Fluid Flow Research Articles

Numerical Simulation of Co-Firing LRC and Ammonia in Pangkalan Susu 3 & 4 Coal-Fired Steam Power Plant (CFSPP) Capacity 210 Megawatts

The effort to reduce CO2 and NOx emissions plays a crucial role in mitigating climate change, improving air quality, complying with environmental regulations, and promoting clean technology innovation. Ammonia, as an emission-free fuel, shows significant potential as a co-firing agent with Coal in coal-fired steam power plants (CFSPP). Previous studies have demonstrated promising results in emission reduction through ammonia co-firing. This research presents a numerical analysis based on Computational Fluid Dynamics (CFD) to investigate the co-firing of ammonia with low-calorific Coal (LRC) in the CFSPP Pangkalan Susu Units 3 and 4, with a capacity of 210 megawatts. The study employs fluid flow modelling and chemical reaction analysis using the Discrete Phase Model (DPM) to provide accurate predictions of temperature distribution and pollutant concentrations in pulverized coal boilers. Cofiring simulations were conducted with ammonia additions of 5% and 15% on a calorific basis. Injection experiments from each burner (A-D) were performed to identify the optimal injection location. The simulation results indicate changes in combustion characteristics, particularly in temperature distribution. The main finding reveals a temperature decrease when ammonia is added as a co-firing material, attributed to the higher H2O content, which leads to increased moisture losses. In terms of efficiency, co-firing showed a decline compared to the baseline combustion of 100% LRC coal due to the more significant moisture losses. The highest reduction in CO2 emissions was observed when 15% ammonia was injected from burner B in case #6, with a mass fraction value of 0.171 at the boiler outlet. Similarly, the most significant reduction in NOx emissions occurred with a 15% ammonia co-firing from burner B, yielding a mass fraction value of 8.81E-04 at the boiler outlet. This co-firing technology is expected to enhance decarbonization efforts and optimize the use of renewable energy in the future.

A three-dimensional curve interface reconstruction algorithm for two-phase fluid flow

A three-dimensional curve interface reconstruction algorithm for two-phase fluid flow

Experimental study of thermoconcentration convection in air–water and air–undecane mixtures

A quantitative experimental comparison was conducted between thermal convection in dry air and thermoconcentration convection in two gas mixtures: air–water vapor and air–undecane vapor, within the temperature range of 0–80 °C at normal atmospheric pressure. Convection in these mixtures is driven by temperature and concentration gradients of water (or undecane) vapor in the air. The stationary thermoconcentration convection is accompanied by continuous phase transitions of the fluids. The quantitative results of the experiments are represented in terms of the Nusselt number Nu and the effective Rayleigh number RaE, which is the sum of the thermal RaT and concentration RaC Rayleigh numbers. Quantitative laboratory measurements were performed using the thermocouple method and were supplemented with qualitative data from visual monitoring of transparent fluid flows using holographic interferometry. The cubic and quadratic temperature dependencies of RaCRaT−1 were determined experimentally for moist air and for the air–undecane vapor mixture, respectively. The significant role of moisture phase transitions in air convection is established. Neglecting these effects at 25 °C and using the ordinary RaT instead of actual RaE would result in an error exceeding 30%. At 38 °C, this error would increase to nearly 100%. At around 80 °C, thermoconcentration convection becomes almost entirely concentration-driven, as the high molecular disordered thermal motion is suppressed by the ordered convective motion generated by evaporation and condensation of water on the opposite heat exchangers of the convective cell.

Hydrothermal Behaviour of Hybrid Nanofluid Flow in Two Types of Shell and Helical Coil Tube Heat Exchangers With a New Design. Numerical Approach

High-efficiency thermal energy systems are very important in meeting the growing demand for thermal energy. As a result, several heat transfer improvements have been proposed. Some promising methods include flow heat transfer in shell and spiral tube exchangers.In a shell-and-coil heat exchanger, utilizing a meticulously designed coil instead of a basic one significantly boosts heat transfer and overall thermal efficiency. This is due to the enhanced fluid dynamics and increased turbulence facilitated by the advanced coil design, making it ideal for space-constrained applications. Moreover, the helical configuration helps minimize fouling and maintenance, and may also provide self-cleaning benefits. Consequently, helical coils are highly regarded in industrial contexts for their superior performance, maintenance ease, and design adaptability.This study conducts a numerical evaluation of the heat transfer and fluid flow properties of two distinct shell-and-coil heat exchangers with specialized designs. The fluids analyzed include water-based hybrid nanofluids, specifically Water/MgO-TiO2and Ag-HEG/water, with results compared to those obtained using pure water. The investigation spans Reynolds numbers from 500 to 2000 and is divided into two segments.The first segment examines the influence of spiral coil geometry and fluid type on the heat exchanger's endothermic performance, utilizing nanoparticle volume concentrations of φ1=φ2=0.3. In the second segment, the optimal geometric and fluid model is chosen based on the findings from the first part. Following this, the impact of various hybrid nanofluids on thermal performance is assessed, comparing fluids with volume concentrations of φ1=φ2=0.3 to pure water (φ1=φ2=0).The findings reveal that Case [A], featuring a unique geometry with Water/Ag_HEG, achieves the highest thermal performance across all examined Reynolds numbers. At the lowest Reynolds number, the thermal efficiency improvements for Case [A], Case [B], and Case [C] were 137%, 113%, and 56%, respectively, compared to the baseline. Additionally, the second part of the study demonstrates that at the lowest Reynolds number, the thermal efficiencies of Water/MgO-TiO2 and Water/Ag_HEG nanohybrid fluids increased by 76% and 49%, respectively.

Unlocking optimal performance and flow level control of three-phase separator based on reinforcement learning: A case study in Basra refinery

This research explores the application of a new reinforcement learning (RL-based) controller for a three-phase separator connected to a gas turbine. The control of flow levels within the separator directly impacts fluid flow turbulence, especially when the equipment is linked to waste heat gas from the turbine to improve gas quality. The study introduces the novel RL-based controller and validates its effectiveness in real-world conditions using three-phase separators in Basra, Iraq, and through a review of relevant literature. The controller can adapt to inlet conditions such as pressure, temperature, mass flow rate, and incoming heat from the gas turbine. Waste heat recovery from the gas can enhance gas purity but also increase turbulence in water and oil. Maintaining a calm flow while ensuring high-speed flow over the baffle in the middle of the separator is crucial for optimal performance. The study considers two geometrical configurations of the vessel for redesigning the separator at the Basra refinery. The controller was implemented using the groovyBC utility within the OpenFOAM software. This model was then utilized to simulate real-world scenarios at the Basra refinery, displaying faster convergence, more rapid response, and more accurate tracking of the target fluid level. This study marks the initial effort to apply the deep deterministic policy gradient (DDPG) controller in computational fluid dynamic (CFD) work. The findings demonstrated a significant enhancement in separation efficiency by more than 36%, as well as smoother streamlines through the control and maintenance of pressure and velocity over the baffle.

Sustainable reuse of oil and gas wells for geothermal energy production: Numerical analysis of deep closed loop solutions in Italy

This study assesses the feasibility of repurposing abandoned oil and gas wells in Italy for geothermal energy production employing a geothermal closed-loop system. A systematic methodology was developed, beginning with raw data collection and progressing to numerical simulations using COMSOL Multiphysics to model a U-shaped deep closed-loop geothermal heat exchanger. The analysis relied on a public database of wells drilled in Italy since the mid-20th century. The Horner plot correction method was applied to measured temperature data to obtain accurate geothermal gradients across Italy, which were then used as input parameters for a numerical sensitivity analysis. The results highlight the critical role of the geothermal gradient and heat carrier fluid flow rate in determining system performance. Regions in Italy with geothermal gradients exceeding 40 °C/km, particularly in the Tyrrhenian area, were identified as having high potential for this technology. A preliminary analysis of a virtual Organic Rankine Cycle (ORC) system estimated power production of 73 kW, with an efficiency of 11.66 % after 25 years of operation under optimal conditions (5 l/s flow rate, 60 °C/km geothermal gradient, and 70 °C evaporation temperature).

Simulasi karakteristik mesin termoakustik pembangkit listrik dengan penambahan model kerugian minor dari dua segmen konis

Acoustic energy output level from regenerator segment of a thermoacoustic engine model is attenuated along it’s loop due to several conditions including minor losses. This article discusses the result of Delta EC simulation of a thermoacoustic engine model acting as simple electric power generator that inserted with two conical segments. The cone segments are capable to lower the energy loss which in turn improve the overall performance of the engine in term of nett heat to acoustic energy conversion efficiency. Combined acoustic energy loss induced by both cones is equivalent to 4.94 watts minor losses. At this condition, regenerator segment amplifies the incoming acoustic energy flow of 57.02 watt up to 93.57 watt, which is equals to 36.55 watts acoustic amplification. It leads to increasing of overall engines heat to acoustic efficiency into 14.05%, which is 1.29% higher than those at the case of without cones. This engine performance improvement addressed to smoother streamline of working fluid flow inside the loop.

Thermal characteristics analysis and cooling model optimization of motorized spindle

Aiming at the influence of poor cooling conditions and traditional cooling control strategy on motorized spindle temperature and machining performance. Firstly, the heat transfer model of the motorized spindle cooling system is studied. Secondly, the influence of coolant inlet flow rate and temperature on the thermal characteristics of the motorized spindle is studied. Then, a thermal network model is established to solve the temperature of each temperature measuring point. Finally, the thermal characteristic experiment of the motorized spindle is carried out, and the cooling fluid flow optimization model is established based on the particle swarm optimization algorithm and simulated annealing algorithm. The results show that the temperature difference of the motorized spindle does not exceed 45°C, the thermal deformation does not exceed 40.2μm, and the thermal elongation is inhibited by 36%. The maximum error of the Thermal Network Method and Finite Element Method（FEM）is 14.24%. The utilization of the average logarithmic temperature difference for assessing the cooling effectiveness of optimal flow rates revealed that the particle swarm optimization algorithm demonstrates a comparatively lower average logarithmic temperature difference in comparison to the simulated annealing algorithm. The heat exchange efficiency of the motorized spindle is higher under the optimal flow rate obtained by the particle swarm algorithm.

Stochastic analysis of the interaction between excess fluid flow and soil deformation in heterogeneous deformable porous media

Stochastic analysis of the interaction between excess fluid flow and soil deformation in heterogeneous deformable porous media

Inertial migration of cylindrical micelles formed by comb-like copolymer in Poiseuille flow

By combining the lattice Boltzmann model of fluid flow with the molecular dynamics model of copolymers, we investigate the inertial migration of cylindrical micelles, which is obtained by controlling the length ratios of hydrophilic and hydrophobic segments in a comb-like copolymer. Our results demonstrate that cylindrical micelles gradually deviate from the center of the nanochannel with increasing Reynolds number (Re). For the same Re, the larger the cylindrical micelle is, the closer it is to the center of the nanochannel. Importantly, we find that the change in the equilibrium position is particularly pronounced at Re less than 0.1, while the trend becomes smoother at Re greater than 0.1, which is because of the transition of micelles from cylindrical to disk-like shapes when Re is smaller than 0.1, and does not change as Re further increases. This work provides an understanding of cylindrical micelles' inertial migration, particularly in identifying the effect of morphological changes on the equilibrium position, which could lead to significant advancements in the inertial migration of polymer micelles.

QFlowS: Quantum simulator for fluid flows

QFlowS: Quantum simulator for fluid flows

Dynamic fluid flow model for phosphate slurry pipeline: OCP main pipeline as case study

The transportation of phosphate slurry through large-scale pipelines presents significant challenges due to the complex behavior of multiphase flows, particularly with varying solid content, density, and dynamic viscosity. Efficient and accurate prediction of flow behavior is critical for optimizing the operation of such pipelines. This work aims to develop a dynamic computational model to simulate phosphate slurry flow in pipelines. The case study focuses on the OCP Group’s main slurry pipeline, which links the mining sites at Khouribga to the industrial plants at Jorf Lasfar, Morocco. This pipeline system spans a total length of 187.124 km, consisting of 5237 pipes with an inner diameter ranging from 0.8546 m to 0.8578 m, and features several elevation changes along its ground level. Using a section-averaged, dynamic approach and the Finite Volume scheme, the model computes essential flow parameters, including density, dynamic viscosity, and pressure, for the incompressible and non-Newtonian slurry and process water flows. The model’s accuracy is validated against on-site measured data, showing an average deviation of ±0.32% for outlet density, and below 10% for pressure along the pipeline, underscoring the model’s reliability. Additionally, a sensitivity analysis was conducted to illustrate the impact of key parameters on the predicted head losses and pressures along the pipeline. This analysis shows that the slurry viscosity is the most critical parameter, significantly influencing these predictions. This model provides high accuracy and reasonable CPU time for real-time simulation and monitoring, while also offering significant potential for optimizing pipeline operations and ensuring the reliability of phosphate transport.

Predicting scale thickness in three-phase flow using neutron activation analysis and deep learning

Calcium carbonate scales occur on the inner walls of the pipes used in oil and gas production, reducing the internal diameter and obstructing fluid flow. This study proposes a method for predicting of scale thickness using a neutron source and gamma radiation analysis with a deep neural network (DNN). The detection system includes a 241Am-Be neutron source and 72 detectors that count particles and record photons. A measurement setup was designed using the MCNP6 code to optimize the detection of scale thickness, utilizing a flat neutron source and strategically positioned detectors. The gamma-ray spectra generated from this setup were used as input data for the DNN to predict scale thickness within a three-phase water–gas-oil annular system. The results indicated that the DNN accurately predicted scale thickness with a mean relative error of 3.01 %, achieving a relative error below 5 % for 92.68 % of the dataset, demonstrating the potential for reliable real-time monitoring in industrial settings.

Comparative study of Eyring–Powell fluid flow with temperature-dependent viscosity in roll-rotating systems: An analytic, numeric, and machine learning approach

Comparative study of Eyring–Powell fluid flow with temperature-dependent viscosity in roll-rotating systems: An analytic, numeric, and machine learning approach

Power-law fluid flow in the entrance region between two parallel plates

Power-law fluid flow in the entrance region between two parallel plates

The Effect of U-bend Zone, Rotation, and Corrugation on Two-Pass Channel Flow

Abstract Further consideration on the two-pass channel flow is still necessary due to the complexity of 180-degree turn, rotation, and wall ribs. Numerical investigations have repeatedly revealed differences from experiments, with the primary focus of the smooth walls. Thus, this work deals with newly added ribs and three-equation variant of the SST $k$-$\omega$ model to the current fluid flow and heat transfer depending on an existing experiment as a reference. The adapted turbulence model thought to be more susceptible to U-bend zone, rotation and wall corrugation is applied using COMSOL Multiphysics program. A two-pass profile with leafy characteristics, derived from a prior work by the first author, is implemented for the first time and contrasted against alternative corrugation designs. The findings demonstrated that applying the suggested model reduces the percentage error between the computational and experimental data to less than 20\%. The Nusselt numbers computed at different leafy-corrugated channel divisions are augmented to 30\% with 70\% surface temperature reduction; however, the friction penalty rises too.

Conjugate heat transfer and fluid flow analysis on printable double-wall effusion cooling with internal topology-optimized TPMS structures

Double-wall effusion is a highly efficient cooling technique in modern gas turbine blades. This study uses topology optimization infilled with triply periodic minimal surface structures (TPMS) to design high-performance internal cooling structures, improving cooling effectiveness and mitigating thermal stress for the double-wall channel. The flow, heat transfer, and static structural characteristics of the topology-optimized TPMS model are compared with the results of the smooth and circular pin fin configurations. Results show that the optimized model provides a uniform flow inside the channel and the effusion holes, reducing the jet lift-off and keeping the coolant attached to the effusion wall. Within the blowing ratios of 0.5–1.7, the optimized model improves impingement heat transfer by 9.5 %–12.5 % compared to the pin fin configuration. The averaged overall cooling effectiveness is also 4.2 %–4.6 % with lower pressure loss. The thermal stress and total deformation are evenly distributed and show 22.9 % and 12.0 % lower than the pin fin model. Moreover, a 3D laser scanning microscope and high-resolution CT scan are used to evaluate the manufacturability of the optimized sample, printed by laser powder bed fusion with an actual gas turbine blade scale. The results benefit the fabrication improvement for next-generation gas turbine blades.

Simulation and performance characteristics of rock with borehole using Visual Finite Element Analysis

Purpose. This study aims to investigate fluid flow and heat transfer within rocks containing boreholes, focusing on the complex mechanisms within hot reservoirs. Non-commercial finite element (FE) software is used to visualize and present the results. Methods. The study involved the use of FE method with Visual Finite Element Analysis (VisualFEA) software to analyze the coupled phenomena of fluid flow and heat transfer in a rock sample. Special attention was given to incorporating material structure and geotechnical analysis in the software, as well as the treatment of cracked elements. In addition, the validation was done by comparing the current numerical solution using VisualFEA with the numerical solution using ANSYS Software. Findings. The study findings highlight the capabilities of VisualFEA software to accurately represent fluid flow, stress, and heat transfer in borehole-containing rocks. The results include insights into flow direction within the borehole, temperature distribution, and the validation of the software performance against expected system behavior. The study demonstrates the effectiveness of VisualFEA in handling complex loading and its ability to visualize multiple flow directions within a 2D model. The results are presented in the form of contours and curves. Originality. This study contributes to the field demonstrating the application of VisualFEA software in analyzing fluid flow and heat transfer in rocks with boreholes. The focus on incorporating material structure, geotechnical analysis, and treatment of cracked elements adds originality to the study, providing a comprehensive understanding of the coupled phenomena in hot reservoirs. Practical implications. The practical significance of this study is in the validation and benchmarking of VisualFEA software for studying fluid flow and heat transfer in geotechnical application. The findings can be utilized by geotechnical engineers and researchers to better understand the behavior of borehole-containing rocks under specific pressure and thermal loading conditions. The insights gained from this study can be used in decision-making processes related to resource mining, reservoir engineering, and geothermal energy use.

Study of hybrid nanofluid flow in a porous medium over an exponentially stretching sheet under Joule heating and thermal radiation: Finite difference

The significant purpose of present investigation of the behavior of a nanofluid's in magneto-hydrodynamics (MHD), mass transfer, Joule heating, and boundary layer transfer characteristics over an exponentially stretching sheet in a porous medium and thermal radiation effects. The article's goal is to look at the fluid flow and heat transmission characteristics from a sheet of hybrid nanoparticles. The partial differential equations (PDEs) that were derived for the mathematical model were converted using the proper similarity transformation into ordinary differential equations (ODEs). The hybrid nanofluid composed of 97 % of ethyl glycol (EG) and the volume concentration of Magnetite (Fe3O4) and Copper(Cu) are ranging from 0.5 % to 2.5 % both respectively. The effects of thermal radiation, stretching rate, Joule heating, porous medium permeability, and nanoparticle volume fraction on the flow and heat transmission properties are investigated by numerical simulations using the finite difference method (FDM). The analysis reveals that the inclusion of nanofluids enhance the thermal conductivity and enhance the heat transfer rate. Additionally, the influence of variable viscosity on the flow behavior and thermal characteristics are examined graphically. The effects of variable viscosity and thermal conductivity are examined, as it has significance in optimizing system under various thermal and magnetic effects. This study offers a pathway to develop more efficient thermal management solutions, by contributing to technological advancement and energy saving. The key findings of present study reveal that the temperature profile rises significantly due to Joule heating effects. The Nusselt number reveal an improvement of about 12 % when the volume fraction of nano particles is increased by 1–4 % indicating the enhancement in heat transfer efficiency. Similarly, the velocity profile was influenced by porous medium permeability as 11 % increase in porosity result a 18 % decrease in velocity profile.By using parametric research, the role of physical parameters in determining the local skin-friction coefficient, temperature, nanoparticle volume percentage, and longitudinal velocity profiles, local Nusselt number, and local Sherwood number are thoroughly examined. A graphic representation of the velocity, temperature, and concentration distribution findings is presented.

An artificial neural network approach to characterizing the behavior of bioconvective nanofluid model using backpropagation of Levenberg–Marquardt algorithm

The study of the behavior of bioconvective nanofluid model (BC-NFM) was explored using bioconvection properties in the computational analysis of magnetized nanofluid flow that was convectively heated, which is critical for applications in energy systems and biomedical devices. We implemented a backpropagated Levenberg–Marquardt neural network approach (BLMNNA) to enhance the analysis and prediction accuracy of such fluid flows. Using the Adams numerical technique, we generated a comprehensive dataset for eight distinct scenarios by variation of thermophoresis parameter, Rayleigh number, Deborah parameter, Hartman number, Prandtl number, bioconvection Lewis number, and Schmidt number to train and test our model. The results demonstrate significant improvements in prediction accuracy and computational efficiency compared to traditional numerical solvers. The steps of training, testing, and validating the developed BLMNNA are used to get the desired numerical solutions of BC-NFM for various instances. The worth and accuracy of the stochastic numerical solutions of BC-NFM are established and authenticated by the outputs of the designed methodology BLMNNA through Adaptation graphs of Mean Square Error, regression studies, plots of error histogram and index of state transition. Excellent measurements of performance in terms of MEAN SQUARE ERROR are achieved at level 9.76E[Formula: see text], 1.41E[Formula: see text], 1.79E[Formula: see text], 1.22E[Formula: see text], 8.49E[Formula: see text], 7.19E[Formula: see text], 9.72E[Formula: see text], and 9.35E[Formula: see text] against 69, 73, 96, 76, 237, 86, 120, and 33 epochs. The significant connection between the proposed and reference findings demonstrates the validity of the BLMNNA based on error analysis, which ranges from E[Formula: see text] to E[Formula: see text] for all situations. The results show that the proposed neural network approach achieves a high level of accuracy, closely matching the outcomes obtained from the numerical solver. The method provides an efficient and precise solution for the analysis of complex fluid flow problems, representing a significant advancement over traditional numerical techniques.