Analysis and assessment of a nanoparticle seeded small scale absorption refrigeration system driven by a low‐grade waste heat source

The thermoeconomic behaviour of a nanoparticle seeded single effect LiBr‐H₂O absorption refrigeration system (ARS) is investigated for a small scale application. In the proposed method, alumina nanoparticles with volume concentrations of 3%, 5%, and 7% are dispersed into an aqua lithium bromide solution. The multiobjective heat transfer search algorithm is employed to examine the design trade‐off between the coefficient of performance (COP) and total annualized cost (TAC). To analyze the overall performance of the system, the influence of five design parameters, namely the temperatures of the generator, absorber, evaporator, condenser and heat exchanger pipe diameter, are studied. It is found that with an increase in the COP, the TAC of the system is initially raised marginally, and after that, raised rigorously with further increment. The comparative results indicate that the COP and TAC of the nanofluid based ARS system are increased by about 7% and decreased by about 3.2%, respectively, corresponding to the Pareto points of the base ARS system. A lower break‐even point of about 2.6 years is achieved for the ARS system containing nanoparticles compared to the base ARS system. Overall, the ARS system containing 5% nanoparticles is the best solution from a thermodynamic and economic point of view.     

Stability of electronic nose (e-nose) as determined by considering date-pits heated at different temperatures

Variability and sensitivity of a portable electronic nose (32 sensors) was assessed by considering different variables for measurement (i.e., reference, standard, first and second purges, sample draw time, waiting time for the volatiles released in the headspace, and mass of sample or headspace volume of the jar containing sample). In this study, dried date-pits were used a model sample. The highest stability was achieved when both reference and standard (i.e., dried date-pits, no heating treatment) were used before test sample measurement. Higher sample draw time more than 10 s significantly decreased the stability, whereas optimum second purge was observed at 50 s. Optimum time to generate volatile was observed as 24 h. A sample of 100 g increased the signal intensity compared to the 50 g sample for the 60°C and 100°C treated samples, while an opposite trend was observed for the 150°C treated sample. Finally, the responses of volatile components in date-pits heated at different temperatures (60°C, 100°C, and 150°C) were measured using the optimum operating conditions. Principal component analysis explored the relationships between the volatile features and classified date-pits heated at different temperatures. The results showed that an electronic nose was able to classify date-pits based on their volatile components generated by different degrees of heating (93.3% accuracy).     

Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic,Wind Turbine,and Battery Systems

Renewable energy sources (RESs) are considered to be reliable and green electric power generation sources. Photovoltaics (PVs) and wind turbines (WTs) are used to provide electricity in remote areas. Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment. The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution. This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization (TLBO) named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system to satisfy the consumer’s load at minimal total annual cost (TAC). The reliability of the system is considered by a maximum allowable loss of power supply probability (LPSP_max) concept. The results obtained from the JLBO algorithm are compared with the original Jaya, TLBO, and genetic algorithms. The JLBO results show superior performance in terms of TAC, and the PV–WT–battery hybrid system is found to be the most economical scenario. This system provides a cost-effective solution for all proposed LPSP_max values as compared with PV–battery and WT–battery systems.     

How vloggers embrace their viewers: Focusing on the roles of para-social interactions and flow experience

Social media allow marketers to directly influence consumers through a diverse set of tools. The characteristics of individual platforms enable the development of creative and innovative strategies for social media marketing. This study aims at examining para-social interactions (PSI), their antecedents (i.e., attitude homophily and physical attractiveness), and their consequences (i.e., consumers’ sense of belonging, flow experience, urge to purchase, addiction, and impulse purchase). The mediating role of flow experience is also examined in this study. Findings, implications, and directions for further research are also presented in this study.     

大涡模拟Smagorinsky模型用于磨粒流精密加工喷嘴的质量控制研究

为研究固液两相磨粒流加工喷嘴小孔过程中的流场分布、涡旋形成规律及涡旋的存在对磨粒流加工的影响机制，采用Smagorinsky亚格子模型对磨粒流加工喷嘴小孔的流道进行大涡数值模拟，并使用磨粒流对变直径喷嘴工件进行加工试验。数值模拟发现磨粒流流体中磨粒与壁面的碰撞与剪切作用随流体的速度增大而增大，同一截面的速度存在速度差，其中还伴随涡旋的存在；通过试验研究发现：经固液两相磨粒流加工后的喷嘴小孔表面质量得到明显提高，喷嘴经过四次不同入口速度的磨粒流加工后大孔处表面粗糙度Ra由1.24 μm降至0.542 μm，小孔处表面粗糙度Ra由1.21 μm降至0.437 μm。结论显示固液两相磨粒流加工技术可有效提高被加工喷嘴工件的内表面质量，加工时同一截面的速度存在速度差，速度差的存在利于涡旋的形成，涡旋的存在利于提高磨粒流加工过程的剪切作用，有助于获得高质量的喷嘴小孔内通道表面。     

浅析混凝土搅拌站的防雷

近年来,我市因雷击而导致搅拌站系统瘫痪停产、影响工程进度的事件时有发生,在针对混凝土搅拌站配料秤的实际检定工作中,也常发现配料秤的称重传感器因雷击损坏而影响计量准确性。因此,对混凝土搅拌站防雷的研究具有重要意义。本文阐述了雷电对混凝土搅拌站的主要危害,分析了混凝土搅拌站常见的雷击原因及其防护措施,旨在引起生产方、安装方以及使用方对混凝土搅拌站防雷的共同重视。     

大坝服役非概率可靠性分析方法

针对大坝服役可靠性影响因子具有非概率不确定特征的情况,建立了定量刻画大坝服役可靠性非概率影响因子的Info-gap模型,运用Info-gap决策理论和体积比非概率可靠性度量方法,提出了大坝服役非概率可靠性分析方法,拟定了大坝服役非概率目标可靠度。应用提出的非概率方法分析了某大坝结构服役可靠性,各功能模式可靠性分析结果说明了该方法的工程适用性,同时也验证了所拟定大坝服役非概率目标可靠度的合理性。     

Quality assurance and quality control of subgrade compaction using the dynamic cone penetrometer

The Dynamic Cone Penetrometer (DCP) is a device that is used in the construction industry for the assessment of in situ soil compaction quality. Over the past few decades, numerous correlations have been developed between the DCP test results and soil strength and stiffness parameters. This paper proposes a comprehensive set of criteria and recommendations for quality control (QC) of compacted subgrade that take into account the inherent statistical variability of DCP test results. For the development of the QC criteria, a new statistical methodology is used to extract representative test values from the raw field DCP test data. In order to use the proposed QC criteria, soils are first classified into two broad categories (fine-grained and coarse-grained) depending on their fabric and response to compaction efforts. Test results indicate that (i) for fine-grained soils, the DCP test values have good correlation with the plasticity index (PI), which is indicative of the type and amount of clay content of the soil and (ii) for coarse-grained soils, the DCP test values have good correlation with the optimum water content of the soil, which is directly related to its in situ density. DCP blow count correlation equations are presented for both soil categories. Recommendations for field DCP testing and data analysis are also provided to highlight the significance of the statistical distribution of the DCP test results in QC testing of compacted subgrade.     

Data quality and uncertainty assessment methodology for pavement LCA

In performing pavement life cycle assessment (LCA), users are facing various reports of energy intensity coefficient (EIC) of pavement materials which differ considerably among data sources and therefore alter the LCA results significantly. Instead of selecting a certain EIC without or of little explanation for the current pavement LCA practices, this study proposed a methodology to build probability density function (PDF) for EIC based on available data-set and their qualities. Each data was first evaluated about the data quality indicator (DQI) through data quality pedigree matrix and converted to PDF in modified Beta distribution form. Three weighting methods, the DQI one, coefficient of variation (COV) one and analytical hierarchy process (AHP) one, were developed to assign weightings for different data. Monte Carlo simulation was run with the weighted PDF of each data as input to obtain the ultimate PDF for EIC. A case study to estimate the bitumen’s EIC with eight data samples were performed using the proposed methodology. It is found (1): the estimates by the proposed methodology is of higher reliability (lower COV) compared to any single data due to utilisation of information of the overall data samples; (2) the AHP weighting method is most favoured despite the results of the three weighting methods are close; (3) the central estimates of bitumen’s EIC are between5.4~5.8 MJ/kg. The proposed methodology is helpful in aiding calculating EICs for pavement materials and capturing uncertainties in LCA results in a statistical sense.     

Development of the method of production of the ultrafine macrohomogeneous composite powder

ABSTRACT

A method of ultrafine macro-homogeneous composite powder – B₄C–ZrO₂ production using a planetary mill was developed. From the macro-homogeneous composite high-density ceramics, B₄C–ZrB₂ was produced by the method of reactive sintering (in situ) at 2000°C under the pressure of 41–42?MPa. The effect of ZrO₂ grain size and of its distribution in the matrix on the consolidation parameters, and the microstructure of the obtained ceramics was studied.