/pdf/knowledge-mapping-of-digital-twin-and-physical-internet-in-3iuv9g4e.pdf

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

A classification proposal of digital twin applications in the safety domain

Digital twins found their genesis in the halls of NASA and the methods of product lifecycle management. Rapidly evolving trends around the proliferation of sensors, the Internet of Things, Industry 4.0, and cyber-physical systems have spurred the growth of digital twins. This paper reviews digital twins and their use in connected and automated vehicles (CAVs). Strictly speaking, digital twins must have communication between a physical system and its model, as opposed to similar methodologies that achieve indirect communication through iteration, or that substitute different parts of a system simulation with bits of hardware or software for testing. In practice, new methodologies for testing CAVs are sufficiently complex and difficult to apply simple labels. This is seen in our review of vehicular digital twins. Several gaps and challenges are apparent for the continued advancement of digital twin applications. We note some developing areas as traffic management centers, digital maps, onboard diagnostics, and logistics. Digital twins foster model reuse and encourage the use of multiple models at different scales of resolution. The role of digital twins will continue to grow as models become more tightly integrated to the physical systems they represent. This will drive such models towards uniqueness (matching a particular vehicle or road), adaptability (evolving with changing conditions and subject to wear and tear), and interpretability (conveying useful information to a human user). A maturing connected infrastructure and the development of smart cities will cause the number of new digital twin services to explode in a myriad of unforeseen ways.

The Role of Digital Twins in Connected and Automated Vehicles

Digital twins found their genesis in the halls of NASA and the methods of product lifecycle management. Rapidly evolving trends around the proliferation of sensors, the Internet of Things, Industry 4.0, and cyber-physical systems have spurred the growth of digital twins. This paper reviews digital twins and their use in connected and automated vehicles (CAVs). Strictly speaking, digital twins must have communication between a physical system and its model, as opposed to similar methodologies that achieve indirect communication through iteration, or that substitute different parts of a system simulation with bits of hardware or software for testing. In practice, new methodologies for testing CAVs are sufficiently complex and difficult to apply simple labels. This is seen in our review of vehicular digital twins. Several gaps and challenges are apparent for the continued advancement of digital twin applications. We note some developing areas as traffic management centers, digital maps, onboard diagnostics, and logistics. Digital twins foster model reuse and encourage the use of multiple models at different scales of resolution. The role of digital twins will continue to grow as models become more tightly integrated to the physical systems they represent. This will drive such models towards uniqueness (matching a particular vehicle or road), adaptability (evolving with changing conditions and subject to wear and tear), and interpretability (conveying useful information to a human user). A maturing connected infrastructure and the development of smart cities will cause the number of new digital twin services to explode in a myriad of unforeseen ways. 

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The integration of sensors, actuators, and real-time control in transportation systems enables intelligent system operation to minimize energy consumption and maximize occupant safety and vehicle reliability. The operating cycle of military ground vehicles can be on- and off-road in harsh weather and adversarial environments, which demands continuous subsystem functionality to fulfill missions. Onboard diagnostic systems can alert the operator of a degraded operation once established fault thresholds are exceeded. An opportunity exists to estimate vehicle maintenance needs using model-based predicted trends and eventually compiled information from fleet operating databases. A digital twin, created to virtually describe the dynamic behavior of a physical system using computer-mathematical models, can estimate the system behavior based on current and future operating scenarios while accounting for past effects. In this manner, the collection of real-time data of the physical vehicle can be compared with the virtual counterpart to assess the likelihood of degraded operation and recommend maintenance. This paper creates a digital twin for an off-road tracked ground vehicle with an accompanying parameter database. A modular architecture enables different design reconfigurations to evaluate various chassis subsystems and vehicle-ground mobility interfaces. A preventive maintenance algorithm is applied to monitor the operating behavior of the vehicle. The prediction tool, operating in parallel with the digital twin, may use model-based and sensory signal condition indicators to monitor vehicle performance. A case study investigates two operating scenarios: a virtual wheeled suspension system is degraded; and a virtual tracked vehicle experiences missing trackpads, which subtly alter the performances. The numerical results will offer insight into the pathway to begin creating a digital twin-based maintenance forecasting strategy.&lt;/div&gt;&lt;/div&gt; 

Kalivaradhan Ramesh Babu

Papers

Design and Implementation of Digital Twin for Predicting Failures in Automobiles Using Machine Learning Algorithms