Effect of Extreme Temperatures and Driving Conditions on Gaseous Pollutants of a Euro 6d-Temp Gasoline Vehicle

Gaseous emissions of modern Euro 6d vehicles, when tested within real driving emissions (RDE) boundaries, are, in most cases, at low levels. There are concerns, though, about their emission performance when tested at or above the boundaries of ambient and driving conditions requirements of RDE regulations. In this study, a Euro 6d-Temp gasoline direct injection (GDI) vehicle with three-way catalyst and gasoline particulate filter was tested on the road and in a laboratory at temperatures ranging between −30 °C and 50 °C, with cycles simulating urban congested traffic, uphill driving while towing a trailer at 85% of the vehicle’s maximum payload, and dynamic driving. The vehicle respected the Euro 6 emission limits, even though they were not applicable to the specific cycles, which were outside of the RDE environmental and trip boundary conditions. Most of the emissions were produced during cold starts and at low ambient temperatures. Heavy traffic, dynamic driving, and high payload were found to increase emissions depending on the pollutant. Even though this car was one of the lowest emitting cars found in the literature, the proposed future Euro 7 limits will require a further decrease in cold start emissions in order to ensure low emission levels under most ambient and driving conditions, particularly in urban environments. Nevertheless, motorway emissions will also have to be controlled well.

Thermal Characterization of a Steam Turbine Casing Including Measuring of Adiabatic Wall Temperatures Using Proprietary Sensors

Modern steam turbines must increasingly be designed for flexible operation. However an increasing amount of cold starts and load changes have a massive impact on fatigue resistance of the material. So the monitoring of thermal parameters of the casing is significant for checking thermally induced stresses and furthermore lifetime calculation. Additionally the measurement data is helpful for CFD validation reasons. This paper presents a new proprietary developed sensor setup and measurement results. The sensors are flush mounted into a steam turbine at different axial and circumferential locations in the recirculation area between the intermediate and the lower pressure turbine. Hence it is possible to detect temperatures, temperature gradients and heat flux in the part of the wall near the fluid. Moreover the field of temperature within the sensor can be modulated by powering an installed heater. So the adiabatic wall temperature can be identified. For measuring the temperature gradient, seven equidistant spaced thermocouples were used in difference circuit. Therefore two different types of thermocouples were applied. Both types have better transfer characteristics compared to a thermocouple of type K. High amplification enables monitoring of small differences in temperature. The temperature measures an integrated resistor thermometer. The sensors are applied on a real 12 MW industrial steam turbine with maximal live steam parameters of 400 °C and 30 bar. The measurements show various operation points and load changes.

Emission Implications of Plug-in Hybrid Electric Vehicles Through an Empirical Exploration of Engine Starts

This paper aims to characterize the engine start activity profiles and emission potential of various plug-in hybrid electric vehicle (PHEV) models by examining the characteristics associated with engine starts, identifying the travel conditions that trigger engine starts, and determining the frequency of different types of engine starts. The study analyzed on-road vehicle data from six PHEV models: Toyota Prius Plug-in, Ford C-Max Energi, Ford C-Max Fusion, Toyota Prius Prime, Chrysler Pacifica, and Chevrolet Volt. An analysis on travel conditions before engine starts revealed that low state-of-charge is the dominant engine start trigger for PHEVs with high all-electric range whereas high vehicle power requirement is the most critical trigger for PHEVs with low all-electric range. For PHEVs with mid-range capabilities, several vehicle specifications, ranging from peak electric motor power to curb weight, could be engine start determinants. A strong inverse correlation exists between battery capacity and the annual frequency of engine starts but this relationship does not hold for cold and high-power cold starts. Both the low and the high battery capacity PHEVs logged fewer cold starts than the mid-sized battery vehicles, indicating that there could be a fundamental tradeoff between engine start emissions and fuel displacement for PHEVs to a certain degree. Despite this tradeoff, all PHEV models in the study logged fewer cold starts than comparable conventional internal combustion engine vehicles, performing the same trips. Ultimately, long-range PHEVs with high battery capacity are found to be ideal for both curbing start emissions and reducing fuel use.

News Recommendation Systems in the Era of Information Overload

The internet has reconstructed information boundaries in the modern world, and along with mobile internet has become the most important source of information for the public. Simultaneously, the internet has brought humanity into an era of information overload. In response to this information overload, recommendation systems backed by big data and smart algorithms have become highly popular on information platforms on the internet. There have already been many studies that attempted to improve and upgrade recommendation algorithms from a technical perspective, but the field lacks a comprehensive reflection on news recommendation algorithms. In our study, we summarize the principles and characteristics of current news recommendation algorithms and discuss “unexpected consequences” that might arise from these algorithms. In particular, technical bottlenecks include cold starts and data sparsity, and moral bottlenecks are presented in the form of information imbalance and manipulation. These problems may cause new recommendation systems to become a “warped mirror”.

Investigations of split injection properties on the spray characteristics using a solenoid high-pressure injector

An on-going challenge with Gasoline direct injection (GDI) engines is achieving rapid activation of the exhaust catalyst during cold starts, in order to reduce the Nitrogen Oxide (NOx) emissions. Injecting late in the compression stroke, in the efforts to form a stratified mixture, provides the fuel insufficient time to be entrained with the surrounding charge. This results in locally fuel rich diffusion combustion and the formation of high levels of particulate matter. Employing a split injection strategy can help tackle these issues. The current study examines the effects of a split injection strategy on the spray characteristics. Varying pulse width (PW) combinations, split ratios and dwell times are investigated using a Solenoid actuated high pressure injector. The injected quantity and the droplet characteristics of a target plume are investigated. The experiments were performed in a constant volume spray chamber. The droplet velocities and sizes were measured using Phase Doppler Particle Anemometry (PDA). Short and large PWs, in the range of 0.3–0.8 ms, were investigated. The results revealed that the highest injected quantity of fuel was measured with the shortest dwell time of 2 ms, owing to increased interactions between the injection events, which led to larger Sauter mean diameters (SMDs) measured. The SMDs for the shorter PW of 0.4 ms were generally larger than 0.8 ms PW. The droplets in this case were affected by the closely spaced opening and closing events of the Solenoid valve.

Matching While Learning

Platforms face a cold start problem whenever new users arrive: namely, the platform must learn attributes of new users (explore) in order to match them better in the future (exploit). How should a platform handle cold starts when there are limited quantities of the items being recommended? For instance, how should a labor market platform match workers to jobs over the lifetime of the worker, given a limited supply of jobs? In this setting, there is one multiarmed bandit problem for each worker, coupled together by the constrained supply of jobs of different types. A solution is developed to this problem. It is found that the platform should estimate a shadow price for each job type, and for each worker, adjust payoffs by these prices (i) to balance learning with payoffs early on and (ii) to myopically match them thereafter.