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To properly simulate the highly anisotropic turbulent engine flows, higher order turbulence model should be used to correctly reproduce flow physics inside the engine. The popular KIVA computer code has been modified to include the Reynolds-stress turbulence model (RSTM) for this purpose. The objective of this paper is to present our recent research on the use of RSTM and the KIVA code for engine flow simulation, which include gas turbine combustors and IC engines.

Abstract In aircraft propulsion as well as stationary power generation, gas turbine engines remain a key energy conversion technology due to their high thermal efficiencies and low emissions. However, as emission requirements become increasingly stringent, engine manufacturers have sought to design combustion systems that operate near the fuel-lean limit of flammability. In this study, superadiabatic matrix-stabilized combustion, also known as porous media combustion, is evaluated as an advanced combustion concept for extending the lean flammability limit to achieve improved efficiency and emissions. To this end, a Brayton cycle analysis is developed and key parameters of the porous matrix are identified for maximizing the extension of the lean flammability limit. It is shown that stabilization of combustion below the nominal lean flammability limit allows for the design of engines with significantly higher pressure ratios and lower dilution ratios without increasing turbine inlet temperatures, thus improving cycle thermal efficiency. Combustor flammability limits were shown to be extendable by up to 32% when employing matrix-stabilized combustion, resulting in thermal efficiency gains of up to 11% compared to a nominal design.

Abstract Marginal costs of electricity vary by time and location. In the past, researchers attributed the variations to factors related to electricity generation and transmission. These authors, however, have not analyzed possible variations in marginal distribution capacity costs (MDCC). The objectives of this paper are: 1. (i) to show that large MDCC variations are due to the dispersion in distribution capital expenditures by time and space, 2. (ii) to propose a method for quantifying the area- and time-specific MDCC in the presence of lumpy investments, and 3. (iii) to compare our MDCC estimates to those commonly used in the electric utility industry. Our proposed method and its results were adopted by the California Public Utilities Commission (CPUC) in 1992 for Pacific Gas and Electric Company (PG&E), the largest privately owned electric utility in the U.S.

Abstract Microscopic observations of the liquid CO2-water-CO2-hydrate system were conducted to measure both the interfacial tensions in these phases and the strength of the CO2-hydrate film. Measurements on hanging drops revealed that the interfacial tension between liquid CO2 and water decreased slightly as the temperature was raised from 266.3 to 284.9 K, and that the average of the interfacial tension was about 29 dyn-cm−1. Contact angles between a water droplet and a teflon plate were measured before and after the thin film of CO2 hydrate was formed on the droplet. Comparisons of these contact angles indicated that the sums of the interfacial tensions between liquid CO2 and CO2-hydrate film and between CO2-hydrate film and water were about 26% less than between liquid CO2 and water. The destructive strength of CO2-hydrate film formed on the pendant drop was examined by using a process for breaking of the film. Measurements indicated that the strength of the CO2-hydrate film depended on the film thickness. The film thickness of CO2 hydrate was then estimated from the film strength.

Abstract Accurate installed capacity forecasting can provide effective decision-making support for planning development strategies and establishing national electricity policies. First, considering the data limitation in quantity and accuracy, this paper proposes a multi-factor installed capacity forecasting framework combining the fuzzy time series method and support vector regression. Compared with four benchmark models, the proposed model shows advantages in installed capacity prediction. Second, the predictability dynamics of national installed capacity are explored from the perspective of country clusters. It is revealed that highly predictable countries usually obtain high forecasting accuracy with all forecasting models and are less sensitive to forecasting models. Using the k-means clustering method, this paper divides 136 sample countries into four categories according to the predictability. Third, based on the mean impact value analysis, this paper differentiates and ranks the importance of input variables on installed capacity development. The two most important factors influencing installed capacity are installed capacity development in the previous period and population. Overall, these results are of practical value to the operating decisions of electric power enterprises and the electricity plans of governments.

Abstract A survey of residential solar hot water heating in the Washington, D.C. area is presented with estimates of the total solar energy contribution per year. These estimates are examined in relation to a local utility's peak-load curves to determine the impact of a substantial increase in solar domestic hot water use over the next 20 yr in the area of utility management. The results indicate that a 10% market penetration of solar water heaters would have no detrimental effect on the utility's peak-load profile and could save several million dollars in new plant construction costs.

Abstract The commercial development of biofuels and bioproducts depends on whether renewable biomass feedstock is available while not directly competing with the production of food. Farmers are one of the most important stakeholders in the biofuel supply chain and confront a range of uncertainties while entering the bioenergy market. Their decision-making process is extremely complex and rarely purely rational. Modeling farmer behavior requires considering a wide range of individual-level factors, socio-temporal dynamics, institutional settings, and their interactions. These characteristics make agent-based modeling a suitable framework for evaluating such systems. We developed a model to simulate farmer bioenergy crop adoption behavior across a 50-county study region in Nebraska, Kansas, and Colorado. The analysis considers adoption decisions for two bioenergy feedstocks, crop residues and energy crops. We examine the influence of individual and farm characteristics, market structure, social networks, and media influence on farmer adoption decisions. Our results indicate that different factors can have varied impacts on the speed of adoption for the crop residues and energy crops. Identifying levers that have the most impact on grower adoption can inform the design of interventions both from policy and private sector standpoints with important implications for the future the bioenergy industry.

Abstract Biomass gasification using lattice oxygen (BGLO) of natural hematite coupling with steam was conducted in a fluidized bed reactor. The presence of hematite particles evidently facilitated to biomass gasification. Comparing with biomass steam gasification (BSG), carbon conversion and gas yield increased by 7.47% and 11.02%, respectively, and tar content lowered by 51.53%, in BGLO with an S/B of 0.85 at 800 °C. In this case, 62.30% of the lattice oxygen in the hematite particles was consumed in the biomass gasification. The reaction temperature, steam-to-biomass ratio (S/B) and reaction time on the performance of hematite particles were extensively investigated, in terms of gas distribution, heating value, yield and carbon conversion. With the reaction temperature increasing from 750 to 850 °C, the gas yield increased from1.12 to 1.53Nm3/kg, and carbon conversion increased from 77.21% to 95.49%. An optimal S/B ratio of 0.85 was obtained in order to maximize the carbon conversion and gas yield of BGLO. At this ratio, the gas yield reached 1.41Nm3/kg with carbon conversion of 92.98%. The gas concentration was gradually close to that of BSG at the end stage of BGLO due to the active lattice oxygen was depleted with the proceeding of reactions.

An operational cost minimisation model is established for a smart energy hub (S.E. Hub) consisting of a combined heat and power (CHP) unit, a heating, ventilation and air-conditioning (HVAC) system, and thermal and electricity storage units. The optimal operation of CHP is combined with the load management of HVAC under a time-of-use (TOU) tariff. The heat and power split ratio of CHP is dynamically determined during the operation. The scheduling of HVAC load and the charging/discharging of energy storage systems are also determined through the optimisation model. The energy management system can therefore shift the load demand and manage energy supply simultaneously. System operation requirements and environment factors including the outdoor air-temperature variation, seasonal variation, and battery degradation are considered. Comprehensive case studies are carried out to examine the effectiveness of the proposed strategy, from which insights are obtained for different energy management strategies and possible upgrade of S.E. Hub. Simulation results reveal that dynamic control of the CHP heat and power split ratio is an effective way to save the total operational cost, and a clear cost saving is shown through the proposed optimal operation strategy.