SafeTrans: A New Software System For Safer Rig Moves

Authors: A.B. Aalbers (MARIN), C.K. Cooper (Chevron), S. Nowak (Santa Fé), J.R. Lloyd (Noble Denton), C.E.J. Leenaars (Dockwise), F. Vollen (SafeTec Nordic),

Abstract

The SafeTrans Joint Industry Project (JIP) started in 1997 with the goal of improving today's tows and installations by utilizing modern probabilistic- and consequence-based methods, improved weather forecasting, and inexpensive but powerful PCs. SafeTrans was funded by 31 companies including 11 oil companies, six warranty surveyors, and six transport companies. The involvement of most of the major players in the offshore transport field has produced a fully vested tooi incorporating much of the technical expertise and practical experience of the JIP members. The software can handie barge and jack-up tows as well as self-propelled (dry) transports. Multi-month trans-ocean transports as well as short installations are covered. SafeTrans has two major software components: an office-based system to pre-plan and design the tow, and an on-board system to provide real-time advice to the captain during the actual tow or installation. This paper describes the common software of the two components including the ship hydrodynamic modeling, the Monte Cario simulator, metocean database, and the risk analysis. The paper also compares SafeTrans results with measured data from the self-propelled transpon of Santa Fé's Galaxy II jack-up on the Dockwise Transshelf from Singapore to Halifax Nova Scotia.

Ackno wledgement The authors wish to thank H, Soma of SafeTec Nordic for his contribution to the paper and M.M.D. Levadou of MARIN for his review of the paper and the presentation at the City University Jack-Up Conference.

1. Introduction

The consequences of having an accident during the tow of a jack-up can be major. There is potential for loss of life, loss of the rig and/or towing vessel, and major delays in spudding a new well with its associated potential delayed production. Design methods for offshore transports may be categorized into the following:

• Rules of Thumb which only take into account primary ship characteristics.

• Design Wave Methodthat determines an n-year (typically 10-yr) wave height and period based on some database, usually visual ship observations, and then calculates the resulting cargo load, or (e.g.) accel-eration due to that n-yr sea state.

• Response-based methods that determine the n-year critical load (e.g. acceleration) on the cargo and sea fastening.

Response-Based methods can use a statistical approach based on wave scatter diagrams (joint frequency probability distributions of wave height and peirod) and uncorrelated long-term extapolation or they can use a Monte Carlo method using correlated weather in many voyage simulations to obtain long term statistics.

Historically, the transport industry has relied heavily on the first two methods. But given the importance of the problem, and the improving computer and analysis technology, there have been several efforts in the last few years to develop more sophisticated software to improve the methodology. For example Vollen (Ref. 1) developed a software tooi known as Safetow to assess the risk of uncontrolled drift during a tow. Aalbers et al. (Ref. 2) developed a comprehensive software package known as VAC that is a Response-Based method utilizing a statistical approach.

SafeTrans is the most recent of these efforts. It began in 1997 as a Joint Industry Project (JIP) involving 31 participants as shown in Tabfe 1. The goal was to develop a comprehensive, integrated software tooi to design and operate marine transports and installations of major facilities (including jack-ups) in a safe and efficiënt manner using modern analysis methods, databases, and instrumentation. The tooi was to capitalize on recent improvements in computer speed, risk assessment, hydrodynamic analysis, and wave forecasting.

The SafeTrans software can be used for planning a transport in the office, on-board the transport as a decision support tooi, and as a training module. It covers dry tows (cargoes on barges), wet tows (floating

units such as jack-ups), and dry transports on heavy-lift vessels. The Maritime Research Institute in the Netherlands (MARIN) was the main contractor for the Project while Chevron Petroleum Technology Company was overall project manager. Major subcontractors were: Safetec Nordic who did the risk analysis, Oceanweather who developed the wave and wind databases, Hydrographic & Marine Consultants (HMC) who did the route plotting software, Argoss who did the cyclone database and wave-forecast correction, and Noble Denton who developed safety factors.

Early in the project formation stage it was realized that the best strategy to develop a comprehensive, credible tooi that would become a de-facto Standard was to include all the major players in the towing industry. Eleven oil companies participated because they ultimately shoulder much of the cost of towing accidents. Six transport companies participated because they are the ones that deal most directly with the details of a tow. Six surveyors and classification societies participated to develop a tooi to better monitor and check proposed tows. Six engineering design companies participated because they wanted to be invofved in development of a tooi that their clients (Transport and Oil companies) were eager to develop.

The JIP companies worked closely during the project to share their combined expertise and data. This was essential since the project encompassed so many different technical expertises.

Table 1: Summary of SafeTrans JIP Participants Oil companies Drilling contractors BPAmoco Noble Neddrill Chevron Santa Fé Conoco Exxon Engineering&consultancy companies Marathon Marin Mobil Dovre safetec Shell Argoss Texaco Breeman Statoil Hydrographic&Marine Consultants Pet rob ras OceanWeather Agip

Surveyors and Authorities Transportation companies Noble Denton Heerema/Dockwise Salvage Association BigLift (Mammoet) Lloyds Register of Shipping International Towing Contractors (I.T.C.) Det norske Veritas Smit Internationale Ha-Ce Marine Kahn Shipping (Jumbo) Health&Safety Executive/ Ministry of Defence (UK) Netherlands Freiqht Aqency (COSCO)

2. Technical description

2.1 Overview

SafeTrans has two modes: a "VMC" mode that works with wave statistics similar to VAC, as described in Aalbers et al. (Ref. 2) and a Monte Carlo mode using time series. The VMC mode can be run in less then an hour and is ideally suited for preliminary sizing and for estimating wind/wave thresholds for the Monte Carlo mode. While the VMC mode is quick, it cannot account for weather-routing.

VMC is an extension of the VAC method (Ref. 2) where the vessel is assumed to travel over the user-defined route. VMC uses wave scatter tables floint frequency of occurrence of significant wave height vs. wave period) from IMDSS. The long-term statistics are computed, assuming Rayleigh distributions to determine motions during the extreme individual wave height. The method applies a weighted summation over all 24 wave directions, 15 wave heights and 16 wave periods, while in each sea state the sustained speed and equiltbrium heading of a towed vessel is computed and taken into account. Towline forces are computed and the effect of wind heel is taken into account; an improvement on the earlier VAC.

The Monte Carlo mode simulates many voyages using a randomly selected start date {within a user-specified range). Cargo response statistics from the multiple voyages are then accumulated to derive

design-level loads. Since a Monte Carlo voyage progresses step-by-step in time it can include weather-routing much as a real-world tow. This is important in many cases because observations have shown that the actual wind/wave height experienced during many tows is often less then the estimates based on the traditional 10-yr criteria. For example Dockwise has found that the actual exposure during an ocean transports is typically 30% less then that calculated using 10-yr criteria. (Leenaars et al, Ref. 3 and 4) This is largely due to the benefits of weather routing that have, before SafeTrans, not been considered effectively in the tow design process.

Figure 1 shows a conceptual schematic of the SafeTrans program as applied to a single voyage in Monte Carlo mode. Of course the initial step (not shown) is for the user to enter the details of the voyage including the ship and cargo geometry, preferred routing, wind and wave thresholds, number of voyages in the Monte Carlo simulation, and the desired load outputs (e.g. 6-degree of freedom acceleration at selected points on the cargo). Once the user has made these inputs, the program starts the first voyage and the first step in Figure 1. The forecast is checked using the weather database and if it shows winds/ waves are above the user-specified threshold then the voyage is delayed {not started). This loop continues until the forecast shows a few days clear sailing along the user-specified route, sufficiënt to get adequately underway.

Once underway, the ship cruises along the preferred route for three hours at a speed dictated by the weather-controlled vessel power characteristics. For tug tows, SafeTrans computes the mean towline force and the equilibrium position of the towed body (jack-up or barge) behind the tug. At the end of the 3-hour step, the program calculates the ship and cargo motion based on the nowcast from the weather database at the ship's 3-hour track.

Fig. 1: Simple Schematic of SafeTrans logic flow for a single voyage using Monte Carlo Simulator.

SafeTrans then begins a series of checks on the new position; the first to see if it has reached the final destination. If not then SafeTrans checks to see if a towline break event occurred (if applicable), in whrch case the tow would be 'out-of-control' and be assumed to drift with the weather where the chances of drifting aground are based on the actual ship position relative to the coast/shallow water. The forecast is checked to assess the tug's capability to reconnect before the tow is washed on-shore. !f so, the tow is booked as a total loss, if not, the voyage continues along the pianned route and the loop repeats. For the other failure modes a risk analysis is carried out, considering fire, grounding, engine failure, etc. (See section 2.5.)

If the forecasted or actual ship behaviour exceeds the thresholds then the power and course settings or route plan is changed. Thereto the Monte Carlo process includes a "Captain's Decision Mimic" which is a probabilistic method using a transformation matrix to convert decision variables to decisions. Rerouting is defined loosely here to mean either weather routing to destination or re-routing to a safehaven (port or leeward coast).

At the end of the voyage, the process begins again with the start of another voyage on a randomly selected but different date. Subsequent voyages will be different then previous voyages because of changes in weather and random occurrence of low-risk failures. At the end of all the Monte Carlo iterations, the total load statistics are accumulated and extreme value analysis is applied to determine the 1 :n voyage loads.

2.2 Metocean database

One of the key factors affecting the success of a tow is the weather, especially waves. Historically tow design has used joint frequency statistics of wave height and period derived from ship observations, e.g. Hogben and Lumb (Ref. 5). There are many limitations with these data including accuracy (especially in the southern oceans), and the lack of continuous time series and forecasts products. In the last 20 years technology has progressed considerably and there are now several global numericai wave models that provide good forecasts out to one week in many parts of the world, e.g. Chen et al (Ref. 6). These models are routinely used for routing tows.

After careful consideration, SafeTrans ultimately chose to use the Integrated Marine Decision Support System (IMDSS) wind/wave database developed by Oceanweather. IMDSS has several advantages over the competitors. First, it includes archived forecasts out to 168 hrs back to 1995 while most competitors have only saved the nowcasts, not forecasts, at least until recently. Second, Oceanweather has added a quality control step that checks that the wind fields accurately reflect ail available wind measurements. Initial wind estimates for IMDSS come from the National Weather Service (NWS) MRF model. The quality controlled winds are feed into a wave model based on a second generation spectral wave model known as ODGP2 further described in Cardone et al (Ref. 7) and Greenwood et al (Ref. 8). Through 1997, the model used a 2.5° square grid but subsequently it has used a rectangular 1.25° latitude by 2.5° longitude grid with finer-scale nested grids in the S. China Sea and Gulf of Mexico.

IMDSS has been validated extensively. Figure 2 shows an assessment based on a storm being defined as an event exceeding a 5 m threshold. Data is for 1995-96 at a site in the central North Atlantic and another in the central South Atlantic. For various forecast time horizons (24-, 48-, 72-, and 120-hr) the pie charts show the percent of occurrence of three forecast outcomes:

1. "correct" forecasts where the IMDSS predicts an Hs in excess of 5 m AND it occurs. 2. "missed" forecasts where the IMDSS predicts no storm (Hs < 5 m) BUT in fact a storm did occur; and 3. "false alarms" where the IMDSS predicts a storm but none materializes.

Of the three, the "missed" forecast is probably the most onerous to a transport captain. False alarms can waste time but won't damage cargoes.

Some trends are obvious from the figure. First, the IMDSS forecasts are correct the majority of the time except for the 120-hr South Atlantic case. For the shorter time horizons, the forecasts are correct the vast majority of the time. Finally forecasts for the North Atlantic are better then for the South Atlantic at all forecast horizons, although not by all that much except at the 120 hr forecast horizon where the South Atlantic forecast wrong the majority of the time.

IMDSS does have some limitations. First, it does not adequately resolve the spatial gradients of most tropical cyclones with the present grid. Second, it started in 1995 so SafeTrans presently has only a four-

year database. This is a marginal length for calculating the typical probability levels of order 0.1 annual exceedence used in tows. The four-year length is definitely inadequate in regions like the Gulf of Mexico where a site is typically affected by a cyclone only every 3 years.

To compensate for these two limitations, SafeTrans developed a tropical cyclone submodel that was merged into IMDSS. The submodel uses historical cyclone tracks and intensity data from NOAA (Global Tropical and Extratropical Cyclone Climatic Atlas) feed into a parametric wind field model by Cooper (Ref. 9) to calculate the wind field on an hourly basis. A 10% modification of the 'Cooper' wind speeds was needed after careful comparisons with observed data. The length of the cyclone data depends on the region and in some cases goes all the way back to 1886. For SafeTrans, the period was taken to be 1972-1995 because of data confidence considerations in the earlier data. The forecast portion of the cyclone database was determined by using linear extrapolation of the data over the past 24 hours. This simplification was necessary because there is no world-wide forecast archive.

Fig. 2: Assessment of IMDSS forecast errors for various forecast horizon at grid points in the North Atlantic and South Atlantic.

SafeTrans also includes currents from the Ocean Drifter database of the National Oceanographic Data Center. Values are binned by seasonal, 2.5° grid s and eight main directions (45° bins).

2.3. Routing

Vessel routing in SafeTrans is handled by a module known as Maritime Geographic Display (MARPLOT). MARPLOT allows the user to define a route between harbours in its database or between 'mouse clicks'. Route restrictions can be defined, such as:

• Hard Waypoints through which the vessel must pass, e.g. meeting points for additional tug assistance and destination

• Wish points used to modify the route to piek up favourable current directions, better climate, etc • Unsafe coasts that will not be accessible during re-routing. • Forbidden areas that will not be entered during re-routing, e.g. iced areas

In Monte Carlo mode, weather rerouting is available using the well known Dijkstra algorithm to find the optimum track through the three-dimensional 'space' of location and time, on the basis of the user-specified wind/wave thresholds. MARPLOT marks the planned and the simulated route as illustrated in Figure 3.

Fk Sofettera Wndow

fiffaPei. Route | F^*Mew UmaJej f[^"DeTuftta(e | |jo Sale Iwem] |l • Maka Kad [ lÏF-TMaka whh |

Fig.3: Route display for simulations with wishpoint west of Portugal

MARPLOT also computes the distances to the coast in eight directions (45° bins) at each new 3-hr position during the voyage. Coastal sites within 3-days sail are considered as possible safehavens unless it is a user-defined 'unsafe coast'. Also, the distance to the downwind coast is computed and used in the risk evaluation of a tow line break when applicable. Finally, it is possible for the user to define other 'safe havens' where the vessel can go for shelter. MARPLOT calculates the routes to these shelters for later consideration by the Captain's Decision Mimic.

2.4 Hydrodynamics

SafeTrans has the capability of handling pre-defined vessel shapes using Standard geometry and Response Amplitude Operator (RAO) files. It can also calculate the RAO's based on user-specified geometry and linearized strip theory.

Linearised Strip theory The strip theory program of MARIN uses diffraction theory to compute the pressures on the 2-dimensional sections. This enables computation of not only the ship motions but also the relative water motions to evaluate shipping green water, and the drift forces in surge, sway and yaw. Drift forces are derived using an approximation method of Salvesen (Ref. 10), to which modifications were made to improve the sway component as function of wave direction.

The strip theory module is extended with routines to compute non-linear roll damping based on the formulations of Ikeda et al (Ref. 11) and Noble Denton (Ref. 12). An additional roll damping contribution was used to take into account water on the deck of a flat top barge using Ref. 2.

The strip theory module computes the RAO's for 5 speeds, 24 wave directions and 5 roll angles. For the motions in irregular waves the 5 different roll responses are interpolated to obtain intrinsically consistent results, wherein the roll motions correspond with the roll damping value.

Motion database On basis of the RAO's, the motions are computed using the non-linear roll data in an iteration process to get consistency between the significant doublé amplitude (SDA) of roll and the roll amplitude in the RAO's. Different types of spectra can be chosen, i.e. Jonswap, Pierson-Moskowitz, and 'Mitsuyasu'. A scatter diagram of spectra with wave heights from 0.5 to 14.5 m and wave peak periods from 3.5 to 18.5 s is used

to compute a database of "SDA's" and "oscillation periods". During simulation, the motions due to wind waves and swell are retrieved by interpolation from the SDA database, and their effect is combined in a total motion SDA value and period.

Fig.4: VOM communication scheme

2.7 Vessel Operating Module (VOM) and the Captain's Decision Mimic

Since the Monte Carlo simulation incorporates weather routing it has a module that controls vessel operation known as the 'Vessel Operating Module' (VOM). Figure 4 shows a schematic of the interrelation of VOM with other program modules. The VOM may be interpreted as the mathematical helmsman on the vessel.

The VOM includes a Captains decision mimic developed from a database of what an experienced captain would do under similar circumstances. VOM collects information to compute the so-called 'decision variables'. There are 13 decision variables describing the 'status' of the vessel relevant to the transport operation. The decision variables were selected on basis of interviews with experienced mariners. It was decided not to consider the mechanical and structural condition of the ship and its systems in the simulation process.

Table 2: Basic decision variables used in the VOM Decision Variables Basic Decisions Forecast on route Continue to destinatton Sheltering potential Seek shelter/head for open sea Rerouting potential Change route Comfort situation Change power/heading for comfort Delay risk Go to survival mode Crew quality Shorten tow line length Value of consequences Reduce tow line tension Vulnerability Apply Rendering Capsize risk Avoid hurricane Shipping green water Wait for weather (when in shelter) Tow line break risk Tow line bottoming risk Hurricane hit risk

The decision variables (DV's) are ranked from 1 to 10, where 10 is extremely bad. The status description is input for the Captain's Decision Mimic module (CDM), which transforms the status into a probability distribution over a range of 10 basic decisions. In Table 2 the DV's and decisions are listed. In addition to

the basic decisions, also probabilities of relevant combinations are computed and finally a weighted draw is made to select the appropriate decision(s).

The transformation of DV's into basic decision probabilities (D) is carried out as follows:

D = K_+ DV. A

where D = the vector with basic decision probabilities K =s a vector of initial values so that (K + t . A) gives the decision for perfect conditions A = a matrix (13 x 10) representing the transfer coeffictents from DVto D

The matrix A has been obtained from multiple regression analysis on results of an enquiry among 23 experienced captains from UK, the Netherlands and Norway. The enquiry was developed with 2 senior captains, and consisted of 15 case descriptions and the initial list of basic decisions. For each case the respondent had to indicate what in his opinion in the next 12 hours "normal captain's" decision(s) would be and with what probability. The answers lead to one modification in the list of basic decisions and allowed to establish the relevant combinations of basic decisions. For all 15 cases the decision variables were computed and multiple regression between the vectors DV and D allowed to determine matrix A. Distinction was made between self propelled transport, dry tow (cargo on a barge) and wet tow.

The probabilities in vector D are scaled for the time difference between 12 hour's and the simulation time step At in such a way that the most probable decision probability is maintained.

(Di.Mpr after scaling)12/at = Di,Mpr before scaling

while 1 - (Dj.Mpr after scaling) = Sum of other decision probabilities

In this way sufficiënt consistency in the decision making is obtained while it is still possible that a less favorable decision is taken. So, the CDM is not "the perfect captain".

Carry-out decisions The VOM receives the CDM's decision (or combination of decisions) and implements them. Checks are made to insure the requests satisfy basic physical constraints, e.g. VOM prevents a requested power reduction to reduce tow line tension when power reduction has afready been applied to reduce speed for comfort. Furthermore, the decision to change speed & heading for comfort may not bring an improvement, and thereto the realisation of that decision is based on a scenario in which the optimum situation is sought. Also the decision to re-route may not be feasible and in such a case the vessel will stay on its original track. The decision 'Wait for Weather' only applies if the vessel is still in port or in shelter.

The build-up ofstatistics When the decision has been carried out the vessel sails on the given power and heading over the track until the next time step. The motions and other relevant parameter's are computed and logged. It is assumed that the wave-induced variables follow the Rayleigh distribution and according to the method given in Ref. 2 the long-term statistics are accumulated by adding weighted probabilities. The distribution of the tow line loads is computed on the assumption that the relative motions between tug and tow are Rayleigh distributed. A joint Rayleigh distribution, given by Ochi and Bolton {Ref. 13), for x and dx/dt (being the relative motion and velocity in the plane of the towline) is input to the dynamic towline program TOWCAP (Ref. 14).

CDM Expert Validation After setting up the decision scheme and establishing the values for the decision transformation matrices A, a number of experienced ship masters were involved in detailed simulations and analysis of self propelled transports and tows. Their experience and 'sailing policy' lead to some modifications in the decision matrix and in the 'wait for weather' policy. Especially for tows it is important to have sufficiënt time to get clear of the coast. After application of the modifications the general judgement was 'realistic'.

2.5 Risk

The SafeTrans system objective is to reduce the risk of ocean transports and tows, reducing exposure to operators, contractors and marine underwriters (J.R. Lloyd, Ref. 15). For engineering, a balance is desired between risk and cost. The risk calculation method in SafeTrans is developed by Safetec Nordic. The purpose of the Risk Module is to calculate the accident risk involved with a transport operation (towed or self-propelled).

The calculations are mainly based on historical data for transport operations. It should be noticed that human error is tmplicit with the historical data. A database was built up from accident data collected by six companies including warranty surveyers and operators. However, because few accidents were recorded by more than one of the companies there appeared to be a severe underreporting. Hence, based on the identified data overlap and on the assumption that all companies had collected their data at random from the total accident population, correction factors were cafculated to adjust for the under-representation.

The accident data are recorded according to initiating events (see Table 3). The frequency of the initiating events, and the conditional probabilities of 5 consequence categories, ranging from total loss to minor damage, are included in the data base. The risk is initially calculated in terms of material damage. Based on data in the database and on expert judgement, transfer matrixes are established for the Human Risk, Economie Risk and Environmental Risk.

The incremental risk is calculated for each time step, and accounts for the conditions at that instance. For the purpose of risk modeling, the events may be split in three categories as shown in Table 3.

Table 3: Initial Accident Event Categories Theoretical Model Scenario Dependent Constant Towline Breakage Capsize (due to wate ingress) Fire

Foundering Machinery Failure Grounding (powered) Collisions Stability Others (e.q. towline fouled) Structural Sea Fasteninq

The probability of towline breakage is based on the sophisticated theoretical Towcap model (Ref. 14), and the consequences are calculated from an Event Tree model, which addresses the likelihood of getting the tow under control after the breakage. The occurrence of the Scenario Dependent events depends upon factors like roll amplitude, proximity to shore, shipping of green water, etc. The risk calculations for these events are in addition based upon the accident data and a calibration approach. The likelihood of the Constant events is the same during the voyage, and taken directly from the historical data.

The risk will be different for each voyage repiication due to the randomness of the weather conditions and decisions taken by the tow master. A probability distribution may be assigned to the voyage risks, and the mean risk as well as the Standard deviation may serve as engineering decision criteria.

3. Validation

3.1. Background

Santa Fe International's Galaxy II, a heavy duty harsh environment jack-up, arrived in Halifax, Nova Scotia, following a 59 day (12,289 NM) transport aboard Dockwise's heavy lift vessel Transshelf. The Rauma Repola built heavy lift vessel Transshelf' was equipped with sponsons as shown in Figure 5 to obtain sufficiënt initial stability for this cargo. The vessel is 162 m long and 40 m wide and with a displacement of over 47000 t it was at deep draft: average 9 m. ABS criteria for static and dynamic stability were applied. Seafastenings were applied at Port and Starboard side symmetrically.

Fig.5: Layout of Galaxy II on Transshelf

The passage from Singapore via the Cape of Good Hope, presented a voyage with high potential for heavy weather, particularly in the vicinity of the South African Coast. In an effort to reduce the exposure of the Galaxy H as much as possible, Santa Fe implemented a number of pre-voyage studies and in-transit measures. In addition to the usual feasibility engineering, other precautions included a hindcast weather study, weather routing advice from multiple firms, and a specialist surveyor for the Cape passage.

The feasibility study indicated that rig leg stresses would be governed by pitch motions, particularly significant in quartering seas. Therefore, as a guide for the ship's master, allowable pitch motion curves were developed, considering limitations for leg strength and other design elements such as sea fastenings and cribbing. These curves served as a tooi for making decisions to minimize motions in the form of steering course changes or taking refuge in a pre-determined "safehaven".

With the risk of heavy weather off the South African coast, a hindcast study was conducted for conditions in September from 1986 to 1997. The report presented the results of a hindcast of 252 simulated voyages around South Africa in a westward direction, 21 voyages considered for each year. Voyages occurred with varying start dates from 31 August to 20 September. An assumed voyage speed of 10 knots was applied, irrespective of weather conditions encountered.

The results clearly demonstrated a considerable year-to-year variation in the severity of conditions during the period investigated. The analysis demonstrated that spells of relatively severe weather and spells of relatively benign weather tend to be quite persistent once they become established. However, the report further highlighted the uncertainty of predicting whether any particular year will be a severe or a benign one.

Weather observations are scarce for the South Atlantic. Although computer models give detailed predictions of wind and wave conditions out to 5 days, the study concluded that a realistic limit for reliable weather forecasting around South Africa in winter is 2-3 days.For this weather sensitive transport, where exceedance of limiting weather thresholds may threaten the safety of the operation, an independent weather consultancy service was employed to compile information and render advice.

In the interest of analyzing leg fatigue, MARIN was commissioned to provide equipment for monitoring motions and conducting a post-voyage evaluation. The TransShelf transport was instrumentation on the vessel consisted of a 6 degree of freedom motion sensor on the bridge of the vessel and a radar sensor at the bow. The radar measures the relative motion somewhat ahead of the vessel and the data acquisition system subtracts the vessel-induced motion so that the wave {onry disturbed by diffraction effects) remains. The Low Frequency cut-off of the measurement filters is 0.2 rad/s, which implies absence of the surge, sway and yaw motion signals components with a longer oscillation period than 30 s. In addition, the ship Master kept a logbook of visually observed wave and winds, and of speed/course setting. Every four hours, half an hour of raw data from the response unit and the wave radar were recorded and stored.

3.2 Results

A validation of the computational methods in SafeTrans has been carried out by comparing the results with actual voyage measurements of the Galaxy II transport. Three types of calculations were carried out: • SafeTrans VMC mode. • SafeTrans Monte Carlo mode 10-voyages over three years. • SafeTrans training mode using the actual route.

Additionally, a Monte Carlo Simulation run with 25 voyages covering 4 years was carried out to obtain indication of statistical stability, while Dockwise carried out runs for wave analysis (Leenaars et al, Ref. 3) and compared long term wave statistics for single, 25 and 100 voyages.

Table 4 compares the actual measurements with results of VMC and MCS. Results for the MCS training mode are discussed later. The remarks in the table are numbered and refer to the discussion items in Section 3.3

Table 4 Item Unit Actual VMC MCS

10 voyages Remarks

See discussion Ensemble statistical analysis valid tor: 1 jouney 1journey

avg spd Kts 9.5 12.0 11.9 Re. 1

Duration hour 1220 1037 1105

(MPr-)Max Ha m 9.8 6.5 7.8 Re. 2

Hmax observed m 8.4 Re. 2

Hs m 5.7 7.4 Re. 2

Hs observed m 8.2 Re. 2

Max. MprMax MprMax

Surge at COG' m 6.48 7.2 Re. 3

Sway at COG m 3.49 3.6 Re. 3

Heave at COG m 4.40 4.6

Roll (incl. wind heel) Roll (excl. wind heel)

6.2 3.4 3.83

5.7 2.3

Re. 4

Heel (max/st.dev.) 0 3.0 /0.8 5.5/0.9

Pitch ° 4.8 4.56 4.8

Yaw ° 1.2 3.05 3.1 Re. 3

Surge at Bridge 148/0/30 m 2.5 6.50 6.5 Re. 3

Sway at Bridge 148/0/30 m 2.4 4.78 4.9 Re. 3

Heave at Bridge 148/0/30 m 8.2 6.64 7.4

Vert. Relmot at Fpp/CL m 8.82 10.0

Long. Acceln at Cargo 55/0/39.4 m/s* 0.79 0.9

Trnsv.Acceln at Cargo 55/0/39.4 m/s* 1.13 1.35

Vert. Acceln at Cargo 55/0/39.4 m/s' 1.40 1.55

Long. Acceln at MQK 148/0/30 m/s' 0.53 0.56 0.65

Transv.Acceln at MQK 148/0/31 m/s' 0.9 1.75 2.0

Vert. Acceln at MQK 148/0/32 m/s' 3.1 2.42 3.1

Avg. Fuel cons. t 2078

MPr is the "Maximum Probable" determined by 1/nolcliiat,<™probability in long term statistics. For multiple voyage MCS computations ensemble statistics are used, which is the weighted sum of all voyage statistics. zCOG is "Center of Gravity" of the vessel at 74/0/23.2 m from aftPP, centreline and keel respectively.

3.3 Discussion

Waves, motions and accelerations

The table shows {Re. 1) that the computed average speed runs roughly 10% higherthan measured and this also shows up in an appropriate discrepancy in the voyage duration. This difference can be explained as follows. On the actual voyage, in good weather periods the vessel runs at lower power settings to save fuel. When weather deteriorates, power is increased to keep steering capacity. SafeTrans computations and simulations do not allow for such a scenario, so the initial power setting in Safetrans is 80% MCR. This means that the vessel goes too fast in good weather. To avoid unrealistic high average speeds, the vessel resistance curve (input value) was increased 30%.

For the table rows invofving wave statistics (Re. 2), some significant differences were found. The differences between VMC and VAC are likefy due to the fact that VAC was based on a 3-year IMDSS with zero-crossing periods while VMC used a 4-year IMDSS database with spectral peak period. The reason was that VMC was run some time later then the VAC runs and so used the most recent version of the IMDSS. Later comparisons suggested the differences in database could account for a 10% difference in wave statistics. Comparing the computations with the measurements and observations the following remarks are made: • The observed wave heights are not very accurate. The highest observed sea state lasted from late

afternoon to early morning, i.e. a 6 m wind sea nearty parallel with a 5 m swell. The swell peaked at midnight at 5.5 m. The square root of the quadratic sum of wind sea and swell was taken for total sea heights which is formally correct but may be too high given the inputs.

• The measurements by downward looking radar on the bow (corrected for local heave) were disturbed by reflected waves from the ship, so the results may be assumed to be somewhat high. The Monte Carlo resufts passing the Cape in the same time slot were about 10 % lower than the observed and measured waves which is a good match in view of above inaccuracies.

For the motions and accelerations, the overall agreement is good. Differences in the surge, sway and yaw motions (Re. 3) between measurements and computations could be partially caused by the filtering process in the measurement system. The low frequency contributions in surge, sway and yaw for seas from aft directions were filtered off, while in the computations these wave directions give large response at low encounter frequency.

The interpretation of the roll motions is difficult due to the effect of wind heel which is present in measurements as well as calculations. The combined effect of heel and (dynamic) roll gave a measured maximum of 6.2° and a simulated most probable maximum of 5.7°. According to the measurements, the maximum wind heel was 3° while the Monte Carlo results give 5.5°. The largest measured (dynamic) roll angle was 3.4°, while the Monte Carlo results show a maximum roll of 2.2°. It has to be noted that the natural roll period of the loaded vessel was about 45 s, so that measured roll may include effects of steering and wave drift roll moments, which are not accounted for in the computations. The Standard deviation of wind heel over the total voyage is given for the VMC and MCS calculations and agrees well: 0.8° and 0.9° respectivety.

Routing and decision making Table 4 shows the main characteristics of the 10 voyages simulated in the MCS calculations.

Table 4: Characteristics of the 10-voyage Monte Carlo simulations Wait for weather At departure: MCS #1 and #4 Durinq vovaqe in 'safehaven': MCS #2, #3 and #9 Vovaqe distance Shortest: 12353 nm Lonqest: 13605 nm Mean: 12887 nm Velocitv Highest mean: 12.68 kn Lowest mean: 11.25 kn Mean mean: 11.9 kn

Waiting for weather occurred twice in the Cape area: in simulation #3 and #9 for a period of resp. 2 and 5 days. So, waiting for weather was according to the CDM not an obvious decision. From a replay in training mode more information was obtained about the conditions near the Cape. The comparison of the voyage in SafeTrans training mode allowed to follow the actual route and Schedule. Results are shown in Figures 6 and 7 with the thin (red) time tracé coming from the measurements and the bold (black) coming from the SafeTrans run. The first figure compares the pitch motion significant doublé amplitude (SDA) and the second compares the measured wave signal (=relative water motion minus local heave) with the computed relative water motion at the bow.

Apparently during the 'waiting for weather' period in Algoa Bay (day 23 - 29) the wave conditions were lower than encountered before entering and after departure from Algoa Bay. This may be a reason for the fact that only in 20% of the simulated voyages the vessel went for shelter.

3.6

3.2 -

2.8

2.4

2.0

1.6

1.2

O.B

0.4

0.0

Pitch motion SDA

Fig.6: Pitch (Significant Doublé Amplitude) time tracé of simulation (bold black) and measurement (thin red)

Bowwave motion 8

20 30

Time (days)

Fig.7: Relative motion (SDA) of simulation (bold black) compared with measured bowwave (thin red)

Comparison of (weather routed) simulations with statistical VMC method The results in Table 4 show that the results form the 10 MCS voyages and the VMC computation are the same within a bandwidth of 10 %. This suggests that the effect of weather routing on the results was marginal. There are two reasons for such a marginal effect: • The voyage is over a very long distince with large percentage in mild sea climates, so in long term

statistics the contribution of storms over Hs=6m (the design criterion) is quite small. • The weather routing simulations avoid tropical cyclones, while the IMDSS wave scatter database used

in VMC doesn't contain the local, high seas of cyclones, so the effect of avoiding cyclones isn't shown.

Statistical stability of results Comparing the '10 voyage 1998' and the '25 voyage 4-years' Monte Carlo simulations in Table 6 below, the conclusion is that the results do not differ much. The main difference is that more years leads to an increased M.Pr. Max. wave and -along with that- pitch and relative water motion at the bow. The vertical acceleration at the bow is not increased because in the simulation the vessel was riding out that storm at almost zero speed, yielding a 3 hour M.Pr. Max. vertical acceleration of 3.0 m/s2. This supports the conclusion which has been drawn before (Ref. 2) that the vertical accelerations are very sensitive to encounter frequency and often are largest in medium sea states where the vessel runs at significant forward speed against the waves.

Item Unit MCS 10 voy. 28/8-3/9

1998

MCS*) 25 voy. 15/8-15/9

1995-1998 Ensemble statistical analysis valid for: 1journey 1journey

avg spd Kts 11.9 12.1

Duration hour 1105 1047

m MprMax MPrMax

Wavecrestheight Ha m 7.8 8.6

SurgeatCOG' m 7.2 6.8

Sway at COG m 3.6 3.4

Heave at COG m 4.6 4.6

Roll (incl. wind heel) ° 5.7 7.0

Heel (st.dev.) ° 0.9 0.95

Pitch ° 4.8 6.5

Yaw ° 3.1 3.2

Vert. Relmot at Fpp/CL m 10.0 12.4

Long. Acceln at MOK 148/0/30 m/sJ 0.65 0.8

Transv.Acceln at MQK 148/0/31 m/sJ 2.0 2.0

Vert. Acceln at MQK 148/0/32 nW 3.1 3.1

Avg. Fuel cons. t 2078 1982

Long term distributions of the wave crest heights of VMC and MCS are shown in Figure 8, demonstrating that for more that 25 MCS simulations the wave statistics in the 4 year are converging to the long term averaged of the scatter diagrams in VMC.

Weibull scale

MCSsingle 25MCS 100MCS VMC

Fig. 8: Wave crest heght distributions on Weibull distribution scale

4. Conclusions

The SafeTrans project has resutted in the development of a software package that allows the user to establish criteria for offshore transports and operations. The validation study showed good agreement between actual voyage and simulations over the same time slot in 1998. The statistical stability of Monte Carlo simulations was investigated by comparing the resufting long term wave statistics and tt can be concluded that for a long voyage the Monte Carlo simulation statistics converge to the wave scatter diagram statistics of VMC if the number of simulated voyages is larger than 25.

The validation case was a weather routed transport, and the simulations demonstrated the capability of the method to take seamanship into account. Yet, the effect on the Most Probable Maximum values for the relevant motions and accelerations could not be demonstrated in this case due relatively high criteria and lack of tropical cyclone data in the IMDSS wave database used for the VMC design method. Further experience and investigations with the programme and actual ship operation will be needed to reach conclusions. Thereto a pilot test program has been initiated in a cooperation between Dockwise and MARIN placing the SafeTrans On-board system on one of the Dockwise vessels.

References

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