Publications

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Typical QRAs provide deterministic estimates and understanding of risks posed but are constructed using significant assumptions and uncertainties due to limited data availability and historical momentum of using nominal estimates. This report presents a hydrogen QRA analysis using HyRAM+ that incorporates uncertainty with Latin hypercube sampling and sensitivity analysis using linear regression.

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Presentation for Expert Workshop on Challenges and Solutions to implementation and reliable operation of Large-Scale Gaseous Hydrogen Infrastructure

Full-scale testing of pipes is costly and requires significant infrastructure investments. Subscale testing offers the potential to substantially reduce experimental costs and provides testing flexibility when transferrable test conditions and specimens can be established. To this end, a subscale pipe testing platform was developed to pressure cycle 60 mm diameter pipes (Nominal Pipe Size 2) to failure with gaseous hydrogen. Engineered defects were machined into the inner surface or outer surface to represent pre-existing flaws. The pipes were pressure cycled to failure with gaseous hydrogen at pressures to match operating stresses in large diameter pipes (e.g., stresses comparable to similar fractions of the specified minimum yield stress in transmission pipelines). Additionally, the pipe specimens were instrumented to identify crack initiation, such that crack growth could be compared to fracture mechanics predictions. Predictions leverage an extensive body of materials testing in gaseous hydrogen (e.g., ASME B31.12 Code Case 220) and the recently developed probabilistic fracture mechanics framework for hydrogen (Hydrogen Extremely Low Probability of Rupture, HELPR). In this work, we evaluate the failure response of these subscale pipe specimens and assess the conservatism of fracture mechanics-based design strategies (e.g., API 579/ASME FFS). This paper describes the subscale hydrogen testing capability, compares experimental outcomes to predictions from the probabilistic hydrogen fracture framework (HELPR), and discusses the complement to full-scale testing.

Gaseous hydrogen is known to embrittle most steels, including the steels used in natural gas pipelines. As injection of hydrogen into the existing natural gas infrastructure is considered globally by the pipeline industry, the structural integrity of pipelines transporting gaseous hydrogen must be investigated. Hydrogen Extremely Low Probability of Rupture (HELPR) is a publicly available and open-source probabilistic fatigue and fracture mechanics toolkit recently developed at Sandia National Laboratories. HELPR is intended to incorporate the influence of hydrogen into structural integrity assessments of natural gas transmission and distribution infrastructure. HELPR utilizes engineering models, such as those specified in ASME B31.12 and API 579, with relatively low computational costs to perform large sample ensembles, enabling estimation of performance distributions including low probability tail estimates. Leveraging the probabilistic capabilities built into HELPR, the sensitivity of fatigue and fracture calculations to specific modeling parameters on performance margins can be quantified. Through applying HELPR’s probabilistic capabilities to realistic scenarios, the impact of uncertainty in specific model parameter descriptions on performance margins, such as cycles to unstable crack growth or rupture in gaseous hydrogen, can be characterized; this same approach can then be used to assess the impact of reducing uncertainty sources on the resulting performance metrics, margins, and associated risks. A few industry-motivated scenarios are used to demonstrate this approach.

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In order to impact physical mechanical system design decisions and realize the full promise of high-fidelity computational tools, simulation results must be integrated at the earliest stages of the design process. This is particularly challenging when dealing with uncertainty and optimizing for system-level performance metrics, as full-system models (often notoriously expensive and time-consuming to develop) are generally required to propagate uncertainties to system-level quantities of interest. Methods for propagating parameter and boundary condition uncertainty in networks of interconnected components hold promise for enabling design under uncertainty in real-world applications. These methods avoid the need for time consuming mesh generation of full-system geometries when changes are made to components or subassemblies. Additionally, they explicitly tie full-system model predictions to component/subassembly validation data which is valuable for qualification. These methods work by leveraging the fact that many engineered systems are inherently modular, being comprised of a hierarchy of components and subassemblies that are individually modified or replaced to define new system designs. By doing so, these methods enable rapid model development and the incorporation of uncertainty quantification earlier in the design process. The resulting formulation of the uncertainty propagation problem is iterative. We express the system model as a network of interconnected component models, which exchange solution information at component boundaries. We present a pair of approaches for propagating uncertainty in this type of decomposed system and provide implementations in the form of an open-source software library. We demonstrate these tools on a variety of applications and demonstrate the impact of problem-specific details on the performance and accuracy of the resulting UQ analysis. This work represents the most comprehensive investigation of these network uncertainty propagation methods to date.

The previous separation distances in the National Fire Protection Association (NFPA) Hydrogen Technologies Code (NFPA 2, 2020 Edition) for bulk liquid hydrogen systems lack a well-documented basis and can be onerous. This report describes the technical justifications for revisions of the bulk liquid hydrogen storage setback distances in NFPA 2, 2023 Edition. Distances are calculated based on a leak area that is 5% of the nominal pipe flow area. Models from the open source HyRAM+ toolkit are used to justify the leak size as well as calculate consequence-based separation distances from that leak size. Validation and verification of the numerical models is provided, as well as justification for the harm criteria used for the determination of the setback distances for each exposure type. This report also reviews mitigations that could result in setback distance reduction. The resulting updates to the liquid hydrogen separation distances are well-documented, retrievable, repeatable, revisable, independently verified, and use experimental results to verify the models.

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There is a dearth in the literature on how to capture the uncertainty generated by material surface evolution in thermal modeling. This leads to inadequate or highly variable uncertainty representations for material properties, specifically emissivity when minimal information is available. Inaccurate understandings of prediction uncertainties may lead decision makers to incorrect conclusions, so best engineering practices should be developed for this domain. In order to mitigate the aforementioned issues, this study explores different strategies to better capture the thermal uncertainty response of engineered systems exposed to fire environments via defensible emissivity uncertainty characterizations that can be easily adapted to a variety of use cases. Two unique formulations (one physics-informed and one mathematically based) are presented. The formulations and methodologies presented herein are not exhaustive but more so are a starting point and give the reader a basis for how to customize their uncertainty definitions for differing fire scenarios and materials. Finally, the impact of using this approach versus other commonly used strategies and the usefulness of adding rigor to material surface evolution uncertainty is demonstrated.

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Thermally activated batteries undergo a series of coupled physical changes during activation that influence battery performance. These processes include energetic material burning, heat transfer, electrolyte phase change, capillary-driven two-phase porous flow, ion transport, electrochemical reactions, and electrical transport. Several of these processes are strongly coupled and have a significant effect on battery performance, but others have minimal impact or may be suitably represented by reduced-order models. Assessing the relative importance of these phenomena must be based on comparisons to a high-fidelity model including all known processes. In this work, we first present and demonstrate a high-fidelity, multi-physics model of electrochemical performance. This novel multi-physics model enables predictions of how competing physical processes affect battery performance and provides unique insights into the difficult-to-measure processes that happen during battery activation. We introduce four categories of model fidelity that include different physical simplifications, assumptions, and reduced-order models to decouple or remove costly elements of the simulation. Using this approach, we show an order-of-magnitude reduction in computational cost while preserving all design-relevant quantities of interest within 5 percent. The validity of this approach and these model reductions is demonstrated by comparison between results from the full fidelity model and the different reduced models.

This paper addresses two challenges in Bayesian calibration: (1) computational speed of existing sampling algorithms and (2) calibration with spatiotemporal responses. The commonly used Markov chain Monte Carlo (MCMC) approaches require many sequential model evaluations making the computational expense prohibitive. This paper proposes an efficient sampling algorithm: iterative importance sampling with genetic algorithm (IISGA). While iterative importance sampling enables computational efficiency, the genetic algorithm enables robustness by preventing sample degeneration and avoids getting stuck in multimodal search spaces. An inflated likelihood further enables robustness in high-dimensional parameter spaces by enlarging the target distribution. Spatiotemporal data complicate both surrogate modeling, which is necessary for expensive computational models, and the likelihood estimation. In this work, singular value decomposition is investigated for reducing the high-dimensional field data to a lower-dimensional space prior to Bayesian calibration. Then the likelihood is formulated and Bayesian inference is performed in the lower-dimension, latent space. An illustrative example is provided to demonstrate IISGA relative to existing sampling methods, and then IISGA is employed to calibrate a thermal battery model with 26 uncertain calibration parameters and spatiotemporal response data.

A strategy to optimize the thermal efficiency of falling particle receivers (FPRs) in concentrating solar power applications is described in this paper. FPRs are a critical component of a falling particle system, and receiver designs with high thermal efficiencies (~90%) for particle outlet temperatures > 700°C have been targeted for next generation systems. Advective losses are one of the most significant loss mechanisms for FPRs. Hence, this optimization aims to find receiver geometries that passively minimize these losses. The optimization strategy consists of a series of simulations varying different geometric parameters on a conceptual receiver design for the Generation 3 Particle Pilot Plant (G3P3) project using simplified CFD models to model the flow. A linear polynomial surrogate model was fit to the resulting data set, and a global optimization routine was then executed on the surrogate to reveal an optimized receiver geometry that minimized advective losses. This optimized receiver geometry was then evaluated with more rigorous CFD models, revealing a thermal efficiency of 86.9% for an average particle temperature increase of 193.6°C and advective losses less than 3.5% of the total incident thermal power in quiescent conditions.

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