January 2011 

NASA/CR–2011-216884 

Open Circuit Resonant Sensors for Composite 

Damage Detection and Diagnosis 

 

John J. Mielnik, Jr. 

Lockheed Martin Corporation, Hampton, Virginia 
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January 2011 

NASA/CR–2011-216884 

Open Circuit Resonant Sensors for Composite 

Damage Detection and Diagnosis 

 

John J. Mielnik, Jr. 

Lockheed Martin Corporation, Hampton, Virginia 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 

Trade names and trademarks are used in this report for identification only. Their usage does not constitute 

an official endorsement, either expressed or implied, by the National Aeronautics and Space 

Administration. 
PREFACE

This document constitutes the final report of the Integrated Vehicle Health Management 

(IVHM) task order entailing Open Circuit Resonant Sensors for Composite Diagnosis. 

The Task order number was 087D3-NNL10AM09T. This research and development 

effort was funded using the American Recovery and Reinvestment Act (ARRA) funding 

with direct guidance from the NASA Technical Monitor. The final completion of the 

report was accomplished under Task Order number NNL10AM25T.

The NASA Technical Monitor for this task is Sandra V. Koppen.

The work was accomplished by engineering support personnel of the Lockheed Martin 

Corporation.
2

Table of Contents

Table of Contents..........................................................................................................................................2

LIST OF FIGURES ......................................................................................................................................3

LIST OF ACRONYNS.................................................................................................................................4

LIST OF SYMBOLS ....................................................................................................................................5

1.0 SUMMARY......................................................................................................................................6

2.0 INTRODUCTION ............................................................................................................................6

2.1 Background ..................................................................................................................................................8

2.2 Problem......................................................................................................................................................10

2.3 Objective ....................................................................................................................................................11

2.4 Scope..........................................................................................................................................................11

2.5 Approach....................................................................................................................................................11

3.0 LITERATURE REVIEW ............................................................................................................... 12

4.0 SENSOR DESIGN.......................................................................................................................... 13

4.1 Sensor Background ....................................................................................................................................13

4.2 Design Factors............................................................................................................................................15

4.2.1 Inductance Value ...................................................................................................................................15

4.2.2 Capacitance Value .................................................................................................................................16

4.2.3 Resistance Value....................................................................................................................................16

4.3 Design Geometry .......................................................................................................................................16

5.0 SENSOR DEVELOPMENT and FABRICATION ........................................................................ 17

5.1 Theoretical Design .....................................................................................................................................17

5.2 Physical Design..........................................................................................................................................18

5.3 Fabrication .................................................................................................................................................18

6.0 TEST PLAN.................................................................................................................................... 19

6.1 Normalization and Background Noise Level .............................................................................................20

6.2 Sensor Free Space Characterization...........................................................................................................20

6.3 Sensor + Substrate Characterization ..........................................................................................................21

6.4 Sensor Measurements of Damaged Panels.................................................................................................21

6.5 Data Comparison........................................................................................................................................21

7.0 TEST METHODS........................................................................................................................... 21

7.1 Experimental Tools....................................................................................................................................21

7.1.1 Sensor Measurements with a Network Analyzer...................................................................................21

7.1.2 Sensor Measurements with a Magnetic Field Response Measurement System.....................................22

7.2 Computational Tools..................................................................................................................................23

8.0 CONCLUSIONS............................................................................................................................. 23

Appendix A................................................................................................................................................. 25

REFERENCES ........................................................................................................................................... 27
3

LIST OF FIGURES

Figure 1. Examples of composite material use. a. Lamborghini Sesto Elemento makes extensive use of composites 

for interior and exterior components [1]; b.Sea Ray 280 composite hull [2]; c. Composite wind turbine blades [3]; d. 

Composite components on F-16 [4] ; e. Composite components on commercial aircraft [5]; f. Composite 

components on satellite [6]............................................................................................................................................7

Figure 2. Carbon fiber weave. ......................................................................................................................................8

Figure 3. Example of a multi-ply composite laminate composed of individual lamina showing orientation of plies at 

right angles to each other [9]. ........................................................................................................................................9

Figure 4. Examples of obvious damage. a. Damage to radome caused by hail; b. Bird strike to horizontal stabilizer; 

c. Jacking incident during maintenance.........................................................................................................................9

Figure 5. Delaminated plies and buckling. .................................................................................................................10

Figure 6. Examples of hidden defects and damage.....................................................................................................10

Figure 7. 1⁄4 inch thick structural fiberglass panel.......................................................................................................12

Figure 8. Simulated damage to fiberglass panels. (a) holes (puncture), (b) crack. ..................................................12

Figure 9. Square spiral sensor.....................................................................................................................................13

Figure 10. Schematic representation of sensor. ..........................................................................................................13

Figure 11. Relationship of Q value to BW. ................................................................................................................15

Figure 12. Spiral geometries utilized for the resonant sensors: (a) triangular, (b) square, (c) hexagonal, and (d) 

circular.........................................................................................................................................................................17

Figure 13. Square spirals ranging in outer dimensions from 2 inches to 9 inches......................................................18

Figure 14. KNK Machine. ..........................................................................................................................................19

Figure 15. Sensor adhered directly to fiberglass panel. ..............................................................................................19

Figure 16. Laminated hexagonal spiral resonant sensor. ............................................................................................19

Figure 17. Measurement using Performance Network Analyzer (PNA) system. .......................................................22

Figure 18. Comparison of the 8 inch square spiral sensor measured data in free space and against the fiberglass 

baseline panel. .............................................................................................................................................................25

Figure 19. Comparison of Square spiral sensors sizes 3 inch through 9 inch against fiberglass panel using a four 

inch antenna.................................................................................................................................................................26

Figure 20. Comparison of the 4 inch hexagonal spiral sensor measurements. ...........................................................26
4

LIST OF ACRONYNS

Acronym Meaning

IVHM Integrated Vehicle Health Management

ESB Electromagnetics & Sensors Branch

HIRF High Intensity Radiated Fields

NASA National Aeronautics and Space Administration

LaRC Langley Research Center

RF Radio Frequency

UV Ultra Violet

NDT Non Destructive Test

EM Electro Magnetic

BW Bandwidth

D.C. Direct Current

CAD Computer Aided Design

KNK Klik-N-Kut

PNA Performance Network Analyzer
5

LIST OF SYMBOLS

Symbol Meaning

L Inductance

C Capacitance

R Resistance

f Frequency

π PI

Q Quality Factor

BW Bandwidth

N Number of turns (of spiral)

μ0 Permeability of free space (4π x 10-7)

μr Relative permeability of medium

a Wire radius
6

1.0 SUMMARY 

Under the Integrated Vehicle Health Management (IVHM) program work was begun to investigate the 

feasibility of sensor systems for detecting and diagnosing damage to aircraft composite structures and 

materials. Specific interest for this study was in damage initiated by environmental storm hazards and the 

direct effect of lightning strikes on the material structures of a composite aircraft in flight. The research 

and development for this work was sponsored by the Electromagnetics and Sensors Branch (ESB) and 

conducted in the High Intensity Radiated Fields (HIRF) Laboratory of the NASA Langley Research 

Center (LaRC).

An internal partnership for design guidance was formed with Dr. Stanley Woodard of NASA’s Research 

Directorate. Dr. Woodard is the inventor of SansECtm resonant sensor technology which was leveraged 

and expanded in notion to develop a proof-of-concept composite damage detection and diagnosis sensor. 

In determining the feasibility of using open circuit resonant sensors for composite aircraft damage 

detection and diagnosis an extensive literature search and review of published works pertaining to 

lightning, composite materials, and open circuit resonant sensor designs and applications was performed. 

Damage in composite materials is theorized to be associated with a change in the electrical impedance in 

the vicinity of the damaged area. Passive open circuit resonant sensors can be used to remotely detect 

such an impedance change by measuring the resonance response of the sensor. The associated resonance 

response is quantifiable in frequency, amplitude, bandwidth, and phase and can be correlated to the level 

of damage inflicted on a composite material. Open circuit resonant sensors provide an added advantage in 

that they will continue to function should the sensor itself become damaged. By additional means of 

geometry and design, the electrically quantifiable information associated with the sensor response can be 

used for composite material lightning damage detection and diagnosis. 

A series of open circuit resonant sensors was designed, fabricated, characterized, and determined to be a 

potentially viable means for damage detection and diagnosis of composite materials. Further 

development of the sensor is needed to provide multifunctional roles which include functioning as 

lightning protection and radio frequency (RF) shielding. The results of this research and development 

effort are documented in this report.

2.0 INTRODUCTION

Composite materials have been in use for several thousands of years and developments in material 

science over the past several decades have resulted in an increased use. Composite materials provide the 

ability to design and produce lighter and stronger structures. In addition to the strength to weight ratio,

the advantage of being able to be formed into complex shapes has made composites a desirable material 

in many industries. Figure 1(a-f) illustrates a small example of the broad spectrum of composite material 

uses from automobiles to satellites.
7

a. b. 

c. d. 

e. f. 

Figure 1. Examples of composite material use. a. Lamborghini Sesto Elemento makes extensive use of composites 

for interior and exterior components [1]; b.Sea Ray 280 composite hull [2]; c. Composite wind turbine blades [3]; d. 

Composite components on F-16 [4] ; e. Composite components on commercial aircraft [5]; f. Composite 

components on satellite [6].

Aviation has seen an increase in the use of composite materials. Modern air vehicles increasingly use 

composite materials for high strength and stiffness with minimal weight. Today’s aircraft incorporate 

substantial amounts of carbon, aramid, or fiberglass composite materials. Airframe manufacturers have 

gone from using composite panels for cosmetic coverings and fairings to empennage, wing, fuselage 

structures, and turbine blades.

The increased usage also means an increased potential for damage to and/or failure of the composite 

materials. While we currently understand and can predict failure modes of composite structures with a 

high degree of accuracy, it is difficult to predict the degree of composite damage or incipient states of 

failure while in use. A means of detection and diagnosis in real time would be a highly desirable resource 

in enhancing safety.
8

The purpose of this report is to document the proof-of-concept exploration in the use of open circuit 

resonant sensors for damage detection and diagnosis of composite materials. This is ultimately focused 

toward future use onboard aircraft, but this is not to be considered a limiting restraint as composites have 

found use in many industries where damage detection and diagnosis would be a viable asset as well. 

Ideally, this sensor would ultimately serve a multifunctional role providing damage detection and 

diagnosis, lightning protection, and RF shielding.

 2.1 Background

Composite materials, often referred to as composites, are a combination comprised of two or more 

materials which are combined on a macroscopic level. The constituent materials retain their individual 

identities and do not merge or blend together [7]. Typically, composites are composed of a 

reinforcement which is embedded in a matrix or resin. The reinforcement materials often are fibers, 

particles, flakes, etc. Figure 2 is an example of a carbon fiber weave which is commonly used as a 

reinforcing material.

Figure 2. Carbon fiber weave.

Polymers, metals, ceramics, etc. act as the matrix and hold the reinforcement material to the desired 

shape. The reinforcement material enhances the mechanical properties of the resin. The combination 

yields a material exhibiting increased strength when compared to the individual components. Concrete 

and plywood are two well known and commonly used composite materials. Composite materials formed 

from reinforcements of fiberglass or carbon (graphite) are becoming widely used in many industries and 

generally have a higher strength to weight ratio compared to metallic structures of comparable dimension. 

Along with strength and weight savings, other factors exhibited which make composites attractive to the 

aviation industry are resistance to fatigue, corrosion, and the ability to be formed into complex shapes [8].

Similar to metallic alloys designed for specific characteristics in metal, composite hybrid materials are 

created with the addition of complimentary materials. The addition of materials to the basic carbon fiber/ 

epoxy matrix aids in obtaining materials with specific properties, such as increased fracture or impact 

resistance. Kevlar and boron fibers are commonly used.

Composites are typically comprised of several layers or plies of the reinforcement material. The 

orientation of the plies is a factor in the strength of the material along its various axes. A typical 

orientation involves the plies at 90° to each other and is referred to as 0°-90°. Figure 3 illustrates the 

layup of a multi-ply laminated composite panel with the individual lamina oriented 90° to the subsequent 

layer.
9

Figure 3. Example of a multi-ply composite laminate composed of individual lamina showing orientation of plies at 

right angles to each other [9].

Like most materials, composites are subject to damage and degradation. Damage may be inflicted 

physically or environmentally. Flaws in production can also cause a decrease in a composite 

component’s integrity. Stress from compression, tension, shear, and torsion are among the possible 

forces acting on a composite structure that can cause damage or lead to failure. Environmental factors, 

such as moisture, lightning, and ultra violet (UV) radiation can result in a degradation of the material over 

time. 

Obvious damage can be detected visually and is readily apparent and requires little equipment. 

Determining the extent of the damage requires the use of specialized test equipment and methods. As 

seen in figure 4, the initial damage is obvious [10]. The extent of transferred stress or the amount of 

hidden damage is not. Surrounding areas can be placed under loads and weakened without exhibiting any 

indications on the outer surface.

a. b. c.

Figure 4. Examples of obvious damage. a. Damage to radome caused by hail; b. Bird strike to horizontal stabilizer; 

c. Jacking incident during maintenance.

Damage that is hidden or not obvious presents a challenge when it comes to the initial detecting and/or 

determining the extent of the damage to the structural integrity of the affected component(s). Composite 

panels can exhibit only minor damage on the exterior surface, but internal damage may exist which can 

cause a loss in structural integrity. Figures 5 and 6 illustrate instances of internal and hidden damage. 
10

Figure 5 is a side view showing delaminated plies and internal buckling, but the top surface is relatively 

free from visual damage indication. The damage was caused by an impact to the upper surface [10].

Figure 5. Delaminated plies and buckling.

Figure 6 depicts examples of defects discovered using various non-destructive test (NDT) methods. The 

examples in figure 6 show defects that most likely occurred during manufacturing (voids, porosity) and 

during use (chemical attack, oxidation cracks, micro-cracking, and buckling) [11].

Figure 6. Examples of hidden defects and damage.

2.2 Problem

Not all composite damage requires an immediate repair. With any type of construction there is a built in 

safety margin. The trouble lies in detecting and diagnosing damage in real time and during use to 

determine the criticality. The current methods for inspecting and determining the degree of damage that 

exists to the composite material are performed when the component is not in use. For aircraft, this is on 

the ground after a flight or during an inspection. Once on the ground, the continued risk to human safety 

is typically minimal. The ability to detect and diagnose the level of damage sustained or the incipient 

stages of hidden damage would be a significant asset to the fight crew and maintenance personnel adding 
11

an additional level of safety. 

Presently, there is no means to accurately detect and diagnose the extent of damage a composite structure 

has sustained and/or indicate the beginning stages of a potential catastrophic failure in real-time. Unless 

pilots have a reason to believe there is a problem they continue to fly. As with other industries, if there is 

no indication of an unsafe condition then operation will continue. Outside surfaces cannot be seen 

entirely during flight. Damage may appear minor on the outside, but the panel may have delaminated 

plies or other damage to the inside. Due to the lightning current path or load stress transfer, damage may 

be in an area away from the initial damage. Damage to surrounding areas could be overlooked or missed 

based on a visual inspection. The component or structure could fail prior to being detected. There is no 

way to determine the level of damage in-flight. This in turn could lead to additional damage to 

surrounding property, structures, and/or loss of life. Detection of the damage or degraded state of the 

composite structure in real-time is the focus of this research.

2.3 Objective

Creating a means to detect and assist in diagnosing damage to composites would allow for informed 

decisions by the user regarding safe continued operations. Open circuit resonant sensors lend themselves 

to filling this need. This task was focused on providing a proof of concept design utilizing an open circuit 

resonant sensor and demonstrating the ability to detect damage to a composite panel. The data gathered 

can be used for subsequent design iterations to further advance the conceptual use of open circuit resonant 

sensors on composites. Further advances may result in a sensor with multifunctional roles providing 

damage detection and diagnosis, lightning protection, and RF shielding.

2.4 Scope

Composite materials have applications beyond aircraft use as presented earlier. The applications of this 

sensor are focused on damage detection and diagnosis of composite materials in use on aircraft with the 

ability to serve a multifunctional role that includes lightning protection and RF shielding. This concept 

may be adaptable to other industries and applications.

2.5 Approach

The approach taken led to a methodical design, development, and test of an open circuit resonant sensor 

for detecting composite panel damage. The steps included a literature search to investigate work 

pertaining to composite materials and open circuit resonant design and applications, the design of a sensor 

based on the literature search, development and fabrication of a sensor based on the initial design, 

development of a test plan, test according to the plan, and documentation of the results.

Modeling of the open circuit resonant sensor design was also accomplished as a parallel effort using 

FEKO. FEKO is an electromagnetic (EM) analysis software suite. Use of the FEKO software was done 

to provide additional data for comparison with the actual measured results. This will also allow for a 

level of confidence for future modeling of the sensors to alleviate the need to fabricate sensors and 

perform testing on each sensor design. Instead, this will allow resources to be applied to the sensor 

designs that predict the intended design characteristics and reduce the need for trial and error setups.

The use of composite materials is ever increasing and the composition of the panel is largely determined 
12

by the intended area of use. A multitude of choices exist and it was determined that a standard 1⁄4” thick 

structural fiberglass composite panel would be used. Much of this was based on the availability and cost 

of the material. The panels were fourteen inches square.

Figure 7. 1⁄4 inch thick structural fiberglass panel.

Damage to the composite panel was limited to a simulated crack and a series of 5/16 inch holes drilled 

simulating a puncture. These types of simulated damage were selected because of the ability to be easily 

recreated for repeatability if needed. Figure 8 illustrates the fiberglass panel with the crack and punctures.

a. b. 

Figure 8. Simulated damage to fiberglass panels. (a) holes (puncture), (b) crack.

3.0 LITERATURE REVIEW

A literature search was performed in order to investigate work pertaining to composite materials, spiral 

inductors, and open circuit resonant design and applications. The information gathered from the literature 

search provided an adequate amount of information to design a series of open circuit resonant sensors and 

establish baseline data to initiate conceptual testing for damage detection.
13

4.0 SENSOR DESIGN

4.1 Sensor Background

The SansECTM is an open circuit resonant sensor technology developed by Dr. Stanley Woodard and his 

colleagues at NASA Langley Research Center. The HIRF Lab at LaRC is leveraging this technology 

with plans to develop it into an aircraft composite material damage detection and diagnosis system 

through focused research and development. The name SansECTM implies the open circuit approach 

through the translation Sans (without) EC (Electrical Connection). The sensor provides all the 

functionality of a traditional closed resonant circuit having, by geometrical design, an inductor element, a 

capacitor element, and a resistor element, but it does so without requiring any direct electrical connections 

to a generating source. The sensor is passive, but it is energized by an electromagnetic field generated 

remotely from the sensor and produces magnetic field responses when electrically stimulated. For 

simplicity, the term SansECTM sensor and open circuit resonant sensors will be used interchangeably and 

referenced as resonant sensors from here on out unless a distinction is necessary.

The sensor baseline design is a single physical component of geometric design constructed of conductive 

material. There is no single point on the circuit that if damaged destroys the functionality of the circuit. 

The resonant sensor is essentially an electrical inductor in the form of a planar spiral. Figure 9 illustrates 

a square spiral resonant sensor. The sensor does not utilize a distinct built in capacitor or resistor, but 

rather the inherent capacitance and resistance of the geometric design. The inductance (L) is the sum of 

all the self and mutual inductances of the individual trace interactions. The capacitance (C) is an inherent 

result of the width between the spiral traces and substrate dielectric and the resistance (R) is largely 

dependent upon the overall trace dimensions. Figure 10 is the schematic representation. If the sensor 

itself is damaged, such as torn or punctured, it still functions but with a different frequency response. 

Depending on the failure and its location, a single sensor or multiple coupled sensors will now exist and 

still maintain function [12]. This robustness of functionality makes these types of sensors highly effective 

in detecting and diagnosing damage.

Placing the sensor upon a non-conductive substrate material, such as a composite panel, alters the 

Figure 9. Square spiral sensor. Figure 10. Schematic representation of sensor.
14

sensor’s free space characteristics of frequency, amplitude, and bandwidth. The substrate material on 

which the sensor is placed or attached acts as a dielectric. Any changes of this modified baseline 

characteristic can be used to detect changes physically to the sensor or the substrate upon which it is 

placed or embedded. Comparisons of these changes from the baseline frequency, amplitude, or 

bandwidth response produce unique signatures that can be correlated to the detection of damage.

The baseline frequency corresponds to the resonant frequency of the sensor. The resonant frequency is 

determined by the interaction of the overall inductance and capacitance of the sensor. As seen by 

equation (1), the equation for resonant frequency of a LC circuit, resonance is inversely proportional to 

the square root of either L or C [13].

 

 

 

Eq. (1)

Where is the frequency in hertz, C is capacitance in farads, and L is inductance in henrys. The resonant 

frequency increases as the inductance decreases. The same effect is true if capacitance varies with 

inductance remaining constant. 

The resistance of the resonant sensor has the greatest affect on the selectivity or quality factor (Q) of the 

sensor which in turn affects the bandwidth. The simple definition of Q is the amount of energy stored 

over the energy loss in one cycle. In terms of inductors, Q becomes the ratio of peak magnetic energy 

minus peak electric energy. The inductance is also a factor in determining the Q, but resistance exerts a 

greater influence with inductance playing a greater role on the center frequency. The higher the quality 

factor the sharper the response curve at the resonant frequency. This sharper response curve in turn 

results in a smaller bandwidth (BW). The Q of a circuit can be determined by calculation or using 

measured data. When using measured data, the Q can be found using equation (2) [13].

 

 

 

 Eq. (2)

Where Q is the quality factor, is the resonant frequency, and is the difference in frequency at the 

3dB power points. The theoretical calculation of the quality factor takes into account the resistance of the 

circuit and is determined using equation (3) [13].

 

 

 

Eq. (3)

Where Q is the quality factor, f is the resonant frequency in hertz, L is the inductance in henrys, and R is 

the resistance in ohms. The effect the Q factor has on the sharpness of the resonant frequency and the 

bandwidth can be seen in figure 11. A higher Q factor results in a narrower bandwidth with the opposite 

true for a low quality factor.
15

Figure 11. Relationship of Q value to BW.

The conductivity of the substrate also has an effect on the Q value. A semi-conductive substrate will tend 

to couple more energy to the substrate material as the frequency increases. This coupling to the substrate 

causes losses in the inductor [14]. The frequency where this begins to occur is dependent on the size of 

the spiral inductor.

4.2 Design Factors

The theoretical design of resonant sensors is an involved process. Several key parameters must be known 

to accurately make calculations and modeling. Characteristics of both the sensor and the substrate on 

which it is to be placed or embedded must be known. The sensor key parameters are inductance, 

capacitance, and resistance. In addition to the sensor’s characteristics, the substrate’s physical properties 

must be known to include not only the physical layup, but also permittivity and conductivity.

The sensor behaves differently when placed against the substrate due to the spiral-to-substrate interaction

as opposed to free space. This interaction has an influence on the inductance, capacitance, and the 

resistance of the sensor. This in turn has an effect on the resonant frequency.

4.2.1 Inductance Value

The open circuit resonant sensor, as stated earlier, is a planar spiral and essentially an inductor. As with 

any inductor, the inductance value is composed of self and mutual inductance. The self-inductance is a 

measure of the magnetic field generated by a time-varying current. Mutual inductance is the measure of 

mutually coupled magnetic fields of adjacent traces with current flowing in the same direction. There are 

various empirical formulas that exist for calculating the inductance of a planar spiral [15]-[21]. These 

methods are mathematically intensive and were beyond the primary objective of a proof-of-concept

design and, therefore, will not be presented. The Missouri Electromagnetic Compatibility Laboratory 

presented several simplified equations for various geometries. Equations (4) and (5) are for a circular 

spiral and a square spiral, respectively [22].
16

Eq. (4)

Eq. (5)

Where (N) is the number of turns, (R) is the outer radius in centimeters, (a) is the wire radius in 

centimeters, (μ0) is the permeability of free space, (μr) is the relative permeability of the medium 

(conductor), and (W) is the length of one side in centimeters. These are approximations and have a 

tolerance of 20% range as determined by comparison of calculated and measured data. These calculations 

are based upon the spiral in free space.

4.2.2 Capacitance Value

The capacitance is an inherent result of the width between the spiral traces and the interaction with the 

substrate which acts as a dielectric. The capacitance is considered to be parasitic and is minimal in a 

basic geometric spiral in free space or with a non-conductive substrate. As with the calculation of the 

inductance values, several equations exist for the calculation of the capacitance [15], [16], and [19]. To 

accurately calculate the capacitance of the sensor when placed upon a substrate the permittivity of the

panel must be known.

The sensors for this project were tested in free space and on fiberglass composite panels. The fiberglass 

panels used were nonconductive and therefore the capacitance was considered to be minimal and more 

parasitic in both cases. The capacitance of the sensor was determined mathematically by manipulating 

the resonant frequency equation, equation (1).

4.2.3 Resistance Value

The total resistance of a planar spiral is a combination of series and parallel resistance. Series resistance 

is both dependent and independent on the frequency. The independent portion is essentially the direct 

current (D.C.) resistance of the wire, or in this case the trace, and is largely dependent on the total length. 

The frequency dependent portion of the overall resistance is due to the effects of eddy currents. The 

parallel resistance is a result of the finite resistance between the substrate material and the spiral 

conductor. Several equations exist to calculate the series and parallel resistance [16] and [19].

4.3 Design Geometry

The overall dimensions of the resonant sensor will play a significant role in inductance, capacitance, and 

resistance of the sensor. The size will factor into the trace width, spacing, and total length. The size of 

the composite material under test will also factor into the final design geometry. This project involved 

structural fiberglass test panels measuring 14 inches by 14 inches. This allowed for a multitude of 

possible sensor geometric layouts.

Sensors for this project were in the shape of polygonal spirals which included triangular, square, 

hexagonal, and circular. Spiral sensors of varying sizes for each shape were created. Figure 12 illustrates 

the four geometric shapes that were used.
17

a. b. c. d.

Figure 12. Spiral geometries utilized for the resonant sensors: (a) triangular, (b) square, (c) hexagonal, and (d) 

circular.

5.0 SENSOR DEVELOPMENT and FABRICATION

After gaining an understanding of the basic elements needed for the design, the development of the 

resonant sensor was undertaken. A process for the development and fabrication of the resonant sensors 

was created. The process outline was as follows:

 Theoretical Design

o Desired resonant frequency

 Geometry of sensor

 Overall shape (circular, square, hexagon, octagon, triangle, etc.)

 Trace width

 Trace spacing

 Physical Design

o Drawn using AutoCAD

 Fabricate

o Klik-N-Kut used to cut copper sheet per design

5.1 Theoretical Design

The theoretical design process began with determining an initial resonant frequency for the sensor. It was 

determined that a variety of sensor shapes and sizes should be fabricated. This was decided upon in order 

to explore and characterize a wider range of sensors to gain further insight into the feasibility of the 

sensor for damage detection and diagnosis. In order to limit the number of variables, the outside 

dimension was the only change made to the sensors when designing sensors of varying size and shape. 

For simplicity, a determination was made to use a trace width and spacing that would result in a sensor’s 

outer dimension incrementally equaling the whole inch.
18

The trace width and spacing was kept constant within all the designs. This allowed each subsequent 

larger sensor to increase the number of spiral turns. A trace width of 3/32 of an inch and a spacing of 

1/32 of an inch was used.

5.2 Physical Design

The designs were done using Autodesk AutoCAD software. The use of computer-aided design (CAD) 

software allowed for an easy means to create sensor layouts of various dimensions and geometries, as 

well as rapid redesigns. Resonant sensors of the chosen geometries were designed with overall outer 

dimensions from two inches to fourteen inches in one inch increments. As stated in the previous section, 

the trace width and spacing between traces was kept constant for all designs. Figure 13 shows sensors 

ranging in size from two inches to nine inches and illustrates the constant stepped increase in the overall 

dimensions of the sensor.

Figure 13. Square spirals ranging in outer dimensions from 2 inches to 9 inches.

5.3 Fabrication

The resonant sensors were fabricated from adhesive backed 1.25 mil thick copper sheets. The CAD 

designs were exported to a Klik-N-Kut (KNK) machine, figure 14, for cutting. The KNK machine 

provided a rapid means of cutting the copper sheet of the various geometries with a higher degree of 

accuracy and repeatability compared to using a straight edge and razor blade.

The first sensors were adhered directly to the fiberglass panel after cutting as seen in figure 17. This 

required a level of delicacy to prevent the sensor from becoming distorted in the transfer from the backing 

to the panel.

9” 8” 7” 6” 5” 4” 3” 2”
19

Adherence of the sensor directly to the panel presented difficulties in testing additional sensors using the 

same panel. Removal of the sensor resulted in damage to the sensor rendering it unusable for repeated 

testing. This increased the time and cost between tests due to having to fabricate new sensors. A means 

to reduce the cost and time was devised. The sensors were laminated after cutting. This allowed for the 

sensors to be used repeatedly and enabled comparisons using the same sensor, not just the same design. 

The rigidity of the laminating plastic also made the sensors more robust which made handling simpler. 

The sensors could be easily reconfigured and the results allowed for a higher degree of repeatability. 

Laminating the sensors provided a cost savings in both time and materials. Figure 16 is an example of a 

hexagonal laminated sensor.

Figure 16. Laminated hexagonal spiral resonant sensor.

6.0 TEST PLAN

A test plan was developed to provide a methodical process to characterize the resonant sensor first in free 

space and then with it attached to the fiberglass composite panel. After characterizing the sensor and 

establishing a baseline measurement, the simulated damaged panels were utilized and the resulting 

measurements compared against the baseline. Conclusions could then be drawn. The test plan was 

broken down as follows:

 Normalize measurement system and determine background noise level for each transmit/receive 

antenna utilized

Figure 14. KNK Machine. Figure 15. Sensor adhered directly to fiberglass panel.
20

 For each sensor prototype:

o Baseline measurements in free space

 Resonant frequency and harmonics

 Amplitude of resonant frequency and harmonics

 Waveform of resonant frequency and harmonics (Q, BW)

o Baseline measurements with sensor attached to fiberglass panel

 Resonant frequency and harmonics

 Amplitude of resonant frequency and harmonics

 Waveform of resonant frequency and harmonics (Q, BW)

o Measurements with sensor attached to damaged panels

 Resonant frequency and harmonics

 Amplitude of resonant frequency and harmonics

 Waveform of resonant frequency and harmonics (Q, BW)

 Comparison of measured data

 Conclusions

6.1 Normalization and Background Noise Level

Normalization of the measurement system was performed to eliminate the transmit/receive antenna 

characteristics from the sensor measurements for each antenna that was used and the background noise. 

This process involved taking a measurement using just the antenna and then subtracting this from future 

measurements. The background noise level measurement provides a baseline for comparison of the 

sensor’s resonant frequency and harmonics amplitude to the background.

6.2 Sensor Free Space Characterization 

Each sensor was characterized in free space. The characterization was used to establish a baseline in 

order to document the interaction of the substrate and the sensor. The baseline measurements included 

the resonant frequency, the harmonic frequencies within the span range, amplitude, and waveform (Q 

factor). This was done using various sized antennas. An example of the measured data can be found in 

Appendix A, Figures 18 and 20.
21

6.3 Sensor + Substrate Characterization

Once the initial baseline measurements were made the next step involved repeating the measurements 

with the various sensors attached to the composite fiberglass test panels. Each sensor was again 

characterized and a baseline was established that included the composite substrate. These baseline 

measurements included the resonant frequency, the harmonic frequencies within the span range, 

amplitude, and waveform (Q factor). This was also done using various sized antennas. An example of 

the measured data can be found in Appendix A, Figures 18,19, and 20.

6.4 Sensor Measurements of Damaged Panels

The data taken from the baseline measurements was analyzed and a determination was made as to which 

sensors would be used for testing of the damaged panels. The characteristics of the sensors selected for 

further testing were again measured utilizing the same methods as the baseline. An example of the 

measured data can be found in Appendix A, Figure 20.

6.5 Data Comparison

The collected data from the baseline free space, baseline undamaged composite panel, and damaged panel 

measurements was analyzed. Shifts in the resonant frequencies and harmonic frequencies were 

compared. Along with the frequency changes, the amplitudes and waveforms were compared for 

deviations. Examples of the measured data is presented in Appendix A.

7.0 TEST METHODS

The test plan was carried out based on experimental and computational methods. The experimental 

methods utilized a network analyzer and a magnetic response measurement system to test the 

functionality of the sensors. The computational method involved the use of computational software. As 

explained previously, the computational method was performed on a limited basis, as an aside, to provide 

a level of confidence for future use of the computational software.

7.1 Experimental Tools

7.1.1 Sensor Measurements with a Network Analyzer

Sensor characterizations and measurements were preliminarily explored with a network analyzer. The 

Agilent Performance Network Analyzer (PNA) system was used to search out the self resonance 

characteristics of several of the earlier developed sensors. The S-parameter model is obtained directly 

from measurements and will be useful for further design steps and analytical and computational model 

validation of planar geometric sensors.

The self resonance frequency of a sensor can be measured by using a network analyzer with a loop probe. 

The RF electromagnetic field is generated by connecting a loop probe to the output of the network 

analyzer set in reflection mode (S11 measurement). The equipment setup will directly display the 

system’s resonant frequency. The network analyzer frequency sweep range was set up to be around the 

expected values of the sensors fundamental response and the first several harmonics. For the E8364C 

PNA, the lowest frequency available is 10 MHz, so this was used as the start frequency for the first 
22

preliminary sensors tested. Ideally for the full range of sensors planned the start frequency would be 

lower. The stop frequency was set to 1000 MHz to establish a broad bandwidth over which to explore the 

fundamental resonance frequency of particular sensors and any associated higher order harmonics. The 

instrument was also configured to display data in the log magnitude format.

An initial background measurement was performed and the system normalized. The sensor under test 

was placed in the vicinity of the loop probe. The sensor and the loop probe were parallel and planar to one 

another. The probe was placed against the sensor. This orientation insures the maximum coupling of the 

electromagnetic field. The illuminating field was generated by the internal source of the network analyzer, 

transmitted through port one over a cable connected to the loop probe and then to the sensor. Upon 

excitation from the network analyzer the sensor under test resonates at a natural resonant frequency 

governed by the sensor’s physical geometry. The sensor reflects, or more accurately re-radiates, a portion 

of the incident signal into the loop antenna connected to the network analyzer. This signal is received and 

processed by the network analyzer as an S11 measurement. 

The resonant frequency corresponds to the minimum observed point on the S11 measurement logarithmic 

magnitude plot displayed on the network analyzer. Figure 17 shows the set-up using the PNA system. 

The sensor was first tested in free space away from the composite panel and then tested again with the 

sensor on the panel. Selected sensors were measured against the damaged panel.

Figure 17. Measurement using Performance Network Analyzer (PNA) system.

7.1.2 Sensor Measurements with a Magnetic Field Response Measurement System

Dr. Stan Woodard and his group developed a Magnetic Field Response Measurement Acquisition System 

which was used as the primary data acquisition instrument for the characterizations of the developed 

resonant sensors. The components of the measurement system consisted of a loop antenna for transmitting 

and receiving magnetic fields, a processor for regulating the magnetic field transmission and reception, 

and software for control of the transmitted signal and for analyzing the responses received. A detailed 

description of the system can be found in NASA Technical Memorandum NASA/TM-2005-213518 

“Magnetic Field Response Measurement Acquisition System” [23].
23

As with using the PNA, a background measurement was made. The sensor was first measured in free 

space and then again against the baseline panel. Select sensors were finally measured against the 

damaged panels.

7.2 Computational Tools

An attractive alternative for the trial and error measurement methodology is based on the use of 

electromagnetic simulation tools that allow predictive design. This is a process where the behavior of the 

electrical spirals can be predicted without the need for expensive and time consuming fabrication or 

measurements. Simulation tools allow a designer to characterize a virtual spiral, which is defined in a 

layout drawing environment. Most electromagnetic simulation tools produce frequency dependent Sparameters and are, therefore, virtual equivalents to the measurement-based technique. The choice of the 

simulation tool used is important.

Advantages of the simulation-based approach are that the designer has more flexibility to try variations of 

the spiral layouts or even optimize the spiral layouts so that a desired behavior is obtained. A typical 

simulation on a spiral, including the setup and interpretation of the results should not take more than half 

an hour. Alternative setups can be used to obtain better performing spirals, as a simulation on a new type 

of spiral is faster and cheaper than actual fabrication and testing. Simulation tools do require an accurate 

setup of the process parameters such as substrate and metallization characteristics. A process

characterization step is therefore advised.

A characterization using simulation offers more flexibility during the design process of the spirals. The 

simulation provides predictive characteristics during the design process, and changes can be made easily 

to optimize and fine-tune the layout of the spiral for an optimal inductance value and quality factor. This 

optimization process can be automated. Parameter studies can reveal sensitivities and insight on how to 

improve the behavior of the spiral. A simulation-based approach requires an accurate, computationally 

efficient and user-friendly tool.

Computational analysis for this effort was done using FEKO. This method was used to validate a 

sensor’s initial characteristics. The computational software was not utilized for simulating the sensor’s 

response to a damaged panel because the altered material properties must be known.

8.0 CONCLUSIONS

A single resonant sensor of each geometric design was measured in free space and compared against each 

other in order to determine the most feasible design to further test and develop for this effort. It was 

determined that the square and hexagonal spirals provided the greatest amplitude above the noise floor as 

compared to the circular and triangular spirals.

The sensor acts as a rudimentary antenna, and it was determined that the resonant frequency increases as 

the size of the sensor decreases. This is important because the ability to penetrate the composite panel 

surface will most likely be dependent upon frequency.

A shift in the resonant frequencies and a decrease in the signal amplitudes occurred when the sensor was 

placed against the fiberglass composite panel. Also, it is important to notice that the greatest notable 

changes occurred to the higher resonant (harmonic) frequency when measuring the damaged panels.
24

The sensors show promise and ability to detect damage in fiberglass composite panels. The fiberglass 

panels are nonconductive and were a good starting point for the initial research into using the sensors for 

damage detection and diagnosis. Further testing will need to be accomplished to determine the ability of 

the sensor to function on carbon and other composite materials that have semi and conductive properties.

 The ability to provide lightning protection and RF shielding needs to be explored. The variability of the 

sensor geometry and experimentation of the trace width and spacing will aide in further refinement. It 

may be possible to direct lightning currents based on the geometry of the sensor.

FEKO has demonstrated ability to accurately predict resonant frequencies and could possibly be used to 

design and verify future designs. This will greatly reduce the overall time required for design and testing. 

Further validation of the software’s ability to be used in simulating shielding effectiveness needs to be 

performed by comparison of actual measured and simulated data.
25

Appendix A

Appendix A contains the measured data for the three inch through nine inch square spiral sensors and the 

four inch hexagonal spiral sensor. The two inch square sensor did not return a signal above the measured 

noise floor. This may be due in part to the receiving/transmitting antenna’s size, initial radiating power 

output, or the scanned frequency range of the measurement system.

The test data is presented in graphical form. The sensor geometry, and transmitting/receiving loop 

antenna size, and measurement conditions (free space, attached to baseline fiberglass panel, or simulated 

damaged fiberglass panel) are indicated.

Figure 18. Comparison of the 8 inch square spiral sensor measured data in free space and against the fiberglass 

baseline panel.

Figure 18 is a comparison of the eight inch square spiral resonant sensor measured in free space and 

against the fiberglass composite panel. The graph shows that the resonant frequencies and the associated 

amplitudes decrease when placed against the fiberglass panel.

6.8 8.4 15.4 18.4 22.8 27.2 36.4 0

10

20

30

40

50

60

S/N (dB)

Resonant Frequencies (MHz)

8" Square Spiral

using 4" Loop Antenna

On Fiberglass Baseline 

Panel

Free Space
26

Figure 19. Comparison of Square spiral sensors sizes 3 inch through 9 inch against fiberglass panel using a four 

inch antenna.

Figure 19 is a comparison of the measured data of the square spiral sensors, ranging in size from three 

inches to nine inches. It can be seen that as the frequency decreases the amplitude increases.

The measurements of the four inch hexagonal spiral sensor using a four inch loop antenna are depicted in 

figure 20. The graph shows the resonant frequency and amplitude variations of the sensor measured in 

free space, against the baseline fiberglass panel, against the fiberglass panel with the simulated crack, and 

against the fiberglass panel with the simulated punctures. Most notably is the lack of change at the lower 

resonant and harmonic frequencies as compared to the second harmonic. The primary resonant and first 

harmonic frequencies show no change from the baseline panel. The third harmonic indicates both a 

frequency and amplitude deviation from the baseline, as well as the different types of damage. This is 

thought to be a result of the inability to penetrate the surface at higher frequencies and the simulated crack 

and punctures provided an “entry.”

Figure 20. Comparison of the 4 inch hexagonal spiral sensor measurements.

0

10

20

30

40

50

60

3 4 5 6 7 8 9

Sensor Size (inches)

Square Spirals on Fiberglass Panel 

using 4" Square Loop Antenna

Fundamental Resonant 

Frequency (MHz)

S/N (dB)

0

10

20

30

40

50

S/N (dB)

Resonant Frequencies (MHz)

4" Hexagonal Spiral Sensor

using 4" Loop Antenna

Double 

Thickness

Crack 

Damage

Baseline

Puncture 

Damage

Free Space
27

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