On the interaction of normal and shear stresses in multiaxial fatigue damage

One of the important issues in assessing multiaxial fatigue damage is interactions between different components of stress such as normal and shear stresses. The present study investigated this interaction effect on the fatigue behavior of materials with shear failure mode when subjected to multiaxial loading conditions. A method is introduced to model this interaction based on the idea that two types of influence are caused by the normal stress acting on the critical plane orientation. These two types of influence are affecting roughness induced closure, as well as fluctuating normal stress which affects the growth of small cracks in mode II. Shear‐based critical plane damage models which use normal stress as a secondary input, such as FS damage model, could then use the summation of these terms. In order to investigate the effect of the method, constant amplitude load paths with different levels of interaction between the normal and shear stresses, as well as variable amplitude tests with histories both taken from service loading conditions and generated using random numbers were designed for an experimental program. The proposed method was observed to result in improved fatigue life estimations where significant interactions between normal and shear stresses exist.     

Parameter Estimation Under Failure-Censored Constant-Stress Life Testing Model: A Bayesian Approach

Abstract

This article compares likelihood and Bayesian estimations for partially accelerated constant-stress life test model under type II censoring assuming Pareto distribution of the second kind. Both maximum likelihood and Bayesian estimators of the model parameters are derived. The posterior means and posterior variances are obtained under the squared error loss function using Lindley's approximation procedure. The advantages of this proposed procedure are shown. Monte Carlo simulations are conducted under different samples sizes and different parameter values to assess and compare the proposed methods of estimation. A noninformative prior on the model parameters is used to make the comparison more meaningful. It has been observed that Lindley's method usually provides posterior variances and mean squared errors smaller than those of the maximum likelihood estimators. That is, Lindley's method produces improved estimates, which is an advantage of this method.     

Nonsmooth model for dynamic simulation of phase changes

Dynamic modeling of processes involving phase changes can be challenging due to changes in the model equations caused by appearance and disappearance of equilibrium phases. Dynamic simulation of these processes requires the ability to detect the change in the number of phases and adapt the model to the new phase regime on the fly. In this work, an easy‐to‐use nonsmooth model for dynamic simulation of processes with vapor‐liquid equilibrium is presented. The presented model does not introduce any auxiliary variables or equations, nor does it require solution of an optimization problem to determine the new phase regime during the dynamic simulation. It can therefore be used for comprehensive simulation of, e.g., distillation columns, where the number of phases present can change during startup and shutdown. The nonsmooth model is illustrated through examples of an evaporator and a distillation column. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3334–3351, 2016     

Phthalocyanine‐Based 2D Conjugated Metal‐Organic Framework Nanosheets for High‐Performance Micro‐Supercapacitors

2D conjugated metal‐organic frameworks (2D c‐MOFs) are emerging as a novel class of conductive redox‐active materials for electrochemical energy storage. However, developing 2D c‐MOFs as flexible thin‐film electrodes have been largely limited, due to the lack of capability of solution‐processing and integration into nanodevices arising from the rigid powder samples by solvothermal synthesis. Here, the synthesis of phthalocyanine‐based 2D c‐MOF (Ni₂[CuPc(NH)₈]) nanosheets through ball milling mechanical exfoliation method are reported. The nanosheets feature with average lateral size of ≈160 nm and mean thickness of ≈7 nm (≈10 layers), and exhibit high crystallinity and chemical stability as well as a p‐type semiconducting behavior with mobility of ≈1.5 cm² V^?1 s^?1 at room temperature. Benefiting from the ultrathin feature, the nanosheets allow high utilization of active sites and facile solution‐processability. Thus, micro‐supercapacitor (MSC) devices are fabricated mixing Ni₂[CuPc(NH)₈] nanosheets with exfoliated graphene, which display outstanding cycling stability and a high areal capacitance up to 18.9 mF cm^?2; the performance surpasses most of the reported conducting polymers‐based and 2D materials‐based MSCs.     

Interfacial activity and micellar morphology of an imidazolium ring containing zwitterionic surfactants

Zwitterionic surfactants based on 3-(1-alkyl-3-imidazolio) propane-sulfonate ([ImS3-R] where R is octyl or dodecyl) is an emerging and important class of amphiphile due to their relevance as nano reactors for the synthesis of metallic nanoparticles and accelerated acid hydrolysis. The physicochemical properties of such synthesized imidazolium ring-containing zwitterionic surfactants have been characterized by surface tension and small-angle neutron scattering (SANS) techniques. Surface tension measurements were used to calculate several thermodynamic parameters over a range of concentrations and temperatures (298–313 K). The results obtained showed a weak signature representing the critical micelle concentration (CMC) for ImS3-8, however, by increasing the alkyl length of the hydrophobic group to dodecyl, that is, ImS3-8 to ImS3-12, the signature of the CMC was much more evident. As expected, the CMC for ImS3-12 shifted to a lower concentration. An increase in temperature increased the surface activity and decreased the CMC of both zwitterionic surfactants, although the changes were small. Compared to classical surfactants, that is, sodium dodecyl sulfate and dodecyl trimethylammonium bromide, the CMC of ImS3-12 is much lower. Modeling of SANS data demonstrated that the morphology of the micelles formed by these amphiphiles may be described by the “classical” model, a central hydrophobic core, with a shell of hydrated headgroups. Due to their widespread applications in colloidal and interfacial science, the present study adds new insight to the fundamental understanding of these interesting imidazolium-based surface-active ionic liquids (ImS3-R).     

Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach

In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and partial least squares (PLS) approaches are applied to predictive control of a drying process. In the proposed approaches, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modeling with the aim at predicting the granule particle size and executing by ANFIS and ANN. ANN holds the promise of being capable of producing non-linear models, being able to work under noise conditions, and being fault tolerant to the loss of neurons or connections. Also, the ANFIS approach combines the advantages of fuzzy system and artificial neural network to design architecture and is capable of dealing with both limitation and complexity in the data set. The efficiencies of ANFIS and ANN approaches in prediction are compared and the superior approach is selected. Finally, by deploying the preferred approach, several scenarios are presented to be used in predictive control of spray drying as an accurate, fast running, and inexpensive tool. This is the first study that presents a flexible intelligent approach for predictive control of drying process by ANN, ANFIS, and PLS. The approach of this study may be easily applied to other production process.     

Synthesis of artificial neural networks by transconductors only

Hardware implementation of artificial neural networks has been attracting great attention recently. In this work, the analog VLSI implementation of artificial neural networks by using only transconductors is presented. The signal flow graph approach is used in synthesis. The neural flow graph is defined. Synthesis of various neural network configurations by means of neural flow graph is described. The approach presented in this work is technology independent. This approach can be applied to new neural network topologies to be proposed or used with transconductors designed in future technologies.     

Resonance condition of a microfiber knot resonator immersed in liquids

Effects of immersing a microfiber knot resonator (MKR) in liquid solutions that have refractive indices close to that of silica are experimentally demonstrated and theoretically analyzed. Significant improvement in resonance extinction ratio within 2 to 10 dB was observed. To achieve a better understanding, a qualitative analysis of the coupling ratio and round-trip attenuation of the MKR is performed by using a curve-fitting method. It was observed that the coupling coefficient at the knot region increased when immersed in liquids. However, depending on the initial state of the coupling and the quantity of the increment in the coupling coefficient when immersed in a liquid, it is possible that the MKR may experience a deficit in the coupling parameter due to the sinusoidal relationship with the coupling coefficient.     

Neuro-fuzzy control strategy for an offshore steel jacket platform subjected to wave-induced forces using magnetorheological dampers

Magnetorheological (MR) damper is a prominent semi-active control device to vibrate mitigation of structures. Due to the inherent non-linear nature of MR damper, an intelligent non-linear neuro-fuzzy control strategy is designed to control wave-induced vibration of an offshore steel jacket platform equipped with MR dampers. In the proposed control system, a dynamic-feedback neural network is adapted to model non-linear dynamic system, and the fuzzy logic controller is used to determine the control forces of MR dampers. By use of two feedforward neural networks required voltages and actual MR damper forces are obtained, in which the first neural network and the second one acts as the inverse dynamics model, and the forward dynamics model of the MR dampers, respectively. The most important characteristic of the proposed intelligent control strategy is its inherent robustness and its ability to handle the non-linear behavior of the system. Besides, no mathematical model needed to calculate forces produced by MR dampers. According to linearized Morison equation, wave-induced forces are determined. The performance of the proposed neuro-fuzzy control system is compared with that of a traditional semi-active control strategy, i.e., clipped optimal control system with LQG-target controller, through computer simulations, while the uncontrolled system response is used as the baseline. It is demonstrated that the design of proposed control system framework is more effective than that of the clipped optimal control scheme with LQG-target controller to reduce the vibration of offshore structure. Furthermore, the control strategy is very important for semi-active control.