=Paper=
{{Paper
|id=Vol-2267/218-222-paper-40
|storemode=property
|title=Usage of the distributed computing system in the recovery of the spectral density of sea waves
|pdfUrl=https://ceur-ws.org/Vol-2267/218-222-paper-40.pdf
|volume=Vol-2267
|authors=Ilya Busko
}}
==Usage of the distributed computing system in the recovery of the spectral density of sea waves==
https://ceur-ws.org/Vol-2267/218-222-paper-40.pdf
Proceedings of the VIII International Conference "Distributed Computing and Grid-technologies in Science and
Education" (GRID 2018), Dubna, Moscow region, Russia, September 10 - 14, 2018
USAGE OF THE DISTRIBUTED COMPUTING SYSTEM IN
THE RECOVERY OF THE SPECTRAL DENSITY OF SEA
WAVES
Ilya Busko
Saint Petersburg State University, Faculty of Applied Mathematics and Control Processes
E-mail: trassae95st@mail.ru
This article presents a task of the recovery of the spectral density of sea waves in the linear case.
Creation of the onboard ship system giving the current information about sea state and weather
forecast in the navigation area is one of the most urgent problem. Weather forecast can be based on the
analysis of the sea waves spectral density change. Evaluation of the sea wave spectral density is solved
on the basis of indirect dynamic measurements of vibrational motion of the marine dynamic object in a
seaway. The first researcher to raise the wave parameter identification problem on the basis of object
behavior was Y. Nechayev. Over the past fifteen years, this problem has become rather popular and
the works of Nielsen U.D., Simons A.N., Pascoal R. and others are of the most significance.
Nevertheless, despite of researches large number it is still impossible to speak of an acceptable
effective solution to this problem. The recovery of the sea waves on the basis of the behavior of the
marine dynamic object requires the analysis and processing of large amounts of information. To
improve the accuracy of identification requires using different algorithm of recovery and a large
number of test calculations. The calculations should be made in real time. The system should also
store processed data and provide access at any time. The software should have the fault-tolerance
property, i.e. the software should continue to work in the case of failure of one of the parts. All these
requirements and features make us to use distributed computing system for developing software of the
solution of the problem.
Keywords: the distributed computing system, the recovery of the spectral density, the wave parameter
identification.
© 2018 Ilya Busko
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Proceedings of the VIII International Conference "Distributed Computing and Grid-technologies in Science and
Education" (GRID 2018), Dubna, Moscow region, Russia, September 10 - 14, 2018
1. Introduction
Creation of the onboard ship system giving the current information about sea state and weather
forecast in the navigation area is one of the most urgent problem. Weather forecast can be based on an
analysis of sea waves spectral density change. Evaluation of sea wave spectral density is solved on the
basis of indirect dynamic measurements of vibrational motion of the marine dynamic object in a
seaway. The first researcher to raise the wave parameter identification problem on the basis of object
behavior was Y. Nechaev (Nechaev, 1990, 1996) [1, 2]. Over the past fifteen years, this problem has
become rather popular and the works of Nielsen, Simons, Pascoal and others are of the most
significance [3-8]. Nevertheless, despite of researches large number it is still impossible to speak of an
acceptable effective solution to this problem. Therefore, in this paper an improvement of the available
methods for the sea waves parameters identification when a ship is used as a buoy is offered.
2. The problem
2.1. The formulation of the problem
At the moment the methods and the analysis is developed only for a linear case. In the linear
case the oscillation equation is represented as:
y(t ) a y(t ) b y(t ) (t ) , (1)
where a is a damping factor, b is a parameter that characterizes the frequency of the ship's own
oscillations, ξ(t) describes disturbance caused by sea waves. It is known by Khinchin theorem [9] that
in the linear case relation between the input and output spectral densities to restore the wave
parameters is represented as:
2 (2)
S y Фxy ( ) S x ,
where Sx is a spectral density of the input process that can be associated with the disturbance, i.e. sea
waves; Sy is a spectral density of the output process, i.e. the registered process of ship vibrations
caused by waves; Фxy ( ) is a transfer function of the linear system.
2.2. The solution of the problem
An algorithm for solving the sea wave parameters identification problem in the linear case is
proposed. The algorithm is based on the iterative algorithm of adaptive identification and use the
concept of “climatic spectrum” [10]. The steps of the solution are the following:
1. Read the acceleration data on the sides and determine the linearity of the process.
2. Read the data of different types of pitching.
3. Calculate the spectral density of the output stream.
4. Calculate a possible set of the input stream spectral densities. The example of such
recovered spectral densities is shown in the pic. 1.
5. Find the best solution using “climatic spectrum” and the values of parameters a and b
from eq. (1).
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Proceedings of the VIII International Conference "Distributed Computing and Grid-technologies in Science and
Education" (GRID 2018), Dubna, Moscow region, Russia, September 10 - 14, 2018
Figure 1: 1.a) the recovery of the roll spectral density, 2.b) the recovery of the pitch spectral density
2.3. The issues of the problem
The formulation of the problem in a detailed form you can see in [11-14]. It should be noted
briefly that there are the following main issues:
1. Read sensor data.
2. Calculate the linearity of the pitch.
3. Calculate the spectral density of the output and the possible set of the input stream
spectral densities.
4. Find the best solution from the set and the values of the parameters of eq. (1).
5. Calculate weather forecast and display it for a user.
6. Storage and backup storage of all data.
7. The requirement to perform all calculations in real time.
8. Requirement of fault tolerance of one of the nodes.
3. The software structure
The software structure satisfying the requirements of paragraph 2.3, can be implemented as a
distributed computing system. The solution of the sea wave parameter identification problem has four
loosely coupled tasks. Each of these tasks has his own features regarding to a hardware:
1. Reading, processing and sending sensor data to the storage. It has no complex
calculations, has no power consumption. It should work permanently and it should have a
special hardware in connection with sensors.
2. Long-term storage of information, the ability to backup and simultaneously transfer data
to multiple nodes.
3. Parallel calculations of the set of possible solutions of the equation (1) and comparison
with the data of the “climatic spectrum” in real time. It requires a multiprocessor
hardware.
4. Displaying the forecast on the user's screen and the ability to configure and work with the
system.
A schematic representation of such structure is shown in Figure 2.
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Proceedings of the VIII International Conference "Distributed Computing and Grid-technologies in Science and
Education" (GRID 2018), Dubna, Moscow region, Russia, September 10 - 14, 2018
Figure 2. The hardware structure
Thus, the software is divided into four hardware nodes, each of which is designed to perform
its individual tasks:
1. Node 1 is designed to display all information for the user. This node is main node in the
system. It knows everything about the location and settings of the system and other
nodes. Communication with other nodes is carried out according to its own protocol
through a separate dedicated module “broker”.
2. Node 2 reads data from sensors, performs their “rough” processing and conversion to the
required format and sends it to the storage.
3. Node 3 is designed to store all recorded data during the navigation of the ship. These
data, recorded over a year or more, can be used for programs adjusting the weather map
in sea navigation regions by other services.
4. Node 4 is designed for calculations. After the data window is read and stored on the node
3, this node receives the necessary information to calculate the weather forecast. Since
model parameters (1) are not exactly known, but have limitations, then the calculated set
of possible solutions will be quite large. This set of solutions should be compared with
the set of possible spectral densities from the “climatic spectrum” characteristic of the
given navigation area and find the best match. The data reading window on node 2 is
sliding. Therefore, this node should be well designed to perform parallel computing both
in terms of hardware and software.
It should be noted that “Solutions Calculation Algorithm” can be applied only in the case of
the linear impact on the ship. So the linearity of the process should be verified before the solutions can
be calculated. In the case of the nonlinear process the linearity can be reached using methods of
statistical linearization proposed by Kazakov I.E. [9] or changing navigation conditions. When the
methods of the statistical linearization can’t be applied the system will inform the user to change
navigation conditions and it will show influence of these changes to change the linearity.
4. Conclusion
The article presents the sea waves spectral density identification problem in the linear case and
the software and hardware structure proposed to make a solution. The solution of the problem has a
range of features that can be divided into four separate loosely coupled tasks: sensor data reading and
processing, data storage, parallel calculations and a user application. These features impose on the
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Proceedings of the VIII International Conference "Distributed Computing and Grid-technologies in Science and
Education" (GRID 2018), Dubna, Moscow region, Russia, September 10 - 14, 2018
construction of software and hardware solutions: they have different calculation complexity, different
construction of the hardware, different power consumption, etc. The most appropriate solution in this
case is the solution developed as a distributed computing system.
References
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[2] Nechaev Y.I., Navigation and Hydrography 3 (1996)
[3] Iseki T., Terada D., Proceedings of the 11th International Ofshore and Polar Engineering
Conference Stavanger (2001)
[4] Nielsen U.D., Marine Structure v. 19, i.1, pp. 33-69 (2006)
[5] Nielsen U.D., Probabilistic Engineering Mechanics v.23, i.1, pp.84-94 (2008)
[6] Nielsen U.D., Stredulinsky D.C., Proceedings of the 12th International Ship StabilityWorkshop,
pp.61-67 (2011)
[7] Pascoal R., C. Guedes Soares., Ocean Engineering v.36, i.6-7, pp.477-488 (2009)
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[11] Degtyarev A.B., Busko I.V., Neurocomputers: development, application 8, pp. 3-10 (2012)
[12] Degtyarev A., Busko I., Nechaev Y., Proceedings of the 11th International Conference on the
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