Chemistry tabulation is a common approach in practical simulations of turbulent combustion at engineering scales. Linear interpolants have traditionally been used for accessing precomputed multidimensional tables but suffer from large memory requirements and discontinuous derivatives. Higher-degree interpolants address some of these restrictions but are similarly limited to relatively low-dimensional tabulation. Artificial neural networks (ANNs) can be used to overcome these limitations but cannot guarantee the same accuracy as interpolants and introduce challenges in reproducibility and reliable training. These challenges are enhanced as the physics complexity to be represented within the tabulation increases. In this manuscript, we assess the efficiency, accuracy, and memory requirements of Lagrange polynomials, tensor product B-splines, and ANNs as tabulation strategies. We analyze results in the context of nonadiabatic flamelet modeling where higher dimension counts are necessary. While ANNs do not require structuring of data, providing benefits for complex physics representation, interpolation approaches often rely on some structuring of the table. Interpolation using structured table inputs that are not directly related to the variables transported in a simulation can incur additional query costs. This is demonstrated in the present implementation of heat losses. We show that ANNs, despite being difficult to train and reproduce, can be advantageous for high-dimensional, unstructured datasets relevant to nonadiabatic flamelet models. We also demonstrate that Lagrange polynomials show significant speedup for similar accuracy compared to B-splines.
Medium scale (30 cm diameter) methanol pool fires were simulated using the latest fire modeling suite implemented in Sierra/Fuego, a low Mach number multiphysics reacting flow code. The sensitivity of model outputs to various model parameters was studied with the objective of providing model validation. This work also assesses model performance relative to other recently published large eddy simulations (LES) of the same validation case. Two pool surface boundary conditions were simulated. The first was a prescribed fuel mass flux and the second used an algorithm to predict mass flux based on a mass and energy balance at the fuel surface. Gray gas radiation model parameters (absorption coefficients and gas radiation sources) were varied to assess radiant heat losses to the surroundings and pool surface. The radiation model was calibrated by comparing the simulated radiant fraction of the plume to experimental data. The effects of mesh resolution were also quantified starting with a grid resolution representative of engineering type fire calculations and then uniformly refining that mesh in the plume region. Simulation data were compared to experimental data collected at the University of Waterloo and the National Institute of Standards and Technology (NIST). Validation data included plume temperature, radial and axial velocities, velocity temperature turbulent correlations, velocity velocity turbulent correlations, radiant and convective heat fluxes to the pool surface, and plume radiant fraction. Additional analyses were performed in the pool boundary layer to assess simulated flame anchoring and the effect on convective heat fluxes. This work assesses the capability of the latest Fuego physics and chemistry model suite and provides additional insight into pool fire modeling for nonluminous, nonsooting flames.
All-solid-state batteries are often assumed to be safer than conventional Li-ion ones. In this work, we present the first thermodynamic models to quantitatively evaluate solid-state and Li-ion battery heat release under several failure scenarios. The solid-state battery analysis is carried out with an Li7La3Zr2O12 solid electrolyte but can be extended to other configurations using the accompanying spreadsheet. We consider solid-state batteries that include a relatively small amount of liquid electrolyte, which is often added at the cathode to reduce interfacial resistance. While the addition of small amounts of liquid electrolyte increases heat release under specific failure scenarios, it may be small enough that other considerations, such as manufacturability and performance, are more important commercially. We show that short-circuited all-solid-state batteries can reach temperatures significantly higher than conventional Li-ion, which could lead to fire through flammable packaging and/or nearby materials. Our work highlights the need for quantitative safety analyses of solid-state batteries.
This is the Sandia report from a joint NSRD project between Sandia National Labs and Savannah River National Labs. The project involved development of simulation tools and data intended to be useful for tritium operations safety assessment. Tritium is a synthetic isotope of hydrogen that has a limited lifetime, and it is found at many tritium facilities in the form of elemental gas (T2). The most serious risk of reasonable probability in an accident scenario is when the tritium is released and reacts with oxygen to form a water molecule, which is subsequently absorbed into the human body. This tritium oxide is more readily absorbed by the body and therefore represents a limiting factor for safety analysis. The abnormal condition of a fire may result in conversion of the safer T2 inventory to the more hazardous oxidized form. It is this risk that tends to govern the safety protocols. Tritium fire datasets do not exist, so prescriptive safety guidance is largely conservative and reliant on means other than testing to formulate guidelines. This can have a consequence in terms of expensive and/or unnecessary mitigation design, handling protocols, and operational activities. This issue can be addressed through added studies on the behavior of tritium under representative conditions. Due to the hazards associated with the tests, this is being approached mainly from a modeling and simulation standpoint and surrogate testing. This study largely establishes the capability to generate simulation predictions with sufficiently credible characteristics to be accepted for safety guidelines as a surrogate for actual data through a variety of testing and modeling activities.
