Fire Modeling

1996 ◽  
Author(s):  
David G. Lilley
Keyword(s):  
Mathematical Modeling ◽  
Experimental Approach ◽  
Simulation Models ◽  
Fire Modeling ◽  
Site Investigations ◽  
Toxic Gases ◽  
Smoke Layer ◽  
Fire Scenarios ◽  
Key Features ◽  
Witness Statements

Abstract The mathematical modeling approach to simulating fire development in a multi-room building complements the experimental approach and/or post-fire on-site investigations. Fire simulation models provide estimates of the amount and temperature of the smoke layer produced, the evolution of toxic gases, and the amount of time available from the onset of fire for the safe departure of occupants. Results can be used to determine the key features of the fire evolution and the corresponding danger to occupants. Studies of this type help to validate or deny the suggested fire scenario and witness statements. Fire modeling thus helps to discriminate between alternative fire scenarios by evaluating the consequences and comparing them with observations.

Computerized Reconstruction of Building Fires

1991 ◽  
Author(s):  
David G. Lilley
Keyword(s):  
Large Scale ◽  
Time Dependent ◽  
Behavioral Interactions ◽  
Toxic Gases ◽  
Smoke Layer ◽  
Fire Scenarios ◽  
Clean Air ◽  
Key Features ◽  
Witness Statements ◽  
Important Time

Abstract A fire development simulation model is described which provides estimates of the amount and temperature of the smoke layer produced, the evolution of toxic gases, and the amount of time available from the onset of fire for the safe departure of occupants. Its results can be used to determine the key features of the fire evolution and the corresponding danger to occupants. Studies of this type help to validate or deny the suggested fire scenario and witness statements. Mathematical modeling thus helps to discriminate between alternative fire scenarios by evaluating the consequences and comparing them with observations. The software consists of data, procedures, and computer programs which simulate important time-dependent phenomena involved in residential fires. Based on sound scientific and mathematical principles, predictions are made of the production of energy and mass (smoke and gases) by one or more burning objects in one room, based on small or large scale measurements. The buoyancy-driven transport of this energy and mass through a series of user-specified rooms and connections is then computed (doors, windows, cracks, etc.). The resulting temperatures, smoke optical densities, and gas concentrations (after accounting for heat transfer to surfaces and dilution by mixing with clean air) are linked to the problem of egress. The evacuation process of a set of occupants may be simulated, accounting for delays in notification, decision making, behavioral interactions, and inherent capabilities.

Computer Programs for Fire Simulation

1996 ◽  
Author(s):  
David G. Lilley
Keyword(s):  
Computer Simulation ◽  
Experimental Approach ◽  
Computer Programs ◽  
Fire Simulation ◽  
Fire Scenario ◽  
Site Investigations ◽  
Fire Scenarios ◽  
Computer Calculations ◽  
Witness Statements ◽  
Rule Out

Abstract Computer programs for simulating fire development in a multi-room building complements the experimental approach and/or post-fire on-site investigations. A variety of computer calculations can help to validate or deny a suggested fire scenario and witness statements. Thus they help to discriminate between alternative fire scenarios by evaluating the consequences and comparing them with observations. In this way computer simulation helps to rule in or rule out the suggested ignition and spread of the fire.

Simulation Modeling and Theoretical Analysis in Sociology

1995 ◽  
Vol 38 (4) ◽  
pp. 457-462 ◽  
Author(s):  
Robert A. Hanneman
Keyword(s):  
Mathematical Modeling ◽  
Simulation Modeling ◽  
Simulation Analysis ◽  
Full Range ◽  
Simulation Models ◽  
Special Issue ◽  
Simulation Experiments ◽  
Scientific Disciplines ◽  
Causal Laws ◽  
Over Time

The use of computer simulation experiments as a tool for working with theories is not as widespread in sociology as it is in most scientific disciplines. This brief article explains how modeling is a distinctive activity from either “theory” or “empirical analysis” but how it informs both and connects them. Simulation models are artificial objects created to translate theoretical generalizations into specific scenarios involving states (variables and/or actors) and rates (the dynamic causal laws hypothesized to generate change over time in the states). Simulation models are analyzed to understand the full range of implications of theoretical statements as they apply to producing historical realizations under particular circumstances. Simulation analysis operates by a method of experimentation, unlike mathematical modeling and statistical modeling. The strengths and limitations of these three approaches to studying models are discussed, and suggested to be complementary rather than competitive with each other. Last, this article briefly points out some of the unique features of the exemplars that compose the remainder of this special issue of Sociological Perspectives.

scholarly journals Experimental Investigation on the Discharge of Pollutants from Tunnel Fires

2020 ◽  
Vol 12 (5) ◽  
pp. 1817
Author(s):  
Lihua Zhai ◽  
Zhongxing Nong ◽  
Guanhong He ◽  
Baochao Xie ◽  
Zhisheng Xu ◽  
...  
Keyword(s):  
Laser Sheet ◽  
Longitudinal Velocity ◽  
Release Rates ◽  
Layer Height ◽  
Tunnel Fires ◽  
Smoke Flow ◽  
Longitudinal Ventilation ◽  
Toxic Gases ◽  
Smoke Layer ◽  
Series Of Experiments

Many pollutants are generated during tunnel fires, such as smoke and toxic gases. How to control the smoke generated by tunnel fires was focused on in this paper. A series of experiments were carried out in a 1:10 model tunnel with dimensions of 6.0 m × 1.0 m × 0.7 m. The purpose was to investigate the smoke layer thickness and the heat exhaust coefficient of the tunnel mechanical smoke exhaust mode under longitudinal wind. Ethanol was employed as fuel, and the heat release rates were set to be 10.6 kW, 18.6 kW, and 31.9 kW. The exhaust velocity was 0.32–3.16 m/s, and the longitudinal velocity was 0–0.47 m/s. The temperature profile in the tunnel was measured, and the buoyant flow stratification regime was visualized by a laser sheet. The results showed that the longitudinal ventilation leads to a secondary stratification of the smoke flow. In the ceiling extract tunnel under longitudinal ventilation, considering the research results of the smoke layer height and the heat exhaust coefficient, a better scheme for fire-producing pollutants was that an exhaust velocity of 1.26–2.21 m/s (corresponding to the actual velocity of 4.0–7.0 m/s) should be used. The longitudinal velocity should be 0.16–0.32 m/s (corresponding to the actual velocity of 0.5–1.0 m/s).

Computer simulation in informatics lessons as a factor of the continuity of school education in teaching mathematical modeling

2021 ◽  
pp. 52-58
Author(s):  
M. E. Korolev
Keyword(s):  
Mathematical Modeling ◽  
Engineering Students ◽  
Applied Mathematics ◽  
Simulation Models ◽  
Interactive Learning Environments ◽  
School Education ◽  
Mathematical Education ◽  
Transport Direction ◽  
Using Data ◽  
Academy Of Sciences

The article actualizes the need to use elements of computer modeling from school to university when teaching mathematics to engineering students, analyzes the role of simulation models of applied mathematics. The experience of organizing classes using data visualization is presented, the use of simulators and games for studying mathematics in the context of mathematical education, in which students interact with interactive learning environments, is discussed, examples of using computer simulations in the classroom are discussed. The study lists and characterizes the types of didactic simulations, examines the process of transition of school education using simulators and games into the didactics of mathematics of technical universities using applied mathematics and mathematical modeling to elements of simulation. A pedagogical experiment of the continuity of school education (section “Information systems and programming” of the Donetsk Republican Small Academy of Sciences for Students) was carried out in teaching applied mathematics, informatics and mathematical modeling (the department “Transport technologies”) students of the engineering and transport direction of Automobile and Road Institute of Donetsk National Technical University.

Smoke Control Models in Building Fire: A Literature Review

2012 ◽  
Vol 193-194 ◽  
pp. 1134-1137
Author(s):  
Xu Tao Zhang ◽  
Song Ling Wang
Keyword(s):  
Numerical Simulation ◽  
Field Model ◽  
Simulation Models ◽  
Analytical Models ◽  
Zone Model ◽  
Smoke Control ◽  
Smoke Movement ◽  
Building Fire ◽  
Fire Scenarios ◽  
Weak Points

The smoke control is of great importance for the life safety of the occupants in the building fire. Many analytical models have been studied for the design of smoke control. The objective of the article is to bring several numerical simulation models about building fire and smoke movement. There are generally three kinds of different models, which are net model, zone model and field model. The characteristics and weak points of each category are discussed, indicating that each model might be applicable to different building fire scenarios.

Calculated Surprises

2019 ◽  
Author(s):  
Johannes Lenhard
Keyword(s):  
Mathematical Modeling ◽  
Scientific Reasoning ◽  
Science And Technology ◽  
Human Capabilities ◽  
Epistemology Of Science ◽  
Simulation Techniques ◽  
Wide Range ◽  
Recent Developments ◽  
Computer Based ◽  
Key Features

In this book, Lenhard concentrates on the ways in which computers and simulation are transforming the established conception of mathematical modeling. His core thesis is that simulation modeling constitutes a new mode of mathematical modeling that is rearranging and inverting key features of the established conception. Although most of these new key features—such as experimentation, exploration, and epistemic opacity—have their precursors, the new ways in which they are being combined is generating a distinctive style of scientific reasoning. Lenhard also documents how simulation is affecting fundamental concepts of solution, understanding, and validation. He feeds these transformations back into the philosophy of science, thereby opening up new perspectives on longstanding oppositions. By combining historical investigations with practical aspects, the book is accessible for a broad audience of readers. Numerous case studies covering a wide range of simulation techniques are balanced with broad reflections on science and technology. Initially, what computers are good at is calculating—with a speed and accuracy far beyond human capabilities. Lenhard goes further and investigates the emerging characteristics of computer-based modeling, showing how this initially simple observation is creating a number of surprising challenges for the methodology and epistemology of science. These calculated surprises will attract both philosophers and scientific practitioners who are interested in reflecting on recent developments in science and technology.

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