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The purpose of this paper is the examination of fluid flow around NACA0012 airfoil, with the aim of the numerical validation between the experimental results in the wind tunnel and the Lattice Boltzmann method (LBM) analysis, for the medium Reynolds number (Re = 191,000). The LBM–large Eddy simulation (LES) method described in this paper opens up opportunities for faster computational fluid dynamics (CFD) analysis, because of the LBM scalability on high performance computing architectures, more specifically general purpose graphics processing units (GPGPUs), pertaining at the same time the high resolution LES approach.

Process starts with data collection in open-circuit wind tunnel experiment. Furthermore, the pressure coefficient, as a comparative variable, has been used with varying angle of attack (2°, 4°, 6° and 8°) for both experiment and LBM analysis. To numerically reproduce the experimental results, the LBM coupled with the LES turbulence model, the generalized wall function (GWF) and the cumulant collision operator with D3Q27 velocity set has been used. Also, a mesh independence study has been provided to ensure result congruence.

The proposed LBM methodology is capable of highly accurate predictions when compared with experimental data. Besides, the special significance of this work is the possibility of experimental and CFD comparison for the same domain dimensions.

Considering the quality of results, root-mean-square error (RMSE) shows good correlations both for airfoil’s upper and lower surface. More precisely, maximal RMSE for the upper surface is 0.105, whereas 0.089 for the lower surface, regarding all angles of attack.

Active flow control on the NACA 0024 airfoil defined as suction-injection jet at the chord-based Reynolds number of 1.5 × 1e + 5 is studied.

The three-dimensional incompressible unsteady Reynolds-averaged Navier–Stokes equations with the SST k-ω turbulence model are used to study the effects of coflow-jet (CFJ) on the dynamic and static stall phenomena. CFJ implementation is conducted with several momentum coefficients to investigate their turnover. Furthermore, the current work intends to analyze the CFJ performance by varying the Reynolds number and jet momentum coefficient and comparing all states to the baseline airfoil, which has not been studied in prior research investigations.

It is observed that at the momentum coefficient (Cµ) of 0.06, the lift coefficients at low attack angles (up to a = 15) dramatically increase. Furthermore, the dynamic stall at the given Reynolds number and with the lowered frequency of 0.15 is explored. In the instance of Cµ = 0.07, the lift coefficient curve does not show a noticeable stall feature compared to Cµ = 0.05, suggesting that a more powerful stronger jet can entirely control the dynamic stall.

Furthermore, the current work intends to analyze the CFJ performance by varying the jet momentum coefficient and comparing all states to the baseline airfoil, which has not been studied in prior research investigations.

This research aims to provide optimization and route safety planning employing the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique.

This research combines the use of graphical, communication tools and simulated models based on building information modeling (BIM) technology and agent-based modeling (ABM) to identify a safe evacuation route. Adopting the multi-criteria decision-making (MCDM) approach, the proposed rescue plan can reduce potential hazards along the evacuation route by selecting a safe route for evacuating residents and entering firefighters to the scene of the incident.

The results show that the use of simulated models along with MCDM methods in the selection of safe routes improves the performance of safe evacuation operations for both relief groups and residents.

The introduced model can improve the performance management of different groups at the time of the incident and reduce casualties and property losses using the information received from sensors at the scene. Moreover, the proposed rescue plan prevents group and individual reactivation at the time of the incident.

Despite many advances in the architecture, engineering and construction (AEC) industry, the number of victims of fire incidents in buildings is increasing compared to other natural disasters. Improving decision management based on effective parameters at the time of incident reduces casualties of residents and rescue workers.