A robust method for collecting and processing the on-road instantaneous data of fuel consumption and speed for motorcycles

ABSTRACT

Using the laboratory-based fuel consumption models for predicting real-world fuel consumption requires the measurement of data under certain conditions to obtain high accuracy of predicted result. Therefore, it is necessary to develop a logging device for measuring the real-time fuel consumption and speed of vehicle on the road. This article presents a study on developing the on-board data logging device to collect real-world data of fuel consumption and speed for motorcycles with the update rate of 1 Hz. The instantaneous speed of the motorcycle was determined based on the rotational speed of the wheel and the wheel radius. Another module was used to determine the instantaneous fuel consumption rate (FR) though measuring the duration injection pulse. The relationship between the duration injection pulse and the injected amount of fuel was established with high correlation coefficient of 0.997. In addition, a filter was designed to remove noise in the dataset collected using the data logging device. The random errors in the speed and the FR profiles were detected and replaced, the percentage of these errors is 1.8% and 2.4%, respectively. The developed method is a precise one for transient fuel consumption and speed measurement. In chassis dynamometer test, the average deviation between steady speed measured by the chassis and the data logging device is only approximately 0.35%. At transient state, the biggest deviation between these two datasets is less than 3.5%. The average FR at steady speed measured by the developed method is slightly different from the one measured by the carbon balance method. The difference is 0.9%, 2.5%, and 0.25% at the speeds of 30 km/h, 50 km/h, and 70 km/h, respectively. Following the transient test cycle, the fuel consumption measured by the developed method is 4.35% lower than that determined by the carbon balance method.

Implications: A robust method for collecting and processing the on-road instantaneous data of fuel consumption and speed was developed for motorcycles. The proposed method can record well the real-world driving data for motorcycles, including the fuel consumption and speed, with the update rate of 1 Hz. The filter was designed to minimize noise while maintaining data integrity of the collected dataset, the percentage of errors in the the speed and the FR profiles is 1.8% and 2.4%, respectively. The proposed method, therefore, can be used as effective tools for future studies relating to the fuel consumption and emission of motorcycles on the road.     

Performance improvement of a double-pass V-corrugated solar air heater under recycling operation

The device performance of double-pass V-corrugated solar air heaters with external recycle was investigated experimentally and theoretically. The comparison between V-corrugated and flat-plate collectors was made to show the thermal efficiency improvement with various operating parameters. The results show that the collector efficiency improvement of the recycling double-pass V-corrugated operation is much higher than those of the other configurations under various recycle ratios and mass flow rates. However, there exists the penalty on the power consumption increment due to implementing V-corrugated channel into the solar air heaters, an economic consideration on both the heat-transfer efficiency enhancement and power consumption increment for the double-pass V-corrugated device was also delineated. The experimental setup was carried out to validate the theoretical predictions, and the fairly good agreement between both results was achieved with the error analysis of 0.48-1.83%.     

Integration of artificial neural network and MADA methods for green supplier selection

In recent years, with the raising of awareness in environmental protection and sustainable development in enterprises, the green issue has become more and more critical in supply chain management. This study intends to develop a green supplier selection model which integrates artificial neural network (ANN) and two multi-attribute decision analysis (MADA) methods: data envelopment analysis (DEA) and analytic network process (ANP). It is called ANN–MADA hybrid method. ANN–MADA hybrid method considers both practicality in traditional supplier selection criteria and environmental regulations. It also overcomes traditional DEA drawbacks, limitations of data accuracy and decision-making units (DMUs) amounts constraint. The model evaluation results of an international well-known camera manufacturer indicate that the ANN–MADA hybrid method outperforms two other hybrid methods, ANN–DEA and ANP–DEA. It was also discovered that ANN–MADA has better power of discrimination and noise-insensitivity in evaluating green suppliers’ performances.     

Use of waste rubber as concrete additive.

For resource reutilization, scrap tyres have long been investigated as an additive to concrete to form 'Rubcrete' for various applications and have shown promising results. However, the addition of rubber particles leads to the degradation of physical properties, particularly, the compressive strength of the concrete. In this study, a theoretical model was proposed to shed light on the mechanisms of decrease in compressive strength due to the addition of rubber particles as well as improvement in compressive strength through modification of particle surfaces. The literature suggests that the compressive strength can be improved by soaking the rubber particles in alkaline solution first to increase the inter-phase bonding between the rubber particles and cement. Instead, we discovered that the loss in compressive strength was due to local imperfections in the hydration of cement, induced by the addition of heterogeneous and hydrophobic rubber particles. Microscopic studies showed that the rubber particles disturbed the water transfer to create channels, which were prone to cracking and led to a loss in the compressive strength. Unexpectedly, no cracking was found along the surfaces of the rubber particles, indicating that the bonding strength between the rubber particles and cement phases was not the critical factor in determining the compressive strength. Therefore, a theoretical model was proposed to describe the water transfer in the Rubcrete specimens to explain the experimental data. In the model, the local water available for hydration (Q) is: Q = -A(slv)/6piv, where Q, A(slv), and v are mass flow rate (kg s(-1)), Hamaker constant (J), and dynamic viscosity (m2 s(-1)), respectively. By maximizing the quantity Q and, in turn, the Hamaker constant A(slv), the compressive strength could be improved. The Hamaker constant A(slv) for water film on rubber particle surfaces was smaller than that for the hydrated cement particles; the water transfer rate was lower in the presence of rubber particles because the Hamaker constant A(slv) for water film on rubber particle surfaces was smaller than that on the hydrated cement particles. Thus, the compressive strength of Rubcrete could be improved by increasing the Hamaker constant of the system. This was achieved by increasing the refractive indices of the solids (n(s)). The refractive indices of materials increase with increases in functional groups, such as OH and SH on the surface. The model provided a possible mechanism for the efficacy of treating rubber particles with NaOH in improving the compressive strength. By using NaOH solution treatment, an oxygen-containing OH group was formed on the rubber surface to increase the Hamaker constant of the system, leading to higher compressive strength. Based on this mechanism, a novel method for modification of the rubber particles was also proposed. In this process, the rubber particles were partially oxidized with hot air/steam in a fluidized bed reactor to produce the hydrophilic groups on the surface of the particles. Preliminary results obtained so far are promising in accordance with the theory.     

A COMPARISON OF MODELS PREDICTING GROUND WATER LEVELS ON HILLSIDE SLOPES1

ABSTRACT: A comparative study of ground water level predictions on hillside slopes using two models is presented. The models are a simplified mass balance model that has components for evapotran-spiration, recharge, and drainage; and a two-dimensional finite difference model that employs kriging to estimate soil parameters and accounts for non-uniform thickness of the soil layer. These models are representative of a wide range of modeling capabilities and are used to illustrate the sensitivity of ground water level predictions to the sophistication of the modeling techniques. The drainage and recharge components of the two models are evaluated and the importance of unsaturated flow in recharge computations is underscored. Piezometric observations in a small drainage depression on the slope of Kennel Creek Valley in Tongass National Forest, Alaska, were used to evaluate the two models. The results show that, although the predictions differ from the field observations, the simple physically-based mass balance model predicts the ground water levels as well as the two-dimensional model. It is suggested that caution should be exercised in using complex models to validate simpler models.     

A model for pulse jet fabric filters

A new model for pulse jet fabric filtration is proposed. In contrast to the earlier model of Ravin and Humphries, which was formulated on the steady state assumption, the present study is aimed at developing a predictive capability for both transient and steady state operations, taking into account the compression effect of filter cakes. The model's relative simplicity allows frequent updating of the model parameter values, thus improving the accuracy of predictions. As a result, the model is particularly useful in developing control algorithms and designing controllers of pulse jet fabric filtration systems.     

Influence of water quality parameters on occurrence of polybrominated diphenyl ether in sediment and sediment to biota accumulation

Polybrominated diphenyl ether (PBDE) concentrations in sediment and fish from 12 principal rivers in Taiwan were investigated to determine their association with water quality parameters as well as the biota-sediment accumulation factor (BSAF) in fish with different living patterns. The highest PBDE concentration in sediment was found in the Bajhang River (261 ng g^?1 dry weight (d.w.)) and the lowest in the Beinan River and the Da-an River (0.17 ng g^?1 d.w.). The PBDE concentrations in fish samples ranged from 1.28 ng g^?1 d.w. (Oreochromis niloticus niloticus) in the Yanshuei River to 33.7 ng g^?1 d.w. (Varico rhinos barbatulus) in the Da-an River. We conclude that PBDEs contamination in sediment was significantly affected by NH₃–N, pH, and DO. The BSAF results showed a parabolic trend from low- to high-brominated BDEs. Fish easily accumulated the congeners BDE-47, -100, -119, -126, and -154 from sediment. The BSAF decreased in the following order: PeBDE > HxBDE > TeBDE > other BDEs. Principle component analysis showed that demersal fish have different PBDE sources than do pelagic fish. We conclude that living and feeding habits are critical factors affecting PBDE accumulation in fish.     

Modelling nature-like fishway flow around unsubmerged obstacles using a 2D shallow water model

In the scope to create efficient nature like fish ramps using large-scale roughness elements, the present study is an audit of modelling such complex 3D free surface flows using an industrial 2D code solving shallow water equations. Validation procedure is based upon the comparison between numerous experimental measurements and numerical runs around large-scale roughness patterns disposed on the flume bottom in order to determine what 2D reliable numerical results can be expected. In this paper, we focused on cases of unsubmerged obstacles. The results demonstrate that 2D shallow water modelling using an industrial code such as TELEMAC-2D can be a convenient way for the hydraulic engineer to help design a nature-like fishway. This article emphasizes the limitations due to 2D depth integration of velocities and turbulence modelling and gives the domain of validity of the method.     

Impact of metal stress on the production of secondary metabolites in <Emphasis Type="Italic">Pteris vittata</Emphasis> L. and associated rhizosphere bacterial communities

Plants adapt to metal stress by modifying their metabolism including the production of secondary metabolites in plant tissues. Such changes may impact the diversity and functions of plant associated microbial communities. Our study aimed to evaluate the influence of metals on the secondary metabolism of plants and the indirect impact on rhizosphere bacterial communities. We then compared the secondary metabolites of the hyperaccumulator Pteris vittata L. collected from a contaminated mining site to a non-contaminated site in Vietnam and identified the discriminant metabolites. Our data showed a significant increase in chlorogenic acid derivatives and A-type procyanidin in plant roots at the contaminated site. We hypothesized that the intensive production of these compounds could be part of the antioxidant defense mechanism in response to metals. In parallel, the structure and diversity of bulk soil and rhizosphere communities was studied using high-throughput sequencing. The results showed strong differences in bacterial composition, characterized by the dominance of Proteobacteria and Nitrospira in the contaminated bulk soil, and the enrichment of some potential human pathogens, i.e., Acinetobacter, Mycobacterium, and Cupriavidus in P. vittata’s rhizosphere at the mining site. Overall, metal pollution modified the production of P. vittata secondary metabolites and altered the diversity and structure of bacterial communities. Further investigations are needed to understand whether the plant recruits specific bacteria to adapt to metal stress.     

Implications for biological invasion of non-native plants for sale in the world's largest online market

Internet trade is increasingly recognized as a dispersal pathway of non-native plant species that is difficult to monitor. We sought to identify non-native flora present in the Chinese online market, the largest e-commerce market globally, and to decipher the effect of existing trade regulations, among other variables, on e-trading patterns and to inform policy. We used a comprehensive list of 811 non-native plant species in China present in 1 of the 3 phases of the invasion continuum (i.e., introduced, naturalized, and invasive). The price, propagule types, and quantities of the species offered for sale were retrieved from 9 online stores, including 2 of the largest platforms. Over 30% of the non-native species were offered for sale in the online marketplaces; invasive non-native species dominated the list (45.53%). No significant price difference was observed across the non-native species of the 3 invasion categories. Among the 5 propagule types, a significantly higher number of non-native species were offered for sale as seeds. The regression models and path analyses consistently revealed a direct positive effect of the number of uses and species’ minimum residence time and an indirect effect of biogeography on the pattern of trade in non-native plant species when minimal phylogenetic signal was detected. A review of the existing phytosanitary regulations in China revealed their inadequacy in managing e-trading of non-native plant species. To address the problem, we propose integration of a standardized risk assessment framework that considers perceptions of stakeholders and is adaptable based on continuous surveillance of the trade network. If implemented successfully, the measures could provide a template for other countries to strengthen trading regulations for non-native plant species and take proactive management measures.