Search

Accurate prediction of temperature distribution during thermotherapy is a significant factor in the thermotherapy process. Thermotherapy equipment produces a different distribution spatial and time-dependent heat fluxes in the thermotherapy processes. This paper presents an exact analytical solution of steady and unsteady Pennes and porous bioheat equations in a cylindrical coordinate system for multi-layer skin with different spatial and time-dependent heat fluxes on the surface. The proposed analytical solutions are useful to make accurate temperature distribution in multi-layer skin tissue with various properties. The results show that the unsteady temperature distributions in both Pennes and porous models are the same in the initial times of process. When the temperature rises, the cooling effect of blood perfusion in the Pennes model increases the difference in temperature distribution for these two models. The skin surface temperature is linear versus time in constant and linear fluxes, and skin temperature increment has a second-degree trend versus time in the quadratic flux. The magnitude of the flux coefficient and the time of applying flux to the tissue are effective in increasing the temperature of the tissue and the skin surface.

Purpose The purpose of this paper is to present detailed algorithms for simulation of individual and group control of production wells in hydrocarbon reservoirs which are implemented in a finite volume-based reservoir simulator. Design/methodology/approach The algorithm for individual control is described for the multi-lateral multi-connection ones based on the multi-segment model considering cross-flow. Moreover, a general group control algorithm is proposed which can be coupled with any well model that can handle a constraint and returns the flow rates. The performance of oil production process based on the group control criteria is investigated and compared for various cases. Findings The proposed algorithm for group control of production wells is a non-optimization iterative scheme converging within a few number of iterations. The numerical results of many computer runs indicate that the nominal power of the production wells, in general, is the best group control criterion for the proposed algorithm. The production well group control with a proper criterion can generally improve the oil recovery process at negligible computational costs when compared with individual control of production wells. Research/limitations/implications Although the group control algorithm is implemented for both production and injection wells in the developed simulator, the numerical algorithm is here described only for production wells to provide more details. Practical/implications The proposed algorithm can be coupled with any well model providing the fluid flow rates and can be efficiently used for group control of production wells. In addition, the calculated flow rates of the production wells based on the group control algorithm can be used as candidate solutions for the optimizer in the simulation-optimization models. It may reduce the total number of iterations and consequently the computational cost of the simulation-optimization models for the well control problem. Originality/value A complete and detailed description of ingredients of an efficient well group control algorithm for the hydrocarbon reservoir is presented. Five group control criteria are extracted from the physical, geometrical and operating conditions of the wells/reservoir. These are the target rate, weighted potential, ultimate rate and introduced nominal power of the production wells. The performance of the group control of production wells with different group control criteria is compared in three different oil production scenarios from a black-oil and highly heterogeneous reservoir.

Different families of the Discrete Vapour Cavity Model (DVCM) are developed, including the frictionless, steady and quasi-steady friction models. A relaxation-dissipation approach is proposed to improve the timing of pressure pulses predicted by the classic DVCM. In this approach, a friction correction factor is introduced into the steady/quasi-steady friction term to reduce the local value of the dissipation term in regions facing with cavitation. The proposed approach is completely consistent with the classical water-hammer framework. The importance of the steady/quasi-steady friction term is investigated by comparing numerical results of different DVCMs with the experimental data for various cavitation problems. Based on a frictionless study, it is shown that there exists an unrealistic attenuation in pressure pulses of the classic DVCM. For problems with high-intensity cavitation, it is shown that the frictionless, steady and quasi-steady friction models generally produce different results, especially in terms of the pressure pulses timing. Within the range described in the manuscript, the timing of the classic DVCM pressure pulses can generally be improved by applying the proposed relaxation-dissipation approach on the steady/quasi-steady friction term.

Regarding their ability to enhance conventional thermal oils' thermophysical properties, oil-based hybrid nanofluids have recently been widely investigated by researchers, especially on lubrication and cooling application in the automotive industry. Thermal conductivity is one of the most crucial thermophysical properties of oil-based hybrid nanofluids, which has been studied in a minimal case of studies on the specific types of them. In this research, for the first time, a comprehensive data-intelligence analysis performed on 400 gathered data points of various types of oil-based hybrid nanofluids using a novel hybrid machine learning approach; the Extended Kalman Filter-Neural network (EKF-ANN). The genetic programming (GP) and response surface methodology (RSM) approaches were examined to appraise the main paradigm. In this research, the best subset regression analysis, as a novel feature selection scheme, was provided for finding the best input parameter among all existing predictive variables (the volume fraction, temperature, thermal conductivity of the base fluid, mean diameter, and bulk density of nanoparticles). The provided models were examined using several statistical metrics, graphical tools and trends, and sensitivity analysis. The results assessment indicated that the EKF-ANN in terms of (R = 0.9738, RMSE = 0.0071 W/m.K, and KGE = 0.9630) validation phase outperformed the RSM (R = 0.9671, RMSE = 0.0079 W/m.K, and KGE = 0.9593) and GP (R = 0.9465, RMSE = 0.010 W/m.K, and KGE = 0.9273), for accurate estimation of the thermal conductivity of oil-based hybrid nanofluids.

In the present work, the performance of proton exchange membrane fuel cells is studied for three cases; A fuel cell with two parallel flow channels (model A), locally baffle restricted flow channels (model B), and metal foam as a flow distributor (model C). The fully coupled thermal-electrochemical equations are numerically solved in three dimensions, based on the macroscopic, single-domain, and finite-volume approaches. While having no significant effect on temperature distribution, the existence of baffles inside flow channels results in more oxygen penetration into gas diffusion and catalyst layers at the cathode side of the cell. This improves the chemical reaction rate, current density and cell performance. Using metal foam increases oxygen concentration and current density at the cathode catalyst surface, and improves the uniformity of their distributions. Furthermore, a more uniform temperature distribution is achieved, when compared with the other cases. For the considered dimensions, it is observed that decreasing the flow channel depth results to an increase in current density and also in pressure drop along channels (models A and C). Moreover, increasing metal foam porosity can increase the current density value and decrease pressure drop in model C, while it has nearly no effects on temperature distribution.

Purpose – The purpose of this paper is to present a detailed algorithm for simulating three-dimensional hydrocarbon reservoirs using the blackoil model. Design/methodology/approach – The numerical algorithm uses a cell-centred structured grid finite volume method. The blackoil formulation is written in a way that an Implicit Pressure Explicit Saturation approach can be used. The flow field is obtained by solving a general gas pressure equation derived by manipulating the governing equations. All possible variations of the pressure equation coefficients are given for different reservoir conditions. Key computational details including treatment of non-linear terms, expansion of accumulation terms, transitions from under-saturated to saturated states and vice versa, high gas injection rates, evolution of gas in the oil production wells and adaptive time-stepping procedures are elaborated. Findings – It was shown that using a proper linearization method, less computational difficulties occur especially when free gas is released with high rates. The computational performance of the proposed algorithm is assessed by solving the first SPE comparative study problem with both constant and variable bubble point conditions. Research limitations/implications – While discretization is performed and implemented for unstructured grids, the numerical results are presented only for structured grids, as expected, the accuracy of numerical results are best for structured grids. Also, the reservoir is assumed to be non-fractured. Practical implications – The proposed algorithm can be efficiently used for simulating a wide range of practical problems wherever blackoil model is applicable. Originality/value – A complete and detailed description of ingredients of an efficient finite volume-based algorithm for simulating blackoil flows in hydrocarbon reservoirs is presented.