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Infrared Signature Modeling and Analysis of Aircraft Plume
Arvind G. Rao
*
Faculty of Aerospace Engineering, Technical University of Delft
2629HS Delft, The Netherlands
ABSTRACT
In recent years, the survivability of an aircraft has been put to task more than ever before. One of the
main reasons is the increase in the usage of Infrared (IR) guided Anti-Aircraft Missiles, especially due
to the availability of Man Portable Air Defence System (MANPADS) with some terrorist groups.
Thus, aircraft IR signatures are gaining more importance as compared to their radar, visual, acoustic,
or any other signatures.
The exhaust plume ejected from the aircraft is one of the important sources of IR signature in
military aircraft that use low bypass turbofan engines for propulsion. The focus of the present work is
modeling of spectral IR radiation emission from the exhaust jet of a typical military aircraft and to
evaluate the aircraft susceptibility in terms of the aircraft lock-on range due to its plume emission, for
a simple case and against a typical Surface to Air Missile (SAM). The IR signature due to the aircraft
plume is examined in a holistic manner. A comprehensive methodology of computing IR signatures
and its affect on aircraft lock-on range is elaborated. A commercial CFD software has been used to
predict the plume thermo-physical properties and subsequently an in-house developed code was used
for evaluating the IR radiation emitted by the plume. The LOWTRAN code has been used for
modeling the atmospheric IR characteristics. The results obtained from these models are in reasonable
agreement with some available experimental data. The analysis carried out in this paper succinctly
brings out the intricacy of the radiation emitted by various gaseous species in the plume and the role of
atmospheric IR transmissivity in dictating the plume IR signature as perceived by an IR guided SAM.
Keywords: - Aircraft Plume, Aircraft Signatures, Gas Emissivity, Infrared Signatures, Lock-on
Range, Plume Radiation, Stealth Technology, Susceptibility
*
Corresponding author
Tel.: +31-15 27 83833
E-mail: a.gangolirao@tudelft.nl
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1.0 INTRODUCTION
Stealth is one of the key factors required for establishing air superiority in modern warfare. Aircraft
Stealth technology primarily deals with reducing aircraft signatures and observables, thus providing
aircraft with the crucial capability of evading enemy’s air defence system. Stealth is now becoming
one of the most sought after feature in military aircraft because aircraft with stealth features can make
successful offensive attacks with much less support [1, 2]. Stealth Technology has given a new
dimension not only to aircraft, but also to other military vehicles like ships, submarines, armoured
tanks, etc. For example, the apparent advantages that sophisticated surface-to-air defences once held
are currently countered by stealth technology. Stealth aircraft have distinct advantages in the
battlefield environment as compared to their non-stealth counterparts [3]. Because of their ability to
penetrate hostile regions, stealth aircraft can provide the much-needed initial breakthrough in the war
by penetrating the enemy’s air defense system and subsequently making the enemy vulnerable to
attacks by conventional aircraft. Stealth technology, which reduces aircraft observables, helps aircraft
to avoid high loss rates, reach their objectives, and operate at a high level of system effectiveness [4].
Infrared signatures of aircraft are one of the passive sources of detecting an aircraft, and hence
forms an important part of aircraft stealth technology [1]. The IR seekers acquire and intercept
airborne targets by passively detecting the IR radiation emitted by an aircraft. One of the main
characteristics of IR missiles is that they do not require active impingement of radio / electromagnetic
frequency on the target, as in case of radar-homing missiles. Present generation IR-detectors are
known to provide radar comparable tracking range, are cheaper to manufacture, easy to use, and have
fire and forget capabilities [5]. Therefore heat-seeking missiles have became popular and proved to be
one of the main dangers to low flying aircraft, such as the shoulder fired heat seeking missiles, like
MANPADS (Man Portable Air Defence System). Between 1979 and 1993, IR guided missiles have
destroyed more than 89% of all downed aircraft and helicopters, out of which many of the missiles
were shoulder launched type [6]. Many of such missiles are available with terrorist groups, posing a
serious threat to aircraft and helicopters even in peacekeeping operations [7]. Hence, IR signatures
and associated technologies will shape the course of developments in aircraft stealth technology.
1.1 Motivation
Due to the growing importance of IR signature relative to other types of aircraft signatures, it is of
utmost importance that IR signatures produced by various sources in an aircraft be investigated
thoroughly. The hot engine exhaust plume emits significant amount of infrared radiation and is one of
the main sources of IR signature in an aircraft. Typically, the plume ejected by an aircraft can be
subtended for hundreds of metres, much larger than the aircraft itself and hence forming a large
radiative surface area. A full flight measurement of IR radiation emitted by an aircraft is prohibitively
expensive and infeasible, especially for a detailed parametric study. Thus, mathematical modelling of
aircraft IR signature is very important and can help in understanding the underlying physics of this
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phenomenon. Plume IR signature modelling is also of interest for some other purposes like, missile
tracking, design and development of anti-missile defense system, tracking of satellite launch vehicles,
assessing effect of aircraft and rocket plumes on environment, evaluating plume composition for
combustion diagnostics and monitoring, etc.
A brief methodology of computing plume signature from naval ship gas turbine engine is
given by Bekker et al. [8]. They used NATO’s plume flow field program ‘NPLUME’, for computing
ship exhaust plume flow field and ‘NIRATAM’ (NATO Infrared Air Target Model), for modeling the
target radiance and atmospheric IR characteristics. The naval ship infrared signature countermeasure
and threat engagement simulator by Vaitekunas et al [9], describes IR signature modelling from
various sources for a naval ship (including plume radiation); and susceptibility assessment of the ship
to IR guided threats. Rapanotti et al [10] described plume IR emission modelling for missiles
(especially anti-tank missiles). Avital et al. [11] describe spectroradiometric measurements on an
experimental rocket exhaust plume. The General Aerodynamic Simulation Program CFD code was
used to model the gas-dynamic and the thermodynamic properties of the flow field. The k-
turbulence model was used for modelling the physical plume flowfield and the statistical narrow band
model was used for computing the rocket exhaust plume spectral radiance.
1.2 Aircraft Plume
The high temperature plume ejected from aircraft exhaust nozzle is a mixture of many species in
gaseous state (solid and liquid phases are negligible) that are produced as a result of hydrocarbon
combustion. Gases with asymmetrical molecular structure like H2O, CO2, and CO, are responsible for
emission of IR radiation from the plume. Other species present in the plume are radiatively inert up to
high temperatures [12]. The species present in the plume emit significant radiation only in the IR
range of the electromagnetic spectrum. While the emissivity of metals is fairly constant over a wide
range of wavelengths, the emissivity of gas changes sharply with wavelength. Hence treating
combustion gases as gray or semi-gray can give rise to substantial errors in the calculation of radiative
heat fluxes [13].
An experimental and numerical investigation of an under-expanded circular jet has been
carried out by Saddington et al. [14]. They used Laser Doppler Velocimetry (LDV) and the pitot tube
measurements to characterise the jet parameters experimentally. The Fluent® CFD code was then
used to numerically simulate and validate the jets. The RNG k- turbulence model was used for
modelling the turbulence. They have reported a good agreement between the experimental and the
numerical results. A preliminary model to evaluate IR emission from a turbofan engine, with an
objective of designing a 2-D rectangular engine exhaust nozzle, is described by Decher [15]. The
model uses empirical correlations for modelling physical flow field of the plume. The spectral
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emissive characteristics of exhaust gases, transmissivity of atmosphere, missile characteristics, etc.,
have not been considered in the analysis.
Levy et al. [16, 17] have worked on modelling the IR radiation from the exhaust plume of a
turbojet combustor for combustion diagnostics and monitoring. They used an in house program called
as INFRAD, which was based on the Statistical Narrow Band (SNB) model for evaluating radiation
from non uniform and non isothermal gas volume. Heragu et al. [18] have described a Generalised
model for IR emission from exhaust plume and nozzle. They have used the GENMIX code for flow
simulation of the exhaust plume. The radiation exchange between nozzle inner surface and gas is
calculated and the SNB model is used for computing the gas radiation.
2.0 PLUME SIGNATURE MODELLING
The comprehensive overview of the complete IR signature modelling of aircraft plume, as perceived
by an IR guided SAM, is shown below in Fig. 1. Each of the modules in the flow chart is
subsequently explained in this sub section. It is important that the plume IR signature be treated in a
holistic manner in order to evaluate the susceptibility of the aircraft against the IR guided SAM.
2.1 Modeling Exhaust Plume Flow field
For computing IR radiation emitted by plume, the plume structure, temperature, pressure, and species
distribution coupled with radiative heat transfer, has to be computed simultaneously; which is
extremely difficult to solve by existing RTE solvers. To simplify this process, the plume structure,
temperature and pressure is computed separately by using commercial CFD solver Fluent®. The
coupled-implicit-scheme with standard k-ε turbulence model is used for solving the aircraft engine
exhaust plume flow field. The nozzle exit conditions, namely the pressure, temperature and the
concentration of gaseous species, for a given flight condition, has been evaluated by using the Gas-
turbine Simulation Program GSP® [19]. Thus using GSP®, the engine nozzle exit conditions for any
mission can be computed.
The operating engine exit parameters calculated from the steady state analysis of GSP® are
used as boundary conditions for the CFD simulations. Since, most aircraft have axisymmetric nozzles,
only half part of the plume is solved to conserve computation time. At the nozzle exit plane, the
“pressure inlet” boundary condition is specified where as the “far field” boundary condition with the
flight Mach number is used for other boundaries. The coupled implicit scheme with standard k-
turbulence model is used. . The exhaust plume Mach number and the static temperature profile from
a typical single engine fighter aircraft, flying at an altitude of 5 km and Mach number of 0.8 is shown
in figures 2(a) & 2(b) respectively. Since the nozzle is under-expanded, the diamond shock within the
exhaust plume can be seen clearly in the figures. A magnified view of the shock structure near the
nozzle exit plane is shown in the figure for better understanding.
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Figure 1: Procedure for evaluating plume IR signature level
Cal. Aircraft nozzle exit
parameters using GSP®
Solve the plume flow-
field using Fluent®
Cal. plume IR Emission using in-
house plume emission solver based
on the Statistical Narrow Band
Model
Compute atm. Transmissivity for the
given mission using LOWTRAN-7
Cal. plume radiance as
transmitted by atm.
Compute atm/sky radiance
using Berger’s Model
Compute plume
irradiance on missile
IR seeker
Cal. aircraft Lock-on
range from Eq.7
SAM detection
system characteristics
STOP
START
Evaluate the contrast between
plume radiation and background
Read the output from Fluent®
and preprocess the grid for
radiation analysis
6
Figure 2a: Plume Mach number contours for a single engine fighter aircraft cruising at 0.8 Mach and
altitude of 5 km (obtained from Fluent®)
Figure 2b: Plume static temperature contours for a single engine fighter aircraft cruising at 0.8 Mach
and altitude of 5 km (obtained from Fluent®)
2.2 Plume Emissivity Solver
The thermophysical properties of the plume as evaluated from the CFD analysis can be used for
modeling the exhaust plume spectral IR emission. As described earlier, unlike a gray body that
radiates in the entire spectrum of the IR radiation, the exhaust plume emits only in specific narrow
bands of IR spectrum depending on the chemical composition and temperature of the plume. The
emissivity of aircraft fuselage (generally made of Aluminum alloys) remains more or less constant,
Nozzle exit
plane
Diamond Shock
Expansion fans
Mach No.
Magnified view
Direction of the Plume
Plume center line
Nozzle exit
plane
Diamond Shock
Static Temperature (K)
Magnified view
Direction of the Plume
Plume center line
7
however, the plume emissivity varies rapidly with wavelength and hence cannot be approximated as a
gray body.
2.2.1 Gas Emissivity Modelling
Radiative heat transfer calculations in combustion gases can be grouped into three methods: line-by-
line calculations, narrow-band calculations, and the global models [20]. The most accurate model is
the line-by-line approach that is based on a direct calculation of all the radiation lines [21, 22]. But
this method requires a huge database to cover the whole of IR range for typical combustion gases and
is impractical for real engineering problems. When calculating spectral radiative heat fluxes from
gases, it is noted that the gas absorption coefficient varies more rapidly across the spectrum than other
quantities like Planck’s function. It is therefore possible to replace the actual absorption coefficient by
smoothened values, appropriately averaged over a narrow spectral range. Hence, band models are
widely used for approximate modeling and evaluation of gas radiative properties.
A comprehensive method of obtaining total emissivity of gases resulting from hydrocarbon
combustion, by using the Statistical Narrow-Band (SNB) model, is given by Saufiani and Taine [23].
The SNB model assumes that the positions of the individual spectral lines occur at random, and that
the intensities can be represented by some probability distribution. The narrow-band calculations are
less accurate as compared to line-by-line calculations, however they are easier to compute. Taine et
al. [24] generated artificial narrow-band properties from their (temperature-extrapolated) line-by-line
data. Using a resolution of 25 cm-1, they observed a maximum 10% error between line-by-line and
narrow-band absorptivities. It is shown by Soufiani et al. [25] that the most accurate statistical model
for the species generated from combustion of hydrocarbon fuels, is the random model with the S1
exponential-tailed distribution of line intensities suggested by Malkmus [26]; which also takes in
account the many weak lines that arise in the bands, especially at higher temperatures.
The plume IR solver developed in-house to compute IR signature level of aircraft exhaust
plume, reads the temperature and pressure profiles as computed by Fluent®. The 2-D grid is then
revolved to create a 3-D axisymmetric plume, which is further discretised radially to create isothermal
gas volume (refer to Fig. 3). For a missile on the ground, the plume radiation is maximum when the
aircraft is at 90 from the horizon (over head of the missile); from this aspect, only the radial emission
is important. Therefore, radiation heat transfer in radial direction is modelled here. The radiation heat
transfer between various discretised isothermal volumes of the plume is then computed.
8
Figure 3: A discretised element of the axisymmetric plume
For an isotropic absorbing and emitting elemental volume of gas, the spontaneously emitted
intensity of radiation in any direction is given by Siegel and Howell [27]
dI(,T) = [(,T,P)eb(,T)dS]/. (1)
A beam with radiation intensity I that passes through a slab of gas undergoes a change in its intensity
by an amount dI, which is a result of the following effects:
i. The I is augmented by emission from the elemental volume by,
dI = [(,T,P)eb(,T)dS] / . (2)
ii. Absorption by gases attenuate I as it passes through dV, which is evaluated as,
dI = (,T,P)I(,T)dS; where, = + S,. (3)
Assuming scattering as negligible, = ; and this equation is written as,
dI = (,T,P)I(,T)dS. (4)
Combining Eqs. 2 and 4, the net change undergone by I in passing through the discretised gas volume
is given as,
dI = (,T,P)dS{[eb(,T)/] I}. (5)
The monochromatic absorptivity of discretised isothermal gas volume () is computed using
the SNB model described by Soufiani and Taine [23]. The concentration of radiation participating
gases is obtained by assuming the Schmidt number as unity, which holds for most gases and their
mixtures; consequently, isothermal zones are the same as iso-concentration zones [5]. The validation
of the solver is presented in next section.
I
I +dI
dV
9
2.3 Atmospheric IR Characteristics
Since the atmosphere surrounds the aircraft and the SAM, the IR characteristics of
atmosphere have a significant role in dictating the perceived aircraft signature by the missile.
Often, studies involving IR signature modelling make simplistic assumptions regarding
atmospheric IR characteristics. Such assumptions can alter the computed aircraft IR signature
by a significant amount, which in turn can alter aircraft susceptibility analysis. Also, the
choice of detector’s operating wavelength band used on IR guided missiles depends on the
environment in which the missile is expected to perform. It is thus important to study
atmospheric IR characteristics and its effect on aircraft IR signatures.
The atmosphere has two important roles in evaluation of aircraft IR signatures.
Firstly, the transmissivity of atmosphere dictates which part of plume IR emission reaches the
SAM and in what amount. Secondly, the atmospheric radiance forms the background IR
radiation against which the SAM distinguishes plume IR emission. Both these phenomena
reduce the total amount of plume signature as perceived by the detector, and hence are
beneficial from the aircraft survivability point of view. Therefore, it is important that the IR
radiation characteristics of atmosphere are considered in the analysis.
Even though N2 and O2 are the major constituents of the atmosphere, they are inert as
far as IR radiation is concerned [28]. The radiation characteristics are primarily governed by
carbon dioxide (CO2), water vapour (H2O) and ozone (O3). These three gases are mainly
responsible for maintaining the atmospheric temperature within limits conducive for the
sustenance of life. There are some other trace gases with asymmetrical molecular structure
affecting atmospheric IR characteristics like, CH4 and NOx, but their contribution is small.
The concentration of O3 is prominent only at an altitude of 20-30 km. The concentration of
water vapour decreases rapidly with altitude, and is absent above 10 km. The pressure,
temperature and concentration of the participating gases influence IR transmission and
emission. Radiation characteristics of atmosphere is a function of many parameters namely -
temperature, humidity, weather conditions, composition, etc. Therefore, considering different
atmospheric conditions leads to different computed predictions about the plume IR signature.
For the purpose of comparison and uniformity, here the aircraft is considered in a clear
cloudless night sky in a tropical atmosphere.
2.3.1 Atmospheric Transmissivity
The atmosphere is a good absorber of IR radiation. Radiant flux from the target aircraft is
selectively absorbed by several atmospheric gases and scattered away by suspended particles
in atmosphere (e.g. aerosols). There are only a few bands in the entire IR spectrum where
10
absorption is low and transmissivity of IR radiation is high, these wavelength bands are
commonly known as atmospheric IR windows [29]. For the purpose of aircraft detection and
tracking as used in the SAMs, the IR detector must operate within one of these IR windows.
The atmospheric transmissivity after 14 µm is negligible, and hence, can not be harnessed for
aircraft detection.
There are several databases available for evaluating atmospheric transmissivity for
various atmospheric conditions, one such well accepted and validated model is LOWTRAN.
The LOWTRAN is a low resolution atmospheric transmissivity model, which can be used for
wavelengths longer than 0.2 µm and at a spectral resolution of 20 cm1 [30, 31]. The
LOWTRAN is based on band models of molecular absorption, and was first released in 1972.
The LOWTRAN-7 code is used in the present analysis for computing atmospheric
transmissivity.
2.3.2 Atmospheric Radiance
As discussed earlier, atmospheric radiance is the background against which the IR detector
detects the aircraft plume. Hence the contrast between the plume and background radiation is
perceived by the detector. The spectral distribution of atmospheric radiation is mainly due to
the thermal emission by atmospheric gases and by the scattering of sunlight. At ground level,
the atmospheric radiance is dominated by H2O (vap.), whose concentration may vary from
0.2-4 % by volume, depending on the temperature and humidity. The peak observed around
9.6 m is due to emission by ozone. Radiance by CO2 is prominent around 4.3 m
wavelength because of its vibrational band, but at this wavelength, the spectral emissive
power of atmosphere is negligible.
Several studies have been carried out to determine the clear sky emissivity. Some
investigators have determined the total equivalent emissivity of clear skies for various
conditions from the statistical analysis of experimental data gathered over a period of time at
many places and in a variety of weather conditions [32]. Berger [33, 34] developed a simple
model for evaluating spectral emissivity, and hence spectral radiance of atmosphere, as a
function of ground level temperature and dew point temperature (a function of relative
humidity). This model can also be used to determine the total and directional spectral
emissivity of clear sky. The model approximates sky radiance to that of a blackbody at
ambient temperature below 7.5 m, between 14.00-16.25 m, and beyond 22.5 μm. Mainly,
the effect of water vapour absorption and ozone (at 9.6 m) is taken into account. The total
11
spectrum is divided into a number of parts and absorption coefficients are tabulated from the
least square fit for data obtained over a period of time, for various atmospheric conditions
2.4 Lock-on Range
The IR signature of plume as perceived by a missile is the contrast in IR emission between
plume (as transmitted by atmosphere) and the background radiance within the detector’s
operating wavelength band (in which the detector has good responsivity). The maximum
detection range of an IR detector depends on its NEI [35] (performance feature of the
detection system). The total radiation incident on the SAM IR detector is the sum of plume
radiance as transmitted by intervening atmosphere and background sky radiance (which is due
to the emission by radiation participating gases in atmosphere). Assuming that the detector’s
field of view is not completely filled by target aircraft (the solid angle subtended by the
aircraft at detector is less than the detector’s field of view), irradiance on the detector is given
as (illustrated in Fig. 4),
H=
pl
N
1i λpl,λpl,bg,λpl,i, τ1II
atm,i,pl + Ibg (d-ac) (6)
The maximum range at which this occurs is the lock-on range (signal from target aircraft
should cross some minimum threshold value for initiation of tracking, determined by min).
Hence, the maximum range where lock-on occurs is calculated as,
RLO,=
pl
N
atm,λ i,pl, bg,pl, pl, i,pl min
i1
τ 1 τ ξI I A NEI
. (7)
The RLO, is a function of aircraft parameters (Ii,pl,, Ai,pl), missile IR-seeker parameters (NEI,
min), atmospheric IR-characteristics (atm,, Ibg,pl,) [36].
12
Figure 4: Schematic of IR-seeking missile’s instantaneous field of view
3.0 VALIDATION
Most of the investigators have not provided any quantitative data from plume IR signature modelling.
Heragu et al. [18] have provided quantitative data from their plume signature analysis, which has been
compared with the experimental investigations of [37] and [38]. The results obtained from the present
analysis are compared with those provided by Heragu et al [18].
The plume flowfiled is simulated using the nozzle exit conditions given in [37]. The nozzle
exit diameter is 0.44m, the exit gas temperature is 635 K and the exit pressure is around 1 atm. The
flow is perfectly expanded in to the ambient air, and the exit Mach No. at the nozzle exit is 1; hence
there no diamond shocks in the plume flow field. Figure 5(a) shows the variation of non-
dimensionalised temperature difference across the plume at an axial distance of X/DN = 10. Figure
5(b) shows the variation of non-dimensionalised concentration profile across the plume at an axial
distance of X/DN = 10. It is seen that results obtained agree well with those provided by Heragu et al.
[18] and Gauffre [39]. Hence for the present problem, Fluent® can be used with confidence for
simulating a perfectly expanded aircraft exhaust plume flowfield.
d
∑pl
Detector
element
Reticle
MISSILE IR SEEKER
IR Transparent
window
R
SAM
Plume
(discretized)
Aircraft
13
Y/Rnz
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.5 1 1.5 2 2.5 3 3.5 4
"temp.txt"
"temp.txt"
"temp_h.txt"
(T-Tamb)/(Tc-Tamb)
Present
Gauffre
Heragu etal
Y/Rnz
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.5 1 1.5 2 2.5 3 3.5
"CO2.txt"
"CO2.txt"
"CO2.txt"
C/CC
C
Present
Gauffre
Heragu etal
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.5 1 1.5 2 2.5 3 3.5 4
"temp.txt"
"temp.txt"
"temp_h.txt"
(T-Tamb)/(Tc-Tamb)
Figure 5(a): Temperature profile across the jet at X/DN =10
Figure 5(b): Concentration profile across the jet at X/DN =10
14
Figure 6 shows the comparison of IR spectral intensity as obtained from the present model
with that reported in [18] and [38]. It can be seen that the results match reasonably well with those
reported earlier. The important peaks and absorption bands in the gas radiation are captured correctly
by the solver. It is seen that the solver over predicts IR intensity at some points, which could be due to
the following reasons
a. the emissivity of an aircraft tailpipe is not constant, as assumed in this analysis;
b. the interation of nozzle wall and gas radiation;
c. the experimental setup, operating conditions and uncertainities are not know.
Figure 6: Comparison of predicted intensity with [18] and [38]
The individual contribution of plume radiation and nozzle radiation for the above described
case is shown in Fig.7. It is seen that the plume contributes mainly in the 4.1-4.8 m and 5-8 m
band, whereas the nozzle radiates in the entire IR band. The figure proves that the plume emission is
very sensitive to the wavelength band; whereas the emission from nozzle wall changes gradually.
0
50
100
150
200
250
300
350
400
450
500
3 4 5 6 7 8
"total.txt"
"total.txt"
"heragu1.txt"
Present
Heragu etal.
Measured
Wavelength (m)
Intensity (W/m2Srm)
15
Figure 7: Contribution of plume and nozzle radiation towards the total radiation
4.0 RESULTS AND DISCUSSIONS
The comprehensive methodology described in Fig. 1 is used for computation of IR signature level
from aircraft plume, as perceived by SAM. For computing plume IR signature level of a typical
fighter aircraft, a simplistic representative case is considered here: an aircraft cruising at an altitude of
5km and 0.8 Mach over a SAM site, as shown in Fig. 8.
Figure 8: Aircraft flying overhead of a SAM site
0
50
100
150
200
250
300
350
400
450
500
1 2 3 4 5 6 7 8
"total.txt"
"total.txt"
"total.txt"
Plume radiation
Nozzle radiation
Total radiation
Wavelength (m)
Intensity (W/m2Srm)
5km
0º (Horizon)
180º
SAM
90º (Zenith)
16
Figure 9 shows the average spectral radiant intensity of the exhaust plume element between 1-
20 μm for the case mentioned in Fig. 8. The plume spectral radiant intensity is prominent only in few
bands of the IR spectrum, the main being the 4.15-4.45 μm band (due to the fundamental vibrational
frequency of CO2 and CO). The gases species responsible for the bands are also shown in the Fig.
Figure 9: Spectral radiant intensity of aircraft plume.
To bring out explicitly the role of atmospheric transmissivity in reducing the plume IR
signature level, Fig. 10 shows the non-dimensionalised plume intensity and atmospheric transmissivity
on the same axis. It is observed that most bands in which plume radiation is significant coincide with
atmospheric absorption bands. This is because aircraft exhaust plume and atmosphere have the same
radiative participating species (CO2, CO, H2O); but differ in temperature, mole fraction, and pressure.
Hence, most of the IR radiation emitted by plume is absorbed by the intervening atmosphere, and only
the radiation emitted by the broadened wings of these emissive bands (due to higher temperature and
species concentration of radiative participating gases of the plume) is transmitted by the atmosphere
and reaches the SAM’s IR detector. This may not be true in case of an Air-to-Air Missile (AAM)
because atmospheric transmissivity is larger at higher altitude; and therefore, some of the emissive
bands absorbed by the atmosphere in case of a SAM are transmitted in case of an AAM. Hence, an
AAM can lock-on to an aircraft from large distance, as compared to a SAM.
0
20
40
60
80
100
120
140
160
180
0 2 4 6 8 10 12 14 16 18 20
"irr_5km.out"
Wavelength (m)
Spectral Intensity (Wm-2Sr-1m-1)
H2O
CO2, CO
H2O, CO2
CO2
17
Figure 10: Plume emissive bands absorbed by the atmosphere
Figure 11 shows the spectral radiant intensity of exhaust plume, as transmitted by atmosphere.
Most of the radiation emitted by the plume is absorbed in the atmosphere. The plume is prominent
only in the 4.15-4.20 m band. The second prominent peak is from 4.3-5.1m.
Figure 11: Plume spectral radiant intensity as transmitted by atmosphere
Wavelength (m)
Non-dimensionalised plume intensity/Atm.
trans
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 2 4 6 8 10 12 14 16 18 20
"temp.txt"
"temp.txt"
Plume intensity
Atm. trans
Atm. transmissivity
Plume
emission
0
5
10
15
20
25
30
35
40
45
2 2.5 3 3.5 4 4.5 5 5.5 6
"irr_5km.out"
Wavelength (m)
Spectral Intensity (Wm-2Sr-1m-1)
18
It can be seen from Figure 11 that unlike in the case of radiation from aircraft rear fuselage
and tailpipe, the exhaust plume does not emit any radiation in the 8-12 m band. Hence, the plume
radiation is not affected by the background radiation (which is dominant in the 6-25 m range). The
plume is transparent to radiation of higher wavelength; and therefore, the infrared radiation from the
sky above the plume is transmitted by the plume, without significant attenuation. Figure 12 shows the
spectral variation of lock-on range for a typical single engine fighter aircraft, against a typical SAM.
The aircraft can be locked-on due to plume radiation, only in the narrow band of 4.15-4.20 μm. For
the particular case under consideration, the lock-on range does exceed the aircraft cruise altitude and
hence the aircraft is susceptible to the ground based SAM.
Figure 12: Spectral variation of lock-on range due to plume, for a single engine fighter aircraft
cruising at 5-km and 0.8 Mach
As a validation exercise for the complete IR signature analysis due to the plume radiation, the
results shown in Fig.11 can be compared with Fig 13, which the plume spectral intensity in a non
dimensional form, of a passenger aircraft (Boeing 707) obtained experimentally [40]. It is seen that
the spectral intensity matches qualitatively well with the results obtained in the present computational
analysis. The operating conditions of the Boeing aircraft, the chemical composition of exhaust gases,
atmospheric conditions, etc., are not known; and hence, there is some discrepancy due to these
uncertainties.
Wavelength (m)
Lock-on Range (m)
0
1000
2000
3000
4000
5000
6000
7000
8000
0 2 4 6 8 10 12 14 16 18 20
"irr_5km.out"
Aircraft altitude (5km)
19
5.0 CONCLUSIONS
A comprehensive methodology is presented in this paper to model the IR signature produced
by the aircraft exhaust plume and to evaluate its susceptibility against an IR guided SAM.
The results qualitatively match well with the results available in the literature.
It is seen that unlike other sources of IR radiation in an aircraft, that the spectral intensity of
exhaust plume is limited to a few narrow bands.
The prominent bands for plume radiation are centered around 2.7 µm, 4.3 µm, 5.5 µm, 6.5 and
15 µm due to the emission by CO2 , CO and H2O present in the plume.
The Statistical Narrow Band model can be used with confidence for modeling the exhaust
plume IR emission.
Since the exhaust plume and the atmosphere have same radiative participating species, namely
H2O, CO2, & CO, most of the IR radiation emitted by the plume is absorbed in the intervening
atmosphere.
Only the radiation emitted from the broadened wings of the plume emissive bands prominent
in the 4.15-4.2 μm band reaches the missile IR detector in the non after burning mode.
The aircraft is susceptible to ground based IR guided SAMs due to the radiation emitted by its
plume.
2 2.5 3.0 3.5 4.0 4.5 5.0 5.5
Wavelength (m)
Relative Radiant Intensity
0
25
50
75
100
Figure 13: Spectral intensity from aircraft plume for Boeing 707 aircraft [40]
20
NOMENCLATURE
A area [m2]
eb
black body emission at a particular wavelength [Wm–2m–1]
C concentration
DN nozzle diameter [m]
H irradiance [Wm-2]
I radiant intensity [W/m2·Sr]
N number of discretised elements [-]
P pressure [bar]
q
rate of heat transfer [W]
R range [m]
S mean thickness[m]
t time [s]
T temperature [K]
V volume [m3]
X axial distance [m]
Greek Scripts
gas absorptivity coefficient [m1]
gas extinction coefficient [m1]
emissivity [-]
wavelength [m]
S gas scattering coefficient [m1]
atmospheric transmissivity [-]
solid angle [Sr]
min minimum signal to noise ratio of detector, necessary for lock-on [-]
Subscripts
ac aircraft
atm atmospheric
amb ambient
bg background
c center
d detector
LO lock-on
21
max maximum
min minimum
pl plume
spectral quantity
Abbreviations
AAM Anti Aircraft Missile
CFD Computational Fluid Dynamics
GSP Gas Turbine Simulation Program
IR Infrared
LDV Laser Doppler Velocimetry
MANPADS Man Portable Air Defense System
NATO North Atlantic Treaty Organization
NEI Noise Equivalent Irrradiance
NIRATAM NATO Infrared Target Model
SAM Surface to Air Missile
SNB Statistical Narrow Band
REFERENCES
1. Mahulikar, S.P., Sonawne, S., Rao, G.A. “Infrared Signature Studies of Aerospace Vehicles”
Progress in Aerospace Sciences, Volume 43, Issues 7-8, October-November 2007, Pages 218-
245.
2. Rao, G.A., and Mahulikar, S.P., “Integrated Review of Stealth Technology and its Role in
Airpower,” Aeronautical Journal, Vol. 106, 2002, 629-641.
3. Dornheim, M.A., “F-117A provides new freedom in attacking ground targets,” Aviation and
Space Technology, 14 May 1990, 132, pp 106-109.
4. Hines, R.N., and Mavris, N.D., “A Parameter Design Environment for Including Signatures
Analysis in Conceptual Design,” AIAA-2000-01-5564, 2001.
5. Mahulikar, S. P., Sane, S. K., Gaitonde, U. N., and Marathe, A. G., “Numerical Studies of
Infrared Signature Levels of Complete Aircraft,” The Aeronautical Journal, Vol. 105, No. 1046,
April 2001, pp. 185-192.
6. Sully, P. R., VanDam, D., Bird, J. and Luisi, D., “Development of a Tactical Helicopter Infrared
Signature Suppression (IRSS) System,” Proceedings of AGARD-FVP Conference, 9605-4-001,
Paper 12, 1996.
7. The MANPADS menace: combating the threat to global aviation from the man-portable air
defense systems. Bureau of Political-Military Affairs and Bureau of International Security and
22
Non-Proliferation, Washington DC, September 20, 2005.
(http://www.state.gov/t/pm/rls/fs/53558.htm).
8. Bakker, E.J., Fair, M.L. and Schleijpen, H.M.A., “Modelling Multi Spectral Imagery Data with
NIRATAM v3.1 and NPLUME v1.6.,” Proceedings of SPIE, Targets and Backgrounds:
Characterization and Representation-V; (Eds: Wendell R. Watkins, Dieter Clement, William R.
Reynolds), Vol. 3699, 1999, pp. 80-91.
9. Vaitekunas, D.A., Alexan, K., and Lawrence, O.E., “SHIPIR/NTCS: A Naval Ship Infrared
Signature Countermeasure and Threat Engagement Simulator”, Proceedings of SPIE –Infrared
Technology and Applications XXII, April 1996, SPIE Vol. 2744, pp. 411-424.
10. Raponotti, J., Gilbert, B., Richer, G., and Stowe, R., “IR Sensor Design Insight from Missile
Plume Prediction Model”, Proceedings of SPIE, Targets and Backgrounds VIII :
Characterization and Representation (Eds: Wendell R. Watkins, Dieter Clement, William R.
Reynolds), Vol. 4718, 2002, pp. 289-300.
11. Avital, G., Cohen, Y., Gamss, L., Kanelbaum, Y., Macales, J., Trieman, B., Yaniv, S., Lev, M.,
Stricker, J., Sternlieb, A., “Experimental and Computational Study of Infrared Emission from
Underexpanded Rocket Exhaust Plumes”, Journal of Thermophysics and Heat Transfer, Vol.15,
No.4, 2001, pp. 377-383.
12. Lefebvre, A.H., Gas Turbine Combustion, McGraw-Hill Series in Energy, Combustion and
Environment, Hemisphere Publishing, Washington, 1983.
13. Edwards, D.K., “Molecular Gas band Radiation,” Advances in Heat Transfer, edited by T.F.
Irvine and J.P. Hartnett, Vol. 12, 1976, pp. 115-193, Academic Press, New York.
14. Saddington, A.J., Lawson, N.J. and Knowles, K., “An experimental and Numerical Investigation
of Under-expanded Turbulent Jets,” 2004, 108 (1081), pp. 145-152.
15. Decher, R., “Infrared Emissions from Turbofans with High Aspect Ratio Nozzles,” Journal of
Aircraft, December 1981, Vol. 18, No. 12, pp. 1025-1031.
16. Levy, Y., Lev, M., and Ovcharenko, V., “Study of Infrared CO2 Radiation From Liquid-Fueled
Combustor”, Journal of Heat Transfer, Vol 128, pp. 478-483, May 2006.
17. Y. Levy, M. Lev and V. Ovcharenko, “Infrared Radiation from Turbojet Exhaust Plume”, ASME
Turbo Expo 2007: Power for Land, Sea and Air, May 14-17, 2007, Montreal, Canada. GT 2007-
27379
18. Heragu, S. S., Rao, K.V.L., and Raghunandan, B.N., “Generalized Model for Infrared
Perception from an Engine Exhaust”, Journal of Thermophysics and Heat Transfer, Vol.16,
No.1, 2002, pp. 68-76.
19. GSP manual
20. Modest, M.F., Zhang, H., “The Full Spectrum Correlated–k Distribution for Thermal Radiation
from Molecular Gas-Particulate Mixtures”, Journal of Heat Transfer, Vol. 124, 2002, pp. 30-38.
23
21. Hartmann, J.M., Levi Di Leon, R., and Taine, J., “Line-by-Line and Narrow-Band Statistical
Model Calculations for H2O”, Journal of Quantitaive Spectroscopy Radiative Transfer, Vol. 32,
No. 2, 1984, pp. 119-127.
22. Rothman, L.S., Gamache, R.R., Tipping, R.H., Rinsland, C.P., Smith, M.A.H., Benner, D.C.,
Devi, V.M., Flaud, J.M., Peyret, C.C., Perrin, A., Goldman, A., Massie, S.T., Brown, L.R. and
Toth, R. A., “The Hitran Molecular Database Editions of 1991 and 1992”, Journal of
Quantitaive Spectroscopy Radiative Transfer, Vol. 48, No. 516, 1992, pp. 469-507.
23. Saufiani, A., and Taine, J., “High temperature gas radiative property parameters of statistical
narrow-band model for H2O, CO2 and CO, and correlated-K model for H2O and CO2”,
International Journal of Heat Mass Transfer, Vol. 40, No 4, 1997, pp 987-991.
24. Taine, J., “A Line-by-Line Calculation of Low-Resolution Radiative Properties of CO2-CO-
Transparent Nonisothermal Gases Mixtures up to 3000 K”, Journal of Quantitaive Spectroscopy
Radiative Transfer, Vol. 30, No. 4, 1983, 371-379.
25. Soufiani, A., Hartmann, J.M., and Taine, J., “Validity of Band-model Calculations for CO2 and
H20 Applied to Radiative Properties and Conductive Radiative Transfer”, Journal of
Quantitaive Spectroscopy Radiative Transfer, Vol. 3, 1985, pp. 243 257.
26. Malkmus, W., “Random Band Lorentz with Exponential Tailed S-1 Line-Intensity Distribution
Function”, Journal of Optical Society of America, Vol. 57, 1967, pp. 323 329.
27. Siegel, R., Howell, J.R., Thermal Radiation Heat Transfer, Hemisphere Publishing,
Washington, 1981.
28. Rao, G.A., and Mahulikar, S.P., “Atmospheric Transmission & Radiance Prediction in
Aircraft Infrared Signature Studies,” Journal of Aircraft, Vol. 42, 2005, 1046-1054.
29. Kondratyev, K. Ya, Radiation in the atmosphere, Academic Press, New York, 1969.
30. Lowtran-7 computer code: user's Manual AFGL-TR-88-0177.
31. Kneizys, F.X., Shettle, E.P., Gallery, W.O., Chetwynd, J.H., Abreu, L.W., Selby, J.E.A., Clough
S.A., and Fenn, R.W., “Atmospheric Transmittance / Radiance: Computer Code LOWTRAN 6,”
AFGL Report, AFGL-TR-83-0187, 1983.
32. Berdahl, P., and Fromberg, R., “The Thermal Radiance of Clear Skies,” Solar Energy, Vol. 29,
No. 4, 1982, pp. 299-314.
33. Berger, X., “A Simple Model for Computing the Spectral Radiance of Clear Skies,” Solar
Energy, Vol. 40, No. 4, 1988, pp. 321-333.
34. Berger, X., and Bathiebo, J., “From Spectral Clear Sky Emissivity to Total Clear Sky
Emissivity,” Solar and Wind Tech., Vol. 6, No. 5, 1989, pp. 551-556.
35. Hudson Jr., R. D., Infrared System Engineering, Wiley, New York, 1969.
36. Rao, G.A., and Mahulikar, S.P., “Aircraft Powerplant and Plume Infrared Signature Modeling
and Analysis,” 43rd AIAA Aerospace Sciences Meeting and Exhibits, Jan 2005, Reno, USA,
AIAA-2005-0221.
24
37. Krishnamoorty, V., and Pai, B.R., “Aerothermodynamics and Infrared Emission Characteristics
of Simulated Aeroengine Jet Plumes,” Propulsion Div., Project Document PR9113, National
Aeronautical Lab., Bangalore, India, July 1991.
38. Murthy, A.N., and Kulkarni, H. D., “Report on Infra Red Spectral Emission Measurements on
Scaled Model Engine and Jaguar Aircraft Engine Plumes”, Defence Electronic Research Lab,
Rept. DLRL : R: 91:026, Hyderabad, India, April 1991.
39. Gauffre, G., “Aircaft Infra-Red Radiation Modelling,” La Recherche Aerospatiale, No. 4, 1981,
pp 21-41.
40. La Rocca, A.J., “Artificial Sources”, The Infrared Handbook, edited by, W.L. Wolfe and G.J.
Zissis, Office of Naval Research, Washington, D.C., 1985, pp. 2.76-2.82.