advantage

TMexcellence in engineering simulation
volume V

issue 1

2011

Sustainable
DesignINNOVATEQUICKLY

REDUCE
PROTOTYPES

SAVE
energypage 6page 12page 17

2X THE DESIGN CANDIDATES.
NO TRADE OFFS.
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Accelerate your design with ANSYS Mechanical 13.0 and NVIDIA® Tesla™ GPUs (graphics
processing units). The GPU-accelerated ANSYS structural mechanics solution enables you
to double your simulation speed, while reducing prototyping time and shortening design
cycles—doubling your design candidates within the same design cycle with no trade offs.
This acceleration is possible when you run ANSYS Mechanical on a workstation equippedwith a Tesla GPU featuring many “must have” capabilities for technical computing.
These Tesla GPU-powered workstations deliver performance increases that accelerate theperformance of computing with CPUs alone.

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©2011 NVIDIA Corporation. NVIDIA, the NVIDIA logo, and Tesla are trademarks or registered trademarks of NVIDIA Corporation in the United States andother countries. Other company and product names may be trademarks of the respective companies with which they are associated. All rights reserved.

TABLE OF CONTENTS

Table of Contents
Sustainable design
4		 Best Practices

		 Green Design without Compromise		 Companies can gain a sustainable competitive advantage and be		 environmental stewards — through robust design and optimization.
6transportation

4

Cleaner, Greener Engine Design

Cummins uses simulation to reduce weight, improve fueleconomy and reduce emissions of engines.

9Construction

Brussels’tainable

Simulation helps to determine potential environmental impact
in preparation for a massive renovation in Brussels.

12

ENERGY

Catch the Next Wave

Hydrodynamic simulation helps to deliver two- to three-times wave
power efficiency improvement.
615

EnergyReacting to Emissions

Fluids simulation helps to speed up research into chemical-looping
combustion capable of reducing fossil fuel emissions.

17

construction

Meeting Green Building Design Goals withEngineering Simulation

9

Simulation is driving innovation in HVAC design for an assembly hall in Saudi Arabia.

19

OffshoreDon’t Rock the Float!

Fluid–structure interaction allows designers to assess impact of waves
on freshwater and offshore systems.

22

12Energy: Wind

Energizing the Wind Industry

Increased complexities require a system-level approachin designing and evaluating wind turbines.

24

Energy: WIND

More Power to You

Simulation helps Indar to design one of the world’s highest-efficiency
permanent magnet wind turbine generators.

17

24

www.ansys.com

ANSYS Advantage • Volume V, Issue 1, 20111

TABLE OF CONTENTS

27 Energy: Wind

Where the Wind BlowsEngineering simulation plays a role in getting the most powerfrom wind farms by predicting the best available locations.
30 ENERGY: Wind

Second Wind

Advanced turbulence models lead to optimized wind turbine spacing.

30

Simulation@Work
32 Aerospace

Fast Lane to Sky High

Fluid flow simulation software co-pilots design ofproduction prototype roadable aircraft.
35 electronics

32

Successful Launch

Circuit and field tools combine to optimize satellite multiplexer design,
reducing time from 10 weeks to two days.
38 Scientific Equipment

Glass Jaw

Simulation helps to solve collimator jaw design problem inthe Large Hadron Collider.

38

Departments
41 ANALYSIS TOOLSDesigning Batteries for Electric Vehicles

Numerical simulation can be used to accelerate battery development
and address safety concerns.
44 Academic

Microbubbles Keep Green Energy Blooming

41

Algae-derived biofuel production gets a strong pulse from flow simulation.
46 Academic

Reforming a Fuel Cell Modeling Process

Coupling flow simulation with complex chemistry tools brings a united
front to analyzing leading-edge energy systems.
48 Tips and Tricks

Accelerating CFD Solutions

Several recent enhancements in ANSYS FLUENT solver
capabilities accelerate convergence and reduce solution time.
46

48

2

ANSYS Advantage • © 2011 Ansys, Inc.

www.ansys.com

TABLE OF CONTENTSIn Memoriam
John Krouse, senior editor and industry analyst
for ANSYS Advantage, passed away on
November 6, 2010. He was editorial director andone of the originators of ANSYS Solutions, thecorporate publication that preceded ANSYSAdvantage. His credits included being editor andpublisher with Penton Media for 18 years,
John Krouse

where he contributed to such publications
as Machine Design and Computer Aided

Engineering. Krouse founded the communications consulting firm
Krouse Associates in 1994. His understanding of CAD/CAM/CAE wasextensive, and he authored many books and articles on the topic.
ANSYS Advantage readers and collaborators alike will misshis ability to explain complex engineering concepts in anentertaining manner, as well as the keen perception he brought tolight in his editorials.

For ANSYS, Inc. sales information,
call 1.866.267.9724
Email the editorial staff at
ansys-advantage@ansys.com.
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Fran Hensler
Managing Editor
Chris Reeves
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Dan Hart
Editors
Erik Ferguson
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Helen Renshaw
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ANSYS North America
Support and Services
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Tom Smithyman
Designer
Miller Creative Group
Circulation Manager
Sharon Everts

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About the Cover

Turbulence can greatly affect theplacement of wind turbines on a farm.
This and other ANSYS Advantage
features focus on solutions forsustainable design.
Simulation image courtesy Fluid andEnergy Engineering GmbH & Co. KG.
Photo © iStockphoto.com/Mienny.

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ANSYS Advantage • Volume V, Issue 1, 20113

Best Practices

Green Design
without CompromiseCompanies can gain a sustainable competitive advantage and beenvironmental stewards — through robust design and optimization.
By Thierry Marchal, Industry Director, ANSYS, Inc.

The Need for Sustainability
Throughout much of the world, people expect that life
will become better, easier and more comfortable forsucceeding generations. Over the past century, privileges
once limited to the wealthy have become commonplace;indeed, many are now considered a necessity. Owning acar, flying around the world, accessing a computer,connecting to the internet from anywhere, and using a cell
phone are routine activities, even in emerging countries.
However, the world can’t sustain such exponential
growth forever. The planet has received a wake-up callabout addressing overconsumption. Many scientists
state emphatically that we are putting our planet, and thehuman species, in danger.
The population is rapidly draining the world’s fossil
energy sources; at the same time, emissions from thesefuels are negatively impacting the environment and its
natural evolution. Huge amounts of waste material arebeing stored with the hope that the earth can cope with it.
Most people are aware that the globe cannot sustain
these practices forever. While a growing number of people
are urging the world’s population to do something
about it, many individuals are unwilling to sacrifice their
comfortable lifestyle.

Engineering for sustainability includes designing more efficient ways
to treat waste, such as studying settling in a wastewater treatment tank.
Courtesy MMI Engineeering.

4

ANSYS Advantage • © 2011 Ansys, Inc.

Reluctance to Sacrifice Current Standards
Without a doubt, cars could be more environmentallyfriendly, but if you surveyed the planet, would you findpeople willing to pay more to achieve this objective? Most
would be reluctant to accept significant reductions in
performance, speed or acceleration. Green building is acommon aspiration, but will the occupants tolerate beinga bit less warm in winter, and a bit more warm insummer? Renewable energy is a “must do” only if theresulting power is less expensive and at least as
stable. An admirable goal: Can we reach it without
personal compromise?
Common sense dictates that we can’t meet suchexpectations using traditional approaches and technologies. Real breakthroughs may be necessary toachieve sustainability goals in these areas:• Better energy management: Fossil fuels remain
an important energy source, but they could be
used more effectively and selectively. Sourcesof renewable energies should be exploited.
Transportation, construction and manufacturingmust become more energy efficient.
• Pollution reduction: Twenty-first century lifestyles
result in the release of huge quantities of various
materials into the environment, making absorption
and recycling of these substances difficult without adirect impact on the planet.
• Product lifecycle: After a lifetime of use, products
quickly become waste material. Shortened productlife increases the amount of waste. Processing
recycled materials into new products in a costeffective manner can benefit greatly from modern
simulation technology.
Virtual Experimentation, Virtual Testing, Virtual Prototyping
If sustainable products and processes today are
merely “nice to have,” it is likely green design will become
mandatory in the future, legislated and forced by market
pressures to develop better solutions. Pioneers andleading companies are already preparing for this evolution
by designing sustainable solutions. But the challenge iswww.ansys.com

Background photo © iStockphoto.com/ooyoo

Best Practices

Engineering simulation can help organizations meet regulations and standards. This study was used to assess the impact of fume cupboard
discharges on surrounding buildings and the environment.
Courtesy BDP Engineering.

daunting. Environmentally friendly products may require
major innovations that could impact both cost and robustness. Innovation usually requires intense experimentationand optimization. Real breakthrough solutions needsystematic testing to ensure that new designs behave
properly throughout the entire lifecycle. Experimentationand testing processes are typically time intensive, costly
and difficult to manage in a highly competitive world.
Best-in-class companies are addressing this issue by
switching to the virtual world.
Numerous reports and industry studies, some foundin this issue of ANSYS Advantage, suggest that an orderof magnitude more experimentation can be done virtuallyfor a smaller cost. Engineering teams can vary numerous
parameters to identify the best combination of sustainable
design, minimal cost and maximal performance. Leading
companies are systematically testing
their virtual product prototypes against
conditions that would be experienced
10
throughout the lifecycle to ensure that
product behavior will satisfy end users.
9
Continually increasing computational
8
power combined with high-efficiency
solvers can contribute to the effort:
7
They might enable the number or6complexity of virtual tests to double
0.35
every 18 months without impact on
P1 0.4
cost or time to market.
0-

standard technology. Designing smart, green products toa high level of product integrity requires investigating a
growing number of parameters — such as dimension,materials and operating conditions — to provide
designers with enough freedom to meet numerous
constraints. Robust design includes identification of
influential parameters and evaluation of design sensitivity
based on performing variations.
The combination of parametric studies and sensitivity
analysis for a large number of parameters using advanced
tools (robust design and optimization) opens the door to a
new era of design.

P12 - FKalotte [N]Engineering Simulation: A Profitable Green Technology
Engineering simulation has proven its costeffectiveness in developing innovative products. Thissame technology can be used
to design greener products
and processes. Pioneering companies are already creating acleaner and more profitable
future by changing their design
processes and widely adopting
simulation to transform thegreen challenge into a major
business opportunity. This issueof ANSYS Advantage illustrates
0.55
h o w c o m p a n i e s a re u s i n g
0.65
pa
r_R
0.75
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comprehensive integrated multiRr_d 0.45a0.85 P6 - p
physics from ANSYS to maintain
Designing smart green products to a high level
corporate responsibility whileof product integrity requires advanced tools that
increasing profit. ninvestigate a growing number of parameters.

Robust Design and Optimization
Sustainability adds a new
dimension that is difficult to handle withwww.ansys.com

ANSYS Advantage • Volume V, Issue 1, 20115

Image © iStockphoto.com/PeskyMonkey

transportation

Cummins ISX15
heavy-duty engine

Cummins ISF3.8 lightcommercial vehicle engine

Cleaner, Greener
Engine Design
Cummins uses simulation to reduce weight,
improve fuel economy and reduce emissions of engines.
By Bob Tickel, Director of Structural and Dynamic Analysis, Cummins Inc., Columbus, U.S.A.

The phrase “environmentally responsible” doesn’t seemto fit into a sentence with the terms 18-wheel tractor trailer
and heavy-duty truck. As a world-leading manufacturer ofcommercial engines and related systems, Cummins Inc. is
working to change that perception one design at a time,developing next-generation technologies that are revolutionizing the international trucking industry.
Using software from ANSYS, Cummins is developing
and testing radical improvements in engine design,
including the use of alternative materials and smaller
engine footprints that reduce weight, improve fueleconomy and reduce emissions — while also boosting
performance. The work of the corporate research andtechnology organization focuses on developing new,environmentally responsible technologies for thecompany’s core engine business.
Cummins’ recent product development efforts target agreat deal of attention on fuel economy and emissions —and with good reason. Government environmental
standards grow more challenging every year. Because
commercial trucking is a low-margin business, every
improvement that Cummins makes in fuel economy adds
to its customers’ successes. Beyond these practicalconsiderations, Cummins invests in environmentally
responsible engine technologies because it is the right

6

ANSYS Advantage • © 2011 Ansys, Inc.

thing to do. The company wants the trucking industry to
be viewed as an environmental steward and champion,
not one of the “bad guys.”

Building the Truck of the Future
In recognition of its environmental technology
leadership, Cummins recently received nearly $54 million
in funding from the U.S. Department of Energy (DOE) tosupport two projects aimed at improving fuel efficiency inboth heavy- and light-duty vehicles.
About $39 million will fund Cummins’ development of
a new “supertruck” — a highly efficient and clean dieselfueled Class 8 (heavy-duty) truck that is expected to set a
new industry standard for green technology. Another$15 million in funding will support the development ofadvanced-technology powertrains for light-duty vehicles.
The resulting improvements in engine system efficiency
will mean significantly lower fuel and petroleum
consumption by these vehicles as well as a significant
reduction in greenhouse gas emissions.
Though details of this work are proprietary, Cummins
is using software from ANSYS to simulate engine
performance and achieve the ambitious goals defined by
the DOE: Improve Class 8 vehicle freight efficiency by
50 percent and achieve a 40 percent improvement in fueleconomy in light-duty vehicles.
www.ansys.com

transportation
Selective catalytic
reduction (SCR) catalyst

Decomposition filter

Cummins particulate filter

Diesel exhaust fluid (DEF)
dosing valve

Cummins new aftertreatment system
aimed at meeting emissions standards

To illustrate how Cummins leverages ANSYS software
on a daily basis, consider this recent product introduction:
the next-generation ISX15 engine design, developed
specifically to meet stringent new emissions standards
from the U.S. Environmental Protection Agency (EPA).
These regulations, which call for near-zero emission
levels of nitrogen oxide (NOx) and particulate matter (PM),place new demands on truck manufacturers andfleet managers.
Cummins used ANSYS technology to develop or
enhance a number of features in the ISX15 design,
including the new Cummins aftertreatment system thatincorporates a revolutionary diesel particulate filter (DPF)
targeted at meeting the new emissions standards.
The DPF removes diesel particulate matter or sootfrom the exhaust gas of a diesel engine. Cummins used
ANSYS software to simulate typical operating loads andmake predictions about the new DPF’s performance andreliability. Engineering simulation predicted both peaktemperatures and temperature distribution inside thiscomponent under a range of operating conditions. Thesethermal analyses were critical as they revealed the peaktemperatures and temperature gradients within the filter,
which ultimately determine the thermal fatigue and life of
the component.
Varying temperatures within the DPF result in thermal
stresses; if this component is not designed properly, temperature variations can lead to component failure. While it
might take months or years for failure to occur in realworld use, software from ANSYS enabled Cummins toquickly simulate the effects of years of field usage andmake predictions about how the DPF would hold up overtime. This gave the engineering team a high degree ofconfidence as they designed the component and installed
it into customer vehicles.
Subsequent field and bench tests confirmed the simulation predictions, as did actual road results. Engineers
can rarely rely on virtual testing alone, but software fromANSYS confirmed that the Cummins design process wasmoving in the right direction — and that the results would
be as team members expected.

www.ansys.com

Using Simulation to Rev Up Ongoing Engine Improvements
Environmental standards and customer needs are anever-evolving target. So the Cummins team uses
engineering simulation to improve engine features andboost performance. Design changes that reduce
emissions and fuel consumption often result in higher
temperatures and pressures within the engine, which
require Cummins engineers to continually test the limits of
conventional engine designs.
Most materials used in diesel engines exhibit reduced
strength as temperature rises. The combination of highpressure/temperature and reduced strength places stresson components such as the cylinder head, a geometricallycomplex casting that serves many functions includingtransferring engine oil and coolant, intaking air andexhausting gas. The head also houses the fuel injector
and valve train components; it must contain hot combustion gases during the cylinder firing event. To address themultiple forces that come into play, Cummins engineers
use multiphysics software from ANSYS to combine
thermal and structural analyses in their work on thissophisticated engine component.
Simulation tools from ANSYS enable the Cumminsengineering team to evaluate the use of new materials
across the entire topological surface of cylinder heads
and other engine components. ANSYS allows theCummins team to quickly and easily answer questions

By using software from ANSYS to predict maximum temperatures
and temperature distribution within its new diesel particulate filter
under diverse conditions, Cummins engineers can ensure that the
manufactured component will have acceptable durability.

ANSYS Advantage • Volume V, Issue 1, 20117

transportationsuch as, “What happens if we manufacture the cylinderh e a d o u t o f t h i s n e w m a t e r i a l ? W h a t a re t h eperformance gains?” without the need to actually machine
and test new components. Working in a simulated
environment gives these engineers the freedom to tweak
existing engine designs — as well as to arrive at some“clean-sheet” designs that may have the power to revolutionize engine performance.
The team recently used software from ANSYS topredict temperature profiles in cylinder heads undervarious operating conditions. The thermal results wereused to determine how this component would perform in
the real world, as well as its threshold for both low-cycle
and high-cycle fatigue.
Since cylinder heads are long-lead-time, expensive
components, the Cummins team must be sure that a new
design is right before moving forward. Using simulation
tools from ANSYS, Cummins engineers can not only
create more exacting test results but also pursue
increased productivity by incorporating capabilities suchas conjugate heat transfer modeling in their designs.
Without analysis-led design to introduce newmaterials and new part configurations, Cummins would

Cummins engineers can easily test the effectiveness of new materials,
new designs and other innovations — and predict their long-term effecton overall engine performance. For example, Cummins engineers usedsoftware from ANSYS to simulate the impact of new materials on acylinder head design.

have to rely instead on very expensive, time-consuming
endurance tests to verify the engineering team’s designs.
ANSYS is a key enabler to Cummins, reducing thecompany’s overall cost of development by minimizing its
investment in physical engine testing. This approachallows the design of a more environmentally friendly
engine without compromising cost or performance. n

Shifting Gears: ANSYS Creates a Cultural ChangeWith so many engine components and performance
aspects to consider, the Cummins engineering teammust perform a wide range of structural simulation
and analysis. According to Bob Tickel, director ofstructural and dynamic analysis at Cummins,
software from ANSYS is the only single-vendor toolwith the technical breadth and depth to meetthis challenge.
Recently, the Cummins corporate researchand technology team began the switch from thetraditional interface for ANSYS software to the
ANSYS Workbench platform, a decision based
primarily on the product’s improved geometry import,
cleanup and meshing capabilities. Tickel anticipates
a 50 percent reduction in throughput time based
upon the move to ANSYS Workbench.
“The ANSYS Workbench environment provides
access to the best multiphysics tools we need to
conduct many types of simulation and analysis,” said
Tickel. “Whether our need is thermal, structural,
dynamic or static engineering analysis, ANSYSWorkbench provides the flexibility and versatility

8

ANSYS Advantage • © 2011 Ansys, Inc.

to accommodate our needs — as well as the multiphysics capabilities to link the results of our various
simulations.”
Tickel noted that the efficiency and cost effectiveness of engineering simulation has resulted in acomplete cultural change at Cummins. “The ease ofusing simulation tools from ANSYS has helped totransform our organization from a test-centric culture
to an analysis-centric one,” said Tickel.
“When investigating a new material or other design
enhancement, traditionally we would build new partsand conduct physical tests as a first step — which
represented a time- and cost-intensive approach,”
he said. “Today, we focus more attention on upfront
analysis, only moving to part-building and testing for
those design improvements that we can verify first using
ANSYS tools. This new cultural approach has not only
saved us time and money, but allowed us to selectively
focus our attention on those design enhancementsthat are shown to hold the greatest promise forrevolutionizing future engine designs.”

www.ansys.com

Construction

Brussels’tainable

The State Administrative City inBrussels was completed in 1983
and is now largely unused.
Photo courtesy Michael Uyttersprot.

Simulation helps to determine potential environmental impact
in preparation for a massive renovation in Brussels.
By Tin Meylemans, Architectural Engineer, and Jean-Pierre Demeure,
Head of Urban Planning, City of Brussels, Belgium
Arnaud Boland and Quentin Hamoir, Support Engineers, ANSYS, Inc.

In 1992 during the Earth Summit in Rio de Janeiro,
200 countries — including Belgium — adopted Agenda
21, an action program for the 21st century to enableglobally sustainable development on the planet. Amongthe program’s main objectives is responsible managementof natural resources and harmonious urban development.
As the federal capital of Belgium and an administrative
center of the European Union, the City of Brussels has
been a leader by example. It is also a prime contributor to
generating awareness of how large cities can naturally
evolve to environmentally friendly urban growth.
G u i d e d b y A g e n d a 2 1 a n d t a rg e t i n g u r b a nrevitalization, the city undertook renovation of the State
Administrative City, or CAE (Cité Administrative de l'Etat),
an important site in downtown Brussels. Often recognized
as the finest architectural achievement in Brussels sinceWorld War II, CAE is nevertheless an urban planningdefeat, as it eliminated an entire district and broke theurban fabric with its 140,000 square meters of officespace. As the Belgian federal government evolved, theadministrative city has not been used since the early
2000s, leaving this huge site unoccupied. Its renovation
had become a city priority. Today the site is undergoing a
massive renovation project that combines housing,offices, shops, parks and a school.
Before any major project is constructed, Belgian law
requires an environmental impact study that includes
economic and social factors, environmental assessments,
mobility studies, health influences and noise level effects.

www.ansys.com

Initial (top) and modified (bottom) geometries of Brussels’ CAE

ANSYS Advantage • Volume V, Issue 1, 20119

Construction

Initial design

Recommended revision
Engineering simulation was performed to determine pedestrian comfort levels (with yellow and red indicating discomfort).

The City of Brussels selected the ANSYS office in Belgium
to investigate wind flow patterns for the new site andcompare different options for building locations — via
engineering simulation.
Wind analysis is useful in studying many parameters
related to a site’s user comfort, in this case including
high-rise buildings and green spaces. In some instances,the building acts as a screen and improves comfort, but inothers the wind pattern could cause significant discomfort— and might even be dangerous — to pedestrians andoccupants. Several important phenomena can occur, suchas channeling and wedge effects, that lead to local wind
acceleration and increased turbulence. These phenomena
can contribute to discomfort for people located at groundlevel, terraces and balconies. The wind effects canalso damage vegetation and generate unwantedpressure effects on buildings that cause whistling ormaterial damage.
A typical comfort criteria from the TNO (anindependent research organization in the Netherlands
whose aim is to apply scientific knowledge to strengthen
the innovative power of industry and government) requiresthat the wind speed in an area 1.75 meters above theground should not exceed 5 meters/second for more than
220 hours per year. In identifying areas of discomfort, thestandard must be expressed in terms of amplification
factor (AF), the ratio between velocity without and with thebuilding. A factor greater than 1 means the flow isaccelerated by the buildings. Considering Brussels’
meteorological data over the last 10 years, an AF of lessthan 0.88 satisfies the most-restrictive comfort criterion.
Project engineers investigated two configurations forenvironmental impact along with a possible need tominimize the negative impacts of wind. The first
configuration, called initial state, contained the layout ofthe project (the volume envelope prescribed by the City of
10

ANSYS Advantage • © 2011 Ansys, Inc.

Brussels in which the building must be situated to satisfy
local planning regulations). A second configuration, called
the modified geometry, was studied to quantify the impactof architectural changes, such as towers being built on
stilts, or architectural details, such as carvings or
openings placed on different levels/stories of thestructure. Because plans for reuse of the area were notcomplete at the time of analysis, this study would show
where changes could have an impact.

Areas of potential discomfort in original (top)and modified (bottom) designs

www.ansys.com

Construction

Pathlines showing wind trajectory colored by velocity magnitude for original (left) and new (right) designs provided increased understanding of wind
behavior near buildings to determine areas of concern.

The ANSYS team determined the amplification factor athuman height for both configurations, illustrating windeffects such as corner effects near tall buildings andchanneling effects between closely spaced buildings. For
the initial state, a map of the most-restrictive values (inwhich AF is greater than critical value) revealed areas where
local wind speed was too high and would lead topedestrian discomfort. The modified configuration reduced
areas of discomfort by approximately a factor of three.
The team used ANSYS engineering simulation todetermine airflow between the buildings, and these results
were used to generate velocity vectors to identify specificpoints of interest, such as recirculation zones in which
dust could accumulate. The details provided informationthat helped in adjusting the building design to avoid thesetypes of zones. Pathlines of wind trajectory provided
increased understanding of wind behavior near buildings,
including turbulence, and identified areas where potential
problems could occur. Finally, computing pressure contours on the buildings identified areas that would require
special care during the design phase to avoid other concerns, including whistling problems due to insufficientinsulation or damage to the facade.
This project demonstrates the use of engineering
simulation for smart urbanization. Such analysis canprovide comprehensive maps of airflow patterns, local
wind velocities and local turbulence intensities — all
useful in planning urban development projects. Simulationdriven building design provides information about how
structures affect the environment, which can be an
important part of discussions and decisions from a
project’s first stages. n

www.ansys.com

Pressure contours for original and new designs identified areas that
required special care during design to avoid insulation whistling
problems or potential façade damage.

ANSYS Advantage • Volume V, Issue 1, 201111

Image © iStockphoto.com/MichaelJay

ENERGY

Catch the Next Wave
Hydrodynamic simulation helps to deliver two- to three-times
wave power efficiency improvement.
By Bradford S. Lamb, President, and Ken Rhinefrank, Vice President of Research and Development,
Columbia Power Technologies, LLC, Corvalis, U.S.A.

If all the ocean’s energy could be harnessed, it would
produce more than 500 times the global energy consumption. The practical potential for wave energy worldwide
is projected to be between 2 trillion and 4 trillion kilowatt
hours per year. The World Energy Council estimates thatabout 10 percent of worldwide energy demand could
realistically be met by harvesting ocean energy.
But wave power is a much less mature technology
than solar or wind power or, especially, fossil fuel.
A tremendous amount of work lies ahead in optimizing thedesign of wave power systems. Researchers must
improve efficiency and reduce costs to the point thatthese systems can make a major contribution to meeting
global energy requirements.
Columbia Power Technologies (COLUMBIA POWER),
LLC, is attempting to harness this potential by developing
commercially viable and scalable wave power generation
systems. In conjunction with Oregon State University, thecompany is working to develop and commercialize
innovative wave energy harvesting devices.
There are several key advantages of wave power:• Power density: Wave power is much denser thanother renewable energy systems, enabling wave
parks to produce large amounts of power from a
relatively small footprint.
• Predictability: The supply of energy from wave
power can be accurately forecast several days inadvance, enabling utilities to make precise sourcing
plans.
• Constancy: Unlike solar power, which produces
energy only when the sun is shining, ocean swells
are available 24 hours per day.
• Proximity to load centers: Wave energy will not
require substantial buildout of transmission
capacity, since 37 percent of the world’s population
live within 60 miles of a shoreline, and 70 percent
reside within 200 miles.

12

ANSYS Advantage • © 2011 Ansys, Inc.

Preparing to test the wave power device

www.ansys.com

ENERGY

Wave direction

1

2

3

4

5

COLUMBIA POWER’s wave power system: The wings and vertical spar react to the shape of the passing ocean swell. Each wing is coupled bya drive shaft to turn its own rotary generator.

The wave power industry, however, faces a major offers both frequency and time domain solutions.
challenge since product developers have much less Frequency domain solutions are faster, which makes themexperience in the design of wave power devices relative ideal for quickly evaluating a large number of shapes,
to other renewable energy systems. Wave power while time domain solutions provide the high level ofcompanies need to rapidly advance efficiency and reduce accuracy needed to refine to the best shapes in the later
costs of their designs to demonstrate viability to potential stages of the design process.
investors and customers. Other industries have taken
COLUMBIA POWER engineers developed an initial
decades or longer to develop technology to the point of concept design in SolidWorks ®, built a prototype andcommercial viability. But the wave power industry does tested it at 1/33 scale in the Tsunami Wave Basin at the
not have that kind of time. To achieve its goals, it needs to Hinsdale Wave Laboratory at Oregon State University. The
team used high-resolution cameras to track light-emitting
rapidly improve designs while conserving limited capital.
COLUMBIA POWER is focusing on development of diodes on the buoy, measuring its motion in the waves.
direct-drive systems, which avoid the use of pneumatic Engineers exported the concept design to ANSYS AQWA
and hydraulic conversion steps and their associated software and performed a time domain simulation while
losses. The company believes that direct-drive systems using a wave climate with the same amplitude andare the future of wave power because they are more frequency as that measured in the wave tank. There was
efficient and reliable as well as easier to maintain. The a very good match between the measurements andnumber-one design challenge was to optimize the design predictions from ANSYS AQWA. Since then, engineersof the buoy to maximize the proportion of wave power have used ANSYS AQWA as their primary design tool totransferred to the buoy. Relative capture width is a optimize the shape of the fiber-reinforced plastic (FRP) buoy.
dimensionless measure of theefficiency of the device in
capturing the available energyof the wave. A relative capture
width of 1 means that the buoy
has captured 100 percent
of available wave energy.
As COLUMBIA POWER
set out to determine theoptimal shape for the buoy,
engineers looked at fivedifferent hydrodynamic simulation software packages. The
company selected ANSYSAQWA software because of its
ease of use, and tests showedthat it provided a better matchwith physical experiments
than did competitive software.
C OLUMB I A PO W ER a l s o
COLUMBIA POWER engineers doubled efficiency of the buoy by using
valued that ANSYS AQWA
ANSYS AQWA to optimize its geometry.

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ANSYS Advantage • Volume V, Issue 1, 201113

ENERGY

COLUMBIA POWER has since evaluated over 350 the power takeoff to the wave climate by changing thedifferent geometries with ANSYS AQWA in an effort to amount of current produced by the generator, which, inmaximize the relative capture width of the buoy. At the turn, changes the mechanical load placed on the system.
same time, the company worked closely with Ershigs Inc., This makes it possible to consider in a single model theits structural partner that produces the FRP floats, to effects of different buoy shapes, power takeoff system
explore the manufacturability of various shapes and to designs and control strategies; it also helps to determine
ensure that the final design can be produced at a low the power that would be generated by each approach in acost. The company also looked at the survivability and variety of different wave climates.
environmental impact of proposed buoy designs.
COLUMBIA POWER recently began using Maxwell
COLUMBIA POWER engineers used a sinusoidal wave electromagnetic simulation software from ANSYS toshape and a suite of wave frequencies ranging from 2 optimize the design of the generator. Engineers evaluatedseconds to 20 seconds for frequency
three different electromagnetic simulation
domain simulations. The response
software packages and concluded that
amplitude operators calculated by ANSYS
Maxwell was the easiest to use and the
AQWA software were used in a postmost stable. Maxwell is being used toprocessing routine written by COLUMBIA
analyze the electromagnetic performance of
POWER engineers that calculates thethe generator while varying the air gaps
relative torque and speed of the buoy as
between the rotor and stator, different
well as the relative capture width.
magnet geometries, different magnet
Once they felt that they were close totypes, and different types of steel. The overan optimal shape for the buoy, COLUMBIA
all goal is to maximize the generator’s
POWER engineers moved to time domainenergy output while minimizing its cost.
modeling, which makes it possible toAs a technology startup with far-fromevaluate the nonlinear effects of theunlimited funding, COLUMBIA POWER
waves. The team evaluated the shapes
must be capital efficient. By focusing itsthat had proven best in frequency domain
development efforts on simulation andmodeling against a variety of wave
using physical testing judiciously as aclimates, including those found at sevenverification tool, COLUMBIA POWER isdifferent coastal locations around themoving forward in the development process
world. At the same time, engineers beganmuch faster than would be possible using
optimizing the power takeoff system thattraditional development methods. ANSYS
converts mechanical energy into electrical
AQWA and Maxwell simulation software
Maxwell software from ANSYS
was used to optimize theenergy. ANSYS AQWA model results fromenable the company to make its mistakes
generator design.
frequency domain models were postin the computer, where they are far lessprocessed in Matlab® Simulink® to incorporate the power expensive than in the ocean. ANSYS AQWA technology,
takeoff reaction torque and to compute power output. in particular, helped to more than double the efficiency of
The ANSYS AQWA time domain models were coupled to COLUMBIA POWER’s wave power system. COLUMBIAa DLL that simulated both linear and nonlinear power POWER has benefitted from the excellent technicaltakeoff operation. The DLL for the power takeoff model support and productive training sessions provided by
was developed in Matlab Real Time®. Engineers used the ANSYS. As a result, the company is on track to soon
output from ANSYS AQWA to drive a numerical model deploy the first ocean demonstration of its technology indeveloped in Simulink that simulates the power takeoff Puget Sound. nsystem and control strategy. The control strategy tunes

14

ANSYS Advantage • © 2011 Ansys, Inc.

www.ansys.com

Energy

Reacting to Emissions
Fluids simulation helps to speed up research into chemical-looping
combustion capable of reducing fossil fuel emissions.
By Schalk Cloete and Shahriar Amini, Flow Technology Research Group, SINTEF Materials and Chemistry, Trondheim, Norway

N2
CO2 +
Metal oxide
The need to reduce CO2 emissions to restrain climate
H2Ochange has never been more urgent. Anthropogenic CO2 ismainly generated in the combustion of fossil fuels, andthese fuels are expected to provide a very large percentage
Oxidizer
ReducerQOof overall world energy consumption for the next several
decades. One thing is clear: Emissions must be lowered.
Qhot
ηrev Qcold
An accepted solution is to separate and sequester the
CO 2 emitted by fuel combustion. Chemical-looping
combustion (CLC) is one of the most promising techW
nologies to carry out CO2 capture at a low cost.
CLC is a combustion process that avoids direct
mixing of fuel and combustion air. The method uses two
Air
Fuel
fluidized bed reactors and circulating metal oxide that isoxidized in an air reactor and reduced in a fuel reactor
Metal
to provide the oxygen required for the fuel. Pure CO 2
Chemical-looping combustion process
is obtained in the fuel reactor exhaust stream after
condensation of water without the need for further gas made much easier by full access to all-important models
separation. CLC will achieve significant CO 2 capture by employing user-defined functions (UDF) compiled within
at a reduced cost when compared to other current tech- the stable, parallelized ANSYS FLUENT solver.
nologies, including post-combustion amine scrubbing.
Heterogeneous reactions, alternate drag laws and wall
The delay in timely commercialization of CLC functions for dilute granular flows were implemented
technology is primarily due to lack of understanding of through UDF along with several other variables, such asreactive multiphase flows in the fluidized beds used in adjusting pressure drop over a periodic section to maintain
CLC systems. Design and scaleup of CLC reactors are a constant superficial gas velocity. Studies were conductedvery complex. Advanced modeling techniqueson coarse graining, implementing filteredare required to capture the intricate couplingdrag, solids viscosity and solids pressure
between the complex reactor hydrodynamics,
formulations by means of UDF.
heterogeneous reaction kinetics and heat
One requirement for modeling thetransfer.
reactive gas–solids flows in CLC processes
With the advent of increased computational
is that the software predicts formation ofcapabilities, the fundamental modeling framethe particle substructures (clusters) thatwork of computational fluid dynamics (CFD) is
often occur in these systems. Theseemerging as a very promising additional tool
structures appear as gas bubbles in dense
for modeling reactive multiphase transport.
regions and particle clusters in dilute regions
Once the model has been validated, CFD canof the fluidized bed reactor, and theybe used for accurate design and scaleup of theinfluence everything happening in the reactor.
process. A team at the Flow Technology group
Fine meshes and small time steps are
at SINTEF, the largest independent researchrequired to resolve particle structures, but aorganization in Scandinavia, has been using
price in computational time has to be paid
ANSYS FLUENT software for this purpose in a
to capture the physics of the system.
project led by Shahriar Amini. The wellThese particle structures increase the slip
established granular flow modeling framework
between particles and fluidizing gas. This
Simulation shows boththe bubble (bottom half)available within the software has been central
effect is analogous to the clustering of tiny
and cluster (top half)
to the progress made in this project.
mist droplets into larger raindrops. A mist of
structures occurring inFundamental model development also was
microscopic droplets does not fall and is very
fluidized beds.

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ANSYS Advantage • Volume V, Issue 1, 201115

Energyeasily blown away by the wind,
reactor domain. Since the reacwhereas a big raindrop certainlytion occurring on the cluster surfalls with considerable force.
face contributes only a small
The same happens in a fluidized
amount to the global reaction
bed reactor: The tiny particles
rate, the correct description of
conglomerate into larger
this interface becomes lessclusters and, therefore, fall much
important.
more readily. If this clustering is
Proper resolution of particle
not modeled correctly, the simustructures along with achieving
lation will predict a bedhydrodynamic and reaction
behaving like a mist, but it
kinetic grid independence require
should actually behave like aengineers to pay particular
thundershower. Clearly, this willattention to grid size compared
A particle cluster (left) influences the concentration oflead to substantial errors.
to particle diameter. To achieve
the reacting gas (right).
In addition, it is believed thathydrodynamic grid independence
particle structures influence reactions. High reaction rates for denser beds, the grid size can be 25 particle diameters
are observed in regions with high volume fractions of or even higher, while the fine structures formed in risers
particles and reacting gas concentrations. A dense need grid sizes of a maximum of 15 particle diameters,
particle cluster represents a region in which lots of surface provided high-order spatial and temporal discretization isarea is available for reaction, and this significantly used. Reaction kinetic grid independence works theincreases the reaction rate. However, the consequence is opposite way. It is very sensitive in dense beds since thethat the reacting gas inside the cluster is used up much volume fraction gradient on a bubble is so large. For very
faster, thereby slowing down the reaction. Clustering is high reaction rates, the grid size can be 10 particle diamactually an unwanted phenomenon in fluidized bed eters or lower, whereas for 50-times lower reaction rate
reactors because it slows down the reaction rate overall the grid size can be 25 particle diameters. Reaction kinetic
by concentrating all the solids in areas with a low amount grid independence in risers is slightly more forgiving, sinceof reacting gas. Incorrect cluster modeling, therefore, the clusters can be more dilute and normally coincide withwill predict better, but unfortunately very wrong, hydrodynamic grid independence.
reactor performance.
Interestingly, simulation results show that the use of
The team at SINTEF has accepted the challenge to a highly reactive particle will not improve reactor
contribute fundamental knowledge in the field of simu- performance as much as might be expected. In the highly
lation of structure resolution on reactor performance. reactive case, no reacting gas species is allowed to
Results have shown that the degree to which the clusters penetrate into the cluster. A large percentage of thehave to be resolved (and therefore theparticles available for reaction, therecomputational cost of the simulation)fore, is being wasted inside the dense
depends largely on the reactivity of theclusters where no reacting gas species
particle used in the reactor. When ais present. When the particle with a low
highly reactive particle is employed, thereactivity is used, fuel gas is available
reaction between the fuel gas and athroughout the reactor, and all particles
dense cluster is almost instantaneous
are involved in the reaction. To quantify
and occurs almost exclusively on thethis concept, simulations have been
surface of the cluster. In this case,used to show that a 50-times decrease
the most important phenomena to bein particle reactivity decreases thecorrectly modeled are the area of theoverall reaction rate by a factor ofcluster on which the reaction can occur
two to three, depending on theand the transport of the reacting gas
fluidization velocity.
species to this interface. Any slight error
Information gained from this study
in the position or sharpness of theis currently being used at SINTEF tocluster surface will lead to significant
perform fluid simulation as a designmodeling errors.
and optimization tool for CLC systems.
When a less reactive particle is used
Fundamental insights offered by thesein CFD simulations, however, themodels make substantial contributions
The interaction between particle clusters
and reacting gas using a particle withresolution of the cluster interface
toward identifying and optimizing
high reactivity (two images on the left)
becomes less important. In this case,important design parameters toand low reactivity (two images on theright). In each pair of images, the one
the reaction rate is slow enough to allow
accelerate the development of thison the left shows the particle volume
reacting gas to penetrate into the partiimportant CO2 capture technology. nfraction, and the one on the right shows
the reacting gas concentration.
cle clusters and react throughout the16

ANSYS Advantage • © 2011 Ansys, Inc.

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construction

Meeting Green Building
Design Goals withEngineering Simulation
Simulation is driving innovation in HVAC design for an assembly hallin Saudi Arabia.
By Sharbel Haber, Senior Mechanical Engineer, Balsam Nehme, Mechanical Engineeringand Adnan Akhdar, Mechanical Engineer, Dar Al-Handasah, Beirut, Lebanon

With the ever-increasing demands for sustainablebuildings, engineers are developing more complex anddiversified designs to reduce loads, boost efficiency andutilize renewable resources. Fluid dynamics simulations
have proven to be a powerful and effective tool, providing
flexible solutions in increasingly complex and demanding
projects. At engineering firm Dar Al-Handasah, thesesimulations are extensively used as an optimization andvalidation tool at an early phase in the design process,since simulation supports implementation of innovative
designs and energy-saving measures geared toward
decreasing the overall facility’s energy costs while maintaining or improving occupant comfort.
Dar Al-Handasah (Shair and Partners) has been apioneering force in the planning, design and implementationof development projects in the Middle East, Africa and Asia
since it was founded in 1956. Today, Dar Al-Handasah is
one of the largest engineering and design firms in the world.
A typical example of building for energy efficiency is thecompany’s recent design for the 31,000-square-meter convention center at Princess Noura Bint AbdulRahman
University for Women in Riyadh, Saudi Arabia.

In theassembly
hall of theconvention center,an underfloor air
distribution (UFAD)
system was coupledwith a conventional ceiling Full-scale 3-D model of the
air supply. The assembly convention center assembly hall
hall that includes the stage
and seating area consists of four levels (basement, ground,mezzanine and first floor) interconnected via one air
continuum, with all levels occupied. Dar Al-Handasah
was charged with reducing the HVAC system energy
consumption while ensuring audience comfort: The total
airflow supplied by the hybrid cooling system should coolthe occupied zones but only temper the upper regions
where maintenance catwalks are located. The distributionof air supply outlets needed to be optimized to ensure
proper air delivery in the 24-meter-high assembly hall while
avoiding disturbance of the thermal stratification of
the air, a key energy-saving measure. Fluid dynamics

Rendering of the convention center at Princess Noura Bint AbdulRahman University for Women in Riyadh. The center assembly hall was studied
to reduce energy costs while maintaining comfort.

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ANSYS Advantage • Volume V, Issue 1, 201117

construction

simulations with ANSYS
design. Spot cooling was
FLUENT software wereused to balance the highfundamental in optimizing
heat emitted by the catthe UFAD system to maxiwalk’s equipment so thatmize energy savings and reduce
disturbances would not beenergy costs.
introduced into the upper
To analyze the performance of
hot stratified air layer.
the proposed system, engineers at
To capture the rising moveDar Al-Handasah built a full-scale,ment of buoyant hot air fromthree-dimensional virtual model ofthe occupied zones, thethe assembly hall, representing theteam added air returns
complex geometry of theabove the catwalks withbuilding. They accurately
an exhaust fan installed
modeled 902 air diffusers
at the highest elevation of
beneath the audience seats inthe stage. This fan also
the hall and 62 ceiling flow bars
assisted in exhausting any
distributed throughout the different
contaminants.
levels. For greater accuracy, theFluid flow simulations
team then meshed the 3-D model
have become an instruwith local mesh refinements appliedmental tool in supporting
Original design scenario (top) and optimized design modelin the occupied zones and near thet h e c o m p a n y ’s d e s i g n(bottom) for temperature distribution across the convention
air outlets. Engineers conducted
process through accurate
center assembly hallsteady-state simulations using theprediction of thermal
ANSYS FLUENT solver to optimize
comfort conditions, design
the performance of the proposed hybrid air conditioning validation enabling design optimization, and energy
system to produce an environment that complies with reduction for the HVAC building systems. Furthermore,
the required comfort conditions. In particular, velocity and the proven breadth and depth of advanced fluid dynamics
temperature distributions were generated and air distri- modeling capabilities from ANSYS have allowed thebution refined so that no disturbances occurred in the company to tackle a wide range of complex problems
hot stratified region.
ranging from HVAC and smoke simulations to dispersion
Initially, the flow simulation indicated a potential for modeling and pumping stations simulations. Dar
improving the temperature distribution at various levels. In Al-Handasah, with the support of ANSYS channel partner
fact, high temperatures were observed at the first-floor- Fluid Codes Ltd for the Middle East, continues to explore
level seating area (around 31 degrees C) and at the stage opportunities to incorporate state-of-the-art tools inarea (around 25 degrees C), implying insufficient supply air building design to continuously improve design quality
flow to these zones. The team observed very high temp- while exceeding client expectations. Fluid dynamics
erature (around 40 degrees C) in the core volume mainly software from ANSYS helps engineers to optimize HVAC
due to high heat loads from the equipment on the catwalk. designs and meet the ongoing challenge of developing
In addition, the mezzanine-level seating area was over- models that are energy efficient, sustainable and compliant
cooled, with average temperature around 21 degrees C, with standards. n
indicating that the supply air flow delivered to that zone
could be reduced.
Using the results from the initial fluid dynamics
simulation, the engineers were able to visualize the airflow
behavior inside the high-ceilinged hall and devise improved
airflow delivery parameters, which were validated in asecond simulation. Specifically, the supply air flow in thefirst-floor seating area was increased and directed
at predefined angles, leading to better air delivery andaccordingly lower temperature. Additionally, the team foundthe side flow bar diffusers at the balconies were inducing
Optimized design model showing flow pattern and velocity distribution
significant disturbances to the hot stratified air layer across the assembly hall. Simulation assisted the designers in meeting
and, thus, engineers removed those diffusers from the energy efficiency goals.

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ANSYS Advantage • © 2011 Ansys, Inc.

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offshore

Sensor float fluid–structure interaction (FSI) transient response
to current flow

Sensor float bow nosing down due to flow on sensor below float,
deck awash

Don’t Rock the Float!
Fluid–structure interaction allows designers to assess
impact of waves on freshwater and offshore systems.
By Richard Grant, President, Grantec Engineering Consultants, Inc., Halifax, Canada

On a recent project commissioned by Environment
Canada, Grantec Engineering Consultants, Inc. wastasked with developing a water quality monitoring float
designed to carry a sensor for capturing environmental
data. The float plays a role similar to — and looks
somewhat like — a catamaran, though it is designed to bemoored rather than driven by an engine or sails. The goalof the analysis was to minimize drag and ensure stabilityof the float as well as to develop specifications for themooring system and structure. To meet this goal, Grantec
used multiphysics simulation software from ANSYS todetermine the fluid–structure interaction (FSI) by modelingthe float and sensor under a wide range of water current
and wave conditions.
Based in the maritime province of Nova Scotia on theeast coast of Canada, Grantec and its engineers have an
extensive background in both structural and fluids analysis
helping customers in the defense, offshore, marine,
manufacturing, energy and aquaculture fields advancenew designs and systems. More recently, however,Grantec has often faced the challenge of how to combine
these two analyses that have historically been performed
separately. Previously, when the interaction between fluid
and structure was critical, Grantec’s engineers needed toenter the results from the fluid dynamics software manually
into the structural analysis software and vice versa. In
contrast, ANSYS offers a solution integrating several of its

www.ansys.com

most powerful and trusted fluids and structures simulation
tools. With its multi-field solver, the ANSYS FSI solution
provided Grantec’s team with a bidirectional capability fortime-transient or steady-state analysis with moving or
deforming geometry. Using ANSYS Multiphysics software,
the Grantec engineers were thus able to evaluate both thestructural part of the analysis and the fluid flow solution
with just a single tool.
In the original float design, the team modeled the floatand sensor as a flow obstruction, which accounted for theflow currents and wave loading on the float as well as
buoyancy forces. They then evaluated the development of
bow and stern waves that result from the resistance of thehull to fluid flow, just as with the hull of a ship. The
software duplicated the vertical heaving and angularpitching of the float in response to different wave andcurrent conditions. The impact forces from the waves
calculated in the fluid simulation were automaticallypassed back to the structural model to more accurately
simulate the stresses and deformations on the hull.
Though they have little effect on fluid flow, the stresses
are important because they make it possible to optimize
the design of the hull to a much higher level than would
be possible without them.
With the FSI solution from ANSYS, Grantec evaluatedthe performance of a wide range of hull profiles and mass
distributions under different flow conditions, and it took

ANSYS Advantage • Volume V, Issue 1, 201119

offshoreadvantage of parallel processing to accommodate larger
models more efficiently than using a single-machine
environment. In the initial series of designs studied, thesensor was fixed to the stern of the float and extended
vertically into the water. The FSI results for these designs
showed the force exerted by water currents on the sensor
combined with the bow wave tended to push the bow of
the float underwater in faster currents. It was not practicalto solve this problem by simply changing the hull design,
so the team tried a hinged connection between the sensor
and the float to reduce the load transmitted from thesensor to the float. The hinged sensor, however, greatly
increased the complexity of the simulation analysis.
Grantec addressed the new challenge of the hingedsensor design by modeling the float with the sensor fixedin different hinge positions using the immersed pipe
element in the structural portion of ANSYS Multiphysics
software. Unlike the extensive approach used for the nonhinge designs, this new method provided a more simplified
way to perform FSI analysis. With the immersed pipe
element, the team applied wave and current loading to
the structural model without the computational load
involved in coupling it to a full fluid dynamics analysis. In
the future, Grantec plans to use a moving mesh to perform
a more complete FSI analysis including full fluid dynamics
simulation that will evaluate the motion of the hinge in
response to hydrodynamic forces.

Finite element mesh (top) and contours of stress (bottom) on a halfmodel of the sensor float. The FSI analysis was performed to look at
the effect of a fixed flexible boom on the float.

20

ANSYS Advantage • © 2011 Ansys, Inc.

Waves washing over top of gravity-based structure of offshore platform(waves traveling to the right)

Beyond its studies of water quality monitoring floats,
the company has done extensive work with engineering
simulation to help create safer and more structurallysound offshore structures and systems. Grantec’s
engineers have also used the ANSYS Multphysics solution
to assess gravity-based structures (GBSs) used to protect
offshore oil drilling and production platforms fromicebergs. GBSs rely on weight to secure them to theseabed, which eliminates the need for pilings in hardseabeds. Concrete GBSs are typically built with hugeballast tanks so they can be floated to the site and, oncein position, sunk by filling the tanks with water. The
Grantec team used FSI from ANSYS to simulate wave
loading a GBS including the effects of massive waves
from storms — also known as green water — coming overits top.
The company believes that its investment in ANSYS
Multiphysics software has made a significant addition
to its analytical capabilities. Clients seek out Grantec
because of its track record in performing advanced
engineering to solve very complex problems. ANSYS
technology has helped put another tool in the Grantec
toolbox that makes it easier to address design challenges
that just a few years ago would have been much moredifficult. n

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Invented the Supercomputer,
Reinvented the Workstation
The Cray CX1 brings high-return, low-risk
benefits to design productivity andefficiency for ANSYS® FLUENT®, ANSYS®
Mechanical™
, HFSS™
, and more.

Visit the Cray and ANSYS
Partner Media Center at
www.cray.com/reinvent

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The leader in
Cluster, Grid and Cloud
Management Software

More Power.
More Possibilities.
Integrate. Simulate. Dominate.

When ANSYS applications are powered by Platform HPC clusters, your engineering
team can deliver more designs, more accurately, and faster. Run more complex andaccurate simulations than previously possible to improve product quality and reducetime to market. Empower yourself.

www.ansys.com

ANSYS Advantage • Volume V, Issue 1, 201121

Energy: Wind

Energizing the Wind Industry
Increased complexities require a system-level approachin designing and evaluating wind turbines.
By Ahmad Haidari, Global Industry Director for Process, Energy and Power, ANSYS, Inc.

Wind energy projects around the globe — from small installations to very large windfarms — have a common goal: to reduce unit energy cost while improving reliability.
From a business perspective, technology contributes to viability by influencing
efficient wind turbine design, manufacture, deployment and operation. Whether
the application is an onshore, offshore or far-shore installation, advancements inscience and engineering will contribute to the industry’s success, especially throughcapabilities related to aerodynamic design, material science, structural design,
electronic mechanical control, site selection and farm layout.
Wind turbines and wind energy projects are becoming increasingly more
complex, so they must operate dependably at levels unimaginable a few years ago.
Installations of very large wind turbines in offshore and floating configurations are amajor technological achievement. Energy companies hope to design, install, andefficiently and reliably operate superstructures whose wind blade spans are over
50 meters and subject to wave and wind loading at different angles of attack.
Historically, wind energy companies have used engineering simulation software
as a point solution, used only to simulate a specific design aspect or analyze acomponent. Successful application of ANSYS solutions ranges across the windenergy industry, including:• Aerodynamic design: thrust coefficient, blade structural integrity, ultimate
loads and fatigue, noise predication, wind gust fluid–structure interaction, bird
strike, icing, boundary layer transition, near-wake and far-field studies• Structural design: tower and rotor structural integrity/safety, power conversion
efficiency, installation cost and maintenance, offshore transport and installation• Component design: blades, gearboxes and bearings, generators, nacelles,
rotors, drivers, motors, electronics cooling• Site selection and farm layout: maximum project potential, power output(both peak and average), wind loads, fatigue• Turbine placement: variable terrain, roughness, forestry, multiple wake effects,
buildings and setbacks• Electromechanical system: electrical machines, variable-speed control
systems, transformers, power electronics, power distribution, sensor andactuator design• Blade manufacturing
Today’s increased complexities require a system-level approach in designingwind turbines and evaluating performance based on real-world conditions.
Advances in engineering simulation software increasingly make this possible: For
example, the ANSYS Workbench environment is designed with capabilities thatenable modeling entire wind turbine systems. Its value is further enhanced throughadvanced solver functionality including turbulence transition models, advanced
contact models, multiphysics capabilities, composites tools, high-performancecomputing and the flexibility to connect to third-party software for wind turbine
blade manufacturing or aero-elasticity calculations.
Engineers can perform electromechanical system-level analysis using Simplorer
software, electromagnetic analysis on electric machines and drives with Maxwell,
wind power analysis via ANSYS CFD, and stress and modal analysis using ANSYS
Mechanical. By leveraging high-level integration and advanced capabilities,

22

ANSYS Advantage • © 2011 Ansys, Inc.

Speed sensor design

Transformer design

Electric machine
analysis

Power electronics design

Generator andshaft designwww.ansys.com

Energy: Wind

Wind Farm and Power Distribution

Site selection, land and sea

engineers over time are extending their once-simplified
simulations to include additional details in overall windturbine design — enabling small efficiency gains,
important in an industry in which a minute efficiency/
performance gain can translate into much larger electricity
production, reduced downtime and greater project profitability. Such details can also improve reliability and enable
better wind energy project operation.
Already, there are many exciting examples of the
expanding use of engineering simulation throughout
the wind energy supply chain. Three separate wind energy
applications are presented in this issue of ANSYSAdvantage — and each highlights the breadth of
ANSYS solutions. Without simulation capabilities, theprojects presented may not have been as successful.

Wind farm configuration for
optimal power generation

Design of floating
offshore wind turbine

With increased demand for wind energy, engineers will
face additional complexities, such as even-larger turbine
blades that will be installed farther offshore and in harsher
environments. Wind farm site selection must continue toreduce risk and overcome proximity and environmental
concerns. New powertrains, lighter towers, multi-accessturbines, floating platforms and quieter machines will be
developed. The industry will innovate to meet thechallenges of increased safety and reliability, improved
remote monitoring, reduced system maintenance andregulatory concerns. ANSYS is keeping pace by providing
high-fidelity integrated, advanced capabilities that meetsingle-physics needs as well as system-level and multidisciplinary requirements of the wind energy industry. n

Software from ANSYS meets the challenges of individual applications as well as of complete systems in the wind energy industry.

Some images courtesy CENER, IMPSA, REpower Systems AG, Trane Inc.,
Xanthus Energy Ltd. and iStockphoto.com/Tore Johannesen.

Power distribution analysis

Blade design

Rotor sizing
and acoustics
Tower design and fluid
structure interaction

Offshore installationand certificationwww.ansys.com

ANSYS Advantage • Volume V, Issue 1, 201123

Energy: Wind

More Powerto YouSimulation helps Indar to design
one of the world’s highestefficiency permanent magnet
wind turbine generators.
By Jon Vaquerizo, Project Manager, and Xabier Calvo, TechnicalManager, Indar Electric, S.L., Beasain Guipuzkoa, Spain

Indar high-speed permanent magnet generator withair–water cooling option

Wind power is the world’s fastest-growing energy source,with 37.5 gigawatts of installed capacity added in 2009.
The Global Wind Energy Council expects this resource togrow by 160 percent over the five-year period ending
2014. One rising trend is permanent magnet generators(PMGs), as they offer higher efficiency and design flexibility. Indar Electric, S.L., set out to develop a 2.5 MW PMG
for wind power applications with the ambitious target ofachieving an unprecedented 97.7 percent level of efficiency at rated load in converting mechanical to electricalenergy using a permanent magnet generator. Another goal
was to increase performance and efficiency at partial
loads, because wind turbines often run at partial load.
Traditional build-and-test methods could not achievethese goals in a reasonable amount of time. Thus, Indar
applied electromagnetic field and fluid flow simulation tofacilitate the process.

Magnetic flux density level in thestator (left) identifies areas of highlosses. The generated voltage (upper
right) and fast Fourier transform (FFT)(lower right) results for a no-loadcondition are also shown.

24

Voltage (%)

Volts

Indar Electric was founded in 1940 as a manufacturerof small electric motors. In 1997, it became part ofIngeteam, a Spanish renewable energy company thatcurrently holds about a 15 percent global market share forwind power components. Indar produces a wide range ofgenerator concepts, including more-traditional double-fed
induction generators (DFIG) and newer PMGs. PMGs generally offer higher efficiency at rated load and even moreat partial loads, since the permanent magnets eliminate
the need for rotor windings that, in turn, remove rotor
ohmic losses. PMGs also eliminate the need for brushes,
which reduces possible problems and maintenanceneeds.
The Indar design team faced several major challenges
in developing its newest PMG. Achieving high efficiency
was the overarching goal, but there were a number of othertargets that had to be simultaneously achieved for reliable
operation. Cogging torque, caused by the interaction
between the rotor’s permanent mag1250
nets and the slots on the stator core,1000
had to be reduced to 0.1 percent of
750
500
overall torque. Voltage harmonics
250(THD) in the output had to be kept
0
0.005
0.01
0.02
0.015
below 0.5 percent. For the cooling-250
system, a demanding goal was set
-500
-750
to maintain magnet temperature-1000
below 100 C to assure good per-1250
Time
formance of the magnets over a
20-year lifetime.
Actual value of 1st order is 99.9
0.7
Indar engineers used Maxwell
0.6
low-frequency electromagnetic field
0.5
simulation software from ANSYS to
0.4
evaluate the effect of different
0.3
geometries and magnet properties
0.2
on the electromagnetic performanceof the generator. Well-known basic
0.1
equations were used to develop
0.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
a preliminary generator design.
Order of Harmonics

ANSYS Advantage • © 2011 Ansys, Inc.

www.ansys.com

Energy: Wind

250
200

100

150
100
Current (%)

Torque (%)

50

0

-50

50
0
-50
-100

-100

-150
-200

-150
0.95

1

1.05

1.1

1.15

Time (s)

1.2

-250

0.95

1

1.05

Time (s)

1.1

1.15

1.2

Magnet behavior in a three-phase short circuit as predicted by Maxwell

Engineers first created a 2-D and, later, a 3-D model of thegenerator, relying primarily on manufacturing drawings to
reproduce the geometry and material properties of rotor
and stator laminations and coils. The time step of thesimulation was adjusted to match the rotating speed of
the generator and the number of poles in the permanent
magnet. Engineers simulated the performance ofthe proposed design under no-load, full-load and shortcircuit conditions.
The results of the simulation included the voltage
wave form produced by the generator, a measurementthat was compared to the design requirement so that
harmonic levels could be evaluated. The voltage fast
Fourier transform output capability in Maxwell provides
the voltage at different frequencies, making it
straightforward to calculate the harmonic levels for aparticular design.
The behavior of the design in the event of a shortcircuit was another important consideration. Short circuits
may be caused by mechanical failure in the generator,
insulation breakdown or power converter malfunction.
Engineers studied the magnetic field generated in eacharea of the permanent magnet in a short circuit, expecting
to ensure that the magnet had the right properties to avoid
any damage. Generally when designing a PMG, Indar
engineers consider a wide range of factors, including thecontribution of magnet temperature, rotational speed,switching frequency and short-circuit (two-phase andthree-phase) performance to achieve ideal magnet
behavior for the entire lifetime of the generator circuit.

Electromagnetic simulation of the balancing operation and inserting therotor in the stator with a cranewww.ansys.com

In the full-load simulation, engineers looked at the
input required by Ingeteam frequency converters for theavailable switching frequencies to achieve nominal torque,high current and low losses. The team examined inductionlevels in the stator because of their important effect on
efficiency. Though high induction levels make it possible
to reduce the size of the generator, they also increase ironlosses. Maxwell results showed the distribution of losses
over the geometry of the stator, providing guidance fordesign changes to improve efficiency. Indar engineers
continually modified the design, attempting to reduce
losses in the stator copper, mechanical losses, and losses
created due to switching frequency when working withfrequency converters — all while achieving the otherdesign requirements.

Flow speed and temperatures through the tubes in the air–air
cooling system

Because of the magnet field’s high strength even when
the PMG is not rotating, Indar engineers simulated theprocess of assembling and balancing the rotor, ensuring it
could be safely accomplished. They determined the level ofmagnetic forces generated while inserting the rotor, which
made it possible to specify assembly tools that could
withstand these forces. The generator rotor is balanced byplacing it on pedestals instrumented with acceleratorsthat detect forces generated by imbalance in the rotor.
The electromagnetic field generated by the rotor during
this process was simulated with Maxwell to ensure
it did not interfere with the cables carrying theaccelerometer signals.

ANSYS Advantage • Volume V, Issue 1, 201125

Energy: Wind

8

Air Flow (%)

7
6
5
4
3

Option 3
Option 2
Option 1

2
1
0
Generator Length

Fluid dynamics results show air flow variations over the length of thegenerator using three different cooling system options. The options
involve modifying the geometry of the slot, windings and magnets.

Indar engineers simultaneously studied the generator’s cooling system because of the interaction between
electrical and thermal performance. The temperature of themagnet plays an important role in its ability to resist
demagnetization, so improvements in cooling performancecan increase the magnet’s ability to handle a short circuit.
Optimization of the cooling circuit helps to improve
efficiency by reducing mechanical and cooling losses.
To optimize the cooling circuit, engineers used ANSYSFLUENT fluid dynamics software to perform a detailed
study of fluid flow and heat transfer in and around thegenerator. Meshing was a challenge because of thedifference in scale between the small 5 mm to 10 mm air
gap between the rotor and stator, where accuracy wascritical, and the large overall 1 meter length of the generator
and cooling system. To minimize computational time, 3-D
steady-state analysis was used during the majority of thedesign process, and the model size was reduced by using
axial symmetry and periodic conditions. Fluid dynamics
results included the local heat transfer coefficients, air flow
velocity at every point in the machine circuit, pressure
drop of the air circuit through the generator, generator
temperature and thermal profile, and magnet working
temperature. Engineers used these results as a guide in
decreasing temperature hotspots by reducing variations
in cooling over the length of the generator.
Using software from ANSYS helped Indar to easily
ex plore multiple automated parametric design
variations. The electromagnetic and fluid flow simulations
provided far more diagnostic information than was available from physical testing. Simulation provides results forany output at any point in the computational domain, while
physical testing provides results only at locations where itis practical to locate sensors. Engineers were able to iterate
to a design that met all their specifications long before a

26

ANSYS Advantage • © 2011 Ansys, Inc.

prototype was built. Pervasive parameter management was
performed quickly and easily by changing design parameters. These parameters were propagated throughout theentire design system, from CAD model through meshingand boundary conditions to generation of updated results.
The next step was building and testing a real-scale
prototype to verify the simulation results and to ensure thegenerator’s functionality and life expectancy. Two types oftesting were performed at the test bench: generator fullload homologation testing to certify generator performanceand durability testing to verify its reliability over time. The
electrical and thermal measurements of the physical prototype matched up very well with the simulation results. For
example, the maximum deviation from the voltage shapeprediction to the measured values was 0.1 percent.
The measured efficiency of the new generator was 97.86
percent, higher than the design target of 97.7 percent, andnearly exactly what was predicted by the simulation. The
rating is one of the highest levels of efficiency for any permanent magnet generator on the market. Simulation madeit possible to achieve this challenging performance goal inless than half the time that would have been required using
conventional build-and-test methods. The simulation predictions correlated well with physical testing, providing
confidence that Indar can use simulation to optimize its
products to deliver high performance under the mostdemanding conditions. n
8.0

Max. Deviation = 1.32%

7.5

Mid. Deviation = 0.5%

7.0
Air Flow (%)

9

6.5
6.0
5.5
5.0

Simulated

4.5

Measured

4.0
Generator Axial Length

Temperature

10

Simulated
MeasuredGenerator Axial Length

The difference between the simulated and measured air flow rates
across the length of the generator was a maximum of 1.32 percent. The
maximum difference in calculated versus measured temperatures was
only +/– 3 degrees C for the stator and +/– 3 degrees C for the rotor
magnets. Software from ANSYS provided very good accuracy, which isessential for these applications and for Indar engineers to trust the new
design variations.

www.ansys.com

Energy: wind

Where the Wind BlowsEngineering simulation plays a role in getting the most power fromwind farms by predicting the best available locations.
By Ian Jones, ANSYS Fellow, and Christiane Montavon, Senior Technical Services Professional, ANSYS, Inc.,and Daniel Cabezón, Wind Specialist, National Renewable Energy Centre (CENER), Sarriguren, Spain

Driven by the need to change the power generation energy
mix, the use of wind turbines is increasing significantly
around the world. The Global Wind Energy Council(GWEC) predicts the global market for wind turbines willgrow from 94 GW in 2007 to 296 GW of total installedcapacity by 2012. Depending on the increase in electricity
demand, wind power could supply 11.5 percent to12.3 percent of global electricity demand in 2020,
according to GWEC, and between 18.8 percent and21.8 percent in 2030.
However, the uptake for land-based installations is not
as great as it could be. Some of this is explained byplanning considerations: People do not want to havewind turbines sited near their homes. Typical objections

include visual intrusion, noise, the effect on nature, anddisturbances caused by construction and operation.
Furthermore, wind farms affect radar signals, acondition that is forcing turbines away from exposed
hilltops to locations that are out of sight of antenna
installations. Consequently, wind farm developers are
considering sites not necessarily ideal for wind power
generation: locations away from hilltops, in regions where
the flow can recirculate and turbulence can be high, andin heavily forested areas. Packing turbines into such
constrained sites means that wake effects can have asignificant impact on the performance of downstream
turbines, reducing power production and considerablyincreasing fatigue loading.

Black Law site
Courtesy Siemens press.

www.ansys.com

ANSYS Advantage • Volume V, Issue 1, 201127

ENergy: Wind

Black Law Wind Farm
A typical wind farm example is Black Law, in central
Scotland, operated by ScottishPower Renewables.
The site contains 54 2.3 MW Siemens turbines andoccupies a former open-cast coal site. In January
2006, it was the largest operating wind farm in the
United Kingdom.
With relatively small height variations across thewind farm (approximately 170 meters), the complexity on this site is less related to topography andmore associated with significant forestry and wake
effects. ScottishPower Renewables and ANSYS
have carried out a collaborative study on this windfarm to understand the interactions between theturbines, the terrain and the forestry, to assess how
well fluid dynamics would perform for this site, andto use the lessons learned in developing new sites.
A comparison of ANSYS results and measurementsof the annual average power for the Black Law site
shows that the software predicts performance well.
The results also indicate which turbines are underperforming, for example, because of their locationin the forest.

To overcome the shortage of land sites, interest is
growing in offshore turbine sites, where low ambient
turbulence is expected to result in reduced rates of wakerecovery. To minimize wake losses and optimize energy
production, it is vital to improve confidence in the abilityof models to accurately capture these effects.
No matter the location, there is considerable
reluctance to invest in expensive wind farms without aguaranteed return on investment (ROI). Using current
prediction tools, developers are unable to provide
sufficiently reliable estimates of projected wind powerfrom challenging sites, failing to satisfy investment criteria.
Developers are now looking for ways to make wind power
estimation more reliable for non-ideal sites.
Turbine blades must withstand significant stresses
caused by strong rotation and wind buffeting. There isconsiderable interest in extending turbine life andreducing operational costs. Understanding the windregime and turbulence levels enables greater reliability inassessing suitable turbine types. Under the International
Electrotechnical Commission (IEC) (the recognizedinternational body for standards development activities)
turbine classification system, the choice of turbine type isdetermined by the levels of turbulence intensity likely to
be experienced at the site. For example a Class IIIB windturbine, designed for lower wind speed (7.5 m/s at hub
height) and lower turbulence intensity (16 percent), will be
subject to lower loads than a Class IA or IIA wind turbine.

“This type of simulation is usefulin highlighting features not picked
Map of Black Law Ordinance Survey® Crown Copyright 2008,
license number 100048580

up in other engineering models
andinbringing

to

light

the circumstances under which
Normalized Power

standard models fail to capture
the detail required. Therefore, it
has a key role to play in managing
the risks associated with modeling
Turbine Number(Highest power on left)

Comparison of average wind power on Black Law, takenfrom operational data and ANSYS simulations, shows close
agreement — giving ScottishPower Renewables increased
confidence in using fluid dynamics simulation for future site
application.

28

ANSYS Advantage • © 2011 Ansys, Inc.

complex wind farms.”– Callum Strachan, ScottishPower Renewables

www.ansys.com

Energy: wind

A Class IIIB machine can therefore be larger both in termsof rotor diameter and hub height to capture a larger
portion of the available energy which will translate inhigher capacity factors compared to Class I and II windturbines. However, they cannot be used at sites thatexperience a high level of turbulence intensity.
Understanding turbulence allows a greater choice ofturbine types and enables matching the type to thesite conditions.
The use of ANSYS CFD solutions has increased
significantly for modeling complex flows that arise in windfarms as a result of the terrain, forest and wake effects
between turbines. Now a customized ANSYS tool helps
designers to understand wind farm efficiency for complex
flows and overcome technology limitations: WindModeller.
The solution assists developers who don’t havesubstantial CFD expertise to benefit from automatedanalysis of complex flows. Advanced users can further
customize the setup and write post-processing scripts tocapture their own expertise.
An add-on tool for ANSYS CFD — CFDWind — wascreated by El Centro Nacional de Energías Renovable(CENER, or National Renewable Energy Centre), a technology center in Spain that specializes in applied research,development and promotion of renewable energies. The
tool contains advanced models for atmospheric boundary
layers, enabling developers to take into account complex
climatic conditions when developing projects. Fluid
dynamics tools from ANSYS provide greater understandingof the wide variety of factors that need to be consideredwhen designing wind farms. The flexible nature of thesoftware provides an excellent platform for customization
specific to this industry. n

References

Alaiz Wind Farm Test Site
The Alaiz hill, located in northern Spain, is situatedin an east–west direction. The site is 4 kilometers
long and approximately 1,050 meters high, and it
comprises very rugged terrain. This hill has beenused as a test site for gathering wind farm data andevaluating different simulation methods. Detailed
calculations have been carried out at CENER
employing ANSYS CFD software as well as using
an industry-standard linearized model. Comparisons
showed significantly lower errors in the predicted
wind speed for the fluid flow simulation, on
average about 1.75 percent, compared with about
5.4 percent for the linear models. This demonstrates
the real benefits that fluid dynamics simulation
can provide.

Simulation results showing velocity vectors colored byvelocity magnitude for Alaiz wind farm test site

1. http://www.wind-energy-the-facts.org/en/factsheets.html.
2. Montavon, C.A.; Jones, I.P.; Staples, C.; Strachan, C.; Gutierrez, I.
Practical Issues In the Use of CFD For Modelling Wind Farms. Proc
European Wind Energy Conference, 2009.
3. British Wind Energy Association: England’s Regional Renewable
Energy Targets: Progress Report, 2009. http://www.bwea.com/pdf/
publications/RRETProgressReport.pdf.
4. Turbine Classifications: IEC-1400-1. http://windwire.blogspot.
com/2009/05/iec-classification-of-turbines.html.
5. BWEA: Calculations for Wind Energy Statistics – Emissions
Reductions. http://www.bwea.com/edu/calcs.html (accessed
16 July, 2009).
6. UK Renewable Energy Strategy (June 2008) HM Government
Department for Business Enterprise & Regulatory Reform (BERR).
7. BWEA UK Wind Energy Database.
8. Climate Change Act 2008 (Chapter 27).
9. Milborrow, D. Dispelling the Myths of Energy Payback Time. Wind
Stats, 1998 (Spring), Vol. 11, No. 2.
10. Cabezon, D.; Iniesta, A.; Ferrer, E.; Marui, I. Comparing Linear andNonlinear Flow Models, Proceedings of EWEC 2006.

www.ansys.com

Fluid dynamics results showing contours of velocity magnitude
for Alaiz site. Fluid dynamics software from ANSYS was able toaccurately model this complex terrain.

ANSYS Advantage • Volume V, Issue 1, 201129

Image © iStockphoto.com/chromatika

ENERGY: wind

Second Wind
Advanced turbulence models leadto optimized wind turbine spacing.
By Thomas Hahm, Fluid & Energy Engineering GmbH & Co. KG,
Hamburg, Germany

With the Global Wind Energy Council projecting that wind
power installations will grow to 409 gigawatts in 2014, theamount of land occupied by wind farms is becoming aserious concern. The issue is already critical in Germany
because of dense population and the country’s leadershipin deploying wind power. Typically the goal is to generate
as much power as possible at the lowest cost from agiven wind farm site. But there are drawbacks to spacing
wind turbines too close together, including lower power
output and wake effects.
Fluid & Energy Engineering (F2E) GmbH & Co. KG ispioneering the use of advanced turbulence models suchas large eddy simulation (LES) to increase the accuracy
with which turbines can be sited within a wind farm. The
end result will be the ability to generate more energy froma given volume of land.
The wake of a turbine has significant effects — including
reduced power output and shorter turbine life — on any
turbine in its path. Extracting energy from incoming windcauses a loss in the kinetic energy and velocity of the windin the wake of a turbine. This energy is recovered over distance as the wake exchanges energy with the surrounding
wind. Turbulence produced in the wake of turbines andturbulence produced by terrain features such as forests orhills can have a substantial impact on turbine life. The
effects of turbulence can vary with the flow angle andinclination of the incoming wind. For example, one of themost damaging scenarios is when one half of a rotor
experiences turbulence from an upwind turbine and theother half is exposed to undisturbed flow.

30

ANSYS Advantage • © 2011 Ansys, Inc.

Atmospheric flow over the Bolund peninsula (Denmark) with winddirection from the escarpment on the west side. The red color
indicates high wind speeds, and blue indicates low speeds.

Wind field behind an ENERCON E-66 wind turbine with hub height of
65 meters. The blue color indicates high wind speeds, and white
indicates low speeds.

Meandering wake behind an ENERCON E-66 wind turbine. Blue
represents high wind speed, and white represents low speed.

www.ansys.com

energy: Wind

Model of a wind turbine used for computational fluid
dynamics simulationwww.ansys.com

0.45
0.4
0.35

TI[-]

0.3
0.25
0.2
0.15
0.1
0.05
0
-150

-125

-100

-75

-50

-25

0

25

50

75

100

125

150

Width of Wake [m]Simulation of local turbulence intensity (blue line) compared tomeasurements (red dots) across width of wake

Ultra Sonic 2-D

LES Simulation 2-D

1098
Velocity [m/s]

In Germany, loan organizations and regulatory authorities require an assessment of every new wind farm projectto ensure that turbulence loads generated by terrain andwind turbine wakes are within acceptable design limits.
Terrain and wind turbine wakes must be considered
together because they have additive effects on turbulence. Typically, the approach to computing these loads is
to use standard empirical formulas to estimate terraingenerated and wake-generated turbulence intensity andto use blade momentum theory to estimate the loading on
the blades.
However, the accuracy of these empirical calculations
can be limited because they do not incorporate the actual
geometry of the rotors and terrain. The result is that thecalculations need to be heavily calibrated to produce
safe wind farm designs. These empirical models showthat wind turbines typically must be spaced at a distanceof about four rotor diameters from each other. Required
spacing can be even greater if terrain-generated
turbulence is a major factor.
Developers of wind farms are demanding moreaccurate methods for calculating the turbulence intensity
generated by wakes and terrains so that they can produce
more energy from a given parcel of land. F2E has beenusing computational fluid dynamics (CFD) over the last
decade to address this important issue. The company’sengineers generally have had little difficulty in determining
the wind velocity in the wake of a turbine, but calculating
the turbulence intensity is much more challenging.
Conventional Reynolds-averaged Navier–Stokes(RANS) models reduce the computational time required tosimulate turbulent flows by time-averaging the velocity
field, pressure, density and temperature over time. Thisapproach eliminates turbulence fluctuations and makes it
possible to model turbulent flow in a reasonable time ondesktop computers. RANS methods are effective at predicting the overall and steady-state behavior of a wind
farm; however, their accuracy suffers in modeling unsteadyturbulent flows that are typically found in wind farms.
Over the past three years, F2E has used the LES
turbulence model with very positive results. It numericallyresolves the larger turbulence scales and models thesmaller scales to provide accurate transient solutions of

7
6
5
4
3
2
1
0
100

120

140

160

180

200

220

240

260

280

I [s]

Measured (green line) and calculated (blue line) values for velocity
magnitude at hub height in center of wake over 180 seconds

the flowfield. Most recently, F2E engineers used ANSYSFLUENT software to model the full geometry of two
ENERCON E-66 wind turbines with 66 meter rotors using
the LES technique. The fluid dynamics results werevalidated with data collected in an actual wind farm using
ultrasonic anemometers. Fluid dynamics accurately
predicted the velocity of the incoming wind along withturbulence intensity at the downwind turbine over thewidth of the wake.
Fluid dynamics did a good job of predicting the variation in the velocity over a period of 300 seconds, a figurethat can be used to calculate turbulence intensity and theaerodynamic loads that turbulence creates. Quick horizontal shifts of the wake from one side to another aredetectable on a 25 second time scale. Another criticalfactor in achieving this level of accuracy was the accurate
modeling of the incoming wind field and, particularly, thevariation in its wind direction and the wind shear.
Fluid dynamics and LES techniques from ANSYS canbe used to calibrate and greatly improve the accuracy of
empirical calculations that are currently used to calculate
blade loads under wake conditions. Calculation of bladeloads should make it possible to substantially increase
the amount of energy that can be generated by newwind farms and to increase the safety and the lifespan ofwind turbines. n

ANSYS Advantage • Volume V, Issue 1, 201131

Aerospace

Computer-generated image of the Terrafugia Transition in flight. The company unveiled a production prototype in 2010.

Fast Lane to Sky High
Fluid flow simulation software co-pilots designof production prototype roadable aircraft.
By Gregor Cadman, Engineer, Terrafugia, Woburn, U.S.A.

Since the earliest days of the aviation industry, inventors
and entrepreneurs — from motorcycle racers to homebuiltaircraft enthusiasts to the largest automakers — havesought to develop the iconic mashup of future transportation technology known as the “flying car.” Some of theirattempts did manage to test successfully and even residein the Smithsonian. Sporting names such as Autoplane,
Aerobile and Airphibian, these machines were impressive
for their time, but they never lived up to their mythologicalpedigrees as conjured by science fiction authors andfilmmakers. The concept is a proven one. But due to theengineering challenges involved in combining a lightweight, aerodynamic aircraft with the stability needed forlong-distance driving, to date none of these vehicles,
which are more accurately called “road-ready” airplanes,
have been successfully brought to market.
Recently, however, the Boston-area startup Terrafugia
revealed its production prototype for the first commerciallyavailable street-legal aircraft with hopes of beginning
manufacturing in 2011. Terrafugia — Latin for “escapefrom land” — used simulation tools from ANSYS to arrive
at a production prototype of its innovative Transition ®
Roadable Aircraft. After earning global attention following
successful test flights in early 2009, the new prototype
was revealed to the industry in 2010 at the annual
Experimental Aircraft Association (EAA) AirVenture
Oshkosh national airshow. It was a critical step incommercializing this one-of-a-kind vehicle.
With a flight range of up to 490 miles and cruising
speed of 105 mph, the Transition can also drive up to
65 mph on the road. It is capable of transforming fromplane to car in less than 30 seconds. The sophisticateddesign features foldable wings that span over 26 feet, arear-wheel-drive system for the road, and a rear pusher
propeller for flight. While the Transition is not designed toreplace anyone’s car, it is intended to drive at normal

Rear view showing pathlines passing by Terrafugia’s beta prototype design flying at 105 mph

32

ANSYS Advantage • © 2011 Ansys, Inc.

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Aerospace

The Transition’s foldable wings allow the aircraft to be driven on the highway as well as parked in a typical residential garage.

highway speeds, so that owners can easily access theirlocal airports. This required Terrafugia’s engineering teamto simultaneously consider the aerodynamics of flying
and driving — activities that exert very different forces on
the vehicle. While physical tests in a wind tunnel helped tovalidate the initial concept design, they were both timeand cost-intensive. For this reason, Terrafugia engineers
turned to ANSYS FLUENT software to make and verify
design modifications for the new production prototype,working in a virtual simulation environment that saved
time and money while also enabling engineers to assess acomplex range of design considerations.
Unlike a typical car or plane, the Transition has a hostof extra components to consider when analyzing air flows
around the vehicle, as wheels, propellers, foldable wings
and other shapes affect dynamic flows whether it isdriving or flying. Using the modeling capabilities of thesoftware, Terrafugia engineers conducted whole-vehicle

Proof-of-concept design of the Transition showing pressure contours
on the vehicle surfaces. Since the Transition was classified as a multipurpose passenger vehicle, a bumper was not legally required, so
engineers conducted simulations to see if they could eliminate thisfeature. ANSYS models confirmed that the canard was not needed, andthis feature has been eliminated in the production prototype design.

www.ansys.com

airflow tests to study the effects of the slightest designchange on overall performance. The team addressedissues such as maximizing wing lift in the air while
simultaneously minimizing the effects of crosswinds
along the road. Without an ability to work in a virtualenvironment, Terrafugia’s team would have had toconstruct complicated physical models, modify or rebuild
them, and conduct hours of real-world testing. Simulations
powered by fluid dynamics software from ANSYS enabled
rapid testing and verification of some modifications to theTransition design, based on the physical performance of
the initial proof-of-concept vehicle.
Wind tunnel tests revealed an adverse interaction
between the vehicle’s front suspension and its canard,
which serves as a wing while flying and a front bumper
while driving. Rather than relying on additional physicaltesting, Terrafugia used ANSYS technology to further
explore this interaction as well as potential solutions. As
the work of the engineering team progressed, it became
clear that, while the canard configuration had initially been
integral to the design, it was undesirable from a number ofstandpoints. With the Transition receiving classification asa multipurpose passenger vehicle, the full-width bumper
requirement for passenger cars — the original reason forthe canard — was no longer applicable. Engineering
simulation software verified that lighter weight, better flightcharacteristics and improved looks were all potential
benefits of a canard-free design.
Another challenging aerodynamic design aspect of theTransition was attaining a wing stall speed — the speed atwhich an aircraft stops flying — of under 52 mph, which isa requirement for the light sport category of aircraft.
Since slower in-air speeds generally create safer flying
conditions, it was important for the Terrafugia engineers

ANSYS Advantage • Volume V, Issue 1, 201133

Aerospace

Production prototype design showing airflow pathlines over the vehicle body. The VBM plug-in to ANSYS FLUENT enabled
Terrafugia engineers to model the vehicle’s propeller under near-stall conditions, which helped to ensure the safety of theaircraft while in flight.

to design the vehicle to operate at a low speed for safety
and stability without stalling. Stall prediction can pose adifficult problem, even for sophisticated CFD tools.
However, with close support from ANSYS experts,
Terrafugia developed a detailed engineering approachnecessary to obtain accurate predictions, including theuse of a virtual blade modeler (VBM) that plugged in to thefluid dynamics software and created additional capabilities to model the Transition’s propeller. After deploying
the specialty VBM tool, the team reshaped the wing andthe remainder of the body as needed to match the weightand center-of-gravity requirements of the vehicle.
The new design improves both the in-air and on-road
performance of the Transition as well as ensures that thevehicle lends itself to full-scale manufacturing. Terrafugia’s
team found that simulation software from ANSYS wascritical in advancing them to the production prototype
stage with a high degree of confidence in their design.
The company credits ANSYS tools for helping to bring
their product to market so quickly. n

Windows®. Life without Walls ™.
HP recommends Windows 7.

Poweringwith theHP Z800 Workstation
with Intel® Xeon® processor

Visit www.hp.com/go/ansys for more information© 2010 Hewlett-Packard Development Company, L.P. The Intel Logo, Xeon and Xeon Inside are trademarks of Intel Corporationin the U.S. and/or other countries. Microsoft and Windows are U.S. registered trademarks of Microsoft Corporation.

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Scientific Equipment

Glass Jaw

5mm

Simulation helps to solve collimator jaw
design problem in the Large Hadron Collider.
By Alessandro Bertarelli and Alessandro Dallocchio, Engineering Department, Mechanicaland Material Engineering Group, European Organization for Nuclear Research, Geneva, Switzerland

The Large Hadron Collider (LHC) at the European
Organization for Nuclear Research (Conseil Européen pourla Recherche Nucléaire, or CERN) is the world’s largest
and most powerful particle accelerator. Two beams ofsubatomic particles called hadrons — either protons orlead ions — travel in opposite directions inside the circular
accelerator, gaining energy with every lap. The LHC’s
collimators scrape away particles that have gone slightly
off track to prevent damage to the highly sensitive superconducting magnets. But the collimators need to be able
to withstand an error that might pound them with asubstantial fraction of the beam itself — considering thatthe beam could melt almost 1 ton of copper.
When researchers tested the LHC collimator prototype
with several shots at different beam intensities, they
discovered that the carbon–carbon collimator jaw nicely
survived the impact, but its metal support suffered apermanent deflection strong enough to put the collimatorout of action. CERN staff immediately set to work to understand what caused the problem and how to correct it.

Cooling pipes – internalpressure 15 bar

3-D contact elementswith friction

Finite element model of collimator

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ANSYS Advantage • © 2011 Ansys, Inc.

SOLID45 – eightnode brick elementwith initial strain
to simulate spring
preload (three bar)

Collimator cross section

Sited near Geneva, Switzerland, the LHC is being
exploited by physicists to study the smallest known
particles that are building blocks of all things. The teamuses the LHC to re-create conditions that existed just after
the big bang by colliding two beams head-on at very highenergy. The target of study is the Higgs boson, a theorized
but not yet discovered particle that may help explain themasses of various subatomic particles. The LHC is being
used to seek out dark matter that is believed to make up
96 percent of the mass of the universe. Scientists hope todiscover the differences between matter and antimatterto help in understanding why so little antimatter is left inthe universe even though the big bang is believed to haveproduced equal amounts of matter and antimatter.
The LHC uses a stored energy of 360 MJ per beam.
This is two to three orders of magnitude above what otherproton colliders can handle. CERN’s collider is containedin an underground circular tunnel, 50 meters to 175 meters
deep, with a circumference of 27 kilometers. The collider
tunnel contains two adjacent parallel beam pipes, andeach contains a proton beam that rotates in opposite
directions around the ring. The parallel beams intersect
at four points, forcing them to collide with each other.
Some 1,232 dipole magnets keep the beams on a circularpath, and an additional 392 quadrupole magnets focus
the beams to increase the chances of collision at the intersection points.
The most critical elements of the collimators are thejaws, which are made of carbon–carbon composites,
which encircle the beam and are designed to block any
stray particles that separate from the beam. The jaws aresupported by a brazed sandwich structure encompassing
the main support bar, cooling pipes and interface plate.

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Scientific Equipment

The collimator is at risk when the beam is moved fromone accelerator line to another. This needs to be done
frequently because the beam is accelerated to maximum
energy by moving it through a family of progressively more
powerful accelerators. Kicker magnets are used to knock
the beam out of its current line and inject it into the newline. If there is an error in the configuration of the kicker
magnets, the beam might be steered too muchin one direction. The collimator must be able to withstand accidents such as an injection error that may cause
3 . 2 x 1 0 1 3 p r o t o n s e a c h w i t h a n e n e rg y o f
450 gigaelectronvolt (GeV) to hit the collimator jaws. An
electron volt is the kinetic energy gained by an electron
when it accelerates through an electric potential differenceof 1 volt.
To validate the collimator design, engineers carriedout tests using another CERN accelerator on a fully
operational prototype of a collimator. The jaws were
submitted to a series of impacts at 450 GeV over 7.2 μs.
Measurements performed on jaw assemblies and metal
supports revealed a permanent deformation of the metal
support of over 300 μm. CERN researchers theorizedthat this deformation was caused by thermally induced
vibrations due to very fast heating. Even when a structureis free to expand, when the heating process is faster than
the typical stress relaxation time, material inertia prevents
free thermal expansion, causing stress waves. Simulation
was clearly required to better understand the problem.
Applications with such great mechanical complexity
are typically addressed with explicit dynamics codes.
But for this case, explicit analysis would have been very
awkward because of the need for multiphysics integrationand the complexity of the mechanical structure, which
includes different materials that are held together withclamps. Since the stresses are well below the elastic
modulus, there are no shock waves that would require an
explicit dynamics code. The problem could instead be
simulated using an implicit finite element model following
the rules of thermo-elasticity. ANSYS Mechanical implicit

Flexural vibrations

Temperature distribution incollimator, where blue is minimum
and red is maximum

finite element analysis software provides multiphysics
capabilities that integrate thermal and mechanicalanalysis. In this application, integration increases
accuracy and reduces the amount of time required toprepare the model. ANSYS Mechanical also offers comprehensive element technology, an extensive library ofmaterial models, auto contact detection for assemblies
and powerful solver capabilities.
The collimation block was modeled as a rectangularbeam, simply supported at its edges. The energy distribution applied to the collimator was determined withFLUKA, a particle physics Monte Carlo simulation
package. The beam energy was introduced to the finite
element model in the form of a 3-D table using the HGEN
command. Transient thermal simulation was used to
calculate the temperature distribution as a function oftime. Researchers performed structural dynamic analysis
by applying the temperature distribution as nodal loading
at different time steps. Elastoplastic analysis used themultilinear kinematic hardening model for metallic
components. The team developed special algorithms toapply a temperature distribution changing in time andspace at different substeps of the elastic–plastic analysis.
The integration time step of 0.1 μs was based on the0

Transverse deformation (mm)

-0.05

-0.1

-0.15

-0.2

-0.25

-0.3

100

200
CFC assy

300

400

CFC assy

500

600

Graphite bloc

700

800

900

1000

Graphite support

Jaw Longitudinal Position (mm)Residual displacement based on physical measurements (left) and simulation (right) matched very closely.

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Scientific Equipment

2.50E-04
2.25E-04

Measured displacement - 1 batch

Measured displacement - 2 batches

FEM - scaled to 1 batch

1dof - no damping

1dof damped

2.00E-04

Displacement [m]1.75E-04
1.50E-04
1.25E-04
1.00e-04
7.50E-05
5.00E-05
2.50E-05
0.00E+00
0.E+00

1.E-02

2.E-02

3.E-02

4.E-02

5.E-02

6.E-02

7.E-02Time [s]Simulation predictions correlate well with measured deflection.

preliminary analytical estimation to avoid numerical
damping. Finally, static analysis evaluated residual plastic
deformation.
The simulation results included the temperature risethroughout the structure, making it possible to use simple
formulas to predict plasticization. The maximum stress iswell above the proportional limit of copper, so the thermal
shock generated plastic strains, as theorized by the
researchers. The largest residual plastic strains of 0.12
percent were seen in the 3–mm thick copper plate. Thesestrains are eccentric with respect to the neutral axisof the metal support, leading to permanent deflection
away from the beam axis.
The simulation results matched the physical measurements remarkably closely. When comparing the physicalmeasurements in the line graph to the simulation
predictions in the contour plot, the transverse residualDeflection was substantially reduced in the new design.

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ANSYS Advantage • © 2011 Ansys, Inc.

deflection of 350 μm predicted by the simulation closely
matched the measured value of 300 μm. The dynamic
response predicted by the simulation also correlated wellwith laser Doppler vibrometer measurements. The
simulations are higher in magnitude than the physicalmeasurements because the simulations do not takedamping into account.
The study team, made up of senior and juniorengineers, fellows, and students, used the validated
simulation model as the primary tool to solve the problem.
CERN researchers evaluated a number of different
geometries and materials to determine their impact on thesimulation of the support structure. Using these results,
they decided to modify the jaw assembly series design bychanging the thin plate material from OFE-copper to thehigher yield strength Glidcop® material. Glidcop is a familyof copper-based metal matrix composite alloys mixed
primarily with aluminum oxide ceramic particles. The
addition of small amounts of aluminum oxide greatly
increases the copper’s resistance to thermal softening
and enhances elevated temperature strength.
An updated model of the series jaw assembly
including Cu-Ni pipes and Glidcop support beam and thinplates showed that deflection was reduced from 300 μm
to 16 μm, which was within acceptable limits. This
figure was confirmed by experimental tests carried out
on a second prototype of the collimator. The use ofsimulation in this application made it possible to rapidly
diagnose the problem and develop an acceptable solution
while eliminating the need to build additional prototypes, excepting the final design. ANSYS Mechanicalsoftware played a key role by providing the full range ofphysics capabilities needed to accurately simulate a very
complex problem. n

www.ansys.com

ANALYSIS TOOLSDesigning Batteries
for Electric Vehicles
Numerical simulation can be used to accelerate battery
development and address safety concerns.
By Xiao Hu, Lead Engineer, ANSYS, Inc.
X=

Current density distribution
within a battery cell

Temperature distribution
within a battery cell

Temperature distributions from this fluid dynamics simulation were shown
to be in good agreement with those from the experimental measurement.

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ᵟ

n

ᵟ

ᵟ

Separator

Positive
Electrode

s

p

X=
L

Current Collector

0

Current Collector

The lithium-ion battery is a preferred candidate as asource of power for the hybrid electric vehicle (HEV) andelectric vehicle (EV) because of high energy density, highvoltage, low self-discharge rate and good stability. HEV
and EV applications require very large lithium-ion
batteries, but, during high power extraction required todrive a vehicle, these large batteries may experience asignificant temperature increase — which can lead tosafety concerns. A properly designed thermal management system is crucial to prevent overheating and unevenheating across a large battery pack, which can lead todegradation, mismatch in cell capacity and thermal
runaway. Design of the thermal management system
requires knowledge of the cooling system as well as theamount of heat that will be generated by cells within
the battery pack.
Simulation can assist in thermal design at both thecell level (a single battery cell) and the system level(a battery module or a complete battery pack).
On the battery cell level, the focus is on detailed heatgeneration and temperature distribution within a battery
cell — information mainly used by manufacturers andbattery researchers. Experimental data reveals that the

Negative
Electrode

Schematic of a lithium-ion cell sandwich consisting ofcomposite negative and positive electrodes and separator

rate of heat generation varies substantially with time overthe course of charging and discharging. Heat can begenerated from internal losses of Joule heating and local
electrode overpotentials, the entropy of cell reaction,
mixing and side reactions. When only the most important
effects of Joule heating and local electrode overpotentials
are considered, heat generation can be expressed byopen-circuit potential and the potential difference between
positive and negative electrodes. By using models to
predict potential and current density distribution on theelectrodes of a lithium-ion battery as a function ofdischarge time, the results can be used to calculate
the temperature distributions of the lithium-ion battery.
These temperature distributions can then be used within
ANSYS CFD software to examine the effect of theconfiguration of the electrodes — the aspect ratio of
the electrodes and the placing of current collecting tabs
— as well as the discharge rates on the thermal behaviorof the battery.
While this type of model provides detailed information
about temperature and current density distribution, it
requires experimental testing data as input. This modelcannot predict the impact of design changes on battery
thermal performance without conducting testing again.
However, a physics-based electrochemistry model canbe used to investigate the impact of battery design
parameters on battery performance. The electrochemistrymodel includes geometry parameters, properties and
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ANALYSIS TOOLS

temperature, with the last being the most important. A
physics-based model also can provide inputs that would
otherwise need experimental testing to obtain. The most
famous physics-based model originally was proposed byProfessor John Newman from UC Berkeley. Such a model
has been implemented in Simplorer software from ANSYS
to allow determination of many important factors inbattery design.
Charge and discharge cycle results can be plottedalong with concentration profiles during discharge. From
this information, optimization of the initial composition ofelectrolytes in the cell can be performed. When this study
was performed, results showed that a higher initialconcentration of electrolytes leads to as somewhat lower
conductivity in the separator but a much higher conductivity in the composite cathode, where this is extremely
important. By plotting concentration profiles under
different temperatures, battery designers can determine
when the limiting current occurs to specify the temperature range that the system needs to maintain to avoid
reaching limiting current. In addition, battery runtime is astrong function of temperature, and battery runtime islonger with higher operating temperature. This can be
confirmed from a physics-based electrochemistry model,
but it presents another problem: Higher temperaturebrings safety concerns, shortens battery life, and becomes
another optimization issue in battery design.
System-level design engineers working at the module
or pack level have a different set of requirements. Typically,
these engineers cannot afford to simulate as many details
as engineers working at the cell level can; they also have

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20

4.3

15

4.1

10
3.9
5
3.7
0
3.5

-5

3.3

-10

3.1

-15
-20

Cell Potential (V)A complete battery thermal–fluid dynamics analysis including
optimization can be done using ANSYS FLUENT software
entirely within the ANSYS Workbench environment.

Current (A)Fluid flow solution for the 16-battery cell module

2.9
0

1

2

3
Time (hr)45Battery charge and discharge cycle results from John
Newman’s electrochemistry model
2600

2400

2200
c(mol/m^3)

Automotive battery module with 16 cells

a different set of simulation goals. Engineers using
computational fluid dynamics (CFD) for battery thermal
management are interested in maintaining thedesired temperature range, reducing pressure drop andmaintaining temperature uniformity. Fluid dynamics has
been widely used to predict flow and heat transfer in manyindustries, and it applies well to battery thermal management. By using the ANSYS Workbench platform with
ANSYS CFD software, a complete battery thermal–fluid
dynamics analysis, including optimization, can be done
entirely within the same environment.
While fluid dynamics simulation can give detailed
thermal information about battery thermal management
systems, it is time consuming to perform many transient
simulations under different drive cycles. Model order
reduction techniques exist for extracting a model from
CFD results, and the extract model, called Foster networkmodel, gives the same solution as that from the full
CFD model — but it runs much faster compared with
CFD. The model order reduction process is handled automatically by Simplorer software using CFD results as
inputs. This process opens the door for simulations thatwould otherwise have been impractical, such as battery
thermal control system analysis.
For electrical engineers, the primary concern is theelectric performance of the battery rather than the thermal

2000
t = 1 min1800

t = 5 mint = 10 min

1600

t = 40 min
1400

0

0.2

0.4

0.6

0.8

1

X

Concentration profiles during galvanostatic discharge

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ANALYSIS TOOLS

performance. An accurate and yet simple-to-use thermal
model that couples with a battery electric circuit model isrequired. This can be accomplished through VHDL-AMS,
an IEEE ® standard hardware simulation language
supported by Simplorer.
Designing batteries for HEV/EV involves many
challenges, and designers focusing on different aspects
have different requirements. The benefit with ANSYS
software is that it offers models ranging from cell-level
electrochemistry to system-level thermal management. n

299

ANSYS FLUENT Cell 3

Cell Average Temperature (K)

ANSYS FLUENT Cell 10298

ANSYS FLUENT Cell 15
Foster Network Cell 3
Foster Network Cell 10297

Foster Network Cell 15296

Reference

295
0

500

1000

1500

Time (Sec)Comparison of CFD results with Foster network results. In
this case, the Foster network model took 20 seconds to run,
whereas traditional CFD required a few hours.

2000

Hu, X.; Lin, S.; Stanton, S.; Lian, W. A Novel Thermal Model for HEV/EV
Battery Modeling Based on CFD Calculation, Proceedings of IEEE Energy
Conversion Congress and Expo, Atlanta, U.S.A., September 12–16, 2010.
Hu, X.; Lin, S.; Stanton, S.; Lian, W. A State Space Thermal Model for HEV/EV Battery Modeling. SAE, 2011, 01-1364.
Fuller, T.F.; Doyle, M.; Newman, J. Simulation and Optimization of the Dual
Lithium Ion Insertion Cell. Journal of Electrochem. Soc., 1994, vol. 141,
pp. 1–110.

5RYITNOW

7ISITWWWMICROSOFTCOMHPC

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Academic

Image © iStockphoto.com/kamil.

Microbubbles Keep
Green Energy Blooming
Algae-derived biofuel production getsa strong pulse from flow simulation.
By William B. Zimmerman, Professor of Chemical and Biological Engineering, University of Sheffield, U.K.

The world faces a shortage of fossil
To reduce these costs, one of thefuels; at the same time, efforts aredeveloping applications being studbeing made to reduce carbon dioxide
ied is to introduce CO2-rich fossil fuel(CO2) emissions. Production of bioexhaust gases into the bioreactor as
fuels from certain kinds of algae offers
microbubbles. Among the groups
a potential solution to both problems.
tackling this problem is a research
Like other photosynthetic organisms, Left: A conventional gas diffuser produces
team from the chemical engineering
relatively large bubbles. Right: A gas diffuseralgae capture CO2 and sunlight and with the fluid oscillator shows tremendous
department at the University ofthen convert the elements to oxygen reduction in bubble size.
Sheffield in the U.K. The groupand the energy required for their life
has made a major step forward incycles. The algae biomass itself includes proteins, carbo- reducing the cost of producing algal biofuels fromhydrates and fatty acids. It is these fatty acids — or natural exhaust gases by creating microbubbles that are aboutoils — that offer great potential as a renewable feedstock 20 microns in diameter. The major advantage of smaller
for refineries, where they can be transformed into fuel to bubbles is that they have a higher surface-area-topower gasoline or diesel engines.
volume ratio. Because these 20 micron microbubbles are
However, most processes to manufacture algae- 50 times smaller than conventional 1 millimeter finederived biofuels are too expensive to be commercially bubbles, they have a correspondingly higher surface area
viable — since the lowest production prices in the United per unit volume. Thus, they provide 50 times greater mass
States are currently in the $20 to $30 per gallon range. transfer rates, which could potentially translate into a
Two of the largest costs involved are the capital expenses tenfold higher yield in algal biofuel production [1].
required to build the bioreactors (producing algae on a
The key to producing bubbles this small is a uniquelarge scale) and the energy required to operate them.
oscillator design developed using ANSYS FLUENT fluid
dynamics software (part of a fluid dynamics Academic
Product bundle from ANSYS) to evaluate the initialdesign concept and perform parametric studies to selectFlow from high-pressure port X2
to low-pressure port X1
appropriate nozzle width and flow rates that provide theright operating conditions. The traditional method of
producing bubbles for reactors has been to force the gas
Air supply

Growing
small airbubbles

Bubblesblown
away

Feedback
loop
Fluidic
diverter
amplifierA

B

Air bleeding
to atmosphere
Water tank

Cover plateshowntransparent

Schematic representation of fluidic oscillator

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ANSYS Advantage • © 2011 Ansys, Inc.

Air flow pulse

Water
flow
pulse

Fluid oscillator driving microbubble generation system

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Academic

Top view of velocity magnitude contours on midplane of oscillator
showing supplied gas flow directed into the upper outlet channel andno flow through the two teardrop-shaped control nozzles

under pressure through a lattice of very small holes. When
finally separated from the nozzle, however, the bubble hasa diameter that is often many times larger than the hole,because its separation is controlled by the surface tensionof the water. The key idea of the Sheffield team’s microbubble generation method was to limit bubble growthtime using an oscillator to supply gas to the bubble
formation holes. Growth is terminated at the end of eachpulse of gas. Bubbles are then removed from the nozzle
using a pulse of water that alternates with the gas pulses
so the growth of new bubbles can begin in the next oscillation period. Using this method, no bubble can reach thesize typically formed from steady gas blowing.
In the oscillator design [2,3], steady gas flow is
supplied to the oscillator inlet. The main flow stream thenexits one of the two outlet channels depending on theaction applied by flow through a bidirectional control loop.
The flow exits through the upper outlet channel, causing
a decrease in pressure at the upper control nozzle. The
upper control nozzle then draws gas through the feedback
loop from the lower control nozzle, where the pressure ishigher. After the flow in the feedback loop tube gains
momentum, the flow exiting the upper control nozzle
causes the main jet — because of inducement (or amplification) effects — to divert to the lower outlet channel.
After a delay needed for the feedback flow to gain
momentum in the opposite direction, the main jet isdiverted back to the upper outlet, resulting in a periodic
switching process. The frequency of the oscillation iscontrolled primarily by the length of the feedback loop
and the gas supply flow rate.
Sheffield’s team performed simulation of an early
oscillator prototype with ANSYS tools. Researchers
meshed the 3-D geometry with 70,000 tetrahedral cells
and used the renormalization group (RNG) k-ε model tohandle turbulence at lower Reynolds numbers. The results
matched the performance parameters of the early prototype, so the team proceeded to use fluid flow simulation to
improve the device’s controllability, including virtual testingof geometries with different control nozzle widths anddifferent input flow boundary conditions. The researchers
compared the results for the different design alternatives
and then selected the final proof-of-concept design.
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With the noted 50-fold increase in mass transfer rateafforded by this oscillator design, CO2 dispersal is accelerated in the bioreactor and should enhance algae growthrate by a factor of 10. The microbubbles more efficiently
strip the oxygen that algae emit during photosynthesis,
thus permitting much higher algae densities in the water.
These improvements should substantially reduce thecapital and operating expense required for a given volume
of biofuel production, as more algae can be grown more
quickly in a smaller amount of space.
Beyond displaying a supercharged growth rate,
the experiments showed an 18 percent reduction in theenergy required for bubble production compared toconventional fine bubbles. Researchers attribute this,in part, to reduced friction losses due to the oscillatory
flow. The team thus predicts that engineering simulation
will play an even greater role in the coming design of acommercial-scale bioreactor, when it will become criticalto optimize all components of the design to minimize
capital expenses. n

References[1] Zimmerman, W.B.; Zandi, M.; Bandalusena, H.C.H.; Tesař, V.; Gilmour,
D.J.; Ying, K. Pilot Scales Studies of Microbubble Mediated Airlift Loop
Bioreactor Growth of Microalgae Dunaliella Salina. Applied Energy,
submitted in 2010.
[2] Tesař, V.; Bandalusena, H.C.H. Bistable Diverter Valve in Microfluidics.
Experiments in Fluids, 2010; doi:10.1007/s00348-010-0983-0.
[3] Zimmerman, W.B.; Hewakandamby, B.N.; Tesař, V.; Bandulasena,
H.C.H.; Omotowa, O.A. On the Design and Simulation of an Airlift
Loop Bioreactor with Microbubble Generation by Fluidic Oscillation.
Food and Bioproducts Processing, 2009; Vol. 97, No. 3, pp. 215–227.

3-D view of gas flow pathlines released from a plane cut in the z
direction and colored by velocity magnitude. The cusped splinter noseat the junction of the outlet channels generates an internal feedback
loop that initially helps to direct the main flow to the upper outlet.

Pathlines later in the cycle showing that flow through the control nozzles
has overcome internal feedback and the main jet is being switched to
the lower outlet channel

ANSYS Advantage • Volume V, Issue 1, 201145

Academic

Reforming a Fuel Cell
Modeling Process
Coupling flow simulation with complex chemistry tools bringsa united front to analyzing leading-edge energy systems.
By Robert J. Kee, George R. Brown Distinguished Professor of Engineering, Colorado School of Mines, Golden, U.S.A.

Since its basic principle was first
demonstrated in the early nineteenth
century, fuel cell technology has
evolved into many different variations.
The underlying mechanism common
to all fuel cells is conversion ofchemical energy into electricity bymeans of reforming the fuel intohydrogen along with the subsequent
electrochemical oxidation of hydrogen
into water. Depending on the type offuel cell and its application, the fuelcan be lighter hydrocarbons — suchas natural gas, propane or methanol— or heavier liquids, like diesel orjet fuel. The key advantages of fuelcells over systems that burn fossil
fuels include fewer moving parts andoverall reduced pollutant emissions.
Some of the challenges in developing
fuel cells for more widespread use
are the high cost of catalyst or otherfabrication materials, the difficulty ofhydrogen storage, and very complex
chemistry.
Solid oxide fuel cells (SOFCs) inparticular have been the subject ofmuch research in recent decades:
They have the ability to reform manydifferent fuels, and their high operating

Reforming agent

temperatures offer theside benefit of using theexhausted heat energy.
With support from the
U.S. Office of Naval
Research, a team fromthe Colorado School of
850˚C
Mines (CSM) has beenworking with ANSYS
simulation tools to
750˚Cmodel the chemistry,Tube-wall temperature andelectrochemistry andOuter shell temperatureshell-interior streamlines
fluid mechanics of an
Results from the simulation of a shell-and-tube reformer
SOFC stack system. simulation for the catalytic partial oxidation of propane in which
Such a system — for an ANSYS FLUENT model of three-dimensional fluid flow andtransfer is coupled with a CHEMKIN-based plug-flow
example, an auxiliary heatmodel. Shown are the temperatures on the outside ofpower unit (APU) used containment shell (left) and catalyst tubes (right).
by a Navy vessel —the air flow surrounding the tubes iscommonly comprises ashell-and-tube design that includes c o m p l e x b u t d o e s n o t i n v o l v einter nal or exter nal reformers, chemical complexity.
To evaluate the full anodedepending on the fuel. The endothermic steam-reforming operation supported SOFC stack configuration
is supported by circulating the — in which the anode side is thee x h a u s t e d h e a t e n e r g y f r o m tube side and the cathode side isthe exothermic electrochemical the shell side — the CSM teamoxidation within the fuel cell. The needed to couple the complex
complex catalytic chemistry is chemistry with the three-dimensional
confined within the tubes, while the fluid mechanics. On the shell side,
three-dimensional fluid mechanics of researchers considered the fluid flow

Waste heat inlet
Catalyst tubes

Fuel
vapor
Reformate

Swirl mixer

Conceptual configuration for a shell-and-tube reformer

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ANSYS Advantage • © 2011 Ansys, Inc.

Waste heat exhaust

Configuration of a 36-tube
anode-supported SOFC stack

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Academic

CathodeElectrolyte
Anode

Tube temperature

Illustration of a single anode-supported SOFC tube. Electric
current is generated on the inside of the tubes (anode) andis discharged on the tube exterior (cathode). The balloon
shows the essential microscale electrochemical phenomenain the porous composite electrode layers.

and heat transfer, including thermalradiation among all tubes and thecontainment shell. For this task, theteam chose ANSYS FLUENT software(a component of the ANSYS
Academic Research CFD productbundle) to model the complex, butnonreactive, fluid mechanics.
On the tube interior (anode) side,
the simulation of chemical kinetics forreforming practical military logistics
fuels — diesel in the case of an
APU — demands hundreds of surface
reactions and thousands of gasphase reactions. One-dimensional
chemistry tools such as CHEMKIN™
or CANTERA can handle the reaction
kinetics and charge transfer, as
long as the fluid mechanics can bemodeled simply. In this case, the fuelreforming chemistry and chargetransfer electrochemistry inside thetubes are all complex, but the fuelflow is indeed simple enough and canbe modeled in a one-dimensional tool
as a plug flow.
The next aspect of the overallsimulation process was coordinating
the iterative coupling of the cathodeside flow simulation model withthe anode-side chemistrymodel. Using the ANSYSF LUE N T u s e r- d e f i n e d

21.0%

530˚C

13.5%

Results from modeling a 66-tube SOFC stack operating on a
mixture of hydrogen, carbon monoxide and methane, which
are produced by reforming hexadecane. The right-side tubes
show gas-phase oxygen mole fraction on the tube surfaces.
The left-side tubes show tube surface temperatures.

function (UDF) capability, the researchteam automated the process of
averaging three-dimensionaltemperature and oxygen mole fraction
data from the shell-side simulationand mapping it onto the tube-side
band mesh. Additionally, the UDF
directed the tube-side chemistrymodel to supply heat flux and oxygenmass flux boundary conditions fromeach of the tubes back to the shellside fluid flow model.
Results of the simulations
revealed that there can be significant
temperature variations between
different tubes. The tubes weregenerally cooler at the bottom, which
was caused by a combination ofinternal fuel reforming and heattransfer to the shell-side air. Because
the outer tubes acted as radiation
shields, the inner tubes generally
operated at higher temperatures.
Shell-side air was introduced frombelow and exhausted at the top, and,
therefore, the oxygen decreased frombottom to top.
When designing and optimizing
a tubular SOFC stack,simulation is
extraordinarily
valuableA three-dimensional ANSYS FLUENT face mesh on an SOFC tube with an overlying
one-dimensional band meshwww.ansys.com

O2 mole fraction

810˚C

because it is important for all tubes todeliver similar performance. The
coupled model is useful forinvestigating the effects of design
considerations, such as tube packing
and air-flow alternatives, on theoverall performance of the stack.
However, since the tube interior
geometries were not fully resolved inthis research due to their complex
microstructure, extensions to thework under consideration include
performing detailed three-dimensional
ANSYS FLUENT simulations of thecharge transport through the porous
anode material on representative tube
volume sections to calculate theeffective electrical conductivity. This
microscale-effective conductivity
could then be used as an input for thefull one-dimensional tube-side
simulation to further improve transportmodeling inside the tubes, thus
enabling the beginnings of amultiscale analysis.
The capability to couple complex
flow and heat transfer using flow
simulation with complex chemistry
and electrochemistry is a powerful
new tool for certain classes ofreacting-flow problems. The CSM
team developed the ANSYS FLUENTUDFs to be sufficiently general so thata range of one-dimensional chemistry
tools could be incorporated. Thisa p p ro a c h t o m o d e l i n g t u b u l a r
configurations can be useful beyond
fuel cells, as it is directly applicable togeometrically related layouts, such asbattery packs, nuclear fuel rods orcracking furnaces. n

ANSYS Advantage • Volume V, Issue 1, 201147

Tips and Tricks

Accelerating
CFD Solutions
Several recent enhancements in ANSYS FLUENT solver capabilities
accelerate convergence and reduce solution time.
By Mark Keating, Principal Engineer, ANSYS, Inc.

Many solver performance enhancements have been
introduced to ANSYS FLUENT fluid dynamics software
over the past few releases. These capabilities candramatically improve the speed and reliability of simulation.
Running the solver out of the box does not always
guarantee optimum solver settings for any particularapplication. So by understanding and using solver technology appropriately, a user can obtain faster results andbetter convergence.

Pressure-Based Coupled Solver
The pressure-based coupled solver (PBCS) was
introduced in 2006, and its usage is growing. This solver
reduces the time to overall convergence, by as much as
five times, by solving momentum and pressure-based
continuity equations in a coupled manner. Though there isa slight increase in associated memory requirements forusing this solver, its benefits far outweigh the drawbacks.
The PBCS is becoming the solver of choice for subsonicapplications. When using it, re-ordering the grid is always
advisable. The default explicit under-relaxation factors( UR F s ) f o r p re s s u re a n d v e l o c i t y o f 0 . 7 5 a regenerally robust values, but they should be reduced for
skewed meshes, when oscillatory convergence isexperienced, or when higher-order discretization is
employed to about 0.4 to 0.5. Taking the turbulence URF
up to 0.95 to 0.99 can help to accelerate viscous cases.
To access the PBCS, change the p–v coupling in thedrop-down list from SIMPLE to Coupled.

Pseudo-Transient Method
The pseudo-transient solution method, introduced inversion 13.0, is a form of implicit under-relaxation forsteady-state cases. It allows users to obtain solutions
faster and more robustly than previous versions of ANSYSFLUENT software, especially for highly anisotropic
meshes, when using PBCS and density-based (DBNS)
implicit solvers. This method uses a pseudo-transienttime-stepping approach. In general, the time per iterationis slightly higher, but in some extreme cases the numberof iterations required for convergence using this method
has dropped by an order of magnitude or more. Usually,
overall speedups of 30 percent to 50 percent can be
expected. Cases with multiple reference frame (MRF)
zones should benefit from using this method. The tableshows the levels of improvement possible.
Courant numberbased coupled(iterations)Pseudo-transient
coupled (iterations)

Backward facing step
(turbulent: SST)

750

75

Film cooling benchmark(turbulent: SA)

2,300

1,350

Flat plate, SST transition
model

1,200

100

Rotor/stator with mixing
plane model

500

250

Cases

Centrifugal pump

220

50

Axial compressor stage

400

110

Solver speedups achieved using the pseudo-transient coupled solver
in ANSYS FLUENT 13.0

48

ANSYS Advantage • © 2011 Ansys, Inc.

www.ansys.com

Tips and Tricks

1e+02

1e+02

Residuals
continuity
x-velocity
y-velocity
z-velocity
energy1e+00

Residuals
continuity
x-velocity
y-velocity
z-velocity
energy

1e+00

1e-02

1e-02
1e-04
1e-04
1e-06
1e-06
1e-08

1e-08

1e-10

1e-10

1e-12
0

50

100

150

200

250

300

Standard Initialization, 279 Iterations

0

20

40

60

80

100

120

Hybrid Initialization, 102 Iterations

Residual plots for a heat exchanger case show the effect on iteration count of hybrid initialization. This case does not converge withdefault under-relaxation and standard initialization, but it converges well with the default under-relaxation factors. It is more than
two times faster when hybrid initialization is used.

Initialization Methods
Providing an initial data field that is close to the final
solution for steady-state cases means the solver has to
do less work to reach the converged result. Therefore, thisreduces simulation time. Typically, many users employ
standard initialization; some use patching for localized
control, especially for moving domains or multiphaseanalyses. Interpolation files are used to initialize cases,
and the interpolation workflows available in ANSYSFLUENT have been improved in recent releases. There arealso other initialization techniques for further accelerating
the simulation convergence.
Full multigrid initialization (FMG), introduced in 2006,
provides the initial and approximate solution at a minimum
cost to overall computational expense. The feature isaccessed via the text user interface (TUI) and re-orders thegrid as part of the process. The commands are:Solve>initialize>set-fmg-initialization
Solve>initialize>fmg-i
The overall initialization time using this approach ismuch longer than that using standard initialization byzone, but it allows a much quicker solve. FMG solves
Euler equations and is available for single-phase flows
only. FMG initialization provides the best-guess initialwww.ansys.com

solution. It is particularly suited to turbomachinery as well
as external and compressible flow problems.
Hybrid solution initialization was introduced atversion 13.0 and uses a collection of recipes and boundaryinterpolation methods to efficiently initialize the solution
based purely on simulation setup — so the user does notneed to provide additional inputs for initialization. The
method can be applied to flows ranging from subsonic tosupersonic. It is the recommended method when using
PBCS and DBNS for steady-state cases in ANSYS FLUENT
13.0. This initialization may improve the convergencerobustness for many cases. Unlike FMG, this initialization
method can be used for multiphase flows.

Summary
A number of solver settings are available to aid
solution acceleration and convergence within the ANSYSFLUENT solver. Individually, these techniques can beused to reduce solution times; combined, they offer evengreater capability. For example, in some cases, movingfrom a segregated solver to PBCS with FMG andthe pseudo-transient method has resulted in up
to 100 times speedup. These features show the benefitsof investigating and taking advantage of newsolver technologies. n

ANSYS Advantage • Volume V, Issue 1, 201149

ANSYS, Inc.
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