bluuebunny/crossref_metadata_2025_split huggingface dataset:
Large Eddy Simulations of In-Nozzle Cavitation Phenomena for Cold Fuel Injection
Authors: Bontitsopoulos Stavros , Hamzehloo Arash , Aleiferis Pavlos G. , Cracknell Roger F. 
Published: October 2020

Abstract
The present work investigates the in-nozzle phenomenology of cold fuel injections. Large Eddy Simulations (LES) were performed using a 3D model of a step nozzle injector with water and iso-octane serving as working fluids and the examined cases spanning across a range of temperatures that is relevant to an engine’s start-up operation. The aim is to shed light on the influence exerted by temperature on the in-nozzle cavitation mechanism, which in turn affects the primary atomization and the structure of the downstream emerging spray.
Results suggest that a decrease in the injected fuel’s temperature induces a reduction of the nozzle’s void fraction and a shrinkage in the streamwise length of the cavitation region. This suggests that the size and intensity of the hydrodynamic cavitation features tend to become suppressed in cold conditions. The phenomenon appears to be driven by the temperature dependence of the injected fluid’s thermophysical properties, primarily the vapour pressure, with lower values hindering phase change.
Thermodynamic Analysis for Cogeneration CSP-MED Cycle Using sCO2 and Sensible Heat Sources
Authors: Caceres Gonzalez Rodrigo A. , Hatzell Marta C. 
Published: October 2020

Abstract
Here we perform a steady state thermodynamic analysis of a cogeneration cycle capable of producing power through a supercritical Carbon Dioxide (sCO2) recompression Brayton cycle and water through a Multi Effect Distillation (MED) plant. The integration considers a demineralized water loop heated by rejected sCO2. Thus, the performance of the power block is independent of the desalination block. Each block of the system is independently analyzed to identify the operational or design parameters that influence the thermodynamic efficiency and water and power production. For a power block producing 112.6 MW, with a fixed turbine inlet temperature, the pressure ratio (PR) is the most influential variable in order to maximize net power production and thermal efficiency. For the desalination block, producing 2659 m3/d of water, the last effect temperature (TN) is the most influential variable for water production. In the integrated cogeneration system, increasing PR beyond the power block optimized value (From PR = 3.5 to 3.96) reduces power (from 112.6 to 112.4 MW), but increases water production (from 35 to 40 kg/s), and decreases the specific energy consumption from 29.4 kWh/m3 of water to 26.5 kWh/m3 of water.
Reactor Network Modeling of Stability and Emissions of Hydrogen and Natural Gas Blends for a Piloted Gas Turbine Combustor
Authors: Hernandez Candy , McDonell Vincent 
Published: October 2020

Abstract
Lean-premixed (LPM) gas turbines have been developed for stationary power generation in efforts to reduce emissions due to strict air quality standards. Lean-premixed operation is beneficial as it reduces combustor temperatures, thus decreasing NOx formation and unburned hydrocarbons. However, tradeoffs occur between system performance and turbine emissions. Efforts to minimize tradeoffs between stability and emissions include the addition of hydrogen to natural gas, a common fuel used in stationary gas turbines. The addition of hydrogen is promising for both increasing combustor stability and further reducing emissions because of its wide flammability limits allowing for lower temperature operation, and lack of carbon molecules. Other efforts to increase gas turbine stability include the usage of a non-lean pilot flame to assist in stabilizing the main flame. By varying fuel composition for both the main and piloted flows of a gas turbine combustor, the effect of hydrogen addition on performance and emissions can be systematically evaluated. In the present work, computational fluid dynamics (CFD) and chemical reactor networks (CRN) are created to evaluate stability (LBO) and emissions of a gas turbine combustor by utilizing fuel and flow rate conditions from former hydrogen and natural gas experimental results. With CFD and CRN analysis, the optimization of parameters between fuel composition and main/pilot flow splits can provide feedback for minimizing pollutants while increasing stability limits. The results from both the gas turbine model and former experimental results can guide future gas turbine operation and design.
Pilot-Scale System With Particle-Based Heat Transfer Fluids for Concentrated Solar Power Applications
Authors: Bonino Christopher A. , Hlebak Joshua , Baldasaro Nicholas , Gilmore Dennis 
Published: October 2020

Abstract
Concentrated solar power (CSP) is a promising large-scale, renewable power generation and energy storage technology, yet limited by the material properties of the heat transfer fluid. Current CSP plants use molten salts, which degrade above 600°C and freeze below 220°C. A dry, particle-based heat transfer fluid (pHTF) can operate up to and above 1,000°C, enabling high-efficiency power cycles, which may enhance CSP’s commercial competitiveness. Demonstration of the flow and heat-transfer performance of the pHTF in a scalable process is thereby critical to assess the feasibility for this technology.
In this study, we report on a first-of-a-kind pilot system to evaluate heat transfer to/from a densely flowing pHTF. This process unit circulates the pHTF at flowrates up to 1 tonne/h. Thermal energy is transferred to the pHTF as it flows through an electrically heated pipe. A fluidization gas in the heated zone enhances the wall-to-pHTF heat transfer rate. We found that the introduction of gas mixtures with thermal conductivities 4.6 times greater than that of air led to a 65% increase in the heat transfer coefficient compared to fluidization by air alone. In addition to the fluidization gas, the particle size also plays a critical role in heat transfer performance. Particles with an average diameter of 270 μm contributed to heat transfer coefficients that were up to 25% greater than the performance of other particles of the same composition in size range of 65 to 350 μm. The considerations for the design of an on-sun system are also discussed. Moreover, the collective work demonstrates the promise of this unique design in solar applications.
Investigation of Soot Formation in Fuel-Rich Premixed Propane/Air Microcombustion at Low Temperatures
Authors: Khalid Abdul H. , Tian Jiashen , Skabelund Brent B. , Milcarek Ryan J. 
Published: October 2020

Abstract
The advantage of micro/meso combustion includes higher efficiency, improved heat and mass transfer, swift startup and shutdown when compared with regular combustion. This study aims to investigate the critical sooting equivalence ratio and soot precursor formation in a micro-flow reactor with a controlled temperature profile of diameter 2.3mm and their dependence on the temperature ranging from 800–1250 °C. The equivalence ratio is varied from 1–13 and flow rates of 10 and 100sccm were investigated. Also, nitrogen is used to study the effect of inert gas dilution. A gas chromatograph is used to study the exhaust gas composition. The reactor is analyzed visually for the traces of soot particles before and after combustion, each time the temperature and/or equivalence ratio is varied. From 750–950°C, no soot is indicted at all equivalence ratios even up to 100. The inert gas dilution helped in raising the critical sooting equivalence ratio as expected because of the lower temperature. The results indicated an opposite trend to what has been well understood for the pre-mixed sooting flames, i.e., decreasing temperature decreases soot formation. The capability of the reactor to examine the effects of temperature on the critical sooting equivalence ratio at different flow rates has been successfully demonstrated.
System Analysis of a 100kW Internal Combustion Engine (ICE) Solid Oxide Fuel Cell (SOFC) Hybrid Configuration
Authors: Colón Rodríguez José , Harun Nor Farida , Zhou Nana , Sabolsky Edward , Tucker David 
Published: October 2020

Abstract
Due to the intermittent nature of the renewable power plants and the rigid operation of existing plans, the need for flexible power production is eminent. Hybrid energy systems have shown potential for flexible power production capable to fulfill the power demands and maintain the efficiency. This work studies different design cases of a 100kW Internal Combustion Engine (ICE) and Solid Oxide Fuel Cell (SOFC) hybrid system.
Anode off-gas from the fuel cell stack provided the chemical energy to run the ICE. Heat management of the anode exhaust was considered to attain the operational limits of the ICE in the present configuration. A turbocharger was used to deliver the necessary air flow for both the fuel cell stack and the engine. A series of 25 design cases were chosen to analyze the performance and the potential flexibility of this cycle. The 25-design points resulted from a matrix composed of the variation of fuel utilization and reformer operating temperature, ranging from 70% to 90% and 600K to 1000K, respectively. At each design point, hardware was re-sized to match the desired conditions.
The cycle performance and fuel cell distributed profiles are discussed in this paper. It is discovered that the system efficiency increases as the fuel utilization increases following a nearly linear behavior. The highest efficiency attained is 62% at a reformer operating temperature of 800K and a 90% fuel utilization. The minimum external fuel required to maintain turbocharger in operation decreases with the increase on the reformer temperature. Power contribution between ICE and SOFC follows a linear behavior closely overlapping each trend at different reforming operational temperatures. The impact of external reforming and internal on-anode reforming is also discussed. It is found that there is an optimal balance between the external and internal reforming. The optimal methane content in this work is shown to be around ∼18 vol%.
A Comprehensive Framework for Distributed Energy Resource Aggregators
Authors: Campbell Nicolas A. , Peinado-Guerrero Miguel A. , Phelan Patrick E. , Villalobos Jesus R. 
Published: October 2020

Abstract
An operational framework is proposed for managing aggregated distributed energy resources (DERs). Currently, aggregators partake in the energy market with minimal coordination or exchange of information with the concerned parties. In particular, demand response (DR) has yet to offer its potential value to the grid. It continues to be utilized as a bulk service for peak-shaving, served with little regard or accountability of the additional effects it brings. This has led to numerous issues surrounding DR events, mainly concerning the distribution system. In both practice and literature, there lacks a structured method for aggregators to operate optimally while addressing the issues observed. Most of the research found in literature pertains to a singular problem, for example, aggregating electric vehicles (EV), optimal bidding strategies, optimal scheduling, and congestion management using DR. The integration of these large concepts is not found in literature but is important in understanding the practical effects additional technical and financial constraints have on finding a practical, close-to-optimal solution. The framework proposed is comprehensive, containing all the components believed to be necessary for an aggregator to operate with respect to the distribution constraints. It is also conceptual and meant to emphasize the benefits the individual components and the complete framework offer.
Digitalisation of Biomass Exploration: A Case Study of Biomass Feedstock Classification
Authors: Olatunji Obafemi O. , Madushele Nkosinathi , Adedeji Paul A. , Akinlabi Stephen 
Published: October 2020

Abstract
Biomass is one of the renewable energy (RE) sources with high prospects in the clean energy strata. The diversity of its application and sources have made digitalisation of its exploration presents an array of attractive opportunity to deploy different categories of biomass in a manner that mitigates climate change, advances economies and reduces power dependency. This study discusses the drivers of digitalisation in bioenergy exploration with the associated opportunities and challenges along the bioenergy value chain. As proof of concept, a case study based on the intelligent classification perspective of digitalisation of biomass was discussed. Two classifiers: Sparse Random Error-Correcting Output-based Support Vector Machine (SRECO-SVM) and Euclidean distance-based k-Nearest Neighbour (KNN-EUM) were elaborated while the procedures for the model development were highlighted. Relevant performance indices were applied to evaluate the models developed. Most significantly, the Accuracy, Sensitivity, Specificity were 0.77, 0.81, 0.97 respectively for SRECO-SVM at the computational time (CT) of 20.41 secs while 0.55, 0.56, 0.96 respectively were reported for KNN-EUM at the computational time (CT) of 20.40 secs. Some other composite metrics, which include G-means, F1-score, Mathews correlation coefficient (MCC), Discriminant power (DP) were reported. On the overall, a synergistic relationship is needed between the data analytics, artificial intelligence and other blockchain technologies in order to unleash the full benefits of digitalisation.
Numerical Investigation of Heat Transfer Characteristics of Different Nanoparticles Using Modified Eulerian-Eulerian Model
Authors: Hussain Muzafar , Tahir Shahbaz 
Published: October 2020

Abstract
Nanofluids are widely adopted nowadays to enhance the heat transfer characteristics in the solar applications because of their excellent thermophysical properties. In this paper, a modified Eulerian-Eulerian model recently developed based on experiments was validated numerically to account for the deviations from the experimental data. The modified Eulerian-Eulerian model is compared with the single-phase model, Eulerian-Eulerian models for TiO2-water at different operating conditions and deviation from the experimental data for each of the model was documented. However, the modified Eulerian-Eulerian model gave much closer results when compared to the experimental data. For the further extension of work, the modified Eulerian-Eulerian model was applied to different nanofluids in order to investigate their heat transfer characteristics. Three different nanoparticles were investigated namely Cu, MgO, and Ag and their heat transfer characteristics is calculated based on the modified Eulerian-Eulerian model as well as the single-phase model for the comparison. For lower values of Reynolds numbers, the average heat transfer coefficient was almost identical for both models with small percentage of error but for higher Reynolds numbers, the deviation got larger. Therefore, single-phase model is not appropriate for higher Reynolds numbers and modified Eulerian-Eulerian model should be used to accurately predict the heat transfer characteristics of the nanofluids at higher Reynolds numbers. From the analysis it is found that the Ag-water nanofluid have the highest heat transfer characteristics among others and can be employed in the solar heat exchangers to enhance the heat transfer characteristics and to further improve the efficiency.
Micromixers and Hydrogen Enrichment: The Future Combustion Technology in Zero-Emission Power Plants
Authors: Hussain Muzafar , Abdelhafez Ahmed , Nemitallah Medhat A. , Habib Mohamed A. 
Published: October 2020

Abstract
The stable and flexible micromixer (MM) gas-turbine technology is coupled with hydrogen (H2) enrichment to present an oxy-methane combustor that can sustain highly diluted flames for application in the Allam cycle for zero-emission power production. MMs have never been tested under oxy-fuel conditions, which highlights the novelty of the present study. The operability window was quantified over ranges of fuel hydrogen fraction (HF) and oxidizer oxygen fraction OF. The MM showed superior stability, allowing for reducing OF down to 21% (by vol.) without H2 enrichment, which satisfies the dilution requirements (23%) of the primary reaction zone within the Allam-cycle combustor. By comparison, swirl-based burners from past studies exhibited a ∼30% minimum threshold. Enriching the fuel with H2 boosted flame stability and allowed for reducing OF further down to a record-low value of 13% at HF = 65% (by vol.) in fuel mixture. Under these highly diluted conditions, the adiabatic flame temperature is 990°C (1800°F), which is substantially lower than the lean blowout limit of most known technologies of lean premixed air-fuel combustion in gas-turbine applications. The results also showed that H2 enrichment has minimal effect on the adiabatic flame temperature and combustor power density (MW/m3/atm), which facilitates great operational flexibility in adjusting HF to sustain flame stability without influencing the Allam cycle peak temperature or affecting the turbine health. MM combustion with H2 enrichment is thus a recommended technology for controlled-emission, fuel/oxidizer-flexible combustion in gas turbines.
Building Energy Prediction Using Artificial Neural Networks (LSTM)
Authors: Goswami Sankhanil 
Published: October 2020

Abstract
Modern buildings account for a significant proportion of global energy consumption worldwide. Therefore, accurate energy use forecast is necessary for energy management and conservation. With the advent of smart sensors, a large amount of accurate energy data is available. Also, with the advancements in data analytics and machine learning, there have been numerous studies on developing data-driven prediction models based on Artificial Neural Networks (ANNs). In this work a type of ANN called Large Short-Term Memory (LSTM) is used to predict the energy use and cooling load of an existing building. A university administrative building was chosen for its typical commercial environment. The network was trained with one year of data and was used to predict the energy consumption and cooling load of the following year. The mean absolute testing error for the energy consumption and the cooling load were 0.105 and 0.05. The percentage mean accuracy was found to be 92.8% and 96.1%. The process was applied to several other buildings in the university and similar results were obtained. This indicates the model can successfully predict the energy consumption and cooling load for the buildings studied. The further improvement and application of this technique for optimizing building performance are also explored.
HRSG Fleet Integrity Management: Lessons Learned From the Field
Authors: Fabricius Andreas , Malloy James , Taylor Mark , Jackson Peter , Moelling David 
Published: October 2020

Abstract
This paper presents key lessons learned from hundreds of field missions for assessing the condition of HRSGs at sites around the world. These involved field inspections, root cause of failure analyses and performance assessments on HRSGs of nearly every design, fuel type and operating mode. Statistics on degradation and failure risk areas are presented. Analysis techniques used in both preparing for field work and in assessing field examination results are also discussed. Trends in the evolution of the principal damage mechanisms over the years are examined, and predictions of future problem areas are considered with a view to guiding asset management actions. The impact on HRSG integrity of changes in unit designs, such as the move to flexible operation in response to the increasing contribution from renewables, is also assessed, and likely future trends are outlined. The use of qualitative and quantitative risk-based methods is also discussed and compared to the use of condition-based inspection methods as a basis for a fleet wide management strategy. A case study including a large IPP with several generating assets in its portfolio is presented and the most cost-effective method with respect to current corporate strategy is discussed. Recommendations for fleet management strategies are made.
Optimization and System Identification of a Variable Pico-Scale Hydro Turbine for Pressure Regulation
Authors: Yu Shi Miao , Ko Youngmok , Hu Han , Seo Jun , Bilton Amy M. 
Published: October 2020

Abstract
Recent studies from the European Commission estimate that more than 20% of global energy is consumed by pumping systems. Significant research has focused on increasing pump efficiency to lower energy consumption; however, few have looked at the energy lost in use of pressure regulating devices (PRDs). This paper proposes a novel pico-scale hydro turbine that could effectively replace PRDs and generate power while regulating pressure. The proposed hydro turbine has an outer diameter of 4″ and a total length of 5.4″. The turbine uses 14 rotating guide vanes and is attached to a generator with a variable load. To maximize power recovery and pressure control range of the turbine, a non-dominated sorting genetic algorithm was used for multi-objective geometry optimization. Then, to build a dynamic model for control system design, parameter identification was conducted using a Gaussian process surrogate model and stochastic search algorithms: particle swarm optimization and genetic algorithm. The optimized turbine showed good agreement between simulated and experimental results and achieved a power output of 120 W, pressure drop range of 6 to 27 psi, and maximum hydraulic efficiency of 75% at the rated flow rate of 27 GPM. The optimized turbine shows the potential of pico-turbines for pressure regulation.
A Review of Cryogenics Applications for Power and Energy
Authors: Rehman Wajiha , Farhan Muhammad , Rehman Fatima 
Published: October 2020

Abstract
Cryogenic technologies use cryogens such as natural gas, nitrogen, helium, hydrogen, argon, and oxygen as main working fluids. These cryogenic technologies and cryogens are emerging in the fields of energy storage, power generation, and eco-friendly fuels. Recently, researchers have proposed to integrate cryogenic systems with power generation systems including Nuclear power plants and wind turbines, to store surplus energy during the off-peak hours and use it during peak hours. The basic idea is to convert air or some other cryogen into liquid during the off-peak hours and expand it during the peak hours to run turbines. Whereas in the shipping industry; LNG is being considered as an alternative fuel to gasoline to meet the future emissions criteria of IMO. LNG not only reduces the vessel’s emissions but it is also cheaper than heavy fuel oils. As every technology has some pros and cons, the efficiency of cryogenics power generation systems is low; about 25%, but work is being done to improve its efficiency and to find alternative applications.
This article discusses the applications, advantages, and drawbacks of cryogenics technologies and fuels for energy generation and storage applications. It also focuses on the challenges which are being faced by this technology and possible ways to overcome them.