Nanofluids thermal conductivity prediction applying a novel hybrid data-driven model validated using Monte Carlo-based sensitivity analysis

Accurate estimation of the thermal conductivity of nanofluids plays a key role in industrial heat transfer applications. Currently available experimental and empirical relationships can be used to estimate thermal conductivity. However, since the environmental conditions and properties of the nanofluids constituents are not considered these models cannot provide the expected accuracy and reliability for researchers. In this research, a robust hybrid artificial intelligence model was developed to accurately predict wide variety of relative thermal conductivity of nanofluids. In the new approach, the improved simulated annealing (ISA) was used to optimize the parameters of the least-squares support vector machine (LSSVM-ISA). The predictive model was developed using a data bank, consist of 1800 experimental data points for nanofluids from 32 references. The volume fraction, average size and thermal conductivity of nanoparticles, temperature and thermal conductivity of base fluid were selected as influent parameters and relative thermal conductivity was chosen as the output variable. In addition, the obtained results from the LSSVM-ISA were compared with the results of the radial basis function neural network (RBF-NN), K-nearest neighbors (KNN), and various existing experimental correlations models. The statistical analysis shows that the performance of the proposed hybrid predictor model for testing stage (R = 0.993, RMSE = 0.0207) is more reliable and efficient than those of the RBF-NN (R = 0.970, RMSE = 0.0416 W/m K), KNN (R = 0.931, RMSE = 0.068 W/m K) and all of the existing empirical correlations for estimating thermal conductivity of wide variety types of nanofluids. Finally, robustness and convergence analysis were conducted to evaluate the model reliability. A comprehensive sensitivity analysis using Monte Carlo simulation was carried out to identify the most significant variables of the developed models affecting the thermal conductivity predictions of nanofluids.

     

Enhanced decision tree ensembles for land-cover mapping from fully polarimetric SAR data

Fully polarimetric synthetic aperture radar (PolSAR) Earth Observations showed great potential for mapping and monitoring agro-environmental systems. Numerous polarimetric features can be extracted from these complex observations which may lead to improve accuracy of land-cover classification and object characterization. This article employed two well-known decision tree ensembles, i.e. bagged tree (BT) and random forest (RF), for land-cover mapping from PolSAR imagery. Moreover, two fast modified decision tree ensembles were proposed in this article, namely balanced filter-based forest (BFF) and cost-sensitive filter-based forest (CFF). These algorithms, designed based on the idea of RF, use a fast filter feature selection algorithms and two extended majority voting. They are also able to embed some solutions of imbalanced data problem into their structures. Three different PolSAR datasets, with imbalanced data, were used for evaluating efficiency of the proposed algorithms. The results indicated that all the tree ensembles have higher efficiency and reliability than the individual DT. Moreover, both proposed tree ensembles obtained higher mean overall accuracy (0.5–14% higher), producer’s accuracy (0.5–10% higher), and user’s accuracy (0.5–9% higher) than the classical tree ensembles, i.e. BT and RF. They were also much faster (e.g. 2–10 times) and more stable than their competitors for classification of these three datasets. In addition, unlike BT and RF, which obtained higher accuracy in large ensembles (i.e. the high number of DT), BFF and CFF can also be more efficient and reliable in smaller ensembles. Furthermore, the extended majority voting techniques could outperform the classical majority voting for decision fusion.     

Nonlinear size-dependent pull-in instability and stress analysis of thin plate actuator based on enhanced continuum theories including nonlinear effects and surface energy

Microsystem Technologies - In recent years, size dependent continuum theories have been commonly used to simulate material discontinuities in micro/nano-scales. In the present article, modified...     

Effect of residual stress on the fracture of chemically strengthened thin aluminosilicate glass

The effect of residual stress on the fracture of chemically strengthened thin aluminosilicate glass was investigated. The large deflection problem on the flexure of thin chemically strengthened glass was solved through finite element analysis. The relationship among compressive stress (CS), central tension (CT), and the modulus of rupture of chemically strengthened thin glass was also discussed. High CS and low CT improved the flexural strength of chemically strengthened glass. However, the effect of residual stress was more complex on Weibull modulus than on strength. The effect of residual stress on the fractography of chemically strengthened thin glass was analyzed. Transparent and opaque zones were observed on the fracture surface of chemically strengthened glass. The relative thickness of the opaque zone (d _Opaque/d ₀), which is a constant in the same fracture zone, linearly decreased with increasing fracture zone. This result indicates that the stored elastic strain energy was released with the number of transverse cracks. These results provide useful information on the failure analysis of chemically strengthened thin glass.     

An improved sum‐of‐squares based approach to fuzzy tracking control design of nonlinear systems

In this paper, using a more general Lyapunov function, less conservative sum‐of‐squares (SOS) stability conditions for polynomial‐fuzzy‐model‐based tracking control systems are derived. In tracking control problems the objective is to drive the system states of a nonlinear plant to follow the system states of a given reference model. A state feedback polynomial fuzzy controller is employed to achieve this goal. The tracking control design is formulated as an SOS optimization problem. Here, unlike previous SOS‐based tracking control approaches, a full‐state‐dependent Lyapunov matrix is used, which reduces the conservatism of the stability criteria. Furthermore, the SOS conditions are derived to guarantee the system stability subject to a given H_∞ performance. The proposed method is applied to the pitch‐axis autopilot design problem of a high‐agile tail‐controlled pursuit and another numerical example to demonstrate the effectiveness and benefits of the proposed method.     

A study of the relation of mobile device attributes with the user-perceived quality of Android apps

The number of mobile applications (apps) and mobile devices has increased considerably over the past few years. Online app markets, such as the Google Play Store, use a star-rating mechanism to quantify the user-perceived quality of mobile apps. Users may rate apps on a five point (star) scale where a five star-rating is the highest rating. Having considered the importance of a high star-rating to the success of an app, recent studies continue to explore the relationship between the app attributes, such as User Interface (UI) complexity, and the user-perceived quality. However, the user-perceived quality reflects the users’ experience using an app on a particular mobile device. Hence, the user-perceived quality of an app is not solely determined by app attributes. In this paper, we study the relation of both device attributes and app attributes with the user-perceived quality of Android apps from the Google Play Store. We study 20 device attributes, such as the CPU and the display size, and 13 app attributes, such as code size and UI complexity. Our study is based on data from 30 types of Android mobile devices and 280 Android apps. We use linear mixed effect models to identify the device attributes and app attributes with the strongest relationship with the user-perceived quality. We find that the code size has the strongest relationship with the user-perceived quality. However, some device attributes, such as the CPU, have stronger relationships with the user-perceived quality than some app attributes, such as the number of UI inputs and outputs of an app. Our work helps both device manufacturers and app developers. Manufacturers can focus on the attributes that have significant relationships with the user-perceived quality. Moreover, app developers should be careful about the devices for which they make their apps available because the device attributes have a strong relationship with the ratings that users give to apps.     

Wave propagation in double-walled carbon nanotube conveying fluid considering slip boundary condition and shell model based on nonlocal strain gradient theory

In this paper, wave propagation in fluid-conveying double-walled carbon nanotube (DWCNT) was investigated by using the nonlocal strain gradient theory. In so doing, the shear deformable shell theory was used, taking into consideration nonlocal and material length scale parameters. The effect of van der Waals force between the two intended walls and the DWCNT surroundings was modeled as Winkler foundation. The classical governing equations were derived from Hamilton’s principle. Results were validated by comparing them to the results of the references obtained through molecular dynamic method, and a remarkable consistency was found between the results. According to the findings, the effects of nonlocal and material length scale parameters, wave number, fluid velocity and stiffness of elastic foundation are more considerable in the nonlocal strain gradient theory than in classical theory.     

Micropatterning of sandwiched FeCuNbSiB/Cu/FeCuNbSiB for the realization of magneto-impedance microsensors

This work presents the fabrication of magnetic field microsensors based on the magneto-impedance phenomenon and dedicated to NDC applications. The multilayer structure, ferromagnetic/conductive/ferromagnetic, is composed of a copper layer sandwiched with two Finemet^? alloy films. The later, initially an amorphous material, is nanocrystallized by heat treatment. The fabrication process has been optimized in order to minimize coercivity and induce transversal anisotropy. The technological defects induced by the lift-off and sputtering processes change the magneto-impedance properties of the sensors. Eliminating these defects permits the sensor to reach to a sensitivity of 1,200 V/T/A at 30 MHz with a bias field larger than the anisotropy field and without hysteresis. The angular dependence of the sensitivity shows that the sensor is only sensitive to the axial component of the magnetic field.     

Reflected shock tube experiments on aeroacoustic signature of hot jets

Journal of Mechanical Science and Technology - We used a reflected shock tube to investigate the acoustic signature of a hot jet at the far-field. Experiments were performed at Mach = 1.4 and a...