COVID-19 spread in a classroom equipped with partition - A CFD approach.

In this study, the motion and distribution of droplets containing coronaviruses emitted by coughing of an infected person in front of a classroom (e.g., a teacher) were investigated using CFD. A 3D turbulence model was used to simulate the airflow in the classroom, and a Lagrangian particle trajectory analysis method was used to track the droplets. The numerical model was validated and was used to study the effects of ventilation airflow speeds of 3, 5, and 7 m/s on the dispersion of droplets of different sizes. In particular, the effect of installing transparent barriers in front of the seats on reducing the average droplet concentration was examined. The results showed that using the seat partitions for individuals can prevent the infection to a certain extent. An increase in the ventilation air velocity increased the droplets' velocities in the airflow direction, simultaneously reducing the trapping time of the droplets by solid barriers. As expected, in the absence of partitions, the closest seats to the infected person had the highest average droplet concentration (3.80 × 10-8 kg/m3 for the case of 3 m/s).

Latest developments in nanofluid flow and heat transfer between parallel surfaces: A critical review.

The enhancement of heat transfer between parallel surfaces, including parallel plates, parallel disks, and two concentric pipes, is vital because of their wide applications ranging from lubrication systems to water purification processes. Various techniques can be utilized to enhance heat transfer in such systems. Adding nanoparticles to the conventional working fluids is an effective solution that could remarkably enhance the heat transfer rate. No published review article focuses on the recent advances in nanofluid flow between parallel surfaces; therefore, the present paper aims to review the latest experimental and numerical studies on the flow and heat transfer of nanofluids (mixtures of nanoparticles and conventional working fluids) in such configurations. For the performance analysis of thermal systems composed of parallel surfaces and operating with nanofluids, it is necessary to know the physical phenomena and parameters that influence the flow and heat transfer characteristics in these systems. Significant results obtained from this review indicate that, in most cases, the heat transfer rate between parallel surfaces is enhanced with an increase in the Rayleigh number, the Reynolds number, the magnetic number, and Brownian motion. On the other hand, an increase in thermophoresis parameter, as well as flow parameters, including the Eckert number, buoyancy ratio, Hartmann number, and Lewis number, leads to heat transfer rate reduction.

Environmentally benign solid catalysts for sustainable biodiesel production: A critical review.

Versatile bio-derived catalysts have been under dynamic investigation as potential substitutes to conventional chemical catalysts for sustainable biodiesel production. This is because of their unique, low-cost benefits and production processes that are environmentally and economically acceptable. This critical review aspires to present a viable approach to the synthesis of environmentally benign and cost-effective heterogeneous solid-base catalysts from a wide range of biological and industrial waste materials for sustainable biodiesel production. Most of these waste materials include an abundance of metallic minerals like potassium and calcium. The different approaches proposed by researchers to derive highly active catalysts from large-scale waste materials of a re-usable nature are described briefly. Finally, this report extends to present an overview of techno-economic feasibility of biodiesel production, its environmental impacts, commercial aspects of community-based biodiesel production and potential for large-scale expansion.

Soft computing-based modeling and emission control/reduction of a diesel engine fueled with carbon nanoparticle-dosed water/diesel emulsion fuel.

This study was set up to model and optimize the performance and emission characteristics of a diesel engine fueled with carbon nanoparticle-dosed water/ diesel emulsion fuel using a combination of soft computing techniques. Adaptive neuro-fuzzy inference system tuned by particle swarm algorithm was used for modeling the performance and emission parameters of the engine, while optimization of the engine operating parameters and the fuel composition was conducted via multiple-objective particle swarm algorithm. The model input variables were: injection timing (35-41° CA BTDC), engine load (0-100%), nanoparticle dosage (0-150 µM), and water content (0-3 wt%). The model output variables included: brake specific fuel consumption, brake thermal efficiency, as well as carbon monoxide, carbon dioxide, nitrogen oxides, and unburned hydrocarbons emission concentrations. The training and testing of the modeling system were performed on the basis of 60 data patterns obtained from the experimental trials. The effects of input variables on the performance and emission characteristics of the engine were thoroughly analyzed and comprehensively discussed as well. According to the experimental results, injection timing and engine load could significantly affect all the investigated performance and emission parameters. Water and nanoparticle addition to diesel could markedly affect some performance and emission parameters. The modeling system could predict the output parameters with an R2 > 0.93, MSE < 5.70 × 10-3, RMSE < 7.55 × 10-2, and MAPE < 3.86 × 10-2. The optimum conditions were: injection timing of 39° CA BTDC, engine load of 74%, nanoparticle dosage of 112 µM, and water content of 2.49 wt%. The carbon dioxide, carbon monoxide, nitrogen oxides, and unburned hydrocarbon emission concentrations were found to be 7.26 vol% , 0.46 vol% , 95.7  ppm, and 36.2 ppm, respectively, under the selected optimal operating conditions while the quantity of brake thermal efficiency was found at an acceptable level ( 34.0 %). In general, the applied soft computing combination appears to be a promising approach to model and optimize operating parameters and fuel composition of diesel engines.

Entropy generation analysis of different solar thermal systems.

The entropy generation analysis is an approach to optimize the performance of different thermal systems by investigating the related irreversibilities of the system. This paper provides a concise review of the entropy generation analysis performed for different solar thermal energy systems including solar collectors, solar heaters, solar heat exchangers, and solar stills. The mathematical formulation and the equations for calculating the entropy generation are briefly presented. Moreover, main passive techniques including the usage of nanofluids, porous materials, and inserts which are used to improve the efficiency of different solar systems are discussed. It is shown that using entropy generation minimization method is an efficient tool to find the optimal design of solar systems. The current review aims to motivate researchers in the field of solar energy for using entropy generation analysis to reduce the lost work and consequently improving the system performance.

Effect of sonication time on the evaporation rate of seawater containing a nanocomposite.

Sonication time has a significant contribution to the stability and properties of nanofluids (mixtures of nanoparticles and a base fluid). Finding the optimum sonication time can help to save energy and ensure optimal design. The present study deals with the sonication time effect on the evaporation rate of seawater containing a nanocomposite (i.e., a mixture of multi-walled carbon nanotubes and graphene nanoplates). For indoor experiments, a solar simulator was employed as the radiation source. At first, the nanofluid with a concentration of 0.01% wt. was sonicated in an ultrasonic bath for different times of 30, 60, 90, 120, 180, 240 min, and the associated zeta potential values were recorded to evaluate the stability. Next, the best time function was used to appraise the effect of concentration variations (0.001, 0.002, 0.004, 0.01, 0.02 and 0.04% wt.) and the light intensities (1.6, 2.6, and 3.6 suns) on the rate of solar steam generation. The results indicate that for a concentration of 0.01% wt. and under 3.6 suns, the highest evaporation efficiency of 61.3% would be achieved at 120 min sonication time.

Effect of sonication characteristics on stability, thermophysical properties, and heat transfer of nanofluids: A comprehensive review.

The most crucial step towards conducting experimental studies on thermophysical properties and heat transfer of nanofluids is, undoubtedly, the preparation step. It is known that good dispersion of nanoparticles into the base fluids leads to having long-time stable nanofluids, which result in having higher thermal conductivity enhancement and lower viscosity increase. Ultrasonic treatment is one of the most effective techniques to break down the large clusters of nanoparticles into the smaller clusters or even individual nanoparticles. The present review aims to summarize the recently published literature on the effects of various ultrasonication parameters on stability and thermal properties of various nanofluids. The most common methods to characterize the dispersion quality and stability of the nanofluids have been presented and discussed. It is found that increasing the ultrasonication time and power results in having more dispersed and stable nanofluids. Moreover, increasing the ultrasonication time and power leads to having higher thermal conductivity and heat transfer enhancement, lower viscosity increase, and lower pressure drop. However, there are some exceptional cases in which increasing the ultrasonication time and power deteriorated the stability and thermophysical properties of some nanofluids. It is also found that employing the ultrasonic horn/probe devices are much more effective than ultrasonic bath devices; lower ultrasonication time and power leads to better results.

Journals Can Persuade Authors to Learn Publishing's Ethics.

Some researchers, even professors in universities, sometimes do unethical actions unintentionally due to lack of a mentor in their academic life. In this opinion piece, we aim to show that journals and publishers can play the role of a mentor for authors of scientific articles, especially young M.Sc. and Ph.D. students, to teach them the ethics in research and publishing. In this way, both journals and researchers will benefit from such a plan.

Energy and Exergy Analyses of a Solid Oxide Fuel Cell-Gas Turbine-Organic Rankine Cycle Power Plant with Liquefied Natural Gas as Heat Sink.

An exergy analysis of a novel integrated power system is represented in this study. A Solid Oxide Fuel Cell (SOFC), which has been assisted with a Gas Turbine (GT) and Organic Rankine Cycle (ORC) by employing liquefied natural gas (LNG) as a heat sink in a combined power system is simulated and investigated. Initially in this paper, the integrated power system and the primary concepts of the simulation are described. Subsequently, results of the simulation, exergy analysis, and composite curves of heat exchangers are represented and discussed. The equations of the exergy efficiency and destruction for the main cycle's units such as compressors, expanders, pumps, evaporators, condensers, reformers, and reactors are presented. According to the results, the highest exergy destruction is contributed to the SOFC reactor, despite its acceptable exergy efficiency which is equal to 75.7%. Moreover, the exergy efficiencies of the ORC cycle and the whole plant are determined to be 64.9% and 39.9%, respectively. It is worth noting that the rational efficiency of the integrated power system is 53.5%. Among all units, the exergy efficiency of the LNG pump is determined to be 11.7% the lowest exergy efficiency among the other investigated components, indicating a great potential for improvements.

Modeling and optimization of thermal conductivity and viscosity of MnFe2O4 nanofluid under magnetic field using an ANN.

This research investigates the applicability of an ANN and genetic algorithms for modeling and multiobjective optimization of the thermal conductivity and viscosity of water-based spinel-type MnFe2O4 nanofluid. Levenberg-Marquardt, quasi-Newton, and resilient backpropagation methods are employed to train the ANN. The support vector machine (SVM) method is also presented for comparative purposes. Experimental results demonstrate the efficacy of the developed ANN with the LM-BR training algorithm and the 3-10-10-2 structure for the prediction of the thermophysical properties of nanofluids in terms of the significantly superior accuracy compared to developing the correlation and employing SVM regression. Moreover, the genetic algorithm is implemented to determine the optimal conditions, i.e., maximum thermal conductivity and minimum nanofluid viscosity, based on the developed ANN.

Is it ethical for journals to request self-citation?

By following the recently published paper in Science titled "Coercive Citation in Academic Publishing", in this paper, we aim to discuss the demand of some journals that request authors to cite recently published papers in that journal to increase the impact factor of that journal. It will be mentioned that some of these demands are not ethical and consequently will diminish the reputation of the journal.

Some opinions on the review process of research papers destined for publication.

The current paper discusses the peer review process in journals that publish research papers purveying new science and understandings (scientific journals). Different aspects of peer review including the selection of reviewers, the review process and the decision policy of editor are discussed in details. Here, the pros and cons of different conventional methods of review processes are mentioned. Finally, a suggestion is presented for the review process of scientific papers.