Sam Lee

ASRC Aerospace, Inc., Cleveland, Ohio 

Andy P. Broeren and Harold E. Addy, Jr.

Glenn Research Center, Cleveland, Ohio

Robert Sills

Sierra Lobo, Inc., Fremont, Ohio

Ellen M. Pifer

St. Louis University, St. Louis, Missouri 

Development of 3–D Ice Accretion 

Measurement Method

NASA/TM—2012-217702

September 2012

AIAA–2012–2938
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Sam Lee

ASRC Aerospace, Inc., Cleveland, Ohio 

Andy P. Broeren and Harold E. Addy, Jr.

Glenn Research Center, Cleveland, Ohio

Robert Sills

Sierra Lobo, Inc., Fremont, Ohio

Ellen M. Pifer

St. Louis University, St. Louis, Missouri 

Development of 3–D Ice Accretion 

Measurement Method

NASA/TM—2012-217702

September 2012

AIAA–2012–2938

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

Atmospheric and Space Environments Conference

sponsored by the American Institute of Aeronautics and Astronautics

New Orleans, Louisiana, June 25–28, 2012
Acknowledgments

This work was funded by NASA Atmospheric Environment Safety Technologies Project and Aeronautics Test Program. The 

authors would like to thank the Icing Research Tunnel personnel and Dr. Judith VanZante. The authors would also like to thank 

the representatives from the scanner manufacturers for demonstrating their systems in the IRT and assisting in the post-processing 

of the data.

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either expressed or implied, by the National Aeronautics and 

Space Administration.

Level of Review: This material has been technically reviewed by technical management. 
NASA/TM—2012-217702 1

Development of 3–D Ice Accretion Measurement Method

Sam Lee

ASRC Aerospace, Inc.

Cleveland, Ohio 44135

Andy P. Broeren and Harold E. Addy, Jr.

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Robert Sills

Sierra Lobo, Inc.

Fremont, Ohio 43420

Ellen M. Pifer

St. Louis University

St. Louis, Missouri 63103

Abstract

A research plan is currently being implemented by NASA to develop and validate the use of a 

commercial laser scanner to record and archive fully three-dimensional (3–D) ice shapes from an icing 

wind tunnel. The plan focused specifically upon measuring ice accreted in the NASA Icing Research 

Tunnel (IRT). The plan was divided into two phases. The first phase was the identification and selection 

of the laser scanning system and the post-processing software to purchase and develop further. The 

second phase was the implementation and validation of the selected system through a series of icing and 

aerodynamic tests. Phase I of the research plan has been completed. It consisted of evaluating several 

scanning hardware and software systems against an established selection criteria through demonstrations 

in the IRT. The results of Phase I showed that all of the scanning systems that were evaluated were 

equally capable of scanning ice shapes. The factors that differentiated the scanners were ease of use and 

the ability to operate in a wide range of IRT environmental conditions.

Nomenclature

AEST Atmospheric Environment Safety Technologies

IRT Icing Research Tunnel

LWC liquid water content

MVD median volumetric diameter

RPM rapid prototype method

SLA stereolithography

SLD supercooled large droplet

SLS selective laser sintering

T0 total air temperature

t icing spray time

V tunnel airspeed

α angle of attack
NASA/TM—2012-217702 2

Introduction

Icing wind tunnels are designed to simulate in-flight icing environments and will ideally conform to 

published standards for calibration and operations to ensure confidence in the environmental simulation 

(Ref. 1). The chief product of such facilities is the ice accretion that forms on various test articles ranging 

from airplane wings, rotor blades, engine inlets, radomes and other flight hardware. Bosetti et al. (Ref. 2)

discuss many important aspects of documenting ice-shape characterization. They point out that ice shapes 

are often used by other groups for follow-on computational or experimental aerodynamic studies; 

development of design criteria or requirements; engineering tool development, improvement or 

validation. Broeren et al. (Ref. 3) describe a recently completed comprehensive study of aerodynamic 

simulation of ice accretion. For some cases such as initial roughness, spanwise ridges and streamwise 

type ice shapes, the fine geometric details can be important to the resulting aerodynamics. Therefore, 

documentation of the resulting ice accretion is a key piece of data in icing-wind-tunnel tests.

There are a number of currently used options for documenting ice accretion in icing-wind-tunnel testing.

They range from simple photographs and pencil tracings to mold and casting methods. Quantitative 

photography has also been used in order to extract roughness size characteristics (Refs. 4 and 5). While all 

of these methods provide useful data, there are many limitations. Simple photographs provide excellent 

qualitative information, but very little quantitative data about the ice accretion. Quantitative photography 

requires multiple cameras or camera locations, calibration against static grids and labor intensive data 

reduction (Ref. 6). It may not be appropriate for very complex ice accretion with areas obscured from 

optical access. The most commonly used method is most likely the cross-sectional tracing. Depicted in 

Figure 1, this method requires melting a section of the ice accretion, inserting a cardboard template and 

tracing around the shape. While this method is relatively fast and simple, there can be variations in the 

results due to individual tracing techniques and subsequent digitization procedure. The latter task can also 

require significant post-test time, particularly for large and complex ice cross-sections. The tracing is only a 

two-dimensional (2–D) piece of information. Many ice accretions, particularly on swept wings are highly 

three-dimensional (3–D). Bosetti et al. (Ref. 2) show a comparison of three tracings of the same ice shape 

along the span of a wing. There were significant differences in these tracings. The best current technology 

for capturing 3–D features of ice accretion is the mold and casting method. This has been used for many 

years, but was improved at NASA Glenn (then Lewis) during the 1980s using more robust materials to 

improve accuracy and durability (Ref. 7). The main disadvantages to the mold and casting method are that it 

can be time consuming both during the icing test and in post-test production. Cost can be significant in some 

cases, but generally scales with the size of the ice accretion to be documented. What is needed is a method 

to accurately and efficiently digitize ice accretion in 3–D.

Figure 1.—Cross-sectional ice shape tracing method.
NASA/TM—2012-217702 3

Laser-based and other optical scanning methods have been investigated to accomplish 3–D

digitization of ice accretion. In 2004, Broughton (Ref. 8) provided background information and a 

corresponding research plan to develop a stationary bench-top laser scanning system at the NASA Glenn 

Research Center. While the system was successfully used to digitize ice accretion, there were a number of 

challenges associated with processing the raw point cloud data into a closed surface that accurately 

represented the ice accretion. A closed surface is critical for most follow-on work such as computational 

or experimental aerodynamic analysis. More recent research in other fields indicated that new advances in 

scanning technology and data processing software were improved such that complex 3–D point cloud data 

could be acquired and closed to form a “water tight” surface (Refs. 9 and 10). Given these advances in 

technology and the motivations described earlier in this Introduction, NASA has incorporated 

development of 3–D ice accretion digitization methods into its current research plans.

The main goal of the Airframe Icing Technical Challenge of the Atmospheric Environment Safety 

Technologies Project of the NASA Aviation Safety Program is to achieve acceptance of experimental and 

computational icing simulation tools in supercooled large droplet (SLD) conditions and on 3–D airframe 

components, including swept wings (Refs. 11 and 12). Since this goal focuses on 3–D geometries, it is 

necessary to develop a suitable means of recording and archiving fully 3–D descriptions of experimental 

ice accretion geometry. A research plan to develop this capability was introduced in the NASA Aviation 

Safety Program Technical Meeting in 2011 (Ref. 13). This research was divided into two phases. The first 

phase considered selection of a laser-scanning system and software. This has been completed and the 

results are described in this paper. The second phase considers validation of the system and a declaration 

of capability for use in future icing experiments. These plans are discussed at the end of this paper.

The roadmap of Phase I is shown in Figure 2. There are numerous commercial 3–D laser scanning 

systems and post-processing software available in the market. It was necessary to develop a process to 

identify the most appropriate system for purchase and further development. Parallel plans were developed 

to identify the most suitable scanner hardware and software systems.

The first task in the plan was to define the criteria that was used to evaluate the scanner hardware and 

software. The hardware selection criteria were based on the ability of the scanner to operate in the IRT 

test section environment under a wide range of temperature and humidity conditions and the ability of the 

system to scan various types of ice accretion. The software selection criteria were based on the 

compatibility of the software with various scanners that were evaluated and the ability to process the data 

effectively and efficiently. The selection criteria (for both hardware and software) will be described in 

more detail later in the paper.

Figure 2.—Phase I of the research plan.

Define 

Scanner 

Selection 

Criteria

Evaluate 

Scanners 

in IRT

Define 

Software 

Selection 

Criteria

DownSelect & 

Purchase 

Scanner

Evaluate 

Software

DownSelect & 

Purchase 

Software

RPM 

Assessment

Phase I Roadmap

Evaluate 

Scan 

Data

Level 3 Milestone:

Selection of Scanner and Software
NASA/TM—2012-217702 4

Several scanner manufacturers were invited to demonstrate the capability of their scanners in the IRT 

test section environment. The evaluation procedure was established such that the scanners were judged 

against the selection criteria. Identical evaluation processes were used when possible, and the scanners 

were operated in a wide range of tunnel conditions and scanned a wide range of ice shape types. The 

results of the IRT scanner evaluation and software evaluation were used to identify the most capable and 

promising system for further development.

Also included in Phase I was the assessment of the current rapid prototype methods that will be used 

to manufacture the ice shapes from the 3–D scan data. Scan data of ice accretion were processed into 

water-tight surfaces, and various RPM (rapid-prototype model) methods were evaluated to ascertain the 

current state of capability to manufacture artificial ice shapes. The outcome of the Phase I of the research 

plan was the selection of the scanner and software to purchase for further development. The results of the 

Phase I of the research plan is detailed in this paper.

Software and Scanner Selection Methodology

The methodology used in Phase I of the research plan to evaluate and down-select a scanner hardware 

system and scanning software is detailed in this section.

Scanner and Software Selection Criteria

In order to evaluate the candidate scanner hardware and software systems, a list of selection criteria

and questions were developed. The software selection criteria consisted of four main categories: Scanner 

Compatibility, Water-Tight Modeling Ability, Noise Filtering, and Efficiency. The Scanner Compatibility 

category was used to determine if the software was compatible with the scanning hardware being 

evaluated. Depending on the ice shapes, the scan data were expected to have significant amounts of the 

gaps and holes. The ability of the software to fill and close these gaps and holes was evaluated using the 

Water-Tight Modeling category. The Noise Filtering category was used to evaluate how well the software 

filters and removes noise in the raw scan data without altering the important features of the ice shape.

Finally, the Efficiency category was used to assess the speed, processing time, and ease of use of the 

software.

Selection Criteria—Software

1. Scanner compatibility—The software was expected to be purchased prior to scanner selection and be 

used to evaluate the candidate scanners. Can the software work with scanners being considered?

2. Water-tight modeling ability—The software will be used to convert the point clouds generated by the 

scanner into a water-tight surface model. How effective is the software in minimizing gaps and holes?

How well does the scanner fill in these gaps and holes? How much user intervention is required in 

filling these holes?

3. Noise filtering—There is expected to be some noise in the raw point cloud (i.e., outliers). How well 

does the software remove these points without making the surface too smooth or round?

4. Efficiency (ease of use, speed, processing time, large file capability)—The process of converting the 

scanned point cloud into a water-tight model should be automated as much as possible with the least 

amount of user intervention. The software should also be easy to use. How much computer processing 

time does it take to convert a point cloud into a water-tight surface? What is the largest, most detailed 

ice shape the software can render?

5. Cost versus capability (final judgment call)—Capabilities of the software will drive the selection.

However, if one system costs substantially less with minimal reduction in capability, this may be 

considered.
NASA/TM—2012-217702 5

The hardware selection criteria consisted of two main categories: IRT Test Section Capability and 

Scanning Capability. As part of the IRT Test Section Capability category the usability of the scanner was 

assessed. The scanner should be easily set up and operated by a single user, with a minimal amount of 

pre-scanning preparatory work. The Scanning Capability category was used to evaluate the ability of the 

system to scan the ice accretion. Some of the criteria in this category were the scan resolution, scan speed, 

ability to scan gaps and holes, and accuracy.

Selection Criteria—Hardware

1. IRT test section capability

a. Environment—The temperature ranges from 32 °F down to 0 °F. The relative humidity can range 

up to 100 percent. This environment must not affect accuracy and durability of the scanner.

b. Usability

(1) Portability—It is expected that the scanner will be moved into test section for the scans and 

removed from the test section during the icing runs. It should be portable enough for one 

person to easily move in and out of the test section.

(2) Ease of Use—The scanner should easily be operated by one user. The scanner should be 

aimed at the target easily by the operator. There should be a real-time feedback of scanning 

status through a computer display.

(3) Convenience of Measurement Procedure—How difficult and time consuming is the scanner 

setup procedure. Does the scanner need reference points on the model/ice accretion? Are 

reference hard points needed? Are any special steps needed in the set-up process? Does it 

require painting the ice?

2. Scanning Capability

a. Scan resolution

b. Scan speed (per resolution and volume)—How long does the scanner take to scan a given volume 

at a given resolution? A typical scan volume is expected to be 24- by 12- by 12-in.

c. Ability to scan gaps and holes—A significant issue with laser-based scanner is its ability to “see” 

into gaps and holes. How far into gaps and holes can the scanner measure? This will be function 

of gap/hole size and may be depended on type of ice.

d. Accuracy—How accurate is the scanner? This may be tested on metal calibration block and metal 

“roughness sample” block of known dimensions.

3. Cost versus capability (final judgment call)—Capabilities of the scanner will be the most important 

criteria in the selection. However, if one system costs substantially less with minimal reduction in 

capability, this may be considered.

Software Evaluation

During the formulation of the research plan, a thorough evaluation and comparison of several 

commercially available 3–D scanning software was planned. However, during the early stage of Phase I

of the plan, it was clear that this was not possible due to cost and time constraints. This would have 

required purchasing and becoming proficient on all of the software that were being considered. Instead, 

two most widely used software packages were considered, Geomagic and Polyworks. Both of these 

software packages were found to be compatible with all of the scanners that were being evaluated. The 

most critical factor in the software selection criteria was the ability to create water-tight surfaces from the 

scan data. Conversations with the scanner factory representatives during the scanner demonstrations 

indicated that Geomagic is better able to process and generate water-tight surfaces of “organic” shapes 

typical of ice accretion shapes. Also, the Facilities Division at NASA Glenn currently uses Geomagic, and 

could provide expertise and on-site support. This led to the decision to select Geomagic as the scanning 

software. 
NASA/TM—2012-217702 6

Hardware Evaluation

IRT Test Procedure

All of the scanners were evaluated in NASA Glenn Icing Research Tunnel. The IRT is capable of 

generating a wide range of icing conditions in both Appendix C and SLD. It has a test section that 

measures 9- by 6-ft with speeds of up to 390 mph. The tunnel can be operated at temperatures between 

–20 and 33 °F. A turntable mounted on the floor of the test section was used to set the airfoil model angle 

of attack. A more detailed description of the facility can be found in the IRT user manual (Ref. 14).

For the scanner evaluation, three straight wing ice shapes identified in the ice shape characterization 

study by Bragg, et al. (Ref. 15) were chosen: horn ice, roughness, and streamwise ice. A swept wing 

scallop ice shape was also chosen. A full-span 21-in. chord NACA 0012 model was used for the three 

straight wing ice shapes and a 36-in. chord semi span NACA 0012 model set to 45° sweep was used for 

the scallop ice shape. Three scanning systems were evaluated in spring 2011 using these ice shapes.

The IRT scanner evaluation procedure consisted of the following six steps:

1. Accrete ice on the test article

2. Photograph the ice

3. Spray the ice with white paint

4. Install and set up the scanner

5. Scan the ice shape

6. Make hand tracings of the ice shape

The first step was to accrete the ice on the test article using predetermined tunnel and spray setting. The 

icing conditions used for the scanner evaluation are shown in Table 1. After the ice was accreted, it was 

photographed to visually document the features for later comparisons to the scan data. A critical step in the 

scanning procedure was the treatment of the ice surface. Due to the transparent and refractive nature of ice, 

the accreted shape must be coated with highly-reflective, diffuse, white paint. Painting the ice allows the 

scanner optics to pick out the surface features of the accreted shape. Even ice that appear to be fully opaque 

(such as rime ice) could not be scanned unless the surface was treated. A titanium-dioxide based paint was 

developed for this purpose and was applied using an airbrush, as shown in Figure 3(a). Figure 3(b) shows 

the painted ice on top and unpainted ice on the bottom. The scanner was then brought into the test section 

and set up, as shown in Figure 4. The ice was scanned until sufficient level of detail had been captured over 

the airfoil model in the center 1 ft section of the test section. A final step in the procedure was to record a 

pencil tracing of a cross-section cut. Currently pencil tracings are the primary means of recording 2–D ice 

shapes in the IRT, and they served as a reference to check the accuracy of the scanning system. Typically, it 

required approximately 20 min to paint the ice, set up the scanner, and scan the ice. For comparison, hand 

tracing of the ice shape at one spanwise location took approximately 10 min.

TABLE 1.—SCANNER EVALUATION ICING CONDITIONS.

Ice Type V,

kn

α,

°

MVD,

μm

LWC,

g/m3

T0,

°F

t,

min

Horn 200 4 20 0.55 25 7

Streamwise 200 4 20 0.55 1 7

Roughness 200 4 20 0.55 25 1

3–D Scallop 200 0 32 0.45 20 19.9
NASA/TM—2012-217702 7

(a) (b)

Figure 3.—Painting the ice prior to scanning. (a) Applying paint (b) Painted ice.

Figure 4.—Scanning ice in the test section

Scanners Evaluated

Three laser scanners were evaluated in the IRT during spring 2011 using the procedure described 

above. They were Faro Quantum, Romer Absolute SI, and NVision HandHeld. They were all arm-based 

laser scanning systems. The laser scan head was mounted on the end of a seven-axis arm which in turn 

was attached to the floor of the tunnel test section using a magnetic base. The operator positioned the scan 

head manually, and the encoders built into the arm tracked the location of the scanner head relative to the 

base of the arm. The scan data from the arm-based scanners were referenced to the location of the base of 

the arm. General specifications of the three arm-based scanners are shown in Table 2. All three of the 

arms could be fitted with a hard-probe for single point measurements.

TABLE 2.—SCANNER SYSTEM GENERAL SPECIFICATIONS

Manufacturer Model Type Max. line 

resolution,

in.

Line width,

in.

Scan rate,

Hz

Arm encoder 

type

Geomagic

plug-in

Faro Quantum Arm 0.002 2.5 30 Relative Yes

Romer Absolute Arm 0.002 2 30 Absolute Yes

NVision HandHeld Arm 0.002 2 30 Absolute No

Creaform HandyScan Armless 0.002 2.5 25 N/A Yes

Creaform MetraScan Armless 0.002 2.7 25 N/A Yes

The Faro Quantum arm was owned by NASA Glenn’s Facilities Division and was operated by NASA 

personnel during the evaluation. At the time of the evaluation, it had been updated in the Faro lineup with 
NASA/TM—2012-217702 8

another model, but was considered similar enough to be a good representation of the newer model. The 

body of the arm was made of aluminum and used a built in temperature senor to compensate for the 

expansion/contraction of the tube. The arm is capable of compensating for a change in temperature of 

0.6 °C/min. The arm used relative position encoders, which required the arm be put through its full range of 

motion prior to use. The Faro arm could be operated directly from Geomagic through the use of a plug-in.

The Romer Absolute SI was a seven-axis arm with a laser scanner built into the handle. The arm was 

constructed of thermally stable carbon fiber and did not require any thermal compensation. The system 

also employed absolute position encoders, so a ranging of the arm was not required prior to use. The 

Romer arm could be operated directly from Geomagic through a plug-in.

The NVision HandHeld system used a Romer arm with an NVision scanner head that was attached to 

the arm handle. Like the Absolute system, it used a carbon fiber arm with absolute encoders and did not 

require any thermal compensation. The NVision system could not be operated directly from Geomagic 

since a plug-in was not available and required the use of a proprietary software. However, the scan data 

could be processed by Geomagic after the scan was completed.

Prior Evaluation

Two scanning systems manufactured by Creaform were evaluated as well, but not as part of the 

standardized evaluation process described above. The first was the HandyScan system, which was 

evaluated during an IRT icing test demonstration in 2008. The MetraScan system was evaluated in the 

summer of 2011. The IRT was undergoing a renovation at that time so the evaluation of the MetraScan 

system was performed with ice castings attached to the leading edge of an airfoil model (which served to 

simulate ice accretions on test article).

Both HandyScan and MetraScan did not require the use of an arm for positioning, allowing much 

greater range of motion for the scanner. The HandyScan system used reflective targeting dots on the 

scanned objects for positioning. The scan data from the HandyScan were referenced to these targeting 

dots. The MetraScan system used a “C-Track” base that tracked the position of the laser scanner optically 

using reflective dots on the scanner itself. The scan data from the MetraScan were referenced to the 

location of the “C-track”. Both Creaform scanners could be operated directly from Geomagic through the 

use of plug-ins. General specifications of the two Creaform scanners are shown in Table 2 as well.

Software and Scanner Evaluation Results

Scanner Comparison

The purpose of the scanner evaluation was not to determine whether one system is generally superior 

to another. The scanners were evaluated against a very specific set of criteria related to digitization of ice 

shapes in the IRT test section. This also meant operating the scanners in an environment that was well 

outside of their designed operating parameters, especially in temperature. The down-selection process was 

used to identify a scanner that best supported the very specific needs of NASA for use in the IRT. The 

findings detailed below should not be viewed as a determination by NASA of one system being superior 

to another for general use.

One of the findings during the scanner evaluation was that the quality of the ice shape scan depended 

greatly on the experience of the scanner operator. None of the company representatives that demonstrated 

the scanners had experience scanning ice shapes. The scanners are normally used to reverse engineer 

items with smooth, regular features in a shop environment. They are not typically used to scan rough, 

irregular shapes in a below freezing environment. All of the scanners demonstrated were able to scan ice 

shapes (or simulations) in the IRT test section. However the quality of the ice shape scans differed among 

the scanners evaluated. Since all of the scanners operate on the same principle (and have very similar 

specifications, as shown in Table 2), the differences were attributed to operator variations, rather than 

scanner hardware differences. The highest quality scans were obtained with the Faro arm, but this was 
NASA/TM—2012-217702 9

likely due to the fact that it was operated by a NASA engineer who had previous experience scanning ice 

shapes in IRT. Because of this, most of the emphasis on the scanner evaluation was placed on usability, 

operability, and software.

The arm-based systems and the two Creaform scanners required different set-up procedures for use in 

the IRT and had different operational issues and limitations. The Creaform HandyScan system required

reflective reference targets near the objects to be scanned (in this case the ice accretion shapes). The 

reference targets could not be placed directly on the ice, so a wired mesh had to be constructed around the 

ice shape, and the reference targets were placed on the wire mesh (as shown in Figure 5). This wire mesh 

had to be installed after each icing spray before the ice shape could be scanned. Different wire-mesh

configurations would also need to be constructed for different airfoil models that are tested in the IRT.

The Creaform MetraScan system did not require the use of reflective reference targets near the ice

shapes. However, because the C-track had to be located outside of the tunnel test section due to 

temperature limitations, a large access panel had to be created on the test section ceiling (Figure 6). This 

was required to provide a clear line of site between the C-track and the handheld scanner head. A 

minimum distance of 6 ft was required between the scanner and the C-track, so the C-track could not be 

mounted close to the ceiling to minimize the size of the access panel. The size and location of the access 

panel also needed to change with different airfoil models. The scanner operator also must be careful not to 

block the line of sight between the scanner and the C-track. The armless systems were easier to 

manipulate and provided a greater freedom of motion for the scanner, when compared to the arm-based 

systems. Another advantage of the two Creaform scanners was that the part of the scanning system that 

was brought into the test section did not have any moving parts. When using the arm-based systems, the 

entire arm (with the complex joints and encoders) must be brought into the test section. Although no 

reliability issues were observed with the arm-based scanners during the IRT evaluations, it is not known 

what effect (if any) repeated exposure to the IRT test section environment would have on the scanners. 

Figure 5.—Creaform HandyScan system.
NASA/TM—2012-217702 10

Figure 6.—Creaform C-track system.

Among the arm-based scanners, there were differences between them as well, some of them 

significant. The Faro Quantum arm was equipped with relative position encoders, which required ranging 

of the arm every time it was turned on. The Romer and NVision arms were equipped with absolute 

encoders and did not require ranging upon power up. Although this was an additional step, it could be 

completed in a few seconds and did not significantly affect the workflow.

Hard-probing of known reference points on the model may be a required step when using an armbased scanner. Faro and NVision system allowed the user to leave the hard-probe on the scanner at all time.

On the Romer system, the hard probe must be removed when the scanner is being used. Again, this is an 

additional operating step, but it could be completed in a few seconds and was not considered significant.

The evaluations of the three arm-based scanners showed that the two Romer based arms were 

significantly easier to position and manipulate than the Faro arm in the IRT. Both arms used counterbalancing systems to lessen the load on the user. However, it was the opinion of the NASA evaluators that 

the Romer system was superior for purposes of this research project. This was considered an important 

factor as it would reduce operator fatigue during the scans and lead to steadier, more accurate scans.

The temperature compensation on the Faro arm worked well for most conditions. However, when the 

scanner was used at a test section temperature near 0 °F, the scanner outputted a temperature fault error. It 

was not known if this was due to the temperature being too low, or the temperature of the arm decreasing 

faster than it could be compensated for. Repeated attempts to use the scanner at this temperature resulted 

in the same fault message. This temperature is well outside of the manufacturer’s stated minimum 

operating temperature of 50 °F. However, all of the other scanners that were evaluated in the IRT test 

conditions were able to operate near 0 °F without any issue (even though none of the scanners was rated 

to operate this cold).

All of the scanners that were evaluated provided a “real-time” feedback on the status of the scan on 

the scanner computer screen. The operator could look at the quality and completeness of the scan as the 

ice shape was being scanned. Additional useful information that was displayed on the computer screen 

was the whether or not the object being scanned was in focus. The Romer scanner provided an additional 

useful feature by projecting a focusing dot on the object being scanned. The operator could be aware of 

the scanner focus state without having to turn and look at the computer screen.

Another significant difference among the scanners was the format of the scan data. All of the armbased scanners outputted point cloud data that could be further processed into a surface data using the

software of choice. However, the two Creaform scanners did not output point-cloud data. Instead, the data 

was already in a semi-processed triangular surface mesh, and the user did not have access to raw, 

unprocessed point-cloud data. This was considered by NASA evaluators to be a significant limitation 

because it does not allow the user to work directly with raw, unprocessed data.
NASA/TM—2012-217702 11

Scanner Selection

After a review of the evaluation results, the consensus among the NASA evaluators was that an armbased scanning system was better suited for further development for IRT. It required minimal modifications 

to the test section, resulting in fewer risks in implementation for use. All of the arm-based systems generated 

“raw” data in an unprocessed state, allowing more control over post-processing of the scan data. Of the arm 

based systems, the Romer Absolute was considered the most promising. The Faro arm had two significant 

deficiencies that the Romer arm did not. Due to temperature compensation errors, it could not be used at 

temperature near 0 °F. Also, its counterbalancing system was found to be less effective than on the Romer 

arm. The NVision system’s arm was nearly identical to that of the Romer Absolute. However, during the 

IRT evaluation, the scanner had difficulties acquiring ice shape features. It was not known if this was due to 

scanner limitations or scanner malfunction. Because it did not demonstrate to satisfactorily acquire ice 

shapes during the IRT demo, the NVision system was not selected.

Lessons Learned

One of the lessons learned during the scanner evaluation was that reference targets were required on 

the airfoil model. Without these reference targets, one cannot determine what part of the airfoil model the 

scans are from. With the two Creaform scanners simple reflective dot stickers were placed on the airfoil 

model where ice was not expected. For the arm-based systems a physical reference features (such as 

holes, spheres, etc.) must be machined into the airfoil model (to be either scanned or hard probed). Since 

the airfoil models used during the scanner evaluation were not specifically built for 3–D scanners, these 

features were not present during the evaluation. The only reference that was taken with the arm-based 

scanners was the test section floor plane. Because of this, only the spanwise location of the ice shape 

(with respect to the airfoil model) was known. The location of the ice shape in the axis of the airfoil chord 

and thickness was not known for the ice shapes that were scanned with the arm-based scanners. When the 

data were post-processed, an attempt was made to visually align the ice shape with the airfoil model by 

matching the portion of the scan that did not have ice (i.e., uniced sections of the airfoil) with the airfoil 

model. However, this was only an approximate fit and the accuracy of this method was uncertain.

For future tests, the airfoil models will have reference targets machined onto the surface at multiple 

locations. Initial tests have shown that counter-sunk holes serve as excellent reference points for use with 

hard probes. If precisely machined, very repeatable hard-probing of the reference points can be achieved.

This method will be utilized during the validation and implementation phase of this research program.

Sample Evaluation Data

Included in this section is a sample of the data from the scanner evaluation. The data shown below 

were obtained during the evaluation of the Faro Quantum Arm. The results are shown only as a general 

indication of typical results one would expect from a modern 3–D laser scanning system and software.

There were four general steps in the post-processing of the scan data: alignment, merge, wrap, and 

hole filling. The first step was to align the sets of point cloud data that make up the scanned object using a 

best-fit method of the overlapping areas. The second step was to merge the aligned data sets into a single 

data set, removing overlapping data points. The third step was to wrap a surface over the point-cloud data, 

turning it into a triangular mesh. The final step was to fill the holes that have formed in the triangular 

mesh due to gaps in the scan data, making the surface water-tight. In Geomagic, all four steps were

largely automated except for the filling of the holes for complex ice geometry, such as the scallop shapes 

that form on swept wings. For these ice shapes, the gaps and holes were too large and numerous for an 

automated method, and a manual intervention was required, significantly increasing the time required for 

the post-processing of the data. Typically, a 1 ft spanwise section of ice shape from the scanner evaluation 

could be post-processed into a water-tight surface in 1 hr. However, large scallop ice shapes required as 
NASA/TM—2012-217702 12

much as 6 hr because many of the holes had to be filled manually. For most ice shapes, the final watertight surface mesh (covering a 1 ft section of airfoil span) contained 1 million triangles.

Figure 7 to Figure 10 show the horn ice shape, roughness shape, streamwise shape, and 3–D scallop 

shape respectively. Figures on the left show the 3–D scan data after they have been fully processed and 

converted into a “water-tight” surface. The figures on the right show the photographs of the same ice 

shapes. These results show the scanner was able to capture many of the details of the ice, even for very 

complicated shapes such as scallop ice shown in Figure 10. An aerodynamic verification study planned in 

the Phase II of this program will determine if it has captured all of the aerodynamically relevant features.

(a) (b)

Figure 7.—Horn ice shape. (a) 3–D Scan. (b) Photograph.

(a) (b)

Figure 8.—Roughness ice shape. (a) 3–D Scan. (b) Photograph.
NASA/TM—2012-217702 13

(a) (b) 

Figure 9.—Streamwise shape. (a) 3–D Scan. (b) Photograph.

(a) (b)

Figure 10.—3–D scallop shape. (a) 3–D Scan. (b) Photograph.

Figure 11 shows the comparison of the scan data to the 2–D pencil tracing. It shows the 2–D section 

cuts of the 3–D scan data at the same spanwise location that the hand tracings were made. Figure 11(a)

shows the horn ice shape, and Figure 11(b) shows the streamwise ice shape. The results show that the 

3–D scan data agreed very favorably with the 2–D hand tracings. It is important to note that the scan data 

was obtained using the Faro arm, and only the spanwise location of the scan was known due to a lack of 

reference locations on the model. The scan data were visually aligned with the airfoil by fitting the clean 

model portion of the scan to the airfoil coordinates. In future work, reference targets will be built into the 

airfoil model so that the location of the scan will be known in all three axis, with respect to the airfoil.
NASA/TM—2012-217702 14

(a) (b)

Figure 11.—Comparison of scan data to hand tracing. (a) Horn ice shape. (b) Streamwise ice shape.

Initial Rapid Prototype Method Assessment

An initial assessment of the ability to accurately reproduce the scanned ice shapes using rapid 

prototype methods (RPM) was performed based largely upon published general information of the 

methods. Three rapid prototype methods were considered for evaluation: selective laser sintering (SLS), 

stereo-lithography (SLA), and PolyJet. All three methods use lasers to build up layers of material until the 

process is completed. Table 3 shows the properties of the three rapid prototype methods. PolyJet had the 

highest resolution and accuracy and was chosen for initial assessment. A 3–D scan file of an ice shape 

was sent to a company specializing in RPM manufacturing and 1/3 and 1/5 scale samples of the ice 

shapes were made. One of the goals of using a 3–D scanner is to develop the ability to geometrically scale 

the ice shapes from the IRT for use in a smaller aerodynamic wind tunnel. It was not known whether 

current RPM processes had enough resolution and accuracy to reproduce an ice shape that captured all of 

the aerodynamically important features. It was also not known if the RPM process could be used to 

geometrically scale down an ice shape while preserving all of the relevant features. This initial assessment 

was performed to answer some of those questions using the scallop ice shape scan shown in Figure 10.

Figure 12(a) shows 1/3 and 1/5 scale RPM shapes next to the full-scale shape obtained using the mold 

and casting method. Figure 12(b) shows the close up view of the RPM shapes. A qualitative visual 

assessment indicated that the RPM process (even at these scales) was able to capture the gross 

aerodynamic features like scallops and large feathers, but not the roughness features (which are on the 

order of 0.5 to 1 mm on the full-scale ice shape). A thorough quantitative assessment is required (and will 

be performed) to fully assess the capability of the current RPM methods.

TABLE 3.—PROPERTIES OF RAPID PROTOTYPE METHODS

Process Min. layer thickness,

in

Tolerance,

in.

SLS 0.03 ± 0.015

SLA 0.005 ± 0.015

PolyJet 0.0003 ± 0.005

x (in)

-1 0 1 2 3 4

y (in)

-2

-1

0

1

2

Airfoil

3D Scan 

Hand Tracing

x (in)

-1 0 1 2 3 4

y (in)

-2

-1

0

1

2

Airfoil

3D Scan 

Hand Tracing
NASA/TM—2012-217702 15

(a) (b)

Figure 12.—PolyJet rapid prototype shapes. (a) Full, 1/3, and 1/5 scale shapes (b) Closeup of 1/3 and 1/5 scale 

RPM shapes.

Figure 13.—Phase II of the research plan.

Future Work

The second phase of this research will focus on the implementation and validation of the 3–D scanning 

system that was selected in the first phase of the program. The Phase II was built on the methodology 

developed by Broughton (Ref. 8) during the previous attempt at developing a 3–D scanning capability for 

the IRT, but it will be greatly expanded upon. The implementation tasks involve developing procedures for 

using the scanner in the IRT as well as for post-processing the data. An expected outcome of this phase is a 

document that describes these procedures. This should include all aspects of the measurement including 

preparation of the ice accretion, set-up of the scanner, scanning of the ice, saving of the data, and finally 

post-processing of the data. This comprehensive document will serve as an internal reference guide for the 

continued use of the scanner in IRT. The roadmap for Phase II is shown on Figure 13.

Benchmark 

Measurements

Rqmts

Met?

Level 1 Milestone:

3D Ice Shape Measurement Capability

Declare 

Swept 

Wing 

Capability

Y

N

Phase II Roadmap

RPM of 

Scan 

Shapes

Mold/Cast 

Ice Shapes

IRT Ice 

Accretion 

Test

Aero test/

Comparisons

Geometric 

Comparisons

Geometric 

Comparisons

RPM of 

Scan 

Shapes

Mold/Cast 

Ice Shapes

2D Airfoil Validation

Swept Wing Validation

IRT Ice 

Accretion 

Test

Declare 2D 

Capability

Phase I: Selection of Scanner and Software
NASA/TM—2012-217702 16

(a) (b)

Figure 14.—Scanner calibration geometries. (a) Metal calibration 

block. (b) Roughness sample block.

The first step in the Phase II of the research is a series of validation exercises that will be conducted 

to help define the measurement capability of the scanning system that was identified and purchased 

during Phase I. Benchmark measurements will be performed on a metal calibration block and a metal 

“roughness sample” block (Figure 14) using the methodology established by Broughton (Ref. 8). The

calibration data will be stored and used as a benchmark reference for future evaluations of the system.

These data can be used as a type of check standard to ensure uniform capability over time.

The main component of the validation (as shown in Figure 13) is the “circular” geometric and 

aerodynamic assessment on a 2–D airfoil geometry. This exercise will consist of comparing scanned ice 

shapes to castings of the same ice shape. Currently the only means of recording fully 3–D ice shapes is 

the mold and cast method. The scan data can also be used to create a RPM artificial shape that can be 

scanned and compared to the original ice accretion. Performing these comparisons will help define the 

system accuracies and the propagation of uncertainties through the manufacture of artificial ice shapes. It

will also help define the limitations of the scanner hardware, software and rapid prototyping. A closely 

related aerodynamic evaluation will also be conducted, where RPM artificial shapes made from ice scans 

are compared against ice-shape castings in an aerodynamic wind tunnel. These validations (both 

geometric and aerodynamic) will be conducted for each of the four basic categories of ice accretion: 

roughness, horn, streamwise and spanwise ridge as differences in measurement accuracy have different 

implications in terms of the resulting aerodynamics for different ice shapes (Ref. 3 and 15).

Finally, a geometric validation test will be performed on a 3–D, swept-wing geometry. This is 

specifically intended to quantify the limitations of capturing very complicated scallop geometries and 

develop methods to ameliorate these limitations. This research will be documented in a final report that 

clearly defines the 3–D ice accretion measurement capability.

Conclusion

A research program is currently being implemented to develop and validate the use of a commercial 

3–D laser scanning system to record ice accretion shapes in the NASA Icing Research Tunnel. Phase I of 

the research was conducted to identify the most suitable laser scanning hardware and software for further 

development. Phase II of the research is to implement the system and validate its capability to record all 

of the aerodynamically relevant features. Phase I has been completed, and Phase II is currently underway.

The results of Phase I showed that commercial 3–D laser scanners were capable of recording many 

details of various types of ice shapes, and post-processing software were capable of generating “water-
NASA/TM—2012-217702 17

tight” surfaces. The scanners that were evaluated in the IRT had similar abilities to digitize ice shapes 

because the scanning methods are identical. The primary differences were the method that each system 

used to position the scanner and the ability to operate in the IRT test section environment. Several 

scanning systems were evaluated against selection criteria, and an arm-based system was found to be the 

most promising. This scanner system will be used to implement and validate the use of this technology 

through a series of icing and aerodynamic tunnel tests.

With continued success of this plan in Phase II, a suitable means of recording and archiving fully 3–D

descriptions of experimental ice accretion geometry will have been developed. This will contribute to 

achieving the main goal of the Airframe Icing Technical Challenge of the NASA Aviation Safety 

Program: acceptance of experimental and computational icing simulation tools in supercooled large 

droplet (SLD) conditions and on 3–D airframe components, including swept wings.

References

1. “Calibration and Acceptance of Icing Wind Tunnels,” SAE ARP 5905, SAE International, 

Warrendale, PA, Sept. 18, 2003.

2. Bosetti, C., Paul, B.P., Jr., Malone, A.M., “Ice Shape Characterization to Aid in Replicating Ice 

Shapes for Subsequent Analysis,” AIAA Paper 2010-7534.

3. Broeren, A.P., Addy, H.E., Jr., Bragg, M.B., Busch, G.T., Guffond, D., and Montreuil, E., 

“Aerodynamic Simulation of Ice Accretion on Airfoils,” NASA/TP—2001-216929, June 2011.

4. Anderson, D.N., Hentschel, D.B., and Ruff, G.A., “Measurement and Correlation of Ice Accretion 

Roughness,” AIAA Paper 98-0486, Jan. 1998, also NASA/CR—2003-211823, June 2003.

5. Shin, J., “Characteristics of Surface Roughness Associated with Leading Edge Ice Accretion,” AIAA 

Paper 94-0799, Jan. 1994, also NASA TM–106459, Jan. 1994.

6. Mikkelsen, K., Juhasz, N., Ranaudo, R., McKnight, R., Freedman, R., and Greissing, J., “In-Flight 

Measurements of Wing Ice Shapes and Wing Section Drag Increases Caused by Natural Icing 

Conditions,” NASA TM–87301, Apr. 1986.

7. Reehorst, A.L., and Richter, G.P., “New Methods and Materials for Molding and Casting Ice 

Formations,” NASA TM-100126, Sept. 1987.

8. Broughton, H., “3D Laser Scanner Project,” Subcontract No. 011422 Report, Feb. 2004.

9. Akca, D., Remondino, F., Novak, D., Hanusch, T., Schrotter, G., and Gruen, A., “Performance 

Evaluation of a Coded Structured Light System for Cultural Heritage Applications,” Videometrics IX, 

Proceedings of the SPIE, Vol. 6491, pp. 64910V, 2007.

10. Remondino, F., Girardi, F., Gonzo, L., Nicolis, F., “Detailed 3D Reconstruction of the Great 

Inscription of Gortyna, Crete: Acquisition, Registration, and Visualization of Multi-Resolution Data,” 

Proc. of 14th Int. Conference on Virtual Systems and MultiMedia (VSMM 2008), pp. 404-412, 2008.

11. Colantonio, R., “Atmospheric Environment Safety Technologies (AEST) Project Overview,” NASA 

Aviation Safety Program Annual Technical Meeting, St. Louis, MO, May 10-12, 2011.

12. Potapczuk, M., “Airframe Icing Technical Challenge,” NASA Aviation Safety Program Annual 

Technical Meeting, St. Louis, MO, May 10-12, 2011.

13. Lee, S., “Status of 3D Ice Shape Measurement Effort,” NASA Aviation Safety Program Annual 

Technical Meeting, St. Louis, MO, May 10-12, 2011.

14. Soeder, R.H., Sheldon, D.W., Ide, R.F., Spera, D.A., and Andracchio, C.R, “NASA Glenn Icing 

Research Tunnel User Manual,” NASA/TM—2003-212004.

15. Bragg, M.B., Broeren, A.P., and Blumenthal, L.A., “Iced-Airfoil Aerodynamics,” Progress in 

Aerospace Sciences, Vol. 41, No. 5, July 2005, pp. 323-418.
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14. ABSTRACT

A research plan is currently being implemented by NASA to develop and validate the use of a commercial laser scanner to record and 

archive fully three-dimensional (3-D) ice shapes from an icing wind tunnel. The plan focused specifically upon measuring ice accreted in 

the NASA Icing Research Tunnel (IRT). The plan was divided into two phases. The first phase was the identification and selection of the 

laser scanning system and the post-processing software to purchase and develop further. The second phase was the implementation and 

validation of the selected system through a series of icing and aerodynamic tests. Phase I of the research plan has been completed. It 

consisted of evaluating several scanning hardware and software systems against an established selection criteria through demonstrations in 

the IRT. The results of Phase I showed that all of the scanning systems that were evaluated were equally capable of scanning ice shapes. The 

factors that differentiated the scanners were ease of use and the ability to operate in a wide range of IRT environmental conditions.

15. SUBJECT TERMS

Icing; Wind tunnel; Aerodynamics 

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