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Int. J. Environ. Res. Public Health 2022, 19, 752. https://doi.org/10.3390/ijerph19020752 www.mdpi.com/journal/ijerph
Article
Impact of Thermal Dissipation on the Lighting Performance
and Useful Life of LED Luminaires Applied to Urban Lighting:
A Case Study
Juan de Dios Unión-Sánchez 1, Manuel Jesús Hermoso-Orzáez 1,2,*, Manuel Jesús Hervás-Pulido 2
and Blas Ogáyar-Fernández 1,3
1 Centre for Advanced Studies in Energy and Environment, University of Jaén, 23071 Jaén, Spain;
junion@ujaen.es (J.d.D.U.-S.); bogayar@ujaen.es (B.O.-F.)
2 Department of Graphic Engineering, Design and Projects, University of Jaén, 23071 Jaén, Spain;
mjhp0001@red.ujaen.es
3 Department of Electrical Engineering, University of Jaén, 23071 Jaén, Spain
* Correspondence: mhorzaez@ujaen.es; Tel.: +34-610-389-020
Abstract: Currently, LED technology is an established form of lighting in our cities and homes. Its
lighting performance, durability, energy efficiency and light, together with the economic savings
that its use implies, are displacing other classic forms of lighting. However, some problems associ-
ated with the durability of the equipment related to the problems of thermal dissipation and high
temperature have begun to be detected, which end up affecting their luminous intensity and the
useful life. There are many studies that show a direct relationship between the low quality of LED
lighting and the aging of the equipment or its overheating, observing the depreciation of the inten-
sity of the light and the visual chromaticity performance that can affect the health of users by alter-
ing circadian rhythms. On the other hand, the shortened useful life of the luminaires due to thermal
stress has a direct impact on the LCA (Life Cycle Analysis) and its environmental impact, which
indirectly affects human health. The purpose of this article is to compare the results previously ob-
tained, at different contour temperatures, by theoretical thermal simulation of the 3D model of LED
street lighting luminaires through the ANSYS Fluent simulation software. Contrasting these results
with the practical results obtained with a thermal imaging camera, the study shows how the phe-
nomenon of thermal dissipation plays a fundamental role in the lighting performance of LED tech-
nology. The parameter studied in this work is junction temperature (Tj), and how it can be used to
predict the luminous properties in the design phase of luminaires in order to increase their useful
life.
Keywords: LED; thermal dissipation; luminaire; CFD (computational fluid dynamics);
FMV (finite volume method)
1. Introduction
Currently, energy saving in public lighting is generating great interest and has be-
come a priority in the management of outdoor lighting in our cities; for this reason, LED
luminaires are becoming more and more frequent [1]. There are studies related to the
thermal dissipation of LEDs applied to automotive lighting that systematically prove that
the properties of thermal interface materials, such as thermal resistance, affect the optical
properties of the luminaire [2]. Other studies demonstrate the loss of chromaticity and
degradation of light, the change in chromaticity, and transmittance loss tested in phos-
phor converted white light emitting diodes (PC-WLED) under accelerated thermal tests
at 150 °C, 200 °C, and 250 °C [3]. On the other hand, the increasing use of LED lights has
Citation: Unión-Sánchez, J.d.D.;
Hermoso-Orzáez, M.J.;
Hervás-Pulido, M.J.;
Ogáyar-Fernández, B. Impact of
Thermal Dissipation on the Lighting
Performance and Useful Life of LED
Luminaires Applied to Urban
Lighting: A Case Study.
Int. J. Environ. Res. Public Health 2022,
19, 752. https://doi.org/10.3390/
ijerph19020752
Academic Editors: Irena Fryc and
Tomasz Ściężor
Received: 27 November 2021
Accepted: 7 January 2022
Published: 10 January 2022
Publisher’s Note: MDPI stays neu-
tral with regard to jurisdictional
claims in published maps and institu-
tional affiliations.
Copyright: © 2022 by the authors. Li-
censee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license
(https://creativecommons.org/licen-
ses/by/4.0/).
Int. J. Environ. Res. Public Health 2022, 19, 752 2 of 23
modified the natural light environment dramatically, posing novel challenges to both hu-
mans and wildlife. Indeed, several biomedical studies have linked artificial light at night
to the disruption of circadian rhythms [4], with important consequences for human health,
such as the increasing occurrence of metabolic syndromes [5], cancer [6], and reduced im-
munity [7]. In this sense, there are many studies that clearly show the adverse effects of
poor lighting on health, including light depreciation as a consequence of deterioration,
faulty thermal dissipation, or the devaluation of the light intensity and chromaticity of the
luminaire [8,9].
The chromaticity, electrical properties and thermal properties of LED devices are
highly dependent on each other [10]; therefore, heat dissipation plays an important role
in improving the efficiency and reliability of LED lighting [11]. Deficiencies in artificial
lighting, loss of light intensity, or changes in chromaticity can have serious consequences
on human health in relation to circadian rhythms [12,13]. This is why it is extremely im-
portant to install energy-efficient outdoor lighting equipment, but that have, at the same
time, a low impact on human health [14,15]. Shorter wavelengths of light preferentially
disturb melatonin secretion and cause circadian phase shifts, even if the light is not bright
[13].
The main source of heat in an LED comes from the union between the p-type and the
n-type semiconductor material that makes up the device [16]. The LEDs disperse 45% of
the energy applied in light and the remaining 55% in heat. High-power chips increasingly
emit more heat, which leads to performance degradation [17] and, ultimately, results in a
shorter life expectancy of LED products. Therefore, the industry is investigating product
design structures to control the heat generated by high power LED chips [18].
The temperature reached at the p–n junction is called junction temperature (Tj) and
is considered a key parameter in LED performance [19]. The junction temperature at
which light-emitting diodes have to operate must be low, as there is an inverse relation-
ship between junction temperature and the lifetime of the LED [20].
The internal thermal resistance and junction temperature (Tj) are the critical parame-
ters of LEDs, which must be maintained at nominal levels for reliable and robust operation
[21].
For a given set of operating conditions, an engineer can calculate the junction tem-
perature of an LED [22] and design a thermal dissipation system to lower the temperature
of electronic devices and extend the life of LEDs [23].
Modern chips are designed with conductive heat pipes to channel heat from the junc-
tion to the “solder point”. The welding point is the part of the LED that comes in contact
with the PCB (printed circuit board) and/or independent heat sink.
The object of this study is to propose an analysis methodology to calculate the theo-
retical thermal dissipation, a priori, depending on the design, the materials and the oper-
ating ambient temperature of the LED luminaires to later compare and discuss the theo-
retical results with the experimental ones made with a thermographic camera. The com-
parison of results between the simulations and the real dissipation in the laboratory allow
to analyze the junction temperature of the LEDs because it is a parameter that cannot be
measured through a thermographic camera. To do this, we thermally analyze, according
to the proposed method, a LED luminaire, carrying out simulations, at different working
environment temperatures [24]. Considering that thermal dissipation is essential to main-
tain the luminous properties of LED luminaires, directly impacting the useful life of the
equipment and its light quality [25], in direct relation to the impact that these deficiencies
can have on the circadian rhythms of people exposed to the light artificial defective [26,27].
Likewise, the useful life of LED luminaires is related to the quality of the equipment and
the ability of the materials to dissipate radiated heat, this aspect directly impacting the
LCA (life cycle analysis) on the carbon footprint [28] and environmental impacts of the
manufacture of new substitute equipment [25,29]. Today, it is very important to invest on
sustainable luminaires with a low carbon footprint and high durability [28].
Int. J. Environ. Res. Public Health 2022, 19, 752 3 of 23
2. Methodology
The methodology applied in this work has two parts. First, a simulation of the ther-
mal dissipation of a LED luminaire model applied to urban lighting under different
boundary conditions was carried out.
The Model luminaire corresponds to a high-power LED luminaire with a specific de-
sign to achieve an adequate lighting performance and optimal thermal dissipation. We
proceeded to analyze the results and observe the theoretical heat dissipation that occurs
in it using the ANSYS Fluent software [30].
Later, this work is complemented with a validation analysis of the methodology with
the practical results realized with a thermographic camera. We analyzed the actual results
of the thermal dissipation of the luminaire with respect to the boundary conditions in the
lighting laboratory [31], trying to assess and contrast the theoretical results, expected a
priori in the design phase, with the real and practical results obtained [32]. We will try to
justify that an adequate design and the choice of materials are key elements that allow a
better heat dissipation in LED luminaires, improving their stability and lighting perfor-
mance [33].
The analysis through CFD (computational fluid dynamics) simulation is represented
in Figure 1, through which we obtained the theoretical results of the thermal dissipation
that were produced by the Model and thus be able to make the comparison between the
data obtained between the theoretical simulations and the experimental data obtained
with the thermal imaging camera [34].
Figure 1. Flow diagram for the analysis of theoretical thermal dissipation. Source: our own elabora-
tion.
Int. J. Environ. Res. Public Health 2022, 19, 752 4 of 23
The computational dynamics of fluids is the science that predicts the flow of a fluid,
the transfer of heat and mass, chemical reactions, and related phenomena through the
numerical resolution of the set of mathematical equations.
CFD analysis complements testing and experimentation by reducing the total effort
and cost required for experimentation and data acquisition.
In the numerical simulation, there are three stages: pre-processed, processed and
post-processed.
2.1. Pre-Processing
2.1.1. Select the Luminaires for the Study
The study was carried out by selecting a high-power luminaire manufactured with a
novel design, specially oriented to the improvement of the thermal dissipation, and is re-
ferred to as Model.
2.1.2. Obtaining the Information on the Luminaire to Be Analyzed
The manufacturer provided the different characteristics and properties of the lumi-
naire, who remains confidential in this project.
The Model consists of different components and materials that are attached in Table
1. The total nominal power of the luminaire is 204 W, of which 192 W is for the operation
of the LEDs and 12 W for the drivers.
The drivers are composed of an electronic circuit that performs the following func-
tions:
- To transform alternating current (AC) into direct current (DC), which is used by
LEDs for their correct operation;
- To adapt the output voltage and current to the LED requirements.
Table 1. Components, materials and powers. Source: ATP lighting S.A.
Components Subcomponent Part Material Information
Cover - - PC Opaque
Heatsink - - Aluminum Confidential
Equipment carrier - - PA66–30FV -
Equipment - - PA66–30FV -
Drivers
Microchips Silicon
2 drivers; 6
W/driver
PCB driver Welding Tin
Base Aluminum
Housing - PC
Diffuser - - PC Transparent
Chassis - - PA66–30FV -
PCB LED - - Aluminum -
LED - - Copper/Material Rth 96 LED; 2 W/LED
2.1.3. Elaboration of the 3D Model of the Equipment
The elaboration of the model was carried out using CATIA (Computer-Aided Three-
Dimensional Interactive Application) [35], which consists of computer-aided design, man-
ufacturing and engineering software (Figure 2).
Int. J. Environ. Res. Public Health 2022, 19, 752 5 of 23
Figure 2. Modelling in 3D of the luminaire Model. Source: our own elaboration.
2.1.4. Discretization of the 3D Model for Study
To execute the discretization, the specific program Altair HyperMesh was used. It is
a computer-aided engineering simulation software (CAE, computer aided engineering)
platform, in which it is possible to create finite element models for the analysis and pre-
pare meshes of high quality efficiently. The method of discretization in the 3D model was
in finite volumes [36].
A two-dimensional mesh was made with triangular elements (triads). Having a con-
trol of the mesh with skew means that an angle between the vector from each node to the
midpoint of the opposite side and the vector between the two adjacent middle sides in
each node of the element is 90 degrees minus the angle found using skew. In this study,
the skew sought is 60 degrees, providing a triangle as similar as possible to an equilateral
triangle for the subsequent generation of 3D elements. See Figures 3 and 4.
Figure 3. Definition of skew. Source: HyperMesh meshing control guide.
Figure 4. Mesh control. Source: HyperMesh meshing control guide.
Luminaire Discretization Model
Next, in the series of Figures 5–7, we represent the discretized components.
Int. J. Environ. Res. Public Health 2022, 19, 752 6 of 23
Figure 5. Discretized luminaire Model. Source: our own elaboration from the software Hy-
perMesh.
Figure 6. Discretized PCB and LED diode. Source: our own elaboration from the software Hy-
perMesh.
Figure 7. Average section of the components and air generated with tetrahedral Model. Source: our
own elaboration from the software HyperMesh.
The mesh was generated in triads and a tetrahedral, although with square meshes
and hexahedrons can be provided higher quality solutions with fewer cells/nodes. The
square and hexahedral meshes show a reduced numerical diffusion when the mesh is
aligned with the flow, but more effort is also required in generating a square mesh and
hexahedrons.
2.1.5. Configuration of the Physical and Solver Characteristics
Definition of the Properties of Materials
It is necessary to define the properties of the materials that are going to be assigned
to the different components of the luminaire to be taken into account during the study
and thus have greater precision in the results when compared with the experimental data,
reflected in Table 2.
Table 2. Properties of the materials of the model. Source: commercial catalogs of the manufacturer’s
Model (ATP).
Material Density
kg/m3
Specific heat
J/kg·K
Thermal conductivity
W/m·K
Aluminum 2750 961 200
Int. J. Environ. Res. Public Health 2022, 19, 752 7 of 23
Silicon 2330 700 148
Tin 7365 228 66.6
PA66–30FV 1370 2290 0.29
PC 1200 1250 0.19
Copper 8900 394 387
Material Rth 3300 780 52.91
The thermal resistance of the LED (Rth) of the luminaire of Model is 2.1 W/K.
Definition of Physical Models
1. Conservation equations.
The ANSYS Fluent solver is based on the finite volume method [37]. The domain is
discretized in a finite set of control volumes and the general conservation equations for
mass, momentum, energy, etc., are solved in this set of control volumes. All the equations
are then solved numerically to represent the solution field.
2. Finite Volume Method.
The general equations of conservation of the mass, amount of movement, energy, etc.
are resolved in this set of control volumes. In the centroid of each control volume, there is
a node where the value of the variables is calculated and, in the borders, their value can
be known through interpolation. Then, we continued with an approximation of the con-
servation equations to determine a system of algebraic equations and obtain their solution
by iterative methods.
Starting from the general transport equation, obeying the Navier–Stokes equations
in the integral form [38]:
∫ρΦdV + ∮ρvΦ ∙ ndA = ∮ρΓ∇Φ ⋅
ndA + ∫SdV
, (1)
where Φ = variable transported by a medium;
ρ = density of the medium through which it is transported Φ;
V = travel speed of Φ through the medium;
Γ = medium diffusion constant;
S = source/sink term of the variable transported;
A = border;
= speed vector of Φ through the medium;
= normal vector to the surface.
The first term corresponds to the temporal variation of the variable transported by
the medium within a volume; the second term corresponds to the convective flows of the
variable transported across the border due to the speed. The first term after equality ex-
presses the diffusive term of the variable transported at the border that depends only on
the gradient of said variable, and the last term is due to the source term of the variable
inside the volume.
The source term, S of Equation (1), contains the radiation terms calculated through
the discrete ordinate model (DO). The DO model transforms the radioactive transport
equation for an absorption, emission and dissipation medium in the position r and in the
direction s in a transport equation for the radiation intensity in the spatial coordinates (x,
y, z). The resolution method is the same as that used for the flow and energy equations,
and represented in Equation (2) [38]:
(
,
)
+(a + σ)I(r, s)= an
+
∫I(r, s′)
Φ(s ∙ s′) dΩ′
(2)
where r = position vector;
s = direction vector;
s’= vector direction dissipation;
s = path length;
Int. J. Environ. Res. Public Health 2022, 19, 752 8 of 23
a = absorption coefficient;
n = refractive index;
σ = dispersion coefficient;
σ = Constant Stefan–Boltzmann (5669 × 10−8 W/m2 K4);
I = Intensity of radiation;
T = local temperature in Kelvin;
Φ = phase function;
Ω′ = solid angle.
For finite volumes and sub-border volumes that correspond to the volume boundary,
we obtained [38]:
V ∙
+∑ρvΦ∙ A
=
∑Γ∇Φ∙ A
+ S+ V
(3)
where Nfaces = number of faces of volume;
f = fluid.
The differential equations were discretized in a system of algebraic equations that are
solved numerically to give a field of solutions.
Implement boundary conditions
The boundary conditions were determined to study the behavior of the luminaire in
certain cases. The boundary conditions were determined in the air, which is found sur-
rounding the luminaire. Several environmental temperatures have been attempted to be
simulated [24], such as, 40 °C, 20 °C and −10 °C, with an initial speed ascending of 0.02
m/s.
3. Determination of the problem solver.
ANSYS Fluent allows one to choose one of the two numerical methods:
- Pressure-based solver;
- Density-based solution.
In both methods, the velocity field is obtained from the moment equations. In the
density-based approach, the continuity equation is used to obtain the density field, while
the pressure field is determined from the state equation.
On the other hand, in the pressure-based approach, the pressure field is extracted by
solving a pressure or pressure correction equation that is obtained by manipulating the
continuity and momentum equations.
In our case, we used the pressure-based solver, which uses an algorithm that belongs
to a general class of methods, called the projection method.
There are two pressure-based solution algorithms available in ANSYS Fluent. A seg-
regated algorithm and a coupled algorithm.
- Segregated algorithm based on pressure: The individual governing equations for the
solution variables (for example, u, v, w, p, T, k, etc.) are solved one after the other.
The convergence of the solution is relatively slow, since the equations are resolved in
an uncoupled way;
- Algorithm coupled based on pressure: The pressure-based coupled algorithm solves
a coupled system of equations that comprises the moment equations and the pres-
sure-based continuity equation. Since the equations of momentum and continuity are
solved in a tightly coupled manner, the convergence rate of the solution improves
significantly compared to the segregated algorithm. The convergence with this algo-
rithm improves with respect to the segregated algorithm, which is taken into account
in the choosing of this method.
4. Monitorization of residuals.
Int. J. Environ. Res. Public Health 2022, 19, 752 9 of 23
In a CFD analysis, the residue measures the local imbalance of a variable stored in
each control volume. For CFD, the residual levels of 10−4 are considered to be slightly con-
vergent, the levels of 10−5 are considered to be very convergent, and the levels of 10−6 are
considered to be closely convergent.
2.2. Processed
The residual definitions that are useful for a problem class are sometimes deceptive
for other kinds of problems. Therefore, it is a good idea to judge convergence not only by
examining residual levels, but also by monitoring relevant integrated quantities, such as
the heat transfer or drag coefficient.
In the present analysis, continuity, velocity in x, velocity in y, velocity in z, energy
and DO with a convergence criterion 10−3, 10−3, 10−3, 10−3, 10−6 and 10−5 were taken into
account, respectively.
2.3. Post-Processing
The results were examined to review the solution and extract the data. The tool used
is Altair HyperView, a complete post-processing and visualization environment for CFD.
It allows to visualize the data in an interactive way.
3. Results
The results section is divided into two parts. On the one hand, the theoretical results
show the data obtained with the simulation software at the three boundary temperatures,
as well as the validation of the luminaire materials by comparing the limit temperatures
of each of the materials that make up the luminaires with the operating temperatures ob-
tained in the simulation for each of them.
Finally, in the practical results, the thermal dissipation of the LED luminaire is tested
at an ambient temperature of 20 °C in order to validate the theoretical results obtained
previously.
3.1. Theoretical Results
3.1.1. Display of Temperature and Air Speed in the Model Luminaire at 20 °C
In the next Figures 8 and 9, we graphically display in color the temperature of differ-
ent parts of the Model at 20 °C.
Figure 8. Temperature medium section of the luminaire at 20 °C. Source: HyperView software.
Int. J. Environ. Res. Public Health 2022, 19, 752 10 of 23
Figure 9. Speed section medium of the luminaire at 20 °C. Source: HyperView software.
3.1.2. Display of Temperature and Air Speed in the Model Luminaire at 40 °C
In the next Figures 10 and 11, we graphically display in color the temperature of dif-
ferent parts of the Model at 40 °C.
Figure 10. Temperature medium section of the luminaire at 40 °C. Source: HyperView software.
Figure 11. Speed section media of the luminaire at 40 °C. Source: HyperView software.
Int. J. Environ. Res. Public Health 2022, 19, 752 11 of 23
3.1.3. Display of Temperature and Air Speed in the Model Luminaire at −10 °C
In the next Figures 12 and 13, we graphically display in color the temperature of dif-
ferent parts of the Model at −10 °C.
Figure 12. Temperature medium section of the luminaire at −10 °C. Source: HyperView software.
Figure 13. Speed section medium of the luminaire at −10 °C. Source: HyperView software.
In the theoretical data obtained from the simulations, a validation of the materials is
carried out followed by an analysis of the LEDs. The validation of the materials consists
of the comparison of the limit temperature that the material used for its operation can
reach without having problems of deterioration with the maximum temperature reached
in each component of the luminaire to observe if the limit temperature of the material is
exceeded.
Regarding the drivers, they are not composed of only one material, as it is a combi-
nation of several subcomponents. The weakest components to the temperature are the
microchips that are assembled on the driver PCB, then in the comparison we have divided
the driver information in two rows (bases and electronics components) and for electronics
we select the temperature limit of the microchips for the evaluation of the driver (Table 3–
5).
Table 3. Maximum temperature limit of the material versus temperature measured in the different
parts of the Model at 20 °C. Source: our own elaboration.
Component Material Maximum Limit Tempera-
ture (°C)
Temperature
Measured
(°C)
Cover PC 145 70
Heatsink Aluminum 460 112
Equipment carrier
PA66–30FV 150 77
Int. J. Environ. Res. Public Health 2022, 19, 752 12 of 23
Equipment PA66–30FV 150 112
Drivers Bases Aluminum 460 112
Driver (Electronic)
Silicon (Weakest)
150 105
Diffuser PC 145 100
Chassis PA66–30FV 150 107
PCB Aluminum 460 117
Table 4. Maximum temperature limit of the material versus temperature measured in the different
parts of the Model at 40 °C. Source: our own elaboration.
Component Material Maximum Limit Tempera-
ture (°C)
Temperature Meas-
ured (°C)
Cover PC 145 90
Heatsink Aluminum 460 131
Equipment carrier PA66–30FV 150 97
Equipment PA66–30FV 150 129
Drivers Bases Aluminum 460 130
Driver (Electronic) Silicon (Weakest) 150 130
Diffuser PC 145 118
Chassis PA66–30FV 150 123
PCB Aluminum 460 137
Table 5. Maximum temperature limit of the material versus temperature measured in the different
parts of the Model at −10 °C. Source: our own elaboration.
Component Material Maximum Limit Temperature
(°C)
Temperature Measured
(°C)
Cover PC 145 42
Heatsink Aluminum 460 82
Equipment carrier PA66–30FV 150 47
Equipment PA66–30FV 150 83
Drivers Bases Aluminum 460 84
Driver (Electronic) Silicon (Weakest) 150 90
Diffuser PC 145 69
Chassis PA66–30FV 150 74
PCB Aluminum 460 88
In the next Figures 14–16, a comparison of some of the different components of the
Model and for the different ambient temperatures of the simulations is shown, giving the
range of temperatures at which, the components are located.
Int. J. Environ. Res. Public Health 2022, 19, 752 13 of 23
Figure 14. Temperature range of the complete luminaire and of the drivers base with respect to the
ambient temperature in each simulation. Source: our own elaboration.
(a) (b)
Figure 15. Temperature range of the complete luminaire (a) and of the drivers base (b) with respect
to the ambient temperature in each simulation. Source: our own elaboration.
-20
0
20
40
60
80
100
120
140
20°C 40°C -10°C
Model temperature range (°C)
Ambient or contour temperature of
the simulation (°C)
Complete luminaire
80
90
100
110
120
130
140
20°C 40°C -10°C
Temperature range of the Drivers (°C)
Ambient or contour temperature of
the simulation (°C)
DRIVERS
Int. J. Environ. Res. Public Health 2022, 19, 752 14 of 23
Figure 16. Comparison of dissipation of the diffuser (left) and of the heatsink (right) for different
simulation temperatures. Source: our own elaboration from the software HyperView.
The heatsink is made of aluminum and has a limit temperature of 460 °C. The maxi-
mum temperature reached by the heatsink in the simulations is much lower than the limit
temperature. The diffuser is composed of PC (polycarbonate), a material that has an op-
erating limit temperature of 145 °C. The results of the simulations show that the maximum
temperatures of these components are within operating temperature range, as shown in
Figure 17.
(a) (b)
Figure 17. Temperature range of the diffuser (a) and the dissipater or heatsink (b) with respect to
the ambient temperature in each simulation of the Model. Source: our own calculations.
3.2. Experimental Results
The prototype high-power LED luminaire was built with the same geometry and di-
mensions than the model in order to validate the simulation. This luminaire corresponds
to Air Series 7 of ATP lighting (Figure 18).
Int. J. Environ. Res. Public Health 2022, 19, 752 15 of 23
Figure 18. Luminaire Model. LED Air Series 7 of ATP Lighting. Source: ATP LED catalog lighting.
In the following Tables 6 and 7, the main power characteristics of LED and driver
features are collected.
Table 6. Luminaire power table. Source: manufacturer’s data.
Model Nominal Power (W)
Number of LEDs Power by LED (W/LED) Power DRIVER (W)
ATP Aire Serie 7
204 96 2 12 (6 W/driver)
Table 7. Driver features. Source: manufacturer’s data.
Model Efficiency (%)
Nominal Power (W) Operating input range (V)
Storage Temperature (°C)
Output current (A)
MP4688 95 2–2.5 4.5–80 −65 to 150 Up to 1 A
The drivers used for control LEDs is the chip MP4688 high power control LEDs. The
luminaire has two drivers PCB, and each driver PCB has three MP4688. Each integrated
MP4688 controls 16 LEDs (96 LEDs in the luminaire).
Each MP4688 has nominal power of 2W. As we have a nominal power of 2 W, two
drivers PCB and three chips in each PCB, we have a total value of 12 W (total power driv-
ers).
For the acquisition of real thermal dissipation data of the analyzed luminaire, a ther-
mographic camera was used at a stabilized temperature of 20 °C and in a climatized en-
vironment with a relative humidity of 70% and air speed of 0 m/s.
The thermal imaging camera used was the FLUKE Ti 25 model. It captures a digital
image with an infrared and fuses it. The temperature range is −20 °C to 350 °C with a
precision of ±2 °C. The thermal sensitivity is 0.1 °C at an ambient temperature of 20 °C,
which describes the smallest difference between two pixels in temperature that the camera
can measure (Table 8).
Table 8. Table of specifications of the thermal imager. Source: the user’s manual of the Ti 25 FLUKE
thermal camera.
Attribute Value
Thermal sensitivity ≤90 mK
Temperature Measurement Range −20 → +350 °C
Maximum Accuracy of Temperature Measurement ±2 °C
Field of vision H × V 23 × 17°
Update frequency 9 Hz
Minimum Focus Distance
15 (Thermal Lens) cm,
46 (Visual Lens) cm
Type of Focus Manual
Int. J. Environ. Res. Public Health 2022, 19, 752 16 of 23
Detector Resolution 160 × 120 pixel
Display size 3.7 plg
Display Resolution 640 × 480 pixel
Model number Ti25
A fundamental parameter when acquiring images with the thermal imaging camera
is the emissivity. All objects radiate infrared energy. The amount of energy radiated is
based on two main factors: the surface temperature of the object and the emissivity of said
surface. Emissivity is a very important issue for measuring surface temperature without
being in contact [39]. Depending on the material to be measured, the emissivity varies
[40]. The emissivity determined to obtain the temperatures of the materials was obtained
from a table of emissivities.
A progressive data collection was made from the ignition to the thermal stabilization
of the equipment, taking data periodically until the temperature remained stable.
To take the measurements, the dissipater and diffuser with the thermal imager and
to be a specific material with a specific alloy or formulation in the material compound,
several types of emissivities were used to obtain the exact temperature (Figures 19–22).
Figure 19. Thermal data of the cover. Blue and green area. Plastic emissivity 0.92. Source: Software
SmartView 4.1 Fluke.
Figure 20. Thermal data of the heatsink. Reddish area: emissivity aluminum alloy 0.5. Source: Soft-
ware SmartView 4.1 Fluke.
Int. J. Environ. Res. Public Health 2022, 19, 752 17 of 23
Figure 21. Thermal data of the heatsink. Reddish area: emissivity aluminum alloy 0.5. Source: Soft-
ware SmartView 4.1 Fluke.
Figure 22. Thermal data of the diffuser. Green zone: plastic emissivity 0.92. Source: Software
SmartView 4.1 Fluke.
4. Discussion
Once the simulation was validated at 20 °C in the laboratory, an analysis of the junc-
tion temperature of the LEDs (Tj) is carried out in order to study how it affects the lighting
performance.
To ensure the useful life, efficiency and color of the LEDs, the junction temperature
of the LEDs must be maintained in a specific range.
After obtaining the junction temperature of the LEDs in the simulations for the dif-
ferent boundary conditions that surround the luminaire, such as 40 °C, 20 °C and −10 °C,
they are compared with the LED junction temperatures found in the market (Table 9, Fig-
ures 23 and 24).
Table 9. Junction temperatures of the LEDs obtained in the simulations (Tj). Source: our own elabo-
ration.
External Ambient Temperature
of the Simulation (°C)
Junction Temperature of the
LEDs Tj (°C)
Temperature 1 40 135
Temperature 2 20 117
Temperature 3 −10 86
Int. J. Environ. Res. Public Health 2022, 19, 752 18 of 23
Figure 23. Summary of the joining temperatures of LEDs during the simulation at ambient temper-
ature of 40 °C. Source: our own elaboration.
Figure 24. Variation of the maximum junction temperature of the LEDs with respect to the ambient
temperature of the simulation, marking the limit junction temperature of each LED. Source: our own
elaboration.
For the Model, an Osram LUW CQAR LED (streetwhite) with its respective data
sheet was used. It was chosen to take note of the dimensions of the LED with the power
of 2 W/LED and, therefore, have an orientation for the features. The maximum junction
temperature of this LED corresponds to a temperature of 135 °C. In this luminaire, the
maximum temperature reached is 10 °C above the value of the temperature of the LED
junction, which causes a decrease in lamp life, possibly causing the LED light output to
decrease irreversibly in the long term at a faster rate than at lower temperatures [41]. Con-
trolling the temperature of LEDs is, therefore, one of the most important aspects of the
optimal performance of LED systems [1].
In the following graphs, it can be observed how important LED parameters vary with
the junction temperature that are included in the data sheet provided by the manufac-
turer. Figure 25 shows how the luminous flux decays as the bonding temperature in-
creases. For the junction temperature at the ambient temperature of 20 °C, which is 117
0
20
40
60
80
100
120
140
160
-20 0 20 40 60
maximum junction temperature of the
LEDs
External ambient temperature of the
simulation (ºC)
Model A
Tj Limit
LED Model
A 125 ºC
Int. J. Environ. Res. Public Health 2022, 19, 752 19 of 23
°C, the LEDs have a 20% decrease in luminous flux. By increasing the LED junction tem-
perature, the power that is perceived as light by the human eye decreases.
Figure 25. Junction temperature of LEDs (Tj) vs. luminous flux. Source: our own elaboration and
data sheet LED Osram LUW CQAR (streetwhite).
Figure 26 shows the variation of the chromaticity coordinates with respect to the
change in the junction temperature. For the junction temperature at the ambient temper-
ature of 20 °C at which the LEDs of the Model are located (green line of Figure 26), the
variation of the chromaticity coordinates is shown.
Figure 26. Junction temperature (Tj) vs. change of chromaticity coordinates. Source: our own elabo-
ration and data sheet LED Osram LUW CQAR (streetwhite).
The variation of the junction temperature of the LED causes the chromaticity coordi-
nates to change. We observed how the color quality of the light becomes worse. For the
junction temperature corresponding to 20 °C ambient temperature, which is 117 °C, the
Int. J. Environ. Res. Public Health 2022, 19, 752 20 of 23
variation of chromaticity varies causing a decrease in the color quality of the LED light,
moving in the chromaticity diagram around 15%.
The theoretical and experimental data vary very slightly comparatively in Figure 27.
In this way, we can verify that, from simulation techniques, we can obtain a very real
approximation to the operating results of the Model without the need to build the proto-
type. The thermal dissipation simulation techniques allow to obtain very precise infor-
mation, always linked to the precision of the geometry of the design and the materials, as
fundamental for the results to be as accurate as possible.
Figure 27. Comparative theoretical data (left) and experimental data (right). Source: our own elab-
oration from the software HyperView and SmartView 4.1.
At the time of favoring heat dissipation, the dissipater is the most important compo-
nent to reduce the binding temperature. Most heatsinks are designed with fins to increase
their contact surface with air and dissipate more heat [42]. It is necessary to improve the
design of the components of the luminaire to facilitate the dissipation of heat and favor
the passage of air through the heatsink of the luminaires to lower the temperature of elec-
tronic devices. To improve cooling, auxiliary ventilation systems could be incorporated,
to improve the flow of air from the interior to the exterior.
A major problem with high power LED luminaires is the heat generated inside. The
distribution of the LEDs and the effect of the number of LEDs lit affect the junction tem-
perature and strongly participate in the degradation of the LED [43]. Most luminaires con-
tain aluminum heatsinks as a solution. Currently, materials with new alloys and formu-
lations for effective thermal dissipation are being investigated. For example, aluminum
nitrate has been developed to be applied as a thin layer in the dissipaters, which has cer-
tain advantages over conventional dielectrics based on polymers or ceramic substrates,
such as excellent thermal dissipation and low thermal resistance [44]. The issue of thermal
dissipation is the order of the day; materials are being investigated to be used as heat sinks
that promote greater durability of LEDs due to an improvement in thermal dissipation
[45].
Int. J. Environ. Res. Public Health 2022, 19, 752 21 of 23
5. Conclusions
With this work, we intend to present an analytical methodology to calculate the ther-
mal dissipation of LED luminaires, previously, in the design phase. The method was val-
idated by comparing the results of the theoretical thermal simulations on the basis of a
previously designed luminaire model with the experimental data obtained in the labora-
tory.
The proposed methodology allows the simulation to be carried out in the design
phase of the LED luminaires, before launching into the construction phase of the proto-
type. This allows to analyze and experiment with the model in a virtual environment,
reducing the time and cost requirements associated with the tests performed.
This study is aimed at the preliminary thermal analysis of LED luminaires, to verify
the design and materials of the luminaires and to check the temperature of the LEDs and
their impact in the face of the good functioning of light-emitting diodes. The study ob-
serves the influence of the junction temperature of the LEDs, as a critical issue of the de-
sign phase that can seriously affect the functionality of the luminaire and causes a decrease
in the useful life and variation of the light properties of the LEDs, such as the decrease in
luminous flux and change in chromaticity coordinates.
All electronic devices and circuits generate excess heat and require greater attention
to avoid premature failures. LEDs convert 45% of the energy applied to light and the re-
maining 55% into heat, which must be dissipated using a design and suitable materials so
that the durability and properties of the LEDs are not affected by the increase in temper-
ature [17].
The thermal simulations give the engineer information about the temperature and
air flow inside the equipment, allowing engineers to design cooling systems to optimize
design and reduce energy consumption, weight, and cost, and verifying that there are no
problems when the equipment is built. In this work, we used thermal simulation software
applying computational fluid dynamics (CFD) techniques to predict the temperature and
air flow of an electronic system [34].
A good design of the luminaire, where the circulation of air is favored for the benefit
of the dissipation of the junction temperature of the LEDs, favors the useful life and im-
proves the lighting efficiency and reliability of the LEDs, which allows them to have better
lighting properties. Apart from the design, a good selection of materials for the compo-
nents of the luminaire where the thermal conductivity is high favors the thermal dissipa-
tion of the luminaire.
Addressing and trying to mitigate the LED fixture overheating issues associated with
poor heat dissipation is key, as they end up affecting light quality, chromaticity, and color
temperature, directly impacting the lifespan of luminaires and the health of users by al-
tering circadian rhythms. That is why we consider it essential to design lighting equip-
ment that allows high thermal dissipation [46]. This work proposes a methodology for the
scientific researcher that satisfies the future need to design luminaires with LED technol-
ogy that are sustainable for the environment, safe for human health, reliable and durable,
supported by the proven results obtained.
Author Contributions: conceptualization, M.J.H.-O. and J.d.D.U.-S.; methodology M.J.H.-O.,
M.J.H.-O. and J.d.D.U.-S.; software, M.J.H.-O., M.J.H.-P. and J.d.D.U.-S.; validation, M.J.H.-O. and
B.O.-F.; formal analysis M.J.H.-O. and J.d.D.U.-S.; investigation, M.J.H.-O., M.J.H.-O. and J.d.D.U.-
S.; resources, writing M.J.H.-O. and J.d.D.U.-S.; writing—review and editing, M.J.H.-O. and
J.d.D.U.-S.; visualization M.J.H.-O. and J.d.D.U.-S. supervision , M.J.H.-O. and B.O.-F.; project ad-
ministration, , M.J.H.-O. and B.O.-F.; funding acquisition. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Int. J. Environ. Res. Public Health 2022, 19, 752 22 of 23
Data Availability Statement: We accept MDPI Research Data Policies Section at
https://www.mdpi.com/ethics (accessed on 26 November 2021).
Conflicts of Interest: The authors declare no conflicts of interest.
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