scholarly journals Mobility Digital Twin with Connected Vehicles and Cloud Computing

2021 ◽  
Author(s):  
Ziran Wang
Keyword(s):  
Cloud Computing ◽  
Twin Studies ◽  
Physical Space ◽  
Building Blocks ◽  
Systematic Research ◽  
Connected Vehicle ◽  
Driver Assistance Systems ◽  
Digital Twin ◽  
Flow Monitoring ◽  
Connected Vehicle Technology

<div>A Digital Twin is a digital replica of a living or non-living physical entity, and this emerging technology has attracted extensive attention from different industries during the past decade. Although a few Digital Twin studies have been conducted in the transportation domain very recently, there is no systematic research with a holistic framework connecting various mobility entities together. In this study, by leveraging both connected vehicle technology and cloud computing, an Mobility Digital Twin (MDT) framework is developed, which consists of three building blocks in the physical space (namely Human, Vehicle, and Traffic), and their associated Digital Twins in the digital space. The cloud architecture is built with Amazon Web Services (AWS) to accommodate the proposed MDT framework and to implement its digital functionalities of storage, modeling, learning, simulation, and prediction. The effectiveness of the MDT framework is shown through the case studies of three digital building blocks with their key microservices: the Human Digital Twin with user management and driver type classification, the Vehicle Digital Twin with cloud-based Advanced Driver-Assistance Systems (ADAS), and the Traffic Digital Twin with traffic flow monitoring and variable speed limit.</div>

scholarly journals Mobility Digital Twin with Connected Vehicles and Cloud Computing

2021 ◽  
Author(s):  
Ziran Wang
Keyword(s):  
Cloud Computing ◽  
Twin Studies ◽  
Physical Space ◽  
Building Blocks ◽  
Systematic Research ◽  
Connected Vehicle ◽  
Driver Assistance Systems ◽  
Digital Twin ◽  
Flow Monitoring ◽  
Connected Vehicle Technology

<div>A Digital Twin is a digital replica of a living or non-living physical entity, and this emerging technology has attracted extensive attention from different industries during the past decade. Although a few Digital Twin studies have been conducted in the transportation domain very recently, there is no systematic research with a holistic framework connecting various mobility entities together. In this study, by leveraging both connected vehicle technology and cloud computing, an Mobility Digital Twin (MDT) framework is developed, which consists of three building blocks in the physical space (namely Human, Vehicle, and Traffic), and their associated Digital Twins in the digital space. The cloud architecture is built with Amazon Web Services (AWS) to accommodate the proposed MDT framework and to implement its digital functionalities of storage, modeling, learning, simulation, and prediction. The effectiveness of the MDT framework is shown through the case studies of three digital building blocks with their key microservices: the Human Digital Twin with user management and driver type classification, the Vehicle Digital Twin with cloud-based Advanced Driver-Assistance Systems (ADAS), and the Traffic Digital Twin with traffic flow monitoring and variable speed limit.</div>

A Distributed Message Delivery Infrastructure for Connected Vehicle Technology Applications

2018 ◽  
Vol 19 (3) ◽  
pp. 787-801 ◽  
Author(s):  
Yuheng Du ◽  
Mashrur Chowdhury ◽  
Mizanur Rahman ◽  
Kakan Dey ◽  
Amy Apon ◽  
...  
Keyword(s):  
Connected Vehicle ◽  
Message Delivery ◽  
Technology Applications ◽  
Connected Vehicle Technology ◽  
Vehicle Technology

AI Assisted High Fidelity Multi-Physics Digital Twin of Industrial Gas Turbines

2021 ◽  
Author(s):  
Senthil Krishnababu ◽  
Omar Valero ◽  
Roger Wells
Keyword(s):  
Gas Turbine ◽  
Data Storage ◽  
Gas Turbines ◽  
Building Blocks ◽  
Data Driven ◽  
High Fidelity ◽  
Test Field ◽  
Operating Characteristics ◽  
Digital Twin ◽  
The Impact

Abstract Data driven technologies are revolutionising the engineering sector by providing new ways of performing day to day tasks through the life cycle of a product as it progresses through manufacture, to build, qualification test, field operation and maintenance. Significant increase in data transfer speeds combined with cost effective data storage, and ever-increasing computational power provide the building blocks that enable companies to adopt data driven technologies such as data analytics, IOT and machine learning. Improved business operational efficiency and more responsive customer support provide the incentives for business investment. Digital twins, that leverages these technologies in their various forms to converge physics and data driven models, are therefore being widely adopted. A high-fidelity multi-physics digital twin, HFDT, that digitally replicates a gas turbine as it is built based on part and build data using advanced component and assembly models is introduced. The HFDT, among other benefits enables data driven assessments to be carried out during manufacture and assembly for each turbine allowing these processes to be optimised and the impact of variability or process change to be readily evaluated. On delivery of the turbine and its associated HFDT to the service support team the HFDT supports the evaluation of in-service performance deteriorations, the impact of field interventions and repair and the changes in operating characteristics resulting from overhaul and turbine upgrade. Thus, creating a cradle to grave physics and data driven twin of the gas turbine asset. In this paper, one branch of HFDT using a power turbine module is firstly presented. This involves simultaneous modelling of gas path and solid using high fidelity CFD and FEA which converts the cold geometry to hot running conditions to assess the impact of various manufacturing and build variabilities. It is shown this process can be executed within reasonable time frames enabling creation of HFDT for each turbine during manufacture and assembly and for this to be transferred to the service team for deployment during field operations. Following this, it is shown how data driven technologies are used in conjunction with the HFDT to improve predictions of engine performance from early build information. The example shown, shows how a higher degree of confidence is achieved through the development of an artificial neural network of the compressor tip gap feature and its effect on overall compressor efficiency.

Platoon recognition using connected vehicle technology

2018 ◽  
Vol 23 (1) ◽  
pp. 12-27 ◽  
Author(s):  
Kamonthep Tiaprasert ◽  
Yunlong Zhang ◽  
Xin Ye
Keyword(s):  
Connected Vehicle ◽  
Connected Vehicle Technology ◽  
Vehicle Technology

The Enabling Technologies for Cloud Computing

2014 ◽  
pp. 29-48
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Multicore Processors ◽  
Network Virtualization ◽  
Building Blocks ◽  
Virtual Private Network ◽  
Point Of View ◽  
Private Network ◽  
Security Network ◽  
Networking Security

This chapter explores emerging technologies centered around cloud computing. From the technological point of view, cloud computing was born as a result of the emergence and the convergence of contemporary technologies. This chapter regards technological aspects of cloud. In the software area, Virtualization Technology and Web Services; in the hardware area, shared compute components (i.e., multicore processors); in networking, security, network virtualization, Virtual Private Network (VPN), virtual firewalls, and network overlay are the promising technologies for the future complex computing infrastructures. In this chapter, the authors review these technologies and describe how they contribute to the anatomy and the characteristics of cloud computing. These technologies constitute the building blocks of cloud computing technologies and infrastructures.

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