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Wire electric discharge machining (WEDM) is one of the most important non-traditional manufacturing methods and widely utilized in aerospace and tooling industry. However, the lack of the accurate numerical description of the WEDM process impedes a better understanding of the WEDM process and its machining mechanism. Since the vibration of the wire significantly affects the discharge location and the temperature distribution of the workpiece, the thermophysical model of the WEDM is much more complex than that of the EDM. In this paper, based on the classical thermal model of the EDM, a novel finite element method (FEM) model of WEDM considering the three dimensional wire vibration was proposed to simulate the machining process and verified by a series of WEDM experiments. Eventually, the effects of the wire vibration on the material removal rate and recast layer thickness were investigated quantitatively by analyzing the simulation results of this new thermophysical model and corresponding experimental results.

The possible existence of a metastable liquid–liquid transition (LLT) and a corresponding liquid–liquid critical point (LLCP) in supercooled liquid water remains a topic of much debate. An LLT has been rigorously proved in three empirically parametrized molecular models of water, and evidence consistent with an LLT has been reported for several other such models. In contrast, experimental proof of this phenomenon has been elusive due to rapid ice nucleation under deeply supercooled conditions. In this work, we combined density functional theory (DFT), machine learning, and molecular simulations to shed additional light on the possible existence of an LLT in water. We trained a deep neural network (DNN) model to represent the ab initio potential energy surface of water from DFT calculations using the Strongly Constrained and Appropriately Normed (SCAN) functional. We then used advanced sampling simulations in the multithermal–multibaric ensemble to efficiently explore the thermophysical properties of the DNN model. The simulation results are consistent with the existence of an LLCP, although they do not constitute a rigorous proof thereof. We fit the simulation data to a two-state equation of state to provide an estimate of the LLCP’s location. These combined results—obtained from a purely first-principles approach with no empirical parameters—are strongly suggestive of the existence of an LLT, bolstering the hypothesis that water can separate into two distinct liquid forms.

Thermophysical model of liquid-steam stream compressor, results of analysis of experimental researches of boiling up streams of liquid in the broadening ducting and running part of liquid-steam stream compressor, description of forming of three critical modes of flow and structural inversion of stream are presented in this article. The mathematical model of working process which allows to determine parameters and characteristics of liquid-steam stream compressor is presented.

We perform interface-resolved simulations of finite-size evaporating droplets in weakly compressible homogeneous shear turbulence. The study is conducted by varying three dimensionless physical parameters: the initial gas temperature over the critical temperature $T_{g,0}/T_c$, the initial droplet diameter over the Kolmogorov scale $d_0/\\eta$ and the surface tension, i.e. the shear-based Weber number, $We_{\\mathcal {S}}$. For the smallest $We_{\\mathcal {S}}$, we first discuss the impact on the evaporation rate of the three thermodynamic models employed to evaluate the gas thermophysical properties: a constant property model and two variable-properties approaches where either the gas density or all the gas properties are allowed to vary. Taking this last approach as reference, the model assuming constant gas properties and evaluated with the ‘1/3’ rule is shown to predict the evaporation rate better than the model where the only variable property is the gas density. Moreover, we observe that the well-known Frössling/Ranz-Marshall correlation underpredicts the Sherwood number at low temperatures, $T_{g,0}/T_c=0.75$. Next, we show that the ratio between the actual evaporation rate in turbulence and the one computed in stagnant conditions is always much higher than one for weakly deformable droplets: it decreases with $T_{g,0}/T_c$ without approaching unity at the highest $T_{g,0}/T_c$ considered. This suggests an evaporation enhancement due to turbulence also in conditions typical of combustion applications. Finally, we examine the overall evaporation rate and the local interfacial mass flux at higher $We_{\\mathcal {S}}$, showing a positive correlation between evaporation rate and interfacial curvature, especially at the lowest $T_{g,0}/T_c$.

Thermal performance of energy conversion systems is one of the most important goals to improve the system’s efficiency. Such thermal performance is strongly dependent on the thermophysical features of the applied fluids used in energy conversion systems. Thermal conductivity, specific heat in addition to dynamic viscosity are the properties that dramatically affect heat transfer characteristics. These features of hybrid nanofluids, as promising heat transfer fluids, are influenced by different constituents, including volume fraction, size of solid parts and temperature. In this article, the mentioned features of the nanofluids with hybrid nanostructures and the proposed models for these properties are reviewed. It is concluded that the increase in the volume fraction of solids causes improvement in thermal conductivity and dynamic viscosity, while the trend of variations in the specific heat depends on the base fluid. In addition, the increase in temperature increases the thermal conductivity while it decreases the dynamic viscosity. Moreover, as stated by the reviewed works, different approaches have applicability for modeling these properties with high accuracy, while intelligent algorithms, including artificial neural networks, are able to reach a higher precision compared with the correlations. In addition to the used method, some other factors, such as the model architecture, influence the reliability and exactness of the proposed models.

The main purpose of the present paper is to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al2O3-MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS-PSO model had a better performance than the ANFIS-GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS-PSO model.

Purpose The purpose of this study is to model and solve numerically the three-dimensional off-centered stagnation point flow and heat transfer of magnesium oxide–silver/water hybrid nanofluid impinging to a spinning disk. Design/methodology/approach The applied effective thermophysical properties of hybrid nanofluid including thermal conductivity and dynamics viscosity are according to the reported experimental relations that would be expanded by a mass-based algorithm. The single phase formulations coupled with experimental-based hybrid nanofluid model is implemented to derive the governing partial differential equations which are then transferred to a set of dimensionless ordinary differential equations (ODEs) with the use of the similarity transformation method. Afterward, the reduced ODEs are solved numerically by bvp4c function from MATLAB that is a trustworthy and efficient code according to three-stage Lobatto IIIa formula. Findings The effect of spinning parameter and nanoparticles masses (mMgO, mAg) on the hydrodynamics and thermal boundary layers behavior and also the quantities of engineering interest are presented in tabular and graphical forms. The recent work demonstrates that the analysis of flow and heat transfer becomes more complicated when there is a non-alignment between the impinging flow and the disk axes. From computational results demonstrate that, the radial and azimuthal velocities are, respectively, the increasing and decreasing functions of the disk spinning parameter. Further, for the greater values of the spinning parameter, an overshoot of the radial velocity owing to the centrifugal forces of the spinning disk is observed. Besides, the quantities of engineering interest gently enhance with first and second nanoparticle masses, while comparing their absolute values illustrates the fact that the effect of second nanoparticle mass (mAg) is greater. Further, it is inferred that the second nanoparticle’s mass enhancement results in the amplification of the heat transfer; although, the high skin friction and the relevant shear stress should be controlled. Originality/value The combination of experimental thermophysical properties with theoretical modeling of the problem can be the novelty of the present work. It is evident that the experimental relations of effective thermophysical properties can be trustable and flexible in the theoretical/mathematical modeling of hybrid nanofluids flows. Besides, to the best of the authors’ knowledge, no one has ever attempted to study the present problem through a mass-based model for hybrid nanofluid.

Double Skin Facades (DSF) are regaining popularity as a way to increase the climate resilience of buildings. Building Performance Simulation (BPS) is commonly used for their assessment, but modelling DSFs with BPS is challenging due to their complex thermophysical behaviour. Several research works have evaluated the capabilities and limitations of BPS for modelling specific DSF configurations. This work presents a validation study based on experimental data from a full-scale naturally ventilated double-skin façade, compared against results from the BPS software IDA-ICE. The study found that in periods with low solar irradiation, the different modelling strategies had a minor influence on the results, with a high agreement between measurements and simulation. In contrast, periods with solar irradiation showed a higher sensitivity to the modelling strategy, with more significant deviations from the measurement results.

In laser powder bed fusion (LPBF), the stability of melt pool dynamics determines the overall quality of a manufactured component. In this work, a numerical model of the LPBF process was developed in order to study and fully understand the behavior of the melt pool dynamics. The numerical model takes into account most of the manufacturing parameters, thermophysical properties, an enhanced thermal conductivity approach, and a volumetric heat source in order to precisely simulate LPBF. This research assumes that the energy emitted by the laser interacts with the metal powder with an absorptivity gradient through the layer thickness in order to calculate the thermal history of the process and the evolution of the melt pool dimensions. The obtained results determined that melt pool dimensions follow a thermal pattern, which is caused by the laser scanning strategy of the LPBF process. A new effective width criterion was proposed in the present research in order to accurately relate both calculated and measured dimensions of the melt pool, reducing the relative error of the model and obtaining data scattering with a standard deviation of ± 7.21 µm and a relative error of 2.92%.

One problematic task in the laser-based powder bed fusion (LB-PBF) process is the estimation of meltpool depth, which is a function of the process parameters and thermophysical properties of the materials. In this research, the effective factors that drive the meltpool depth such as optical penetration depth, angle of incidence, the ratio of laser power to scan speed, surface properties and plasma formation are discussed. The model is useful to estimate the meltpool depth for various manufacturing conditions. A proposed methodology is based on the simulation of a set of process parameters to obtain the variation of meltpool depth and temperature, followed by validation with reference to experimental test data. Numerical simulation of the LB-PBF process was performed using the computational scientific tool “Flow3D Version 11.2” to obtain the meltpool features. The simulation data was then developed into a predictive analytical model for meltpool depth and temperature based on the thermophysical powder properties and associated parameters. The novelty and contribution of this research are characterising the fundamental governing factors on meltpool depth and developing an analytical model based on process parameters and powder properties. The predictor model helps to accurately estimate the meltpool depth which is important and has to be sufficient to effectively fuse the powder to the build plate or the previously solidified layers ensuring proper bonding quality. Results showed that the developed analytical model has a high accuracy to predict the meltpool depth. The model is useful to rapidly estimate the optimal process window before setting up the manufacturing tasks and can therefore save on lead-time and cost. This methodology is generally applied to Inconel 718 processing and is generalisable for any powder of interest. The discussions identified how the effective physical factors govern the induced heat versus meltpool depth which can affect the bonding and the quality of LB-PBF components.