Combined Computational Fluid Dynamics Research Articles

Heat transfer optimisation using novel biomorphic pin-fin heat sinks: An integrated approach via design for manufacturing, numerical simulation, and machine learning

With the availability of advanced manufacturing techniques, non-conventional shapes and bio-inspired/biomorphic designs have shown to provide more efficient heat transfer. Consequently, this research investigates the heat transfer performance and fluid flow characteristics of novel biomorphic scutoid pin fins with varying volumes and top geometries. Numerical simulations were conducted using four hybrid designs for Reynolds Number 5500–13500. The impact of pin fin 'top' geometrical features on the heat transfer coefficient (HTC) was evaluated by combining computational fluid dynamics (CFD), experimental data, and machine learning. The results highlighted that the new pin fins saved 6.3 % to 14.3 % volume/material usage but produced around 1.5 to 1.7 times more heat transfer than conventional square/rectangular fins. Also, manipulating pin fins via the top geometrical properties can lead to more uniform velocity and temperature distributions while demonstrating the potential for increased thermal efficiency with reduced thermal resistance. Furthermore, six machine learning models accurately predict HTC using volume and surface area as key variables, achieving less than 5 % mean absolute percentage error (MAPE). Overall, this research introduces innovative biomorphic designs with unconventional geometries, emphasising resource optimisation and efficient HTC prediction using machine learning. It simplifies design processes, supports agile product development, calls for re-evaluation of conventional heat sink geometries, and provides promising directions for future research.

Optimization Based on Computational Fluid Dynamics and Machine Learning for the Performance of Diffuser-Augmented Wind Turbines with Inlet Shrouds

A methodology that could reduce computational cost and time, combining computational fluid dynamics (CFD) simulations, neural networks, and genetic algorithms to determine a diffuser-augmented wind turbine (DAWT) design is proposed. The specific approach used implements a CFD simulation validated with experimental data, and key parameters are analyzed to generate datasets for the relevant mathematical model established with the backpropagation neural network algorithm. Then, the mathematical model is used with the non-dominant sorting genetic algorithm II to optimize the design and improve the DAWT design to overcome negative constraints such as noise and low energy density. The key parameters adopted are the diffuser’s flange height/angle, the diffuser’s length, and the rotor’s axial position. It was found that the impact of the rotor’s axial position on the power output of the DAWT is the most significant parameter, and a well-designed diffuser requires accelerating the airflow while maintaining high-pressure recovery. Introducing a diffuser can suppress the wind turbine’s noise, but if the induced tip vortex is too strong, it will have the opposite effect on the noise reduction.

Stability Analysis of Cofferdam with Double-Wall Steel Sheet Piles under Wave Action from Storm Surges

Double-wall steel sheet piles (DSSPs) are widely used in large-span cofferdams for docks due to their good performance against wave action during storm surges. This paper describes a study of the dynamic behavior of a DSSP cofferdam under wave action through flume tests and a numerical simulation that combined computational fluid dynamics (CFD) and the finite element method. The influences of the water level and wave height on the DSSP cofferdam were investigated experimentally and numerically. Tall waves in shallow water broke upon and impacted the seaside pile with large dynamic wave pressure, dramatically increasing the stress and displacement of the seaside pile. The overlap of the traveling and reflected waves increased the excess pore water pressure near the seaside pile due to taller overlapped waves and higher wave frequency. The DSSP cofferdam failed under the combined actions of the dynamic wave pressure and erosion of the landside seabed. The leakage and overflow of the breaking waves resulted in significant erosion of the landside seabed and greatly weakened the support of the seabed. The dynamic wave pressure then pushed the DSSP cofferdam until it failed. The simulation with the combined methods of CFD and FEM resulted in trends that were similar to those of the test measurements. Compared to the quasi-static method and pseudo-dynamic method, the results of the simulation via the present method were much closer to the test results because the simulation included the effects of breaking waves. The reinforced measure worked well to prevent the DSSP cofferdam in a sandy seabed foundation from continuous failures of deformation–leakage–erosion–tilting. However, it failed in a clay interlayer seabed foundation due to the large settlement.

Optimization of olive pomace dehydration process through the integration of computational fluid dynamics and deep learning

ABSTRACT This study introduces an innovative approach to optimize the dehydration process of olive pomace by combining computational fluid dynamics (CFD) and deep learning. Through CFD, it identifies the optimal air inlet velocity in a prototype of a passive direct solar dehydrator for olive pomace, which allows for the reduction of its moisture content for subsequent use as biomass. The prototype was simulated in ANSYS software, and this simulation consisted of the following steps: prototype design, meshing, selection of physical models, material assignment, boundary condition simulation, and validation of results with data obtained from the prototype. Following this process, it was concluded that the optimal air inlet velocity to the dehydration chamber is 0.1 m/s. Concurrently, an artificial neural network model was used to analyze data from sensors in the physical prototype, revealing that solar radiation and ambient temperature are the most influential variables on the temperature of the dehydration chamber. This analysis resulted in a predictive model for the optimization of the dehydration process, with a correlation coefficient of 0.9699 for temp_up and 0.9710 for temp_down, and a Willmott coefficient of 0.9999, demonstrating a high concordance between the model’s predictions and the experimental data. The model’s input variables include solar radiation, ambient temperature, and both external and internal air humidity. This integration of CFD and deep learning offers a promising methodology for improving olive pomace dehydration systems and the industry in general.

CFD-DEM-based evaluation of main-channel sediment transport processes subject to supplement from a steep tributary

The accumulation of sediments within mountainous gullies serves as a significant source for the main channel. Understanding the transport characteristics is crucial for comprehending reach-scale sediment supply features and channel morphodynamics. This study addresses the previously unexplored challenges in accurately quantifying variations in sediment transport rates, particularly during critical transitions such as the mobilization of static sediment pulses into continuous motion. To accomplish this objective, a combined Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) computational framework is employed to directly simulate the detailed trajectories of all particles. After validating the CFD-DEM model, we examine the effects of three major influential factors (particle size, channel slope and channel width) on sediment transport characteristics. The results indicate that the sediment transport rate exhibits rapid rise to peak followed by gradual attenuation process, described by a long-tail effect. The cross-sectional averaged sediment transport rate positively correlates with the width of sediment transport belt. The downstream propagation of sediment is enhanced by steeper channel slope and narrower channel width. In addition, we find that cross-sectional averaged sediment transport rate controlled by these factors exhibits inherent self-similarity. The temporal variation of dimensionless sediment transport rate for a certain section can be modeled by a modified two-branch Weibull function encompasses three model parameters dependent on influential factors and longitudinal location. The parameter variation of the functional model, combined with linear variation of the three main influencing factors, provides support for explaining sediment transport processes in channels of dimensions different from those studied. These findings enhance the understanding of the formation and evolution mechanism of sediment transport induced by upstream loose deposit destruction.

Numerical analysis of coarse particle two-phase flow in deep-sea mining vertical pipe transport with forced vibration

Exploring the dynamics and flow characteristics of coarse particles within vertical lifting pipes is essential to ensure safe and efficient pipeline transportation. This study combines computational fluid dynamics (CFD) and the discrete element method (DEM) to investigate the behavior of coarse particle solid–liquid two-phase flow in a vertical pipe utilized for deep-sea mining under forced vibration and compares it with that in stationary pipes. This study examined the effects of pipe amplitude, vibration frequency, and initial internal flow velocity separately. The amplitude values tested were 0D (static), 1D, 2D, 3D, and 4D, where D represents pipe diameter. The vibration frequencies of 0 Hz (static), 0.25 Hz, 0.5 Hz, 1 Hz, and 2 Hz were tested, respectively. Finally, initial velocities of 1 m/s, 2 m/s, 3 m/s, and 4 m/s were tested. Compared with stationary pipes, particle-induced turbulence gradually disrupts the flow field, leading to a more dispersed particle volume distribution during pipe vibration. An increase in the amplitude and vibration frequency of the pipe results in an augmentation of the pressure drop within the pipe. By comparing the results of the vibrating pipe with those of the stationary pipe, the effect of the pipe vibration gradually weakened as the initial velocity increased.

Topography prediction of high-aspect ratio features milled with overlapping abrasive slurry jet footprints considering fluid confinement effects

Overlapping adjacent passes of abrasive slurry jets can be used to fabricate micro-pockets or blind micro-slots. This study investigates the propagation of surfaces milled using overlapping passes of a high-pressure abrasive slurry jet on a ductile 6061-T6 target over a wide range of aspect ratios. Features such as shallow pockets with low aspect ratios to deep slots with aspect ratios ∼1.5 were fabricated to investigate the correlations between the aspect ratio, degree of pass overlap, and number of repeating passes with the machined surface evolution characteristics. A comprehensive model was presented that combined computational fluid dynamics (CFD) with surface evolution to predict the evolving machined feature topography after each of the overlapping and repeating passes, and to provide insights into experimental observations. The model explicitly considered the CFD-predicted local particle impact angles and velocities as well as effects due to secondary impacts, high sidewall slopes, and stagnation effects. For larger degrees of overlap, measurements revealed a more than linear rate of increase in depth after each repeat of two adjacent overlapped passes, as opposed to a linear depth increase for smaller overlaps and blind pockets. Large overlaps were found to result in asymmetric features. Up to a critical aspect ratio of ∼0.65, the model predicted the surface evolution of the features to within <8.6% of those measured. Beyond that, randomness in the machined feature shape and size made the process challenging to control and the surface evolution difficult to predict. Nevertheless, the model was able to elucidate the reasons for the randomness and other observed phenomena such as the more than linear growth in etch rate, and the occurrence of feature asymmetry. The findings emphasize that flow confinement, increases in the jet's turbulent kinetic energy, and the formation of vortices at the bottom of the machined features are critical factors influencing the machining process, and that in these cases explicit modeling approaches like the one presented in this study must be used.

Influence of rotor impeller structure on performance improvement of suspended axial flow blood pumps.

The hydrodynamic suspension structure design of the axial blood pump impeller can avoid the problems associated with using mechanical bearings. However, the particular impeller structure will impact the hydraulic performance and hemolysis of the blood pump. This article combines computational fluid dynamics (CFD) with the Lagrange particle tracking method, aiming to improve the blood pump's hydraulic and hemolysis performance. It analyzes the flow characteristics and hemolysis performance inside the pump. It optimizes the taper of the impeller hub, the number of blades, and the inclination angle of the circumferential groove at the top of the blade. Under certain rotational speed conditions, an increase in the taper of the impeller hub or the number of blades can increase the pumping pressure of a blood pump, but an increase in the number of blades will reduce the flow rate. The design of circumferential grooves at the top of the blade can increase the pumping pressure to a certain extent, with little impact on the hemolysis performance. The impeller structure is optimized based on the estimated hemolysis of each impeller model blood pump. It could be seen that when the pump blood pressure and flow rate were reached, the optimized impeller speed was reduced by 11.4%, and the estimated hemolysis value was reduced by 10.5%. In this paper, the rotor impeller structure of the blood pump was optimized to improve the hydraulic and hemolytic performance effectively, which can provide a reference for the related research of the axial flow blood pump using hydraulic suspension.

Combining artificial intelligence and computational fluid dynamics for optimal design of laterally perforated finned heat sinks

The efficient design of heat sinks is a severe challenge in thermo-fluid engineering. A creative and innovative way is applying lateral perforations to parallel finned heat sinks. The significance of achieving an optimal design for perforated finned heat sinks (PFHSs) has inspired the present authors to introduce a novel hybrid designing approach that combines computational fluid dynamics (CFD), machine learning (ML), multi-objective optimization (MOO), and multi-criteria decision-making (MCDM). The design variables considered include the size (0.25<φ<0.5) and shape (square, circular, and hexagonal) of the perforations, as well as the airflow Reynolds number (2000<Re<5000). The design objectives have been redefined as dimensionless parameters to assess heat dissipation, pressure drop, and heat sink weight. These modified objectives encompass thermal performance (TP), thermo-hydraulic performance (THP), and thermo-volumetric performance (TVP). The modeling process showed that both stepwise mixed selection (SMS) and GMDH-NN techniques exhibited comparable performance in most modeling scenarios. Nevertheless, the SMS approach demonstrated more reliability in modeling diverse objectives. Furthermore, the optimization results demonstrated that the optimal size of the perforations is strongly dependent on their shapes. In PFHSs with square perforations, approximately 54% of the Pareto points had a φ-value greater than 0.45. Meanwhile, in PFHSs based on circular perforations, more than 50% of the optimal points have φ less than 0.4. The MCDM-based analysis on various real-world scenarios indicated that using PFHSs with square-shaped perforations with Reynolds numbers around 2000 and considering a wide range of perforations' sizes could result in optimal designs.

Optimization of a thermal energy storage system enhanced with fins using generative adversarial networks method

Optimizing fin configurations is an effective method to improve the performance of a thermal energy storage system. The present research aims to determine the optimal fin spacing and inclination angle to minimize the melting process of a phase change material (PCM) in a rectangular enclosure, which is exposed to a constant temperate finned wall while other walls are adiabatic. Oleic acid (OA) is used as a PCM with a melting temperature of 287–288 K. Computational fluid dynamics (CFD) simulations are employed to comprehensively investigate the thermal behavior of the system by solving continuity, momentum, and energy equations simultaneously. Initially, fifteen different datasets are simulated using CFD methods to explore various fin configurations. After that, fifteen additional datasets are generated to verify the reliability of the generative adversarial networks (GANs) method, which supplements the CFD approach. An ensemble learning approach is implemented to create a hybrid dataset consisting of thirty datasets, and then the optimum dataset is determined. To validate this approach, a single numerical simulation is conducted at the optimum dataset, and the obtained optimum value, minimum melting time of PCM, is compared with the result obtained from CFD simulation. It is identified as the optimal fin configuration, resulting in a fin spacing of 15.75 mm and an inclination angle of −11.6°, highlights the efficacy of the combined CFD and GANs approach. The melting time captured from the numerical simulation and the one produced with GANs differed by about 4.7 %. The current study concludes that the GANs method might be effectively worthwhile in many energy storage applications.

Simulation Analysis and Experimental Study on Airfoil Optimization of Low-Velocity Turbine

By combining computational fluid dynamics (CFD) and surrogate model method (SMM), the relationship between turbine performance and airfoil shape and flow characteristics at low flow rate is revealed. In this paper, the flow velocity tidal energy airfoil model is designed based on the Kriging model, and the original airfoil with a relative thickness of 12% and a relative curvature of 2.5% is obtained. The parameter optimization is carried out by setting the 4th CST equations through the surrogate model; the maximum lift-drag ratio is the optimization goal, the optimization design variable is 10, the maximum number of iterations is 100, and the maximum number of sub-optimization iterations is 200. The results show that the hydrodynamic performance of the airfoil with thinner thickness and more curvature is better, the maximum thickness part is shifted forward by 4.58%, and the lift-drag ratio is improved by 4.03%. The flow field and the efficiency are more stable, which provides an engineering reference for the optimal design of hydraulic turbine airfoils under low flow velocity. It supplements the research on the performance of turbine blades in low velocity.

Optimization of Impeller Structure Parameters of a Centrifugal Fan in a Powered Air-Purifying Respirator Power System

As the core power element of a centrifugal fan, the impeller’s structural parameters are important factors affecting the aerodynamic performance of the fan. Therefore, to improve the aerodynamic performance of centrifugal fans, in this study, we take the Powered Air-Purifying Respirator (PAPR) power system as the object of research and use a combination of computational fluid dynamics (CFD) and experimental validation to investigate the effects of the number of blades, blade inlet angle, blade outlet angle, blade height, and blade thickness on the aerodynamic performance of the fan. A five-factor, four-level orthogonal test table L16 (45) was selected to obtain the optimal combination of structural parameters for the impeller. In addition, in order to identify and visualize the features of the vortex, Q Criterion Normalized is applied to the simulation on the basis that the minimum pressure appears in the vortex core. In this study, Q Criterion Normalized is used to compare the internal vorticity of the prototype with that of the optimized prototype. The results show that (i) the order of influence of each parameter on the aerodynamic performance of the centrifugal fan is blade height &gt; blade outlet angle &gt; blade inlet angle &gt; number of blades &gt; blade thickness; (ii) the optimal combination of the structural parameters is number of blades 48, blade inlet angle 80°, blade outlet angle 120°, blade thickness 0.6 mm, and blade height 23 mm; the optimized prototype has an increase in air pressure of about 10%, an increase in air volume of about 31%, and an increase in efficiency from 49.61% to 53.57%; (iii) the intensity of internal vortices in the optimized prototype is weakened, the size of vortices and the number of vortices are reduced, and the homogeneity of the flow field is also improved.

Water-exit dynamics of a ventilated underwater vehicle in wave environments with a combination of computational fluid dynamics and machine learning

A ventilated vehicle exiting water in a wave environment is a complex nonlinear process, and the mechanism by which the wave conditions influence this process remains poorly understood. This paper describes realistic simulations of a ventilated vehicle exiting a water body under various wave conditions. Comprehensive analysis is conducted for a range of distinct wave scenarios, and a machine learning-based method is developed for the rapid forecasting of vehicle-related parameters. A three-layer backpropagation neural network is constructed, and its prediction performance is verified. Subsequently, predictive and optimization procedures are employed to determine the optimal wave phase for the water exit of the vehicle. Different wave conditions are shown to significantly affect the evolution of the ventilated cavity as well as the kinematic and loading characteristics of the vehicle. The pitch angular velocity and angle at the moment when the head of the vehicle reaches the free surface exhibit a positive cosine trend under different wave conditions. No regularity of the pitch angular velocity at the moment when the tail reaches the free surface is evident. The neural network exhibits exceptional proficiency in predicting the motion parameters and load characteristics of the vehicle. The optimal point for the vehicle to exit the water is determined to be at a wave phase of 0.125π, while the most hazardous point occurs when the wave phase is 1.1875π.

On the hysteresis characteristics and compensation control strategy of a pneumatic hydrodynamic retarder

As the core component of the hydrodynamic retarder braking system, pneumatic proportional solenoid valve plays an important role in the process of braking torque control. However, the hysteresis characteristic caused by friction and air compressibility will lead to the deviation of braking torque. In order to solve this problem, the hysteresis characteristic of pneumatic proportional solenoid valve is experimentally studied, and the causes of hysteresis phenomenon are analyzed through mathematical modeling of the valve core. Then, a high-precision prediction model of braking torque is obtained by combining Computational Fluid Dynamics (CFD) and response surface method, which is used to construct the mapping relationship between control air pressure and braking torque. Furthermore, a feedforward controller based on Prandtl–Ishlinskii inverse model is designed to compensate the hysteresis characteristics. On this basis, a compound hysteresis characteristics compensation control strategy combined with Proportional Integral Derivative (PID) feedback control is proposed to realize the accurate control of braking torque. Finally, the test verification of braking torque control is carried out. The average error of compound control, feedforward control, and PID control under torque step working condition is 3.16%, 5.97%, and 6.23%, and the response time is 3.75, 8.75, and 3.2 s, respectively. The compound control strategy also has smaller torque error and shorter response time under both ramp torque tracking and constant torque conditions. To sum up, this compound control strategy can effectively compensate the hysteresis characteristics of the pneumatic hydrodynamic retarder and ensure the driving safety by improving the control accuracy and response speed of braking torque.

Application of DMD method to eccentric self-excited vibration of tubular turbine runner

Tubular turbines are widely used in the development and utilization of low-head marine energy. The cantilever type runner inevitably leads to gap between the chamber and blade tip, and non-standard installation can easily lead to eccentric runner, affecting the safe and stable operation of turbine. The overall process of self-excited vibration is an obvious nonlinear process, so the applicability of traditional methods needs to be discussed. In recent years, reduced-order modelling methods based on linear dynamic systems have gradually been introduced into the analysis of hydraulic machinery, and have achieved certain results. Therefore, this study combines computational fluid dynamics (CFD) and dynamic mode decomposition (DMD) methods to conduct low-dimensional mode decomposition and comparative analysis of self-excited vibration caused by different runner eccentricities of tubular turbine. The research shows that as the runner eccentricity continues to increase, the modal frequency distribution of self-excited vibration becomes complex. The DMD method disassembles the low-level representations of self-excited vibration signals, greatly enriching the understanding of eccentric self-excited vibration of the runner, and providing assurance for the safe and stable operation of tubular turbines.

3D modelling of the hydraulic performance of open-graded asphalt using the DEM and CFD methods

In this study, we propose a modelling approach that combines computational fluid dynamics (CFD) and the discrete element method (DEM) to simulate the hydraulic performance of three-dimensional (3D) porous asphalt (PA) specimens. Initially, realistic aggregate appearances are captured through the image analysis technique. This information is then transferred to DEM software for the reconstruction of pore structure with distinct aggregate shapes following the real particle size distribution (PSD) as identified by local construction standards. The pore structure, reproduced via DEM, is used as input for CFD simulation to assess the hydraulic performance. To capture the dynamic of water motion simulation, the multiphase model, volume of fluid method (VOF), within CFD is applied to track and predict the interactions between different material phases, and the effect of porous structure to the hydraulic behaviour and performance. Results of numerical simulations indicate that PA samples with higher total porosity (air voids) have better pore connectivity and more effective porosity. For samples with the total porosity of 20%, the difference between the total porosity and the effective porosity was 14.8%; while a much lower difference of 8.3% was found for the higher porosity (25%) specimen. Compared with effective porosity, the reduction in active porosity reaches 58.8% in deeper layers for the sample at 20% total porosity, and only 32.2% for the sample at 25% total porosity. Overall, the results show that there is about a 4.57 times difference in hydraulic performance represented by mass flow rate among PA samples with 5% total porosity difference.

A Computational Fluid Dynamics–Discrete Element Method Model for Physics‐Based Simulation of Structure Formation during Battery Electrode Drying

Drying is a critical process step during battery electrode production due to its microstructure defining nature. Distribution and interconnectivity of active material particles, pores, and the overall porosity significantly influence the later cell performance. Knowledge about structure formation as well as electrode property prediction are crucial for optimization and targeted electrode design. However, the exact microprocesses during electrode drying are not yet understood well and very difficult to access experimentally. Therefore, in this study, a combination of computational fluid dynamics (CFD) and discrete element method (DEM) simulation models for investigating structure formation considering particle–particle as well as fluid‐particle interactions and vice versa is presented. , The volume of fluid method is used for taking into account the fluid‐fluid interface, evaporation and capillary interactions. The simulations reveal the formation of a top–down consolidation front which interacts with the fluid leading to a backflow of liquid. The results show good agreement with experimental measurements for NMC622 cathodes. Furthermore, the influence of production parameters such as mass loading and solids content is examined. The findings demonstrate the modeling tool's suitability for process engineers to anticipate and enhance electrode characteristics and facilitate scientists to understand complex structure formation relationships.

Experimental assessment of modelling VOC emissions from particleboard into a ventilated chamber

The interactions between the emission of Volatile Organic Compounds (VOCs) and airflow play a key role in determining indoor pollutant concentrations. In the framework of Indoor Air Quality (IAQ) prediction, numerical models based on fluid dynamics are useful tools to address typical cases and then make decisions to improve both public and occupational health. This requires an accurate description of VOC emission processes from building materials, products and furniture, taking into account the diffusion of VOCs into these materials instead of using constant surface emission rates. A predictive model combining Computational Fluid Dynamics (CFD) for airflow and kinetics modelling for VOC emissions is described. The validation of airflow and VOC transfer models is carried out by comparing experimental and simulation results into a 128 L ventilated experimental chamber.Our results show that the Reynolds-Averaged Navier Stokes (RANS) approach, together with a standard k-ω turbulence model, provide good agreement between numerical and experimental airflows regarding both air velocity and age of air profiles. The overall VOC dynamics is also reproduced by numerical simulation for the eight VOCs monitored. The predicted absolute VOC concentrations are in agreement with experimental data for acetone, formaldehyde, hexanal and terpenes. However, numerical simulations underestimate the concentration of pentanal, propanal, butanal, acetaldehyde, which is discussed in terms of representativeness of emission parameters. Our results highlight that CFD simulations combined with VOC emission kinetics modelling is a promising tool for IAQ prediction, but it requires further investigation to fully understand the observed behaviour for some VOCs. This finally demonstrates the crucial need of tracking various VOCs simultaneously in the framework of assessing an IAQ prediction model.

Numerical simulation and structural optimization of transpiration cooling with gradient porous matrix in supersonic condition

Transpiration cooling with gradient porous matrix is employed to address the non-uniform distribution heat flux in the applied environment. The cooling characteristics in supersonic condition are analyzed by developing porous matrix with varied coolant mass rates, structure schemes and coolant injection methods. The simulation results indicate that temperature on the trailing edge changes less compared with the leading edge due to the film lifting outside the structure during coolant mass rate adjustment. Furthermore, it is discovered that the segment with greater permeability does indeed result in more coolant allocation, but it does not lead to a better cooling effect. Meanwhile, the coolant injection pressure is primarily influenced by the leading edge in the gradient porous matrix, despite its highest permeability. Based on the above analysis, a new optimization process combining Computational Fluid Dynamics (CFD), Artificial Neural Network (ANN) and Multi-Objective Genetic Algorithm (MOGA) is proposed. This optimization process is implemented through the coupled simulation of commercial software ANSYS FLUENT and MATLAB using User Defined Function (UDF). The configuration parameters are set as design variables, and cooling efficiency at the stagnation point and dimensionless injection pressure are selected as optimization objectives. The optimization results demonstrate an improvement of 8.77% in cooling efficiency and a reduction of 10.31% in dimensionless injection pressure compared to the original structure. This procedure provides a reliable and accessible reference for solving a series of similar optimization problems.

Performance analysis of several EVs batteries thermal management systems under hot-arid climatic conditions of Morocco

The transition towards a 100 % renewable energy society has become imperative to mitigate the impacts of climate change and reduce CO2 emissions. To attain this objective, the use of electric vehicles (EVs) is crucial, particularly considering that the transportation sector is a major contributor to pollution and energy consumption. However, for optimal performance, EV batteries need to operate within a temperature range of 15 to 35 °C, which poses challenges in hot desert locations like Morocco. This paper aims to evaluate the effectiveness of three Thermal Management Systems (TMS): (i) aluminum minichannel, (ii) aluminum cold plate and (iii) phase change material under various climate conditions in Morocco. All three TMSs are commonly used and easily manufacturable for electric vehicle batteries, specifically in the hot-arid climate of Morocco. The assessment combines computational fluid dynamics (CFD) with on-site meteorological data collected from different climatic zones. The findings reveal that among the examined TMS options, the aluminum mini-channel system proves to be the most suitable for EVs operating in Moroccan climates. This system effectively maintains the battery temperature within the desired range, with maximum temperatures (Tmax) of 27.30 °C in Oujda, 27.34 °C in Errachidia, and 27.29 °C in Agadir. Furthermore, the aluminum mini-channel TMS exhibits the lowest pumping power requirement (3.89e-7 W) and introduces the least additional mass (13.63 %) compared to all the other examined TMS systems.