Risk assessment in hydrogen systems relies heavily on predictive models to evaluate hazards such as dispersion and combustion. These models must account for uncertainties to ensure robust safety protocols. Uncertainty quantification (UQ) is a critical component, distinguishing between epistemic (knowledge-based) and aleatory (randomness-based) uncertainties while employing probabilistic frameworks to bound confidence in predictions. This article explores these concepts in the context of hydrogen risk modeling, focusing on dispersion and combustion scenarios.

Hydrogen dispersion models predict how released gas spreads in the atmosphere, influenced by factors like wind speed, turbulence, and release conditions. Combustion models estimate flame behavior, ignition probabilities, and overpressure effects. Both domains face inherent uncertainties, necessitating probabilistic approaches to capture variability and incomplete knowledge.

Epistemic uncertainties arise from gaps in scientific understanding or insufficient data. For example, the accuracy of turbulence models in computational fluid dynamics (CFD) simulations for hydrogen dispersion depends on empirical constants derived from limited experimental datasets. Similarly, combustion models rely on reaction kinetics parameters that may not fully represent real-world conditions. Epistemic uncertainties can be reduced with additional research, improved measurements, or refined models.

Aleatory uncertainties stem from natural variability in physical processes. Wind fluctuations, leak rates, or ignition source locations are inherently random and cannot be eliminated. These uncertainties require statistical treatment, often through Monte Carlo simulations or stochastic sampling, to propagate variability through risk models.

A key tool in UQ is confidence interval estimation, which quantifies the range within which a model prediction is likely to fall. For hydrogen dispersion, a 95% confidence interval might bound the predicted gas concentration downwind of a leak, accounting for both epistemic and aleatory uncertainties. Experimental data from hydrogen release trials show that concentration predictions can vary by ±20% under identical release conditions due to turbulence variability. Probabilistic frameworks assign probability distributions to input parameters, enabling outputs to reflect aggregated uncertainty.

In combustion modeling, flame speed predictions exhibit uncertainty due to variations in mixture composition and ignition energy. Experimental studies indicate that laminar flame speeds for hydrogen-air mixtures can range between 1.8 and 3.3 m/s depending on equivalence ratios and pressure conditions. Probabilistic methods, such as Bayesian inference, update predictions by incorporating empirical data, narrowing confidence intervals as evidence accumulates.

Sensitivity analysis complements UQ by identifying which inputs contribute most to output uncertainty. For dispersion models, parameters like atmospheric stability and release velocity often dominate uncertainty. In combustion models, reaction rate coefficients and radiative heat loss assumptions are critical. Ranking these inputs guides resource allocation for data collection or model refinement.

Advanced UQ techniques include polynomial chaos expansions (PCE) and Gaussian process emulators. PCE represents uncertain model outputs as expansions of orthogonal polynomials, enabling efficient propagation of input uncertainties. Gaussian processes interpolate between sparse simulation or experimental data points, providing probabilistic predictions with quantified error margins. These methods are computationally efficient compared to brute-force Monte Carlo sampling.

In practical applications, UQ informs safety distances for hydrogen refueling stations or ventilation requirements in enclosed spaces. Regulatory frameworks increasingly demand probabilistic risk assessments (PRA) over deterministic margins, as they provide a more realistic basis for decision-making. For instance, a PRA might reveal that the likelihood of a hydrogen flame exceeding a certain length is less than 1e-6 per year, justifying reduced buffer zones.

Challenges persist in UQ, particularly in validating models against rare or high-consequence events. Large-scale hydrogen explosions are infrequent, limiting empirical validation opportunities. Hybrid approaches combine limited experimental data with physics-based simulations, using UQ to bridge gaps. Collaborative efforts like the Hydrogen Safety Panel and international research consortia pool data to improve uncertainty estimates.

Future directions include integrating machine learning for surrogate modeling, accelerating UQ for complex simulations. Neural networks trained on high-fidelity CFD results can predict dispersion or combustion outcomes with quantified uncertainty in real-time, enabling dynamic risk management. However, these methods require rigorous validation to avoid introducing new epistemic uncertainties.

In summary, uncertainty quantification is indispensable for credible hydrogen risk assessments. By systematically addressing epistemic and aleatory uncertainties, employing probabilistic frameworks, and leveraging advanced computational techniques, models can deliver reliable predictions with transparent confidence bounds. This probabilistic rigor supports safer hydrogen deployment, ensuring risks are neither underestimated nor overly conservatively bounded.