2012 Iowa Statewide Greenhouse Gas Emissions Inventory Report

This policy-neutral report is the seventh statewide greenhouse gas inventory conducted for Iowa as required by Iowa Code 455B.104. Note: This report was amended on 12/11/14 to correct minor typographical errors.

2012 Iowa Statewide Greenhouse Gas Emissions Inventory Report Technical Support Document

This technical support document is an addendum to the 2012 Iowa Statewide Greenhouse Gas Emissions Inventory Report. Note: This report was amended on 12/11/14 to correct minor typographical errors.

2013 Iowa Statewide Greenhouse Gas Emissions Inventory Report

This policy-neutral report is the eighth statewide greenhouse gas inventory conducted for Iowa as required by Iowa Code 455B.104.

2013 Iowa Statewide Greenhouse Gas Emissions Inventory Report Technical Support Document.

This technical support document is an addendum to the 2013 Iowa Statewide Greenhouse Ga Emissions Inventory.

Electromagnetic Emissions from Wireless Power Transfer System

In this paper, the measurement and analysis of the electromagnetic radiated emissions from the wireless power transfer system is reported. The aim is to evaluate the level of the electromagnetic field produced by the magnetic resonance wireless power transfer system. Due to the advances of the wireless power transfer technology, it becomes feasible to apply the wireless power transfer in the electric vehicles charging. Among the existent wireless power transfer technologies, the magnetic resonant coupling is proven to be the most suitable for this task. Because of strong electromagnetic field generated by wireless power transfer system the electromagnetic compatibility has become an important issue.

Do Global CO2 Emissions from Fossil-Fuel Consumption Exhibit Long Memory? A Fractional Integration Analysis

In this article we use an autoregressive fractionally integrated moving average approach to measure the degree of fractional integration of aggregate world CO2 emissions and its five components – coal, oil, gas, cement, and gas flaring. We find that all variables are stationary and mean reverting, but exhibit long-term memory. Our results suggest that both coal and oil combustion emissions have the weakest degree of long-range dependence, while emissions from gas and gas flaring have the strongest. With evidence of long memory, we conclude that transitory policy shocks are likely to have long-lasting effects, but not permanent effects. Accordingly, permanent effects on CO2 emissions require a more permanent policy stance. In this context, if one were to rely only on testing for stationarity and non-stationarity, one would likely conclude in favour of non-stationarity, and therefore that even transitory policy shocks

A baseline projection of methane emissions by the Brazilian beef sector: preliminary results .

2007

A new sward management strategy to mitigate the methane (CH4) emissions by grazing ruminants.

2016

Soil N²O emissions from long-term agroecosystems: interactive effects of rainfall seasonality and crop rotation in the Brazilian cerrado.

2016

Effect of seasons on enteric methane emissions from cattle grazing Urochloa brizantha.

Abstract: The objective of this study was to evaluate the effect of seasons under a tropical climate on forage quality, aswell the effect of an Urochloa brizantha cv. Marandu grazing system on enteric methane (CH4) emissions fromNellore cattle in the Southeast region of Brazil. Sixteen Nellore steers (18 months old and initial weight 318.0 ± 116.59 kg of LW; final weight 469 ± 98.50 kg of LW) were used for a trial period of 10 months, with four collection periods in winter (August), spring (December), summer (February) and autumn (May). Each collection period consisted of 28 days, corresponding to the representative month of each season where the last six days were designed for methane data collection. Animals were randomly distributed within 16 experimental plots, distributed in four random blocks over four trial periods. CH4 emissions were determined using the sulphur hexafluoride (SF6) tracer gas technique measured by gas chromatography and fluxes of CH4 calculated. The forage quality was characterized by higher CP and IVDMD and lower lignin contents in spring, differing specially from winter forage. Average CH4 emissions were between 102.49 and 220.91 g d-1 (37.4 to 80.6 kg ani-1 yr-1); 16.89 and 30.20 g kg-1 DMI; 1.35 and 2.90 Mcal ani-1 d-1; 0.18 and 0.57 g kg-1 ADG-1 and 5.05 and 8.76% of GE. Emissions in terms of CO2 equivalents were between 4.68 and 14.22 g CO2-eq-1 g-1 ADG. Variations in CH4 emissions were related to seasonal effect on the forage quality and variations in dry matter intake.

Modeling nitrous oxide emissions in grass and grass-legume pastures in the western Brazilian Amazon.

Mineral nitrogen (N) dynamics in soil and the exchange of N gaseous in the interface soil-atmosphere are intimately associated with animal manure in pastures. According to soil inorganic-N pools and the site studied, forest or pasture, and pastures age the soil inorganic-N pools of ammonium and nitrate can be similar in the forest or ammonium dominated in the pasture. Also annual average net nitrification rates at soil surface in forest can be higher than in pasture suggesting a higher potential for nitrate-N losses either through leaching or gaseous emissions from intact forests compared with established pastures (NEILL et al., 1995).

Emissions from cattle farming in Brazil.

2016

Real-Time Engine Diagnostic and Control Based on Acoustic Emissions Analysis

Engine developers are putting more and more emphasis on the research of maximum thermal and mechanical efficiency in the recent years. Research advances have proven the effectiveness of downsized, turbocharged and direct injection concepts, applied to gasoline combustion systems, to reduce the overall fuel consumption while respecting exhaust emissions limits. These new technologies require more complex engine control units. The sound emitted from a mechanical system encloses many information related to its operating condition and it can be used for control and diagnostic purposes. The thesis shows how the functions carried out from different and specific sensors usually present on-board, can be executed, at the same time, using only one multifunction sensor based on low-cost microphone technology. A theoretical background about sound and signal processing is provided in chapter 1. In modern turbocharged downsized GDI engines, the achievement of maximum thermal efficiency is precluded by the occurrence of knock. Knock emits an unmistakable sound perceived by the human ear like a clink. In chapter 2, the possibility of using this characteristic sound for knock control propose, starting from first experimental assessment tests, to the implementation in a real, production-type engine control unit will be shown. Chapter 3 focus is on misfire detection. Putting emphasis on the low frequency domain of the engine sound spectrum, features related to each combustion cycle of each cylinder can be identified and isolated. An innovative approach to misfire detection, which presents the advantage of not being affected by the road and driveline conditions is introduced. A preliminary study of air path leak detection techniques based on acoustic emissions analysis has been developed, and the first experimental results are shown in chapter 4. Finally, in chapter 5, an innovative detection methodology, based on engine vibration analysis, that can provide useful information about combustion phase is reported.

Virtual Calibration of CO2 and Pollutant Emissions of a High-Performance PHEV using Model-in-the-Loop Methodology

The study analyses the calibration process of a newly developed high-performance plug-in hybrid electric passenger car powertrain. The complexity of modern powertrains and the more and more restrictive regulations regarding pollutant emissions are the primary challenges for the calibration of a vehicle’s powertrain. In addition, the managers of OEM need to know as earlier as possible if the vehicle under development will meet the target technical features (emission included). This leads to the necessity for advanced calibration methodologies, in order to keep the development of the powertrain robust, time and cost effective. The suggested solution is the virtual calibration, that allows the tuning of control functions of a powertrain before having it built. The aim of this study is to calibrate virtually the hybrid control unit functions in order to optimize the pollutant emissions and the fuel consumption. Starting from the model of the conventional vehicle, the powertrain is then hybridized and integrated with emissions and aftertreatments models. After its validation, the hybrid control unit strategies are optimized using the Model-in-the-Loop testing methodology. The calibration activities will proceed thanks to the implementation of a Hardware-in-the-Loop environment, that will allow to test and calibrate the Engine and Transmission control units effectively, besides in a time and cost saving manner.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.