Theoretical and Experimental Investigation of a Novel Hybrid Heat-Pipe Solar Collector

Abstract

This article describes a novel flat plate heat-pipe solar collector, namely, the hybrid heat-pipe solar collector. An analytical model has been developed to calculate the collector efficiency as well as simulate the heat transfer processes occurring in the collector. The effects of heat pipes/absorber, top cover, flue gas channel geometry, and flue gas temperature and flow rate, on the collector efficiency were investigated based on three modes of operation, i.e., solar only operation, solar/exhaust gas combined, and solar, exhaust gas and boiler combined. Experimental testing of the collector was also carried out for each of these modes of operation under real climatic conditions. The results were used to estimate the efficiency of the collector and determine the relation between the efficiency and general external parameter. The modeling and experimental results were compared and a correlation factor was used to modify the theoretical predictions. It was found that the efficiency of the collector was increased by about 20–30% compared to a conventional flat-plate heat pipe solar collector.     

Comparison Between Heat Pipe and Direct Heat Exchange Solar Louvre Collectors

International concern about the environmental implications of climate change coupled with increasing demand for energy to fuel modern society has lead to growing interest in using renewable energy sources as alternatives to conventional sources. The work presented in this paper compares two types of solar collector integrated into louvred shading devices. In addition to protecting glazed spaces in buildings from excessive solar gain, the collector would provide the flexibility to produce systems customized for collecting heat over a temperature-range appropriate to particular building services applications at various climates/locations. This would allow considerable savings to be made in primary energy consumption and lead to a reduction in global warming impact. Two solar absorbers, based on different techniques of heat exchange, were tested experimentally. The first was based on a direct heat exchange technique, and the second used heat pipe technology. Various comparisons were made and it was concluded that the heat pipe solar louvre collector was the preferred device.     

EFFICIENCY OF HYDROLOGIC DATA COLLECTION SYSTEMS ROLE OF TYPE I AND II ERRORS1

ABSTRACT The efficiency of hydrologic data collection systems is relevant to solution of environmental problems, scientific understanding of hydrologic processes, model-building and management of water resources. Because these goals may be overlapping and non-commensurate, design of data networks is not simple. Identified are four elements of error or risk in such networks: (a) choice of variables and mathematical model for the same process, (b) accuracy of model parameter estimates, (c) acceptance of wrong hypothesis or rejection of correct hypothesis and (d) economic losses associated with error. Of these four, the classical hypothesis testing problem is specifically evaluated in terms of costs of type I and II errors for simple and composite hypotheses; mathematical models for these economic analyses also include costs of sample data and costs of waiting while new data is obtained. An illustrative computational example focuses on the hypothesis that natural recharge might be augmented by a system of pumping wells along an ephemeral channel. The relationship of the hypothesis testing problem to Bayesian decision theory is discussed; it is felt that the latter theory offers a more comprehensive framework for design and use of hydrologic data networks.     

Comparative study on performances of a heat-pipe PV/T system and a heat-pipe solar water heating system

The heat-pipe solar water heating (HP-SWH) system and the heat-pipe photovoltaic/thermal (HP-PV/T) system are two practical solar systems, both of which use heat pipes to transfer heat. By selecting appropriate working fluid of the heat-pipes, these systems can be used in the cold region without being frozen. However, performances of these two solar systems are different because the HP-PV/T system can simultaneously provide electricity and heat, whereas the HP-SWH system provides heat only. In order to understand these two systems, this work presents a mathematical model for each system to study their one-day and annual performances. One-day simulation results showed that the HP-SWH system obtained more thermal energy and total energy than the HP-PV/T system while the HP-PV/T system achieved higher exergy efficiency than the HP-SWH system. Annual simulation results indicated that the HP-SWH system can heat the water to the available temperature (45°C) solely by solar energy for more than 121 days per year in typical climate regions of China, Hong Kong, Lhasa, and Beijing, while the HP-PV/T system can only work for not more than 102 days. The HP-PV/T system, however, can provide an additional electricity output of 73.019 kWh/m², 129.472 kWh/m², and 90.309 kWh/m² per unit collector area in the three regions, respectively.     

SOIL SALINITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site.     

ARTIFICIAL NEURAL NETWORKS FOR SUBSURFACE DRAINAGE AND SUBIRRIGATION SYSTEMS IN ONTARIO,CANADA1

ABSTRACT: Artificial neural network (ANN) models were developed to simulate fluctuations in midspan water table depths (WTD) given rainfall, potential evapotranspiration, and irrigation inputs on a Brookston clay loam in Woodslee, Ontario, having a dual‐purpose subsurface drainage/subirrigation setup. Water table depths and meteorologic data collected at this site from 1992 to 1994 and from 1996 to 1997 were used to train the ANNs. The ANNs were then used for real‐time control and time series simulations. The lowest root mean squared errors (RMSE) for the various ANNs were 60.6 mm for real‐time control simulation, and 88.4 mm for time‐series simulation of water table depths. It was possible to simulate WTD for the different modes of water table management in one network by incorporating an indicator for switching from one to the other. The ANN simulations were quite good even though the training data sets had irregular measurement intervals. With fewer input parameters and small network structures, ANNs still provided accurate results and required little time for training and execution. ANNs are therefore easier and faster to develop and run than conventional models and can contribute to the proper management of subsurface drainage and subirrigation systems.     

Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks

This study investigates the feasibility of artificial neural networks (ANNs) to retrieve root zone soil moisture (RZSM) at the depths of 20 cm (SM20) and 50 cm (SM50) at a continental scale, using surface information. To train the ANNs to capture interactions between land surface and various climatic patterns, data of 557 stations over the continental United States were collected. A sensitivity analysis revealed that the ANNs were able to identify input variables that directly affect the water and energy balance in root zone. The data important for RZSM retrieval in a large area included soil texture, surface soil moisture, and the cumulative values of air temperature, surface soil temperature, rainfall, and snowfall. The results showed that the ANNs had high skill in retrieving SM20 with a correlation coefficient above 0.7 in most cases, but were less effective at estimating SM50. The comparison of the ANNs showed that using soil texture data improved the model performance, especially for the estimation of SM50. It was demonstrated that the ANNs had high flexibility for applications in different climatic regions. The method was used to generate RZSM in North America using Soil Moisture and Ocean Salinity (SMOS) soil moisture data, and achieved a spatial soil moisture pattern comparable to that of Global Land Data Assimilation System Noah model with comparable performance to the SMOS surface soil moisture retrievals. The models can be efficient alternatives to assimilate remote sensing soil moisture data for shallow RZSM retrieval.     

Impact of utilizing reflector,single-axis and two-axis sun trackers on the performance of an evacuated tube solar collector

ABSTRACT

In this paper, a novel evacuated tube solar collector (ETSC) is first designed and built. Then, the impact of adding reflector, reflector plus single-axis sun tracker and reflector plus two-axis sun tracker to the built ETSC on the thermal efficiency of the ETSC is evaluated both theoretically and experimentally. In this regard, four identical versions of the proposed ETSC have been built and utilized in four collectors built and presented in this research work. The first collector is the same proposed built ETSC, the second collector is a parabolic trough solar collector comprising one built ETSC and a reflector (ETSC+R), the third collector is composed of one built ETSC, a reflector and a single-axis sun tracker all built in this study (ETSC+R+ ST), and the fourth collector consists of one built ETSC, a reflector and a two-axis sun tracker all built in this study (ETSC+R+ DT). Theoretical basis and concepts of the four collectors are formulated and analyzed in separate subsections. Theoretical results are outlined and highlighted at the end of each subsection. Experimental measurements and data obtained from the operation of the four collectors in the four seasons are presented that point by point verify theoretical results obtained in this study. To provide a comprehensive view, a techno-economic numerical comparison is performed between the four collectors. The following points, which are also the novelty and contributions of this work, are deduced from theoretical concepts, experimental data, and comparison provided in this study:

?There is no technical and economic justification for adding a reflector to an ETSC that results in forming a parabolic trough solar collector (ETSC+R) without any sun tracker.

?There is no economic justification for adding a single-axis sun tracker to a parabolic trough solar collector (ETSC+R).

?There is no economic justification for adding a two-axis sun tracker to a parabolic trough solar collector (ETSC+R).

?Comparing between a two-axis sun tracker and a single-axis sun tracker, adding the single-axis type to a parabolic trough solar collector (ETSC+R) is more advantageous.     

Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique

Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resources' variables. The feed-forward neural network modeling technique is the most widely used ANN type in water resources applications. The main purpose of the study is to investigate the abilities of an artificial neural networks' (ANNs) model to improve the accuracy of the biological oxygen demand (BOD) estimation. Many of the water quality variables (chemical oxygen demand, temperature, dissolved oxygen, water flow, chlorophyll a and nutrients, ammonia, nitrite, nitrate) that affect biological oxygen demand concentrations were collected at 11 sampling sites in the Melen River Basin during 2001-2002. To develop an ANN model for estimating BOD, the available data set was partitioned into a training set and a test set according to station. In order to reach an optimum amount of hidden layer nodes, nodes 2, 3, 5, 10 were tested. Within this range, the ANN architecture having 8 inputs and 1 hidden layer with 3 nodes gives the best choice. Comparison of results reveals that the ANN model gives reasonable estimates for the BOD prediction.     

AN EVALUATION OF THE AVAILABLE TECHNIQUES FOR ESTIMATING MISSING FECAL COLIFORM DATA1

ABSTRACT: This paper presents the findings of a study aimed at evaluating the available techniques for estimating missing fecal coliform (FC) data on a temporal basis. The techniques investigated include: linear and nonlinear regression analysis and interpolation functions, and the use of artificial neural networks (ANNs). In all, seven interpolation, two regression, and one ANN model structures were investigated. This paper also investigates the validity of a hypothesis that estimating missing FC data by developing different models using different data corresponding to different dynamics associated with different trends in the FC data may result in a better model performance. The FC data (counts/100 ml) derived from the North Fork of the Kentucky River in Kentucky were employed to calibrate and validate various models. The performance of various models was evaluated using a wide variety of standard statistical measures. The results obtained in this study are able to demonstrate that the ANNs can be preferred over the conventional techniques in estimating missing FC data in a watershed. The regression technique was not found suitable in estimating missing FC data on a temporal basis. Further, it has been found that it is possible to achieve a better model performance by first decomposing the whole data set into different categories corresponding to different dynamics and then developing separate models for separate categories rather than developing a single model for the composite data set.     

Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler

The volcanic plate made pillar cooler system is designed for outdoor spaces as a heat exchanging medium and reduces the outcoming air temperature which flows through the exhaust port. This paper proposes the use of artificial neural networks (ANNs) to predict inside air temperature of a pillar cooler. For this purpose, at first, three statistically significant factors (outside temperature, airing and watering) influencing the inside air temperature of the pillar cooler are identified as input parameters for predicting the output (inside air temperature) and then an ANN was employed to predict the output. In addition, 70%, 15% and 15% data was chosen from a previously obtained data set during the field trial of the pillar cooler for training, testing and validation, respectively, and then an ANN was employed to predict inside air temperature. The training (0.99918), testing (0.99799) and validation errors (0.99432) obtained from the model indicate that the artificial neural network model (NARX) may be used to predict inside air temperature of pillar cooler. This study reveals that, an ANN approach can be used successfully for predicting the performance of pillar cooler.     

SOIL MOISTURE PREDICTION USING SUPPORT VECTOR MACHINES1

ABSTRACT: Herein, a recently developed methodology, Support Vector Machines (SVMs), is presented and applied to the challenge of soil moisture prediction. Support Vector Machines are derived from statistical learning theory and can be used to predict a quantity forward in time based on training that uses past data, hence providing a statistically sound approach to solving inverse problems. The principal strength of SVMs lies in the fact that they employ Structural Risk Minimization (SRM) instead of Empirical Risk Minimization (ERM). The SVMs formulate a quadratic optimization problem that ensures a global optimum, which makes them superior to traditional learning algorithms such as Artificial Neural Networks (ANNs). The resulting model is sparse and not characterized by the “curse of dimensionality.” Soil moisture distribution and variation is helpful in predicting and understanding various hydrologic processes, including weather changes, energy and moisture fluxes, drought, irrigation scheduling, and rainfall/runoff generation. Soil moisture and meteorological data are used to generate SVM predictions for four and seven days ahead. Predictions show good agreement with actual soil moisture measurements. Results from the SVM modeling are compared with predictions obtained from ANN models and show that SVM models performed better for soil moisture forecasting than ANN models.     

Solar Assisted Small-Scale Biomass District Heating System in the Northern Part of Sweden

Abstract

This article presents a case study of a projected solar assisted biomass district heating system in the north of Sweden. It is generally known that a biomass district heating system combined with solar heat brings many important benefits. The most common system solution is to install a heat store and a large solar collector field near the heating central. No plant of this type is however in operation in the northern part of Sweden. The main reason for this is that the solar irradiation at these latitudes is very low when the demand for heat is high. Solar heat could however be useful during summer in order to generate hot tap water. One problem is that the heat losses, calculated as percentage of the delivered heat, become very large during these months. This article presents the idea of allowing the connected households to generate their own hot tap water using solar collectors and heat stores installed in each house. The district heating network can therefore be closed in summer, which eliminates the heat losses outside the heating period. A case study of a projected plant has been carried out and it is shown that it is possible to reduce the heat losses by 20% compared to a conventional system. This idea also provides many other important technical and economic benefits.     

Predicting Carbon Monoxide Concentrations in the Air of Pardis City,Iran, Using an Artificial Neural Network

To date, several methods have been proposed to explain the complex process of air pollution prediction. One of these methods uses neural networks. Artificial neural networks (ANN) are a branch of artificial intelligence, and because of their nonlinear mathematical structures and ability to provide acceptable forecasts, they have gained popularity among researchers. The goal of our study as documented in this article was to compare the abilities of two different ANNs, the multilayer perceptron (MLP) and radial basis function (RBF) neural networks, to predict carbon monoxide (CO) concentrations in the air of Pardis City, Iran. For the study, we used data collected hourly on temperature, wind speed, and humidity as inputs to train the networks. The MLP neural network had two hidden layers that contained 13 neurons in the first layer and 25 neurons in the second layer and reached a mean bias error (MBE) of 0.06. The coefficient of determination (R²), index of agreement (IA), and the Nash–Scutcliffe efficiency (E) between the observed and predicted data using the MLP neural network were 0.96, 0.9057, and 0.957, respectively. The RBF neural network with a hidden layer containing 130 neurons reached an MBE of 0.04. The R², IA, and E between the observed and predicted data using the RBF neural network were 0.981, 0.954, and 0.979, respectively. The results provided by the RBF neural network had greater acceptable accuracy than was the case with the MLP neural network. Finally, the results of a sensitivity analysis using the MLP neural network indicated that temperature is the primary factor in the prediction of CO concentrations and that wind speed and humidity are factors of second and third importance when forecasting CO levels.     

Thermal Modeling and Performance Evaluation of Photovoltaic Thermal (PV/T) Systems: A Parametric Study

Conventional solar photovoltaic (PV) module converts the light component of solar radiation into electrical power, and heat part is absorbed by module increasing its operating temperature. Combined PV module and heat exchanger generating both electrical and thermal powers is called as hybrid photovoltaic/thermal (PV/T) solar system. The paper presents the design of a PV/T collector, made with thin film PV technology and a spiral flow absorber, and a simulation model, developed through the system of several mathematical equations, to evaluate the performance of PV/T water collectors. The effect of various parameters on the thermal and electrical efficiency has been investigated to obtain optimum combination of parameters. Finally, a numerical simulation has been carried out for the daily and annual yield of the proposed PV/T collector, and comparison with a standard PV module is discussed.     

Is Learning without teaching possible? The productive tension between network governance and reflexivity

ABSTRACT

Theories of reflexive governance are closely linked with the claim that more traditional modes of coordination have been replaced by networked structures, allowing reflexivity to emerge and reflexive learning to function as a steering mechanism in rapidly changing policy contexts. This paper explores this connection between reflexivity, governance, learning and networks in societal transitions, focusing particularly on the claim that networks will deliver reflexive learning. Using network theories from both policy networks and network governance and a case study of the Canadian agricultural biotechnology (agbiotech) policy network, it suggests that the kind of learning produced in networks will be a function of network structure. In particular, higher order reflexive learning will be compromised by the inevitability of the political struggle for nodality or central place in networks and the ensuing distribution of opportunities for bridging and bonding activities. Networks such as the Canadian agbiotech policy network that may promote learning but not necessarily reflexive learning are increasingly disadvantaged in contemporary policy settings.     

Numerical studies on the thermal regimes of the horizontal tube falling film evaporation under varying feeder height

ABSTRACT

This paper discusses about the effect of feeder height and heat flux on the heat transfer characteristics of horizontal tube falling film evaporation in the thermal regimes. In order to investigate this, a two- dimensional CFD model was developed to perform simulation and results were compared and validated with published data available in the literature. Heat transfer co-efficients in the thermal regimes were determined from the CFD simulation and the results were recorded, analyzed and validated with the mathematical models available in the literature. The novelty of the current study is to predict the commencement of the fully developed thermal region over the tube from the simulation model under varying feeder height and heat flux. An effort was also made to measure the liquid film thickness around the tube from the CFD model in the thermal regimes. It is observed that angle of thermally developing region contracts and fully developed thermal region extends with the increase of the feeder height and heat flux. It is observed from the study that increase of heat flux by 10 kW/m² resulted in increase of heat transfer co-efficient value by 10–12% average in thermally developing region and 12–15% average in fully developed region. Thinnest liquid film thickness observed between 85 and 127°angle. Shifting of thinnest region of liquid film upward from the mid tube with the increase of the feeder height and heat flux is noted.     

Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction

Artificial neural networks (ANNs) are suitable for modeling solid waste generation. In the present study, four training functions, including resilient backpropagation (RP), scale conjugate gradient (SCG), one step secant (OSS), and Levenberg–Marquardt (LM) algorithms have been used. The main goal of this research is to develop an ANN model with a simple structure and ample accuracy. In the first step, an appropriate ANN model with 13 input variables is developed using the afore-mentioned algorithms to optimize the network parameters for weekly solid waste prediction in Mashhad, Iran. Subsequently, principal component analysis (PCA) and Gamma test (GT) techniques are used to reduce the number of input variables. Finally, comparison amongst the operation of ANN, PCA-ANN, and GT-ANN models is made. Findings indicated that the PCA-ANN and GT-ANN models have more effective results than the ANN model. These two models decrease the number of input variables from 13 to 7 and 5, respectively.     

Effect of pipes in heat pump system on electric vehicle energy saving

ABSTRACT

Refrigerant pressure drop and temperature change in pipes are normally ignored in the thermodynamic analysis of traditional vehicle air conditioning system, this will result in serious errors. In this Paper, pressure drop and temperature difference are simulated in different pipes of electric vehicle (EV) heat pump system to analysis the effects of pipes in the actual EV heat pump system. The results indicate that the greater the mass flow, the faster pressure drop increases, the temperature difference decreases. Pressure drop of saturated liquid refrigerant is smaller than that of saturated gas refrigerant at the same saturation pressure and mass flow rate. The higher the refrigerant pressure (no phase change), the slower pressure drop decreases, the faster the temperature difference decreases. Pressure drop decreases with the increment of bending angle of the pipe. For EV heat pump system, suitable valves and less branches are helpful for energy saving of the system. Shortening the pipe between compressor and condenser can reduce temperature change obviously. Pressure drop per unit length in the pipe between evaporator and compressor is large especially in heating mode because of lower refrigerant density. It even reaches to over 100 times of that in the pipe between condenser and throttle valve in heating mode and has negative effects on the performance of the system. If the evaporator is closer to the compressor and the number of branches is less, then pressure drop will decrease a lot, which will be advantageous for energy saving of the heat pump system.     

Evaluating the “Catch‐Can” Test for Measuring Lawn Sprinkler Application Rates1

Abstract: The effectiveness of measuring lawn sprinkler application rates using the catch‐can test was evaluated. A survey of sources recommending the catch‐can test for measuring application rates show that catch‐can test procedures differ in the collector type, collector placement, number of collectors, and test duration. Analyses of catch‐can tests were performed to address these procedural differences, with emphasis on the type and number of collectors required to provide a reasonable level of confidence in test results. The accuracy of the catch‐can test generally improves as the number of randomly placed collectors increases. In order to achieve an accuracy of ±25% for 90 out of 100 catch‐can tests, the number of randomly placed collectors required ranged from 6 to over 50 for hand‐move systems, while for in‐ground systems, the number of randomly placed collectors required ranged from 2 to 8, depending on the pressure and percent overlap of the water distribution pattern. As long as a reasonable number of collectors were used when performing a catch‐can test, no consistent differences were observed in catch‐can test results due to type of collectors when using tuna fish cans, soup cans, or coffee mugs.