• Energy Research
• other engineering and technologies close
• SA close
• PK close
• BE close

In this contribution, the magnetohydrodynamic non-Newtonian nanofluid flow through a porous medium in eccentric annuli with peristalsis is investigated. This has been done under the combined effect of viscous dissipation and radiation. The inner annulus is rigid and at rest, while the outer annulus has a sinusoidal wave traveling down its wall. The fundamental equations are modulated under the long wave length assumptions, and a closed form of solution is obtained for the axial velocity. While, homotopy perturbation solution is obtained, which satisfies the energy and nanoparticles equations. Numerical results for the axial velocity, temperature, and nanoparticles phenomena distributions as well as the reduced Nusselt and Sherwood numbers are obtained and tabulated for various parametric conditions.

For high-voltage (symmetric and non-symmetric) transmission networks, detecting simultaneous faults utilizing a single-end-based scheme is complex. In this regard, this paper suggests novel schemes for detecting simultaneous faults. The proposed schemes comprise two different stages: fault detection and identification and fault classification. The first proposed scheme needs communication links among both ends (sending and receiving) to detect and identify the fault. This communication link between both ends is used to send and receive three-phase current magnitudes for sending and receiving ends in the proposed fault detection (PFD) unit at both ends. The second proposed scheme starts with proposed fault classification (PFC) units at both ends. The proposed classification technique applies the Clarke transform on local current signals to classify the open conductor and simultaneous faults. The sign of all current Clarke components is the primary key for distinguishing between all types of simultaneous low-impedance and high-impedance faults. The fault detection time of the proposed schemes reaches 20 ms. The alternative transient program (ATP) package simulates a 500 kV–150-mile transmission line. The simulation studies are carried out to assess the suggested fault detection and identification and fault classification scheme performance under various OCFs and simultaneous earth faults in un-transposed and transposed TLs. The behavior of the proposed schemes is tested and validated by considering different fault scenarios with varying locations of fault, inception angles, fault resistance, and noise. A comparative study of the proposed schemes and other techniques is presented. Furthermore, the proposed schemes are extended to another transmission line, such as the 400 kV–144 km line. The obtained results demonstrated the effectiveness and reliability of the proposed scheme in correctly detecting simultaneous faults, low-impedance faults, and high-impedance faults.

Sometimes, an increased reaction is caused by an immune system of the body to a non-toxic agent (e. g. eggs, dust, pollens or some drugs). This is called allergy or hypersensitivity. The regulatory T cells decrease these allergic reactions. Nowadays, it is noticed that immune system has a deep relationship with micro-organism present in the intestine, that can be explained by the example, that some bacteria of intestine increase production of Treg cells by producing butyric acid like fatty acids. This can also understand that sufficiently different types of T cell receptors of Treg cells are needed to stop the inflammatory response produced by intestinal bacteria. In this study, the dynamic relation of T helper cells, intestinal bacteria and Treg cells are illustrated by a conformable mathematical model. Memory effects are figured out and displayed through graphs. Different plots also show the effects of increasing/decreasing amount of Treg induction efficiency on the whole system.

Abstract This study presents an overview of footprints as defined indicators that can be used to measure sustainability. An overview of the definitions and units of measurement associated with environmental, social, and economic footprints is important because the definitions of footprints vary and are often expressed unclearly. Composite footprints combining two or more individual footprints are also assessed. These combinations produce multi-objective optimisation problems. Several tools for footprint(s)' evaluation are presented, including some of the numerous carbon footprint calculators, available calculators for other footprints, some ecological footprints-based, graph-based, and mathematical programming tools. A comprehensive overview is offered of footprint-based sustainability assessment.

Various supervised machine-learning algorithms for wind power forecasting have been developed in recent years to manage wind power fluctuations and effectively correlate to energy consumption; Meanwhile, the performance of the model does suffer from missing values. To address the issue of missing values in wind power forecast, this paper proposes two methods: Clue-based missing at random (CMAR) and patterned k-nearest Neighbor (PkNN). In addition, a hybrid wind energy forecasting system has been created that is built on 1D-Convolutional neural networks, which are used to extract features from raw input, and Long Short-Term Memory, which employs time series data internal representation learning to improve the accuracy of month-wise wind power forecasting. The efficacy of the proposed model on generated datasets is also compared to the classic machine learning model to check the generalization ability. The strength of the Convolutional LSTM has been estimated in terms of different performance metrics such as mean absolute error (MAE), root mean squared error (RMSE), R2, and explained variance score (EVS). The experimental results show that the use of the PkNN algorithm for data imputation; integrated with regression-based Convolutional LSTM is much more efficient in prediction over other deep neural network models.

It is crucial to produce potable water from salt water in arid places using a stepped solar still desalination process. This work presents the temperature distribution and phase transformation of lauric acid PCM in inclined stepped solar still desalination system. A second-order finite volume method is employed for discretizing the two-dimensional geometry, while energy, continuity, and momentum equations are solved and then coupled using the second-order PRESTO algorithm. The solidification and melting CFD models have been used to capture the melting stages of lauric acid PCM. Four steps with an inclination of 30° to ground and two different cases with saline water levels of 10 mm and 40 mm have been reported. The results reveal that the temperature and mass fraction of lauric acid PCM strongly depends on the saline water level at each step, the number of steps, the angle of inclination, and the location of the solar still step. The maximum percentage increase in PCM melting fraction is 50%, and a PCM temperature rise of 4 °C is observed in the case of SWL-max compared to SWL-min. The total enthalpy of lauric acid PCM is increased by 30% for the SWL-max water level compared to the SWL-min water level.

This study tests binary and ternary n-pentanol, ethanol, and petrol blends to increase spark-ignition (SI) engine performance and minimize CO and HC emissions. To improve brake thermal efficiency (BTE) and reduce emissions, adding ethanol into gasoline is one of the practices used in Automobiles. But the literature reported some performance limitations and problems with adding a high ethanol concentration to gasoline as phase separation problem occurs in fuel tank due to the hygroscopic nature of ethanol, a higher ethanol concentration may corrode some parts of the fuel supply system. Ethanol has a lower calorific value than gasoline, so a higher ethanol concentration in blends beyond a specific limit reduces BTE. N-pentanol as a fuel additive in gasoline can better solve these problems due to its high caloric value compared to ethanol. Also, when n-pentanol is mixed with gasoline and exposed to moisture, it does not separate in stages as in the ethanol case. Accordingly, this research aims to evaluate n-pentanol's viability as a fuel additive in petrol and ethanol-gasoline blends at different compression ratios. The experiments were carried out on a single-cylinder, four-stroke spark-ignition engine running at a constant speed. Different blends of n-pentanol with gasoline and ethanol were tested, and results were compared against gasoline and E10 (the optimal blend of ethanol-gasoline, 10/90 v/v %). Various parameters were examined, including BTE, brake-specific fuel consumption (BSFC), and different exhaust pollutants. The effects of compression ratio values on these parameters were also recorded. The 1.5 vol% pentanol with E10 (E10P1.5) mix has the best overall characteristics, including low BSFC, high BTE, and low CO and HC emissions compared to petrol and E10 fuels. E10P1.5 shows a maximum enhancement in BTE, low BSFC, CO reduction, and HC reduction by 23.79%, 19.80%, 37.79%, and 19.46%, respectively, over gasoline. Compared to E10, the improvement is 3.64% for BTE, 4.59% for BSFC, 8.88% for CO emission reduction, and 4.13% for HC emission reduction.