Performance predictions and parametric analysis of a valved linear compressor using a mathematical model

A mathematical model for a linear compressor with valves is developed. A linear compressor consists of an oscillating motor and a piston rigidly coupled to it. The mathematical model deals with the dynamic equation of a linear compressor consisting of inertia, motor force, and damping. It also solves the electrical circuit equation to estimate the current flowing through the motor circuit. The model considers the dynamic operation of suction and discharge valves. The refrigerant properties are estimated by interfacing the programme with REFPROP to predict the cylinder instantaneous pressure and temperature. Finally, the model predicts the net cooling capacity, compressor input power, and COP for the given compressor geometry. The mathematical model is validated with the experimental results reported by Lee et al. (2004. ‘Linear Compressor for Air-Conditioner.’ L. G. Electronics, international compressor engineering conference, Purdue University, C047, 1–7). Parametric analysis of the compressor is done by varying parameters like input voltage, refrigerant, and superheating temperature of the refrigerant. Results generated by the mathematical model are presented and discussed.     

Simulation of dynamic linear thermal bridges using a boundary element method model in the frequency domain

Heat losses through thermal bridges often lead to building pathologies generated by moisture condensation. Thus, thermal bridges need to be considered in the building design phase in order to avoid both heat loss and the occurrence of these pathologies later on. The linear thermal bridge is often taken into account at the design stage by using a pre-defined ψ coefficient. This ψ factor is listed in several national regulation/standards for various types of linear thermal bridges on the assumption of a steady state condition.This paper studies linear thermal bridges more realistically by assuming a dynamic behavior that allows the simulation of transient states where the external and internal temperatures may vary over time. The problem is solved by a boundary element model (BEM), formulated in the frequency domain. Time solutions are obtained afterwards by means of inverse Fourier transformations, which can simulate any external temperature variations. After an experimental validation of the BEM model, a series of linear thermal simulations was performed to illustrate the applicability of the proposed model. It further allows the importance of computing thermal bridges to be verified dynamically.     

Mapping and indicator approaches for the assessment of habitats at different scales using remote sensing and GIS methods

The paper presents a case study for the application of satellite remote sensing and GIS data and methods in the context of habitat monitoring and landscape assessment at different scales. The range of work covers the production of overview maps for land covers, techniques of classification for detailed habitat maps, change detection as a management support tool for the updating of existing habitat databases and an integrative GIS model to delineate habitat suitability for key species. Furthermore the role of comprehensive indicators and historical satellite data in investigating landscape change over two decades on a regional scale is discussed. Future activities for transferring the respective approaches onto a pan-European scale are presented in a concluding discussion.     

An innovative land use regression model incorporating meteorology for exposure analysis

The advent of spatial analysis and geographic information systems (GIS) has led to studies of chronic exposure and health effects based on the rationale that intra-urban variations in ambient air pollution concentrations are as great as inter-urban differences. Such studies typically rely on local spatial covariates (e.g., traffic, land use type) derived from circular areas (buffers) to predict concentrations/exposures at receptor sites, as a means of averaging the annual net effect of meteorological influences (i.e., wind speed, wind direction and insolation). This is the approach taken in the now popular land use regression (LUR) method. However spatial studies of chronic exposures and temporal studies of acute exposures have not been adequately integrated. This paper presents an innovative LUR method implemented in a GIS environment that reflects both temporal and spatial variability and considers the role of meteorology. The new source area LUR integrates wind speed, wind direction and cloud cover/insolation to estimate hourly nitric oxide (NO) and nitrogen dioxide (NO(2)) concentrations from land use types (i.e., road network, commercial land use) and these concentrations are then used as covariates to regress against NO and NO(2) measurements at various receptor sites across the Vancouver region and compared directly with estimates from a regular LUR. The results show that, when variability in seasonal concentration measurements is present, the source area LUR or SA-LUR model is a better option for concentration estimation.     

Particulate phosphorus bioavailability as a function of stream flow and land cover

Using total phosphorus concentrations to estimate eutrophication risk is problematic for management purposes, as only some forms of phosphorus are biologically available for phytoplankton growth. This study estimated the bioavailability of particulate phosphorus, in forested, urban, agricultural (i.e. dairy farm) and mixed land cover streams. Sixteen stream sites were sampled during base and storm flow conditions and the following parameters were determined: total suspended solids, total phosphorus, total dissolved phosphorus, particulate phosphorus, percent bioavailable particulate phosphorus (%BAPP), total bioavailable phosphorus and sediment particle size distribution. Algal assays with Pseudokirchneriella subcapitata were used to measure %BAPP. Percent BAPP averaged 17%, 26% and 24% for streams draining catchments with forested, mixed use and agricultural land cover, respectively, and %BAPP did not vary significantly between base and storm flow conditions in these stream types. In contrast, %BAPP averaged 73% in the urban streams during baseflows but declined to an average of only 19% during storms. Particle size distributions did not correlate with %BAPP in these samples. During storm events, particulate phosphorus concentrations increased in all streams by an average of 614% and total phosphorus increased by 200%, whereas total BAP (i.e. total dissolved phosphorus+%BAPP x particulate phosphorus) only increased by 72% because on average only 20% of the particulate phosphorus transported during these events was biologically available.     

Regional land pattern assessment: development of a resource efficiency measurement method

Debate on the sustainability of human settlements has recently been focused primarily on the urban portion of the land use pattern. However, urban areas rely on suburban, rural, and other less densely settled lands for their existence. In order to quantify the impacts of various land patterns on their supporting resources, these exurban lands must be included in any sustainability assessment. This need for a regional view has resulted in a measurement method that enables comparisons of relative sustainability between various regional land use patterns. Existing methods employed to assess urban sustainability are reviewed and compared with the regional characteristic curves method, introduced here, that takes a more holistic regional view. Results from the application of the method are presented, displaying the spatial dimension it brings to the analysis of illustrative primary metrics as well as demonstrating its ability to spatially quantify change in these metrics over time.     

Monitoring and predicting land use change in Beijing using remote sensing and GIS

Rapid land use change has taken place in many mega cities of China such as Beijing over the past two decades. In this paper, land use change dynamics were investigated by the combined use of satellite remote sensing, geographic information systems (GIS). The results indicated that there had been a notable and uneven urban growth and a major loss of cropland loss between 1986 and 2001. Most of the urban growth and loss of agriculture land occurred in inner and outer suburbs. Land use change was projected for the next 20 years using Markov chains and regression analyses. The further integration of remote sensing and GIS technologies with Markov model and regression model was found to be useful for describing, analyzing and predicting the process of land use change.     

Assessment of Tc-99 monitoring within the western Irish Sea using a numerical model

Water circulation patterns and associated material transport within a highly dynamic system such as the Irish Sea are complex phenomena. Although Tc-99 monitoring programme undertaken by the Radiological Protection Institute of Ireland provides a good insight to the material distribution on the east coast of Ireland, transport patterns within the Irish Sea have not been fully explored. In this study a validated transport model was used to hindcast transport of Tc-99 discharged from the Sellafield plant to determine extents of Tc-99 migration within the Irish Sea and reassess transit times to east coast of Ireland. Transit times are also estimated within a context of changes in meteorological conditions and fluctuations in discharges. Additionally, seasonal and inter-annual circulation patterns were examined.Relationships between discharge times and timing of far field concentrations are highly variable and are dependant on sea dynamics controlling the accumulation and removal of Tc-99 mass. Transport towards the Irish east coast, and consequently transit times, vary intra- and inter-annually, and depend on the prevailing hydrodynamic conditions resulting from meteorological conditions. The transit times from Sellafield to Balbriggan fall within the wide range of 30-240 days; with summer releases resulting in the shortest transit times. The model also indicated a strong relationship between summer concentration peaks on the east coast of Ireland and the strength of the Western Irish Gyre. Sudden increases of Tc-99 concentrations at Balbriggan coincide with peak of sea surface temperatures when the gyre is strongest and when advection is fastest.The adequacy of the current radionuclide monitoring programme within the western Irish Sea is evaluated, and recommendations are made for the development of a more optimised monitoring programme.     

Performance of a new refrigeration cycle using refrigerant mixture R32/R134a for residential air-conditioner applications

In this paper, a new refrigeration cycle (NRC) using the binary non-azeotropic refrigerant mixture R32/R134a is presented, which can be an alternative refrigeration cycle applied in residential air-conditioner. In the NRC, refrigerant circuit of the evaporator is separated into two branches. Because the non-azeotropic mixture has the characteristic of temperature glide, an important benefit of such configuration is that the Lorentz cycle can be realized. Compared with that of conventional cycle configuration, the new cycle efficiency can be improved. The calculating results show that, in the conventional refrigeration cycle (CRC), the mixture R32/R134a has a close performance to that is obtainable with pure refrigerant R22. However, the mixture R32/R134a in the NRC will result in a better performance. The maximal COP can be improved in a range of 8–9% over that of the CRC, and the volumetric refrigerating capacity can be approximately increased by 9.5%.     

Identification of land use with water quality data in stormwater using a neural network

To control stormwater pollution effectively, development of innovative, land-use-related control strategies will be required. An approach that could differentiate land-use types from stormwater quality would be the first step to solving this problem. We propose a neural network approach to examine the relationship between stormwater water quality and various types of land use. The neural network model can be used to identify land-use types for future known and unknown cases. The neural model uses a Bayesian network and has 10 water quality input variables, four neurons in the hidden layer, and five land-use target variables (commercial, industrial, residential, transportation, and vacant). We obtained 92.3 percent of correct classification and 0.157 root-mean-squared error on test files. Based on the neural model, simulations were performed to predict the land-use type of a known data set, which was not used when developing the model. The simulation accurately described the behavior of the new data set. This study demonstrates that a neural network can be effectively used to produce land-use type classification with water quality data.     

Examining spatially varying relationships between land use and water quality using geographically weighted regression I: model design and evaluation

Traditional regression techniques such as ordinary least squares (OLS) can hide important local variations in the model parameters, and are not able to deal with spatial autocorrelations existing in the variables. A recently developed technique, geographically weighted regression (GWR), is used to examine the relationships between land use and water quality in eastern Massachusetts, USA. GWR models make great improvements of model performance over OLS models, which is proved by F-test and comparisons of model R² and corrected Akaike Information Criterion (AIC_c) from both GWR and OLS. GWR models also improve the reliabilities of the relationships by reducing spatial autocorrelations. The application of GWR models finds that the relationships between land use and water quality are not constant over space but show great spatial non-stationarity. GWR models are able to reveal the information previously ignored by OLS models on the local causes of water pollution, and so improve the model ability to explain local situation of water quality. The results of this study suggest that GWR technique has the potential to serve as a useful tool for environmental research and management at watershed, regional, national and even global scales.     

On the treatment of finite element structures in stochastic linear dynamics using a mode-based meta-model

This contribution presents a meta-model of the structural modes, which can be used for efficient dynamic analysis of finite element structures in the presence of uncertainties in the loading and structural parameters. The main goal consists in cutting down the computational time required for the repeated number of analyses in order to assess the impact of the uncertainties. The meta-model allows for an efficient evaluation of the structural modes which are represented as a linear combination of the modes of a reference solution. The calibration of the meta-model is based on the modes of about 30-50 samples of the structure obtained with Monte Carlo simulation. The mode-based meta-model can always be advantageously employed when a limited number of modes are required for analysis, such as, e.g. for mode superposition analysis. The numerical examples show the utilization of the meta-model for various applications such as uncertainty analysis, reliability assessment and design optimization.     

Improving spatial concentration estimates for nitrogen oxides using a hybrid meteorological dispersion/land use regression model in Los Angeles, CA and Seattle, WA

Predictions from a simple line source dispersion model, Caline3, were included as a covariate in a land use regression (LUR) model for NO_X/NO₂ in Los Angeles, CA and Seattle, WA. The Caline3 model prediction assumed a unit emission factor for all roadway segments (1.0 g/vehicle-mile). The NO_X and/or NO₂ measurements for LA and Seattle were obtained from a comprehensive measurement campaign that is part of the Multi-Ethnic Study of Atherosclerosis Air Pollution Study (MESA Air). The measurement campaigns in both cities were approximately 2 weeks in duration employing approximately 145 measurement sites in Greater LA and 26 sites in Seattle. The best “standard” LUR model (obtained without the inclusion of the Caline3 predictions) in LA had R² values of 0.53 for NO_X and 0.74 for NO₂. The leave-one-out cross-validated R² values for NO_X and NO₂ were 0.45 and 0.71, respectively. The equivalent “standard” NO₂ model for Seattle had an R² of 0.72 and a leave-one-out cross-validated R² of 0.63. When the Caline3 variable was included in the LA hybrid model, the R² values were 0.71 and 0.79 for NO_X and NO₂, respectively. The corresponding cross-validated R² values were 0.66 and 0.77, for NOX and NO2, respectively. In Seattle, the inclusion of the Caline3 variable resulted in a NO₂ model with an R² of 0.81 and a corresponding cross-validated R² of 0.67. In LA, hybrid model performance was not affected by excluding roadways with annual average daily traffic volumes (AADT) < 100,000. When the Caline3 predictions for heavy-duty trucks and lighter-duty vehicles were modelled as separate terms, the estimated fleet average NO_X emission factors were 8.9 (SE = 0.7) and 0.16 (SE = 0.12) grams NO_X/vehicle mile for heavy-duty and lighter-duty vehicles, respectively. These values are consistent with fleet average emission factors computed for LA with EMFAC 2007.     

Predicting performance of EPB TBMs by using a stochastic model implemented into a deterministic model

The current study is an attempt to address the stochastic nature of the rock excavation process by suggesting a stochastic performance prediction model implemented into a deterministic model developed for hard rock TBMs. Full-scale linear cutting experiments using constant cross-section and V-type of disc cutters are performed on two different limestone samples to provide the basic input required for the deterministic model used for estimation of instantaneous penetration rate, daily advance rate, thrust and torque requirements of TBMs. Stochastic estimation is performed by using a Monte Carlo simulation program by applying iterations to implement the probabilistic distribution of each model parameter and provide knowledge of a confidence level. Results of the suggested model are verified by measuring the field performance of two earth pressure balance (EPB) TBMs excavating competent rocks in semi-closed mode. The results indicate that the suggested model works well for prediction of instantaneous cutting/penetration rate for both TBMs and both types of disc cutters. However, an improvement on the model is required for estimation of cutterhead torque and thrust of EPB TBMs. The stochastic model implemented into the deterministic model results in almost similar predictions with the deterministic model in 50% (best guess) probability. However, the stochastic modeling provides a tool for exploring the full implications of linear cutting experiments and allows assessing the probability of occurrence and predicting variations of the TBM performance parameters, covering the uncertainties/risks.     

城镇化背景下基于时序TM/NDVI的建成区检测方法——以福州市为例

对1990—2010年49景福州市TM时间序列影像进行处理,采用MODTRAN4+模型进行大气校正,得到研究区土地覆盖类型的NDVI值的多时相轨迹图。分析城镇化背景下建成区的变化特征和NDVI时间序列数据的季节特征,添加耕地发展为建设用地的地物特征到学习样本,比较不同数据组合对最大似然法、支持向量机、神经网络法、面向对象法对分类和检测城镇化背景下建设用地精度的影响,以及比较添加样本特征后对城镇化进程中建设用地检测方法的影响。结果表明,对于小样本数据集,面向对象法具有最高的分类精度,不同的数据组合与不同季节对面向对象法分类精度的影响分别达3.49%和5.22%,引入NDVI时间序列数据和添加变化地物的学习样本,总体分类精度提高了3.54%,建设用地的制图精度提高了4.24%。     

国家游泳中心子结构模型往复水平加载试验

为研究国家游泳中心“水立方”内墙受力最大部分的抗震性能,而完成了1/3缩比的子结构模型在重力荷载代表值和往复水平力作用下的试验。子结构模型的破坏形态为杆件局部屈曲和杆件母材拉断,破坏的杆件主要位于墙面上;杆端贴板使塑性铰和破坏位置转移至杆件上,避免了杆件与节点连接焊缝的破坏。子结构模型受推和受拉的名义屈服位移角分别为1/127及1/100左右,水平荷载分别为880kN和746kN,为顶部附加重量的1.73倍和1.47倍;加载至水平力为附加重量的2倍以上、顶点位移角为1/50左右,承载力尚未下降,水平位移仍能增大。试验结果表明,子结构模型具有大的承载能力、良好的变形能力和耗能能力。     

Fire detection in video sequences using a generic color model

In this paper, a rule-based generic color model for flame pixel classification is proposed. The proposed algorithm uses YCbCr color space to separate the luminance from the chrominance more effectively than color spaces such as RGB or rgb. The performance of the proposed algorithm is tested on two sets of images, one of which contains fire, the other containing fire-like regions. The proposed method achieves up to 99% fire detection rate. The results are compared with two other methods in the literature and the proposed method is shown to have both a higher detection rate and a lower false alarm rate. Furthermore the proposed color model can be used for real-time fire detection in color video sequences, and we also present results for segmentation of fire in video using only the color model proposed in this paper.     

Building temperature regulation using a distributed model predictive control

This paper presents a predictive control structure for thermal regulation in buildings. The proposed method exploits the intermittently operating mode of almost all types of buildings. Usually the occupation profile can be known in advance and this fact will be used to reduce the energy consumption without decreasing the thermal comfort during the occupation. For that purpose, the predictive control strategy is first presented for a single zone building then extended to a multizone building example. Two opposite control strategies commonly exists: the decentralized control structure, which does not offer good performances especially when the thermal coupling among adjacent rooms is not negligible, and on the other hand, the centralized control for which the computational demand grows exponentially with the size of the system, being very expensive for large scale buildings. Our solution is based on a distributed approach which takes the advantages of both methods mentioned above. A distributed MPC algorithm with one information exchange per time step is proposed with good control performances and low computational requirements. Simulations and a comparison performance table end the article.