**4. Sensor design**

The present chapter is a consequence of the previous considerations analysis, for which the characteristics of the designed smart sensor are analyzed in order to be used in some gas measurement alarms, as well as in applications that seek enhancement of hydrogen combustion motors.

There were prepared samples based in AAO because of the simplicity of getting organized nanoholes by anodization, previously an electrochemical cleaning that was given by electropolishing. The porosity is a good property according to warrant the nanoholes obtained by the anodization [12, 13]; in this context, Eqs. (23)–(26) can give the mathematical explanation to achieve an approximation for the porosity measurement. Hence, Eq. (23) is the volume for a cylindrical solid with side base "l" and height "L":

**Figure 3.** *Block diagram for the algorithm of the designed smart sensor.*

*Optimal Analysis for the Enhancement in the Thermal Variables Measurement by Smart… DOI: http://dx.doi.org/10.5772/intechopen.112676*

$$V\_b = \frac{6\sqrt{3}}{4}l^2L\tag{23}$$

Furthermore, the porous volume is given by Eq. (24), for which the porous diameter is "*Dp*", and height "L":

$$V\_p = \frac{\pi}{4} D\_p^2 L \tag{24}$$

Hence, the porosity is achieved from Eqs. (23) and (24), which is shoed by Eq. (25):

$$P = \frac{\frac{\pi}{4}D\_p^2 L}{\frac{6\sqrt{3}}{4}l^2 L} \tag{25}$$

In fact, the diameter of the porous "*Dp*" and the base diameter "*Db*" proportionate the porosity calculation. In which, it was replaced "*l*" as a function of "*Db*" which is described by the Eq. (26):

$$P = \frac{2\pi D\_p^2}{3\sqrt{3}D\_b^2} \tag{26}$$

**Figure 4** depicts a nanohole prepared in a sample of aluminum, the relation of the diameters "*Dp*" and "*Db*" are also represented in this figure, moreover the expected hexagon of the AAO holes, which are references to get optimal designs during the anodization.

The chemical equations can explain the hexagon geometry of the nanoholes obtained after the anodization. Hence, Eq. (27) gives the information about the aluminum composition after taking three electrons from its last orbital:

$$Al = Al^{3+} + \text{3e} \tag{27}$$

The chemical equation for water composition is given by Eq. (28):

**Figure 4.** *Representation of a nanohole prepared in samples of aluminum.*

$$H\_2O = O^{-2} + 2H^{+1} \tag{28}$$

Moreover, it is obtained Eq. (29) by the reaction between aluminum and oxygen:

$$\text{2Al}^{+3} + \text{3O}^{-2} = Al\_2O\_3 \tag{29}$$

From Eqs. (28) and (29), it is achieved Eq. (30):

$$2Al^{+3} + 3H\_2O = Al\_2O\_3 + 6H^{+1} \tag{30}$$

Replacing Eq. (27) in (30) helped to get Eq. (31), which gives the information of the AAO obtaining:

$$2Al + 3H\_2O = Al\_2O\_3 + 6H^{+1} + 6e^{-1} \tag{31}$$

After designing nanoholes over aluminum samples, there were prepared structures (amorphous nanostructures) based on electrochemical deposition by materials such as titanium, silver, gold, and silicon. **Figure 5** shows a picture that was taken from a microscope Litz on a scale of 25 μm. In which are shown some amorphous nanoparticles with an average diameter of 1000 nm.

The **Figure 6** depicts the designed smart sensor representation. "*NS*" is the component of the sensor that is covered by nanostructures around the sections "*TR*" and the samples based in nanotubes to receive the static pressures "*P1*" and "*P2*", these samples are integrated as part of the transducer to get the static pressure and they are represented by "*NT*". Therefore, the transduced signal is used by the microcontroller of the designed smart sensor "*SS*" aiming to be organized by matrices of static difference of pressure "*ΔP*", as well as to obtain the optimal estimations of the measured fuel flow and detector alarm that it is measured hydrogen flow and presence of other molecules, such as for example oxygen or mixing of hydrocarbons. In fact, the measured signal can be transmitted by wireless signals "*EW1*" to external users in order to get diagnostic of the ICM, and the interpretation of the ICM operation that can be achieved by the integration of the solid components of the designed smart sensor.

The nanotubes of the designed transducers are based on Anodic Aluminum Oxide, achieved by electropolishing and anodizing aluminum in high purity. Over the AAO

**Figure 5.** *Amorphous nanostructures prepared over AAO, a photo taken by microscope Litz.*

*Optimal Analysis for the Enhancement in the Thermal Variables Measurement by Smart… DOI: http://dx.doi.org/10.5772/intechopen.112676*

**Figure 6.** *Designed smart sensor representation.*

**Figure 7.** *Static curve analysis.*

samples were focused nano holes according to prepare nanotubes by chemical load deposition, getting transducer samples based in titanium, carbon, silver, gold, and silicon. In order to represent the relation between the fuel flow with the difference of pressure, as it was analyzed in the chapter above, the static curve is given on the **Figure 7** (as well as there are also shown the curves of the flow in dependence on the data quantity), where the blue color curve is based on the measurement supported by the automobile own sensors in which was made the tests (Nissan Frontier 2003). The red color curve is obtained by the theoretical analysis described in this chapter earlier that is supported by Bernoulli analysis; furthermore, the green color curve is the optimal estimation achieved from the designed smart sensor. Hence, in the error comparison of the **Figure 7**, the green color curve is obtained from the comparison between the fuel flow (by the blue color curve in the static analysis of **Q versus ΔP**) with the theoretical fuel flow (by the red color curve in the static analysis of **Q versus ΔP**). Therefore, the green color curve of the error analysis shows that the designed sensor gives optimal fuel flow measurement that also can be used for prediction tasks.

**Figure 8.** *Dynamic curve analysis.*

Finally, it was possible to choose an appropriate model for the dynamic analysis of the designed smart sensor response, which was consequently the previous static curve of **Figure 7**, it implies to choose either a linear system analysis by transfer functions or a nonlinear analysis by adaptive modulating functions. The described proposition on the paragraph above is an advantage due to the smart sensor receiving the measured signal in short response time and high robustness for the boundary conditions of the ICM operating point. Therefore, **Figure 8** shows the response of the fuel flow measurement in the time domain (the experiments were done over adaptation in an ICM), in which the blue color curve is obtained by the calibrated sensor (that belongs to the Nissan Frontier), the red color curve is the result of the theoretical analysis, and the green color curve is the optimal flow measurement achieved by the designed smart sensor. Furthermore, there are shown the flow error curves for the dynamical analysis evaluation, in which the red color curve was taken as a consequence of the comparison between the data obtained from the calibrated sensor with the theoretical flow, and the green color curve is the result of the comparison between the data achieved from the calibrated sensor with the data achieved by the designed sensor that got an approximation of 50% less than the error obtained by the theoretical model.

In other hand, there were obtained many alarms of presence of oxygen molecules and hydrocarbon residues in the nonfiltered fuel flow, because of evaluating the performance of the alarm sensor.
