Blood Flow in a Coronary Bifurcation, Paper Numerical Validation, ANSYS FluentDescriptionThis project is based on the reference paper "Numerical investigation of blood flow in a deformable coronary bifurcation and non-planar branch," which numerically investigates pulsatile blood flow through a coronary bifurcation featuring a non-planar branch, with the vessel wall assumed compliant to reflect more realistic physiological behavior.Identifying and assessing hemodynamic characteristics is critical to understanding and preventing cardiovascular disease, since stenosis development depends heavily on local blood flow behavior — and given the high mortality and morbidity associated with coronary artery disease, these hemodynamic characteristics warrant close attention. This simulation examines the effects of wall compliance and blood's non-Newtonian rheology on flow behavior using ANSYS Fluent, with results compared and validated against the reference article's published data.The study evaluates how non-Newtonian blood behavior, wall compliance, and varying bifurcation angles together shape hemodynamic flow characteristics, with blood's shear-thinning behavior captured using the Carreau-Yasuda model. Since atherosclerosis develops predominantly at bifurcations, this research focuses specifically on flow behavior in these regions, identifying the low-shear-stress zones most prone to stenosis development. The pulsatile inlet velocity profile was derived using MATLAB and implemented in Fluent through a custom UDF.Geometry & MeshThe geometry was designed in Gambit. Meshing used a starting element size of 0.25, a growth rate of 1.25, and a maximum size of 0.5, producing a mesh of 397,388 elements.MethodologySeveral assumptions were applied: a pressure-based solver was used, the energy equation was disabled, the simulation was run as unsteady, and gravitational effects were included.Key simulation settings included:Material properties: Blood modeled with a density of 1050 kg/m³ and viscosity governed by the Carreau modelBoundary conditions: Both outlets set to 0 Pa gauge pressure; vessel wall with no-slip condition; inlet velocity defined via UDF to capture the pulsatile profileTurbulence model: Laminar, with the energy equation disabledDynamic mesh: Smoothing method using a linearly elastic solid formulation, with the vessel wall defined as a deforming boundary to capture wall complianceSolution methods: SIMPLE pressure-velocity coupling, with second-order upwind discretization for both pressure and momentumInitialization: Hybrid methodRun settings: 35 time steps at a step size of 0.01, with a maximum of 200 iterations per stepConclusionValidation was performed using Figure 6 of the reference article, which presents wall shear stress along the coronary vessel walls as a function of distance from the bifurcation. The wall shear stress values from this simulation were extracted along Line 3 (as defined in Figure 1 of the article) and compared directly against the reference results, with the comparison presented in the accompanying image set — confirming that the current numerical setup successfully reproduces the hemodynamic behavior reported in the original study.

Arterial Stent CFD Simulation, Improving Blood Flow and Reducing Shear Stress, ANSYS FluentDescriptionThis project simulates blood flow through arteries both with and without a stent present, using ANSYS Fluent, investigating how stent implantation affects hemodynamics — particularly shear stress and pressure distribution, both of which play critical roles in cardiovascular health.Two geometries were built in SpaceClaim: the first representing a stenotic artery without a stent, using a defined equation to construct the clogged (narrowed) region, and the second representing the same artery with a stent implanted. The stenotic artery mesh totaled over 220,000 cells, while the stented artery mesh totaled approximately 250,000 cells, both generated in ANSYS Meshing.MethodologyThe simulation used a pressure-based solver with a transient formulation to capture the time-dependent nature of blood flow, with the Eulerian multiphase model enabled to distinguish between the blood and arterial wall interfaces.Pulsatile blood flow was applied at the inlet through a custom UDF, with velocity ranging from 0 to 0.33 m/s to mimic realistic arterial flow conditions, with outlet velocity profiles monitored throughout the simulation. The Coupled algorithm handled pressure-velocity coupling, with the PRESTO! scheme used for pressure calculations. Simulations were initialized using standard methods, run with a time step of 0.001 seconds over a total of 200 time steps.ConclusionThe results reveal clear differences between the two cases across several key metrics. Shear stress distribution differed substantially: the stenotic artery without a stent exhibited high shear stress regions — conditions that can potentially damage endothelial cells and raise thrombosis risk — while the stented case showed a marked reduction in shear stress, indicating improved hemodynamics and reduced complication risk.Pressure distribution also varied meaningfully, with the stenotic artery showing higher overall pressure than the stented case — consistent with expectations, since the stent alleviates the arterial narrowing and facilitates smoother blood flow, easing the pressure burden.Outlet velocity profiles further supported this trend: the stented artery displayed a more uniform, consistent velocity pattern over time, reflecting improved flow characteristics compared to the unstented stenotic case.Together, these results demonstrate the clear hemodynamic benefits of arterial stent implantation — lower shear stress, more favorable pressure distribution, and more stable outlet flow — underscoring both the clinical value of stenting in managing cardiovascular disease and the usefulness of computational simulation for evaluating and optimizing such medical interventions.

Hyperthermia Therapy of a Cancer Tissue, ANSYS Fluent CFD Simulation TrainingDescriptionCommon cancer treatment methods include surgery, radiotherapy, and chemotherapy, each carrying disadvantages such as aggressiveness, irreversibility, and significant side effects. Hyperthermia therapy offers an alternative approach: applying warming to prevent oxygen and nutrients from reaching unhealthy tissue, causing the proteins within that tissue to change nature in a way that surrounds cancer cells and makes them identifiable to the immune system.This project examines blood flow through capillaries passing through tissue containing cancerous tumors, simulating hyperthermia therapy using ANSYS Fluent. The model represents a spherical region of healthy body tissue through which blood flows slowly, containing several veins arranged uniformly along the x-axis for simplification (real vein structures within body tissue more closely resemble a branching, bush-like pattern). Four spherical cancerous tumors of varying diameters are positioned at the center of this tissue, on the surface of the modeled veins. While healthy and cancerous tissue actually differ in their thermophysical properties, this simulation simplifies the problem by treating both with the same properties.Each cancerous tumor acts as a heat source, releasing thermal energy per unit volume to drive heat transfer and substantially increase local blood flow — the central mechanism this study investigates in terms of how blood vessels and surrounding tissue respond to hyperthermia treatment.Incoming blood flow through the capillary was set to 0.08 m/s at 310.15 K, while blood flow surrounding the capillary was set to 0.000035 m/s, also at 310.15 K. The geometry was designed in Design Modeler and meshed in ANSYS Meshing using an unstructured grid totaling 717,087 cells.MethodologyThe energy equation was activated to capture the heat transfer effects central to hyperthermia therapy. The tissue itself was modeled as a porous medium, since blood flows through the capillaries via the empty cavities within the tissue structure, with a porosity coefficient of 0.05 defined as the ratio of void space to total tissue volume.Each of the four spherical cancer tissues was heated via simulated ultrasonic waves over a 10-second period, implemented using the Source Term option. A custom UDF defined the heat generation rate per unit volume within each sphere: approximately 10,000,000 W/m³ at each sphere's center, decreasing progressively at greater distances from that center point. The simulation was run as unsteady (transient) to capture this time-dependent heating process.ConclusionResults include volume rendering (3D contours) alongside standard contours of velocity, pressure, and temperature throughout the domain. The temperature distribution surrounding the cancerous tumors — the central focus and primary challenge of this simulation — is clearly captured in the resulting figures, illustrating how the applied heat source elevates local tissue temperature and drives the increased blood flow response central to the hyperthermia treatment mechanism.

Monte Carlo Radiation, CT Scan CFD SimulationDescriptionThis project simulates radiation patterns and absorption within a Computerized Tomography (CT) scan environment using the Monte Carlo (MC) radiation model in ANSYS Fluent, focusing on patient safety and image quality optimization. The simulation captures how radiation interacts with the human body and surrounding medical equipment — knowledge directly relevant to medical physicists and radiologists working to balance diagnostic image quality against radiation exposure.The 3D geometry represents a full CT scan room, including the CT machine, patient bed, and patient body, meshed using a high-fidelity unstructured grid totaling 4,390,045 cells.MethodologyThe Monte Carlo radiation model was used to accurately track individual photons from their source through to either absorption within the body or exit from the domain, solving the Radiative Transfer Equation (RTE) to capture photon-environment interaction throughout the scan room. This approach establishes a direct correlation between radiation intensity and photon angular flux, with radiant heat flux calculated based on the local photon incidence rate.Simulation setup involved configuring the Monte Carlo radiation model parameters, defining the CT scanner's radiation source characteristics, assigning material properties for both the patient's body and the surrounding medical equipment, and specifying boundary conditions governing radiation absorption and reflection throughout the domain.ConclusionResults include volumetric absorbed radiation dose within the patient's body, incident radiation patterns across various surfaces, radiation intensity distribution throughout the CT scan environment, and temperature changes resulting from radiation absorption.Two key regions were examined in detail: the radiation path and intensity distribution in the air between the CT scanner and the patient before body contact, and the penetration depth and absorption pattern of radiation once inside the patient, across different body regions. Together, these results characterize how radiation dose is distributed and absorbed throughout the scanning process — information directly applicable to optimizing CT scan protocols for reduced patient radiation exposure while maintaining diagnostic image quality.

COVID-19 Patient Transient Breathing in the Operating Room, ANSYS Fluent TrainingDescriptionThis project simulates transient breathing airflow from a COVID-19 patient's mouth within an operating room using ANSYS Fluent. The operating room is equipped with ventilation and air conditioning systems, and the patient continuously inhales oxygen and exhales carbon dioxide throughout the simulation. The primary objective is to evaluate how effectively fresh, oxygen-carrying air continuously flows into the room's interior, diluting and displacing contaminated exhaled air from the patient's mouth rather than allowing it to accumulate.Ventilation and air conditioning systems positioned on the room's ceiling and floor circulate fresh air throughout the interior, directing it out through side vents to the exterior. The room is modeled as a cubic space measuring 2.9 m × 2.23 m × 3.7 m, containing a hospital bed and patient, with six circular fresh air inlets and five rectangular outlet vents positioned along the side walls.The 3D geometry was designed in Design Modeler, with the patient's oral surface defined as the inlet boundary, since the primary focus is the exhaled airflow from the patient's mouth. The domain was meshed in ANSYS Meshing using an unstructured grid totaling 4,354,238 elements, with finer mesh resolution applied near internal boundaries to improve accuracy. Given the time-dependent nature of respiration, the simulation was run as unsteady with a time step of 0.01 s.MethodologyThe incoming fresh air consists of oxygen and nitrogen in a 3.76 ratio, while the patient's exhaled air additionally contains carbon dioxide — requiring the Species Transport model to track these species throughout the domain. Fresh air entering through the room's air conditioning system carries an oxygen mass fraction of 0.23 and nitrogen mass fraction of 0.77, with no carbon dioxide, entering at 1 m/s and 293.15 K.Since the exhaled airflow from the patient's mouth acts as a discrete source of gaseous species, the Discrete Phase Model (DPM) was used to track these particles from a Lagrangian perspective. Exhaled air was defined with an oxygen mass fraction of 0.16, a carbon dioxide mass fraction of 0.04, and a temperature of 310.15 K.Since real breathing involves inhaling and exhaling through the same oral opening, a custom UDF defined the mouth's airflow velocity as alternating between positive (exhalation, air leaving the mouth) and negative (inhalation, air entering the mouth) every 2.5 seconds, with a velocity magnitude of 0.25 m/s throughout.ConclusionBy tracking the exhaled particles — oxygen, nitrogen, and carbon dioxide — using unsteady DPM particle tracking, this simulation captures how airborne particles exhaled from the patient's mouth behave and disperse over time within the operating room. This approach enables a detailed, time-resolved analysis of coronavirus particle dispersion inside the operating room under the influence of the installed ventilation and air conditioning systems — directly informing infection control strategy for clinical settings during patient care.

Corona Virus Spread in a Car Due to the Cough of the Driver, CFD Simulation Tutorial by ANSYS FluentDescriptionThis project simulates coronavirus spread within a car's interior resulting from the driver's cough, using ANSYS Fluent. COVID-19 has posed one of the greatest global health challenges, largely due to its high contagion rate — coughing or sneezing without a mask can readily spread the virus within enclosed spaces. Maintaining social distance in closed environments has consistently been recommended as a key preventive measure, and the interior of a passenger car represents exactly this kind of confined space where virus transmission between occupants becomes a concern.This simulation models the release of virus particles from the mouth of an infected driver within a car's interior, aiming to investigate how strongly these particles diffuse throughout the enclosed cabin space.The geometry was designed in Design Modeler, representing a car interior with a driver modeled seated in the driver's seat. The domain was meshed in ANSYS Meshing using an unstructured grid totaling 290,403 cells.MethodologyThe Discrete Phase Model (DPM) was used to track the virus-laden particles individually within the continuous airflow inside the car. The wet virus particles secreted from the driver's mouth were treated as the discrete phase, with the surrounding cabin airflow as the continuous phase. Several physical sub-models were applied to these discrete particles: two-way turbulence coupling (capturing the mutual interaction between the continuous and discrete phases), stochastic collision (irregular droplet-to-droplet collisions), coalescence (droplet merging), and breakup (droplet disintegration). The discrete phase behavior was tracked using unsteady particle tracking with a time step of 0.001 s.Injected particles were defined as water droplets, with water vapor modeled as an evaporating gas species — this droplet-based approach required activating the Species Transport model alongside DPM, tracking three gas species throughout the domain: oxygen (O₂), nitrogen (N₂), and water vapor (H₂O), with air serving as the primary fluid throughout the cabin.ConclusionResults include particle tracking of the virus particles at multiple time intervals, based on each particle's residence time and diameter. This tracking clearly illustrates the spreading pathway of the virus particles throughout the car's interior, revealing the strength and extent of the spread. After a brief period, these particles begin to disperse further or settle onto the car's interior surfaces — offering insight into both the transmission risk posed by an infected driver and the practical timeframe over which airborne virus concentration within the vehicle diminishes.

Plastic Cover Effect in Banks Regarding COVID-19, ANSYS Fluent SimulationDescriptionThis project simulates the release of virus particles from a patient's mouth within a bank setting, using ANSYS Fluent. The geometry represents a bank interior containing a table, two chairs, and two people — one of whom is defined as infected, serving as the virus source through coughing, with the mouth treated as the reference surface for discrete virus particle release.Two configurations were modeled: one with no barrier present, and a second incorporating a thin plastic cover positioned between the two people across the table. The domain was meshed in ANSYS Meshing, totaling 1,570,219 elements for the no-cover case and 1,618,366 elements for the case with the plastic cover. Given the particle/virus dispersion nature of the problem, a transient solver was used throughout.MethodologyThis study investigates coronavirus transmission from a bank customer to a healthy employee, comparing scenarios with and without a plastic barrier positioned between them to evaluate its effectiveness at blocking virus particle transmission.The Discrete Phase Model (DPM) tracked virus-laden particles individually within the continuous cabin airflow. Several physical sub-models were applied: two-way turbulence coupling, stochastic collision, coalescence, and breakup, with unsteady particle tracking using a time step of 0.001 s. Particles were injected as water droplets from the surface of the infected patient's mouth, representing the physical expulsion of virus-laden droplets during a cough, defined at 310 K, 32 m/s, and a flow rate of 0.018 kg/s, released over an interval of 0 to 0.1 seconds. Since droplet size varies during propagation, a Rosin-Rammler logarithmic distribution was used to represent the resulting range of particle diameters.This droplet-based approach required activating the Species Transport model alongside DPM, tracking three gas species throughout the domain — oxygen (O₂), nitrogen (N₂), and water vapor (H₂O) — with turbulence resolved using the RNG k-epsilon model and the energy equation enabled to capture temperature distribution throughout the space.ConclusionResults include particle tracking of the virus particles across multiple time intervals, based on each particle's residence time and diameter, compared directly between the with-cover and without-cover configurations. The results clearly demonstrate the plastic cover's effectiveness at blocking coronavirus particle dispersion — confirming that the modeled barrier meaningfully reduces virus transmission risk between the customer and bank employee compared to the uncovered scenario.

ICU Ventilation Design Improvement, Industrial ApplicationDescriptionThis project models and optimizes ICU ventilation system design using ANSYS Fluent's Discrete Phase Model (DPM), balancing two critical requirements: preventing respiratory disease transmission and maintaining optimal thermal comfort for patients and staff.The simulation examines airflow patterns and particle dispersion within the ICU environment, modeling the spread of virus-laden aerosols from infected patients and analyzing particle residence time as an indicator of infection risk. Alongside disease transmission, thermal comfort is evaluated using the Predicted Mean Vote (PMV) and Percentage People Dissatisfied (PPD) indices, capturing how different HVAC configurations affect occupant comfort throughout the space.MethodologyThree distinct ICU ventilation designs were evaluated and compared iteratively:A conventional configuration with inlet and outlet positioned on opposite wallsA cross-ventilation configuration with strategically placed outletsAn optimized configuration featuring a ceiling inlet paired with patient-specific outletsEach design was assessed for its ability to balance airflow-driven infection control against maintaining stable, comfortable thermal conditions, with inlet and outlet placement adjusted across iterations to identify the configuration offering the best overall performance on both fronts.ConclusionThis iterative design process reveals the trade-offs inherent to each ventilation configuration, highlighting how airflow pattern, particle residence time, and thermal comfort indices shift depending on inlet and outlet placement. The optimized ceiling-inlet, patient-specific-outlet configuration emerges as the strongest candidate for minimizing respiratory disease transmission risk while preserving favorable PMV and PPD comfort conditions — offering practical, evidence-based guidance for ICU ventilation design, retrofit planning, and infection control strategy in real-world healthcare facilities.

Fluidized Bed Bio-Reactor, ANSYS Fluent TrainingDescriptionThis project simulates a fluidized bed bio-reactor (FBR) using ANSYS Fluent, applying the Eulerian multiphase model to capture the interaction between air and silicon particles within the reactor.Fluidized bed bio-reactors are versatile devices used across a range of industries, including biomedical research, food processing, and chemical and pharmaceutical manufacturing. Their defining principle — fluidization, where an upward gas flow suspends solid particles in a fluid-like state — offers advantages in mixing, heat transfer, and reaction efficiency that make FBRs a widely adopted technology across these sectors.The bio-reactor geometry was designed in Design Modeler and meshed in ANSYS Meshing using a structured mesh, with mesh quality specifically optimized to accurately capture the complex multiphase behavior characteristic of fluidized systems.MethodologyThe Eulerian multiphase model was configured with the Granular sub-model activated to represent the particle phase, including phase property models for calculating granular temperature and drag and virtual mass forces defined between the air and particle phases.Heat transfer between the air and particles was captured using the Ranz-Marshall model, with the energy equation enabled to resolve temperature distribution throughout the reactor and the standard k-epsilon model applied for turbulence. Particle-particle interactions were captured through defined restitution coefficients, with gravitational effects included to properly represent particle motion, and the simulation run to capture the fluidized bed's inherently transient behavior.ConclusionResults include particle distribution and motion patterns throughout the reactor, along with the resulting temperature changes driven by air-particle heat transfer — together illustrating how fluidization influences the reaction kinetics within the bed.These results offer practical insight into bio-reactor design optimization, informing process efficiency improvements and providing a foundation for scaling fluidized bed bio-reactors up to industrial production levels — directly relevant to bioprocess engineering across biomedical, food, and pharmaceutical manufacturing applications.

Crystallization CFD Simulation of Loratadine Using the PBM in ANSYS FluentDescriptionThis project simulates the crystallization process of Loratadine within a dual-inlet nozzle, using the Population Balance Model (PBM) in ANSYS Fluent. The primary objective is to study particle size distribution and how mixing affects crystal formation as ethanol and water streams interact — these fluids mix to create supersaturation conditions that drive nucleation and subsequent growth of Loratadine crystals. The simulation offers useful insight into the relationship between hydrodynamics, turbulence, and crystallization kinetics, with direct relevance to designing and controlling similar industrial crystallization processes.The computational domain represents a dual-inlet nozzle, with ethanol and water entering through separate inlets and mixing before exiting through a single outlet. The geometry was built in Design Modeler, and the domain was meshed in ANSYS Meshing using a tetrahedral mesh, with refinement concentrated in the mixing region to improve accuracy. The total mesh contained 506,677 elements, balancing accuracy against computational cost, with mesh quality verified to maintain low skewness and support stable convergence.MethodologyThe simulation combined the Eulerian multiphase model with the Realizable k-epsilon turbulence model to capture both phase interaction and turbulent mixing behavior. The Discrete Population Balance Model tracked Loratadine crystal sizes across 10 discrete bins, ranging from 1×10⁻⁸ m to 8.1681×10⁻⁸ m. A custom UDF defined the nucleation and growth kinetics governing crystal formation, allowing these rates to depend on local supersaturation and flow conditions throughout the domain.The primary phase — an ethanol-water mixture — was modeled using the Species model, while the secondary phase represented the dispersed Loratadine particles themselves. The solver was pressure-based and transient, using a time step of 1×10⁻⁷ s with second-order upwind discretization applied for improved accuracy.ConclusionThe simulation captured effective mixing and crystallization of Loratadine throughout the nozzle. Particle size distribution across the 10 defined bins showed smaller particles dominating the early stages of the process, consistent with active nucleation, while larger particles emerged later as growth progressed:BinSize (m)Bin-08.168097 × 10⁻⁸Bin-16.4680612 × 10⁻⁸Bin-25.1218559 × 10⁻⁸Bin-34.0558379 × 10⁻⁸Bin-43.2116915 × 10⁻⁸Bin-52.5432383 × 10⁻⁸Bin-62.0139111 × 10⁻⁸Bin-71.5947534 × 10⁻⁸Bin-81.2628355 × 10⁻⁸Bin-91 × 10⁻⁸The UDF successfully captured crystal growth's dependence on local flow conditions, with flow results revealing strong mixing zones near the inlets and a progressively more uniform crystal distribution toward the outlet. Together, these results confirm that the combined PBM-UDF approach reliably represents the coupled interaction between flow dynamics and particle growth throughout the crystallization process.