ASME 2020 Power Conference最新文献

{"title":"Combining a Fuel Cell and Ultracapacitor Bank to Power a Vertical Take-Off and Landing Unmanned Aerial System","authors":"Justin A. Laddusaw, A. Pollman, O. Yakimenko, A. Gannon","doi":"10.1115/power2020-16560","DOIUrl":"https://doi.org/10.1115/power2020-16560","url":null,"abstract":"\u0000 This research investigated the combination of a fuel cell and ultracapacitors to create a hybrid powertrain for a vertical take-off unmanned aerial system (UAS). This replaced the more common battery-only powertrain or the hybrid fuel cell-battery powertrain. A secondary power source, such as a battery or ultracapacitors, is required to assist a fuel cell with immediate load requests because fuel cells are unable to supply instantaneous power. The fuel cell-ultracapacitor was tested using a power profile that was experimentally determined using a battery-powered vertical take-off UAS during take-off, hover, and landing. This tabletop experiment is meant to lead to a more refined solution that can be easily scaled to fit into a smaller future vertical take-off UAS. Two separate ultracapacitor banks were made to be put in parallel with the fuel cell. The first was a series of 14, 650 Farad ultracapacitors and the second was a series of 14, 350 Farad ultracapacitors. Both fuel cell-ultracapacitor powertrains were able to meet the power requirements while also supplying power to the fuel cell itself, without an external power supply. Future work opportunities include scaling for implementation into a UAS platform and coding the power management software to optimally manage the proposed hybrid powertrain.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124711526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal Performance Evaluation of a Solar Collector Utilizing a Novel Resistance Network Model","authors":"A. Nokhosteen, Sarvenaz Sobhansarbandi","doi":"10.1115/power2020-16579","DOIUrl":"https://doi.org/10.1115/power2020-16579","url":null,"abstract":"\u0000 Heat pipe evacuated tube solar collectors (HPETCs) are a type of solar collectors with appealing characteristics for the application in solar water heating (SWH) technologies. In order to better understand the heat transfer phenomena in HPETCs and improve their efficiency, there is a need for a fast and robust numerical tool. Due to the complexity of the heat transfer processes involved in modeling a collector’s performance, direct numerical analysis solutions (DNS) are computationally cumbersome. Recent studies have shown that resistance network (RN) models are suitable tools for studying the performance and thermal behavior of HPETCs. In this work, a novel method of resistance network based proper orthogonal decomposition (RNPOD) is presented which can not only consider the geographical and meteorological characteristics of the ambient surroundings, but also take into account the peripheral temperature distribution of a single HPETC. Once the temperatures at each instance in time have been calculated, a POD method is used to predict the thermal behavior of the collector with desired temporal accuracy. The obtained results of this study are cross-validated with the previous experimental work of the authors, illustrating that the model is able to predict the peripheral temperature distribution with a maximum error of 10%.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124957774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building Energy Prediction Using Artificial Neural Networks (LSTM)","authors":"Sankhanil Goswami","doi":"10.1115/power2020-16828","DOIUrl":"https://doi.org/10.1115/power2020-16828","url":null,"abstract":"\u0000 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.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116828540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Micromixers and Hydrogen Enrichment: The Future Combustion Technology in Zero-Emission Power Plants","authors":"M. Hussain, A. Abdelhafez, M. Nemitallah, M. Habib","doi":"10.1115/power2020-16809","DOIUrl":"https://doi.org/10.1115/power2020-16809","url":null,"abstract":"\u0000 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.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125277302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Modeling and Simulation of a Solar Air Heater Assisted by a Dehumidification System for an Agriculture Greenhouse","authors":"F. Almehmadi, K. Hallinan","doi":"10.1115/power2020-16189","DOIUrl":"https://doi.org/10.1115/power2020-16189","url":null,"abstract":"\u0000 Appropriate greenhouse microclimate control is essential for optimizing plant growth and food production. But, maintenance of a greenhouse microclimate generally requires an excessive amount of energy. According to a report published by Scott Sanford [1], the energy cost for greenhouses is considered the third highest annual cost, behind labor and plant materials. At northern latitudes, heating is the primary energy requirement needed in an agriculture greenhouse, comprising 70 to 80% of a typical greenhouse energy consumption [1]. A reduction of heating energy is necessary to ensure the economic viability of a greenhouse.\u0000 This research investigates the potential energy savings associated with integrating a solar air heater assisted with a desiccant wheel in an agriculture greenhouse. This study has two main thrusts. The first is to demonstrate the energy effectiveness a solar air heater with a dehumidification system to maintain the internal climate. The second thrust is to develop a multi-linear regression model that can be used to predict the hourly heating requirement. Thereafter, the developed regression model can be used to conduct a parametric analysis to investigate the impact of changing greenhouse parameters on the total heating requirements.\u0000 A case study has been considered for a greenhouse that is 30 m long and 24 m wide. The climate condition of the city of Dayton, OH was selected for this case. The predicted performance of the integrated system is compared with two other heating systems: electric and gas furnaces. The study reveals that heating energy savings in the proposed system is 51% and 30% when compared with the electric and gas furnaces, respectively. Aside from heating energy savings, the proposed system can be efficiently used to control indoor humidity in a way that ensures better crop yield.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134179064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pilot-Scale System With Particle-Based Heat Transfer Fluids for Concentrated Solar Power Applications","authors":"Christopher A. Bonino, Joshua Hlebak, Nicholas G. Baldasaro, Dennis Gilmore","doi":"10.1115/power2020-16588","DOIUrl":"https://doi.org/10.1115/power2020-16588","url":null,"abstract":"\u0000 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.\u0000 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.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128056007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and Control of Subcritical Coal-Fired Power Plant Components for Fault Detection","authors":"S. Agbleze, F. Lima, Natarianto Indrawan, R. Panday, Paolo Pezzini, Harry Bonilla-Alvarado, K. Bryden, D. Tucker, L. Shadle","doi":"10.1115/power2020-16571","DOIUrl":"https://doi.org/10.1115/power2020-16571","url":null,"abstract":"\u0000 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.\u0000 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.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129235848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning","authors":"Doeun Choe, Gary Talor, Changkyun Kim","doi":"10.1115/power2020-16557","DOIUrl":"https://doi.org/10.1115/power2020-16557","url":null,"abstract":"\u0000 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.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123027856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent Dynamics in Siting Onshore Wind Energy Farms: A Case of a Wind Farm in South Africa","authors":"P. Adedeji, S. Akinlabi, N. Madushele, O. Olatunji","doi":"10.1115/power2020-16726","DOIUrl":"https://doi.org/10.1115/power2020-16726","url":null,"abstract":"\u0000 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.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122486904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison Study of Two Different Integrated Solar Combined Cycle Systems","authors":"Liqiang Duan, Wang Zhen, Liu Yulei, L. Pang","doi":"10.1115/power2020-16695","DOIUrl":"https://doi.org/10.1115/power2020-16695","url":null,"abstract":"\u0000 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.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"487 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122749498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}