Optimal Heat Transfer Research Articles

Stagnation point flow of hybrid nanofluid driven towards a sinusoidal-radius permeable circular cylinder

ABSTRACT Recent advancements in nanofluid technology have emphasized the potential of ternary hybrid nanofluids (THNFs) to significantly enhance thermal and hydrodynamic properties. The implementation of a wavy sinusoidal cylinder is proposed as a novel approach to optimize heat transfer in a variety of engineering applications. This study focuses on analysing the flow and thermal behaviour of TiO2-Al2O3-MoS2/Kerosene oil a THNF around a circular cylinder with a sinusoidal radius, demonstrating enhanced heat conduction capabilities compared to conventional hybrid nanofluids. The governing equations, formulated as partial differential equations, are solved using the bvp4c solver. The study reveals that the shape of nanoparticles plays a crucial role in heat transfer efficiency. Specifically, when the solid volume fraction increases from 1% to 3%, brick-shaped particles exhibit the lowest heat transfer enhancement of 25.80%, while blade-shaped particles achieve the highest enhancement of 68.24%. Additionally, the heat transfer coefficient improves from 1.5% to 1.8% across different nanoparticle shapes under varying dissipation parameters. Similar trends are observed in the reduction of drag force with changes in the velocity slip parameter. These findings underscore the significant impact of nanoparticle shape and volume fraction on the thermal performance of THNFs, offering valuable insights for optimizing heat transfer systems in engineering applications.

Effect of stacking pattern of multilayered polyetheretherketone/boron nitride composites on the mechanical and thermal properties: Experiments and molecular dynamics simulations

The effect of stacking patterns in multilayered polyetheretherketone (PEEK)/boron nitride (BN) composites was investigated to improve the thermal conductivity and mechanical properties. The thick PEEK and BN layers in the multilayered composite were the best multilayer structure, resulting in high mechanical properties and in-plane thermal conductivity due to the many strong electrostatic interaction sites between boron nitride nanosheets (BNNSs). Molecular dynamics simulations were used to clarify the enhanced mechanism of multilayered structure on thermal conductivity. The multilayered structure with combinations of PEEKs and thick BN layers composed of large BNNSs led to the optimization of heat transfer due to the effective phonon transfer path. The best multilayered composite had the highest in-plane thermal conductivity, which was 471% higher than that of a PEEK. This study provides information about the filler size and stacking patterns for more effective multilayer structures with polymer and filler layers to achieve high performance on mechanical and thermal properties.

Optimizing heat transfer in solar air heater ducts through staggered arrangement of discrete V‐ribs

AbstractThis research paper details an experimental study on airflow dynamics in a solar air heater. The heater's design incorporates unique, discrete V‐shaped ribs with staggered elements to enhance thermal performance. The study investigates the influence of various roughness parameters on flow characteristics. These parameters include a relative coarseness pitch (P/e) ratio of 12, a rib inclination angle (α) of 60°, a relative coarseness height (e/Dh) of 0.043, and a staggered element arrangement with a positioning ratio (p′/P) of 0.65. Additionally, the investigation includes scenarios with three gaps (Ng) between elements and a gap‐to‐rib width (g/e) ratio of 4. The research focuses on how changes to the Reynolds number, ranging from 3000 to 14,000, and alterations to the ratio of staggered element positioning to rib height (r/e), from 2 to 5, impact the flow dynamics. The outcomes indicate a significant boost in heat transfer performance, with the Nusselt number rising to 3.76 compared with a conventional smooth duct. The highest thermal efficiency recorded was 86%, at an r/e ratio of 3.5. These results underscore the potential of using discrete V‐ribs with staggered elements in rectangular ducts to improve heat transfer efficiency.

Optimization of entropy and heat transfer in a magnetohydrodynamic marangoni convection flow of biviscosity bingham hybrid nanofluid through convergent channel

This study examines the entropy generation and heat transfer performance in a Jeffrey-Hamel flow of biviscosity Bingham fluid with the addition of Al2O3 and Cu nanomaterials in the presence of a thermal Marangoni convective process. An emerging Jeffrey-Hamel problem is extended with the implementation of the Bingham fluid stress tensor in a Naiver Stokes equation. The governing equations with Marangoni boundary conditions due to surface tension are solved computationally utilizing the Keller-Box methodology. The findings demonstrate the complex interplay of entropy production processes, fluid-solid interfaces, and thermal Marangoni convection. Findings demonstrated that increasing Marangoni, Bingham parameters, and thermal radiation enhances the rate of heat transmission. The influence of the Marangoni and Bingham parameters on skin friction is conflicting in a narrow channel. System entropy and flow field uplift with a higher Marangoni convection parameter. Increasing the Reynolds number significantly increases the drag force, whereas the effect of the magnetic variable is the reverse. Velocity is an increasing function of the Reynolds and Bingham parameters and deteriorates with the load of nanomaterials. Temperature is a rising function of nanomaterial load, Eckert, and Bingham parameter. The results of this investigation have important ramifications for improving heat transfer, coolant systems, and nozzles design.

Thermal distillation of hypersaline waters toward zero liquid discharge via spontaneously siphon-channeling crystallized salt

Membrane distillation of hypersaline solution with low-grade waste heat is particularly outstanding to address the challenge of water-energy nexus. However, the high causticity of hypersaline solution always makes this system complicated and expensive. Here, we design a passive membrane-free thermal distiller for hypersaline desalination with high stability, easy operation and low cost. A siphon channel is constructed with the capillary wick and the crystallized salt, which can self-flush the crystallized salt and assure the stability of this passive system. Together with optimizing heat transfer process to suppress temperature polarization, the integrated distiller system can efficiently collect water and salt, achieving zero liquid discharge. The system can stably desalinate 70 g/L NaCl solution, with the water yield of 1.33 kg m−2h−1 and salt collection of about 0.34 kg m−2h−1 at 60 °C. This passive system is expected to desalinate hypersaline water in large-scale commercial production, for example, integrating with desalination industry.

Entropy generation in newtonian vs non-newtonian nanofluid flow under vibration

Numerical investigation into the effects of vibration on heat transfer and entropy generation in Newtonian and Non-Newtonian nanofluid flows through pipes reveals enhanced heat transfer via intensified fluid agitation and improved particle dispersion. Thermal entropy generation analysis shows reduced irreversibility in vibrated flow, indicating improved flow mixing. Vibration enhances heat transfer by intensifying fluid agitation and promoting particle dispersion near the wall, resulting in a significantly more uniform temperature distribution along the pipe, approximately 100 times more than steady-state flow. This study underscores vibration’s potential to optimize heat transfer and reduce entropy generation in nanofluid systems, emphasizing velocity and rheological impacts. Comparison of vibrated flow to steady-state flow for Newtonian and non-Newtonian fluids reveals significant improvements under vibration, particularly at lower Reynolds numbers where non-Newtonian fluids exhibit pronounced effects. Future research directions include exploring thermal radiation’s impact on entropy generation, analyzing different nanofluid compositions, and investigating varied boundary conditions and geometries to advance understanding in this field. This study provides valuable insights into the complex interplay among vibration, fluid dynamics, and heat transfer in nanofluid flows. Its findings have practical implications for optimizing thermal management systems in diverse engineering applications.

Optimizing thermal performance in tube-in-tube heat exchangers with metal foam inserts: experimental and RSM analysis

ABSTRACT This study investigates tube-in-tube heat exchanger performance under steady-state conditions, examining co-current and counter-current flows. It explores various configurations, including metal foam enhancements, to optimize heat transfer efficiency and minimize pressure drop. Driven by the potential of metal foam to enhance heat transfer, the study aims to demonstrate significant improvements in thermal performance compared to conventional designs. Experimental methods involve testing four configurations with Nickel foam (10 PPI, 0.90 porosity) inserts, evaluating temperatures and pressure drops across flow rates of 25 to 50 LPH. Hot water at 65°C flows in the annulus, while normal water at 31°C flows in the outer annulus. Results show metal foam’s substantial enhancement in heat transfer, with counter-current metal foam configurations achieving up to 3.71 times greater efficiency than parallel flow setups. Moreover, comprehensive performance evaluation reveals that the counter-flow heat exchanger with metal foam is 2.03 times more efficient than the conventional co-current flow without metal foam. Conclusions highlight metal foam’s effectiveness in improving heat transfer performance and optimizing thermal systems through systematic parameter optimization using response surface methodology.

Influence of mass flux ratio and phase difference on impingement heat transfer characteristics of coaxial synthetic jet

The aim of the present study is to introduce and investigate the performance of a novel independently controlled coaxial synthetic jet (CSJ) designed for enhancing impinging heat transfer applications. The CSJ features a unique configuration with two coaxial cavities, each featuring inner and annular orifices of the same hydraulic diameter, all aligned at 0∘ The independently controlled CSJ allows precise manipulation of jet conditions and phase differences between its inner and outer cavities, offering a versatile platform for optimizing heat transfer performance under varying operational scenarios. This capability not only enhances our understanding of the heat transfer characteristics of the CSJ but also underscores its potential as an innovative tool for advancing thermal management in engineering systems. Here, the actuators are operated at a Reynolds number (Re) of 1260 to perform the jet impingement experiment. The study explores mass flux ratios, including 0.5, 1, and 2, to understand how these ratios affect impinging heat transfer characteristics. The investigation also explores the influence of different phase differences (∅) between the inner and outer diaphragms, ranging from 0∘ to 180∘ for different mass flux ratios. It has been observed that the percentage enhancement of the peak stagnation Nusselt number (Nustag) for Mr=0.5 is approximately 30.36░% and 53.68░% compared to Mr=1 and Mr=2, respectively. The peak Nustag enhancement of CSJ operated at Mr=0.5 is approximately 63.9░% and 58.6░% compared to the inner jet and annular jet operated separately. In the far field, when a phase difference of ∅=135∘ is provided, the peak Nustag is enhanced by approximately 17.29░% compared to ∅=0∘ for Mr=0.5. Based on the present study, it is recommended to operate the CSJ at a lower mass flux ratio (Mr=0.5) and a higher phase difference between the actuators (∅=135∘) to achieve maximum cooling effect from the heated plate.

Optimizing heat transfer with electro-osmotic ciliated propulsion in a non-uniform canals with Cu-blood characteristics considering thermal casson nano fluid

Electroosmosis peristaltic flows find promising applications in electro-fluid thrusters in space propulsion, polymer injection systems, ion exchange membrane designs, microbe fuel cells, biochip fabrication and fossil fuel energy process. In light of this, the current work aims to investigate blood based electroosmotic peristaltic flow in a ciliated, non-uniform propagation channel when copper nanoparticles are present. For that purpose, mathematical modeling is done in two dimensional frame and then governing partial differential equations are simplified and converted in ordinary differential equations using dimensionless quantities and long wave length and low Reynolds number approximations. In result we got coupled nonlinear boundary value problem that is solved numerically using three-stage Lobatto IIIa formula known as bvp4c invoking shooting technique. Peristaltic ciliated waves are thought to pass along the channel wall. The properties of both fluid phase and particle phase flow are modeled and investigated. Plotting against various parameters, streamline contours, temperature contours, concentration and temperature profiles, and pressure rise graphs are examined in-depth from a physical perspective. The obtained results revealed that concentration profile α upsurges for rising values of Casson fluid parameter β, nanoparticle volume fraction ϕ and flow rate Q while it drops with an increase in particulate volumetric fraction C, Eckert number Ec, Soret number Sr, Schmidt number Sc and viscosity constant. A decrease in temperature is also caused by an increase in the volumetric percentage of nanoparticles. In the pumping portion, there is a decrease in pressure rise; however, this decrease changes to an increase in the increased pumping zone.

Optimizing heat transfer predictions in HCNG engines: A novel model validation and comparative study via quasi-dimensional combustion modeling and artificial neural networks

Heat transfer from the walls of engine has a significant role on engine combustion, performance and emission characteristics. The study objectives to showcase the efficacy of the new model through an analysis by comparison with existing heat transfer models. New model is based on woschni model in which pressure and temperature is replaced by other fluid properties like density, thermal conductivity and dynamic viscosity. A series of experiments were conducted on a compressed natural gas internal combustion engine across varying hydrogen fractions, EGR ratios, engine speeds and different loads under stoichiometric conditions. The study demonstrates the efficacy of the proposed model by constantly achieving high prediction quality across a wide range of engine calibration coefficients by using Quasi-dimensional Combustion Model (QDCM) on MATLAB. Comparative analyses of new model with different heat transfer models were undertaken to validate the heat transfer rates with experimental results across a broad spectrum of operational conditions. It is observed that heat transfer rate is increased by increasing the engine load as 25%, 50%, 75% and 100% as 90.57J/deg, 130.12J/deg, 200.02J/deg, and 260.26J/deg with new model correspondingly. Heat transfer rate reduced by rise in engine speed with 1100 rpm, 1200 rpm, 1500 rpm and 1700 rpm is as 32.91 kW, 32.16 kW, 25.36 kW and 18.03 kW by new heat transfer model respectively. Artificial neural network (ANN) popular backpropagation algorithm is adopted to predict the heat transfer rate of HCNG engine, the five-input and one-output network structure are used. The values of correlation coefficient (R) and mean square error (MSE) were 0.99957 and 0.22667, 0.99998 and 0.010776, 0.99253 and 4.4762, 0.9961 and 1.2329, 0.99994 and 0.025108 for Woschni, New_Model, Chang, Sitkei and experimental respectively. This research work offers that ANN is a wise option for conventional modeling systems. In this way, the heat transfer rate of hydrogen-added CNG engines may be precisely predicted using ANN modeling.

Research on enhancing power plant net power by integrating modeling heat transfer and operation optimization of once-through cooling water system

For low power generation efficiency and water source thermal pollution caused by irrational operation of once-through cooling water systems (OCWSs) in condensing power plants, this paper advances a method to maximize the power plant net power by OCWS operation optimization and a 2 × 330 MW power plant is taken as a study case. First, the power plant thermodynamic system is simulated by Ebsilon to obtain exhausted steam parameters. Then, a heat transfer analysis method for the whole OCWS is investigated to calculate the system water temperature. Next, a maximum net power optimization model is deduced and established. Particularly, the model sets the temperature difference between OCWS outlet and inlet as a constraint. Finally, an improved firefly algorithm is employed to solve the model to get the numbers of constant-speed pumps and variable-speed pumps in operation and the frequency conversion ratio of the latter. This paper analyses the optimization under various scenarios and the sensitivity of optimization results. After optimization, the net power of the case power plant is increased up to 2080.5 kW, approximately 0.524 %. The OCWS temperature difference is decreased under most conditions. The method effectively enhances power plant net power and reduces drainage impact on the environment.

A hybrid machine learning-CFD method for the innovative analysis of Al2O3 nanoparticle deposition in shell-and-tubes heat exchangers

This study examines the intricate dynamics surrounding the deposition of Al2O3 nanoparticles within a heat exchanger, with the aim of optimizing heat transfer efficiency and gaining insights into gas dynamics. A comprehensive investigation of various parameters is conducted, including nanoparticle diameter ranging from 10 to 100 nm, heat flux variations from 500 to 3000 W/m2, Reynolds numbers spanning from 308 to 1540, and mass fractions ranging from 0.5 to 8 %. The methodology integrates machine learning algorithms with Eulerian and Lagrange methods, leveraging Python programming to deepen the understanding of complex deposition processes. Through the integration of random forest algorithms and SHAP values, the study achieves a model accuracy of 96.74 %, supported by minimal mean absolute error (6E-06) and root mean square error (2.5E-03). Key findings reveal the profound impact of heat flux, particularly at 3000 W/m2, on enhancing nanoparticle deposition. Furthermore, a direct correlation is observed between mass fraction and sedimentation, peaking at a mass fraction of 8 %. In laminar flow regimes, the Reynolds number profoundly influences sedimentation, with the sedimentation rate reaching its apex as the Reynolds number decreases. The diameter of nanoparticles also emerges as a crucial factor, with larger diameters correlating with increased sedimentation.

Assessment of the synergy of hydrophobicity and thermal conductivity in epoxy/graphite oxide composite coatings

AbstractIn various industrial applications, especially within the internal pipes of heat exchanger devices, there is a crucial need for surface coatings that offer both superhydrophobic properties and high thermal conductivity. Achieving the balance between these two characteristics is essential for optimizing heat transfer performance along metal pipe walls and mitigating the formation of water droplets on the surface. This research focuses on the development of polymer composite coatings to address these dual requirements, providing protection against humid environments, resistance to dew formation, and simultaneous enhancement of thermal conductivity. The key challenge lies in selecting a coating type that provides low surface energy and polarity, thereby achieving the desired hydrophobic properties while also maintaining adequate thermal conductivity. This study formulates polymer composite coatings utilizing laser‐modified epoxy resin and strategically integrates graphite oxide particles. These graphite particles undergo modification through oxidation to enhance compatibility with epoxy. In conjunction with graphite oxide modification, the resulting laser‐modified coatings exhibit super‐hydrophobic characteristics with an enhanced water contact angle of 162° and a low contact angle hysteresis (<5°). Furthermore, the epoxy/graphite oxide composite coatings demonstrate improved thermal conductivity, marking a significant 261% increase compared to pure epoxy, elevating it from .234 to .846 W/mK.

Analysis of natural convection heat transfer in a rectangular cavity with discrete heat flux: Implications for building thermal management using artificial neural networks

This study numerically investigates free convection within a rectangular air-filled cavity, simulating real-romm conditions. The top, bottom, and one sidewall are at constant temperatures, while the opposite sidewall has a constant discrete heat flux, akin to heater appliances. The impact of heating intensity, length, and position on temperature distribution is explored. Artificial Neural Networks (ANN) are utilized to correlate the average Nusselt number, providing a model for engineering applications in building thermal management. The dataset includes 2436 simulation runs with varying parameter: Rayleigh number (103 to 106), aspect ratio (0.5 to 2), heating surface length (0.1 to 1), and elevation (0.05 to 0.95). Results show increased Rayleigh numbers intensify the stream function and promote uniform temperature distribution. The elevation of the heating surface influences temperature distribution, with placement closer to the floor or ceiling optimizing heat transfer. ANN modeling predicts the average Nusselt number with high precision (±3%).

Numerical study of a novel battery thermal management system coupled with heat pipe and cold plate

This study addresses the critical issue of thermal management in lithium-ion batteries, presenting an innovative battery thermal management system that integrates heat pipes and a cold plate (IHPCP). Effective thermal management is crucial for enhancing the performance and longevity of lithium-ion batteries. Using three-dimensional numerical simulations, the study evaluates the impact of various parameters, including the number of heat pipes, the ratio of the plate runner’s width to the heat pipe’s thickness (ω), and the ratio of the cold plate’s thickness to the battery’s thickness (ζ), on battery temperature regulation. The results demonstrate that incorporating heat pipes significantly reduces the maximum battery temperature and improves temperature uniformity. Optimal heat transfer efficiency was achieved with seven heat pipes, resulting in a 21.7% reduction in maximum dimensionless temperature and a 16.4% decrease in temperature variance at a Reynolds number of 100. The Performance Evaluation Criterion (PEC) reached 3.1, indicating superior thermal management performance. This work surpasses previous efforts in the literature by demonstrating the enhanced thermal regulation capabilities of the IHPCP system, offering a compact and efficient solution for battery thermal management.

Optimising shapes of multiple pin fins in a microchannel using deep reinforcement learning and mesh deformation techniques

The utilisation of advanced pin fin designs in microchannels is useful for enhancing cooling efficiency. Advancements in machine learning and processing power have sparked interest in shape optimisation techniques. This research employs a novel framework that integrates Deep Artificial Neural Networks and Reinforcement Learning with a Computational Fluid Dynamics (CFD) solver to optimise multiple pin fin shapes within a microchannel. By incorporating Radial Basis Function interpolation and Proximal Policy Optimisation alongside FLUENT, acting as the CFD environment, the reinforcement learning agent adeptly explores the design space to enhance the thermohydraulic performance factor (TPF), aiming to maximise Nusselt number while minimising pressure loss. Unlike previous heat transfer optimisation studies, which typically required mesh regeneration at each step, the proposed framework could bypass the meshing step and alter the geometry directly by relying on the RBF interpolation technique to deform the mesh directly. Three distinct scenarios investigated in this study are uniform deformation of all pin fins, deformation of all pin fins arranged in two rows, and individual deformation of each pin fin. Extensive simulations, exceeding 90,000 different cases, demonstrate that although the optimisation process for the individual deformation of each pin fin requires more iterations compared to others, it surpasses them in terms of TPF improvement. Notably, significant improvements are achieved, such as a 49 % enhancement in Nusselt number and a 33 % reduction in pressure drop, culminating in an impressive 63 % increase in TPF compared to the initial geometry.

An experimental and optimization of heat transfer between two-disk systems

An experimental and optimization of heat transfer between two-disk systems

Response surface methodology-based new model to optimize heat transfer and shear stress for ferrites/motor oil hybrid nanofluid

PurposeThis study aims to optimize heat transfer efficiency and minimize friction factor and entropy generation in hybrid nanofluid flows through porous media. By incorporating factors such as melting effect, buoyancy, viscous dissipation and no-slip velocity on a stretchable surface, the aim is to enhance overall performance. Additionally, sensitivity analysis using response surface methodology is used to evaluate the influence of key parameters on response functions.Design/methodology/approachAfter deriving suitable Lie-group transformations, the modeled equations are solved numerically using the “spectral local linearization method.” This approach is validated through rigorous numerical comparisons and error estimations, demonstrating strong alignment with prior studies.FindingsThe findings reveal that higher Darcy numbers and melting parameters are associated with decreased entropy (35.86% and 35.93%, respectively) and shear stress, increased heat transmission (16.4% and 30.41%, respectively) in hybrid nanofluids. Moreover, response surface methodology uses key factors, concerning the Nusselt number and shear stress as response variables in a quadratic model. Notably, the model exhibits exceptional accuracy with $R^2$ values of 99.99% for the Nusselt number and 100.00% for skin friction. Additionally, optimization results demonstrate a notable sensitivity to the key parameters.Research limitations/implicationsLubrication is a vital method to minimize friction and wear in the automobile sector, contributing significantly to energy efficiency, environmental conservation and carbon reduction. The incorporation of nickel and manganese zinc ferrites into SAE 20 W-40 motor oil lubricants, as defined by the Society of Automotive Engineers, significantly improves their performance, particularly in terms of tribological attributes.Originality/valueThis work stands out for its focus on applications such as hybrid electromagnetic fuel cells and nano-magnetic material processing. While these applications are gaining interest, there is still a research gap regarding the effects of melting on heat transfer in a NiZnFe_2O_4-MnZnFe_2O_4/20W40 motor oil hybrid nanofluid over a stretchable surface, necessitating a thorough investigation that includes both numerical simulations and statistical analysis.

Heat transfer optimization for MH reactor using combined taguchi design and data-driven optimization method

Adding the heat transfer enhancement components into the MH reactor can improve the hydrogen absorption/desorption rate, but the vital indicators as the gravimetric and volumetric hydrogen storage density decrease at the same time, which is hardly considered in the published researches. In this study, a comprehensive hydrogen storage performance (CHSP) indicator for the MH reactor has been proposed considering the above contradiction, which can be applied to evaluate the comprehensive performance of the MH reactor and compare the reactor performances with different heat transfer configurations. Then, with the constraint of the CHSP, the heat transfer structure of the classical finned-tube MH reactor is optimized using a novel sieving optimization strategy which combines the Taguchi design and the data-driven optimization (ANN model + GA) to increase the optimization efficiency and the reliability of optimization solutions. The optimized results indicate that the optimal structural parameters of the finned-tube MH reactor are the diameter of heat exchange tube d of 5.13 mm, the fin thickness h of 0.28 mm, the fin radius r of 21.73 mm and the fin number N of 11, and the optimal CHSP is 0.547 mg/s.

Heat transfer optimization via finned surface with the use of CFD

Heat transfer optimization via finned surface with the use of CFD