The MaxEnt method for probabilistic structural fire engineering : performance for multi-modal outputs

Probabilistic Risk Assessment (PRA) methodologies are gaining traction in fire engineering practice as a (necessary) means to demonstrate adequate safety for uncommon buildings. Further, an increasing number of applications of PRA based methodologies in structural fire engineering can be found in the contemporary literature. However, to date, the combination of probabilistic methods and advanced numerical fire engineering tools has been limited due to the absence of a methodology which is both efficient (i.e. requires a limited number of model evaluations) and unbiased (i.e. without prior assumptions regarding the output distribution type). An uncertainty quantification methodology (termed herein as MaxEnt) has recently been presented targeted at an unbiased assessment of the model output probability density function (PDF), using only a limited number of model evaluations. The MaxEnt method has been applied to structural fire engineering problems, with some applications benchmarked against Monte Carlo Simulations (MCS) which showed excellent agreement for single-modal distributions. However, the power of the method is in application for those cases where ‘validation’ is not computationally practical, e.g. uncertainty quantification for problems reliant upon complex modes (such as FEA or CFD). A recent study by Gernay, et al., applied the MaxEnt method to determine the PDF of maximum permissible applied load supportable by a steel-composite slab panel undergoing tensile membrane action (TMA) when subject to realistic (parametric) fire exposures. The study incorporated uncertainties in both the manifestation of the fire and the mechanical material parameters. The output PDF of maximum permissible load was found to be bi-modal, highlighting different failure modes depending upon the combinations of stochastic parameters. Whilst this outcome highlighted the importance of an un-biased approximation of the output PDF, in the absence of a MCS benchmark the study concluded that some additional studies are warranted to give users confidence and guidelines in such situations when applying the MaxEnt method. This paper summarises one further study, building upon Case C as presented in Gernay, et al

Numerical modelling of heat generated by electroosmotic flows in micro-channels

This paper was presented at the 2nd Micro and Nano Flows Conference (MNF2009), which was held at Brunel University, West London, UK. The conference was organised by Brunel University and supported by the Institution of Mechanical Engineers, IPEM, the Italian Union of Thermofluid dynamics, the Process Intensification Network, HEXAG - the Heat Exchange Action Group and the Institute of Mathematics and its Applications.In this paper, numerical modeling of Joule heating in electroosmotic flows is described in some detail. The finite element method is used for the spatial discretization along with the characteristic based split (CBS) time discretization. A new non-dimensional scaling is also introduced. In addition to standard problems of micro channel flows, flow and heat generation in a T-mixer are also discussed in this paper

Global sensitivity analysis of the single particle lithium-ion battery model with electrolyte

The importance of global sensitivity analysis (GSA) has been well established in many scientific areas. However, despite its critical role in evaluating a model’s plausibility and relevance, most lithium ion battery models are published without any sensitivity analysis. In order to improve the lifetime performance of battery packs, researchers are investigating the application of physics based electrochemical models, such as the single particle model with electrolyte (SPMe). This is a challenging research area from both the parameter estimation and modelling perspective. One key challenge is the number of unknown parameters: the SPMe contains 31 parameters, many of which are themselves non-linear functions of other parameters. As such, relatively few authors have tackled this parameter estimation problem. This is exacerbated because there are no GSAs of the SPMe which have been published previously. This article addresses this gap in the literature and identifies the most sensitive parameter, preventing time being wasted on refining parameters which the output is insensitive to

A numerical model for the fractional condensation of pyrolysis vapours

Experimentation on the fast pyrolysis process has been primarily focused on the pyrolysis reactor itself, with less emphasis given to the liquid collection system (LCS). More importantly, the physics behind the vapour condensation process in LCSs has not been thoroughly researched mainly due to the complexity of the phenomena involved. The present work focusses on providing detailed information of the condensation process within the LCS, which consists of a water cooled indirect contact condenser. In an effort to understand the mass transfer phenomena within the LCS, a numerical simulation was performed using the Eulerian approach. A multiphase multi-component model, with the condensable vapours and non-condensable gases as the gaseous phase and the condensed bio-oil as the liquid phase, has been created. Species transport modelling has been used to capture the detailed physical phenomena of 11 major compounds present in the pyrolysis vapours. The development of the condensation model relies on the saturation pressures of the individual compounds based on the corresponding states correlations and assuming that the pyrolysis vapours form an ideal mixture. After the numerical analysis, results showed that different species condense at different times and at different rates. In this simulation, acidic components like acetic acid and formic acids were not condensed as it was also evident in experimental works, were the pH value of the condensed oil is higher than subsequent stages. In the future, the current computational model can provide significant aid in the design and optimization of different types of LCSs

Grey-Box Modeling for Photo-Voltaic Power Systems Using Dynamic Neural-Networks

There exists various ways of modeling and forecasting photo-voltaic (PV) systems. These methods can be categorized, in board-way, under either definite equations models (white or clear-box) or heuristic data-driven artificial intelligence models (black-box). The two directions of modeling pose a number of drawbacks. To benefit from both worlds, this paper proposes a novel method where clear-box model is extended to a grey-box model by modeling uncertainities using focused time-delay neural network models. The grey-box or semi-definite model was shown to exhibit enhanced forecasting capabilities