Advanced CFD topics

Transient CFD, also known as unsteady or time-dependent CFD, is the simulation of fluid flow and heat transfer problems where the fluid properties and flow characteristics change with time. In transient CFD, the governing equations include time derivatives, accounting for the variations in fluid behavior over time.

Unsteady analysis in Computational Fluid Dynamics (CFD) has increasingly gained importance as the need for more accurate and realistic simulations has grown. While steady-state solvers are still widely used due to their lower computational cost, transient analysis provides a wealth of benefits that cannot be overlooked. Some of these benefits include:

• Time-dependent behavior: Transient analysis captures the time-dependent behavior of fluid flows, providing a more accurate representation of real-world scenarios. This is especially important when studying phenomena that exhibit time-varying characteristics, such as the formation and dissipation of vortices, oscillating flows, or transient heat transfer.
• Improved accuracy for complex systems: Transient analysis is critical for simulating complex systems where the flow field changes over time or is affected by moving parts. Examples include the opening and closing of valves, the rotation of wind turbine blades, or the unsteady operation of pumps and compressors.
• Flow instability analysis: Unsteady analysis enables the study of flow instabilities and their potential impact on system performance. This information is crucial for designing systems that can withstand or mitigate the effects of instabilities, such as cavitation in pumps or flutter in aircraft wings.
• Better understanding of flow physics: Transient analysis provides detailed insights into the flow physics, allowing researchers and engineers to identify the underlying mechanisms driving specific flow phenomena. This understanding can lead to improved design and optimization of fluid systems, as well as enhanced predictive capabilities for future applications.
• Comprehensive multiphase flow simulations: Transient analysis is essential for simulating multiphase flows, as it can capture the dynamic interactions between different phases, such as gas bubbles in a liquid or solid particles in a gas. This capability is crucial for applications like bubble column reactors, fluidized bed reactors, and erosion analysis.
• Enhanced performance prediction: Unsteady analysis enables the simulation of real operating conditions, such as varying loads or boundary conditions. This information can be used to predict the performance of fluid systems under a wide range of conditions, leading to better design and operation strategies.

In conclusion, while steady-state solvers have their place in CFD analysis due to their lower computational cost, transient analysis offers a more comprehensive and accurate view of fluid behavior. By capturing the time-dependent nature of flows, unsteady analysis provides valuable insights into complex systems, flow instabilities, and multiphase flows, ultimately leading to better design, optimization, and performance prediction for a variety of applications.

High-resolution meshes offer a plethora of advantages in Computational Fluid Dynamics (CFD) simulations, allowing engineers and researchers to obtain more accurate and reliable results. Some of the key benefits of using high-resolution meshes in CFD simulations are as follows:

• Improved Accuracy: High-resolution meshes provide a more detailed representation of the geometry and flow behavior of the physical domain. This results in better accuracy when simulating complex phenomena, such as turbulence, boundary layers, and flow separation. Accurate simulations are essential in applications where even small deviations from the actual flow behavior can have significant consequences, such as in the design of aircraft, automobiles, and power plants.
• Enhanced Convergence: The use of high-resolution meshes can improve the convergence rate of CFD simulations. With a fine mesh, the numerical solution converges more quickly to the true solution, leading to reduced simulation times and more efficient use of computational resources.
• Superior Visualization: High-resolution meshes enable better visualization of the flow field and its characteristics. This can be particularly beneficial in understanding complex flow phenomena and identifying areas of interest, such as recirculation zones, vortices, and flow separation points.
• Increased Sensitivity to Boundary Conditions: High-resolution meshes allow for a more accurate representation of boundary conditions, which can have a significant impact on the overall flow behavior. This is particularly important when dealing with intricate geometries or when simulating multiphase flows, where the interaction between phases and the boundary conditions plays a crucial role.
• Greater Flexibility: With high-resolution meshes, it becomes easier to refine specific regions of interest within the computational domain. This allows for targeted mesh refinement, where areas of high flow gradients or complex geometries are represented with a higher level of detail while maintaining coarser meshes in regions with simpler flow behavior. This results in a more efficient use of computational resources and faster simulation times without sacrificing overall accuracy.

In conclusion, the use of high-resolution meshes in CFD simulations provides numerous benefits, including improved accuracy, enhanced convergence, superior visualization, increased sensitivity to boundary conditions, and greater flexibility. By using accurate models and high-resolution meshes, engineers and researchers can obtain high-quality and reliable results, enabling them to optimize designs and make informed decisions in various industries and applications.

A multiphase CFD (Computational Fluid Dynamics) simulation is a numerical method used to analyze the flow and interactions between two or more phases within a fluid system. In this context, a „phase“ refers to a distinct quantity of matter with its own physical properties, such as different densities, sizes, or shapes. The phases can be in the form of solid, liquid, or gas, and may have distinct interfaces or be dispersed within each other.

Multiphase CFD simulations are employed to model complex flow behavior involving multiple phases, such as the mixing of liquids, the suspension of solid particles in a fluid, or the interaction between gas bubbles and a liquid medium. These simulations are essential for understanding and predicting the behavior of fluid systems in a wide range of industrial applications, including oil and gas, chemical engineering, power generation, food processing, and environmental engineering.

In a multiphase CFD simulation, different modeling techniques are used to accurately represent the interactions between the different phases. Some of the most common methods include:

• Eulerian-Eulerian method: This approach treats each phase as an interpenetrating continuum, with separate conservation equations for mass, momentum, and energy being solved for each phase simultaneously. The interactions between the phases are accounted for through source terms in the conservation equations.
• Eulerian-Lagrangian method: In this technique, one phase (typically the continuous phase, such as a liquid or gas) is modeled using an Eulerian approach, while the other phase (usually the dispersed phase, such as particles or bubbles) is modeled using a Lagrangian approach. The dispersed phase is tracked individually or in groups, and its interaction with the continuous phase is calculated through various force models.
• Volume of Fluid (VOF) method: The VOF method is particularly suited for simulating immiscible fluids with distinct interfaces, such as liquid-liquid or liquid-gas systems. In this approach, a single set of conservation equations is solved for the entire domain, and the interface between the phases is tracked using a volume fraction variable.

Some of the key aspects and benefits of multiphase modeling in CFD simulations are:

Realistic representation of complex systems: Multiphase modeling enables the simulation of real-world systems, where the interaction between different phases (such as liquids, gases, and solids) can significantly impact the overall flow behavior. It allows for the accurate representation of phenomena like phase separation, phase change, and flow-induced forces on particles or bubbles.

Enhanced understanding of flow behavior: Multiphase modeling provides detailed insights into the flow patterns, pressure and velocity distributions, and other critical parameters within a system. This knowledge is essential for optimizing processes, predicting system performance, and identifying potential issues that could arise during operation.

Multiphase modeling is applicable across a wide range of industries and applications, including oil and gas, chemical processing, power generation, food processing, and environmental engineering. Some typical examples of multiphase flow phenomena include:

• Mixing of different liquids in chemical reactors
• Sedimentation and settling of solid particles in wastewater treatment plants
• Bubble formation and gas dispersion in flotation processes
• Particle-laden flows in pneumatic conveying systems
• Spray formation and droplet dynamics in fuel injection systems

Large Eddy Simulation (LES) is a cutting-edge computational fluid dynamics (CFD) technique that has gained significant traction in recent years for its ability to accurately model turbulent flows. Unlike the Reynolds-Averaged Navier-Stokes (RANS) turbulence models, LES offers a more detailed and precise representation of the complex turbulence phenomena that occur in fluid flows.

The primary difference between LES and RANS models lies in their approach to modeling turbulence. While RANS models focus on calculating the time-averaged effects of turbulence, LES resolves the larger, energy-containing eddies directly and models only the smaller, subgrid-scale (SGS) eddies. This allows LES to capture the unsteady, transient behavior of the flow, providing a more realistic representation of the turbulence.

There are several benefits to using LES over RANS turbulence models:

• Improved Accuracy: LES captures the large-scale, unsteady flow structures that RANS models often fail to represent, resulting in a more accurate prediction of turbulence and its effects on the flow.
• Better Representation of Complex Flows: LES can model complex, transient flows with greater fidelity than RANS, such as flows with strong separation, vortex shedding, or shear layer interactions.
• Enhanced Understanding of Flow Physics: By resolving the larger eddies directly, LES provides valuable insight into the underlying flow physics, enabling researchers to develop a deeper understanding of turbulence and its impact on engineering applications.
• Reduced Model Uncertainty: LES has fewer empirically derived closure models compared to RANS, reducing the uncertainty associated with model selection and calibration.

The Lattice Boltzmann Method (LBM) is a numerical technique used in computational fluid dynamics (CFD) for simulating fluid flow and other related physical phenomena. It is an alternative to the traditional Navier-Stokes equations-based methods, offering several advantages in specific applications.

The Lattice Boltzmann Method (LBM) is grounded in the Boltzmann equation, a fundamental equation in statistical mechanics that describes the behavior of particles in a fluid. This equation provides a more general description of fluid behavior, accounting for the statistical properties of individual particles. In contrast, the Navier-Stokes equations describe the macroscopic behavior of fluids, focusing on the conservation of mass and momentum in a continuous medium.

LBM discretizes the fluid domain into a lattice or grid, with each node representing a discrete point in space. At these nodes, a limited set of particle distribution functions is defined, representing the probability of particles moving in different directions. Interestingly, the Navier-Stokes equations can be derived from the Boltzmann equation, establishing a connection between the statistical mechanics of individual particles and the macroscopic fluid behavior described by the Navier-Stokes equations.

One of the key advantages of LBM is its inherent parallelizability, as the computations at each lattice node can be performed independently, making it well-suited for high-performance computing platforms. Additionally, the LBM is adept at handling complex geometries, porous media, and multiphase flows, making it a versatile tool in the field of CFD .

Viscosity is a measure of a fluid’s resistance to flow or deformation under applied shear stress or shear strain. In simpler terms, it describes how „thick“ or „sticky“ a fluid is. Rheology is the broader study of the flow and deformation of matter, specifically focusing on the relationship between stress, strain, and time. While it primarily deals with fluids, it can also be applied to semi-solids, gels, and even some solid materials. Rheology encompasses a variety of material behaviors, such as elastic (recoverable deformation), viscous (non-recoverable deformation), and viscoelastic (a combination of both). Rheological properties can be characterized by rheograms, which graphically represent the relationship between stress and strain, and rheometers, which are instruments used to measure these properties.

Complex rheology refers to the study of fluids that exhibit non-Newtonian behavior, meaning their viscosity changes with applied stress or strain rate. Examples of such fluids include blood, polymers, slurries, and foams. CFD simulations, when combined with advanced rheological models, provide valuable insights into the flow behavior and performance of systems involving these complex fluids.

Complex rheological fluids pose unique challenges for engineers and scientists due to their non-linear and often time-dependent flow properties. This makes predicting their behavior in various applications and systems more complicated than for Newtonian fluids. CFD simulations, through the incorporation of appropriate rheological models, enable the accurate representation of these fluids‘ characteristics and behavior under different flow conditions.

In CFD simulations, the choice of a suitable rheological model is critical for capturing the relevant fluid behavior. Common models for non-Newtonian fluids include the Bingham plastic, power-law, Herschel-Bulkley, and Carreau-Yasuda models, among others. These models consider factors such as shear-thinning, shear-thickening, and viscoelastic properties, enabling the simulation of complex fluid dynamics in a wide range of applications.

Stirred tank modeling with accurate 3-dimensional impeller geometry is crucial for achieving a detailed representation of the flow dynamics and the mixing patterns. This kind of application requires employing special solution approaches to capture the motion of the impeller within a stationary tank. Two widely used models for stirred tank simulations are the Multiple Reference Frames (MRF) model and the Sliding Mesh model.

The MRF model is a steady-state approach, allowing for the simulation of baffled stirred tanks and tanks with other complex internals, such as multiple rotating components. In this model, the computational domain is divided into stationary and rotating frames. The rotating frame contains the impeller, while the stationary frame houses the tank walls and baffles. The momentum equations are solved separately within each frame, and a steady transfer of information occurs at the MRF interface.

However, the MRF model has its limitations. It is best suited for simulations where the impeller-baffle interaction is weak, as the impeller’s position remains static throughout the calculation. For applications where the interaction is strong, the MRF model may not provide an accurate representation of the flow field. To overcome these limitations, engineers often perform multiple solutions with different impeller positions and average the results.

The Sliding Mesh model offers a more rigorous solution method for stirred tank simulations. This time-dependent approach provides a realistic representation of impeller motion by allowing the grid surrounding the impeller to physically move during the simulation. This results in a time-accurate simulation of impeller-baffle interactions and captures low-frequency oscillations in the flow field. However, the Sliding Mesh model requires more computational resources due to its transient nature and the need for interpolation when passing information between the rotating and stationary grid regions.

In summary, stirred tank modeling with 3D simulations offers valuable insights into the performance of industrial processes involving impellers. The choice between the MRF and Sliding Mesh models depends on the specific application and the desired level of accuracy. The MRF model provides a computationally efficient solution for cases with weak impeller-baffle interactions, while the Sliding Mesh model delivers a more rigorous and informative representation of the flow field, capturing time-dependent phenomena at the cost of increased computational resources