Impact of Lake Michigan water level rise on complex bidirectional flow in the Chicago Area Waterway System (CAWS)

In the past decade, continuously rising water levels in Lake Michigan have been threatening lakefront areas, especially in metropolitan regions like the Greater Chicago area. This provides the motivation to analyze the impact that high lake levels have on the Chicago Area Waterway System (CAWS). As the only primary free connection between the CAWS and Lake Michigan, the Calumet Area waterway subsystem plays a key and unique role. In this work, a numerical model covering the Calumet subsystem and having Lake Michigan as a boundary condition, is set up, calibrated, and validated using limited field observations. It is found that the Calumet subsystem has become bidirectional, where both discharge and flow directions are controlled by lake levels. When lake levels are below −0.15 m (-0.5 ft, Chicago City Datum, CCD), the discharge in the Grand Calumet River is around zero, with water flowing along its east branch towards Indiana. When lake levels are above +0.46 m (+1.5 ft, CCD), the flow reverses direction and drains west into Illinois. In 2020, the mean lake-level was at +1.07 m (+3.5 ft, CCD), and the base discharge in the Grand Calumet River was approximately 8.5 m³/s (300 ft³/s). The higher Lake Michigan’s level is, the larger the discharge would be into Illinois. Potential impact of this extra discharge on Lake Michigan Diversion Accounting (LMDA) of the State of Illinois and flood management in the Chicago Sanitary and Ship Canal (CSSC), is analyzed; while the nature of the bidirectional flows is characterized with the intent of shedding light on this complex phenomenon.     

Thermodynamic modeling of hydrogen refueling for heavy-duty fuel cell buses and comparison with aggregated real data

The foreseen uptake of hydrogen mobility is a fundamental step towards the decarbonization of the transport sector. Under such premises, both refueling infrastructure and vehicles should be deployed together with improved refueling protocols. Several studies focus on refueling the light-duty vehicles with 10 kg_H2 up to 700 bar, however less known effort is reported for refueling heavy-duty vehicles with 30–40 kg_H2 at 350 bar. The present study illustrates the application of a lumped model to a fuel cell bus tank-to-tank refueling event, tailored upon the real data acquired in the 3Emotion Project. The evolution of the main refueling quantities, such as pressure, temperature, and mass flow, are predicted dynamically throughout the refueling process, as a function of the operating parameters, within the safety limits imposed by SAE J2601/2 technical standard. The results show to refuel the vehicle tank from half to full capacity with an Average Pressure Ramp Rate (APRR) equal to 0.03 MPa/s are needed about 10 min. Furthermore, it is found that the effect of varying the initial vehicle tank pressure is more significant than changing the ambient temperature on the refueling performances. In conclusion, the analysis of the effect of different APRR, from 0.03 to 0.1 MPa/s, indicate that is possible to safely reduce the duration of half-to-full refueling by 62% increasing the APRR value from 0.03 to 0.08 MPa/s.     

Hydrogen production by silica membrane reactor during dehydrogenation of methylcyclohexane: CFD analysis

A comprehensive computational fluid dynamic model has been developed using COMSOL Multiphysics 5.4 software to predict the behavior of a membrane reactor in dehydrogenation of methylcyclohexane for hydrogen production. A reliable reaction kinetic of dehydrogenation reaction and a permeation mechanism of hydrogen through silica membrane have been used in computational fluid dynamic modeling. For performance comparison, an equivalent traditional fixed bed reactor without hydrogen removal has been also modeled. After model validation, it has been used to evaluate the operating parameters effect on the performance of both the silica membrane reactor and the equivalent traditional reactor as well. The operating temperature ranged between 473 and 553 K, pressure between 1 and 2.5 bar, sweep factor from ?6.22 to 25 and feed flow rate from 1 to 5 × 10^?6 mol/s. The membrane reactor performed better than the equivalent traditional reactor, achieving as best result complete methylcyclohexane conversion and 96% hydrogen recovery.     

旗袍三围规格的优化设计研究

旗袍需要拥有合身的廓形,贴体程度要求高且强调人体曲线美感,而旗袍的松量设计直接影响其外观造型的优美程度及穿着舒适感。文章首先采用正交实验法针对相同款式旗袍设计了9种不同水平组合的三围松量设置方案,并制作成相同款式不同三围规格的旗袍样衣;然后利用心理学标尺,对9件旗袍样衣的静态造型和穿着舒适感分别进行主观评价,并利用直观分析法和方差分析法综合分析主观评价结果;最后对分析结果进行验证,最终找出外观造型优美程度高和穿着舒适感佳的三围松量水平组合,为旗袍三围规格的优化设计提供理论依据。     

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

Computational Investigation of Effects of Grain Size on Ballistic Performance of Copper

Numerical simulations were conducted to compare ballistic performance and penetration mechanism of copper (Cu) with four representative grain sizes. Ballistic limit velocities for coarse-grained (CG) copper (grain size ≈ 90 µm), regular copper (grain size ≈ 30 µm), fine-grained (FG) copper (grain size ≈ 890 nm), and ultrafine-grained (UG) copper (grain size ≈ 200 nm) were determined for the first time through the simulations. It was found that the copper with reduced grain size would offer higher strength and better ductility, and therefore renders improved ballistic performance than the CG and regular copper. High speed impact and penetration behavior of the FG and UG copper was also compared with the CG coppers strengthened by nanotwinned (NT) regions. The comparison results showed the impact and penetration resistance of UG copper is comparable to the CG copper strengthened by NT regions with the minimum twin spacing. Therefore, besides the NT-strengthened copper, the single phase copper with nanoscale grain size could also be a strong candidate material for better ballistic protection. A computational modeling and simulation framework was proposed for this study, in which Johnson–Cook (JC) constitutive model is used to predict the plastic deformation of Cu; the JC damage model is to capture the penetration and fragmentation behavior of Cu; Bao–Wierzbicki (B-W) failure criterion defines the material's failure mechanisms; and temperature increase during this adiabatic penetration process is given by the Taylor–Quinney method.     

Zero-Sequence Voltage-based Method for Determination and Classification of Unloaded Overhead Line Operating Conditions at the Moment of Energization

Overhead line energization can be performed under normal or faulty conditions. The latter indicates an occurrence of a fault along the line that exists at the moment of energization. This can lead to significant over-voltages that could endanger proper line operation. The issue gets more complex to protective devices when it comes to high-resistance faults, which impair the ability of relays to react promptly and accurately. Consequently, installation of additional detection devices becomes necessary. This paper proposes a novel method that properly detects and classifies line operating conditions at the moment of energization. The method is designed to be useful for utilities and it can be considered as a low-cost, fast, and accurate detection and classification approach, suitable for dealing with both low-resistance and high-resistance faults. Through comprehensive mathematical modeling it was found that both normal and faulty conditions during line energization are accompanied by the zero-sequence voltages of specific characteristics. The differences between zero voltage sequences are reflected in harmonic content, magnitudes of dominant frequencies, and their phase angles in regard to supply voltage. These findings are taken as the method's detection and classification criteria. The validity of the proposed model is verified by simulations and by field measurements.     

Numerical studies on heat and fluid flow of nanofluid in a partially heated vertical annulus

A numerical investigation on natural convective heat transfer of nanofluid (Al₂O₃+water) inside a partially heated vertical annulus of high aspect ratio (352) has been carried out. The computational fluid dynamics solver Ansys Fluent is used for simulation and results are presented for various volume fraction of nanoparticles (0‐0.04) at different heat flux values (3‐12 kW/m²). Two well‐known correlations for evaluating thermal conductivity and viscosity have been used. Thus different combinations of the available correlations have been set to form four models (I, II, III, and IV). Therefore, a detailed analysis has been executed to identify effects of thermophysical properties on heat transfer and fluid flow of nanofluids using different models. The results show enhancement in heat transfer coefficient with volume fraction of nanoparticles. Highest enhancement achieved is found to be 14.17% based on model III, while the minimum is around 7.27% based on model II. Dispersion of nanoparticles in base fluid declines the Nusselt number and Reynolds number with different rates depending on various models. A generalized correlation is proposed for Nusselt number of nanofluids in the annulus in terms of volume fraction of nanoparticles, Rayleigh number, Reynolds number, and Prandtl number.