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## Section: Research Program

### Thermal methods

#### Infrared thermography and heat transfer

This section introduces the infrared radiation and its link with the temperature, in the next part different measurement methods based on that principle are presented.

Figure 1. Electromagnetic spectrum - Credit MODEST, M.F. (1993). Radiative Heat Transfer. Academic Press.

For scientific purposes, infrared can be divided in three ranges of wavelength in which the application varies, see Table 1.

Our work is concentrated in the mid infrared spectral band. Keep in mind that Table 1 represents the ISO 20473 division scheme, in the literature boundaries between bands can move slightly.

The Plank's law, proposed by Max Planck in 1901, allows to compute the black body emission spectrum for various temperatures (and only temperatures), see Figure 2 left. The black body is a theoretical construction, it represents perfect energy emitter at a given temperature, cf. Equation (20).

With $\lambda$ the wavelength in m and $T$ as the temperature in Kelvin. The ${C}_{1}$ and ${C}_{2}$ constants, respectively in W.m${}^{2}$ and m.K are defined as follow:

with

• $c$, the electromagnetic wave speed (in vacuum $c$ is the light speed in m.s${}^{-1}$).

• $k=1.381{e}^{-23}$ J.K${}^{-1}$ The Boltzmann (Entropy definition from Ludwig Boltzmann 1873). It can be seen as a proportionality factor between the temperature and the energy of a system.

• $h\approx 6,62606957{e}^{-34}$ J.s The Plank constant. It is the link between the photons energy and their frequency.

Figure 2. Left: Plank's law at various temperatures - Right: Energy spectrum of the atmosphere

By generalizing the Plank's law with the Stefan Boltzmann law (proposed first in 1879 and then in 1884 by Joseph Stefan and Ludwig Boltzmann), it is possible to address mathematically the energy spectrum of real body at each wavelength depending on the temperature, the optical condition and the real body properties, which is the base of the infrared thermography.

For example, Figure 2 right presents the energy spectrum of the atmosphere at various levels, it can be seen that the various properties of the atmosphere affect the spectrum at various wavelengths. Other important point is that the infrared solar heat flux can be approximated by a black body at 5523,15 K.

##### Infrared Thermography

The infrared thermography is a way to measure the thermal radiation received from a medium. With that information about the electromagnetic flux, it is possible to estimate the surface temperature of the body, see section 3.2.1.1. Various types of detector can assure the measure of the electromagnetic radiation.

Those different detectors can take various forms and/or manufacturing process. For our research purposes, we use uncooled infrared camera using a matrix of microbolometers detectors. A microbolometer, as a lot of transducers, converts a radiation in electric current used to represent the physical quantity (here the heat flux).

This field of activity includes the use and the improvement of vision system, like in [3].

#### Heat transfer theory

Where $k$ is the thermal conductivity in W.m${}^{-1}$.K ${}^{o}$, $\nabla$ is the gradient operator and $\varphi$ is the heat flux density in Wm${}^{-2}$. This law illustrates the first principle of thermodynamic (law of conservation of energy) and implies the second principle (irreversibility of the phenomenon). From this law it can be seen that the heat flux always goes from hot area to cold area.

An energy balance with respect to the first principle yields to the expression of the heat conduction in all point of the domain $\Omega$, cf Equation (23). This equation has been proposed by Joseph Fourier in 1811.

With $\nabla .\left(\right)$ the divergence operator, $C$ the specific heat capacity in J.kg${}^{-1}$.${}^{o}$K${}^{-1}$, $\rho$ the volumetric mass density in kg. m${}^{-3}$, $X$ the space variable $X=\left\{x,y,z\right\}$ and $P$ a possible internal heat production in W.m${}^{-3}$.

To solve the system (23), it is necessary to express the boundaries conditions of the system. With the developments presented in section 3.2.1.1 and the Fourier's law, it is possible, for example, to express the thermal radiation and the convection phenomenon which can occur at $\partial \Omega$ the system boundaries, cf Equation (24).

Equation (24) is the so called Robin condition on the boundary $\partial \Omega$, where $n$ is the normal, $h$ the convective heat transfer coefficient in W.m${}^{-2}$.K${}^{-1}$ and ${\varphi }_{0}$ an external energy contribution W.m${}^{-2}$, in cases where the external energy contribution is artificial and controlled we call it active thermography (spotlight etc...), otherwise it is called passive thermography (direct solar heat flux).

The systems presented in the different sections above (3.2.1 to 3.2.2) are useful to build physical models in order to represents the measured quantity. To estimate key parameters, as the conductivity, model inversion is used, the next section will introduce that principle.

#### Inverse model for parameters estimation

Lets take any model $A$ which can for example represent the conductive heat transfer in a medium, the model is solved for a parameter vector $P$ and it yields another vector $b$, cf Equation (25). For example if $A$ represents the heat transfer, $b$ can be the temperature evolution.

To do that it is important to respect the well posed condition established by Jacques Hadamard in 1902

• A solution exists.

• The solution is unique.

• The solution's behavior changes continuously with the initial conditions.

Unfortunately those condition are rarely respected in our field of study. That is why we dont solve directly the system (27) but we minimise the quadratic coast function (28) which represents the Legendre-Gauss least square algorithm for linear problems.

Where $ℱ$ can be a product of matrix.

$ℱ={\left[AP-ℳ\right]}^{T}\left[AP-ℳ\right]$

Where $J$ is the sensitivity matrix of the model $A$ with respect to the parameter vector $P$.

Until now the inverse method proposed is valid only when the model $A$ is linearly dependent of its parameter $P$, for the heat equation it is the case when the external heat flux has to be estimated, ${\varphi }_{0}$ in Equation (24). For all the other parameters, like the conductivity $k$ the model is non-linearly dependant of its parameter $P$. For such case the use of iterative algorithm is needed, for example the Levenberg-Marquardt algorithm, cf Equation (30).

Equation (30) is solved iteratively at each loop $k$. Some of our results with such linear or non linear method can be seen in [4] or [2], more specifically [1] is a custom implementation of the Levenberg-Marquardt algorithm based on the adjoint method (developed by Jacques Louis Lions in 1968) coupled to the conjugate gradient algorithm to estimate wide properties field in a medium.