Modelling and Simulation in Engineering

Proton exchange membrane fuel cell (PEMFC) has a promising future in the power generation and transportation fields. Recirculation of unused anodic gases is fundamental to achieve a high-performance energy system, and this is usually attained employing ejectors or pumps. With respect to the latter, ejectors present no moving parts, thus resulting in both higher overall efficiency of the system and lower maintenance cost. Their main drawback is represented by the narrow optimal operative range: the entrainment ratio (ER) greatly depends on primary pressure, working pressure, and operative condition in general. In the last decade, numerous authors focused their efforts on fully comprehending and correctly simulating their working principles and analyzing how geometrical parameters influence ER and design different geometries to enlarge the operative range. The aim of this paper is to present in an ordered and clear manner the state of the art of ejector design, both from simulative (turbulence model, single or multiphase stream, etc.) and empirical (commonly used “rule of thumb”) points of view.

In this study, we introduce an innovative model for evaluating the impact of environmental factors on drone-to-ground control station datalink communications. Our approach integrates both deterministic and stochastic processes to account for small-scale and large-scale fading effects, encompassing propagation attenuation, the Rician fading model, and Gaussian noise to accurately reflect real-world conditions. The model is implemented on signals transmitted using spread spectrum modulation. Through a comparative analysis of the model’s predictions against actual signals received in three distinct environments, the model’s efficacy in diverse scenarios is affirmed. Error metrics obtained from Monte Carlo simulations are employed to validate the theoretical results against experimental data. The proposed approach is pivotal for predicting the transmission range and understanding the electromagnetic susceptibility of the datalink, offering a substantial contribution to the optimization of remote drone control.

In this paper, finite element simulation of asymmetric rolling in relief rolls of C11000 copper alloy in order to analyze the effectiveness of possible roll profiles and technological schemes of deformation was performed. The scientific innovation of this work lies in determining the patterns of development of stress-strain state parameters for various configurations of rolls, as well as determining the effectiveness of metal processing using various technological schemes. It was found that the use of trapezoidal relief makes it possible to increase the level of metal processing by almost 5 times compared with the use of radial relief. Comparison of technological schemes of deformation showed that deformation with 180° workpiece turning between passes significantly reduces the influence of the asymmetry factor. Deformation without changing the workpiece position between the passes has the opposite effect, and such a scheme significantly increases the influence of the asymmetry factor. Deformation with a transverse workpiece shift for the relief period between passes has the effect of a “golden mean.” The conducted laboratory experiment for lead billet showed that the shape change of lead billet during computer simulation has a high level of convergence with real conditions. At each stage of deformation, the difference in the geometric parameters of the workpiece between the model and the experiment did not exceed 3-5%. When deforming a copper billet, the maximum difference level was 8%, which is the result of the low rigidity of the rolling cage with smooth rolls.

Failure to control crisis conditions leads to irreparable damage to many supply chains around the world, including blood supply chains (BSCs) as critical networks in the health system. Consequently, it significantly reduces the supply of blood and its products, as vital materials, and exerts detrimental effects on the activities of blood organizations and facilities as well as the health of individuals in society. In the present study, the proposed model seeks to simultaneously minimize the operating costs and the shortage of blood products with the aim of improving the attractiveness of blood centers during the COVID-19 pandemic. Accordingly, by optimizing the overall cost and the attractiveness of blood donation centers, an attractive efficient environment is provided. It can help to remove barriers to blood donation and improve blood health. To this end, the model takes certain strategies into account for the proper establishment of new local blood collection centers (BCCs) and mobile BCCs. It also arranges suitable transportation vehicles for the efficient transfer of blood products to the provincial centers of the candidate country and sets various incentive policies for blood donation. In order to minimize the costs of the entire supply chain network and maximize the attractiveness of the BCCs, a two-objective mathematical model is developed. It produces Pareto solutions using the -constraint method. Finally, the efficiency of the proposed approach and the sensitivity of the corresponding parameters are analyzed through a practical case study. The obtained results represent that a growth in the attractiveness of blood centers induces a raise in the number of donors, and, consequently, the amount of the donated blood grows. This depends on more investment at all levels of the supply chain, including collection, production, storage, and transportation. Moreover, the performance and attractiveness of a BSC can be enhanced significantly if the number of collection centers and the amount of blood sent from the receiving centers to the demand nodes are increased.

The flow regime is essential in the photoreactor’s performance in pollutant degradation in the aqueous medium, especially in fluidized systems. Therefore, this study is focused on determining the fluidization conditions of a granular catalyst based on TiO₂-CuO nanoparticles (1 wt.% CuO) immobilized on beach sand granules using an FBP photoreactor. COMSOL Multiphysics 6.0 was employed for inlet velocities between 0.1 m/s and 1.0 m/s, mainly from the Reynolds averaged Navier–Stokes (RANS) turbulence model and the Stokes drag law. The results indicated that the average velocities in the annular section are much higher ( and ) than the required particle terminal velocity. Moreover, the pressure contour lines revealed that these flow velocities do not represent excessive pressures in the concentric cylinders, with maximum gauge pressures of 740.52 Pa and 1310 Pa for inlet velocities and 1.0 m/s, respectively. Finally, it was determined that the Reynolds number adjusted () values lower than or equal to allow high fluidization after 2 seconds. This information makes it possible to adapt and assemble the FBP equipment for future photocatalytic evaluation.

This study provides insights into the challenges involved in predicting the Reid vapor pressure (RVP) of gasoline-oxygenate blends (GOB), which is an important indicator of fuel quality and compliance with environmental and performance standards. Given the enormous variety of gasoline compositions and ratios available, there is a significant demand for a fast, straightforward, and cost-effective technique to predict RVP without relying on costly instruments or complicated spectral measurements that involve numerous input variables. A comparative performance analysis has been performed for different regression modelling strategies for predicting RVP in GOB, which is valuable for researchers and practitioners in the petroleum industry for saving time and money. Parametric and nonparametric approaches were compared using partial least squares regression (PLSR), nonlinear regression (NLR), and nonparametric regression (NPR) models. Locally weighted scatterplot smoothing (LOWESS) approach was applied to the NPR model. The gasoline’s physical characteristics (distillation curves and density) formed the basis for the analysis of these models’ performances. Acceptable error metrics have been reached for root mean square error of calibration and prediction (RMSEC and RMSEP) values, for the PLSR, NLR, and NPR models, which are 4.790, 6.235, 4.739, 6.149, 3.968, and 6.029, respectively, which are close for those reported in literature. The NPR model eliminates parametric constraints and allows for a different kind of data structure to emerge. The established models here demonstrate a sound ability to overcome barriers by omitting the use of inconvenient spectral measurements to save expense and simplify data calibration, making them a promising approach for RVP detection of GOB. This finding aids in the development of more accurate RVP prediction models and contributes to the optimization of fuel formulations.