Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation

Partially Detected Intelligent Traffic Signal Control (PD-ITSC) systems that can optimize traffic signals based on limited detected information could be a cost-efficient solution for mitigating traffic congestion in the future. In this paper, we focus on a particular problem in PD-ITSC - adaptation to changing environments. To this end, we investigate different reinforcement learning algorithms, including Q-learning, Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), and Actor-Critic with Kronecker-Factored Trust Region (ACKTR). Our findings suggest that RL algorithms can find optimal strategies under partial vehicle detection; however, policy-based algorithms can adapt to changing environments more efficiently than value-based algorithms. We use these findings to draw conclusions about the value of different models for PD-ITSC systems.Comment: Accepted by ICMLA 201

Predicting the outcomes of traumatic brain injury using accurate and dynamic predictive model

Predictive models have been used widely to predict the diseases outcomes in health sector. These predictive models are emerged with new information and communication technologies. Traumatic brain injury has recognizes as a serious and crucial health problem all over the world. In order to predict brain injuries outcomes, the predictive models are still suffered with predictive performance. In this paper, we propose a new predictive model and traumatic brain injury predictive model to improve the predictive performance to classifying the disease predictions into different categories. These proposed predictive models support to develop the traumatic brain injury predictive model. A primary dataset is constructed which is based on approved set of features by the neurologist. The results of proposed model is indicated that model has achieved the best average ranking in terms of accuracy, sensitivity and specificity

Introducing a precise system for determining volume percentages independent of scale thickness and type of flow regime

When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent

Competition of ANN and RSM techniques in predicting the behavior of the CuO-liquid paraffin

Please read abstract in the article.The Taif University Researchers Supporting grant of Taif University, Taif, Saudi Arabia.http://www.tandfonline.com/loi/gcec20hj2024Mechanical and Aeronautical EngineeringSDG-09: Industry, innovation and infrastructur

Applying artificial neural network and response surface method to forecast the rheological behavior of hybrid nano‐antifreeze containing graphene oxide and copper oxide nanomaterials

In this study, the efficacy of loading graphene oxide and copper oxide nanoparticles into ethylene glycol-water on viscosity was assessed by applying two numerical techniques. The first technique employed the response surface methodology based on the design of experiments, while in the second technique, artificial intelligence algorithms were implemented to estimate the GO-CuO/water-EG hybrid nanofluid viscosity. The nanofluid sample’s behavior at 0.1, 0.2, and 0.4 vol.% is in agreement with the Newtonian behavior of the base fluid, but loading more nanoparticles conforms with the behavior of the fluid with non-Newtonian classification. Considering the possibility of non-Newtonian behavior of nanofluid temperature, shear rate and volume fraction were effective on the target variable and were defined in the implementation of both techniques. Considering two constraints (i.e., the maximum R-square value and the minimum mean square error), the best neural network and suitable polynomial were selected. Finally, a comparison was made between the two techniques to evaluate their potential in viscosity estimation. Statistical considerations proved that the R-squared for ANN and RSM techniques could reach 0.995 and 0.944, respectively, which is an indication of the superiority of the ANN technique to the RSM one.https://www.mdpi.com/journal/sustainabilitydm2022Mechanical and Aeronautical Engineerin

Optimizing the Gamma Ray-Based Detection System to Measure the Scale Thickness in Three-Phase Flow through Oil and Petrochemical Pipelines in View of Stratified Regime

As the oil and petrochemical products pass through the oil pipeline, the sediment scale settles, which can cause many problems in the oil fields. Timely detection of the scale inside the pipes and taking action to solve it prevents problems such as a decrease in the efficiency of oil equipment, the wastage of energy, and the increase in repair costs. In this research, an accurate detection system of the scale thickness has been introduced, which its performance is based on the attenuation of gamma rays. The detection system consists of a dual-energy gamma source ( 241 Am and 133 Ba radioisotopes) and a sodium iodide detector. This detection system is placed on both sides of a test pipe, which is used to simulate a three-phase flow in the stratified regime. The three-phase flow includes water, gas, and oil, which have been investigated in different volume percentages. An asymmetrical scale inside the pipe, made of barium sulfate, is simulated in different thicknesses. After irradiating the gamma-ray to the test pipe and receiving the intensity of the photons by the detector, time characteristics with the names of sample SSR, sample mean, sample skewness, and sample kurtosis were extracted from the received signal, and they were introduced as the inputs of a GMDH neural network. The neural network was able to predict the scale thickness value with an RMSE of less than 0.2, which is a very low error compared to previous research. In addition, the feature extraction technique made it possible to predict the scale value with high accuracy using only one detector

Simulation of nanofluid flow in a micro-heat sink with corrugated walls considering the Effect of Nanoparticle Diameter on Heat Sink Efficiency

In this numerical work, the cooling performance of water–Al2O3 nanofluid (NF) in a novel microchannel heat sink with wavy walls (WMH-S) is investigated. The focus of this article is on the effect of NP diameter on the cooling efficiency of the heat sink. The heat sink has four inlets and four outlets, and it receives a constant heat flux from the bottom. CATIA and CAMSOL software were used to design the model and simulate the NF flow and heat transfer, respectively. The effects of the Reynolds number (Re) and volume percentage of nanoparticles (Fi) on the outcomes are investigated. One of the most significant results of this work was the reduction in the maximum and average temperatures of the H-S by increasing both the Re and Fi. In addition, the lowest Tmax and pumping power belong to the state of low NP diameter and higher Fi. The addition of nanoparticles reduces the heat sink maximum temperature by 3.8 and 2.5% at the Reynolds numbers of 300 and 1800, respectively. Furthermore, the highest figure of merit (FOM) was approximately 1.25, which occurred at Re 1800 and Fi 5%. Eventually, it was revealed that the best performance of the WMH-S was observed in the case of Re 807.87, volume percentage of 0.0437%, and NP diameter of 20 nm.Taif University, Taif, Saudi Arabiahttp://www.frontiersin.org/Energy_Researcham2022Mechanical and Aeronautical Engineerin

Correlations for total entropy generation and Bejan number for free convective heat transfer of an eco-friendly nanofluid in a rectangular enclosure under uniform magnetic field

In this paper, focusing on the study of entropy generation (EGN), the convection flow of an eco-friendly nanofluid (N-F) in a rectangular enclosure is studied numerically. The nanoparticles (N-Ps) used are silver N-P, which are obtained in an eco-friendly manner from natural materials. By suspending these N-Ps in an equal mixture of water and ethylene glycol (E-G), the N-F has been prepared. There are two constant-temperature triangular obstacles with height w and base H that are placed on the hot wall. There is a magnetic field (M-F) in the x-direction. To simulate the N-F flow, eco-friendly N-P relations are used, and the equations are solved using the volume control method and the SIMPLE algorithm. The variables include Rayleigh number (Ra), Hartmann number (Ha), H, W, and the volume fraction of silver N-Ps. The effect of these parameters is evaluated on the EGN and Bejan number (Be). Finally, a correlation is expressed for the EGN for a range of variables. The most important results of this paper demonstrate that the addition of silver eco-friendly N-Ps intensifies the EGN so that the addition of 3% of N-Ps enhances the EGN by 3.8%. An increment in the obstacle length reduces the Be barrier while increasing the Ha, which enhances the Be when the convection is strong. Increasing the height of the obstacle intensifies entropy generation.Taif University, Taif, Saudi Arabia.https://www.mdpi.com/journal/processesam2022Mechanical and Aeronautical Engineerin

The influence of forced convective heat transfer on hybrid nanofluid flow in a heat exchanger with elliptical corrugated tubes : numerical analyses and optimization

The capabilities of nanofluids in boosting the heat transfer features of thermal, electrical and power electronic devices have widely been explored. The increasing need of different industries for heat exchangers with high efficiency and small dimensions has been considered by various researchers and is one of the focus topics of the present study. In the present study, forced convective heat transfer of an ethylene glycol/magnesium oxide-multiwalled carbon nanotube (EG/MgO-MWCNT) hybrid nanofluid (HNF) as single-phase flow in a heat exchanger (HE) with elliptical corrugated tubes is investigated. Three-dimensional multiphase governing equations are solved numerically using the control volume approach and a validated numerical model in good agreement with the literature. The range of Reynolds numbers (Re) 50 < Re < 1000 corresponds to laminar flow. Optimization is carried out by evaluation of various parameters to reach an optimal case with the maximum Nusselt number (Nu) and minimum pressure drop. The use of hybrid nanofluid results in a greater output temperature, a higher Nusselt number, and a bigger pressure drop, according to the findings. A similar pattern is obtained by increasing the volume fraction of nanoparticles. The results indicate that the power of the pump is increased when EG/MgO-MWCNT HNFs are employed. Furthermore, the thermal entropy generation reduces, and the frictional entropy generation increases with the volume fraction of nanoparticles and Re number. The results show that frictional and thermal entropy generations intersect by increasing the Re number, indicating that frictional entropy generation can overcome other effective parameters. This study concludes that the EG/MgO-MWCNT HNF with a volume fraction (VF) of 0.4% is proposed as the best-case scenario among all those considered.Taif University Researchers Supporting Granthttps://www.mdpi.com/journal/applsciMechanical and Aeronautical Engineerin

Simulation of alumina/water manofluid flow in a micro-heatsink with wavy microchannels : impact of two-phase and single-phase nanofluid models

In this article, alumina/water nanofluid (NF) flow in a heatsink (H-S) with wavy microchannels (W-MCs) is simulated. The H-S is made of aluminum containing four similar parts. Each part has an inlet and outlet. Constant heat flux is applied on the bottom of the H-S. The study is based on two-phase (T-P) mixture and single-phase (S-P) models to determine the difference between these two types of simulations. FLUENT software and the control volume method were used for simulations. The volume control method is employed to solve equations. The effective variables include the volume fraction 0 < φ < 5% of alumina and Reynolds number (Re) 300 < Re < 1800. The maximum H-S bottom temperature, the required amount of pumping power (PP), the temperature uniformity, and the heat resistance of the H-S are the outputs studied to simulate the S-P and T-P models. The results show that the use of the T-P model has less error in comparison with the experimental data than the S-P model. An increment in the Re and φ reduces the maximum temperature (M-T) of the H-S. The S-P model, especially at a higher value of φ, leads to a lower M-T value than the T-P model. The S-P model shows a 0.5% greater decrease than the T-P model at the Reynolds number of 300 by enhancing the volume percentage of nanoparticles (NPs) from 1 to 5%. Temperature uniformity is improved with Re and φ. The reduction of H-S thermal resistance with Re and φ is the result of this study. Adding NPs to water, especially at higher amounts of φ, enhances the required PP. The T-P model predicts higher PP than the S-P one, especially at a high value of φ. The T-P model shows 4% more PP than the S-P model at Re 30 and a volume fraction of 4%.The German Research Foundation (DFG) and Taif University, Taif, Saudi Arabia.http://www.frontiersin.org/Energy_Researcham2022Mechanical and Aeronautical Engineerin