Prediction and Optimization of Truss Performance for Lightweight, Intelligent Packaging and Deployable Structures

Metadata Updated: February 28, 2019

Recent advances in fabrication techniques have enabled the creation of large metallic, polymeric, or ceramic truss lattices with the smallest beam dimension on the micron- and nanometer-scales, which form a new class of cellular meta-materials with tunable macroscopic properties. These lattice materials, which can bridge many length scales, offer desirable mechanical properties such as high stiffness and strength while having extremely low density. However, by manipulating the arrangement and architecture of trusses, fabricable lattices have been shown to exhibit many other novel properties, including large nonlinear recoverability and large energy absorption due to the wide mechanical hysteresis produced by the buckling of truss members. Because of the above properties, microlattices are excellent candidates for applications ranging from impact absorption in sandwich cores to deployable space structures.

Fabrication and testing of these multiscale structures are available, but the prediction of the response of complex trusses to large inelastic deformation, large rotations, inelasticity and failure requires accurate computational tools that severely restrict the number of truss members that can be modeled on realistic computing resources. Since these lattices span many length scales, thousands to millions of truss members need to be simulated in order to resolve the behavior of the structures at the largest and smallest scales. The inability to predict the response of these structures through simulation leads to a trial-and-error design cycle, which is extremely inefficient. The proposed research will fill the void in the design process by creating a computational tool capable of predicting the complex nonlinear response of truss lattices containing extremely large numbers of beams and nodes. The technique will borrow concepts from the traditional quasicontinuum (QC) method, which is a powerful multiscale modeling method originally designed to drastically lower the computational cost of simulating atomistic lattices through coarse graining. My research advisor, Professor Dennis Kochmann, already has a massively parallel QC code, which will be extended to TrussQC, a high-performance computational toolbox for the simulation of large trusses.

We will focus on metallic and polymeric trusses with micron-sized or larger truss members whose response is sufficiently well described by a continuum representation (i.e., well above nano-scale size effects). This has been shown to even apply for nanolattices when loaded in the linear elastic regime. Although the proposed techniques are equally applicable to all scales (as long as a model for the response of individual beams and nodes is available), we focus on micro-to-macrolattices due to the scalability of current manufacturing methods. The proposed research will develop and implement computational tools to understand the effect of microstructural nonlinearities and predict and optimize the large-deformation, dynamic, inelastic macroscopic performance of complex cellular truss structures in situations relevant to NASA, e.g. impact energy absorption in sandwich cores or the deployment of lightweight foldable structures. Lastly, non-destructive sensing to assess the mechanical integrity of these periodic structures will be investigated by computationally comparing the wave attenuation profiles of damaged and undamaged lattices.

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Public: This dataset is intended for public access and use. License: U.S. Government Work

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Dates

Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Publisher Space Technology Mission Directorate
Unique Identifier TECHPORT_88613
Maintainer
TECHPORT SUPPORT
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 55b80a2d-6a8c-4281-9afc-0bc0f287799f
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2020-08-01
Homepage URL https://techport.nasa.gov/view/88613
License http://www.usa.gov/publicdomain/label/1.0/
Data Last Modified 2018-07-19
Program Code 026:027
Source Datajson Identifier True
Source Hash f5360019398090143fe99643f1a668209293b4b4
Source Schema Version 1.1

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