Digital twin architectures for industrial pumps: Integrating data perception, reduced-order modeling, and implementation paradigms
TL;DR
Comprehensive analysis indicates that the interdisciplinary integration of multisource data fusion and non-intrusive reduced-order modeling synergistically improves real-time responsiveness while preserving the physical fidelity of fluid-dynamics knowledge.
Abstract
As the core energy-driven general machinery, pumps play an irreplaceable role in safeguarding national economic development and national defense security. However, the structural contradiction between the high energy consumption of traditional pump systems and the global “Dual Carbon” goals has become increasingly prominent. With the evolution of digital transformation and the continuous elevation of industrial internet security standards, the pump maintenance paradigm has undergone a profound shift from reactive responses to proactive, forward-looking interventions. To facilitate this transition, Digital Twin (DT) technology provides a system-level informatics solution by constructing virtual mirrors of physical entities. This paper aims to provide a comprehensive review of the lifecycle operation and maintenance of pumps, focusing specifically on how cutting-edge data perception and twin model frameworks serve as imperative elements to overcome bottlenecks in industrial deployment. Comprehensive analysis indicates that the interdisciplinary integration of multisource data fusion and non-intrusive reduced-order modeling synergistically improves real-time responsiveness while preserving the physical fidelity of fluid-dynamics knowledge. Furthermore, by leveraging the latest data mining and physical mechanism fusion technologies, this paper constructs a diversified paradigm for the “Four-Pre” functions, including pre-forecasting, pre-warning, pre-simulation, and pre-planning, which provides a critical reference framework for the architectural design and engineering application of subsequent industrial-grade pump DT. To further advance this paradigm, the deep coupling of real-time perception with reduced-order models and the construction of system-level dynamic architectures should be identified as future directions to empower the real-time, system-wide coordination of on-site industrial operations.