Free vibration analysis and design optimization of SMA/Graphite/Epoxy composite shells in thermal environments

Composite shells, which are being widely used in engineering applications, are often under thermal loads. Thermal loads usually bring thermal stresses in the structure which can significantly affect its static and dynamic behaviors. One of the possible solutions for this matter is embedding Shape Memory Alloy (SMA) wires into the structure. In the present study, thermal buckling and free vibration of laminated composite cylindrical shells reinforced by SMA wires are analyzed. Brinson model is implemented to predict the thermo-mechanical behavior of SMA wires. The natural frequencies and buckling temperatures of the structure are obtained by employing Generalized Differential Quadrature (GDQ) method. GDQ is a powerful numerical approach which can solve partial differential equations. A comparative study is carried out to show the accuracy and efficiency of the applied numerical method for both free vibration and buckling analysis of composite shells in thermal environment. A parametric study is also provided to indicate the effects of like SMA volume fraction, dependency of material properties on temperature, lay-up orientation, and pre-strain of SMA wires on the natural frequency and buckling of Shape Memory Alloy Hybrid Composite (SMAHC) cylindrical shells. Results represent the fact that SMAs can play a significant role in thermal vibration of composite shells. The second goal of present work is optimization of SMAHC cylindrical shells in order to maximize the fundamental frequency parameter at a certain temperature. To this end, an eight-layer composite shell with four SMA-reinforced layers is considered for optimization. The primary optimization variables are the values of SMA angles in the four layers. Since the optimization process is complicated and time consuming, Genetic Algorithm (GA) is performed to obtain the orientations of SMA layers to maximize the first natural frequency of structure. The optimization results show that using an optimum stacking sequence for SMAHC shells can increase the fundamental frequency of the structure by a considerable amount

The association among cytochrome P450 3A, progesterone receptor polymorphisms, plasma 17-alpha hydroxyprogesterone caproate concentrations, and spontaneous preterm birth

Background Infants born <37 weeks’ gestation are of public health concern since complications associated with preterm birth are the leading cause of mortality in children <5 years of age and a major cause of morbidity and lifelong disability. The administration of 17-alpha hydroxyprogesterone caproate reduces preterm birth by 33% in women with history of spontaneous preterm birth. We demonstrated previously that plasma concentrations of 17-alpha hydroxyprogesterone caproate vary widely among pregnant women and that women with 17-alpha hydroxyprogesterone caproate plasma concentrations in the lowest quartile had spontaneous preterm birth rates of 40% vs rates of 25% in those women with higher concentrations. Thus, plasma concentrations are an important factor in determining drug efficacy but the reason 17-alpha hydroxyprogesterone caproate plasma concentrations vary so much is unclear. Predominantly, 17-alpha hydroxyprogesterone caproate is metabolized by CYP3A4 and CYP3A5 enzymes. Objective We sought to: (1) determine the relation between 17-alpha hydroxyprogesterone caproate plasma concentrations and single nucleotide polymorphisms in CYP3A4 and CYP3A5; (2) test the association between progesterone receptor single nucleotide polymorphisms and spontaneous preterm birth; and (3) test whether the association between plasma concentrations of 17-alpha hydroxyprogesterone caproate and spontaneous preterm birth varied by progesterone receptor single nucleotide polymorphisms. Study Design In this secondary analysis, we evaluated genetic polymorphism in 268 pregnant women treated with 17-alpha hydroxyprogesterone caproate, who participated in a placebo-controlled trial to evaluate the benefit of omega-3 supplementation in women with history of spontaneous preterm birth. Trough plasma concentrations of 17-alpha hydroxyprogesterone caproate were measured between 25-28 weeks of gestation after a minimum of 5 injections of 17-alpha hydroxyprogesterone caproate. We extracted DNA from maternal blood samples and genotyped the samples using TaqMan (Applied Biosystems, Foster City, CA) single nucleotide polymorphism genotyping assays for the following single nucleotide polymorphisms: CYP3A4*1B, CYP3A4*1G, CYP3A4*22, and CYP3A5*3; and rs578029, rs471767, rs666553, rs503362, and rs500760 for progesterone receptor. We adjusted for prepregnancy body mass index, race, and treatment group in a multivariable analysis. Differences in the plasma concentrations of 17-alpha hydroxyprogesterone caproate by genotype were evaluated for each CYP single nucleotide polymorphism using general linear models. The association between progesterone receptor single nucleotide polymorphisms and frequency of spontaneous preterm birth was tested using logistic regression. A logistic model also tested interaction between 17-alpha hydroxyprogesterone caproate concentrations with each progesterone receptor single nucleotide polymorphism for the outcome of spontaneous preterm birth. Results The association between CYP single nucleotide polymorphisms *22, *1G, *1B, and *3 and trough plasma concentrations of 17-alpha hydroxyprogesterone caproate was not statistically significant (P =.68,.44,.08, and.44, respectively). In an adjusted logistic regression model, progesterone receptor single nucleotide polymorphisms rs578029, rs471767, rs666553, rs503362, and rs500760 were not associated with the frequency of spontaneous preterm birth (P =.29,.10,.76,.09, and.43, respectively). Low trough plasma concentrations of 17-alpha hydroxyprogesterone caproate were statistically associated with a higher frequency of spontaneous preterm birth (odds ratio, 0.78; 95% confidence ratio, 0.61–0.99; P =.04 for trend across quartiles), however no significant interaction with the progesterone receptor single nucleotide polymorphisms rs578029, rs471767, rs666553, rs503362, and rs500760 was observed (P =.13,.08,.10,.08, and.13, respectively). Conclusion The frequency of recurrent spontaneous preterm birth appears to be associated with trough 17-alpha hydroxyprogesterone caproate plasma concentrations. However, the wide variation in trough 17-alpha hydroxyprogesterone caproate plasma concentrations is not attributable to polymorphisms in CYP3A4 and CYP3A5 genes. Progesterone receptor polymorphisms do not predict efficacy of 17-alpha hydroxyprogesterone caproate. The limitations of this secondary analysis include that we had a relative small sample size (n = 268) and race was self-reported by the patients

Constitutive Behavior of Shape Memory Alloys: One dimensional thermomechanical derivation with non-constant material functions and redefined martensite internal variable.

A one-dimensional constitutive model for the thermomechanical behavior of shape memory alloys is developed based on previous work by Liang and Tanaka. An internal variable approach is used to derive a comprehensive constitutive law for shape memory alloy materials from first principles without the assumption of constant material functions. This constitutive law is of such a form that it is well suited to further practical engineering applications and calculations. A separation of the martensite fraction internal variable into temperature-induced and stress-induced parts is presented and justified which then allows the derived constitutive law to accurately represent both the pseudoelastic and shape memory effects at all temperatures. Several numerical examples are given which illustrate the ability of the constitutive law to capture the unique thermomechanical behavior of shape memory alloys due to their internal phase transformations with stress and temperature

Finite Element Analysis of the Behavior of Shape Memory Alloys and their Applications.

A nonlinear finite element procedure is developed which incorporates a thermodynamically derived constitutive law for shape memory alloy material behavior. The constitutive equations include the necessary internal variables to account for the material transformations and are utilized in a one-dimensional finite eelement procedure that captures the unique shape memory alloy responses of pseudoelasticity and of the shape memory effect at all temperatures, stress levels andloading conditions. Detailed material properties for the alloy used are necessary for the analysis. The solution of the geometrically andphysically nonlinear problem is achieved by application of a Newton'smethod in which a sequence of linear problems is numerically solved. Due to consistent linearization, a quadratic rate of convergence is obtained. Several test cases are presented to illustrate the potential of the finite element procedure. Cases simulating the stress-strain behavior of a bar of shape memory alloy under simple uniaxial loading as well as restrained recovery responses at different temperatures compare well with experimental and analytical results. Two further generalizued applications are examined: the use of a shape memory alloy ring as a pipe connector and eigenfrequency tuning of a composite beam with embedded shape memory wires. The results of these analyses correlate well with analytical results and the methodology for the incorporation of the finite element procedureinto general cases is demonstrated. The finite element procedure is thus shown to be a powerful tool for studying various applications ofshape memory alloys

Micro and macromechanical observation of polycrystalline NiTi using in situ optical microscopy

An experimental investigation of the micro and macromechanical transformation behavior of binary NiTi was undertaken using in situ optical microscopy. Special attention was paid to macroscopic banding, variant microstructure, effects of cyclic loading, strain rate and temperature effects. The experiments were accomplished with a custom-built loading stage designed to allow simultaneous loading and viewing of transformation behavior on the specimen surface with an optical microscope. The results show clearly that martensitic transformation occurs throughout the material at all strain levels. Macroscopic bands are regions of more intense transformation, but areas outside the bands are not martensite-free. The bands themselves are shown to contain regions (stripes) of higher and lower transformation, especially in the earlier transformation stages. Even at full transformation of the specimen, our results show that a polycrystalline NiTi material is far from being 100 % martensitic. Low level cyclic loading of the NiTi specimens was also pursued which revealed significant microstructural changes in the material after as few as 10 cycles. Furthermore was observed that the variants activated remained the same at the various strain rates tested, however at the highest strain rate possible in our set-up, small redistributions of martensitic plates within grains were seen after loading

Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning

Advancements in additive manufacturing (AM) technology and three-dimensional (3D) modeling software have enabled the fabrication of parts with combinations of properties that were impossible to achieve with traditional manufacturing techniques. Porous designs such as truss-based and sheet-based lattices have gained much attention in recent years due to their versatility. The multitude of lattice design possibilities, coupled with a growing list of available 3D printing materials, has provided a vast range of 3D printable structures that can be used to achieve desired performance. However, the process of computationally or experimentally evaluating many combinations of base material and lattice design for a given application is impractical. This research proposes a framework for quickly predicting key mechanical properties of 3D printed gyroid lattices using information about the base material and porosity of the structure. Experimental data was gathered to train a simple, interpretable, and accurate kernel ridge regression machine learning model. The performance of the model was then compared to numerical simulation data and demonstrated similar accuracy at a fraction of the computation time. Ultimately, the model development serves as an advancement in ML-driven mechanical property prediction that can be used to guide extension of current and future models