This study investigated numerically the characteristics of laminar flow around two identical circular cylinders placed in tandem, with slits of the same width through their respective axis. The center to center distance between the cylinders and the slit orientation were varied to study their effects on the flow structure, lift and drag, and vortex shedding characteristics. It was found that three flow regimes could be distinguished, the transitions between which could be indicated by the sudden changes in drag and lift. Asymmetrically, configured slits destabilized the stagnant region between cylinders; whereas in-line slits connect the two cylinders to act as a single elongated bluff body, even at large cylinder separation, by stabilizing the stagnant region in between. These in turn strongly modified the transition between flow regimes. Vortex shedding was also strongly influenced by both slit configuration and cylinder separation.

The evaluation of the mixing effect of gas-liquid two-phase flow during the top-blown gas agitation mixing is one of the difficulties in the testing field, especially in the process of using the model method to study the metallurgical top-blowing process. In order to evaluate the effect of gas-liquid two-phase flow mixing, a gas chromatography simulation based on capacitance tomography was used to visualize the flow pattern and analyze the mixed characteristics. A gas top-blown agitation test rig was set up, the gas phase was air-selected, and the liquid phase was selected from synthetic heat-conducting oil. The top-blown stirring test process was measured and imaged by electrical capacitance tomography (ECT) equipment from ECT Instruments Ltd (UK). The MATLAB program was used to identify the mixing areas of the images to obtain the distribution of gas-liquid two-phase. The flow pattern of the gas-liquid mixing region was obtained. The chaotic detection of the gas-liquid mixing process was performed by the three-state test method; the images were processed by the counting box dimension-corrosion method to obtain the mixing uniformity time of gas-liquid flow. Results show that it is feasible to use the capacitance tomography technique to visualize the gas-liquid two-phase distribution. The uniformity time quantification of the gas-liquid mixing process is also achieved.

The recently proposed interface propagation-based method has shown its advantages in obtaining the thermal conductivity of phase change materials during solid-liquid transition over conventional techniques. However, in previous investigation, the analysis on the measurement error was qualitative and only focused on the total effects on the measurement without decoupling the influencing factors. This paper discusses the effects of influencing factors on the measurement results for the interface propagation-based method. Numerical simulations were performed to explore the influencing factors, namely model simplification, subcooling and natural convection, along with their impact on the measurement process and corresponding measurement results. The numerical solutions were provided in terms of moving curves of the solid-liquid interface and the predicted values of thermal conductivity. Results indicated that the impact of simplified model was strongly dependent on Stefan number of the melting process. The degree of subcooling would lead to underestimated values for thermal conductivity prediction. The natural convection would intensify the heat transfer rate in the liquid region, thereby overestimating the obtained results of thermal conductivity. Correlations and experimental guidelines are provided. The relative errors are limited in ±1.5%, ±3%and ±2% corresponding to the impact of simplified model, subcooling and natural convection, respectively.

The knowledge of bubble profiles in gas-liquid two-phase flows is crucial for analyzing the kinetic processes such as heat and mass transfer, and this knowledge is contained in field data obtained by surface-resolved computational fluid dynamics (CFD) simulations. To obtain this information, an efficient bubble profile reconstruction method based on an improved agglomerative hierarchical clustering (AHC) algorithm is proposed in this paper. The reconstruction method is featured by the implementations of a binary space division preprocessing, which aims to reduce the computational complexity, an adaptive linkage criterion, which guarantees the applicability of the AHC algorithm when dealing with datasets involving either non-uniform or distorted grids, and a stepwise execution strategy, which enables the separation of attached bubbles. To illustrate and verify this method, it was applied to dealing with 3 datasets, 2 of them with pre-specified spherical bubbles and the other obtained by a surface-resolved CFD simulation. Application results indicate that the proposed method is effective even when the data include some non-uniform and distortion.

Submerged gas injection into liquid leads to complex multiphase flow, in which nozzle geometries are crucial important for the operational expenditure in terms of pressure drop. The influence of the nozzle geometry on pressure drop between nozzle inlet and outlet has been experimentally studied for different gas flow rates and bath depths. Nozzles with circular, gear-like and four-leaf cross-sectional shape have been studied. The results indicate that, besides the hydraulic diameter of the outlet, the orifice area and the perimeter of the nozzle tip also play significant roles. For the same superficial gas velocity, the average pressure drop from the four-leaf-shaped geometry is the least. The influence of bath depth was found negligible. A correlation for the modified Euler number considering the pressure drop is proposed depending on nozzle geometric parameter A_oL_o⁻² and on the modified Froude number gd_o⁵Q⁻² with the hydraulic diameter of the nozzle d_o as characteristic length.

Natural convection heat transfer from annular finned tubes was studied numerically. Effects of fin spacing, temperature difference and tube diameter on flow pathlines and local heat transfer were also studied. It was shown that pathlines remain mostly circular for different geometries. Moreover, the contributions of fin periphery, fin side and bare tube to heat transfer were specified. It was shown that the heat transfer per unit area of fin periphery can be several times that of other parts. Moreover, in higher finspacing, the heat transfer from the bare tube can be more important than fin sides.

Knowledge of transport phenomena and keyhole evolution is important for controlling laser welding process. However, it is still not well understood by far due to the complex phenomena occurring in a wide temperature range. A transient 3D model including heat transfer, fluid flow and tracking of free surface is built in this study. The transport phenomena are investigated by calculating the temperature and velocity fields. The 3D dynamic keyhole evolution process is revealed through tracking free surface using volume-of-fluid method. The results show that the keyhole deepening speed decreases with laser welding process before the quasi-steady state is reached. The plasma can greatly affect the keyhole depth through absorbing a great amount of laser energy and thus lowering the recoil pressure. Moreover, the relationship between keyhole depth and weld penetration is also discussed. This paper can help to better understand the dynamics in molten pool and then improve laser welding process.

In this paper, a novel calibration integral equation is derived for resolving double-sided, two-probe inverse heat conduction problem of surface heat flux estimation. In contrast to the conventional inverse heat conduction techniques, this calibration approach does not require explicit input of the probe locations, thermophysical properties of the host material and temperature sensor parameters related to thermal contact resistance, sensor capacitance and conductive lead losses. All those parameters and properties are inherently contained in the calibration framework in terms of Volterra integral equation of the first kind. The Laplace transform technique is applied and the frequency domain manipulations of the heat equation are performed for deriving the calibration integral equation. Due to the ill-posed nature, regularization is required for the inverse heat conduction problem, a future-time method or singular value decomposition (SVD) can be used for stabilizing the ill-posed Volterra integral equation of the first kind.

In this article, we considers the thermodynamics analysis of creeping viscous nanofluid flow in a horizontal ciliated tube under the effects of a uniform magnetic field and porous medium. Moreover, energy analysis is performed in the presence of an internal heat source and thermal radiation phenomena. The thermal conductivity of base fluid water is strengthened by considering the carbon nanotubes (CNTs). Mathematical formulation operated, results in a set of non-linear coupled partial differential equations. The governed differential system is transformed into an ordinary differential system by considering suitable similarity variables. Exact solutions in the closed form are computed for the temperature, momentum and pressure gradient profiles. In this study, special attention is devoted to the electrical conductivity of the CNTs. Streamlines patterns are also discussed to witness the flow lines for different parameters. Thermodynamics analysis shows that entropy of the current flow system is an increasing function of Brinkmann number, magnetic parameter, nanoparticle concentration parameter and Darcy number.

In the feeding process of aluminum electrolytic, feeding quantity of alumina affects eventually dissolved quantity at the end of a feeding cycle. Based on the OpenFOAM platform, dissolution model coupled with heat and mass transfer was established. Applying the Rosin-Rammler function, alumina particle size distribution under different feeding quantities was obtained. The temperature response of electrolyte after feeding was included and calculated, and the dissolution processes of alumina with different feeding quantities (0.6, 0.8, 1.0, 1.2, 1.4, 1.6 kg) after feeding were simulated in 300 kA aluminum reduction cell. The results show that with the increase of feeding quantity, accumulated mass fraction of dissolved alumina decreases, and the time required for the rapid dissolution stage extends. When the feeding quantity is 0.6 kg and 1.2 kg, it takes the shortest time for the electrolyte temperature dropping before rebounding back. With the increase of feeding quantity, the dissolution rate in the rapid dissolution stage increases at first and then decreases gradually. The most suitable feeding quantity is 1.2 kg. The fitting equation of alumina dissolution curve under different feeding quantities is obtained, which can be used to evaluate the alumina dissolution and guide the feeding quantity and feeding cycle.

There is a lack of thermophysical data of heat transfer oil and nano-oil in the high temperature range of 50–300°C for designing and developing heat transfer oil furnace and its heating systems. In the present study, the thermal conductivity values of heat transfer oil and TiO₂ nano-oil in the above high temperature range were measured by a newly developed high-temperature thermal conductivity meter. Based on the principle of least square method, the thermal conductivity values obtained from experiments were fitted separately, and the correlation between thermal conductivity and temperature of heat transfer oil and TiO₂ nano-oil was obtained. The results show that the thermal conductivity and the increased percentage of thermal conductivity of TiO₂ nano-oil are proportional to the increase of particle size and mass fraction of nanoparticles, but thermal conductivity is in reverse proportion to the increase of temperature and the increased percentage of thermal conductivity is less affected by temperature.

An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.

The copper flash smelting process is characterized by its involvement of wide energy sources and high energy consumption, so the energy conservation is usually a highly concerned topic for the flash smelting enterprises. However, due to the complexity of the system, it is quite difficult to perform a timely comprehensive analysis of the energy consumption of the whole production system. Aiming to realize an online assessment of the energy consumption of the system, great effort was first made in Jinguan Copper, Tongling Nonferrous Metals Group Co. Ltd. Methods were proposed to solve technical difficulties such as the acquisition and processing of data with different sampling frequencies, the online evaluation of the electricity consumption, and timely evaluation of product output in the periodic process. As a result, a software system was developed to make the online analysis of the energy consumption and efficiency from the three levels ranging from the system to the equipment. The analytical results at the system level was introduce. It’s found that electricity is the most consumed energy in the system, accounting for 77.3% of the total energy consumption. The smelting unit has the highest energy consumption, accounting for 52.8% of the total energy consumed in the whole enterprise.

It is the basic requirement of the synergetic exploitation of deep mineral resources and geothermal resources to arrange the heat transfer tube in filling body. The heat release performance of filling body directly impacts on the exploiting efficiency of geothermal energy. Based on heat transfer theory, a three-dimensional unsteady heat transfer model of filling body is established by using FLUENT simulation software. Taking the horizontal U-shaped buried pipe as research object, the variation of temperature field in filling body around buried pipe is analyzed during the heat release process of filling body; the initial temperature of filling body, the diameter of buried pipe, the inlet temperature and inlet velocity of heat transfer fluid influencing of coupling heat transfer, which exists between heat transfer fluid and surrounding filling body within a certain axial distance of buried tube, and influencing of temperature difference between inlet and outlet of heat transfer fluid and on heat transfer performance of filling body are also discussed. It not only lays a theoretical foundation for the synergetic exploitation of mineral resources and geothermal energy in deep mines, but also provides a reference basis for the arrangement of buried pipes in filling body as well as the selection of working conditions for heat transfer fluid.

The pre-warning of abnormal energy consumption is important for energy conservation of industrial engineering. However, related studies on the lead smelting industries which usually have a huge energy consumption are rarely reported. Therefore, a pre-warning system was established in this study based on the intelligent prediction of energy consumption and the identification of abnormal energy consumption. A least square support vector regression (LSSVR) model optimized by the adaptive genetic algorithm was developed to predict the energy consumption in the process of lead smelting. A recurrence plots (RP) analysis and a confidence intervals (CI) analysis were conducted to quantitatively confirm the stationary degree of energy consumption and the normal range of energy consumption, respectively, to realize the identification of abnormal energy consumption. It is found the prediction accuracy of LSSVR model can exceed 90% based on the comparison between the actual and predicted data. The energy consumption is considered to be non-stationary if the correlation coefficient between the time series of periodicity and energy consumption is larger than that between the time series of periodicity and Lorenz. Additionally, the lower limit and upper limit of normal energy consumption are obtained.

Damage in rock salt has significant implication on permeability, which affects the tightness of underground salt cavern gas storage in further. During the leaching of a salt cavern, the brine with formation temperature and pressure can promote the self-healing of rock salt in the excavation damage zone (EDZ). Laboratory tests were conducted to study the promoting effect. The permeability of two intact rock salt specimens was tested. Then they were damaged into two kinds of the state respectively through uniaxial compression. After that, they were put in saturated brine (with a temperature of 50 °C and pressure of 12 MPa, which we called the repair environment in this paper) for 7 d. Finally, the permeability and mechanical properties were obtained after the damaged specimens being repaired. The results show that the permeability of intact rock salt is below 10⁻¹⁹ m²; the permeability increases by more than two orders because of damage; the permeability decreases significantly after being repaired, which can be comparable to its intact state. Discussions of the repair mechanisms are presented (especially the mechanism of recrystallization), which may help to provide significant guidance for the study of the tightness and stability of gas storage facilities in China.

The standard k-ε turbulence model and discrete phase model (DPM) were used to simulate the heat and mass transfer in a liquid-desiccant evaporator driven by a heat pump using FLUENT software, and the temperature field and velocity field in the device were obtained. The performance of the liquid-desiccant evaporator was studied as the concentration of the inlet solution varied between 21% and 30% and the pipe wall temperature between 30 and 50 °C. Results show that the humidification rate and the humidification efficiency increased with the inlet air temperature, the solution flow rate, the solution temperature, and the pipe wall temperature. The humidification rate and humidification efficiency decreased with increasing moisture content in inlet air and the concentration of inlet solution. The humidification rate increased substantially but the humidification efficiency decreased as the inlet air flow rate increased. The error between the simulations and experimental results is acceptable, meaning that our model can provide a theoretical basis for optimizing the performance of a humidifying evaporator.

In order to enhance catalytic combustion efficiency, a premixed hydrogen /air combustion model of the micro turbine engine is established under different excess air ratio, inlet velocity and heat transfer coefficient. And effects of inlet velocity, excess air coefficient and heat transfer coefficient on the catalytic combustion efficiency of the hydrogen have been analyzed by the FLUENT with CHEMKIN reaction mechanisms and the fuzzy grey relation theory. It is showed that inlet velocity has a more intuitive influence on the catalytic combustion efficiency of the hydrogen. A higher efficiency can be obtained with a lower inlet velocity. The optimum excess air coefficient is in the range of 0.94 to 1.0, the catalytic combustion efficiency of the hydrogen will be declined if the excess air coefficient exceeded 1.0. The effect of heat transfer coefficient on the catalytic combustion efficiency of the hydrogen mainly embodies in the case of the excess air coefficient exceeded 1.0, however, the effect will be declined if the heat transfer coefficient exceeded 4.0. The fuzzy grey relation degrees of the inlet velocity, heat transfer coefficient and excess air coefficient on the catalytic combustion efficiency of the hydrogen are 0.640945, 0.633214 and 0.547892 respectively.

Flame is prone to lose its stability in micro-combustors due to the large amount of heat loss from the external walls. On the other hand, heat recirculation through the upstream combustor walls can enhance flame stability. These two aspects depend on the structural heat transfer, which is associated with the thickness and thermal conductivity of the combustor walls. In the present study, the effects of wall thickness and material on flame stability were numerically investigated by selecting two thicknesses (δ=0.2 and 0.4 mm) and two materials (quartz and SiC). The results show that when δ=0.2 mm, flame inclination occurs at a certain inlet velocity in both combustors, but it happens later in SiC combustor. For δ=0.4 mm, flame inclination still occurs in quartz combustor from a larger inlet velocity compared to the case of δ=0.2 mm. However, flame inclination in SiC combustor with δ=0.4 mm does not happen and it has a much larger blowout limit. Analysis reveals that a thicker wall can enhance heat recirculation and reduce heat loss simultaneously. Moreover, SiC combustor has larger heat recirculation ratio and smaller heat loss ratio. In summary, the micro-combustor with thicker and more conductive walls can harvest large flame stability limit.

The group-contribution (GC) methods suffer from a limitation concerning to the prediction of process-related indexes, e.g., thermal efficiency. Recently developed analytical models for thermal efficiency of organic Rankine cycles (ORCs) provide a possibility of overcoming the limitation of the GC methods because these models formulate thermal efficiency as functions of key thermal properties. Using these analytical relations together with GC methods, more than 60 organic fluids are screened for medium-low temperature ORCs. The results indicate that the GC methods can estimate thermal properties with acceptable accuracy (mean relative errors are 4.45%–11.50%); the precision, however, is low because the relative errors can vary from less than 0.1% to 45.0%. By contrast, the GC-based estimation of thermal efficiency has better accuracy and precision. The relative errors in thermal efficiency have an arithmetic mean of about 2.9% and fall within the range of 0–24.0%. These findings suggest that the analytical equations provide not only a direct way of estimating thermal efficiency but an accurate and precise approach to evaluating working fluids and guiding computer-aided molecular design of new fluids for ORCs using GC methods.

Effects of butanol isomers on characteristics of combustion and emission were studied on PFI SI engine. Experiments were operated under the condition of 3 and 5 bar brake mean effective pressure (BMEP) engine loads and different equivalence ratios (φ=0.83−1.25) with engine speed of 1200 r/min using blends made of 70 vol.% gasoline and 30 vol.% butanol isomers (N30, S30, I30 and T30). The results indicated that compared with gasoline, all butanol isomer blends have higher cylinder pressure. N30 has the highest and most advanced peak pressure, and T30 shows a higher brake specific fuel consumption (BSFC) and lower brake thermal efficiency (BTE). N30 presents a lower UHC emissions and I30 has slightly higher CO emissions than other blends. For unregulated emissions, compared with gasoline, butanol isomer blends have higher acetaldehyde, and N30 produces a higher emission of 1,3-butadiene than other blends. A reduction in benzene, toluene, ethylbenzene and xylene (BTEX) has been found with butanol isomer blends.

The micro-combustion chamber is the key component for micro-TPV systems. To improve the combustor wall temperature level and its uniformity and efficiency an improved flat micro-combustor with a front cavity is built, and the combustion performance of the original and improved combustors of premixed H₂/air flames under various inlet velocities and equivalence ratios is numerically investigated. The effects of the front cavity height and length on the outer wall temperature and efficiency are also discussed. The front cavity significantly improves the average outer wall temperature, outer wall temperature uniformity, and combustion efficiency of the micro-combustor, increases the area of the high temperature zone, and enhances the heat transfer between the burned blends and inner walls. The micro-combustor with the front cavity length of 2.0 mm and height of 0.5 mm is suitable for micro-TPV system application due to the relatively high outer wall temperature, combustion efficiency, and the most uniform outer wall temperature.

Alternating current electrical dynamometer is a common device to measure the torque of engines, such as the gasoline engine. In order to solve the problems such as high cost, high energy consumption and complicated measurement system which exists in the direct measurement on the torque of alternating current electrical dynamometer, copper loss and iron loss are taken as two key factors and a soft-sensing model on the torque of alternating current electrical dynamometer is established using the fuzzy least square support vector machine (FLS-SVM). Then, the FLS-SVM parameters such as penalty factor and kernel parameter are optimized by adaptive genetic algorithm, torque soft-sensing is investigated in the alternating current electrical dynamometer, as well as the energy feedback efficiency and energy consumption during the measurement phase of a gasoline engine loading continual test is obtained. The results show that the minimum soft-sensing error of torque is about 0.0018, and it fluctuates within a range from −0.3 to 0.3 N-m. FLS-SVM soft-sensing method can increase by 1.6% power generation feedback compared with direct measurement, and it can save 500 kJ fuel consumption in the gasoline engine loading continual test. Therefore, the estimation accuracy of the soft measurement model on the torque of alternating current electrical dynamometer including copper loss and iron loss is high and this indirect measurement method can be feasible to reduce production cost of the alternating current electrical dynamometer and energy consumption during the torque measurement phase of a gasoline engine, replacing the direct method of torque measurement.

In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.