International Journal of Thermofluids最新文献

Biodiesel has emerged as a compelling substitute for conventional diesel fuel, providing a sustainable and eco-friendly choice for fueling compression ignition engines. This comprehensive study investigates the influence of biodiesel, specifically derived from sunflower oil, through the esterification method, on crucial engine performance parameters and environmental effects. The study examines the impact of varying engine torque on the performance of a single-cylinder, four-stroke compression ignition engine, encompassing parameters such as brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC), as well as exhaust emissions, including unburned hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NOx). Four distinct biodiesel blends (B10, B20, B30, B40) with varying sunflower oil content are systematically compared to pure diesel (B0). The engine operates at a consistent speed of 1700 rpm, while the torque undergoes controlled adjustments from 0 to 10 Nm. Subsequently, this study explores the application of an alternating model tree (AMT) machine learning algorithm to establish relationships between independent factors, specifically torque and biodiesel volume (%vol), and dependent variables, including BTE, BSFC, CO, and NOx in a combustion engine. Additionally, the study employs a multi-objective ameliorative whale optimization algorithm (AWOA) to optimize the model's output. The objective is to identify optimal values for torque and%vol that maximize engine performance (BTE) while minimizing engine emissions (CO and NOx) and reducing fuel consumption (BSFC). The optimization process yields noteworthy results, with AWOA achieving peak BTE at 29.714 %, BSFC at 0.262 kg.kWh^-1, and NOx emissions at 992 ppm at torque 7.3 N.m and 13% vol. In contrast, particle swarm optimization (PSO) secured the minimum CO level at 0.123 %, with torque set at 7.6 N.m and 26% vol. The AMT models demonstrate high prediction accuracy, with coefficient of determination (R²) values exceeding 0.98.

This review aims to comprehensively consolidate the knowledge and understanding of the development of thermal insulation materials for buildings using lignocellulosic waste materials. The building sector currently accounts for approximately 40 % of global energy consumption. Lignocellulosic waste materials are abundantly available worldwide, with their effective repurposing imperative to achieve positive environmental impacts. Lignocellulosic waste can be directly employed as a thermal insulator with minor modifications and binders. These materials consistently exhibit thermal conductivities below 0.1 W/m.K, the defining criterion for effective thermal insulation. An alternative strategy involves incorporating lignocellulosic waste as a filler or reinforcement agent within thermoplastics and thermosets to enhance their physicochemical attributes and render them suitable as thermal insulators. This approach aligns with sustainability principles and enables the replacement of 30–50 % of the polymer content with renewable material, with the resultant thermal conductivity values in these investigations also below 0.1 W/m.K. Moreover, with interest centered on the integration of lignocellulosic waste into the modification of biodegradable polymers to produce thermal insulation materials, prospects are promising for utilizing treated lignocellulose, cellulose, and their derivatives (such as nanocellulose and microcrystalline cellulose) as economical materials for developing biodegradable insulators. This review presents a thorough analysis and comparison of these approaches, providing researchers with insights for designing future research methodologies and addressing existing gaps in knowledge.

The current research explores the useful impact of artificial neural networks (ANN) back-propagation along Levenberg-Marquardt Method (ANN-BLMM), for findng the influence of dimensionless numbers on the flow distribution pertaining magnetohydrodynamics (MHD) Jeffery–Hamel fluid amid two plates which are enclined at angles 2α. We have employed a numerical approach, ANN-BLMM to assess various aspects of our data, including testing, training, validation, Mean Square Errors (MSE), performance, and fitting. The methodology being used has been tested and validated through comparison with other results obtain numerically, showing extreme level of accuracy. Moreover, we have confirmed our findings through error histograms and regression tests. We used OHAM for the data set. Furthermore, we have also explored the influence of Reynolds number (R) on both flow and pressure distribution, visually representing our findings through graphical analysis. We have discussed about the nature and variations of velocity profiles within MHD Jefery–Hamel flow (MHDJHF), taking into account various values of Ha and Re in both convergent and divergent channels. It was discovered that a significant stabilizing influence of an increase in the magnetic field intensity was observed for both diverging and converging channel geometries and as the Hartman numbers rise, so does the fluid velocity. The absolute error is reduced to 10^–2 to 10^–6.

This study presents a novel investigation into the thermal behavior of a hybrid nanofluid (MgO − Ag − H₂O) in natural convection around a porous fin in a triangular enclosure under the influence of radiation and magnetohydrodynamic effects. The research uniquely combines these complex phenomena, addressing a significant gap in the literature. This configuration has potential applications in advanced solar thermal collectors, electronic cooling systems for high-power devices, and compact heat exchangers in various industries. The main objectives are to understand how various parameters influence heat transfer and fluid flow behavior and to optimize the design for enhanced thermal performance. The study considers a range of variables including Rayleigh number (10³  − − 10⁶), Hartmann number (0 – 50), Aspect ratio (0.3 – 0.6), radiation parameters (Rd = 1  − − 5,  λ =  1 − 5), and volume concentration (0- 0.05), which have been numerically analyzed using the finite element method (FEM). The findings reveal that increasing the Darcy number (Da) enhances heat transfer at low Rayleigh numbers (Ra  =  10³,10⁴). However, at higher Ra (Ra  =  10⁶), the impact of Da becomes more complex, with a critical Da beyond which heat transfer efficiency decreases due to an increase in flow resistance. The nanoparticle volume concentration plays a vital role, as higher concentrations lead to improved heat transfer efficiency, especially at higher Ra, through enhanced thermal conductivity and thermal dispersion. The length of the porous fin greatly impacts fluid flow patterns and heat transfer rates, with longer fins creating more complex flow patterns, promoting enhanced heat transfer and stronger thermal plumes. Thermal radiation, represented by the radiation parameters (Rd and λ), significantly influences both the heat transfer rate and the convective flow patterns within the enclosure. This study also incorporates a comprehensive entropy generation analysis, providing novel insights into system irreversibilities and optimization potential. The entropy analysis reveals the complex interplay between various parameters and their impact on system efficiency, offering valuable guidance for designing high-performance thermal management systems.

The proposed research is an attempt to advance the state-of-the-art of the numerical modelling of RPB by combining Computational Fluid Dynamics and Machine Learning approaches. The latter creates an accurate framework that should help quantify the potential of Rotating Packed Beds (RPB) technology to intensify conventional CO₂ capture processes. To this end, a direct sensitivity analysis is detailed to supplement a machine-learning (ML) algorithm built for calibrating resistance coefficients needed for porous media modelling. The algorithm is used to improve CFD predictions of dry pressure drop in rotating packed beds (RPBs) for a wide range of operating conditions. The sensitivity derivatives with respect to the packing resistance coefficients are demonstrated for the first time in RPBs, which is not available in the current CFD open source and commercial codes. In this regard, sensitivity differential equations are derived from three-dimensional Navier-Stokes equations for porous media in a rotating reference frame. These sensitivity equations are discretized using a finite volume scheme and solved to obtain the sensitivity pressure drop differences at the packing edges. The results are validated against the predictions of the analytical sensitivity analysis and the finite difference approximation. After, the Newton – Gauss method that employs the sensitivity pressure drop derivatives, is used to minimize the error (cost function) between the pressure drop obtained from CFD simulations and the available experimental data. This is achieved by tuning the packing resistance coefficients to the RBPs' operating conditions (gas flowrate and rotating speed) and correlate them using an artificial neural network (ANN). The results of the proposed approach show a significant improvement in porous media-based CFD predictions of RPBs' pressure drop across a wide range of operating conditions and this over conventional porous media-based CFD models. This is necessary for CFD models to be reliably used as a tool that can efficiently improve existing RPBs' designs and/or participate in RPBs' design innovation.

This study investigates the complex thermal-fluid behavior within a tilted porous enclosure filled with a Cu−Al₂O₃-water hybrid nanofluid, subject to segmented magnetic fields, wavy cooling segments, and distributed heat sources. The research explores the intricate interplay between geometric factors and thermal-magnetic forces to enhance heat transfer in industrial applications. The finite volume method (FVM), coupled with the SIMPLE algorithm and a TDMA solver, is employed to solve the governing transport equations. A comprehensive parametric analysis examines the effects of key dimensionless parameters: Darcy-Rayleigh number (10–10⁴), Darcy number (10^-4–10^-1), Hartmann number (0–70), magnetic field angle (0°-180°), nanoparticle volume fraction (0–2 %), porosity (0.1–1.0), wavy cooler undulation height (0–0.3), magnetic segment width (0–1), number of segmental magnetic fields (0–4), and enclosure tilting angle (0°–180°). The study elucidates the physical mechanisms underlying the transition from uniform to segmented heating scenarios. Results reveal a remarkable enhancement of up to 38 % in heat transfer performance when transitioning from a conventional square enclosure to the proposed configuration with partial waviness on opposing walls. This improvement stems from increased surface area and disrupted thermal boundary layers, promoting better fluid mixing. The application of a segmented magnetic field with strategic orientation resulted in up to 26 % enhancement by modulating flow patterns and creating localized convection cells. The segmented heating generates thermal plumes that interact with the magnetic field-induced Lorentz forces, further improving thermal transport. The findings provide valuable insights into the design and optimization of efficient heat transfer systems in various industries, including electronics cooling, solar thermal collectors, and nuclear reactors, demonstrating significant potential for energy savings and improved thermal management through the strategic integration of hybrid nanofluids, magnetic fields, and geometric modifications in porous media applications.

The aviation industry accounts for part of the CO₂ emissions contributing to climate change. The industry has established a target to reduce 2050 net aviation carbon emissions by 50 % relative to 2005 levels. With this in mind, waste heat recovery is a key pathway to achieve reduced emissions and improve system efficiency. The waste heat may potentially be converted to electric power using a supercritical CO₂ Brayton power cycle. The sCO₂ power system offers the advantage of compactness owing to the high working fluid density, which is an important consideration for aircraft performance. The present work focuses on the integration of the sCO₂ power system into the aircraft propulsion system and evaluation of its performance. Detailed optimization of the sCO₂ waste heat system will be evaluated with a focus on cycle efficiency and net power under different operating conditions, including ground, takeoff, climb, cruise, and landing operations. The study is divided into two parts with two different turbofan engines, one with a nominal thrust of 30 kN and the other with a nominal thrust of 9 kN. The first part shows the effect and operation of the waste heat recovery unit under the different operating conditions. The second part is focused on cycle optimization and performance evaluation. The results demonstrate the potential of waste heat recovery during a range of operational conditions. The sCO₂ cycle efficiency can reach between 25 and 39 % (depending on aircraft engine) with net power output in the range of 100 to 260 kW.

This research attempts to study the thermal activity of suspension consisting of water and NEPCM elements inside a cavity with a trapezoidal cross-section. In addition, the cavity center contains a cold circular object rotating at a constant speed. The cavity is thermally characterized by the following: the upper and lower walls are thermally insulated (adiabatic), while the two lateral walls have a high temperature. The purpose of this study is to find out how the suspension interferes with the transfer of heat from hot walls to the cold body through the intervention of some initial conditions, which are: The effects of rotating cylinder speed (Re = 1 -500), medium permeability (Da = 10^–5 – 10^–2) and the magnetic field strength (Ha= 0 -100). The study used a digital simulation by solving the differential equations related to fluid mechanics and heat transfer using the Galerkin finite element (GFEM) method.to understand the influences of studied parameters (Re, Da and Ha numbers), the contours of isotherms, heat capacity and pathlines are presented in terms of these parameters. The findings indicated that as the rotation speed of the obstacle increased, the forced convection became dominant, and the thermal transmission rate improved. The improvement of the thermal transfer rate was also observed for higher Da numbers. Increasing the strength of the magnetic field hiders the fluid motion and reduces the thermal activity. At the highest studied Re, increasing Da from 10 ^{to 5} to 10^–2 augmented the Nusselt number by 10 times, while augmenting Ha from 0 to 100 reduced the Nu by 46 %.

This study investigates the complex behavior of Jeffrey nanofluid flow in a porous oscillating microchannel under the influence of magnetohydrodynamic (MHD) effects. The research explores how magnetic field distortions lead to diverse accumulation patterns of nanofluid particles, a phenomenon attributed to homogeneous magnetization in fluid dynamics. Nanoparticles ranging from 0.25 % to 0.5 % (low and inexpensive concentrations) are remarkably consistent for best results in most machining procedures. Different concentrations of various nanomaterial's is utilized (φ₁ = φ₂ = 0.01,  0.02,  0.03,  0.04) to make the simple nanfluid and hybrid nanofluid suspensions. By employing fractal-fractional derivatives governed by power law, a mathematical model developed to describe the time-varying, compressible MHD flow of Jeffrey nanofluid. The model incorporates the effects of heat transfer, pressure, and magnetic fields on the fluid dynamics. A novel fractional approach utilizing the Laplace transform is applied to solve the fractal MHD hybrid-fluid model integrated into a porous medium. The study reveals velocity flow decrease with increasing Reynolds numbers but increase with channel inclination. Additionally, both the Darcy number and magnetic field orientation enhance heat transfer rates. In addition, the velocity profile enhanced by the hybrid nanofluid suspension as compared to simple nanofluid flow. The research validates its findings by demonstrating the convergence of fractional and numerical solution methods. Furthermore, the study compares the performance of different hybrid nanofluids, concluding that water-based (H₂O + GO + MoS₂) hybrid fluids exhibit slightly superior characteristics compared to (CMC + GO + MoS₂) hybrid nanofluids.

Models designated to predict flow patterns in microscale geometries with enhanced surfaces such as micro-finned tubes are scarce in the literature and new low-GWP HFOs could benefit from the utilization of such geometries. Therefore, HFO1234ze(E)’s condensation flow patterns were subjected to visualization inside micro-finned tubes ranging from 4 to 7 mm outer diameters with various geometrical figures. The saturation temperature was set to 30 °C, vapor qualities ranged in the scope of 0.01 to 0.9, and mass fluxes in the magnitude of 100 to 400 kg m^-2 s^-1. Four distinctive flow patterns are observed, namely intermittent, annular, wavy-stratified, and transitional. Stratification only transpired at low mass fluxes (mainly below 100 kg m^-2 s^-1) and intermittent flow was only present at vapor qualities close to full condensation. The range in which transitional flow is observable shrinks with a progressive trend of mass flux. The impact of diameter was observed to be negligible, however, a more meticulous assessment highlights smaller ranges of vapor qualities in which transitional flow is present for the tube of 5 mm OD whose helix angle is substantially larger. Datapoints were juxtaposed to previous models of Doretti et al., Chen et al., Mandhane et al., and Jige et al. and the results attested to an inadequacy of accurate predictions made for tube of 4 mm OD. Noting the absence of surface tension force in the aforementioned maps, a novel flow pattern map based on modified Froude versus modified Weber numbers provided an accurate prediction for the three cases under study. Ultimately the model was also deemed fairly suitable for visualization datasets collected from literature.