Numerical investigation of nanofluid flow using CFD and fuzzy-based particle swarm optimization.

This paper is focused on the application and performance of artificial intelligence in the numerical modeling of nanofluid flows. Suspension of metallic nanoparticles in the fluids has shown potential in heat transfer enhancement of the based fluids. There are many numerical studies for the investigation of thermal and hydrodynamic characteristics of nanofluids. However, the optimization of the computational fluid dynamics (CFD) modeling by an artificial intelligence (AI) algorithm is not considered in any study. The CFD is a powerful technique from an accuracy point of view. However, it could be time and cost-consuming, especially in large-scale and complicated problems. It is expected that the machine learning technique of the AI algorithms could improve such CFD drawbacks by patterning the CFD data. Once the AI finds the CFD pattern intelligently, there is no need for CFD calculations. The particle swarm optimization-based fuzzy inference system (PSOFIS) is considered in this study to predict the velocity profile of Al2O3/water turbulent flow in a heated pipe. One of the challenging problems in CFD modeling is the lost data for a specific boundary condition. For example, the CFD data are available for wall heat fluxes of 75, 85, 105, and 125 w/m2, but there is no data for the wall heat flux of 95 w/m2. So, the PSOFIS learns the available CFD data, and it predicts the velocity profile for where the data is not available (i.e., wall heat flux of 95 w/m2). The intelligence of PSOFIS is checked by the coefficient of determination (R2 pattern) for different values of accept ratio (AR) and inertia weight damping ratio (IWDR). The best intelligence is obtained for the AR and IWDR of 0.7 and 0.99, respectively. At this condition, the velocity profile predicted by both CFD and PSOFIS is compatible. As the performance of the PSOFIS, for learning time of 268 s, the prediction of the CFD data lost was negligible (~ 1 s). In contrast, the CFD calculation takes around 600 s for each simulation.

Computational simulation and modelling of uranium extraction using tributylphosphate through membrane extractor.

Non-disperse solvent extraction is an effective technique for the extraction of metal ions from aqueous solution. In this study, uranium extraction using n-dodecane solvent containing tributylphosphate extractant in a membrane contactor was investigated. A 2D mathematical model was developed for the fluid flow and mass transfer in the hollow fibre membrane extractor. The equations of the created model were solved using the finite element method. The uranium concentration distribution in the extractor at different extractant concentrations as well as feed acidity was studied. The results showed that there is reasonable good agreement between experimental uranium extraction and modelling outputs at different extractant concentrations. Increasing extractant concentration from 5 to 30% led to the enhancement of uranium extraction from 2.60 to 34.13%. Also, there was an increase in the uranium extraction with increasing feed acidity in the range of 1-3 M. Furthermore, based on the radial uranium concentration distribution, it was found that the main mass transfer resistance in the system was microporous membrane section. Finally, it was obtained that the uranium extraction efficiency could be improved significantly by increasing porosity-to-tortuosity ratio. It was concluded that the membrane specification plays the most important role as the dominant mass transfer resistant was in the membrane subdomain.

Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm.

Due to focal liberality in electricity market projection, researchers try to suggest powerful and successful price forecasting algorithms. Since, the accurate information of future makes best way for market participants so as to increases their profit using bidding strategies, here suggests an algorithm for electricity price anticipation. To cover this goal, separate an algorithm into three steps, namely; pre-processing, learning and tuning. The pre-processing part consists of Wavelet Packet Transform (WPT) to analyze price signal to high and low frequency subseries and Variational Mutual Information (VMI) to select valuable input data in order to helps the learning part and decreases the computation burden. Owing to the learning part, a new Least squares support vector machine based self-adaptive fuzzy kernel (LSSVM-SFK) is proposed to extract best map pattern from input data. A new modified HBMO is introduced to optimally set LSSVM-SFK variables such as bias, weight, etc. To improve the performances of HBMO, two modifications are proposed that has high stability in HBMO. Suggested forecasting algorithm is examined on electricity markets that has acceptable efficiency than other models.

A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources.

The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non-linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi-objective hybrid algorithm is proposed to solve this problem by considering the linear and non-linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses' search function to destroy host cells and cause the penetration of the best virus into a cell for reproduction. In the proposed model, the PSO is used to enhance local and global search. In addition, the non-dominated sort of the Pareto criterion is used to sort the data. The optimization results on different scenarios reveal that the combined method of the proposed hybrid algorithm can improve the parameters such as convergence time, index of voltage stability, and absolute magnitude of voltage deviation, and this method can reduce the total transmission line losses. In addition, the presence of wind resources has a positive effect on the mentioned issue.

Author Correction: Transcriptome and functional analysis reveals hybrid vigor for oil biosynthesis in oil palm.

A correction has been published and is appended to both the HTML and PDF versions of this paper. The error has been fixed in the paper.

Transcriptome and functional analysis reveals hybrid vigor for oil biosynthesis in oil palm.

Oil palm is the most productive oil crop in the world and composes 36% of the world production. However, the molecular mechanisms of hybrids vigor (or heterosis) between Dura, Pisifera and their hybrid progeny Tenera has not yet been well understood. Here we compared the temporal and spatial compositions of lipids and transcriptomes for two oil yielding organs mesocarp and endosperm from Dura, Pisifera and Tenera. Multiple lipid biosynthesis pathways are highly enriched in all non-additive expression pattern in endosperm, while cytokinine biosynthesis and cell cycle pathways are highly enriched both in endosperm and mesocarp. Compared with parental palms, the high oil content in Tenera was associated with much higher transcript levels of EgWRI1, homolog of Arabidopsis thaliana WRINKLED1. Among 338 identified genes in lipid synthesis, 207 (61%) has been identified to contain the WRI1 specific binding AW motif. We further functionally identified EgWRI1-1, one of three EgWRI1 orthologs, by genetic complementation of the Arabidopsis wri1 mutant. Ectopic expression of EgWRI1-1 in plant produced dramatically increased seed mass and oil content, with oil profile changed. Our findings provide an explanation for EgWRI1 as an important gene contributing hybrid vigor in lipid biosynthesis in oil palm.