Multi-disciplinary Optimizations on Flexural Behavioural Effects on Various Advanced Aerospace Materials: A validated investigation

Barmavatu, Praveen; Deshmukh, Suhas; Das, Mukunda P.; Aepuru, radhamanohar; Ragireddy, Venkat Reddy; Sravanthi, banoth

doi:10.37358/mp.22.1.5575

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…Comparative studies have been done on stainless steel, titanium alloy, tungsten carbide, GFRP, and CFRP. In Figures 58-60, the example computational outcomes are displayed [44][45][46][47][48][49][50]. All of the short-listed materials' structural outputs were created using a FEA software tool, and the data are given in detail in Figures 61-63.…”

Section: Automotive Disc Brakementioning

confidence: 99%

Multi-Disciplinary Computational Investigations on Asymmetrical Failure Factors of Disc Brakes for Various CFRP Materials: A Validated Approach

Raja¹,

Gnanasekaran²,

Kaladgi

et al. 2022

Symmetry

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Finite element analyses (FEA) are flexible and advanced approaches, which are utilized to address difficult problems of aerospace materials that exhibit both structural symmetrical and structural asymmetrical characteristics. Frictional behavior effects are used as a crucial element in this multidisciplinary study, and other structural, thermal properties are computed using FEA. Primary lightweight materials such as glass fiber reinforced polymer (GFRP), carbon fiber reinforced polymer (CFRP), kevlar fiber reinforced polymer (KFRP), titanium alloy, tungsten carbide, steel alloys, and advanced lightweight materials, such as silicon carbide (SiC) mixer, based on aforesaid materials underwent comprehensive investigations on aircraft disc brake, two-wheeler disc brake, and ASTM general rotating test specimen (G-99). Standard boundary conditions, computational sensitivity tests, and theoretical validations were conducted because the working nature of FEA may impair output dependability. First, FEA calculations were performed on a standard rotating disc component with two separate material families at various rotational velocities such as 400 RPM, 500 RPM, 600 RPM, 800 RPM, and 10 N of external frictional force. Via tribological experiments, frictional force and deformation of FEA outcomes were validated; the experimental outcomes serve as important boundary conditions for real-time simulations. Second, verified FEA was extended to complicated real-time applications such as aircraft disc brakes and automobile disc brakes. This work confirms that composite materials possess superior properties to conventional alloys for aircraft and vehicle disc brakes.

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Section: Automotive Disc Brakementioning

confidence: 99%

Multi-Disciplinary Computational Investigations on Asymmetrical Failure Factors of Disc Brakes for Various CFRP Materials: A Validated Approach

Raja¹,

Gnanasekaran²,

Kaladgi

et al. 2022

Symmetry

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“…Novel materials have a significant impact on our daily lives and modern industries such as the aerospace, biomedical, and energy sectors [1][2][3][4][5] . However, the conventional research and design (R&D) of novel materials utilizes a "trial-and-error" approach, which is challenging due to the complexity and diversity of materials.…”

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confidence: 99%

MLMD: a programming-free AI platform to predict and design materials

Ma,

Cao,

Dong

et al. 2024

npj Comput Mater

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Accelerating the discovery of advanced materials is crucial for modern industries, aerospace, biomedicine, and energy. Nevertheless, only a small fraction of materials are currently under experimental investigation within the vast chemical space. Materials scientists are plagued by time-consuming and labor-intensive experiments due to lacking efficient material discovery strategies. Artificial intelligence (AI) has emerged as a promising instrument to bridge this gap. Although numerous AI toolkits or platforms for material science have been developed, they suffer from many shortcomings. These include primarily focusing on material property prediction and being unfriendly to material scientists lacking programming experience, especially performing poorly with limited data. Here, we developed MLMD, an AI platform for materials design. It is capable of effectively discovering novel materials with high-potential advanced properties end-to-end, utilizing model inference, surrogate optimization, and even working in situations of data scarcity based on active learning. Additionally, it integrates data analysis, descriptor refactoring, hyper-parameters auto-optimizing, and properties prediction. It also provides a web-based friendly interface without need programming and can be used anywhere, anytime. MLMD is dedicated to the integration of material experiment/computation and design, and accelerate the new material discovery with desired one or multiple properties. It demonstrates the strong power to direct experiments on various materials (perovskites, steel, high-entropy alloy, etc). MLMD will be an essential tool for materials scientists and facilitate the advancement of materials informatics.

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Theoretical study of GRX-810 material on a hypersonic engines: on lock heed Martin’s J-58 engine

Selvam,

Harish,

Dhivya

et al. 2024

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Multi-disciplinary Optimizations on Flexural Behavioural Effects on Various Advanced Aerospace Materials: A validated investigation

Cited by 6 publications

References 7 publications

Multi-Disciplinary Computational Investigations on Asymmetrical Failure Factors of Disc Brakes for Various CFRP Materials: A Validated Approach

Multi-Disciplinary Computational Investigations on Asymmetrical Failure Factors of Disc Brakes for Various CFRP Materials: A Validated Approach

MLMD: a programming-free AI platform to predict and design materials

Theoretical study of GRX-810 material on a hypersonic engines: on lock heed Martin’s J-58 engine

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