Slobodan R. Savić

Using multi-criteria decision making for selection of the optimal strategy for municipal solid waste management.

Multi-criteria decision making (MCDM) is a relatively new tool for decision makers who deal with numerous and often contradictory factors during their decision making process. This paper presents a procedure to choose the optimal municipal solid waste (MSW) management system for the area of the city of Kragujevac (Republic of Serbia) based on the MCDM method. Two methods of multiple attribute decision making, i.e. SAW (simple additive weighting method) and TOPSIS (technique for order preference by similarity to ideal solution), respectively, were used to compare the proposed waste management strategies (WMS). Each of the created strategies was simulated using the software package IWM2. Total values for eight chosen parameters were calculated for all the strategies. Contribution of each of the six waste treatment options was valorized. The SAW analysis was used to obtain the sum characteristics for all the waste management treatment strategies and they were ranked accordingly. The TOPSIS method was used to calculate the relative closeness factors to the ideal solution for all the alternatives. Then, the proposed strategies were ranked in form of tables and diagrams obtained based on both MCDM methods. As shown in this paper, the results were in good agreement, which additionally confirmed and facilitated the choice of the optimal MSW management strategy.

Application of Computational Fluid Dynamics to flow next to high-rise buildings in Hong Kong due to air-conditioner heat rejection

This paper presents the results of several studies done for high-rise residential buildings in Hong Kong that involved Computational Fluid Dynamics (CFD) simulation. During summer, a number of Window Air-Conditioners (WACs) could be rejecting condenser heat into a recessed space of these high-rise residential buildings. FLUENT 5.0, a CFD code can be used to predict temperatures and flow field of a powerful rising hot air stream formed in the recessed space. In these cases, for simulations, we use κ-e turbulence model, and 2D and 3D model of the building. A CFD code can also be used to predict temperature and flow fields inside the recessed spaces that differ in heights and condenser-unit locations. The CFD code also employed to predict the airflow patterns. The papers study the worst-case scenario in summer when all the WAC units reject condenser heat into this space.

Simulation and Measurements of Small Arms Blast Wave Overpressure in the Process of Designing a Silencer

Abstract Simulation and measurements of muzzle blast overpressure and its physical manifestations are studied in this paper. The use of a silencer can have a great influence on the overpressure intensity. A silencer is regarded as an acoustic transducer and a waveguide. Wave equations for an acoustic dotted source of directed effect are used for physical interpretation of overpressure as an acoustic phenomenon. Decomposition approach has proven to be suitable to describe the formation of the output wave of the wave transducer. Electroacoustic analogies are used for simulations. A measurement chain was used to compare the simulation results with the experimental ones.

Application of feedforward neural network in the study of dissociated gas flow along the porous wall

This paper concerns the use of feedforward neural networks (FNN) for predicting the nondimensional velocity of the gas that flows along a porous wall. The numerical solution of partial differential equations that govern the fluid flow is applied for training and testing the FNN. The equations were solved using finite differences method by writing a FORTRAN code. The Levenberg-Marquardt algorithm is used to train the neural network. The optimal FNN architecture was determined. The FNN predicted values are in accordance with the values obtained by the finite difference method (FDM). The performance of the neural network model was assessed through the correlation coefficient (r), mean absolute error (MAE) and mean square error (MSE). The respective values of r, MAE and MSE for the testing data are 0.9999, 0.0025 and 1.9998.10^-^5.