Generally, the proposed expressions for pressure em- anate from field of thermodynamics [1, 2, 19]. In the approach presented here, we propose to deduce pres- sure expression from that of internal energy “potential” [21]. Thus, we shall consider the energy concept as the starting point of the traffic **flow** **modelling**. Indeed, it may be helpful to highlight the existence of several dynamics for traffic **flow** and to determine the pressure expression necessary to use from the analogy with fluid **flow**. Of course, the expression of this energy must take into account the specificity of the traffic. Furthermore, the model must respect the quasi-totality of the con- ditions (mentioned in [2, 4, 15]) to be physically valid. Moreover, it has to show that coming back to the free **flow** phase of vehicles at downstream front of jam must be done intrinsically (without exogenous action).

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The work described here represents only the initial step in the integration of electrical systems **modelling** within building simulation. The ESP-r capability is currently being improved and expanded upon, with the addition of new component models and user interface facilities. Work is also underway to address the important issue of power quality in embedded generation. Voltasge, current and power **flow** data from an ESP- r thermal/electrical simulation can be supplied to the EMTP † tool, which is used to model high frequency electrical phenomena (e.g. harmonic distortion) associated the embedded power sources. Finally, particular attention is being focused on the possible applications of control. This is perhaps the richest area for exploitation using integrated thermal and electrical **modelling**. The addition of electrical power **flow** **modelling** adds a new range of variables (voltage, power **flow**, current, etc.) around which new control algorithms can be constructed, particularly in the development and testing of techniques to match local supply to demand. Such algorithms would be responsible for making more efficient use of heat and power, minimising energy usage conflicts (e.g. between artificial lighting, daylighting and cooling), scheduling energy storage, limiting energy demand and controlling energy supply. In effect, the control described here would be a global energy management system, managing both the supply and demand of energy in a building.

The fourth goal concerns the time dependency in the overland **flow** computations. The **flow** of runoff should be related to a time scale. Such an improvement can give more information about the durations of floods. The Manning-based model has reached this goal. The model is able to compute overland flows based on Manning‟s **flow** equations. The model is able to compute overland **flow** with a time step with a maximum of 1 second. A larger time step gives unreliable results. The comparison of the Manning-based model with the other models showed that the runtime is significantly larger, about 10 times larger compared to the intuitive distribution model and even larger compared to WOLK. It takes more runtime for the Manning-based model to reach the same final **flow** state as the intuitive distribution model and the WOLK-AML. The advantage of the Manning-based model compared to the intuitive distribution model is that the **flow** is more constant, thus more reliable to use in micro scale analysis. On the other hand, the nature of the analysis is uncertain in itself. The models try to simulate a particular rainfall event with a chance of occurrence of once every one hundred years. The assumptions which lie at the basis of all models, a constant infiltration and sewer parameter, causes uncertainty, which is not eliminated with a more accurate model. Therefore the question should be asked whether or not a theoretical based overland **flow** model pretends to give accurate results, while in reality this is not possible. The illusion of confidence is created with a Manning-based model. In that sense are the results of WOLK good, because people know that the model does not give 100% accurate results.

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There are several factors that can influence the air **flow** pattern inside the nasal cavity. First factor is the unique geometry inside the nasal cavity. The geometry can increase and decrease the velocity and pressure of the air **flow** inside the nasal cavity. Second factor is the pressure difference of the inlet and the outlet of the nasal cavity. The pressure difference can influence the airflow nasal cavity. The relation is the higher pressure difference of the inlet and outlet of nasal cavity, the higher the velocity of the air **flow** inside the nasal cavity.

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speed computers (digital computers). This method is an approach to computational fluid dynamics (CFD) and very effective in groundwater **flow** **modelling**. Ground- water is an important resource in so many areas for its use as a source of drinking water and irrigation water. In many areas, groundwater is threatened by leaching of pesticides and other agricultural chemicals and the leaching of industrial chemicals from hazardous-waste sites. Because of the importance of this resource, and because the degradation of groundwater cannot be easily reversed, the assessment of threat to groundwater quality from human activities is often required. So groundwater models are increasingly used as part of this assessment. The major aim of this research work is to discuss the principles of Finite Difference Method and its applica- tion in groundwater modeling

In many engineering applications (e.g. fuel spray injection and mixing, two-phase flows in combustion chambers), it is important to have information about the structure of local particle / droplet accumulation zones to estimate the rates of possible droplet collisions and the e ff ect of droplet accumulation on heating and evaporation of droplets and combustion of fuel vapour / air mixtures (76). This information can be inferred only from Lagrangian tracking of the dispersed phase. In the standard Eulerian-Lagrangian approach for gas-particle / droplet **flow** **modelling** (e.g. Crowe et al. (16); Sazhin (76)), the carrier-phase **flow** parameters are calculated on a fixed Eulerian mesh and the particles / droplets are tracked along chosen Lagrangian trajectories. Direct **modelling** of individual droplet trajectories in the carrier phase leads to satisfactory results for the droplet velocity field (e.g. Sazhina et al. (77)). However, the correct calculation of the droplet number density field presents serious di ffi culties. This was instructively demonstrated by Healy and Young (35), who showed that to reach a satisfactory accuracy in the calculation of the droplet number density in a laminar **flow** it is necessary to have about 10 3 Lagrangian droplet trajectories per Eulerian cell.

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7. Hamimed A., Nehal L., M. Benslimane M., Khaldi, A., “Contribution To The Study Of The **Flow** Resistance In A Flume With Artificial Emergent Vegetation”, Larhyss Journal, ISSN 1112-3680, pp. 55-63, 2013. 8. Huthoff, F., Straatsma, M. W., Augustijn, D. C. M. and Hulscher, S. J. M. H., “ Evaluation of a Simple Hydraulic Resistance Model Using **Flow** Measurements Collected in Vegetated Waterways”, Open Journal of Modern Hydrology, vol. , pp. 28-37, 2012.

The blood **flow** behavior are shown in figs. 28 and 29 with various **flow** rates for all cases. It is clear that the figure the blood shear -thinning behavior is more intensified at stenosis area compare to non-stenosis. The velocity is slight lower for generalized Oldroyd-B model compare to other models. At **flow** rate 5 cm 3 /s, the Newtonian and Oldroyd-B model very close and same result for rest of two models. On the other hand, the pressure distribution along vessel axis are presented in fig. 29 with different **flow** rates. The pressure is decreased with respect to vessel axis for all four models. At second stenosis, we obtained lowest value of pressure. For higher **flow** rate, the pressure profile almost same for all models.

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Vladimir Stefuca et.al [12] described the principles and applications of **flow** calorimetry (FC) in the investigation of the IMB properties. The FC can be used practically for every enzyme-substrate system, under the condition that a sufficient reaction heat is produced and the substrate is in soluble form [13]. Wide applications of glucoamylase in starch industry research focused in the improvement of the enzyme properties by methods of enzyme screening, molecular biology and enzyme engineering. Research in this area can be facilitated by developing suitable methods for the investigation of kinetic properties of immobilized glucoamylase.

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Although the concept of IO analysis was developed for national economic systems, the principles have been extended to specific products. Essengun et al. [59,60] explored the energy **flow** of dry apricot and tomato pro- duction in Turkey, by collecting primary energy input, quantities and costs of inputs and outputs. The authors employed various multipliers to construct a relatively simple model that allows the modeller to track the energy and monetary flows in the agricultural produc- tion. Such model determined the energy efficiency and intensity of the production and suggested different energy improvement measures. Kuswardhani et al. [61] studied the energy and economic IO of greenhouse and open-field production in Indonesia. The authors ob- tained primary data from surveys and used appropriate conversion factors to obtain the energy values. The study identified the linkages between energy input and crop yield for greenhouse and open-field production, and depicted the energy efficiency ratio of different products. A number of other studies also employed simple adapta- tion of IO analysis for the agricultural sector [62-68]. The primary motivation for choosing the product- specific IO method was the flexibility in collecting pri- mary data, determining energy efficiency ratios and extending the analysis to cost-benefit analyses.

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The numerical model is defined on the basis of various physical phenomena in complex differential equations. In this case, we will observe the **flow** of air in a rather complex geometry. In view of the fact that this is the 3D model of **flow** which can be described as a system of partial differential equations that can be solved by numerical methods. The calculation of the following equations are typically designed for a number of cells.

So far the Chen’s [2] correlation has quite commonly used in a variety of applications. He divided the heat transfer into two parts namely, the nucleate boiling contribution and non-boiling forced convection. There are numerous modifications of this correlation depending mainly on the employed in it formula to determine the "pool-boiling" coefficient. Shah [3] proposed a correlation in a graphical form introducing to it the convective number, Co, and the boiling number, Bo. Instead of taking the sum of two above mentioned contributions he recommends to pick the higher value of heat transfer coefficient. In the development of correlation approximately 800 experimental points have been used. In some later publications, [4], Shah supplemented his correlation with the relations enabling analytical calculation of constituent coefficients. Shah’s correlation has been widely used in engineering practice. Kandlikar and Thakur [5] put forward another widely acknowledged correlation, which included both the bubble boiling and the convective evaporation process, taking advantage for its construction of the same data as Shah did. A later publication by Kandlikar [6] presented a correlation for a number of agents. In effect, the heat transfer coefficient is expressed in terms of the parameter dependent on the type of refrigerant. A large bank of data amounting to over 5000 testing points for water, R11, R12, R114, R13B1, R22, R113, R152a, nitrogen and neon has been used in development of correlation. As it follows from the construction of the Kandlikar’s correlation, it does not provide accurate values for liquid- only or vapour-only **flow** of fluid in a channel. The correlation proposed by Gungor and Winterton [7] is a modification of Chen’s correlation. The correlation is based on the same parameters used by Shah and Kandlikar. Bjorge [8] suggests superposition of heat flux as the base for elaborating the correlation. The constants of Bjorge’s correlation depend also on the type of refrigerant. As can be deduced from the above survey none of the above correlations, despite their wide acceptance, does not feature theoretical foundations.

Urban flooding is an inevitable problem for many cities around the world. South-East Asia regions have more severe problems because of much heavier local rainfall and lower drainage standards (Mark et al., 2004). In order to understand and reduce the flooding, it is important to simulate the urban flooding mechanisms, which consist of **flow** on surface area, **flow** in drainage system and **flow** in underground structure to describe the real flooding process in urban area. Now in developing countries, afford has been made to use advances computer technology to tackle this problem. Local and minor flooding problems are illustrated using computer-based solutions by building the computer models for their drainage and sewer system. These models are then used to give more understanding of the complex interaction between rainfall and urban flooding. Good understanding of the existing condition gives advantages to evaluate alleviation schemes and also to choose the most optimal scheme to be implemented to solve the flooding problem. In conventional models, usually only one part of the urban drainage system is simulated, this is either surface **flow** or underground sub-system. These models do not represent the real situation, which should have a dynamic interaction between surface and sub-surface **flow**. In order to improve these conventional models, accurate input data and advanced **modelling** techniques such as 1D-2D coupling model is really needed.

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A closer examination of the speed index of the quasi-steady solution shows that there is a smooth transition from sub- to supercritical **flow** followed by an elastic jump back to subcritical velocity (figure 14a). The unusual double peak in the speed index curve is due to the fact that the wave-speed curve (see figure 15) is not monotonic. For large values of α, the wave speed is large. As α decreases to α ≈ 0.7, the wave speed decreases as well. Further decrease in α however now causes the wave speed to increase again. Now consider figure 14(b). In the region in which the cross-sectional area varies so that there is a smooth transition from sub- to supercritical **flow**, the area varies from approximately 0.01 to about 1.2. Thus the wave speed decreases (as shown in figure 14b) until the cross-sectional area is ≈ 0.7 at which point the wave speed begins to increase for a short distance until the cross-sectional area is at its maximum at approximately α = 1.2. Then as α decreases from this value, the wave speed decreases too until α = 0.7 again at which point it once more increases. In the region in which α goes through 0.7 (0.095 < ξ < 0.105), the fluid velocity is approximately constant (see figure 14b) and therefore the speed index S = u/c varies in the same manner as the wave speed, causing the double peak seen in figure 14(a).

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The model of the 3-path flowmeter body is very complex and calculation detailed maps **flow** behaviour under various conditions, and it is further continued. It has been prepared the comparison between simulations and experimental testing of **flow** perturbations effects. The research should continue to bring as much information about the field described.

In both cases the sufficiently uniform distributions of absolute pressure along impeller channels are observed, Figs. 5 - 6. Obviously, the distributions of density are visually similar to distributions of absolute pressure, Figs. 7 - 8, because these two quantities are directly related by the state equation of ideal gas and they are proportional. The temperature distributions for both cases are presented in Figs. 9 - 10. Similarly as with the previous two quantities, the temperature distributions are uniform, and clearly visible areas of increased temperature are close to the walls, which results in the viscose friction close to the walls. The distributions of relative velocities for both types of impellers are presented in Figs. 11 - 12. In Fig. 11 in case of “the first type of impeller” an area near the shroud is observed, which starts from the location of transition from axial to radial direction, of accumulation of low momentum fluid and with high losses. This is explained by the intensive secondary **flow** which displaces the low momentum fluid toward shroud (i.e. toward casing), and which is, as other investigators have indicated, characteristic for impellers with axial inducer >5@, >6@. As shown in Fig. 12, with “the second type of impeller”, the existence of the secondary flows is not observed at all. Therefore it may be concluded that the replacement of normal blades by “S” shape blades effectively eliminates the occurrence of the secondary flows.

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generalisation leading to errors of both omission and commission. The resolution of 1 kilometre grid leads to further generalisation. The reliability of key datasets varies spatially between countries particularly when relying on national census data for information on population density. The Hydro1k global used here aims to provide comprehensive and consistent global coverage of topographically derived data sets, but is based on the GTOPO30 global terrain model which at 30 arc -second equates to roughly 1 kilometre grid on the ground. While it is theoretically possible to use a terrain model of this resolution, the coarse resolution inevitably leads to errors in defining **flow** paths and watersheds especially in low relief areas where low variability between adjacent cells in the terrain data make determining the correct **flow** paths difficult and prone to error. The

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This Project introspects the effects of incorporation of load models i.e. the variation of active and reactive power demands with magnitude of voltage at different buses in load **flow** analysis. The simulation of a standard IEEE 14 bus bar system was conducted and the effects of load **modelling** were also incorporated in the experiment. The effect of load **modelling** could be observed with the pronounced difference in fuel cost. The heavier the loading of the system, the lower is the fuel cost difference[3]. Implementation of load model brings a significant reduction in the generation cost for the whole year. The calculations become more accurate and system security and stability increase by incorporating the voltage dependent load models.

The concept of the experiment was to try to measure for soil what a fan pressurisation test measures for a building, that is the overall leakage through all possible **flow** paths. In a fan pressurisation test, [Stephen 88 ], a fan is installed in the outer wall of a building, usually in a doorway. The rates of **flow** required to produce a series of pressure differences between inside and out are measured. These pressures are generally -50 to +50 Pa, with steps of 10 Pa. From the plot of these results the characteristic leakiness of the building is estimated. It is usual to express it as the number of air changes per hour (ach) at 50 Pa pressure, often called n^g. The air change rate is the volume **flow** rate of gas divided by the volume of the building, so it has units s'^ or more usually h'* or ach.

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Simply reducing the value of the refresh period is not the right approach, however. Indeed, doing so would increase the control traffic associated with every **flow**, thus increasing the required capacity of the signalling channel of the network while threatening to pose severe scalability problems. Consequently, reducing the refresh period at establishment time only 5 (including local repair conditions) is considered a better solution. In [5], it is suggested that a node could, at establishment, temporarily send control messages more often than dictated by the refresh period. However, the question of how many, as well as how often, such messages should be sent has not been addressed. This is precisely what we propose to do in this section.

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