Exergy analysis of vapor compression refrigeration cycle with two-stage and intercooler

In this study, exergy analyses of vapor compression refrigeration cycle with two-stage and intercooler using refrigerants R507, R407c, R404a were carried out. The necessary thermodynamic values for analyses were calculated by Solkane program. The coefficient of performance, exergetic efficiency and total irreversibility rate of the system in the different operating conditions for these refrigerants were investigated. The coefficient of performance, exergetic efficiency and total irreversibility rate for alternative refrigerants were compared.     

Estimation and optimization of thermal performance of evacuated tube solar collector system

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy (ANFIS) in order to predict the thermal performance of evacuated tube solar collector system have been used. The experimental data for the training and testing of the networks were used. The results of ANN are compared with ANFIS in which the same data sets are used. The R²-value for the thermal performance values of collector is 0.811914 which can be considered as satisfactory. The results obtained when unknown data were presented to the networks are satisfactory and indicate that the proposed method can successfully be used for the prediction of the thermal performance of evacuated tube solar collectors. In addition, new formulations obtained from ANN are presented for the calculation of the thermal performance. The advantages of this approaches compared to the conventional methods are speed, simplicity, and the capacity of the network to learn from examples. In addition, genetic algorithm (GA) was used to maximize the thermal performance of the system. The optimum working conditions of the system were determined by the GA.     

Experimental two-phase pressure gradients during evaporation of pure and mixed refrigerants in a smooth horizontal tube. Comparison with correlations

The paper reports the results of an experimental study on pressure drop during horizontal flow boiling of refrigerants R22, R507, R404A, R134a, R407C and R410A. The test section is a smooth, horizontal, stainless steel tube (6 mm I.D., 6 m length) uniformly heated by Joule effect. The experimental tests are carried out at an almost constant evaporating pressure of 7.0 bar varying the mass flow rate in the range 280–1,080 kg/m² s. The experimental comparison clearly shown that the pressure drop of R22 is significantly higher as compared to all the other fluids. The results are compared against well-known pressure drop prediction methods. The available correlations can be used for both pure fluids and mixtures with no corrective factors, provided the mixture properties are evaluated at local compositions. The Chawla friction correlation is the best-fitting of our experimental data in combination with the heterogeneous momentum pressure drop model on the basis of the Rouhani-Axelsson void fraction correlation.     

Prediction of nonlinear viscoelastic behavior of polymeric composites using an artificial neural network

Creep tests at constant stress are performed for the carbon-fiber reinforced epoxy composite at various temperatures and initial stresses. A nonlinear viscoelastic constitutive model is developed, and its material parameters are determined by fitting it to creep test data. Model results are found to agree very well with the experimental data at low temperature and low stress conditions. However, the agreement deteriorates at high temperatures, particularly in the vicinity of the glass transition temperature.An alternative model based on an artificial neural network (ANN) is developed to predict the stress relaxation of the polymer matrix composite. The ANN model is trained and validated with 9000 experimental data sets obtained from stress relaxation tests performed at various constant strain (initial stress) and constant temperature conditions. Training of the ANN employs a scaled conjugate gradient method. The optimal brain surgeon algorithm is employed to optimize the topology. The optimal ANN configuration has 88 processing elements (3 in the input layer, 45 in the first hidden layer, 39 in the second hidden layer, and 1 in the output layer) and 410 links. The predictions of the ANN model are found to be more accurate over a wider range of stress and temperature conditions than those of the explicit nonlinear viscoelastic model, in particular near the glass transition temperature.     

Efficiency comparison of subcritical OTEC power cycle using various working fluids

This paper presents an investigation into the thermal efficiency and main component size of the subcritical ocean thermal energy conversion (OTEC) power cycle using various working fluids under different operation conditions. The analysis procedure was performed with a simulation program written in Engineering Equation Solver. With the given analysis conditions, efficiencies of three types of working fluids were evaluated and compared. It was found that the thermal efficiencies of the subcritical OTEC power cycle depend strongly on the evaporating and condensing temperature, and turbine efficiency, while not roughly depending on superheating degrees and pump efficiencies. With a thorough grasp of these results, an efficient OTEC power cycle can be designed. R717 and R404A yielded the highest and lowest thermal efficiencies among the wet fluids, and R22 showed the largest efficiency among the dry fluids. For the iso-entropic fluids, R245fa provided the highest thermal efficiency. In comparison of main component sizes, R404A and R744 had the largest and smallest condenser size, respectively. Also, R744 exhibited the smallest evaporator size, and R404A and R227ea show the largest size. And R744 and R245fa gave the largest and smallest pump size, respectively. From the results of thermal efficiency and main components for various working fluids in the OTEC power cycle, R717 in the subcritical OTEC power cycle is the preferred working fluid, except for its toxicity and flammability.     

多孔介质中天然气水合物注热+降压开采的实验研究

为研究注热与降压相结合的开采方式是否更加有利于天然气水合物的开采,在自制的天然气水合物开采模拟实验系统上进行了实验研究.在一维填砂模型中人工生成天然气水合物之后,进行先注热盐水然后再降低压力(注热+降压)的开采模拟实验,分析了开采过程中系统温度、电阻率变化规律以及产气量、能量效率等.结果表明:产气规律具有明显的阶段性,...     

基于人工神经网络的非结构网格尺度控制方法

网格自动化生成和自适应是制约计算流体力学发展的瓶颈问题之一, 网格生成质量、效率、灵活性、自动化程度和鲁棒性是非结构网格生成的关键问题. 在非结构网格生成中, 网格空间尺度分布控制至关重要, 直接影响网格生成质量、效率和求解精度. 采用传统的背景网格法进行空间尺度分布控制需要在背景网格上求解微分方程得到背景网格上的尺度分布, 再将网格尺度从背景网格插值到真实空间点, 过程十分繁琐且耗时. 本文从效率和自动化角度提出两种网格尺度控制方法, 首先发展了基于径向基函数(RBF)插值的网格尺度控制方法, 通过贪婪算法实现边界参考点序列的精简, 提高了RBF插值的效率. 同时, 还采用人工神经网络进行网格尺度控制, 初步引入相对壁面距离和相对网格尺度作为神经网络输入输出参数, 建立人工神经网络训练模型, 采用商业软件生成二维圆柱和二维翼型非结构三角形网格作为训练样本, 通过训练和学习建立起相对壁面距离和相对网格尺度的神经网络关系. 进一步实现了二维圆柱、不同的二维翼型的尺度预测, RBF方法和神经网络方法的效率与传统背景网格法相比提高了5~10倍, 有助于提高网格生成的效率. 最后, 将方法推广应用于各向异性混合网格尺度预测, 得到的网格质量满足要求.      

Transient heat transfer in a functionally graded convecting longitudinal fin

The study investigates the transient thermal performance of a constant area longitudinal fin made of a functionally graded material. Such a fin offers advantages that are not attainable with a traditional fin made of a homogeneous material. A numerical approach has been used to study the transient response of the fin with a step change in its base temperature. The fin is assumed to have an adiabatic tip. Three types of variations in the thermal conductivity with the longitudinal distance along the fin are considered: (a) linear, (b) quadratic, and (c) exponential. New analytical solutions for the steady state performance of the fin are derived in terms of the Bessel functions for cases (a) and (c) and in terms of the Legendre functions for case (b). These solutions provide a check on the accuracy of the transient numerical predictions for large times. The thermal performance of the fin is governed by the classical fin parameter, N _c, and the fin thermal conductivity grading parameter, a. Results are presented for the transient temperature distribution, base heat flow, convective heat loss, the energy stored in the fin and the fin efficiency for representative values of N _c and a. It is found that the transient, as well the steady state performance of the fin, is significantly affected by the functional grading of the fin material. The results presented are not only of fundamental interest but can also be used to design a functionally graded fin with the desirable steady and transient thermal characteristics.     

A theoretical model for the prediction of thermal expansion behaviour of particulate composites

The thermal expansion coefficient of particle-reinforced polymers was evaluated using a theoretical model which takes into account the adhesion efficiency between the inclusions and the matrix — an important factor affecting the thermomechanical properties of a composite. To measure the adhesion efficiency a boundary interphase, i.e. a layer between the matrix and the fillers having a structure and properties different from those of the constituent phases, was considered. This layer is assumed to have varying properties.To obtain information concerning the properties and extent of the interphase, an experimental study of the thermal behaviour of aluminium-epoxy composites was undertaken. Differential Scanning Calorimetry (DSC) measurements were performed to evaluate heat capacity with respect to temperature. In addition, the effects of different factors, such as heating rate and filler concentration on the glass transition temperature of the composite, were examined. The sudden changes in heat capacity values in the glass transition region were used to estimate the extent of the boundary interphase according to an existing theory.Finally, the values of the thermal expansion coefficient, predicted by this model, were compared with theoretical results obtained by other authors and with experimental results.     

Artificial neural network prediction to the hot compressive deformation behavior of Al–Cu–Mg–Ag heat-resistant aluminum alloy

The behavior of the flow stress of Al-Cu-Mg-Ag heat-resistant aluminum alloys during hot compression deformation was studied by thermal simulation test. The temperature and the strain rate during hot compression were 340-500 °C, 0.001 s⁻¹ to 10 s⁻¹, respectively. Constitutive equations and an artificial neural network (ANN) model were developed for the analysis and simulation of the flow behavior of the Al-Cu-Mg-Ag alloys. The inputs of the model are temperature, strain rate and strain. The output of the model is the flow stress. Comparison between constitutive equations and ANN results shows that ANN model has a better prediction power than the constitutive equations.     

Nonlinear constitutive models from nanoindentation tests using artificial neural networks

This paper presents a new approach using Artificial Neural Networks (ANNs) models to simulate the response during nanohardness tests of a variety of materials with nonlinear behavior. The ANNs continuous input and output variables usually include material parameters, indentation deflection, and resisting force. Different ANN models, including dimensionless input/output variables, are generated and trained with discrete finite-element (FE) simulations with different geometries and nonlinear material parameters. Only the monotonic loading part of the load–displacement indentation response is used to generate the trained ANN models. This is a departure from classical indentation simulations or tests where typically the unloading portion is used to determine the stiffness and hardness. The experimental part of this study includes nanoindentation tests performed on a silicon (Si) substrate with and without a nanocrystalline copper (Cu) film. The new ANN models are used to back-calculate (inverse problem) the in situ nonlinear material parameters for different copper material systems. The results are compared with available data in the literature. The proposed FE–ANN modeling approach is very effective and can be used in calibrating and predicting the in situ inelastic material properties using the monotonic part of the indentation response and for depths above 50 nm where the overall resisting force represents a continuum response.     

Condensation pressure drop of R22, R134a and R410A in a single circular microtube

The condensation pressure drop characteristics for pure refrigerants R22, R134a, and a binary refrigerant mixture R410A without lubricating oil in a single circular microtube were investigated experimentally. The test section consists of 1,220?mm length with horizontal copper tube of 3.38?mm outer diameter and 1.77?mm inner diameter. The experiments were conducted at refrigerant mass flux of 450–1,050?kg/m²s, and saturation temperature of 40°C. The main experimental results showed that the condensation pressure drop of R134a is higher than that of R22 and R410A for the same mass flux. The experimental data were compared against 14 two-phase pressure drop correlations. A new pressure drop model that is based on a superposition model for refrigerants condensing in the single circular tube is presented.     

Prediction of temperature performance of a two-phase closed thermosyphon using Artificial Neural Network

Here, the temperature performance of a two-phase closed thermosyphon (TPCT) was investigated using two synthesized nanofluids, including carbon nano-tube (CNT)/water and CNT-Ag/water. In order to determine the temperature performance of a TPCT, the experiments were performed for various values of weight fraction and input power. To predict the other experimental conditions, a reliable and accurate tool should be applied. Therefore Artificial Neural Network (ANN) was applied to predict the process performance. Using ANN, the operating parameters, including distribution of wall temperature (T) and the temperature difference between the input and the output water streams of condenser section (?T) were determined. To achieve this goal, the multi-layer perceptron network was employed. The Levenberg–Marquardt algorithm was chosen as learning algorithm of this network. The results of simulation showed an excellent agreement with the data resulted from the experiments. Therefore it is possible to say that ANN is a powerful tool to predict the performance of different processes.     

脉冲爆轰发动机的系统性能分析

采用简化的脉冲爆轰推进装置模型,利用热循环效率分析方法,推导了包含进气道总压恢复系数的热循环效率公式. 并在特定来流条件下,考察了一个爆轰循环中进气道总压恢复系数和燃烧室初始温度对热循环效率和比冲的影响. 研究发现,降低来流总压损失有助于提高热循环效率,而提高燃烧室初始温度能更有效地提高热循环效率. 据此,提出了多级重起爆脉冲爆轰发动机概念,利用在突扩截面上解耦的爆轰波的前导激波去再次压缩工质,进一步提高工质的热力学参数,从而提高脉冲爆轰装置的热循环效率. 推导了此种构型PDE的热循环效率计算公式,并对多级重起爆脉冲爆轰发动机进行了原理性论证. 研究结果表明,多级重起爆方法提高了燃烧室的爆前温度,从而有效地提高脉冲爆轰发动机热循环效率. 最后,关于出口工质的非完全膨胀的情况,做了定性的阐述,认为只有降低工质的出口压力,才能更有效增加工质的出口动能,从而提高热循环效率.      

TEC结构的三维非线性瞬态温度场分析

热电制冷器(TEC)以其体积小、作用速度快及无噪音等机械制冷无法替代的优点在航空航天和电子工业等领域得到了越来越广泛的应用。本文根据TEC的导热特点,推导了TEC结构稳态温度场的解析解,建立了其瞬态非线性温度场分析的微分方程。利用伽辽金法导出TEC结构热分析的有限元方程,对非线性热分析的有限元方程进行了求解,得到了TEC的稳态温度场和瞬态响应温度场。算例结果表明,本文提出的TEC结构热分析有限元模型具有较高的精度,能够有效地分析TEC的非线性瞬态温度场。     

Evaluation analysis of prediction methods for two-phase flow pressure drop in mini-channels

Two-thousand and ninety-two data of two-phase flow pressure drop were collected from 18 published papers of which the working fluids include R123, R134a, R22, R236ea, R245fa, R404a, R407C, R410a, R507, CO₂, water and air. The hydraulic diameter ranges from 0.506 to 12 mm; Re_l from 10 to 37,000, and Re_g from 3 to 4 × 10⁵. Eleven correlations and models for calculating the two-phase frictional pressure drop were evaluated based upon these data. The results show that the accuracy of the Lockhart–Martinelli method, Mishima and Hibiki correlation, Zhang and Mishima correlation and Lee and Mudawar correlation in the laminar region is very close to each other, while the Muller-Steinhagen and Heck correlation is the best among the evaluated correlations in the turbulent region. A modified Chisholm correlation was proposed, which is better than all of the evaluated correlations in the turbulent region and its mean relative error is about 29%. For refrigerants only, the new correlation and Muller-Steinhagen and Heck correlation are very close to each other and give better agreement than the other evaluated correlations.     

Drop trajectory predictions and their importance in the design of spray dryers

A knowledge of the trajectories of atomized droplets in both the nozzle zone (where the droplets are rapidly decelerating from their initial high velocity) and in the free-entrainment zone (where the droplets are conveyed by the drying gas) is required for the design of spray dryers, since it governs the evaporative capacity and thermal efficiency of the chamber, while affecting the moisture content and general quality of the product through the control of the drying time.

The trajectories of droplets in three-dimensional motion were determined theoretically in both zones. In the case of two-fluid pneumatic atomizers, the characteristics of the jet of atomizing fluid were found to be important in both the zones.

Predictions of droplet trajectories were tested in an experimental circular cocurrent spray-drying chamber with a conical bottom, in which the drying air was introduced tangentially near the top. Water was used as the feed material. A study was made of the effects of liquid feed rate and temperature, drying air flowrate and temperature, and of nozzle position on the thermal efficiency and evaporative capacity of the chamber. The results were interpreted in the light of the droplet trajectories predicted.     

不同水合物饱和度下注热水开采实验研究

为研究不同水合物藏饱和度对注热开采动态的影响,采用自制的一维天然气水合物(NGH)开采模拟实验装置,模拟地层多孔介质的条件,首先在填砂模型中生成不同饱和度的NGH,然后进行注热盐水分解实验。结果表明:不同饱和度的NGH注热分解产气都可划分为三个阶段,不同的是NGH饱和度越高,水合物分解阶段的产气速率越大,且该阶段持续的时间越长;NGH饱和度越高,注热分解阶段电阻率增大的幅度越大,系统各测点温度升高的幅度越小。注热分解过程中产水速率围绕注水速率而上下波动,且NGH饱和度越高,产水速率波动幅度越大;在实验研究范围内,随初始水合物饱和度的升高,注热开采的能量效率逐渐升高。因此,从能量效率来说,高饱和度的水合物藏更适宜于注热开采。     

Experimental study of water droplet boiling on hot, non-porous surfaces

In this paper, the results of a series of experimental tests on single- and multi-droplet boiling systems are presented and discussed. The main objectives of the present study are: a) to investigate experimentally the effect of the boiling onset on the evaporation rate of water droplets; b) to measure the evolution of the solid surface temperature during evaporation; c) to examine the possibility of improving spray cooling efficiencies. The behavior of small water droplets (from 10 to 50 μl) gently deposited on hot, non-porous surfaces is observed. The evaporation of multi-droplet arrays (50 and 100 μl) under the same conditions of the single-droplet tests is analyzed. In particular, the conditions which determine the onset of nucleate and film boiling are stressed out. In the experimental tests, the interaction of different materials with several multi-droplet systems is monitored by infrared thermography. The spray cooling efficiency is related to the solid temperature decrease as a function of the water mass flux. In the present study, the effect of varying the droplet volume and the mass flux is also analyzed and discussed. The results on the droplets evaporation time and on the solid surface transient temperature distribution are also compared with the data obtained by the same authors during the analysis of droplet evaporation in total absence of nucleate and film boiling. In order to analyze the different behavior of the evaporating droplet as a function of the solid surface thermal conductivity, evaporative transients on aluminum, stainless steel and macor (a glass-like, low-conductivity material) are considered. Received on 20 February 1998     

Enhanced bioavailability and biosafety of cannabidiol nanomicelles for effective anti-inflammatory therapy

Cannabidiol (CBD) shows great anti-inflammatory potential; however, the hydrophobicity and strong first-pass effect of CBD leads to its extremely low oral bioavailability. Poloxamer 407 (P407) is a triblock copolymer composed of (poly)ethylene oxide (PEO) and (poly)propylene oxide (PPO) sections. It has a PEO-PPO-PEO structure, which is widely used in the preparation of drug delivery systems that are highly biocompatible. When it reaches a certain concentration in water, P407 can self-assemble into a micelle structure containing a hydrophobic core and a hydrophilic shell. A potential approach to enhancing the oral bioavailability of hydrophobic drugs incorporating them into the hydrophilic carrier. We prepared CBD nanomicelles with a drug loading of 14.29% by a cosolvent evaporation method using P407 with appropriate antioxidants. Cell experiments indicated that anti-inflammatory markers (IL-4 and IL-10) increased, while inflammatory markers (TNF-α and IL-6) decreased. Moreover, animal experiments showed that inflammatory cells were inhibited by CBD nanomicelles, and the anti-inflammatory effect of micelles was better than that of CBD, while no obvious evidence indicated toxicity to the liver and kidney.