Plasma Internal Inductance in the Presence of External Resonant Fields in IR-T1 Tokamak

In this paper we presented experimental investigation of effects of local limiter biasing (V_biasing = +200 v, V_biasing = +320 v) on the plasma parameters as plasma current, loop voltage, poloidal beta, plasma pressure, plasma energy, plasma resistance, plasma temperature, plasma displacement, Shafranov parameter and plasma internal inductance in IR-T1 tokamak. For these purposes, array of magnetic probes and also a diamagnetic loop have been used. The results show that applied biased voltage V_biasing = +200 v causes to decrease of about 40 % in plasma internal inductance. The plasma resistance and the plasma displacement have been decreased by V_biasing = +200 v. The main result of the application of V_biasing = +200 v is flatting the plasma parameters profiles. In other words, the addition of biasing voltage V_biasing = +200 v to plasma could be effective for improving the quality of tokamak plasma discharge by creating the steady state plasma. The plasma current, plasma pressure, plasma energy, plasma temperature and shift parameter have increased after the application of limiter biasing with V_biasing = +320 v but they decrease rapidly.     

Modified Cuckoo Optimization Algorithm (MCOA) to solve Precedence Constrained Sequencing Problem (PCSP)

In recent years, new meta-heuristic algorithms have been developed to solve optimization problems. Recently-introduced Cuckoo Optimization Algorithm (COA) has proven its excellent performance to solve different optimization problems. Precedence Constrained Sequencing Problem (PCSP) is related to locating the optimal sequence with the shortest traveling time among all feasible sequences. The problem is motivated by applications in networks, scheduling, project management, logistics, assembly flow and routing. Regarding numerous practical applications of PCSP, it can be asserted that PCSP is a useful tool for a variety of industrial planning and scheduling problems. However it can also be seen that the most approaches may not solve various types of PCSPs and in related papers considering definite conditions, a model is determined and solved. In this paper a new approach is presented for solving various types of PCSPs based on COA. Since COA at first was introduced to solve continuous optimization problems, in order to demonstrate the application of COA to find the optimal sequence of the PCSP, some proposed schemes have been applied in this paper with modifications in operators of the basic COA. In fact due to the discrete nature and characteristics of the PCSP, the basic COA should be modified to solve PSCPs. To evaluate the performance of the proposed algorithm, at first, an applied single machine scheduling problem from the literature that can be formulated as a PCSP and has optimal solution is described and solved. Then, several PCSP instances with different sizes from the literature that do not have optimal solutions are solved and results are compared to the algorithms of the literature. Computational results show that the proposed algorithm has better performance compared to presented well-known meta-heuristic algorithms presented to solve various types of PCSPs so far.     

Comparison and evaluation of dust detection algorithms using MODIS Aqua/Terra Level 1B data and MODIS/OMI dust products in the Middle East

Dust storms have a major impact on air quality, economic loss, and human health over large regions of the Middle East. Because of the broad extent of dust storms and also political–security issues in this region, satellite data are an important source of dust detection and mapping. The aim of this study was to compare and evaluate the performance of five main dust detection algorithms, including Ackerman, Miller, normalized difference dust index (NDDI), Roskovensky and Liou, and thermal-infrared dust index (TDI), using MODIS Level 1B and also MODIS Deep Blue AOD and OMI AI products in two dust events originating from Iraq and Saudi Arabia. Overall, results showed that the performance of the algorithms varied from event to event and it was not possible to use the published dust/no-dust thresholds for the algorithms tested in the study area. The MODIS AOD and OMI AI products were very effective for initial dust detection and the AOD and AI images correlated highly with the dust images at provincial scale (p-value <0.001), but the application of these products was limited at local scale due to their poor spatial resolution. Results also indicated that algorithms based on MODIS thermal infrared (TIR) bands or a combination of TIR and reflectance bands were better indicators of dust than reflectance-based ones. Among the TIR- based algorithms, TDI performed the best over water surfaces and dust sources, and accounted for approximately 93% and 90% of variations in the AOD and OMI AI data.     

An experimental study of forced convective heat transfer for fully developed Al2O3–water nanofluid in an annulus tube

Nanofluids have been known as practical materials to ameliorate heat transfer within diverse industrial systems. The current work presents an empirical study on forced convection effects of Al₂O₃–water nanofluid within an annulus tube. A laminar flow regime has been considered to perform the experiment in high Reynolds number range using several concentrations of nanofluid. Also, the boundary conditions include a constant uniform heat flux applied on the outer shell and an adiabatic condition to the inner tube. Nanofluid particle is visualized with transmission electron microscopy to figure out the nanofluid particles. Additionally, the pressure drop is obtained by measuring the inlet and outlet pressure with respect to the ambient condition. The experimental results showed that adding nanoparticles to the base fluid will increase the heat transfer coefficient (HTC) and average Nusselt number. In addition, by increasing viscosity effects at maximum Reynolds number of 1140 and increasing nanofluid concentration from 1% to 4% (maximum performance at 4%), HTC increases by 18%.     

Spatio-Temporal Analysis of Regional Trends and Shift Changes of Autocorrelated Temperature Series in Urmia Lake Basin

Recently, the Urmia Lake located in northwestern Iran which is the second largest hyper saline in the world suffers from the significant fluctuations of water level and surface area. The current study tries to investigate the spatiotemporal trends of mean (T_mean), maximum (T_max) and minimum (T_min) temperatures of monthly, seasonal and annual time-series. To do so, the data of 15 temperature gauge stations within the Urmia Lake basin, for the period 1972–2011 was employed. The pre-whitening approach was applied to remove the effects of serial correlation in the air temperature series based on the Mann-Kendall (MK) test. The results of Ljung-Box test showed positive serial correlation in the T_mean and T_max series for all of the stations at the 0.05 significance level. In the monthly series, the significant warming trends in the T_mean series were more perceptible than the same ones in T_max series; however, T_max trend was found more than T_min series. The Mann–Whitney (MW) test detected a significance upward shift changes in the annual T_mean, T_max and T_min series of about 86, 73 and 80 % of the stations, respectively. The average magnitude of significant warming trends in annual T_mean, T_max and T_min series were (+) 0.58 °C, (+) 0.52 °C and (+) 0.69 °C per decade, respectively. Furthermore, the interpolation maps showed that warming trends in the east and west of Urmia Lake were more than southern area. Therefore, the results showed that the basin has suffered from increasing trends in the T_mean, T_max and T_min over the recent decades. Finally, significant changes were found in 1980s and 1990s based on the Mann-Kendall ranks and change point tests. In this study, it is interesting that the period of significant changes in warming trends were close to the beginning of decreasing water level of the Lake.     

Analysis of nonpremixed multiregion laminar flames through biofuel porous particles considering the effect of heat recirculation

In this study, a comprehensive analysis is conducted to evaluate the effects of heat recirculation and particle porosity on combustion characteristics of multizone counterflow nonpremixed flames fed with porous biofuel particles. For this purpose, the structure of flame contains preheat, postvaporization, and oxidizer zones. Additionally, Lycopodium is considered as the volatile biofuel, especially due to its appreciable flammability and dispensability. Dimensionalized and nondimensionalized forms of mass and energy conservation equations are scrutinized in each zone. To explore of the thermal recirculation effect, a specific term is included in the energy conservation equation. The variation of several parameters, including flame temperature, particle radius, mass fraction of the gaseous fuel and oxidizer, mass particle content, equivalence ratio, and particle porosity, is studied in this work considering and ignoring the thermal recirculation impact. As a result, increasing heat recirculation coefficient from $k=0$ to 1 will rise the flame temperature and shift the flame position to the left side (fuel nozzle). Furthermore, consideration of the thermal heat recirculation will improve the gaseous fuel production in the preheat and postvaporization zones. Additionally, an increase in mass concentration and reduction of particles radius and porosity would lead to a rise in the flame temperature.     

Micromechanical machining of soda lime glass

Micromechanical machining, which is the mechanical removal of materials using miniature cutting tools, is one of the fabrication methods in the microrealm that has recently attracted a great deal of attention because it has the advantage of being able to machine complex shapes from brittle materials. The most challenging problem in the mechanical machining of brittle material is the fabrication of fracture-free surfaces. To avoid brittle fractures, a thorough investigation is required to find the machining parameters in the ductile cutting regime, which is characterized by plastic deformation of the material when the chip thickness is smaller than the critical value. In this study, cutting forces and surface characteristics of soda lime glass are examined in detail. Conical scratch tests are performed to identify the critical chip thickness, and the cutting forces in the ductile regime are modeled. In addition, coated ball end mill cutters were used to perform machining on inclined soda lime glass to investigate the feed rate effects, up and down milling, and depth of cuts on the surface finish and to examine tool wear.     

Low-cost optical approach for noncontact predicting moisture content of apple slices during hot air drying

Absorption and scattering properties of product change as moisture content is reduced, but it has not been investigated how these changes are correlated. This study was aimed to measure and test the feasibility of using optical properties in predicting the moisture content of sliced apple samples during hot air drying. In this investigation, the noninvasive backscattering laser imaging technique at three wavelengths (650, 780, and 880?nm) and Farrell’s diffusion theory were used to determine absorption and reduced scattering coefficients. Artificial neural network model was applied to correlate the optical coefficients and moisture content of samples. The highest correlation between above-mentioned parameters was found at 780?nm. The best moisture content prediction result was obtained when absorption and reduced scattering coefficients were combined at three wavelengths with R_p?=?0.984. The results suggested that this method can be effectively used to predict the moisture content and control the drying process.