In order to make design decisions, engineers may seek to identify regions of the design domain that are acceptable in a computationally efficient manner. A design is typically considered acceptable if its reliability with respect to parametric uncertainty exceeds the designer’s desired level of confidence. Despite major advancements in reliability estimation and in design classification via decision boundary estimation, the current literature still lacks a design classification strategy that incorporates parametric uncertainty and desired design confidence. To address this gap, this works offers a novel interpretation of the acceptance region by defining the decision boundary as the hypersurface which isolates the designs that exceed a user-defined level of confidence given parametric uncertainty. This work addresses the construction of this novel decision boundary using computationally efficient algorithms that were developed for reliability analysis and decision boundary estimation. The proposed approach is verified on two physical examples from structural and thermal analysis using Support Vector Machines and Efficient Global Optimization-based contour estimation.
As large systems of Li-ion batteries are being increasingly deployed, the safety of such systems must be assessed. Due to the high cost of testing large systems, it is important to extract key safety information from any available experiments. Developing validated predictive models that can be exercised at larger scales offers an opportunity to augment experimental data In this work, experiments were conducted on packs of three Li-ion pouch cells with different heating rates and states of charge (SOC) to assess the propagation behavior of a module undergoing thermal runaway. The variable heating rates represent slow or fast heating that a module may experience in a system. As the SOC decreases, propagation slows down and eventually becomes mitigated. It was found that the SOC boundary between propagation and mitigation was higher at a heating rate of 50 °C/min than at 10 °C/min for these cells. However, due to increased pre-heating at the lower heating rate, the propagation speed increased. Simulations were conducted with a new intra-particle diffusion-limited reaction model for a range of anode particle sizes. Propagation speeds and onset times were generally well predicted, and the variability in the propagation/mitigation boundary highlighted the need for greater uncertainty quantification of the predictions.
A jet is formed from venting gases of lithium-ion batteries during thermal runaway. Heat fluxes to surrounding surfaces from vented gases are calculated with simulations of an impinging jet in a narrow gap. Heat transfer correlations for the impinging jet are used as a point of reference. Three cases of different gap sizes and jet velocities are investigated and safety hazards are assessed. Local and global safety hazard issues are addressed based on average heat flux, average temperature, and average temperature rise in a cell. The Results show that about 40% to about 70% of venting gases energy can leave the module gap where it can be transferred to other modules or causes combustion at the end of the gap if suitable conditions are satisfied. This work shows that multiple vents are needed to increase the temperatures of the other modules’ cells to go into thermal runaway. This work is a preliminary assessment for future analysis that will consider heat transfer to the adjacent modules from multiple venting events.
Many engineering design problems can be formulated as decisions between two possible options. This is the case, for example, when a quantity of interest must be maintained below or above some threshold. The threshold thereby determines which input parameters lead to which option, and creates a boundary between the two options known as the decision boundary. This report details a machine learning approach for estimating decision boundaries, based on support vector machines (SVMs), that is amenable to large scale computational simulations. Because it is computationally expensive to evaluate each training sample, the approach iteratively estimates the decision boundary in a manner that requires relatively few training samples to glean useful estimates. The approach is then demonstrated on three example problems from structural mechanics and heat transport.
The inverse methods team provides a set of tools for solving inverse problems in structural dynamics and thermal physics, and also sensor placement optimization via Optimal Experimental Design (OED). These methods are used for designing experiments, model calibration, and verfication/validation analysis of weapons systems. This document provides a user's guide to the input for the three apps that are supported for these methods. Details of input specifications, output options, and optimization parameters are included.
Thermal runaway and its propagation are major safety issues in containerized lithium-ion battery energy storage systems. While conduction-driven propagation has received much attention, the thermal hazards associated with propagation via hot gases vented from failing cells are still not fully understood. Vented gases can lead to global safety issues in containerized systems, via heat transfer to other parts of the system and potential combustion hazards. In this work, we validate the characteristics of vented gases from cells undergoing thermal runaway in the thermal propagation model LIM1TR (Lithium-ion Modeling with 1-D Thermal Runaway). In particular, we assess the evolution of vented gases, venting time, and temperature profiles of single cell and multi-cell arrays based on experiments performed in Archibald et al (Fire Technology, 2020). While several metrics for estimating the venting time are assessed, a metric based on the CO2 generation results in consistent predictions. Vented gas evolution, and venting times predicted by the simulations are consistent with those estimated during the experiments. The simulation resolution and other model parameters, especially the use of an intra-particle diffusion limiter, have a large role in prediction of venting time.
Propagating thermal runaway events are a significant threat to utility-scale storage installations. A propagating thermal runaway event is a cascading series of failures in which energy released from a failed cell triggers subsequent failures in nearby cells. Without intervention, propagation can turn an otherwise manageable single cell failure into a full system conflagration. This study presents a method of mitigating the severity of propagating thermal runaway events in utility-scale storage systems by leveraging the capabilities of a module-interfaced power conversion architecture. The method involves strategic depletion of storage modules to delay or arrest propagation, reducing the total thermal energy released in the failure event. The feasibility of the method is assessed through simulations of propagating thermal runaway events in a 160 kW/80 kWh energy storage system.
Propagating thermal runaway events are a significant threat to utility-scale storage installations. A propagating thermal runaway event is a cascading series of failures in which energy released from a failed cell triggers subsequent failures in nearby cells. Without intervention, propagation can turn an otherwise manageable single cell failure into a full system conflagration. This study presents a method of mitigating the severity of propagating thermal runaway events in utility-scale storage systems by leveraging the capabilities of a module-interfaced power conversion architecture. The method involves strategic depletion of storage modules to delay or arrest propagation, reducing the total thermal energy released in the failure event. The feasibility of the method is assessed through simulations of propagating thermal runaway events in a 160 kW/80 kWh energy storage system.
This report describes an assessment of flamelet based soot models in a laminar ethylene coflow flame with a good selection of measurements suitable for model validation. Overall flow field and temperature predictions were in good agreement with available measurements. Soot profiles were in good agreement within the flame except for near the centerline where imperfections with the acetylene-based soot-production model are expected to be greatest. The model was challenged to predict the transition between non-sooting and sooting conditions with non-negligible soot emissions predicted even down to small flow rates or flame sizes. This suggests some possible deficiency in the soot oxidation models that might alter the amount of smoke emissions from flames, though this study cannot quantify the magnitude of the effect for large fires.