Parametric combustion modeling for ethanol-gasoline fuelled spark ignition engines

Ethanol-gasoline fuel blends are increasingly being used in spark ignition (SI) engines due to continued growth in renewable fuels as part of a growing renewable portfolio standard (RPS). This leads to the need for a simple and accurate ethanol-gasoline blends combustion model that is applicable to one-dimensional engine simulation. A parametric combustion model has been developed, integrated into an engine simulation tool, and validated using SI engine experimental data. The parametric combustion model was built inside a user compound in GT-Power. In this model, selected burn durations were computed using correlations as functions of physically based non-dimensional groups that have been developed using the experimental engine database over a wide range of ethanol-gasoline blends, engine geometries, and operating conditions. A coefficient of variance (COV) of gross indicated mean effective pressure (IMEP) correlation was also added to the parametric combustion model. This correlation enables the cycle combustion variation modeling as a function of engine geometry and operating conditions. The computed burn durations were then used to fit single and double Wiebe functions. The single-Wiebe parametric combustion compound used the least squares method to compute the single-Wiebe parameters, while the double-Wiebe parametric combustion compound used an analytical solution to compute the double-Wiebe parameters. These compounds were then integrated into the engine model in GT-Power through the multi-Wiebe combustion template in which the values of Wiebe parameters (single-Wiebe or double-Wiebe) were sensed via RLT-dependence. The parametric combustion models were validated by overlaying the simulated pressure trace from GT-Power on to experimentally measured pressure traces. A thermodynamic engine model was also developed to study the effect of fuel blends, engine geometries and operating conditions on both the burn durations and COV of gross IMEP simulation results.

CAN CHINA USE ALTERNATIVE ENERGIES INSTEAD OF COAL TO PROVIDE MORE ELECTRICITY BY 2030?

Following the rapid growth of China's economy, energy consumption, especially electricity consumption of China, has made a huge increase in the past 30 years. Since China has been using coal as the major energy source to produce electricity during these years, environmental problems have become more and more serious. The research question for this paper is: "Can China use alternative energies instead of coal to produce more electricity in 2030?" Hydro power, nuclear power, natural gas, wind power and solar power are considered as the possible and most popular alternative energies for the current situation of China. To answer the research question above, there are two things to know: How much is the total electricity consumption in China by 2030? And how much electricity can the alternative energies provide in China by 2030? For a more reliable forecast, an econometric model using the Ordinary Least Squares Method is established on this paper to predict the total electricity consumption by 2030. The predicted electricity coming from alternative energy sources by 2030 in China can be calculated from the existing literature. The research results of this paper are analyzed under a reference scenario and a max tech scenario. In the reference scenario, the combination of the alternative energies can provide 47.71% of the total electricity consumption by 2030. In the max tech scenario, it provides 57.96% of the total electricity consumption by 2030. These results are important not only because they indicate the government's long term goal is reachable, but also implies that the natural environment of China could have an inspiring future.

Price risk management in the copper market using commodity derivatives and options strategies

Metals price risk management is a key issue related to financial risk in metal markets because of uncertainty of commodity price fluctuation, exchange rate, interest rate changes and huge price risk either to metals’ producers or consumers. Thus, it has been taken into account by all participants in metal markets including metals’ producers, consumers, merchants, banks, investment funds, speculators, traders and so on. Managing price risk provides stable income for both metals’ producers and consumers, so it increases the chance that a firm will invest in attractive projects. The purpose of this research is to evaluate risk management strategies in the copper market. The main tools and strategies of price risk management are hedging and other derivatives such as futures contracts, swaps and options contracts. Hedging is a transaction designed to reduce or eliminate price risk. Derivatives are financial instruments, whose returns are derived from other financial instruments and they are commonly used for managing financial risks. Although derivatives have been around in some form for centuries, their growth has accelerated rapidly during the last 20 years. Nowadays, they are widely used by financial institutions, corporations, professional investors, and individuals. This project is focused on the over-the-counter (OTC) market and its products such as exotic options, particularly Asian options. The first part of the project is a description of basic derivatives and risk management strategies. In addition, this part discusses basic concepts of spot and futures (forward) markets, benefits and costs of risk management and risks and rewards of positions in the derivative markets. The second part considers valuations of commodity derivatives. In this part, the options pricing model DerivaGem is applied to Asian call and put options on London Metal Exchange (LME) copper because it is important to understand how Asian options are valued and to compare theoretical values of the options with their market observed values. Predicting future trends of copper prices is important and would be essential to manage market price risk successfully. Therefore, the third part is a discussion about econometric commodity models. Based on this literature review, the fourth part of the project reports the construction and testing of an econometric model designed to forecast the monthly average price of copper on the LME. More specifically, this part aims at showing how LME copper prices can be explained by means of a simultaneous equation structural model (two-stage least squares regression) connecting supply and demand variables. A simultaneous econometric model for the copper industry is built: {█(Q_t^D=e^((-5.0485))∙P_((t-1))^((-0.1868) )∙〖GDP〗_t^((1.7151) )∙e^((0.0158)∙〖IP〗_t ) @Q_t^S=e^((-3.0785))∙P_((t-1))^((0.5960))∙T_t^((0.1408))∙P_(OIL(t))^((-0.1559))∙〖USDI〗_t^((1.2432))∙〖LIBOR〗_((t-6))^((-0.0561))@Q_t^D=Q_t^S )┤ P_((t-1))^CU=e^((-2.5165))∙〖GDP〗_t^((2.1910))∙e^((0.0202)∙〖IP〗_t )∙T_t^((-0.1799))∙P_(OIL(t))^((0.1991))∙〖USDI〗_t^((-1.5881))∙〖LIBOR〗_((t-6))^((0.0717) Where, Q_t^D and Q_t^Sare world demand for and supply of copper at time t respectively. P(t-1) is the lagged price of copper, which is the focus of the analysis in this part. GDPt is world gross domestic product at time t, which represents aggregate economic activity. In addition, industrial production should be considered here, so the global industrial production growth that is noted as IPt is included in the model. Tt is the time variable, which is a useful proxy for technological change. A proxy variable for the cost of energy in producing copper is the price of oil at time t, which is noted as POIL(t ) . USDIt is the U.S. dollar index variable at time t, which is an important variable for explaining the copper supply and copper prices. At last, LIBOR(t-6) is the 6-month lagged 1-year London Inter bank offering rate of interest. Although, the model can be applicable for different base metals' industries, the omitted exogenous variables such as the price of substitute or a combined variable related to the price of substitutes have not been considered in this study. Based on this econometric model and using a Monte-Carlo simulation analysis, the probabilities that the monthly average copper prices in 2006 and 2007 will be greater than specific strike price of an option are defined. The final part evaluates risk management strategies including options strategies, metal swaps and simple options in relation to the simulation results. The basic options strategies such as bull spreads, bear spreads and butterfly spreads, which are created by using both call and put options in 2006 and 2007 are evaluated. Consequently, each risk management strategy in 2006 and 2007 is analyzed based on the day of data and the price prediction model. As a result, applications stemming from this project include valuing Asian options, developing a copper price prediction model, forecasting and planning, and decision making for price risk management in the copper market.

Afghanistan connection : heroin production, distribution, and consumption

Heroin prices are a reflection of supply and demand, and similar to any other market, profits motivate participation. The intent of this research is to examine the change in Afghan opium production due to political conflict affecting Europe’s heroin market and government policies. If the Taliban remain in power, or a new Afghan government is formed, the changes will affect the heroin market in Europe to a certain degree. In the heroin market, the degree of change is dependent on many socioeconomic forces such as law enforcement, corruption, and proximity to Afghanistan. An econometric model that examines the degree of these socioeconomic effects has not been applied to the heroin trade in Afghanistan before. This research uses a two-stage least squares econometric model to reveal the supply and demand of heroin in 36 different countries from the Middle East to Western Europe in 2008. An application of the two-stage least squares model to the heroin market in Europe will attempt to predict the socioeconomic consequences of Afghanistan opium production.

Car ownership modeling and forecasts for China

A great increase of private car ownership took place in China from 1980 to 2009 with the development of the economy. To explain the relationship between car ownership and economic and social changes, an ordinary least squares linear regression model is developed using car ownership per capita as the dependent variable with GDP, savings deposits and highway mileages per capita as the independent variables. The model is tested and corrected for econometric problems such as spurious correlation and cointegration. Finally, the regression model is used to project oil consumption by the Chinese transportation sector through 2015. The result shows that about 2.0 million barrels of oil will be consumed by private cars in conservative scenario, and about 2.6 million barrels of oil per day in high case scenario in 2015. Both of them are much higher than the consumption level of 2009, which is 1.9 million barrels per day. It also shows that the annual growth rate of oil demand by transportation is 2.7% - 3.1% per year in the conservative scenario, and 6.9% - 7.3% per year in the high case forecast scenario from 2010 to 2015. As a result, actions like increasing oil efficiency need to be taken to deal with challenges of the increasing demand for oil.

ELECTRICITY GENERATION MODELING AND PHOTOVOLTAIC FORECASTS IN CHINA

With the economic development of China, the demand for electricity generation is rapidly increasing. To explain electricity generation, we use gross GDP, the ratio of urban population to rural population, the average per capita income of urban residents, the electricity price for industry in Beijing, and the policy shift that took place in China. Ordinary least squares (OLS) is used to develop a model for the 1979-2009 period. During the process of designing the model, econometric methods are used to test and develop the model. The final model is used to forecast total electricity generation and assess the possible role of photovoltaic generation. Due to the high demand for resources and serious environmental problems, China is pushing to develop the photovoltaic industry. The system price of PV is falling; therefore, photovoltaics may be competitive in the future.