A Semi-Analytical and Data-Calibrated Hybrid Model for Predicting Residual Deformation of Shape Memory Alloy Honeycombs

Future lunar missions, like the International Lunar Research Station (ILRS), demand single-launch multi-point operations, urgently requiring reusable energy-absorbing structures. Integrating shape memory alloy (SMA) into honeycombs offers a promising solution; however, deformation exceeding the SMA’s recoverable limit induces structural residual deformation, altering the configuration and degrading subsequent energy absorption. To address this, we propose a semi-analytical, data-calibrated hybrid model predicting SMA honeycomb residual deformation. A four-stage linear constitutive model is established capturing superelasticity and martensitic yielding. Cell walls are idealized as equivalent beams. Using layered fiber integration and numerical interpolation, a nonlinear moment–curvature relationship is constructed, enabling rapid structural residual deflection evaluation from material residual strains. Finite element results confirm that initial residual deformation stabilizes the honeycomb into a reusable configuration, governing subsequent plateau stresses. Calibrated by uniaxial test data, the proposed model accurately predicts residual deformation ratios and reusable plateau stresses with errors within 8%. By bridging material-level strain with structural-level deformation, this approach circumvents computationally expensive full-scale simulations and costly experimental trials, providing a highly efficient tool for designing reusable SMA absorbers.

Authors

Institutions

Publication Details

Journal
Buildings
Published
2026-06-17
DOI
https://doi.org/10.3390/buildings16122406
Primary Topic
Shape Memory Alloy Transformations
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Semi-Analytical and Data-Calibrated Hybrid Model for Predicting Residual Deformation of Shape Memory Alloy Honeycombs

Haohang Li, Jiayue Zhai, Jian Feng, Yi-Xiang Wang et al.
Buildings
Shape Memory Alloy Transformations
article

A Semi-Analytical and Data-Calibrated Hybrid Model for Predicting Residual Deformation of Shape Memory Alloy Honeycombs

Haohang Li, Jiayue Zhai, Jian Feng, Yi-Xiang Wang, Chengbo Cui, Jianguo Cai, Meng Li
article en

Abstract

Future lunar missions, like the International Lunar Research Station (ILRS), demand single-launch multi-point operations, urgently requiring reusable energy-absorbing structures. Integrating shape memory alloy (SMA) into honeycombs offers a promising solution; however, deformation exceeding the SMA’s recoverable limit induces structural residual deformation, altering the configuration and degrading subsequent energy absorption. To address this, we propose a semi-analytical, data-calibrated hybrid model predicting SMA honeycomb residual deformation. A four-stage linear constitutive model is established capturing superelasticity and martensitic yielding. Cell walls are idealized as equivalent beams. Using layered fiber integration and numerical interpolation, a nonlinear moment–curvature relationship is constructed, enabling rapid structural residual deflection evaluation from material residual strains. Finite element results confirm that initial residual deformation stabilizes the honeycomb into a reusable configuration, governing subsequent plateau stresses. Calibrated by uniaxial test data, the proposed model accurately predicts residual deformation ratios and reusable plateau stresses with errors within 8%. By bridging material-level strain with structural-level deformation, this approach circumvents computationally expensive full-scale simulations and costly experimental trials, providing a highly efficient tool for designing reusable SMA absorbers.

BuildingsVol. 16(12)
China Academy of Space Technology (CN), China University of Petroleum, Beijing (CN), Xinxiang University (CN), Southeast University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 14%
Shape Memory Alloy Transformations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.