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This project explores new classes of shape memory alloys (SMAs), through integration of novel processing, manufacturing, and composition to modulate the SMA thermomechanical response.
High-temperature shape memory NiTiPd and NiTiPdX (X=Au, Pt, Hf) alloys were produced with titanium equivalent (Ti+Hf) compositions of 50.5, 50.0, 49.5, and 49.0 at.%. Thermo-mechanical testing in compression was used to evaluate the transformation temperatures, transformation strain, work output, and permanent deformation behavior of each alloy to study the effects of quaternary alloying and stoichiometry on high-temperature shape memory alloy behavior. Microstructural evaluation showed the presence of second phases for all alloy compositions. No load transformation temperatures in the stoichiometric alloys were relatively unchanged by Au and Pt substitutions, while the substitution of Hf for Ti causes a drop in transformation temperatures. The NiTiPd, NiTiPdAu and NiTiPdHf alloys exhibited transformation temperatures that were highest in the Ti-rich compositions, slightly lower at stoichiometry, and significantly reduced when the Ti equivalent composition was less than 50 at.%. For the NiTiPdPt alloy, transformation temperatures were highest for the Ti-rich compositions, lowest at stoichiometry, and slightly higher in the Ni-rich composition. When thermally cycled under constant stresses of up to 300 MPa, all of the alloys had transformation strains, and therefore work outputs, which increased with increasing stress. In each series of alloys, the transformation strain and thus work output was highest for stoichiometric or Ti-rich compositions while permanent strain associated with the constant-load thermal cycling was lowest for alloys with Ni-equivalent-rich compositions. Based on these results, basic rules for optimizing the composition of NiTiPd alloys for actuator performance will be discussed.
Pt additions substituted for Ni in NiTi alloys are known to increase the transformation temperature of the alloy but only at fairly high Pt levels. However, until now only ternary compositions with a very specific stoichiometry, Ni50-xPtxTi50, have been investigated and then only to very limited extent. In order to learn about this potential high-temperature shape memory alloy system, a series of over twenty alloys along and on either side of a line of constant stoichiometry between NiTi and TiPt were arc melted, homogenized, and characterized in terms of their microstructure, transformation temperatures, and hardness. The resulting microstructures were examined by scanning electron microscopy and the phase compositions quantified by energy dispersive spectroscopy."Stoichiometric" compositions along a line of constant stoichiometry between NiTi to TiPt were essentially single phase but by any deviations from a stoichiometry of (Ni,Pt)50Ti50 resulted in the presence of at least two different intermetallic phases, depending on the overall composition of the alloy. Essentially all alloys, whether single or two-phase, still under went a martensitic transformation. It was found that the transformation temperatures were depressed with initial Pt additions but at levels greater than 10 at.% the transformation temperature increased linearly with Pt content. Also, the transformation temperatures were relatively insensitive to alloy stoichiometry within the range of alloys examined. Finally, the dependence of hardness on Pt content for a series of Ni50-xPtxTi50 alloys showed solution softening at low Pt levels, while hardening was observed in ternary alloys containing more than about 10 at.% Pt. On either side of these "stoichiometric" compositions, hardness was also found to increase significantly.
An overview of smart structures research currently underway at the NASA Langley Research Center in the areas of aeroservoelasticity and structural dynamics is presented. Analytical and experimental results, plans, potential technology pay-offs, and challenges are discussed. The goal of this research is to develop the enabling technologies to actively and passively control aircraft and rotorcraft vibration and loads using smart devices. These enabling technologies and related research efforts include developing experimentally-validated finite element and aeroservoelastic modeling techniques; conducting bench experimental tests to assess feasibility and understand system trade-offs; and conducting large-scale wind- tunnel tests to demonstrate system performance. The key aeroservoelastic applications of this research include: active twist control of rotor blades using interdigitated electrode piezoelectric composites and active control of flutter, and gust and buffeting responses using discrete piezoelectric patches. In addition, NASA Langley is an active participant in the DARPA/ Air Force Research Laboratory/ NASA/ Northrop Grumman Smart Wing program which is assessing aerodynamic performance benefits using smart materials. Keywords: aeroelasticity, smart structures, piezoelectric actuators, active fiber composites, rotorcraft, buffet load alleviation, individual blade control, aeroservoelasticity, shape memory alloys, damping augmentation, piezoelectric power consumption
A method is developed to predict the vibration response of a composite panel and the resulting far-field acoustic radiation due to acoustic excitation. The acoustic excitation is assumed to consist of obliquely incident plane waves. The panel is modeled by a finite element analysis and the radiated field is predicted using Rayleigh's integral. The approach can easily include other effects such as shape memory alloy (SMA) ber reinforcement, large detection thermal postbuckling, and non-symmetric SMA distribution or lamination. Transmission loss predictions for the case of an aluminum panel excited by a harmonic acoustic pressure are shown to compare very well with a classical analysis. Results for a composite panel with and without shape memory alloy reinforcement are also presented. The preliminary results demonstrate that the transmission loss can be significantly increased with shape memory alloy reinforcement. The mechanisms for further transmission loss improvement are identified and discussed.
Shape memory polymers (SMPs) have been found to be promising biomaterials for a variety of medical applications; however, the clinical translation of such technology is dependent on tailorable properties such as gravimetric changes in degradation environments. For SMPs synthesized from amino-alcohols, oxidation resulting in rapid mass loss may be problematic in terms of loss of material functionality as well as toxicity and cytocompatibility concerns. Control of gravimetric changes was achieved through the incorporation of small molecule antioxidants, either directly into the polymer matrix or included in microparticles to form a SMP composite material. Direct incorporation of small molecule antioxidants, such as phenolic alcohols, was found to alter shape memory attributes and increase elastic modulus at the expense of the strain to failure. Such changes could not ensure retention of the antioxidants and therefore did not increase oxidative stability. However, the inclusion of antioxidants in microparticles was found to produce materials with similar thermomechanical and shape memory properties while increasing oxidative resistance compared to controls. The microparticle composite SMPs also act as a platform for environmental sensing, such as pH-dependent fluorescence shifts and payload release, as demonstrated by fluorescent dyes and the release of antioxidants. The use of polyurethane-urea microparticles in porous SMPs is demonstrated to increase biostability of the materials, by approximately 25%, and ultimately extend their lifespan for use in aneurysm occlusion as determined through calculated in vivo degradation rates corresponding to a porcine aneurysm environment.
The metamagnetic shape memory Heusler alloy Ni 50 Mn $50–x$ In $x$ exhibits a rich phase diagram featuring competing magnetic states, coupled magnetic–structural phase transitions, and strong compositional sensitivity. Existing first-principles approaches struggletocapturetheintertwinedchemical, magnetic, andvibrationaleffectsinthesealloys, necessitating a more integrated modeling framework. We develop a spin cluster expansion (spin-CE) framework augmented by a quasi-harmonic phonon model to capture both configurational (chemical and magnetic) and vibrational contributions to the free energy of Ni 50 Mn $50–x$ In $x$ over the full compositional range 0 ≤ x ≤25. The spin-CE includes both chemical clusters and composition-dependent Ising spin interactions, with parameters fit to a first-principles density functional theory (DFT) dataset. Using this approach, we predict the complete magnetostructural phase diagram and transformation temperatures of Ni 50 Mn $50–x$ In $x$ across the composition space. We find that vibrational entropy alone is insufficient to reproduce the martensitic transformation in the magnetic shape memory alloy regime, highlighting the essential role of magnetism. Incorporating both magnetic and vibrational contributions allows us to reproduce all experimentally known phases, including the disappearance of the stable martensite phase at a critical In concentration and the Curie temperature of the austenite phase. The method also captures the transition with increasing In in martensite from antiferromagnetic to ferromagnetic order and predicts re-entrant ferromagnetism, though the latter occurs at higher In content than reported experimentally. We discuss possible sources of this discrepancy and highlight the broader applicability of the method to other magnetostructurally complex materials, where it may offer mechanistic insight and predictive design capabilities.
NASA’s approach to computational materials modeling is detailed in the NASA Vision 2040 Roadmap for Multiscale Modeling and Simulation of Materials and Systems. This report is in the spirit of national initiatives such as the Material Genome Initiative (MGI), Integrated Computational Materials Engineering (ICME), and others. We utilize a combination of fundamental modeling, computational high-throughput screening, and data science methods, e.g., machine learning, are used to find innovative solutions to NASA or national technology challenges. Applications of interest are wide ranging from advanced alloys to batteries to coatings, among others. In this talk, we present three examples for recent work related to NASA applications. First, doping advanced sulfur battery cathodes with selenium boosts electrical conductivity important for electric aircraft applications. First principles calculations will be discussed that result in compositional design maps for these materials. Second, development of icephobic coatings is important to mitigate safety hazards associated with icing for aircraft. Molecular dynamics simulations are reported for ice-surface interfaces to understand adhesion mechanisms and help screen optimal ice-phobic coatings. Third, shape memory alloys have numerous applications as actuators, superelastic materials, etc. for aerospace. We report machine learning models that predict martensitic transition temperatures across a broad swath of compositional space.
In binary NiTi alloys, it has long been known that Ni-rich alloys can be heat treated to produce precipitates which both strengthen the matrix against dislocations and improve the behavior of the material under thermal and mechanical cycling. Within recent years, the same effect has been observed in Ni-rich NiTiHf high temperature shape memory alloys and heat treatment regimens have been defined which will reliably produce improved properties. In NiTiPd alloys, precipitation has also been observed, but studies are still underway to define reliable heat treatments and compositions which will provide a balance of strengthening and good thermomechanical properties. For this study, a series of NiTi-32 at.Pd alloys was produced to determine the effect of changing nickeltitanium content on the transformation behavior and heat treatability of the material. Samples were aged at temperatures between 350C and 450C for times up to 100 hours. Actuation type behavior was evaluated using uniaxial constant force thermal cycling (UCFTC) to determine the effect of composition and aging on the material behavior. TEMSEM was used to evaluate the microstructure and determine the types of precipitates formed. The correlation between composition, heat treat, microstructure, and thermomechanical behavior will be addressed and discussed.
Ni-49Ti and Ni-30Pt-50Ti (at.%) shape memory alloys were oxidized isothermally in air over the temperature range of 500 to 900 C. The microstructure, composition, and phase content of the scales were studied by SEM, EDS, XRD, and metallography. Extensive plan view SEM/EDS identified various features of intact or spalled scale surfaces. The outer surface of the scale was a relatively pure TiO2 rutile structure, typified by a distinct highly striated and faceted crystal morphology. Crystal size increased significantly with temperature. Spalled regions exhibited some porosity and less distinct features. More detailed information was obtained by correlation of SEM/EDS studies of 700 C/100 hr cross-sections with XRD analyses of serial or taper-polishing of plan surfaces. Overall, multiple layers exhibited graded mixtures of NiO, TiO2, NiTiO3, Ni(Ti) or Pt(Ni,Ti) metal dispersoids, Ni3Ti or Pt3Ti depletion zones, and substrate, in that order. The NiTi alloy contained a 3 at.% Fe impurity that appeared in embedded localized Fe-Ti-rich oxides, while the NiPtTi alloy contained a 2 v/o dispersion of TiC that appeared in lower layers. The oxidation kinetics of both alloys (in a previous report) indicated parabolic growth and an activation energy (250 kJ/mole) near those reported in other Ti and NiTi studies. This is generally consistent with TiO2 existing as the primary scale constituent, as described here.
Ab initio simulations of phase stability and martensitic phase transitions are performed for NiTi-based ternary shape memory alloys (SMAs). Specifically, we considered NiTiHf and NiTiZr, which are highly studied for high temperature SMA applications. Previously, we performed investigations of ordered NiTi and related binaries [1,2]. However, similar approaches for chemically disordered compounds present additional difficulties. In this work, special quasi-random structures (SQS) were generated for various compositions, x∈[0,0.5], of Ni0.5Ti(0.5-x)Hfx and Ni0.5Ti(0.5-x)Zrx to capture chemical disorder of off-stoichiometric compounds. Phase stability was evaluated through analysis of finite temperature phonon spectra using temperature dependent effective potential (TDEP) method. Free energies for the cubic B2 phase of NiTiHf and NiTiZr were computed using ab initio thermodynamic integration (AITI) developed previously [1,2]. Free energies for monoclinic B19’ and orthorhombic B33 phases were evaluated via quasi harmonic approximations (QHA). Our results show a critical composition (xc) where the three phases of B2, B19’ and B33 meet, i.e. there is a tri-critical point. For x xc, the transition is between B33 and B2, i.e. it is not a shape memory transition. The approach presented here opens the door to ab initio based predictions of MTT for arbitrary ternary SMAs.
Shape memory alloys (SMAs) exhibit unique thermomechanical properties due to a reversible martensitic transformation which can be controlled via chemical composition and microstructural features. Among the latter, grain boundaries (GBs) are key in determining how the transformation nucleates and propagates. We performed molecular dynamics simulations to examine the roles of a few special GBs in the austenite phase of NiTi. The GBs can act as preferential host sites for martensite nuclei which can substantially lower the nucleation barrier for transformation and thereby result in reduced thermal hysteresis, an important SMA property for cyclic actuation applications. Free energy calculations show that a characteristic of these GBs is a negative entropy which drops sharply close to the transformation temperature, an anomalous behavior for GBs with fixed composition which is a direct result of the martensitic transformation. We discuss implications of these results with respect to SMA processing techniques for achieving improved properties.
Shape memory polymers (SMPs) represent a paradigm shift in material science, uniquely capable of undergoing reversible shape transformations triggered by external stimuli, positioning them as pivotal in developing next-generation biomedical devices, aerospace components, and adaptive structures. Extensive research has been done on SMPs with a major focus on high-temperature programming methods, which can limit energy efficiency and applicability with temperature-sensitive materials. Additionally, while various SMP blends have demonstrated great potential, limited work has been done on the suitability for 3D printing these materials, particularly under high-strain and ambient temperature programming conditions. In this study, a three-component optimized SMP composition was evaluated by 3D printing via the Material Extrusion (MEX) technique and investigating its ambient temperature-programming behavior at high strains. The SMP formulation studied was a tailored blend of thermoplastic polyurethane (TPU), polycaprolactone (PCL), and an octadecane diol-based copolymer (OBC) that exhibits robust shape memory behavior, high strain tolerance, and efficient force generation. Rigorous thermal, mechanical, and shape recovery analyses, along with optimized printing parameters and consistent shape recovery rates of up to 90%, were achieved under dynamic mechanical analysis (DMA), even under ambient programming conditions. This work demonstrates the SMP composition’s potential for adaptive, self-deployable systems with 4D printing characteristics ideal for bio-inspired structures and artificial muscle fibers.
Customizable Digital Receivers for Radar Two-Camera Acquisition and Tracking of a Flying Target Visual Data Analysis for Satellites A Data Type for Efficient Representation of Other Data Types Hand-Held Ultrasonic Instrument for Reading Matrix Symbols Broadband Microstrip-to-Coplanar Strip Double-Y Balun A Topographical Lidar System for Terrain-Relative Navigation Programmable Low-Voltage Circuit Breaker and Tester Electronic Switch Arrays for Managing Microbattery Arrays Topics covered include: Lower-Dark-Current, Higher-Blue-Response CMOS Imagers; Fabricating Large-Area Sheets of Single-Layer Graphene by CVD; Support for Diagnosis of Custom Computer Hardware; Providing Goal-Based Autonomy for Commanding a Spacecraft; Dynamic Method for Identifying Collected Sample Mass; Optimal Planning and Problem-Solving; Attitude-Control Algorithm for Minimizing Maneuver Execution Errors; Grants Document-Generation System; Heat-Storage Modules Containing LiNO3 3H2O and Graphite Foam; Precipitation-Strengthened, High-Temperature, High-Force Shape Memory Alloys; Improved Relief Valve Would Be Less Susceptible to Failure; Safety Modification of Cam-and-Groove Hose Coupling; Using Composite Materials in a Cryogenic Pump; Using Electronic Noses to Detect Tumors During Neurosurgery; Producing Newborn Synchronous Mammalian Cells; Smaller, Lower-Power Fast-Neutron Scintillation Detectors; Rotationally Vibrating Electric-Field Mill; Estimating Hardness from the USDC Tool-Bit Temperature Rise; Particle-Charge Spectrometer; Automated Production of Movies on a Cluster of Computers; FIDO-Class Development Rover; and Tone-Based Command of Deep Space Probes Using Ground Antennas.
NiTiHf type shape memory alloys have been studied for use in high temperature actuator applications, the most studied of which is the NiTi-20Hf atomic % composition. In this study, six samples of NiTi-Hf including 20Hf, 30Hf, 35Hf, 40Hf, 45Hf, and 50Hf were evaluated by both 1D and 2D X-ray diffraction (XRD) to elucidate what phases and crystal structures were present at room temperature. Given small sample sizes and relatively large grains, multiple cross-sections were measured to increase the sampling statistics, and 2D XRD frames were collected to corroborate the 1D XRD data. Rietveld and structureless refinements were both performed, allowing for adjustments in orientation lattice constant, and structure factor. At room temperature, the primary martensitic phase in the NiTiHf alloys with 20 at% and lower Hf is a monoclinic B19’ phase (P1121/m space group). This phase was identifiable, even in small amounts, in the higher Hf samples. The primary phase in the 30 -50 at% Hf samples is an orthorhombic B33 (Cmcm space group) phase. This work supported thermal analyses indicating the phase transition paths from the two different room temperature martensitic phases to the B2 austenitic phase.
Self-actuating thermal coupling device is light, and transfers heat efficiently. Coupling is thermostatic, making contact as necessary to maintain nearly steady temperature at hot side of interface. Hot pins on hot plate poised near cold pins on cold-plate. As temperature of hot plate rises, shape-memory wire contracts, drawing hot pin against cold pin. When temperature falls below critical level, wire expands, allowing hot pin to pull away. Critical temperature determined by composition of alloy.
Upcoming crewed space missions will involve large internal and external heat loads and require advanced thermal control systems to maintain a desired internal environment temperature. Radiators with at least 12:1 turndown ratios (the ratio between the maximum and minimum heat rejection rates) will be needed. However, current technologies are only able to achieve turndown ratios of approximately 3:1. A morphing radiator capable of altering shape could significantly increase turndown capabilities. Shape memory alloys offer qualities that may be well suited for this endeavor; their temperature-dependent phase changes could offer radiators the ability to passively control heat rejection. In 2015, a morphing radiator prototype was constructed and tested in a thermal vacuum environment, where it successfully demonstrated the morphing behavior and variable heat rejection. Newer composite prototypes have since been designed and manufactured using two distinct types of SMA materials. These models underwent temperature cycling tests in a thermal vacuum chamber and a series of fatigue tests to characterize the lifespan of these designs. The focus of this paper is to present the design approach and testing of the morphing composite facesheet. The discussion includes: an overall description of the project background, definition of performance requirements, composite materials selection, use of analytic and numerical design tools, facesheet fabrication, and finally fatigue testing with accompanying results.
Shape memory alloys (SMAs) are a unique class of materials with several remarkable properties including shape recovery, superelasticity, etc. Nickel-titanium (NiTi) based alloys are the most widely studied of this class, with compositions including ternary, quaternary, or higher additions being considered. Especially important for many NASA applications is the ability to tune the martensitic phase transition temperature of NiTi alloys by varying the alloy composition and processing conditions. In addition, low hysteresis and an acceptable recoverable transformation strain are required. Over the past several years, a significant database of SMA properties has been assembled by NASA researchers. Such a database is ideal for data science-based approaches. We present results from our machine learning approach for designing new SMAs with target properties within our range of interest. Our developed models are capable of accurately predicting the transition temperature, hysteresis, and transformation strain of SMAs across a wide range of compositions. This approach has the potential to significantly accelerate the discovery and design of new SMA materials.