A-Evac: The Evacuation Simulator for Stochastic Environment

Krasuski, Adam; Kreński, Karol

doi:10.1007/s10694-019-00827-7

Cited by 10 publications

(12 citation statements)

References 42 publications

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“…A-EVAC: simulation of human movement and states. A fully featured evacuation model has been developed, 25 which is based on navigation meshes 26 for global wayfinding and the optimal reciprocal collision avoidance model 27 for collision avoidance. In addition, evacuees are affected by smoke which restricts visibility and affects their speed.…”

Section: A-mc: Stochastic Producer Of Input Files For A-fire and A-evacmentioning

confidence: 99%

“…The toxic injury model is based on the calculation of FED 31 and its further ranking. 25 The thermal injury model is based on the FED of convected heat accumulated per minute as defined in. 32 The traumatic injury model is of a rather rough nature and is based on an exceeding of the temperature of construction stability with respect to evacuation time.…”

Section: Risk Model and Metricsmentioning

confidence: 99%

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AAMKS—Integrated cloud‐based application for probabilistic fire risk assessment

Krasuski

Hostikka

2020

Fire and Materials

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Available models of fire risk assessment can still hardly be used for practical engineering problems. They are mostly loosely integrated, complex, computationally demanding applications, and require additional pre-or post-hand-calculations. However, the latest achievements in computer science enable establishing an easy-to-use webapplication with access to enormous computing power in a cloud requiring minimal management effort. In this article, we present a new tool for probabilistic fire risk assessment called Aamks. Our goal is to build an easy-to-use, science-based, practical engineering tool to support building design in day-to-day work. To meet the overall goal, we aim at working out an integrated, web-based application with computational efficiency and scalability allowed by cloud computing. Aamks performs a stochastic analysis of life safety in building fires using deterministic models for fire and evacuation and stochastic sampling of the uncertain input parameters.

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Section: A-mc: Stochastic Producer Of Input Files For A-fire and A-evacmentioning

confidence: 99%

Section: Risk Model and Metricsmentioning

confidence: 99%

AAMKS—Integrated cloud‐based application for probabilistic fire risk assessment

Krasuski

Hostikka

2020

Fire and Materials

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“…The design of the systems is performance-based, which means that tenability criteria must not be exceeded on the evacuation routes during the evacuation time. This approach is more commonly known as the Available and Required Safe Evacuation Time (ASET/RSET) analysis [5,6]. Finally, the technical requirements are accompanied by organisational requirements, related to the fire-fighting equipment, personnel training and safety procedures.…”

Section: Life Safety Of Underground Club Venuesmentioning

confidence: 99%

Smart Smoke Control as an Efficient Solution for Smoke Ventilation in Converted Cellars of Historic Buildings

2020

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The paper is focused on the topic of smoke control in a confined, underground cellar area of a historical building, that is undergoing conversion to a restaurant. Similar venues were host to some of the most devastating fires in history. We have investigated the performance of a novel solution, “smart smoke control (SSC)”, and compared its performance with “traditional” smoke venting solution. The investigation was based on CFD simulations performed in a commercial code ANSYS Fluent, modified with user-defined functions to include for the steering algorithm of the SSC. The simulation results for a traditional system were unsatisfactory, while the SSC removing substantially more smoke (approx. 50% more) has provided tenable conditions over the whole course of the analysis. The results indicate that smoke control solutions based on the adaptive performance of the smoke exhaust fan, based on the conditions within the protected premise, can be favourable for compartments with the limited size of the smoke reservoir or limited space available for ducts. These findings are also true for existing buildings, where it may be advantageous to replace the existing smoke control solution with SSC. Future investigations should include full-scale experiments, and improvements in the simple algorithms used so far.

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“…These simple techniques allowed mapping of small-scale fire tests performed in full-size geometries, as well as verification of mathematical models known at that time. Currently, deterministic CFD simulations or stochastic calculations based on multi-simulations are used to predict smoke flow in rooms [9], [20]. A comparison of the older method using the water-filled model and the newer method of solving computer-based fluid mechanics equations is shown in Figure 2.…”

Section: Physical Modelingmentioning

confidence: 99%

Physical modeling of a fire with the use of the Froude number

Zimny

2019

Bud-Arch

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Ever since the field of Fire Safety Engineering first came into existence, small-scale (model scale) fire tests were the source of much valuable information about the phenomena occurring during combustion. Over the years, several computational methods to determine the criteria for the similarity of fires on both a real and small geometric scale have been developed. The purpose of this article is to present a method of performing fire tests on a model scale using the Froude number. The basics of scaling, similarity conditions that must be preserved, ways of describing fire phenomena, as well as methods of calculating fire parameters from the real and model scale have been discussed. An example of physical tests with the use of reduced research models is also presented. What is particularly emphasized is how important these types of experiments are and what responsibility rests with the people who carry them ouvol. The motivation to conduct research is the safety of people who reside in buildings with fire protection based on model scale test.

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A-Evac: The Evacuation Simulator for Stochastic Environment

Cited by 10 publications

References 42 publications

AAMKS—Integrated cloud‐based application for probabilistic fire risk assessment

AAMKS—Integrated cloud‐based application for probabilistic fire risk assessment

Smart Smoke Control as an Efficient Solution for Smoke Ventilation in Converted Cellars of Historic Buildings

Physical modeling of a fire with the use of the Froude number

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