سعید محبی

In this paper, the effect of a fixed twisted-tape placed in the inner tube of a double-pipe heat exchanger is investigated using numerical simulation. A twisted-tape with three and four blades with different twist ratios is placed in the inner tube of the heat exchanger, and the Nusselt number, friction coefficient, and overall performance coefficient of the heat exchanger are studied. The flow velocity in the outer tube is considered constant, but the Reynolds number of the flow at the inlet of the inner tube is changed in the range of 5000 to 12000. The results showed that in all cases, placing a twisted-tape inside increases the Nusselt number and friction coefficient. The highest increase of these two parameters is 244 and 490 percent, respectively. By increasing the Reynolds number, the friction coefficient and the overall performance coefficient decreased, while the Nusselt number increased. This increase reaches about 80% in the case that has a twisted-tape with a twist ratio of 4. Also, reducing the twist ratio increases the turbulence of the flow and thus increases the Nusselt number, pressure drop, friction coefficient, and the overall performance coefficient. The maximum increase in the friction coefficient due to the reduction of the twist ratio from 8 to 4 was equal to 43%, occurred in the Reynolds number of 5000 and the three-bladed twisted-tape. Increasing the number of blades of the twisted tape has a direct effect on the pressure drop and friction coefficient and causes a decrease in the overall performance coefficient.

In recent years, different studies, using heuristic approaches for portfolio optimization, have been done to maximize the return and minimize the risk. In this study, we employed nine useful indicators, including SMA, EMA, ROC, OBV, RSI, MACD, TSI, HMA, Fibonacci Retracement, and genetic algorithm (GA), to construct an expert system that optimizes the portfolio automatically. In this system, Buy, Sell or Hold signals are produced based on the weighted combination of mentioned indicators and under the specified thresholds for each one. After that, signals (Buy/Sell/Hold) feed to the GA to optimize portfolio return/risk by changing indicator weights. This algorithm repeats continuously to optimize indicators weights for system and qualified stocks are selected according to optimized weights. Outputs of this expert system have been compared with Tehran Stock Exchange Index (Value-weighted and Equal-weighted Indices) from March 2013 until June 2021.The empirical results show that this expert system outperforms the Buy-and-Hold strategy (TSE indices) in terms of risk and return.