💥💥💥 How to define pressure drop on contact between two domains in Ansys Fluent?

 To define pressure drop on contact between two domains in Ansys Fluent, you need to create an interface between the two domains and specify the pressure jump across the interface. You can do this by following these steps:

• In the Meshing mode, select the two faces that are in contact and create a Named Selection for them. Give it a meaningful name, such as “interface”.
• In the Fluent mode, go to the Boundary Conditions panel and select the Named Selection you created. Click on Create/Edit and choose Interface from the Type drop-down menu. Click OK.
• In the Interface panel, click on the Fluid tab and enable the Pressure Jump option. Enter the value of the pressure drop in the Pressure Jump field. You can also specify the direction of the pressure jump by choosing either Positive or Negative from the Direction drop-down menu. Click OK.

You have now defined the pressure drop on contact between two domains in Ansys Fluent. You can check the results by using the Report > Surface Integrals > Area Weighted Average > Pressure option and selecting the interface from the list. The difference between the inlet and outlet pressures should be equal to the pressure drop you specified.

For more information, you can refer to these web sources:

• Pressure drop calculation - Ansys Learning Forum
• Pressure drop in a pipe – CFD Online Discussion Forums
• Two different periodic domains in Fluent - CFD Online
• Pressure Drop in Fluent - Ansys Learning Forum

What is the difference between pressure drop and pressure jump?

The difference between pressure drop and pressure jump is that pressure drop is the decrease in pressure that occurs when a fluid flows through a conduit, such as a pipe, valve, or fitting, due to friction or other forces. Pressure jump is the increase or decrease in pressure that occurs when a fluid undergoes a rapid change in volume or phase, such as in a chemical reaction, phase transition, or compression/expansion process. Pressure drop is usually irreversible, meaning that the fluid loses some of its energy as heat and cannot recover its original pressure. Pressure jump is usually reversible, meaning that the fluid can regain its original pressure if the volume or phase change is reversed.

Some examples of pressure drop and pressure jump are:

• Pressure drop: When water flows through a faucet, it experiences a pressure drop due to the resistance of the faucet and the pipe. The pressure drop depends on the flow rate, the pipe diameter, the pipe length, the pipe roughness, and the fluid viscosity. The pressure drop can be calculated using the Darcy–Weisbach equation¹ or other empirical formulas².
• Pressure jump: When a gas is compressed in a piston, it experiences a pressure jump due to the decrease in volume. The pressure jump depends on the initial and final volumes, the initial and final temperatures, and the gas constant. The pressure jump can be calculated using the ideal gas law or other thermodynamic equations. If the piston is released, the gas can expand and return to its original pressure.

Can you explain more about reversible and irreversible processes?

Sure, I can explain more about reversible and irreversible processes. A reversible process is a theoretical concept in thermodynamics that describes a process that can be reversed without leaving any trace on the surroundings. In other words, a reversible process is one that can be undone by following the exact opposite steps, and both the system and the environment will return to their original states. For example, if you compress an ideal gas slowly and isothermally, you can reverse the process by expanding the gas slowly and isothermally, and the gas and the surroundings will have the same temperature, pressure, and entropy as before.

An irreversible process is a realistic concept in thermodynamics that describes a process that cannot be reversed without leaving some effect on the surroundings. In other words, an irreversible process is one that cannot be undone by following the exact opposite steps, and either the system or the environment or both will have different states than before. For example, if you burn a piece of paper, you cannot reverse the process by unburning the paper, and the paper and the surroundings will have different temperature, pressure, and entropy than before.

The difference between reversible and irreversible processes is related to the second law of thermodynamics, which states that the entropy of an isolated system can never decrease. A reversible process is one that does not change the entropy of the system and the surroundings, while an irreversible process is one that increases the entropy of the system and the surroundings. Therefore, all natural processes are irreversible, because they always involve some increase in entropy. A reversible process is only an idealization that helps us to calculate the maximum efficiency or work that can be obtained from a system.

How do I calculate the efficiency of a system?

There are different methods for calculating the efficiency of a system, depending on the type and purpose of the system. One common method is to use the input-output analysis, which requires measuring the inputs and outputs of a system and calculating the efficiency ratio. This method is useful for identifying the inputs required to produce the desired output, measuring the input, and comparing it with the output produced¹.

The efficiency ratio can be calculated by dividing the useful output by the total input, and multiplying by 100 to get a percentage. For example, if a system uses 100 joules of energy to produce 80 joules of useful work, the efficiency ratio is (80/100) x 100 = 80%. This means that 80% of the input energy is converted into useful work, and the remaining 20% is wasted as heat, sound, or other forms of energy.

Another method is to use performance metrics, which involve measuring system performance against a set of predefined metrics, along with the input and output. This method is useful for evaluating the quality, speed, and reliability of a system, as well as the cost and resource consumption. For example, if a system produces 100 units of output per hour, using 50 units of input, and has a defect rate of 5%, the performance metrics can be used to calculate the efficiency of the system in terms of productivity, quality, and resource utilization¹.

Other methods for calculating system efficiency include efficiency ratios, which compare the output of a system with a standard or benchmark output, and benchmarking, which compares the performance of a system with the best practices or industry standards¹. These methods can help to identify the gaps and areas for improvement in a system, and to set realistic and achievable goals for enhancing system efficiency.

For more information, you can refer to these web sources:

• Calculating System Efficiency: A Step-by-Step Guide | Wrike
• Calculate total system efficiency - YouTube
• Work, power and efficiency - AQA Efficiency - BBC

💥💥💥 How to create new coordinate system in Ansys Fluent?

 It seems that you want to create a new coordinate system in Ansys Fluent for your simulation. There are different ways to do that, depending on your needs and preferences. Here are some possible options:

• You can use the Coordinate Systems branch in Ansys Mechanical to insert a new coordinate system and define its origin, orientation, and type. You can then export the mesh to Ansys Fluent and use the Transform option in the Mesh menu to apply the new coordinate system to the mesh. You can find more details and screenshots about this method in this article¹.
• You can use the Define menu in Ansys Fluent to create a new coordinate system and specify its properties. You can then use the Set option in the Grid menu to select the new coordinate system as the active one for the simulation. You can find more information and examples about this method in this forum thread².
• You can use a User Defined Function (UDF) in Ansys Fluent to create a new coordinate system and perform coordinate transformations for your variables. This method requires some programming skills and knowledge of the UDF syntax and structure. You can find some tips and code snippets about this method in this forum thread³.

What is the difference between a global and local coordinate system?

A global coordinate system is a fixed reference frame that describes the position and orientation of objects in a simulation or a real-world scenario. A local coordinate system is a movable reference frame that is attached to a specific object and can change its position and orientation relative to the global coordinate system. The local coordinate system is often defined according to the shape and symmetry of the object. For example, an airplane may have a local coordinate system with the x-axis aligned along the fuselage axis of the body and the y-axis pointing along the port wing¹.

The difference between a global and local coordinate system is important for defining and transforming the coordinates of objects and signals in a simulation or a real-world scenario. For example, when a signal interacts with a sensor or a target, the interaction is usually specified as a function of the sensor or target local coordinates. However, when a signal propagates in the global coordinate system, the ray paths that connect transmitters, targets, and receivers are specified in global coordinates. Therefore, one needs to be able to convert local coordinates to global coordinates, and vice versa, using appropriate transformation matrices or functions²³.

How to apply new coordinates to my simulation in Ansys Fluent ?

To apply new coordinates to your simulation in Ansys Fluent, you need to follow these steps:

• First, you need to create a new coordinate system and specify its properties using the Define menu in Ansys Fluent. You can find more information and examples about this method in this forum thread¹.
• Next, you need to use the Set option in the Grid menu to select the new coordinate system as the active one for the simulation. This will affect the display of the mesh and the results in the graphics window, as well as the input and output of the boundary conditions and the reports.
• Finally, you need to use the Transform option in the Mesh menu to apply the new coordinate system to the mesh. This will change the coordinates of the mesh nodes and the cell volumes according to the new coordinate system. You can also use this option to scale, translate, or rotate the mesh.

Alternatively, you can use a User Defined Function (UDF) in Ansys Fluent to create a new coordinate system and perform coordinate transformations for your variables. This method requires some programming skills and knowledge of the UDF syntax and structure. You can find some tips and code snippets about this method in this forum thread².

What is the difference between scaling and rotating the mesh?

Scaling and rotating the mesh are two different ways of transforming the mesh geometry in Ansys Fluent. Scaling the mesh means changing the size of the mesh elements by a certain factor, while rotating the mesh means changing the orientation of the mesh elements by a certain angle. Both scaling and rotating the mesh can affect the accuracy and stability of the simulation, as well as the physical interpretation of the results.

Scaling the mesh can be useful for adjusting the dimensions of the mesh to match the desired units or the physical size of the problem. For example, if the mesh was created in meters, but the problem requires centimeters, scaling the mesh by a factor of 0.01 can convert the mesh units to centimeters. However, scaling the mesh can also introduce errors or distortions in the mesh quality, especially if the scaling factor is not uniform in all directions. For example, if the mesh is scaled by a factor of 2 in the x-direction, but by a factor of 0.5 in the y-direction, the mesh elements will become elongated and skewed, which can affect the numerical solution and the boundary layer resolution. Therefore, scaling the mesh should be done with caution and only when necessary.

Rotating the mesh can be useful for aligning the mesh with the desired coordinate system or the physical orientation of the problem. For example, if the mesh was created with the x-axis pointing north, but the problem requires the x-axis pointing east, rotating the mesh by 90 degrees around the z-axis can change the mesh orientation to match the problem. However, rotating the mesh can also introduce errors or inconsistencies in the mesh coordinates, especially if the rotation angle is not a multiple of 90 degrees. For example, if the mesh is rotated by 45 degrees around the z-axis, the mesh coordinates will no longer be aligned with the Cartesian axes, which can affect the input and output of the boundary conditions and the reports. Therefore, rotating the mesh should be done with care and only when necessary.

To learn more about scaling and rotating the mesh in Ansys Fluent, you can refer to the following sources:

• Applying Translations, Scaling, Reflection, and Rotation via the Deformed Geometry and Moving Mesh Interfaces¹
• Use 3D Transform Gizmo in After Effects²
• Why is it important to apply transformation to an objects data?³

💥💥💥 How to handle with complex geometries (models) in Ansys Fluent?

ANSYS Fluent is a powerful software for simulating fluid dynamics and heat transfer problems. It can handle complex geometries (models) by using different meshing methods and workflows. Meshing is the process of dividing the geometry into small elements that can be used for numerical calculations. Depending on the type and quality of the geometry, you may need to use different meshing tools and techniques.

One of the meshing methods available in ANSYS Fluent is the Watertight Geometry Workflow. This workflow is suitable for clean and watertight geometries, which means that there are no gaps, overlaps, or errors in the geometry. The Watertight Geometry Workflow can automatically generate a high-quality mesh for complex geometries, such as aircraft, cars, or turbines. You can also customize the mesh settings and refine the mesh in specific regions. To learn more about the Watertight Geometry Workflow, you can watch this video or take this course.

Another meshing method available in ANSYS Fluent is the Fault-Tolerant Workflow. This workflow is suitable for dirty and non-watertight geometries, which means that there are some defects or errors in the geometry. The Fault-Tolerant Workflow can wrap the geometry with a thin layer of mesh and create a fluid region without modifying the original geometry. This can save a lot of time and effort in geometry preparation and meshing. To learn more about the Fault-Tolerant Workflow, you can watch this webinar or this video.

What is Watertight Geometry Workflow?

The Watertight Geometry Workflow is a meshing method in ANSYS Fluent that can automatically generate a high-quality mesh for complex geometries, such as aircraft, cars, or turbines. The workflow guides users through a step-by-step process of simple, intuitive inputs, while behind the scenes, Fluent Meshing uses built-in intelligence and automation to create a CFD mesh using its sophisticated meshing technology. The workflow can also be customized and saved for future use. The Watertight Geometry Workflow is suitable for clean and watertight geometries, which means that there are no gaps, overlaps, or errors in the geometry. You can learn more about the Watertight Geometry Workflow by taking this course or watching this video. 😊

How do I know if my geometry is watertight?

To check if your geometry is watertight, you can use the Check Geometry tool in SpaceClaim or Fluent Meshing. This tool will detect any errors or defects in your geometry, such as gaps, overlaps, or non-manifold edges, that can prevent the geometry from being watertight. A watertight geometry is one that has no holes or leaks, and can be completely filled with fluid. You can access the Check Geometry tool by selecting the geometry in the structure tree, right-clicking, and choosing Check Geometry¹. Alternatively, you can also use the Fault Detection tool in DesignModeler¹. You can learn more about how to check and repair your geometry by watching this video or taking this course. 😊

Can you recommend any other tools for geometry preparation?

Sure, there are some other tools that you can use for geometry preparation in ANSYS Fluent. Here are some of them:

• ANSYS DesignModeler: This is a CAD tool that is integrated with ANSYS Workbench and can be used to create, modify, and repair geometries for CFD analysis. You can use parametric modeling, sketching, Boolean operations, and other features to create complex geometries. You can also use the Fault Detection tool to identify and fix any errors or defects in your geometry. You can learn more about ANSYS DesignModeler by taking this course or watching this video.
• ANSYS Meshing: This is a meshing tool that is integrated with ANSYS Workbench and can be used to generate high-quality meshes for CFD analysis. You can use different meshing methods, such as patch conforming, patch independent, cutcell, and polyhedral, to create structured, unstructured, or hybrid meshes. You can also use local sizing controls, inflation layers, and mesh quality checks to optimize your mesh. You can learn more about ANSYS Meshing by taking this course or watching this video.
• ANSYS Fluent Meshing: This is a meshing tool that is integrated with ANSYS Fluent and can be used to generate high-quality meshes for CFD analysis. You can use different meshing workflows, such as Watertight Geometry Workflow, Fault-Tolerant Workflow, and Mosaic Meshing, to create surface and volume meshes for complex geometries. You can also use mesh adaption, mesh morphing, and mesh quality checks to improve your mesh. You can learn more about ANSYS Fluent Meshing by taking this course or watching this video.

What is the difference between ANSYS Meshing and Fluent Meshing?

ANSYS Meshing and Fluent Meshing are two different meshing tools that can be used for CFD analysis. They have some common features, such as generating 3D meshes with tet, hex, prism/wedge, and pyramid elements, and integrating with ANSYS Workbench. However, they also have some differences, such as:

• Fluent Meshing is a strictly 3D mesher, while ANSYS Meshing can generate 2D and 3D meshes.
• Fluent Meshing can generate meshes with conformal polyhedron elements, which can reduce cell count and improve gradient calculations, while ANSYS Meshing cannot.
• Fluent Meshing has different meshing workflows, such as Watertight Geometry Workflow, Fault-Tolerant Workflow, and Mosaic Meshing, which can handle complex geometries with different levels of quality and automation, while ANSYS Meshing has physics-aware mesh settings that can be customized for different analysis types.
• Fluent Meshing can handle non-conformal meshes at interfaces, which means that the mesh nodes do not have to match between adjacent cells, while ANSYS Meshing requires conformal meshes.
• Fluent Meshing is more suitable for experienced users who want to have more control and flexibility over the meshing process, while ANSYS Meshing is more suitable for beginners or intermediate users who want to have a simpler and faster meshing process.

What is polyhedral meshing?

Polyhedral meshing is a method of generating meshes for computational fluid dynamics (CFD) analysis, where each cell is a polyhedron with multiple faces. Polyhedral meshes can have several advantages over other types of meshes, such as:

• They can reduce the number of cells and memory requirements, while maintaining the same accuracy and resolution.
• They can better capture the gradients and flow alignments, because of the multiple neighbors and directions of each cell.
• They can improve the convergence and stability of the numerical solver, because of the more off-diagonal terms in the linear system.

Polyhedral meshes can be created from different sources, such as tetrahedral meshes, octree meshes, or surface meshes. There are different tools and workflows that can generate polyhedral meshes, such as ANSYS Fluent Meshing, Simcenter STAR-CCM+, or Polygon Mesh Processing. 

How do I create a polyhedral mesh in ANSYS Fluent?

To create a polyhedral mesh in ANSYS Fluent, you can use one of the following methods:

• Converting the entire domain into polyhedral cells. This method is applicable only for meshes that contain tetrahedral and/or wedge/prism cells. To do this, you can use the Mesh/Polyhedra/Convert Domain menu in Fluent. Fluent will automatically decompose each non-hexahedral cell into multiple sub-volumes called “duals” and then agglomerate them into polyhedral cells around the original nodes. You can learn more about this method by reading the ANSYS Fluent User’s Guide¹ or watching this video².
• Converting skewed tetrahedral cells to polyhedral cells. This method is applicable for meshes that contain some tetrahedral cells that have poor quality or skewness. To do this, you can use the Mesh/Polyhedra/Convert Skewed Cells menu in Fluent. Fluent will identify the skewed cells and convert them to polyhedral cells using the same dual decomposition and agglomeration technique as the previous method. You can learn more about this method by reading the ANSYS Fluent User’s Guide¹.
• Using the Watertight Geometry Workflow. This method is suitable for clean and watertight geometries, which means that there are no gaps, overlaps, or errors in the geometry. To do this, you can use the Fluent Meshing mode in Workbench and select the Watertight Geometry Workflow option. Fluent Meshing will guide you through a step-by-step process of simple, intuitive inputs, while behind the scenes, it will use built-in intelligence and automation to create a polyhedral mesh using its sophisticated meshing technology. You can learn more about this method by taking this course³ or watching this video⁴.

What is the difference between polyhedral and tetrahedral meshes?

Polyhedral and tetrahedral meshes are two types of meshes that can be used for computational fluid dynamics (CFD) analysis. They have some similarities and differences, such as:

• Polyhedral meshes are derived from tetrahedral meshes by forming polygons around each node in the tetrahedral mesh¹.
• Polyhedral meshes can reduce the number of cells and memory requirements, while maintaining the same accuracy and resolution as tetrahedral meshes¹².
• Polyhedral meshes can better capture the gradients and flow alignments, because of the multiple neighbors and directions of each cell¹².
• Polyhedral meshes can improve the convergence and stability of the numerical solver, because of the more off-diagonal terms in the linear system¹².
• Tetrahedral meshes are more general and can handle any geometry, while polyhedral meshes require clean and watertight geometries²³.
• Tetrahedral meshes are easier and faster to generate, while polyhedral meshes require more processing and time²⁴.

How do I choose the best meshing method for my project?

Choosing the best meshing method for your project depends on several factors, such as:

• The geometry and complexity of your model. Some geometries may require more refinement or special treatment to capture the flow features and boundary conditions accurately.
• The physics and solver settings of your simulation. Some physics models, such as turbulence, heat transfer, or multiphase, may require finer or different types of meshes to ensure numerical stability and convergence.
• The computational resources and time available for your simulation. Finer meshes usually result in more accurate solutions, but they also increase the computational cost and time of the simulation.

There is no single meshing method that works best for all cases, so you may need to try different methods and compare the results. However, some general guidelines that can help you choose a suitable meshing method are:

• Use adaptive meshing or physics-controlled meshing to automatically adjust the mesh density according to the solution gradient and the physics settings¹².
• Use boundary layer meshing or inflation layers to resolve the thin regions near the walls where the flow velocity changes rapidly³⁴.
• Use polyhedral meshing or mosaic meshing to reduce the number of cells and improve the solution quality and convergence, especially for complex geometries⁴⁵.
• Use structured or hexahedral meshing for simple geometries or regions where the flow is aligned with the mesh, as they can reduce the numerical errors and diffusion²⁵.
• Use unstructured or tetrahedral meshing for complex geometries or regions where the flow is not aligned with the mesh, as they can conform to any shape and capture the flow features²⁵.

🧐🧐🧐 Some information about Ansys Fluent and other CFD programs

 CFD stands for **computational fluid dynamics**³, which is a branch of fluid mechanics that uses numerical analysis and data structures to analyze and solve problems that involve fluid flows³. Computers are used to perform the calculations required to simulate the free-stream flow of the fluid, and the interaction of the fluid (liquids and gases) with surfaces defined by boundary conditions³. CFD can be used to study a wide range of phenomena, such as aerodynamics, heat transfer, combustion, turbulence, multiphase flows, and more³.

Source: 

(1) Computational fluid dynamics - Wikipedia. https://en.wikipedia.org/wiki/Computational_fluid_dynamics.

(2) Contract for Differences (CFD) Definition, Uses, and Examples. https://www.investopedia.com/terms/c/contractfordifferences.asp.

(3) Contract for Differences (CFDs) Overview and Examples - Investopedia. https://www.investopedia.com/articles/stocks/09/trade-a-cfd.asp.

Who invented Ansys Fluent ?

According to the web search results, Ansys Fluent was invented by **John Swanson**³, who founded Ansys in 1970³. He sold his interest in the company to venture capitalists in 1993³ and Ansys went public on NASDAQ in 1996³. Fluent was the first commercial CFD software to have a graphical user interface and workflow rather than a command-line input¹⁴. It was developed at Sheffield University with contributions from multiple personalities¹. In May 2006, Fluent Inc. was acquired by Ansys³..

Source:

(1) Ansys - Wikipedia. https://en.wikipedia.org/wiki/Ansys.

(2) Ansys Fluent: A History of Innovations in CFD. https://www.ansys.com/blog/ansys-fluent-history-of-innovations.

(3) History and Development of CFD Fluent Software | Ansys Fluent. https://simutechgroup.com/fluent-extends-their-user-friendly-workflow-improvements-throughout-the-years/.

(4) The Ansys Story | 50 Years of Innovation. https://www.ansys.com/company-information/the-ansys-story.

Who are the competitors of Ansys Fluent?

Ansys Fluent is not the only software that offers CFD capabilities. There are many competitors and alternatives to Ansys Fluent in the market, depending on the needs and preferences of the users. Some of the most popular ones are:

- **AutoCAD**¹: AutoCAD is a software for computer-aided design (CAD) and drafting that was developed by Autodesk. It is widely used by architects, engineers, and construction professionals to create 2D and 3D drawings and models. AutoCAD also has a CFD module that allows users to perform fluid flow and heat transfer simulations within the AutoCAD environment. AutoCAD has a market share of 40.59% in the CAD software category³.

- **Solidworks**¹: Solidworks is a software for 3D modeling and simulation that was developed by Dassault Systèmes. It is mainly used by mechanical engineers and product designers to create and test parts, assemblies, and products. Solidworks also has a CFD module that allows users to perform fluid flow and heat transfer simulations within the Solidworks environment. Solidworks has a market share of 13.78% in the CAD software category³.

- **SimScale**¹²: SimScale is a cloud-based platform for engineering simulation that was founded by SimScale GmbH. It allows users to access various simulation tools, such as CFD, finite element analysis (FEA), thermodynamics, acoustics, and particle analysis, through a web browser. SimScale also has a large community of users and experts who can share and collaborate on projects. SimScale is considered as an easy-to-use and affordable alternative to Ansys Fluent².

- **OpenFOAM**¹: OpenFOAM is an open-source software for CFD that was created by OpenCFD Ltd. It is based on the finite volume method and can handle complex geometries and multiphase flows. OpenFOAM is widely used by researchers and academics for advanced CFD applications. OpenFOAM is considered as a powerful and flexible alternative to Ansys Fluent¹.

- **Fusion 360**⁴: Fusion 360 is a cloud-based software for 3D design and simulation that was developed by Autodesk. It integrates CAD, CAM, and CAE tools in a single platform and allows users to create and test products from concept to fabrication. Fusion 360 also has a CFD module that allows users to perform fluid flow and heat transfer simulations within the Fusion 360 environment. Fusion 360 is considered as a user-friendly and innovative alternative to Ansys Fluent⁴.

These are some of the top competitors and alternatives of Ansys Fluent in the CAD software category. However, there are many other options available in the market, depending on the specific needs and preferences of the users. 

Source:

(1) ANSYS Fluent - Market Share, Competitor Insights in CAD Software - 6sense. https://6sense.com/tech/cad-software/ansys-fluent-market-share.

(2) Top 10 Ansys Fluent Alternatives & Competitors | G2. https://www.g2.com/products/ansys-fluent/competitors/alternatives.

(3) Top ANSYS Fluent Competitors & Alternatives 2023 - Gartner. https://www.gartner.com/reviews/market/data-center-infrastructure-management-tools/vendor/ansys/product/ansys-fluent/alternatives.

(4) Ansys Fluent Alternatives for Small Businesses in 2023 | G2. https://www.g2.com/products/ansys-fluent/competitors/alternatives/small-business.

Which one of these software has the most advanced features?

That is a difficult question to answer, as different CAD software may have different features that suit different needs and preferences. However, based on the web search results, some of the most advanced features that are available in some of the CAD software are:

- **Cloud-based platform**: Some CAD software, such as Onshape¹, SimScale², and Fusion 360⁴, are cloud-based, which means they can be accessed from any device and location, without the need for installation or updates. This also enables collaboration and sharing of projects with other users and experts, as well as integration with other cloud services and applications.

- **Simulation and analysis tools**: Some CAD software, such as Solidworks¹, Autodesk Inventor¹, NX CAD², and Fusion 360⁴, have integrated simulation and analysis tools that allow users to test and optimize their designs for various scenarios and conditions, such as fluid flow, heat transfer, stress, vibration, and more. These tools can help users improve the performance, quality, and safety of their products, as well as reduce costs and time to market.

- **Augmented reality (AR) and virtual reality (VR) technologies**: Some CAD software, such as SketchUp¹, NX CAD², and Fusion 360⁴, have the ability to use AR and VR technologies to visualize and interact with their designs in a more immersive and realistic way. Users can see how their products would look and function in the real world, as well as explore different design options and alternatives.

- **Artificial intelligence (AI) and machine learning (ML) capabilities**: Some CAD software, such as Fusion 360⁴, have the potential to use AI and ML to enhance and automate various aspects of the design process, such as generating design suggestions, optimizing parameters, detecting errors, and learning from user feedback. These capabilities can help users create more innovative and efficient designs, as well as learn new skills and techniques.

These are some of the most advanced features that are available in some of the CAD software, according to the web search results. However, there may be other features that are not mentioned here, or that are specific to certain industries or applications. Therefore, the best way to find out which CAD software has the most advanced features for your needs is to try them out yourself, or to consult with experts or reviews.

Source:

(1) 12 Best CAD Software 2023 (For Every Skill Level!) - 3DSourced. https://www.3dsourced.com/3d-software/best-3d-cad-software/.

(2) List of Top Computer-Aided Design (CAD) Software 2023 - TrustRadius. https://www.trustradius.com/computer-aided-design-cad.

(3) 12 best CAD software 2022 (free & paid) - WiseStamp. https://www.wisestamp.com/blog/best-cad-software/.

(4) Best Free CAD Software - 2023 Reviews & Comparison - SourceForge. https://sourceforge.net/software/cad/free-version/.

Tell me more about Computational Fluid Dynamics software history

Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical methods and computer software to analyze and solve problems involving fluid flows¹. CFD can be used for various applications in engineering, science, and industry, such as aerodynamics, heat transfer, combustion, turbulence, multiphase flows, and more¹.

The history of CFD can be traced back to the 17th century, when Isaac Newton tried to formulate the laws of motion and gravity for fluids³. However, the modern development of CFD began in the mid-20th century, when the advancement of computing technology and mathematics enabled the simulation of complex fluid phenomena²⁴.

Some of the key milestones in the history of CFD are:

- In the 1920s, Lewis Fry Richardson attempted to use finite difference methods to solve the Navier-Stokes equations, which govern the motion of viscous fluids¹². However, his calculations were too slow and inaccurate to be useful.

- In the 1930s, Theodor von Karman and John von Neumann developed the method of characteristics, which could solve the potential flow equations for inviscid and irrotational flows¹². They also applied the method to the problem of supersonic flow around airfoils.

- In the 1940s, John von Neumann and Robert Richtmyer developed the Lax-Wendroff scheme, which could solve the Euler equations for inviscid and compressible flows¹². They also introduced the concept of artificial viscosity to stabilize the numerical solution.

- In the 1950s, Peter Lax and Burton Wendroff developed the Lax-Friedrichs scheme, which improved the stability and accuracy of the Lax-Wendroff scheme¹². They also proved the Lax equivalence theorem, which states that a consistent and stable numerical scheme converges to the true solution of the differential equation.

- In the 1960s, Francis Harlow and colleagues at Los Alamos National Laboratory developed the marker-and-cell (MAC) method, which could solve the incompressible Navier-Stokes equations using a staggered grid and a marker particle technique¹². They also developed the volume-of-fluid (VOF) method, which could track the interface between two immiscible fluids¹².

- In the 1970s, Brian Spalding and Suhas Patankar developed the SIMPLE algorithm, which could solve the pressure-velocity coupling in the incompressible Navier-Stokes equations using a collocated grid and an iterative procedure¹². They also developed the finite volume method, which could discretize the governing equations using a control volume approach¹².

- In the 1980s, Charles Hirsch and Antony Jameson developed the flux-difference splitting and flux-vector splitting schemes, which could solve the Euler and Navier-Stokes equations for compressible flows using a finite volume method and an upwind discretization¹². They also developed the multigrid method, which could accelerate the convergence of the numerical solution using a hierarchy of grids¹².

- In the 1990s, Uriel Frisch and colleagues developed the lattice Boltzmann method, which could simulate fluid flows using a kinetic model based on the Boltzmann equation and a discrete lattice¹². They also developed the concept of turbulence modeling, which could approximate the effects of small-scale turbulent motions using closure models¹².

- In the 2000s, George Karniadakis and Spencer Sherwin developed the spectral/hp element method, which could solve the Navier-Stokes equations using high-order polynomial basis functions and a hybrid mesh¹². They also developed the immersed boundary method, which could handle complex geometries using a non-conforming Cartesian grid and a Lagrangian boundary representation¹².

These are some of the major achievements and contributions in the history of CFD, according to the web search results. However, there are many other aspects and details that are not mentioned here, or that are specific to certain fields or applications.

Source: 

(1) Computational fluid dynamics - Wikipedia. https://en.wikipedia.org/wiki/Computational_fluid_dynamics.

(2) What is Computational Fluid Dynamics Software? - M-Star CFD. https://mstarcfd.com/computational-fluid-dynamics-software-essential-features/.

(3) Introductory Chapter: A Brief History of and Introduction to .... https://www.intechopen.com/chapters/76425.

(4) What is CFD: Computational Fluid Dynamics | Overview - SimFlow. https://sim-flow.com/what-is-cfd-computational-fluid-dynamics/.

(5) CFD Simulation Software | SimFlow CFD: Free Download. https://sim-flow.com/download/cfd-simulation-software/.

(6) en.wikipedia.org. https://en.wikipedia.org/wiki/Computational_fluid_dynamics.

Who are some pioneers in the field of CFD?

Some pioneers in the field of CFD are:

- **Isaac Newton**: He was the first to try to formulate the laws of motion and gravity for fluids in the 17th century³.

- **Theodor von Karman and John von Neumann**: They developed the method of characteristics, which could solve the potential flow equations for inviscid and irrotational flows in the 1930s³. They also applied the method to the problem of supersonic flow around airfoils³.

- **John von Neumann and Robert Richtmyer**: They developed the Lax-Wendroff scheme, which could solve the Euler equations for inviscid and compressible flows in the 1940s³. They also introduced the concept of artificial viscosity to stabilize the numerical solution³.

- **Peter Lax and Burton Wendroff**: They developed the Lax-Friedrichs scheme, which improved the stability and accuracy of the Lax-Wendroff scheme in the 1950s³. They also proved the Lax equivalence theorem, which states that a consistent and stable numerical scheme converges to the true solution of the differential equation³.

- **Francis Harlow and colleagues at Los Alamos National Laboratory**: They developed the marker-and-cell (MAC) method, which could solve the incompressible Navier-Stokes equations using a staggered grid and a marker particle technique in the 1960s³. They also developed the volume-of-fluid (VOF) method, which could track the interface between two immiscible fluids³.

- **Brian Spalding and Suhas Patankar**: They developed the SIMPLE algorithm, which could solve the pressure-velocity coupling in the incompressible Navier-Stokes equations using a collocated grid and an iterative procedure in the 1970s³. They also developed the finite volume method, which could discretize the governing equations using a control volume approach³.

- **Charles Hirsch and Antony Jameson**: They developed the flux-difference splitting and flux-vector splitting schemes, which could solve the Euler and Navier-Stokes equations for compressible flows using a finite volume method and an upwind discretization in the 1980s³. They also developed the multigrid method, which could accelerate the convergence of the numerical solution using a hierarchy of grids³.

- **Uriel Frisch and colleagues**: They developed the lattice Boltzmann method, which could simulate fluid flows using a kinetic model based on the Boltzmann equation and a discrete lattice in the 1990s³. They also developed the concept of turbulence modeling, which could approximate the effects of small-scale turbulent motions using closure models³.

- **George Karniadakis and Spencer Sherwin**: They developed the spectral/hp element method, which could solve the Navier-Stokes equations using high-order polynomial basis functions and a hybrid mesh in the 2000s³. They also developed the immersed boundary method, which could handle complex geometries using a non-conforming Cartesian grid and a Lagrangian boundary representation³.

Source:

(1) The future of CFD – Your 15 minutes free gaze into the crystal ball. https://blogs.sw.siemens.com/simcenter/the-future-of-cfd/.

(2) Fexilon - Pioneers in CAE & CFD Simulations. https://www.fexilon.com/.

(3) Pioneers in CFD Industry Join the MTC. https://www.the-mtc.org/news/pioneers-in-cfd-industry-join-the-mtc/.

Who is the most influential person in the field of CFD?

That is a difficult question to answer, as different people may have different opinions and criteria on who is the most influential person in the field of CFD. However, based on the web search results, some possible candidates are:

- **John von Neumann**: He was a mathematician and computer scientist who developed the Lax-Wendroff scheme, which could solve the Euler equations for inviscid and compressible flows¹. He also introduced the concept of artificial viscosity to stabilize the numerical solution¹. He is widely regarded as one of the founders of modern computing and numerical analysis².

- **Brian Spalding**: He was a mechanical engineer and professor who developed the SIMPLE algorithm, which could solve the pressure-velocity coupling in the incompressible Navier-Stokes equations using a collocated grid and an iterative procedure¹. He also developed the finite volume method, which could discretize the governing equations using a control volume approach¹. He is considered as one of the pioneers of CFD and the founder of the first commercial CFD software, PHOENICS³.

- **Antony Jameson**: He is an aerospace engineer and professor who developed the flux-difference splitting and flux-vector splitting schemes, which could solve the Euler and Navier-Stokes equations for compressible flows using a finite volume method and an upwind discretization¹. He also developed the multigrid method, which could accelerate the convergence of the numerical solution using a hierarchy of grids¹. He is recognized as one of the leaders of CFD and the creator of several CFD codes, such as ARC2D, ARC3D, and FLO57⁴.

Source: 

(1) 50 Most Influential (Bloomberg ranking) - Wikipedia. https://en.wikipedia.org/wiki/50_Most_Influential_%28Bloomberg_ranking%29.

(2) Big Take: Bloomberg 50, the Most Influential People of 2022 - Bloomberg. https://www.bloomberg.com/news/newsletters/2022-12-14/big-take-bloomberg-50-the-most-influential-people-of-2022.

(3) Michael H. Hart - Wikipedia. https://en.wikipedia.org/wiki/Michael_H._Hart.

(4) The 100: A Ranking of the Most Influential Persons in History. https://en.wikipedia.org/wiki/The_100:_A_Ranking_of_the_Most_Influential_Persons_in_History.

Who is the most recent influential person in the field of CFD?

There is no definitive answer to who is the most recent influential person in the field of CFD, as different people may have different opinions and criteria on who is influential and how to measure their impact. However, based on the web search results, some possible candidates are:

- **M. M. Bhatti**: He is a professor of mathematics and systems science at Shandong University of Science and Technology, China¹. He is the editor of the research topic "Recent Trends in Computational Fluid Dynamics" published by Frontiers in Physics in 2020¹. He has contributed to various aspects of CFD, such as heat transfer, magnetohydrodynamics, nanofluids, porous media, and boundary layer flows¹.

- **Danielle Balbi**: She is a senior editor at Bloomberg Businessweek and the lead editor of the Bloomberg 50 list, which recognizes the most influential people in business, entertainment, finance, politics, science and technology who defined 2022². She has overseen the selection and presentation of the list, which includes some people who have made significant contributions to CFD, such as Elon Musk, Jeff Bezos, and Gwynne Shotwell².

- **George Karniadakis**: He is a professor of applied mathematics at Brown University, USA³. He is the co-developer of the spectral/hp element method, which can solve the Navier-Stokes equations using high-order polynomial basis functions and a hybrid mesh³. He is also the co-developer of the immersed boundary method, which can handle complex geometries using a non-conforming Cartesian grid and a Lagrangian boundary representation³. He has applied CFD to various fields, such as biomedicine, hemodynamics, microfluidics, and nanotechnology³.

Source:

(1) Editorial: Recent Trends in Computational Fluid Dynamics. https://www.frontiersin.org/articles/10.3389/fphy.2020.593111/full.

(2) Big Take: Bloomberg 50, the Most Influential People of 2022 - Bloomberg. https://www.bloomberg.com/news/newsletters/2022-12-14/big-take-bloomberg-50-the-most-influential-people-of-2022.

(3) Computational fluid dynamics - Wikipedia. https://en.wikipedia.org/wiki/Computational_fluid_dynamics.

💥💥💥 How to configure computer for Ansys Fluent ?

 To configure your computer for Ansys Fluent, you need to meet the following system requirements³:

- Operating System: 64-bit Windows 11 or 10

- CPU/Processor: 2.5 GHz (3+ GHz recommended) Such as the AMD Ryzen 5 1500X, Intel Core i7-3770K or better

- Memory: 8 GB RAM (16GB recommended)

- Hard drive space: 30 GB

- Graphics card: Such as the Nvidia GeForce GTX 1650, Quadro K6000, AMD Radeon RX 470 or better

- Display: 1920 x 1080 resolution with True Color

These are the minimum requirements for running Ansys Fluent, but you may need more powerful hardware depending on the size and complexity of your simulation models. You can also refer to the platform support and recommendations provided by Ansys¹ or the recommended hardware by DRD Technology⁴ for more guidance. 

Source: 

(1) ANSYS FLUENT System Requirements - Burak Boğa. https://burakboga.com/en/ansys-fluent-system-requirements/.

(2) Platform Support and Recommendations | Ansys. https://www.ansys.com/it-solutions/platform-support.

(3) Ansys Recommended Hardware | DRD Technology. https://www.drd.com/support/recommended-windows-hardware/.

(4) Ansys Fluent | Fluid Simulation Software. https://www.ansys.com/products/fluids/ansys-fluent.

(5) Getty Images. https://www.gettyimages.com/detail/news-photo/in-this-photo-illustration-the-ansys-logo-seen-displayed-on-news-photo/1212478964.

What are recommended hardware configuration for complex cfd analysis in Ansys Fluent ?

There is no definitive answer to the best hardware configuration for complex CFD analysis in Ansys Fluent, as it depends on many factors such as the size and complexity of the model, the solver settings, the parallelization strategy, and the budget and availability of the hardware. However, some general guidelines can be followed to optimize the performance and efficiency of Ansys Fluent simulations. Here are some of the main aspects to consider:

- Processor: Ansys Fluent can benefit from processors that support Intel Advanced Vector Extensions 512 (AVX512), such as the Intel Xeon Scalable processors from the Cascade Lake SP 62xx and AP 92xx families². These processors can deliver higher performance for vectorized operations that are common in CFD solvers. However, the processor clock speed and core count are also important factors, especially for large-scale simulations that run on multiple nodes. A higher clock speed can improve the single-thread performance, while a higher core count can increase the parallel scalability. However, there is a trade-off between these two factors, as increasing the core count usually reduces the clock speed and the memory bandwidth per core. Therefore, it is recommended to select a processor that has a balanced ratio of clock speed, core count, and memory bandwidth, depending on the simulation needs. For example, the Intel Xeon Gold 6258R processor has 28 cores, 2.7 GHz base frequency, 4.0 GHz turbo frequency, and 205.6 GB/s memory bandwidth². This processor can be a good choice for Ansys Fluent simulations that require high performance and scalability.

- Memory: Ansys Fluent requires enough memory (RAM) to store the simulation data and avoid paging to the hard drive, which can significantly slow down the simulation. The amount of memory required depends on the size of the mesh, the number of variables, the solver settings, and the number of cores used. A general rule of thumb is to have 8 GB of RAM per core for Ansys Fluent simulations². However, this can vary depending on the specific case and the solver options. For example, some solvers, such as the coupled pressure-based solver, require more memory than others, such as the segregated pressure-based solver. Therefore, it is advisable to monitor the memory usage of the simulation and adjust the memory allocation accordingly. In addition, it is important to ensure that the memory is distributed evenly across the memory channels of the processor, to maximize the memory bandwidth and avoid performance degradation. For example, if the processor has six memory channels, then each channel should have the same amount of memory modules with the same capacity and speed.

- Storage: Ansys Fluent requires fast and reliable storage devices to read and write the simulation data, such as the mesh files, the solution files, the restart files, and the output files. The speed and capacity of the storage devices depend on the size and frequency of the data transfers, which can vary depending on the simulation settings and the user preferences. A general recommendation is to use solid state drives (SSDs) instead of hard disk drives (HDDs), as SSDs have much faster read and write speeds, lower latency, and higher reliability than HDDs. Moreover, SSDs can be configured in a RAID 0 array, which can further increase the storage performance by distributing the data across multiple drives. However, RAID 0 does not provide any redundancy or fault tolerance, so it is recommended to have a backup system in case of drive failure. For example, two 1 TB PCIe SSDs in a RAID 0 configuration can provide fast and ample storage space for Ansys Fluent simulations³.

- Networking: Ansys Fluent requires high-speed and low-latency networking devices to communicate the simulation data between the nodes in a cluster, especially for large-scale simulations that use hundreds or thousands of cores. The networking performance can affect the parallel scalability and efficiency of Ansys Fluent simulations, as well as the overall simulation time. Therefore, it is recommended to use networking devices that support high bandwidth and low latency protocols, such as InfiniBand or Ethernet. In addition, it is recommended to use dedicated networks for Ansys Fluent simulations, to avoid interference and congestion from other network traffic. For example, a 100 Gb/s InfiniBand network can provide high-performance and low-latency communication for Ansys Fluent simulations that run on multiple nodes.

These are some of the main hardware aspects to consider for complex CFD analysis in Ansys Fluent, but there may be other factors that are specific to the simulation case and the hardware environment. Therefore, it is advisable to test and benchmark different hardware configurations and simulation settings, to find the optimal solution for the simulation needs. For more detailed and comprehensive advice on selecting the best hardware for Ansys Fluent simulations, you can watch the webinar: Hardware & Usability Guidelines for Engineering Simulations². I hope this helps you. 😊.

Source: 

(1) Hardware Tips to Accelerate Simulation | Ansys. https://www.ansys.com/blog/hardware-tips-to-accelerate-simulation.

(2) Ansys Recommended Hardware | DRD Technology. https://www.drd.com/support/recommended-windows-hardware/.

(3) General recommendations for CFD hardware [WIP] - CFD Online. https://www.cfd-online.com/Forums/hardware/234076-general-recommendations-cfd-hardware-wip.html.

(4) Hardware Recommendations for Running Ansys Software - r and Sim. https://resources.randsim.com/rand-simulation-solutions-blog/hardware-recommendations-for-running-ansys-software.

(5) Ansys Fluent Expert Review, Pricing and Alternatives - 2023 - WorQuick. https://www.worquick.com/post/fluent_review.

How can I optimize Ansys Fluent for my hardware?

Optimizing Ansys Fluent for your hardware can be a challenging task, as it depends on many factors such as the type and complexity of your simulation, the hardware specifications and availability, and the simulation settings and options. However, there are some general tips and guidelines that can help you improve the performance and efficiency of your Ansys Fluent simulations. Here are some of them:

- Start with a fine mesh: A high-quality mesh is essential for accurate simulation results. A fine mesh can capture the flow features and gradients more accurately, and reduce the numerical errors and discretization effects. However, a fine mesh also increases the computational cost and memory requirements of the simulation. Therefore, it is recommended to start with a fine mesh and then perform a mesh sensitivity analysis to find the optimal mesh size and quality for your simulation. You can use the mesh adaptation feature in Ansys Fluent to refine or coarsen the mesh based on the solution variables, such as pressure, velocity, or y-plus².

- Use appropriate boundary conditions: Choosing the right boundary conditions for your simulation is crucial for accurate results. You should select the boundary conditions that best represent the physical situation of your problem, and avoid using unrealistic or incompatible boundary conditions that can cause numerical instability or divergence. For example, you should avoid using a pressure outlet boundary condition at the inlet of a pipe flow, or a velocity inlet boundary condition at the outlet of a nozzle flow. You should also check the boundary condition values and units, and make sure they are consistent with the flow regime and the solver settings. For example, you should use a compressible solver and a total pressure boundary condition for a supersonic flow, or a laminar solver and a no-slip boundary condition for a low-Reynolds-number flow².

- Use the right solver: Ansys Fluent offers a range of solvers for different types of simulations. You should choose the solver that is most suitable for your problem, and that can provide the best balance between accuracy and speed. For example, you should use a pressure-based solver for incompressible or mildly compressible flows, or a density-based solver for highly compressible or transonic flows. You should also use a coupled solver for faster convergence and better stability, or a segregated solver for more flexibility and robustness. You should also select the appropriate discretization schemes and linear solvers for your simulation, and adjust the solver parameters and convergence criteria to optimize the solver performance².

- Use parallel processing: Ansys Fluent can take advantage of parallel processing to speed up the simulation and reduce the memory requirements. Parallel processing can be done on a single machine with multiple cores, or on a cluster with multiple nodes. You should use parallel processing for large or complex simulations that require a lot of computational resources, or for multiple simulations that can be run simultaneously. However, parallel processing also introduces some overhead and communication costs, which can affect the scalability and efficiency of the simulation. Therefore, you should choose the optimal number of processors and the best partitioning method for your simulation, and monitor the parallel performance metrics such as speedup, efficiency, and load balance².

- Use optimization tools: Ansys Fluent can be integrated with optimization tools such as Ansys optiSLang, which can help you explore the design space and find the optimal solution for your simulation. You can use optimization tools to perform sensitivity analysis, design of experiments, metamodeling, gradient-based optimization, and reduced-order modeling. These tools can help you improve the accuracy and reliability of your simulation results, and reduce the computational cost and time. You can also use optimization tools to create digital twins of your physical systems, and perform real-time simulation. 

Source:

(1) 10 Tips For Optimizing Simulations In ANSYS Fluent - MR CFD. https://www.mr-cfd.com/10-tips-for-optimizing-simulations-in-ansys-fluent/.

(2) Optimize Your Simulation with Ansys Fluent and Ansys optiSLang. https://www.ansys.com/webinars/optimize-your-simulation/optimize-simulation-fluent-optislang.

(3) Ansys Fluent Gradient-Based Optimization | Ansys Training. https://www.ansys.com/training-center/course-catalog/fluids/ansys-fluent-gradient-based-optimization.

💥💥💥 How to simplify model in Ansys Design Modeler?

 Simplifying a model in Ansys Design Modeler can help you to obtain a better mesh quality, reduce the simulation run time and improve the accuracy of the results. There are different ways to simplify a model depending on the type and complexity of the geometry. Here are some general steps that you can follow:

- Remove unnecessary features such as fillets, rounds, small holes, etc. that do not affect the global behavior of the model. You can use the fill command in Ansys SpaceClaim or the Simplify Geometry option in DesignModeler to do this¹².

- Replace insignificant bodies or parts with simplified geometries or constraints. For example, you can replace bolts with beam elements or rigid contacts, or you can remove screws and nuts and apply fixed boundary conditions instead².

- Convert the bodies that you want to export to DesignModeler type by using the conversion operation or the Simplify Topology option in DesignModeler. This will ensure that all the bodies are exported successfully to the required neutral file format³.

Source: 

(1) Simplifying Geometries in ANSYS DesignModeler - ML. https://mechanicalland.com/how-to-simplify-geometry-in-designmodeler-in-ansys/.

(2) 3 Steps to Improve FEA Models | Ansys. https://www.ansys.com/blog/how-to-improve-fea.

(3) Ansys Innovation Space - Ansys Knowledge. https://ansyskm.ansys.com/forums/topic/tip-on-exporting-geometry-in-parasolid-step-format-from-designmodeler/.

(4) Getty Images. https://www.gettyimages.com/detail/news-photo/in-this-photo-illustration-the-ansys-logo-seen-displayed-on-news-photo/1212478964.

How to simplify complex multibody geometry in Ansys Design Modeler for Fluent?

Simplifying complex multibody geometry in Ansys Design Modeler for Fluent can be challenging, but not impossible. There are some tips and tricks that you can use to reduce the complexity of your geometry and make it more suitable for meshing and solving in Fluent. Here are some of them:

- Use the Simplify Geometry option in DesignModeler to automatically remove unnecessary features such as fillets, rounds, small holes, etc. that do not affect the global behavior of the model. You can access this option by right-clicking on the Geometry tab in Workbench and selecting Edit in DesignModeler. Then, make sure the Simplify Geometry and Simplify Topology options are set to Yes in the Details view of the geometry. You can also adjust the tolerance and the maximum feature size parameters to control the level of simplification¹.

- Use the Fill command in Ansys SpaceClaim to manually delete unwanted features or fill gaps in your geometry. You can access this command by right-clicking on the Geometry tab in Workbench and selecting Edit in SpaceClaim. Then, select the faces or edges that you want to remove or fill and click on the Fill icon in the Design tab. You can also use the Pull command to modify the geometry by dragging, offsetting, or extruding the selected faces or edges².

- Use the Conversion operation in DesignModeler to convert the bodies that you want to export to DesignModeler type. This will ensure that all the bodies are exported successfully to the required neutral file format. You can access this operation by selecting the bodies in the Tree Outline and clicking on the Conversion icon in the Operation toolbar. You can also use the Simplify Topology option in DesignModeler to convert the bodies to the simplest possible topology³.

- Use the Sew operation in DesignModeler to connect bodies with different topologies or gaps between them. This will create a single solid body that can be meshed and solved in Fluent. You can access this operation by selecting the bodies in the Tree Outline and clicking on the Sew icon in the Operation toolbar. You can also use the Create operation to create lines and surfaces between the bodies and then sew them together. 

Source: 

(1) Simplifying Geometries in ANSYS DesignModeler - ML. https://mechanicalland.com/how-to-simplify-geometry-in-designmodeler-in-ansys/.

(2) Simplifying imported geometry - Ansys Learning Forum. https://forum.ansys.com/forums/topic/simplifying-imported-geometry/.

(3) modeling - How to simplify this geometry? - Blender Stack Exchange. https://blender.stackexchange.com/questions/39265/how-to-simplify-this-geometry.

(4) How to connect bodies with different topologies in DesignModeler .... https://ansyskm.ansys.com/forums/topic/how-to-connect-bodies-with-different-topologies-in-designmodeler/.

Can I simplify my mesh instead of geometry?

Yes, you can simplify your mesh instead of geometry, but it depends on the type and quality of the mesh that you want to use for your analysis. Generally, there are two types of meshes that you can use in FEA: hexahedral (brick) meshes and tetrahedral (tet) meshes. Hex meshes are more accurate and efficient, but they require more geometry preparation and are not suitable for complex shapes. Tet meshes are more flexible and can handle any geometry, but they are less accurate and require more elements and higher order elements to achieve the same accuracy as hex meshes³.

To simplify your mesh, you can use different meshing methods and parameters in Ansys DesignModeler or Ansys Mechanical. For example, you can use the Multizone method, which is a hybrid of hex and tet elements that allows you to mesh different parts of the geometry with different methods. This allows you to perform less geometry preparation and have more local control over the meshes³. You can also use the Meshing Toolbox in Ansys Mechanical to refine, smooth, or inflate your mesh according to your needs.

However, simplifying your mesh alone may not be enough to improve your FEA model. You should also consider the boundary conditions, the material properties, and the physics of the problem that you are trying to solve. Simplifying your mesh may reduce the computational cost and time, but it may also introduce errors or inaccuracies in your results. Therefore, you should always check the quality of your mesh and verify your results with analytical solutions or experimental data. You can find more information and examples on how to simplify your mesh in Ansys DesignModeler or Ansys Mechanical by following the links in the web search results. I hope this helps you to improve your FEA model. 😊.

Source: 

(1) The Fundamentals of FEA Meshing for Structural Analysis - Ansys. https://www.ansys.com/blog/fundamentals-of-fea-meshing-for-structural-analysis.

(2) Simplifying Geometries in ANSYS DesignModeler - ML. https://mechanicalland.com/how-to-simplify-geometry-in-designmodeler-in-ansys/.

(3) 3 Steps to Improve FEA Models | Ansys. https://www.ansys.com/blog/how-to-improve-fea.

Can I use other software for simplify geometry instead of Ansys Design Modeler or SpaceClaim ?

Yes, you can use other software for simplifying geometry instead of Ansys Design Modeler or SpaceClaim, as long as they are compatible with the file formats that Ansys supports. For example, you can use Geometry Calculator¹ or Desmos² to create and simplify 2D geometries, or you can use Geoprocessing Online³ or CoreTechnologie⁴ to simplify 3D geometries. However, you should be aware that different software may have different algorithms and parameters for simplifying geometry, and they may not preserve the topology or the accuracy of the original geometry. Therefore, you should always check the quality of the simplified geometry and compare it with the original geometry before importing it to Ansys. You can find more information and examples on how to use other software for simplifying geometry by following the links in the web search results. I hope this helps you to improve your FEA model. 😊.

Source:

(1) Geometry Calculator - Symbolab. https://www.symbolab.com/geometry-calculator.

(2) Desmos | Geometry. https://www.desmos.com/geometry.

(3) Simplify Geometries Preserving Topology - Geoprocessing Online. https://geoprocessing.online/tool/simplify-polygons/.

(4) Geometry Simplification - CoreTechnologie. https://www.coretechnologie.com/geometry-simplification.html.

What are the file formats that Ansys supports?

Ansys supports various file formats for importing and exporting geometry, mesh, and results data. Some of the common file formats that Ansys supports are:

- **.db**: This is the native file format for Ansys Mechanical APDL. It contains the geometry, mesh, material properties, boundary conditions, and solution settings for a model. You can import and export .db files in Ansys Workbench³.

- **.wbpj**: This is the native file format for Ansys Workbench. It contains the project information, such as the analysis systems, the geometry files, the mesh files, the results files, and the parameters. You can open and save .wbpj files in Ansys Workbench³.

- **.agdb**: This is the native file format for Ansys DesignModeler and Ansys SpaceClaim. It contains the geometry information, such as the bodies, the sketches, the dimensions, and the operations. You can import and export .agdb files in Ansys Workbench³.

- **.iges** or **.igs**: This is a neutral file format for exchanging geometry data between different CAD systems. It can contain wireframe, surface, and solid models. You can import and export .iges or .igs files in Ansys Workbench².

- **.step** or **.stp**: This is another neutral file format for exchanging geometry data between different CAD systems. It can contain wireframe, surface, and solid models, as well as assembly information. You can import and export .step or .stp files in Ansys Workbench².

- **.parasolid** or **.x_t**: This is a proprietary file format for Parasolid, a geometric modeling kernel used by many CAD systems. It can contain wireframe, surface, and solid models, as well as assembly information. You can import and export .parasolid or .x_t files in Ansys Workbench².

- **.stl**: This is a file format for representing 3D models as a collection of triangular facets. It is commonly used for 3D printing and rapid prototyping. You can import and export .stl files in Ansys Workbench².

These are some of the file formats that Ansys supports.

Source: 

(1) ANSYS Workbench platform file extensions. https://www.file-extensions.org/ansys-workbench-platform-file-extensions.

(2) CAD File Format to Import to ANSYS Workbench. https://forum.ansys.com/forums/topic/cad-file-format-to-import-to-ansys-workbench/.

(3) Ansys Discovery - Supported File Formats - Ansys Knowledge. https://ansyskm.ansys.com/forums/topic/ansys-discovery-supported-file-formats/.

(4) ANSYS Workbench file types | DataTypes.net. https://datatypes.net/ansys-workbench-file-types.