Recent researches in indirect evaporative cooler V: relative thermal performance of buildings coupled to direct and indirect evaporative cooler

A mathematical model has been developed to evaluate the relative thermal performance of a building coupled with an indirect or direct evaporative cooler. Using periodic analysis for taking into account thermal storage of building envelope, explicit expressions have been obtained for room air temperature and room air humidity. For comparing their performance under different climatic conditions, numerical calculations have been made taking meteorological parameters for a typical day for Delhi (composite climate), Jodhpur (hot-dry climate) and Madras (hot-humid climate). It is found that the indirect evaporative cooler is a more effective and energy efficient system than the air-conditioner; it can hence be commercially used for computer and electronic exchange applications as well as for human comfort in a variety of climatic conditions, whereas direct evaporative cooler has limited use (only in hot-dry and composite climates). © 1997 by John Wiley & Sons, Ltd.     

Recent researches in indirect evaporative cooler III: optimization of the cooling potential of a room–coupled indirect evaporative cooler

A mathematical model based on heat transfer principles, for characterizing the cooling performance of a room coupled indirect evaporative cooler (tube type) has been developed. Two dimensionless parameters, i.e. environment factor, ϕ and cooling factor, CF have been defined to characterize the performance of IEC coupled with a room. The optimum values of these parameters have been obtained for different environmental and thermal load conditions. In addition to this, a linear relationship has been obtained for the optimum size of a cooler to remove maximum heat from a room of given size. © 1998 John Wiley & Sons, Ltd.     

Modelling of a cascade refrigeration system using artificial neural network

This study investigates the applicability of artificial neural networks (ANNs) to predict various performance parameters of a cascade vapour compression refrigeration system. For this aim, an experimental cascade system was set up and tested in steady‐state operating conditions. Then, utilizing some of the experimental data for training, an ANN model for the system based on the standard back propagation algorithm was developed. The ANN was used for predicting the evaporating temperature in the lower‐temperature circuit, compressor power for the lower and higher circuits and coefficients of performance for both the lower circuit and the overall cascade system. Afterwards, the performances of the ANN predictions were tested using new experimental data. The ANN predictions usually agreed well with the experimental results with correlation coefficients in the range of 0.953–0.996 and mean relative errors in the range of 0.2–6.0%. Furthermore, the ANN yielded acceptable predictions for the system performance outside the range of the experiments. The results suggest that the ANN approach can alternatively and reliably be used for modelling cascade refrigeration systems. Copyright © 2006 John Wiley & Sons, Ltd.     

板式间接蒸发冷却器热工特性的实验研究

对自行研制设计的间接蒸发冷却换热试件开展了实验,研究了影响换热器换热性能的因素。结果表明：板式间接蒸发冷却器换热效率随二次空气入口的速度升高、一次空气入口的温度、二次空气入口的湿球温度升高而变大,随一次空气入口的速度变大而变小。实验结果对于深入认识间接蒸发冷却器的换热机理及开展换热器的优化设计有着很大的指导意义。     

Exergy analysis of direct expansion solar‐assisted heat pumps using artificial neural networks

Artificial neural network (ANN) is applied for exergy analysis of a direct expansion solar‐assisted heat pump (DXSAHP) in the present study. The experiments were conducted in a DXSAHP under the meteorological conditions of Calicut city in India. An ANN model was developed based on backpropagation learning algorithm for predicting the exergy destruction and exergy efficiency of each component of the system at different ambient conditions (ambient temperature and solar intensity). The experimental data acquired are used for training the network. The results showed that the network yields a maximum correlation coefficient with minimum coefficient of variance and root mean square values. The results confirmed that the use of an ANN analysis for the exergy evolution of DXSAHP is quite suitable. Copyright © 2009 John Wiley & Sons, Ltd.     

燃气轮机进气喷雾蒸发冷却的经济性分析

以轮南燃气轮机电站喷雾蒸发冷却器为例,分析了在不同气候条件下蒸发冷却器运行的经济性。结果表明,对于联网运行机组,蒸发冷却器的投运可提高燃气轮机出力,从而获得经济效益,在高温干燥地区尤为突出,且机组在保持T5温度(燃气轮机第三级透平喷嘴入口处温度)不变的情况下投运所带来的经济效益更为明显。     

Recent research on an indirect evaporative cooler (IEC) Part 1: optimization of the COP

The paper presents an analysis for optimizing the coefficient of performance of an tube type indirect evaporative cooler (IEC) in terms of process stream air velocity and wet stream air velocity. Theoretical predictions of the optimum COP value have been compared with the experimental results obtained for an IEC at Indore in the summer months of May and June; the agreement is satisfactory. © 1997 John Wiley & Sons, Ltd.     

基于BP神经网络的油冷器压降及换热量预测

油冷器作为发动机散热部件之一,压降和换热量是评估其性能的重要指标,但油冷器中传热与流动规律错综复杂,所以对其压降和换热量进行预测存在一定难度。本研究提出了一种基于BP神经网络和特征工程的预测方法。该方法通过实验获得不同结构类型下冷油器数据,对样本数据进行插值和增强等方法解决样本量分布不均的问题,并根据相关性计算Shah-Focke关联式、Gray and Web关联式、A.R.Wieting关联式等相关经验公式与本文实验结果相关性,并筛选出相关性最高的关联式来构造新特征,最后利用BP神经网络模型进行预测。结果表明,Shah-Focke关联式与本文实验结果相关性最高,且该经验公式特征的引入对模型有积极影响,预测精度提升50%,令压降预测误差为6%,换热量预测误差为4%。     

Heat transfer prediction for radiant floor heating/cooling systems using artificial neural network (ANN)

Radiant floor cooling and heating systems (RHC) are gaining popularity as compared with conventional space conditioning systems. An understanding of the heat transfer capacity of the radiant system is desirable to design a space conditioning system using RHC technology. In the present work, a simplified heat flux model for RHC is developed for both cooling and heating modes of operation. The Artificial Neural Network (ANN) technique is used for the development of the simplified model. Experimental data from literature covering a wide operating range of the RHC is considered for model development and validation. Operating parameters such as mass flow rate (m_f), heat resistance (R_s), mean temperature of water flowing through the pipe (T_m), and operative temperature (T_op) are considered independent variables influencing the heat flux (q_t). The neural network consists of four input layers, one output layer, and one hidden layer with a feed-forward-back-propagation algorithm. A study on the selection of the optimum number of neurons in the range of 1–9 for the hidden layer is also performed. On the basis of the performance parameters, namely, average-absolute-relative-deviation (AARD = 0.11283) percentage, mean-square-error (MSE = 0.00055), and the coefficient of determination (R² = 0.9984), a hidden layer is modeled with five neurons.     

Experimental study of a direct evaporative cooling approach for Li-ion battery thermal management

A desirable operating temperature range and small temperature gradient is beneficial to the safety and longevity of lithium-ion (Li-ion) batteries, and battery thermal management systems (BTMSs) play a critical role in achieving the temperature control. Having the advantages of direct access and low viscosity, air is widely used as a cooling medium in BTMSs. In this paper, an air-based BTMS is modified by integrating a direct evaporative cooling (DEC) system, which helps reduce the inlet air temperature for enhanced heat dissipation. Experiments are carried out on 18650-type batteries and a 9-cell battery pack to study how relative humidity and air flow rate affect the DEC system. The maximum temperatures, temperature differences, and capacity fading of batteries are compared between three cooling conditions, which include the proposed DEC, air cooling, and natural convection cooling. In addition, a DEC tunnel that can produce reciprocating air flow is assembled to further reduce the maximum temperature and temperature difference inside the battery pack. It is demonstrated that the proposed DEC system can expand the usage of Li-ion batteries in more adverse and intensive operating conditions.     

Modelling the SOFC behaviours by artificial neural network

The Artificial Neural Network (ANN) can be applied to simulate an object's behaviour without an algorithmic solution merely by utilizing available experimental data. The ANN is used for modelling singular cell behaviour. The optimal network architecture is shown and commented. The error backpropagation algorithm was used for an ANN training procedure.     

Prediction of selected biodiesel fuel properties using artificial neural network

Biodiesel is an alternative fuel to replace fossil-based diesel fuel. It has fuel properties similar to diesel which are generally determined experimentally. The experimental determination of various properties of biodiesel is costly, time consuming and a tedious process. To solve these problems, artificial neural network (ANN) has been considered as a vital tool for estimating the fuel properties of biodiesel, especially from its fatty acid (FA) composition. In this study, four ANNs have been designed and trained to predict the cetane number (CN), flash point (FP), kinematic viscosity (KV) and density of biodiesel using ANN with logsig and purelin transfer functions in the hidden layer of all the networks. The five most prevalent FAs from 55 feedstocks found in the literature utilized as the input parameters for the model are palmitic, stearic, oleic, linoleic and linolenic acids except for density network with a sixth parameter (temperature). Other FAs that are present in the biodiesels have been considered based on the number of carbon atom chains and the level of saturation. From this study, the prediction accuracy and the average absolute deviation of the networks are CN (96.69%; 1.637%), KV (95.80%; 1.638%), FP (99.07%; 0.997%) and density (99.40%; 0.101%). These values are reasonably better compared to previous studies on empirical correlations and ANN predictions of these fuel properties found in literature. Hence, the present study demonstrates the ability of ANN model to predict fuel properties of biodiesel with high accuracy.     

CFD方法与间接蒸发冷却换热器的三维数值模拟

采用计算流体力学(CFD)和数值传热学方法,对间接蒸发冷却器内流体流动与热质交换过程进行简化和假设,建立了换热器内三维层流流动与传热的数学物理模型。采用交错网格离散化非线性控制方程组,编制了三维simple算法程序。对间接蒸发冷却器内的流场、温度场及浓度场进行数值模拟研究,得到换热器内的流体流动状态和热流分布,并分析了通道宽度变化对换热器内流体流动与换热的影响。     

Modelling of solar energy potential in Nigeria using an artificial neural network model

In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4–14°N, log. 2–15°E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983–1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01–5.62 to 5.43–3.54 kW h/m² day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.     

Recent research on an indirect evaporative cooler. Part VI: evolution of design pattern for indirect evaporative cooler

A rule of thumb for indirect evaporative cooler has been derived in terms of the size of floor area to be cooled and design parameters of tube type IEC, viz. process stream air flow rate and number of tubes; the three Indian climate zones, namely hot–dry, composite and warm–humid, represented by three cities of Jodhpur, Delhi and Madras, respectively, have been considered. Copyright © 1999 John Wiley & Sons, Ltd.     

应用神经网络方法预测生物质的热值

应用人工神经网络方法对生物质的热值进行了预测,网络的训练数据集来自美国Biomass Feedstock Composition and Property Database of U．S．Department of Energy。神经网络以生物质的工业分析结果作为输入数据．采用56组数据对网络进行训练,以7组数据对网络进行验证,对网络输出值与实际值进行比较,相对误差在0．08％以内。人工神经网络成功地预测各种生物质的热值,说明人工神经网络能够处理生物质的热值与工业分析各组分间的非线性关系。     

Recent research on an indirect evaporative cooler (IEC) Part 2: thermal performance of a non-conditioned building coupled with an IEC

The thermal performance of a non-conditioned building fitted with an indirect evaporative cooler (IEC) has been investigated in terms of hourly, monthly and seasonal discomfort index. The effect of various design parameters of the IEC on the discomfort index has been investigated for three different climatic areas of India, i.e. hot–dry, warm, humid and composite. The analysis has shown that the IEC is effective for creating thermal comfort conditions in buildings in dry–hot and composite climates. © 1997 by John Wiley & Sons, Ltd.     

A novel application of artificial neural network for wind speed estimation

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.