Using Big Data in the pursue of asustainable and safe maritime future
Ulrik Dam Nielsen
WISE kick-off symposium
Online meeting hosted by TUMSAT Tokyo, Japan
16th February, 2021

Outline

1. Introduction
2. An overview about recent and ongoing projects at DTU
Mechanical Engineering
A. Sea state estimation (SSE) using multiple observations
platforms
B. Evaluation of retrofits for increased fuel efficiency
C. Voyage optimization benefits for different shipping
stakeholders
3. Final remarks2

Prologue

NB: The research projects target goals 9, 12, 13, 14, and 173

Introduction
Using Big Data and machine learning…
To ensure SAFE, GREEN and EFFICIENT
marine design and operations! Examples are,oShips in transit, adverse weather with a focus on safety (e.g.,the of decision support system)oCrane and tow operations during installation and maintenanceof windfarms, fish cages, etc. (re operational windows, control
systems for DP)oVessel performance monitoring (re added-wave resistance)

oFor ‘black box’ purposes (re accidents and other post-event
investigations)oImprovement of large-scale, spectral wave models leading tobetter weather forecast, and more accurate wave hindcasts (rethe need for calibration, data assimilation)oBesides, wave measuring is important to understand themechanisms of surface water mixing and air-sea fluxes (reclimate changes!)4

Introduction
Using Big Data and machine learning…
This presentation gives highlights about three(research) projects where the use of large
amounts of data is fundamental!

AIS data
5Sea state estimation based on data frommultiple observation platforms
Why do we want to measure waves?
• Because waves are the fundamental driver of most of theprocesses* we are concerned about as maritime engineers,
naval architects, or ocean scientists.
*including wave-structure interactions aqua farming developments, mixing ofwater and air sea-fluxes responsible for weather and climate

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SSE from multiple observation platforms
Well-known means for sea state estimation (SSE)
In-situ buoys

Navigational radarused as wave radar

Remote sensing (aircraft or satellite)

All with their own pros and cons;
but keep in mind we want the sea
state at the exact position at any
moment in time...
7SSE from multiple observation platforms
An alternative means for sea state estimation
The wave buoy analogy

What is its estimation principle? It is based on measurements ofthe buoy’s motions that are processed to give the wave spectrum(in real-time and at the buoy’s exact position). We will do the samefor a ship...
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SSE from multiple observation platforms“Problems”
 The single estimate from the individual platform is valid in a confined
geographical area at current time, in fact, a bit back in time Associated uncertainty may be large; not to mention that it is difficult
to quantify since the ground truth is unknown“Answer”:• Merge all means in a combined (heterogeneous) network of wavesensors• Leading to improved estimates on both a local and global
geographical scale, and with better forecasted estimates ahead oftime (spatially as well as temporally)
A network of ‘observation platforms’!

9SSE from multiple observation platforms
Objective of this study• Introduce the idea to apply several platforms simultaneously (in situ buoys,fleet of ships, remote sensing, ...) to make SSE• This conceptual study “kicks off” by focusing on just one of the means (ship asa wave buoy)• A PhD-study has be initiated in October 2020 (2020-2023).
• Results are presented by Nielsen, Brodtkorb and Sørensen (2019); limited tofocus on numerical simulations as no experimental results exist (yet).

10SSE from multiple observation platforms
Why it is particularly appealing to use ships as sailing wave
buoys:
1. Sensor instrumentation is already in place on most of today’sships
2. The number of ships...

11Multiple ships…
Network of ‘wave recorders’ (Nielsen et al., 2019)

Nielsen, U.D., Brodtkorb, A.H., Sørensen, A.J. (2019). Sea state estimation using multiple ships simultaneously as sailing wave buoys Applied Ocean Research, 83,
pp. 65-76.

A snapshot of vessel positions around the world’s ocean based on data from AIS (green: terrestrial,red: satellites)

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Open questions and future studies
Potential for sea state estimation using networked and heterogeneous
sensor systems; ranging from remote sensing, fleet of ships to in situbuoys and offshore/ocean structures.
 In a network, the use of ships as wave buoys is appealing (extensive amount of data
is available in real-time)! But there are many open questions: How should/could weighting be done, for ships?, for other platforms?
 How can estimates be forecasted, spatially and temporally? What about inclusion ofeffects from sea current, wind, bathymetry, ...?
 How to communicate and transfer the vast amount of data between the single
platforms? (connectivity is still low at sea)?
 How could the results be linked and integrated to applications (performancemonitoring, operability assessment, DSS, ...)?
 Available literature is part of the solution (de Souza, 2019; Ardhuin et al., 2019; Longet al., 2019; Mak et al., 2019; Nielsen et al., 2019; …)
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Evaluating the effect of retrofits using in-service data(MSc study)

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Evaluating the effect of retrofits
Research question: Can in-service
data be used to evaluate the effectof retrofits while correcting for
operational and environmental
factors?
Motivation: RINA (2018) is calling for aframework that will make it possible tocompare technologies in a fair andreliable way in order to increase theuptake of technologies that work, andspeed up development of newtechnologies.
This project was made together with Maersk

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Evaluating the effect of retrofits

In-service data: Engine (RPM, ME power, fuel flow) andloading condition (including trim), wave-induced motions,weather and wave conditions, …

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Evaluating the effect of retrofits
Does the retrofit work?
Data representing
average over 24 hours…

The key performance indicator (KPI) informs about efficiency;
hence, it will be a measure of the retrofit’s performance.
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Evaluating the effect of retrofits
Regression Discontinuity Design (RDD)This project investigates how operational data collected on board ships can beused to evaluate the effect of a retrofit. The method Regression Discontinuity
Design (RDD) is identified as a possible method for this task.
o Econometric framework.
o Evaluate the causal effect of an event.
o Controls for other factors

Two casestudies:18

Evaluating the effect of retrofits
A lot of data was analysed based on regression discontinuity design…

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Evaluating the effect of retrofits
Two case studies
A lot of data analysis based on regression discontinuity design…

Conclusions and future work Data analysis showed little effect the retrofits; however, more importantly,
the method itself, RDD, proved useful for the analysis.
 Additional methods should be tested on same data; is the analysis
sensitive to the selected KPI; improved model for hull fouling is needed.

20Analysis of voyage optimization benefits for different
shipping stakeholders (MSc thesis)

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Analysis of voyage optimization benefits…

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Analysis of voyage optimization benefits…
Case study with TORM A/S and NAPA

All available AIS data of TORM ship from January 2018 to end of March 2019.

The study made a comparison between the actually made voyages (using AISdata), and corresponding simulated-optimized ones, with account to variousperformance metrics.

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Analysis of voyage optimization benefits…
Case study with TORM A/S and NAPA

Main findings from the study:
All available AIS data of TORM ship from January 2018 to end of March 2019.
• Review of the benefits of voyage optimization processes• An overview of ship performance calculations
Simulation
results by using
different
voyageoptimization
strategies
The• study
made a comparison
betweenthe actually
madevoyages (usingAISdata),
correspondingones,with accountto various• andA benefit
sharing simulated-optimized
estimate by different
benefit
distribution
schemes
performance metrics.

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The end
A number of other projects related to the use of BIG data:


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Data-driven prediction of added resistance in waves (MSc and PhD studies, 20202023)
Inference of transfer functions and prediction of vessel responses using machinelearning (MSc study, 2020)
Improved accuracy of speed-through-water readings using sensor fusion (PhDstudy, 2019-2022)
Short-time deterministic motion prediction using process correlation structures(PostDoc study, 2019-2020)
A design tool for early evaluation of compliance with minimum propulsion power
requirements (2019-2020)
A decision support tool based on virtual structural hull monitoring (2020)
Investigation of vessel performance benchmarking at speed intervals below thedesign speed (2021)…

Contact:
Ulrik Dam Nielsen
Email: udn@mek.dtu.dk25Acknowledgements
 Financial support from the Danish Maritime Fund and Orient’s Fund ishighly appreciated In this presentation, data from Maersk and TORM has been considered Collaboration with and support by NTNU AMOS26