Towards Collaborative Analysis of Computational Fluid Dynamics
using Mixed Reality
Thomas Schweiß
, Deepak Nagaraj
, Simon Bender and Dirk Werth
August-Wilhelm Scheer Institut, Uni Campus Nord D 5.1, Saarbrücken, Germany
Keywords: Computational Fluid Dynamics, Mixed Reality, Collaborative Virtual Environments, Artificial Intelligence,
Machine Learning.
Abstract: Computational fluid dynamics is an important subtopic in the field of fluid mechanics. The associated
workflow includes post processing simulation data which can be enhanced using Mixed Reality to provide an
intuitive and more realistic three-dimensional visualization. In this paper we present a cloud-based proof of
concept Mixed Reality system to accomplish collaborative post processing and analysis of computational
fluid dynamics simulation data. This system includes an automated data processing pipeline with a ML-based
3D mesh simplification approach and a collaborative environment using current head mounted Mixed Reality
displays. To prove the effectiveness and accordingly support the workflow of engineers in the field of fluid
mechanics we will evaluate and extend the system in future work.
In the field of fluid mechanics, computational fluid
dynamics (CFD) represents an important subtopic to
optimize product design workflows, reduce the need
for costly prototypes and eliminate rework. With
CFD simulations of either liquid or gas passing
through or around an object, engineers can analyse
the flow’s impact on the object. Therefore, CFD
simulations are used in several fields of application,
such as aerodynamics and aerospace analysis,
industrial system design and analysis, biological
engineering as well as engine and combustion
analysis. The process of CFD simulation and analysis
consists of three main steps: Pre-processing, which
includes identifying the fluid domain of interest and
set up the geometry of the object, which will be
analysed. The solving step to solve physical equations
related to the fluid flow and the post processing step,
where appropriate visual representations of the results
are being generated for analysis purposes. The latter
can be done by post processors to visualize the
resulting solutions represented as contour and vector
plots or 3D models. The analysis of this data is mostly
done on pc monitors, which restricts the graphical
representations on two dimensions. Thus, an intuitive
and realistic data analysis is prevented. Additionally,
these post processors and CFD simulation software
often rely on proprietary data formats, so that
incompatibilities on different systems can occur.
Especially when distributed and interdisciplinary
teams collaborate.
We suppose that current Mixed Reality (MR)
systems can support and enable collaborative CFD
post processing by outsourcing this process
completely into mixed reality. Results of a CFD
solver will be uploaded into our system in an open file
format to process them further in three dimensions
without restricting users to be completely separated
from reality and manipulate data intuitively with hand
gestures, eye gaze and speech control.
Current systems used for CFD post processing
lack of collaborative features. Especially when it
comes to multidisciplinary and distributed teams. It is
not uncommon for third-party systems such as video
conferencing tools to be used. Therefore, users share
their desktop screens or presentation slides to present
resulting data, which means, that the integration of
new ideas and the communication of feedback is
often limited through an abstraction of the actual
three-dimensional data to static images or animations,
based on pre-defined camera angles. As a result,
individual inspection of components is restricted.
Schweiß, T., Nagaraj, D., Bender, S. and Werth, D.
Towards Collaborative Analysis of Computational Fluid Dynamics using Mixed Reality.
DOI: 10.5220/0010321602840291
In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 1: GRAPP, pages
ISBN: 978-989-758-488-6
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
With the help of cloud computing, AI technology
and current MR hardware, a distributed MR
environment can be implemented to accomplish
collaborative and interdisciplinary post processing
and analysis of CFD data. Thus, the workflow of
engineers in the field of fluid mechanics can be
actively supported by minimizing times of analysis
and reducing travel costs. It also leads to enhanced
coordination and collaboration between team
members by providing natural exploration of
multidimensional data paired with enhanced social
interaction due to digital avatars and collaboration
features like object annotations or digital
whiteboards. Accordingly, we assume that this will
result in a better product and system design.
In this paper we present a proof of concept system
to manipulate and analyse the results of CFD
simulations in a collaborative and distributed MR
environment. The goal is to integrate this system in
the daily workflow of engineers and scientists. To
achieve this, a cloud infrastructure used for
automated data computation based on an open data
format is necessary, so that users can upload their
simulation results and view them on an MR head
mounted display (HMD). The latter can be
computationally very intensive for complex CAD
models due to the large number of polygons within
the meshes. Accordingly, an artificial intelligence
(AI) model, which can implicitly learn the shape and
simplify the meshes in a goal-oriented manner, needs
to be developed, so that also complex models can be
computed by the HMDs. In the collaborative MR
environment, users will be able to control the post
computing process by intuitive interactions via
gestures or speech, as they can set filters to redefine
visualization results and move or scale an object
freely. With the given cloud infrastructure,
distributed collaboration including virtual avatars,
can also be made possible as mentioned above.
According to the sensor data of current HMDs, those
avatars can be equipped with eye gaze and hand
visualization as proposed in (Piumsomboon et al.
2017) and also spatial sound to enhance
The following sections are structured as follows:
In section 2, we will present related work according
to collaborative MR systems in the field of CFD and
AI based mesh simplification. Section 3 will describe
the overall system design including the automated
data computation (Section 3.1), the AI based model
optimization approach (Section 3.2) and the
capabilities of the collaborative MR environment
(Section 3.3). In section 4 we will conclude and
present future work.
CFD simulations and analysis are widely used in the
field of fluid mechanics. The visualisation of CFD
results with MR technology was proposed in several
projects. (J. Moreland et al. 2013) for e.g. developed
an MR representation of simulated fluid flow within
a power plant for training purposes. Vectors,
streamlines and colour gradients were used to
visualize CFD data. (Zhu et al. 2020) created a
system, used in early building-design processes
including the modelling of information about the
building, mesh generation and CFD simulation to
visualize animated thermal activities in a full-scale
room using MR. Both studies rely on precomputed
data, which must be manually changed on a desktop
pc to visualize the MR projections. Functionalities to
dynamically update models were proposed in
(Malkawi und Srinivasan 2005). In this project
different wireless temperature sensors were used to
update the CFD simulation and visualize projections.
These projects provide visualizations of simulation
data but lack adequate user interfaces (UIs) and
therefore an intuitive user experience while
performing CFD post processing in MR in order to
create new visualisations for HMDs.
As described in (Cheng et al. 2020), there is a lack
of MR systems based on cloud storage and cloud
computing, which are current trends for MR systems.
They also mention that current systems are limited in
terms of number of gestures or stability of voice
recognition. Additionally, collaborative multiuser
systems should be more promoted in the industry. For
collaborative and distributed CFD analysis, cloud
computing is also an essential part in order to
accomplish efficient data manipulation, computation
and visualisation. (García et al. 2015) presented a
cloud-based system to monitor and alter CFD
simulations for collaborative solution analysis. This
includes pre-processing, solving and post-processing
data within a server environment. The latter enables
the user to set new basic filters such as stream tracers
or colormaps to manipulate the simulations.
Simulation results can be displayed in 3D
environments, but are visualised on 2D screens,
which, in contrast to MR systems, abstracts the
outgoing data into unintuitive WIMP interactions.
Furthermore, according to the literature research,
there is a lack of MR environments to accomplish
collaborative analysis of CFD simulation data by
distributed working teams, which agrees with the
statements provided by (Cheng et al. 2020).
Polygonal meshes effectively represent 3D shapes
by capturing both surfaces and topology, and leverage
non-uniform elements to represent large flat regions
as well as sharp, intricate features. However, naive
Towards Collaborative Analysis of Computational Fluid Dynamics using Mixed Reality
Figure 1: Architecture.
application of MR for CFD data visualization to
create an immersive design environment, for example
for automobile shape designing, requires huge
memory demand and accordingly it would be difficult
to render those graphics in real-time. Numerous
algorithms have been proposed for mesh
simplification (aka polygonal simplification). Most of
such conventional algorithms can be grouped under
Manifold-only simplification and Nonmanifold
simplification algorithms (D. P. Luebke 2001; P.
Cignoni et al. 1998). Manifold-only algorithms are
limited in their application, as they are not capable of
handling nonmanifold meshes, which are typical for
CAD models created manually (D. P. Luebke 2001).
The most commonly used approach in simplifying
nonmanifold meshes is to use a quadric error metric
algorithm (QEM) (Garland und Heckbert 1997). Over
the years, many variations to the original QEM
algorithm (Garland und Heckbert 1997) were
developed. Each of these variations are suitable for
different and specialized applications, but produce
undesirable results when used for other types of
meshes (e.g., no boundaries, lots of boundaries,
textured, etc.) (Bahirat et al. 2018). However,
machine learning (ML) based techniques which
implicitly learn how to retain or collapse edges,
depending on the overall task being undertaken, are
still in nascent stage. In the study (Hanocka et al.
2019), a novel convolutional neural network (CNN)
called MeshCNN has been introduced. They have
demonstrated the ability of MeshCNN in task-driven
pooling to collapse redundant edges and expand
important features on various 3D meshes. However,
their approach works only on triangular meshes.
After considering the limitations of past and current
systems, and future trends for MR in section 2, in this
section, we propose a system which has the potential
to overcome these limitations by providing a cloud
based approach that makes dynamical CFD post
processing, based on user inputs, possible. With this
system, users can change the data and its visualization
through a post processor working in the cloud and
share all information and 3D components within an
immersive and collaborative distributed MR
environment. Including three-dimensional avatars
and abilities to e.g. create annotations in the
environment, we assume to improve efficiency of MR
remote collaboration.
The overall system consists of three main
components visualised in Erro! A origem da
referência não foi encontrada.. First, resulting CFD
simulation data is exported from the simulation
software system to an open file format to ensure data
compatibility. These files will then be uploaded to the
cloud and verified to minimize computation errors.
The second part includes the cloud based automated
data computation, the interfaces for handling MR
control commands and the ML based mesh
simplification. The computed data will then be sent to
the last component, a collaborative and distributed
MR system. This includes UIs and interaction
techniques for communication between users as well
as for data manipulation.
GRAPP 2021 - 16th International Conference on Computer Graphics Theory and Applications
3.1 Automated Data Computation
As described in section 1, the process of generating
CFD simulations and analysing them is divided into
three basic steps: Pre-processing, solving and post
processing. Our system will be part of the post
processing stage, in which simulation data is
transferred into 3D data for computation in MR.
Therefore, data must be exported from the CFD
simulation software into an open file format.
Proprietary data formats are difficult to handle when
it comes to partners or team members with different
software applications. Based on this, an open file
format, which can be handled by different CFD
applications such as STAR-CCM+, ANSYS or
OpenFOAM, is required. The CFD General Notation
System (CGNS) has established itself as such an open
file format and data model to store CFD simulation
results. It is capable of storing several types of
auxiliary data, such as generic, discrete or integral
data, dimensional unity and exponents or
nondimensionalization information (Poirier et al.
1998). This also includes mesh data of CAD models.
In order to compute and visualise CGNS data
models, a post processing software is needed. This
software must be capable of converting CGNS into a
readable data format for 3D engines in order to send
this computable data to the HMD. Additionally, it
must handle all necessary tools and commands which
are required in the workflow of CFD engineers to
perform CFD post processing. The post processing
software should be included in the cloud
infrastructure to directly connect it to the MR system
and therefore, be able to benefit from cloud storage as
well as cloud computing power in terms of CPUs,
GPUs and RAM storage. A tool which is capable of
these requirements is ParaView (Ahrens et al. 2005).
As an open source post processing software,
including the Visualization Toolkit, it provides a
variety of algorithms to process CFD simulation data
such as isosurfacing, cutting, clipping and
streamlines. With the ability to access its filter
pipeline by a Python API, ParaView can be run in a
docker container. Based on this, resulting data can
easily be processed on server side via HTTP requests.
For further processing in the 3D engine and for
computation on the MR device, different export
formats are available. Currently, the Wavefront OBJ
format is used to send data from ParaView to the MR
devices. Indeed, this data format is not directly
capable of processing additional visualization data
like surface textures. The GL Transmission Format is
an open data format providing efficient transmission
of 3D scenes and models between applications. It is
capable of handling large datasets such as needed for
CFD data processing and supported by ParaView.
Also it includes binary files such as images and
shaders (Schilling et al. 2016) and therefore makes it
suitable for our approach.
To visualize the resulting data from ParaView, the
Mixed Reality HMD HoloLens 2 from Microsoft is
used. With its hand- and eye-tracking capabilities as
well as voice recognition and spatial audio within a
stand-alone device, it provides a variety of different
visualization and interaction possibilities for the user.
Although it has some limitations as mentioned in
(Cheng et al. 2020), it is still the most advanced
standalone MR device with improved performance,
wearing comfort and especially an renewed
interaction design compared to its predecessor.
In order to connect the MR device to the cloud,
the game engine Unity 3D is used. Based on its
modular structure, open accessibility by utilising
.NET scripting with either C# or JavaScript and
together with the Mixed Reality Toolkit, which
provides a variety of predefined interaction scripts
and connection services, the engine is a good matter
of choice for this system.
The 3D engine establishes a connection to the
cloud, as the user logs into the system. After logging
in, its uploaded and optimized models will
automatically be fetched and loaded. Therefore, a
network manager receives the optimized meshes from
the cloud. These meshes will be included in the
following interaction sequence.
Figure 2: Hand menus.
To facilitate a user-centred object interaction and
provide a good ease of use, we have designed two
hand menus (see Figure 2). The object menu on the
left hand provides the processed meshes as 3D objects
around a sphere. The second menu on the right hand
lets the user access the current implemented filters of
ParaView. This is a modular approach in which more
filters can be added in future versions. The interaction
sequence is visualized in Figure 3, pictures (a) to (f).
In order to apply one or more filters to an object, the
user can place objects into the room via drag and drop
with hand gestures (a). Also, the scale of an object can
easily be changed by grabbing the object with both
hands (thumb and forefinger) and move the hands
away from each other. After the object is placed, a
Towards Collaborative Analysis of Computational Fluid Dynamics using Mixed Reality
Figure 3: MR interaction sequence to apply filters.
filter from the filter menu can be selected (b) and
applied onto the object, utilizing the drag and drop
method (c). With adjusted filter settings via a menu
near the current selected object (d, e), a new mesh
including the resulting data is generated by ParaView
and Unity (f). If the user wants to remove an object,
it can be dragged and released into the object menu
again. Additionally, filters like the stream tracer can
be animated to allow the visualization of a local
stream flow (f). Besides the stream tracer, also filters
like the clip, slice and contour (isosurfaces) filter
together with colour gradients are implemented in a
simple form.
3.2 AI for Model Optimization
With reference to section 2, another edge collapsing
algorithm which works effectively and efficiently on
big CAD files, is NSA (Silva 2007). Unlike other
QEM based methods, which are based on
minimization of error associated with each new
vertex, the NSA algorithm follows a geometric
criterion which implies, that the region around the
collapsing edge be nearly coplanar. An edge is only
collapsed if the variation of the face normal around
the target edge is within a given tolerance. This makes
NSA to arrive at a good compromise between shape
preservation, time performance, and mesh quality
(Silva 2007).
Another interesting approach for mesh
simplification would be, to use neural mesh
autoencoders, which have been recently applied for
many 3D tasks (Ranjan et al., 2018). With this
approach, the task is, that the autoencoder implicitly
learns the mesh structure and the encoder component
of the autoencoder compresses the input mesh into a
latent space representation. Later, the decoder
component of the autoencoder decodes / reconstructs
the latent representation into a mesh structure,
neglecting the redundant elements from the original
mesh. (Ranjan et al. 2018) for example, proposed
such a convolutional mesh autoencoder called
CoMA, which used spectral convolution layers
accompanied by quadric up-and-down sampling
methods to achieve promising results in aligned data
of a 3D human face. However, such learning-based
methods would also require additional heuristics (or
operators) to make them work with different number
of neighbouring elements, yet maintaining the weight
sharing property of CNNs. In this direction, the study
(Zhou et al. 2020) has proposed a template-free fully
convolutional autoencoder, empowered by novel
convolution and (un)pooling operators, which works
for arbitrary registered meshes like tetrahedrons and
non-manifold meshes. The spatially varying
convolution kernel is especially interesting for our
application, as every vertex will have its own
convolution kernel, which accounts for irregular
sampling and connectivity in the dataset. Their
method of jointly learning the global kernel weight
basis and a low dimensional sampling function for
each individual kernel, would greatly reduce the
number of parameters and accordingly would be less
computationally expensive (Zhou et al. 2020).
Although, our task here is restricted only to carry out
GRAPP 2021 - 16th International Conference on Computer Graphics Theory and Applications
mesh simplification with this approach based on an
autoencoder, the capability of obtaining semantically
meaningful localized latent codes would further assist
in better semantic manipulation of the given original
3D mesh, if desired by the designer. Although the
application of autoencoder based algorithms seems
very interesting, owing to goal-oriented mesh
simplification, the main drawback here is, that the
technique requires a sufficient number of datasets for
each kind of mesh and long training times to be
effective. However, the research in this direction is
ongoing and with recent advances, particularly in
hardware technology, learning based algorithms have
a great potential for this application.
3.3 Distributed and Collaborative XR
Due to complex characteristics of the human body,
the human perception of physical quantities like the
behaviour of light as well as social interaction
between multiple users, creating collaborative and
distributed MR environments is a challenging task.
They have to provide different key aspects, such as
telepresence via co-present active communication,
immersion achieved through interactivity or include
tacit knowledge such as cultural and contextual cues
to enhance distributed collaboration (Raybourn et al.
2019). Although collaborative MR environments
have been developed and studied in different fields of
application like architectural design (Ahn et al. 2019)
or analysis of geo-spatial data (Mahmood et al. 2019),
currently there is a lack of such systems for
collaborative CFD analysis as proposed in section 2.
In order to close this gap and extend the MR system,
we propose a cross reality (XR) system to achieve
collaborative CFD post processing and data analysis,
in which additional features like virtual whiteboards
will be provided, in order to enhance collaboration.
Indeed, the system will also be capable of
including other devices, such as Virtual Reality (VR)
headsets to make the system accessible for different
team members, who prefer to view the simulation data
in a fully immersed environment. Advanced Mixed
Reality hardware like the HoloLens 2 is currently
quite expensive (3500€) but offers more
functionalities like hand and eye tracking in
standalone devices and advanced communication
aspects based on see-through displays in comparison
to VR systems. Nevertheless, VR headsets can also
provide adequate and immersive environments for
CFD analysis. To accomplish such analysis, the user,
according to section 3, must be able to upload the
CGNS data for further computation. This can be done
by a web application, including a user management
system. In order to do so, the user can sign up to the
system, which generates a unique user ID and a certain
amount of cloud storage for the simulation data. After
this step, the files can be uploaded and verified to
ensure for e.g. that the data format is correct, which
minimizes further computation errors. After the file
upload is completed, an automated mesh generation
process will be initiated to create the basic meshes for
the 3D objects in the object menu by ParaView.
Further processing is described in section 3.1.
To enhance user collaboration and provide
telepresence, automatically generated 3D avatars of
each connected user will be included. Based on the
sensor technology of current MR headsets, these
avatars and the MR environment can be equipped
with different kinds of features to enrich collaboration
and communication of distributed users. Hand
recognition and eye tracking can be used to create
realistic hand and finger movement as well as eye
gaze visualizations, which enhanced multi-user
collaboration (Piumsomboon et al. 2017) and create
significantly stronger sense of co-presence (Bai et al.
2020). With spatial audio, the task of finding points
in three dimensions, that are located outside the users
Field of View (FoV), is less time consuming
(Hoppenstedt et al. 2019). This can help users to find
avatars of currently speaking team members, which
can’t be seen in the FoV at that moment, and therefore
improve communication.
To further enhance virtual collaboration,
annotations can be added to the virtual scene by the
user. When it comes to CFD analysis and workflows
of engineers, it is important to show other team
members, at which point a simulation is defective or
where enhancements can be applied. Especially,
interdisciplinary teams can benefit from annotations
as the task of collaborative CFD analysis in expert to
non-expert relationships can be accomplished faster
(Gauglitz et al. 2014). Therefore, an annotation
system with simple annotations will be implemented
to visualize important or critical parts of objects.
Thereby, users will be able to mark objects partially
or in complete in an annotation mode using their
hands in MR or controllers in VR systems.
The paper proposes a proof of concept for
collaborative post processing and analysis of
computational fluid dynamics using Mixed Reality. It
recommends a cloud based and automated data
computation pipeline with a user management system
to allow CFD engineers uploading simulation results
in the open data format CGNS. With a CFD post
processing software, which is located in the backend,
Towards Collaborative Analysis of Computational Fluid Dynamics using Mixed Reality
and an MR application for visualization, analysis of
resulting 3D fluid flow models is possible. The
system is partially implemented in the ongoing
For future work, we plan to implement a first
working prototype and evaluate the collaboration
aspects and effectiveness of the proposed system with
different user tests. This includes implementation of
the ML algorithms for mesh simplification as well as
the collaboration aspects such as avatars and the
annotation system. Additionally, it is planned to
examine if GLTF, as an internal format for 3D data
exchange, is an appropriate alternative for simple data
formats such as the Wavefront OBJ format.
This Work is based on HoloSim, a project partly
founded by the German ministry of education and
research (BMBF), as part of the “KMU-innovativ:
Informations- und Kommunikationstechnologien”
program, reference number 01IS18020D. The authors
are responsible for the content of this publication.
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