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Immersive Particle Advection: Through the Scales of Renewable
Energy
Nicholas Brunhart-Lupo
Kenny Gruchalla
nicholas.brunhart-lupo@nrel.gov
kenny.gruchalla@nrel.gov
NREL
Golden, Colorado, USA
Figure 1: A photograph of a scientist exploring the airow inside the cabin of an electric vehicle using immersive particle
advection. The trajectories of the particles reveal the complex dynamics of air circulation, with the colors indicating temper-
ature gradients throughout the cabin. By understanding these dynamics, we can improve energy eciency and increase the
range of electric vehicles.
ABSTRACT
We describe the benets of immersive ow analysis for three large-
scale computational science studies in the eld of renewable energy.
The studies encompass a range of scales, spanning from the large
atmospheric scale of a wind farm to the human scale of an electric
vehicle cabin down to the microscopic scale of battery material
science. In these studies, users explored the ow patterns and dy-
namics through immersive particle advection. The integration of
high-performance computing with immersive analysis provided
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government. As such, the Government retains a nonexclusive, royalty-free right to
publish or reproduce this article, or to allow others to do so, for Government purposes
only.
PEARC ’23, July 23–27, 2023, Portland, OR, USA
©2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-9985-2/23/07. . . $15.00
https://doi.org/10.1145/3569951.3603641
a deeper understanding of these systems, helping develop more
eective solutions for a sustainable energy future.
CCS CONCEPTS
•Applied computing →Chemistry
;
Earth and atmospheric
sciences
;
•Human-centered computing →Virtual reality
;
•
Computing methodologies →Physical simulation.
KEYWORDS
Immersive Analytics, Computational Fluid Dynamics, Particle Ad-
vection
ACM Reference Format:
Nicholas Brunhart-Lupo and Kenny Gruchalla. 2023. Immersive Particle Ad-
vection: Through the Scales of Renewable Energy. In Practice and Experience
in Advanced Research Computing (PEARC ’23), July 23–27, 2023, Portland, OR,
USA. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3569951.
3603641
PEARC ’23, July 23–27, 2023, Portland, OR, USA Brunhart-Lupo and Gruchalla
1 INTRODUCTION
Particle advection is a fundamental technique for ow visualiza-
tion [
2
], where researchers can analyze computational uid dy-
namics (CFD) models through the trajectories of massless particles
released in the simulated ow. Particle advection is the process of
placing a particle in a vector eld that models a ow and displacing
the particle’s position through numerical integration. Particle ad-
vection can be used to analyze both time-varying and steady-state
vector elds and is particularly useful for understanding complex
uid dynamics, as these trajectories can reveal the structure of
the ow, like areas of convergence or divergence. However, two-
dimensional projections of three-dimensional particle ow may
be insucient to perceive the complex structure in these point
clouds, requiring some combination of depth cues to aid in per-
ception and understanding [
9
]. Additionally, seeding particles in
three-dimensional space can be challenging using two-dimensional
input devices. When coupled with real-time rendering, immersive
technologies can address both of these issues, allowing researchers
to seed points directly in three-dimensional space and naturally ob-
serve the resulting complex three-dimensional particle ow paths
with an embodied perspective.
Immersive analytics merges immersive technologies with data
analytics and visualization techniques to analyze complex data
sets. With its focus on embodied perception and interaction, im-
mersive analytics has shown potential in multiple scientic and
engineering contexts [
7
]. The concept of immersive analysis has
long been of interest to researchers seeking to gain a deeper un-
derstanding of CFD data; one of the rst immersive visualization
applications was the creation of a virtual wind tunnel [
3
] using
a boom-supported cathode-ray-tube head-mounted display. And
since that early introduction, immersive ow analysis has been ap-
plied to aerodynamics [
12
,
15
,
18
], geophysics [
1
], blood ow [
4
,
10
],
and even paleontology [14].
We add to this body of work by describing three renewable
energy applications: wake analysis of wind turbines, heating and
cooling optimization of an electric vehicle, and the analysis of elec-
trolyte ow through the electrode structure of a lithium-ion battery.
In these three renewable energy applications, domain scientists
have used immersive particle advection to inform real-world anal-
yses. Across all three applications, immersive analyses have led
to a deeper understanding of these complex systems. Our work
contributes to the call [
5
] to provide evidence of the use and e-
cacy of immersive analytics by domain experts by documenting
the outcomes of these three real-world applications.
2 APPLICATION
We implemented the immersive particle rendering application us-
ing C++, OpenGL, and MPI and deployed it in the custom-designed
large-scale six-projector immersive virtual environment at the Na-
tional Renewable Energy Laboratory (NREL). We utilized instance
rendering of the particles to ensure real-time interaction and ren-
dering capabilities. We stored each particle’s state in a 4
𝑥
4matrix
containing information about its position, rotation, color, and scale,
processed through vertex and fragment shaders. Rather than al-
locating and garbage-collecting particles as they are seeded, we
pre-allocated a xed number of particles (20,000 to 60,000) then
updated and rendered their attributes in parallel. New particles are
allocated from the xed number in a least-recently-used fashion,
overwriting the oldest particles rst. We sized inactive particles to
a zero radius until the user activates them, and particle radii decay
as a function of time. Particles exiting the domain are returned to
the inactive state. We introduced the ability to distort particles by
scaling along the motion vector, resulting in a simple blur-like eect
without requiring computationally expensive motion sampling.
To support real-time particle advection, the particle state vector
requires per-frame updates. Particle advection can be done e-
ciently on GPUs using compute shaders to integrate the vector
elds stored in a 3D texture; however, for a large-scale immersive
environment with multiple GPUs driving multiple displays, this
requires synchronization between cards. To avoid these complexi-
ties, we implemented the advection on the CPU, advecting all the
particles in parallel with a multi-core threading implementation
to meet frame budget targets. This implementation successfully
supports the real-time advection and rendering of 60,000 particles.
User interaction plays a crucial role in our immersive ow analy-
sis system, facilitated by an optically tracked game controller. Users
can seed particles by pressing a button on the controller, and the
particles are seeded from a point located just forward of the joystick,
visually indicated by a cursor to assist with precise positioning. In
addition to particle seeding, users can create a persistent generation
point, allowing them to drop a generator and freely move away
while observing and following the particles generated from that
point. We incorporated radial dials accessible through controller
buttons to provide users with control over visualization parameters.
Users manipulate these dials by twisting the joystick to set various
properties, such as colormaps, isosurface values, and advection
elds.
3 CASE STUDIES
3.1 The Kilometer Scale
Wind energy is a critical component in the transition to renewable
energy sources. However, the optimal placement of wind turbines
within a wind plant is not always straightforward, and the complex
physical interactions between turbines (see Fig. 2-
km
) in the plant
can impact power generation and overall eciency. Computational
modeling can be used to analyze these interactions [
16
] and im-
prove wind plant siting, control systems, and turbine design [
13
]. In
order to capture atmospheric boundary layer conditions, these sim-
ulations have domain sizes of at least
9 km3
, generating large-scale
results that can reach hundreds of terabytes [
8
]. In this case study,
we use particle advection to examine the dynamics of turbine wakes
and their impact on wind farm control and design. This research
can inform the development of more ecient and eective wind
plant systems, ultimately contributing to the goal of a sustainable
energy future.
The wind farm application presented turbines sitting within
three volumetric elds: the velocity vector eld, the vorticity vector
eld, and the Q criterion scalar eld. This integration allows for the
visualization of the turbines’ state with isosurfaces of the scalar eld
and the vector magnitudes of the vector elds, complemented by the
advection of particles within these vector elds. A typical starting
point for users involves visualizing an isosurface of the velocity
Immersive Particle Advection: Through the Scales of Renewable Energy PEARC ’23, July 23–27, 2023, Portland, OR, USA
Figure 2: Scales of renewable energy analysis investigated across our case studies. (km scale) Rendering of wakes forming
behind wind turbines in a wind farm. Understanding the wake dynamics is critical to the ecient siting and operation of a
wind turbine array. (mscale) Streamline rendering of airow through the cabin of an electric vehicle. Optimizing the heating
and cooling system can signicantly extend the range of the vehicle. (µmscale) Streamline visualization of a morphology-
resolved battery electrochemistry simulation shows the current ow within an electrode microstructure during a fast charge.
A better understanding of the current ow through the electrode structure can improve battery performance and safety.
magnitude at
4.5 m/s
, which represents the low-velocity turbine
wakes, alongside an isosurface of the Q criterion, representing the
vortices shed from the blades. The isosurfaces provide an overview
of the ow, and particle advection provides details. By seeding
particles in specic locations and integrating forward or backward
(in either the velocity or vorticity vector elds), users track where
particles are going or where they came from. Users can optionally
color the particles by applying a continuous colormap of one of the
three elds or a categorical colormap indicating when particles are
inside or outside the turbine wakes.
The trajectories generated by particle advection have proven in-
valuable to domain experts—physicists and mechanical engineers—
seeking to understand the formation of the shape of the turbine
wakes. They observed that the wake shape under yawed conditions
is not circular but curled. Using the immersive advection, they were
able to ascertain that two counter-rotating vortices forming behind
a yawed turbine caused this distortion in shape [
6
]. The immer-
sive particle advection has proven to be an indispensable tool for
discussions between technical and non-technical stakeholders by
providing an unambiguous representation of the ow dynamics.
First, the relative speed of the particles allows us to clearly perceive
the fast-moving incoming air and delineates it from the low-velocity
wakes that form behind the turbines. Then following the paths of
the lower-velocity particles, stakeholders can see the correlation
between the low-power and high-stress values observed on waked
turbines.
3.2 The Meter Scale
We studied with the airow around a driver inside the cabin of
an electric car. The eciency of heating and cooling systems in
electric vehicles is a critical area of research, as these loads directly
aect the vehicle’s driving range. However, visualizing the intri-
cate airow patterns within a vehicle’s cabin poses a signicant
challenge. The complex nature of the airow (see Fig. 2-
m
), inu-
enced by factors such as temperature, venting design, and occupant
presence, demands a visualization solution that can provide a clear
and comprehensive view of the ow dynamics. Vehicle engineers
used the immersive particle advection to inform the analysis of a
zonal venting design simulation [
11
], which aimed to optimize the
cooling system for a single occupant. The objective was to enhance
the comfort and thermal experience of the driver while maximizing
energy eciency by ne-tuning the distribution of cool air. This
required a detailed analysis of the airow patterns inside the cabin
to assess the eectiveness of the proposed cooling strategy.
The immersive application embedded air velocity and tempera-
ture inside the cabin with the geometry of the car and driver. We
advected the particles by velocity and colored them by temperature.
The immersive analysis provided a signicant value-add to the
vehicle engineering process, as it revealed previously unnoticed
ow features that they had missed in the traditional desktop analy-
ses. Prior to the immersive environment, the engineers had relied
on two-dimensional slices and projections of three-dimensional
streamlines (see Fig. 2-
m
) to analyze these ows. However, through
the immersive visualization, they interactively explored the ow
and gained a deeper understanding, nding vortical structures and
the areas of undesirable ow. This enhanced interactivity and im-
mersion allowed the engineers to uncover important ow charac-
teristics not apparent in the two-dimensional representations.
3.3 The Micro Scale
Lithium-ion batteries have become integral to our daily lives, pow-
ering many essential electronics. The performance and safety of
these batteries are dependent on the complex electrode microstruc-
tures that interface with the electrolyte. To optimize battery life and
charge-discharge rates, researchers need a better understanding of
the electrolyte potential ux, or current ow, through the electrode
structure. In a recent case study, researchers focused on a simulated
battery electrode material with a
14.5µm
x
6.3µm
x
6.3µm
volume
under a fast-charge scenario, presenting a challenging data analysis
due to its spatial complexity (see Fig. 2-
µm
) [
17
]. At the micrometer
scale, the electrode structure exhibits complex morphology, with
numerous interconnected voids that the electrolyte penetrates.
The immersive battery analysis visualizes this complex surface
morphology of the lithium electrode with the current ow. Users
PEARC ’23, July 23–27, 2023, Portland, OR, USA Brunhart-Lupo and Gruchalla
can colormap the surface based on a variety of scalar values such
as charge magnitude or charge rate. And then, seed particles in
or around the structure, advecting them through the electrolyte
potential ux.
The immersive analysis provided a mechanism to explore the cur-
rent ow in relation to the lithium-ion battery electrode microstruc-
ture. The complexity of the geometry presented a signicant chal-
lenge for researchers using traditional visualization techniques.
With most of the ow occluded inside the material pathways, tra-
ditional desktop tools are limited in their ability to visualize these
intricate structures, much less the uid-structure interaction. How-
ever, with immersive analysis techniques, researchers gain a unique
perspective. By naturally navigating through the microstructure
and following the particles, they can observe and understand the
intricate interplay between the ow, the surface, and the surface
values. Immersive particle advection provides a level of visibility
and interaction that is simply not achievable with conventional
desktop tools, enabling researchers to unlock deeper insights and
make more informed decisions in their microstructure analysis.
4 CONCLUSIONS
We presented three renewable energy case studies that used immer-
sive particle advection to analyze complex ows. The embodied
visualization transformed these
µm
to
km
ows to the human scale,
where domain experts could reason about the ow patterns and spa-
tial structures at a familiar scale with natural body movements. The
domain experts could directly seed particles in the ow, advecting
them forward and backward to understand complex dynamics.
In each case study, the immersive analysis provided unique in-
sights. Researchers gained a deeper understanding of the ow dy-
namics, identied vortical structures, and discovered areas of un-
desirable ow. Immersive visualization enabled the exploration of
uid-structure interaction with complex shapes. The interactive
seeding of particles directly in three-space appears to catalyze un-
derstanding, promoting the role of action in building knowledge.
Additionally, the application has improved communication among
technical and non-technical stakeholders. The value of immersive
particle advection was evident in its ability to reveal previously
unnoticed features and enhance the analysis and decision-making
processes.
Overall, our work contributes to the growing body of evidence
supporting the use and ecacy of immersive analytics in domain-
specic applications. By leveraging immersive technologies and
particle advection, researchers have improved their understanding
of complex ow phenomena in large-scale CFD data, leading to
advancements in these renewable energy systems.
ACKNOWLEDGMENTS
This work was authored by the National Renewable Energy Labo-
ratory, managed and operated by Alliance for Sustainable Energy,
LLC for the U.S. Department of Energy (DOE) under Contract No.
DE-AC36-08G028308. The research was performed using computa-
tional resources sponsored by the Department of Energy’s (DOE)
Oce of Energy Eciency and Renewable Energy (EERE) located
at the National Renewable Energy Laboratory, and used resources
at the Energy Systems Integration Facility, which is a DOE EERE
User Facility. The views expressed do not necessarily represent the
views of the DOE or the U.S. Government. The U.S. Government
retains and the publisher, by accepting the article for publication,
acknowledges that the U.S. Government retains a nonexclusive,
paid-up, irrevocable, worldwide license to publish or reproduce
the published form of this work, or allow others to do so, for U.S.
Government purposes.
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