The plan is organised into six best-practice planning
clusters spanning 18 weeks. Each cluster states its focus, key activities and a
milestone that must be reached before the next cluster begins.
Weeks 1-3
Cluster 1 - Onboarding & Foundations
Settle in, set up the working environment, and agree the detailed plan and success criteria with the academic and industrial supervisors.
Key activities
- Onboarding at Graha International: tooling, data-governance and NDA briefing.
- Familiarisation with digital-twin concepts, vehicle subsystems and existing Graha assets.
- Set up a reproducible environment: version control, experiment tracking and a containerised workspace.
- Refine scope, success criteria and the detailed 18-week work plan with the supervisor.
Milestone, Approved internship work plan and a running, reproducible development environment.
Weeks 4-6
Cluster 2 - Literature Review & Requirements
Build the scientific foundation through a structured literature review and a precise requirements and evaluation specification.
Key activities
- Structured literature review on digital twins, data fusion and hybrid modelling.
- Survey of digital-twin reference architectures and simulation tooling.
- Stakeholder and requirements analysis; definition of the core use cases and KPIs.
- Draft the conceptual approach and the evaluation methodology with metrics and baselines.
Milestone, Literature-review report and an agreed requirements and evaluation plan.
Weeks 7-9
Cluster 3 - Data Engineering & System Architecture
Prepare the data assets and design the digital-twin platform architecture.
Key activities
- Acquire, profile and clean the sensor telemetry for the chosen subsystem.
- Select the twin scope and fidelity; define the state representation.
- Design the platform architecture: ingestion, twin state, modelling, simulation and view layers.
- Specify the synchronisation strategy and the interfaces between components.
Milestone, Architecture design document and a prepared, documented telemetry dataset.
Weeks 10-13
Cluster 4 - Implementation & Modelling
Implement the platform and the physics- and data-driven twin models.
Key activities
- Implement the real-time data-fusion pipeline that updates the twin state.
- Develop the physics-based and data-driven models and combine them into a hybrid twin.
- Implement the simulation and what-if analysis engine.
- Build a monitoring dashboard that visualises the twin and its predictions.
Milestone, Working Digital Twin prototype covering the core monitoring-and-simulation use case.
Weeks 14-16
Cluster 5 - Evaluation, Validation & Hardening
Evaluate, validate and harden the prototype against measured behaviour.
Key activities
- Define and run experiments on twin fidelity, synchronisation latency and prediction error.
- Validate simulated behaviour against measured telemetry.
- Assess robustness to missing or noisy data; check reproducibility of results.
- Iterate on the models and the synchronisation logic based on the findings.
Milestone, Evaluation report with quantitative results and a validated, hardened prototype.
Weeks 17-18
Cluster 6 - Documentation, Thesis & Final Defence
Consolidate the documentation, draft the thesis material and present the results.
Key activities
- Consolidate code, documentation and reproducibility instructions.
- Write the thesis-ready report covering method, results and limitations.
- Prepare and deliver the final presentation and a live demo.
- Hand over the platform, datasets and the backlog of future work to Graha.
Milestone, Final thesis-ready report, final presentation and a complete handover package.
