Numerical studies on an accidental flashfire at a water fun park by FLACS software

Abstract

A severe accidental fire with ?explosion? resulting from spraying coloured corn flour powder occurred at a water fun park in Taiwan. The possible fire scenarios were studied in this paper using Flame Acceleration Simulator(FLACS). Environmental conditions including wind action and solid boundary conditions were deduced based on government fire investigation and video records, and were used as the input parameters in simulation. Simulation results indicate that upon ignition of the sprayed powder, the maximum overpressure in the open space was only 0.03 bar gauge, with a dust flash fire generated without having an explosion. These environmental conditions or parameters and the simulation results together would give a fire scene that agrees with the accident observed, indicating that appropriate environmental parameters had been identified. Therefore, CFD simulation with carefully selected parameters can be used to reproduce explosion scenarios and to identify key factors in supporting accident investigations.

The Effects of Thermophoresis and Brownian Motion on the Flow of a Nanofluid in Rectangular Channels Using Buongiorno’s Model

“…The laminar mixed convection heat transfer of a copper-water nanofluid inside a microtube with a curvature angle of 90° was numerically investigated by Akbari et al [24] In this research conducted by Saghir et al [26], a cavity-based approach for the numerical modeling (single-phase & two-phase) of thermo fluidic flow for an Al2O3-water nanofluid has been conducted to compare to the experimental data of Ho et al [27]. The numerical simulation was performed using the finite element method for a range of nanoparticle concentrations of 1 %vol-3%vol.…”

Prediction of some physical properties of nanofluids including various metal oxides using artificial neural network

“…A general correlation was provided by Vafai and Khanafer 14 for the thermal conductivity of CuO/water nanofluids and Al2O3/water as: (12) This correlation is developed for nanoparticle size within the range of 13-80 nm and volume fractions of over 15%. The following empirical correlation was proposed by Ho et al 15 for alumina/water nanofluids' thermal conductivity with the size of nanoparticles just as 33 nm:…”

“…Based on the experimental data, Ho et al 15 offered a correlation for predicting the viscosity of alumina/water nanofluids in which the nanoparticles'size is 33 nm (valid for Φ 4%):…”

“…Because of the high effect of thermal conductivity and viscosity on the convection heat transfer coefficient, the required power for pumping, and heat transfer rate, an artificial neural network (ANN) was implemented to predict the mentioned parameters in nanofluids more accurately. 0.0005854 valid for volume fractions of less than 4% Batchelor 18 0.0005915 considered the effect due to the Brownian motion of particles for an isotropic suspension of rigid and spherical particles Maiga 19 0.001331 Al2O3-water and Al2O3-EG nanofluids Ho 15 0.001672 valid for Al2O3-water nanofluids, dp= 33 nm Φ  4% Williams 16 0.0403 γ-Al2O3/water nanofluid Table 3 Comparing the predicted values of thermal conductivity of 𝛾-Al2O3/water nanofluids using different models…”

“…0.866 spherical particles -Φ  1% Bruggeman 7 1.082 considering the interactions between spherical particles Hamilton and Crosser 5 0.866 developing the Maxwell model by adding a shape factor Yu and Choi 8 1.1068 liquid nanolayer Xue et al 11 1.115 nanosuspensions containing carbon nanotubes (CNTs) Maiga 12 0.868 Al2O3/water nanofluid, dp= 28 nm Khanafer and Vafai 14 1.551 CuO/water -Al2O3/water nanofluids, dp = 13-80 nm Williams et al 16 0.953 γ-Al2O3/water nanofluid Ho et al 15 0.979 Al2O3/water nanofluid, dp= 33 nm -(Φ  4%)…”