Performance Evaluation of Small-channel Pulsating Heat Pipe Based on Dimensional Analysis and ANN Model

The pulsating heat pipe is a very promising heat dissipation device to address the challenge of higher heat-flux electronic chips,as it is characterised by excellent heat transfer ability and flexibility for miniaturisation.To boost the application of PHP,reliable heat transfer performance evaluationmodels are especially important.In this paper,a heat transfer correlation was firstly proposed for closed PHP with various working fluids(water,ethanol,methanol,R123,acetone)based on collected experimental data.Dimensional analysis was used to group the parameters.It was shown that the average absolute deviation(AAD)and correlation coefficient(r)of the correlation were 40.67%and 0.7556,respectively.For 95%of the data,the prediction of thermal resistance and the temperature difference between evaporation and condensation section fell within 1.13K/Wand 40.76K,respectively.Meanwhile,an artificial neural networkmodelwas also proposed.The ANN model showed a better prediction accuracy with a mean square error(MSE)and correlation coefficient(r)of 7.88e-7 and 0.9821,respectively.

Study on Residual Oil HDS Process with Mechanism Model and ANN Model

Based on the Residual Oil Hydrodesulfurization Treatment Unit (S-RHT), the n-order reaction kinetic model for residual oil HDS reactions and artificial neural network (ANN) model were developed to determine the sulfur content of hydrogenated residual oil. The established ANN model covered 4 input variables, 1 output variable and 1 hidden layer with 15 neurons. The comparison between the results of two models was listed. The results showed that the predicted mean relative errors of the two models with three different sample data were less than 5% and both the two models had good predictive precision and extrapolative feature for the HDS process. The mean relative error of 5 sets of testing data of the ANN model was 1.62%—3.23%, all of which were smaller than that of the common mechanism model (3.47%— 4.13%). It showed that the ANN model was better than the mechanism model both in terms of fitting results and fitting difficulty. The models could be easily applied in practice and could also provide a reference for the further research of residual oil HDS process.

Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market

In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.

ANN model of subdivision error based on genetic algorithm

According to the test data of subdivision errors in the measuring cycle of angular measuring system, the characteristics of subdivision errors generated by this system are analyzed. It is found that the subdivision errors are mainly due to the rotary-type inductosyn itself. For the characteristic of cyclical change, the subdivision errors in other measuring cycles can be compensated by the subdivision error model in one measuring cycle. Using the measured error data as training samples, combining GA and BP algorithm, an ANN model of subdivision error is designed. Simulation results indicate that GA reduces the uncertainty in the training process of the ANN model, and enhances the generalization of the model. Compared with the error model based on the least-mean-squared method, the designed ANN model of subdivision errors can achieve higher compensating precision.

ANN模型与分段线性插值及回归模型的比较及应用

对ANN模型、分段线性插值模型和非线性回归模型从原理上进行了比较,ANN模型易于构建各影响因素与因变量间复杂关系,非线性回归模型和分段线性插值模型可以将自变量与因变量间的关系通过表达式直观表达。以荆江三口分流量与枝城流量的关系为应用算例,采用相关系数、纳什效率系数、均方根误差和平均绝对误差等4个评价指标对3个模型的拟合精度和误差大小进行了比较。结果表明:3个模型均可应用于模拟枝城流量与荆江三口分流量的关系,但3个模型的计算值与实际值间的误差大小存在差异,从4个评价指标综合来看,ANN模型计算值与实测值的误差最小,分段线性插值模型次之,回归模型计算精度相对较低。

基于ANN-CA模型的F县季节性闲置耕地模拟及预测

为保护耕地和提高耕地利用率,促进农业可持续发展,本研究利用ANN-CA模型对F县的季节性闲置耕地情况进行模拟预测。模拟结果表明,在α=2,T=0.8的参数组合下,各类用地变化的模拟精度较高,模拟出的用地变化情况与2020年的实际用地情况较为贴近;根据季节性闲置耕地识别规则模拟出F县未来耕地季节性闲置现象呈现明显好转趋势,预测2025年F县季节性闲置耕地主要集中在东北部和南部,面积为25.8318km^(2)。生产中,注意对耕地进行科学合理的养护和利用,以保障农作物的产量和质量,确保农业可持续发展。

基于BP-ANN与RBF-ANN的钢筋与混凝土黏结强度预测模型研究

为研究神经网络对钢筋与混凝土黏结强度的预测能力以及神经网络的输出性能,基于大量的试验数据,提出一种基于改进神经网络的变形钢筋与混凝土黏结强度预测模型,对混凝土结构的研究与实际工程应用均有着重要的意义。收集290组黏结锚固试验数据,引入基于反向传播人工神经网络(BP-ANN)与径向基函数神经网络(RBF-ANN)算法,揭示混凝土强度、保护层厚度、钢筋直径、锚固长度及配箍率对变形钢筋与混凝土黏结性能的影响规律,建立基于改进神经网络算法的钢筋与混凝土黏结强度预测模型。对比分析不同数据预处理方法和训练神经元个数对建议模型预测结果的影响,评估各经典模型与建议模型的预测精度和离散性,提出临界锚固长度计算公式。结果表明:BP-ANN预测值与试验值比值的均值、标准差及变异系数分别为1.009、0.188、0.86,其预测精度略高于RBF-ANN;建议模型能够更准确、更稳定地预测钢筋与混凝土的黏结强度,该方法为解决钢筋与混凝土黏结问题提供了新思路。

Modeling of hot deformation behavior and prediction of flow stress in a magnesium alloy using constitutive equation and artificial neural network(ANN)model

The aim of the present study was to investigate the modeling and prediction of the high temperature flow characteristics of a cast magnesium(Mg-Al-Ca)alloy by both constitutive equation and ANN model.Toward this end,hot compression experiments were performed in 250-450℃and in strain rates of 0.001-1 s^(−1).The true stress of alloy was first and foremost described by the hyperbolic sine function in an Arrhenius-type of constitutive equation taking the effects of strain,strain rate and temperature into account.Predictions indicated that unlike low strain rates and high temperature with dominant DRX activation,in relatively high strain rate and low temperature values,the precision of the models become decreased due to activation of twinning phenomenon.At that moment and for a better evaluation of twinning effect during deformation,a feed-forward back propagation ANN was developed to study the flow behavior of the investigated alloy.Then,the performance of the two suggested models has been assessed using a statistical criterion.The comparative assessment of the gained results specifies that the well-trained ANN is much more precise and accurate than the constitutive equations in predicting the hot flow behavior.

Development of processing map for InX-750 superalloy using hyperbolic sinus equation and ANN model

The aim of this study is to develop processing maps based on two models and compare them with conventional processing maps.The hyperbolic sinus constitutive equation and artificial neural network(ANN)approaches were used in this investigation to predict flow stress and to develop processing maps in various conditions.The hot compression tests of InX-750 superalloy were carried out above the gamma prime phase temperature and within the temperature range of 1000-1150℃and strain rate of 0.001-1.000 s^(-1).The processing maps were conducted based upon dynamic material model(DMM)for data by experimental,constitutive equation and ANN approaches.The processing maps drawn by either of the prediction methods show that the method developed by ANN data does not significantly differ from the experimental processing map.The ANN approach is thus a suitable way to predict the flow stress as well as hot working processing map of engineering metals and materials.

Predicting the capacity of perfobond rib shear connector using an ANN model and GSA method

Due to recent advances in the field of artificial neural networks(ANN)and the global sensitivity analysis(GSA)method,the application of these techniques in structural analysis has become feasible.A connector is an important part of a composite beam,and its shear strength can have a significant impact on structural design.In this paper,the shear performance of perfobond rib shear connectors(PRSCs)is predicted based on the back propagation(BP)ANN model,the Genetic Algorithm(GA)method and GSA method.A database was created using push-out test test and related references,where the input variables were based on different empirical formulas and the output variables were the corresponding shear strengths.The results predicted by the ANN models and empirical equations were compared,and the factors affecting shear strength were examined by the GSA method.The results show that the use of ANN model optimization by GA method has fewer errors compared to the empirical equations.Furthermore,penetrating reinforcement has the greatest sensitivity to shear performance,while the bonding force between steel plate and concrete has the least sensitivity to shear strength.

Research on Optimizing the Hidden Layer Structure of ANN-Based Model and Its Application in Predicting End-Quench Curves of Steels

In this paper, a method of optimizing the number of hidden layer neurons has been put forward. This optimizing method is suitable for three layers B-p network. The purpose of this optimizing method is to reduce the predicting errors when the model is used as predicting model. As an example of application, a predicting model of steel end-quench curves has been designed by using this optimizing method. The result shows that the optimization of ANN hidden layer architecture has an effect on reducing predicting errors.

基于RSEI和ANN-CA-Markov模型的伊宁市生态环境质量动态监测及预测研究

伊宁市位于中国新疆西北边陲伊犁河谷内,土地肥沃,水资源和生物资源丰富,具有发展农、林、牧业的优越自然条件,但由于城市化进程过快,导致生态遭受破坏,生态环境质量不断下降,因此依托遥感生态指数(Remote-sensing ecological index,RSEI)及ANN-CA-Markov模型,科学、合理地利用Landsat TM5/OLI-TIRS8遥感数据对开展伊宁市2006—2021年生态环境动态评价及预测具有重要意义。结果表明:(1)绿度和湿度对伊宁市生态水平具有正面影响,干度和热度对伊宁市生态水平具有负面影响,影响伊宁市生态环境质量的主要因素依次为绿度、热度、干度、湿度,符合伊犁河谷地区所表现出来的生态状况。(2)伊宁市RSEI平均值为0.451,总体处于中等水平,生态环境质量变化呈现两极逐渐缩小的趋势,但RSEI指标中等区域及较差区域的面积正在逐年增大,总体生态环境呈现稳中向差的发展趋势。(3)预计2026年和2031年伊宁市北坡地区生态得到一定程度改善,结合《新疆伊宁市城市2018—2035年总体规划》,城区在未来生态环境质量依旧保持在中等水平,城市将继续向外扩张,可耕地面积将继续减少。

A Comparison of ANN and HSPF Models for Runoff Simulation in Balkhichai River Watershed, Iran

In this study, the capability of two different types of models including Hydrological Simulation Program-Fortran (HSPF) as a process-based model and ANN as a data-driven model in simulating runoff was evaluated. The considered area is the Balkhichai River watershed in northwest of Iran. HSPF is a semi-distributed deterministic, continuous and physically-based model that can simulate the hydrologic cycle, associated water quality and quantity and process on pervious and impervious land surfaces and streams. Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. Statistical approach depending on cross-, auto- and partial-autocorrelation of the observed data is used as a good alternative to the trial and error method in identifying model inputs. The performances of ANN and HSPF models in calibration and validation stages are compared with the observed runoff values in order to identify the best fit forecasting model based upon a number of selected performance criteria. Results of runoff simulation indicated that the simulated runoff by ANN was generally closer to the observed values than those predicted by HSPF.

Optimization of the Conceptual Model of Green-Ampt Using Artificial Neural Network Model (ANN) and WMS to Estimate Infiltration Rate of Soil (Case Study: Kakasharaf Watershed, Khorram Abad, Iran)

Determination of the infiltration rate in a watershed is not easy and in empirical and theoretical point of view, it is important to access average value of infiltration. Infiltration models has main role in managing water sources. Therefore different types of models with various degrees of complexity were developed to reach this aim. Most of the estimating methods of soil infiltration are expensive and time consuming and these methods estimate infiltration with hypothesis of zero slope. One of the conceptual and physical models for estimating soil infiltration is Green-Ampt model which is similar to Richard model. This model uses slope factor in estimating infiltration and this is the power point of Green-Ampt model. In this research the empirical model of Green-Ampt was optimized with integrating artificial neural network model (ANN) and a model of geographical information system WMS to estimate the infiltration in Kakasharaf watershed. Results of the comparison between the output of this method and real value of infiltration in region (through multiple cylinders) showed that this method can estimate the infiltration rate of Kakasharaf watershed with low error and acceptable accuracy (Nash-Sutcliff performance coefficient 0.821, square error 0.216, correlation coefficient 0.905 and model error 0.024).

基于GM-ANN模型的生态足迹时间序列预测分析——以甘肃省为例

基于生态足迹、生态承载力及其变化时间动态特征的分析,应用灰色系统与神经网络理论与方法,组建了GM-ANN预测模型,以甘肃省为例进行了评价与预测分析.结果表明:1991-2009年期间,甘肃省的生态经济发展一直处于不可持续状态,人均生态足迹为1.517 hm^2/人,人均生态承载力为1.077 hm^2/人,人均生态冗余为-0.44hm^2/人.预测结果显示,到2015年和2020年,甘肃省人均生态足迹将分别达到2.503 hm^2/人和2.870 hm^2/人,而人均生态承载力将分别降至0.985 hm^2/人和0.930 hm^2/人,人均生态冗余则分别为-1.518hm^2/人和-1.940 hm^2/人.这说明未来该省生态经济仍处于不可持续状态,急需调整经济结构与消费模式,以降低其人均生态足迹和增加生态冗余值.另外,通过内插拟合能力检验分析证明,新建立的GM-ANN模型与常用的GM(1,1)模型相比,可使预测精度提高1.7%,在分析和预测不确定系统中有明显的优越性.因此,GM-ANN模型在研究生态足迹动态的过程中,有着较为广泛的应用前景.

基于GM-ANN模型的建筑物沉降量变化趋势预测方法

建筑物沉降观测结束之后,为降低和预防因地基不均匀沉降等因素造成的不安全事故发生率,准确预测建筑物沉降量变化趋势已引起相关科研单位的重视。首先,将人工神经网络数据分析与灰色GM(1,1)模型相结合,提出GM-ANN预测模型。然后,结合工程实例验证模型对监测沉降危险点数据变化的准确性,形成Matlab拟合曲线和预测趋势图。最终,结果表明仅考虑时间因素,GM-ANN模型明显优于灰色GM(1,1)模型,可使预测精度提高将近三倍。因此,利用GM-ANN预测模型可以对建筑物安全性进行有效预测。

基于ANN贡献分析及GEP算法的地铁车站土建造价预测模型

科学地选取工程特征因素及预测方法对于构建一个好的造价预测模型十分关键。在选取的14个影响地铁车站土建造价特征因素中,利用神经网络贡献分析的变量选取方法,筛选出12个主要特征因素。并针对这选定的14个全部特征因素和12个主要特征因素分别组合BP神经网络和GEP 2种预测方法构建4个不同的造价预测模型,应用18组地铁车站土建造价和特征因素的历史数据进行实例探究,通过R2,MSE,RMSE和MaxRE 4个指标的评价,结果表明:用主要特征因素为模型输入变量能显著提高模型的预测精度,且和GEP算法组合建立的造价预测模型为最优。将主要特征因素选取和预测方法选取相结合构建求解的最优模型很好地解决了已有相关研究中选取特征因素主观性多科学性不足及未考虑特征因素选取对预测方法选取的影响问题。

GA-PLS结合PC-ANN算法提高奶粉蛋白质模型精度

提出一种偏最小二乘法(PLS)和人工神经网络(ANN)结合用于近红外光谱(NIRS)的分析方法,以提高奶粉蛋白质模型的预测精度。首先采用基于遗传算法的波长选择法(RS-GA)优化光谱数据,建立GA-PLS模型预测奶粉蛋白线性部分;然后在RS-GA法选择的波段上进行主成分分析(PCA),以主成分的得分矩阵作为ANN模型输入层,以GA-PLS预测值与真实值之差作为输出层,建立PC-ANN模型预测其非线性部分。最终预测结果为两个模型预测值之和,以模型的预测标准偏差(RMSEP)作为评价指标,以便考察新方法的有效性。同时建立线性的全谱模型(Fr-PLS),其Fr-PLS、GA-PLS和GA-PLS+PC-ANN模型的RMSEP分别为0.511,0.440和0.235。结果表明:考虑奶粉蛋白含量近红外模型的非线性部分,可以显著提高模型的预测精度,该方法也可为其它复杂体系模型精度的提高提供借鉴。

利用BP-ANN模型进行深基坑变形预测分析的探讨

利用LM算法和自适应学习速率的动量梯度下降法对BP-ANN模型进行改进,发现LM算法在建立深基坑变形预报模型时具有更高的学习效率,且输入量间的相关程度对网络效率无显著影响,隐含层节点数可通过仿真误差分布图确定和优化。仿真与预测结果表明,在平稳观测时间序列内该模型具有更高的预测精度,在实际深基坑监测预报中值得推广应用。

应用人工神经网络(ANN)分析热泵型海水淡化系统产水特性

利用人工神经网络来模拟仿真热泵型海水淡化系统的性能。以空气入口干球温度和湿球温度、预冷器进口冷却水温度、预冷器出口冷却水温度、海水喷淋温度作为输入参数,建立了海水淡化系统产水(淡水)模型。对建好的神经网络模型经训练学习后,用来模拟预测预冷器和蒸发器的产水值,并且与数值模拟产水值、实验产水值进行了比较,误差较小。表明利用人工神经网络(ANN)建立的热泵型海水淡化系统仿真模型取得了满意的结果。