Predicted Velocity Profiles Research Articles

A numerical study on effects of current density distribution, turbulence, and magnetohydrodynamics (MHD) on electrolytic gas flow with application to alkaline water electrolysis (AWE)

A three-phase Eulerian model is proposed to investigate the induced flow due to the generation of gas bubbles between two parallel plates without forced convection with application to alkaline water electrolysis (AWE). Earlier models, assuming a laminar regime, accurately predicted the multiphase flow near electrodes but struggled to calculate bulk liquid electrolyte flow away from them. Herein, we study the influences of electric current density distribution, turbulence effects, and the interaction between flow and the magnetic field known as magnetohydrodynamics (MHD). Based on our modeling results, the traditional method using an averaged uniform current density along electrodes (e.g. here 2000 A m−2) is feasible, as incorporating calculated non-uniform current distribution minimally affects the multiphase velocity field. The Lorentz force, originating from flow interaction with the (self-induced) magnetic field, is negligible compared to forces like drag or bubble dispersion. Consequently, MHD effects only become relevant upon introducing an external magnetic field. Including turbulence in the model, being minor in magnitude but non-negligible, significantly improves the predicted velocity profile. Modeling results are validated against an experiment.

On robust boundary treatments for wall‐modeled LES with high‐order discontinuous finite element methods

SummaryTo robustly and accurately simulate wall‐bounded turbulent flows at high Reynolds numbers, we propose suitable boundary treatments for wall‐modeled large‐eddy simulation (WMLES) coupled with a high‐order flux reconstruction (FR) method. First, we show the need to impose an auxiliary boundary condition on auxiliary variables (solution gradients) that are commonly introduced in high‐order discontinuous finite element methods (DFEMs). Auxiliary boundary conditions are introduced in WMLES, where the grid resolution is too coarse to resolve the inner layer of a turbulent boundary layer. Another boundary treatment to further enhance stability with under‐resolved grids, is the use of a modal filter only in the wall‐normal direction of wall‐adjacent cells to remove the oscillations. A grid convergence study of turbulent channel flow with a high Reynolds number () shows that the present WMLES framework accurately predicts velocity profiles, Reynolds shear stress, and skin friction coefficients at the grid resolutions recommended in the literature. It was confirmed that a small amount of filtering is sufficient to stabilize computation, with negligible influence on prediction accuracy. In addition, non‐equilibrium periodic hill flow with a curved wall, including flow separation, reattachment, and acceleration at a high Reynolds number (), is reported. Considering stability and the prediction accuracy, we recommend a loose auxiliary wall boundary conditions with a less steep velocity gradient for WMLES using high‐order DFEMs.

Adaptive Sound Velocity Profile Prediction Method Based on Deep Reinforcement Learning

Adaptive Sound Velocity Profile Prediction Method Based on Deep Reinforcement Learning

Flow velocity adjustment in a channel with a floating vegetation canopy

Floating vegetation with unanchored and connected roots can completely cover the water surface of rivers, ditches, and streams. Eichhornia crassipes is a typical macrophyte with a stable network of roots that is present in aquatic environments, forming a complicated flow velocity adjustment. This study constructed model floating canopies and real E. crassipes canopies to investigate flow velocity adjustment and verify a new proposed velocity model. Laboratory experiments showed that flow velocity was modified in both the streamwise (x) and vertical (z) directions and impacted by the mean channel velocity, canopy density, and relative flow depth. The penetration distance δe of the Kelvin–Helmholtz vortices penetrating into the floating canopy increased to its maximum beyond the flow adjustment distance XD, where XD was the distance from the canopy leading edge to the position at which the flow was fully developed. The thickness of the bed boundary layer δb was related to the vegetation drag (FD(x)), the bed shear stress (τb(x)), and the height of the free-flow region below the canopy hg. New estimators for δe and δb were proposed and validated for floating canopies. Combined with the new estimators (δe and δb), flow continuity equation, momentum equation and four-layer divided method, a new analytical model was proposed to predict vertical profiles of velocity within and below a floating canopy in both the flow adjustment region (XD > x > 0) and fully developed flow region (x > XD). Fourteen groups of velocity data from this study and published literature were used to verify the proposed model. The predicted vertical profiles of velocity in flow adjustment and fully developed flow regions agreed with measurements under a wide range of vegetation and flow conditions, suggesting that the proposed model was capable of accurately predicting velocity profiles in a channel with a floating canopy. Finally, the model was validated in a channel with a real E. crassipes canopy. A comparison between predicted and measured velocities confirmed that the new model is valid for application in a channel with real floating macrophytes.

Mapping Mean Velocity Field over Bed Forms Using Simplified Empirical-Moment Concept Approach

The log-wake law was successful in mapping velocity fields for uniform flow over flat surfaces, even in cases of wake effects (velocity dips, wall effects, and secondary currents). However, natural riverbeds with undulations and bedforms challenge these models. This study introduces a moment-based empirical method for rough estimation of the velocity fields over stationary 2D bedforms. It proposes three polynomial velocity profile templates (first, fifth, and eighth orders) with coefficients deduced analytically while taking into account an array of flow conditions and assumptions, including slip velocity at the bed, mass and moment of momentum conservations, imposing inviscid potential flow near the water surface, and incorporation of near-bed shear stress utilizing a moment-based Chezy formula. Remarkably, the coefficients of these polynomials are primarily reliant on two crucial velocity scales, the depth-averaged velocity (uo) and the moment-derived integral velocity (u1), along with the dimensionless reattachment coefficient (Kr). Validation of the proposed approach comes from ten lab experiments, spanning Froude numbers from 0.1 to 0.32, offering empirical data to validate the obtained velocity profiles and to establish the relationship of the spatial variation in the normalized u1 velocity along bedforms. This study reveals that the assumption of a slip boundary condition at the bed generally enhances the accuracy of predicted velocity profiles. The eighth-order polynomial profile excels within the eddy zone and close to reattachment points, while the fifth-order profile performs better downstream, approaching the crest. Importantly, the efficacy of this approach extends beyond water flow to encompass airflow scenarios, such as airflow over a negative step. The research findings highlight that linear velocity, as employed in Vertically Averaged and Moment models (VAM), exhibits approximately 70% less velocity mismatch compared to constant Vertically Averaged (VA) models. Moreover, the utilization of the fifth-order and eighth-order velocity profiles results in substantial improvements, reducing velocity mismatch by approximately 86% and 90%, respectively, in comparison to VA models. The insights gained from this study hold significant implications for advancing vertically averaged and moment-based models, enabling the generation of approximate yet more realistic velocity fields in scenarios involving flow over bedforms. These findings directly impact applications related to sediment transport and mixing phenomena.

A virtual flow meter downstream of various elbow configurations

When flow meters are installed in disturbed flow conditions caused by pipe configurations such as junctions or elbows, significant measurement errors can occur. With existing knowledge of the flow conditions, those errors can be corrected using suitable calibration factors. This paper presents a surrogate model based on computational fluid dynamics simulation results that can predict velocity profiles downstream of single and double elbows in- and out-of-plane. The simulated flow profiles are decomposed into a fully-developed and a disturbed part, which allows changing the Reynolds number and roughness of the pipe wall without performing additional simulations. Furthermore, proper orthogonal decomposition and interpolation using piecewise linear splines enables the real-time evaluation of the model for a wide range of curvature radii and distances between the elbows. By incorporating a measurement principle, calibration factors for different flow meter designs can be predicted. The model is validated on the example of an ultrasonic flow meter by comparison with measurement data obtained from a clamp-on flow meter.

Velocity distribution characteristics for rigid vegetation model with spherical canopy: An analytical study adopting multiple mathematical methods

Vegetation exists widely in natural rivers, resulting in unique characteristics of flow movement, affecting river flood processes and water quality. The flow movement of vegetated channels is defined as vegetated flow, and is currently receiving close attention. Influenced by vegetation, the classical logarithmic velocity profile turn into a more complex function according to the vegetation morphology and flow pattern. Several research teams have studied analytical models for simple cylinder-type vegetation; however, few studies have been conducted on analytical velocity models for vegetation with spherical canopy, and previous models cannot make accurate prediction of velocity profile in this case. To address this issue, new analytical models are proposed adopting the power series, exponential and trigonometric models. A comparison between the measured velocity profile and the analytical solution confirms that the newly proposed models are suitable for vegetation with spherical canopy. This analytical model can capture the vertical non-monotonic characteristics of velocity profile, which is what the previous models lack. Moreover, in contrast to numerical models, which require a lot of computational cost, this analytical model allows a quick and intuitive description of the flow profile, which provides hydrodynamic support for the restoration of vegetated channels.

Modeling and simulation of nanofluid in low Reynolds numbers using two-phase Lattice Boltzmann method based on mixture model

In the present work, a novel two-phase scheme was implemented for modeling and simulation of Al2O3/water nanofluid within a rectangular channel with the Lattice Boltzmann method based on the mixture model. In this proposed model, a two-phase approach was adopted to consider the effect of the drift velocity of each phase. The simulations are performed in two cases at laminar flow regime and low Reynolds numbers (Re=0.5 in this case). In the first case, the results of the velocity distribution of the nanofluid through the channel were numerically validated with the finite element method results, and the predicted velocity profile at Reynolds numbers of 1, 10, and 50 showed good agreement with the velocities extracted from FEM. In the second case, the velocity distribution and particle volume fraction of the nanofluid were investigated at Reynolds numbers 0.5, 1, and 10. The results showed that considering the drift velocity in the modeling has remarkable effects only at low Reynolds numbers (Re=0.5 in this case), when the Reynolds number increases, the effect of drift velocity can be ignored. The proposed Lattice Boltzmann model is promisingly strong and suitable for engineering and scientific problems.

How to accurately predict nanoscale flow: Theory of single-phase or two-phase?

Accurate evaluation and recognition of nanoscale flow is the premise of the extension of classical theories of fluid mechanics to nanoscales. Despite the widely reported nonuniform characteristics of nanoconfined fluids, nanoscale flow is still considered as a single-phase flow in general, resulting in large deviations in theoretical predictions of velocity profile and flow rate. Considering the significant characteristics of a two-phase flow in nanoscales and the similarity between nanoscale flow and gas–liquid two-phase annular flow, we put forward a novel viewpoint that nanoscale flows should be described based on the theory of a two-phase flow. To support this idea, nanoscale flows under different fluid types, densities, temperatures, fluid–solid interactions, and driving pressures are extensively tested using molecular dynamics simulations. The results demonstrate that nanoscale flows can be divided into an adsorption phase and a bulk phase, and the characteristics of a two-phase flow are especially obvious under low fluid density, strong fluid–solid interaction, and low fluid temperature. The reasonability is further demonstrated by systematically analyzing the interphase density difference, interphase velocity difference, interphase mass exchange, and interfacial fluctuation, which are typical characteristics of a two-phase flow at macroscales. Finally, we present a series of theoretical descriptions of nanoscale flow from the perspective of a two-phase flow. By adopting different viscosity and density in the adsorption phase and bulk phase, the new model can better capture the physical details of nanoscale flow, such as velocity distribution and flow rate.

Chute flows of dry granular media: Numerical simulations by a well-posed multilayer model and comparisons with experiments

Debris flows and avalanches are dangerous natural phenomena, characterized by the gravity-driven motion of granular media immersed in a fluid. For an appropriate hazard assessment or disaster mitigation by scenario investigation, it is crucial to capture the underlying dynamics of the granular solid phase. For this purpose, a multilayer depth-averaged approach represents a promising and computationally efficient tool over fully three-dimensional models. Here we use a mathematically well-posed multilayer model, which implements the µ(I)-rheology and a dilatancy law depending on the inertial number, I, and compare the numerical results of the model with laboratory experiments of steady uniform chute flows over an erodible bed. The well-posedness of the model for any value of I, which is essential to get convergent numerical solutions, is achieved by considering an approximation of the in-plane stress gradients, directly emerging from the µ(I)-rheology. The predicted velocity profiles show a very good agreement with the experimental ones, measured by particle image velocimetry (PIV). The volume fraction profiles by the multilayer model are also in good qualitative agreement with those measured by using the stochastic-optical method (SOM), while they tend to overestimate the volume fraction measurements in the more dilute upper region, closer to the free surface.

A Semi-empirical correlation for stratified two-phase flow friction factors

The optimal design of two-phase flow pipes depends on various variables. Pressure drop and liquid hold-up are essential parameters in the appropriate design of pipes. These parameters can be estimated by numerical simulation of pipes. However, the simulation strongly depends on both phase frictions. Frictions are commonly calculated using friction factors, and various empirical correlations have been proposed to calculate friction factors, but most of these correlations are dependent on the experimental conditions.The present study proposes a semi-empirical friction factor model by modifying an existing model in the literature for stratified two-phase flows. Experimental data from various laboratories are taken to validate the interface shear stress, velocity profile and pressure gradient. Compared with previous models, the predicted velocity profiles and shear stresses are in better agreement with the experimental data.

Sparse Bayesian Learning of Explicit Algebraic Reynolds-Stress models for turbulent separated flows

A novel Sparse Bayesian Learning (SBL) framework is introduced for generating stochastic Explicit Algebraic Reynolds Stress (EARSM) closures for the Reynolds-Averaged Navier–Stokes (RANS) equations from high-fidelity data. Building on the recently proposed SpaRTA (Sparse Regression of Turbulent Stress Anisotropy) algorithm of Schmelzer et al. (2020), corrections to an underlying Linear-Eddy-Viscosity Models (LEVM) (namely, the k−ω SST model) are formulated as physically-interpretable, frame-invariant tensor polynomials and built from a redundant dictionary of candidate functions. The SBL-SpaRTA algorithm yields a sparse model structure (which favors interpretability and reduces overfitting), while endowing model coefficients with a measure of uncertainty, namely, posterior probability distributions. The framework is used to learn customized stochastic closure models for three separated flow configurations, characterized by different geometries but similar Reynolds number. The resulting stochastic models are then propagated through a CFD solver for all three configurations by means of probabilistic chaos collocation (Loeven et al., 2007). SBL-SpaRTA predictions of velocity profiles and friction coefficient distributions outperform those of the baseline LEVM, for training as well as for test cases. Furthermore, the prediction uncertainty intervals encompass reasonably well the reference data and tend to become large in regions of large discrepancy between RANS and high-fidelity predictions, thus warning the user about model reliability. Finally, a global sensitivity analysis of the stochastic models is carried out, illustrating the role of the dominant corrective terms and providing insights for future improvement of data-driven turbulence models.

Analytical model for predicting the lateral profiles of velocities through a partially vegetated channel

In an open channel, an emergent canopy of vegetation enhances local resistance, producing a complicated velocity distribution within and around the canopy. Laboratory experiments were performed to understand the flow velocity variations in a partially vegetated channel, revealing both streamwise and lateral velocity variations as the flow entered the canopy. An analytical model was proposed to predict the lateral velocity profiles across the vegetated region and bare channel in both the flow adjustment region and the fully developed flow region. The flow velocity variations in the streamwise and lateral directions were parameterized and included in the new proposed model, allowing the model to capture both the streamwise and the lateral velocity variations. Twenty sets of experimental data from our experiments and data from published studies were used to verify the new model. The predicted velocity profiles exhibited good agreement with the measurements under a wide range of vegetation and flow conditions, suggesting that the proposed model is capable of accurately predicting the velocity profiles in a partially vegetated channel. Finally, the proposed model was applied to evaluate the critical frontal area per canopy volume at which Kelvin–Helmholtz vortices form along the emergent canopy side edge. With the same spatially averaged velocity, the critical frontal area exhibited no dependence on stem diameter.

Impact of spatially varying flow conditions on the prediction of fatigue loads of a tidal turbine

Site development for tidal turbines relies upon a good understanding of the onset flow conditions, with disk averaged velocity typically used as a reference to define turbine power and mean loading. This work investigates the variation of onset flow conditions which occur for the same disk averaged velocity. Analysis builds upon data previously acquired during the measurement campaign conducted for the ReDAPT project using bed mounted ADCPs \cite{Sellar2018}. These measurements define the turbulence characteristics and vertical shear profiles over the rotor plane which are incorporated into an efficient blade element method for prediction of unsteady blade loads. This method allows efficient calculation of blade loading for multiple onset shear and turbulence profiles, each with the same disk average velocity, to determine the cyclic loading which contributes towards fatigue. Predictions of fatigue loads from measured profiles are compared with predictions from profiles predicted for the same location with a MIKE3 model \cite{Gunn2014}. Within the water depth two vertical positions are analysed, with vertical shear profiles from measurements and a multi-parameter model used to define the onset. For a near-bed location, use of the averaged predicted velocity profiles neglecting variation of turbulence intensity with flow-speed provides fatigue loads to within 1\% of predictions obtained using all measured profiles of velocity and corresponding turbulence intensity. For the near-surface location, the same approach under predicts fatigue loads by 16-19\%. This is partly due to the occurrence of a wider range of turbulence intensities. Since this is nearly constant with flow-speed a scaling factor is applied to load cycles from predicted profiles to estimate the aggregated fatigue load obtained using all measured conditions, providing confidence that accumulated fatigue loads can be predicted efficiently from velocity profiles obtained from shallow water models.

Integrated Velocity Prediction Method and Application in Vehicle-Environment Cooperative Control Based on Internet of Vehicles

Rapid progress has been gained in the field of advanced communication technologies, which also promote parallel developments in the Internet of Vehicles (IoVs). In this context, vehicle-environment cooperative control can be integrated into next-generation vehicles to further improve the vehicle's performance, in particular energy efficiency. Accurate prediction of future velocity profiles on basis of IoVs can be a critical breakthrough, which can contribute much to vehicle operation efficiency promotion. In this paper, an integrated velocity prediction (IVP) method fully taking advantage of IoVs is proposed and demonstrated through a case study. In the IVP method, both the macroscopically and microscopically predicted velocity profiles are considered. The macroscopic velocity profiles are predicted via traffic flow analysis (TFA) in multi-access edge computing units (MECUs) which are situated alongside the route. Microscopic velocity profiles are forecasted through Mondrian forest (MF) algorithm in the on-board vehicle control unit (VCU). Final velocity prediction is generated through combination of the macroscopic and microscopic profiles in frequency domain in on-board VCU through fast Fourier transform (FFT) and inverse FFT. A case study validates the distinguished performance of IVP method and demonstrates its significant contribution to vehicle performance improvement.

An URANS Simulation of the Kelvin-Helmholtz Aerodynamic Effect over the Ahmed Body

The reduction of energy consumption of cars is always a significant issue in automotive design. Turbulent flow around a car body is very difficult to simulate accurately due to the complexity of the flow conditions around the body, such as complex flow separation and laminar to turbulent flow transition. In particular, flow over the Ahmed body with a rear angle of 25o is considered a challenging problem for the RANS approach with two-equation turbulence models. In this study, we aim to analyze the Kelvin-Helmholtz instability associated with this flow with a URANS approach. Methodology for utilizing the URANS method is fully discussed. The predicted velocity profiles and drag coefficient are com-pared with experimental results. Three turbulence models, such as the k-ε, k-ω and SST models, are assessed and validated with experimental data. The aim of the study is to evaluate the performance of these models for the study of the Kel-vin-Helmholtz instability over the Ahmed body and for car bodies generally us-ing experimental data for their validations. It is found that the URANS approach with the turbulence models with proper numerical treatment can perform as well as or even better than the LES. And the SST model shows the best performance compared with other turbulence models.

Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

The heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

Analytical Formula for the Mean Velocity Profile in a Pipe Derived on the Basis of a Spatial Polynomial Vorticity Distribution

The derivation of the mean velocity profile for a given vorticity distribution over the pipe cross-section is presented in this paper1. The velocity profile and the vorticity distribution are axisymmetric, which means that the radius is the only variable. The importance of the vortex field for the flow analysis is discussed in the paper. The polynomial function with four free parameters is chosen for the vorticity distribution. Free parameters of this function are determined using boundary conditions. There are also two free exponents in the polynomial. These exponents are determined based on the comparison of this analytical formula with experimental data. Experimental data are taken from the Princeton superpipe data which consist of 26 velocity profiles for a wide range of Reynolds numbers (Re). This analytical formula for the mean velocity profile is more precise than the previous one and it is possible to use it for the wide range of Reynolds number <31,577; 35,259,000>. This formula is easy to use, to integrate, or to derivate. The empirical formulas for the profile parameters as a function of Re are also included in this paper. All information for the mean velocity profile prediction in the mentioned Re range are in the paper. It means that it is necessary to know the average velocity v(av), the pipe radius R, and Re to be able to predict the turbulent mean velocity profile in a pipe.

Validation of a morphology adaptive multi-field two-fluid model considering counter-current stratified flow with interfacial turbulence damping

Stratified flows are one of the most important multiphase flow regimes for safety analyses of the loss-of-coolant accident in pressurized water reactors. The present paper considers simulations of an isothermal counter-current stratified flow case in the channel of the WENKA (Water Entrainment Channel Karlsruhe) experiment using a morphology adaptive multi-field two-fluid modelling framework. A consistent momentum interpolation approach is applied together with the partial elimination algorithm, as it is required for strong momentum coupling, which enforces the no-slip condition at the interface and mirrors the behaviour of a homogeneous model. To model the turbulent flow conditions near an interface, the framework is extended with a turbulence damping model based on the damping scale formulation from the literature, which is introduced into the k-ω SST (Shear Stress Transport) turbulence model. The presented modelling approach is validated with experimental data for the pressure difference and vertical profiles of volume fraction, velocity and turbulent kinetic energy. Results of a mesh sensitivity study of the model are presented. Simulations were performed on two- and three-dimensional models of the channel geometry. Two turbulence damping strategies are investigated: symmetric, with damping in both phases, and asymmetric with damping only in the gas phase. The comparison shows that the asymmetric approach offers improved prediction of turbulent kinetic energy on the liquid side of the interface, but with a cost of diminished accuracy of the predicted velocity profiles on the gas side.

Investigations on Predictions and Characteristics of Flow Field in the Pipelines of Chillers for Measured Locations of Ultrasonic Flowmeters by CFD Approach

The flow velocity profiles in most of the central air-conditioning pipelines are, in general, not fully developed flow and difficult to obtain the accurate flow rates by flowmeters, which are used for measuring average velocity. Especially for being at the outlet of an elbow, the accuracy of flow rate by measurement is quite low. Therefore, there are some limitations for measurements of flow rate and velocity profile by the present flow measuring technologies. The objective of this study was to establish an approach on accurate predictions of velocity profiles at different measured locations of central air-conditioning pipelines for nonuniform flow measurements by simulations of computational Fluid Dynamics (CFD). All the velocity profiles will integrate as a database for predictions by neural network algorithm for smart measurement further. In the present work initially, international experiments were employed to validate the accuracy of CFD approach. The calculations were carried out by different turbulence models. The results compared with the experimental data by Realizable [Formula: see text]-[Formula: see text] turbulence model with less computing resources have great agreements. Realizable [Formula: see text]-[Formula: see text] turbulence model was, therefore, determined for the predictions of central air-conditioning pipeline. According to various pipings and pipe sizes, the results for three cases show that the velocity profiles in the pipelines would not be symmetrical and has strong secondary flow. Therefore, all of the flow profiles would be integrated and analyzed as a database and assist to get accurately the measured locations of ultrasonic flowmeters. Further, this database will be combined with algorithm of artificial neural network for smart predictions.