scholarly journals COUPLED SIMULATION FOR FIRE-EXPOSED STRUCTURES USING CFD AND THERMO-MECHANICAL MODELS

2017 ◽  
Vol 13 ◽  
pp. 121
Author(s):  
Stanislav Šulc ◽  
Vít Šmilauer ◽  
František Wald
Keyword(s):  
Fluid Dynamics ◽  
Temperature Field ◽  
Mechanical Model ◽  
Data Transfer ◽  
Thermal Strain ◽  
Dynamics Simulation ◽  
Coupled Simulation ◽  
Fire Dynamics ◽  
Mechanical Models ◽  
Fire Loading

Fire resistance of buildings is based on fire tests in furnaces with gas burners. However, the tests are very expensive and time consuming. This article presents a coupled simulation of an element loaded by a force and a fire loading. The simulation solves a weakly-coupled problem, consisting of fluid dynamics, heat transfer and mechanical model. The temperature field from the computational fluid dynamics simulation (CFD) creates Cauchy and radiative boundary conditions for the thermal model. Then, the temperature field from element is passed to the mechanical model, which induces thermal strain and modifies material parameters. The fluid dynamics is computed with Fire Dynamics Simulator and the thermo-mechanical task is solved in OOFEM. Both softwares are interconnected with MuPIF python library, which allows smooth data transfer across the different meshes, orchestrating simulations in particular codes, exporting results to the VTK formats and distributed computing.

scholarly journals Prediction of fire loading on the structures using computational fluid dynamics

2020 ◽  
Vol 313 ◽  
pp. 00033
Author(s):  
Romana Erdelyiová ◽  
Lucia Figuli ◽  
Matúš Ivančo
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Structure Design ◽  
Gas Temperature ◽  
Fire Load ◽  
Fire Dynamics ◽  
Large Space ◽  
Space Objects ◽  
Fire Loading ◽  
A Performance

The development of a fire in a large-space fire section differs significantly from the development in a small fire section. In large-space objects, to design structures under the fire load often proceeds through a performance-based approach. Advanced methods can be used in all parts of the design in predicting of the scatter of temperature field, in calculating of the heat transfer to the structure and in assessing of the mechanical behaviour of the structure or its part under the fire load. The prediction of the gas temperature in the fire compartment is crucial for the structure design. The paper is focused on selection of different fire scenarios in the large-space building. The aim is to provide background for structural design in a fire using a performance-based design. The problem is solved by using FDS (Fire Dynamics Simulator) software based on the CFD (Computational Fluid Dynamics) method.

Development of a new algorithm for fluid sloshing-vehicle dynamic coupled simulation and its experimental validation

2017 ◽  
Vol 24 (24) ◽  
pp. 5867-5879 ◽  
Author(s):  
Mohammad Hossein Abbasi ◽  
Mohammad Durali
Keyword(s):  
Fluid Dynamics ◽  
Excellent Agreement ◽  
Experimental Validation ◽  
Vehicle Dynamic ◽  
Coupled Simulation ◽  
New Methods ◽  
Fluid Sloshing ◽  
Mechanical Models ◽  
Position And Orientation ◽  
Mass Spring

Traditional methods for analyzing the dynamics of fluid-carrying vehicles such as pendulum or mass–spring models do not yield accurate results, thus seeking new methods for investigating the dynamics of these vehicles is desirable. The objective of this study is to find a new methodology for handling the dynamics of such systems. In this paper, an algorithm is proposed by which a data bridge is implemented between two commercial software programs such that a coupled simulation of the carrier and fluid dynamics is achieved. Unlike alternative mechanical models, the most important feature of this method is its validity during all events regardless of carrier position and orientation. Finally, this approach is validated by experiments. Experimental results are in excellent agreement with simulations. In addition, unlike existing algorithms, the proposed algorithm is numerically stable.

scholarly journals VALIDATION OF LINKED SIMULATION FOR FIRE-EXPOSED CONCRETE STRUCTURES

2018 ◽  
Vol 15 ◽  
pp. 120-125
Author(s):  
Stanislav Šulc ◽  
Vít Šmilauer ◽  
František Wald
Keyword(s):  
Temperature Field ◽  
Mechanical Model ◽  
Thermal Strain ◽  
Concrete Block ◽  
Material Model ◽  
Standard Material ◽  
Material Parameters ◽  
Eurocode 2 ◽  
Simulation Based ◽  
Computational Fluid Dynamics Cfd

This article presents linked computational approach for fire simulation and its effects on structure using adiabatic surface temperature. The simulation solves a weakly-linked problem, consisting of computational fluid dynamics (CFD), heat transport and mechanical model. The temperature field from the CFD creates Cauchy and radiative boundary conditions for the thermal model. The temperature field from an element is passed further to the mechanical model, which induces thermal strain and modifies material parameters. This article also brings a validation of the linked simulation, based on experiment with a concrete block exposed to fire in a furnace. The material model uses standard material properties given in Eurocode 2 - EN 1992-1-2.

scholarly journals Integrated IEW-TOPSIS and Fire Dynamics Simulation for Agent-Based Evacuation Modeling in Industrial Safety

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 47
Author(s):  
Wattana Chanthakhot ◽  
Kasin Ransikarbum
Keyword(s):  
Emergency Evacuation ◽  
Industrial Sector ◽  
Industrial Safety ◽  
Dynamics Simulation ◽  
Entropy Weight ◽  
Fire Dynamics ◽  
Evacuation Simulation ◽  
Evacuation Modeling ◽  
Agent Based ◽  
The Impact

Emergency events in the industrial sector have been increasingly reported during the past decade. However, studies that focus on emergency evacuation to improve industrial safety are still scarce. Existing evacuation-related studies also lack a perspective of fire assembly point’s analysis. In this research, location of assembly points is analyzed using the multi-criteria decision analysis (MCDA) technique based on the integrated information entropy weight (IEW) and techniques for order preference by similarity to ideal solution (TOPSIS) to support the fire evacuation plan. Next, we propose a novel simulation model that integrates fire dynamics simulation coupled with agent-based evacuation simulation to evaluate the impact of smoke and visibility from fire on evacuee behavior. Factors related to agent and building characteristics are examined for fire perception of evacuees, evacuees with physical disabilities, escape door width, fire location, and occupancy density. Then, the proposed model is applied to a case study of a home appliance factory in Chachoengsao, Thailand. Finally, results for the total evacuation time and the number of remaining occupants are statistically examined to suggest proper evacuation planning.

scholarly journals Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David R. Rutkowski ◽  
Alejandro Roldán-Alzate ◽  
Kevin M. Johnson
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Velocity Fields ◽  
Dynamics Simulation ◽  
Patient Specific ◽  
4D Flow Mri ◽  
4D Flow ◽  
Flow Phase ◽  
Flow Mri ◽  
Trained Network

AbstractBlood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.

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