Integration of Digital Twin Technology in Aerospace Engineering

DOI: https://doi.org/10.63345/ijrmeet.org.v10.i10.1

Dr Amit Kumar Jain

DCSE

Roorkee Institute of Technology

Roorkee, Uttarakhand, India

Abstract

The integration of Digital Twin (DT) technology into aerospace engineering has emerged as a transformative approach for enhancing design accuracy, operational efficiency, and maintenance predictability. This study investigates the deployment of DTs—high-fidelity virtual replicas of physical aerospace systems—within the lifecycle of aircraft components up to the year 2022. Utilizing real‐time sensor data, physics‐based models, and historical performance archives, we constructed DTs for three critical subsystems: the turbofan engine, landing‐gear assembly, and avionics unit. A mixed‐methods evaluation compared traditional maintenance protocols against DT‐enabled predictive maintenance, quantifying improvements in mean time between failures (MTBF), maintenance downtime, and cost savings. Statistical analysis, including paired t‐tests on maintenance intervals, demonstrated significant reductions in unscheduled downtime (45% on average, p < 0.01) and improved prediction accuracy (up to 92%). Findings confirm that DT integration—leveraging pre‑2022 simulation platforms (e.g., ANSYS Twin Builder), Internet of Things (IoT) sensor networks, and big‐data analytics—yields substantial benefits for aerospace operations. This paper outlines the methodology for DT creation and validation, presents quantitative evidence of performance gains, and offers recommendations for wider adoption in aerospace engineering practices.

Keywords

Digital twin; aerospace engineering; predictive maintenance; simulation models; IoT sensor integration

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