Autonomous AI-Native Digital Twins for Mission-Critical Data Centres: A Multi-Layer Framework for Predictive Commissioning, Operational Readiness, Resilience and Lifecycle Optimization

Abstract

The rapid convergence of artificial intelligence (AI) workloads, high-density computing, advanced liquid and air cooling, increasingly distributed electrical architectures, and stringent availability requirements is transforming the data centre from a predominantly passive infrastructure environment into a highly dynamic cyber-physical system. Conventional data centre infrastructure management (DCIM) platforms remain largely dependent on fragmented telemetry, threshold-based alarms, static visualization, and human interpretation, limiting their ability to continuously correlate physical infrastructure behaviour with computational workload, commissioning state, operational risk, and lifecycle constraints. This paper proposes AIDCDT-X, an AI-native autonomous data centre digital twin framework that extends the conventional digital twin concept from visualization and monitoring toward predictive, context-aware, and governance-controlled infrastructure intelligence. The proposed architecture evolves the original five-layer AIDCDT model into an integrated cyber-physical intelligence stack comprising physical asset representation, synchronized multi-domain telemetry, contextual digital-twin state estimation, predictive and prescriptive intelligence, decision orchestration, and governance/audit control. The framework establishes a continuous information loop connecting building management systems (BMS), electrical power management systems (EPMS), DCIM, information-technology telemetry, environmental sensing, commissioning records, asset configuration, and operational events. A central contribution is a lifecycle-aware twin maturation model linking Design, Commissioning, Operational Readiness, and Steady-State Operation. Rather than treating commissioning documentation as a terminal project artifact, the proposed approach converts Level 1–5 commissioning tests, integrated systems testing, defect registers, burn-in observations, and as-built configuration data into structured training and calibration evidence for the operational twin. This creates a facility-specific intelligence baseline capable of continuously updating its representation of infrastructure health, thermal behaviour, electrical resilience, capacity margin, and operational risk. The paper further introduces a proposed multi-objective cyber-physical optimization formulation in which latency, energy efficiency, thermal stability, availability, predictive uncertainty, maintenance risk, lifecycle cost, and governance constraints are jointly evaluated. A bounded-autonomy principle is incorporated to explicitly separate prediction from authorized physical action, thereby preventing unrestricted AI control of mission-critical infrastructure. The resulting framework provides a pathway toward explainable and auditable autonomous operations while retaining human authorization at safety-critical decision boundaries. Unlike a conventional conceptual digital twin, AIDCDT-X is designed to support measurable validation through predictive lead time, anomaly-detection performance, commissioning defect detection, decision latency, false-positive and false-negative rates, availability impact, energy efficiency, and human-override metrics. The present work establishes the architecture and analytical methodology; it does not claim empirical performance from a live facility. Future experimental validation will therefore focus on a controlled pilot or high-fidelity digital-twin environment using facility-specific commissioning and operational datasets.