TY - JOUR A1 - Mathiazhagan, Akilan A1 - Kim, Dongsuk A1 - Vegini, George A1 - Montemurro, Marco A1 - Baltag, Serghei A1 - Asli, Majid A1 - Höschler, Klaus T1 - Design strategies for enhancement of mechanical behaviour of hybrid cellular structures based on schwarz primitive geometry JF - Materials & Design N2 - Hybrid cellular structures are gaining attention for their multi-functionality in load-bearing, heat-transfer, and energy-absorption applications. Schwarz Primitive (SCP)-based geometries, a subclass of Triply Periodic Minimal Surfaces (TPMS), with their high surface area, tunable properties, and continuous topology, provide strong potential for such applications. To further enhance mechanical performance and stability, this work conducts a thorough investigation of hybrid designs that integrate SCP TPMS with Kelvin truss-based geometries, combining the high connectivity of TPMS with the reinforcement and directional stiffness benefits of struts. A theoretical strain-based homogenization framework was implemented over a design matrix that independently varies the relative densities of the SCP and the truss geometries, quantifying how each constituent proportion influences the overall mechanical response of the hybrid. These predictions were corroborated by finite element analyses in ANSYS 2023 and by mechanical testing of 3D-printed polylactic acid samples, providing independent validation of homogenization trends across orientations. The resulting formulation enables rapid property mapping, sensitivity analysis, and optimization of SCP-Kelvin hybrids, thereby reducing the prototyping effort while guiding application-based design selection and customization. KW - Lightweight design KW - Anisotropy control KW - Homogenization KW - Triply periodic minimal surfaces KW - Periodic open cellular structures Y1 - 2025 U6 - https://doi.org/10.1016/j.matdes.2025.115161 SN - 0264-1275 VL - 240 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Asli, Majid A1 - Kim, Dongsuk A1 - Höschler, Klaus T1 - On the potentials of the integration of pressure gain combustion with a hybrid electric propulsion system T1 - Über die Möglichkeiten der Integration der Druckverstärkungsverbrennung in ein hybrides elektrisches Antriebssystem N2 - As the issue of pollutant emissions from aviation propulsion escalates, research into alternative powertrains is gaining momentum. Two promising technologies are the Hybrid Electric Propulsion System (HEPS) and Pressure Gain Combustion (PGC). HEPS is expected to reduce pollutant emissions by decreasing fuel consumption, whereas PGC uses detonation in the combustor to increase the thermal efficiency of engines by elevating the total pressure during combustion. This study extensively explores the integration of these two emerging technologies, thoroughly assessing the advantages that arise from their combination. First, the renowned turboprop engine PW127 is benchmarked and modeled using Gasturb software. The model is integrated into Simulink using the T-MATS tool, with HEPS and pressure gain components added to analyze the thermodynamics of various configurations under different pressure gain values and HEPS parameters. The analysis, conducted up to the cruise phase of the baseline aircraft, reveals that applying pressure gain combustion through Rotating Detonation Combustion (RDC) results in a more significant increase in efficiency and decrease in fuel consumption compared to HEPS with conventional gas turbines. However, HEPS helps maintain a more uniform combustor inlet condition and reduces the Turbine Inlet Temperature (TIT) at the takeoff phase, where the highest TIT otherwise occurs. The results suggest that integrating HEPS with PGC can be beneficial in maintaining optimal combustor conditions and mitigating turbine efficiency degradation. N2 - Da das Problem der Schadstoffemissionen von Flugantrieben eskaliert, gewinnt die Forschung an alternativen Antriebssträngen zunehmend an Dynamik. Zwei vielversprechende Technologien sind das Hybrid Electric Propulsion System (HEPS) und die Pressure Gain Combustion (PGC). Es wird erwartet, dass HEPS die Schadstoffemissionen durch einen geringeren Kraftstoffverbrauch reduziert, während PGC die Detonation in der Brennkammer nutzt, um den thermischen Wirkungsgrad von Motoren durch Erhöhung des Gesamtdrucks während der Verbrennung zu erhöhen. Diese Studie untersucht ausführlich die Integration dieser beiden neuen Technologien und bewertet gründlich die Vorteile, die sich aus ihrer Kombination ergeben. Zunächst wird das renommierte Turboprop-Triebwerk PW127 mithilfe der Gasturb-Software einem Benchmarking unterzogen und modelliert. Das Modell wird mit dem T-MATS-Tool in Simulink integriert, wobei HEPS- und Druckverstärkungskomponenten hinzugefügt werden, um die Thermodynamik verschiedener Konfigurationen unter verschiedenen Druckverstärkungswerten und HEPS-Parametern zu analysieren. Die Analyse, die bis zur Reiseflugphase des Basisflugzeugs durchgeführt wurde, zeigt, dass die Anwendung der Druckverstärkungsverbrennung durch Rotating Detonation Combustion (RDC) im Vergleich zu HEPS mit herkömmlichen Gasturbinen zu einer deutlicheren Effizienzsteigerung und Reduzierung des Treibstoffverbrauchs führt. HEPS trägt jedoch dazu bei, einen gleichmäßigeren Brennkammereinlasszustand aufrechtzuerhalten und reduziert die Turbineneinlasstemperatur (TIT) in der Startphase, wo sonst die höchste TIT auftritt. Die Ergebnisse legen nahe, dass die Integration von HEPS mit PGC dazu beitragen kann, optimale Brennkammerbedingungen aufrechtzuerhalten und die Verschlechterung der Turbineneffizienz abzumildern. KW - Pressure gain combustion KW - Hybrid electric propulsion KW - Rotating detonation combustion KW - Druckverstärkungsverbrennung KW - Rotierende Detonationsverbrennung KW - Hybrid-Elektroantrieb KW - Propeller-Turbinen-Luftstrahltriebwerk KW - Hybridantrieb KW - Verbrennung KW - Schadstoffemission KW - Emissionsverringerung Y1 - 2023 U6 - https://doi.org/10.3390/aerospace10080710 ER - TY - JOUR A1 - Kim, Dongsuk A1 - Gerstberger, Ulf A1 - Asli, Majid A1 - Höschler, Klaus T1 - U-Net Driven Semantic Segmentation for Detection and Quantification of Cracks on Gas Turbine Blade Tips JF - Results in Engineering N2 - Crack detection and quantification on gas turbine blades is crucial for component validation during the development phase and for operational efficiency in service, as unexpected cracks can compromise blade integrity and lead to early engine removals. Gas turbine blades operate under extreme thermal and mechanical stresses, making them particularly susceptible to crack formation. At the same time deterministic predictions of crack formation are subject to high uncertainty in material data and actual loading conditions. Accurate detection and quantification of cracks, therefore, is essential for the validation and calibration of life predictions in order to prevent in-service failures, to extend component lifespan, and to reduce maintenance costs. This study introduces a U-Net based semantic segmentation model designed to automate crack detection on turbine blade tips. The model was trained on a dataset of 210 surface images with and without evidence of cracks, each divided into 128  ×  128 pixel patches. Data augmentation techniques were applied to address the class imbalance between cracked and non-cracked pixels. The U-Net architecture, optimized with a Dice loss function, achieved a validation IoU of 0.7557, along with approximately 85% recall and precision in identifying cracked pixels. The pixel-based accuracy of the model primarily affects the quantification of cracks rather than their identification. A sliding window pipeline was implemented to extend the model’s applicability, enabling segmentation of entire blade tip images for comprehensive crack localization. While the model may occasionally miss low-contrast cracks, it holds potential as a supplementary tool for manual inspection as part of the life prediction validation. By providing automated crack localization and quantification, the model can assist in analyzing crack characteristics relative to engine operating conditions. KW - Crack detection KW - Semantic segmentation KW - Gas turbine blade KW - U-Net KW - Deep learning KW - Convolutional neural network Y1 - 2025 U6 - https://doi.org/10.1016/j.rineng.2025.108864 SN - 2590-1230 VL - 29 PB - Elsevier CY - Amsterdam ER -