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Natural or buoyant convection flow is an exemplary heat transfer phenomenon, with growing applications in various industries. This article develops a new algorithm, which models and solves the buoyancy-driven turbulent flows in enclosures more accurately than the past similar solvers. A careful literature review shows that the past existing approaches have mostly had serious limitations to apply their algorithms to buoyancy-driven flows with high temperature differences magnitude because of employing the classical Boussinesq approximation. As the novelty of this study, it benefits from a momentum-based variable approach in the context of the semi-implicit method for the pressure linked equations (SIMPLE) algorithm, which lets it accurately solve the strong compressible buoyant flows with high temperature differences. The algorithm is applied to both the Navier-Stokes and the accompanied turbulent flow governing equations using OpenFOAM 4.1 as the platform. To validate the developed algorithm, the current results are compared with experimental data in both square and tall cavities considering low (8.6 × 105), high (1.43 × 106), and very high (1.58 × 109) Rayleigh numbers. As the major contribution of this work, it improves the accuracy of the thermo-buoyant turbulent flow prediction at both low and high Rayleigh numbers. All test cases are carried out employing two different turbulence models of k-ω and k-ε. Furthermore, comparing the results of the present non-Boussinesq algorithm and those of the past developed methods with the experimental data, it is shown that the present algorithm provides a more accurate prediction for the temperature field, that is, <10% differences with the experimental data. Moreover, the present maximum velocity results surpass the solution of the past numerical methods and show <3% differences with the experimental data. [ABSTRACT FROM AUTHOR]

The heat recovery steam generator (HRSG) can be regarded as a heat exchanger, which uses the heat rejected from the gas cycle to supply steam for the vapor cycle. HRSGs are consisted of many tube bundles (or harps). Supplying a duct burner in the upstream of an HRSG necessitates constructing the first bundle using different finned-tube materials with dissimilar fin configurations including unequal fin-pitch sizes. The computational fluid dynamics (CFD) has been widely used to study the flow through tube bundles; however, considering a fixed fin-pitch size for all rows of the finned-tubes. There is no trace of numerical analyses to treat tube bundles with unequal fin pitch sizes, apparently, because there are major challenges for the classical strategies, say unified-domain approach, to simulate tube bundles constructed from tubes with different fin configurations. This work introduces the splitted-domain approach as an innovative strategy to pass over the challenges with using the unified-domain approach. Then, these two approaches are used to model the superheater module of a real HRSG with available online measured data. The calculated Nusselt numbers and pressure drops indicate that the splitted-domain approach provides more accurate results than the unified one in simulating tube bundles constructed from tubes with dissimilar fin configurations. [ABSTRACT FROM AUTHOR]

One main concern of this work is to develop an efficient particle‐tracking‐managing algorithm in the framework of a hybrid pressure‐based finite‐volume/probability‐density‐function (FV/PDF) Monte‐Carlo (MC) solution algorithm to extend the application of FV/PDF MC methods to absolutely incompressible flows and speedup the convergence rate of solving the fluctuating velocity‐turbulent frequency joint PDF equation in turbulent flow simulations. Contrary to the density‐based algorithms, the pressure‐based algorithms have stable convergence rates even in zero‐Mach number flows. As another contribution, literature shows that the past developed methods mostly used mesh searching techniques to attribute particles to cells at the beginning of each tracking time‐step. Also, they had to calculate the linear basis functions at every time‐step to estimate the particle mean fields and interpolate the data. These calculations would be computationally very expensive, time‐consuming, and inefficient in computational domains with arbitrary‐shaped 3D meshes. As known, the barycentric tracking is a continuous particle tracking method, which provides more efficiency in case of handling 3D domains with general mesh shapes. The barycentric tracking eliminates any mesh searching technique and readily provides the convenient linear basis functions. So, this work benefits from these advantages and tracks the particles based on their barycentric coordinates. It leads to less computational work and a better efficiency for the present method. A bluff‐body turbulent flow case is examined to validate the present FV/PDF MC method. From the accuracy perspective, it is shown that the results of the present algorithm are in great agreement with experimental data and available numerical solutions. The present study shows that the number of particle time‐steps required to reach the statistically steady‐state condition is at least one‐sixth less than the previously developed algorithms. This also approves a faster convergence rate for the present hybrid pressure‐based algorithm. [ABSTRACT FROM AUTHOR]

AbstractNowadays, heat-exchangers in general and shell-and-tube heat-exchangers (STHEs) in particular have crucial influences on the performances of various industry processes. Apparently, the STHE designers wish to design STHEs with optimal performances under various operating conditions. Literature shows that previous research works relied entirely on either experiments or numerical simulations to calculate the hydrothermal performances of STHEs. Alternatively, this study introduces an innovative efficient procedure, which benefits from both numerical simulations and analytical approaches to accurately predict the STHE thermal performance at its wide ranges of proposed working conditions. This procedure involves three main steps. First, numerical simulations are utilized to develop accurate numerical-based correlations for both smooth and dimpled tubes for 3,000 < Re < 30,000 and 7 < Pr < 70 ranges. Second, the P-NTU analytical approach is used to construct a full STHE model based on its specific geometrical characteristics. Third, the developed correlations are suitably inserted in the P-NTU model to produce correlation for the entire STHE and calculate its thermal performance under various operating conditions. It is shown that the achieved results are as accurate as the experimental data. As the novelty, this study eliminates the need for expensive experimental setups and reduces the computational costs due to benefiting from analytical correlations. Since the present procedure is cost-effective, the designers are encouraged to design STHEs with more optimum thermal performances within their working condition ranges via suitable choices of various their design parameters, such as the tube type and size and the STHE general configuration. Fortunately, the present procedure can be similarly extended to design and optimize the thermal performances of other similar thermal equipment. [ABSTRACT FROM AUTHOR]

It is very common in the heat transfer area to analyze and design heat equipment using the past available heat correlations. Basically, demanding higher‐accuracy correlations enforces the heat laboratories to test and collect larger banks of laboratory data. However, this conversely affects the laboratory cost. Therefore, it becomes challenging to create new approaches that let the correlation developers use smaller experimental datasets and provide correlations with sufficient accuracies. To surmount this challenge, the present work develops a new approach that benefits from the computational fluid dynamics method as a reliable and cheap tool and adequately enriches the original, insufficient dataset. Then, suitable enhanced correlations are developed using the new enriched experimental‐numerical‐based dataset. In parallel, the artificial neural network (ANN) is used to enrich the original insufficient dataset separately. Using this experimental‐ANN‐based dataset, it provides a totally ANN‐based correlation. It is shown that the results of enhanced correlations are as accurate as those of the ANN‐based correlation. However, the point is that the use of the present approach is about 100 times faster than using the ANN. The typical forced convection heat transfer through a pipe is examined here to show the capabilities of the current approach. [ABSTRACT FROM AUTHOR]

The heat transfer simulations of turbine blades with internal cooling are faced with so many uncertainties, of which some originate from the secondary air system, including the inlet hot gas temperature and pressure and the cooling side boundary conditions, and the blade material. The main objective of this work is to carry out a suitable sensitivity analysis on a specific novel turbine vane to improve the thermal performance of its internal cooling system and to quantify how the uncertainties on the designed/calculated values can desirably/undesirably affect the maximum blade surface temperature, which can consequently affect the gas turbine engine efficiency. Furthermore, the sensitivity analysis is carried out to find the effects of uncertainties of a number of key parameters on the resulting blade temperature distribution. To arrive at trustworthy conclusions, the conjugate heat transfer (CHT) method is used to analyze the heat and fluid flow behavior. This work suitably develops a CHT-based method/solver to perform the proposed study. This method/solver uses a segregated iterative procedure, in which the outer hot gas region is simulated using the computational fluid dynamics (CFD) solver, the flow passing through the connected internal cooling passages is calculated using a 1D correlated-based solver, and the vane conduction is predicted using a 3D finite-element solver, which are fully coupled. The results show that the cooling channel wall temperature has a direct impact on the convective coefficient magnitude; especially in lower temperature regions. As a novel contribution, this work takes into account the cooling wall temperature influence on the 1D code calculations. To implement this, an artificial neural network is suitably trained to predict better convective coefficient. The results of this developed CFD-CHT code are validated against experimental data available for a benchmark vane. Eventually, the sensitivity analysis is carried on the present specific novel turbine vane. [ABSTRACT FROM AUTHOR]

The complex nature of the convective terms in the fluid flow governing equations has caused many researchers to work on estimating more accurate advection term magnitudes by employing better approximate expressions. The trend of progress has been to include more physics of flow in the approximations. However, there is no archival evidence that reports the direct implementation of buoyancy as a physical influence in the advection term modeling. In this work, the influence of buoyancy in an efficient advection term expression is investigated in a control volume context with a collocated grid arrangement. The accuracy of the results obtained by both excluding and including the buoyancy term is evaluated through application to the natural convection heat transfer problem in a cavity at different Rayleigh numbers. [DOI: 10.1115/1.1569508]