哈尔滨市住宅小区供热管网的设计

根据哈尔滨市住宅小区五年来的设计经验，分析了住宅小区供热管网的特点，阐述了设计中应注意的一些问题。     

供热管网的设计及施工管理

阐述了在供热管网的设计及施工管理上,如何运用最优的设计方案,搞好科学的上行调节,降低工程造价,是保证供热质量的关键问题。     

供热管网腐蚀的成因及预防

分析了产生供热管网腐蚀的原因，并提出了预防供热管网腐蚀的有效措施。     

GIS技术在供热管网中的应用综述

简述GIS的概念,通过对供热管网现状的分析,结合GIS软件的特点,使GIS运用到供热管网中,实现供热管网管理的标准化、科学化和合理化。     

供热管网水力失调及其防止

从调查情况出发,对单位面积采暖能耗居高不下,煤耗不减,电耗增加,冷热不均现象加剧的原因进行了分析,阐述了自力式流量控制阀的工作原理和作用并通过实例介绍自力式流量控制阀的应用效果。     

基于图论的复杂供热管网设计

供热管网系统规模日益扩大,管网结构也更加复杂,具有较强的非线性,为避免供热过程中不出现水力失调以及降低管网建设成本,必须进行管网设计优化。采用图论方法,构建复杂供热管网,建立了管网水力计算数学模型,构造出水力计算的矩阵方程组,并利用MATLAB软件采用迭代法进行求解。针对辽宁某地区热力管网进行分析,计算结果和供热管网实际运行数据进行对比,基于图论的复杂管网水力计算模型精度满足工程要求。     

供热管网水力平衡问题分析

介绍了供热管网常用的敷设方式 ,水力失调的现象 ,分析了管网产生水力失调的原因 ,正确地敷设管路 ,选用合适的敷设方式 ,以控制失调现象 ,保证供热管网的水力平衡 ,并提出了解决方法。     

浅谈供热管网分段阀的设置

利用现行的计算公式,考虑供热管网的修复性等因素,对太原市供热管网的分段阀设计进行了优化分析,力求降低系统的初投资。     

除去循环水中的溶解氧防止供热管网腐蚀

本文通过对供热管网腐蚀机理和影响因素的论述，介绍了现行的除氧方法及运行结果而得出结论。     

An approximate integral model with an artificial neural network for heat exchangers

In order to improve predicting precision and increase computation speed of simulation for heat exchangers, a novel method is presented in this paper, whereby an approximate integral model is used to simplify the original distributed parameter model and an artificial neural network is combined to reflect the nonlinear relations. This model is applied in actual calculations of fin‐and‐tube condensers and high precision is achieved. Where the calculated outlet temperature of refrigerant and that of air, the average errors are both less than 0.2 °C. For the heat exchange of the condenser, the average error is less than 1 0.2 °C. For the heat exchange of the condenser, the average error is less than 1%. The calculation speed of the approximate integral model is two orders of magnitude faster than that of the distributed‐parameter model. © 2004 Wiley Periodicals, Inc. Heat Trans Asian Res, 33(3): 153–160, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/htj.20006     

基于人工神经网络的机车当量模型的研究

研究了当前我国机车检修的现状,在传统的机务检修基础上建立了机车当量公里的模型,将机车运行过程中的公里量、负荷量和时间量通过人工神经网络训练进行学习,形成了一种确定机车检修周期的新方法,解决了确定我国铁路机车检修周期问题。     

Reconstruction model for heat release rate based on artificial neural network

Optimizing the distribution of heat release rate (HRR) is the key to improve the performance of various combustors. However, limited by current diagnostic techniques, the spatial measurement of HRR in many realistic combustion devices is often difficult or even impossible. HRR prediction is theoretically possible through establishing correlations between HRR and other quantities (e.g., chemiluminescence intensity) that can be experimentally determined; however, up to now, few universal correlations have been established. A novel artificial neural network (ANN) approach was adopted to build the mapping relationship between the combustion heat release rate and the measurable chemiluminescent species. Proper orthogonal decomposition (POD) technology is used to extract the combustion physics and reduce the data of the spatial-temporally high-resolution combustion field. The correlation between the reduced-order HRR and chemiluminescent species is built using an ANN model. A unique segmentation approach was proposed to improve the training efficiency and accuracy. Validation in a supersonic hydrogen-oxygen nonpremixed flame proves the accuracy and efficiency of the proposed HRR reconstruction model based on the reduced-order POD method and data-driven ANN model.     

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。     

An energy benchmarking model based on artificial neural network method with a case example for tropical climates

Energy benchmarking is an important step in evaluating a buildings energy use and comparing it with similar buildings in similar climates. Depending on the benchmarking results, extra measures can be taken to reduce energy consumption when the subject building has been assessed to consume more than other similar buildings. This study presents the current state of energy benchmarking‐related research and available tools. An artificial neural networks (ANN)‐based benchmarking technique is presented as a highly effective method. The model specifically focuses on predicting a weighted energy use index (EUI) by taking into consideration various building variables, such as plug load density, lighting type and hours of operation, air conditioning equipment type and efficiency, etc. Data collected from laboratory, office and classroom‐type buildings and mixed use buildings in Hawaii are used to present the ANN‐based benchmarking technique. The developed model successfully predicted the benchmarking EUI for the buildings considered in the study. The model coefficient of correlation was 0.86 for the whole building benchmarking analysis, indicating a good correlation between the measured EUI and the ANN predictions. Additionally, the use of ANN benchmark model for predicting potential energy savings from retrofit projects was evaluated. Some of the benchmarking input variables were modified to reflect a potential energy savings from a retrofit project and the new input set was simulated with the ANN model. The preliminary results show that the developed ANN model can be used to predict energy savings from retrofit projects. Copyright © 2006 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.     

Experimental study for predicting the specific heat of water based Cu-Al2O3 hybrid nanofluid using artificial neural network and proposing new correlation

In this study, an artificial neural network model has been created in order to estimate the specific heat of Cu-Al₂O₃/water hybrid nanofluid based on temperature (T) and volume concentration (φ). Specific heat values of the Cu-Al₂O₃/water hybrid nanofluid prepared in five-volume concentration were measured experimentally in the 20°C to 65°C temperature range. The dataset was reserved into three primary parts, with the inclusion of 901 (70%) for the training, 257 (20%) for the test and 129 (10%) for the validation. As a result of comparison with experimental values, it is concluded that this model predicts specific heat with R-value of 0.99994 and an average relative error of approximately 5.84e-9. In addition, a mathematical correlation has been developed to estimate the specific heat of the Cu-Al₂O₃/water hybrid nanofluid. The data acquired from the mathematical correlation, developed, were in great correlation with all the experimental values with an average deviation of −0.005%. This result has revealed that the developed mathematical correlation is an ideal design for estimating the specific heat of the Cu-Al₂O₃/water hybrid nanofluid.     

An energy benchmarking model based on artificial neural network method utilizing US Commercial Buildings Energy Consumption Survey (CBECS) database

This study focuses on development of an energy benchmarking model utilizing U.S. Commercial Buildings Energy Consumption Survey (CBECS) Database. An artificial neural networks (ANN) method based approach was used in the study. Office type buildings in the CBECS database were used in the benchmarking model development and weighted energy use intensity (EUI) was selected as the benchmarking index. The benchmarking model included input variables describing building's physical properties, occupancy and climate. Yearly electricity consumption per square meter, or EUI, was estimated by the ANN model. The correlation coefficient for each census division benchmarking model varied between 0.45 and 0.73, and mean squared error (MSE) varied between 9.60 and 15.25. It was observed that when the data set for a census division was grouped by different climate zones, ANN benchmarking model provided more accurate predictions. It was also observed that ANN model provides more accurate estimations when compared with predictions obtained with multi-linear regression models. For comparison, the MSE values varied between 10.24 and 40.43. Overall, the ANN model proved itself a better prediction model for energy benchmarking. Copyright © 2006 John Wiley & Sons, Ltd.     

基于GA-BP神经网络的土壤-空气换热器换热量预测分析

文章对位于太原市一个日光温室内的土壤-空气换热器进行夏季工况试验,获得了不同运行工况下换热管内空气的温度和湿度的分布数据.试验结果表明:土壤-空气换热器具有一定的除湿效果;当换热管长度为17.2 m,换热管内空气流速为2 m/s时,土壤-空气换热器潜热换热量占全热换热量的31.37％,且潜热换热量在全热换热量中的占比随...     

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.