Abstract Two-terminal adaptive materials and circuit elements that mimic the signal processing, learning, and computing capabilities of biological synapses are essential for next-generation computing systems. To this end, we have recently developed resistive (ion channel) and capacitive (lipid bilayer) memory elements that mimic the composition, structure, and plasticity of biological synapses. Unlike solid-state counterparts, these biomolecular systems are low-power, analog, less noisy, biocompatible, and capable of exhibiting multiple timescales of short-term synaptic plasticity. However, lipid membranes lack structural stability and modularity necessary for a long-lasting adaptive material system. To address this issue, we propose the replacement of phospholipids with amphiphilic polymers to create artificial membranes, which have been demonstrated to be more durable than phospholipids. With the focus on memory capacitors, we demonstrate that polymeric bilayers can exhibit pinched hysteresis in the Q-v plane because of voltage-induced geometrical changes. Further, we demonstrate that the memcapacitive response is altered based on the surrounding oil medium; smaller oil molecules are retained at higher volume in the membrane, so that thicker bilayers have lower nominal capacitance but can vary this value by over 400%. Finally, we present a physics-based model that enables us to predict the device’s areal voltage-dependent response. Polymeric bilayers represent a significant enhancement in the field of soft-matter, geometrically-reconfigurable memcapacitors, and their highly customizable compositions will allow for a finely tuned electrical response that has a future in brain-inspired materials and circuits.

Shape Memory Alloys (SMAs), known as an intermetallic alloys with the ability to recover its predefined shape under specific thermomechanical loading, has been widely aware of working as actuators for active/smart morphing structures in engineering industry. Because of the high actuation energy density of SMAs, compared to other active materials, structures integrated with SMA-based actuators has high advantage in terms of tradeoffs between overall structure weight, integrity and functionality. The majority of available constitutive models for SMAs are developed within infinitesimal strain regime. However, it was reported that particular SMAs can generate transformation strains nearly up to 8%–10%, for which the adopted infinitesimal strain assumption is no longer appropriate. Furthermore, industry applications may require SMA actuators, such as a SMA torque tube, undergo large rotation deformation at work. Combining the above two facts, a constitutive model for SMAs developed on a finite deformation framework is required to predict accurate response for these SMA-based actuators under large deformations. A three-dimensional constitutive model for SMAs considering large strains with large rotations is proposed in this work. This model utilizes the logarithmic strain as a finite strain measure for large deformation analysis so that its rate form hypoelastic constitutive relation can be consistently integrated to deliver a free energy based hyper-elastic constitutive relation. The martensitic volume fraction and the second-order transformation strain tensor are chosen as the internal state variables to characterize the inelastic response exhibited by polycrystalline SMAs. Numerical experiments for basic SMA geometries, such as a bar under tension and a torque tube under torsion are performed to test the capabilities of the newly proposed model. The presented formulation and its numerical implementation scheme can be extended in future work for the incorporation of other inelastic phenomenas such as transformation-induced plasticity, viscoplasticity and creep under large deformations.

Memristors are solid-state devices that exhibit voltage-controlled conductance. This tunable functionality enables the implementation of biologically-inspired synaptic functions in solid-state neuromorphic computing systems. However, while memristors are meant to emulate an intricate signal transduction process performed by soft biomolecular structures, they are commonly constructed from silicon- or polymer-based materials. As a result, the volatility, intricate design, and high-energy resistance switching in memristive devices, usually, leads to energy consumption in memristors that is several orders of magnitude higher than in natural synapses. Additionally, solid-state memristors fail to achieve the coupled dynamics and selectivity of synaptic ion exchange that are believed to be necessary for initiating both short- and long-term potentiation (STP and LTP) in neural synapses, as well as paired-pulse facilitation (PPF) in the presynaptic terminal. LTP is a phenomenon mostly responsible for driving synaptic learning and memory, features that enable signal transduction between neurons to be history-dependent and adaptable. In contrast, current memristive devices rely on engineered external programming parameters to imitate LTP. Because of these fundamental differences, we believe a biomolecular approach offers untapped potential for constructing synapse-like systems. Here, we report on a synthetic biomembrane system with biomolecule-regulated (alamethicin) variable ion conductance that emulates vital operational principals of biological synapse. The proposed system consists of a synthetic droplet interface bilayer (DIB) assembled at the conjoining interface of two monolayer-encased aqueous droplets in oil. The droplets contain voltage-activated alamethicin (Alm) peptides, capable of creating conductive pathways for ion transport through the impermeable lipid membrane. The insertion of the peptides and formation of transmembrane ion channels is achieved at externally applied potentials higher than ∼70 m V. Just like in biological synapses, where the incorporation of additional receptors is responsible for changing the synaptic weight (i.e. conductance), we demonstrate that the weight of our synaptic mimic may be changed by controlling the number of alamethicin ion channels created in a synthetic lipid membrane. More alamethicin peptides are incorporated by increasing the post-threshold external potential, thus leading to higher conductance levels for ion transport. The current-voltage responses of the alamethicin-based synapse also exhibit significant “pinched” hysteresis — a characteristic of memristors that is fundamental to mimicking synapse plasticity. We demonstrate the system’s capability of exhibiting STP/PPF behaviors in response to high-frequency 50 ms, 150 mV voltage pulses. We also present and discuss an analytical model for an alamethicin-based memristor, classifying that later as a “generic memristor”.

Based on a recently developed shakedown theory for non-smooth nonlinear materials, we derive a criterion for high-cycle fatigue in shape memory alloys (SMAs). The fatigue criterion takes into account phase transformation as well as reorientation of martensite variants as the source of fatigue damage. The mathematical derivation of the criterion is based on the requirement of elastic shakedown for a given structure to achieve unlimited fatigue endurance. Elastic shakedown is defined as an asymptotic state in which damage due to time-varying load becomes confined at the mesoscopic scale, or the scale of the grain, with no discernable inelasticity at the macroscopic scale. From an energy standpoint, elastic shakedown corresponds to a situation where energy dissipation becomes bounded and the response elastic after a certain number of loading cycles. A sufficient condition to achieve this state was established by Melan (1936) [1] and Koiter (1960) [2] for elastoplastic materials and later generalized to hardening plasticity by Nguyen (2003) and to non-smooth non-linear materials by Peigney (2014). The latter formulation is applicable to SMAs obeying the ZM constitutive model (Zaki & Moumni, 2007) and is shown here to allow the derivation of a high-cycle fatigue criterion analogous to the one proposed by Dang Van (1973) for elastoplastic materials. The criterion allows establishing a safe domain in stress deviator space at the mesoscopic scale consisting of a hypercylinder with axis parallel to the direction of martensite orientation. The hypercylinder is delimited along its axis by two transverse hyperplanes representing bounds on admissible stress states consistent with the loading conditions for phase transformation. Safety with regard to high-cycle fatigue, upon elastic shakedown, is conditioned by the persistence of the macroscopic stress path, as the load varies and at every material point, strictly within the hypercylinder. The size of the hypercylinder is shown to strongly depend on the relative amount of martensite present in the SMA.

The paper presents a new constitutive model for iron-based shape memory alloys (Fe-SMAs) adapted from the ZM model initially proposed for Nitinol by Zaki and Moumni [JMPS2007]. The model introduces nonlinear hardening terms to account for interactions between the grains, martensite variants and slip systems that may exist within a volume element of the material. The expressions used for the hardening terms are similar to those in (Khalil et al. [JIMSS2012]). The equations of the model are derived from the expression of a Helmholtz free energy potential, with complementary loading conditions obtained within the framework of generalized standard materials with internal constraints. A detailed derivation of the implicit algorithm used for the integration of the model is provided and used for numerical simulations that are shown to agree with experimental data.

The continuous implementation of shape memory alloys’ (SMAs’) actuation capabilities in various applications from aerospace to biomedical tools has attracted researchers’ interests into design optimization of active systems. Traditional methods of optimization have mostly relied on several iterations of altering and testing different possible design of prototypes seeking the best configuration. This trial and error experimentation method is usually expensive and time consuming. In the recent years the availability of computational analysis has facilitated the optimization process by avoiding the developments of many prototypes in the whole design space. In this work an automated design optimization frameworks is presented especially for the systems including active components. Design exploration of a recently proposed medical device was considered as a case study to elaborate this iterative technique. SMA activated needle is an innovative medical tool to be used in needle-based surgeries aiming the enhancement of the needle tip placement inside the tissue. Different configurations have been assessed by altering the design variables in the assigned domain seeking the maximum needle tip deflection to assure the maximum flexibility of the structure where all the analyses were constrained to the stress level of SMAs to be in the safe range preventing plasticity. A commercially available finite element package was used for the iterative assessments in the optimization approach. The challenging part in any analysis of active components is the incorporation of a suitable material model. For this purpose three experimental setups were developed to get the material properties of SMAs through different responses of the wires. These material properties along with the implementation of Brinson model led to the generation of the isothermal stress strain curves which were defined as material model of the active components in the FE analyses. The FE model was then linked to the iterative engine of direct optimization to iterate through the whole domain and determine the best configuration. The Design of Experiments (DOE) and the Multi-Objective Genetic Algorithm (MOGA) were used for the case study optimization. Both the design optimization and the design sensitivity studies were described. The results showed the length of the needle and the offset between the neutral axis of needle and the actuator were the most sensitive variables. The best five configurations with the maximum tip deflection was also presented.

Dielectric Elastomers (DEs) are deformable dielectrics, which are currently used as active materials in mechatronic transducers, such as actuators, sensors and generators. Nonetheless, at the present state of the art, the industrial exploitation of DE-based devices is still hampered by the irregular electro-mechanical behavior of the employed materials, also due to the unpredictable effects of environmental changes in real world applications. In many cases, DE transducers are still developed via trial-and-error procedures rather than through a well-structured design practice, one reason being the lack of experimental data along with reliable constitutive parameters of many potential DE materials. Therefore, in order to provide the practicing engineer with some essential information, an open-access database for DE materials has been recently created and presented in [1]. Following the same direction, this paper addresses the temperature effect on the visco-hyperelastic behavior of two DE candidates, namely a natural rubber (ZRUNEK A1040) and a well-known acrylic elastomer (3M VHB 4905). Measurements are performed on pure shear specimens placed in a climactic chamber. Experimental stress-strain curves are then provided, which makes it possible to predict hyperelasticity, plasticity, viscosity, and Mullins effect as function of the environmental temperature. Properties of these commercial elastomeric membranes are finally entered in the database and made available to the research community.

For the past decade, wind turbines have become the largest source of installed renewable-energy capacity in the United States. Economical, maintenance and operation are critical issues when dealing with such large slender structures, particularly when these structures are sited remotely. Because of the chaotic nature of non-stationary rotating-machinery systems such as the horizontal-axis wind turbines (HAWTs), in-operation modeling and computer-aided numerical characterization is typically troublesome, and tends to be imprecise while predicting the real content of the actual aerodynamic loading. Loading environment under operation conditions is usually substantially different from those driven by modal testing or computer-aided model characterization and difficult to measure directly in the field. In addition, rotational machinery such as HAWTs exhibit complex and nonlinear dynamics ( i.e. , precession and Coriolis effects, torsional coupling, nonlinear geometries, plasticity of composite materials); and are subjected to nonlinear constrained conditions ( i.e. , aeroelastic interaction). For those reasons, modal-aeroelastic and computer-aided models reproduced under controlled conditions may fail to predict the correct non-stationary loading and resistance patterns of wind turbines in actual operation. Operational techniques for extracting modal properties under actual non-stationary loadings are needed in order to (1) improve computer-aided elasto-aerodynamic models to better characterize the actual behavior of HAWTs in operational scenarios, (2) improve and correlate models, (3) monitor and diagnose the system for integrity and damage through time, or even (4) optimize control systems. For structural health monitoring (SHM) applications, model updating of stochastic aerodynamic problems has gained interest over the past decades. For situations where optimizing objective functions are not differentiable, convex or continuous in nature that is the case of gradient methods such as Modal Assurance Criterion (MAC), global optimization (metaheurstic) methods based on probability principles have emerged. These search engine techniques are promising suitable to cope with non-stationary-stochastic system identification methods for model updating of HAWT systems. A probability theory framework is employed in this study to update the wind turbine model using such a stochastic global optimization approach. Structural identification is addressed under regular wind turbine operation conditions for non-stationary, unmeasured, and uncontrolled excitations by means of the eigensystem realization theory (ERA). This numerical framework is then tied up with an adaptive simulated annealing (ASA) numerical engine for solving the problem of model updating. Numerical results are presented for an experimental deployment of a small HAWT structure. Results are benchmarked and validated with other empirical mode-decomposition and time-domain solutions.

In this work we present a constitutive model for High Temperature Shape Memory Alloys (HTSMAs), where the appearence of viscoplastic mechanisms during transformation influences the cyclic response of the actuator performance. Based on previous models developed for conventional SMAs, a Gibbs free energy potential is defined and the evolution equations for forward, reverse transformation, plasticity occuring during transformation, retained martensite and viscoplasticity are properly chosen. The calibration of the model is achieved with the help of experimental tests performed on TiPdNi alloy. The transformation behavior of the material is calibrated using fast load biased thermal cycling tests at selected stress levels with fast cooling/heating rate. The viscoplastic behavior of the HTSMA is captured with creep and uniaxial tests at appropriate temperature levels. Predictions of the model are compared with load biased thermal cycling tests at slow cooling/heating rate, where viscoplastic strains are significant.

This work discusses the increased capabilities of a three-dimensional analysis tool for shape memory alloy engineering components. As the number and complexity of proposed SMA applications increases, engineers and designers must seek out or develop more capable predictive methods. Three-dimensional models implemented in a continuum finite element analysis (FEA) framework can be applied to most SMA component geometries. However, such methods may require fine meshes in 3-D space, resulting in many degrees of freedom and potentially long analysis times. On the other hand, constitutive models implemented in one dimension can be simple and fast, but are restricted to a limited class of problems for which such reductions are appropriate (e.g., rods and beams). More recently, engineers have begun investigating more complex SMA bending components for which 2-D shell elements might provide a computationally efficient FEA discretization. Here we consider a single modeling tool (a material subroutine) that combines 1-D, 2-D, and 3-D implementations for use in a general FEA framework. As an example analysis case, we consider an SMA bending element that has been adhesively bonded to a carbon fiber-reinforced polymer (CFRP) laminate and is subjected to thermally-induced actuation. The active SMA and passive composite components are bonded in a pre-stressed configuration such that the elastic laminate provides a variable restoring force to the SMA during transformation, resulting in repeatable actuation cycles. This two-part bonded configuration is analyzed using different types of finite elements (1-D beam, 2-D shell, and full 3-D continuum elements). The constitutive behavior of the shape memory alloy is defined using an established three-dimensional model based on continuum thermodynamics and motivated by the methods of classical plasticity. A user material subroutine (UMAT) in an Abaqus Unified FEA framework is used to implement the model. The methodology for capturing 1-D, 2-D, and 3-D thermomechanical response in a single such UMAT is described. The run times of the various analyses are compared, and the relative accuracies of the results are discussed.

As active structures become more prominent, the use of more capable numerical modeling has gained importance as an aid to the design process. The need to accurately account for the response of Shape Memory Alloys (SMAs) under complex loading paths has become increasingly important. Such paths are general in a stress-temperature space and may induce irreversible deformation (plasticity). In addition, the structural utilization of active SMA components often includes large deformations, specifically large rotations. This is especially important in beam bending and torsional applications. This work proposes a new method for implementing a phenomenological SMA model originally formulated using small strains into a numerical framework which preserves objectivity given large rigid body rotations. The implementation is shown to be straightforward, and example analyses are performed which demonstrate the usefulness of this capability. An extension of the model to include the generation of plastic strains is also discussed. Alterations to the numerical algorithms are addressed which allow the analysis of simultaneous transformation and yielding. Additional analyses are performed on structural members undergoing transformation and yielding while at the same time moving through large rotations.