Engineering topics

Vishnu Saseendran

Publications and source records attributed to Vishnu Saseendran.

3 records

NASA NTRS2024

Characterizing Face Sheet/Core Disbonding Using the Single Cantilever Beam Test: Results from an International Round Robin

Test results from an international round robin are presented. The single cantilever beam (SCB) test was used to characterize face sheet/core disbonding in sandwich components in an effort to help standardization. Each of the seven participating laboratories performed a set of five baseline tests using the same protocol, however, using their own specific equipment. In addition, each laboratory performed two sets of tests with altered tests conditions which included using a test fixture with a translating carriage and performing the tests with different loading and unloading speeds. The orientation of the disbond front with respect to the honeycomb core cells was varied and the effect on the fracture toughness was also studied. Additional factors that could influence test results were investigated, such as the use of a saw cut starter disbond as an alternative to Teflon® release film, and the effects of using a face sheet doubler to increase the bending stiffness. For each set of tests, summary results such as load/displacements plots, observed disbond growth location and calculated energy release rates are reported and a comparison of results between labs is presented. Critical strain energy release rate measurements from SCB tests are also compared with measurements made from an alternative test, namely the double cantilever beam with uneven bending moments (DCB-UBM) test, which has been studied extensively at the Technical University of Denmark and proposed as a mixed-mode test standard. A set of recommendations are made with respect to improving the SCB test and a path to standardization is presented.

Ronald Krueger↗

NASA NTRSDate not supplied

Multiscale Modeling of Reconstructed Tricalcium Silicate using NASA Multiscale Analysis Tool

To study microstructure characteristics of cementitious materials hydrated in space; previously, cement binder formations were processed under microgravity conditions and was further compared against ground-based experiments. For accurate estimation of process-structure-property linkage, particularly on samples hydrated in the microgravity environment, it is desired to have a high-fidelity volumetric representation of the microstructure. However, owing to small sample size and high porosity of the space-returned samples, conventional experimental characterization techniques are not viable. Hence, a deep learning-based reconstruction algorithm was employed to obtain high fidelity 3D volumes from sparse high resolution 2D Scanning Electron Microscopy (SEM) images, as inputs to micromechanics-based modeling. This machine learning-based reconstruction methodology validated against low-order statistical descriptors, captured the microstructural topology of both sample types (ground, 1g and microgravity, μg). Due to the lack of gravity, hydration products of the samples processed in space differed from those processed-on ground. Such AI-generated virtual samples were analyzed in a multiscale recursive micromechanics approach using the NASA Multiscale Analysis Tool (NASMAT). Here, we present a methodology to rapidly integrate and evaluate these AI-generated volumes in NASMAT. The synthesized microstructural volumes are directly employed as Representative Volume Elements (RVEs) to preserve the fidelity (1 pixel = 0.54 m). Invariably, analysis of such largescale problems (5123 voxels) requires huge amount of computational resources. By taking advantage of the NASMAT architecture, we also focused on systematic multiscale integration of these AI-reconstructed virtual volumes to reduce the computational demands. In this work, this methodology is demonstrated on the ground-based, 1g samples. The estimated stiffness value of 15.90 GPa is comparable to experimentally obtained modulus of hydrated tricalcium silicate sample. The workflow presented here paves the way for utilizing the NASMAT tool to perform multiscale analyses of other multi-phase material systems using either 3D virtual datasets synthesized using AI or obtained via micro-CT.

Machine Learning↗

NASA NTRSDate not supplied

Micromechanics-based Modeling of Cement Solidified in the Microgravity

Given the prohibitive expense of transporting raw materials into space, in-situ resources alongside cement-like binders is envisioned to realize large-scale habitats on celestial bodies like the Moon and Mars. However, understanding the mechanical properties of cementitious materials in reduced gravity environments is crucial for effective design and engineering in extraterrestrial construction projects. Using micromechanics-based approach, this research focuses on analyzing the impact of microgravity on the mechanical properties of cementitious materials by employing distinctive Scanning Electron Microscopy (SEM) images of tricalcium silicate (C 3 S) samples hydrated aboard the International Space Station (ISS).

Namiko Yamamoto↗