The need for full scale measurements

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THE NEED FOR FULL SCALE MEASUREMENTS

T A Dinham-Peren, BMT Defence Services Ltd, UK

I W Dand, BMT Isis Ltd, UK SUMMARY

In the period 1868 to 1870, William Froude persuaded the Admiralty of the value of experiments on models compared to measurements at full scale. His raison d’être lay mainly in the development of optimum hull forms, with resistance to forward motion the parameter being optimised.

The success of Froude’s vision led to model testing as the most cost-effective way to produce good hull designs and its universal adoption for ship hydrodynamics research and development is testimony to this. It is now supplemented by computer modelling which may, in the future, make the physical model test redundant.

However, Froude’s goal of producing hull forms optimised for purpose is still relevant; indeed with the drive toward “greener ships”, it can be argued that it is more relevant today than it was in Froude’s time. The advent of accurate and comprehensive measurements of in-service performance data at full scale can now provide an accurate picture of ship behaviour throughout a voyage, potentially allowing not only the determination of the true full size vessel performance of the vessel, but also the effects of such factors as; fouling, wind and waves and the benefits of performance improving technologies such as new anti-fouling paints and new hull form and propulsor design, including retro-fit technologies. But problems emerging from these measurements demonstrate how difficult it is to be sure that predicted benefits are gained overall. This paper discusses the analysis of in-service ship powering performance data to determine the actual performance of the vessel. Issues to do with the measurement of the performance data and the environmental conditions are discussed and two possible methods for analysis the data are examined; ‘data normalisation’ and ‘data bucketing’. The overall precision of such approaches are investigated and the implications for the utility of in-service performance measuring systems are discussed. Possible ways forward to give increased precision are outlined.

1. INTRODUCTION

In the period between 1868 and 1870, William Froude negotiated with the Admiralty for the provision of a purpose-built towing tank [1]. The success of these negotiations, combined with Froude’s brilliantly intuitive Law of Comparison, led to the adoption of model testing as the primary means of determining the performance of a full size ship at sea.

For 140 years this cost-effective means of prediction has been developed and improved throughout the world, its essentially analogue approach lately having been joined by its digital offspring, CFD, to provide a vast body of knowledge about why ships behave in the way they do and how their hydrodynamic design can be optimised. But in spite of the undoubted success of model testing, the question most often posed by both the initiated and the uninitiated relates to the certainty with which predictions from models can be made; in other words, how successfully can scale effects be accounted for? The answer to this question, of course, requires accurate and believable full scale measurements. These have been obtained since before Froude’s day from full scale trials of various forms ranging from measured mile and steering trials to data collection from ships sailing on the oceans of the world.

Nowadays, with the emphasis on hydrodynamic and powering efficiency, combined with the reduction of emissions and the economic drivers associated with optimum dry-docking intervals, the need for reliable and accurate full scale measurements of vessel behaviour has seldom been greater. Perhaps most pressing is the need to ensure that various innovative devices proposed as means to save power, do actually provide the savings in the real world that are predicted by model tests or numerical predictions.

The development of on-board measurement packages in recent years means that it is now possible to measure many parameters on board ship either during a formal trial or over a whole voyage. It should, one would suppose, be a simple matter to analyse this data in order to get a clear picture of ship behaviour. Of particular interest are the trends in behaviour over time which allows either decisions to be made regarding hull maintenance, or the effects of improvements to be determined.

It is the purpose of this paper to show that this simple goal is not all that easy to achieve and special techniques are needed to deal with the data obtained from the real world. Indeed one might reasonably pose the thought that perhaps the profession has neglected, to a degree, techniques for dealing with full scale measurements to the extent that it has developed those for model test measurement and prediction. One wonders whether © 2010: The Royal Institution of Naval Architects Page 1 of 13

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Froude himself would have allowed such a situation to develop.

2. TRIAL AND VOYAGE ANALYSIS

It should not be supposed, of course, that analysis of full scale measurements has been entirely neglected over the past 140 years. Builders’ and owners’ trials have, of course, long been the standard method by which ship performance is judged. But they are, in a sense, special cases; the hull surface is usually in a good, trial, condition, the weather is, ideally, benign, the ship may or may not be at its design load condition and the trials area should have been specially chosen or surveyed so that water depths are reasonably constant and significantly greater than the trial draught of the ship.

The concern of this paper, however, is the analysis of voyage data collected in conditions which may be far from ideal, but which nevertheless reflect conditions a real ship experiences for most of its life. Voyage analysis has been carried out by well-known practitioners in the past ([2] to [4] for example) and the ITTC has done much to summarise such work over a number of years while also setting its own standards for full-scale trials [5]. The focus of the ITTC, however, has been devoted to powering performance and scale effect, the correct conduct of trials etc. and it has done much to aid in scaling model test results to full scale; the (1+x) factor used in powering prediction is a case in point [6].

There are problems with voyage data analysis and they arise from the data itself; a simple case with in-service measured data is used to demonstrate.

2.1 VOYAGE DATA ANALYSIS: AN INITIAL CASE STUDY

Assume it is necessary to determine the speed/rpm curve from a set of data collected over a number of consecutive voyages covering an area from the Baltic to the Mediterranean and across the Atlantic to the Caribbean, taking the best part of a year. Figure 1 shows a plot from the complete data set of measured ground speed against shaft speed.

Figure 1: Ship Speed/shaft Speed for Complete Voyage Dataset

The difference between a similar plot from speed trials data and the voyage data is obvious. From a trial, one might reasonably expect the ship speed to be roughly linear with shaft speed (depending on resistance and power train characteristics, of course); such a relationship is not apparent from the voyage data.

The data is widely spread with long “tails” parallel to both axes. These imply:

• A wide range of measured ground speeds for a given shaft speed, some of which are due to the effects of severe metocean conditions (see [3]). • A wide range of shaft speeds for a given ground

speed, some of which would undoubtedly arise from those times when shaft and ship speed are out-of-balance because the ship has reduced shaft speed to slow or stop, and the over ground speed has not yet reduced to match.

Ground speed was measured by a DGPS system and shaft speed by a standard shaft revolution counter. Data was sampled every two or three minutes and, when looked at in detail, does not appear to be randomly dispersed, but suggests instead that the ship speed was seldom steady. It transpired that the ship, a medium-sized tanker, had passed through a number of storms during the period of data collection which could have accounted, at least in part, for some of the data “tails” parallel to the ship speed axis as mentioned above. To study this further, the database was supplemented by satellite-derived metocean data for the same time period and voyage locations. This enabled the results to be windowed under a number of constraints and Figure 2 shows the result for straight line running in benign conditions. The following constraints were applied to the data:

• Wind speed less than 10 knots

• Significant wave height less than 2 metres • Drift angle less than 2 degrees

• “Current” (i.e. the difference between overgound speed and speed through the water) less than 1 knot

• Shaft speed between 0 and 120 rpm (i.e. no astern running)

• Overground speed gradient between successive samples less than 0.5 knot/minute. (This constraint was aimed at searching out those times when the ship was moving at a reasonably steady speed).

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Figure 2: Ship Speed/Shaft Speed for Constrained Voyage Dataset

It is seen that the data now suggests the expected roughly linear relationship between overground ship speed and shaft speed across the range of shaft speeds, albeit with some outliers and spread still apparent in the data. The spread of ship speed at a given shaft speed may be due to measurement scatter, but it might also show a change with time. Such a trend, if valid, is perhaps one of the most important pieces of information to be derived from voyage data. To explore the variation of overground ship speed with time throughout the year, Figure 3 has been prepared. This shows the data, subject to the same constraints as Figure 2, but plotted against days elapsed for a roughly constant shaft speed of 108 ≤ N ≤ 109.

Figure 3: Overground Ship Speed at Shaft Speed 108-109 rpm: Variation with Time.

While it is true that the overground speed seems to be reducing with time after 200 days, it would appear to have increased (or at least remained constant) before then, a period when the ship experienced a number of storms.

If, however, the shaft speed constraint is changed to 119 ≤ N ≤ 121 to encompass the MCR shaft speed, the plot Figure 4 is obtained. This time it is seen that no obvious change of speed with time is apparent.

Figure 4: Overground Ship Speed at Shaft Speed 119-121 rpm: Variation with Time.

It is clear from this simple example that voyage data can, and will, produce results which do not accord with intuition. Why is the downward trend of ship speed at one shaft speed not reflected at a higher shaft speed? Is it due to the ship moving from cold to hot latitudes in its voyages, thereby affecting the performance of its engine? No air or sea temperatures were measured in the exercise described above, but it is known that from day 295 to 362 (in the months of November and December) the ship went from the Mediterranean to the St Lawrence and the Great Lakes, then to the English Channel before sailing in the Baltic Sea by day 362.

There may be other reasons for the behaviour of the data, but this exercise has shown enough to indicate that the interpretation of voyage data is not a trivial task. We now discuss this in more detail and introduce techniques and results with which this task can be tackled.

3. PERFORMANCE RECORDING AND

MONITORING

3.1 THE DIFFERENCE BETWEEN RECORDING AND MONITORING

There are main two reasons for making in-service performance measurements.

The first and most straightforward is Performance

Recording to quantify the actual fuel usage and engine

duty for each voyage and part thereof. Such data is useful in checking actual fuel used as a check against fraudulent bunkerage claims and in settlement of chartering disputes. The measurement of the required data for these purposes is relatively straightforward and can readily be fulfilled with existing fuel flow instrumentation, temperature sensors, shaft torsion meters, revolution counters and the like. Little data reduction is required as the requirement is first and foremost an accurate record of actual fuel use.

The second is Performance Monitoring to determine how well the ship is performing. In principle this can give information on the hydrodynamic condition of the hull and the propeller, the benefit of retro-fit propulsion enhancing devices, the benefit of new types of

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fouling paint and the optimum trim at which to operate the vessel for any given draught and speed.

But in order to be able to realise these benefits it is necessary to:

• Know the actual vessel performance, the environmental conditions and the vessel loading condition at a given point in time.

• Have a method to correct the actual measured performance to what the performance would be for the same vessel loading conditions but in calm deep water with no wind.

• Have a method to compare this performance with some reference performance, eg what the vessel performance should be when the vessel is in good hydrodynamic condition (no fouling or additional roughness).

The comparison with the reference performance can be done in one of two ways:

• Either: Calculate the reference performance at the in-service loading condition.

• Or: Correct the in-service performance data to a standard reference loading condition.

Both of these methods require an underlying performance model of the vessel and ideally this should take account of speed, draught and trim. In practice unless a bespoke set of draught and trim model tests have been performed, the data is usually limited to speed, power and rev curves at two load conditions only. This means that a simplified reference model has to be used which relies on interpolating the data on the basis of draught only and thus does not take account of any effects due to trim.

It is thus clear that for Performance Monitoring a number of extra measurements, correction procedures and performance reference models are required over and above those needed for Performance Recording. 3.2 SYSTEM PRECISION

At this point the concept of System Precision is introduced and should be understood to be the accuracy of the ultimate vessel performance determined after analysis of the in-service data. No formal method for assessing this has been specified, but it is introduced here as a general concept in order to discuss what might be achieved using Performance Monitoring. We consider four levels of precision:

Low

This level corresponds to general monitoring of vessel performance by fuel receipts etc and does not constitute any serious effort to monitor the performance of the vessel.

Medium

This level corresponds to what can be achieved with careful use of manual methods based on daily or four hourly manual recording of key performance data. It is assumed that this data is then screened and assessed by an experienced ship manager and the required trends extracted. This level of performance monitoring requires a significant input of effort on behalf of the ships crew and shore staff and corresponds to existing state-of-the art ship management techniques.

Methods based on automatic data recording are also believed to be at about this level. While continuous and automatic acquisition of data offers significantly greater potential System Precision because errors due to manual recording of data at possibly varying time intervals are eliminated, in practice other uncertainties due to systematic and random errors in water speed log readings, wind speed measurements and other underdetermined variables such as sea conditions and current speed actually mean that this higher potential is difficult to achieve.

High

This would represent a significant improvement over present day practice and if achieved would go a long way to fulfilling the expectations of Performance Monitoring Systems. To achieve these levels the quality of the input data will need to be high and linking of the shipboard data with metocean data sets will probably be required in order to make corrections for current, wind and waves.

Perfect

This level of precision is unlikely to be achieved in reality but is mentioned here as a useful concept when discussing precision levels. The level may be considered as equivalent to the repeatability/accuracy levels achieved in model testing at the model scale. If this level could be achieved at the ship size this would represent a major technical achievement and would give real world results more accurate than model based results as these latter need to be scaled to ship size via scaling methods which themselves involve some level of uncertainty. 3.3 THE BENEFITS OF MONITORING

The main benefit and objective of monitoring is clearly the evaluation of how well any given vessel is performing which allows management decisions to be made on any maintenance or upgrade options for that vessel. For example the need for hull cleaning, painting, propeller scrubbing or the optional fit of a modified propeller or retro-fit propulsion device can be evaluated on a cost/benefit basis.

Other potential benefits which may arise if the appropriate analysis techniques can be developed include:

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conditions which would allow the optimum trim for any given draught and speed to be determined, but would require the vessel to be operated at a range of conditions to calibrate the system.

• The determination of the wind added resistance model and the wave added resistance model. • The fouling trend with time which would lead to

an understanding of fouling characteristics in relation to paint, vessel and service type and geographical location.

If these factors can be determined from the in-service data directly this leads to the prospect of a virtuous circle whereby the corrections required for the normalisation of the data can be determined from the data itself. This gives the prospect of self-calibrating performance monitoring systems.

This information would also provide data on speed loss characteristics of different vessel types which could be used to decide which routes to operate the vessels on, as input to voyage planning systems and as information that can be used to develop better power margin policies and improved hull design for better in-service performance for future vessels.

The realisation of many of these potential benefits remains speculative until it can be shown that suitable analysis techniques can be developed.

Our experience to date is that changes in performance due to factors such as hull cleaning and painting are detectable from in-service data. But it is difficult to detected changes in power of 5% or less and thus determining the effects of, for example, trim changes or retro-fit devices is problematic.

Properly conducted double run speed trials in controlled conditions appear to be the only way of reliably assessing these items at the present time.

4. RESEARCH and DEVELOPMENT STUDY

The hydrodynamics consultancy team in BMT frequently analyses in-service data in order to investigate issues such as whether a vessel is under-performing against contract, is suffering undue speed loss due to environmental conditions or whether any benefits can be detected due to retro-fit propulsion devices, modified anti-fouling schemes or modified operating policies (such as running at a different trim).

In order to support this work we are working on a research project into the analysis of in-service data. As part of this work a Visual Basic program is being developed which can handle large quantities of in-service data and provides a data environment for the development and evaluation of analysis methods.

We are using as a start point our experience to date with a view to developing methodologies to give more rapid and precise evaluations of the true vessel performance and to investigate the options of determining aspects such as wave and wind added resistance functions from the data itself.

We have a large set of in-service data for several vessels which we can use in this project, but at the time of writing this paper only a limited number have been looked at in any depth and these are used in this paper. Due to confidentiality requirements the full details of the vessels cannot be given here and some of the data is of necessity presented in arbitrary scaled units. But the plots do serve to show the levels of precision or uncertainty present in the analysis.

Unfortunately the research project is still underway and so only preliminary results are included in this paper, but these do serve to show the issues and problems involved and maybe give some indication of a possible way forward.

5. A SECOND CASE STUDY

5.1 ORIGINAL DATA SET

The problems with the degree of scatter experienced with in-service data are outlined in Section 2 and they are further illustrated by Figures 5.1 and 5.2 below which show data collected at 5 minute intervals over several months for the study vessel in the load condition yielding about 14,000 data points. Also shown on the plot is the load power curve as estimated from the trial results.

0 5 10 15 20 25 30 35 0 2 4 6 8 10 12 14 16 18 Speed Po w e r

Figure 5.1 – Original Data Set, Speed and Power

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0 10 20 30 40 50 60 70 80 0 2 4 6 8 10 12 14 16 18 Speed Rev s

Figure 5.2 – Original Data Set, Speed and Revs

The problem we have here is that while it is clear that some of these points will fall on the correct power speed curve (which is not necessarily the one shown if the vessel is fouled), the question is how do we find which are the correct points?

5.2 INITIAL DATA FILTERING

As shown in Section 2, some simple filtering methods serve to reduce the scatter considerably and an underlying set of curves emerge. The filters used are:

• Both log speed and ground speed should be greater than 0.5 knots.

• The shaft power should be greater than 0.5 units and the revs should be greater than 1 RPM • The true wind speed, the shaft power and the

shaft revs should not have varied by more than 5% from the mean value over the last hour. Filtering is this way reduced the number of data points to 3470 to give Data Subset A. Figure 6.1 shows the shaft power plotted against speed and it is clear that a lower bounding trend close to the assumed base line performance curve is emerging. Some horizontal ‘tails’ or ‘structure’ can be seen and these are likely to be due to an increase in resistance from metocean conditions with the engine power setting kept nearly constant. These tails or structures therefore reflect the fact that wind and wave data were not included in the filtering. They correspond to what would be expected and the fact that there is this structure in the data gives some degree of confidence that it does indeed contain information about vessel performance in wind and waves, rather than simply being a set of random data.

0 5 10 15 20 25 30 35 0 2 4 6 8 10 12 14 16 18 Speed Po w e r

Figure 6.1 – Power and Speed after Initial Data Filtering

0 10 20 30 40 50 60 70 80 0 2 4 6 8 10 12 14 16 18 Speed R evs

Figure 6.2 - Revs against Speed after Initial Filtering Figure 6.3 shows the Performance Ratio (the measured power divided by the baseline power at the same loading condition and speed) plotted against time. It will be seen that there is a lot of scatter and it is difficult to form an opinion as to how well the vessel is performing.

0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 0 50 100 150 200 250 300 350 Time (Days) (M e a s u re P o w e r) /( B a s e lin e P o w e r)

Figure 6.3 – Performance Ratio

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5.3 WATER SPEED DETERMINATION 5.3 (a) Differences in Log Speed and Over Ground Speed

Accurate determination of the speed through the water is a key ingredient when analysing in-service data. BMT’s past experience with similar data sets has shown there can be significant error in the water speed measurements obtained from the ships log (Vlog). If accurate metocean data is available and it includes reliable current data, it may be possible to determine the speed through the water by taking the difference of the speed over ground (SOG) vector and the current vector. However, in cases where metocean data is not available, some other method needs to be used.

The SOG is determined from the GPS and is usually fairly accurate, but includes the effect of any tidal or ocean currents. It is known from tide charts and metocean data that the currents can be significant, in the order of up to 2 knots or more. This can also be demonstrated from the in-service data itself, provided it is accepted that the log speed, which may be in need of calibration, will be proportional to the true water speed for the case where the water is deep and the environmental conditions are good.

The plot below shows the difference between the log and over ground speeds (Vlog-SOG) plotted against the log speed (Vlog) for the data set shown in Figure 6.1 but further filtered for cases where the water depth is greater than 100m and the true wind speed is less than 10 knots.

Line Fit: (Vlog-SOG) = -0.00210 x Vlog

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 8 10 12 14 16 Vlog (knots) (V lo g -S O G ) ( k n o ts ) 18

Figure 7 – Comparison of Vlog and SOG

It will be seen that the SOG can differ from the un-calibrated log speed by about ±1.5 knots in this case and it is assumed that this is largely due to tidal and ocean currents.

5.3 (b) Calibration of Log Speed

A method which can be used to calibrate the water speed is to observe that, if the vessel regularly makes return voyages (rather than a round trip between three or more

places) and the voyage timings are not synchronised with tides or seasonal currents, the current speed experienced by the vessel will be randomly distributed. Therefore, if sufficient data over a long enough time is available it should be possible to determine the log speed calibration by fitting an assumed log calibration function to the data by a best fit approach.

Such a log speed calibration function might take account of time out of dock (in the event of hull fouling affecting the log) and vessel loading condition. Some initial investigations were done using this data and no such dependencies where found in this case.

If it is assumed that the true speed through the water is related to the log speed by:

VCOR = k.Vlog

and that s is the slope of the curve fitted through the data and the origin in Figure 7, it may be shown that the log calibration k is given by:

k = 1/(1-s).

In this case a value of -0.0021 for s gives a log calibration of k=0.9979, that is the speed log is in error by some -0.2%. The correction is deemed small in this case the log speed is used with a calibration of 1 for the remainder of the study for this vessel.

5.3 (c) Water Depth

Doppler speed logs can operate in a range of modes include sensing speed from the sea bed in shallow water or from water at various distances away from the hull depending on the amount of particulate matter in the water. This means that in shallow water the log speed is likely to be the same as the SOG. In deeper water the measurement may be either from water close to the hull (which may be affected by the hull itself), or it may be from water some distance down, in which case the local current speed may differ from that which the vessel is experiencing. Ideally the speed log mode should be recorded by the performance monitoring system. In addition, the speed log mode for its initial calibration in the speed trials must be recorded.

5.3 (d) Environmental Conditions

In the course of checking for any load condition or elapsed time effects, the effect of the angle between the vessel heading and the true wind direction (Wind Angle) on the log speed calibration was investigated. The result is shown in Figure 8. This shows the ratio Vlog/SOG plotted against the Wind Angle.

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0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 0 30 60 90 120 150 180

Wind Angle (0=from Ahead, 180=from Astern)

V

log/

SO

G

Figure 8 - Log Readings and Wind Angle

This can be interpreted as showing a dependency on the true wind direction relative to the ship heading. However, what this actually means is not so clear. It might imply a dependency on how the log works with different vessel motions, or it may just be showing that the wind causes a small surface current. The latter interpretation is used here and so no additional log speed correction is applied for true wind direction relative to the ship.

5.3 (e) Effect of Speed Log Error

As already mentioned, BMT has past experience of speed logs being considerably in error. We have determined this from both in-service data using techniques like that given in section 5.3 (b) and also from a full five double run speed power trial performed by us on a tanker while in service. This showed the water speed log was under reading by some 0.6 knots.

The effect of this on the assessed vessel performance is shown in Figure 9 below. This shows:

a) A nominal base line curve for a vessel in good condition,

b) A curve with the power increased by 17% due to the effects of fouling and

c) The same curve plotted, but at a 0.6 knot slower speed.

It will be seen that if curve c) is compared with curve a), the power would appear to be increased by some 32% due to fouling, whereas in this example we know that it was only increased by some 17%.

0 5 10 15 20 25 30 35 10 11 12 13 14 15 16 Speed (knots) Sh a ft Po w e r Clean Hull 17% Power Increase 17% Power increase and 0.6 knot Log Speed Error

17% Actual Power Increase 32% Aparrent

Power Increase

Figure 9 - Effect of Log Error on Assessed Power

This accounts for at least one reputable performance monitoring system reporting similarly high values of power increase which were ultimately found to be due to an incorrect log calibration.

5.4 ANALYSIS OPTIONS 5.4 (a) Bucket Analysis

One approach to data reduction of in-service data is to apply a technique which may be termed ‘Bucket Analysis’. As the name implies, the data is bucketed into subsets based on a number of criteria, for example on discrete ranges of:

• Load condition, • Log speed,

• Wind speed and direction and • Wave height and direction.

The hope and objective is that the data contained in any one of these buckets will show little scatter and will then confirm the ‘true’ performance value which applies for the combination of parameters that defines that bucket. While this approach is simple and powerful, it also has a number of drawbacks, the main one being that a large amount of data is required. It can be the case that a vessel operates for several voyages in a row where the weather conditions are so bad that no ‘fine weather’ data is available, or, alternatively, it may have operated with a range of load conditions so that it is impossible to determine if power changes are due to changes in either hull condition or loading condition. In all these cases bucket type analysis will not yield an assessment of whether the calm water powering has changed over time. It should be noted that if the data is further filtered to obtain only data for what is thought to be fine weather, that this is equivalent to doing a bucket analysis and taking only the fine weather buckets.

5.4 (b) Normalisation Analysis

The objective of Normalisation Analysis is to produce a larger final data set for examination by correcting the measured data to a “normalised”, or datum, state for the

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effects of wind, waves and load condition. If we had complete understanding of the factors involved and the appropriate correction formulae, most of the measured in-service data could in principle be corrected back to the equivalent calm condition values to give a reliable assessment of the actual performance of the vessel more or less instantaneously from real time data.

These two kinds of analysis are looked at in the next two sections.

5.5 BUCKET ANALYSIS 5.5 (a) Available Environmental Data

The data set used in Section 5.1 to 5.4 does not at the present time include wave and current data. For the work presented here an estimate for the wave conditions was prepared for each data sample using the true wind speed and an assumed relationship between wind speed, significant wave height and modal period, as summarised in the table below.

Wind Speed Significant Wave Height Modal Period (knots) (m) (s) 0 0.00 7.50 5 0.24 7.50 10 0.62 7.43 15 1.13 7.73 20 1.78 8.67 25 2.56 9.30 30 3.48 9.94 40 5.73 13.47 50 8.52 15.32 60 11.85 16.76

Table 1 - Wind Speed, Wave Height and Period

The direction of these waves is assumed to be the same as that of the true wind. This means that in the calculations made here, the significant wave height and wind are tied together effectively making the wave and wind resistance components into a single function as both are dependant directly on wind speed and wind angle only.

In practice the wave conditions, while related to the wind speed time history at the ship, are subject to lag and lead because it takes time for waves to develop after the wind starts to blow or strengthen and similarly they will persist for a time after the wind drops.

One way around this problem is to look for portions of the data set where the wind has been nearly constant over a period of time. In this case it can be assumed to some extent that the waves will have developed to match the ambient wind speed and that the wave height will follow some suitable relationship with wind speed, such as that assumed here and given in Table 1.

5.5 (b) Further Data Filtering

In order to investigate the effect of performing a bucket type analysis, the data set shown in Figure 6.1 (Data Subset A) was further filtered for:

• True wind speed less than 10 knots and

• Wind speed within 10% of mean value for 3 hours.

This gives Data Subset B which has 300 data points (that is 2.1% of the original data set) and is shown in Figure 5.1. 0 5 10 15 20 25 30 35 0 2 4 6 8 10 12 14 16 18 Speed Po w e r

Figure 10.1 - Further Filtered Data Set

If the Performance Ratio is plotted against time, Figure 10.2 results. 0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 1.25 1.3 0 50 100 150 200 250 300 350 Time Days (M eas u red P o w er )/ (Refer en c e P o we r)

Figure 10.2 - Performance Ratio

This would appear to show that at the start of the period the vessel was performing some 5% worse than the baseline curve and this was maintained up to about day 270. For some reason not yet identified, the performance then seems to jump to some 15% worse than base line at around 320 days.

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This plot shows that the data is very sparse over time and applies for a few very short periods of time. This of course makes the overall analysis conducted in this way reliant on factors which are not recorded by the data acquisition system and which may have unusual values at the points in time for which the limited data applies. 5.6 DATA NORMALISATION

5.6 (a) Wind and Wave Corrections

Wind and wave corrections are introduced in this study as follows.

Firstly, the effective resistance (Rteff) is calculated for each data point using the propeller open water characteristics and calm water values of thrust deduction (t), wake fraction (Wt) and relative rotative efficiency (EtaR) along with an assumed shaft loss efficiency (EtaT) via a Kqo/Jo3 analysis The values for the propulsive elements are interpolated from the model test results for load and ballast conditions.

Estimates are then made for the added wind resistance (using standard wind coefficients) and for the wave added resistance. For the wave added resistance a method based on Loukakis [7] combined with weather heading reduction factors given by Townsin [8] are used.

The effective resistance is then corrected by the wind and wave added resistance to obtain an estimate of what the resistance in calm conditions would be (RtCalm). This is then used with the propulsive elements and open water data via a Kt/J2_{analysis to get an estimate of what the}

shaft power and shaft revs would be in calm conditions. Finally, this is compared with the estimated baseline shaft power for this load condition and vessel speed to get a Corrected Performance Ratio PsCor/PsBase. 5.6 (b) Evaluation of corrections

The validity of the combined wind and wave corrections was evaluated by comparing the calculated corrections with the deduced power increase due to wind and waves determined by comparing the measured data with the estimated baseline performance.

Figures 11.1, 11.2 and 11.3 show the comparison for head, beam and stern seas. These show broad agreement between the predicted and actual values. However, it is also clear that the agreement is far from perfect and there is still some effects unaccounted for in the measured data. It should be noted that the estimated corrections do not fall on a single line as the value of the correction also depends on vessel speed which is different for each data point.

Data for Head Seas

0.8 1 1.2 1.4 1.6 1.8 2 0 5 10 15 20 25 30 35

True Wind Speed (knots)

(M eas u red P o w e r)/ (B a se li n e P o w e r) Measured Values

Estimated from Wind and Wave Corrections

Figure 11.1 – Evaluation of Corrections, Head Seas

Data for Beam Seas

0.8 1 1.2 1.4 1.6 1.8 2 0 5 10 15 20 25 30 35 40 45

(M e a s u re d P o we r) /( B a s e li n e P o we r) Measured Values

Figure 11.2 – Evaluation of Corrections, Beam Seas

Data for Stern Seas

0.8 1 1.2 1.4 1.6 1.8 2 0 5 10 15 20 25 30 35

(M ea su red P o w e r) /( B as el in e P o w e r) Measured Values

Figure 11.3 – Evaluation of Corrections, Stern Seas These plots also serve to show that it may be possible to reverse the process and by using similar but more advanced techniques to determine the wind and wave correction functions from the data itself, particularly if actual wave information is available in the data set.

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5.6 (c) Results with a Larger Data Set

Wind and wave corrections were then applied to the larger data set given in Figure 6.1 (Data Subset A) and Figures 12.1 and 12.2 result.

0 5 10 15 20 25 30 35 0 2 4 6 8 10 12 14 16 18 Speed Po w e r

Figure 12.1 – Data Subset A, Corrected Data, Speed and Power 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 0 50 100 150 200 250 300 350 Time (Days) (m eas u re P o wer )/ (B ase li n e P o w er )

Figure 12.2 – Data Subset A, Corrected Data, Performance Ratio

These show that in many cases the data has been over-corrected resulting in a larger scatter in the over-corrected data. It is not clear whether this is due to:

• The prediction formula for wind and wave added resistance are too high or

• The assumed wind speed to wave height relationship is incorrect or

• Whether the low level of filtering used in this case has allowed data through where short time scale variations in wind, waves or some other factor has affected the results.

Assuming that the over-correction was due to the estimates for the wind and wave added resistance being too large, an arbitrary factor was introduced on the total

added resistance and by trial and error a value of about 0.45 appeared to be most suitable.

Figures 13.1 and 13.2 give the results when this factor is applied and it will be seen that the scatter is reduced on the Performance Ratio (Compare Figure 13.2 with 6.3).

0 5 10 15 20 25 30 35 0 2 4 6 8 10 12 14 16 18 Speed Po w e r

Figure 13.1 - Data Subset A, Modified Normalisation, Speed and Power

0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 0 50 100 150 200 250 300 350 Time Days (M easu red P o w er )/ (Refer en ce P o wer )

Figure 13.2 - Data Subset A Modified Normalisation, Power Ratio

This correction factor was then applied to the analysis for Data Subset B and the results are given in Figures 14.1 and 14.2. Again the scatter on the Performance Ratio is reduced (Compare Figure 14.2 with 10.2).

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0 5 10 15 20 25 30 35 0 2 4 6 8 10 12 14 16 18 Speed Po w e r

Figure 14.1 - Data Subset B, Modified Normalisation, Speed and Power

0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 1.25 1.3 0 50 100 150 200 250 300 350 Time Days (M easu red P o w er )/ (Refer en ce P o wer )

Figure 14.2 - Data Subset B, Modified Normalisation, Power Ratio

This shows that data filtering combined with appropriate data normalisation has some prospect of improving the precision of in-service data analysis.

6. FUTURE WORK

As mentioned in Section 4, the information given here is the result of the first part of an R&D study being conducted by BMT. The next phase will look at what can be done to improve the analysis and will include:

• Using additional ship data sets.

• Using metocean data on wind, waves and currents.

• Obtaining speed through the water using the current speed and the GPS data.

• Refining the wind and wave correction functions, taking account of the improved wind and wave data and using the in-service data to check the validity of the corrections.

• Investigating further the effect of filtering given that there will be additional data fields to filter

on including actual values for significant wave height and wave direction.

• Spending more time to ensure that the input data is correct, particularly data such as the loading condition data which is entered manually by the crew is sometimes inn error.

7. CONCLUSIONS

The matter of reducing in-service data to determine the actual ship performance is not trivial. The potential benefits of being able to do this are large, giving the prospect of being able to determine when best to clean a vessel and what actual speed or power margins are required for a given level of in-service performance. In this paper some filtering and normalisation techniques have been investigated and have shown some promise, but further development is required if all the goals of in – service monitoring are to be achieved.

The results given here are the early output of an ongoing R&D study which it is hoped will yield some further positive results.

Finally, we can ask the question “What would Froude be doing in this area if he were alive today?” No doubt he would be pursuing the matter with his characteristic vigour and insight and taking a keen interest in the precision of the instrumentation and making sure that the required data were recorded accurately, including, of course, that from speed logs and anemometers. He would doubtless be at pains to acquire good quality sea state data as well as ensuring that the proper mix of model tests and theory was employed to analyse and understand full-scale data and ship behaviour. We would do well to emulate his example.

8. ACKNOWLEDGEMENTS

The Authors would like to acknowledge the BMT R&D Committee which has generously funded the R&D study and those ship owners that allowed the data to be recorded.

9. REFERENCES

1 BROWN, D. K., ‘The Way of the Ship in the Midst of the Sea’, Periscope Publishing, 2006.

2 KENT, J L: ‘Ships in Rough Water’, Thomas Nelson and Sons, 1958.

3 AERTSSEN, G., ‘Further Sea Trials on the Lubumbashi’, Trans IMarE (TM) Vol 69, pp 411-439,

1957.

4 AERTSSEN, G., ‘New Sea Trials of the Sandblasted Lubumbashi’Trans IMarE (TM) Vol 73, 1961.

5‘ITTC Full Scale Trials Code of Practice’.

6 MOOR, D. I. and SILVERLEAF, A., ‘Standard Procedure for Resistance and Propulsion Experiments

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with Ship Models’, Proceedings of the Ninth ITTC,

Paris,1960.

7 LOUKAKIS, T.A. and CHRYSSOSTOMIDIS, C, ‘Seakeeping Standard Series for Cruiser-Stern Ship’,

SNAME, 1975.

8 TOWNSIN, R.L, KWON, Y. J. and BAREE, M. S., ‘Estimating the Infleunce of Weather on Ship Performance’, RINA, 1993.

9. AUTHORS’ BIOGRAPHIES

Tom Dinham-Peren is the Chief Hydrodynamicist at

BMT Defence Services. He has over 28 years experience of ship hydrodynamics with particular expertise in the fields of hydrodynamic hull design, resistance, powering and seakeeping. He has been involved in a number of projects looking at in-service ship performance, full scale ship trials and development of methods for predicting speed loss at sea. Recent work has included investigation of options for improving tanker fuel efficiency both by using retro-fit energy saving devices for existing ships but also what can be done if a clean sheet design approach is adopted for new build vessels. Prior to his current role he worked as a Hydrodynamicist for BMT SeaTech. He has a BSc in Naval Architecture from the University of Newcastle upon Tyne.

Dr. Ian Dand was Director of Hydrodynamics at BMT

SeaTech Limited where, before his retirement, he carried out work of both a research and consultancy nature, concentrating mainly on aspects of ship behaviour with particular emphasis on ship safety and ship dynamics in confined waters.

He is a Fellow of The Royal Academy of Arts and Manufacturing and The Royal Institution of Naval Architects and was elected a Fellow of the Royal Academy of Engineering in 1994. He is also a Companion of the Nautical Institute.