Computational Fluid Dynamics Model Research Articles

Estimating daily road traffic pollution in Hong Kong using CFD modelling: Validation and application

Road traffic is a major source of urban air pollution, exposing residents to harmful levels of pollutants, particularly in densely populated urban centers. In this study, Computational Fluid Dynamics (CFD) modelling is applied to investigate the impact of road traffic emissions on air pollution within a dense urban environment. The model, applied in Mong Kok, Hong Kong, incorporates high-resolution traffic activity, meteorological, and background concentration data. It demonstrates strong agreement with roadside measurements, deviating by 18.8 % for NOx, 15.5 % for CO, and 0.7 % for PM10, with potential underestimation linked to unmodeled NOx-O3 photochemistry during midday hours (12:00–17:00). Results reveal that local road traffic significantly increases background NOx levels (41 % average increase), highlighting the importance of dispersion modelling for accurate pollution assessments. Traffic-induced increases in CO and PM10 background levels are less pronounced. Two applications demonstrate the model's utility. Firstly, analysis of daily pollution distribution reveals that approximately 10 % of the studied area experiences moderate to very high pollution levels during peak hours. Secondly, an investigation into pollution sources within a leisure park identifies heavy-duty and light-duty vehicles as the primary NOx polluters. This modelling approach provides a valuable decision-making tool for policymakers and local authorities. It enables the assessment of urban design changes, strategic placement of sensitive structures, and future traffic fleet composition choices to mitigate pollution exposure. As air quality regulations tighten, this study offers a robust method for guiding impactful mitigation strategies.

Computational investigation of combustion and emission characteristics of HCCI engine fueled with Carbon-Free NH3 –H2O2 blend

A computational investigation is provided of the utilization of blends of NH3 and H2O2 as carbonless fuels for HCCI combustion. A Computational Fluid Dynamics (CFD) model is developed and validated to assess the engine’s performance under varying proportions of NH3 and H2O2, equivalence ratios, and inlet temperatures. The heat release rate profiles demonstrated dual peaks, which were rationalized as an initial fuel decomposition followed by a slow thermal explosion and then a fast depletion of the fuel after the generation of a pool of OH radicals through decomposition of H2O2. Findings suggest that an increase in mole fraction of H2O2 advances combustion. Increasing the H2O2 volume fraction also decreases combustion duration, while increasing the IMEP. In-cylinder peak pressure and Maximum Pressure Rise Rate (MPRR) increase with H2O2 mole fraction, leading to reduced thermal efficiency, particularly at higher equivalence ratios, and an increased danger of uncontrolled combustion (knocking). Additionally, higher inlet mixture temperatures were shown to result in an advancement in the start of combustion and a reduction in combustion duration, with a corresponding increase in peak pressure. This points to the possibility of potentially controlling the notoriously difficult-to-control HCCI combustion through intake temperature for NH3/H2O2 fuel blends. However, increased NOx emissions are observed for increased intake temperature and in any case the NOx emissions are orders of magnitude above current regulatory limits, which means that practical engine operation will only be possible with an aftertreatment scheme based on the de-NOx activity of NH3.

Examination of Thermal Dispersion and Airflow within a Refrigerator

A blast freezer, characterized by its capability to diminish the core temperature of cooked food from 100 °C to -18 °C within 270 minutes, constitutes a critical component in this preservation process. This study endeavors to model a blast freezer system employing Computational Fluid Dynamics (CFD) methodologies, subsequently validating the CFD analysis through empirical investigations. The pressure-based k-ε turbulence model is employed to solve the Navier-Stokes and energy equations. The ensuing analyses encompass airflow assessments and temperature evaluations for unloaded and fully loaded blast freezers. Results gleaned from experiments and analyses indicate a temperature escalation within the cabin as it approaches the enclosure walls. Maximum velocities of 31.1 m/s and 26.9 m/s are recorded for unloaded and fully loaded freezers. The average disparity between the CFD and experimental models is computed as -0.7 °C, signifying a close alignment between the simulated and actual outcomes.

Computational fluid dynamics investigation of pesticide spraying by agricultural drones

Precise control over pesticide spraying by agricultural drones requires fundamental research of multiphase flow, heat and mass transfer of liquid droplets in downwash flow. In this study, water was used as a carrier agent in pesticide formulations, in which the liquid droplets exhibit a Newtonian fluid behavior. A general computational fluid dynamics (CFD) model that characterizes propeller motion coupled with droplet evaporation and deposition was developed. The downwash flow generated by the propeller rotation was simulated using a multiple reference frame method along with the SST-k-ω turbulence model. Spraying of liquid droplets was simulated using a discrete phase model. The flow model validation was conducted by comparing the simulated velocities with experimental data, and the spray model validation was completed by a comparison of droplet depositions from CFD predictions and experiments. The results indicated that the droplet deposition fluctuates with an increase of propeller speed, and that both temperature and relative humidity affect the droplet deposition. In addition, the relation between the number of particles and the impact velocity was identified quantitatively, impact velocity with respect to particle diameter was predicted, and the effects of surface inclination, surface roughness, and crosswind speed on droplet deposition were analyzed. Moreover, an in-depth discussion about the intrinsic relation between downwash flow and droplet spraying was conducted.

Investigation on the influence of particle size and porosity on solid phase fluidization and heat transfer in carbon fibre porous media

AbstractIn this paper, the 3D pore structure was reconstructed and solid phase fluidization in porous media was investigated. Based on the two fluid model, a computational fluid dynamics (CFD) model of gas–solid fluidization was established and the influence of particle size and porosity were investigated. When porosity was constant, during the fluidization process, the particle velocity and solid concentration standard deviation gradually decreased, and the bed height increased. At the same time, the smaller the particle size was, the smaller the solid concentration standard deviation was, the faster the bed height increased, and the more the particle temperature decreased. Based on the fixed value of particle size, when studying the effect of porosity on fluidization, it was found that with the enhancement of solid‐phase fluidization, particle velocity, solid concentration standard deviation, and particle temperature decreased, and bed height increased. Moreover, when porosity was large, particle velocity decreased rapidly, and solid concentration standard deviation reached a smaller minimum value, which took a longer time. Within the same time, the particle temperature also decreased less.

Hydrogen enrichment in methanol SI engine at varying injection timing during compression stroke

This study investigates the effect of hydrogen enrichment in a direct-injected methanol-fuelled SI engine, with combustion and emission performance analysed by varying the methanol injection timing from 150° to 60° CA bTDC. A three-dimensional computational fluid dynamic model of the hydrogen-enriched methanol engine was developed to predict the engine performance, in-cylinder, and emission characteristics. The numerical model was solved using three-dimensional Reynolds-Averaged Navier Stokes, the RNG k-epsilon model for turbulence, the O'Rourke and Amsden sub-model for heat transfer, the extended Zeldovich mechanism for nitric oxide emissions, and the Hiroyasu-NSC model for soot emissions. The results indicated that retarding the injection timing resulted in a decrease in the indicated specific CO and soot emissions along with a rise in the indicated specific NOx emissions. Furthermore, hydrogen enrichment with methanol enhanced the hydroxyl radical concentration, reduced the combustion duration, reduced also the indicated specific CO and soot emissions while increasing the indicated specific NOx emissions. The study indicates that hydrogen enrichment could extend the late injection timing limit of methanol by enhancing fuel-air mixing, which improves and controls the combustion process more effectively.

CFD modeling of CO2 fixation by microalgae cultivated in a lab scale photobioreactor

The green microalga Chlorella vulgaris has been studied for efficient fixation of carbon dioxide, CO2, from elevated concentrations of in-feed gas flows. In this work, a Computational Fluid Dynamics (CFD) model for algae cultivation including the dependence on CO2 concentration in liquid has been derived based on cultivation experiments of Chlorella vulgaris. The experiments were performed in a laboratory scale cylindrical stirred tank reactor with a range of enriched CO2 concentrations in-feed (0.04–50 %). The model provided by Béchet et al. (2015) for algae cultivation considering dependence on local light, algae concentration and temperature has been implemented for CFD and modified for comprising CO2 dependence. The model has been verified with experimental results for the algae productivity showing the proper trends for dissolved CO2 concentrations and algae concentrations. Thus, the developed CFD model can serve as a tool for evaluating photobioreactor performance for CO2 capture

Calculating the local and overall view factors of a multi-segment human model

In non-uniform environments, such as spaces with radiant ceilings or floors, or with hot or cold windows, view factors (VFs) of a person towards these surfaces are needed for calculating the heat exchanges between the person and the surroundings. Multi-body segmented human models can simulate the heat exchanges of individual body parts with the environment. Simulating heat transfer at the local body parts is important for assessing comfort in non-uniform environments. These models require VFs for each individual body part. Accurately determining these VFs is time-consuming, often requiring methods such as ray-tracing techniques. A fast calculation method to accurately determine the view factor (VF) for each segment has yet to be investigated. This study employed a 16-segment human model into various architectural radiation scenarios. The surface-to-surface model in computational fluid dynamics was utilized to simulate VFs for the 16 body segments in relation to radiant surfaces at different locations. Through a series of simulations, comprehensive formulas for VFs were derived for each individual at various positions within a room, encompassing 16 body segments and computing parameters for 24 building surfaces. The accuracy of these general formulas was validated by comparing the whole-body VF with results from several available studies. Additionally, this study developed a visualization tool for calculating VF and mean radiant temperatures with different room space dimension and embedded radiant surfaces. The users can use this online tool to easily calculate VFs and mean radiant temperatures for each body parts, and for the whole-body. The tool offers valuable insights for predicting thermal comfort in asymmetric radiant environments and facilitating control over building environments.

Analysis of flow boiling incipience models in computational fluid dynamics

Recent advances in the computational fluid dynamics and heat transfer (CFD/HT) modeling of flow boiling have enabled a more detailed analysis and understanding of thermal management in relatively complex microsystems. Most advanced modeling approaches allow the transient analysis of two-phase flow and phase-change processes, providing the capability to post-process in detail the temperature, phase and pressure distributions in microscale applications with the use of moderate computational resources. Although these models have significantly evolved in recent years, there are still several idealizations and assumptions that have yet to be improved or better justified, the boiling incipience treatment being a commonly questioned topic. In the present investigation, a boiling incipience model is incorporated into an existing CFD/HT model for the analysis of flow boiling mechanisms, where the superheat temperature is derived from fundamental thermodynamic relations and compared to the original CFD/HT model that does not account for such effects. Results are compared using a non-trivial case of a silicon micro-cooling layer with variable density of pin fins that has been demonstrated to be an efficient thermal control device in high-power applications. The dielectric fluid HFE-7200 is used as the coolant in flow boiling conditions. The two-phase flow regimes and heat transfer results for both models are compared.

Coupled Modeling of Computational Fluid Dynamics and Granular Mechanics of Sand Production in Multiple Fluid Flow

Summary Sand production is a significant issue in oil and gas fields with poorly consolidated formations, often involving the multiphase flow of reservoir fluids and solid particles. The multiscale mechanisms of sand production, particularly fluid flow and particle movement, remain poorly understood. This study investigates these mechanisms using a coupled computational fluid dynamics and discrete element method (CFD-DEM) modeling approach. Single and multiple fluid flows of water and heavy oil were simulated with increasing fluid injection velocities, leading to different sand production patterns. The simulation results were compared with experimental results from a large cylindrical specimen of weak artificial sandstone under similar loading conditions. The multiphase conditions created various localized flow and deformation patterns that influenced both fluid and solid production, resulting in shorter transient sand production periods. Microstructures and phenomena such as fingering and water coning were observed, associated with a critical flow rate below which oil displacement was uniform and no water breakthrough occurred. Higher fluid injection velocities and fluid viscosities resulted in greater drag forces, leading to progressive damage zones and explaining the occurrence of single or multiple staged sand production events. The evolution of the microscopic granular structure was visualized under the effect of transient sand production.

Development of a Multi‐Scale Meteorological Large‐Eddy Simulation Model for Urban Thermal Environmental Studies: The “City‐LES” Model Version 2.0

AbstractTo bridge the gaps between meteorological large‐eddy simulation (LES) models and computational fluid dynamics (CFD) models for microscale urban climate simulations, the present study has developed a meteorological LES model for urban areas. This model simulates urban climates across both mesoscale (city scale) and microscale (city‐block scale). The paper offers an overview of this LES model, which distinguishes itself from standard numerical weather prediction models by resolving buildings and trees at the microscale simulations. It also differs from standard CFD models by accounting for atmospheric stratification and physical processes. Noteworthy features of this model include: (a) the calculation of long‐ and short‐wave radiations in three dimensions, incorporating multiple reflections within urban canopy layers using the radiosity method, and accounting for building and tree shadows in the simulations; (b) the provision of various heat stress indices (Universal Thermal Climate Index, Wet Bulb Globe Temperature, MRT, THI); (c) the assessment of the efficacy of heat stress mitigation measures such as dry‐mist spraying, roadside trees, cool pavements, and green/cool roofs strategies; (d) the capability to run on supercomputers, with the code parallelized in a three‐dimensional manner, and the model can also run on a graphics processing unit cluster. Following the introduction of this model, the study confirms its basic performance through various numerical experiments, including simulations of thermals in the convective boundary layer, coherent structure of turbulence over urban canopy, and thermal environment and heat stress indices in urban districts. The model developed in this study is intended to serve as a community tool for addressing both fundamental and applied studies in urban climatology.

U-Net architecture for high-resolution thermal image segmentation: Enhancing CFD models in smart HVAC design

This paper presents an enhanced U-Net architecture for accurately segmenting high-resolution thermal images to facilitate the design of intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems using Computational Fluid Dynamics (CFD) models. Indoor thermal comfort for occupants will improve under the same energy load by imposing comfort demands. Segmented data is fed into CFD simulations using a U-Net variant for thermal imagery. Integrating the segmented thermal data into the CFD simulations increases the model’s accuracy by 15 % for segmentation and 20% for prediction when compared to a traditional U-Net architecture. Experimental findings demonstrate the potential to achieve energy savings of up to 25 % in the simulated scenario by using the trained and validated model on a dataset of 1,500 high-resolution thermal images of various building styles. Compared to existing methods, the proposed method saves 30% in total simulation time for complex building models. Deep learning enables building the next generation of HVAC systems by pushing the boundaries of energy-efficient and environmentally friendly buildings. This study shows that advanced computer vision can be leveraged to create more innovative and sustainable built environments, leading to drastic improvements in energy efficiency.

Integration of CFD Analysis and Artificial Neural Networks for Estimation of Erosion Rate in Oil and Gas Pipelines

Transporting oil and gas via pipelines has been creating countless challenges; sand particles deposit and erode the pipelines wall. Generally, conventional erosion rate prediction models are conservative due to numerous generalizations and theories. Finite Element Analysis via computational fluid dynamics (CFD) approach has proven to be useful to solve sand deposition problems and to estimate the eroded pipe due to its capability to precisely determine erosion deficiencies and make predictions with great precision. In this study, a CFD model was developed to calculate sand erosion rates in a 2-inch, 90° elbow pipe. Effects of particle sizes, sand flowrates, fluids velocities and pipe diameters on erosion rates were studied. The model was validated against literature data, and it was then used to generate data via sensitivity analysis simulations. The data became the basis for developing artificial neural network (ANN) models, which were then deployed in the environment of a process simulation software called Symmetry. Based on this approach, a variety of pipelines can be modelled, and maximum erosion rates of pipelines are calculated using deployed ANN models. Hence, a comprehensive study on the fluid flow dynamics and erosion rates of the pipelines can be evaluated simultaneously.

Indoor thermal environment assessment of a historic building for a thermal and energy retrofit scenario using a CFD model

Research on retrofit scenarios for the energy efficiency of historical buildings is growing. Despite their potential to improve the energy consumption of a building, retrofit scenarios for indoor thermal environments and the thermal comfort of historical buildings has not been sufficiently studied. Therefore, in this study, indoor airflow, heat distribution, and thermal comfort levels of indoor residents were predicted using computational fluid dynamics (CFD) modeling of historic buildings based on measured values. Although the heating was turned on in the meeting room in winter, heat loss caused by low thermal performance, airtightness, and inflow of cold air caused local thermal discomfort. Individuals seated near the windows had a temperature difference of up to 2.6 °C between the head and ankle levels. Heat loss was identified from the exterior walls and windows, with air infiltration significantly affecting indoor thermal comfort. The airtightness of the window frames of historic buildings was low, thus necessitating related improvement in heat and energy retrofit scenarios. This study propose a suitable approach to resolve this problem.

Three-dimensional CFD modeling for hollow fiber gas separation membrane modules based on a dual porous media model

Mathematical modeling is crucial to optimizing the design of the hollow fiber membrane (HFM) module. A Computational Fluid Dynamics (CFD) model enables efficient three-dimensional (3D) simulations of HFM modules, which integrates a dual porous media approach, significantly reducing computational costs. Validation against alternative simulations and experimental data confirms its prediction capability (<7% deviation). Comparative analyses demonstrate the superior reliability of the 3D approach over one-dimensional simulations. Detailed velocity, pressure, and concentration profiles for the shell and tube sides reveal optimization opportunities such as dead zones within the module. Aerometric studies show significant pressure drops (>30%) for smaller fiber diameters (<100μm) on the permeate side. The model's adaptability suggests broader applications in membrane gas separation processes with modifications. This research highlights the efficacy of CFD modeling in enhancing the design and optimization of HFM modules, highlighting the cost savings of the dual porous media approximation.

Numerical modeling assisting in surgical treatment of total anomalous pulmonary venous connection in children

ObjectiveTo develop a model using patient-specific computational fluid dynamics (CFD) to predict the required anastomotic size for total anomalous pulmonary venous connection (TAPVC) surgery and to forecast surgical outcomes.MethodsBased on...

Numerical Analysis of Spray Evaporation Process in Desuperheater

Abstract The cooling water is sprayed into the desuperheater to regulate the temperature of the superheated steam. Computational fluid dynamics (CFD) was applied to investigate the spray evaporation process in the desuperheater. Discrete phase model (DPM) was applied to describe the gas-liquid two-phase flow characteristics based on the Eulerian-Lagrangian approach. The Rosin-Rammler distribution and TAB model were used for the description of the primary and secondary droplet breakup, respectively. The coupling effect of gas-liquid two-phase, influence of temperature on latent heat, the stochastic collision and coalescence of droplets were considered in the CFD model. The numerical results show good agreement with the plant data. The hydrodynamics and temperature distribution characteristics were analysed. The influence of water mass flow rate and orifice dimensions on temperature distribution and temperature non-uniformity was further investigated. The results indicate that increasing the water flow rate can improve the desuperheating ability, but it will make the temperature uniformity worse. Under the same operating conditions, smaller orifice diameters are beneficial for production of droplets with smaller size, leading to better desuperheating ability.

Data-driven multi-objective optimization of aerodynamics and aeroacoustics in dual Savonius wind turbines using large eddy simulation and machine learning

Savonius rotor is a popular form of vertical-axis wind turbine (VAWT) for small-scale and urban applications because of its straightforward design and self-starting ability. Dual VAWTs present challenges in terms of wake interactions and noise, particularly in urban areas. Optimizing these parameters is essential for future wind energy adoption. This research is the first to analyze how the interaction of wakes from adjacent rotors, combined with a deflector, affects both the aerodynamic performance and noise levels of dual Savonius rotors. Large Eddy Simulation is applied, as it effectively captures detailed turbulent wind flows and their interactions with wind turbines. A multi-objective optimization method combining Machine Learning and Computational Fluid Dynamics (CFD) is developed to optimize rotors for maximum power efficiency and minimum noise, considering their wake interactions with a unique deflector system. First, the influence of geometric parameters on aerodynamics and aeroacoustics characteristics of rotors is analyzed, and the database is generated using Design of Experiment approach. Next, the CFD model is replaced by Artificial Neural Network (ANN) model established for predicting rotor performances. A Multi-Objective Genetic Algorithm method is used to optimize aerodynamics and aeroacoustics characteristics of rotors. Finally, optimal design parameters are identified from the Pareto front using the technique for order of preference by similarity to ideal solution decision-making method. The ANN model demonstrated high accuracy with an RANN2 of 0.995 and 0.971 for the average power coefficient (CP) and overall sound pressure level (OSPL) predictions, respectively. Multi-objective optimization revealed the best configuration of the deflector with bleed jets, improving the average CP up to 57.5% and reducing OSPL to an almost 5.2% compared to the dual rotor case at TSR = 0.8.

Estimating pulmonary arterial remodeling via an animal-specific computational model of pulmonary artery stenosis.

Pulmonary artery stenosis (PAS) often presents in children with congenital heart disease, altering blood flow and pressure during critical periods of growth and development. Variability in stenosis onset, duration, and severity result in variable growth and remodeling of the pulmonary vasculature. Computational fluid dynamics (CFD) models enable investigation into the hemodynamic impact and altered mechanics associated with PAS. In this study, a one-dimensional (1D) fluid dynamics model was used to simulate hemodynamics throughout the pulmonary arteries of individual animals. The geometry of the large pulmonary arteries was prescribed by animal-specific imaging, whereas the distal vasculature was simulated by a three-element Windkessel model at each terminal vessel outlet. Remodeling of the pulmonary vasculature, which cannot be measured in vivo, was estimated via model-fitted parameters. The large artery stiffness was significantly higher on the left side of the vasculature in the left pulmonary artery (LPA) stenosis group, but neither side differed from the sham group. The sham group exhibited a balanced distribution of total distal vascular resistance, whereas the left side was generally larger in the LPA stenosis group, with no significant differences between groups. In contrast, the peripheral compliance on the right side of the LPA stenosis group was significantly greater than the corresponding side of the sham group. Further analysis indicated the underperfused distal vasculature likely moderately decreased in radius with little change in stiffness given the increase in thickness observed with histology. Ultimately, our model enables greater understanding of pulmonary arterial adaptation due to LPA stenosis and has potential for use as a tool to noninvasively estimate remodeling of the pulmonary vasculature.

Parametric study of combustion characteristics of diesel/methanol dual fuel engine using global sensitivity analysis and multi-objective optimization

The pursuit of clean and efficient internal combustion engine power necessitates the substitution of fossil fuels with methanol and the adoption of advanced combustion strategies. This study examines the combustion and emission characteristics of methanol/diesel dual-fuel compression ignition engines. To understand the intricate interplay between critical parameters and engine performance, the global sensitivity analysis (GSA) method is employed, utilizing Sobol sensitivity as the quantitative metric. The investigated parameters include methanol substitution rate, diesel injection parameters, and five thermodynamics parameters concerning in-cylinder conditions. A validated computational fluid dynamics (CFD) model, coupled with a chemical kinetic mechanism, is developed to predict the combustion behavior of the dual-fuel engine accurately. This predictive capability forms the basis for utilizing the tree-based process optimization (TPOT) algorithm. By implementing TPOT, a significant reduction in the mean square error (MSE) of the predicted characteristics model is achieved, averaging a decrease of 73.59%. Subsequently, a multi-objective optimization is conducted, exploring the impact of varying target weights on the optimal outcomes. Results show the initial pressure significantly impacts the engine's economy, power and emission characteristics, and NOx formation should be carefully considered in the combustion optimization of dual-fuel engines.