Nondestructive Testing of the Integrity of Solid Oxide Fuel Cell Stack Elements by Ultrasound and Thermographic Techniques

Abstract

Single planar fuel cell elements consisting of metallic interconnectors that are bonded and sealed by a thin glass solder layer form the core of a solid oxide fuel cell. For reliable operation, the bonding layer has to adhere well and must be without voids or foreign material inclusions, which might cause gas leakage, electrical shorts or mechanical weakening and structural failure. Nondestructive testing (NDT) by the high-frequency ultrasound in immersion technique and by air-coupled ultrasound was optimized to find such defects. Another technique was flash light excited thermography for detection of voids. The NDT techniques used are complementary to each other, as they are based on different physical principles. Voids and small steel platelets of different sizes were prepared in the glass solder layer before the high-temperature bonding process and then monitored by the NDT techniques through the interconnector plates. Two selected NDT techniques were then validated in a probability of detection (POD) study. The study resulted in detection limits for the two main types of defects. As a step towards production testing, a demonstrator was built combining testing by air-coupled ultrasound and that by flash thermography. During the testing steps, the cell elements were handled by a collaborative robot.

Ultrasonic imaging in highly heterogeneous backgrounds

“…Examples include (a) single- and polycrystalline superalloys deployed in aeroengine and gas turbine blades [3], (b) interpenetrating phase metamaterials such as SiC-SiC composites used in accident-tolerant nuclear fuel claddings [4,5] and (c) multifunctional polymer matrix composites with a wide spectrum of applications thanks to their exceptional mechanical properties [6,7]. The topology and characteristics of such materials at micro- and meso-scales are often unknown, or only known to a limited extent because of variabilities in the manufacturing process [8,9] and/or ageing [10]. In addition, mechanisms of deterioration via corrosion, fatigue, irradiation and thermal cycling are yet to be fully understood.…”

Recent Advances in Machine Learning Applied to Ultrasound Imaging

“…Ultrasound imaging is employed in a wide variety of applications, including sonar [9][10][11], non-destructive evaluation (NDE) [12][13][14], indoor positioning systems (IPS) [15,16] and biometric recognition [17][18][19]. However, its best known application is medical diagnostics, where it has been found to be very attractive and interesting compared to other imaging modalities, such as magnetic resonance (MR), X-rays and computed tomography(CT), because it enables the acquisition of organ images at low cost and in a safe and noninvasive way.…”

Ultrasonic imaging in highly heterogeneous backgrounds

“…Examples include (a) single-and polycrystalline superalloys deployed in aeroengine and gas turbine blades [6], (b) interpenetrating phase metamaterials such as SiC-SiC composites used in accident-tolerant nuclear fuel claddings [1,46], and (c) multifunctional polymer matrix composites with a wide spectrum of applications thanks to their exceptional mechanical properties [25,47]. The topology and characteristics of such materials at micro-and meso-scales are often unknown, or only known to a limited extent because of variabilities in the manufacturing process [35,39] and/or aging [49]. In addition, mechanisms of deterioration via corrosion, fatigue, irradiation, and thermal cycling are yet to be fully understood.…”