Uncertainty-aware AI-enabled framework for predicting hydrogen dispersion in confined spaces to enhance operator situational awareness

Niclas Flehmig; Mary Ann Lundteigen; Shen Yin

cs.LGOct 16, 2026

Uncertainty-aware AI-enabled framework for predicting hydrogen dispersion in confined spaces to enhance operator situational awareness

Authors: Niclas Flehmig, Mary Ann Lundteigen, Shen Yin

Organizations: Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway · Department of Cybernetics Engineering, Trondheim, Norway

Abstract

We present a two-stage AI-enabled framework for enhancing operator situational awareness during compressed hydrogen bunkering in maritime confined spaces. In the event of a leak, the framework first estimates the unknown leakage rate from early sensor readings and then forecasts how the hydrogen cloud will disperse over the next time steps, providing operators with a predicted concentration map together with confidence bounds. The framework is based on two independent AI models building one AI system. The AI system is designed as a decision-support tool. It augments sensor readings, and thus enhances operator situational awareness and supports decision-making. This enables preventive safety barriers to be deployed before hazardous concentrations are reached, while an independent emergency shutdown system remains the ultimate safety layer. The system design incorporates safeguards to ensure the AI system operates within its competence envelope. Trained and tested on an open dataset of confined-space hydrogen releases, the framework achieves a mean absolute error of $0.052$ ~g/s in leakage-rate estimation and $\approx0.018$ in hydrogen-concentration prediction, approaching the sensor precision bound of $\pm0.01$ . The hydrogen-concentration prediction provides well-calibrated uncertainty estimates that allow operators to judge the reliability of each forecast. These results suggest that the framework may provide early information about hazardous concentration build-up, potentially supporting preventive actions by operators.

Figures & tables

Figure 1: Generic system diagram for compressed $H_{2}$ bunkering system based on [ 37 , 30 , 8 ]

Figure 2: Generic architectural pattern for AI system(s) in safety-related systems based on [ 9 ] . Raw data inputs first pass through an input guard that validates format, range, and integrity. The data then feed the AI system(s), a monitoring component that observes both inputs and internal AI processes, and a supervisory component representing the human operator. The AI outputs are aggregated by a voting component before being relayed to the downstream process. The monitoring component informs the supervisor, who retains authority to override the output of the AI system(s).

Figure 3: The placement of the hydrogen sensors in the experimental setup based on [ 11 ] .

Figure 4: Data processing steps for the hydrogen concentration dataset with their practical safety implications. Top row: (1) Raw data ( $n=429\,514$ ) showing extreme right skew. (2) Trimmed data ( $n=182\,181$ ) after removing idle periods before and after the exposed leakage interval; transformation step to remove non-leakage related data that would introduce a bias. (3) Log-transformed trimmed data revealing a bimodal structure with distinct not-exposed (near-zero) and exposed sensor populations; transformation reveals high number of not-exposed sensors that should be excluded from the dispersion forecast. Bottom row: (4) Not-exposed vs. exposed split using a threshold of 0.009 H 2 volume fraction, consistent with the sensor accuracy of $\pm$ 1%. (5) Distribution of the exposed sensors before transformation; represents relevant sensors for the forecast. (6) Log-transformed exposed subset showing an approximately Gaussian distribution; transformation enables Gaussian likelihood within the GPyTorch framework but comes with information loss.

Feature	Mean	Std	Min	Max
time $t$ (s)	74.28	55.98	0.00	239.95
leakage rate $\hat{m}$ (g/s)	0.35	0.32	0.03	1.27
$x$ (m)	0.46	0.20	0.00	0.90
$y$ (m)	2.17	1.61	0.00	4.68
$z$ (m)	0.49	0.31	0.00	0.80
H 2 conc. $c$ (%)	0.06	0.06	0.00	0.31

Table 1: Descriptive statistics of the trimmed dataset (

n=182\,181

Figure 5: Control structure and system design of the safety-related system within a bunkering system for compressed $H_{2}$ . Filled boxes represent the ESD system that is independent and the safety function of the system. Cross-hatched boxes represent component that are introduced to enhance reliability of the AI system based on [ 9 ] . The sensors are part of the ESD system and their readings are replicated for the use of the AI system.

Figure 6: Performance of the Stage 1 MLP ensemble for leakage rate estimation. Left: Predicted versus true mass flow for all test-set samples (MAE = 0.052 g/s); the dashed line marks perfect agreement. Middle: Per-experiment MAE, showing reliable estimation for low-to-moderate leaks but elevated error for a few high-flow scenarios. Right: Distribution of prediction errors (true minus predicted), with mean $\mu=-0.023$ g/s; the negative bias indicates a slight tendency toward under-prediction.

Panel of Figure 7 — Figure 7: Predictive mean (solid line) and 95% confidence interval (shaded) for the two best SVGP models at three sensor positions, with ground truth (green crosses). (a) Gaussian likelihood: accurate mean but overconfident uncertainty. (b) Beta likelihood: wider, better-calibrated bands that capture the ground truth more reliably, especially at $\hat{m}\approx 1.0$ g/s where training data are sparse.

Method	Likelihood	MAE	RMSE	NLL
VNNGP ( $k=16$ )	Gaussian	0.0248	0.0359	17.5418
SVGP ( $M=6000$ )	Gaussian	0.0207	0.0255	12.6712
SVGP ( $M=4000$ )	Beta	0.0209	0.0267	$-1.2255$
SVGP ( $M=6000$ )	Beta	0.0176	0.0226	$\mathbf{-1.5340}$

Table 2: Test-set performance of the best Stage-2 GP models with light additive kernel.

Figure 8: Calibration of the GP dispersion model (Beta likelihood) on the held-out test experiments (exposed sensor readings, n = 14,694; 1000 predictive Monte Carlo samples per point). (a) Empirical versus nominal coverage of the predictive quantiles. Red markers show PICP for the central 50–99% intervals. The curve above the diagonal indicates conservative prediction intervals. (b) PIT histogram centered at 0.508 but narrower than uniform, confirming mild over-coverage.

Nominal coverage	Empirical coverage	MPIW [conc.]
0.50	0.883	0.044
0.68	0.949	0.065
0.80	0.974	0.084
0.90	0.987	0.108
0.95	0.990	0.128
0.99	0.993	0.166

Table 3: Calibration results showing nominal coverage, empirical coverage, and MPIW.

Threshold	Total	Exposed	Train.	Val.	Test
0.0085	182 181	116 768	78 444	23 589	14 735
0.0090	182 181	116 306	78 133	23 479	14 694
0.0095	182 181	115 868	77 835	23 388	14 645

Table 4: Sample counts for datasets generated with different exposure thresholds. The exposed share is 64.1 %, 63.8 %, and 63.6 % for thresholds 0.0085, 0.0090, and 0.0095, respectively.

Configuration	MAE	RMSE	NLL
Two-stage (MLP est. $\hat{m}$ )	0.0249	0.0318	$-1.421$
Oracle (true $m$ )	0.0176	0.0236	$-1.534$

Table 5: Performance comparison of two-stage approach versus oracle.

Figure 9: Hydrogen concentration measured at six representative sensors (1, 8, 10, 15, 20, 24) over all tests. All sensors exhibit a rapid transient rise during the first approximately 50–100 s, followed by a quasi-steady-state plateau. The post-transient behavior is largely independent of time, indicating that temporal evolution is primarily relevant during the initial dispersion phase. For the majority of the leakage duration, sensor position and mass flow rate dominate the concentration mapping.

Figure 10: Overall distribution of leakage rates in the trimmed dataset. The empirical distribution is heavy-tailed, with dominant peaks near 0.1 g/s and in the 0.4–0.5 g/s band. Data become scarce above 0.8 g/s and at the lowest rates, which limits model accuracy at the extremes.

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How Mobile Gas Sensor Trajectories Govern Hydrogen Leak Detection: A Safety Gap in Manual Leak Inspection of Hydrogen System Components

Christian Masuhr, Arne Wendt, Thorsten Schüppstuhl

Institute of Aircraft Production Technology (IFPT), Hamburg University of Technology (TUHH), Denickestraße 17, Hamburg, 21073, Germany

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The integrity of hydrogen infrastructure relies on reliable leak detection, performed almost exclusively via manual tracer gas sniffing in electrolyzer manufacturing. Although mandated by standards, the lack of spatial probe guidance instructions leaves detection reliability entirely to operator execution, further compromised by sensor signal delays. This study quantifies how sniffer trajectory kinematics affect detection reliability at small-scale pipes and fittings, a near-field regime largely neglected by macroscopic dispersion research. Using a robotically guided test bench to eliminate operator variability, static concentration fields and dynamic trajectory passes were acquired across representative geometries under standardized leak rates (5 vol% hydrogen in nitrogen) and varying scanning velocities. Results demonstrate that scanning velocity and spatial probe orientation strongly dictate detectability. Conventional linear trajectories frequently miss leaks under dynamic conditions, causing severe false negatives. Conversely, geometry-specific routing, such as circumferential plunging paths around sealing points, maintains a high safety margin. From these observations, geometry-specific routing rules and a reduction-factor model for dynamic signal loss are derived. The findings reveal that current standard operating procedures pose a tangible safety risk. To operationalize these rules, a proof-of-concept software pipeline is presented, generating validated trajectories directly from 3D models for visualization in assistance systems.

Abstract

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