Search NASA⌕ Search

SEARCH · Search NASA

Results for “Transformation strain”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Phase Transformation and Creep Behavior in Ti50Pd30Ni20 High Temperature Shape Memory Alloy in Compression

The creep behavior and the phase transformation of Ti50Pd30Ni20 High Temperature Shape Memory Alloy (HTSMA) is investigated by standard creep tests and thermomechanical tests. Ingots of the alloy are induction melted, extruded at high temperature, from which cylindrical specimens are cut and surface polished. A custom high temperature test setup is assembled to conduct the thermomechanical tests. Following preliminary monotonic tests, standard creep tests and thermally induced phase transformation tests are conducted on the specimen. The creep test results suggest that over the operating temperatures and stresses of this alloy, the microstructural mechanisms responsible for creep change. At lower stresses and temperatures, the primary creep mechanism is a mixture of dislocation glide and dislocation creep. As the stress and temperature increase, the mechanism shifts to predominantly dislocation creep. If the operational stress or temperature is raised even further, the mechanism shifts to diffusion creep. The thermally induced phase transformation tests show that actuator performance can be affected by rate independent irrecoverable strain (transformation induced plasticity + retained martensite) as well as creep. The rate of heating and cooling can adversely impact the actuators performance. While the rate independent irrecoverable strain is readily apparent early in the actuators life, viscoplastic strain continues to accumulate over the lifespan of the HTSMA. Thus, in order to get full actuation out of the HTSMA, the heating and cooling rates must be sufficiently high enough to avoid creep.

Kumar, Parikshith K.↗
DOE OSTI2026

Experimental determination of critical resolved shear stress for strain-induced phase transformation

The current investigation reports the first attempt to measure the critical resolved shear stress to initiates strain-induced phase transformation in a three-dimensional polycrystalline aggregate using high-energy diffraction microscopy (HEDM). In situ tensile testing was performed on a Fe38.5Mn20Co20Cr15Si5Cu1.5 metastable alloy exhibiting transformation-induced plasticity (TRIP). Grains with a higher propensity for strain-induced phase transformation were tracked using HEDM. A sharp drop in grain specific stress, coinciding with the appearance of a new ε-hcp grain oriented relative to the parent γ-fcc phase, was taken as evidence of grain-specific stress required to trigger phase transformation. The highest resolved shear stress on the potential transformation systems was identified as the critical resolved shear stress (CRSS) for strain-induced phase transformation. For the studied metastable alloy, CRSS for transformation was determined to be 86±3 MPa which was very close to theoretical CRSS value determined using Schmid factor (89±12 MPa) compared to the value determined using Taylor factor (62±7 MPa).

Jain, Roopam↗
NASA NTRS2010

Characterization of Ternary NiTiPd High-Temperature Shape-Memory Alloys under Load-Biased Thermal Cycling

While NiTiPd alloys have been extensively studied for proposed use in high-temperature shape-memory applications, little is known about the shape-memory response of these materials under stress. Consequently, the isobaric thermal cyclic responses of five (Ni,Pd)49.5Ti50.5 alloys with constant stoichiometry and Pd contents ranging from 15 to 46 at. pct were investigated. From these tests, transformation temperatures, transformation strain (which is proportional to work output), and unrecovered strain per cycle (a measure of dimensional instability) were determined as a function of stress for each alloy. It was found that increasing the Pd content over this range resulted in a linear increase in transformation temperature, as expected. At a given stress level, work output decreased while the amount of unrecovered strain produced during each load-biased thermal cycle increased with increasing Pd content, during the initial thermal cycles. However, continued thermal cycling at constant stress resulted in a saturation of the work output and nearly eliminated further unrecovered strain under certain conditions, resulting in stable behavior amenable to many actuator applications.

Bigelow, Glen S.↗
DOE OSTI2022

Adaptive evolution of Methylotuvimicrobium alcaliphilum to grow in the presence of rhamnolipids improves fatty acid and rhamnolipid production from CH4

Abstract Rhamnolipids (RLs) are well-studied biosurfactants naturally produced by pathogenic strains of Pseudomonas aeruginosa. Current methods to produce RLs in native and heterologous hosts have focused on carbohydrates as production substrate; however, methane (CH4) provides an intriguing alternative as a substrate for RL production because it is low cost and may mitigate greenhouse gas emissions. Here, we demonstrate RL production from CH4 by Methylotuvimicrobium alcaliphilum DSM19304. RLs are inhibitory to M. alcaliphilum growth (<0.05 g/l). Adaptive laboratory evolution was performed by growing M. alcaliphilum in increasing concentrations of RLs, producing a strain that grew in the presence of 5 g/l of RLs. Metabolomics and proteomics of the adapted strain grown on CH4 in the absence of RLs revealed metabolic changes, increase in fatty acid production and secretion, alterations in gluconeogenesis, and increased secretion of lactate and osmolyte products compared with the parent strain. Expression of plasmid-borne RL production genes in the parent M. alcaliphilum strain resulted in cessation of growth and cell death. In contrast, the adapted strain transformed with the RL production genes showed no growth inhibition and produced up to 1 μM of RLs, a 600-fold increase compared with the parent strain, solely from CH4. This work has promise for developing technologies to produce fatty acid-derived bioproducts, including biosurfactants, from CH4.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Large room-temperature elastocaloric effect in a bulk polycrystalline Ni-Ti-Cu-Co alloy with low isothermal stress hysteresis

Creation of high-performance advanced elastocaloric materials is essential for the execution of elastocaloric refrigeration that can be an environment-friendly and high-efficiency substitute for the widely used traditional vapor-compression cooling technology. Here we have developed a bulk polycrystalline Ni-Ti-Cu-Co shape memory alloy exhibiting large elastocaloric effect, low stress hysteresis and room temperature working temperature, all of which are of great importance to and urgently demanded for high-efficiency room-temperature elastocaloric refrigeration. This newly developed (Ni 42.5 Ti 50 Cu 7.5 ) 99 Co 1 alloy shows a large room-temperature elastocaloric effect with directly measured adiabatic temperature change up to -14.4 K during unloading. The stress hysteresis of the isothermal superelastic stress-strain curve is as low as 60 MPa when the maximum tensile strain is 2.7%. Owing to the large elastocaloric effect and low stress hysteresis, a very high coefficient of performance up to 19 is achieved on the material level. This newly developed (Ni 42.5 Ti 50 Cu 7.5 ) 99 Co 1 alloy is a robust candidate for efficient elastocaloric refrigeration. Advanced in-situ synchrotron high-energy X-ray diffraction technique was employed to reveal the phase transformation sequence and to accurately determine the crystal structure of different phases, based on which the lattice compatibility between the transforming phases was evaluated and the phase transformation strain was predicted, providing in-depth fundamental understanding of the martensitic transformation in this newly developed alloy. This work could be useful for designing high-performance elastocaloric materials for solid-state cooling applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Uncovering hidden bias in neutron diffraction residual strain measurements

When calculating residual strain via neutron or X-ray diffraction, uncertainties propagated from the peak fit are often inadequate to describe the true scatter of measurements about a singular strain state, such as one that should describe a macroscopic continuum. Because diffraction is inherently a selective process, orientation-dependent scatter arises from the sub-sampling of strong microstructure and strain gradients. This paper investigates the appropriateness of propagated uncertainties with reference to their original intention, i.e. noise about a mean value. Thirty-six unique orientations of strain measurements are taken at multiple locations within an additive friction-stir deposition component with fine-scale gradients (∼200 µm) of plastic strain, texture and residual elastic strain. Multiple strain and stress calculation pathways are compared: direct substitution of three measurements into Hooke's law, direct inversion of any six unique orientations into the strain state tensor and thirty-six measurement least-squares estimation. For the last two cases, the appropriateness of the uncertainty interval is statistically evaluated on the basis of a physical constraint: common agreement under the strain transformation law. For this sample, the direct inversion of six measurements retains a conservative estimate of the uncertainty. However, propagated uncertainties in the least-squares solution greatly underestimate the true experimental scatter. A simple pathway to estimate appropriate uncertainty intervals is suggested. These results demonstrate that the interpretation of uncertainty in residual strain is strongly dependent on intrinsic sample-dependent effects, and that oversampling orientations and statistical analysis can give more accurate results with realistic uncertainties.

36 MATERIALS SCIENCE↗
NASA NTRS2011

Thermal Cycling and Isothermal Deformation Response of Polycrystalline NiTi: Simulations vs. Experiment

A recent microstructure-based FEM model that couples crystal-based plasticity, the B2<-> MB190 phase transformation and anisotropic elasticity at the grain scale is calibrated to recent data for polycrystalline NiTi (49.9 at.% Ni). Inputs include anisotropic elastic properties, texture and differential scanning calorimetry data, as well as a subset of recent isothermal deformation and load-biased thermal cycling data. The model is assessed against additional experimental data. Several experimental trends are captured - in particular, the transformation strain during thermal cycling monotonically increases and reaches a peak with increasing bias stress. This is achieved, in part, by modifying the martensite hardening matrix proposed by Patoor et al. [Patoor E, Eberhardt A, Berveiller M. J Phys IV 1996;6:277]. Some experimental trends are underestimated - in particular, the ratcheting of macrostrain during thermal cycling. This may reflect a model limitation that transformation-plasticity coupling is captured on a coarse (grain) scale but not on a fine (martensitic plate) scale.

Manchiraju, Sivom↗
NASA NTRSDate not supplied

Data-Driven Study of Shape Memory Behavior of Multi-Component Ni-Ti Alloys

Ni-Ti based shape memory alloys (SMAs) have found wide-spread use in aerospace, automotive, biomedical, and commercial applications owing to their favorable properties and ease of operation. Especially important for many NASA applications is the ability to tune the martensitic transformation temperature of Ni-Ti alloys by varying the alloy composition and processing conditions. Recently, researchers at NASA have compiled an extensive database of shape memory properties of materials, including over 8,000 multi-component Ni-Ti alloys containing 37 different alloying elements. Using this dataset, machine learning models are trained to predict transformation temperatures, hysteresis, and transformation strain with extremely low mean absolute errors. These models are used to learn relationships between shape memory behavior and input parameters in the composition and processing space. ML predictions are validated through new experiments. The combination of an extensive experimental dataset and accurate learning models, together, make our approach highly suitable for the rapid discovery and design of novel SMAs with targeted properties. We are not aware of any current approaches capable of predicting SMA transformation behavior over such a wide range of compositions and processing conditions.

Shape memory alloys↗
DOE OSTI2024

A crystal-plasticity-informed Gaussian Process Regression model to capture anisotropy in single crystal shape memory alloys

This work presents a machine learning (ML) framework that model the anisotropic actuation responses in a shape memory alloy. A Gaussian Process Regression (GPR) based ML model is trained on a set of different crystal orientations subjected to different actuation conditions. The training employed thermo-mechanical responses from a crystal-plasticity model that captures phase-transformation, stress-induced plasticity, and transformation-induced plasticity. Further, on training the GPR-ML model at fixed stress level for different orientations, it captured the thermo-mechanical responses accounting for the anisotropy, and predicted responses for new orientations with good accuracy. The GPR-ML model is able to capture the transformation temperature variations even when trained using multiple stress levels, and the transformation strain showed significant deviations. The developed GPR-ML model gave reasonable predictions for an unexplored sample set of orientations and loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Evolution of microstructure and strength of a high entropy alloy undergoing the strain-induced martensitic transformation

In a recent work, we have reported outstanding strength and work hardening exhibited by a metastable high entropy alloy (HEA), Fe 42 Mn 28 Co 10 Cr 15 Si 5 (in at. %), undergoing the strain-induced martensitic transformation from metastable gamma austenite (γ) to stable epsilon martensite (ε). However, the alloy exhibited poor ductility, which was attributed to the presence of the brittle sigma (σ) phase in its microstructure. The present work reports the evolution of microstructure, strength, and ductility of a similar HEA, Fe 38.5 Mn 20 Co 20 Cr 15 Si 5 Cu 1.5 (in at. %), designed to suppress the formation of σ phase. A cast and then rolled plate of the alloy was processed into four conditions by annealing for 10 and 30 min at 1100 °C and by friction stir processing (FSP) at tool rotation rates of 150 and 400 revolutions per minute (RPM) to facilitate detailed examinations of variable initial grain structures. Neutron diffraction and electron microscopy were employed to characterize the microstructure and texture evolution. The initial materials had variable grain size but nearly 100% γ structure. Diffusionless strain induced γ→ε phase transformation took place under compression with higher rate initially and slower rate at the later stages of deformation, independent on the initial grain size. The transformation facilitated part of plastic strain accommodation and rapid strain hardening owing to a transformation-induced dynamic Hall-Petch-type barrier effect, increase in dislocation density, and texture. Here, the peak strength of nearly 2 GPa was achieved under compression using the structure created by double pass FSP (150 RPM followed by 150 RPM). Remarkably, the tensile elongation exhibited by the alloy was nearly 20% with fracture surfaces featuring a combination of ductile dimples and cleavage.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to enable and understanding of their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transfected with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produce. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of novel DB positions and enzymatic specificity.

mass spectrometry, deep learning, Lipidomics, doub↗
DOE OSTI2022

Formation of misfit dislocation arrays and helium nanochannels near copper surface assisted by high-temperature graphene deposition

Misfit dislocation arrays (MDAs) at semi-coherent heterogeneous interface have been demonstrated effectiveness to trap and then outgas helium (He) for alleviating void swelling in metals, but limited by atomic-level thin film deposition that is difficult to scale-up for industrial production. In this work, we proposed an innovative strategy that could generate semi-coherent homophase interfaces in a same grain by varying surface energy density assisted by high-temperature graphene (Gr) deposition. This strategy transforms strain energy and surface energy, two originally distinctive processes into successive processes, and demonstrates experimentally and theoretically by introducing {111} copper (Cu) in a {100} Cu grain. Subsequent He implantation and electrical resistivity measurement results suggest that high-density MDAs originated at this {111}/{100} interface could form stable He nanochannels near Cu surface with Gr assistance, manifesting by ultra-low increased ratio of the electrical resistivity (i.e. ~3800% of Cu vs. ~90% of Gr/Cu) after He implantation. Finally, the present findings may provide a new strategy for efficiently managing He and achieving surface protection of advanced metals, merely by depositing a super-rigid material on metal surface to greatly reduce surface energy density.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Magnetic semiconductors from functionalized Cr 2 ⁢C MXenes

Here, we report an ab initio investigation of functionalized and 3⁢𝑑-electron-doped Cr 2 ⁢C MXenes. Upon functionalization, the Cr 2⁢ C becomes chemically, dynamically, and mechanically stable, and it exhibits magnetic semiconducting behavior. Cr 2 ⁢CF 2 stands out as a wide band gap semiconductor, possessing superexchange interaction mediated by F atoms within the layer; however, the applied strain transforms it from an indirect to a direct band gap semiconductor. Strong spin-phonon coupling found in Cr 2 ⁢CH 2 is supported by the distorted Cr spin density due to the hydrogen environment. Two magnon branches, associated with two sublattice spins, are found in the ferromagnetic Cr 2 ⁢CO 2 and antiferromagnetic Cr 2 ⁢CF 2 . Depending on the types of 3⁢𝑑-electron dopants and functionalization, Cr 2 ⁢C MXenes (except for Cr 2 ⁢CO 2 ) change from an indirect band gap magnetic semiconductor to different states of electronic and magnetic matter, including an exotic direct band gap magnetic bipolar semiconductor. In addition, we reveal a band inversion between the two highest valence bands in the Fe-doped Cr 2 ⁢CCl 2 .

2D materials↗
NASA NTRS2011

Simulating Thermal Cycling and Isothermal Deformation Response of Polycrystalline NiTi

A microstructure-based FEM model that couples crystal plasticity, crystallographic descriptions of the B2-B19' martensitic phase transformation, and anisotropic elasticity is used to simulate thermal cycling and isothermal deformation in polycrystalline NiTi (49.9at% Ni). The model inputs include anisotropic elastic properties, polycrystalline texture, DSC data, and a subset of isothermal deformation and load-biased thermal cycling data. A key experimental trend is captured.namely, the transformation strain during thermal cycling is predicted to reach a peak with increasing bias stress, due to the onset of plasticity at larger bias stress. Plasticity induces internal stress that affects both thermal cycling and isothermal deformation responses. Affected thermal cycling features include hysteretic width, two-way shape memory effect, and evolution of texture with increasing bias stress. Affected isothermal deformation features include increased hardening during loading and retained martensite after unloading. These trends are not captured by microstructural models that lack plasticity, nor are they all captured in a robust manner by phenomenological approaches. Despite this advance in microstructural modeling, quantitative differences exist, such as underprediction of open loop strain during thermal cycling.

Manchiraju, Sivom↗
NASA NTRS2017

Additive Manufacturing of NiTiHf High Temperature Shape Memory Alloy

Additive manufacturing of a NiTi-20Hf high temperature shape memory alloy (HTSMA) was investigated. A selective laser melting (SLM) process by Phenix3D Systems was used to develop components from NiTiHf powder (of approximately 25-75 m particle fractions), and the thermomechanical response was compared to the conventionally vacuum induction skull melted counterpart. Transformation temperatures of the SLM material were found to be slightly lower due to the additional oxygen pick up from the gas atomization and melting process. The shape memory response in compression was measured for stresses up to 500 MPa, and transformation strains were found to be very comparable (Up to 1.26 for the as-extruded; up to 1.52 for SLM).

NiTiHf; High Temperature Shape Memory Alloys; Addi↗
NASA NTRS2017

Functionally Graded Metal-Metal Composite Structures

Methods and devices are disclosed for creating a multiple alloy composite structure by forming a three-dimensional arrangement of a first alloy composition in which the three-dimensional arrangement has a substantially open and continuous porosity. The three-dimensional arrangement of the first alloy composition is infused with at least a second alloy composition, where the second alloy composition comprises a shape memory alloy. The three-dimensional arrangement is consolidated into a fully dense solid structure, and the original shape of the second alloy composition is set for reversible transformation. Strain is applied to the fully dense solid structure, which is treated with heat so that the shape memory alloy composition becomes memory activated to recover the original shape. An interwoven composite of the first alloy composition and the memory-activated second alloy composition is thereby formed in the multiple alloy composite structure.

Brice, Craig A.↗
NASA NTRSDate not supplied

Data-Driven Study of Shape Memory Behavior of Multi-component Ni-Ti Alloys

Ni-Ti based shape memory alloys (SMAs) have found wide-spread use in aerospace, automotive, biomedical, and commercial applications owing to their favorable properties and ease of operation. Especially important for many NASA applications is the ability to tune the martensitic transformation temperature of Ni-Ti alloys by varying the composition and processing conditions. Recently, researchers at NASA have compiled an extensive database of shape memory properties of materials, including over 8,000 multi-component Ni-Ti alloys containing 37 different alloying elements. Using this dataset, machine learning models are trained to predict transformation temperatures, hysteresis, and transformation strain with extremely small errors. These models are used to learn relationships between shape memory behavior and input parameters in the composition and processing space. ML predictions are validated through new experiments. The combination of an extensive dataset and accurate learning models, together, make our approach highly suitable for the rapid discovery of novel SMAs with targeted properties.

Shape Memory Alloys↗