Heat Flux Research Articles

Achieving Ultra‐High Heat Flux Transfer in Graphene Films via Tunable Gas Escape Channels

AbstractGraphene films have been applied in the thermal management of electronic devices due to their high thermal conductivity. However, the ever‐increasing power and local heat flux density of electronic chips require graphene films with excellent heat flux carrying capacity. Enhancing the heat flux carrying capacity is highly challenging, and the key is to maintain high thermal conductivity while increasing film thickness. Gases released during film assembly and the resulting catastrophic structural destruction should be responsible for the trade‐off between film thickness and thermal conductivity. Herein, the evolution of the pore structure is investigated during the assembly of graphene films and propose the construction of gas escape channels for the preparation of thick graphene films. The process involves using humidification treatment and freeze‐drying GO films to pre‐construct the ordered flat pore structure. The microstructure optimization of graphene films with more order, fewer wrinkles and defects, and larger grain size is achieved. After optimization, graphene films with ultra‐high thermal conductivity (1781 W m−1 K−1) and a thickness over 100 µm are realized. These films exhibit exceptional heat dissipation and cooling capabilities in high heat flux density (≈2000 W cm−2). This finding holds significant potential for guiding the thermal management of high‐power devices.

A Physics Informed Neural Network for Solving the Inverse Heat Transfer Problem in Gas Turbine Rotating Cavities

Abstract Recently, Physics-Informed Neural Networks have displayed great potential in delivering swift and accurate solutions for inverse problems. Here, a Physics-Informed Neural Network (PINN) was used to solve the inverse heat conduction problem in the rotating cavities of a aeroengine high-pressure compressor internal air system. The neural network was designed to receive experimentally captured radially distributed temperature profiles as inputs and predict the associated surface heat fluxes. The correctness of these predicted heat fluxes are assessed by numerically solving the direct heat conduction equation using a 2D Finite-Element model, thereby recovering the original temperature profiles. A comparative analysis is conducted between the predicted temperature profiles and the initial inputs. The physics informed neural network is trained using noise free synthetic data, created from a range of radial temperature curve fit coefficients, and subsequently tested on noisy experimental data at engine representative conditions. The predicted temperature values exhibit some good agreement with their respective actual counterparts. Furthermore, the sensitivity of model hyperparameters are explored to showcase the capability of the proposed approach. The results show that Physics- Informed Neural Networks exhibit reduced susceptibility to experimental uncertainties when addressing inverse problems, in contrast to inverse solution methods, and offer a possible new approach for analysis of experimental data if trained on a sufficiently large dataset.

ALTP heat flux sensor: the compensation of the ordinary Seebeck effect through a new structure

Abstract This technical note highlights the detrimental impact of the ordinary Seebeck effect (SE) on the accuracy of atomic layer thermopile (ALTP) heat flux sensors and presents an innovative structural design and correction approach. ALTP sensors are based on the Transverse Seebeck effect (TSE), linking their output directly to the temperature difference (ΔTz ) across the sensor’s sensitive film layers. Experimental analysis reveals that a temperature gradient (ΔTx ) along the film’s tilt direction, via the SE, introduces a bias voltage, causing a substantial maximum relative error of 56.4% in measurements. To counteract this issue, we introduce a novel structural configuration and correction strategy, effectively reducing the relative error to 4.1%, and thereby significantly enhancing the precision of ALTP heat flux sensors.

Deep learning based hybrid POD-LSTM framework for laminar natural convection flow in a rectangular enclosure

Abstract Laminar natural convection in side-heated enclosures is characterized by transient phenomena of the working fluid till it reaches steady state. The side heating can done in several ways the most common way being heating one end at constant temperature and cooling the other end. One of the other ways is heating both sides of the enclosure at a constant heat flux. Mathematical modeling of such problems using Computational Fluid Dynamics (CFD) essentially involves considerable amount of computational time and power to predict the flow phenomena observed in actual experimentation. In the last few years, data driven model frameworks have proven to be anefficient way in saving both time and computational cost in several applications. In the present study, a data driven model framework using a combination of unsupervised machine learning (using Proper Orthogonal Decomposition [POD]) and supervised deep learning models (using Long Short Term Memory [LSTM]) has been developed and referred to as POD-LSTM framework. The selection of a few dominant spatial bases and accompanying temporal modes provides us with a reduced order model of the system. The flow is then reconstructed and compared with results of CFD simulations. The Rayleigh number (Ra) chosen for the study is 3.27 × 1010. The estimated time to reach stedy state for this Ra number is 15,000 s. The POD-LSTM framework is trained using data obtained from a validated CFD model for the first 1,000 s. The trained model was then tested to predict temporal dynamics for the entire 15,000 s. The predictions provided by POD-LSTM framework were found upto 98 % accurate compared to the ones predicted by CFD. The computational time and power was however an order of magnitude lower for the POD-LSTM framework than that required for the CFD model.

The effects of nanoparticles on pool boiling and critical heat flux

The effects of nanoparticles on pool boiling and critical heat flux

Investigation of bubble interaction in a temporal and spatial heat flux partitioning model for subcooled flow boiling

CFD methodology for subcooled flow boiling is significantly affected by the heat flux partitioning model, which plays a crucial role in predicting the mass source term generated by wall boiling of the liquid phase. Although numerous models have been proposed, most research efforts have focused on the spatial dimensions of boiling heat transfer mechanisms, neglecting the temporal aspect. This study presents a heat flux partitioning model for subcooled flow boiling that considers both temporal and spatial dimensions. The model accounts for the contribution of heat flux from the superheated liquid layer during the bubble growth stage, liquid convection during the bubble wait stage within the bubble influence area in the temporal dimension, and heat flux from the overlapping area of bubble influence in the spatial dimension. An approach was developed to determine the bubble influence area by considering the bubble interaction based on the stochastic nature of nucleation sites on the heated wall, verified by the Monte Carlo method. The bubble dynamics parameters were experimentally derived to reduce the uncertainty associated with the boiling sub-models on the calculation. A comparative analysis of boiling curves and wall heat flux partitioning was conducted between the present model and the RPI model under various flow conditions. The results indicate that both models could reasonably predict the boiling curve. However, the RPI model tends to over-predict the proportion of evaporative heat flux, which is considered a weakness. Based on physical principles, the present model accurately captures the fundamental trend of wall heat flux partitioning. This research enhances the understanding of subcooled flow boiling and increases confidence in multiphase CFD methodology predictions.

Numerical Study on Composite Multilayer Insulation Material for Liquid Hydrogen Storage

This study investigated the heat transfer characteristics of composite multilayer insulation (MLI) materials used for the storage and transport of liquid hydrogen at cryogenic temperatures. This research focused on analyzing the effects of thermal boundary temperature, total layer count, and vacuum level on the heat flux through the insulation material. Based on the layer-by-layer model, a heat transfer model of composite MLI was constructed. This research introduces a novel method for analyzing the heat transfer properties of composite MLI in the liquid hydrogen temperature range. Results indicate that heat flux increases with higher thermal boundary temperatures, with the MLI layers near the cold boundary playing a critical role in overall insulation performance. Additionally, numerical analysis was conducted to examine the impact of different material combinations and variations in vacuum level on heat transfer characteristics. Findings reveal that adding spray-on foam insulation reduces heat flux by 20.76% compared to using MLI alone. Furthermore, increasing the total number of MLI layers effectively mitigates heat flux increase, achieving an optimal heat flux of 0.5377 W/m2 with a total of 50 layers.

Correction of Sensible Heat Flux from Flux-Gradient Method to Eddy Covariance Method Based on Multi-Layer Perceptron

Abstract. Understanding surface-atmosphere interactions is critical for environmental and meteorological studies. Sensible heat flux, a key component in this interaction, is typically measured using methods such as Eddy Covariance (EC) and Flux-Gradient (FG). The EC method, known for its high temporal resolution and direct measurement capabilities of wind speed, temperature, and humidity changes, requires the use of expensive and complex equipment, making it costly and challenging to implement. On the contrary, the FG method is more accessible and economical, relying on simpler instruments, but often lacks the precision of the EC method. To harness the benefits of both methods, this article uses the Multi-Layer Perceptron (MLP) machine learning method to enhance the accuracy of the FG method's sensible heat flux calculations. Through the MLP model, this paper aims to determine the optimal parameter settings for the specific measurement environment, thereby improving the FG methods accuracy. The data was measured from Guandu Village, Anhui Province, China. This research seeks to demonstrate that the trained MLP model can be applied to similar measurement environments, thus enhancing the FG method's applicability and precision.

The cause of an extreme sea surface warming in the midlatitude western North Pacific during 2012 summer

This study investigated an extreme sea surface warming in the midlatitude western North Pacific (MLWNP) during the summer of 2012. The 2012 extreme event was characterized by warm sea surface temperature anomaly (SSTA) extending from the East/Japan Sea to central North Pacific. The SSTA box–averaged over the MLWNP (130–180°E, 33–50°N) in 2012 ranked as the third warmest in recent four decades, which has caused intense marine heatwaves in this region. During the summer of 2012, a positive Indian Ocean Dipole event co-occurred with El Niño, favoring anomalous moisture transport between the two basins that caused enhanced convection in the South China and Philippine Seas and western–to–central subtropical Pacific. The enhanced convective activities triggered two meridional atmospheric Rossby wave trains to form strong atmospheric blocking high–pressure systems in the MLWNP. This reduced the total cloud cover and surface wind speed, enhancing insolation and reducing the release of latent heat flux. In addition, the weakened wind strengthened the stratification and shoaled the mixed layer. As a result, the increased net heat flux into the ocean accompanied by a shallower mixed layer contributed to the upper ocean warming in the MLWNP. Meanwhile, the North Pacific was dominated by a negative phase of Pacific Decadal Oscillation (PDO), significantly contributing to warm SSTAs in the MLWNP in 2012. Consequently, the 2012 extreme warming in the MLWNP was the results of the combination of atmospheric Rossby waves and PDO. Our study highlighted the roles of high–frequency atmospheric teleconnection and low–frequency PDO in extreme sea surface warming in the MLWNP.

Thermophoretic particle deposition effect on a squeezed flow of radiative Jeffrey fluid past a sensor surface with uniform heat source/sink and chemical reaction

Abstract This anticipated model investigates the time-dependent, and incompressible 2D magnetized Jeffrey liquid flowing on a sensor surface placed between two infinite parallel plates with the existence of a uniform heat source/sink. The lower plate remains fixed while the upper plate is subject to squeezing. For the heat and mass transmission processes, the consequences of radiative heat flux, chemical reaction, and thermophoretic particle deposition are applied and analyzed. The envisioned model is supported by prescribed heat and mass flux conditions. The uniqueness of the proposed model is the analysis of the unsteady squeezed flow of Jeffrey liquid over a sensor surface, accounting for radiative heat flux, thermophoretic particle deposition, and variable thermal conductivity. The model possesses applications in microfluidic sensors, biomedical devices, thermal management systems, and inkjet printing. The equations comprising the model are subject to similarity transformations. The bvp4c package is utilized to analyze the dynamic flow system. The connotation of arising parameters with the associated profiles is depicted through graphs and tables. It is examined that fluid velocity, temperature, and concentration profiles are dwindling functions of squeezed parameter. It is also witnessed that the concentration of liquid drops with an enhancement of thermophoretic and chemical reaction parameters. The validation of the truthfulness of the envisioned model is an added feature of this study.

Dakar Niño under global warming investigated by a high-resolution regionally coupled model

Abstract. In this study, we investigated interannual variability in sea surface temperature (SST) along the northwestern African coast, focusing on strong Dakar Niño and Niña events and their potential alterations under the RCP8.5 emission scenario for global warming, using a high-resolution regional coupled model. Our model accurately reproduces the SST seasonal cycle along the northwestern African coast, including its interannual variability in terms of amplitude, timing, and the position of maximum variability. Comparing Dakar Niño variability between the 1980–2010 and 2069–2099 periods, we found that it intensifies under a warmer climate without changing its location and timing. The intensification is more pronounced during Dakar Niñas (cold SST events) than during Dakar Niños (warm SST events). In the future, SST variability will be correlated with ocean temperature and vertical motion at deeper layers. The increase in Dakar Niño variability can be explained by the larger variability in meridional wind stresses, which is likely to be amplified in the future by enhanced land–sea thermal contrast and associated sea-level-pressure anomalies extending from the Iberian Mediterranean area. A heat budget analysis of the mixed layer suggests that surface heat flux and horizontal-advection anomalies are comparably important for Dakar Niño and Niña events in the present climate. However, the future intensification of Dakar Niños and Niñas is likely to be driven by surface heat flux (latent heat flux and shortwave radiation). While horizontal- and vertical-advection anomalies also contribute to the intensification, their roles are secondary.

Large eddy simulation of the combined effect of heat fluxes and wave forcing of summer monsoon on a diurnal ocean mixed layer in the north Arabian Sea

Large eddy simulation of the combined effect of heat fluxes and wave forcing of summer monsoon on a diurnal ocean mixed layer in the north Arabian Sea

Numerical Assessment of the Thermal Performance of Microchannels with Slip and Viscous Dissipation Effects

Microchannels are widely used across various industries, including pharmaceuticals and biochemistry, automotive and aerospace, energy production, and many others, although they were originally developed for the computing and electronics sectors. The performance of microchannels is strongly affected by factors such as rarefaction and viscous dissipation. In the present paper, a numerical analysis of the performance of microchannels featuring rectangular, trapezoidal and double-trapezoidal cross-sections in the slip flow regime is presented. The fully developed laminar forced convection of a Newtonian fluid with constant properties is considered. The non-dimensional forms of governing equations are solved by setting slip velocity and uniform heat flux as boundary conditions. Model accuracy was established using the available scientific literature. The numerical results indicated that viscous dissipation effects led to a decrease in the average Nusselt number across all the microchannels examined in this study. The degree of reduction is influenced by the cross-section, aspect ratio and Knudsen number. The highest reductions in the average Nusselt number values were observed under continuum flow conditions for all the microchannels investigated.

Numerical investigation on boiling crisis characteristic of a 7-rod HCF assembly in hexagonal lattice

Helical cruciform fuel (HCF) has the advantages of larger heat transfer area, enhanced coolant mixing and self-supporting, which contribute to increasing power density and safety margins. Compared with the square lattice configuration, the hexagonal arrangement of HCF assembly is more compact, which can help achieve a higher power density. In this paper, the flow characteristics and heat transfer behaviors of HCF in hexagonal lattice were predicted at high and low vapor quality during boiling crisis based on Eulerian two-fluid model. The influence of twist pitches and cross-sections of the fuel rod on heat transfer efficiency and fuel temperature was also studied. The cross-flow intensity changed periodically with a 30° cycle at low vapor quality, and did not fluctuate periodically at high vapor quality, which decreased with the increase of flow resistance. The highest heat flux of HCF rod was the at the blade root and the lowest was at the blade tip, and the maximum to average heat flux ratio was about 1.8. The peak vapor fraction and temperature occurred at leeside side of the fuel rods. The increase of the twist pitch reduced the critical heat flux (CHF), and the increase of blade length enhanced the non-uniformity of heat flux distribution. During boiling crisis, the maximum temperature of the fuel was lower than the phase transition temperature of U-50 wt%Zr alloy, which means the cladding meltdown caused by boiling crisis will occur before phase transition of the fuel.

Nonlinear Rayleigh wave propagation in a three-layer sandwich structure in dual-phase-lag

Plane, nonlinear Rayleigh wave propagation is investigated in a three-layer sandwich structure of a thermoelastic medium, within the frame of dual-phase-lag theory. The thermal conductivity is taken as a linear function of temperature. This induces nonlinearity in the evolution equations for the heat flux components. A particular solution is found in the form of Poincaré expansion in a small parameter that reflects the fluctuations of temperature about a steady value. This solution is discussed and plots are provided for a special case when both external faces of the structure are traction-free and under prescribed temperature regime. It is noted, in particular, that some quantities of practical interest suffer jumps at the interfaces. Some of the jumps appear only starting from the second order of approximation. The existence of jumps may be favourably used to carry out some measurements of material parameters.

Analytical generalized thermal resistance model for conductive heat transfer in pebble beds based on heat flux weighted temperature difference

A novel model for inter-particle contact thermal resistance based on analytical solutions is proposed. By applying the concept of heat flux weighted temperature difference in thermal resistance modeling for the first time, this model offers greater physical significance than traditional thermal resistance models. It effectively describes the dissipation in the heat transfer process and the influence of particle radius on thermal resistance, making it a generalized thermal resistance model suitable for multi-dimensional systems. The impact of applying different levels of uniform heat flux boundary conditions on thermal resistance is analyzed from the perspective of energy dissipation, revealing that a more uniform heat flux input results in lower thermal resistance. The effects of contact radius and particle size on the dimensionless generalized thermal resistance of inter-particle contact were studied, showing that the dimensionless resistance increases with larger particle contact radius and smaller particle size. Using the thermal discrete element method, the thermal resistance model with uniform heat flux density boundary conditions was applied to calculate the effective thermal conductivity of the pebble bed. Considering the distribution of contact radius, differences in effective thermal conductivity at various heights of the pebble bed due to different contact radii were examined and compared with the traditional fixed-coefficient contact resistance model. It was found that the difference between the two models decreases as the contact radius decreases. Finally, multiple sets of fixed contact radii were established under three different particle sizes, revealing that as the contact radius increases, the deviation between the new generalized resistance model and the traditional fixed-coefficient model rises from 8.18 % to 14.64 % for different particle radii.

Intelligent adaptive nonlinear autoregressive eXogeneous neuro‐structure for ferromagnetic Powell‐Eyring fluidic involving cubic autocatalysis chemical reaction

AbstractThe scope of artificial intelligence in the field of fluid mechanics has been expanded with the development sophisticated technology to enhance the efficiency, reliability, solve complexities, introduced alternate transformation and enabling more dependable solutions with their analysis. The goal of this study is to investigate the ferromagnetic Powell‐Eyring fluids (FMPEFs) model with non‐Fourier heat flux by using artificial intelligence‐based scheme by exploiting the adaptive nonlinear autoregressive eXogenous (NARX) neuro‐architecture with backpropagation of Levenberg Marquart (LM), that is, NARX‐LM. The developed NARX‐LM methodology applied on synthetic datasets acquired with the help of Adams numerical method for FMPEF system by prudently changing physical quantities that is, material parameters of Eyring Powell, homogeneous reaction, heterogeneous reaction, dimensionless thermal relaxation time, Prandtl number, Schmidt number with fixed values parameter of ferrohydrodynamic interaction, rate of diffusion coefficient. Outcomes of NARX‐LM are regularly overlapping with the numerical results for the FMPEFs system having reasonable small error magnitude for each variant. The proficiency of intelligent computing anticipated on FMPEFs is depicted exhaustively with iterative mean squared error based iconvergence curves, analysis of adaptive controlling factors, error frequency distribution on the histograms, auto‐correlation, and correlation measures.

Optimization of condensing tray shapes for a multi‐effect solar still coupled with parabolic solar collectors for domestic wastewater treatment: A comparative study

AbstractThe issue of water depletion emerged as a life‐threatening problem for the world due to urbanization, groundwater over‐extraction, industrialization, and global warming. Current study aimed to address this issue by proposing a solution to distill wastewater using a multi‐effect solar still. The study optimizes two condensation tray shapes, U and V, based on the yield produced and the temperatures achieved. Two solar still units with four stages of U and V‐shaped trays were connected to a parabolic solar collector. To determine the optimal tray shapes, a rigorous optimization process was carried out using SolidWorks 2020. The optimization process yielded optimum heat flux values of 3.04e01 and 2.84e01 W/m2 for U and V‐shaped trays, respectively. Higher the value of heat flux suggested more energy available for condensation. Which was corroborated by physical findings that the U‐shaped condensation tray was more efficient, producing an average per day yield of 2.519 L/m2 h compared to the V‐shaped tray's yield of 2.041 L/m2 h. The findings provide strong evidence that the U‐shape is a better tray shape for condensation purposes. Maximum yield for both units was observed during the peak temperature time between (13:00–15:00 h), indicating a direct correlation between yield and atmospheric temperature.

Processes Driving the Intermodel Spread of the Southern Hemisphere Hadley Circulation Expansion in CMIP6 Models

AbstractThe Hadley circulation (HC) has been expanding poleward in recent decades. The Coupled Model Intercomparison Project Phase 6 (CMIP6) models predict that the expansion will accelerate in the future, more so in the Southern Hemisphere (SH). However, the extent of the expansion varies widely among the models. We investigate the mechanisms driving the intermodel spread in SH HC expansion predictions. The intermodel spread is obtained by an empirical orthogonal function analysis on the SH HC trend patterns of 16 CMIP6 model simulations using the historical and shared socioeconomic pathway 5–8.5 scenarios. The leading mode, showing a mean meridional stream function anomaly at the poleward SH HC extent, explains 49.73% of the variance and significantly correlates (r = 0.94) with the SH HC expansion. By analyzing the extended Kuo‐Eliassen equation, we find that the intermodel difference in the representation of diabatic heating is responsible for about 14% of the intermodel spread. The meridional eddy momentum and heat fluxes contribute to about 21% and 18% of the intermodel spread, respectively. The models simulating a relatively large SH HC expansion tend to show increased precipitation in the Southern Pacific Convergence Zone, reduced baroclinic instability in the subtropics, and an enhanced poleward shift of jet stream in the midlatitudes. This suggests that the uncertainty in the HC projection may be constrained by reducing the bias in the trend of the mean fields.

Analysis of Optical Window for Constant Cooling Heat Flux‐Based Spectral Splitting in Concentrator Photovoltaic System

The solar cells cooled to constant temperature at different concentration ratios (CR) and spectral bands (SB) require the same cooling heat flux (CHF), but the output power varies significantly. Thus, it is necessary to clarify the relationship of CHF‐CR‐SB to provide a reference for photovoltaic systems to select the output power generation of the spectral band under constant cooling heat flux. In this article, a selecting spectra model of a centralized photovoltaic (CPV) system is established and the selection of the spectrum based on CHF and the CR is analyzed. Theory shows that the wider the spectral division of the same CR, the larger the cooling heat flux consumed. The narrower the spectrum division, the higher the CR that the cell receiver can withstand. The experiments show that the higher the photoelectric conversion efficiency (PCE), the lower the cooling heat flux to be consumed. The cooling heat flux of 650 filter consumes 22.74% more than the UV700 filter, which means the demand for CHF is more severe when the heat spectrum distribution is wide. The spectral division should be carried out according to the requirements of high‐energy flux but a small thermal energy proportion to achieve efficient PCE.