of the sheathing at a 3.5 in. [89 mm]
distance and immediately saw that, yes,
the radar was seeing the sheathing,”
Boudreaux said.
The researchers then had to deter-
mine if the radar could distinguish the
moisture content of the sheathing. By
applying mathematical algorithms they
had developed, results showed that
the radar reflection signals could be
correlated to moisture content.
“We are able to predict the reflected
microwave pulse shape from moist
oriented strand board,” Boudreaux
said. “But the pulse can also be
analyzed empirically by correlating
pulse characteristics to moisture
content.”
Next they transformed the setup
into a portable electronic system, oper-
ating at 10–15 GHz, to enable field
measurements.
With promising results, ORNL aims to
license the technology for commercial
production, making a portable radar
system available for inspectors and
homeowners. The tool could also assess
roofs and foundations, helping home-
buyers avoid surprises by identifying
issues that could go unnoticed during an
inspection.
“When developing the detector, we
made a system specifically applicable
to walls in residential homes, and for
general public access,” Boudreaux said.
“It’s small, portable, and lightweight,
with easy setup, and can be adapted to
transmit within frequency regulations.”
Next the team plans to test full wall
assemblies with different exterior mate-
rials, such as vinyl siding and brick.
“With early detection, a small issue
can be repaired before major damage
occurs,” Boudreaux said. “We’ve found
what can help locate that moisture
early, but we still have work to do
and more materials to test and more
boundaries to explore with microwave
radar reflection.”
The study was published in
IEEE Xplore and presented at the 2024
IEEE Radar Conference in Denver,
Colorado. Researchers included
Boudreaux, Stephen Killough, and Rui
Zhang, with guidance from Diana Hun,
ORNL’s building envelopes subpro-
gram manager. The project was funded
by the US DOE’s Building Technologies
Office.
SCANNER
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INDUSTRYNEWS
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M AT E R I A L S E V A L U AT I O N • M A R C H 2 0 2 5

DIGITAL TWINS: AT THE CORE OF NDE 4.0
Background
According to MedicineNet, a twin is one
of two children produced from the same
pregnancy. Twins who develop from a
single ovum are called monozygotic,
or identical, twins. They share identical
genomes. As a result, the study of twins
has become a key tool in behavioral
science. This inspired a parallel approach
to understanding the behavior of physical
assets at the turn of this century.
An (ideal) digital twin is a virtual repre-
sentation of an asset and—like a mono-
zygotic twin—it shows the same behavior
and development as the asset. An asset
could be a manufacturing device, sensor,
component, product, system, process,
service, operation, inspection, plant,
business, enterprise, software, or even a
person, operator, inspector, or engineer.
A digital twin connects the physical world
with the cyber world. Digital twins can
be used for behavioral or developmental
studies of the asset represented.
Digital twins can be differentiated by
the type of asset they represent, such as
production facilities, production equip-
ment, assembled products, components,
inspection systems, devices, sensors,
or operators. Digital twins can also
be layered and allow inheritance. For
example, a digital twin of a production
facility can contain the digital twins of all
the production equipment and inspec-
tion systems within that facility. Similarly,
the digital twin of an inspection system
may include the digital twins of sources,
sensors, detectors, and manipulators [1].
Progress Up Until Now
Computer-aided design (CAD) models
can be viewed as early-stage digital
twins, as they started to integrate the
component design data with drawings
and visualization processes. Over the
last few decades, these models have
evolved into sophisticated PLM (product
life-cycle management) systems,
handling all sorts of data (materials,
process, integration, assembly) and
enabling concurrent engineering (simu-
lation, multidisciplinary optimization,
design verification, and validation). PLM
systems provide the context, security,
traceability, and processes needed
across enterprise teams and systems to
ensure that the product data is acces-
sible and trustworthy.
The concept of the digital twin was
first introduced in 2002 by Michael
Grieves at an executive course on PLM
at the University of Michigan, where it
was referred to as the “mirrored spaces
model.” It was later published in a journal
article [2]. By 2011, the term “digital twin”
had evolved through collaboration with
John Vickers of NASA [3].
Today, digital twins provide value on
many fronts, offering capabilities such as:
Ñ processing all sorts of data based
on stored data
Ñ simulating asset usage using stored
data
Ñ interfacing with all relevant data
of an asset or component (or
accessing data stored in multiple
computer systems/databases
through the IIoT using semantic
interoperability)
Ñ establishing virtual relationships
across components, systems,
processes, products, and product
inspections through common
events
Ñ enabling users to visualize data and
data-processing results
Ñ creating knowledge from stored data
Ñ triggering actions based on stored
data and
Ñ doing all of this in real time, wher-
ever possible.
The National Academies of Sciences,
Engineering, and Medicine (NASEM)
define digital twin as “a set of virtual
information constructs that mimics the
structure, context, and behavior of a
natural, engineered, or social system is
dynamically updated with data from its
physical twin has a predictive capability
and informs decisions that realize value.”
The bidirectional interaction between the
virtual and the physical is central to the
digital twin [4].
NASEM also emphasizes that digital
twins must be fit for purpose, requiring
verification, validation, and uncertainty
quantification (VVUQ)—which, when
applied to NDE, translates to the need
for appropriate probability of detection
(POD) assessments.
NDEOUTLOOK
|
SCANNER
From physical to virtual
Sensor fusion, data assimilation,
inverse problems
From virtual to physical
Automated action and
decision-making Data
repository
VVUQ
Virtual asset
Modeling and simulation,
AI, physical principles, and
empirical models
Physical asset
Sensors, data acquisition,
and data integration
Visualization
Visualization of
a digital twin.
M A R C H 2 0 2 5 • M AT E R I A L S E V A L U AT I O N 15