Methods for Hull and Propeller Performance Monitoring (Part 1)
In this blog post, divided into three parts, we discuss some of the recent trends in performance monitoring, such as the emerging ISO-19030 standard. In addition, we unveil some of our own recent developments in performance tracking, which lean heavily on new propulsion power modelling ideas. We present various real-data examples and compare different approaches using data collected via the Eniram platform. The main conclusion is that with our new methodology we can raise the accuracy of performance tracking to a new level, which allows for more detailed assessment of individual hull and propeller treatment effects. Moreover, we can derive interesting novel fouling indicators that enable, for instance, sister vessel comparisons and “fleet-wide” performance tracking, etc.
It has been estimated that around 10% of the energy cost and emissions of the global fleet are due to poor hull and propeller performance, which translates into billions of dollars annually [Soyland and Oftedahl 2016]. With these numbers in mind, it is not surprising why so much effort is put in tracking vessels’ performance as accurately as possible. By monitoring the performance, it is possible to get some insight into what drives the complicated processes that lead to hull and propeller degradation. Accurate performance tracking techniques enable us, for instance, to make educated decisions about hull treatments and to assess the efficiency of various coating techniques.
Back in 2013, a team of some 50 experts representing various universities, coating manufacturers, energy efficiency companies, ship owners, shipping associations, class societies, etc., was formed to work towards defining an ISO standard for carrying out performance tracking (Eniram was part of the team). The result of 3 years of work has led to the ISO-19030 standard, which will be released in the fall of 2016. The goal of the standard is to provide a generally accepted yardstick for hull and propeller performance monitoring, see [Soyland and Oftedahl 2016] for a nice introduction into the standard.
The ISO-19030 standard defines a practical approach for hull and propeller performance monitoring. The approach is based on calculating so called Performance Values (PVs) that track the speed loss (or power loss) compared to a reference speed-power curve. The PV time series are then used to calculate various Performance Indicators (PIs), such as dry-docking performance and maintenance effect. The basics of the ISO-19030 method are discussed below.
The ISO-19030 approach is quite simple and can be implemented with data that is generally available via different vessel performance monitoring solutions and data platforms. Simplicity, however, comes with a cost; the level of accuracy of the method seems good enough for tracking general long-term trends in the vessel performance, but detailed assessment of individual hull treatment effects, for instance, can be difficult. In addition, since the standard measures relative performance (and not absolute), comparing vessels is difficult.
The next-generation Eniram performance tracking technique helps in solving the afore mentioned issues. In the method, we use our own propulsion power model as the reference, and describe hull and propeller fouling by introducing a time-evolving resistance component on top of the power model. In addition, we replace the problematic STW log data with our novel Virtual Speed Log technology.
Let us here briefly recap the ISO-19030 methodology. The general idea is simple; compare the speed and power data obtained from the vessel to a speed-power reference curve. Implementation details such as methods for obtaining the reference curves and how to filter the data can be read in the standard text and, for brevity, are not reproduced here.
In practice, implementing the ISO-19030 approach requires five main steps:
- Obtain speed-power reference curves from sea trial data, tank test data, CFD calculations or via curve fitting based on collected in-service data.
- Filter the data a) for outliers and b) according to certain reference conditions (e.g. take out high winds).
- Do a wind correction to the obtained power measurements.
- Calculate the PVs (either speed loss V_d or power loss P_d) which simply compare the obtained speed and power measurements (filtered and wind corrected) to the reference curves: where the subscript m denotes a measured value and e denotes the expected value read from the speed-power reference curve.
- Calculate different PIs using the PVs (dry-docking performance, in-service performance, maintenance trigger and maintenance effect).
An example of a speed-power reference curve for a cruise vessel obtained using the curve fitting approach is given in Fig. 1 below. The method works simply by filtering the data according to the ISO-19030 specifications, calculating the wind correction, and then fitting your favorite curve to the results. For cargo vessels, where draft changes play a larger role, this technique could be implemented by looking at different loading conditions separately.
Fig 1. Original speed-power data, the data remaining after the filtering (and wind correction), and the estimated speed-power reference curve.
In Fig. 2 below, we visualize the Performance Values given by the ISO-19030 method for this specific cruise vessel, just to give an idea about how the time series looks like and what is the typical noise level. The quality of the obtained data is good for this vessel, so this example gives an indication of the “best case scenario”. It is clear that while the method does reveal the main trends in hull performance, the noise level is rather high so that assessing the effect of individual hull treatments is difficult.
Fig. 2. Speed (top) and Power (bottom) Performance Values. Lines indicate various hull treatments.Color indicates different sea areas.
The main benefit of the ISO-19030 is obviously that it provides a unified way to measure vessel performance. Secondly, the method is simple in the sense that relies on a rather small number of input variables, which means that it is robust against data errors. It can also be implemented using generally available data, and coding up the method is also rather straightforward.
On the downside, the accuracy of the ISO-19030 is limited. The main reasons are:
- Reliance on STW logs that are known for their inaccuracy,
- Extensive filtering and crude normalization (only a simple wind correction) and
- Possible inaccuracies in the reference curves.
The next-generation Eniram performance tracking approach can help to solve these issues: STW log problems are alleviated with our Virtual Speed Log technology, and the Eniram Propulsion Power Decomposition model built specifically for each vessel is used in the normalization. These topics are discussed in Part 2 and Part 3 of this blog series.