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Representative solution scenario

Digital Twin Feasibility and Architecture

This hypothetical feasibility scenario examines whether a synchronized digital representation could support observation, simulation, planning, or optimization for a defined physical process. It does not claim that AARHIT has built or operated such a twin.

Digital twin feasibility blueprint
Problem context

Define the decision before defining the twin

An organization is considering a digital twin but has not yet established the target decision, required model fidelity, data availability, calibration method, integration scope, or operating responsibility.

Feasibility depends on the smallest useful physical boundary, justified synchronization, visible model assumptions, and an owner who can maintain the representation as conditions change.

Functional scope

Assets, states, scenarios, and decisions in scope

  • Define the twin purpose and physical boundary
  • Represent approved assets, states, events, and relationships
  • Synchronize available operational data at a justified frequency
  • Run bounded scenarios or analytical calculations
  • Present assumptions, uncertainty, and reviewable recommendations
Data needs

Operational evidence needed to represent the asset

  • Asset structure and engineering definitions
  • Telemetry, process events, and operating context
  • Maintenance history and known state changes
  • Calibration observations and model assumptions
Reference architecture

From physical state to a reviewable scenario

01

Physical assets, sensors, and operational source systems

02

Governed ingestion and state-synchronization services

03

Asset, process, simulation, or analytical models

04

Scenario management and visualization workspace

05

Access, provenance, calibration, and monitoring controls

Human approval points

Twin assumptions and actions requiring acceptance

  • Model assumptions and calibration acceptance
  • Scenario configuration used for a material decision
  • Any recommendation affecting a physical process
Security and governance

Separate simulation insight from physical control

  • Read-only integration during early feasibility work
  • Visible assumptions, uncertainty, and data freshness
  • Isolated simulation separated from live control
  • Versioned calibration and model-change records
  • Role-based access and approved feedback boundaries
Delivery phases

Resolve feasibility before expanding model fidelity

  1. 01

    Define purpose, decision, scope, and stakeholders

  2. 02

    Audit data, models, integrations, and constraints

  3. 03

    Specify value, risk, and feasibility hypotheses

  4. 04

    Build a bounded prototype for selected questions

  5. 05

    Review evidence and prepare an architecture decision

Validation criteria

Does the twin represent enough reality to help?

  • State representation accuracy
  • Synchronization delay and missing-data behavior
  • Model calibration against observed conditions
  • Scenario usefulness for the stated decision
  • Integration and operating feasibility
Principal risks

Ways a twin can create false confidence

  • Undefined purpose or excessive scope
  • Incomplete asset and operational data
  • False precision or poorly communicated uncertainty
  • Unsafe coupling with physical control
Measurement framework

Signals for a twin architecture decision

  • State accuracy against approved observations
  • Synchronization freshness
  • Calibration error for defined variables
  • Scenario review usefulness
  • Estimated operating effort and cost

No fidelity target, synchronization interval, or operating value is assumed. Those thresholds belong to the defined decision, physical process, and available evidence.

Frame a digital twin around one accountable decision.

Bring the physical boundary, intended decision, source systems, modelling assumptions, and maintenance responsibilities that determine twin feasibility.

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