Journal of Thermal Analysis and Calorimetry最新文献

The heat transport efficiency inside an enclosure has been harnessed in different applications including heat exchangers, solar collectors, and electronic devices. Hybrid nanoliquids conduct higher heat transport than mono nanoliquid. This research deals with the performance of heat transport within square chamber filled with hybrid nanofluid featuring a heated inner circular obstacle. The used hybrid nanofluid is amalgamation of uniform and equal amount of MgO and Ag nanoadditives in water as a host liquid. The square chamber is under the Lorentz force effect. Further, the chamber is isothermally heated/cooled from the lower surface and vertical surfaces, respectively, while upper wall is thermally insulated. The coupled partial differential equations have been solved using a CFD (Computational fluid dynamics) package named Comsol Multiphysics, advanced simulation software. Comsol Multiphysics is solver of finite element problems. The impact of key parameters such as the Rayleigh and Hartmann numbers, nanoparticles concentration fraction, and inclined angle of Lorentz force on thermal convection is studied in details. The computational examination has been conducted in a wide range of key characteristics viz. Ra = 10³–10⁶, Ha = 0–100 and ϕ = 0.01, 0.02. The performed validation has shown a good agreement with numerical outcomes of other authors. The streamlines strength is raised rapidly for higher Ra. Highest velocity magnitude is near arc length L = 1.5. With rise of Ha, the isotherms formed at the left and right wall are reduced. The inclination angle of Lorentz force is significantly affecting the energy transport. The study is relevant in optimizing the cooling of electronic system within device where reducing temperature within the device is essential in order to prevent overheating and make sure the reliability of the electronic devices. The findings hold promise for different applications including electronics cooling and heat exchangers.

In recent years, modeling of heat exchanger is increased due to transient prediction, optimization, and performance calculations. Nanofluids play a vital role in increasing heat transfer performance of heat exchangers. This review gives an open knowledge on predicting heat transfer performance of various heat exchanger with nanofluid as coolant using various machine learning techniques. Machine learning is a promising data-driven approach for estimating heat exchanger parameters through regression classification, demonstrating promising prediction capabilities. This review article provides exemplary guidance on selecting suitable model to predict important criteria such as heat transfer coefficient, Nusselt number, overall heat transfer performance, and provides restrictions, and loopholes of machine learning techniques for heat transfer applications.

Designing the chemical composition of new heat-resistant materials, including a new group of cobalt-based materials strengthened with the L1₂ phase with the general formula Co₃(Al,X), requires the introduction of numerous alloy components, stabilizing the strengthening phase into the base composition. Typically, these are elements of high melting metals, whose main role is to stabilize the interphase boundary between the matrix and L1₂ precipitates and, in some way, strengthen the solid solution. These elements are characterized by high bond energy. On the other hand, the high content of this type of alloy addition increases the tendency of the alloy to release undesirable topologically compact phases, which rapidly deteriorate the mechanical properties of the alloy. These phases usually form in interdendritic areas, generating the so-called melting onset temperature, which deviates significantly from the solidus value. Therefore, the ability to predict the type and number of topologically compact phases being formed allows for the skilful design of the chemical composition of the alloy and its heat treatment, ensuring full dissolution of the mentioned phases in the matrix. This topic is the area of research in this article and concerns the Co–20Ni–9Al–7W–3Re–2Ti alloy in its immediately as-cast state. The scope of the research included simulations using the CALPHAD method and prediction of the phase composition of precipitates using two-dimensional structure maps. The obtained theoretical results were verified in microstructural tests using the STEM method and correlated with the results of DTA tests.

In recent years, mine conveyor belt fire accidents have occurred frequently, and an enormous amounts of toxic smoke generated during fire accidents seriously threaten the lives and safety of miners. To evaluate the smoke toxicity from mining conveyor belt fires and determine the characteristic gases for early warning of the fire, the combustion performance test and smoke density infrared spectroscopy combined test were employed to qualitatively and quantitatively analyze the change patterns of combustion products, and the index toxicity (CIT) and fractional effective dose, toxicity evaluation models, were used to evaluate the toxicity of combustion products. The results of this study showed that the heat release rate and smoke release rate of the conveyor belt had similar trends under different thermal radiation intensities, and the peak values of the heat release rate and smoke release rate increase with the increase in thermal radiation intensity. A total of 19 gas-phase substances were detected in the combustion products, among which the conventional index of toxicity of CO and HCl were 1.007 and 10.43 times the reference value, respectively, which are the leading causes of casualties in mine conveyor belt fires. In addition, the CIT of SO₂, HCN, NO₂, and CO₂ was 38.79%, 36.98%, 26.05%, and 9.00% of the reference value, respectively, all of which are highly toxic. Therefore, the above six types of gases can be used as indicator gases for conveyor belt fire early warning. The results of this study have vital guiding significance for the planning of escape routes and early monitoring and warning for personnel in mine conveyor belt fires.

This research investigates the unsteady flow of rotating Casson tetra hybrid nanofluid with dual stratification, examining the impact of variable thermal conductivity and thermal stratification on heat transfer. This study shows the unsteady behavior of tetra and ternary hybrid nanofluid flow over two different surfaces, i.e., linear and exponential surfaces. The thermophysical properties of the tetra nanoparticles, i.e., ({text{Ag}},;{text{Cu}},;{text{SWCNTs}},,,{text{and}},,{text{MWCNTs}}), comprise in C₂H₆O₂–H₂O (50–50%) base fluid. Similarity constraints are applied to convert the flow model equations into a system of coupled ordinary differential equations, which are then solved using MATLAB’s iteration-based bvp4c code. Results show that rotation and stretching significantly affect the velocity and temperature profiles. A comparison with existing literature indicates strong agreement, validating the current findings. Additionally, response surface methodology (RSM) for multiple linear regression is used to statistically analyze the impact of relevant parameters on the drag coefficient and rate of energy transfer of fluid over the linear elongated surface, including 3D plots illustrating these effects. Thermal stratification is utilized in energy-efficient buildings to maintain comfortable temperatures by keeping warmer air near the ceiling and cooler air near the floor. It is also employed in water reservoirs to optimize energy storage and distribution.

This work aims to analyze the behavior of the Cattaneo–Christov heat flux theory on hybrid nanofluid flow, and the heat transportation that occurs across a stretchable porous space. In this study, energy equation incorporates the combined effects of thermal radiation and Cattaneo–Christov heat flux. Because of their potential uses in various domains, hybrid nanofluids—a more sophisticated type of nanofluids recognized for their improved thermal properties—are being studied. A two-dimensional hybrid nanofluid system with copper (Cu) and alumina oxide (AlO₂) nanoparticles distributed throughout a base fluid of water (H₂O) is described by the mathematical model created here. The study includes other elements like viscous dissipation and changing viscosity. Similarity transformations are used to turn the governing partial differential equations (PDEs) into ordinary differential equations, which are then numerically solved using a shooting approach and the MATLAB Bvp4c solver. The impact of crucial parameters on velocity and temperature profiles is carefully investigated in this study. These parameters include the Weissenberg number, magnetic parameter relaxation time, Prandtl number, thermal radiation parameter, velocity slip parameter, Biot number, convection parameter, suction parameter, heat source parameter, and Eckert number. In comparison to conventional nanofluids, the hybrid nanofluid's temperature profile shows a notable rise, according to the data. In certain circumstances, the results also closely match existing solutions. By outperforming traditional nanofluids, this discovery holds promise for improving the performance of industrial heat exchangers, automobile radiators, and electrical gadgets.

The primary subject of this article is the study of the viscous flow of nanofluids consisting of copper-methanol and water in the presence of a three-dimensional stretched sheet, which is subjected to magnetohydrodynamic effects (3D-MHD-NF) by employing artificial recurrent neural networks that are optimized using a Bayesian regularization technique (ARNN-BR). The viscosity effect is recognized to be dependent on temperature, with methanol and water being used as the base fluid. The presented model is employed in the manipulation and creation of surfaces within the field of nanotechnology. Its applications include stretching, shrinking, wrapping, and painting devices. The Adams method was employed to generate a dataset for the 3D-MHD-NF model for four scenarios by varying the Hartmann number (H), volume fraction of nanoparticle ((varphi)), and viscosity parameter (α). The ARNN-BR technique employed a random selection of data 70% for training, 20% for testing, and 10% for validity. It has been found that boundary layer becomes thinner as the volume percentage of nanoparticle increases. Additionally, it is observed that augmentation in the viscosity parameter results in a proportional rise in temperature. Moreover, it is observed that increment in the variables H, φ, and α have an impact on the velocity boundary thickness in both the x- and y-directions. The newly introduced ARNN-BR technique's dependability, stability, and convergence were assessed using a fitness measure based on mean squares errors, histogram drawings, regression, input-error cross-correlation, and autocorrelation analysis for each scenario of the 3D-MHD-NF model.

A closed-loop pulsating heat pipe (CLPHP) can provide effective and adaptable thermal solutions for various applications. This work presents extensive experimental studies on CLPHP to enhance thermal performance using nanofluid. The experimental studies are conducted using two different heat transfer fluids: deionized (DI) water and a nanofluid (Al₂O₃-DI water with 0.1 mass/% nanoparticles). Parametric studies are performed with different combinations of filling ratios (FR) and heat input values. To analyze the experimental data, an in-house Python library named PyPulseHeatPipe is developed, which facilitates statistical analysis, data visualization, and process data for machine learning from raw experimental data. Furthermore, the experimental datasets are used to train various machine learning (ML) models, including random forest regressor (RFR), extreme gradient boosting regressor, gradient boosting regressor, support vector machine, and K-nearest neighbors (KNN) to determine the thermal parameters for a given CLPHP. These models precisely predict the thermal performance of CLPHP using two novel approaches. The first approach predicts thermal resistance under given thermal properties such as evaporator temperature, pressure, FR, heat input, and heat transfer fluid, while the second approach predicts thermal parameters such as evaporator temperature, pressure, and heat input to achieve the desired thermal resistance. For the first approach, the RFR model performs the best among the trained ML models, with the lowest root mean square error (RMSE) of 0.0175 and the highest goodness of fit, with R² score and R²-adjusted (R²-adj.) of 0.9873 and 0.9872, respectively. For the second approach, the KNN model achieves the highest goodness of fit (R²-adj.) for evaporator temperature, pressure, and heat input values of around 0.9889, 0.9524, and 0.8149, respectively. This study establishes a foundation for the more efficient thermal design of CLPHP in various engineering systems by integrating experimental research with data-driven solutions through ML.

In the current study, the performance of a dimple-roughened solar thermal collector (DRSTC) is investigated within a (({text{Re}}_{text{xx}})) range that spans from 3000 to 48,000. Under constant solar intensity (({I}_{text{sr}})=1000 ({text{Wm}}^{-2})), relative roughness height (({e}_{text{d}}/{D}_{text{h}})) varied from 0.021 to 0.036, relative roughness pitch ((p/{e}_{text{d}})) from 10 to 20, arc angle (({alpha }_{text{a}})) from 45 to 60°, and temperature rise parameter from 0.003 to 0.02, and the proposed model predicts exergy efficiency of the SAH, and the results obtained can be used as reference for the design of new solar thermal systems. The assessment makes use of advanced MATLAB simulations in order to evaluate the exergetic efficiency ({(}eta_{{{text{ex}}}} )) of a DRSTC. At lower ({text{Re}}_{text{xx}}) values, ({(}eta_{{{text{ex}}}} )) increases uniformly; however, stabilization and decline occur at higher ({text{Re}}_{text{xx}}) values. The highest ({(}eta_{{{text{ex}}}} )) for the DRSTC is 1.47% under a temperature rise parameter ((Delta T/I_{{{text{sr}}}} )) of 0.0071 ({text{Km}}^{2}{text{W}}^{-1}) for obtaining optimum values of ({e}_{text{d}}/{D}_{text{h}}) = 0.036, (p/{e}_{text{d}}) = 10, and (alpha_{a}) = 60°. This research illustrates the usefulness of MATLAB for solar energy system analysis and optimization by integrating simulation and experimental data. This investigation further supports the feasibility of the proposed collector design.

The impact of Stefan blowing on the stagnation point flow of HNF (hybrid nanofluid) across a sheet containing gyrotactic microorganisms under local thermal non-equilibrium conditions (LTNECs) is briefly discussed in this paper. The present work uses a simplified mathematical model to inspect the characteristics of heat transfer in the absence of LTNECs (local thermal equilibrium conditions). LTNECs, traditionally provide two distinct fundamental temperature gradients for the liquid and solid phases simultaneously. A hybrid nanofluid is a mixture of water as the base fluid and single-walled carbon nanotubes and multi-walled carbon nanotubes . Gyrotactic microorganisms are included into nanoparticles to increase their thermal efficiency in a variety of systems, including microbial fuel cells, enzyme biosensors, bacteria powered micromixers, chip-shaped microdevices like bio-microsystems, and micro-volumes like microfluidic devices. This model can also help environmental engineering by enhancing wastewater treatment procedures by allowing microorganisms to break down pollutants more effectively. It advances the development of more productive photo bioreactors, increasing the output of biofuels in the field of renewable energy. Material scientists can utilize this concept to develop controlled nanostructured materials with consistent composition and thermal properties. The considerable similarity transformation is used to build ordinary differential equations for the nonlinear dimensionless system. This problem is solved numerically by using the Bvp4c method. The results determine that when the Stefan blowing parameter increases, fluid flow increases but temperature, mass transfer rate, and heat transfer are decreased.

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