Assessment of convective heat transfer coefficient and mass of water evaporated from a single-slope passive solar still by different thermal models: an experimental validation

In this paper, an effort has been made to evaluate the convective heat transfer coefficient and mass of water evaporated from a single-slope passive solar still for different water depths (0.01, 0.015, 0.02 and 0.025?m) by various thermal models, namely Dunkle’s model, Chen et al.’s model, Clark’s model, Adhikari et al.’s model, Kumar and Tiwari’s model, Zheng Hongfei et al.’s model and Tsilingiris’s model. These models were studied and compared with our experimental work. Also, the energy and exergy efficiency were calculated and the percentage deviation between experimental and theoretical prediction is also listed out. The experimental validation of energy and exergy efficiency of single slope passive solar still using different thermal models was carried out at Chennai, Tamil Nadu, India. By comparing the theoretical values of the hourly yield with the experimental data it was found that Dunkle’s, Chen et al.’s and Tsilingiris’s models gave better conformity between the forecasted and experimental results. Kumar and Tiwari’s model yield is very high, whereas Clark’s model, Adhikari et al.’s model and Zheng Hongfei et al.’s model predict lower values as compared with our experimental results.     

Simulating one of the CIB W14 round robin test cases using the SMARTFIRE fire field model

Numerical predictions produced by the SMARTFIRE fire field model are compared with experimental data. The predictions consist of gas temperatures at several locations within the compartment over a 60 min period. The test fire, produced by a burning wood crib attained a maximum heat release rate of approximately 11 MW. The fire is intended to represent a non-spreading fire (i.e. single fuel source) in a moderately sized ventilated room. The experimental data formed part of the CIB Round Robin test series. Two simulations are produced, one involving a relatively coarse mesh and the other with a finer mesh. While the SMARTFIRE simulations made use of a simple volumetric heat release rate model, both simulations were found capable of reproducing the overall qualitative results. Both simulations tended to over-predict the measured temperatures. However, the finer mesh simulation was better able to reproduce the qualitative features of the experimental data. The maximum recorded experimental temperature (1214°C after 39 min) was over-predicted in the fine mesh simulation by 12%.     

Kinetic modeling of U.V. disinfection of water

The response of pure cultures of Escherichia coli, Candida parapsilosis, and bacterial virus f2 to ultraviolet light radiation was studied in a batch reactor and in a completely mixed, flow-through annular reactor. Two kinetic models were tested as to their ability to scale between batch results and flow-through reactor results. Using the respective best-fit kinetic parameters for each model from batch data, the response of the organisms in the flow-through reactor could be predicted by using either multi-target kinetics or series-event kinetics. The series-event model was judged to be superior to multi-target kinetics because it better represents the known mechanism of u.v. inactivation than does multi-target kinetics. Since two different models can be used to describe the data, the simple agreement between experimental data and model predictions does not necessarily prove that either model is mechanistically correct.     

A Comprehensive Validation of Two Airflow Models — COMIS and CONTAM

Abstract Several airflow and contaminant dispersion models have been developed to study air distribution in buildings. This paper reports the results of a comprehensive validation of two models: COMIS and CONTAM. The validation process was carried out at three different levels; inter-program comparison; validation with experimental data which was collected in a controlled environment; and finally, validation with field measurement data. At the inter-program level, the airflow rates and pressure values predicted by COMIS and CONTAM for a four-zone paper building were compared with the airflow rates and pressures predicted by CBSAIR, AIRNET and BUS. The results show good agreement between these software programs. The second level of validation compares the models’ predictions with measured data collected in a controlled environment. Fan pressurisation, smoke and tracer gas tests were conducted to estimate the permeability of building envelope components, to locate cracks, and to determine the interzonal airflow rates between rooms. The results confirm that there is good agreement between predictions made by COMIS and CONTAM; there are, however, some differences between these models’ predictions and the measured data. The predictions made by these models were also compared with the results of a tracer gas measurement carried out in a residential building. The predicted and measured values were in good agreement.     

Examining the applicability of empirical models using short-term VOC emissions data from building materials to predict long-term emissions

Chamber testing is a common method to evaluate volatile organic compound (VOC) emissions from building materials. Empirical models based on short-term testing (typically less than 28 days) are frequently used to estimate long-term emissions (up to years). However, the applicability of the empirical models for long-term prediction remains unclear in practice. Four empirical models, i.e., two constant models with and without a prerequisite (M1 and M2), a power-law model (M3), and an exponential model (M4), were used to test the applicability of predicting year-long emissions using emission data that were less than one month. The diffusion-based mass-transfer model was used to generate reference emission data with random variations involved to represent measurement errors, etc. For M1 and M2, the discrepancy ratios between the constant emissions and the characteristic average emissions are quantified. For M3 and M4, an additional measure, i.e., normalized mean square error (NMSE), was adopted to statistically study the applicability of using empirical models to predict long-term emissions. The results shown that, first, the NMSE values indicate that M3 prefers slow emissions and generally performs better than M4. However, M4 performs better for predicting year-long emissions for cases with characteristic emission time of one year. Second, both M3 and M4 predict the average life-long emissions reasonably well for most scenarios. Third, while the effects of test duration are less significant for M3 than M4, the early-stage sampling points are more important for better long-term predictions. Additionally, experimental data by National Research Council Canada (NRC) were used to validate the applicability of the empirical models in year-long emission predictions, with the results similar to those from the simulated data. This paper can be used as a reference to select appropriate empirical model(s), as well as the testing duration, to simulate long-term VOC emissions from building materials using short-term testing data.     

Thermal sensation and comfort models for non-uniform and transient environments, part II: Local comfort of individual body parts

A three-part series presents the development of models for predicting the local thermal sensation (Part I) and local comfort (Part II) of different parts of the human body, and also the whole-body sensation and comfort responses (Part III). The models predict these subjective responses to the environment from thermophysiological measurements or predictions (skin and core temperatures). The models apply to a range of environments: uniform and non-uniform, transient and stable. They are based on diverse results from literature and from body-part-specific human subject tests in a climate chamber. They were validated against a test of passengers in automobiles. This series is intended to present the rationale, structure, and coefficients for these models so that others can test them and develop them further as additional empirical data becomes available. The experimental methods and some measured results from the climate chamber tests have been published previously.Part II describes a thermal comfort model with coefficients representing 19 individual local body parts. For each part, its local comfort is predicted from local and whole-body thermal sensations. These inputs are obtained from the sensation models described in Part I and III, or from measurements.     

碳纤维—硫铝酸盐水泥复合材料的制备及其性能研究

本文以硫铝酸盐水泥为基体材料,以改性后的碳纤维为功能导电组分,采用浇筑成型制备出碳纤维—水泥基导电复合材料,并对该种材料的力电性能进行了研究。扫描电子显微镜分析发现,改性后的碳纤维表面纤维层部分被腐蚀,并在表面出现了凹坑。能谱分析发现,改性后碳纤维表面成分中多了钾、钠、钙等元素。该复合材料的抗压强度随碳纤维的掺入量增加而增加。当掺入量为0.6%时,强度达到最大值。试样的电阻率随碳纤维掺入量增加而逐渐降低,当掺入量达到0.8%时,其电阻率达到最小值。其中,重铬酸钾改性后碳纤维的性能优于高锰酸钾改性后碳纤维的性能。     

Mechanism of phenol adsorption onto electro-activated carbon granules

The main purpose of this paper is to determine the mechanisms which govern the adsorption of the phenol onto electro-activated carbon granules. This new activation technique allowed an increase of the performance of the adsorbent. Two models were utilised to understand the improvement in the performance of electroactivated carbon granules. The first, a simple external resistance model based on film resistance, gave acceptable predictions, with an error of less than 15%, between the theoretical results and experimental data independent of the activation potential and phenol initial concentration. The second linear model, based on diffusion phenomena, was more representative in describing the experiment than the first model. It was observed that the electro-activation method did not change the mechanism which governs phenol adsorption onto granular carbon. Indeed, the same mathematical model based on diffusion phenomena made it possible to predict with a very low error (less than 5%) the experimental data obtained for the favourable activation potential, without activation potential and with an unfavourable activation potential. The electro-activation technique makes it possible to increase the number of active sites that improve the performance of the electro-activated granular carbon compared with conventional granular activated carbon.     

Analytical prediction of transfer length in prestressed self-consolidating concrete girders using pull-out test results

While self-consolidating concrete (SCC) is comparable to conventional concrete (CC) in terms of strength, the comparability of SCC’s bond to steel is less well-defined. A keen understanding of SCC’s bond strength is essential to advance SCC within the prestressed concrete industry. This study presents an analytical method for predicting the transfer length of steel strands in prestressed girders using pull-out test results. The experimental data from a series of 56 pull-out tests is utilized to derive bond stress–slip relationships for 12.7 mm steel strands embedded in SCC and CC. Modification factors are used to correlate pullout bond stresses to transfer bond stresses in prestressed members, and the modified relationships are integrated in three-dimensional finite element models to predict transfer lengths in prestressed SCC girders. The analytical predictions correlate well with experimental results and transfer length requirements of current US design codes.     

Estimating moment capacity of ferrocement members using self-evolving network

In this paper, an empirical model based on self-evolving neural network is proposed for predicting the flexural behavior of ferrocement elements. The model is meant to serve as a simple but reliable tool for estimating the moment capacity of ferrocement members. The proposed model is trained and validated using experimental data obtained from the literature. The data consists of information regarding flexural tests on ferrocement specimens which include moment capacity and cross-sectional dimensions of specimens, concrete cube compressive strength, tensile strength and volume fraction of wire mesh. Comparisons of predictions of the proposed models with experimental data indicated that the models are capable of accurately estimating the moment capacity of ferrocement members. The proposed models also make better predictions compared to methods such as the plastic analysis method and the mechanism approach. Further comparisons with other data mining techniques including the back-propagation network, the adaptive spline, and the Kriging regression models indicated that the proposed models are superior in terms prediction accuracy despite being much simpler models. The performance of the proposed models was also found to be comparable to the GEP-based surrogate model.     

ETFE薄膜气枕模型试验研究

设计制作了2个不同矢跨比的ETFE薄膜气枕模型,进行了气枕形状测试、加压以及铺砂加压试验,得到了气枕形状坐标以及膜面随气压的变形量。对ETFE薄膜进行单向拉伸试验,测得材料的屈服强度、切线弹性模量和割线弹性模量。利用几何非线性有限元进行了数值分析,将数值结果与试验值进行了比较分析。结果表明:ETFE薄膜可按各向同性材料分析,数值计算结果与试验值吻合较好;在同样内压作用下,气枕矢高较高时膜面位移与膜面应力较小;ETFE薄膜采用割线弹性模量计算得到的膜面位移与试验值比较吻合,采用切线弹性模量计算得到的变形小于试验值。     

Leveraging the analysis of parametric uncertainty for building energy model calibration

Calibrated energy models are used for measurement and verification of building retrofit projects, predictions of savings from energy conservation measures, and commissioning building systems (both prior to occupancy and during real-time model based performance monitoring, controls and diagnostics). This paper presents a systematic and automated way to calibrate a building energy model. Efficient parameter sampling is used to analyze more than two thousand model parameters and identify which of these are critical (most important) for model tuning. The parameters that most affect the building’s energy end-use are selected and automatically refined to calibrate the model by applying an analytic meta-model based optimization. Real-time data from an office building, including weather and energy meter data in 2010, was used for the model calibration, while 2011 data was used for the model verification. The modeling process, calibration and verification results, as well as implementation issues encountered throughout the model calibration process from a user’s perspective are discussed. The total facility and plug electricity consumption predictions from the calibrated model match the actual measured monthly data within ±5%. The calibrated model gives 2.80% of Coefficient of Variation of Root Mean Squared Error (CV (RMSE)) and ?2.31% of Normalized Mean Bias Error (NMBE) for the whole building monthly electricity use, which is acceptable based on the ASHRAE Guideline 14–2002. In this work we use EnergyPlus as a modeling tool, while the method can be used with other modeling tools equally as well.     

Modelling of emission of volatile organic compounds from building materials—estimation of gas-phase mass transfer coefficient

A mathematical model is developed to predict Volatile Organic Compound (VOC) emission rates from homogeneous materials. The model considers both mass diffusion and mass convection processes in the boundary layer between the material surface and the air flow. Establishing the relationship between the surface air flow and emission rate; the model, therefore can predict the material emission rate under different environmental conditions. The other feature of the model is that all the parameters have clear physical meaning and can be either found in literature or calculated using known theories and/or equations.The prediction of the mathematical model was validated at three different levels; with experimental results from the CBS specially designed test chamber, with experimental results from the EPA which were carried out in an ASTM chamber, and finally with the predictions made by other models. The results indicate that there is, in general, good agreement between the model predictions and the experimental results. The main advantage of this model is that the model does not require any experimental data as input.     

An improved theoretical model of a metal tube under free external inversion

Due to the long stroke and stable reaction force, circular tubes under inversion are always adopted in the design of energy absorbers. Although a few studies were conducted, the existing theoretical predictions on the knuckle radius and the reaction force of the circular tube during its free inversion displayed large discrepancy from the experimental observations [1], [2], [3].In this paper, currently existing two-dimensional theoretical models of the free inversion tube are carefully reviewed. After realizing the defects of these models, Reddy׳s model [2] is improved by adopting the appropriate external work rate and material yield condition, for tubes made of rigid, perfectly plastic material. In order to validate the improved theoretical model, experiments of the free inversion of circular aluminum tubes, and finite element simulations of tubes made of the elastic, perfectly plastic material are all carried out. It is confirmed that compared with the experimental data and the FE simulations, the current model provides more accurate predictions on both the knuckle radius and the steady inversion load for the quasi-static free inversion of circular tubes.     

Prediction of shear strength of steel fiber RC beams using neural networks

This paper presents the development of artificial neural network models for predicting the ultimate shear strength of steel fiber reinforced concrete (SFRC) beams. Two models are constructed using the experimental data from the literature and the results are compared with each other and with the formula proposed by Swamy et al. and Khuntia et al. It is found that the neural network model, with five input parameters, predicts the shear strength of beams more closely than the network with four input parameters. Moreover, the neural network models predict the shear strength of SFRC beams more accurately than the above-mentioned formulas. Further, the accuracy of predicted results is found not biased with concrete strength, shear span to depth ratio and the beam depth. Limited parametric studies show that the network model captures the RC beam’s underlying shear behavior very well.     

Modelling of geosynthetic-reinforced column-supported embankments using 2D full-width model and modified unit cell approach

Soil-cement deep mixing (DM) columns combined with geosynthetic basal reinforcement are an accepted technique in geotechnical engineering to construct road and railway embankments over soft foundations. Both full-width and unit cell models have been used to numerically simulate the performance of geosynthetic-reinforced and column-supported (GRCS) embankments. However, the typical unit cell model with horizontally fixed side boundaries cannot simulate the lateral spreading of the embankment fill and foundation soil. As a result, the calculated reinforcement tensile loads using typical unit cell models are much less than those from matching full-width models. The paper first examines GRCS embankments using a full-width model with small- and large-strain modes in FLAC and then compares the calculated results from the full-width model with those using a typical unit cell model, a recently proposed modified unit cell model, and a closed-form solution. The paper also examines the influence of the soft foundation soil modulus, reinforcement tensile stiffness, and DM column modulus on the reinforcement tensile loads. Numerical analyses show that the reinforcement tensile loads from the modified unit cell model are in good agreement with those from the full-width model for zones under the embankment crest for all cases and conditions examined in the paper. Both the full-width model and modified unit cell model perform better than the typical unit cell model for the prediction of the reinforcement tensile load when compared to the closed-form solution. However, while the modified unit cell developed by the writers is shown to be more accurate than the typical unit cell when predictions are compared to results using full-width numerical simulations, the benefit of using this approach to reduce computation times may be limited in practice.     

A simple model for screening the local impacts of atmospheric ammonia

The dry deposition of ammonia from the atmosphere to the surface can lead to eutrophication of sensitive ecosystems and acidification of the soil. A large proportion of the ammonia emitted from agricultural sources can be deposited within a few kilometres and, therefore, impacts of ammonia dry deposition often occur near to the source. To assess these impacts, short-range atmospheric dispersion models are often applied to simulate the emission, dispersion and deposition of ammonia. However, these models can be time-consuming to run and often require detailed input data and, therefore, for multiple assessments it is useful to have a method of screening to discard scenarios where impacts are expected to be negligible. The SCAIL model (Simple Calculation of Ammonia Impact Limits) has been developed for this purpose. SCAIL estimates the atmospheric concentration and dry deposition at the nearest edge of a sensitive ecosystem (receptor) downwind of an ammonia source. These estimates are calculated based on simple meteorological data, the emission rate of the source, land cover type and distance to the receptor. Analysis of the model predictions showed that uncertainty in the model input data leads to an uncertainty in concentration and dry deposition estimates of 25–30% and 40–45% respectively. Detailed atmospheric dispersion models will also have similar uncertainties since they use similar types of input data. Comparison of the concentration predictions with previous measurements made around eight farms showed that the model significantly underestimated concentrations although the model performance was similar to existing screening techniques. The measurement dataset was used to calibrate the SCAIL model which subsequently performed better, using independent verification data, than existing models calibrated in a similar way. The benefits of the SCAIL model are already being seen in the UK, where it is used to screen farms for potential impacts on statutory nature conservation areas.     

Effects of roughness blocks on atmospheric boundary layer flow over a two-dimensional low hill with/without sudden roughness change

Wind tunnel experiments were carried out to investigate the effects of roughness blocks on the atmospheric boundary layer flow over a two-dimensional low hill with maximum slope 0.21. Roughness blocks whose heights were one eighth of the hill height were arranged in a staggered pattern with a roughness density of 4.1% to model the rough conditions. Four situations with/without roughness change in the flow direction, i.e. a smooth hill in smooth flow, a rough hill in rough flow, a smooth hill in rough flow and a rough hill in smooth flow, were considered. The effects of the roughness blocks were clarified by comparing the flow characteristics over hill models, with emphasis on speedup ratio and turbulence structure. Experimental results were compared with the predictions obtained from linear models. The results show that speedup ratio depends strongly on the surface condition in the middle layer, the inviscid but rotational part of the outer layer defined by HLR theory [Hunt, J.C.R., Leibovich, S., Richards, K.J., 1988. Turbulent shear flow over low hills. Quart. J. Roy. Meteorol. Soc. 114, 1435-1471]. Adding or removing roughness blocks on the hill surface or inflow area changes the velocity deficit and creates a completely different turbulence structure in the wake.     

基于两相复合材料的再生混凝土弹性模量预测模型

与采用再生混凝土抗压强度计算弹性模量的公式相比,基于两相复合材料的再生混凝土弹性模量预测模型能更好地反映再生粗骨料对混凝土弹性模量的影响,预测结果更为精确。但现有再生混凝土两相复合材料弹性模量预测模型是由再生粗骨料与砂浆的弹性模量按体积加权平均得到的,该种计算方法预测结果偏于保守。目前针对天然骨料混凝土已提出多种两相复合材料弹性模量预测模型,但各模型预测结果有较大差异。基于上述模型,考虑再生粗骨料中残余砂浆的影响,推导得到了6种再生混凝土弹性模量预测模型,并搜集了100组再生混凝土弹性模量试验数据,用于评价各模型的可靠性。在此基础上,统计分析了模型不确定性及其与关键参数之间的相关性,为后续再生混凝土构件与结构的可靠度分析提供统计数据。研究表明,在常见参数范围内,各模型预测结果之间最大可相差23.3%;所提出的BNC(RAC)模型预测精度最高,其预测结果与试验结果比值的均值为1.014,变异系数为8.2%,该模型的不确定性服从正态分布。     

填充墙对框架结构抗侧刚度的影响分析

建立12个带填充墙的10层框架结构有限元模型,研究不同地震波作用下填充墙的材料、空间布置、各层墙数量等因素对结构抗侧刚度的影响。分析表明:填充墙分别采用标准砖、空心砖、加气混凝土砌块时,"薄弱层"刚度突变的影响逐渐减小,但不宜忽略不计;某层填充墙截面面积与其相邻上层填充墙截面面积之比γw≤45%时,应将该层视为薄弱层,实际设计过程中建议γw最小取60%;在数值分析基础上,提出了考虑填充墙影响的框架结构层间抗侧刚度计算模型。