Wednesday, May 18, 2016

Non-Inertial Reference Frame Visualization

Adapting non-inertial reference frame model results to enable visualization from a stationary frame of reference is a nifty new feature coming in the upcoming releases of FlowSightTM for FLOW-3D Cast v4.2 (next month) and FLOW-3D v11.2 (late 2016). This brief note describes this feature and gives a couple examples.

Many real-world processes happen in accelerating or non-inertial reference frames. Examples of such processes include sloshing of fuel in satellite tanks and centrifugal casting. FLOW-3D has long incorporated in its solver the ability to model fluid and solid motion using a non-inertial reference frame model (NIRF), but lacked the ability to visualize the motion depicted from a stationary frame of reference. After the introduction of the General Moving Objects (GMO) model, users could have their cake and eat it, too: modeling the coupled fluid-solid motion and visualizing the resulting motion in a realistic way. Unfortunately, the GMO model comes with a price in terms of computational time. While the NIRF may be more computationally convenient for solving large problems, the inability to analyze the solution from a stationary frame of reference frustrated many users. FlowSight’s recent development makes it easy to apply the NIRF feature to all parts of a case, such as iso-surfaces, clips and streamlines, allowing the user to view the rigid body motion and the fluid flow from a stationary reference frame.

NIRF motion can be captured in animations that are created by FlowSight. The user can activate only one or both of the possible motions – rotation and translation. The scale of the translational motion can be adjusted for better visualization in cases where the translations are huge, like aerospace applications where the moving body can translate for miles.

Below are the two animations with NIRF motions for tilt pour and centrifugal casting.

Tilt pour casting with NIRF motion on the left and stationary motion on the right

Centrifugal casting with NIRF motion on the top and stationary motion on the bottom left

In the upcoming few blogs, I will discuss the featured developments of FLOW-3D Cast v4.2.

Tuesday, April 26, 2016

Microfluidic Circuit - Pneumatic Latching Valve

In this final post in the series of Flow Science’s 35th anniversary simulation contest, I will talk about a case study simulating a part of a microfluidic circuit – pneumatic latching valve. These devices are a relatively new industry application that Flow Science is exploring, in the context of a broader exploration of the use of CFD in microfluidics applications, and results have been very encouraging.

There have been recent developments in the field of microfluidic circuit devices, also called Lab-on-a chip. These circuits are used in biological sciences to transport matter from one place to another, or to perform hundreds of assays in parallel, based on certain logic. Hence these circuits are also known as microfluidic logic circuits. Analogous to an electronic circuit, the fluid runs through channels and is driven by pressure differentials (as opposed to the traditional potential/voltage differentials in an electronic circuit). Combinations of valves and the application of correct pressures forms logic gates like AND, OR, XNOR, etc. Eventually the combination of these logic gates forms a microprocessor chip that can be used as a positive edge detector, toggle, clock, demultiplexer, etc. One such example of a 4-bit microfluidic demultiplexer is shown in Figure 1. The demultiplexer has 16 (4-bit implies 16 possible combinations or outputs) pneumatic valves that work in parallel to attain a desired output. Therefore, understanding these latching valves is central to the correct operation of the circuit. CFD simulations can play a vital role in reducing costs by testing the design of these types of circuits before fabrication.


Figure 1. A microfluidic 4-bit demultiplexer for routing pressures and vacuum pulses. Inside the red box is a single latching valve that will be simulated in FLOW-3D.

Pneumatic Latching valve

A latching valve, as the name indicates, holds (latches) a valve in open/closed position without continuous application of external pressure. Latching valves are used for energy efficiency and are analogous to electrical solenoid valves. Details of the working of a latching valve system are shown in Figure 2. Stages 1-7 show how the system changes from a closed state to a latched open state, and then back to a closed state again. An open state is one where the fluid can flow through the valve, and in a closed state fluid cannot flow through the valve.

Figure 2. The 7 stages of latching valve system as it evolves from closed to latched open to closed again. NC means not connected*

Latching valve setup in FLOW-3D

The latching system primarily comprises of 3 types of features – valves, inlet channels and control channels. Valves and inlet channels are made from solid components in FLOW-3D, while the control channels are directly represented through meshes (seen in black in the figure below). Each valve has an inlet channel and a control channel except for valve 3. Valve 3 has an inlet channel and two output channels.  The inlet channel brings in fluid and the control channel allows the pressure to be manually controlled externally by the user/designer. Setup of the entire latching valve system in FLOW-3D is shown in Figure 3. 

Figure 3. Setup of the latching valve system (currently in stage-7) in FLOW-3D

Time dependent pressure boundary conditions

Being pneumatic valves, the functioning of the latching system is totally dependent on the application of pressures at the boundaries of the system. The inlet boundary condition is a time-varying pressure boundary condition with vacuum (below atmospheric pressure) and pressure pulses (Figure 4). The control channel for valve 1 has a pressure pulse twice the atmospheric pressure (Figure 5). The control channel for valve 2 is maintained at atmospheric pressure. The outlet channel is at atmospheric pressure. Notice that eventually all the pressures fall back to atmospheric pressure, which means that no additional external pressure is required by the latching system to stay in its state (closed in this case).

Figure 4. Time-varying pressure boundary condition for the inlet channel to Valve 1.

Figure 5. Time-varying pressure boundary condition for the control channel of Valve 1.

Simulation results

Stages 3-7 were simulated using FLOW-3D and the results post-processed in FlowSight. The latching mechanism has been accurately simulated, as shown in the reference paper, by starting at the  open stage (stage 3) and ending at the closed (stage 7) stage. Pressure pulses in the inlet channel are 500 Pa, positive or negative and the pulses span over 50 milliseconds. Water is used as the fluid, and compressibility of water is used to allow some propagation time for the pressures in the system. Opening and closing of the individual valves can be seen in top three viewports of the animation below. The simulation below shows the evolution of the system from stages 3-7.
Simulation of a pneumatic latching valve used in microfluidic demultiplexer. The animation starts at stage 3 – the open stage, and finally evolves to stage 7 – the closed stage.

An accurate simulation of the working of the pneumatic latching valve can help designers reduce the cost of trials and errors in the design phase, ensuring that the best design goes to the fabrication stage. Notice that in the final stage, the valves are in a closed state and would remain so for a certain period of time, in spite of the absence of external pressures through control channels.

In the upcoming post I will talk about our new optimization and parametric study capabilities using CAESES, an optimization software by Friendship Systems.

*Reference: William H. Grover, Robin H. C. Ivester, Eric C. Jensen, Richard A. Mathies, Development and multiplexed control of latching pneumatic valves using microfluidic logical structures, 2006


Tuesday, April 5, 2016

Simulating Pelton Turbines

In this third post of the Flow Science simulations contest blog series, I will be talking about the simulation of a Pelton turbine using FLOW-3D. This work was done by our associate in Italy, XC Engineering.

Pelton turbines are used for electricity generation in hydraulic power plants. They are suitable for operation when water energy is available at high head and low flow rate.  In a Pelton turbine, the energy extracted from the kinetic energy of the water is used for the rotation of the impeller. Water, coming from an upper basin, is accelerated and ejected from the surface of the Pelton paddles. Paddle geometry is designed to absorb as much kinetic energy of the fluid as possible for the rotation of the paddle. The rotational speed of the turbine is then converted to electric power using an electricity generator with a rotor and a stator. The aim of this study is to analyze the initial transient of the turbine, where water impacts the Pelton’s paddle at around 120 m/s, providing torque and angular acceleration.

Modelling a Pelton turbine in FLOW-3D

The geometry used in the simulation is shown below. All geometries and data used in the simulation are realistic and in line with the real phenomena: the wheel geometry has a real shape and mass property, the fluid is water with a reasonable speed, and the nozzle contains a Doble valve (not visible here), used in real turbines to adjust the flow rate of the water.
Pelton turbine (red), inlet (blue), inner casing (yellow), outer casing (pink) and probes (grey spheres)

Moving objects

Many kinematics are involved in this simulation, which makes FLOW-3D a very good choice for this study. The motion of an object can have all six degrees of freedom (3 rotational + 3 translational), or it can be constrained in a prescribed way. For this simulation, the Pelton turbine is allowed to have only fixed x-axis coupled rotation while staying constrained in every other direction (both rotational and translational). The other components do not move.

Gravity and non-inertial reference frame

The figure below shows that the acceleration due to gravity is not inclined to any of the axes. This is because in the original CAD geometry, the axes are defined relative to the inlet such that inlet is parallel to the y-axis and perpendicular to the z-axis. However, for this simulation, gravity has to be in the direction shown below (pink vector) and not along any of the axes. FLOW-3D’s gravity and non-inertial reference frame model allows users to overcome such difficulties. Instead of defining a value of gravity (G) along one axis, the user can define multiple values of accelerations along multiple axes such that the net resultant is equal to G and is along the desired direction. The figure below highlights how this was done in FLOW-3D. Acceleration in the –y direction was set to 3.35 m2/s and in the –z direction to 9.209 m2/s such that the resultant is 9.8 m2/s in the desired direction.
Net resultant gravity with correct magnitude and desired direction based on prescribed acceleration vectors in –y and –z directions. (Vectors are not to scale. Directions of vectors, however, are exact)

Results

For Pelton turbines, it is known that the top efficiency is reached when the peripheral speed of the wheel is about half the speed of the water at the nozzle. For this purpose, a probe was located at the center of the nozzle to monitor the fluid speed, while another probe was attached to a paddle’s wheel, to track the peripheral speed. The two quantities are shown in the animation below.

Pelton turbine simulation showing the fluid velocity (blue) plot and the corresponding peripheral velocity (red). Also shown is the sectional view highlighting the coupled motion of paddles and water.

The plots above show that by the end of the simulation, the peripheral speed is asymptotically becoming steady at more than half the velocity of the impacting fluid. Half of the impacting fluid velocity is 60m/s, but the peripheral speed reaches 75m/s by the end of the simulation. This difference (which is desirable) arises because currently the turbine is not receiving any rotational resistance from a rotor. A higher peripheral speed ensures higher kinetic energy to overcome losses in case a rotor was connected to the turbine. The final goal is to adjust, for each water velocity exiting from the nozzle, the resistance from a rotor in order to reduce the rotational speed at its maximum efficiency point and extract the energy.

Understanding the results of this study was made significantly easier by the advanced post-processing features of FlowSightTM, such as alpha transparency based on variable value, moving camera, fine tuning of light and reflections, multi-plots and multi-viewport visualization. One of the many such post-processed results is shown below to highlight the moving camera and slow motion recording of FlowSight.
Pelton turbine simulation showing slow motion and moving camera animation

FLOW-3D’s robust moving objects model backed by multi-directional acceleration prescription and state-of-the-art post-processor, FlowSight, yields good results for this case study. In the upcoming post, I will be talking about another Flow Science contest entry based on a relatively new field of research, microfluidic circuits.

Tuesday, March 22, 2016

Turbulent Dispersion of Environmental Discharges

Continuing the blog series on Flow Science’s 35th anniversary contest, I will cover the case study from the winner of the contest, Daniel Valero Huerta from FH Aachen University of Applied Sciences in Germany. His research focuses on understanding the dispersion of contaminants/discharges in rivers and estuaries. In this research, FLOW-3D has been extensively used as the computational model for studying the turbulent dispersion of the discharges.

Environmental discharges and outfall structures have been traditionally designed by means of complex, cost-intensive and time-consuming experimental studies. Models based on an integral approach are commonly employed despite their limitations, but contaminant re-entrainment or strong adverse discharges fall outside the hypothesis of such models. Thus, using a full 3D model for contaminant dispersion may improve knowledge on the real contaminant dispersion in rivers and estuaries. Similarly, bounded jets can be modeled and different diffusor locations can be tested in order to improve the overall environmental water quality and biotic conditions.

Relevant physics and the case study

In this study, a jet discharge was modeled both experimentally and numerically. Then, an estimation of the turbulent dispersion in the shear region was obtained. For the turbulence modeling, the Renormalized Group (RNG) model was employed together with the TruVOF method for tracking the free surface. A monotonicity-preserving, second-order scheme was employed for contaminant advection ensuring proper modeling of turbulent transport. FLOW-3D is a very good choice for the numerical modeling of such engineering problems because it offers a comprehensive turbulence modeling suite and accurately estimates the free surface. Another advantage of FLOW-3D is the ability to use a one-fluid approach because modeling air (a two-fluid problem) is not important for river contaminant transport problems for practical applications. One-fluid modeling is a more natural, and efficient approach for hydraulic problems. Figure 1 shows a snapshot of the simulation results.

Figure 1. Top view (top) and side view (bottom) showing the discharge ejected from an outfall. Complex flow patterns are seen along with circulation zones between groins (pink blocks). 

Turbulence modeling
FLOW-3D offers a comprehensive set of turbulence models. They can be broadly divided into two categories – Reynold’s Averaged Navier Stokes (RANS) models and Large Eddy Simulation (LES) models. As the name suggests, RANS models average out the fluctuating quantities in the governing equations. LES models, on the other hand, solve for the turbulent motions at scales resolved by the mesh. RANS models are good for understanding the average behavior of a flow over a period of time, while LES models are used to describe individual experiments or significant transient behavior. For this study, the RNG model, which falls into the category of RANS type models, was used. RNG is an improved k-ԑ model, with coefficients determined through rigorous statistical analysis. Other options that could have been used as RANS models are the classical k-ԑ model or the Wilcox k-ω model. RNG was chosen because it is typically good for transitional flows.

Free surface tracking
FLOW-3D uses an enhanced variant of Volume-of-Fluid (VOF) technique called TruVOF®. TruVOF provides a natural way to capture free surfaces and their evolution with great efficiency. More details on the free surface fluid flow can be found here.

Momentum advection
FLOW-3D offers three options for momentum advection based on the order of accuracy desired. The first order is the simplest and fastest method. The second order is preferred for minimizing numerical dissipation. The third option is called second order monotonicity preserving. This method is second order accurate in space and first order accurate in time. It was used for this study to properly model the turbulent transport of the contaminant. Preservation of monotonicity ensures that the quantity gradients are limited to avoid non-physical oscillations. 

Simulation results

The animation below shows that the upstream channel flow deforms the jet, pushing it to the side groin fields where re-circulation takes place.
Simulation showing the deformation of the jet and circulation zones in two different views.

This bounded jet shows unsteady behavior even for the statistically steady final solution. For ease of visualization, two iso-concentration surfaces are shown:  C=0.01 (red) and  C=0.001 (grey), which act as a representative envelope of the contaminant reach. The grid space was set to 5 cm for visual comparison with the experimental results (see animations below).


Animations showing a visual comparison of experimental results (top) and numerical results (bottom)

We see that FLOW-3D captured all the relevant physics important in modeling turbulent dispersion of environmental discharges. The results match the experiment to a good degree of accuracy both visually and numerically.

In my next blog, I will be talking about another entry from our 35th anniversary simulation contest, which focuses on modeling Pelton turbines.

Tuesday, March 8, 2016

Dynamic Response of a Constrained Floating Structure

This is the first in a series of blog posts in which I feature some of the entries for Flow Science's 35th anniversary contest.

Floating structures on an ocean surface, restrained by mooring ropes, are a common sight in the oil and gas industry. Understanding the dynamic response of these structures in the presence of ocean waves is crucial for their structural design. Additionally, each structure is typically made of multiple elements (sub-structures) that interact with each other and the waves. The problem of understanding the dynamic response of a floating structure becomes challenging due to the complex dynamics of the system. A good CFD model should be able to accurately calculate the dynamic response of the floating structure with all of its complex variables in place. In this article, I will talk about the dynamic response of a floating structure made of 3 elements hinged together, followed by a presentation of FLOW-3D simulation results. FLOW-3D can simulate moving objects, generate desired wave types, calculate very accurate free surfaces, and has a robust mooring lines model. All these features make FLOW-3D an excellent tool for estimating the dynamic response of constrained floating structures.

Details of the floating structure

Figure 1 (a) shows the details of the floating structure with its 3 elements. The blue and the yellow elements float on the ocean surface, while the red element is submerged. The 3 elements are connected via axial hinge support. The relative motion of the elements is governed by the motion constraints from the axial hinge support, which allows rotational motion along the longitudinal axis of the hinge.

Figure 1 (a) Details of the floating structure with its 3 elements colored in blue, yellow and red.  

       Figure 1 (b) Floating structure relative to the ocean floor and connected to the ocean bottom with a mooring/catenary rope.

The model setup of the system is shown in Figure 1 (b). The floating structure is shown in the context of the ocean surface. Also, the red element is tied to the bottom floor using a mooring rope. A wave train is applied at the left boundary.

Physics and Simulation

As discussed earlier, FLOW-3D is a natural choice to model such scenarios. In the following sections, I will discuss the details of the important physics involved and FLOW-3D‘s ability to simulate them.

Moving objects
FLOW-3D’s moving and simple deforming objects model allows the user to prescribe a type of motion to the elements in a structure. Users can constrain the motion of elements in a certain direction or let the system evolve in a completely coupled way. In this example, motion is constrained in the direction perpendicular to the plane of paper. However, in every other direction, rotations and translations are set to evolve per coupled forces from other elements and the fluid.

Waves 
Users can set a particular type of wave train in FLOW-3D as a boundary condition. Available wave types are Linear, Stokes, Stokes and Cnoidal, Solitary and Random waves. Each of these wave types can be fully defined by specifying wave height, mean fluid depth, wavelength/wave period, and current velocities. Random waves, however, are defined based on a power/energy spectrum and can be allowed to evolve based on the wind speed. In this example, Stokes and Cnoidal waves were used with a wave height of 6m, mean fluid depth of 50m and wave period of 6s. The current velocity is unidirectional from left to right and is set to 0.25m/s. Figure 2 shows the definition of a Stokes and Cnoidal wave.

Figure 2. Stokes and Cnoidal wave definition from FLOW-3D.

Free Surface estimation
FLOW-3D uses an enhanced variant of Volume-of-Fluid (VOF) technique called TruVOF®. TruVOF provides a natural way to capture free surfaces and their evolution with great efficiency. More details on the free surface fluid flow can be found here.

Mooring lines
FLOW-3D’s Mooring Lines model provides an effective, flexible and robust implementation to accurately simulate the transient dynamic response of floating structures in moored configurations. In addition to basic parameters like free length, linear density, material density and diameter, advanced options such as drag coefficients in normal and tangential directions, and deep water behavior of mooring lines can be set. In this example, a mooring line with a free length of 28m, diameter of 0.2m and a tangential drag coefficient of 0.3 is used. A spring coefficient of 10 6 N/m is provided to imitate the slacking and extension behavior of a rope.

Simulation results

The animation below shows the simulation results from FLOW-3D, post-processed in FlowSightTM. The animation captures all the complex physics involved in the process. The top right frame shows the evolution of the velocity of waves. Notice the complex fluid velocity fields around the hinge and at the bottom of the red element. The left frame shows streamlines colored by their relative magnitude. Also, a plot of catenary extension from its free state is shown as the entire structure sways to and fro, causing mooring rope to stretch and slacken. After 20 seconds the catenary extension starts following a steady oscillatory motion.

Dynamic hinged motion of three structural elements using FLOW-3D and the oscillating behavior of the constraining catenary extension from the free state.
Watch the YouTube video >

Simulating floating structures involves complex physics and requires a CFD solution that is capable of capturing these physics easily and accurately. FLOW-3D not only gives good results in this case, but the ease with which the entire process can be set up is amazing.

In my next post, I will be talking about another 35th anniversary contest entry, applying FLOW-3D to the dispersion of environmental discharges.

Tuesday, February 23, 2016

FlowSight Key Improvements, Part III

In this third and final blog post about the latest improvements to FlowSight™, I will discuss the key features of the new Preferences Dialog followed by the new calculation options on history data and extra menu options for sampling volumes. Finally, I will give a brief overview of existing and new features to demonstrate the advanced post-processing capabilities of FlowSight.

Reducing Redundancy using the Preferences Dialog

An important part of a CFD study is post-processing the simulations. A strong CFD software can generate high-quality data, but it needs to be coupled with a good post-processor that will let the user easily extract useful and compelling information from that data in an efficient manner. In this section, I will talk about reducing, or eliminating altogether, the repetition of tasks using the Preferences Dialog in FlowSight.

The goal of the new Preferences Dialog is to allow the user to set preferences in FlowSight so that they do not need to be reset for future simulations. A preference can be a timeline, viewport background color, color scale setting, text font, etc. Users have the following Preferences options:
  • Load Preferences: Includes timeline preferences, geometry preference, and iso-surface preferences.
  • Legend/Color Preferences: Includes legend text font, color and number of levels. Allows setting a preference to a particular variable.
  • Image Saving Preference: Allows images to be saved in the same format (resolution and file type) every time.
  • Viewport Preference: Includes preferences for viewport background color, border visibility, height and width.

Other preferences include Views, Mouse Actions and Annotations. An example of a Legend/Color Preferences Dialog is shown below.


A Legend/Color Preferences Dialog box (left) and the applied preferences to a simulation (right)

Calculations on History Data

In addition to visualization capabilities, FlowSight has numerous ways to perform calculations on History data. The History data calculated by the solver includes important time-dependent quantities such as fluid volume, fill fraction and solid fraction. Also included is the output from cooling channels, sampling volumes, history probes, flux surfaces, moving object data, and the pressure and shear force output on the geometry components. Users can perform mathematical calculations on the history data to extract even more information, such as scaling, integration, derivation, summation, multiplication, division, combination, equation specification and fast Fourier transform. An example of the “Sum” query on cooling channels is shown below.

Sum query of total heat flow rate from cooling channels

Extra Menu Options on Sampling Volumes

In addition to the existing sampling volume options like Transparency, Shading, Fill pattern, etc., users now have two new features – Plot and Display of the history queries associated with the sampling volume. Users can plot fluid forces, moments, volume, etc. and then display them on the FlowSight window as shown in the example below.


Example showing the options in a Sampling Volume drop down menu (left) and the plotted query of fluid force in X-direction (right)
FlowSight Recap

In the last few posts, we have seen how FlowSight enhances the user experience during the post-processing phase. In addition to its core capabilities of volume rendering, CFD calculators, animated streamlines and pathlines, 2D and 3D slicing, animated time-dependent plots and vortex core generation, these latest improvements have taken FlowSight to another level. With improvements to the visualization of baffles, sampling volumes, probes, open volumes and iso-surfaces; reducing the computational burden during volume rendering using Stencils; stitching multiple simulations into one seamless simulation; or, simply being able to set preferences to reduce redundancy and speed up workflows; FlowSight continues to provide increased flexibility and ease of use.

Readers who are interested in seeing more of FlowSight and its capabilities can go to our website or contact our sales team. And, as a reminder, FlowSight is available in all FLOW-3D products: FLOW-3D, FLOW-3D Cast, and FLOW-3D/MP for no additional cost. 

Tuesday, February 2, 2016

FlowSight Key Improvements, Part II

In my last post, I talked about the new developments in FlowSightTM that provide a better connection between simulation setup and post-processing in relation to visualizing geometry features. Continuing the theme, I will discuss the improvements to volume rendering and the new case linking features in FlowSight.

Volume rendering improvements

Volume rendering is a very powerful way of looking at simulation results. However, it is computationally intensive as it directly displays 3D volume data as a pixel map instead of drawing a surface by creating polygons or triangles.  This computational burden can be reduced in FlowSight using a new feature called Stencil. Another improvement, Filters, provide the ability to create a volume render that only shows a specified region. I will explain more about how you can use these features to improve your workflow in the following sections.

Stencils

Stencils control the resolution of a volume render. The default value of the stencil is 1 in all the directions, which means that all mesh cells will be used. Increasing the stencil size coarsens the volume rendering, reducing the time to create the rendering and providing comparable visualization depending on the original mesh resolution. Stencils can only be set during their creation and cannot be modified later. A comparison of two volume renderings with different stencil sizes is shown below followed by a table that shows performance improvements as the stencil size varies.


Volume render stencil 1:1:1 (finer) vs. 2:2:1 (coarser)





Volume render time comparison showing performance improvement with increasing stencil size. Comparison has been done for mesh cell count of 15 million.

Filters

The Filters option allows the user to define the region where a volume render should be created. Using Filters, the user can specify to create a volume render only in Fluid 1 or only in the Solid region. There are seven predefined filter options shown below. The options include 6 surfaces: fluid 1, void, solid volume, open volume, liquid fluid, solidified fluid and auto. This last is the default option, and it selects the filter based on color by variable.


Volume render - predefined filters

In addition, FlowSight allows the user to create up to five custom filters (shown below) that work in combination of and /or logical operators.


Volume render - custom filters

Volume render comparison with(right) and without (left) filter. Fluid-1 has been chosen for filtering.

Case linking
Sometimes a simulation is intentionally (or unintentionally, for example, due to a computer crash), broken into a series of restart simulations. But, the user may still want to see several data sets in one continuous timeline/animation. The user can use Case Linking feature to create a single animation from a simulation and associated restart simulations so that the entire timeline/process can be seen from the start of the first simulation to the end of the last restart simulation.

The Case Linking feature controls the overall visibility of a case. Based on the link times, only the case valid at a current time will have its parts available in the display.  The user needs to correctly set the Viewport visibility. So, in the example below, we want to a create a single animation from three cases with the left viewport showing fluid isosurface colored by the velocity and the right viewport showing fluid isosurface colored by the temperature. So, to achieve this, “Isosurface-1” for all the three cases must be made visible only in the left viewport and similarly “Isosurface-2” for all the cases must be made visible only in the right viewport.

For new FlowSight users, I would like to briefly explain how viewports work. The FlowSight window can be divided into multiple sub-sections or viewports. Each viewport can have a different view, different iso-surface, etc. Multiple viewports provide flexibility to the user to study and perform an action on the same simulation in different ways while visualizing them simultaneously. For example, a velocity isosurface can be seen in one viewport, a temperature isosurface can be seen in another viewport, a volume render can be seen in the third viewport, and so on.
    
 


Setting part viewport visibility:
  • Isosurface-1 colored by velocity visible in the Left viewport
  • Isosurface-2 colored by temperature visible in the Right viewport
Once the viewport visibility is set correctly, an animation can be captured from the Create animation dialog. One such animation is shown below that uses the Case Linking feature. 

Video highlighting the new Case Linking feature of FlowSight where multiple simulations were sewn together to generate one seamless animation

This post focused on reducing the computational burden while creating volume renders and linking multiple simulations to create a single seamless animation. In my next post, I will talk about the improved Preferences option in FlowSight.