bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phase Change Thermal Diode Using Al2O3-Cu/Water Hybrid Nanofluids for Thermal Rectification Enhancement
Authors: Wong M. Y. , Tso C. Y. , Ho T. C. 
Published: October 2020

Abstract
A thermal diode, a device to manipulate the heat flow in different directions, is useful in various thermal systems, such as solar thermal storage systems. It is noted that the performance of phase change thermal diodes shows the highest thermal rectification performances in the literature. The performances of the phase change thermal diode can be further improved by utilizing a working fluid with enhanced thermal properties. Since hybrid nanofluids are proven to have better thermal properties than the base fluid (i.e. water), in this study, a thermal diode using Al2O3-Cu/water hybrid nanofluid is fabricated and tested to investigate the feasibility of using hybrid nanofluid to enhance the performance of the thermal diode. The heat transfer and thermal rectification performances of the thermal diode using Al2O3-Cu/water hybrid nanofluid are compared experimentally, to a thermal diode using water. The effect of temperature on the heat transfer and thermal rectification performances of the thermal diode is also examined. The results indicate that the effective thermal conductivity in the forward direction and the diodicity of the thermal diode using Al2O3-Cu/water hybrid nanofluid are improved by 42.4% and 30.8%, respectively, compared to that of the thermal diode using water. The findings not only reveal a new direction for future research in enhancement of the thermal rectification performance of the phase change thermal diode but also provide an alternative research path for improving the performance of existing solar thermal storage systems.
Novel Chemical Heatpump Technology for Thermoelectric Powerplants Cooling Needs
Authors: Zhou Gong , Shaaban Aly 
Published: October 2020

Abstract
With an increasing need to find ways to reduce water consumption in industrial cooling system, dry cooling has been of a great interest. To eliminate the dependence of powerplant cooling needs on water, a novel technology, called ACTIVE, has been developed. ACTIVE, is a novel chemical heatpump cycle, cools below the ambient wet bulb temperature with coefficient of performance (COP) reaching up to 30, outperforming current dry cooling technology especially during hot summer days. ACTIVE employs a cyclic depolymerization-polymerization process, based on readily available polymers and catalysts. In this effort the ACTIVE cycle was developed starting with a concept and was reduced to practice. Here the cycle is explained and test results are presented.
Wind Turbine Blade Coating Fatigue Induced by Raindrop Impact
Authors: Hu Weifei , Chen Weiyi , Wang Xiaobo , Liu Zhenyu , Tan Jianrong , Wang Yeqing 
Published: October 2020

Abstract
With the increase of wind energy production demand, the need to manufacture larger wind turbine blades is on the rise. Because of the high tip speed of the large blade, the blade could be impacted by high-speed objects such as raindrops. This research focuses on developing a computational model for analyzing wind turbine blade coating fatigue induced by raindrop impact. A stochastic rain texture model is used to simulate a realistic rain event determined by a rain intensity and a rain duration. A smoothed particle hydrodynamic approach is implemented to calculate the impact stress considering a single raindrop. A stress interpolation method is proposed to accurately and efficiently estimate the impact of stress under a random rain event. Besides, a crack growth law is used to explain the process of coating shedding. Through a method for calculating crack growth length based on stress, this paper analyzes crack growth life as a function of the rain intensity and the rain duration. This function, together with the statistics of rainfall history, provides a new approach for estimating the expected fatigue life of the blade coating.
Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning
Authors: Choe Do-Eun , Talor Gary , Kim Changkyu 
Published: October 2020

Abstract
Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.
Modeling and Control of Subcritical Coal-Fired Power Plant Components for Fault Detection
Authors: Agbleze Selorme , Lima Fernando V. , Indrawan Natarianto , Panday Rupendranath , Pezzini Paolo , Bonilla-Alvarado Harry , Bryden Kenneth M. , Tucker David , Shadle Lawrence J. 
Published: October 2020

Abstract
Due to the increased penetration of renewable power sources into the electric grid, the current number of existing coal-fired power plants shifting from baseload to load-following operations has also increased. This shift creates challenges especially for the power industry as coal-fired power plants were not designed for ramping situations, leading to added stress on major components of these plants. This stress causes the system to degrade over time and eventually develop faults. As boilers are still the primary component that fails and causes forced outages, accurate characterization of faults and fractures of boilers is now becoming increasingly critical to reduce plant downtime and extend the plant life during cycling operations. This work focuses on modeling sections of a subcritical coal-fired power plant and proposes algorithms for fault detection in MATLAB/Simulink. The developed model simulates the process dynamics including steam and feedwater flow regulating valves, drum-boiler, and heat rate on the regulation of pressure, drum level and production of saturated steam. The model also simulates the dynamics of superheaters for increasing the energy content of steam, and a spray section for regulating the temperature of steam upstream of the high-pressure turbine to allow for power output adjustment within a given valve operating range.
Furthermore, an extension to a leak detection framework proposed by co-authors in previous work is explored. The new framework includes a modification to the threshold analysis portion of the previous work. The extended framework is then applied to a subcritical coal-fired power plant model for leak detection. In particular, this framework analyzes mismatches or deviations in expected plant dynamics with an identified transfer function model. The mismatch is flagged after it exceeds a threshold. The developed algorithm thus aids in rapid detection of faults to reduce impeded plant performance. The results of this work will support real plant operations by providing an accurate characterization of faults in the operation of coal-fired power plants.
Theoretical Analysis of Solar Thermal Desalination Performance Limitation
Authors: Zheng Yanjie , Hatzell Kelsey B. , Caceres Gonzalez Rodrigo , Hatzell Marta C. 
Published: October 2020

Abstract
Solar thermal desalination systems utilize concentrated or non-concentrated sunlight to produce heat to drive a phase change separation process and produce freshwater. It could be an effective solution for increasingly scarce freshwater resources and energy shortages across the globe. In order to explore the performance limits and operating parameters that affect specific water production (SWP), this paper proposes a thermodynamic model of the ideal solar-driven thermal desalination process. The model compares two different heating configurations of solar collector system and considers surface temperature of solar collector, concentration ratio, recovery ratio and inlet saline water salinity to find maximum specific water production. The results show that under reversible condition, a flat plate collector with inlet saline water salinity of 35 g/kg will experience an increase in SWP from 29.9 gs−1m−2 to 52.7 gs−1m−2 if the recovery ratio decrease from 70% to 10%. For a system with concentration ratio of 10, when the surface temperature of solar collector is 507K, the maximum specific water production can reach 166.3 gs−1m−2 as the recovery ratio approaches zero. Reduction in incoming fluid salinity can further increase these performance limitations. The work fundamentally demonstrates the thermodynamic process of solar thermal desalination, and proposes a method to evaluate the performance limitation.
Using Machine Learning to Increase Model Performance for a Gas Turbine System
Authors: Hipple Samuel M. , Reinhart Zachary T. , Bonilla-Alvarado Harry , Pezzini Paolo , Bryden Kenneth Mark 
Published: October 2020

Abstract
With increasing regulation and the push for clean energy, the operation of power plants is becoming increasingly complex. This complexity combined with the need to optimize performance at base load and off-design condition means that predicting power plant performance with computational modeling is more important than ever. However, traditional modeling approaches such as physics-based models do not capture the true performance of power plant critical components. The complexity of factors such as coupling, noise, and off-design operating conditions makes the performance prediction of critical components such as turbomachinery difficult to model. In a complex system, such as a gas turbine power plant, this creates significant disparities between models and actual system performance that limits the detection of abnormal operations.
This study compares machine learning tools to predict gas turbine performance over traditional physics-based models. A long short-term memory (LSTM) model, a form of a recurrent neural network, was trained using operational datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. The LSTM turbine model was trained to predict shaft speed, outlet pressure, and outlet temperature. The performance of both the machine learning model and a physics-based model were compared against experimental data of the gas turbine system. Results show that the machine learning model has significant advantages in prediction accuracy and precision compared to a traditional physics-based model when fed facility data as an input. This advantage of predicting performance by machine learning models can be used to detect abnormal operations.
Design of Fast and Reliable Steam Surface Condensers
Authors: Nadig Ranga 
Published: October 2020

Abstract
Power plants operating in cyclic mode, standby mode or as back up to solar and wind generating assets are required to come on line on short notice. Simple cycle power plants employing gas turbines are being designed to come on line within 10–15 minutes. Combined cycle plants with heat recovery steam generators and steam turbines take longer to come on line.
The components of a combined cycle plant, such as the HRSG, steam turbine, steam surface condenser, cooling tower, circulating water pumps and condensate pumps, are being designed to operate in unison and come on line expeditiously. Major components, such as the HRSG, steam turbine and associated steam piping, dictate how fast the combined cycle plant can come on line. The temperature ramp rates are the prime drivers that govern the startup time. Steam surface condenser and associated auxiliaries impact the startup time to a lesser extent.
This paper discusses the design features that could be included in the steam surface condenser and associated auxiliaries to permit quick startup and reliable operation. Additional design features that could be implemented to withstand the demanding needs of cyclic operation are highlighted.
Comparison Study of Two Different Integrated Solar Combined Cycle Systems
Authors: Duan Liqiang , Wang Zhen , Liu Yulei , Pang Liping 
Published: October 2020

Abstract
The thermodynamic performances of the two different integrated solar combined cycle (ISCC) systems are compared in this paper. Different from the previous comparison researches of ISCC systems based on different solar energy collecting technologies, the goal of this paper is to compare the integration characteristics of two different configurations of integrating concentrated solar energy into a gas turbine combined cycle (GTCC) system based on the same solar collector system. For the first kind of integrated solar gas-steam combined cycle system (ISCC1), the solar energy is introduced to the topping cycle of the gas-steam combined cycle system, while for the second kind of integrated solar gas-steam combined cycle system (ISCC2), the solar energy is introduced to the bottoming cycle of the GTCC system. The detailed system models are developed and their thermal performances are compared under different conditions. For ISCC1, the solar-to-electricity efficiency is higher than that of ISCC2 at the design condition when both the direct normal irradiation and ambient temperature are high due to more efficient energy conversion to electricity. However, the ISCC2 offers the advantages of higher solar-to-electricity efficiency and more solar power output when both the direct normal irradiation and ambient temperature are low. Two ISCC systems are good for energy saving, the ISCC1 consumes 4.412 × 108 kg of fuel a year, which is 2.803 × 106 kg less than that of ISCC2, and the ISCC1 has an annual solar-to-electricity efficiency of 23.93%, 0.88% higher than that of ISCC2. Detailed daily and monthly simulation results show that two systems have advantages of saving energy, and the simulations results show the obvious effects of different solar energy integration modes on the overall IGCC system performance. The achievements of this paper can offer valuable references for the design and operation optimization of ISCC system.
Effect of Magnetic Field on the Laminar Heat Transfer Performance of Hybrid Nanofluid in a Lid Driven Cavity Over Solid Block
Authors: Nimmagadda Rajesh , Matta Durga Prakash , Reuven Rony , Asirvatham Lazarus Godson , Wongwises Somchai , Yerramilli Anjaneyulu , Adusumilli Srinath 
Published: October 2020

Abstract
A 2D numerical investigation has been carried out to obtain the heat transfer performance of hybrid (Al2O3 + Ag) nanofluid in a lid driven cavity over solid block under the influence of uniform as well as non-uniform magnetic field. The geometrical domain consists of a cavity containing nanofluid that is driven by means of lid moving in one direction. This circulating nanofluid will extract enormous amount of heat from the solid block underneath the cavity resulting in conjugate heat transfer. A homogenous solver based on the finite volume method with conjugate heat transfer was developed and adopted in the existing study. The heat efficient hybrid nanofluid (HyNF) pair (2.4 vol.% Ag + 0.6 vol.% Al2O3) obtained by Nimmagadda and Venkatasubbaiah [1] is used in the present investigation. Moreover, efficient non-uniform sinusoidal magnetic field identified by Nimmagadda et al. [2] is also implemented and compared with uniform magnetic field. Furthermore, the magnetic field is applied over the geometrical domain along the two axial directions separately and the effective heat transfer performance is obtained. The significant impact of extensive parameters like Reynolds number, nanoparticle type, nanoparticle concentration, magnetic field type, magnetic field location and the strength of the magnetic field on heat transfer performance are systematically analyzed and presented.
Latent Dynamics in Siting Onshore Wind Energy Farms: A Case of a Wind Farm in South Africa
Authors: Adedeji Paul A. , Akinlabi Stephen , Madushele Nkosinathi , Olatunji Obafemi O. 
Published: October 2020

Abstract
Siting a renewable energy facility entails several latent but influential quantitative and qualitative variables. Empirical and analytical models often fail to unravel the dynamics of these variables however; prior knowledge of their existence and dynamics offers knowledge-based decision-making during the plant siting process. This article examines the significance and dynamics of land ownership, avian environment, and renewable energy policies. Asides the literature survey, review of government policy, and regulations, a semi-structured interview-based method was used in this study using a wind power plant in the Eastern Cape Province of South Africa as a case study. A qualitative content analysis was used for response analysis. From our findings, dynamics around land ownership could be complex depending on the land category and existing contracts between a landowner and the developer. Also, an in-extensive study of avian habitat in seemingly viable land could lead to forced-downtime of wind turbine generators at periods where production is notably high. Lastly, careful examination of prevailing renewable energy policies and a projection on future policies culminates into the viability of the investment. Trivializing these variables before site development could lead to investment loss through low-productivity or force-majeure in the investment. On the overall, the proposed solutions to these barriers can be useful for wind developers in solving similar problems in other renewable energy resources both in South Africa and other countries.
Study on Effects of Compressor Inlet Air Cooling on GTCC System Performance Under Different Environmental Conditions
Authors: Liqiang Duan , Yaofei Guo , Pan Pan , Yongxia Li 
Published: October 2020

Abstract
The environmental conditions (air temperature and relative humidity) have a great impact on the power and efficiency of gas turbine combined cycle (GTCC) system. Using the intake air cooling technology can greatly improve the performance of GTCC system. On the base of the PG9351FA gas turbine combined cycle system, this article builds the models of both the GTCC system and a typical lithium bromide absorption refrigeration system using Aspen Plus software. The effects of compressor inlet air cooling with different environmental conditions on the GTCC system performance are studied. The research results show that using the inlet air cooling technology can obviously increase the output powers of both the gas turbine and the combined cycle power. When the ambient humidity is low, the efficiency of GTCC changes gently; while the ambient humidity is high, the GTCC system efficiency will decline substantially when water in the air is condensed and removed with the progress of cooling process. At the same ambient temperature, when the relative humidity of the environment is equal to 20%, the gas turbine output power is increased by 35.64 MW, with an increase of 16.32%, and the combined cycle output power is increased by 39.57 MW, with an increase of 11.34%. At an ambient temperature of 35°C, for every 2.5 °C drop in the compressor inlet air, the thermal efficiency of the gas turbine increases by 0.189% compared to before cooling.
Modeling and Analysis of a Thermal Management System With Thermoelectric Cooling for the Application in Li-Ion Batteries
Authors: Mostafavi Amirhossein , Jain Ankur 
Published: October 2020

Abstract
Lithium-ion (Li-ion) batteries have recently become the main source of power in portable devices due to advantages such as high energy density. However, Li-ion cells operate well only in a specific temperature range. Degraded preperformance is a consequence of low temperature operation, and potential fire risk originates from thermal runaway at elevated temperatures. Efficient thermal management of Li-ion cells and battery packs is essential to ensure safe and durable performance in wide temperature range. Thermoelectric coolers (TECs), which have been used widely for electronics cooling may also be appropriate for battery cooling due to size compactness, working with direct current. This paper presents experimental characterization of cooling of a prismatic test cell with TECs on two sides. Cooling effect of TEC on the cell core and surface temperatures is investigated at different TEC power rates. Results show core and surface temperatures of the test cell decrease significantly. The obtained results show that by applying the TEC, a temperature drop of 10 °C was achieved for 0.75A TEC current. The optimum TEC current can be selected based on the application. In addition, numerical simulations are carried out to compare with experimental measurements. Heating effect of mounted TECs can be easily achieved just by changing current direction. Experimental results reveal TECs can heat up a cell in cold climate shortly. In addition, thermo electric module may also offer insulating effect in cold climate. Results presented in this paper illustrate potential application of thermoelectric cooling for thermal management of Li-ion cells.
Application of GE Low Load Package on an Existing District Heating Power Plant: A Case Study
Authors: Mambro Antonio , Congiu Francesco , Piraccini Francesco 
Published: October 2020

Abstract
The continuous increase of variable renewable energy and fuel cost requires steam turbine power plants to operate with high flexibility. Furthermore, the reduction in electricity price is forcing many existing and new district heating power plants to further optimize the heat production to maintain a sustainable business. This situation leads to low pressure steam turbines running at very low volume flow for an extended time.
In this work, a case study of an existing 30 MWel district heating power plant located in Europe is presented. The customer request was the removal of the steam turbine last two stages along with the condenser to maximize steam delivery for district heating operations. However, based on the experience gained by GE on low load during the last years, the same heat production has been guaranteed without any significant impact on the existing unit, excluding any major modification of the plant layout such as last stage blading and condenser removal. Making use of the latest low flow modeling, the minimum cooling flow through the low-pressure turbine has been reduced by more than 90% compared to the existing unit. Optimization of the hood spray system and logic will reduce trailing edge erosion during low load operation leading to a significant extension in the last stage blade lifetime. These modifications, commercialized by GE as the Advanced Low Load Package (ALLP), provide a cheap, flexible and effective solution for the customer.
With today’s knowledge, GE has the capability to guarantee low load operation minimizing the mass flow through the low-pressure turbine to the minimum required for safe operation. As a benefit to the customer, this option allows a gain in operational income of about 1.5 M€ per year.