Academic literature on the topic 'Power-interest grid method'
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Journal articles on the topic "Power-interest grid method"
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Tuzikova, Valeriya, Josef Tlusty, and Zdenek Muller. "A Novel Power Losses Reduction Method Based on a Particle Swarm Optimization Algorithm Using STATCOM." Energies 11, no. 10 (2018): 2851. http://dx.doi.org/10.3390/en11102851.
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In the modern electric power industry, Flexible AC Transmission Systems (FACTS) have a special place. In connection with the increased interest in the development of “smart energy”, the use of such devices is becoming especially urgent. Their main function is the ability to manage modes in real time: maintain the necessary level of voltage in the grids, control the power flow, increase the capacity of power lines and increase the static and dynamic stability of the power grid. The problem of system reliability and stability is related to the task of definitions and optimizations and planning indicators, design and exploitation. The main aim of this article is the definition of the best placement of the STATCOM compensator in case to provide stability and reliability of the grid with the minimization of the power losses, using Particle Swarm Optimization algorithms. All calculations were performed in MATLAB.
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Wen, Huixian. "Power flow analysis of 110kV power supply system based on PowerWorld." Journal of Physics: Conference Series 2495, no. 1 (2023): 012025. http://dx.doi.org/10.1088/1742-6596/2495/1/012025.
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Abstract With the growing global interest in various economic and environmental benefits of the power system, it is essential to ensure the safe and stable operation of the power grid. The optimal power flow calculation method is studied using the PowerWorld and Newton-Ralfsnn methods. The results calculated by the Simulator LP OPF function are compared with the manual calculation of the 110 kV power supply system grid. After optimization, the total cost is saved by 623.05 ¥/h. The simulation results can directly and vividly reflect the power distribution and optimize the power supply and distribution power flow.
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Peng, Dong, Yawei Xue, Tianzhi Li, Lina Wang, and Jinchao Li. "Research on Operation Benefit Evaluation of Power Network Project Based on Combination Weighting Method." E3S Web of Conferences 118 (2019): 01055. http://dx.doi.org/10.1051/e3sconf/201911801055.
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Firstly, on the basis of economic efficiency, equipment and operational safety, public interest and other dimensions, this paper constructs an evaluation index system for operational benefit of power grid projects, and then a combined weighting method based on Analytic Network Process (ANP) and entropy weight method is established. Finally, an empirical study is carried out based on the actual data of power grid projects, and the research results prove the effectiveness of the model.
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Joseph, C. Attachie, K. Amuzuvi Christian, and Diamenu Godwin. "Large-scale wind power grid modelling and stability evaluation using stochastic approaches." International Journal of Applied Power Engineering 11, no. 3 (2022): 237~250. https://doi.org/10.11591/ijape.v11.i3.pp237-250.
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Due to dwindling fossil fuel reserves, there is a global demand to increase the level of low-carbon based renewable energy resources (RERs) for electric power generation. Coupled with concerns that emissions from fossil fuels is leading to climate change with possible disastrous consequences, effort is seriously under way to discover the probable usage of RERs on large-scale without being integrated into an existing power grid. It is envisaged that such a large-scale RER power grid will operate solely on its own and expected to be stable and reliable comparable to a conventional power grid. The impact of wind power generation as part of the electric power grid is no longer negligible. Wind energy generation is one of the most established renewable energy resources to help ensure low carbonbased renewable energy (RE) self-sufficiency, yet it is also one of the most volatile RERs. Despite its disadvantages, wind power generation is expected to continue its strong growth in the coming years as result of high interest in clean energy to curb the global warming. Various studies are looking at the prospects for solely RER power grids for usage on large-scale. However, the issue has been the stability and the reliability of such power grids. Variability of power output, intermittency and load mismatch are wind farms’ unique characteristics potentially harmful to grid voltage stability. In response to this problem, A large-scale wind power system was modelled using stochastic approach and the results analyzed using Lyapunov method, matrix exponential and Hurwitz criterion to ascertain the stability behavior of an entirely 100% RE grid being envisaged for the near future.
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Burtsev, Anton V. "Practical application of the method for calculating lightning impact levels on overhead power lines." Transactions of the Kоla Science Centre of RAS Series Engineering Sciences 14, no. 6/2023 (2023): 66–72. http://dx.doi.org/10.37614/2949-1215.2023.14.6.008.
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The article presents examples of the practical application of the method for calculating the level of lightning effects on overhead power lines. The method is based on calculation of distances between coordinates of lightning and overhead line supports. The method was tested on overhead lines of Murmansk region. The proposed method is of interest for power grid companies, as it allows identifying the most vulnerable sections of power lines to lightning.
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Moon, Guk-Hyun, Rakkyung Ko, and Sung-Kwan Joo. "Integration of Smart Grid Resources into Generation and Transmission Planning Using an Interval-Stochastic Model." Energies 13, no. 7 (2020): 1843. http://dx.doi.org/10.3390/en13071843.
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In the power industry, the deployment of smart grid resources in power systems has become an issue of major interest. The deployment of smart grid resources represents an additional uncertainty in the integrated generation and transmission planning that raises uncertainties in investment-related decision making. This paper presents a new power system planning method for the integration of electric vehicles (EVs) and wind power generators into power systems. An interval-stochastic programming method is used to account for the heterogeneous uncertainties attributable to natural variability and lack of knowledge. The numerical results compare the multiple integration scenarios and verifies the effectiveness of the proposed method in terms of cost distribution and regret cost.
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An, Chang-Gyun, Hoon Lee, Tae-Gyu Kim, Junsin Yi, and Chung-Yuen Won. "A Study on Energy Management and Cooperative Control Considering LVRT in a Hybrid Microgrid." Energies 16, no. 11 (2023): 4372. http://dx.doi.org/10.3390/en16114372.
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Recently, the establishment of technical standards for grid connection has gained interest in academia and industry. These standards have focused on the reactive power control function of the grid-connected inverter and maintenance of grid operation, and include detailed information about the grid support function. However, remote control communication and control devices for grid support functions, and other distributed sources, such as wind power and energy storage systems, other than inverters have not been addressed. In this paper, the control of the interlinking converter (ILC) in a hybrid microgrid considering low voltage ride-through (LVRT) among grid support functions is investigated. The proposed method consists of an energy management system considering LVRT and a cooperative control scheme. In the energy management system, an algorithm capable of mode selection was constructed by applying the LVRT curve. Then, considering the LVRT situation, the allowable reactive power range of the ILC was mathematically analyzed through the cooperative control of the energy storage device and the ILC. The proposed method enables us to perform active and reactive power control of the ILC in a hybrid distribution network, considering the power factor under various conditions. This functionality, such as supplying reactive power, significantly contributes to the enhanced grid resilience with distributed power sources, including renewable energy. The proposed strategies were verified through experiments after configuring an experimental set of distributed power sources.
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Liang, Kun, Baoxian Zhou, Yiying Zhang, Yiping Li, Bo Zhang, and Xiankun Zhang. "PF2RM: A Power Fault Retrieval and Recommendation Model Based on Knowledge Graph." Energies 15, no. 5 (2022): 1810. http://dx.doi.org/10.3390/en15051810.
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Energy saving and emission reduction have become common concerns in countries around the world. In China, with the implementation of the new strategy of “carbon peak and neutrality” and the rapid development of the new smart grid infrastructure, the amount of data of actual power grid dispatching and fault analysis show exponential growth, which has led to phenomena such as poor supervision effectiveness and difficulty in handling faults in the process of grid operation and maintenance. Existing research on retrieval recommendation methods has had a lower accuracy rate at cold-start due to a small sample of user interactions. In addition, the cumulative learning of user personalization during general retrieval results in a poor perception of potential interest. By constructing a power knowledge graph, this paper presents a power fault retrieval and recommendation model (PF2RM) based on user-polymorphic perception. This model includes two methods: the power fault retrieval method (PFR) and the user-polymorphic retrieval recommendation method (UPRR). First, we take the power grid fault dispatching business as the core and reconstruct the ontology layer of the power knowledge graph. The PFR method is used to design the graph-neighbor fault entity cluster to enhance the polymerization degree of a fault implementation scenario. This method can solve the search cold-start recommendation problem. At the same time, the UPRR method aims to form user retrieval subgraphs of the past-state and current-state and make a feature matching for the graph-neighbor fault entity cluster, and then realize the accurate prediction of the user’s general search intention. The model is compared with other current classical models through the evaluation of multiple recommendation evaluation metrics, and the experimental results show that the model has a 3–8% improvement in the cold-start recommendation effect and 2–10% improvement in regular retrieval. The model has the best average recommendation performance in multiple metrics and has good results in fault analysis and retrieval recommendation. It plays a helpful role in intelligent operation and maintenance of the power grid and auxiliary decision-making, and effectively improves the reliability of the power grid.
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Nazir, Muhammad Shahzad, Fahad Alturise, Sami Alshmrany, et al. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend." Sustainability 12, no. 9 (2020): 3778. http://dx.doi.org/10.3390/su12093778.
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To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.
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Jo, Haesung, Jaemin Park, and Insu Kim. "Environmentally Constrained Optimal Dispatch Method for Combined Cooling, Heating, and Power Systems Using Two-Stage Optimization." Energies 14, no. 14 (2021): 4135. http://dx.doi.org/10.3390/en14144135.
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The reliance on coal-fired power generation has gradually reduced with the growing interest in the environment and safety, and the environmental effects of power generation are now being considered. However, it can be difficult to provide stable power to end-users while minimizing environmental pollution by replacing coal-fired systems with combined cooling, heat, and power (CCHP) systems that use natural gas, because CCHP systems have various power output vulnerabilities. Therefore, purchasing power from external electric grids is essential in areas where CCHP systems are built; hence, optimal CCHP controls should also consider energy purchased from external grids. This study proposes a two-stage algorithm to optimally control CCHP systems. In Stage One, the optimal energy mix using the Lagrange multiplier method for state-wide grids from which CCHP systems purchase deficient electricity was calculated. In Stage Two, the purchased volumes from these grids were used as inputs to the proposed optimization algorithm to optimize CCHP systems suitable for metropolitan areas. We used case studies to identify the accurate energy efficiency, costs, and minimal emissions. We chose the Atlanta area to analyze the CCHP system’s impact on energy efficiency, cost variation, and emission savings. Then, we calculated an energy mix suitable for the region for each simulation period. The case study results confirm that deploying an optimized CCHP system can reduce purchased volumes from the grid while reducing total emissions. We also analyzed the impact of the CCHP system on emissions and cost savings.
Book chapters on the topic "Power-interest grid method"
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Xing, Liyuan, Gleb Sizov, and Odd Erik Gundersen. "Digital Transformation in Renewable Energy: Use Cases and Experiences from a Nordic Power Producer." In Digital Transformation in Norwegian Enterprises. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05276-7_5.
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AbstractThe electric power system is changing. The changes include the integration of renewable resources, such as wind farms and solar plants, making the grid smarter so that it can react and adapt to changes and increase customer engagement. These changes of the power system have radical effects, which can only be tackled if it is digitized, so digital transformation of the power system is of paramount concern.Electrical energy management systems are therefore an integral part of the digitization process. Such systems typically provide the fundamental information and computation capability to perform real-time network analyses, to provide strategies for controlling system energy flows, and to determine the most economical mix of power generation, consumption, and trades. Currently, the maturity of digitization is at different levels for various parts of the electrical power system. Machine learning has been suggested as a tool for making smart grids that can adapt to sudden changes and long-term distributional shifts and recover from errors. The interest in implementing machine learning methods into energy management systems has grown in recent years, and many companies are taking the first steps.TrønderEnergi is a Norwegian power generation company that does exactly this. It aims at increasing the value of renewable energy and at the same time reducing the cost. In the context of hydropower and wind power, there are several use cases that undergo digital transformation in TrønderEnergi. Examples of such use cases are (1) hydropower trading, (2) wind power trading, and (3) predictive maintenance on wind farms and hydro plants. These use cases as well as the digital transformation processes are introduced in detail in this chapter along with our practical experience. We discuss how machine learning helps to improve the functioning of the existing systems and optimize operations. Inspired by these use cases, we believe digital transformation will continue to make inroads in other applied areas in energy management systems and form the digital electric power ecosystem.
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Mallick, Nirmalya, Ayani Nandi, and Ranadip Roy. "Profit-Based Unit Commitment Using Local and Global Search Methods." In Robotics and Automation in Industry 4.0. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815223491124010014.
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The availability of clean energy is crucial for both the environment and human health. Numerous harmful gasses released by conventional automobiles cause illnesses and ailments in people all over the world. Nevertheless, there is growing interest in Plug-in Electric Vehicles (PEVs) to help with the energy and climate emergency. It has been noted that the manufacturing of PEVs has dramatically increased over the past ten years. The PEVs may supply the power grid with electricity while both consuming it and storing it in batteries. By effectively managing the electric demand profile and integrating electricity from PEVs into the electric grid, operating expenses can be reduced overall. The study recommends the course of action, which, in this case, is to apply the chaotic mapped Sine Cosine Algorithm advancement method and combine chaotic maps with the Harris Hawks Optimizer. It also evaluates how well the suggested better technique is implemented while taking PEVs into account.
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Pandey, Anant P., Jitendra K. Yadav, and Ambesh Dixit. "LiFePO4 as a Cathode Material." In Advancement in Oxide Utilization for Li Rechargeable Batteries. Royal Society of Chemistry, 2025. https://doi.org/10.1039/9781837673612-00318.
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The lower toxicity, affordable synthesis, and high cyclability make LiFePO4 (LFP) a promising material that has attracted considerable interest in Li-ion batteries (LIBs). These characteristics with high theoretical capacity ∼170 mA h g−1 make it the ideal cathode material, with a possibility of ultrafast charging/discharging. The benefits of choosing LFP include its cheap cost, extended cycle life, ability to handle high charge and discharge rates, and more interestingly its higher structural stability. The chapter discusses the intriguing progress made thus far using LFP as a cathode for cleaner, more sustainable energy. Briefly, the chapter provides a detailed discussion on the crystal and morphological structure, and possible uses in energy storage before concentrating on the development and advancement of LFP for commercial usage. An LFP cathode is the ideal alternative over other cathode materials for Li-ion batteries as it now has commercially large-scale applications such as in hybrid electric vehicles (HEV), and in the power sector. The primary disadvantage of LFP is its ionic diffusion restrictions, which include low Li-ion diffusion kinetics and also lower electronic conductivity. The chapter covered ways to overcome these disadvantages, improve electrochemical performance as well as a method for reducing capacity losses that happen while discharging at high rates. The possible future prospects of LFP and how to scale it up for the power grid and other significant applications are also highlighted in this chapter.
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"Bluetooth-Tracing RSSI Sampling Method as Basic Technology of Indoor Localization for Smart Grid." In Smart Grid Test Bed Using OPNET and Power Line Communication. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2776-3.ch005.
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In recent years, Smart Grid have become the center of interest for IT companies and construction companies and various types of Smart Grids have been made currently available on the market. Yet, equipment is costly and it is not easy to convert existing equipment for Smart Grid application as they may require additional resources which could also inflict much costs. The extra costs involving the remodeling of existing housing structure and installment of new equipment can be avoided by using advanced wireless technologies. As an example, this book proposed an indoor localization system that adopts Bluetooth technology and uses RSSI (Received Signal Strength Indication) values for localization. Researchers have configured a system where the central control device will recognize all other devices or equipment in the system, communicate with each other, and respond to the commands or the information provided. However, despite the efforts of many researchers, existing RSSI-based indoor localization systems do not show a satisfactory level of accuracy such that we have devised a system that traces the trend in the RSSI samples.
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Ceyda Yelgel, Övgü, and Celal Yelgel. "The Role of Machine Learning Methods for Renewable Energy Forecasting." In Advances in Energy Recovery and Efficiency Technologies [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1007556.
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Across the world, countries are placing greater emphasis on transitioning to cleaner energy sources, while also becoming increasingly concerned about the worsening climate crisis. With the cost-effectiveness and eco-friendly nature of renewable energy (RE) sources, there has been a considerable amount of interest. Nevertheless, the unpredictable nature of RE sources presents significant challenges to the security and stability of power grids, adding complexity to the operation and scheduling of power systems. Consequently, the widespread adoption of RE applications becomes more challenging. Accurately forecasting the efficiency of RE is essential for effective system management and operation. By improving the accuracy of these forecasts, we can minimise risks and enhance the stability and reliability of the network. Machine learning (ML) has the potential to greatly assist in achieving the future objectives of RE by comprehending complex correlations within data and providing accurate predictions. This review offers valuable insights into the prediction of RE generation using ML techniques. It explores a wide range of RE sources, such as solar, wind, hydroelectric, geothermal, biomass, and marine-based energies. In addition, the assessment offers a detailed analysis of the latest research findings, along with comprehensive information on performance metrics and ML techniques utilised in RE forecasting.
Conference papers on the topic "Power-interest grid method"
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Eça, Luís, Filipe S. Pereira, Guilherme Vaz, Rui Lopes, and Serge Toxopeus. "On the Role of Discretization Errors in the Quantification of Parameter Uncertainties." In ASME 2020 Verification and Validation Symposium. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/vvs2020-8825.
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Abstract The independence of numerical and parameter uncertainties is investigated for the flow around the KVLCC2 tanker at Re = 4.6 × 106 using the time-averaged RANS equations supplemented by the k–ω two-equation SST model. The uncertain input parameter is the inlet velocity that varies ±0.25% and ±0.50% for the determination of sensitivity coefficients using finite-difference approximations. The quantities of interest are the friction and pressure coefficients of the ship and the Cartesian velocity components and turbulence kinetic energy at the propeller plane. A grid refinement study is performed for the nominal conditions to allow the estimation of the discretization error with power series expansions. However, for grids between 6 × 106 and 47.6 × 106 cells, not all the selected quantities of interest exhibit monotonic convergence. Therefore, the estimates of the sensitivity coefficients of the selected quantities of interest using the local sensitivity method and finite-differences performed for refinement levels that correspond to 0.764 × 106, 6 × 106 and 47.6 × 106 cells lead to significantly different values. Nonetheless, for a given grid, negligible differences are obtained for the sensitivity coefficients obtained with two different intervals in the finite-differences approximation. Discrepancies between sensitivity coefficients are compared with the estimated numerical uncertainties. Results obtained in the study suggest that uncertainty quantification performed in coarse grids may be significantly affected by discretization errors.
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Katsuno, Eduardo Tadashi, Gustavo de Goes Gomes, Felipe Santos de Castro, and Joao Lucas Dozzi Dantas. "Numerical Analysis of Debris Containment Grid Fluid-Body Interaction." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-78106.
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Debris containment grid is an important part of hydroelectric power plant, since it retains objects, preventing damage to the turbine. In the case of the Santo Antonio hydropower plant, located in the Amazon rainforest, in the north of Brazil, the most significant debris are logs. This paper aims to analyze the interaction between several log boom modules (type of debris containment grids developed specifically for containing logs) present in a debris containment line present in Santo Antonio hydropower plant, as well as its interactions with the fluid, varying the advance velocity and side-slip angle. The analysis of the fluid-body interaction is performed using CFD software with Finite Volume Method approach. The problem is divided into steps. Firstly, one log boom module is simulated with several velocities and side-slip flow angle, obtaining a relation between forces, moments and movements. Next, in order to save the expected computational cost, the module is analyzed and compared through the porosity approach. Finally, the analysis of a line with several log boom modules, including the interaction between each module, is carried out. The results of the simulations will allow to perform an analysis of the line stability, obtaining the forces, moments and movements of each log boom module, observing its influence in the log boom line. With a fluid-body hydrodynamic analysis of several modules in a line, data are provided for a structural analysis. Since the porosity approach is used to reduce the computational cost, this paper also contributes to similar cases, with a main interest in larger scales of forces and movements.
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Jiang, Leiyong, and Ian Campbell. "An Attempt at Large Eddy Simulation for Combustor Modeling." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-22257.
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Large eddy simulation (LES) is recognized as a promising method for numerical simulation in combustion systems. A LES attempt in a model combustor has been made, and a few important issues including grid size, inflow condition, wall boundary conditions, physical sub-models and data sampling, have been carefully considered. It is found that the turbulence forcing with the vortex method at the air/fuel inlets does not affect the LES results for the present configuration and the turbulence can develop naturally in the inlet section. Moreover, significant computing power is required for LES to capture both the high and low frequencies of interest in a turbulent reacting flow. In the paper, some of the numerical results are presented and compared with a comprehensive experimental database, which indicates that LES can provide reasonable predictions for the mean axial velocity and temperature distributions inside the combustion chamber. However, in order to make LES a valuable and cost-effective tool in the development of advanced combustion systems, some fundamental questions remain to be addressed and more validation efforts are required.
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Spring, J. P., and D. M. McLaughlin. "Rod Bundle Heat Transfer: Steady-State Steam Cooling Experiments." In 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/icone14-89734.
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Through the joint efforts of the Pennsylvania State University and the United States Nuclear Regulatory Commission, an experimental rod bundle heat transfer (RBHT) facility was designed and built. The rod bundle consists of a 7×7 square pitch array with spacer grids and geometry similar to that found in a modern pressurized water reactor. From this facility, a series of steady-state steam cooling experiments were performed. The bundle inlet Reynolds number was varied from 1 400 to 30 000 over a pressure range from 1.36 to 4 bars (20 to 60 psia). The bundle inlet steam temperature was controlled to be at saturation for the specified pressure and the fluid exit temperature exceeded 550 °C in the highest power tests. One important quantity of interest is the local convective heat transfer coefficient defined in terms of the local bulk mean temperature of the flow, local wall temperature, and heat flux. Steam temperatures were measured at the center of selected subchannels along the length of the bundle by traversing miniaturized thermocouples. Using an analogy between momentum and energy transport, a method was developed for relating the local subchannel centerline temperature measurement to the local bulk mean temperature. Wall temperatures were measured using internal thermocouples strategically placed along the length of each rod and the local wall heat flux was obtained from an inverse conduction program. The local heat transfer coefficient was calculated from the data at each rod thermocouple location. The local heat transfer coefficients calculated for locations where the flow was fully developed were compared against several published correlations. The Weisman and El-Genk correlations were found to agree best with the RBHT steam cooling data, especially over the range of turbulent Reynolds numbers. The effect of spacer grids on the heat transfer enhancement was also determined from instrumentation placed downstream of the spacer grid locations. The local heat transfer was found to be greatest at locations immediately downstream of the grid, and as the flow moved further downstream from the grid it became more developed, thus causing the heat transfer to diminish. The amount of heat transfer enhancement was found to depend not only on the spacer grid design, but also on the local Reynolds number. It was seen that decreasing Reynolds number leads to greater heat transfer enhancement.
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Seto, Mae L., Rubens Campregher, Stefan Murphy, and Julio Militzer. "Prediction of Ship Acoustic Signature Due to Fluid Flow." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43343.
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The contribution of flow noise to the radiated acoustic signature of CFAV Quest is of interest. Quest is the research ship used by Defence R&D Canada as a quiet platform. It is difficult to identify the flow noise component in an acoustic ranging so there is interest in predicting the flow noise as a first step towards extracting it from range measurements. Below propulsor cavitation inception speeds, machinery-induced noise dominates. While flow noise does not usually dominate in the presence of machinery-induced noise or propulsor cavitation, it is unclear what fraction of the total noise power flow noise constitutes. A direct numerical simulation for a complex ship geometry was impractical so an alternative approach was sought. An immersed boundary method was used to model the presence of the ship in the flow domain. The unsteady flow field was calculated using a finite volume method over an unstructured Cartesian grid. The flow field around Quest in straight and level flight was calculated at Reynolds numbers between 1.8×108 and 4.3×108, corresponding to a full-scale speed range of 4 to 10 knots. Results from such flow field predictions become the hydrodynamic sources in the integrals of Lighthill’s acoustic analogy to predict the far-field acoustic signature from the flow past the hull. These far-field predictions consist of computing the propagation and radiation of the hydrodynamic sources. This assumes noise generation and its propagation are decoupled. Under certain circumstances, knowledge of the predicted flow component helps to extract it from a standard acoustic ranging.
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Porter, Nathan W., Maria N. Avramova, and Vincent A. Mousseau. "Validation of CTF Void Predictions Using the BFBT Database." In 2018 26th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icone26-81869.
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A quantified validation of CTF void fraction and equilibrium quality predictions is performed using the NUPEC BFBT database. Standard code modeling options are used, and there is no calibration to the experimental data. Each of the 392 steady state BFBT void distribution experiments has a specified geometry, power distribution, and spacer grid design. All experiments are modeled in CTF, but some cases that have oscillations are excluded from the final analyses. Three quantities of interest are compared between the experiment and the code predictions: exit equilibrium quality, average exit void fraction, and subchannel void fractions. The results indicate that, in general, CTF accurately predicts equilibrium quality but overpredicts void content. By splitting the subchannels into different groups, it is shown that subchannels near unheated surfaces are the least accurately modeled, especially for cases which have a high concentration of unheated surfaces. Though the BFBT database is the most complete source of experimental data for fine-mesh void distributions, the measurements are asymmetric even for symmetric geometry and boundary conditions. A method is proposed to approximate the experimental uncertainties which led to these asymmetries.
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Piacentino, Antonio, and Fabio Cardona. "A Thermoeconomics-Based Approach to the Integrated Optimization of Design and Operation for Decentralised Energy Systems and Variable Load Conditions." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95096.
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The many comprehensive approaches formulated for the optimization of large industrial energy systems have been rarely applied to small and medium scale units, because of the difficulties in handling a continuously variable energy demand and of the lower margins for energy and emissions saving. Today, the growing interest for decentralised energy systems in the civil sector stimulates major efforts for the optimization of such plants, with a particular focus on the control system and on a management strategy able to exploit the opportunities existing in the free energy market. In this paper a methodology is proposed for the optimization of design and operation of variable demand systems supplying different non-storable products. In such systems, efficiency penalty due to off-design operation is usually assumed as a key issue; the proposed method, however, introduces an original and meaningful interpretation of the capital depreciation cost and keeps into account the possibility for grid connected power systems to produce surplus electricity to be sold. The proposed optimization process, based on the Lagrange multipliers method, assumes either an economic indicator (the Net Present Value, NPV or the Net Cash Flow, NCF) or a function depending only on fuel consumption (as usually proposed in literature) as objective function. Main advantages of the proposed method are the high level of integration between the optimization of design and operation and the possibility to automate the algorithm in order to drive a real-time optimized control system aiming to achieve the maximum profitability or the maximum primary energy saving.
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Eça, Luís, Rui Lopes, Filipe Soares Pereira, and Guilherme Vaz. "Numerical and Parameter Uncertainties: Are They Independent?" In ASME 2019 Verification and Validation Symposium. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/vvs2019-5125.
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Abstract In the Validation procedure proposed by the ASME V&V 20 Standard, the quantification of the modeling error of mathematical models solved by Computational Fluid Dynamics (CFD) requires the estimation of the experimental, numerical and parameter uncertainties. In its simplest form, these uncertainties are assumed independent. Parameter uncertainty is typically a consequence of lack of exact values for the boundary conditions and/or fluid properties. Its evaluation is handled by the broad field of uncertainty quantification. However, this evaluation requires simulations that are unavoidably affected by numerical errors. Therefore, it may be questionable if parameter and numerical uncertainties are really independent or, alternatively, how small must be the numerical uncertainty to estimate parameter uncertainties without its influence. In this paper we address the relation between parameter and numerical uncertainties using two test cases: the transition from laminar to turbulent flow on a flat plate at a Reynolds number of 107; the flow around the Eppler 387 airfoil at an angle of attack of 1o and Re = 3 × 105. The mathematical model for the three cases is the Reynolds-Averaged Navier-Stokes equations supplemented by the k–ω SST eddy-viscosity turbulence model and the one-equation γ transition model. Sensitivity coefficients are determined for the selected quantities of interest using the local sensitivity method and finite-differences. These estimates are performed for different levels of grid refinement to check its dependency on the estimated numerical error obtained from power series expansions. Results obtained in this study suggest that the evaluation of the parameter uncertainty only becomes independent of the numerical error when the simulation results are in the asymptotic range.
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Porumb, Ioana. "ANALYSIS OF THE INFLUENCE OF THE TECHNOLOGICALLY-ENHANCED SOFTWARE ON TEACHING GRID-CONNECTED RENEWABLE ENERGY PLANTS BEHAVIOR." In eLSE 2015. Carol I National Defence University Publishing House, 2015. http://dx.doi.org/10.12753/2066-026x-15-189.
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The increased adoption of both the renewable energy sources and energy efficiency has pushed European Union into adopting the EU Directive 20-20-20, which constitutes a decisive action towards renewable energy and energy efficiency adoption within electric power systems. The area of interest for electrical engineering students is very broad, it covers a series of topics for the electric power systems, like calculation of losses, reliability, and power quality. The power generation of grid-connected photovoltaic plants wind farms is continuously increasing all over Europe, with capacities attaining values of hundreds of megawatts, thus making these plants a crucial part of the future electric energy system and Smart Grids. Advanced electrical devices can be used to increase transmission capacity, improving stability and dynamic behavior, and ensure a better quality of energy by controlling several parameters. Electrical engineers need to solve the issue of operating traditional electric power systems using the latest technological developments, thus leading to an inherent bottle neck if not using additional power systems analysis software. Current paper deals with the issue of enhancing electrical engineers analytical capabilities using friendly-interfaced software, with respect to traditional analytical methods. In the first section, a general analysis was performed, aimed at identifying the current state-of-the art of analytical methods and software available for both scholars and professionals. An example was built, depicting a real electric distribution system, in order to create an array of possible issues and solutions which were analyzed both traditionally and with dedicated software, and of which differences were highlighted.
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Vishwakarma, Shubham Kumar, Adhip Srivastava, Pramod Kumar, Pradip Dutta, and Nagendra Somanath. "Topological Shape and Performance Optimisation of Microchannel Diffusion-Bonded Heat Exchangers Used in sCO2 Brayton Cycle." In ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/gt2024-124223.
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Abstract In recent years, Micro-Channel Heat Exchangers (MCHEs) have received significant interest in the context of energy systems and devices for supercritical carbon dioxide (sCO2) compact power blocks. The unprecedented ability of MCHEs to sustain extremities of both temperature and pressure makes them an ideal choice as recuperators/gas-cooler in a sCO2 Brayton cycle. The proposed approach uses a cylindrical-shaped heat exchanger with circular/non-circular channel holes that are topologically optimised. The channels are laser drilled into thin discs instead of electrochemically etched. The length is achieved by stacking and joining prepared discs with diffusion bonding. The MCHEs propose channels with different cross-sectional shapes and configurations defined in a cylindrical geometry as a novel alternative for conventional heat exchangers. The analysis focuses on enhancing heat transfer by arriving at suitable optimised forms and numbers of hot and cold channels based on the specified operating, manufacturing, and design constraints. The numerical analysis uses a three-dimensional computational fluid dynamics (CFD) model, which accommodates the nonaxisymmetric behavior that arises in a global meshing system. The Navier Stokes (N-S) and energy equations are solved in cylindrical coordinates. The steady-state energy and momentum transport equations are discretised in a Finite Volume (FV) framework utilising a collocated grid. The approach provides flexibility to vary radial distribution of channels and the number of holes based on specified operating, manufacturing, and design constraints. The full-scale CFD model, which is an accurate method for performing property evolution studies, is computationally expensive. Hence, a parallel computing approach is adopted for a real, efficient, and versatile numerical model for evaluating the analyses and then performing optimization. Enhancement in heat exchanger performance is achieved using an Elite Genetic Algorithm (EGA), resulting in an optimised heat exchanger geometry. The geometric parameters used to guide the EGA in generating multiple improved heat exchanger geometries, allow for design trade-offs along the Pareto front between heat exchanger efficiency and geometric location of channels. The current approach offers a promising method for optimising heat exchanger performance, resulting in nonintuitive designs that were previously unattainable. The process is demonstrated with an optimized design. The results predicted are validated using a commercial CFD solver. Test cases have shown a close agreement in temperature and velocity distributions.