5. Conclusion

Optimization of energy harvesters is a system-level problem that involves several design requirements on the power processing stages. Deploying an energy harvester on its own will yield poor power densities, which is why additional circuitry is needed to implement features such as synchronized switch harvesting or impedance match between harvester and load electronics, energy storage capabilities, and output voltage regulation.

the task to be performed. This is the key approach to adapt the sensing tasks in applications where the ambient energy levels are low, variable, or intermittent. Energy harvesting based on vibrational and thermal effects has been chosen for the module development, thanks to their better reliability and performances than others. In the case of vibrational energy, the production of micro-generators using either electromagnetic or piezoelectric conversion elements seems the most promising. Both conversion principles are complementary although the electromagnetic

For thermal energy harvesting, the system operation is based on the principle of the Seebeck effect. Such technologies are investigated because of their robustness and resistance to environmental stresses, i.e., mechanical and thermal. Nonetheless, a number of challenges must be solved before achieving efficient wireless autonomous sensors for real aeronautical applications, such as long-lasting self-sufficient

According to Figure 16, the goal of maximizing the amount of the harvested energy involves several factors, including electronics optimization, characterization of the available ambient energy, selection and configuration of energy harvesting materials, and integration with storage mechanisms, along with the power optimization and power awareness design. This project tried to address these issues in an integrated manner from the multidisciplinary engineering perspective. The performance of thermal and vibration harvester prototypes had been validated and tested

Future perspectives are optimization of the transduction mechanism of the harvester, power awareness, and storage element to validate an upgrade of the prototype in experimental environment derived from the real measurements.

Work in the project has been done in the EPICE-CORALIE program, a funded

project from French CORAC. Project was done in partnership with SAFRAN. Experimental work was supported by LGEF-INSA Lyon, Université de Lyon.

one is more effective in the low frequency range (100 Hz).

Ideal workflow for developing an autonomous system.

Structural Health Monitoring from Sensing to Processing DOI: http://dx.doi.org/10.5772/intechopen.86758

operation of sensors, selection of wireless protocols, etc.

in real environment.

Figure 16.

Acknowledgements

19

Each energy harvester is differentiated by its transduction mechanism, and therefore the equivalent source impedance model must be derived for different harvesters and available energy densities variation from the environment. By matching the source impedance to that of the load or by applying appropriate switching, the maximum power transfer is achieved from the harvester to the load under optimal conditions Figure 15.

The project provided key data to identify the gaps to be solved for developing energy harvesting module in engine environment.

Despite several energy sources available in airplanes and helicopters, engineers and scientists have to face numerous challenges when trying to achieve reliable devices able to capture sufficient energy to perform any useful work.

Energy sources available must be characterized in terms of level, availability, and frequency range. This is a prerequisite before sizing the harvester module to ensure capability to produce enough energy.

Energy management is also key: the best practice approach is to use a comparator on the energy storage module to prevent the system from drawing energy from the storage element, unless enough energy was stored to perform the programmed sensing/processing/logging communication tasks. This allowed the energy storage elements to store up enough energy to perform for the specific task, before allowing

Figure 15. Constraints in designing an autonomous system.

Structural Health Monitoring from Sensing to Processing DOI: http://dx.doi.org/10.5772/intechopen.86758

#### Figure 16.

5. Conclusion

Figure 14.

Figure 15.

18

Constraints in designing an autonomous system.

ties, and output voltage regulation.

Thermal and vibration harvesters' characteristics.

Advances in Structural Health Monitoring

under optimal conditions Figure 15.

energy harvesting module in engine environment.

ensure capability to produce enough energy.

Optimization of energy harvesters is a system-level problem that involves several design requirements on the power processing stages. Deploying an energy harvester on its own will yield poor power densities, which is why additional circuitry is needed to implement features such as synchronized switch harvesting or impedance match between harvester and load electronics, energy storage capabili-

Each energy harvester is differentiated by its transduction mechanism, and therefore the equivalent source impedance model must be derived for different harvesters and available energy densities variation from the environment. By matching the source impedance to that of the load or by applying appropriate switching, the maximum power transfer is achieved from the harvester to the load

The project provided key data to identify the gaps to be solved for developing

Despite several energy sources available in airplanes and helicopters, engineers and scientists have to face numerous challenges when trying to achieve reliable

Energy sources available must be characterized in terms of level, availability, and frequency range. This is a prerequisite before sizing the harvester module to

Energy management is also key: the best practice approach is to use a comparator on the energy storage module to prevent the system from drawing energy from the storage element, unless enough energy was stored to perform the programmed sensing/processing/logging communication tasks. This allowed the energy storage elements to store up enough energy to perform for the specific task, before allowing

devices able to capture sufficient energy to perform any useful work.

Ideal workflow for developing an autonomous system.

the task to be performed. This is the key approach to adapt the sensing tasks in applications where the ambient energy levels are low, variable, or intermittent.

Energy harvesting based on vibrational and thermal effects has been chosen for the module development, thanks to their better reliability and performances than others. In the case of vibrational energy, the production of micro-generators using either electromagnetic or piezoelectric conversion elements seems the most promising. Both conversion principles are complementary although the electromagnetic one is more effective in the low frequency range (100 Hz).

For thermal energy harvesting, the system operation is based on the principle of the Seebeck effect. Such technologies are investigated because of their robustness and resistance to environmental stresses, i.e., mechanical and thermal. Nonetheless, a number of challenges must be solved before achieving efficient wireless autonomous sensors for real aeronautical applications, such as long-lasting self-sufficient operation of sensors, selection of wireless protocols, etc.

According to Figure 16, the goal of maximizing the amount of the harvested energy involves several factors, including electronics optimization, characterization of the available ambient energy, selection and configuration of energy harvesting materials, and integration with storage mechanisms, along with the power optimization and power awareness design. This project tried to address these issues in an integrated manner from the multidisciplinary engineering perspective. The performance of thermal and vibration harvester prototypes had been validated and tested in real environment.

Future perspectives are optimization of the transduction mechanism of the harvester, power awareness, and storage element to validate an upgrade of the prototype in experimental environment derived from the real measurements.

### Acknowledgements

Work in the project has been done in the EPICE-CORALIE program, a funded project from French CORAC. Project was done in partnership with SAFRAN. Experimental work was supported by LGEF-INSA Lyon, Université de Lyon.

Advances in Structural Health Monitoring

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