Skidding is a primary cause of rolling element bearing (REB) failure, which is influenced by operating conditions, lubricant properties, and bearing design. This paper presents an improved nonlinear dynamic model of REBs to analyze the impact of lubricant characteristics on REB skidding at different speed states. The model considers time-varying traction coefficient, cage flexibility, and cage pocket clearance. The selected tested lubricants include lithium grease (LGT2), compound calcium sulfonate (CCS) grease, and 4109 Chinese aviation oil. Simulation results show that CCS grease effectively reduces cage skidding, with the lowest load limits (300 N at 2000 RPM) under constant speed conditions and the highest stable limit (2932 RPM) during acceleration. LGT2 grease exhibits minimal sensitivity to acceleration and fluctuation speed parameters change.

The rolling element bearing is a fundamental component of any rotating machinery. During operation, wear debris and lubricant impurities create dents and bumps on the bearing raceway surfaces. Such localized defects produce transient vibration impulses at one of the bearing characteristic frequencies. Having a combination of multiple types of point defects on the raceway results in superimposed vibration patterns, which reduce the ability to recognize these defects’ effects. In this paper, a 6-DOF dynamic model is developed to accurately investigate the vibration characteristic of a ball bearing with a multipoint defect comprising a dent and bump on its raceway surface. The model considers the effects of time-varying contact force produced due to defects, lubricant film damping, bearing preload, and the inertia effect of rolling elements. The simulation results reveal the vibration behavior of multipoint defect bearings. In addition, bearing vibration response is affected by the number of defects, the angle between them, and the type and size of each defect. Furthermore, it is challenging to predict bearing defects parameters such as the numbers, types, sizes, and angles between adjacent defects from acceleration signal analysis without jerk signal analysis. The validation of the model is proved using signals from the Case Western University test setup.

In vitro modeling of the left heart relies on accurately replicating the physiological conditions of the native heart. The targeted physiological conditions include the complex fluid dynamics coming along with the opening and closing of the aortic and mitral valves. As the mitral valve possess a highly sophisticated apparatus, thence, accurately modeling it remained a missing piece in the perfect heart duplicator puzzle. In this study, we explore using a hydrogel-based mitral valve that offers a full representation of the mitral valve apparatus. The valve is tested using a custom-made mock circulatory loop to replicate the left heart. The flow analysis includes performing particle image velocimetry measurements in both left atrium and ventricle. The results showed the ability of the new mitral valve to replicate the real interventricular and atrial flow patterns during the whole cardiac cycle. Moreover, the investigated valve has a ventricular vortex formation time of 5.2, while the peak e- and a-wave ventricular velocities was 0.9 m/s and 0.4 m/s respectively.

In this study, the data analysis technique of proper orthogonal decomposition (POD) is applied to the numerical simulation solutions of two-dimensional unsteady cellular detonation. As a first stage to introduce the idea, the analysis is performed on the simulation results obtained numerically with the reactive Euler equations with a one-step Arrhenius kinetic model. Cases with different activation energies Ea are considered, yielding different degrees of cellular instability of the detonation frontal structure. The POD modes are obtained by performing a singular value decomposition (SVD) of the full ensemble matrix whose columns are the snapshots of time-dependent pressure fields from the stored numerical solutions. The dominant spatial flow features behind the detonation front with varying Ea are revealed by the resulting POD modes that represent flow structures with decreasing flow energy content. The accuracy of the pressure flow field reconstructed using different levels of POD basis modes for reduced-order modeling is demonstrated. The coherent structures and increasing complexity of the flow fields with higher Ea are elucidated with the use of Lagrangian descriptors (LD). The potential of the methods described in this work is discussed.

In recent years, the use of waste plastic materials such as low-density polyethylene (LDPE) to modify asphalt binders and enhance mixture performance has garnered significant attention. One major concern with using such materials is the higher production temperature required, which necessitates the use of a bitumen extender agent, such as waste engine oil (WEO), to reduce the viscosity and mixing temperature. Therefore, using these stabilizing additives can reduce the consumption of virgin binder, especially for Stone Matrix Asphalt (SMA) mixtures, which require higher asphalt content. This study aimed to optimize the design of SMA modified by LDPE and WEO to minimize the optimum asphalt content (OAC) and mixing temperature while maximizing SMA performance. To achieve this, Response Surface Methodology (RSM) was utilized to develop the necessary models for optimizing design and predicting performance. The selected independent variables (factors) for the design include LDPE content, WEO content, OAC, and mixing temperature. Meanwhile, the responses (dependent variables) consist of Marshall stability, rut depth, tensile strength ratio, and resilient modulus, which were used to determine the best mix design. The findings demonstrated that the suggested models for the output variables can predict performance with a higher level of confidence. The Analysis of variance (ANOVA) demonstrated that predictive models were significant and well-fitted, with a coefficient of determination (R²) of higher than 0.80, an adequate precision value of greater than 4, and a low p-value (less than 0.05). The error percentage between the RSM-predicted and actual values was less than 5 %, indicating that RSM-established models can accurately and efficiently predict the SMA performance. The best mix design of the SMA mixture modified by LDPE and WEO was found to be 10 % LDPE, 3.91 % WEO, 5.92 % OAC, and mixing temperature of 152.24°C with a combined desirability of 82.8 %.

Recycled aggregate concrete (RAC) is commonly used to lessen the environmental effect of concrete building and demolition waste. The compressive strength of the RAC is one of the most critical factors influencing concrete quality. The compressive strength is assessed by a compression test, which takes a large number of materials and is expensive and time-consuming. With the development of novel concrete mixes and applications, academics are obliged to seek accurate models for forecasting mechanical strength. A significant source of difficulty in compressive strength modeling is that there are many mixture components and testing conditions whose variation significantly influences the predicted values. To this end, this study explores the mixture design of sustainable concrete in order to generate eco-friendly concrete mixes. Tests are conducted on 18 different mixtures comprising different proportions of waste tires, plastic, cement, and red brick to experiment with new green RAC mixtures. For the modeling part, the deep residual neural networks (DRNNs) method is first presented to the problem, aided by a database from the literature for a pretraining task. The proposed DRNNs structure uses shortcuts (i.e., residual connections) that bypass some layers in the deep network structure to alleviate the problem of training with high accuracy. The performance of the proposed DRNNs is evaluated using different goodness of fit measures and compared with well-known machine learning tools. The findings showed that the suggested model could provide credible predictions about the desired mechanical parameter, saving the required lab efforts by 40 %. Finally, a variance-based global sensitivity analysis is performed with the Latin hypercube simulation method to help rank/prioritize each mixture component's impact on determining the compressive strength in practice while mitigating the potential misrepresentation of results due to the correlations between the input parameters. The analysis showed that cement and waste contents are the most significant ones in their first and total order effects.

Pumping water for irrigation systems using renewable energy is one of the broad and viable uses. The use of renewable energy for pumping water is a perfect solution in places far from the electrical grids because the cost of connecting an electric pump to the utility network becomes expensive and sometimes difficult. Photovoltaic systems are the most common and abundant type of renewable energy used to overcome this challenge. Therefore, this paper presents a methodology for improvement the size of the PV system and calculating all the elements of this system from the PV water pumping system, water quantity needed for irrigation, the sort of plant, the microclimate, the soil, and the irrigation method to obtain the optimum electric power needed to pump the water. In the proposed work, the Farafra Oasis was chosen as a case study, an Egyptian oasis located in the Western Desert within the borders of the New …

¹ The potential to integrate multicarrier energy systems that fulfill the rapidly increasing energy demand is being made possible by combining renewable energy sources (RES) encompassing wind and solar into massive-scale fossil fuel power generating stations. This article provides a thorough overview of the energy hub while highlighting its benefits, such as optimizing energy consumption and reducing greenhouse gas emissions. Energy hub systems are considered the future trendsetter for energy systems. A number of their merits over conventional energy systems are reported in this article. Solar farms, wind turbines, boilers, power-to-gas (P2G) units, fossil-fueled combined cycle power plants (CCPPs), and electric and thermal storing units are components of an energy hub that generates and transforms energy. Levelized cost of energy (LCOE), Levelized C02 emission (LC02), and Levelized Cost of …