Prediction Of Heat Transfer Performance Research Articles

Predicting heat transfer Performance in transient flow of CNT nanomaterials with thermal radiation past a heated spinning sphere using an artificial neural network: A machine learning approach

An efficient heat transfer phenomenon using nanofluid have greater challenges in various industries, engineering application the recent trend. Keeping this in present scenario, this study aims to optimize the heat transmission rate in the magnetized flow of nanomaterials through a rotating, spinning sphere. The heat transfer phenomena in the time-dependent fluid are enhanced by the incorporation of nonlinear radiation and a variable heat source. Additionally, the free convective flow is influenced by the effects of thermal buoyancy and a transverse magnetic field. The proposed model along with several factors is standardized through adequate transformation rules. Further, shooting-based Runge-Kutta technique is adopted with the help of built-in MATLAB function bvp4c for the solution of the transformed system. The prime focus of the proposed work is the optimizing heat transfer rate combined with regression analysis using artificial neural network and then it uses Levenberg Marquardt algorithm with well-posed training, testing, and validation data. The error analysis also presented briefly and the variation of characterizing parameters is depicted via graphs. Further, the important outcomes are; the particle concentration of carbon nanotubes contributes to decelerating the velocity profiles, leading to an increase in boundary layer thickness. In contrast, increasing magnetization has the opposite effect. Both nonlinear radiative heat and an additional heat source enhance the heat transfer phenomenon.

Modeling and predicting heat transfer performance in bioconvection flow around a circular cylinder using an artificial neural network approach

The current research aims to study the heat transfer rate in mixed convection flow of viscous fluid along a vertical cylinder containing swimming microorganisms and velocity slip effects. The thermal and solutal processes are examined using gyrotactic microorganisms, chemical reaction and slip conditions. The non-linear (PDEs) of the governing flow are transformed into set of highly nonlinear (ODEs) by employing appropriate similarity transformations, which are then resolved computationally by Runge-Kutta-Fehlberg 4th order toward with shooting approach. The predicted solution is derived using the Levenberg-Marquardt scheme combined with a backpropagation neural network (LMS-BPNN). The labelled data sheet is divided into three parts: 80% data is used for training, 10% for validation and 10% for testing. The performance of the proposed AI-based LMS-BPNN is examined in term of MSE for considered scenarios. An optimal solution is achieved at 675, 397, 727, 647, and 711, epochs, while performance in terms of MSE for these epochs is to be 1.06×E−9, 8.47×E−10, 9.20×10−9, 9.94×E−10,and 9.09×10−10. The assigned computational valuations and ANN valuations are in excellent alignment than those existing studies on numerous special cases. The predicted solution with AI-based technique LMS-BPNN is reliable, well-organized, and easy to handle the thermal and solutal transport analysis of flow across a stretchable cylinder. The validity of proposed LMS-BPNN algorithm is examined with absolute error analysis and error is found to be approximately 10−4.

Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials

This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge–Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space.

Machine-assisted quantification of droplet boiling upon multiple solid materials

The intricate nature of droplet wetting and boiling on heated nano-/micro-structured surfaces has significant implications for practical applications. However, the multitude of factors influencing these liquid-vapor-solid interactions complicates the understanding of boiling behaviors across a wide range of operating conditions. In this study, we examined droplet impingement boiling over a large range of Weber numbers (We) on various surfaces, from smooth to nano-micro hierarchical ones, and from stationary to vibrated ones. The results indicate that the nano-micro hierarchical surface possesses the greatest heat transfer ability due to its superior superhydrophilicity, enhanced by nano-structures, and the vapor buffer effect caused by micro-structures. Additionally, vibration significantly improves boiling performance by 55.4%. However, contrary to previous findings, a higher We does not always enhance heat transfer performance due to a pronounced water hammer effect. We discovered that when the net pressure reaches a threshold value (∼ 90 kPa), a new phenomenon - jet flow - occurs, leading to a deterioration in heat transfer. Utilizing our database, we trained a machine learning framework to generate accurate droplet boiling data, providing a droplet boiling law that can extensively predict heat transfer performance. This paper proposes a fusion of physics and artificial intelligence to enhance our understanding and application of droplet boiling phenomena. Our findings are expected to contribute to the development of more efficient droplet-based boiling heat transfer devices and configurations across a wider range of industrial settings.

Numerical investigation of the flows and heat transfer characteristics of internal cooling channels with separated ribs in gas turbine blades

Gas turbine engines play a crucial role in numerous industrial domains, including power generation, aviation, and marine propulsion. One of the major challenges in designing gas turbine engines is managing the high temperature generated by the combustion process. Internal cooling is a commonly used technique to maintain the temperature of critical components, such as turbine blades, within a safe operating range. Rib turbulators are widely used in internal cooling systems to enhance heat transfer performance by promoting turbulence in the fluid flow. Nevertheless, the existence of a continuous rib within the cooling channel can result in elevated temperatures near the rib section, potentially diminishing the overall system efficiency. In response to this challenge, a new rib turbulator design, denoted as the “separated rib,” has been introduced to mitigate the high-temperature zone. Through the utilization of the passing-gap design in the separated rib configuration, the coolant flow passes through the gap, effectively eliminating the region of extreme heat and augmenting the secondary flow. Consequently, it results in a notable enhancement of heat transfer performance within the ribbed channel. The numerical simulations are performed by solving three-dimensional (3D) Reynolds-averaged Navier–Stokes equations using the commercial software ANSYS CFX. The working fluid is steam, and the heat transfer performance is evaluated in terms of the Nusselt number (Nu), friction factor (f), and thermal performance factor (TPF). The results show that the separated rib configuration has approximately 17.3% higher Nusselt number than the original ribbed configuration when the Reynolds number (Re) changes from 5000 to 60 000. The separated rib configuration consistently shows higher TPF values between about 1.6 and 1.9 than the original rib configuration, where TPF is smaller than 1.35. Furthermore, the heat transfer correlation related to the Reynolds number was developed to predict heat transfer performance. The heat transfer correlations align closely with the numerical simulation results, showing about 17.4% and 34.3% improvements in Nu and TPF, respectively, for our newly designed system compared to the old version.

Analysis of heat transfer enhancement of passive methods in tubes with machine learning

This study investigates the efficacy of machine learning techniques and correlation methods for predicting heat transfer performance in a dimpled tube under varying flow conditions, including the presence of nanoparticles. A comprehensive numerical analysis involving 120 cases was conducted to obtain Nusselt numbers and friction factors, considering different dimple depths and velocities for both pure water and water-Al2O3 nanofluid at 1%, 2%, and 3% volume concentrations. Utilizing the data acquired from the numerical simulations, a correlation equation, SVM ANN architectures were developed. The predictive capabilities of the statistical approach, ANN, and SVM models for Nusselt number distribution and friction factor were meticulously assessed through mean average percentage error (MAPE) and correlation coefficients ( R2). The research findings reveal that machine learning techniques offer a highly effective approach for accurately predicting heat transfer performance in a dimpled tube, with results closely aligned with Computational Fluid Dynamics (CFD) simulations. Particularly noteworthy is the superior performance of the ANN model, demonstrating the most precise predictions with an error rate of 2.54% and an impressive R2 value of 0.9978 for Nusselt number prediction. In comparison, the regression model achieved an average error rate of 6.14% with an R2 value of 0.8623, and the SVM model yielded an RMSE value of 2.984% with an R2 value of 0.9154 for Nusselt number prediction. These outcomes underscore the ANN model’s ability to effectively capture complex patterns within the data, resulting in highly accurate predictions. In conclusion, this research showcases the promising potential of machine learning techniques in accurately forecasting heat transfer performance in dimpled tubes. The developed ANN model exhibits notable superiority in predicting Nusselt numbers, making it a valuable tool for enhancing thermal system analyses and engineering design optimization.

Machine learning-based modelling using ANN for performance prediction of a solar air heater design with jet impingement

The prediction of heat transfer performance for solar air heater (SAH) designs is important to assess its potential without relying on extensive experimental studies. This work aimed to predict the heat transfer characteristics of a SAH design with jet impingement using an extensive artificial neural network (ANN) study and covering a complete year-long ambient and various operating parameters (commensurate and non-commensurate). Total 84 ANN models were developed using different network types, transfer functions, and training functions. They were tested on 174 representative datasets collected throughout a year. The best ANN model was found to be the feed forward backpropagation model with tangent-sigmoid transfer function and LM training algorithm, which produced accurate predictions for thermal efficiency, exergy efficiency, and thermo-hydraulic efficiency, with sample errors within ±5 %. The numerical values of R2, MAE, RMSE, and COV from the ANN model results were respectively 0.9972, 0.9053, 1.1884, and 2.0618 for thermal efficiency, 0.9947, 0.2631, 0.3363, and 1.7157 for exergy efficiency, and 0.996, 0.0139, 0.0193, and 0.8076 for thermo-hydraulic efficiency. The study concluded that ANN models can accurately predict the heat transfer characteristics of an SAH design, requiring less time and less computational resources compared to conventional experimental or computational methods.

Predicting heat transfer performance of Fe3O4-Cu/water hybrid nanofluid under constant magnetic field using ANN

In this study, the experimental results using mono (Fe3O4/water and Cu/water) and hybrid (Fe3O4-Cu/water) type nanofluid with nanoparticle volume concentrations of (0≤φ≤0.02) under laminar flow conditions (994≤Re≤2337) were compared with the results obtained by ANN. While the Reynolds number (Re), hydraulic diameter (Dh), thermal conductivity (k) of working fluid, and volume concentration of the nanoparticles (φ) were selected as input layers, the Nusselt number (Nu) were considered as output layers. The %75 of the findings obtained from experiments were used to train Artificial Neural Network (ANN). The estimated data by ANN is in perfect agreement with the experimental data. The success of ANN was deter-mined by comparing it with SVM, Dec Tree, and their variations. Mean square error (MSE), root mean square error (RMSE), R-sq (R2), and mean absolute error (MEA) were considered in evaluating the results obtained. According to findings, MAE 0.00088274, MSE 1.4106e-06, RMSE 0.0011877 and R2 1.00 were measured. These findings show that the use of ANN is a feasible way to predict the convective heat transfer performance of hybrid nanofluid under a magnetic field (MF).

열회로망을 이용한 판형 히트 파이프 열전달 성능 예측

전자장비 열 관리에 요구되는 냉각 용량이 점차 증가하면서 냉매의 증발-응축 순환을 이용한 히트파이프가 폭넓게 사용되고 있다. 히트 파이프는 레이더 등 군용 장비의 냉각에도 사용되는데, 군용 장비 특성상 냉각 장치인 히트 파이프에도 다품종 소량 생산이 요구된다. 다품종 생산을 위해서는 설계변수 변화 시의 열전달 성능 예측이 요구된다. 본 연구에서는 히트 파이프의 구성 요소를 등가 열회로 망으로 단순화하여 히트 파이프의 구조 변화에 따른 열전달 성능을 예측하였다. 모델 신뢰성 확보를 위해서 실험값과 계산을 비교하여 검증하고, 히트 파이프의 증기 코어 두께, 금속 벽 두께, 금속 윅 두께와 윅 구조를 변수로 하여 증발면 온도와 압력비 등 열전달 성능 지수를 계산하고 변수에 따른 변화 경향을 분석하였다.

Predicting heat transfer performance in a complex heat exchanger for LNG FGSS development

Predicting heat transfer performance in a complex heat exchanger for LNG FGSS development

Computational Fluid Dynamics and Fuzzy Logic for Modeling Conical Spiral Heat Exchangers

AbstractComputational fluid dynamics and fuzzy logic (FL) modeling methods were used to estimate heat transfer and fluid flow characteristics in conical spiral tubes. The effects of coil pitch p and cone angle θ at Reynolds numbers Re of 1500–5500 on flow and heat transfer were investigated. Water with an inlet temperature of 298 K was considered as the working fluid, and a constant wall temperature of 398 K was used in the study. FL was used to predict the Nusselt number and friction factor in conical spiral tubes as functions of Re, θ, and p. FL is an efficient method for predicting complex systems and is a valuable complement to classical hard computing techniques. The purpose of the study was to find a relationship between heat exchanger parameters and performance characteristics. It was also investigated how the FL expert system plays an important role in predicting heat transfer performance.

Heat transfer distribution and flow characteristics in a channel with perforated-baffles

The current paper reports the prediction of heat transfer performance of a channel with a modified perforated-baffle in a square wings (SW-PB) form. Numerical results include flow/temperature field, local heat transfer distribution, and thermal performance of a channel installed with the transverse solid baffle (TB), perforated-baffle (PB), and perforated-baffle with square wings (SW-PB). The simulation results demonstrated that TB brought a large recirculation flow, PB induced small recirculation and SW-PB produced several impinging jets as well as recirculation flows. Heat transfer rates given by PB were lower than those provided by TB and SW-PB by around 6.8% and 7.3%, respectively which were accompanied by lower friction losses by about 11.8% and 3.6%, respectively. Although, SW-PB gave higher heat transfer, the assessment of thermal performance factor (TPF) showed that the benefit from lower friction losses was more important. Under the same pumping power, TPFs assisted by SW-PB were higher than that assisted by PB and TB by 6.0% and 3.3%, respectively. The highest TPF of 1.2 was captured by SW-PB at Re = 9,000.

Numerical prediction of heat transfer performance of plate heat exchanger based on experimental data assimilation to calibrate turbulence model constants

• Apply Data Assimilation to the study of heat transfer characteristics. • Use the ensemble Kalman filter to calibrate turbulence model constants. • Realize the perfect integration of experimental and numerical research. • The application of Data Assimilation in engineering is expanded. The heat transfer effect of plate heat exchangers is different due to its various plate structures. To accurately predict the heat transfer characteristics of a plate heat exchanger, this paper makes an important attempt to apply data assimilation technology, namely the data-driven model constant optimization method, to the study of the flow and heat transfer characteristics of a plate heat exchanger. Based on the experimental data, the ensemble Kalman filter algorithm is used to calibrate turbulence model constants and predict the heat transfer performance of a plate heat exchanger. The results show that the data assimilation technology maximizes the value of experimental data, realizes the perfect integration of experimental research and numerical simulation research, and gives full play to the advantages of experimental research and numerical simulation research of plate heat exchangers. At the same time, the reliability of predicting heat transfer characteristics of a plate heat exchanger is improved, the application of data assimilation in engineering is expanded, and the application potential of data assimilation in future turbulence research is highlighted.

Numerical simulation of falling film sensible heat transfer over round horizontal tubes

Numerical simulations are performed to explore the heat transfer characteristics of falling films over horizontal round tubes with uniform heat flux imposed for a range of Reynolds numbers spanning the droplet, jet, and sheet regimes. Simulations results agree well with available experimental measurements. The study analyzes the local as well as average heat transfer behavior under the different flow modes. The numerical results show that the local Nusselt number (Nu) distribution depends on the flow features in each mode and varies substantially in all directions for the respective mode. In the droplet mode, the Nu value varies significantly as the droplet impinges and the remnant liquid-bridge retracts (peak instantaneous Nu near 6), followed by wave propagation over the tube surface with peak Nu around 0.25. For the jet modes, the local maximum in the heat transfer occurs off-center to the impingement location with magnitudes of peak Nu = 3.1 for the inline jet mode and Nu = 2.7 for the staggered jet mode, while on the rest of the tube surface, it has an inverse relation with the liquid film thickness. Substantial variations in the heat transfer value are also recorded in the middle of the two impinging jets with Nu = 0.95 in the inline jet mode where the neighboring jets do not interact, and Nu = 0.60 in the crest region of the staggered jet mode where the neighboring jets interact with each other. In the sheet mode, the Nu was seen to depend on the thickness of the liquid waves traversing over the tube surface. Lower Nu values were recorded beneath the crest location of the liquid waves, which increases (1.4–11.6% depending on circumferential location) abruptly in magnitude at the advancing fronts of the waves. The temperature distribution in the liquid film in each of the modes was examined to evaluate the mechanism of heat transfer process. This study also compares the local heat transfer coefficient distribution with the analytical heat transfer models derived to predict heat transfer performance over horizontal tube surfaces.

Assessment and Prediction of Heat Transfer Performance of Oscillating Heat Pipe using Acetone

Heat pipe is a device to enhance the performance of heat transfer, in particular to transition phase change process. The present investigation is carried out to measure the thermal performance of a closed loop acetone filled oscillating heat pipe with four turns is fabricated and tested. The test was carried out for the inner diameter of 1.7 mm copper tube by varying the acetone of 50%, 60%, 70% and 80% filler ratio with a heat input of 15 W to 40 W in steps of 5 W has been considered. The results shows that 80% acetone filled OHP provides the better thermal performance with an average minimum temperature difference between condenser and evaporator by 6.03%, heat transfer coefficient increased by 77% and the thermal resistance decrease by 43% respectively. The mathematical modelling was developed to predict the performance of the heat transfer. The model clearly shows that the independent variables are statistically significant with a p-value of 0.05 and the uncertainty analysis between the experimental and predicted values are less than 10%.

An Experimental Study of ZrO2‐CeO2 Hybrid Nanofluid and Response Surface Methodology for the Prediction of Heat Transfer Performance: The New Correlations

This article experimentally and statistically reports the convective heat transfer performance of a cylindrical mesh‐type heat pipe apparatus filled with ZrO2‐CeO2/water‐ethylene glycol nanofluids. In this regard, ZrO2‐CeO2 nanoparticles were synthesized and characterized through the Scanning Electron Microscope and Powder X‐ray diffraction methods followed by the preparation of hybrid ZrO2‐CeO2 nanofluids of various concentrations ranging from 0.025 to 0.1%. The heat transfer features of a tubular heat pipe with a mixture of the ZrO2‐CeO2 nanofluid were evaluated. A 5.33% decrease in thermal resistance value and a 41.16% increase in heat transfer ability with increased power input were observed. The potent regression models were proposed to estimate heat transfer features of the heat pipe. The ANOVA statistical method has been employed to determine the P value and the F value of the models towards enhancing the reliability and accuracy of the developed models. The outcome revealed that the proposed models are reliable and have the best fit with the experimental data for 30–60 W power. The correlations’ results were validated against the experimental data and showed high accuracy. Moreover, the accuracy of the developed models was ensured through R‐squared and adjusted R‐squared values.

Numerical Prediction of Heat Transfer Performance of Plate Heat Exchanger Based on Experimental Data Assimilation to Calibrate Turbulence Model Constants

Numerical Prediction of Heat Transfer Performance of Plate Heat Exchanger Based on Experimental Data Assimilation to Calibrate Turbulence Model Constants

Flow condensation heat transfer performance of natural and emerging synthetic refrigerants

There is great interest in predicting flow condensation heat transfer for lower global warming potential (GWP) fluids. This paper analyzes the efficacy of common flow condensation correlations developed for particular fluids in order to identify their suitability to predict heat transfer performance of low GWP fluids. Condensation heat transfer data were extracted from the literature, including 19 papers and 1,473 data points for natural refrigerants [i.e., ammonia (R717), CO2 (R744), propane (R290), isobutane (R600a)] and 35 papers and 5,030 data points for synthetic refrigerants [i.e., R12, R1234yf, R1234ze(E), R1234ze(Z), R22, R32, R41, R123, R125, R134a, R142b, R152a, R161, R404A, R410A, R448A, R449A, R450A, R452B, R454C, R455A, R513A] encompassing tube diameters of 0.1–11.5 mm, mass fluxes of 55–1200 kg/m2s, and saturation temperatures of -25 °C–65 °C. Correlations analyzed included (Akers et al., 1959; Cavallini et al., 2006; 2011; Kim and Mudawar, 2013; Macdonald and Garimella, 2016; Shah, 1979, 2009, 2013, 2016) and (Traviss et al., 1973) for smooth tubes and (Chamra et al., 2005) and (Kedzierski and Goncalves, 1999) for enhanced tubes. Since most studies did not report wall temperature, correlations which relied on wall temperature directly or indirectly were excluded from the analysis. For synthetic refrigerants, mean average error (MAE) ranged from 6% to 257%, and (Cavallini et al., 2011) and (Kim and Mudawar, 2013) were the best predictors for emerging synthetic refrigerants. The (Kim and Mudawar, 2013) correlation was found to best predict the heat transfer performance for propane and R600a data, but most correlations did not accurately predict ammonia and CO2 flow condensation.

Genetic Algorithm and Deep Learning to Explore Parametric Trends in Nucleate Boiling Heat Transfer Data

Abstract Exploring parametric effects in pool boiling is challenging because the dependence of the resulting surface heat flux is often nonlinear, and the mechanisms can interact in complex ways. Historically, parametric effects in nucleate boiling processes have been deduced by fitting relations obtained from physical models to experimental data and from correlated trends in nondimensionalized data. Using such approaches, observed trends are often influenced by the framing of the analysis that results from the modeling or the collection of dimensionless variables used. Machine learning strategies can be attractive alternatives because they can be constructed either to minimize biases or to emphasize specific biases that reflect knowledge of the system physics. The investigation summarized here explores the use of machine learning methods as a tool for determining parametric trends in boiling heat transfer data and as a means for developing methods to predict boiling heat transfer. Results are presented that demonstrate how a genetic algorithm and deep learning can be used to extract heat flux dependencies of a binary mixture on wall superheat, gravity, Marangoni effects, and pressure. The results provide new insight into how gravity and Marangoni effects interact in boiling processes of this type. The results also demonstrate how machine learning tools can clarify how different mechanisms interact in the boiling process, as well as directly providing the ability to predict heat transfer performance for nucleate boiling. Each technique demonstrated clear advantages depending on whether speed, accuracy, or an explicit mathematical model was prioritized.

Potential of Fuzzy Methodology for Investigation in Nanofluids Heat Transfer

In this paper, the Fuzzy Nanofluid Model (FNFM) used to develop a fuzzy analysis investigation on heat transfer optimal performance at different Nanofluids flow rate. The fuzzy Nanofluid model is applied to examine the effects of heat transfer parameters on heat transfer performance. Silicon Oxide SiO2 Nanofluid is used to explain their effects on heat transfer by two methods traditional and fuzzy (with two shapes of member ship function triangular and trapezoidal). This study evaluates the effects of nanoparticles SiO2 with different value of particle concentration PC (0.0-4.0%) using the water as a base fluid. This investigation covers a Reynolds number (Re) in the range of (100-500) as a flow rate (FR) for laminar flow. The main objective of present research, first one, compared a developed FNFM model with traditional model (TM) and determines how fuzzy model plays a significant role in prediction of Heat Transfer performance. Second one, to provide developed methodology for performance evaluation of heat transfer by connecting more than one parameter to a single output which is invaluable supplements relative to classical models. Third one, a developed FNFM can be used as a help tool for decision making to get the best judge (optimum) the performance of any system. The results of fuzzy model showed the heat transfer of SiO2/H2O Nanofluids significantly increased the PC compared with the increase in FR. However, however, using this method, there will be no need to resort to solving complex equations to arrive at a representation of the performance of any system. Finally, the study shows that fuzzy model plays significant role in prediction of heat transfer investigation without the complexity of mathematical tradition models. The correlations coefficients R2 between TM and FNFM models for heat transfer coefficient (0.97) and the average relative error (ε) is ((4.4%). FNFM models can predict heat transfer characteristics with higher accuracy than that of the traditional model.