter 3, the simulation model is structured as a combination of three subsystems: the power supply system, the propulsion system and the dredging system; while in Chapter 4, all subsystems are verified, matched and validated. Furthermore, the simulation model is also validated as a whole to obtain a certain degree of confidence.
Chapter 5 explores the response of the hopper dredger’s energy system to the external disturbances and the control commands by means of linear models. By implementing a linear approximation, both the propulsion system and the dredging system can be linearized in such a way that the parameters can still be linked to the original non-linear model. The linear models give a clear route to trace how the external disturbances and the control commands propagate through the energy system. It provides an opportunity to detect the key parameters which determine the system responses. In addition, by means of two numerical examples, it is demonstrated that, in the case of small variation around a specific operation point, around which the system is linearized, the two linear models give similar results (in terms of amplitude and phase) to the non-linear model.
Chapter 6 further evaluates the possible applications and limitations of the linear models. The already matched and validated non-linear model is used to investigate how the linear model is influenced by the amplitude of the variations and by varying the operation point.
In the final Chapter 7, conclusions are drawn and recommendations given.
In all chapters, the important information, such as, the measurement results, the model structure, the matching results and validation results of the time domain simulation model, the overview of structure of the linear models, etc., are included in the main text, while details, such as the post processing of the measured data, the theoretical background of the simulation model, the derivation of the transfer functions for the linearization, etc. are addressed in separate appendices.
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CHAPTER 2
8
2 ONBOARD MEASUREMENT
2.1. Introduction
2.1.1. Characteristics of a dredge cycle
A Trailing Suction Hopper Dredger (TSHD) is a complex and special type of ship. It operates in cycles, transporting dredge material both vertically and horizontally. In each dredge cycle, there are four main operations, i.e. sailing empty from the discharging location to the dredging location, dredging, sailing loaded from the dredging location to the discharging location and discharging (by means of dumping, pumping ashore via pipes or rainbowing), an indication is given in Figure 2. 1. Between the main operations, there are additional activities, such as manoeuvring the ship, starting or stopping the dredge pump, releasing or lifting the suction pipes, etc.
Figure 2. 1 Dredge cycle of a TSHD (reproduced from (Braaksma, 2008))
From the energy management point of view, the activities in a dredge cycle can be divided into two categories: the activities of the propulsion system and the activities of the dredging system. Descriptions are given in Table 2. 1. For the propulsion system, the activities are determined by control commands (the engine speed setting and the propeller pitch setting) and are affected by payload condition and external disturbances, i.e. waves, current, wind and extra resistance from suction pipes and drag head (when applicable). For the dredging system, the activities are determined by the control commands (the suction pipe angle setting, the speed control or flow control of the dredge pump) and are affected by the dredge conditions, i.e. the dredge depth (adapted by adjusting suction pipe angle), the properties of the dredge materials, the excavation process and etc.
2.1.2. Objectives of onboard measurements
The scarcity of actual performance data on TSHD's has made it difficult to capture the characteristics of the load to the energy system and consequently, to investigate the impact of the dynamic load on the energy system behaviour, in particular to the fuel consumption and the exhaust emissions. Therefore, the necessarily first step is to perform real time measurements on board, for which three objectives must be achieved:
- In various operating conditions, the influence of the load upon the energy system must be captured
- In various operating conditions, the behaviour of the energy system, in particular the dynamic behaviour must be uncovered
- An extensive database of time signals must be collected, providing a solid basis for matching and validating the simulation model (details are given in Chapter 4)
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dredging low ship speedmedium or high propeller load variable payload
dredging mode high pump load
manoeuvring
manoeuvring mode increasing ship speed increasing propeller load full payload
dredge pump stops
sailing loaded
sailing mode high ship speed high propeller load full payload
n.a.
manoeuvring
manoeuvring mode reducing ship speed reducing propeller load full payload
dredge pump starts
in the case of dumping, jet pumps start as well
discharging
dumping
sailing mode medium ship speed medium propeller load variable payload
high jet pump load
pumping ashore
idle mode zero ship speed low propeller load variable payload
discharging mode high pump load
rainbowing
idle mode zero ship speed low propeller load variable payload
discharging mode high pump load
manoeuvring
manoeuvring mode increasing ship speed increasing propeller load no payload
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2.2. Design of measurement
2.2.1. Selected energy systems
Measurements on three different energy systems were executed real time onboard. The related drive-train configurations are illustrated in Figure 2. 2 to Figure 2. 4. The general specifications of their nominal operating points are given in Table 2. 2.
Figure 2. 2 Energy system A: diesel engine directly drives CPP
Figure 2. 3 Energy system B: diesel directly drives CPP + diesel electrically drives dredge pump
Figure 2. 4 Energy system C: diesel engine directly drives dredge pump Table 2. 2 Nominal point and applications of the test energy system
Energy system A Energy system B Energy system C
Nominal point Peng [kW] Neng [rpm] Nprop [rpm] Npump [rpm] 2021 1000 177 n.a. 8700 600 128 180~200 & 270 2021 1000 n.a. 195 & 279
Application Diesel direct driven CPP
Diesel direct driven CPP Diesel electrical dredge
pump drive
Diesel direct dredge pump drive (variable speed)
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In Table 2. 5, a summary is given. For a complete list of measured variables refer to (Shi, 2011b, Shi, 2011c). A list of the measured variables used in this thesis can be found in Appendix A.1. In addition, two separate exhaust gas measurement systems (System H and System F) were installed for both Energy system A and Energy system B to ensure the quality of the measurements and create redundancy. Specifications are given in Table 2. 6.
Table 2. 3 Description of different operations
commands to energy system ship status
steady state operation remains remains
transient operation changes changes
dynamic operation fluctuating fluctuating
Table 2. 4 Measured operating conditions of the considered energy systems measured operating conditions category
Energy system A1
(trial operations)
benchmark points2
(14%, 32%, 47%, 62%, 77%, 101% of nominal power) steady state
ship acceleration transient
ship deceleration transient
dredging dynamic
crash stop transient
Energy system B1 (real life operations)
benchmark points2
(35%, 60%, 84%, 90% of nominal power) steady state
entire dredging cycle steady state + dynamic
Energy system C1
(trial operations) dredging dynamic
Note:
1. For the Energy system A and the Energy system C, the measurements were performed during external collaboration projects; while the measurements of Energy system B were performed by the author himself specifically for this PhD research
2. Because of the effects of external disturbances, i.e. waves, current and wind, ideal steady state operations cannot be achieved. Instead, measurements of benchmark points were performed during sailing with fixed speed in good weather conditions (negligible wind and waves)
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Table 2. 5 Selected measuring variables of considered energy systems and their applications
measured variables applications
Energy system A
system inputs and outputs understanding dynamic behaviour engine operational variables model matching and validation
Energy system B system inputs and outputs understanding dynamic behaviour
Energy system C dredge pump operational variables
pipeline system operational variables model matching and validation Table 2. 6 Specifications of exhaust gas measurement system
System H System F
measured components CO, CO2, O2, NO (as NOx) O2, NO (as NOx)
sample gas condition dry wet
response time (T90) approx. 45s for NOx approx. 1.5s for NOx
2.3. Data post-processing
Post-processing converts the raw measurement data into an organized dataset. It consists of five steps: signal synchronization, correction of sensor time lag, filtering, signal organization and unit conversion. By means of signal synchronization and signal organization, the measured time signals are, on the time axis, corrected (time events are reconstructed); while by means of filtering and unit conversion, the measured variables themselves are corrected quantitatively (i.e. their amplitudes). Correction of the sensor time lag results in both corrections on the time axis and a change of the amplitude of the signal (sensor with slow response behaves like a filter, the output signal is therefore smoother than the original. By correction, the actual fluctuation or the original larger amplitude is recovered).
2.3.1. Signal synchronization
With respect to measuring a large amount of variables real time onboard, the simultaneity of measured signals is important. Firstly, the measured variables were logged by multiple logging systems, for which the starting points (the ZERO of every time event) may have a time shift. Secondly, sensors were installed at convenient locations instead of appreciated locations, from which transport delays occur at locations of interest, in particular for the exhaust gas measurements. Therefore, signal synchronization is a necessary first step of time events reconstruction.
To avoid subjective decisions on signal synchronization, cross-correlations are employed to detect and examine the time shift/time delay between any two time signals. They are performed with tstool© from Matlab®.The demonstration of the cross-correlation method and examples of applying the method to the measured signals are given in Appendix A.2. In this section, only the results are presented.
Synchronization of time shift
The signal of fuel rack position, being one of the control commands (the inputs) to the energy system, is selected as the reference signal for synchronization of time shift between different logging systems. After comparing and shifting the zero point, examples of synchronized signals are given in Figure 2. 5 and Figure 2. 6.
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Figure 2. 6 Synchronized fuel rack signal of Energy system B(left: an example period and right: a 1sec zoom in)
Note:
- All the measured signals from Energy system C were logged by a single logging system, this
step is therefore skipped
- In Figure 2. 5, the measured fuel rack varies in different logging systems. Actually, only the
signals given by logging system 3 gives the real fuel rack position, the other two are not calibrated and therefore only may be used for synchronization. In Figure 2. 6, the signals from both logging system 1 and 2 give the real fuel rack position, the differences shall be considered as measuring errors, which are within approximately 0.5%
Synchronization of time delay
To measure the composition of the exhaust gas, a certain amount of exhaust gas is sampled and transported to the analysis equipment. Ideally, the sample location should be right after the engine exhaust valve in order to detect the ‘real’ composition of the engine-out exhaust gas. However, practically, the sample location is selected somewhere on the exhaust gas pipe. As addressed in (IMO, 2008): ‘The sampling probes for the gaseous emissions shall be fitted at least 10 pipe diameters after the outlet of the engine, turbocharger, or last after-treatment device, whichever is furthest downstream, but also at least 0.5 m or 3 pipe diameters upstream of the exit
of the exhaust gas system, whichever is greater’. Then, from the engine exhaust valve to the
sample location, there is a time delay due to exhaust gas transportation. Meanwhile, from the sample location to the gas analysis device, there is always a connection tube, in which an additional transport time delay occurs. Moreover, inside the analysis equipment, from the sample inlet to the sensor(s), because of internal arrangement, there is another transport time delay. To correct those transport time delays, a step approach is derived. The full description of this
3-14
step approach is given in Appendix A.3. Example of results from Energy system A are shown here while detail results can be found in (Shi, 2011a and Shi, 2011c).
Note: For better visualization, the signals are presented as the ratio to its mean.
Step 1, the internal gas transportation from the device inlet to each sensor is corrected. The results are given in Figure 2. 7.
Figure 2. 7 Synchronization within System H and System F, after step 1
Step 2, the transport delay between the sample location and the inlet of the gas analysis equipment is corrected. The results are shown in Figure 2. 8. Up to now, the exhaust emission measurements from two independent measuring systems are synchronized, respectively.
Note: in Figure 2. 8, the measured values of NOx volumetric ratio from System H and System F are still different. The reason is two-fold. First System H is a so-called dry measurement and System F a wet measurement and second they exhibit different sensor dynamics. Details will be given later in this section.
Figure 2. 8 Synchronization between System H and System F, after step 2
Step 3, the transport delay from the diesel engine (after turbocharger) to the sample location is corrected. The assumption is made that, along the time axis, when the injected fuel to the engine varies, the NOx volumetric ratio in the exhaust gas would immediately change. Then, the fuel flow is selected as an indicator of the time events associated with engine performance, while the NOx signal from System F is selected to indicate the time events of the exhaust gas measurements. After synchronization, the results are given in Figure 2. 9.
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filter is developed. The block diagram is given in Figure 2. 10. The comparisons of NOx signals between System H and System F before and after signal recovering are given in Figure 2. 11.
1 1 H s s (2.1)Where is the time constant of the sensor.
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Figure 2. 11 Comparisons of NOx from System H and System F
Note: As addressed previously, in Figure 2. 8, the differences of NOx signals from System H and System F are caused by both the dry/wet measurement difference and the sensor dynamics. In Figure 2. 11, to avoid the influence from the dry/wet difference, after correction, the sample gas of System H has been further converted to the same condition (wet exhaust) as System F, again for details see later in this section.
Be aware that in principle the NOx sensor could behave like a second order system or even a more complicated system other than the assumed simple first order system. Then theoretically, a more complex anti-filter system needs to be implemented. But, as shown in Figure 2. 11, the assumption of a first order system gives good similarity between the NOx signals from System H and System F. Therefore, the simple anti-filter as shown in Figure 2. 10 has been used.
2.3.3. Filtering
The shaft torque measurements were performed with very high frequency. Therefore, torsional vibrations are captured. An example is given in the left-hand side of Figure 2. 12, which is the measured torque from the propeller shaft of the Energy system A. Due to torsional vibration, the measured time signal of shaft torque has in fact a band-shape. To detect torsional vibrations, the method of Fast Fourier Transform (FFT), which is considered as an efficient algorithm to compute the Discrete Fourier Transform (DFT), is implemented. Details can be found in (Smith, 1999). The resulted frequency distribution is given in the right-hand side of Figure 2. 12. Clearly, the torsional vibrations are related to both the engine performance and the propeller performance. Although the gearbox and the flexible coupling between the diesel engine and the propeller shaft already absorbed most of the vibrations from engine side, the influence from engine still remains.
Figure 2. 12 Example measurements in time domain and in frequency domain
Concerning the objectives of the onboard measurements, which are uncovering the unknown features of dynamic behaviour and providing an extensive dataset for model matching and validation, neither of them requires information on torsional vibration of the shaft. Therefore, it is decided to filter them out.
17
Figure 2. 13 Results of frequency filtering2.3.4. Signal organization
The measuring signals were logged with different sampling frequencies by different logging systems. In order to produce a well-organized dataset, all the measured parameters are further brought under a fixed frequency.
The rule of thumb of capturing dynamic behaviour is that, there need to be at least five measuring points during one single transient (step) change. According to an analysis of the sensitivities of the diesel engine to a dynamic load (see Chapter 5), the frequency band of interest is about < 2 Hz, from which, the period of transient changes that one is interested in is about > 0.5 s. With five points per single transient change, it was decided to select 10 Hz as the basic frequency for the dataset. On the one hand, most of the variables were measured onboard with frequencies equal or higher than 10 Hz, reducing the frequency to 10 Hz is feasible and accurate; on the other hand, all the state variables, e.g. the ambient conditions, were measured with a frequency in an order of magnitude of 1 Hz. Because they are relatively stable compare to the operational variables, increasing the frequency to 10 Hz by interpolating is also feasible and acceptable. Therefore, it is decided that, the signals are organized to the fixed frequency of 10 Hz.
2.3.5. Unit conversion of NOx emission
Both System H and System F give NOx volumetric ratio , yNOx in ppm (often, ppmv is used to
specify the volumetric (or number) ratio as opposed to the mass ratio , but since throughout this thesis, no mass ratio is considered, ppm is used all over). From a cost/benefit point of view, volumetric ratio gives no straightforward information and by legislation, NOx emission is regulated as g/kWh. Therefore, the power specific NOx, spNOx in g/kWh, is a more attractive measure. The conversion of spNOx from yNOx is presented in this section.
The definition of spNOx is given in Eq. (2.2). 1000 def NOx eng m spNOx P (2.2)
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NOx
m is the mass flow of NOx (kg/h) calculated by Eq. (2.3), while Peng is the engine power (kW),
calculated from measured torque and speed (in the case of no torque and speed being measured, it can be roughly derived from the fuel rack position, as presented in (Shi, 2010c).
2 6 10 NO NOx NOx exh exh y m m (2.3)
with yNOx the volumetric ratio of NOx in exhaust gas in ppm which is measured onboard; ρNO2
(kg/m3) and ρexh (kg/m 3
) the density of NO2 and exhaust gas which are known constants.
Note: normally, the volumetric ratio of NO (sometimes NO2 is also converted to NO by
de-oxidation) is measured as an indicator of NOx. While in Eq. (2.3), the density of NO2 is used for
calculation of NOx mass flow. This approach is following the procedure introduced in (IMO, 2008)
to measure NOx as NO2 as it ultimately is converted to that form in the atmosphere.
Because exhaust gas flow (mexh) cannot be measured on board (any obstruction of the gas exhaust flow or inlet flow is not allowed as it would impair the turbocharger), it has to be estimated.
In (IMO, 2008), a method (which is called IMO method in this thesis) of calculating mNOx (in g/h) from the measured yNOx (in ppm) is published. But, there are difficulties when implementing the
published formulas:
- They are complex and no introduction of the physical background is given, making them barely transparent
- Volumetric ratio of dry CO2, dry CO and wet HC in the raw exhaust gas must be known. For
some measuring systems, e.g. System F, there is not enough information to complete the calculation
To solve the problems, a separate method, which was developed at NLDA (Netherlands Defence Academy) is used in this thesis (the NLDA method). Details are referring to (Stapersma, 2005; Stapersma, 2010)) and augmented in this PhD research is considered as well. Similar to the IMO method, the NLDA method also calculates mNOxfrom the measured exhaust gas composition. It first gives the air excess ratio (λ) by means of a carbon balance or oxygen balance approach from either dry exhaust or wet exhaust condition. (Note: it is the total air excess ratio as measured in the exhaust rather than the air excess ratio in the cylinders). Then together with the stoichiometric air/fuel ratio (σ, which is constant for a given type of fuel oil), it calculatesmNOx. The derivation is presented in Appendix A.4. Compared to the IMO method, it has several advantages:
- The derivation has full physical background, making the method transparent and easy to modify or improve
- The approach of λ calculation can be achieved by either carbon balance or oxygen balance from either dry exhaust or wet exhaust condition. It can therefore easily be implemented to different measuring systems
The exhaust gas composition was measured independently by System H and System F. Then, there are several options of calculating mNOx, as shown in Table 2. 7 and Figure 2. 14.
Table 2. 7 Available options of NOx mass flow calculation
IMO method NLDA method dry exhaust carbon balance dry exhaust oxygen balance wet exhaust carbon balance wet exhaust oxygen balance System H √ √ √ x x System F x x x x √
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,
NOx IMO
An example is given in Figure 2. 15. It shows that, IMO method and NLDA method give slightly different mNOx. Because of both the IMO method and the NLDA method are dealing with the same piece of measurement data and performing carbon balance for λ calculation, these small differences are most likely from the theoretical errors introduced during formula derivations. However, since the formulas from IMO method are not transparent, it is not possible to detect precisely how and where these theoretical errors are introduced.
Figure 2. 15 Calculation results from IMO method and NLDA method
- Dry exhaust vs. Wet exhaust
To avoid additional influences from the calculation method and the calculation of λ, for comparison mNOxis calculated from now on by the NLDA method with the oxygen balance for λ calculation. In order to compare the dry and wet method another NOx mass flow ratio is therefore defined by Eq. (2.5).
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, , , , , ,
def
NOx NLDA dry oxygen exhaust
NOx NLDA wet oxygen m
r
m
(2.5)
An example is given in Figure 2. 16. Since the mNOx from both measuring conditions are calculated by NLDA method with oxygen balance for λ calculation, the contribution of theoretical errors to rexhaust is cancelled out. The fluctuation of rexhaust around unity comes mainly from the
measuring errors. For instance, due to limited information, corrections on sensor time lags are not performed for O2 measurements. The measuring errors of oxygen volumetric ratio are propagated
to the λ calculation, and eventually to the calculation of mNOx.
Figure 2. 16 Calculation results from dry exhaust measurements and wet exhaust measurements
- Carbon balance vs. Oxygen balance
To avoid additional influences from the calculation method and the exhaust conditions, yet another NOx mass flow ratio is defined by Eq. (2.6) in order to compare the carbon versus oxygen balance. An example is given in Figure 2. 17. The difference comes from two parts: first,
the λ calculation based on carbon balance and on oxygen balance requires different measured
variables (carbon balance needs CO2 volumetric ratio, while oxygen balance needs O2 volumetric
ratio), which involves measuring errors; second, the deviations of the λ calculation on carbon balance and on oxygen balance is based on different assumptions, which result in different theoretical errors.
, , , , , ,
def
NOx NLDA dry carbon balance
NOx NLDA dry oxygen m
r
m
(2.6)
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transparency of a method, the decision is open to the user
In this thesis, the decision is made that, the NOx volumetric ratio measured from System H, the dry exhaust measurement, is used (although ambient humidity was measured, employing dry exhaust measurement can just avoid an additional measuring error from ambient humidity). Further the unit conversion from ppm to g/kWh will employ the NLDA method (because of its transparency) with a carbon balance based calculation of λ (the CO2 volumetric ratio in exhaust
gas only depends on the combustion process but O2 volumetric ratio in the exhaust gas depends
on both the combustion process and the O2 volumetric ratio in ambient air, the latter could be an
extra error source).
Then, Eq. (2.2) is rewritten as:
, , ,
1000 NOx NLDA dry carbon
eng m spNOx
P
(2.7)
In addition, NOx emission depends strongly on ambient air conditions as well, as proposed in (Boot, 1994). In (IMO, 2008), it is regulated that, the calculated spNOx must be further corrected for ambient air temperature and humidity by a so-called humidity correction factor khd, as shown
in Eq. (2.8). However, the mechanism of the influence of ambient air conditions on the NOx emission is not fully understood. Therefore, the formula proposed by in (IMO, 2008) is directly implemented.
hd hd
spNOx k spNOx (2.8)
Simplified approximation
In Appendix A.4, Eq. (A.24) and Eq. (A.41) are the derived formulas of calculating the mass flow of exhaust gas. With respect to the facts that:
- The fuel mass flow (mfuel) only accounts for about 2 ~2.5% of the total exhaust gas mass flow; - The hydrogen content of fuel ( fuel
H
x ) is small, accounts for about 10 ~ 15% of the fuel mass; - The water content of real air ( wet_air
water
x ) is small, accounts for about 1% of the air mass.
_ _ 1
1
wet exh wet air fuel
water m m x ref.(A.24) 2 _ 1 1 2 H O fuel
dry exh H fuel
H M m x m M ref.(A.41)
Then, by ignoring minor factors, Eq. (A.24) and Eq. (A.41) are simplified and united as Eq. (2.9).
1
exh fuel
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Substitute Eq. (2.9) into Eq. (2.3), a useful approximation of mNOx (for a given fuel) is achieved:
2 6 1 10 NO NOxNOx fuel NOx fuel
exh y m m C y m (2.10)
2 6 1 10 NO NOx NOx exh y spNOx sfc C y sfc (2.11)
2 6 1 10 NO NOx hd hd hd NOx exh y spNOx k sfc C k y sfc (2.12)with C a constant for a given fuel type.
The use of this simplified approximation for explanatory purposes as will be addressed later in this chapter.
2.4. Results and discussions (constant engine speed)
2.4.1. Dynamic load to the energy system
As observed from the measurements, the controlling commands, the payload condition, the environmental conditions (such as the weather condition) and the operational conditions (i.e. the dredge conditions) have impacts on the load to the energy system, in particular of the main diesel engine. In this section, from measurements of Energy system B, details are explored.
Normal distribution (which is produced by the Statistic© Toolbox from Matlab®) is used to describe mean values and standard deviations. In addition, Eq. (2.13) is introduced to calculate the relative deviation, which indicates the ratio between the standard deviation and the mean value.
def r (2.13)
where μ and σ are the mean value and the standard deviation of the normal distribution.
Dynamic load to the propulsion system
Impacts from weather conditions. Figure 2. 18 gives the propulsion power (calculated by
measured propeller speed and the torque measured behind the gearbox) during operations with no payload in three different weather conditions (described in Table 2. 8).
Note: the ship performance has to adapt local environment conditions and operating conditions, therefore when measuring in different weather conditions, the ship speeds were different, which can be observed from the mean values in Figure 2. 18. This is also one of the reasons that, relative deviations are introduced to characterise the amplitude of measured signals.
Table 2. 8 Description of weather conditions during onboard measurements WT1: good weather with no wind and small waves (approximately sea state 1) WT2: weather condition with medium wind and waves (approximately sea state 2~3) WT3: bad weather with strong wind and large waves(approximately sea state 4~6)
Observed from the left side of Figure 2. 18, when operating in different weather conditions, not only the amplitudes of the load but also the frequencies are different. Since the measurements were all performed with no payload, which implies no change of the ship stability, the frequency differences are mainly caused by wave conditions. Then, it may be concluded from the right side of Figure 2. 18 that, bad weather conditions with strong winds and large waves cause a larger load amplitude to the propulsion system.
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Figure 2. 19 Results from Energy system B sailing in WT3 with full payload and no payloadEnergy system B operates as the Starboard propulsion system. When carrying full payload, the ship was sailing at a reduced speed (for stability reasons) while the waves came from Starboard. When carrying no payload, the ship was sailing at (nearly) full speed with the waves coming from Portside. Firstly, due to the position of propeller to the (approximately) cross waves, the impact of the waves on the energy system are different. Secondly, in full payload condition, the total weight of the ship was more than twice as much compared to the no payload condition, while the speed was about 85%, so that, in similar wave conditions, the frequency of ship motion tends to decrease. Thirdly, in full payload condition, the restoral moment of the ship increases, from which the frequency of ship motions tends to increase while the amplitude decreases. Fourthly, although based on visual observation, during these measurements, the wave conditions were similar and the directions were perpendicular to the ship sailing direction, the actual wave spectrum and direction is unknown and they would affect the propulsion load as well. Therefore, combining the above four aspects, it is concluded that, both the variations of amplitude and frequency were caused by combined effects from the payload condition, the ship speed, the wave condition and the propeller location. But it is believed that, the payload conditions (which determine the total inertia of ship motion and the restoral moment of the ship) dominate the amplitude. However, to understand this observed phenomenon requires deep knowledge on ship motions in waves (which is beyond the current research scope) and details on wave conditions, therefore, no more details are presented.
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Impacts from ship speed. Sailing in waves, the ship motions as a result of the wave conditions
depend also on the ship speed. The measured results are given in Figure 2. 20. From the left side of Figure 2. 20, the results show that, when operating in the same wave with the same payload condition, the amplitude of the load on the propulsion system is somehow independent of the ship speed, but, looking at the right side of Figure 2. 20, the relative amplitude increases at low ship speed.
Note: since the real ship speed was not measured, the propulsion load as a percentage of the propulsion power to the nominal engine power is used as an indication.
Figure 2. 20 Results from Energy system B sailing in WT3 with full payload but different speed
Impacts from propeller. Although no measurement was delegated specifically to explore the
dynamic loads caused by varying the propeller pitch, it is believed that, in particular during manoeuvring, the more intensive the change of propeller pitch, the more dynamic the load to the propulsion system.
Conclusions. Summarising the above observations, it can be concluded that, bad weather
(strong wind and large wave), low payload and intensive control of propeller pitch result in large, both absolute and relative, amplitude of the load to the propulsion system, while low speed sailing leads to only large relative amplitude of load on the propulsion system.
Dynamic load to the dredging system
During dredging and discharging, the load to the dredging system depends on the controlling commands, i.e. the pump speed and the suction pipe angle (in order to adapt the dredge depth), and the dredge conditions, i.e. the dredge material and the dredge depth. Measurements during dredging and discharging were performed with two different dredge materials, and the results are given in Figure 2. 21 and Figure 2. 22. It is demonstrated that, for both dredging operation and discharging operation, the finer the dredge material, the lower the level of dynamic load to the dredging system.
Note: the dredging and discharging operations were tested under operations executed by the same dredge operator, and from the reading on the operation bridge, the operations were similar between dredging operations and between discharging operations. Therefore, even it is unknown if although during measurements the automation system was active or not, the load variations caused by controlling commands (not measured) are ignored.
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Figure 2. 22 Results from Energy system B discharging with different dredge materials Dynamic load to the main diesel engineEnergy system B is a combination of diesel direct propulsion drive-train and diesel electrical dredge pump drive-train (shown in Figure 2. 3), implying that for each operational stage of a dredge cycle, the load on the main diesel engine is the sum of the load to the propulsion system and the electrical load (to the dredging system and the auxiliary systems), including losses. Note: the propulsion power is measured behind the gearbox (in fact is calculated as the production of measured speed and torque), the electrical power is directly measured as the active power from the generator. The engine power is calculated as the sum of the propulsion power (including a calculated gearbox efficiency, see Chapter 3) and the electrical power (including an estimated generator efficiency, 96%).
During sailing, as shown in Table 2. 9, the load variation at the generator side (electrical output) remains at the same level in all situations (the electrical output feeds only the auxiliary systems), but the load variation at the propulsion side is affected by the weather conditions, the payload condition and the ship speed. Particularly, the total load to the main diesel engine fluctuates with large amplitudes in bad weather with no payload.
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Table 2. 9 Dynamic load on Energy system B during sailing in different conditions
WT PL SPD Pprop Pelec Peng
μ [kW] σ [kW] σ/μ [%] μ [kW] σ [kW] σ/μ [%] μ [kW] σ [kW] σ/μ [%] SAIL 1 E H 6806.9 101.8 1.49 635.4 73.6 11.59 7608.0 65.9 0.87 SAIL 1 F H 6874.8 72.5 1.05 575.6 55.8 9.70 7614.8 63.3 0.83 SAIL 1 F H 6828.6 92.0 1.35 605.7 51.8 8.55 7599.0 62.3 0.82 SAIL 1 F H 6872.7 49.4 0.72 542.6 30.7 5.65 7578.3 37.3 0.49 SAIL 1 E M 4411.1 33.2 0.75 465.2 20.3 4.36 4991.0 35.9 0.72 SAIL 1 E L 2081.0 17.4 0.84 522.9 23.3 4.45 2678.5 32.2 1.20 SAIL 1 E VL 1185.8 11.6 0.98 548.7 10.1 1.85 1793.7 15.3 0.85 SAIL 2 E H 7254.5 167.6 2.31 451.3 130.2 28.85 7872.0 149.8 1.90 SAIL 2 F M 4053.5 28.4 0.70 548.2 38.0 6.93 4740.9 61.4 1.30 SAIL 3 E H 6243.9 343.7 5.50 1254.1 130.7 10.42 7679.1 339.5 4.42 SAIL 3 F M 4096.9 154.0 3.76 401.4 88.2 21.97 4604.6 162.2 3.52 SAIL 3 F H 7186.4 215.8 3.00 514.7 134.2 26.08 7868.6 205.4 2.61 SAIL 3 F H 6701.2 149.2 2.23 447.8 139.4 31.12 7304.9 179.4 2.46 SAIL 3 F M 4603.5 165.0 3.58 502.9 144.0 28.62 5226.2 180.0 3.44 SAIL 3 F L 2491.1 169.7 6.81 460.5 151.5 32.91 3031.0 141.6 4.67 Abbreviations:
WT: weather condition; PL: payload condition; SPD: speed condition F: full payload; E: no payload;
H: high speed; M: medium speed; L: low speed; VL: very low speed
During dredging, as shown in Table 2. 10, the load variation at the generator side is determined by the dredge material (there should be also impacts from dredge depth, but it was not measured therefore cannot be specified here). The load variation from the propulsion side is influenced not only by the weather conditions, the payload condition, the controlling commands, but also by the dredging condition, which determine the major part of the ship resistance. In combination, the amplitude of the load on the main diesel engine shows no clear trends.
Table 2. 10 Dynamic load to the Energy system B during dredging in different conditions
WT PL SPD MAT Pprop Pelec Peng
μ [kW] σ [kW] σ/μ [%] μ [kW] σ [kW] σ/μ [%] μ [kW] σ [kW] σ/μ [%] DRED 1 V L SA 1877.9 127.4 6.78 3508.3 56.8 1.62 5581.4 153.9 2.76 DRED 1 V L SA 2187.8 185.6 8.48 3435.2 58.0 1.69 5820.8 201.1 3.45 DRED 3 V L ST 1998.9 294.6 14.74 3030.5 143.7 4.74 5206.8 276.7 5.32 DRED 3 V L ST 2893.8 103.6 3.58 3264.4 75.7 2.32 6361.8 116.4 1.83 Abbreviations:
WT: weather condition; PL: payload condition; SPD: speed condition: MAT: material V: variable payload; L: low speed; SA: sand; ST: stone
During discharging, as shown in Table 2. 11, the load variation at the generator side is dependent on the dredge material. But, since the ship speed is nearly zero, the load variation from the propulsion side is only affected by the weather conditions and the payload conditions. Particularly,
