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Abstract The effect of modulated air jets, introduced through the combustor shell, on the temperature distribution and nitric oxide emissions is investigated. Temperature and emissions measurements have been made at a number of forcing frequencies in the range of 100–850 Hz, blowing ratios in the range of 4–10 and equivalence ratios between 0.6 and 1.0. Open-loop flame response to forcing has also been acquired by recording pressure spectra. Results show that substantial reductions in nitric oxide emissions index (15–30%) can be obtained over a wide range of flow conditions with side-air jet forcing. In addition, forcing also alters the time averaged temperature field, with higher mean temperatures close to the dump plane, due to enhanced fuel-air mixing. The higher temperatures and volumetric heat release obtained with forcing can enable more compact combustor designs. The lower emissions are potentially linked to greater unsteadiness with forcing.

Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.

Photosynthetic chromatophore vesicles found in some purple bacteria constitute one of the simplest light-harvesting systems in nature. The overall architecture of chromatophore vesicles and the structural integration of vesicle function remain poorly understood despite structural information being available on individual constituent proteins. An all-atom structural model for an entire chromatophore vesicle is presented, which improves upon earlier models by taking into account the stoichiometry of core and antenna complexes determined by the absorption spectrum of intact vesicles in Rhodobacter sphaeroides, as well as the well-established curvature-inducing properties of the dimeric core complex. The absorption spectrum of low-light-adapted vesicles is shown to correspond to a light-harvesting-complex 2 to reaction center ratio of 3:1. A structural model for a vesicle consistent with this stoichiometry is developed and used in the computation of excitonic properties. Considered also is the packing density of antenna and core complexes that is high enough for efficient energy transfer and low enough for quinone diffusion from reaction centers to cytochrome bc(1) complexes.

Abstract Thermal energy storage systems based on Phase change materials (PCM) are an attractive option to bridge the temporal and spatial gap between the energy demand and supply. But, these systems possess poor thermal conductivity causing reduced rate of heat transfer. The objective of the present study is to numerically analyze the melting process in an optimized finned latent heat storage system dispersed with varying volume fraction of Graphene nano plates (GNP). The individual effect of incorporating fins, GNP and a combination of both at different volume fraction has been studied. Effective thermal conductivity of nano-composite PCM has been theoretically evaluated including the effect of aspect ratio, interfacial thermal resistance, anisotropy, non-linear effects as well as concentration for the dispersed GNP. In this work, Dynamic differential scanning calorimetry tests are performed to evaluate the phase change temperature, latent heat and specific heat of the sugar alcohol (d- mannitol). Transient variation of liquid fraction, average temperature and radial/longitudinal temperature differentials are presented which would be useful for designing medium temperature (160–200 °C) storage systems for various applications. Fin height is varied to obtain an optimum fin size such that natural convection currents are not impeded. Various heat transfer models (including natural convection) are analysed using the actual plant data of a double effect solar absorption system at different arrangements of fins and GNP. Effect of Reynolds number and inlet temperature of HTF on the system performance have also been studied. A reduction of 68% in total melting time is observed in finned LHSS with 5% GNP as compared to a conventional system.

Abstract Integration of solar photovoltaic (PV) and battery storage systems is an upward trend for residential sector to achieve major targets like minimizing the electricity bill, grid dependency, emission and so forth. In recent years, there has been a rapid deployment of PV and battery installation in residential sector. In this regard, optimal planning of PV-battery systems is a critical issue for the designers, consumers, and network operators due to high number of parameters that can affect the optimization problem. This paper aims to present a comprehensive and critical review on the effective parameters in optimal planning process of solar PV and battery storage system for grid-connected residential sector. The key parameters in process of optimal planning for PV-battery system are recognized and explained. These parameters are economic and technical data, objective functions, energy management systems, design constraints, optimization algorithms, and electricity pricing programs. A timely review on the state-of-the-art studies in PV-battery optimal planning is presented. The challenges, trends and latest developments in the topic are discussed. At the end, scopes for future studies are developed. It is found that new guidelines should be provided for the customers based on various electricity rates and demand response programs. Also, several design considerations like grid dependency and resiliency need further investigation in the optimal planning of PV-battery systems.

AbstractThe adoption of a bioenergy crop is affected by various factors, including but not limited to the characteristics of farmers, farm economics, market forces, and physical environment. This study develops a spatially explicit agent‐based model for ascertaining the adoption rate of carinata (Brassica carinata) among the farmers in the Little River Experimental Watershed located in the southern state of Georgia in the United States. Each farmer's adoption behavior is modeled using the profitability difference between traditional crop rotations (with and without carinata at different contract prices), the adoption rate of neighboring farmers, and their land allocation decisions from managing a risky portfolio of enterprises. Carinata production in the winter season once every 3 years has no conflict with the most profitable and popular traditional row crop rotations, such as cotton‐cotton‐cotton and cotton‐cotton‐peanut, to a larger extent. The results show that 28% and 85% of farmers in the watershed will adopt carinata after 33 years at a contract price of $13/bushel (bu) under two different assumptions of low (2.5%) and high (5%) initial neighborhood adoption rates. The proportions of land allocated to carinata to the total farmland under field crops are 38% and 85% after 33 years under the same low and high neighborhood adoption rates, respectively. Our results suggest that fixing the appropriate contract price of carinata will bring additional profits to farmers without any significant foreseeable agronomic risks, thereby increasing the adoption rate of carinata at a regional level.

The IEEE Power and Energy Society (PES) has a long history of numerous educational activities that benefit members as well as future members and the global community. This panel presentation provides a summary of the latest activities that are being led by the society and ideas for the future. These activities include the work of the Power Engineering Education Committee (PEEC), The U.S. Power and Energy Engineering Workforce Collaborative, IEEE eLearning (formerly Expert Now), Plain Talk Courses, the PES Distinguished Lecturer Program, PES-Careers, and other novel educational resources that are available through the IEEE/PES web site.

Abstract Hybrid nanofluids are a novel class of colloidal fluids which have drawn significant attention due to potential tailoring of their thermo-physical properties for heat transfer enhancement by a combination of more than one nano-additive to meet specific requirements of an application. In the present work, ceramic copper oxide/carbon (SiO2-CuO/C) nanoparticles in 80:20 (wt%) composition were prepared by ultrasonic-assisted wet mixing technique. The hybrid nanofluid was formulated by dispersing the nanoparticles into a base fluid mixture of 60:40 (% by mass) glycerol and ethylene glycol (G/EG) using the two-steps method. The influence of nanoparticles on the augmentation of specific heat, thermal conductivity and viscosity was examined in the volume concentration range of 0.5–2.0% in the temperature range of 303.15–353.15 K. The results demonstrate that the synthesized SiO2-CuO/C hybrid nanoparticles enhanced the thermo-physical properties of the base fluid mixture which is higher than using SiO2 alone. In the case of SiO2–G/EG nanofluid, the specific heat capacity decremented by a maximum value of 5.7% whereas the thermal conductivity and viscosity incremented by 6.9% and 1.33-times as compared with G/EG at maximum volume concentration of 2.0% at a temperature of 353.15 K. Comparatively, a reinforcement of 80% SiO2 with 20% CuO/C in G/EG mixture led to thermal conductivity and viscosity enhancement by 26.9% and 1.15-times, respectively with a significant reduction of specific heat by 21.1%. New empirical correlations were proposed based on the experimental data for evaluation of thermophysical properties.

Abstract Kenya has made considerable policy efforts to expand its renewable energy portfolio to meet energy demand and mitigate greenhouse gas (GHG) emissions. Development of proper policies requires a robust framework for analyzing the benefits of renewable energy investments. Towards this end, this study applied a choice experiment analysis to determine how attributes (type of energy, ownership, impact on environment, distance and visibility, community job creation, and yearly renewable energy tax) impact the public willingness to pay for renewable energy development in Kenya. A nationwide survey of 1020 households was conducted in nine counties using conditional logit (MNL) and random parameter logit (RPL) frameworks. The results reveal that the Kenyan public places a high value on environmental impact, followed by type of renewable energy and community job creation, respectively. On the other hand, respondents do not place much emphasis on ownership or distance and visibility. Policy simulation suggests that while renewable energy adoption is highly valued by households, the total willingness to pay is not enough to cover the higher capital cost for the development of various renewable energy technologies.