Analyzing the structural integrity of ferritic steel structures subjected to large temperature variations requires the collection of the fracture toughness (K_Jc) of ferritic steels in the ductile-to-brittle transition region. Consequently, predicting K_Jc from minimal testing has been of interest for a long time. In this study, a Windows-ready K_Jc predictor based on tensile properties (specifically, yield stress σ_YSRT and tensile strength σ_BRT at room temperature (RT) and σ_YS at K_Jc prediction temperature) was developed by applying an artificial neural network (ANN) to 531 K_Jc data points. If the σ_YS temperature dependence can be adequately described using the Zerilli–Armstrong σ_YS master curve (MC), the necessary data for K_Jc prediction are reduced to σ_YSRT and σ_BRT. The developed K_Jc predictor successfully predicted K_Jc under arbitrary conditions. Compared with the existing ASTM E1921 K_Jc MC, the developed K_Jc predictor was especially effective in cases where σ_B/σ_YS of the material was larger than that of RPV steel.

Physics-based simulations of lithium-ion battery thermal runaway (TR) and thermal propagation (TP) enable the assessment of diverse temperature behaviors among individual cells. These behaviors are primarily driven by variations in thermal properties and the amount of heat released during thermal decomposition. However, given the inherent variability in thermal property measurements, the specific values adopted can lead to substantial differences in predicted temperature behavior. In this study, we developed a 1-dimensional TP model for an array of three prismatic lithium-ion battery cells, in consideration of the uncertainty of key thermal parameters including specific heat, thermal conductivity, activation energy, and the latent heat of the thermal decomposition reaction. The validity of the model and the identification of calibration parameters are ensured through comparison with experimentally measured temperatures. We evaluated the influence of these parameter variations on the temperature and thermal runaway behavior of each cell. Our findings indicate that the variation in thermal runaway timing increases with distance from the trigger cell, and the probability of thermal runaway in the end cell was significantly higher than in the center cell. A sensitivity analysis using a surrogate model revealed that cell temperature is more sensitive to variations in thermal conductivity and latent heat than to variations in specific heat and activation energy.

Analytical solutions for the temperature change of a lithium-ion battery during thermal runaway were derived by the equation of linearizing thermal decomposition reaction. This study focuses on the representative temperature of the battery cell (zero-dimensional) during the heating test. First, the thermal decomposition reaction was modeled from DSC tests data of the electrode assuming Arrhenius-type temperature dependency. Subsequently, the reaction was simplified by a linear function of temperature and the analytical solution was derived as the exponential function with respect to time. The validity and applicability of the analytical solution are discussed by comparing it with a one-dimensional thermal runaway simulation. Further study was carried out for multiple batteries in consideration of cell-to-cell propagation of the thermal runaway and the applicability was discussed. As results, the single-cell predictions agreed generally with numerical results, especially with higher heating and lower latent heat. A delay in thermal runaway onset in multiple cells, linearly dependent on inter-cell conductivity, was quantified analytically. Parameter adjustments improved the alignment of analytical and numerical results for multiple cells, enabling quick thermal assessments. While numerical simulation is needed for high accuracy, this analytical framework offers new insights and facilitates initial analyses.