An Immersive Digital Twin with Virtual Agent Interface for Pipeline Leak Simulation and Monitoring
Bibliographic record
Abstract
This paper presents a work-in-progress project that integrates an immersive digital twin simulation with an interactive virtual agent for hydrogen pipeline monitoring and training. Built upon the University of Calgary’s Advanced Pipeline Research and Innovation Laboratory (APRIL) facility, the project emulates realistic pipeline flow and failure scenarios using a Real-Time Transient Model (RTTM) and artificial intelligence (AI)-assisted leak detection techniques. To enhance user interaction and decision making in pipeline operation and training, an embodied conversational agent is embedded in the 3D digital twin pipeline environment, delivering naturalistic feedback through speech, gaze, and gesture. The agent can explain sensor anomalies, guide responses to simulated leaks, and support operator training based on the simulated pipeline data. A planned user study will evaluate how agent design factors, e.g., priming, accuracy, and dominance, affect user trust, situational awareness, and decision making. This work advances the integration of eXtended Reality (XR), digital twins, and explainable AI for critical infrastructure management and training applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".