Chapter 5 - An introduction to digital twins and big data analytics
Kabbaj, Narjisse
Kabbaj, Narjisse
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2026-03-09
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Abstract
Digital twins and big data analytics are revolutionizing industries by providing real-time insights, predictive capabilities, and optimization frameworks for complex systems. Digital twins create virtual representations of physical assets, integrating data from sensors, simulations, and historical records to enable enhanced monitoring, predictive maintenance, and operational efficiency. This chapter introduces the fundamental concepts of digital twins, their evolution, and their integration with big data analytics. It explores the architecture of digital twins and key enabling technologies. The discussion extends to the data lifecycle within digital twins, covering data acquisition, harmonization, processing, and predictive modeling. Challenges such as interoperability, scalability, and data governance are also addressed, along with emerging trends like autonomous digital twins, blockchain integration, and sustainable applications. To illustrate the practical implementation of digital twins, this chapter presents two case studies: one on hydrogen leakage detection and another on turbulent jet analysis. The hydrogen leakage case study demonstrates how digital twins integrate real-time sensor data with physics-based simulations and machine learning to predict and mitigate risks. The turbulent jet case study highlights the role of digital twins in fluid dynamics, showcasing their ability to enhance predictive modeling and system optimization.
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Mathematical Modeling for Big Data Analytics