For SCR (selective catalytic reduction) applications both aqueous ammonia and urea reagents are used for NOx reducing agents in exhaust systems. For both diesel engines and small boilers, the reagent injection systems often consist of a few, and in some cases, a single injector, located on the wall of the exhaust pipe or duct. Often numerical modeling is performed to determine the location and orientation of the injectors and to predict the NRMS (normalized root mean square) of the gas phase reducing specie distribution prior to the catalyst. Aqueous ammonia and aqueous urea have significantly different processes from the point of injection to the formation of the gas phase reducing species. Evaporation characteristics are important, but for urea, the molecule must also decompose into ammonia and isocyanic acid. For modeling purposes, a simplifying assumption is frequently made to treat the liquid reagent as liquid water and assume that the evaporation of liquid water satisfactorily emulates the processes of forming the gas phase reducing species for both aqueous ammonia and aqueous urea reagents. In reality, the reagent droplets are binary component mixtures and treating the droplets as a single component, namely water, may significantly depart from reality. Additionally, the evaporation processes for aqueous ammonia and aqueous urea have significantly different behaviors. This paper addresses the potential errors associated with using a single component water drop for emulating the evaporation of aqueous ammonia and aqueous urea. This is accomplished by analyzing binary component evaporation for both aqueous ammonia and aqueous urea. Additionally, the time for the gas phase chemical decomposition of urea into ammonia and isocyanic acid is evaluated for various conditions. Typical decomposition times are compared to droplet evaporation times. Finally, the paper attempts to provide guidelines for determining when treating the drops as a single component may be sufficiently accurate, and when the complexity of modeling binary component evaporation is necessary.

The design of optimal control architectures and the optimal selection of controlled variables represents a critical task for maximizing the economic profitability of operating plants. Traditionally, controlled variables are selected based on heuristics or past experiences. For cyber-physical systems (CPS), where physical and virtual components can be integrated in the same unit to evaluate the dynamic response of innovative power cycles past experiences may not be available. Trial-and-error methods can be time-consuming and costly. Besides, the selected controlled variables may not be optimal from the economic perspective. A systematic approach to the selection of controlled variable for cyber physical systems is presented here. The method is applied to a solid oxide fuel cell (SOFC)-gas turbine (GT) CPS at the National Energy Technology Laboratory (NETL) in Morgantown, WV. The main result of this work is the synthesis of optimal controlled variable sets for a cyber physical system exhibiting trade offs between economics as well as controllolability of variables.

In this work, the combustion characteristics of semi-coke, coal, and their blends under air conditions were studied. The influence of blending ratio on the combustion characteristics of blended fuels were investigated by thermogravimetric analysis. It was found that the co-combustion of semi-coke and bituminous coal was a complicated process rather than a simple linear superposition, with interaction effect occurring in the co-combustion process. The synergy occurred in the whole combustion process and it was analyzed quantitatively by comparing the interaction coefficient f and the relative root mean square error RMS . The combustion of semi-coke and the blends can be divided to three stages, as well as two stages of coal. In addition, the blends show better combustion behavior with enhancing bituminous coal proportion, and bituminous coal can improve the combustion behavior of semi-coke.

The influence of mesh resolution, the abilities of various eddy viscosity models, and near wall flow treatments on predicting the flow coefficients of poppet valves, operating in water are investigated in this paper. The computational fluid dynamics (CFD) models are solved using STAR-CCM+ 12.04. Grid-convergence is studied first, followed by quantitative assessments of the ability of standard k-ε model, realizable k-ε model, EB k-ε model, Lag EB k-ε model, V2F model and k-ω-sst model, and different wall treatments, such as high y + wall treatment, two-layer wall treatment and all y + wall treatment, embedded in the solver. The flow discharge coefficient (C q ) of poppet valves predicted by CFD models are compared to physical measurements. It was demonstrated in the study that grid resolutions normal to the wall and mesh quality are key factors. Advanced near wall flow treatments produce similar or worse predictions when using the standard k-ε model, and the effects of the near wall flow treatments are marginal for the realizable k-ε model. The ability of turbulence models varies greatly in predicting flow in different valves and lift levels. The realizable k-ε model is the optimal option for the considered valve flows giving an acceptable error within ±5%.

Compressed air and gas are the lifeline of power plants. Deficient or unstable supply of air and gas can result in huge costs and revenue losses of plants. Thus, the accurate determination of performance of the compressors plays important role in prediction of the plant performance. In order to provide reliable and low cost operation for end users, an uncertainty analysis of volume flow, pressure ratio, and power consumption have been investigated and implemented to accurately determine the effect of the compressors to the plant performance. Mathematical models for the uncertainty treatments are proposed based on the ASME test code, and both the systematic errors originating from the measurement instruments and random errors rooted from the raw data are taken into consideration. Moreover, the approach of the uncertainty propagation is also presented through data reduction equations in this paper to evaluate the final performance. Both the rigorous numerical model and sophisticated data acquisition system instrumented in the test facility are employed to conduct the uncertainty analysis for a multi-stage centrifugal compressor. Comprehensive error sources such as ambient conditions, inter-stage pressures and temperatures, and rotational speeds are identified and studied for the final tolerance of the pressure ratio and power consumption of the whole compressor. The test uncertainty results of the compressor can help to improve the power plant field design and demonstrate quality assurance and quality control. Moreover, the tolerance analysis introduced in this paper can be extended to each component of the power plant system to optimize the performance of the whole power plant.

Wind energy and especially offshore wind energy faces an uphill battle in the United States to become a mainstream source of energy generation due to its high price relative to fossil fuels. The wind industry is looking for methods to reduce the costs of energy production by improving the efficiency of wind turbines and reducing their operation and maintenance costs. Correction of yaw error is one way to lower the price of wind energy. Yaw error is the angle between the turbine’s central axis in horizontal plane and the wind flow direction. LIDAR devices are used to correct yaw error, however they are expensive. Therefore, there is a need to develop a return on investment model (ROI) to calculate the cost trade-offs of using such systems. This work reviews how yaw error affects the performance and maintenance costs of wind turbines, discuss the development of an ROI model and provide a case study with two scenarios where LIDAR is used to correct the yaw error of an onshore and an offshore wind farm.

Human error accounts for about 60% of the annual power loss due to maintenance incidents in the fossil power industry. The International Atomic Energy Agency reports that 80\% of industrial accidents in the nuclear industry can be attributed to human error and 20\% to equipment failure. The Personal Augmented Reality Reference System (PARRS) is a suite of computer-mediated reality applications that looks to minimize human error by digitizing manual procedures and providing real-time monitoring of hazards present in an environment. Our mission is to be able to provide critical feedback to inform personnel in real-time and protect them from avoidable hazards. PARRS aims to minimize human error and increase worker productivity by bringing innovation to safety and procedural compliance by leveraging technologies such as augmented reality, LiDAR, computer machine learning and particulate mapping using remote systems.

Pipelines are widely used in energy power system including thermal power plants and nuclear power plants. In the power system, especially nuclear power plants, as thermal stratification characteristic of nonuniform temperature distribution in the pipe and thermal fatigue such as the low cycle thermal fatigue due to existence of the cycle thermal stress are inevitable, the pipe line system can be destroyed easily and thus affect the normal operation of the power plants. In order to study the pipeline thermal fatigue, the pipeline thermal stress needs to be calculated and therefore the temperature distributions especially the inner wall temperature was needed. The outer wall temperature and working fluid temperature can be obtained with installing measuring tools. The key and difficult point is the estimation of the inner temperature distribution of the pipe. At the same time, in the practical engineering, the pipeline structure with special safety requirements or higher requirements for structural completeness are not allowed to be destroyed by the measuring equipment. Based on the consideration above, this paper presents a method of solving the inverse heat conduction problem, which means the inner temperature distributions can be derived by the outer wall temperature distributions. For the pipe inverse heat conduction problem, this paper applies numerical analysis as the main way. Firstly, the method for transient inverse heat conduction problems applying separation of variables and Duhamel’s theorem is established. As the effects of the random error on the measured outer wall temperature are inevitable, the measured data need to be smoothed before used as an input. The Gram orthogonal polynomial method based on the digital filtering theory is applied in this paper to accomplish the smoothing process. The inverse process is accomplished by using MATLAB programming. Then this method is verified in the experiment with high temperature and high pressure. In order to directly validate the accuracy of the inverse analysis, for the test section, not only the transient outer wall temperature and fluid temperature were measured, but also the time dependent middle layer temperature were measured. Then the middle layer temperature obtained from inverse calculation was compared with the measured data from the experiment. The calculated results show that the accuracy of this method is high. The temperature distributions along the radical direction can be obtained quickly and accurate instantaneous heat load for the structural stress analysis and thermal fatigue analysis can be provided using this method. (CSPE)

Effective Plume-Chimney Height (EPCH) was a factor engineers used to design and analyse the performance of natural convection in air-cooled heat exchangers particularly in the event of power outage. To date the number of papers in the open literature presenting data on natural convection performance of air-cooled heat exchangers is scarce. The aim of this study is to corroborate the experimental results and theoretical predictions of Effective Plume-Chimney Height (EPCH) using Computational Fluid Dynamics (CFD) in a laboratory-scale air cooled heat exchanger of 457mm × 457mm face area and an industrial-scale test rig of 2.4m × 6.0m face area forced draft air-cooled heat exchanger comprising of a bundle with 4 rows of annular finned tubes in staggered formation operating under natural convection. The CFD software Phoenics 2015 was employed to simulate the electrically-heated air-cooled heat exchanger fitted with a top screen which was built to study the aerodynamics of natural convection of air-cooled heat exchangers. The CFD geometry arrangement and dimensions were schematic in nature, where errors introduced were considered reasonably negligible. The laboratory-scale exchanger model experimental pressure drop data was found to have an insignificant effective plume-chimney height, as predicted by a theoretical equation. It was found that EPCH values calculated from CFD results agree closely to within −0.11m and +0.06m with both experiments and the theoretical prediction, confirming the same conclusion reached in an earlier report. However, for an industrial-scale test rig (ITR) in forced draft mode of large face dimensions the EPCH had been found to be non-negligible in an earlier work. Significant values of theoretical effective plume-chimney height were inserted in the heat transfer and pressure drop simulation that appeared to yield results that agreed with the experimental heat loads. The CFD simulations on the ITR have confirmed the existence of significant effective plume-chimney heights at more than 100 percent of the bundle depth, or the chimney height. The implication is that a solid-walled chimney can appear to have an efficiency of more than 100 per cent, if cold inflow can be prevented or the penetration to the central core hindered. Since the validation of the existence of EPCH by CFD here has used only a set of data from a single source, it is worthwhile to produce more experimental data and analysis to establish the concept for better predictions of air-cooled heat exchanger natural convection performance.

This paper established a thermal and air leakage calculation model of quad-sectional rotary air preheater, which considered the axial heat conduction of the matrix and the effect of three kinds of air leakage-radial, axial and circumferential air leakage. Based on control equation of rotary air preheater, thermal calculation model was simplified and solved by numerical finite difference method. Air leakage model was solved by nonlinear equations based on mass and energy equations. With the coupling calculation of two models, temperature and air leakage distribution were given. Compared with a quad-sectional rotary air preheater, the error value was less than 5%. The results showed that air leakage of quad-sectional air preheaters was much less than tri-sectional ones .The temperature distribution of fluids in different air channels were continuous and were quite consistent with matrix. Declining the inlet gas flow along the direction of rotary can reduce the low-temperature area rate.

For the natural oil circulation power transformer, the hot spot temperature and winding temperature should be within prescribed limits so as to ensure its life and reliability. Temperature rise of inner windings not only depends on the transformer loss, but relates to oil flow closely. Therefore, whether the oil flow of transformer and oil flow distribution rules among the three windings can be predicted or not, relates to success or failure of the winding temperature rise research. According to the structural characteristics of transformer, a three-dimensional computational model including windings and external radiators was built by a three-dimensional modeling software. Initially, from the view of flow, the flow resistance of natural oil circulation power transformer windings has been calculated and analyzed using the CFD methodology, and the resistance characteristic curves about high voltage windings, middle voltage windings, low voltage windings, radiators and pipelines were fitted respectively by the least square method. And then, on the basis of above, the porous media model was applied to simplify the integral model of transformer so as to build a three-dimensional coupled porous media-pure fluid computational model. Meanwhile, the author calculated porosities and linear resistance coefficients of high voltage, middle voltage and low voltage windings. At the end of this paper, to obtain the relationship of oil flow distribution among three windings, the pressure loss was calculated by numerical simulation. Compared with the theoretical calculation results which was based on the part of experimental data, the numerical calculation results are in agreement with these data. And the error between them is within 6 percent. Therefore, the feasibility and accuracy of calculation method, which was used to calculate oil flow of natural oil circulation power transformer windings by the porous media model, has been verified as well. There are two kinds of conventional method to calculate oil flow, including an empirical method and an experimental method. On the one hand, although the former kind of method can be applied conveniently, it cannot consider all the factors so that the accuracy cannot be guaranteed. On the other hand, experimental method to calculate the flow can present high accuracy, but the cost of carrying out experimental study is high. Therefore, this paper presents another calculation method aimed to the flow resistance and oil flow by CFD. And the calculation method presented has higher calculation accuracy compared to above. Meanwhile, the new method provides a theoretical reference about oil flow distribution for the design of the power transformer.

As one kind of serious environmental problems, flow-induced noise in centrifugal pumps pollutes the working circumstance and deteriorates the performance of pumps, meanwhile, it always changes drastically under various working conditions. Consequently, it is extremely significant to predict flow-induced noise of centrifugal pumps under various working conditions with a practical mathematical model. In this paper, a three-layer back propagation (BP) neural network model is established and the number of input, hidden and output layer node is set as 3, 6 and 1, respectively. To be specific, the flow rate, rotational speed and medium temperature are chosen as input layer, and the corresponding flow-induced noise evaluated by average of total sound pressure level (A_TSPL) as output layer. Furthermore, the tansig function is used to act as transfer function between the input layer and hidden layer, and the purelin function is used between hidden layer and output layer. The trainlm function based on Levenberg-Marquardt algorithm is selected as the training function. By using a large number of sample data, the training of the network model and prediction research are accomplished. The results indicate that good correlation is established among the sample data, and the predictive values show great consistence with simulation ones, of which the average relative error of A_TSPL in process of verification is 0.52%. The precision of the model can satisfy the requirement of relevant research and engineering application.

Prior to choosing a site for a wind farm, its wind resources must be known. On-site measurement of wind speed, using an anemometer or any other appropriate measuring device or the use of historical meteorological data for the site (if they exist) enhance the knowledge of the site’s wind resources. Typically, the use of 50-year historical data is recommended by Wind Energy Engineering Standards. For the offshore site in study, only the 24-year historical data from the National Oceanic and Atmospheric Administration (NOAA) data base is available. Wind speed determined from NOAA’s error bars is used to plot Rayleigh probability distribution curves for each month of the year, based on the operational limit of the 5MW NREL reference wind turbine. The site’s average wind speed and gust are determined based on average wind energy capture. A Gumbel probability distribution curve is plotted based on the operational range of the wind turbine in study, using NOAA’s error bars for the 24year historical hourly wind gust for the site. This study uses the estimated mean wind speed and mean gust, to implement BEMT simulations to investigate the aerodynamic forces caused by the wind or gust on the blades of the HAWT rotor. The wind power captured and the power coefficient are estimated for each scenario. Empirical formulae are developed for the estimation of the rotor blade airfoil’s chord length in terms of blade element radius and the axial induction factor for each scenario, in terms of blade element radius.

In a central receiver solar power plant, heliostats are arranged with respect to the central receiver so as to reflect the rays from the sun onto the power tower with high precision by tracking the sun in both the azimuth and elevation directions. The master control system of a solar power plant consists of different levels. The first level is local control; it takes care of the positioning of the heliostats when the aiming point and the time are given to the system, and informs upper level about the status of the heliostats field. The second logic level makes some important dispatch calculations of heliostats field. The most popular linear two-axis local driving system of heliostat consists of two linear driving actuators, the driving mechanism with rotary joints, and the controller. Traditional methods for heliostat design are often based on a sequential approach in which the mechanical structure is designed first and then the control system is advised. In order to reach the optimal design of heliostats, an integrated design approach that concurrently considers the interactions between the mechanical and control subsystems is necessary. In this article, an integrated design methodology of heliostat drive system is presented. The methodology is based on modeling and simulation. The dynamic models that describe the behavior of the mechanical and control components are presented. These models involve mechanical and control design variables such as the motor parameters, power screw (including back lash), heliostat mass, load forces, and wind forces. Matlab, Solidwork, and Simulink are chosen to apply PID tracking control to heliostats, due to the ability to arbitrarily model complex mechanical systems, directly import properly constructed, third-party 3D CAD models, simulate integrated control, handle a variety of robotics nomenclature, and other features. The present methodology is employed for integrated design of a single facet small size heliostat with mirror area of 3 m 2 .The methods described in this article also show a way to rapidly simulate novel and complex heliostat geometries. Analysis of the heliostat drive system performance and dynamic characteristics according to mechanical and control design variables is conducted for the purpose of control system design and performance optimization. The drive system performance is evaluated in terms of positioning tracking errors, system response, and control system behavior. It is shown that the mechanical characteristics of the ball power screw actuator such as ball-screw diameter, lead, overall flexibility, stiffness, backlash, and inertia significantly influence the performance of drive system.

Laminar mixed convection in a three-dimensional lid driven cavity is numerically investigated. The top lid of the cavity is moving rightwards with a constant speed at a cold temperature. The bottom wall is maintained at an isothermal hot temperature, while the other vertical walls of the cavity are assumed to be insulated. In this study the mass diffusion was not taken into account and the fluid used was air. The flow and heat transfer behavior is studied for various Richardson number ranging from 5 × 10 −5 to 3 × 10 −4 at a fixed Prandtl number of 0.71 through analyzing the local Nusselt number distribution at different sections inside the cavity. Lewis number Le is assumed to be unity and the buoyancy ratio parameter N is equal to zero. Computations were done using an in-house code based on a finite volume method. The results showed a good agreement with previous two dimensional studies, while the three dimensional study gives different results at different sections inside the cavity. It is observed that, the average Nusselt number “Av Nu” on top and bottom surfaces decreases for all sections inside the cavity with increasing Richardson number. A correlation was formulated for each section on both walls for “Av Nu” as a function of “Ri” with a maximum error of 7.3%.

The power output and heat rate (or efficiency) of a combined cycle power plant are expressed in the Power Industry at a specified set of “reference conditions”. Some of these reference conditions pertain to the test boundary (eg. ambient air temperature, barometric pressure etc.) while some others pertain to the operating condition (eg. baseload, evaporative cooler status, etc.) within the plant boundary. The process of measuring the actual thermal performance of a combined cycle plant involves conducting a test wherein the plant is operated at the pre-determined set of operating conditions that enable minimizing deviations from the “reference conditions”. It is a well-known fact that despite all efforts made during such a test, the actual boundary and operating conditions that prevail at the time of the test will not necessarily be identical to the pre-defined set of “reference conditions”. Hence, in order to evaluate the performance levels of the plant, one of the essential steps in the testing process is to “correct” the measured power output and heat consumption (or heat rate) for differences that persist between the actual test conditions and the corresponding set of “reference conditions”. This “correction” can be performed by using either a correction curve-based approach or a model-based approach. When a correction curve-based approach is used, the effects of the boundary conditions on the relevant performance parameter (output, heat consumption or heat rate) can be depicted as an additive correction term or as a multiplicative correction term. As such, the corrections to the boundary conditions can be applied as either a) additive or b) multiplicative or c) a combination of additive and multiplicative referred to as “hybrid”. The prevailing industry code for testing combined cycle power plants, ASME PTC 46, has adopted the “hybrid” method while the codes for testing individual equipment (such as PTC 22, PTC 6.2, PTC 6) have adopted either the additive philosophy or the multiplicative philosophy or a “hybrid” philosophy similar to PTC 46. The purpose of this paper is to present the outcome of a study that compares the three different correction methods utilizing the correction curve approach for a combined cycle power plant. The studies were based on thermodynamic simulations performed on different plant configurations. A key result will be the quantification of the errors associated with the different methods, which are primarily a function of the ability of the different methods to inherently capture the interactions between the various boundary parameters in the correction process and are a representation of the uncertainty associated with the particular correction method. Furthermore, the paper will introduce a new calculation method and provide recommendations that will help improve the accuracies of test results.

The present work reviews a comparison between calculations of a steady and unsteady three dimensional (3D) flow past the diffuser channels of a centrifugal pump. The commercial software ANSYS Fluent has been used. The considered domain is one of the three stages, since each has exactly the same design. In the first part, simulations are carried out at the best efficiency point (BEP) both steady and transient state, single phase flow and four different turbulence models. Results are compared with the performance report from the manufacturer. In the second part, only the realizable k-ε turbulence model has been taken into account. The simulations have been repeated for different mass flows and the results were again compared with the data from the manufacturer. The comparison performed in the first part shows that integral quantities results are not sensibly influenced by the turbulence model. The comparison at different mass flow shows that the steady state simulations demonstrated to be a good approximation of the transient state, always containing the error within an acceptable limit. The minor computational effort needed makes it attractive to be used for further investigations which will involve two-phase flow studies on the same pump.

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.

Capacity measures a system’s ability to survive stress. For example, structures are engineered in part to have the capacity to survive the worst wind loads expected over the life of the structure. Likewise wind electric power systems should have the capacity to reliably survive the worst combination of high load and low wind. A superior approach for quantifying wind’s contribution to system capacity is well known. It is to view wind as a negative load and use the Effective Load Carrying Capacity (ELCC) methodology for a given year. A frequent mistake is to average these annual ELCC estimates. A main contribution of this paper is to explain why the system design criteria should take the worst of the annual ELCC estimates over a number of years and not an average of annual ELCC estimates. Based on extreme events, wind generation contributes little to system capacity (<6.6% of wind nameplate). The empirical evidence shows that wind generation is an energy source, not a capacity resource.

This paper examines the concepts and methods involved in the uncertainty analysis of a differential pressure flow measurement, including which variables have the greatest influence on the uncertainty, and how to effectively manage the influence of error sources on the measurement. An uncertainty analysis will provide the user with a good measure of actual and/or potential system performance and can be instrumental in identifying potential areas of improvement in system performance. The wide availability of instruments of all types and levels of performance presents a difficult decision to those who are responsible for project success and budgets. Optimizing the investment in instrumentation is possible when the required performance is known based an appropriate uncertainty analysis.