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Immersive Particle Advection: Through the Scales of Renewable Energy



We describe the benefits of immersive flow analysis for three large- scale computational science studies in the field 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 flow patterns and dy- namics through immersive particle advection. The integration of high-performance computing with immersive analysis provided a deeper understanding of these systems, helping develop more effective solutions for a sustainable energy future.
Immersive Particle Advection: Through the Scales of Renewable
Nicholas Brunhart-Lupo
Kenny Gruchalla
Golden, Colorado, USA
Figure 1: A photograph of a scientist exploring the airow 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 eciency and increase the
range of electric vehicles.
We describe the benets 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
Publication rights licensed to ACM. ACM acknowledges that this contribution was
authored or co-authored by an employee, contractor or aliate of the United States
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
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
a deeper understanding of these systems, helping develop more
eective solutions for a sustainable energy future.
Applied computing Chemistry
Earth and atmospheric
Human-centered computing Virtual reality
Computing methodologies Physical simulation.
Immersive Analytics, Computational Fluid Dynamics, Particle Ad-
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.
PEARC ’23, July 23–27, 2023, Portland, OR, USA Brunhart-Lupo and Gruchalla
Particle advection is a fundamental technique for ow visualiza-
tion [
], 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 insucient to perceive the complex structure in these point
clouds, requiring some combination of depth cues to aid in per-
ception and understanding [
]. 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 scientic and
engineering contexts [
]. 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 [
] using
a boom-supported cathode-ray-tube head-mounted display. And
since that early introduction, immersive ow analysis has been ap-
plied to aerodynamics [
], geophysics [
], blood ow [
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 [
] 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.
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
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 eect
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
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-
) in the plant
can impact power generation and overall eciency. Computational
modeling can be used to analyze these interactions [
] and im-
prove wind plant siting, control systems, and turbine design [
]. 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 [
]. 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 ecient and eective 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 ecient siting and operation of a
wind turbine array. (mscale) Streamline rendering of airow through the cabin of an electric vehicle. Optimizing the heating
and cooling system can signicantly 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 specic 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 [
]. 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
3.2 The Meter Scale
We studied with the airow around a driver inside the cabin of
an electric car. The eciency of heating and cooling systems in
electric vehicles is a critical area of research, as these loads directly
aect the vehicle’s driving range. However, visualizing the intri-
cate airow patterns within a vehicle’s cabin poses a signicant
challenge. The complex nature of the airow (see Fig. 2-
), inu-
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 [
], 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 eciency by ne-tuning the distribution of cool air. This
required a detailed analysis of the airow patterns inside the cabin
to assess the eectiveness 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 signicant 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-
) 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
under a fast-charge scenario, presenting a challenging data analysis
due to its spatial complexity (see Fig. 2-
) [
]. 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 signicant 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.
We presented three renewable energy case studies that used immer-
sive particle advection to analyze complex ows. The embodied
visualization transformed these
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, identied 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
Overall, our work contributes to the growing body of evidence
supporting the use and ecacy of immersive analytics in domain-
specic 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.
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)
Oce of Energy Eciency 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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In our daily usage of the large-scale immersive virtual environment at the National Renewable Energy Laboratory (NREL), we have observed how this VR system can be a useful tool to enhance scientific and engineering workflows. On multiple occasions, we have observed scientists and engineers discover features in their data using immersive environments that they had not seen in prior investigations of their data on traditional desktop displays. We have embedded more information into our analytics tools, allowing engineers to explore complex multivariate spaces. We have observed natural interactions with 3D objects and how those interactions seem to catalyze understanding. And we have seen improved collaboration with groups of stakeholders. In this chapter, we discuss these practical advantages of immersive visualization in the context of several real-world examples.
We present the results of a two-year design study to developing virtual reality (VR) flow visualization tools for the analysis of dinosaur track creation in a malleable substrate. Using Scientific Sketching methodology, we combined input from illustration artists, visualization experts, and domain scientists to create novel visualization methods. By iteratively improving visualization concepts at multiple levels of abstraction we helped domain scientists to gain insights into the relationship between dinosaur foot movements and substrate deformations. We involved over 20 art and computer science students from a VR design course in a rapid visualization sketching cycle, guided by our paleontologist collaborators through multiple critique sessions. This allowed us to explore a wide range of potential visualization methods and select the most promising methods for actual implementation. Our resulting visualization methods provide paleontologists with effective tools to analyze their data through particle, pathline and time surface visualizations. We also introduce a set of visual metaphors to compare foot motion in relation to substrate deformation by using pathsurfaces. This is one of the first large-scale projects using Scientific Sketching as a development methodology. We discuss how the research questions of our collaborators have evolved during the sketching and prototyping phases. Finally, we provide lessons learned and usage considerations for Scientific Sketching based on the experiences gathered during this project.
Electric vehicles (EVs) need highly optimized thermal management systems to improve range. Climate control can reduce vehicle efficiency and range by more than 50%. Due to the relative shortage of waste heat, heating the passenger cabin in EVs is difficult. Cabin cooling can take a high portion of the energy available in the battery. Compared to internal combustion engine-driven vehicles, different heating methods and more efficient cooling methods are needed, which can make EV thermal management systems more complex. More complex systems typically allow various alternative modes of operation that can be selected based on driving and ambient conditions. A good system simulation tool can greatly reduce the time and expense for developing these complex systems. A simulation model should also be able to efficiently co-simulate with vehicle simulation programs, and should be applicable for evaluating various control algorithms. The MATLAB/Simulink dynamic system simulation environment, widely used in the automotive industry, effectively meets these criteria. To model the full EV thermal management system, the National Renewable Energy Laboratory's air-conditioning model now incorporates liquid-coolant system components. In the full system model, lookup tables were used to characterize the components' performance. Predicted data obtained with the system simulation model were compared against experimental data. An agreement within 5% for most of the system parameters was achieved. The validated system model was then used to determine which of two possible locations for the power electronics and electric motor in the system is better for quick cabin heating starting from cold soak.
Medical education, training and preoperative diagnostics can be drastically improved with advanced technologies, such as virtual reality. The method proposed in this paper enables medical doctors and students to visualize and manipulate three-dimensional models created from CT or MRI scans, and also to analyze the results of fluid flow simulations. Simulation of fluid flow using the finite element method is performed, in order to compute the shear stress on the artery walls. The simulation of motion through the artery is also enabled. The virtual reality system proposed here could shorten the length of training programs and make the education process more effective.