Innovative sensor-based network for autonomous on-orbit structural health monitoring

Abstract

The increasing density of space debris in the Low Earth Orbit and the need to face long-term manned missions have raised the interest on vehicle health monitoring systems. Active monitoring techniques involving a network of sensors may assist in the impact detection and evaluation process, reducing the cost and risk of such activities. However, any space technology must deal with stringent mass, volume and power requirements. In the framework of the two-days NASA Space Apps Challenge 2018, the student team ARACNE from Politecnico di Milano has faced the design of a system aimed to monitor the structural health of the external skin of a spacecraft. The proposed system would consists on a network of self-powered Triboelectric Sensors whose measurements are processed by two pre-trained Artificial Neural Networks (ANNs). The networks are designed to estimate both impact position and magnitude. An extended literature research on Triboelectric Nanogenerators (TENGs) has shown that it is possible to design a proper device capable to operate in the space environment and provide the desired dynamic signal with low power and mass budgets. A case study is finally introduced and analysed for the ISS in order to assess the feasibility of the system.

Full text

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IAC-19- D1.1.8.x53464
Innovative sensor-based network for autonomous on-orbit structural health monitoring
Salvatore Andrea Bellaa,*, Renato Cirellib,*, Álvaro Romero-Calvoc,*, Aloisia Russod,*, Francesco Ventree,*
a Department of Aerospace Sciences and Engineering, Politecnico di Milano, Italy, salvatoreandrea.bella@mail.polimi.it
b Department of Aerospace Sciences and Engineering, Politecnico di Milano, Italy, renato.cirelli@mail.polimi.it
c Department of Aerospace Sciences and Engineering, Politecnico di Milano, Italy, alvaro1.romero@mail.polimi.it
d Department of Aerospace Sciences and Engineering, Politecnico di Milano, Italy, aloisia.russo@mail.polimi.it
e Department of Aerospace Sciences and Engineering, Politecnico di Milano, Italy, francesco.ventre@mail.polimi.it
* Corresponding Author Abstract
The increasing density of space debris in the Low Earth Orbit and the need to face long-term manned missions have
raised the interest on vehicle health monitoring systems. Active monitoring techniques involving a network of sensors
may assist in the impact detection and evaluation process, reducing the cost and risk of such activities. However, any
space technology must deal with stringent mass, volume and power requirements. In the framework of the two-days
NASA Space Apps Challenge 2018, the student team ARACNE from Politecnico di Milano has faced the design of a
system aimed to monitor the structural health of the external skin of a spacecraft. The proposed system would consists
on a network of self-powered Triboelectric Sensors whose measurements are processed by two pre-trained Artificial
Neural Networks (ANNs). The networks are designed to estimate both impact position and magnitude. An extended
literature research on Triboelectric Nanogenerators (TENGs) has shown that it is possible to design a proper device
capable to operate in the space environment and provide the desired dynamic signal with low power and mass budgets.
A case study is finally introduced and analysed for the ISS in order to assess the feasibility of the system.
Keywords: triboelectric sensors, neural network, structural health monitoring, spacecraft monitoring, impacts
detection.
Nomenclature
 TENG cross area
 Detected TENG acceleration
 Speed of sound in material 
 Equivalent TENG circuit impedance
 Impacting particle diameter
 Impact time
 Elastic modulus of silicon rubber thread
 Vibration frequency
 TENG natural frequency
 Material group
ISC Short Circuit Current
 Length of silicon fiber
 Equivalent TENG circuit impedance
 Inner TENG inertial mass
 Penetration depth
 Impact pressure
 Density of the impactor
 Density of the impacting particle
R2 Regression index
 TENG surface area
 - slope in material 
 tribo-charge surface density
 Impact time
 Thickness of the target plate
 Particle velocity inside the target
 Impact velocity
Voc Open Circuit Voltage
Acronyms/Abbreviations
Al Aluminium
Al2O3 Aluminium oxide
ANN Artificial Neural Network
Cu Copper
FOS Fibre Optic Sensor
FSTENG Free-standing TENG
HVI Hypervelocity Impact
ISS International Space Station
LMRO Launch and Mission Related Objects
LSTENG In-plane Sliding TENG
MLI Multi-Layer Insulation
MMOD Micro Meteoroid and Orbital Debris
NGs Nanogenerators
PA Polyamide
PDMS Polydimethylsiloxane
PENGs Piezoelectric Nanogenerators
PMMA polymethyl methacrylate
PTFE Polytetrafluoroethylene
RH Relative Humidity
SETENG Single Electrode TENG
SHM Structural Health Monitoring
SiO2 Silica
SRM Solid Rocket Motor
TA Triboelectric Accelerometer
Ti Titanium
TiO2 Titanium dioxide

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TENG Triboelectric Nanogenerator
VCSTENG Vertical Contact Separation TENG
VHM Vehicle Health Monitoring
1. Introduction
Space debris and micrometeorites fluxes are a major
concern for spacecraft operators [1]. The impact of
millimetre-size particles can lead to catastrophic
damages and typically require avoidance manoeuvres
and passive shielding [2]. Periodical inspections have to
be carried out in manned missions, resulting in high costs
in terms of time and resources for both the astronauts and
the on-ground segment [3]. Extra-Vehicular-Activities
aimed to inspect the damage also imply an increased
safety risk for the astronauts [4]. Advanced embedded
systems for anomaly detection are then highly desirable
[5]. The development of Vehicle Health Monitoring
(VHM) systems has the potential of increasing the
robustness and reliability of space vehicles as well as
maximizing the safety and survivability of the crew on
manned missions [6]. Moreover, the required vehicle
maintenance practices could be scheduled and performed
according to the data collected by the system, resulting in
reduced costs and a safer operation [7].
Structural Health Monitoring (SHM) plays a central
role in VHM. It consists of a sensor network, integrated
into the vehicle design process, capable of providing
useful real-time information upon the structural
condition. Typically, SHM systems also aim to detect,
locate and measure possible impact events on the
monitored structure [8]. Nowadays, the concept of SHM
is widely applied to various forms of infrastructures such
as bridges and skyscrapers and it relies on signal
processing and statistical analysis to correlate the
measured data with the state of the structure [9]. The
main functional blocks of a SHM system are (i) Data
acquisition from a network of sensors (ii) Data filtering
(iii) Feature extraction (iv) Deterministic/Machine
Learning-based model [8]. This framework can be
implemented on any type of aerospace structures,
extending the range of application to space manned
vehicles.
Triboelectric Nanogenerators (TENGs) [10, 11],
may be particularly well suited for space applications
[12]. In contrast with previous technologies, TENGs are
self-powered and can hence operate without a dedicated
power supply network.
In the following sections authors discuss about the
application of TENGs as spacecraft SHM sensors. The
effects of pressure, relative humidity, temperature and
radiation on TENGs are summarized based on the
existing bibliography and particularized for the
operational range of interest. A case study is then
introduced and analysed.
This paper is divided as follows: an overview of the
proposed SHM procedure is given in Section 2. Section
3 presents the TENGs and a feasibility study for their
employment in the selected space environment. In
Section 4 a case study is proposed in order to give a
practical application to the procedure. The results
obtained in the impact modelling context are illustrated
in Section 5. Finally, Section 6 summarizes the
conclusions of the work.
2. Project overview
From the previous considerations it emerges the need
of developing active detection systems for spacecraft
SHM. Already existing systems rely on gyroscopic
measurements and other data sources. Also, many of
them employs Piezoelectric Nanogenerators (PENGs).
However, the usage of PENGs implies a lower output
power density and limited operational modes [13]. The
system proposed in this paper consists of a sensors
network composed of an array of TENGs uniformly
distributed on the surface to be monitored. The sensors
are able to measure indirectly the response (e.g. local
stress, displacements, etc.) of the structure due to an
external solicitation (e.g. MMOD impact). However,
their output is highly influenced by the operating
environmental conditions.
This kind of sensors may be suitable for the
implementation of a wireless net that conveys the
measured data to the on-board data handling system [14].
This allows to reduce the mass budget thanks to the
absence of feeding cables, which represents a 4-7% of the
dry mass of a spacecraft [13], and the power consumption.
Nevertheless, with the current technology, TENGs are
not able to supply enough power to allow a wireless data
transfer. This could be done by using a battery to store
the energy, but this would imply an increment in mass
[15].
In the case of limited available energy for the sensor data
transmission, an efficient way to proceed would be to
locally process the acquired signal with the aim of
extracting characteristic information. The latter are then
transmitted to a central unit that feeds pre-trained ANNs
with the aim to estimate useful data for SHM. The usage
of ANNs, with normalized inputs and outputs, allows the
procedure to be applied on any type of structure (e.g.
shells, sandwich panels, etc.). Moreover, its versatility is
connected to the capability of mapping highly non-linear
links between the input and the output layers.
3. Sensors overview
Different types of sensors are suitable to be used in
space for SHM. Their working principles are based on
acoustic or microwave emission detection, thermography
or calorimetry. Accelerometers, Fiber Optic Sensor (FOS)
[16], resistor-based sensors or surface inspection cameras
have been employed in the past [17].

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Two of the main problems that engineers must face
when dealing with the design of a space system are the
limited mass and power availability. This leads to prefer
compact and efficient electronics, since power sources
are needed for the independent and continuous operation
for such devices [18].
In last decades, nanogenerators (NGs) have been
developed, and the piezoelectric and triboelectric effects
have been exploited to harvest small-magnitude
mechanical energies while preserving reduced
dimensions [19, 20, 21, 22]. In this scenario, self-
powered sensors can largely simplify the design and
operation of a given system [23].
3.1 Triboelectric Nanogenerators (TENGs)
First proposed in 2012 [10], TENGs have gained an
increasing attention due to their capability of being used
both as energy harvesters and self-powered sensors [19,
20, 24, 15]. TENGs working principle relies on two
combined physical effects: contact electrification and
electrostatic induction. The former describes a surface
phenomenon where charges form with opposite polarity
(triboelectric pair) due to the contact between two
different materials which exhibit distinct surface electron
affinity. The latter takes place when two electrodes are
mounted on the backside surface of the triboelectric pair:
moving the pair allows the charges to flow due to the
electrification induction effect [10, 25].
There are many prototypes of TENGs based on four
fundamental working modes (Fig.1): (i) Vertical contact-
separation mode (VCSTENG), (ii) In-plane sliding mode
(LSTENG), (iii) Single electrode mode (SETENG), and
(iv) Free-standing triboelectric-layer mode (FSTENG)
[15].
Fig. 1: Four operational modes of TENGs. (a) Vertical
contact separation mode. (b) Contact sliding mode. (c)
Single electrode mode. (d) Free-standing triboelectric
layer mode.
In the VCSTENG (Fig. 1a) the electrodes are located on
the top and bottom of the structure. Separating the layers
by means of an external solicitation creates a drop of
potential. If the electrodes are electrically connected to
each other, the free electrons flow creates an opposite
potential. Once the gap is closed the triboelectric charge
potential disappears and the electrons flow back [26, 27].
VCSTENG is the most known operational mode, due to
its simple architecture and its capability to generate
relatively high outputs [28, 27]. This mode has been
investigated for various applications, including energy
harvesting [29] and pressure sensing [30]. The structure
of LSTENG (Fig. 1b) is the same as VCSTENG with the
exception that the charge generation is activated with a
relative sliding movement of the layers [31] which
creates triboelectric charges on the contact surfaces. The
sliding can be induced with a planar motion, a cylindrical
rotation, or a disc rotation [32, 33]. SETENG
architectures (Fig. 1c) typically consists of two parts: a
grounded bottom electrode and a free-to-move
triboelectric surface. When the surface approaches or
leaves the bottom electrode the local electrical field
distribution is changed enabling the electron transfer
between ground and electrode. In this way, the
mechanical movement is transformed into an electric
current [25]. The relative movement can be either contact
separation motion or sliding motion [34, 35]. The single
electrode mode has lower output than other TENGs
architectures. Therefore, many applications have focused
on energy harvesting from human body movement [36,
28]. Two symmetric electrodes and a freely moving
triboelectric layer compose instead the architecture of the
FSTENG (Fig. 1d). The triboelectric charging takes
place between the moving TENG layer and the electrodes
[37]. The movement of the free-standing triboelectric
layer causes an asymmetric charge distribution on
electrodes, inducing a charge flow between them to
balance the potential difference [38]. This mode can
operate using both vertical motion and sliding motion.
Different TENG architectures have been
investigated for sensing applications due to their
capability of being self-powered, flexible and highly
sensitive. Those include motion tracking [39], human
health monitoring systems [40, 41], self‐powered wind
velocity [42], pressure [43, 30], force [21] and
temperature and weight [44] sensors. Each operational
mode has its own peculiarities which make it suitable for
different applications. Moreover, evolved configurations
and combination of TENGs with other sensing devices
have led to the creation of hybrid sensors/energy
harvesters such as TENG/Electromagnetic Generators
(EMG) [45, 46], Coaxial Hybrid TENG (CHTENG) [47]
, Spherical Hybrid TENG [48] or Tribo-Pyro-
Piezoelectric devices [49].
3.2 Feasibility study in the space environment
The influence of the environment on the generation
of charges in triboelectric sensors is currently under
investigation. An overview on the environmental effects
on nanogenerators is given in [50] while optimization
strategies to minimize the influence of the environmental
variables on the power output of different TENGs
configurations are widely discussed in [51]. In the
following subsections, a bibliography review is presented

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with a focus on the employment of TENGs in space. The
operational conditions of the International Space Station
(ISS) [52, 53, 54] listed in Table 1 have been taken as a
reference for the study.
Table 1: Reference conditions for the ISS employed in
the feasibility study
Variable
Range of operation
External temperature
173.15-373.15 K
Internal temperature
294.15-297.15 K
External pressure
10-4 Pa
Internal pressure
9.79 -10.27 · 105 Pa
External humidity
0%
Internal humidity
60%
Radiation
4.4-10.5 rad/year
3.2.1 Pressure
Atmospheric pressure has been shown to affect the
contact electrification process of TENGs [55, 56].
Many researches have concluded that pressure influences
two main effects: equilibrium of the adsorbed charge
species and thickness of the adsorbed nanoscale water
layer [50].
Adsorption process is a surface phenomenon by
which a multi-component fluid (gas or liquid) mixture is
attracted to the surface of a solid adsorbent and forms
attachments via physical or chemical bonds [57].
Regarding the adsorption effect, in [56] has been
concluded that, for a charged dielectric surface exposed
to different pressure, decreasing pressure decreases the
charge density on the dielectric surface. However, this
effect can be reduced choosing materials with proper
adsorption energies.
To describe the adsorbed water layer effect, in [55] it
has been experimentally quantified the effect of the RH
and pressure on the charge generated from a VCSTENG
built using Aluminium and Polydimethylsiloxane
(PDMS) as contact surface materials. When RH is kept
constant at ~0% across the tested pressure range, the
output charge decreases monotonically with pressure.
Increasing both pressure and RH rises the output until a
maximum value (around 60% RH) and then lowers it.
This peak can be explained considering that a change in
the atmospheric pressure changes the chemical potential
of the vapor. This change shifts the equilibrium and thus
changes the charge density of the adsorbed layer [56]. In
a more recent study [58], using a VCSTENG with layers
of Cu and Polytetrafluoroethylene (PTFE) and changing
the atmospheric pressure to high-vacuum (~ 10-4 Pa) an
increment of the surface charge density from 50 μC/m2
up to 660 μC/m2 has been obtained. Moreover, coupling
the PTFE layer with a ferroelectric material, the charge
density has been increased up to 1003 μC/m2. Since the
charge density is directly proportional to the outputs of
TENGs [59], the current, voltage and output power
increased significantly. All these studies have confirmed
the capability of TENGs of working both in high-vacuum
and atmospheric pressure environment.
3.2.2 Relative Humidity (RH)
Even if for outer space the effects linked to humidity
could be considered negligible (RH ~ 0%), since the
sensorial network in study could also be installed in
internal ambient it is worth noting the influence of this
environmental parameter on TENGs performances.
Liquid or vapour water can penetrate the TENG
package, affecting the contact electrification effect. This
influence depends on the characteristics of the material
as well as the RH content of the environment [60]. In [60]
it has been investigated the behaviour of hydrophilic
polyamide (PA) and hydrophobic PTFE TENGs exposed
to water in form of droplets and vapour. For both
experiments, the PTFE-based TENG voltage output
remained unaltered while the PA-based TENG voltage
output reduced to a negligible value. This study has
proven the capability of polymer based TENGs to be
used as self-powered active sensors for monitoring
activities in humid environments.
As stated in the previous paragraph, the analysis
conducted increasing both pressure and RH in [55] has
shown that the p-p Charge increases until RH value
reaches 60% and then is lowered due to the concurrent
effect described in [56]. This suggest that TENGs could
also be able to work under a wide range of RH conditions
without relevant losses in terms of output. However,
some studies suggest the importance of maintaining
substantial humidity conditions due to the charge
stabilizing effect of moisture present in air [61].
It can be concluded that, based on the humidity level,
properly constructed TENGs can be employed for in-
space applications, both in internal and external
environment.
3.2.3 Temperature
Temperature is known to influence the output
behaviour of TENGs directly affecting triboelectric
charging and electrostatic induction process [62, 63, 64].
An investigation on the temperature influence on the
triboelectric charging has been conducted in [62] using
Ti-SiO2 and Ti-Al2O3 VCSTENGs. Results have shown
that increasing temperature from 293 K to 473 K
decreases the TENGs charge and output current/voltage.
This behaviour has been explained using an electron
cloud potential well model, where atoms of the materials
have been represented by a potential well with outer shell
electrons loosely bound, forming an electron cloud.
When the two TENG layers contact, clouds overlap.
After the separation, according to the model, an energy
barrier appears. This barrier can be overcome by

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electrons considering that the thermal energy of the
material increases with temperature. This effect enables
electrons to go back to the atom of the material where
they have come from (or to the air), which results in the
reduced triboelectric charging at sufficiently large
temperatures.
A more general correlation between TENG outputs
(i.e. Voltage and Current) and temperature variation is
presented in [63]. The behaviour of a VCSTENG made
of PTFE and Al layers has been investigated over the
range 77 – 500 K. The results have shown that the outputs
are maximised around 260 K and decrease significantly
when the temperature is further increased. The first trend
(77-260 K) is due to the softening effect which increases
the contact surface, enhancing the triboelectric charge
transfer. The following decreasing trend depends on
thermal fluctuations on the contact surface, which forces
the charge density to reduce. It can be concluded that a
change in temperature influences TENG outputs
depending on the temperature effect on the triboelectric
charge density, the material and the structural
characteristics of the TENG layers [63].
The low ambient temperature dependence of different
TENGs has been analysed in [65] making use of a testing
chamber kept at 8.67 ∙ 10-4 Pa to preserve thermal
insulation. One of the investigated devices, Al-PTFE
based, has shown increasing voltage and current
(respectively 2 V and 5 A) as temperature is decreased
from 320 to 77 K, confirming the capability of increased
performance under low temperature conditions for
certain TENGs type. This behaviour has been described
in an analytical way in [66] making use of a Schottky
model.
All these researches have widely demonstrated the
usability of TENGs in low and relatively high
temperature environment, compatibly with the reference
operating conditions listed in Table 1.
3.2.4 Radiation
Since a large number of TENGs are made using
polymeric layers, it is worth noting that the exposure of
this kind of materials to Low Earth Orbit (LEO)
environment may result in different damaging effects by
modification of the polymers chemical, electrical,
thermal, optical, mechanical and surface properties [67].
Some researchers investigated the behaviour of different
TENGs subjected to Ultraviolet (UV) and gamma
radiations. In [68] an integrated and active UV
photodetector based on TENG configuration has been
developed. The built TiO2 - polymethyl methacrylate
(PMMA) TENG has shown an excellent responsivity and
a good linearity in a wide light intensity range. An
interesting application for TENG as Martian wind energy
harvester has been studied in [12]. A VCSTENG made of
Al (which served both as layer and electrode) and PDMS,
has been inserted into a Mars analogue weather chamber
to analyse its Voc performances under appropriate
environmental conditions. In such test the UV light has
been irradiated with a wavelength of 254 nm and an
intensity of 45 W/m2. A rapid increment of the Voc has
been noted. The slope gradually slowed down during the
2000 s of irradiation and saturated thereafter.
In another study PTFE, one of the most vulnerable
polymers to gamma rays, has been employed as contact
surface to estimate the effect of gamma radiation. The
surface has been exposed to 10 krad which is equivalent
to 379 years of operation on earth. The output
characteristics of the TENG did not change significantly.
The average Voc and Short Circuit Current (Isc) increased
by only 3.6 % and 1.9 % after the exposition,
respectively.
The effects of solar and cosmic radiation still must be
addressed in depth, but previous results indicate that UV
radiation may improve the performance of TENGs, while
gamma radiation causes negligible effects.
3.3 Mechanical applications of TENGs
Sensors suitable for monitoring and detecting
movement, distance, velocity, acceleration and angles are
important for mechanical systems. TENGs can serve as
self-powered active motion sensors. They relate
instantaneously frequency, amplitude and total period of
the generated electrical signals to the input of mechanical
motions even if those are concealed, since output voltage
and current provides static and dynamic information to
the mechanical triggering [11].
In the past years, different types of TENGs have been
developed to detect displacements [69], rotations [70],
moving objects (i.e. Aluminum balls) [71], acceleration
[72] or vibrations (i.e table/ball impact, human
movement) [73].
Even if their performance variation due to
environmental effects still has to be addressed in detail, a
suitable device for the proposed study could be the self-
powered Triboelectric Accelerometer (TA), due to its
capability of monitoring continuously the dynamic
displacement [74] for SHM. The implementation
proposed in [74] is composed of two different parts: an
outer sleeve tube and an inner cylindrical inertial mass
suspended through a stretchable silicon fiber. From
experimental test its high sensitivity and its advantage on
being dominated only by base resonance mode have been
demonstrated. The resonance condition can be properly
designed for any application, allowing a wide variety of
analysable frequency ranges. This represents another
advantage if compared to the high-order harmonics
domination of piezoelectric sensors. Also, the latter
sensors generate as an output low voltage which could be
affected to the electromagnetic noise, therefore the
system cost for accurate measurements increase
significantly [75].

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To avoid damages to the TA caused by large
vibrational displacements, a real-time system monitoring
could be developed, while for intrinsic energy losses due
to the layers sliding or friction [24], an high stretch
silicon fibre between the inertial mass and the sleeve-tube
has been used as a noncontact structure.
One of the major advantages of TA is that VOC depends
linearly on the displacement of the inertial mass through
[76]:
  

(1)
 


(2)
Moreover, its natural frequency depends on size,
materials, elastic modulus of the silicon rubber, cross-
sectional area, diameter and length.



(3)


(4)
The dynamic displacement could be computed by a
double integration of the acquired acceleration. To avoid
the drift error due to the unknown initial condition it is
possible to use a high pass filtering technique after each
integral operation. This approach also permits to avoid
the acquisition of the signal due to the external noise. It
is possible to detect dynamic displacements for SHM
using TENGs properly designed to avoid problems with
natural and detected frequency. However, a suitable
TENG for the application proposed in this study must be
designed and tested according to the presented criteria, in
order to assess its operational performance in the space
environment.
4. Case study: ISS MMODs impact detection
After addressing the performance of TENGs in a
space-like environment, a possible application for space
debris impact location and intensity estimation is here
presented. In this framework a discrete array of TENGs
sensors is distributed on the monitored surface.
The critical point in modelling the considered
problem is related to the description of an HVI happening
on the external surface of the ISS. Many authors [77, 78,
79, 80] employ a properly bounded flat plate as a
reference surface, which can be considered as a solar
panel or a shell component of an ISS module, on which
impacting events are happening.
Statistical information about MMOD population at
ISS orbit can be obtained by dedicated database, such as
ESA MASTER* tool, and used with suitable models of
the HVI (e.g. hydrodynamic model) to describe the load
applied over the structure itself (i.e. target). The
mechanical response of the target is then registered and
manipulated in order to feed an artificial neural network
(ANN) group capable of addressing in real time the entity
of the event and correlated risks.
4.1 FEM
The FEM approach is suitable due to its adaptability
to different kind of problems, independently from their
characteristics (e.g. geometry, material, external loads,
etc.). Since ANNs require large datasets, that cannot be
obtained through experimental processes due to their
costs in terms of founds and time, FEM allows to
overcome this drawback. Moreover, multiphysics
simulations can be implemented in order to test the
influence of the sensors dynamics on the measured data.
This can be done comparing standard FEM results with
an augmented version that takes into account sensors.
Nevertheless, FEM simulations require particular
attention to the selection of the integration and mesh
parameters.
HVI problem peculiarities demand specific
integration methods, among these, the most used is
ABAQUS®/Explicit [78, 79]. The integration time step
shall be chosen accordingly to the problem dynamic
properties:
 

(5)
Where  is the maximum natural circular frequency,
and  is the integration step that grants numerical
stability [79]. A proper solution is also related to a correct
mesh sizing, it is suggested to refine the mesh in the
region close to the impact point. Moreover, the
characteristic element length (e.g. distance between two
nodal points) shall be selected in order to reduce the mesh
dependency during strain softening [78].
4.2 MMODs impact modelling
Among the available methods to describe HVI in case
of MMODs problem, a suitable one is the hydrodynamic
model built upon ESA MASTER tool results which is
here addressed.
The European Space Agency Meteoroid and Space
Debris Terrestrial Environment Reference (ESA
MASTER) is a widely extended software for the
assessment of the debris or meteoroids flux on Earth

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IAC-19- D1.1.8.x53464 Page 7 of 13
Orbits
*
. This tool has been employed to generate the
distribution of diameter () - velocity () pairs of the
particles impacting the target spacecraft.
The flux of particles was simulated for the ISS orbit
between the 21st and 25th of October 2019. Explosion
fragments (a), collision fragments (b), Launch and
Mission Related Objects (LMRO) (c), ejecta particles
(d), NaK droplets (e), Solid Rocket Engines (SRE) slag
(f), SRM dust (g), detached paint (h), Multi-Layer
Insulation (MLI) debris (i) and meteoroids (j) were
considered in the analysis. The combined flux of particles
is approximated by representative materials. The
selection not only obeys to technical reasons, but also to
the availability of experimental correlations (e.g. the
NaK droplets were modelled as liquid hydrogen due to
the lack of NaK impact data). The impact properties of
representative materials were extracted from the LASL
Shock Hugoniot Data [81] and summarized in Table 2.
Table 2: Classification of space debris subgroups with
approximate Hugoniot properties and distribution at the
ISS orbit.
a: Explosion fragments, b: Collision fragments, c: LMRO,
d: Ejecta, e: NaK droplets, f: SRM slag, g: SRM dust, h:
Paint, i: MLI, j: Meteoroids.
4.2.1 Hydrodynamic impact modelling
During a ballistic impact the response of the target
is given by the superposition of local and global
contributions. The modelling of such events involves
geometrical, physical and energetic considerations, with
the strike velocity being the most important of them. For
high-velocity impacts (>100 m/s) like the ones
commonly produced in space, the target experiences a
local response dominated by the hydrodynamic effect, on
which the penetration is controlled by the density of the
projectile and target [82, 83]. Hydrodynamic impact
models assume that the energy dissipated in the impact is
enough to melt or even vaporize the material. The
hypothesis of inviscid flow (no shear stress) is then
employed, and the penetration is considered to be a
steady-state, one-dimensional process [84].
*
Further information and resources related to ESA
MASTER can be found at https://sdup.esoc.esa.int.
The classical hypervelocity impact mechanics is based on
the theory of shock waves. Within this framework, the
pressure exerted by the impacting particle and its speed
in the impactor are given by [85]

(6)
 
 
(7)
where , ,
,  is the impact velocity,  is
the particle velocity in the impactor,  is the density of
the target,  is the density of the particle, and the pairs
of material parameters - relate the shock speed with
the particle speed through
 
(8)
The solution of  is selected by imposing the
conditions   and  . The penetration depth
 can be computed under the previous assumptions
through [85]
 

(9)
with  being the length of the particle in the impact
direction. Since in this work spherical impact particles
are assumed,  is also the diameter of the particle. The
impact time is finally computed as


(10)
If the penetration depth is greater than the thickness  of
the plate, the impact time becomes .
Based on this theoretical framework, the impact of space
debris or meteoroids in the target is modelled as a step
pressure load with width  and pressure  applied in a
circle with diameter . The values of  and  and the
population characteristics of impacting particles required
to generate those values is obtained from ESA MASTER
as described in Section 4.2.
4.3 Artificial Neural Networks (ANNs)
In the context of SHM, deterministic methods are
available to retrieve some information (e.g. triangulation
for impact location estimation). However, another
convenient choice is to use deep learning approaches.
ID
Contains
Material

()

()

1
a, b, c, d
AL7075
2810
5.2
1.26
2
e
 (liq.)
72
2
1.28
3
f, g

3977
8.75
0.98
4
h
Rubber
1372
1.84
1.44
5
i
AL1100
2712
5.38
1.34
6
j
Iron
7174
4
1.59

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A suitable tool for building ANNs is MATLAB®
Deep Learning Toolbox™ which allows to implement
different types of architectures. The networks receive as
input characteristic parameters related to the physical
phenomenon and correlate them to an output of interest
(i.e. impact location and magnitude). Among the
different kind of ANNs, the feedforward ones are the
simplest, nevertheless, the most used for impact
estimation problems. These networks elaborate the input
through a series of hidden layers which consists of
different numbers of neurons. Each neuron of a layer
exchange information with every neuron of the adjacent
layers by means of weighted connections. Before giving
the output, the final layer of the net processes the
information using an activation function.
As shown in [86, 87, 88], applying ANNs (i.e. for
impact estimation) leads to reasonable estimation errors.
In particular, in [86] the percentage errors in terms of
length and width of the impact area are around 3%, in
[87] the x and y position errors resulted, respectively,
around 9% and 10%, while in [88] they reach values
around 10% and 27%. These differences may be due to
the choice of the characteristic parameters used to train
the ANNs. The most common parameters for the
investigated problem are: (i) maximum and minimum
values of the signal and their corresponding times, (ii)
maximum and minimum values of the signal envelope
and their corresponding time, (iii) beginning and end
times of each envelope, (iv) characteristic data extracted
from the signal spectrum. A combination of these data
can be used to feed the networks.
5. Results
In this section are presented the results obtained in the
impact modelling context.
5.1 Space environment
The probability distribution of impacts was
calculated in ESA MASTER for the ISS orbit between
the 21st and 25th of October 2019.
Fig. 2 shows the total flux of particles as a function
of the particle diameter and velocity. The total flux is
subdivided into groups as presented in Table 2. Fig. 3
represents the predominant group for each diameter-
velocity pair. It should be noted that the distribution is
concentrated in the region below 1 mm and between 5
and 25 km/s, which is related with a greater presence of
groups 3, 4 and 6.
A 97.54% of the impacting particles belonged to
group 6, while 1.56% was assigned to group 3, 0.75% to
group 4 and 0.15% to group 1. That is, most of the
impacting particles are meteoroids, whose mechanical
properties have been assumed to be equivalent to iron.
Fig. 2. Total flux density at the proposed orbit as a
function of the particle diameter and velocity.
Fig. 3. Predominant particle group as a function of the
object diameter and impact velocity.
5.2 Impact analysis
A 1 mm thickness Al 7075 target plate was
employed to simulate the space debris / meteoroids
impacts by making use of equations (6-10). The
distribution of impact and particle velocities is depicted
in Fig. 4, while the distribution of pressures is given in
Fig. 5. A 95.24% of the impacting particles had a
diameter inferior to 0.12 mm, while a 3.92% was in the
range between 0.4 and 0.42 mm. 0.01% of the impacting
particles was able to cross the aluminium plate.

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Fig. 4. Distribution of impact and particle velocities for
the configuration under analysis.
Fig. 5. Distribution of impact pressures for the
configuration under analysis.
6. Conclusions
In this paper a case study to test the performance of a
sensorial network for SHM has been presented. From a
bibliographic review the behaviour of different TENGs
subjected to space environment conditions has been
investigated. It has emerged that TENGs are capable to
operate in space at the conditions of internal/external
environment of the ISS. This last conclusion shall be
validated with the design of a proper TA for MMOD
detection, tested within the aforementioned
environmental conditions.
Along with these final considerations, the most important
result is that it could be possible to extend the procedure
to more complex structures. In order to do that, the
presented procedure has to be validated for the case under
study and a better modelling of HVI and of the coupling
between TENGs and structure shall be investigated.
Furthermore, the non-deterministic nature of ANNs,
suggests implementing a nested topological optimization
study over the position and distribution of the sensors.
This may improve the performances, by removing over
feeding data, and reduce the required power and mass
budget, by minimizing the number of sensors.
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