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This paper presents an idea of using carbonized electrospun Polyacrylonitrile (PAN) fibers as a sensor material in a structural health monitoring (SHM) system. The electrospun PAN fibers are lightweight, less costly and do not interfere with the functioning of infrastructure. This study deals with the fabrication of PAN-based nanofibers via electrospinning followed by stabilization and carbonization in order to remove all non-carbonaceous material and ensure pure carbon fibers as the resulting material. Electrochemical impedance spectroscopy was used to determine the ionic conductivity of PAN fibers. The X-ray diffraction study showed that the repeated peaks near 42° on the activated nanofiber film were α and β phases, respectively, with crystalline forms. Contact angle, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and Fourier transform infrared spectroscopy (FTIR) were also employed to examine the surface, thermal and chemical properties of the carbonized electrospun PAN fibers. The test results indicated that the carbonized PAN nanofibers have superior physical properties, which may be useful for structural health monitoring (SHM) applications in different industries.

The main purpose of the present paper is to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al2O3-MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS-PSO model had a better performance than the ANFIS-GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS-PSO model.

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A steady laminar flow over a vertical stretching sheet with the existence of viscous dissipation, heat source/sink, and magnetic fields has been numerically inspected through a shooting scheme based Runge—Kutta–Fehlberg-integration algorithm. The governing equation and boundary layer balance are expressed and then converted into a nonlinear normal system of differential equations using suitable transformations. The impact of the physical parameters on the dimensionless velocity, temperature, the local Nusselt, and skin friction coefficient are described. Results show good agreement with recent researches. Findings reveal that the Nusselt number at the sheet surface augments, since the Hartmann number, stretching velocity ratio A, and Hartmann number Ha increase. Nevertheless, it reduces with respect to the heat generation/absorption coefficient δ.

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Summary The recent attention in the applications of the fiber reinforced thermoplastic composite have raised some concerns because of the ability and performance of the fiber reinforced thermoplastic composite after long‐term exposures to environmental weathering (e.g., UV radiation, moisture, and oxygen). This weathering can be very destructive to the thermoplastic polymers; hence, a systematic study of the UV radiation effects on the properties of fiber‐reinforced thermoplastic composite is crucial for industrial applications. The major objective of the project was to study the correlation between thermoplastic fabrication parameters and the final properties of composites. In this study, carbon fiber (CF) reinforced polyphenylene sulfide (PPS) thermoplastic composites were manufactured using high temperature press after optimizing the parameters. Subsequently, the effects of long‐term UV exposures on the thermal and mechanical properties of CF/PPS thermoplastic composites were investigated in detail. The test results showed that the correlation between the processing parameters and the physical properties of the laminate composites were in all good agreements. The test results also revealed a significant decrease in the glass transition temperature, as well as storage modulus, and tensile strengths. Furthermore, short (200 hrs) and long (300 hrs) term UV exposures changed various thermal and mechanical properties of the thermoplastic PPS composites. This study can provide some preliminary knowledge to engineers and scientists in the field and develop new set of structural composites.

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