The Effects of Magnetization Saturation on Thermomagnetic Convection in a Locally Heated Square Enclosure

Steady-state simulation study of thermomagnetic convection is presented for a square cavity with localized heat-sources. The external magnetic field conforms to Maxwell's equations. Effects of magnetization saturation of the ferrofluid medium on heat transfer enhancement are studied by invoking Langevin's law. Thermal interactions between the heaters and the fluid at convection-dominated regimes are visualized through streamline, heatline, and isotherm plots. The variation of Nu_avg,heater is depicted for increasing dipole strength of the magnetic field sources. Fluid-magnetization contours explain the layout of the Kelvin force-field. The positions of the magnets for maximum value of Nu_avg,heater are determined. For the same heat generation rate, effects of enclosure dimensions on heat transfer augmentation are studied.     

Penetration depth in concrete for nondeformable missiles

The ability of the NDRC equations to estimate the penetration depth in concrete structures has been reevaluated using the presently available test results. This study confirms the industry notion that the NDRC equations overpredict the penetration depth. The NDRC equations, when used with a safety factor, can give a penetration depth about two standard deviations away from the mean value. In a probabilistic formulation the use of this penetration depth is over-conservative and unrealistic. The NDRC equation is updated here statistically using the presently available test results. This statistical NDRC equation significantly improves the predicted penetration depth. However, the form of the NDRC equation may not be ideal. A new relationship is proposed here to estimate the penetration depth by introducing a dimensionless impact factor. It has been shown here that the proposed relationship is better than the NDRC or even the statistical NDRC equations.     

Improved infrared photoluminescence characteristics from circularly ordered self-assembled Ge islands

The formation of circularly ordered Ge-islands on Si(001) has been achieved because of nonuniform strain field around the periphery of the holes patterned by focused ion beam in combination with a self-assembled growth using molecular beam epitaxy. The photoluminescence (PL) spectra obtained from patterned areas (i.e., ordered islands) show a significant signal enhancement, which sustained till 200 K, without any vertical stacking of islands. The origin of two activation energies in temperature-dependent PL spectra of the ordered islands has been explained in detail.     

Analysis of entropy generation due to natural convection in square enclosures with multiple discrete heat sources

Entropy generation due to natural convection in an enclosure heated locally from below with two isoflux sources was investigated. The flow and temperature fields were determined by numerical simulation of two-dimensional laminar conservation equations for mass, momentum and energy. For heaters of equal length and strength, the effects of Rayleigh number and heater position on flow and temperature fields and local entropy generation were examined. For heaters of unequal heater length and heater strength, the effects of heater length and strength ratios were investigated. Minimum entropy generation rate was achieved for the same condition at which the minimum peak heater temperature was obtained.     

Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Compressive strength is the most important metric of concrete quality. Various nondestructive and semi-destructive tests can be used to evaluate the compressive strength of concrete. In the present study, a new image-based machine learning method is used to predict concrete compressive strength, including evaluation of six different models. These include support-vector machine model and various deep convolutional neural network models, namely AlexNet, GoogleNet, VGG19, ResNet, and Inception-ResNet-V2. In the present investigation, cement mortar samples were prepared using each of the cement:sand ratios of 1:3, 1:4, and 1:5, and using the water:cement ratios of 0.35 and 0.55. Cement concrete was prepared using the cement:sand:coarse aggregate ratios of 1:5:10, 1:3:6, 1:2:4, 1:1.5:3 and 1:1:2, using the water:cement ratio of 0.5 for all samples. The samples were cut, and several images of the cut surfaces were captured at various zoom levels using a digital microscope. All samples were then tested destructively for compressive strength. The images and corresponding compressive strength were then used to train machine learning models to allow them to predict compressive strength based upon the image data. The Inception-ResNet-V2 models exhibited the best predictions of compressive strength among the models tested. Overall, the present findings validated the use of machine learning models as an efficient means of estimating cement mortar and concrete compressive strengths based on digital microscopic images, as an alternative nondestructive/semi-destructive test method that could be applied at relatively less expense.     

Fault Diagnosis of Dynamically Loaded Bearing with Localized Defect Based on Defect-Induced Excitation

Journal of Failure Analysis and Prevention - The defect-induced excitations causing the bearing to vibrate have significant contribution in vibration generated from rolling element bearings....     

Structural damage prognosis of three-dimensional large structural systems

A novel procedure for the health assessment of large three-dimensional (3D) structures with several significant attractive features and improved implementation potential is proposed. Structures are represented by 3D finite elements and a substructure concept is used so that acceleration time histories can be measured only at small part(s) of the structure. Just by measuring relatively few noise-contaminated responses in the substructure, the health of the whole structure can be assessed by the system identification (SI) concept by tracking the stiffness parameter of all the elements using a significantly improved unscented Kalman filter (UKF) algorithm. Since measuring excitation time histories can be very problematic and expensive, the UKF algorithm is integrated with 3D iterative least-squares with unknown input algorithm. UKF fails to identify large structures due to convergence-related issues. The authors used short duration responses and multiple global iterations with weight factor and objective function instead of one long duration response generally used in UKF. For the preselected excitation, short duration eliminates multiple sources of excitation beyond the control of inspector. The weight factor helps accurately locate the defect spot. With informative examples, it is documented that the proposed method is superior to various other forms of Kalman filter-based algorithms.     

Polymers from renewable resources. XII. Structure property relation in polyamideimides from rosin

Polyamideimides were synthesized from rosin maleic anhydride adduct either by reacting the acid chloride of the latter with different diamines or by self-polycondensation of rosin imido amino acid in the presence of thionyl chloride. The rosin imido amino acids were prepared by condensing rosin-maleic anhydride adduct with diamines. The presence of lithium chloride was found to have pronounced effect on yield and molecular weight of the polymers. The polyamideimides were characterized by infrared, elemental analysis, and viscosity measurements. The polymers were either amorphous or poorly crystalline in nature and soluble only in highly polar solvents. The thermal stability of the polymers has been studied. Some generalizations about structure-property relation in polyamideimides were made.     

Performance of a constructed wetland with a sulfur/limestone denitrification section for wastewater nitrogen removal

The effectiveness of a nonvegetated lab-scale subsurface flow constructed wetland for wastewater treatment had been evaluated with the feed ammonium concentration of approximately 20-40 mg of NH4(+)-N L(-1) and a hydraulic retention time of approximately 10 d. The present system had a nitrification zone plus a sulfur/limestone (S/L) autotrophic denitrification zone followed by an anaerobic polishing zone and was operated with and without aeration. The wetland had only 80% organics removal and no net nitrogen removal when there was no artificial aeration. However, almost 100% organics removal and approximately 81-90% total inorganic nitrogen (TIN = NH4(+)-N + NO2(-0-N + NO3(-)-N) removal were achieved when the oxic zone of the system was aerated with compressed air. S/L autotrophic denitrification contributed 21-49% of total NO3(-)-N removal across the whole wetland and 50-95% across the S/L column. TIN and NH4(+)-N in the effluent were always < 5.5 and < 0.7 mg L(-1), respectively, when the feed had NH4(+)-N < or = 35 mg L(-1). Sulfate removal of approximately 53-69% was achieved in the anaerobic polishing zone. The position of the S/L column was changed (1.78, 2.24, and 2.69 m from the inlet), and no remarkable difference in nitrogen removal was observed. However, without the S/L column, TIN removal decreased to approximately 74%, and the effluent NO3(-)-N increased about two times (9.13 mg of N L(-1)). The present study has demonstrated the possible use of S/L autotrophic denitrification for nitrate removal in a constructed wetland.