Abstract:
By gathering data from the actual environment and simulating it, digital twin (DT) technology portrays the twinning behavior of a tangible thing or process, i.e., the current and future behavior. In a variety of fields and industries, including industrial, automotive, medical, smart cities, etc., it is utilized for predictive analysis. In the age of a growing urban population, it is a challenge not only to maintain water quality but also to develop a system that manages water logging’related problems like sewage overflows and flash floods. Numerous initiatives have failed to meet the needed performance standards because there is insufficient ongoing monitoring of data in real time and a poor knowledge of self’propelled systems. To address these issues, engineers are seeking to build a network embedded with data sensors and online models using digital twin technology for real’time monitoring of system dynamics. Operators can use this technology to spot odd sewer network conditions and then dispatch a maintenance crew to conduct repairs before damage is done. It results in reducing the operational cost of the system and preventing sewer overflows and waterlogging to a large extent. The main challenge of DT technology in social adoption is the lack of standardization of definitions and characteristics. Performance digital twin (PDT) methodology can be utilized to monitor the information from physical counterparts and produce actionable data for optimizing product design, generating strategy, and drawing conclusions. Instead of achieving economic efficiency, the methodology has provided quality of life and services to the citizens. The DT’s infrastructure includes various data mining and modeling techniques for prediction and optimization, and sensors, actuators, and IoT can be used for data acquisition methods. © 2024 Scrivener Publishing LLC.
