Prediction Of Heat Transfer Research Articles

Prediction of Heat Transfer During Condensation in Annuli

Many applications involve condensation in annuli; therefore, accurate prediction of heat transfer is important. While there have been a large number of experimental studies on condensation in tubes and several well-verified correlations are available for them, there have been very few experimental studies on annuli, and no well-verified correlation is available for prediction of heat transfer during condensation in annuli. This research was done to identify reliable correlations for this purpose and to develop a new one if needed. Literature was surveyed to identify experimental studies, test data, and predictive methods. Test data was compared to general correlations which have had considerable verification with data for condensation in channels. None of them was found fully satisfactory. A new correlation was developed by modifying the present author’s published correlation for condensation in tubes. It gives a MAD of 19.2% with available data from eight sources. Deviations of other correlations were much higher. The occurrence of surface tension effects and mini/macro channel boundary are investigated. The results of this research are presented and discussed.

Research on heat transfer characteristics and prediction methods of supercritical liquified natural gas flowing horizontally under the influence of buoyancy

An exhaustive elucidation of the heat transfer performances and mechanisms of supercritical LNG under water-bath heating conditions is paramount for optimizing heat transfer and designing related heat exchangers. This study employs experiments and numerical simulations to explore the heat transfer mechanisms and prediction methods of supercritical LNG flowing horizontally under buoyancy. The experimental investigation was executed within a small SCV using the N2-Ar mixture as a substitute medium for safety considerations. Concurrently, the accuracy of the numerical model employed is rigorously authenticated through a comparative analysis with the empirical findings obtained from the experimental endeavor. The investigation delves into the impacts of operating conditions, structural parameters, and medium composition on heat transfer through numerical simulations. The influence mechanism of buoyancy on heat transfer is meticulously explicated by examining the distribution of flow fields and thermophysical properties within the boundary layer. The introduction of a novel criterion, Bufull, derived from the analysis of wall shear stress variation caused by buoyancy, facilitates a quantitative assessment of the impact of buoyancy on heat transfer. Additionally, a simplified buoyancy criterion, FBu, is introduced, dependent solely on fundamental operating parameters and tailored for engineering applications, with a determined critical value of 1/ FBu = 60.3. Based on the dramatically variable thermophysical and transport properties, that occurs near the critical point, as well as buoyancy correction, a correlation is formulated for predicting mixed convection heat transfer, exhibiting an MAE of merely 3.04% and accurately predicting 81.28% of the data within a ± 5% margin of error, surpassing existing correlations. Finally, the proposed heat transfer calculation method is validated using five groups of operational data from two in-service SCVs. The maximum absolute discrepancy between the computed outlet temperature and the monitored value is 1.95K. The findings of this paper can support practical applications.

Deep learning assisting construction of heat transfer constitutive relationships for porous media

Predicting heat transfer in porous materials is challenging due to their complex microstructure, and traditional experimental and theoretical methods often fall short. To address this, we present a machine learning-based approach using a single Descriptor-to-Property Network (D2P-Net) to identify the relationships between the effective thermal conductivity (ETC) of porous media with the structural descriptors. The D2P-Net, an ensemble of decision trees, uses eleven structural descriptors, such as porosity, pore size, and connectivity, derived from four distinct types of three-dimensional porous structures. These structures are computationally generated, and their ETC is computed using the Lattice Boltzmann Method (LBM) to create a robust dataset for training. The model is trained using mean squared error (MSE) employed as the loss function, and achieves an R2 value of 0.994 and a root mean square error (RMSE) of 0.229, indicating high predictive accuracy. Additionally, SHapley Additive exPlanations (SHAP) values are employed to analyze the importance of each structural descriptor, offering valuable insights into their contributions to ETC predictions. This approach provides a reliable and interpretable method for understanding and optimizing the thermal properties of porous materials, with potential applications in areas requiring efficient thermal management, such as hypersonic vehicles.

Modelling and numerical simulation of heat transfer and hydrodynamic performance of multi-pass parallel flow condensers – A novel algebraic method to determine flow distribution

In multi-pass parallel flow condensers (MPFCs), refrigerant flow maldistribution poses a significant challenge, deteriorating heat transfer performance. A robust flow distribution prediction method is crucial for the accuracy and applicability of an MPFC model. This study originally develops a new algebraic method to determine refrigerant distribution in tube-passes for MPFCs. This algorithm can be directly integrated with the authors' previously developed 2-D distributed-parameter model for MPFCs with liquid-vapor separation (MPFCs-LS). The novel algorithm applies to conditions where: 1) the inlet tube of the first tube-pass is positioned arbitrarily along the vertical inlet header, and 2) the mediate headers have or lack liquid-vapor separation. The new condenser models are validated by comparing the predicted results with the experimental data of an MPFC and an MPFC-LS. The predicted heat transfer capacity and pressure drop show deviations within ±5% and ±35%, respectively. Results also reveal that the optimal position of the inlet tube is one tube pitch downward from the top of the inlet header under the given configuration and conditions. The best position of the inlet tube, compared with the worst case, improves the flow distribution by 67%, reduces the pressure drop by 68% and increases the heat transfer by 0.5%.

Condensation heat transfer and applicability assessment of a printed circuit heat exchanger as a condenser in a cryogenic CO2 capture and storage system

Global warming is accelerating due to the rapid increase in CO2 emissions and cold energy. CO2 capture and storage systems (CCSSs) using discarded cold energy have been suggested to prevent this problem. To utilize cold energy, a printed circuit heat exchanger (PCHE) with high structural integrity was applied as a condenser in the CCSS. However, studies on condensation heat transfer in PCHE are still lacking, and conventional condensation heat transfer correlations developed in circular-channels do not sufficiently predict heat transfer performance in PCHE. Additionally, evaluating the local heat transfer performance is difficult because PCHE is an all-in-one type heat exchanger. Therefore, in this study, a CO2 condensation experiment in PCHE was conducted using an optical fiber sensor as a measurement, which can evaluate the local heat transfer performance, and a new condensation heat transfer correlation for CO2 in PCHE was developed. The correlation was approximately 20 % smaller than the conventional correlations developed in circular-channels, indicating that CO2 would not be fully condensed if the condenser was designed by conventional correlations. Based on the developed correlation, in-house code was developed and the CCSS using cold energy was compared with the conventional CCSS, which condenses CO2 after pressurizing it to a high pressure. As a result, cold energy based CCSS could replace the conventional CCSS because the CCSS using cold energy was approximately 5.7 million-USD per year more economical than the conventional CCSS when condensing CO2 at 200 tons/h. These results contribute to the design of PCHE-type condensers used in the CCSS.

Friction factor and heat transfer prediction of rocket engine cooling channels in numerical simulations with high roughness Reynolds number

In literature, it is widely recognized that surface roughness significantly influences friction and heat transfer. This effect is especially pronounced in additive manufactured components, which are increasingly important in the space sector. However, using numerical approaches to predict their performances is challenging because the correlations and the numerical models available for high roughness conditions have to be tuned with respect to the specific case. This study aims to benchmark existing modeling strategies and to compare the results against the experimental results of additively manufactured rocket engine cooling channels. To achieve this, the Spalart–Allmaras model equipped with the Boeing roughness correction was implemented in ANSYS Fluent and tested on various test conditions. The goal is to determine the best-fitting sand-grain parameter values that match experimental data in terms of friction factor and compare the heat transfer predictions regarding coolant temperature, wall temperature, and Nusselt number. Eventually, a correction of the turbulent Prandtl number was demonstrated to be needed. Data from literature and empirical correlations for high values of roughness Reynolds number are gathered and compared with the outcomes from this work, giving satisfying results.

Rotationally Induced Local Heat Transfer Features in a Two-Pass Cooling Channel: Experimental–Numerical Investigation

Turbine blades for modern turbomachinery applications often exhibit complex twisted designs that aim to reduce aerodynamic losses, thereby improving the overall machine performance. This results in intricate internal cooling configurations that change their spanwise orientation with respect to the rotational axis. In the present study, the local heat transfer in a generic two-pass turbine cooling channel is investigated under engine-similar rotating conditions (Ro={0…0.50}) through the transient Thermochromic Liquid Crystal (TLC) measurement technique. Three different angles of attack (α={−18.5°;+8°;+46.5°}) are investigated to emulate the heat transfer characteristics in an internal cooling channel of a real turbine blade application at different spanwise positions. A numerical approach based on steady-state Reynolds-averaged Navier–Stokes (RANS) simulations in ANSYS CFX is validated against the experimental method, showing generally good agreement and, thus, qualifying for future heat transfer predictions. Experimental and numerical data clearly demonstrate the substantial impact of the angle of attack on the local heat transfer structure, especially for the radially outward flow of the first passage, owing to the particular Coriolis force direction at each angle of attack. Furthermore, results underscore the strong influence of the rotational speed on the overall heat transfer level, with an enhancement effect for the radially outward flow (first passage) and a reduction effect for the radially inward flow (second passage).

Particle resolved DNS and URANS simulations of turbulent heat transfer in packed structures

An accurate prediction of turbulent heat transfer in packed-bed reactors is fundamental for the design and performance of such reactors. Various classes of Reynolds-Averaged Navier-Stokes models are available in the literature for predicting turbulent heat transfer via closing the Reynolds stress and Turbulent Heat Flux terms. In this work, we investigate the accuracy of representative unsteady RANS turbulence models in modeling heat transfer characteristics in packed-bed reactors. For this accuracy assessment, the performance of the examined unsteady RANS models is compared with particle-resolved Direct Numerical Simulations including heat transfer. The simulations are conducted in a three dimensional periodic domain consisting of randomly arranged cylindrical particles for Reynolds number Rep=1000. The extracted mean quantities (e.g. velocity and temperature), turbulent heat flux components and turbulent kinetic energy are used as a reference for the RANS models' accuracy assessment. The SST k−ω and Spalart-Allmaras models performed well in predicting mean fields. However, all models tended to mispredicted the turbulent quantities including the turbulent viscosity and heat flux vector. The thermal field calculations rely on the turbulent viscosity based Simple Gradient Diffusion Hypothesis for modeling turbulent heat flux, which introduce challenges in capturing the coupling between thermal and turbulent fields.

Experimental Investigation and Calculation of Convective Heat Transfer in Two-Component Gas–Liquid Flow Through Channels Packed with Metal Foams

This paper presents a study on heat transfer in two-phase mixtures (air–water and air–oil) flowing through heated horizontal channels filled with open-cell aluminum foams characterized by porosities of 92.9–94.3% and pore densities of 20, 30, and 40 PPI. The research included mass flux densities ranging from 2.82 to 284.7 kg/(m2·s) and heat flux densities from 5.3 to 35.7 kW/m2. The analysis examined the effects of flow conditions, fluid properties, and foam geometry on the intensity of heat transfer from the heated walls of the channel to the fluid. Results indicate that the heat transfer coefficient in two-component non-boiling flow exceeds that of single-phase flow, primarily due to fluid properties and velocities, with minimal impact from flow structures or foam geometry. An assessment of existing methods for predicting heat transfer coefficients in gas–liquid and boiling flows revealed significant discrepancies—up to several hundred percent—between measured and predicted values. To address these issues, a novel computational method was developed to accurately predict heat transfer coefficients for two-component non-boiling flow through metal foams.

Comparative Analysis of Heat Transfer Coefficient Using Experimental Data and Empirical Expressions

This study aims to determine the heat transfer coefficient by comparing results obtained from both experimental methods and empirical expressions. A controlled experiment involving the flow of water through a polyethylene hose was conducted, and the heat transfer coefficient was calculated based on inlet and outlet temperatures. These findings were then compared with values derived from empirical correlations, specifically the Sieder-Tate equation, to assess accuracy and reliability. The experimental setup maintained consistent boundary conditions, including water inlet temperature, airflow rate, and hose orientation. The results indicated that the experimentally determined heat transfer coefficient was 48.30 W/(m²·K), while the empirical calculation yielded a value of 53.44 W/(m²·K). The slight discrepancy between these values highlights minor experimental errors and assumptions inherent in empirical models. Overall, the close agreement between the experimental and empirical values validates the use of empirical correlations for predicting heat transfer coefficients in similar configurations. This study provides valuable insights for optimizing heat transfer processes in various industrial and engineering applications, emphasizing the importance of experimental validation. Future work could explore extended parameter ranges, advanced measurement techniques, and numerical simulations to further enhance the accuracy and applicability of the findings.

Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant

This study focuses on enhancing heat and mass transfer in an electronic cooling system such as a rectangular cavity that contains equidistant heated chips along the bottom wall. The cavity of the present study is filled with ethylene glycol (30:70) based hybrid nano coolants of different volume fractions (ϕ) of Multi-walled Carbon Nanotube (MWCNT), Aluminum Oxide (Al2O3), and Copper Oxide (CuO). The set of equations controlling the thermo-solutal natural convection within the enclosure is simulated using the Galerkin weighted residual finite element method (FEM). The study has shown a good agreement of numerical results with different experimental reports within the framework of the present study. A solution space is constructed based on governing parameters such as Rayleigh number, buoyancy ratio, and Lewis number. A hybrid nano-coolant containing ϕMWCNT = 1.5 %, ϕCuO = 0.5 %, and ϕAl2O3 = 2 % showed a 3.11% improvement in heat transfer rate compared to the base fluid, highlighting its potential for thermal management applications. This study also investigates various machine learning models for predicting the heat and mass transfer rate, and an error analysis is conducted on the K-Nearest Neighbour Regressor, Random Forest Regressor, Decision Tree Regressor, and ANN model. The ANN model with 6-50-100-50-2 architecture showcases the best fit with the mean squared error of 0.8923 and an R² value of 99.96 % on testing data. The ANN model exhibits its capability to predict heat transfer and mass transfer rates within the error ranges from 1–2 % and 2–3 %, respectively, even at a strong thermal buoyancy force (Ra = 10⁵). This accuracy showcases a novel use of ANN to efficiently predict thermo-fluidic transport behaviors of hybrid nanofluids, offering a faster alternative to resource-intensive simulations. The present study opens new possibilities for real-time, cost-effective cooling solutions, particularly in microelectronics and renewable energy, while maintaining high prediction accuracy by integrating machine learning with the nanotechnology approach.

Prediction of Heat Transfer in a Hybrid Solar–Thermal–Photovoltaic Heat Exchanger Using Computational Fluid Dynamics

Solar energy is one of the main renewable energy resources due to its abundance. It can be used for two purposes, thermal or photovoltaic applications. However, when the resource obtained is mixed, it is called photovoltaic thermal hybrid, where the solar panels generate electricity and are provided with a heat exchanger to absorb energy through a water flow. This is one of the techniques used by the scientific community to reduce the excess temperature generated by solar radiation in the cells, improving the electrical efficiency of photovoltaic systems and obtaining fluid with higher temperature. In this work, the thermal behavior of a heat exchanger equipped with fins in its interior to increase the thermal efficiency of the system was analyzed using CFD (Computational Fluid Dynamics). The results showed that the average fluid outlet temperature was 75.31 °C, considering an incident irradiance of 1067 W/m2 and a fluid inlet temperature of 27 °C. The operating conditions were obtained from published experimental studies, achieving 97.7% similarity between the two. This was due to the boundary conditions of the heat flux (1067 W/m2) impinging directly on the coupled cells and the heat exchanger in a working area of 0.22 m2.

A fast quadrupole-based analytical model for solving transient heat transfer in ventilated double-skin walls and assessing their energy saving potential

This research proposes a fast and accurate method for modeling transient heat transfer in ventilated double-skin façades (VDFs) with forced ventilation to predict their energy-saving potential. Using the thermal quadrupole formalism, the method solves the VDF problem in the Laplace domain while considering the full transient nature of the heat transfer; the only approximation is in space. The solution involves obtaining a general transfer function that predicts heat transfer rates or air temperatures at ventilated cavity's exit. The quadrupole method is validated against computational fluid dynamic numerical simulations conducted under similar meteorological, design and boundary conditions. A very good agreement was found (less than 3 % average deviation) with a significant reduction in computation time (less than 3s against 6h for CFD calculations). The model is computationally efficient and can consider important factors such as the opacity of the walls, construction materials, and air cavity design parameters. Finally, the model allows the assessment of the energy-saving potential of VDFs under various scenarios compared to conventional systems, which helps contributing to more sustainable building design practices. The energy efficiency of a VDF configuration was compared against a conventional wall system. Energy savings of 8.4 and 5.2 % were obtained, in cold and hot climates respectively.

Optimizing thermal performance in internal passage cooling with extended rib: Applying response surface method with artificial neural networks

Efficient cooling technology is crucial for high-temperature gas turbines which are significant applications in thermal processes particularly in industries such as power generation aerospace and marine propulsion. Enhancing cooling efficiency in these systems not only improves energy utilization but also plays a critical role in reducing fuel consumption increasing operational reliability and minimizing environmental impacts. This research focuses on optimizing the thermal performance of internal passage cooling channels with extended ribs using a combination of the response surface method and artificial neural networks. By varying the extended rib length and rib width the study aims to enhance heat transfer and minimize flow loss leading to improvements in overall system efficiency. Latin Hypercube Sampling is employed to strategically select variables and automated numerical simulations are conducted to perform a thorough parametric analysis. A steady-state k-ω shear stress transport turbulence model is applied in the simulations and the numerical results are validated against experimental data ensuring accuracy. The combined response surface method–artificial neural network approach integrated with TensorFlow-based modeling allows for the prediction of heat transfer and friction factor ultimately generating a comprehensive response surface to identify the optimal design point. Applying the optimized design leads to a 23.99 % improvement in the thermal performance factor compared to the reference case. The findings of this study highlight a significant enhancement in the thermal performance of the cooling channel structure particularly in high heat flux environments. This research provides valuable insights into the design of more efficient thermal management systems for gas turbines and has broader implications for other high-temperature thermal processes. The proposed optimization methodology offers a reliable approach for improving cooling technologies contributing to the development of the next generation of energy-efficient high-performance gas turbines and other industrial applications where effective thermal management is essential.

Thermo-hydraulic performance of concentric tube heat exchangers with turbulent flow: Predictive correlations and iterative methods for pumping power and heat transfer

This research addresses the problem of predicting the thermo-hydraulic performance of concentric tube heat exchangers (CTHE) under turbulent flow conditions, a critical aspect in energy-efficient industrial systems such as HVAC, power generation, and chemical processing. Existing studies often lack accurate predictive methods for balancing heat transfer performance with pumping power requirements. To tackle this issue, novel correlations and an iterative Newton–Raphson method were developed for predicting pumping power and heat transfer rates. Three-dimensional CFD simulations of a water-to-water counter-flow CTHE were conducted, with Reynolds numbers ranging from 4000 to 8000 for both the hot and cold fluids. The simulations employed the Reynolds-Averaged Navier–Stokes (RANS) equations with the k−ω SST turbulence model. The results demonstrated that increasing the Reynolds number enhances both heat transfer rates and pumping power, with the cold fluid requiring consistently higher pumping power. New correlations were developed to predict pumping power, capturing the impact of both entry and fully developed flow regions. These correlations showed an average error of less than 2.33% when compared with the CFD data. The iterative Newton–Raphson method for predicting heat transfer rates demonstrated high accuracy, with an average error of 0.66% for heat transfer rate, 0.03% for hot fluid outlet temperature, and 0.01% for cold fluid outlet temperature. Additionally, we identified optimal operating conditions for efficient cooling and heating based on the heat capacity ratio (Cr). The novelty of this work lies in the development of new, highly accurate predictive correlations and iterative methods for optimizing CTHE performance, going beyond existing literature by providing comprehensive insights into the relationship between pumping power, heat transfer efficiency, and flow conditions.

Heat transfer characteristics of a turbulent swirl jet issuing from a circular nozzle

Experimental and numerical studies on the flow field and cooling performance of a swirling jet, impinging on a flat surface is presented. A new nozzle that resembles the swirl injector of a liquid propellant rocket engine is used. This study considers different parameters including swirl number(S), Reynolds number(Re), normalised orifice to target spacing (Z/D) and confinement. The performance of various RANS and LES turbulence models is assessed using the data obtained from present experimental studies and that reported in literature. RNG k-ϵ turbulent model is successful in predicting heat transfer in non swirling flow. In the case of swirling flow, LES WALEM(Wall Adapting Local Eddy viscosity Model) predicts the heat transfer better than the other models. The performance of Swirling Impinging Jets(SIJ) and Cylindrical Impinging Jets (CIJ) is compared. At higher Z/D, introduction of swirl results in reduction in heat transfer and this is because of the increased spreading of the jet and reduction in velocity of the jet impinging on the target. Better performance is achieved at lower Z/D when a cylindrical jet is introduced with a small amount of swirl. For Z/D = 2 and S = 0.2, the peak Nu increases by 10 % and average Nu in the stagnation region increases by 6 % in comparison to CIJ. The position of the peak Nu moves away from the axis of the jet and there is better uniformity in Nu in the stagnation region. For Z/D = 2 swirling flow creates secondary peak(which usually occurs at high Re)even at low Re. At very low Z/D, top wall confinement causes increase in both peak heat transfer and average heat transfer in the stagnation region.

Innovative simplified approach and numerical investigation for flow and heat transfer in internally threaded tubes

Internally threaded tubes exhibit exceptional heat transfer capabilities, holding paramount significance in enhancing energy efficiency, reducing energy consumption, and tackling thermal challenges across aerospace, automotive, and environmental domains. Nonetheless, their intricate structural features, software modelling intricacies, extensive grid requirements, and challenges in maintaining grid quality render numerical simulation investigations a demanding endeavor. This study elucidates the influence mechanisms of thread spacing on the distribution of single-phase and two-phase flows. It delves into the condensation heat transfer of the R32 refrigerant and the single-phase heat transfer of water within internally threaded tubes. At the same Reynolds number, the heat transfer rate can be improved by approximately 19.61% with the optimized internally threaded parameters. When the Reynolds number varies from 18,626 to 51,222, the heat transfer rate per unit length for Tube-5 remains around 2,478.9 W/m, with a variation of less than 5.19%. Furthermore, it presents a simplified numerical simulation approach tailored for internally threaded pipes. This method manipulates wall roughness and regulates wall rotation speed to mimic fluid dynamics phenomena akin to conventional numerical simulation techniques. The findings reveal that the proposed simplified numerical simulation approach yields heat transfer and pressure drop predictions with less than 3% discrepancies compared to traditional numerical simulation predictions. Simultaneously, compared to directly simulating threaded pipes through conventional means, this approach significantly mitigates grid generation complexities, slashing computational time costs by at least 85% and facilitating efficient thermal design and structural optimization processes.

Influence of the equation of state for moderate to high pressure gaseous systems

For transportation and defense systems, equations of state describe the thermodynamical behavior of gases, as a function of pressure, density, and temperature and are often used to close systems of equation (such as Navier–Stokes for fluid mechanics). For low pressure system (under a few dozen MPa), the ideal gas equation can be used, whereas for high pressure system (above 1000 MPa), dedicated equations of state are used (Jones-Wilkins-Lee: JWL or Becker-Kistiakowsky-Wilson: BKW). For intermediate pressure systems, such as interior ballistics or pyromechanisms, where the pressure is around a few hundreds MPa and many phenomena interact closely and simultaneously (multiphysics system: combustion, fluid dynamics, thermics, fluid-structure interaction, thermodynamics…), the ideal gas equation is not valid anymore and nor are the JWL or BKW equations. A dedicated equation of state is then necessary for intermediate level of pressures, and its choice is not obvious. This paper presents a comparison of equations of state for this range of pressure (Noble-Abel, Van der Waals, virial), details the calculations of the involved coefficients and their effects on uncertainties, and studies the influence of the equation of state on the predictions of pressure, density, pressure coefficient, and heat transfer, with both analytical and numerical approach. The results highlight the importance of the choice of the equation of state, especially for a multiphysics system, as the equation of state influences not only thermodynamical properties (pressure, density, temperature) but also thermical properties of a system, such as heat transfer.

Evaluation and Research on Correlation for Spray Cooling

Abstract Spray cooling is influenced by many related parameters, making analysis of heat transfer mechanisms and prediction of cooling performance challenging. After summarizing and analyzing fifteen different forms of correlation in spray cooling literature, it appears that there are four expressions for spray cooling correlation. 2989 experimental data under different experimental conditions in twelve studies were collected to form an experimental database to evaluate the prediction of correlations. The prediction accuracy of Nusselt number correlations based on droplet diameter is considered the best, while that of correlations based on dimensionless heat flux is considered the worst. The factor analysis method was used to analyze the prediction differences of correlations and investigate the correlation between parameters and the heat transfer process. We found that the correlation with the best evaluation accuracy must contain more parameters with high correlation. A new correlation was derived based on this conclusion to predict the data better.

Improving heat transfer prediction across catalytic SiC interface through a multiscale framework leveraging machine learning and data fusion techniques

Accurate aerothermal prediction under hypersonic flow conditions is crucial for any thermal protection materials design and engineering. The surface catalytic effect, where dissociated atoms recombine into their molecular forms at the air-solid interface, is an exothermic reaction and plays an important role in high-enthalpy aerodynamic environment prediction. Taking SiC, a widely recognized high-temperature thermal protection material as an example, a multiscale fusion approach for aerothermal prediction is proposed. The approach utilizes reactive molecular dynamics method to construct an interface catalysis model, and the Bayesian maximum entropy to integrate experimental and simulational data into an optimized database. Subsequently, using the radial basis function neural network algorithm, a machine learning-based reaction kinetics model with precise analysis of surface catalysis is trained. The obtained catalytic recombination efficiency is used as a boundary input for computational fluid dynamics simulation, enabling rapid and accurate prediction of hypersonic thermal environment. The result shows that the predicted surface heat flux by this novel approach is consistent with the benchmark studies, but comes with significantly reduced computation time. The stagantion heat flux predictions based on the assumptions of full catalytic wall, finite rate catalytic wall (from the proposed multiscale fusion method), and non-catalytic wall are determined to be 8.65×106 W/m2, 7.51×106 W/m2, and 4.02×106 W/m2, respectively. Comparing these with the wind tunnel benchmark value of 7.48×106 W/m2, the error from the proposed multiscale fusion method is 0.30 %, indicating significant enhancement in prediction accuracy through the proposed upscaling method. The new approach could not only reveal complex exothermic reaction processes at the interface, but also enhance the prediction accuracy at much higher computational efficiency, providing an alternative for multiscale modeling of complex flow and heat transfer at the interface.