This work investigates the integration of solid oxide fuel cells (SOFCs) and a small methanol/nitromethane fueled piston engine as a prospective hybrid powertrain for small unmanned aerial systems (UASs). The increased chemical energy density of a liquid fuel when compared to traditional batteries, along with ease of storage, accessibility, and refuel time make the use of a liquid fuel powered UAS preferable when compared to battery only power UAS’. Currently small UAS’ of increasing interest as a research area, as they have a wide application to a variety of fields. UAS’ are currently being used for precision agricultural crop management and water resource visual inspection. UAS’ are a cost effective avenue to survey water resources and track water runoff that is contaminating water resources. UAS’ can be easily automated and fitted with sensors and cameras capable of providing actionable feedback to the user. The use of UAS’ for land management and survey is expected to continue to expand. However, nearly all UAS’ are powered by a typical lithium polymer battery pack, giving an average endurance of approximately twenty minutes. This is acceptable to most hobbyists and for short filming duration; however, it limits UAS’ to only being able to be operated in close proximity to the user. Current power plants for UAS’ are not suited for long duration missions, such as the survey of water resources. Therefore, the development of a hybrid power plant is crucial for UAS’ to be utilized to their full potential as a survey tool. This work introduces a small internal combustion engine to act as a partial oxidation fuel reformer, producing high temperature exhaust and syngas. The exhaust of this engine is then analyzed as a fuel source for tubular SOFC’s. The SOFC is integrated into the exhaust of a 3.3 cm 3 nitromethane fueled two-stroke engine, achieving a maximum power of 680 mW/cm 2 . A theoretical comparison of flight time indicates that the modular hybrid system could increase a typical small UAS’ flight time beyond 1 hour. The system is capable of achieving a significantly higher energy density than traditional lithium polymer batteries.

Significant research is ongoing on several fronts in smart sensor technologies for optimizing the performance of power generating assets. The initiatives include: 1. Real-time models with advanced computational algorithms, embedded intelligence at sensor and component level for reducing operating costs, improving efficiencies, and lowering emissions. 2. Optical sapphire sensors for monitoring operation and performance of critical components in harsh environments, for improving accuracy of measurements in combustion monitoring, and lowering operating costs. 3. Wireless technologies using (a) microwave acoustic sensors for real-time monitoring of equipment in high temperature/pressure environments (b) integrated gas/temperature acoustic sensors for combustion monitoring in diverse harsh environment locations to improve combustion efficiency, reduce emissions, and lower maintenance costs (c) sensors for sensing temperature, strain and soot accumulation inside coal-fired boilers for detailed condition monitoring, better understanding of combustion and heat exchange processes, improved designs, more efficient operation. 4. Distributed optical fiber sensing system for real-time monitoring and optimization of high temperature profiles for improving efficiency and lowering emissions. 5. Smart parts with embedded sensors for in situ monitoring of multiple parameters in existing and new facilities. 6. Optimizing advanced 3D manufacturing processes for embedded sensors in components for harsh environments to reduce costs and improve efficiency of power generation facilities with carbon capture capabilities. 7. New energy-harvesting materials for powering wireless sensors in harsh environments, improving reliability of wireless sensors in demanding environments, and in-situ monitoring and performance of devices and systems. 8. Real-time, accurate and reliable monitoring of temperature at distributed locations of sensors in harsh environments for improving operations and reducing operating costs. 9. Algorithms and methodologies for designing control systems utilizing distributed intelligence for optimal control of power generation facilities. 10. Gas sensors for monitoring high temperatures in harsh environments for lowering operating costs and better control of operations. 11. Optimizing placement of smart sensors in networks for cognitive behavior and self-learning. This paper provides an overview of the initiatives in smart sensor technologies and their applications in optimizing the performance of power generating facilities.

Increased accuracy of solids flow meters can be achieved with sensor fusion; the combination of solids velocity measurement with other process sensors to define the process state of the solids while they flow. In a circulating fluidized bed aeration flow, temperature measurements, and pressure differentials are routinely measured to ensure stable process operations. These process measurements were used as input to real-time model of the standpipe where the velocity measurement was taken to determine the actual solids bed density. The model development and implementation are described with the objective of updating the measured flows continuously in real time. Real time optimization techniques use the pressure measurements for inferring voidage and apply the inferred values to estimate the pressure profile and aeration split. In essence, the approach becomes solving nonlinear least squares problem and closure of mass balance of gas in real time.

A well-designed battery management system along with a set of voltage and current sensors is required to properly measure and control the battery cell operational variables for Hybrid Electric Aircrafts (HEAs). Some critical functions of the battery including State-Of-Charge (SOC) and State-Of-Health (SOH) estimations, over-current, and over-/under-voltage protections are mainly related to current and voltage sensor measurements. Therefore, in case of battery faults occur in HEA, designing a reliable and robust diagnostic procedure is essential. In this study, for Li-ion batteries, a new and fast fault diagnosis technique via collecting data is proposed. Finally, the effectiveness of the proposed diagnostic method is validated, and the results show how overcharge, over-discharge and sensor faults can be accurately detected.

Thermoacoustic technology potentially offers a sustainable and reliable solution to help address the continuing demand for electric power. Thermoacoustic devices, operating on the principle of standing or traveling acoustic waves, can be designed as a heat pump or a prime mover system. This technical strategy is environmentally friendly as it utilizes noble gases, or air, as the working fluid and does not directly produce harmful emissions. However, heating and cooling sources are required to create the required thermal gradient. Due to the inherit simplicity and limitation of moving components, thermoacoustic devices require little maintenance and have a forecasted long operational lifespan. This paper will present the design considerations necessary to construct a traveling wave thermoacoustic heat engine. The modeling, analysis, fabrication, and testing with integrated sensors will be discussed to offer insight into the capabilities and subtleties. A case study with system operation at 54 Hz and 7.8% thermal to acoustic efficiency will be presented.

Solar trackers are rising in popularity; they benefit a wide range of applications since distributed solar energy generation can reduce electricity costs and support energy independence. In this paper, a simple solar tracking system is introduced. The system is a package unit that can be mounted on any solar panel. The system consists of an electrical motor connected directly to a sliding mass on a linear bearing. The electrical motor is controlled to slide the weight along the shafts in controlled steps. As a result, the photovoltaic panels are rotated automatically under the effect of controlled weight unbalance in fine angle increments to track solar trajectory without the need for traditional complex or costly mechanisms. Two light dependent resistors (LDR) sensors, mounted onto the surface of the solar photovoltaic panel, are exposed to solar irradiance and used to feed signals to a controller. A model of the solar tracking system is developed using ordinary differential equations, and numerically solved by MATLAB/Simulink™. The power consumption and tracking strategy of the proposed tracking system are estimated under realistic operating conditions (e.g. wind and brakes), and the power consumption is compared to the power generated by the photovoltaic panels. Optimum values for the sliding mass are suggested. Two photovoltaic modules are used to calculate the output parameters of the proposed tracking mechanism.

This paper proposes new seismic-based methods for use in the wind energy industry with a focus on wind turbine condition monitoring. Fourteen Streckeisen STS-2 Broadband seismometers and two 3D sonic anemometers are deployed in/near an operating wind farm to collect the data used in these proof-of-principle analyses. The interquartile mean (IQM) value of power spectral density (PSD) of the seismic components in 10-minute time series are used to characterize the spectral signatures (i.e. frequencies with enhanced variance) in ground vibrations deriving from vibrations of wind turbine subassemblies. A power spectral envelope approach is taken in which the probability density function of seismic PSD is developed using seismic data collected under normal turbine operation. These power spectral envelopes clearly show the energy distribution of wind-turbine-induced ground vibrations over a wide frequency range. Singular PSD lying outside the power spectral envelopes can be easily identified, and are used herein along with SCADA data to diagnose the associated sub-optimal turbine operating conditions. Illustrative examples are given herein for periods with yaw-misalignment and excess tower acceleration. It is additionally shown that there is a strong association between drivetrain acceleration and seismic spectral power in a frequency band of 2.5–12.5 Hz. The long-term goal of the research is development of seismic-based condition monitoring (SBCM) for wind turbines. The primary advantages of SBCM are that the approach is low-cost, non-invasive and versatile (i.e., one seismic sensor monitoring for multiple turbine subassemblies).

The U.S. electric utility industry continues to undergo dramatic and accelerating transformation. Reliability and resiliency are a key focus. A number of important issues including cyber and physical security challenges, aging infrastructure, and low natural gas prices continue to be of concern. Significant advances in technology, and prolonged regulatory uncertainty are also contributing factors. Electric utilities are now making substantial investment in renewable resources and other technologies needed for renewables integration. This means a reduction in investment in generation assets and an increase in the transmission and distribution grids. There is also increased investment in providing customers with solutions to lower their costs, reduce their carbon footprint and provide control over their energy management. The transformation ultimately demands significant increases in power plant generation operating capabilities and higher levels of equipment reliability while reducing O&M and capital budgets. Achieving higher levels of equipment reliability, with such tightening budget and resource constraints, requires a very disciplined approach to maintenance and an optimized mix of the following maintenance practices: • Preventative (time-based) • Predictive (condition-based) • Reactive (run-to-failure) • Proactive (combination of 1, 2 and 3 + root cause failure analysis) Preventive maintenance (PM) is planned maintenance actions taken to ensure equipment is capable of performing its required functions. PM tasks are generally time-based, depending on the availability of condition monitoring data through a predictive maintenance (PdM) program. Traditionally, PdM is largely performed by maintenance technicians in the field with handheld devices. Resource constraints usually mean that often weeks or even months elapsed between readings on the same piece of equipment. This approach has limitations with data volume, velocity, variety, and veracity. Significant recent advances in sensor and technology associated with the Industrial Internet of Things (IIoT) have enabled the transformation of critical power plant assets such as steam turbines, combustion turbines, generators, and large balance-of-plant equipment into smart, connected power plant assets. These enhanced assets, in conjunction with analysis and visualization software, provide a comprehensive on-line conditioning monitoring solution that enables both a reduction in time-based PM tasks and also automation of PdM tasks. This paper describes an approach by Duke Energy to apply smart, connected power plant assets to greatly enhance its fossil generation equipment reliability program and processes. It will outline the value that is currently being realized and will also examine additional opportunities.

Artificial intelligence (AI) has played an increasingly important role in condition monitoring and machinery fault diagnosis in power generation plants. However, the accuracy and reliability of any AI-based machinery fault diagnosis is highly dependent on the quality and quantity of the input data fed to the AI model. The hypothesis of this paper is that AI-based fault diagnosis can be further improved by taking into account all the available sensor inputs of the machine. In short, the more sensor inputs fed into the AI model, the more accurate and reliable the outcome of the fault diagnosis. This paper proposes an application of Dempster-Shafer (DS) evidence theory for sensor fusion to improve the accuracy of decision-making in machinery fault diagnosis, by fusing all the available vibration signals measured on different axes and locations of the test machine. Vibration signals from different axes and locations of a machinery faults simulator were collected by multiple accelerometers simulating various machinery health conditions, namely healthy, unbalance, misalignment and foundation looseness. The accuracy of fault diagnosis using a different number of sensor inputs was then investigated. Analysis results showed that by combining more sensor inputs using a DS-based algorithm can improve fault detection accuracy from an average of 63% to 83%. In conclusion, the multi-sensor fusion algorithm can be applied to increase the accuracy and reliability of AI-based fault diagnosis.

The U.S. electric utility industry continues to undergo dramatic change due to a number of key trends and also prolonged uncertainty. These trends include: • Increasing environmental regulations uncertainty • Natural gas supply uncertainty and price • Economic / decoupling of electricity demand growth from GDP • Aging coal and nuclear generation fleet / coal retirements • Aging workforce • Increasing distributed energy resources • Increasing customer expectations The transformation ultimately demands significant increases in power plant generation operating capabilities (e.g. flexibility, operating envelop, ramp rates, turn-down etc.) and higher levels of equipment reliability, while reducing O&M and capital budgets. Achieving higher levels of equipment reliability and flexibility, with such tightening budget and resource constraints, requires a very disciplined approach to maintenance and an optimized mix of the following maintenance practices: • Reactive (run-to-failure) • Preventive (time-based) • Predictive (condition-based) • Proactive (combination of 1, 2 and 3 + root cause failure analysis) Many U.S. electric utilities with fossil generation have adopted and implemented elements of an equipment reliability process consistent with Institute of Nuclear Power Operations (INPO) AP-913. The Electric Power Research Institute has created a guideline modeled from the learnings of AP-913, that consists of six key sub-processes [1]: 1. Scoping and identification of critical components (identifying system and component criticality) 2. Continuing equipment reliability improvement (establishing and continuously improving system and component maintenance bases) 3. Preventive Maintenance (PM) implementation (implementing the PM program effectively) 4. Performance monitoring (monitoring system and component performance) 5. Corrective action 6. Life cycle management (long-term asset management) A significant proportion of Duke Energy’s coal fleet is of an age where individual components have reached their design intent end-of-life thereby creating an increased need for performance monitoring. Until recent times this was largely performed by maintenance technicians with handheld devices. This approach does not allow regular data collection for trending and optimization of maintenance practices across the fleet. Significant and recent advances in sensor technology, microprocessors, data acquisition, data storage, communication technology, and software have enabled the transformation of critical power plant assets such as steam turbines, combustion turbines, generators, transformers, and large balance-of-plant equipment into smart, connected power plant assets. These enhanced assets, in conjunction with visualization software, provide a comprehensive conditioning monitoring solution that continuously acquires sensory data and performs real time analysis to provide information and insight. This advanced condition monitoring capability has been successfully applied to obtain earlier detection of equipment issues and failures and is key to improving overall equipment reliability. This paper describes an approach by Duke Energy to create and apply smart, connected power plant assets to greatly enhance its fossil generation continuous condition monitoring capabilities. It will discuss the value that is currently being realized and also look at future possibilities to apply big data and analytics to enhance information, insight, and actionable intelligence.

Over the past years there has been a dramatic increase in the regulatory requirements for low emissions. Renewable energy targets and CO 2 emissions markets drive the transition to a cleaner and renewable energy production system. In addition to increasing the overall plant cycle efficiency, there two principal means of the reduction of the CO 2 from coal fired power plants: by coal and biomass co-firing and by the capture and long term storage of the CO 2 emitted from power plant. Carbon dioxide capture and storage will involve substantial capital investment, accompanied by a significant power plant cycle efficiency penalty, and is not currently available on a fully commercial basis. Co-firing biomass, in comparison with other renewable sources, is the main contributor to technologies meeting the world’s renewable energy target. However, the impact of biomass co-firing on boilers performance and integrity has been modest. Operational problems associated with the deposition and retention of ash materials can and do occur on all the major gas-side components of combustion and boilers. The process occurs over a wide range of flue gas and surface temperatures, and dependent both on the characteristics of the ash and on the design and operation conditions of the furnace and boiler. Development and validation of the predictive models have been hindered significantly by the practical difficulties in the obtaining reliable data from the boilers operated with coal and biomass. Although specialized on–line deposition monitoring and sootblowing control systems are commercially available, but they are based on a very simple estimates of the fouling factors, which results in crude and not reliable approach to optimization of sootblowers operation. In the present paper an alternative approach and a new technique based on electro-optical sensor are demonstrated. The long term experience with the system attached to the furnace wall and capable to move the compact sensor in and out of the furnace, allowing to measure simultaneously deposits thickness and reflectivity, is described in details. Results of our study show that dynamics of both parameters on the operated power unit can be registered simultaneously in real time and then interpreted separately. Experiments have been carried out with different coal types at 575MW unit equipped with CE tangential boiler and 550 Mw equipped with B&W boiler with opposite fired burners. The measurements were performed in different locations of the furnace. It was shown that dynamics of thickness and reflectivity variation just after the wall cleaning activation are quite different. Situations have been registered where changes of reflectivity have a significant impact on heat transfer, comparable and sometimes even greater than that of growing fouling thickness. Technique and device exploited in this study appears to be a very useful tool for sootblowing optimization and, as a result, for improvement of boiler efficiency and reduction of water wall erosion and corrosion in both pulverized coal and co-firing boilers.

Human cognition, bias and error have been studied significantly over the past few decades and are utilized in several fields, including reliability and safety engineering. Research has indicated that both man-machine interfaces and training are critical during human intervention. Additionally, it has been shown that humans contribute significantly to failures, and thus downtime. This trend is likely to continue as systems become more complex. Several methods, such as Human Reliability Assessment (HRA) and Probabilistic Risk Assessment (PRA), had been proposed and utilized throughout industry. These methods are both qualitative and quantitative and aim to understand, and thus improve, human performance within the system. Additionally, much of the research is focused on risk reduction — for example, design of a power plant to maximize redundancy in human performance during a mishap. Human error is a complicated process in itself and closely tied to cognition, information processing, system automation, team dynamics and biases inherent to humans. It cannot be eliminated by training and familiarity alone, and system design plays a major role in susceptibility to error. The digital age has spurred many advances in processing power, sensor technology and data capture. These advances have resulted in situations where a very large amount of data can be captured and presented to the user. The large amount of information has to be processed with limited attention resources, which can result in human error. This contribution will discuss human error and information processing along with the role of humans in modern power plants. Finally, trends in information overload will be discussed with applications to reducing human error in power plants.

Automatic fault location on the distribution system is a necessity for a resilient grid with fast service restoration after an outage. Motivated by the development of low cost synchronized voltage phasor measurement units (PMUs) for the distribution system, this paper describes how PMU data during a fault event can be used to accurately locate faults on the primary distribution system. Rather than requiring many specialized line sensors to enable fault location, the proposed approach leverages a PMU data stream that can be used for a variety of applications, making it easier to justify the investment in fault location. The accuracy of existing automatic fault location techniques are dependent either on dense deployments of line sensors or unrealistically accurate models of system loads. This paper demonstrates how synchronized voltage measurements enable sufficiently accurate fault location with relatively few instrumentation devices and relatively low fidelity system models. The IEEE 123 bus distribution feeder is examined as a test case, and the proposed algorithm is demonstrated to be robust to variations in total load and uncertainty in the response of loads to voltage sags during a sample set of varied fault conditions.

World experience shows that important factor in the calculations for natural gas consumption between suppliers and consumers is not only the volume of natural gas, but the quality indicators. With gas market liberalization, gas properties are expected to vary more frequently and strongly (composition, heating value etc.). Quality of natural gas is currently a topical issue, considering the steady increase of gas consumption in the world in recent decades. Existent chromatographs and calorimeters are very accurate in gas quality determination, but general expenditure and maintenance costs are still considerable. Market demands alternative lower cost methods of natural gas quality determination for transparent energy billing and technological process control. Investigation results indicate that heating value (HV) is a nonlinear function of such parameters as sound velocity in gas, N 2 and CO 2 concentration. Those parameters show strong correlation with natural gas properties of interest (HV, density, Wobbe index), during analysis conducted on natural gas sample database. For solving nonlinear multivariable approximation task of HV determination, artificial neural networks were used. Proposed approach allowed excluding N 2 concentration from input parameters with maintenance of sufficient accuracy of HV determination equal to 3.7% (with consideration of N 2 concentration – 2.4%) on sample database. For validating of received results corresponding experimental investigation was conducted with reference analysis of physical and chemical parameters of natural gas samples by gas chromatography and followed superior HV calculation according to ISO 6976:1995. Developed experimental setup consist of measuring chamber with ultrasonic transducer, reflector, pressure, temperature and humidity sensors, ultrasonic inspection equipment for sound velocity measurements and CO 2 concentration sensor with relevant instrument. The experimental setup allows measurement of sound velocity at 1MHz frequency and CO 2 concentration in natural gas sample along with parameters control (temperature, humidity, pressure). The HV calculation algorithm was based on specially designed and trained artificial neural networks. Experimental investigation of proposed approach was conducted on 40 real samples of locally distributed natural gas. Obtained results, in comparison to reference values, showed absolute error in Lower HV (net calorific value) determination equal 166 kJ/m 3 , while relative error was equal 4.66%. Developed technology allows construction of autonomous instrument for instant natural gas quality determination, which can be combined with volume meters in order to provide transparent energy flow measurement and billing for gas consumers. Additionally it can be used for gas sensitive technological process control.

This study investigated experimentally the performance of turbojet engine fueled by biodiesel obtained from different feedstocks. The engine is equipped with measuring sensors for pressure, temperature, thrust, shaft speed in addition to flow meter, data acquisition system and a control unit. The results of the effect of biodiesel fuel type and its blends on turbojet engine performance are presented. Three biodiesel fuels which are Cotton methyl ester (CTME), Corn methyl ester (CRME) and Sunflower methyl ester (SME) and their blends of B10, B20 and B50 (10%, 20% and 50% biodiesel/Jet A1 by volume) are used and compared with the engine recommended fuel (Jet A1). Moreover, in this study, the Biodiesel fuel is produced through transesterification process in which the triglyceride (oil) reacts with alcohol (methanol) to form the mono-alkyl ester (biodiesel) and glycerol. Physical and chemical properties of all produced and tested fuels are measured. The results clearly indicate that the produced biodiesel fuels have a higher density, kinematic viscosity, than JetA-1 fuel, while, the calorific value of biodiesel fuels is very close to JetA-1 fuel. Moreover, JetA-1 fuel has higher sulfur content than other biodiesel fuels. Also, the experimental results show that Engine speed for the cases of using biodiesel fuels is lower than JetA-1 fuel at the same fuel throttle valve opening. Moreover, the Biodiesel fuels have a lower fuel volume flow rate compared to JetA-1 at the same throttle valve opening that lead to decrease the engine static thrust as well as lower value of TSFC.

Power plant owners have been monitoring the performance of their power plant processes, equipment and instrumentation for several years. In recent years, however, they have been increasingly confronted with several challenges in maximizing the value of their generating plant assets. The challenges include: deregulation, increased competition, aging of power plant equipment, inadequate instrumentation, aging workforce, lack of experienced/qualified personnel and tools to effectively monitor the operation, performance and condition of plant processes and equipment. These constraints have adversely impacted the capacity, availability and reliability and efficiency of the generating plant asset as well as the total cost of ownership and return on investment. On a positive note, however, technological innovations and tools such as the advent of the Internet, web-based and wireless technologies, advanced software applications and tools, automation, smart sensors and instruments with diagnostic capabilities, are all now making it possible for power plant owners to do more with less. Using case studies, this paper discusses various initiatives and technological advances that are being utilized to maximize power generating asset value through performance management.

Impulse Response (IR) is a nondestructive testing technique in which “mobility” of the structural member under investigation is determined. Mobility is a complex ratio of particle velocity and the applied force. The test involves striking the surface of the structure with an instrumented hammer and measuring vibration response of the member. The time-domain data of the instrumented hammer (applied force) and the vibration sensor are collected and transformed into the frequency domain by the signal analyzer. The mobility of the member is obtained by calculating the transfer function of particle velocity and the applied force. The value of the mobility can be used to assess the condition of the member on a comparative basis. Tests can be performed on a variety of construction materials such as concrete, masonry, ceramic, thin steel or aluminum plates. The method has been successfully used to determine slab-on-grade support conditions, delaminations, voids, existence of grout under the machinery base plates or in concrete blocks or clay bricks. This paper presents a description and the equipment used in the method. In addition, case studies will be presented to show how the method was applied to document the internal conditions.

In conducting the siting analysis of a possible on-shore or off-shore wind farm, computational tools are required to analyze the extensive wind data collected over long periods of time in order to estimate the energy that can be harnessed at that particular location. The major parameters that play a crucial role in this are the wind speed, wind direction, and presence of turbulence in the upcoming wind. However, estimation of the potential for electrical energy generation from wind at a particular site is quite complex and prone to error due to the uncertain nature of the wind. The yaw error, which is the difference between the direction of wind and the normal to the face of the rotor, can reduce the power output of a wind turbine significantly. Zero inertia assumption for the turbine rotor used by multiple assessment tools result in overestimation of the power output. For an accurate estimation of the energy that can be harnessed, the effect of directional change of the wind should be incorporated along with the other obvious parameters such as the wind speed, the effect of landscape and altitude. Most modern utility-scale wind turbines are equipped with yaw motion controller and direction measuring sensors that help change the yaw angle of the wind turbine to adjust for the wind direction. A dynamic control model and the corresponding scheme have to be incorporated in the energy estimation process. A wind energy assessment analysis for a potential off-shore wind farm in Lake Michigan is currently under way. An unmanned marine buoy, equipped with LIDAR-based data acquisition system, is deployed in Lake Michigan and data are measured at six different altitudes starting from 55 m and up to 175 m. As project participants, the authors have been working on developing a versatile, flexible and precise model and software tool to evaluate the potential for electrical energy generation. A MATLAB based program has been developed for this purpose, equipped with the capability of working with different data formats and different time averaged data sets. A dynamic model capable of considering the change in wind direction and adjusting the yaw angle has been developed as a part of the MATLAB program. The dynamic model evaluates the yaw error and implements a scheme for the adjustment of the orientation of the wind turbine in order to provide an accurate estimate of the amount of wind energy that can be converted into electrical energy. The algorithm for this dynamic model and the results obtained are discussed in this paper.

An experiment has recently been completed at Xi’an Jiaotong University (XJTU) to obtain thermo-physical characteristics of supercritical pressure water at supercritical pressures with upward flow of water inside a vertically-upward bare tube. The thermo-physical characteristics of supercritical pressure water were investigated experimentally by means of a non-intrusive measurement method, using a single-beam gamma densitometer generated from the radioactive sources (Cs-137). The purpose of this experiment is to investigate accurately the changes drastic thermo-physical characteristics process of supercritical pressure water at pseudocritical point, comparing and analyzing in-depth the experiment results at different pressures and mass fluxes. Several factors influencing the performance of the gamma densitometer are examined, including the distance between the source and the detector, the distance between the source and the test section, the measuring time, the intensity of source, and the ambient temperature.

The Pressure Water Reactor (PWR) primary flow rate is determined using an enthalpy balance between the primary and the secondary circuits. Hot leg bulk temperature is necessary to estimate the enthalpy of the primary circuit. This bulk temperature is difficult to evaluate as the temperature is measured in the hot leg using a small number of local sensors in a region where its heterogeneousness is large. Measuring the bulk temperature in a real-life configuration is, to the authors’ knowledge, still a challenging task. Thus, Computational Fluid Dynamics (CFD) is an interesting approach to quantify the difference which may exist between the measured temperature and its bulk value. Deciding which turbulence model one has to use in order to have a satisfactory prediction of the fluid motion and the temperature field is not obvious. Indeed, the flow in the hot legs has strong secondary motions which may need a high order turbulence closure. The main objective of the present work is to compare a standard model such as the k-ε with a more sophisticated one (the Reynolds Stress Model from Speziale, Sarkar and Gatski (SSG)). EDF in-house CFD tool Code_Saturne is used for all the computations. The data coming from the so called “Banquise” experiment (1/5 th scale, Re = 100.000, PIV, thermal tracing) representing the upper plenum of a 1300 MWe PWR and its hot legs are used for comparisons. It is, at the present stage, difficult to clearly distinguish the quality of the two models. Both give globally satisfactory results with the criteria used in the present article. One finds however that the SSG model is superior at the beginning of one of the two hot legs which has a direct impact on the prediction of the temperature field.