scholarly journals Evaluation of Gasoline Evaporative Emissions from Fuel-Cap Removal after a Real-World Driving Event

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1110
Author(s):  
Hiroo Hata ◽  
Syun-ya Tanaka ◽  
Genta Noumura ◽  
Hiroyuki Yamada ◽  
Kenichi Tonokura

This study evaluated gasoline evaporative emissions from fuel-cap removal during the refueling process (or “puff loss”) for one gasoline vehicle in the Japanese market. Specifically, the puff loss emissions were measured after a real-world driving event in urban Tokyo, Japan for different seasons and gasoline types. The experimental results indicated higher puff loss emissions during summer than in winter and spring despite using low vapor pressure gasoline during summer. These higher puff loss emissions accounted maximally for more than 4 g of the emissions from the tested vehicle. The irregular emission trends could be attributed to the complex relationships between physical parameters such as fuel-tank filling, ambient temperature, ambient pressure, and gasoline vapor pressure. Furthermore, an estimation model was developed based on the theory of thermodynamics to determine puff loss emissions under arbitrary environmental conditions. The estimation model included no fitting parameter and was in good agreement with the measured puff loss emissions. Finally, a sensitivity analysis was conducted to elucidate the effects of three physical parameters, i.e., fuel tank-filling, ambient pressure, and gasoline type, on puff loss emissions. The results indicated that fuel tank-filling was the most important parameter affecting the quantity of puff loss emissions. Further, the proposed puff loss estimation model is likely to aid the evaluation of future volatile organic compound emission inventories.

Author(s):  
Dale Purves

Although understanding neural functions has progressed at a remarkable pace in recent decades, a fundamental question remains: How does the nervous system relate the objective world to the subjective domain of perception? Everyday experience implies that the neural connections on which we and other animals depend link physical parameters in the environment with useful responses. But that interpretation won't work: biological sensory systems cannot measure the physical world. Whereas something is linking sensory inputs to useful responses, it is not the physical world that instruments measure. How, then, have we animals met this challenge, and what is it that we end up perceiving? The purpose of this chapter is to suggest how nervous systems have evolved to deal with the inability to convey the objective properties of the real world.


Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5226
Author(s):  
Subhrasankha Dey ◽  
Stephan Winter ◽  
Martin Tomko

All established models in transportation engineering that estimate the numbers of trips between origins and destinations from vehicle counts use some form of a priori knowledge of the traffic. This paper, in contrast, presents a new origin–destination flow estimation model that uses only vehicle counts observed by traffic count sensors; it requires neither historical origin–destination trip data for the estimation nor any assumed distribution of flow. This approach utilises a method of statistical origin–destination flow estimation in computer networks, and transfers the principles to the domain of road traffic by applying transport-geographic constraints in order to keep traffic embedded in physical space. Being purely stochastic, our model overcomes the conceptual weaknesses of the existing models, and additionally estimates travel times of individual vehicles. The model has been implemented in a real-world road network in the city of Melbourne, Australia. The model was validated with simulated data and real-world observations from two different data sources. The validation results show that all the origin–destination flows were estimated with a good accuracy score using link count data only. Additionally, the estimated travel times by the model were close approximations to the observed travel times in the real world.


2014 ◽  
Vol 1061-1062 ◽  
pp. 1140-1143
Author(s):  
Dong Jie Liu

The numerical study of the influence of the ambient pressure of the fuel tank on the inerting effect of an aircraft fuel tank inerting system was carried out. The mathematical model of ullage equilibrium oxygen concentration has been established using the differential time calculation method based on the mass conservation and ideal gas state equations. The variations of ullage oxygen concentration and dissolved oxygen concentration in the fuel with time under different working conditions have been obtained. The results have shown that the as the ambient pressure of the fuel tank became lower, the speed of the decreasing of oxygen concentration of the fuel tank ullge and the dissolved oxygen concentration of the fuel was slower.


2005 ◽  
Vol 12 (3) ◽  
pp. 156-163 ◽  
Author(s):  
Lyn D. English ◽  
Jillian L. Fox ◽  
James J. Watters

In recent years, we have introduced elementary school children to the powerful world of mathematical modeling. Models are used to interpret real-world situations in a mathematical format. For example, graphs and tables model complex relationships among various phenomena.


2013 ◽  
Vol 6 (2) ◽  
pp. 382-392
Author(s):  
Johanna Dolch ◽  
Aaron Reek ◽  
Gerard Glinsky ◽  
Dominic Dicicco ◽  
Valerie Ughetta

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 708 ◽  
Author(s):  
Samuel Marcos-Pablos ◽  
Francisco García-Peñalvo

Applying the concepts of technological ecosystems to the care and assistance domain is an emerging field that has gained interest during the last years, as they allow to describe the complex relationships between actors in a technologically boosted care domain. In that context, this paper presents a systematic review and mapping of the literature to identify, analyse and classify the published research carried out to provide care and assistance services under a technological ecosystems’ perspective. Thirty-seven papers were identified in the literature as relevant and analysed in detail (between 2003–2018). The main findings show that it is indeed an emerging field, as few of the found ecosystem proposals have been developed in the real world nor have they been tested with real users. In addition, a lot of research to date reports the proposal of platform-centric architectures developed over existing platforms not specifically developed for care and services provision. Employed sensor technologies for providing services have very diverse natures depending on the intended services to be provided. However, many of these technologies do not take into account medical standards. The degree of the ecosystems’ openness to adding new devices greatly depends on the approach followed, such as the type of middleware considered. Thus, there is still much work to be done in order to equate other more established ecosystems such as business or software ecosystems.


Author(s):  
Krishnamurty Muralidhar ◽  
Rathindra Sarathy

Simulation is often used as a tool to analyze and understand complex systems in supply chain management research. Supply chains involve complex relationships between different variables. Hence, it is necessary to simulate related non-normal distributions to simulate these systems. The simulation of related normal distributions is relatively easy and can be found in most simulation texts. However, when the marginal distributions under investigation do not have a normal distribution, it becomes very difficult to generate values from these related distributions. In this study, the authors illustrate a method based on copulas that allows for the generation of related distributions with arbitrary marginals. The procedure suggested in this study is simple and easy to implement. Using this procedure will enable researchers in supply chain management to more effectively simulate complex real-world scenarios resulting in better analysis and understanding of supply chains.


1996 ◽  
Vol 30 (10) ◽  
pp. 3001-3009 ◽  
Author(s):  
Robert McLaren ◽  
Alan W. Gertler ◽  
Dave N. Wittorff ◽  
Wayne Belzer ◽  
Tom Dann ◽  
...  

Author(s):  
Jacob Holden ◽  
Harrison Van Til ◽  
Eric Wood ◽  
Lei Zhu ◽  
Jeffrey Gonder ◽  
...  

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages roughly one million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rate look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to a wide range of driving data. The model has been trained on vehicle travel profiles from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. When compared against the detailed on-road conventional vehicle fuel consumption test data, the energy estimation model accurately predicted which route would consume less fuel over a dozen different tests. When compared against a larger set of real-world origin–destination pairs, it is estimated that implementing the present methodology should accurately select the route that consumes the least fuel 90% of the time. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.


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