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Shanyi Du

Researcher at Harbin Institute of Technology

Publications -  232
Citations -  10407

Shanyi Du is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Ceramic & Shape-memory polymer. The author has an hindex of 48, co-authored 221 publications receiving 8891 citations. Previous affiliations of Shanyi Du include Beihang University.

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Three-dimensional Cure Simulation of Stiffened Thermosetting Composite Panels

TL;DR: In this article, a nonlinear transient heat transfer finite element model was developed to simulate the curing process of stiffened thermosetting composite panels and a simulation example was presented to demonstrate the use of the present finite element procedure for analyzing composite curing process.
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Assessment of F-III and F-12 aramid fiber/epoxy interfacial adhesions based on fiber bundle specimens

TL;DR: In this article, the normal bonding and longitudinal shear adhesion properties of F-III and F-12 aramid fiber/epoxy interfaces were estimated by transverse fiber bundle tension (TFBT) test and 45° Fiber Bundle Tension (45FBT), respectively.
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Cure Characterization of a New Bismaleimide Resin using Differential Scanning Calorimetry

TL;DR: In this article, the cure characterization of a new BMI resin was investigated to ascertain a suitable cure model for the material, which was expressed by nth-order cure reaction, and all the parameters of this new BMI system were calculated.
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Thermal ablation resistance of melt-infiltrated titanium diboride-(copper, nickel) composites

TL;DR: In this paper, the thermal shock and plasma ablation behavior of TiB 2 /(Cu, Ni) composites were conducted by plasma arc heater and the initial microstructure, interfacial bonding and ablated morphology were investigated.
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A deep learning approach for efficient topology optimization based on the element removal strategy

TL;DR: The deep-learning model outperforms the traditional methods in terms of lower time cost and broader applicability, demonstrating the potential of such a data-driven approach to accelerate the process of preliminary structural design.