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ECOS 2011 Pariotis Kosmadakis Rakopoulos | Internal Combustion Engine | Computational Fluid Dynamics
ECOS 2011 Pariotis Kosmadakis Rakopoulos
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Me 6404 Assignment Topic
Comparative Analysis of Three Simulation Models Applied on a Motored Internal Combustion Engine
Efthimios Pariotis a, George Kosmadakis b and Constantine Rakopoulos c
a Laboratory of Naval Propulsion Systems, Section of Naval Architecture and Marine Engineering, Department of Naval Sciences, Hellenic Naval Academy, End of Hatzikyriakou Ave., Hatzikyriakio, 18539 Piraeus, Greece, pariotis@snd.edu.gr, CA b Internal Combustion Engines Laboratory, Thermal Engineering Department, School of Mechanical Engineering, National Technical University of Athens, 9 Heroon Polytechniou St., Zografou Campus, 15780 Athens, Greece, gkosmad@central.ntua.gr c Internal Combustion Engines Laboratory, Thermal Engineering Department, School of Mechanical Engineering, National Technical University of Athens, 9 Heroon Polytechniou St., Zografou Campus, 15780 Athens, Greece, cdrakops@central.ntua.gr
The motivation of the present work is to comparatively evaluate the results obtained using three computational models of increasing complexity, for the simulation of the closed part of the cycle of an internal combustion engine running under motoring conditions. The first model is a singlezone thermodynamic model, the second one is a hybrid quasi-dimensional model and the third one is a computational fluid dynamics (CFD) model. All of them have been originally developed by the authors, and each of these models has certain limitations due to their inherent features, while their usage has a different scope. The three models have been applied to simulate the closed part of the cycle of a high-speed diesel engine with a bowl in piston design, running under motoring conditions at four engine speeds, in the range of 1200 to 3000 rpm. From the analysis conducted, it is concluded that the single-zone model calculates with reasonable accuracy the in-cylinder pressure, while it rather underestimates the peak in-cylinder mean gas temperature near the top dead centre (TDC), compared to the other two models, needing only 1 sec on a personal computer (PC), to simulate each case. On the other hand the computational time required by the quasi-dimensional model is approximately 6 minutes to simulate the closed part of the engine cycle, whereas the CFD model needs up to 20 hours (all tested on a personal computer with a CPU running at 2.2 GHz). At the same time the quasidimensional model, provides information concerning the local in-cylinder temperature distribution and describes qualitatively correctly how the cylinder design affects the in-cylinder flow and temperature field, as revealed by comparing its results with the corresponding ones obtained by the more accurate and time consuming CFD model. Highlighting the special features of each type of simulation model examined on a comparative basis, provides useful information for the engineer to decide which type of model is best for each application, taking into account not only the accuracy but also the computational time.
Computational fluid dynamics, Internal combustion engine, Quasi-dimensional, Single-zone, Motoring.
Engine simulation models are valuable tools for the engineers who are working on the automotive industry or belong to the research community, aiming to design engines that comply with the strict emission legislation and offer high performance. During the last decades various simulation models for the internal combustion engines have been proposed. In general there are two main categories of simulation models: the phenomenological and the computational fluid dynamic (CFD) ones. The main difference is that in the phenomenological models, empirical or semi-empirical relations are used to describe the physical and chemical processes taking place inside the cylinder, while in CFD models, special iterative techniques are used to solve the conservation equations of mass, momentum, energy and species, coupled to a turbulence model at the discretised computational
domain which covers the whole cylinder volume. The phenomenological models are further divided depending on their complexity to one or two dimensional or multi-zone models. Recently, a new sub-category of simulation model has been proposed [1-3], signed between the multi-zone phenomenological and CFD models, named hybrid quasi-dimensional model, which combines features of the multi-zones and the CFD models. This type of model, offers a more fundamental description of the physical processes taking place inside the combustion chamber compared to the multi-zone models, while it is less accurate compared to the more detailed and more CPU demanding CFD models, since the momentum equations are not taken into consideration for the calculation of the local flow-field. It is clear that depending on the specific needs of the research, the engineer has to decide which type of model should best fit his needs, in order to obtain the required results for his application and at the same time spend the lowest possible computation resources. Although the differences between a single zone and a CFD model are wide and their purpose of usage might be clear, sometimes a quasi-dimensional model can substitute in a certain extent the usage of a CFD model and this can be translated in gaining significant amount of effort and time. The present authors have developed in the past and validated against experimental or published data the quasi-dimensional and the CFD model [4-9], while the single-zone (1-D) model has been used to support the development of various sub-models incorporated into the CFD model. The main scope of the present work is to compare the results obtained using a 1-D, a quasi-dimensional and a CFD model, applied to simulate a high speed diesel engine with a bowl in piston running under motoring conditions. The study was intentionally restricted to the motoring conditions to understand how the complexity of the model affects the description of the physical processes. When combustion takes place, each type of model follows totally different approach to simulate the airfuel mixing and the chemical processes involved, which makes the comparison of the models more complex. Apart from estimating the mean in-cylinder thermodynamic properties, the quasi-dimensional model has the advantage of predicting the spatial distribution of temperature inside the whole cylinder volume at each computational step, following a procedure resembling to the one used in pure CFD codes, with a major difference: in the hybrid quasi-dimensional model, a phenomenological sub-model is used for the estimation of the three dimensional velocity vector at the cells faces of the computational domain, avoiding the solution of the momentum equations, as done in pure CFD codes. This leads to significant CPU power saving, however the predicted velocity field is only a rough estimation (although qualitatively correct), being inferior compared to the one provided by the CFD code. The predicted velocities are used to calculate the convention terms of the energy conservation equation, affecting the spatial temperature distribution. Comparing the estimated temperature fields using the quasi-dimensional and the CFD model at certain time instants (corresponding to 160 and 220 CA degs ABDC, at 2000 rpm engine speed) it is observed that the quasi-dimensional model manages to predict qualitatively correct the spatial temperature distribution. It is important to notice, that using the quasi-dimensional model, it is possible to capture qualitatively the effect of piston bowl geometry on in-cylinder temperature and velocity field.
The experimental tests were conducted on a Ricardo/Cussons Hydra, single cylinder, four-stroke, water-cooled, high-speed, experimental standard engine located at the authors laboratory. The fully automated test bed includes facilities to monitor and control engine variables such as engine speed, load, static injection timing, water, lubricating oil and exhaust gas temperatures, fuel and air flows etc. The engine is coupled to a McClure DC motoring dynamometer, having load absorbing and motoring capabilities. The test bed control and engine operating conditions adjustment is achieved through an electronic console. Electrically driven pumps assure the circulation of coolant and lubricating-oil, while heaters are used to maintain their temperatures during warm-up conditions.
In the present work the Hydra engine is used as a naturally aspirated, DI diesel engine having a reentrant (torroidal) bowl-in-piston combustion chamber. In Table 1 is shown a summary of the basic engine data. Table 1. Diesel engine specifications Engine model and type Ricardo/Cussons Hydra, single cylinder, diesel, naturally aspirated, four-stroke, water-cooled, high-speed Bore 80.26 mm Stroke 88.90 mm Swept volume 0.4498 l Connecting rod length 158 mm Compression ratio 19.81:1 Squish height 0.82 mm Bowl-in-piston volume 19.737 cm3 Swirl ratio 3.57 Speed range 1000 4500 rpm Maximum cylinder pressure 120 bar Valve Inlet valve opening 8 oCA BTDC Inlet valve closure 42 oCA ABDC timing Exhaust valve opening 60 oCA BBDC Exhaust valve closure 12 oCA ATDC The measuring set-up comprises the following two transducers: Tektronix TDC marker (magnetic pick-up) Kistler 6125B piezoelectric transducer for measuring the cylinder pressure, flush mounted to the cylinder head and connected to a Kistler 5008 charge amplifier. The output signals of these two transducers, while being continuously monitored on a dual beam Tektronix oscilloscope, are fed to the input of the data acquisition module, which is a Keithley KUSB 3102 ADC card connected to a Pentium Dual Core PC via USB interface. This specific type has a maximum sampling rate of 100 ksamples/sec, with a 12-bit resolution for its 8 differential (or 16 single-ended) input, and 2 output analogue channels. Control of this high-speed acquisition system is achieved by using the quickDAQ data collecting software, supplied by Keithley, together with the KUSB 3102 ADC card. For the present study 20 cycles, with 1 CA resolution, were recorded for every engine speed. The mean measured motored pressure history was computed, though the cyclic variability was negligible, while the measurement error was estimated to be smaller than 1%.
In the current study three numerical models have been applied for the simulation of the motored Diesel engine. These are namely a single-zone (1-D) model, a quasi-dimensional (Q-D) model, and a computational fluid dynamics (CFD) model, which have been originally developed by the authors, and have been applied in the past for the simulation of various engines and operating conditions. These three models are briefly described next.
3.1. Single-zone model
The single-zone model is based on the first law of thermodynamics. It incorporates a simple turbulence model based on the k- principle, according to [10,11]. This turbulence model is actually coupled with the heat transfer model. For the current application the Annand heat transfer model has been chosen [12], in which the Reynolds number includes the effective conductivity (keff) and viscosity (eff), and a characteristic velocity. The heat transfer surface includes the real surface of
the engine under investigation, which is the sum of the surface surrounding the main chamber and the piston bowl. For the rest of the current study, this model will be referred to as 1-D model.
3.2. Quasi-dimensional model
The quasi-dimensional model used is a hybrid one which means that it uses phenomenological submodels to describe the various processes taking place inside the combustion chamber, combined with methods used in CFD models to calculate the local values of the various properties. Specifically, phenomenological sub-models are used to describe the following: heat transfer through the cylinder walls, fuel injection rate, fuel spray penetration, evaporation, combustion, and pollutants formation. In the present study, all the simulations are done in motoring conditions, thus the sub-models related to fuel injection and combustion have been deactivated. In contrast to what is done in most multi-zone models [13], the whole cylinder volume is divided in computational cells which consist the computational domain and the local value of each characteristic property of the field (specific enthalpy and temperature, velocity and species concentrations) is calculated at each computational cell, by solving the general conservation equation of property (1).
1 ( zpiston ) 1( u r ) 1( v ) ( w ) + + + = zpiston r r t r z r 1r z 1 r + +S = + r r r z
where, u, v, and w are the components of the local velocity vector at each computational node, the gas density, is the diffusivity of the dependent variable and S is the volumetric source rate. At motoring conditions, the dependent variable is the local gas enthalpy, and the source rate S, represents the contribution on the energy equation of the heat transfer through the cylinder walls (according to the Annand heat transfer model) and the power due to the pressure variation. Equation (1) is solved using the finite volume method, similarly to what is done in most CFD codes. One important difference with the CFD models is the methodology followed for the calculation of the velocity field, where a new phenomenological sub-model is used, to avoid solving the equation of momentum conservation, which is highly CPU demanding, since the convective terms of the energy conservation equation contain non-linear quantities, and the momentum equation (containing the local pressure) and the continuity equation are intricately coupled. Following this approach, the physical phenomenon of mass transfer between the computational cells is simulated in a realistic way, based on the assumption that the in-cylinder pressure must be practically spatially uniform at each crank angle. The velocity is estimated at the boundaries of each computational cell, by calculating the mass that should be transferred through each cells faces from the neighbouring cells to achieve a uniform pressure field inside the cylinder. The velocity field is obtained following an iterative procedure. First, at each crank angle the energy conservation equation is solved, and a spatial distribution of the temperature is obtained assuming that the velocity of the gas relative to the grid is zero. Then the pressure distribution is obtained using the perfect gas state equation. Given that the pressure must practically be uniform, an amount of mass dmcell is calculated that should be transferred inside each computational cell through its boundaries from its neighbouring cells to eliminate the pressure difference and make its pressure practically equal to the mean pressure of the cylinder. However, the sum of the transferred mass for all computational cells at each crank angle should be equal to zero, given that the total mass of the gas inside the cylinder is constant. In this way the amount of mass dmcell transferred to each computational cell is estimated. This amount of mass is distributed to each cells faces using an empirical method, based on the local pressure difference at each direction. In this way, the
velocities at the boundaries of all the computational cells are determined. These velocities are used when solving the energy equation (1). Following this procedure, the solution of the momentum equation is by-passed, and the in-cylinder velocity field is estimated faster but less accurately compared to the pure CFD models. Taking into account that the accuracy of a simulation model depends on the less accurate sub-model used, in the proposed quasi-dimensional model, semi-empirical relations are used for the simulation of the heat transfer, fuel injection rate, fuel spray penetration, evaporation, combustion, and pollutants formation, as done in most multi-zone models. According to the aforementioned methodology for the estimation of the in-cylinder velocity field, the developed model describes in a more fundamental way the transport phenomena of the incylinder gas compared to existing multi-zone phenomenological models, being able to capture the effect of cylinder geometry (i.e. piston bowl design). Furthermore, it overcomes basic difficulties experienced in existing multi-zone models, as far as air-fuel mixing is concerned (in firing conditions), where only empirical relations are used. A more detailed description of the model can be found in previous publications [1-3]. In the present work where motoring conditions are examined, the general transport equation is solved only for enthalpy, to estimate the local value of temperature at each computational cell inside the cylinder. For the rest of the current study, this model will be referred to as Q-D model.
3.3. Computational fluid dynamics model
The CFD code developed can simulate three-dimensional curvilinear domains using the finite volume method in a collocated grid. It incorporates the RNG k- turbulence model with some slight modifications to introduce the compressibility of a fluid in generalized coordinates, as described in [14]. All the constants used in the RNG k- turbulence model and the enthalpy equation, are in accordance with [15]. These constants retain their values at all cases examined in this study. The following transport equations for the conservation of mass, momentum, energy and turbulence properties are solved:
( ) + (u ) = ( ( )) + S + S cr t
where Scr is a source term due to crevice regions [6] and the generalized variable is replaced by the appropriate variable, shown in Table 2. Table 2. Variables that represent the generalized variable Generalized variable Equation 1 Continuity u umomentum v vmomentum w wmomentum h Enthalpy k Turbulent kinetic energy Dissipation of turbulent kinetic energy
0 eff eff eff eff /h eff /k eff /
The solution follows the PISO algorithm for the velocity-density-pressure coupling, according to the methodology found in [16], which is more robust than the SIMPLE algorithm [17]. The spatial discretization is based on the hybrid-differencing scheme, while the temporal one on the backward second-order Euler scheme. The transport properties (specific heat capacity under constant pressure, thermal conductivity and laminar viscosity) are calculated from least-squares fits proposed by NASA [18]. The heat transfer model used has been developed by the authors and validated under
motoring and firing conditions [7,8], which provides more reliable calculations, especially during the compression stroke. The expression of the local heat flux (qw) includes the compressible form of the wall-functions and a pressure term (dP/dt) [7], while it is given by the following equation:
dP y + 40 T u T c p Tln W + 117.31 T dt u T 0.4767 + 1 Pr qW = 1 + 1 1 ln y + 0.4767Pr ln 40 + 0.4767Pr + 10.2384 0.4767
where uT is the friction velocity, cp the heat capacity under constant pressure, TW the wall temperature, the kinematic viscosity, y+ the non-dimensional distance from the wall, and Pr the Prandtl number. The CFD model incorporates also a phenomenological crevice model, in order to simulate the mass transfer between the combustion chamber and the crevice regions [6], predicting with a higher accuracy especially the peak cylinder pressure. The mesh generation model used belongs to the wide family of algebraic grid generation methods, and has been originally developed by the authors. This model is capable of creating 3D structured grids consisting of hexahedral cells. Additionally, mesh layers are removed and added, during the compression and expansion stroke respectively, in order for the cells dimension along the axial direction to keep an almost constant value. Further details of the in-house CFD model, its evaluation and validation, together with the mesh generation model and the crevice model used, can be found in previous published studies of the authors [4-9]. For the rest of the current study, this model will be referred to as CFD model.
4.1. Test cases examined / Numerical Data
The three numerical models have been applied to simulate the closed part of the engine cycle of a diesel engine, with a bowl in piston configuration, running under motoring conditions, at 1200, 1500, 2000 and 3000 rpm engine speed. The measured cylinder pressure traces are used to evaluate the predictive accuracy of each model, as far as the engine performance is concerned. Based on the fact that the CFD code used has already been validated [4,6,7] at various types of engines, it is used as a reference to evaluate the calculated results obtained using the other two models, regarding the in-cylinder mean gas temperature and the wall heat losses during the closed part of engine cycle. The same also applies, as far as the spatial distribution of temperature and the velocity field is concerned calculated from the quasi-dimensional model, at two time instants corresponding to 160 and 220 CA degs ABDC at 2000 rpm engine speed. Table 3 summarizes the test conditions examined in the present study. Table 3. Diesel engine test conditions (motored) Engine operation Engine speed Ambient pressure / temperature Lubricating oil temperature Coolant (water) temperature Motored 1200, 1500, 2000, 3000 rpm 1 bar / 23 oC 80 oC 80 oC
Some initial and boundary conditions used, which are common for all three models are listed in Table 4. The Q-D and the CFD model use a mesh, covering the entire cylinder volume, where the conservation equations are solved. When applying the CFD model the computational mesh at the beginning of the simulations consists of 30x30x40 grid lines along the x,y,z axis respectively (z-
axis corresponds to the axial direction). This mesh configuration is selected based on previous grid independence studies [6]. Table 4. Initial/boundary conditions used Swirl ratio at IVC Air temperature at IVC Wall temperature Turbulent kinetic energy at IVC 3.57 310 K 373 K 0.8 x (mean piston speed)2
On the other hand, when applying the quasi-dimensional model the area inside the cylinder is divided into cylindrical computational cells, using a structured grid that contracts and expands to account for the variation of the swept volume. Since in the present study only motoring conditions are examined, there is no need to divide the cylinder volume into the circumferential direction, assuming that the piston bowl is centrally located and symmetric. A schematic view of the combustion chamber geometry assumed, together with the computational mesh used with the Q-D model is shown in Fig. 1. The number of cells used in the radial direction above the piston crown is equal to 16, and inside the piston bowl is equal to 8, while in the axial direction the number of cells inside the piston bowl is equal to 7 and in the outer region varies from 37 to 1 depending on the distance of the piston from the cylinder top. With this configuration, mesh independent solutions are derived, and the dimensions of the computational cells are similar with those when the CFD model is applied. During all the simulations, the same computational time-step has been used, corresponding to 0.5 oCA. This decision is supported by a time-step independence study that has already been conducted in the past [6].
Dcyl r direction
Fig. 1. Computational domain used in the quasi-dimensional model at a vertical plane (r-z) It should be mentioned that the two phenomenological models (1-D and Q-D) need to be calibrated at one operating condition to match the calculated cylinder pressure traces with the measured one. This is done by changing the exponential constant of the Reynolds number in the Annands equation which is used to estimate the heat transfer through the cylinder walls. After having calibrated this constant, it retains its value at all the operating conditions examined. On the contrary, the CFD model has no calibration constants, since the wall-function formulation used to calculate the wall heat losses is based on the one-dimensional energy equation applied on the boundary cells. This is an advantageous feature of the CFD model, indicating that it is preferable for conducting fundamental research [5].
z direction dbowl
4.2. Models Validation Calculated Cylinder Pressure Traces
At first, the predictive ability of the three simulation models is examined as far as the cylinder pressure trace is concerned, at four engine speeds i.e. 1200, 1500, 2000 and 3000 rpm, since the accurate prediction of the cylinder pressure is a prerequisite for any simulation model used in a real engine application. To this scope, the calculated cylinder pressure during the closed part of engine cycle is compared with the corresponding measured one. As shown in Fig. 2, at all operating conditions examined all the simulation models manage to predict well the cylinder pressure traces. The higher discrepancies are observed near the TDC, where the maximum difference is approximately 5 bar between the measured and the calculated peak cylinder pressure using the 1-D model. On the other hand the quasi-dimensional model, predicts with high accuracy the measured peak cylinder pressure at all cases examined, performing slightly better than the CFD model, with the maximum difference observed at 3000 rpm engine speed, which is approximately equal to 2 bar (although in this case, the predicted cylinder pressure is identical with the one obtained using the CFD model).
Meas. 1-D Q-D CFD
40 30 20 10 0 140
Crank Angle [degs aBDC]
Fig. 2. Comparison of calculated pressure obtained from the three numerical models with the measured one for various engine speeds.
4.3. Comparison between the calculated results of simulation models
After having validated that the three simulation models manage to predict the cylinder pressure traces at all test cases examined, the investigation proceeds with the comparison of the calculated results of the three simulation models, as far as the mean cylinder temperature, and the heat transfer through the cylinder walls during the closed part of the engine cycle, at 1200, 1500, 2000 and 3000 rpm engine speed, is concerned. Since no experimental data are available, the calculated results of the CFD model will be used as a reference to evaluate the other two phenomenological models. This decision is based on the fact that the CFD model calculates in a more fundamental and detailed way the physical processes taking place inside the combustion chamber, which consequently affect the heat transfer process. Besides, the validity of this model has already been confirmed by the authors in previous publications [4,9].
4.3.1. Mean Cylinder Gas Temperature
In Fig. 3 is shown the mean in-cylinder gas temperature during the closed part of the engine cycle at the four engine speeds examined, as calculated from the three simulation models.
1-D Q-D CFD
Mean Gas Temperature [Kelvin]
800 700 600 500 400 300 200 120 140
Fig. 3. Calculated mean cylinder gas temperature using the three numerical models (1-D, Q-D and CFD) for various engine speeds.
As observed, at all cases examined the predicted mean gas temperature at each CA deg. is higher when using the CFD code. At all engine speeds examined, the difference between the peak mean gas temperature of the cycle, as predicted by the CFD and the quasi-dimensional model, is almost the half of the corresponding one, when comparing the CFD and the 1-D model (especially at higher engine speeds), which is approximately equal to 50 oC. It is noticed that the difference on the predicted peak in-cylinder mean gas temperatures using the three simulation models remains practically constant at all the range of engine speeds examined, with a slight increase at 3000 rpm. From the previous it can be concluded that, although the prediction of the cylinder pressure traces is made with relatively similar accuracy, the predicted mean gas temperature profiles vary in a wider extent when switching between the three simulation models examined.
4.3.2. Wall heat losses
As far as the wall heat transfer is concerned, in Fig. 4 are shown the calculated values of the heat transferred through the cylinder walls at each CA deg. using the three simulation models, at all engine speeds examined. As observed, there is a general similarity on the predicted profiles between the CFD and the quasi-dimensional model, with a small discrepancy on the absolute values mainly
Wall Heat Transfer [W]
-4000 -6000 -8000 -10000 -12000 -14000 -16000 -18000 40 80 120 160 200 240
-2000 -4000 -6000 -8000 -10000 -12000 -14000 -16000 -18000 40 80 120 160 200 240
Fig. 4. Wall-heat losses calculated using the three numerical models (1-D, Q-D and CFD) for various engine speeds.
during the expansion phase at all engine speeds examined. However, using the 1-D model a significant under prediction of the wall heat losses is made during the compression and the first part of the expansion phase, compared to the other two more sophisticated models. More specifically, during the compression stroke the in-cylinder temperature rises and the in-cylinder gas velocities increase, which consequently lead to more intense heat transfer to the cylinder walls. As shown in Fig. 4 the quasi-dimensional model manages to predict precisely the aforementioned physical mechanism which affects the heat transfer through the cylinder walls. As the piston approaches the TDC, the predicted values of the heat losses to the cylinder walls using the quasi-dimensional model, resemble to the ones predicted using the CFD one, at almost all the engine speeds examined. The only exception occurs at 3000 rpm engine speed, where a relatively higher difference occurs, but again the prediction is much more accurate than the one obtained using the 1-D model. The aforementioned better prediction of the wall heat transfer mechanism obtained using the quasidimensional model compared to the 1-D model, should be attributed to the fact that in the quasidimensional model a fundamental description of the physical process is made. The heat is transferred from the hot in-cylinder core to the outer cells located next to the cylinder walls through conduction and convection (taking into account the effect of the calculated 3-D velocity field). In turn, the cells located at the boundary of the cylinder volume transfer heat to the cylinder walls through convection according to Annands law [12]. Thus it is taken into account the stratification of the in-cylinder local temperatures at each time step, which is not possible when using the 1-D model. This is one of the main attractive features of the proposed quasi-dimensional model. To further extend the comparative evaluation of the quasi-dimensional model and the CFD one, the local heat fluxes are calculated at four locations on the cylinder head as shown in Fig. 5.
Fig. 5. Position of the four locations (HT1, HT2, HT3, HT4) where the local heat flux is calculated Due to the different computational mesh used at the quasi-dimensional and the CFD model, it was not possible to calculate the local heat flux at exactly the same points, however, the differences where small. In Table 5, is shown the radial distance of each point from the cylinder axis, when using the CFD and the quasi-dimensional model. Table 5. Radial Distance from the cylinder axis of the locations where local heat flux is calculated Location CFD (mm) Quasi-dimensional (mm) HT1 0 1.25 HT2 28.80 26.29 HT3 30.33 30.06 HT4 32.92 32.58
In Figure 6 are shown the calculated local heat fluxes using the CFD and the quasi-dimensional model at the four locations (HT1, HT2, HT3, and HT4) respectively, at 2000 rpm engine speed. As observed, with the exception of point HT1, the quasi-dimensional model predicts similar heat flux at the other three points with the CFD model, during the compression stroke. On the expansion phase, it seems that the quasi-dimensional model, slightly over predicts the local heat flux compared to the CFD one, which is in accordance to what has already been observed when examining the total wall heat transfer in Fig.4. On the other hand, at location HT1, significant differences are observed between the calculated heat fluxes, especially near the TDC. This may be attributed to the fact that these heat fluxes do not correspond exactly to the same location (as shown in Table 5) and that the local temperature as being calculated by the CFD model seems to be higher at HT1 point, compared to the one calculated using the quasi-dimensional model, as it will be shown in Figs. 7a,b and 8a,b. Moreover, the CFD model predicts high level of turbulence close to the cylinder axis (HT1), which enhances the heat transfer rate. This is not possible to be captured by the quasi-dimensional model, since turbulence is not taken into account.
0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 -1.1 -1.2 -1.3 -1.4 -1.5 40 80 0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 -1.1 -1.2 -1.3 -1.4 -1.5 40 80
Local Heat Flux [MW/m2]
Point: HT1
Point: HT2
RQ-D=1.25mm RCFD=0.00mm
Q-D CFD
RQ-D=26.29mm RCFD=28.80mm
120 160 200 240 280 320
0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 -0.9 -1.0 -1.1 -1.2 -1.3 -1.4 -1.5 40 80
Point: HT3
RQ-D=30.06mm RCFD=30.33mm
Point: HT4
RQ-D=32.58mm RCFD=32.92mm
Fig. 6. Calculated local heat fluxes at locations HT1, HT2, HT3, HT4 using CFD and Quasidimensional model
However, taking into account that the local heat flux is affected by both the temperature and the flow field, it seems that the quasi-dimensional model predicts with relative good accuracy the incylinder physical processes (heat and mass transfer).
4.3.3. In-cylinder Spatial Distribution of Temperature
The quasi-dimensional model solves the energy conservation equation at each computational cell, according to the finite volume method using the velocity field which has been calculated with a phenomenological velocity sub-model [1]. In this way it becomes feasible to capture how each parameter (operating or design) affects the physical processes taking place inside the combustion chamber, without the need to use the more accurate but also much more CPU demanding CFD models. In Figures 7 a,b are shown the calculated temperature fields by the quasi-dimensional and the CFD model corresponding to 160 CA degs ABDC, at 2000 rpm engine speed. As observed the temperature range used in Fig. 7a is slightly shorter than the one used in Fig. 7b, in order to improve the contrast of the stratification of the local temperature. Comparing these two figures, it can be concluded that the calculated temperature field using the quasi-dimensional model is similar to the one obtained using the CFD model. The gas with the higher temperature is restricted in the inner portion of the cylinder, while near the cylinder and piston walls the gas has lower temperature. This is due to the heat transfer, from the hot gas to the colder cylinder walls. However, using the CFD model, as observed in Fig. 7b, the hot core of the cylinder gas is predicted to be slightly nearer to the bottom of the piston bowl compared to what is predicted using the quasi-dimensional model. Moreover, the maximum value of the local temperature in Fig. 7b is approx. 15 oC higher than the one predicted by the quasi-dimensional model, Fig. 7a.
Fig. 7a. Calculated spatial temperature distribution at 160 CA degs ABDC, at 2000 rpm engine speed using the quasi-dimensional model
Fig. 7b. Calculated spatial temperature distribution at 160 CA degs ABDC, at 2000 rpm engine speed using the CFD model
On the other hand, in Figs. 8a,b are shown the calculated temperature fields, during the expansion phase, at 220 CA degs ABDC, at 2000 rpm engine speed, using the quasi-dimensional and the CFD model respectively. At the same figures, the velocity vectors at each computational node are also presented, in order to show a sample of the predicted velocity field using the two models and make a qualitative comparison. It is noticed that the vectors shown in Fig. 8a,b have a uniform length and they correspond to the local velocity of the air relative to the computational mesh (which follows the piston motion). As observed both models predict similar temperature fields. It is interesting to notice that the shape of the contour surfaces is similar in both figures, with the hot core of the gas being restricted in the middle of the piston bowl and the temperature drops when moving towards the cylinder walls. As far as the velocity field is concerned, it is noticed that the quasi-dimensional model predicts the existence of a vortex in the region upon the cylinder crown and the flow follows a path from the periphery of the cylinder to the inner of the piston bowl (as sketched in Fig. 8a).
Fig. 8a. Calculated Temperature and Velocity field (uniform vector length) with indicative flow path lines at 220 CA degs ABDC, at 2000 rpm engine speed, using the quasi-dimensional model
Fig. 8b. Calculated Temperature and Velocity field (uniform vector length) at 220 CA degs ABDC, at 2000 rpm engine speed, using the CFD model These results are in accordance to the predictions of the CFD model (Fig. 8b), although the comparison is only qualitative. Taking into account the simplicity of the phenomenological submodel used to calculate the velocity field when using the quasi-dimensional model and comparing it to the detailed procedure followed by the CFD model, the produced result is considered satisfactory. It should be mentioned that the alternative procedure followed by most phenomenological models in order to describe the flow field inside the combustion chamber of an engine with bowl in piston design, would be to pre-define that the flow would have only a radial component in the region
above the piston crown and an axial component inside the piston bowl. In previous studies, it has been shown by the authors [19], that the calculated mean mass flow rates through certain reference planes using the quasi-dimensional and the CFD model are similar, however the predicted velocity field using the quasi-dimensional model could by no means thought to be as accurate as the one obtained using the CFD model.
4.4. Comparison of computational time.
A significant parameter taken into account in the present investigation is the computation time required by each model to complete the engine closed cycle simulation. As mentioned earlier, the limitations and advantages of the 1-D models are well known to the research community, however, there are applications where the significantly lower computational time required by the quasidimensional model makes it preferable compared to the more accurate CFD model. This does not mean that the quasi-dimensional model can directly substitute the CFD model, but sometimes (i.e. engine cycle parametric studies) it can be proved to be a fair compromise. In Table 6 are given the actual simulation times required by each model examined to fully complete the engine closed cycle simulation, at all engine speeds. It should be mentioned that for the CFD model the computational time decreases as the engine speed increases. All the simulation runs have been accomplished on a PC with a CPU running at 2.2 GHz. Table 6. Simulation times of the 1-D, Q-D and CFD model at various engine speeds Engine speed 1-D (sec) Q-D (sec) CFD (sec) 1200 rpm ~1 360 72x103 1500 rpm ~1 360 58x103 2000 rpm ~1 360 52x103 3000 rpm ~1 360 43x103 As was expected, the CFD model requires much more computational time than the other two models (it is 120 to 200 times more time consuming than the quasi-dimensional model), but it is the most detailed one, proper for fundamental research. The 1-D model is very fast, it is ideal for quick parametric investigations, however it has certain limitations on the extent of its usage. Finally, the quasi-dimensional model is 360 times more time consuming than the 1-D model, but in a generally acceptable time interval it manages to predict reliably the thermodynamic properties of the gas on a cycle basis and also provides a rough estimation of the in-cylinder temperature and velocity field.
In the present study, three engine simulation models (1-D, Q-D and CFD) have been applied to simulate the closed part of the engine cycle of a HSDI Diesel engine, running under motoring conditions at various engine speeds. The single zone model calculates with reasonable accuracy the in-cylinder pressure, while it rather underestimates the mean gas temperature near the top dead centre (TDC), compared to the other two models (quasi-dimensional and CFD one) at all engine speeds examined. Among the positive features of this type of model is definitely the very low computational time, which is approximately equal to 1 sec for the simulation of the engine closed cycle, when running on a personal computer. On the other hand the computational time required by the quasi-dimensional model is approximately 6 minutes to simulate the closed part of the engine cycle, whereas the CFD model needs up to 20 hours. Additionally, the quasi-dimensional model provides information for the local in-cylinder temperature distribution and describes qualitatively correctly how the cylinder geometrical design affects the in-cylinder flow field and spatial temperature distribution, as revealed by comparing its results with the pertinent obtained by the more accurate and time consuming CFD model.
Moreover, comparing the quasi-dimensional with the 1-D model, it calculates the cylinder pressure trace closer to the corresponding measured one, while the calculated mean cylinder temperature is closer to the one obtained using the CFD model, at all cases examined. Concerning the heat transfer process, the calculated heat transferred through the cylinder walls during the compression phase is almost identical between the quasi-dimensional and the CFD model at all cases examined (with the exception of the case of 3000 rpm engine speed, where a small deviation is observed). Thus, the timing and the peak value of the heat transferred is accurately predicted. During the expansion phase, a discrepancy is observed between the calculated heat transferred through the cylinder walls using the CFD and the other two models, with the quasi-dimensional giving much closer predictions to the ones obtained using the CFD model, than the 1-D model. Based on the previous analysis, it can be concluded that the usage of the quasi-dimensional model might be beneficial as an intermediate tool to appropriately analyze, on an engine cycle basis, the effect of combustion chamber geometry on the physical processes taking place inside the cylinder. In these applications, the 1-D model fails, since it does not take into account the design of combustion chamber, although it seems capable of predicting the effect of engine operating parameters (engine speed in the present investigation) on engine performance (cylinder pressure trace). On the other hand, for a detailed investigation of how each design parameter of the combustion chamber affects the in-cylinder flow-field, and consequently all the relevant physical processes, the CFD model is the most appropriate. On a real case, where a parametric investigation is to be conducted, it could be preferable to use the quasi-dimensional model, to determine a limited number of cases with higher interest, and then focus on these cases, by applying the CFD code, to produce more detailed and accurate results. In a future work it is interesting to compare the quasi-dimensional model with a multi-zone phenomenological model and a CFD one, applied to simulate a diesel engine running under firing conditions. In this situation it will be possible to explore the beneficial characteristics of the quasidimensional model, on the description of the air-fuel mixing mechanism compared to the conventional multi-zone model, and determine to which extent its usage would be preferable.
The authors would like to thank A.M. Dimaratos for his significant contribution in the experimental facilities of the present study. Also, G.M. Kosmadakis wishes to thank the Greek State Scholarships Foundation for granting him a Ph.D. research scholarship.
ABDC after bottom dead center ATDC after top dead center BBDC before bottom dead center BTDC before top dead center o CA degrees of crank angle CFD computational fluid dynamics CPU central processing unit IVC inlet valve closure PISO pressure implicit splitting of operators rpm revolutions per minute SIMPLE Semi-Implicit Method for Pressure Linked Equations TDC top dead center
[1] [2] Hountalas DT, Pariotis EG. A simplified model for the spatial distribution of temperature in a motored DI diesel engine. SAE Paper no. 2001-01-1235; 2001. Pariotis EG, Hountalas DT. A new quasi-three dimensional combustion model for prediction of DI diesel engines performance and pollutant emissions. SAE Paper no. 2003-01-1060; 2003. Pariotis EG, Hountalas DT. Validation of a newly developed quasi-dimensional combustion model application on a heavy duty DI diesel engine. SAE Paper no. 2004-01-0923; 2004. Rakopoulos CD, Kosmadakis GM, Pariotis EG. Critical evaluation of current heat transfer models used in CFD in-cylinder engine simulations and establishment of a comprehensive wall-function formulation. Appl Energy 2010;87(5):161230. Rakopoulos CD, Kosmadakis GM, Demuynck J, De Paepe M, Verhelst S. A combined experimental and numerical study of thermal processes, performance and nitric oxide emissions in a hydrogen-fueled spark-ignition engine. Int J Hydrogen Energy 2011;36(8):516380. Rakopoulos CD, Kosmadakis GM, Dimaratos AM, Pariotis EG. Investigating the effect of crevice flow on internal combustion engines using a new simple crevice model implemented in a CFD code. Appl Energy 2011;88(1):11126. Rakopoulos CD, Kosmadakis GM, Pariotis EG. Evaluation of a new computational fluid dynamics model for internal combustion engines using hydrogen under motoring conditions. Energy 2009;34(12):215866. Rakopoulos CD, Kosmadakis GM, Pariotis EG. Evaluation of a combustion model for the simulation of hydrogen spark-ignition engines using a CFD code. Int J Hydrogen Energy 2010;35(22):1254560. Rakopoulos CD, Kosmadakis GM, Pariotis EG. Simulation of a motored internal combustion engine using an improved CFD code. In: Proceedings of the 23rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2010), 2010 June 1417, Lausanne, Switzerland, Paper No. 295. Borgnakke C, Arpaci VS, Tabaczynski VS. A model for the instantaneous heat transfer and turbulence in a spark ignition engine. SAE Paper no. 800287; 1980. Nefischer A, Hallmannsegger M, Wimmer A, Pirker G. Application of a flow field based heat transfer model to hydrogen internal combustion engines. SAE Int J Engines 2009;2(1):1251 63. Annand WJD. Heat transfer in the cylinders of reciprocating internal combustion engines. Proc Inst Mech Engrs 1963;177:97390. Rakopoulos CD, Antonopoulos KA, Rakopoulos DC, Hountalas DT. Multi-zone modeling of combustion and emissions formation in DI diesel engine operating on ethanol-diesel fuel blends. Energy Convers Manage 2008;49(4):625-43. Demirdzic I, Gosman AD, Issa RI, Peric M. A calculation procedure for turbulent flow in complex geometries. Computer Fluids 1987;15(3):25173. Han Z, Reitz RD. Turbulence modeling of internal combustion engines using RNG k- models. Combust Sci Technol 1995;106(4):26795. Jennings MJ, Morel T. Observations on the application of the k- model to internal combustion engine flows. Combust Sci Technol 1988;58(1):17793. Ferziger JH, Peric M. Computational methods for fluid dynamics. Berlin, Germany: Springer; 2002.
[18] McBride BJ, Zehe MJ, Gordon S. NASA Glenn coefficients for calculating thermodynamic properties of individual species. NASA/TP2002-211556; September 2002 Available at: <http://gltrs.grc.nasa.gov/reports/2002/TP-2002-211556.pdf> [accessed 04.02.2011]. [19] Rakopoulos CD, Kosmadakis GM, Pariotis EG. Investigation of piston bowl geometry and speed effects in a motored HSDI diesel engine using a CFD against a quasi-dimensional model. Energy Convers Manage 2010;51(3):47084.
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Working at AEG MANAGEMENT BROOKLYN LLC: Employee Reviews | Indeed.com
AEG MANAGEMENT BROOKLYN LLC Employee Reviews
(all)Security Guard - 4Security Supervisor - 1Shift Manager - 1Usher/Ticket Taker - 1
(all)United States - 7 Brooklyn, NY - 6 New York, NY - 1
Don't work at barclays center
Security Supervisor (Former Employee) – Brooklyn, NY – May 5, 2018
you won't be there for much long.
you'll be a target working there.
they don't value their security guards at all.
they value people in the venue more than the people protecting the company.
for example a jay z concert will have magnetometers but special performers don't let their people get checked.
it was really dangerous working there for low pay.
meeting celebrities, networking, free concerts tickets to events
horrible management no team work between other departments
Security Guard (Former Employee) – Brooklyn, NY – October 21, 2018
AEG Management Brooklyn LLC is dedicated to mange part of the Human resources at the Barclay's Center that provides entertainment to the community. Its a fast moving environment that requires dedication and alertness.
Shift Supervisor (Former Employee) – Brooklyn, NY – September 11, 2018
Very nice place to work at but I'm afraid thats where it stops. not flexible with scheduling. all of my corrective actions came from hr errors in scheduling me on days i went to schools, & with little room to grow inside i wouldn't see myself there unless is aw a change professionally
everything else: short breaks, last minute extended shifts
Nice place with world class entertainment
Ticket Taker (Current Employee) – Brooklyn, NY – January 4, 2018
It’s ok, very friendly co- workers and management. Easy going environment with friendly guest from all around the world. Can be used as a stepping stone towards a promising future in customer service.
Security Guard (Former Employee) – New York, NY – April 18, 2017
this job is one of the best job anybody can have if they like entertainment.what i love about this job, was meeting new people everyday and also got to meet a lot of celebrity that i though i will never meet and also i got to talk to some of them.
Entertaining work atmosphere
Security Guard (Current Employee) – Brooklyn, NY – November 20, 2016
The employment area is fun to be around because of constant involving entertaining activities, but their hours of employment is limited for part-time employees.
Invovlement with guests and visitors
Reduced hours for monthly schedule.
Security Officer/ Fire Guard (Current Employee) – Brooklyn, NY – August 22, 2015
This is not a career it is a need job. I will have like a full time positionwith them but it is very hard to go into. I like the events schedule grreat part time job.
Ask a question about working or interviewing at AEG MANAGEMENT BROOKLYN LLC. Our community is ready to answer.
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Lowest price$147$122
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A gorgeous Get Well or Good Luck card to wish your special person better or Good Luck for an operation .
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Hands On Learning-Cynthia Andrews Otr-L - Pleasant Hill, CA | Groupon
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Natural & Organic Solid Gold Dog Food - Walmart.com
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Canadian man Jean Béliveau travelled the world on FOOT for over 11 years | Daily Mail Online
Canadian globetrotter Jean Béliveau left Montreal and visited 64 countries
He wore 54 pairs of shoes and stayed in temples, parks and even jail
The trip was taken to promote peace and non-violence towards children
Journey believed to be longest uninterrupted circumnavigation on foot
Published: 03:02 EST, 16 September 2015 | Updated: 07:46 EST, 16 September 2015
Into the wild: After a 'midlife crisis' while walking across Jacques Cartier Bridge in Montreal, Jean realised he wanted to walk the whole world. Pictured in Cape Town, South Africa, in 2003
The call of the road was both a blessing and a hardship for Jean at times. Pictured in Genu, Iran, in 2007
Great walker: The 75,500km route Jean took on foot, during his 11 year, two-month odyssey
JEAN BELIVEAU'S 11 YEAR JOURNEY
Travelled to: 64 countries
Walked: 75,554 kilometres
Wore: 54 pairs of shoes
Journey took: 11 years and 2 months
Stayed with: 1600 families
Left: 45 years old on Aug 18th 2000
Route: Montreal > Brazil >South Africa >Egypt >Morocco >Europe( Portugal,Spain, France, Ireland, Scotland, England, Germany) >Turkey >Iran > India > China > Japan >Taiwan > Borneo> Java >Australia
Exotic life of the kings of Benin: The land where subjects... Could you shoot the cover for National Geographic? Iconic...
Multiple honeymoons: Luce travelled 11 times to meet Jean describing each trip as a new honeymoon. Pictured is a visit to Kande Beach, Malawi
Jean walked to promote peace and raise awareness of non-violence for children. This picture of people he met on the way was taken in Morocco in 2005
Wonders of the world: In March 2005, Jean was mesmerised by the Great Sphinx and the Pyramids in Egypt
Reunion: Luce and Jean met up for Christmas in 2008 in Kenting, Taiwan
Remarkable encounters: Jean met four Nobel Prize winners during his journey including Nelson Mandela in Durban, South Africa, in 2003 (pictured)
Self doubt: Pictured in July 2004 in Ethiopia, Jean felt extremely lonely and as though he wanted to give up
Jean covered approximately 20 miles a day and crossed six deserts over the years. He is pictured in Shiraz, Iran in 2007
Jean's wife Luce, family and a few friends gathered to witness his first departing steps (right) At the US-Mexico border in 2001 (left) during the early days of his adventure
Family time: Jean met up with his family during his journey in June 2005, his son Thomas-Eric went with him through Sfax, Tunisia
In August 2006, his son Thomas-Eric also joined him for a walk around Hass, Scotland
Saying goodbye to America before heading to Africa: Pictured in São Paulo, Brazil in June 2003, Ron Davidson, Consul General of Canada helps Jean transfer his luggage in his new buggy
Luck of the Irish: In 2006, Jean entered the green land of Ireland. Pictured he walks up hill in Kinsale
On the adventure Jean visited Dasuz Azerbaijan in 2007 (pictured). Taking on many terrains, he wore out 54 pairs of shoes during his trip
Walk around the Earth: The circumference of the planet at the Equator is 40.075 km. When Jean reached that symbolic mark in Hungary, locals organised festivities to celebrate the feat
Jean said he was hosted by 1600 families and also slept in temples, parks and even jail cells. Pictured is his tent pitched in the wilderness near Baku Azerbaijan in 2007
Of the 64 countries visited, Jean's journey took him to Darab, Iran in October 2007
The grand return: 11 years and 2 months later, Jean walks home to Montreal, to his home and his family
WWWalk | Jean B�liveau » World Wide Walk for Peace and Children

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Pear Apple Crisp #summerfest | Let's Pull Up a Chair | Pinterest | Pear, Apples and Crisp recipe
Pear Apple Crisp - Recipe is Not completely gluten free but I'm sure it can be adapted
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Easy, Festive Pear Crisp -- Very tasty!
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Cranberry-Pear Crisp This looks great! I can't wait to try it!
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The finished product! Step Open up pantry. Take out canned pears, and let the fun begin.
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I modified a traditional Spanish apple cake recipe to make this grain-free summer treat. You can use any summer fruit in it instead of peaches, or you can saute apples or pears in some of the butter for a warm autumn delight.
Granola-Apple-Cherry Crisp
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granola apple cherry crisp modify with fresh apples
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Pear-Cranberry Pie with Oatmeal Streusel from Cooking Light. If you can't find fresh cranberries, use thawed frozen ones. A prepared pie shell yields a stellar dessert with little effort. Serve with vanilla ice cream.
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Spicy pear cake - you can find this recipe in the book No Naughties at Christmas: Sweet treat without sugar, wheat, gluten, yeast and lactose
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Cherry and Plum Crisp

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Patent US20030142810 - Automated phone priorities - Google Patents
A method of automatically routing a telephone call in a pharmacy, comprising the steps of: activating a telephone when a pharmacy employee signs onto a workstation; identifying a position type for the pharmacy employee, wherein the position type is associated with a set of functions available to the...http://www.google.com/patents/US20030142810?utm_source=gb-gplus-sharePatent US20030142810 - Automated phone priorities
Publication number US20030142810 A1
Application number US 10/056,641
Also published as US6891946
Publication number 056641, 10056641, US 2003/0142810 A1, US 2003/142810 A1, US 20030142810 A1, US 20030142810A1, US 2003142810 A1, US 2003142810A1, US-A1-20030142810, US-A1-2003142810, US2003/0142810A1, US2003/142810A1, US20030142810 A1, US20030142810A1, US2003142810 A1, US2003142810A1
Inventors Dejan Kozic, Karl Meehan
Original Assignee Dejan Kozic, Meehan Karl E.
US 20030142810 A1
activating a telephone when a pharmacy employee signs onto a workstation;
identifying a position type for the pharmacy employee, wherein the position type is associated with a set of functions available to the pharmacy employee;
assigning a phone priority to the pharmacy employee after the pharmacy employee signs onto the workstation;
identifying a type of phone call associated with a caller;
evaluating a set of resources available to handle the type of phone call;
directing the caller to an available pharmacy employee having the position type preferred to handle the call type and having the highest phone priority;
placing the caller in a hierarchical holding cue if no pharmacy employee having the position preferred to handle the call type is available until a pharmacy employee having the position type preferred to handle the call type becomes available; and
deactivating the telephone when the pharmacy employee signs off of the workstation.
2. The method of claim 1, wherein the step of activating the telephone when the pharmacy employee signs onto the workstation is repeated to allow the pharmacy employee to sign onto a plurality of workstations, thus activating a plurality of telephones.
4. The method of claim 1, wherein the type of call is selected from the group of calls consisting of a prescriber call and a patient call.
5. The method of claim 1, wherein the position type is selected from the group of position types consisting of pharmacists and technicians.
6. The method of claim 1, wherein the workstation comprises a controller, a display, a data input apparatus, and a telephone.
7. The method of claim 1, wherein the step of identifying the type of phone call associated with the caller is performed with the use of an automated attendant.
8. The method of claim 1, further comprising the step of automatically updating the phone priority for at least one pharmacy employee when the pharmacy employee signs off of the workstation.
9. The method of claim 1, further comprising the step of automatically assigning another position type, that is associated with another set of functions available to the pharmacy employee, to the pharmacy employee when there is only one pharmacy employee of each position type signed onto the system.
10. A method of automatically routing a telephone call in a pharmacy, comprising the steps of:
identifying whether the pharmacy employee is a pharmacist or a technician;
identifying whether the telephone call is from a prescriber or from a patient;
evaluating a set of resources capable of handling the telephone call, wherein the set of resources comprises one or more pharmacy employees that have each signed onto a workstation;
directing the telephone call to an available pharmacist if the telephone call is from a prescriber;
directing the telephone call to one of an available technician and an available pharmacist if the telephone call is from a patient;
placing the caller in a hierarchical holding cue if the set of resources capable of handling the telephone call does not have an available pharmacy employee, until a pharmacy employee from the set of resources capable of handling the telephone call becomes available; and
11. The method of claim 10, wherein the step of activating the telephone when the pharmacy employee signs onto the workstation may be repeated to allow the pharmacy employee to sign onto a plurality of workstations, thus activating a plurality of telephones.
12. The method of claim 10, further comprising the step of allowing a manual increase or a manual decrease of the phone priority assigned to the pharmacy employee.
13. The method of claim 10, wherein the workstation comprises a controller, a display, a data input apparatus, and a telephone.
14. The method of claim 10, wherein the step of identifying whether the telephone call is from a prescriber or from a patient is performed with the use of an automated attendant.
15. The method of claim 10, further comprising the step of automatically updating the phone priority for at least one pharmacy employee when the pharmacy employee signs off of the workstation.
16. The method of claim 10, wherein the step of directing the telephone call to one of an available technician and an available pharmacist if the telephone call is from a patient has a preferred order comprising first attempting to direct the telephone call to a technician if a technician is available, then directing the telephone call to a pharmacist if the set of resources capable of handling the call includes a pharmacist that has agreed to receive calls from patients.
17. A system for automatically routing a telephone call in a pharmacy for an organization having a processor, the system comprising:
a first software routine stored in the memory and adapted to be executed on the processor to execute the step of activating a telephone when a pharmacy employee signs onto a workstation;
a second software routine stored in the memory and adapted to be executed on the processor to execute the step of identifying a position type for the pharmacy employee, wherein the position type is associated with a set of functions available to the pharmacy employee;
a third software routine stored in the memory and adapted to be executed on the processor to execute the step of assigning a phone priority to the pharmacy employee after the pharmacy employee signs onto the workstation;
a fourth software routine stored in the memory and adapted to be executed on the processor to execute the step of identifying a type of phone call associated with a caller;
a fifth software routine stored in the memory and adapted to be executed on the processor to execute the step of evaluating a set of resources available to handle the type of phone call;
a sixth software routine stored in the memory and adapted to be executed on the processor to execute the step of directing the caller to an available pharmacy employee having the position type preferred to handle the call type and having the highest phone priority;
a seventh software routine stored in the memory and adapted to be executed on the processor to execute the step of placing the caller in a hierarchical holding cue if no pharmacy employee having the position preferred to handle the call type is available until a pharmacy employee having the position type preferred to handle the call type becomes available; and
an eighth software routine stored in the memory and adapted to be executed on the processor to execute the step of deactivating the telephone when the pharmacy employee signs off of the workstation.
18. The system of claim 17, wherein the first software routine stored in the memory and adapted to be executed on the processor to execute the step of activating a telephone when a pharmacy employee signs onto a workstation is repeated to allow the pharmacy employee to sign onto a plurality of workstations, thus activating a plurality of telephones.
19. The system of claim 17, wherein the system further comprises a ninth software routine stored in the memory and adapted to be executed on the processor to execute the step of allowing a manual increase or a manual decrease of the phone priority assigned to the pharmacy employee.
20. The system of claim 17, wherein the fourth software routine stored in the memory and adapted to be executed on the processor is adapted to identify the type of call from the group of calls consisting of a prescriber call and a patient call.
21. The system of claim 17, wherein the second software routine stored in the memory and adapted to be executed on the processor is adapted to select the position type from the group of position types consisting of pharmacists and technicians.
22. The system of claim 17, wherein the fourth software routine stored in the memory and adapted to be executed on the processor is adapted to be performed with the assistance of an automated attendant.
23. The system of claim 17, further comprising a ninth software routine stored in the memory and adapted to be executed on the processor to execute the step of automatically updating the phone priority for at least one pharmacy employee when the pharmacy employee signs off of the workstation.
24. The system of claim 17, further comprising a ninth software routine stored in the memory and adapted to be executed on the processor to execute the step of automatically designating another position type, that is associated with another set of functions available to the pharmacy employee, to the pharmacy employee when there is only one pharmacy employee of each position type signed onto the system.
[0004]FIG. 1 is a block diagram of an embodiment of an intelligent network system in accordance with the invention;
[0005]FIG. 2 is a schematic diagram of an embodiment of one of the facilities shown schematically in FIG. 1;
[0006]FIG. 3 is a flowchart of some of the steps used in assigning phone priorities for an automatic telephone routing system in accordance with an embodiment of the invention;
[0007]FIGS. 4A and 4B are two parts of a flowchart of an embodiment of a main routine that may be performed during operation of an automatic telephone routing system; and
[0008]FIG. 5 is an exemplary graphic representation of a user interface in accordance with an embodiment of the invention.
[0009]FIG. 1 illustrates an embodiment of a pharmacy data network 2 in accordance with the invention. Referring to FIG. 1, the pharmacy data network 2 may include a first group of stores or facilities 20 operatively coupled to a network computer 30 via network data link 32. The plurality of stores 20 may be located in separate geographic locations from each other and may include an automatic telephone routing system 10. The stores 20 may be located in different areas of the same city, or they may be located in different states. The network 32 may be provided using a wide variety of techniques well know to those skilled in the art for the transfer of pharmacy data. For example, the network 32 may comprise dedicated access lines, plain, ordinary telephone lines, satellite links, combinations of these, etc. Additionally, the network 32 may include a plurality of network computers or server computers (not shown), each of which may be operatively interconnected. Where the network 32 comprises the Internet, data communication may take place over the communication link 32 via an Internet communication protocol.
[0012]FIG. 2 is a schematic diagram of one possible embodiment of an automatic telephone routing system 10 located in one or more of the stores 20 from FIG. 1. Although the following description addresses the design of the stores 20, it should be understood that the design of one or more of the stores 20 may be different than the design of other stores 20. Also, each store 20 may have various different structures and methods of operation. It should also be understood that the embodiment shown in FIG. 2 illustrates some of the voice and data connections present in a pharmacy section of a store, however it does not illustrate all of the voice and data connections present in a typical store. For exemplary purposes, various designs of the stores are described below, but it should be understood that numerous other designs may be utilized.
[0022]FIG. 3 illustrates some of the steps of an operating routine or subroutine 175 that may be stored in the memory of the controller 100. The routine 175 may be performed in preparation of the pharmacy receiving telephone calls from customers and may continue running during normal operating hours of the pharmacy. The routine 175 may begin operation at a block 180 during which a telephone is activated when a pharmacy employee signs onto a workstation. The sign on process may include entering a user name and a password associated with the pharmacy employee.
[0027]FIGS. 4A and 4B are two parts of a flow chart of a main operating routine 200 that may be stored in the memory of the controller 100. The main routine 200 may be utilized for automatic telephone routing systems that are designed to channel customers to the appropriate technicians and to channel prescribers to the appropriate pharmacist, in order to maximize the internal efficiency of the pharmacy portion of the store 20. Referring to FIG. 4A, the main routine 200 may begin operation when a customer calls a store and is directed to an automated attendant that is connected to a telephone switch, as shown at a block 202. For the purpose of this description, the term “customer” is intended to include all callers to the pharmacy, for example, patients, physicians, nurses, physician assistants, insurers, etc.
[0032]FIG. 4B is a second half of the flow chart of the main operating routine 200. After it is determined at the block 210 that the telephone call is not from a prescriber, (i.e., a patient, an insurer, etc.) the system may evaluate a set of resources at a block 250 that are available or capable of handling the type of telephone call. The set of resources may comprise one or more pharmacy employees that have each signed onto a work station. If it is determined at a block 252 that the technician with the highest phone priority is signed onto a work station and available, then the system may direct the patient or other caller to the available technician having the highest priority as shown at a block 254. After directing the telephone call to the technician with the highest phone priority at the block 254, the system may be updated to set the technician's status to “busy” at a block 256, check at a block 254 to see if there are multiple technicians available.
U.S. Classification 379/265.04
Cooperative Classification G06Q10/02, H04M3/5232, G06Q50/24, G06Q50/22, G06Q40/08, H04M2242/30
European Classification G06Q40/08, G06Q50/22, G06Q10/02, G06Q50/24, H04M3/523D
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:KOZIC, DEJAN;MEEHAN, KARL E.;REEL/FRAME:012789/0119

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