PMDG makes awesome airliners. The 737s are my favorite by far. I've been flying it for over a decade through the various iterations of simulators, and it is by far my favorite line. And I'm still learning new tricks lol. Youtube is your friend in that regard. Many real-life Boeing pilots willing and eager to share their knowledge.
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Abstract:Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.Keywords: deep learning; machine learning; structural health monitoring; crack detection; damage detection; data science; computer vision
Maintenance of engineering assets and industrial equipment (such as aircraft, jet engines, wind turbines, and so on) is critical for safety as well as enhanced profitability in the services and warranties of these assets. Effective preventive maintenance requires the knowledge of the various operating parameters and their impact on the wear and tear of equipment. Simulations, advanced analytics, and deep learning algorithms enable the predictive modeling of complex systems and their operating environment.
Full-fledged finite element analysis configured for crack growth simulation is extremely expensive. Therefore, it is simply not feasible for digital twin applications. This is true even if we were to run simulations for hundreds of aircraft many times over, as we optimized inspections and decided how to swap routes for a few aircraft. A parameterized physics-driven AI model is constructed in Modulus that satisfies the governing laws of linear elasticity, as follows:
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