Dr. Samir Mustapha Profile

Dr. Samir Mustapha

at American Univ. of Beirut

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Publications (4)

Proceedings Article | 22 March 2021 Paper

Analytical and experimental verification of environmental barrier coating (EBC) covering crack propagation, failure mechanisms, and life modeling

Ali Abdul-Aziz, Samir Mustapha, Ali Fakha

Proceedings Volume 11592, 115920R (2021) https://doi.org/10.1117/12.2585384

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Gas turbine engines undergo very harsh operating environmental conditions, and this leads to various issues related to components materials strength limitations, degradation, cracking, and other durability problems. Under such circumstances, a robust material design is required to prevent these critical components from failing in service and preventing catastrophic events from taking place. The robust design must enhance component durability, which could be degraded due to material processing defects, variability in material properties, in-service loads, and operating environment. To encounter and manage these durability issues, materials scientists and engineers that are involved in this field along with engine makers are continuously working on developing protective materials to alleviate and increase materials damage tolerance and prevent components failure. Ceramic matrix composites (CMC) are now materials of choice for gas turbine engine design and manufacturing. The CMC has a good capability in operating at high temperatures up to 1500 °C which is within the norm of gas turbine engine operation and it is much lighter compared to metals. Good impact resistance and stability at high operating temperatures make the silicon carbide (SiC) ceramic matrix composite system a desirable option for jet engines [1]. However, CMC’s when they undergo the degradation process that typically includes coating interface oxidation as opposed to a moisture-induced matrix which is generally seen at a higher temperature. Additionally, other factors such as residual stresses, coating process-related flaws, and casting conditions may influence the degradation of their mechanical properties. These durability considerations are being addressed by introducing a highly specialized form of environmental barrier coating (EBC) that is being developed and explored in particular for hightemperature applications greater than 1100 °C [2]. In this paper, a CMC substrate is being evaluated for failure under supportive protection of EBC coatings. The primary aim is to identify the crack propagation phenomenon, the sequence of failure of the EBC and assess the life the CMC substrate. An analytical simulation applying the extended finite element method (XFEM) in ABAQUS software [3] is used to perform this analyses. Analytical results obtained are discussed and checked against test data.

Proceedings Article | 22 April 2020 Paper

Numerical validation using finite element to assess the performance of microwave sensor in detecting blade tip displacement

Ali Abdul-Aziz, Samir Mustapha, Khaled Aboumerhi

Proceedings Volume 11382, 1138202 (2020) https://doi.org/10.1117/12.2556194

KEYWORDS: Sensors, Microwave radiation, Finite element methods, Sensor performance, Safety, Data acquisition, Distance measurement, 3D modeling, Structural health monitoring, Denoising

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Gas turbine engine manufacturers are in continuous strive to improve the durability and the technology behind engine development to help monitor engine health and performance. Such technologies are confined to employing highly specialized sensors within the engine compartment. The role of the sensors is to screen and track the structural response of the engine components and in particular the rotor disk due to its venerability to endure failure since it is subject to complex and harsh loading conditions. Detecting unexpected or excessive blade vibration before failure is critical to ensure safety and to achieve projected component life. Nondestructive Evaluation has been the traditional method of detection in addition to relying exclusively on visual inspections as well as other means. These methods require time and cost and do not provide accurate feedback on the health when the engine is in operation. At NASA Glenn Research Center, efforts are undergoing to develop, and test validates microwave-based blade tip timing sensors in support of these concerns and to investigate their application for propulsion health monitoring under the Transformational Tools and Technologies Project (TTTP). A set of prototype sensors is used to assess their ability and applicability in making blade tip clearance measurements in an attempt to extract the blade tip timing from the acquired raw data. The sensors are non-contact type and microwavebased technology. The study covers an experimental task to define the optimum set-up of these sensors, determine their sensitivity in making blade tip deflection measurements and validate their performance against realistic geometries in a spin rig. It also includes finite element analysis base calculations to compare with the experimental data. Data pertaining to the findings obtained from the testing as well as the analytical results are presented and discussed. This work is an extension of a prior combined experimental and computational study that is available in reference [1].

Proceedings Article | 12 April 2017 Presentation + Paper

Detection and assessment of flaws in friction stir welded metallic plates

Mohammad Ali Fakih, Samir Mustapha, Jaafar Tarraf, Georges Ayoub, Ramsey Hamade

Proceedings Volume 10168, 101682L (2017) https://doi.org/10.1117/12.2258701

KEYWORDS: Ultrasonics, Computed tomography, Inspection, Structural health monitoring, Magnesium, Nondestructive evaluation, Defect inspection, CT reconstruction, Defect detection, Damage detection, 3D modeling, Signal attenuation, Sensors, Ferroelectric materials

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Proceedings Article | 12 April 2017 Presentation + Paper

Detection of surface cracking in steel pipes based on vibration data using a multi-class support vector machine classifier

S. Mustapha, A. Braytee, L. Ye

Proceedings Volume 10168, 101682K (2017) https://doi.org/10.1117/12.2258232

KEYWORDS: Sensors, Data modeling, Feature extraction, Feature selection, Semiconducting wafers, Machine learning, Statistical analysis, Sensing systems, Binary data, Structural health monitoring, Statistical modeling

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