Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

Maritime piracy is posing a genuine threat to maritime transport. The main purpose of simulation is to predict the behaviors of many actual systems, and it has been successfully applied in many fields. But the application of simulation in the maritime domain is still scarce. The rapid development of network and measurement technologies brings about higher accuracy and better availability of online measurements. This makes the simulation paradigm named as dynamic data driven simulation increasingly popular. It can assimilate the online measurements into the running simulation models and ensure much more accurate prediction of the complex systems under study. In this paper, we study how to utilize the online measurements in the agent based simulation of the maritime pirate activity. A new random finite set based data assimilation algorithm is proposed to overcome the limitations of the conventional vectors based data assimilation algorithms. The random finite set based general data model, measurement model, and simulation model are introduced to support the proposed algorithm. The details of the proposed algorithm are presented in the context of agent based simulation of maritime pirate activity. Two groups of experiments are used to practically prove the effectiveness and superiority of the proposed algorithm.

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Application of dynamic data driven application system in environmental science

Environmental Reviews ◽

10.1139/er-2013-0071 ◽

2014 ◽

Vol 22 (3) ◽

pp. 287-297 ◽

Cited By ~ 5

Author(s):

Jingwei Song ◽

Bo Xiang ◽

Xinyuan Wang ◽

Li Wu ◽

Chun Chang

Keyword(s):

Data Assimilation ◽

Real Time ◽

Computational Models ◽

Model Simulation ◽

Spatial Scales ◽

Data Driven ◽

Time Data ◽

Environmental Sciences ◽

Dynamic Data ◽

Application System

The paradigm of dynamic data driven application system (DDDAS) has been proposed as a framework to analyze and predict the character and behavior of complex systems that influence computational models significantly. Its accuracy and efficiency lies in its ability to integrate observations on different temporal and spatial scales from real-time sensors, and in its measurement steering and controlling capabilities. Many problems in environmental sciences are nonlinear and complex, impossible to solve by using input/output sequence flows without feedback control. Nonlinear system efficiency depends on measurement control and steering, on-line data assimilation, and model selection with dynamic optimization. Compared with traditional methods, DDDAS possesses the capacity to overcome these limitations. This paper discusses DDDAS and classifies typical cases of its application in environmental sciences into three levels of paradigm. Short reviews of multi-model simulation and data assimilation are provided for practical use. Recent developments and future perspectives are reviewed. Future work may address determining automatically where, when, and how to acquire real-time data, and its integration with GIS, to improve efficiency and accuracy. User-generated content will find wide application in the future. Considering the differences between DDDAS and other data-driven methods in solving the same nonlinear complex system problems, a combination of nonlinear science and chaos theory is advocated.

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The Use of Data Assimilation to Improve Wave Hindcast Results

Coastal Engineering 1996 ◽

10.1061/9780784402429.371 ◽

1997 ◽

Author(s):

Jon M. Hubertz

Keyword(s):

Data Assimilation ◽

Wave Hindcast ◽

Use Of Data

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Prospect of Dynamic Data Driven Application System in agricultural and environmental applications

CHINESE JOURNAL OF ECO-AGRICULTURE ◽

10.3724/sp.j.1011.2008.01597 ◽

2009 ◽

Vol 16 (6) ◽

pp. 1597-1602

Author(s):

Ying OUYANG

Keyword(s):

Data Driven ◽

Environmental Applications ◽

Dynamic Data ◽

Application System

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Dynamic-Data Driven Modeling of Uncertainties and 3D Effects of Porous Shape Memory Alloys

10.21236/ada597368 ◽

2014 ◽

Author(s):

Craig C. Douglas

Keyword(s):

Shape Memory Alloys ◽

Shape Memory ◽

Data Driven ◽

Dynamic Data ◽

3D Effects ◽

Data Driven Modeling

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On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

Energies ◽

10.3390/en14051310 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1310

Author(s):

Pablo Torres ◽

Soledad Le Clainche ◽

Ricardo Vinuesa

Keyword(s):

Physical Phenomenon ◽

Urban Pollution ◽

Pollutant Dispersion ◽

Urban Environments ◽

Machine Learning Algorithms ◽

Data Driven ◽

Thermal Fields ◽

Use Of Data ◽

Sustainability Challenges ◽

New Research

Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

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