Digital Twin of Regenerative Pump for Performance Evaluation

Abstract

In an era marked by rapid technological advancements, digital twin technology has emerged as a revolutionary concept, bridging the gap between physical and digital realms. A digital twin is a dynamic, virtual representation of a physical object or system, updated with real-time data and capable of simulating, analyzing, and predicting its real-world counterpart's performance. digital twins have transitioned from theoretical frameworks to practical applications, offering unprecedented insights into the operational status and efficiency of various systems. This thesis focuses on the application of digital twin technology to a regenerative pump, highlighting its potential to transform performance evaluation and maintenance practices in industrial settings. The methodology involves developing a comprehensive digital model of the pump using advanced simulation software such as ANSYS, integrating IoT sensors for real-time data acquisition, and processing this data with sophisticated algorithms to replicate the pump's behavior digitally. The integration of software tools and hardware components ensures the accuracy and reliability of the digital twin, with a circuit diagram illustrating the system's configuration. This setup enables real time monitoring, simulation, and control of the pump, offering a holistic view of its performance. The implementation of the digital twin demonstrated significant improvements in performance evaluation, with real-time monitoring allowing for immediate anomaly detection and enhanced predictive maintenance strategies. The digital twin accurately mirrored the physical pump's behavior, providing insights into operational efficiency and potential failure points. A graphical dashboard facilitated user interaction, enabling informed decision-making based on real-time data visualizations and performance metrics. The successful development and implementation of a digital twin for a regenerative pump underscores the transformative potential of this technology. The methodologies and insights gained from this research can be applied to other systems, paving the way for widespread adoption of digital twins across various industries. Future research can explore the application of digital twins to complex systems, further expanding their capabilities and benefits.