ASME 2020 Power Conference最新文献

{"title":"Integration of Novel Geometry Solid Oxide Fuel Cells Into a Residential Furnace/Boiler","authors":"Alex Hartwell, J. Ahn","doi":"10.1115/power2020-16607","DOIUrl":"https://doi.org/10.1115/power2020-16607","url":null,"abstract":"\u0000 Increasing prevalence of extreme weather events and other climate related natural disasters is leading to the increased frequency of power outages. Resilient non-grid dependent power supply for residences is becoming increasingly desirable in order to maintain building management system operation during these events. One potential option for low-maintenance on-site power generation comes from the integration of solid oxide fuel cells (SOFCs) into the combustion chamber of a residential furnace/boiler yielding a combined heat and power (CHP) system. Fuel-rich combustion of natural gas or propane within furnaces/boilers provides the necessary heat as well as fuel for the SOFCs. As a result, the addition of fuel cells into this chamber is possible. The combustion chamber/heat exchanger geometry, however, introduces issues with existing fuel cell geometries that must be addressed before integration is possible.\u0000 This work presents the development of novel anode supported tubular SOFCs with internal cathode and the study of their subsequent integration into a furnace/boiler including model exhaust tests as well as individual cell testing. The proposed system has tremendous potential to effect power distribution to residences, and the novel fuel cell designed in this project has many potential applications.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"25 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":"125209771","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":"Application of GE Low Load Package on an Existing District Heating Power Plant: A Case Study","authors":"Antonio Mambro, F. Congiu, F. Piraccini","doi":"10.1115/power2020-16885","DOIUrl":"https://doi.org/10.1115/power2020-16885","url":null,"abstract":"\u0000 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.\u0000 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.\u0000 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.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"28 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":"122593186","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 and Fluid Analysis of Test Canister for Spent Nuclear Fuel","authors":"Marwan Charrouf, Allen T. Williams","doi":"10.1115/power2020-16244","DOIUrl":"https://doi.org/10.1115/power2020-16244","url":null,"abstract":"\u0000 The absence of a long-term solution for the storage of spent nuclear fuel prompts utilities in the United States to establish on-site storage for used fuel. The challenges associated with placement of spent fuel in dry cask storage on the power plant’s Independent Spent Fuel Storage Installations (ISFSI’s) include aging management of the stainless steel canisters and monitoring for the possible onset of stress corrosion cracking (SCC). The San Onofre Nuclear Generating Station (SONGS) has initiated a test program to examine the effects of heat generation variations inside a test canister using an electric heater rather than spent fuel on the shell temperatures. The test helps in the evaluation of external environmental factors and shell temperature, and to monitor for SCC. This paper presents the computational fluid dynamics (CFD) modeling developed in support of the test to analyze the air natural circulation in the subgrade enclosure and within the test canister with the electrical heating. The thermal analysis is performed using ANSYS CFX and integrally simulates the flow behavior and heat transfer mechanisms both inside and outside the test canister. Comparison of results from different heat loads that represent the decay heat time-profile, sensitivity to the turbulence model, and modes of heat dissipation are discussed. The CFD results are also compared to in-situ temperature measurements to validate the analysis.","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":"121912431","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":"The Change in Boiler and Steam Turbine Failure Modes With Minimum Load Operation: Using Modeling to Predict Susceptibility With Validation Through Plant Testing","authors":"T. Reid, J. Malloy, M. Scoffone, S. Reid, A. Fabricius","doi":"10.1115/power2020-16905","DOIUrl":"https://doi.org/10.1115/power2020-16905","url":null,"abstract":"\u0000 Reducing the minimum load at which a unit can reliably operate is one method to manage changes in market demands and avoid inherent concerns over frequent on and off cycling. For this reason, it is now becoming common practice for plants to develop new lower minimum load levels that are well below conventional targets provided when the unit was first commissioned. For many plants, the criteria for successful operation were not based on optimizing minimum load levels. In fact, most conventional steam plants were commissioned during an era when full base load operation was expected throughout the life of the plant. Base load availability was the key driver not parameters that promoted unit flexibility. As a result, there are opportunities for plants to lower minimum load levels, but it is important for owners to understand the trade-offs and risks that come with such operation.\u0000 TG Advisers (Turbine - Generator) and Tetra Engineering (Boiler) partnered on an analytical assessment and process simulation for a US site with four vintage boilers and steam turbines, the boilers having been converted from coal to gas-firing some years earlier. The boilers were modeled at different load points using boiler and power plant process simulation software. Key issues analyzed were superheat steam temperature, stability of natural circulation, and maintenance of minimum flow velocities. Secondary factors included cold end condensation and the potential for accumulation of dissolved solids in the circuit.\u0000 Utilizing the results of Tetra’s boiler model, TGA completed off-design modeling and calculations for the steam turbine and balance of plant equipment. Examples of primary interest was the impact of the predicted steam conditions and superheat, resulting thermal transient cycles, and LP blading concerns influenced by moisture content and back pressure control. Finally, balance of plant equipment was reviewed to ensure acceptable operating points for key equipment such as boiler feed pumps, feedwater heaters, and hood spray systems.\u0000 Following computer simulations, a plant testing plan was developed, and plant testing was completed. The paper will review analytical predictions and actual plant testing as well as overall lessons learned from the project. Through these analytical and testing efforts the minimum load was reduced from the current practice of 65 MW to 31 MW.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"1 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":"129846161","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":"Phase Change Thermal Diode Using Al2O3-Cu/Water Hybrid Nanofluids for Thermal Rectification Enhancement","authors":"M. Wong, C. Tso, T. C. Ho","doi":"10.1115/power2020-16278","DOIUrl":"https://doi.org/10.1115/power2020-16278","url":null,"abstract":"\u0000 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.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"67 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":"127411117","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":"Economic Analysis of Trigeneration Systems Considering Participations of Energy Storage","authors":"Hongkun Lv, Gaoyan Han, Xutao Guo, Hang Ma, Menglian Zheng","doi":"10.1115/power2020-16807","DOIUrl":"https://doi.org/10.1115/power2020-16807","url":null,"abstract":"\u0000 Distributed trigeneration has been regarded as one of the leading solutions for the future energy production. Unlike centralized energy systems, trigeneration typically recovers otherwise wasted energy and supplies combined cooling, heating, and power products to end users simultaneously, which however causes difficulties in meeting weak temporal-correlated energy demands of end users. Inspired by the success in electric energy systems, energy storage may provide effective solutions to the challenges with respect to trigeneration by decoupling energy generation and consumption. However, multiple key questions are yet fully understood for planning storage-integrated trigeneration systems. The present study aims to answer the following questions: (i) what roles of energy storage are going to play in a trigeneration system? And (ii) how would energy storage affect the performance of the trigeneration system? A self-coded trigeneration system planning model is developed via Python programming to optimize capacities of different devices in the trigeneration system with the presence of energy storage to meet variable multi-energy demands. The effects of the energy storage on the performance of the trigeneration system are investigated. The underlying mechanisms of the energy storage affecting the system’s performance are also explored based on the feasibility region analysis and wasted energy analysis.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"1 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":"128962268","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":"Study on Basic Coal Consumption Characteristics in Dynamic Process of 660MW Ultra-Supercritical Coal-Fired Unit","authors":"Junjie Yin, Ming Liu, Junjie Yan, Yongliang Zhao","doi":"10.1115/power2020-16227","DOIUrl":"https://doi.org/10.1115/power2020-16227","url":null,"abstract":"\u0000 With the spreading of intermittent renewable power, coal-fired power units should cycle frequently to balance the load between power supply side and demand side. Coal consumption of coal-fired units operating in dynamic processes is influenced by many factors, including thermal system, control system, heat storage variation, etc. Therefore, it is very difficult to evaluate the energy efficiency of coal-fired units operating in dynamic processes. It is important to ascertain the basic coal consumption rate in dynamic processes, which is the basis to evaluate the operation performance of coal-fired units.\u0000 In this study, an off-design calculation model of 660MW ultra-supercritical coal-fired unit is developed and validated with design parameters. The developed model can be used to predict the coal consumption rates under steady-state off-design conditions. The basic coal consumption means the coal consumption of coal-fired units with operating parameters the same as target values. To calculate the basic coal consumption rate, a load cycling process is differentiated into lots of short time periods and every period is regarded as a steady-state condition with constant load, therefore the coal consumption rates in every period are equal to that of the corresponding steady-state condition. The calculation formula of basic coal consumption rates in is derived for load cycling processes.\u0000 On the basis of the off-design calculation model and assumption of idealized condition, average coal consumption rates during different processes with constant load cycling rates can be calculated and analyzed. Results show that if the initial and final loads are both settled, the basic coal consumption rate remains unchanged and is independent of load cycling rate. If the load cycling amplitude remains unchanged, the basic coal consumption rate increases as the initial load decrease. The study aims to provide benchmark values for the energy consumption analysis in actual dynamic processes, and further study on coal consumption characteristics in dynamic processes will be developed based on it.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"9 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":"128793010","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":"Reactor Network Modeling of Stability and Emissions of Hydrogen and Natural Gas Blends for a Piloted Gas Turbine Combustor","authors":"Candy Hernández, V. McDonell","doi":"10.1115/power2020-16568","DOIUrl":"https://doi.org/10.1115/power2020-16568","url":null,"abstract":"\u0000 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.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"198 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":"123358664","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":"Wind Turbine Blade Coating Fatigue Induced by Raindrop Impact","authors":"Weifei Hu, Weiyi Chen, Xiaobo Wang, Zhen-yu Liu, Jianrong Tan, Yeqing Wang","doi":"10.1115/power2020-16510","DOIUrl":"https://doi.org/10.1115/power2020-16510","url":null,"abstract":"\u0000 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.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"11 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":"122454715","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":"Using Machine Learning to Increase Model Performance for a Gas Turbine System","authors":"Samuel M. Hipple, Zachary T. Reinhart, Harry Bonilla-Alvarado, Paolo Pezzini, K. Bryden","doi":"10.1115/power2020-16580","DOIUrl":"https://doi.org/10.1115/power2020-16580","url":null,"abstract":"\u0000 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.\u0000 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.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"70 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":"122609612","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}