SMARTFIRE: an intelligent CFD-based fire model

This paper describes a project aimed at making Computational Fluid Dynamics (CFD) based fire simulation accessible to members of the fire safety engineering community. Over the past few years, the practise of CFD based fire simulation has begun the transition from the confines of the research laboratory to the desk of the fire safety engineer. To a certain extent, this move has been driven by the demands of performance based building codes. However, while CFD modelling has many benefits over other forms of fire simulation, it requires a great deal of expertise on the user’s part to obtain reasonable simulation results. The project described in this paper, SMARTFIRE, aims to relieve some of this dependence on expertise so that users are less concerned with the details of CFD analysis and can concentrate on results. This aim is achieved by the use of an expert system component as part of the software suite which takes some of the expertise burden away from the user. SMARTFIRE also makes use of the latest developments in CFD technology in order to make the CFD analysis more efficient. This paper describes design considerations of the SMARTFIRE software, emphasising its open architecture, CFD engine and knowledge based systems.

SMARTFIRE: an intelligent CFD based fire model

This paper describes a project aimed at making Computational Fluid Dynamics (CFD)- based fire simulation accessible to members of the fire safety engineering community. Over the past few years, the practice of CFD-based fire simulation has begun the transition from the confines of the research laboratory to the desk of the fire safety engineer. To a certain extent, this move has been driven by the demands of performance based building codes. However, while CFD modeling has many benefits over other forms of fire simulation, it requires a great deal of expertise on the user’s part to obtain reasonable simulation results. The project described in this paper, SMARTFIRE, aims to relieve some of this dependence on expertise so that users are less concerned with the details of CFD analysis and can concentrate on results. This aim is achieved by the use of an expert system component as part of the software suite which takes some of the expertise burden away from the user. SMARTFIRE also makes use of the latest developments in CFD technology in order to make the CFD analysis more efficient. This paper describes design considerations of the SMARTFIRE software, emphasizing its open architecture, CFD engine and knowledge-based systems.

Directed percolation in a two dimensional stochastic fire-diffuse-fire model

In this paper we establish, from extensive numerical experiments, that the two dimensional stochastic fire-diffuse-fire model belongs to the directed percolation universality class. This model is an idealized model of intracellular calcium release that retains the both the discrete nature of calcium stores and the stochastic nature of release. It is formed from an array of noisy threshold elements that are coupled only by a diffusing signal. The model supports spontaneous release events that can merge to form spreading circular and spiral waves of activity. The critical level of noise required for the system to exhibit a non-equilibrium phase-transition between propagating and non-propagating waves is obtained by an examination of the \textit{local slope} $\delta(t)$ of the survival probability, $\Pi(t) \propto \exp(- \delta(t))$, for a wave to propagate for a time $t$.

Sensitisation waves in a bidomain fire-diffuse-fire model of intracellular Ca²⁺ dynamics

We present a bidomain threshold model of intracellular calcium (Ca²⁺) dynamics in which, as suggested by recent experiments, the cytosolic threshold for Ca²⁺ liberation is modulated by the Ca²⁺ concentration in the releasing compartment. We explicitly construct stationary fronts and determine their stability using an Evans function approach. Our results show that a biologically motivated choice of a dynamic threshold, as opposed to a constant threshold, can pin stationary fronts that would otherwise be unstable. This illustrates a novel mechanism to stabilise pinned interfaces in continuous excitable systems. Our framework also allows us to compute travelling pulse solutions in closed form and systematically probe the wave speed as a function of physiologically important parameters. We find that the existence of travelling wave solutions depends on the time scale of the threshold dynamics, and that facilitating release by lowering the cytosolic threshold increases the wave speed. The construction of the Evans function for a travelling pulse shows that of the co-existing fast and slow solutions the slow one is always unstable.

The FIREDASS (Fire Detection and Suppression Simulation) Model

The FIREDASS (FIRE Detection And Suppression Simulation) project is concerned with the development of fine water mist systems as a possible replacement for the halon fire suppression system currently used in aircraft cargo holds. The project is funded by the European Commission, under the BRITE EURAM programme. The FIREDASS consortium is made up of a combination of Industrial, Academic, Research and Regulatory partners. As part of this programme of work, a computational model has been developed to help engineers optimise the design of the water mist suppression system. This computational model is based on Computational Fluid Dynamics (CFD) and is composed of the following components: fire model; mist model; two-phase radiation model; suppression model and detector/activation model. The fire model - developed by the University of Greenwich - uses prescribed release rates for heat and gaseous combustion products to represent the fire load. Typical release rates have been determined through experimentation conducted by SINTEF. The mist model - developed by the University of Greenwich - is a Lagrangian particle tracking procedure that is fully coupled to both the gas phase and the radiation field. The radiation model - developed by the National Technical University of Athens - is described using a six-flux radiation model. The suppression model - developed by SINTEF and the University of Greenwich - is based on an extinguishment crietrion that relies on oxygen concentration and temperature. The detector/ activation model - developed by Cerberus - allows the configuration of many different detector and mist configurations to be tested within the computational model. These sub-models have been integrated by the University of Greenwich into the FIREDASS software package. The model has been validated using data from the SINTEF/GEC test campaigns and it has been found that the computational model gives good agreement with these experimental results. The best agreement is obtained at the ceiling which is where the detectors and misting nozzles would be located in a real system. In this paper the model is briefly described and some results from the validation of the fire and mist model are presented.

The development of a CFD based simulator for water mist suppression systems: the development of the fire submodel

The FIRE Detection and Suppression Simulation (FIREDASS) project was concerned with the development of water misting systems as a possible replacement for halon based fire suppression systems currently used in aircraft cargo holds and ship engine rooms. As part of this program of work, a computational model was developed to assist engineers optimize the design of water mist suppression systems. The model is based on Computational Fluid Dynamics (CFD) and comprised of the following components: fire model; mist model; two-phase radiation model; suppression model; detector/activation model. In this paper the FIREDASS software package is described and the theory behind the fire and radiation sub-models is detailed. The fire model uses prescribed release rates for heat and gaseous combustion products to represent the fire load. Typical release rates have been determined through experimentation. The radiation model is a six-flux model coupled to the gas (and mist) phase. As part of the FIREDASS project, a detailed series of fire experiments were conducted in order to validate the fire model. Model predictions are compared with data from these experiments and good agreement is found.

The status and challenge of global fire modelling

Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, either using well-founded empirical relationships or process-based models with good predictive skill. A large variety of models exist today and it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project - FireMIP, an international project to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we summarise the current state-of-the-art in fire regime modelling and model evaluation, and outline what essons may be learned from FireMIP.

Periodicities in the Hodgkin-Huxley model and versions of this model with stochastic input

A field of computational neuroscience develops mathematical models to describe neuronal systems. The aim is to better understand the nervous system. Historically, the integrate-and-fire model, developed by Lapique in 1907, was the first model describing a neuron. In 1952 Hodgkin and Huxley [8] described the so called Hodgkin-Huxley model in the article “A Quantitative Description of Membrane Current and Its Application to Conduction and Excitation in Nerve”. The Hodgkin-Huxley model is one of the most successful and widely-used biological neuron models. Based on experimental data from the squid giant axon, Hodgkin and Huxley developed their mathematical model as a four-dimensional system of first-order ordinary differential equations. One of these equations characterizes the membrane potential as a process in time, whereas the other three equations depict the opening and closing state of sodium and potassium ion channels. The membrane potential is proportional to the sum of ionic current flowing across the membrane and an externally applied current. For various types of external input the membrane potential behaves differently. This thesis considers the following three types of input: (i) Rinzel and Miller [15] calculated an interval of amplitudes for a constant applied current, where the membrane potential is repetitively spiking; (ii) Aihara, Matsumoto and Ikegaya [1] said that dependent on the amplitude and the frequency of a periodic applied current the membrane potential responds periodically; (iii) Izhikevich [12] stated that brief pulses of positive and negative current with different amplitudes and frequencies can lead to a periodic response of the membrane potential. In chapter 1 the Hodgkin-Huxley model is introduced according to Izhikevich [12]. Besides the definition of the model, several biological and physiological notes are made, and further concepts are described by examples. Moreover, the numerical methods to solve the equations of the Hodgkin-Huxley model are presented which were used for the computer simulations in chapter 2 and chapter 3. In chapter 2 the statements for the three different inputs (i), (ii) and (iii) will be verified, and periodic behavior for the inputs (ii) and (iii) will be investigated. In chapter 3 the inputs are embedded in an Ornstein-Uhlenbeck process to see the influence of noise on the results of chapter 2.

An Overview of Modelling Bulgarian Wildland Fire Behaviour by Application of a Mathematical Game Method and WRF-Fire Models

This paper presents the main achievements of the author’s PhD dissertation. The work is dedicated to mathematical and semi-empirical approaches applied to the case of Bulgarian wildland fires. After the introductory explanations, short information from every chapter is extracted to cover the main parts of the obtained results. The methods used are described in brief and main outcomes are listed. ACM Computing Classification System (1998): D.1.3, D.2.0, K.5.1.

A bidomain threshold model of propagating calcium waves

We present a bidomain fire-diffuse-fire model that facilitates mathematical analysis of propagating waves of elevated intracellular calcium (Ca) in living cells. Modelling Ca release as a threshold process allows the explicit construction of travelling wave solutions to probe the dependence of Ca wave speed on physiologically important parameters such as the threshold for Ca release from the endoplasmic reticulum (ER) to the cytosol, the rate of Ca resequestration from the cytosol to the ER, and the total [Ca] (cytosolic plus ER). Interestingly, linear stability analysis of the bidomain fire-diffuse-fire model predicts the onset of dynamic wave instabilities leading to the emergence of Ca waves that propagate in a back-and-forth manner. Numerical simulations are used to confirm the presence of these so-called "tango waves" and the dependence of Ca wave speed on the total [Ca]. The original publication is available at www.springerlink.com (Journal of Mathematical Biology)

Sample size considerations and augmentation of computer experiments

Computer Experiments, consisting of a number of runs of a computer model with different inputs, are now common-place in scientific research. Using a simple fire model for illustration some guidelines are given for the size of a computer experiment. A graph is provided relating the error of prediction to the sample size which should be of use when designing computer experiments. Methods for augmenting computer experiments with extra runs are also described and illustrated. The simplest method involves adding one point at a time choosing that point with the maximum prediction variance. Another method that appears to work well is to choose points from a candidate set with maximum determinant of the variance covariance matrix of predictions.

Some practical issues in the design and analysis of computer experiments

Deterministic computer simulations of physical experiments are now common techniques in science and engineering. Often, physical experiments are too time consuming, expensive or impossible to conduct. Complex computer models or codes, rather than physical experiments lead to the study of computer experiments, which are used to investigate many scientific phenomena of this nature. A computer experiment consists of a number of runs of the computer code with different input choices. The Design and Analysis of Computer Experiments is a rapidly growing technique in statistical experimental design. This thesis investigates some practical issues in the design and analysis of computer experiments and attempts to answer some of the questions faced by experimenters using computer experiments. In particular, the question of the number of computer experiments and how they should be augmented is studied and attention is given to when the response is a function over time.