Combined Cycle Performance Test Uncertainty Validation by Comparing Monte Carlo Analysis With Monovariate Perturbation Results

2006 ◽  
Author(s):  
Kerri L. Spencer ◽  
Jeffrey R. Friedman ◽  
Terry B. Sullivan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Performance Test ◽  
Performance Testing ◽  
Monte Carlo Analysis ◽  
Combined Cycle ◽  
Plant Performance ◽  
The Monte Carlo Method ◽  
The Impact ◽  
Test Uncertainty

This paper focuses on the calculation of the test uncertainty of an ASME PTC 46 [1], overall plant performance test of a combined cycle by two separate methods. It compares the combined cycle corrected plant output and heat rate systematic uncertainty results that are generated using monovariate perturbation analysis with the Monte Carlo method. The Monte Carlo method has not been used widely in power plant performance testing applications. It offers insights into the results of the Monte Carlo analysis method, which is less intuitive than the conventional method. This study shows that utilizing two distinctly different methods of calculation of test uncertainty serves to corroborate assumptions, or to isolate flaws in one or both methods. In developing the method for calculation of test uncertainty, the authors conclude that it is prudent to validate the calculation method of choice of test uncertainty, and to consider the correlations in measurement uncertainties. Also discussed in detail are the impact of correlated uncertainty assumptions, and recommendations on their application. Correlated uncertainty has not been extensively discussed in the literature concerning specific applications in performance testing, although it should be a critical consideration in any uncertainty analysis. Details of determination of instrumentation uncertainty, measurement uncertainty of a parameter, and calculation of sensitivity factors are included in this paper.

scholarly journals Simulation Modeling in Assessing the Effectiveness and Risk of Investment Projects

2020 ◽  
Vol 22 (1) ◽  
pp. 119-124
Author(s):  
Volodymyr Kharchenko ◽  
◽  
Hanna Kharchenko ◽  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Agricultural Production ◽  
Random Number ◽  
Investment Project ◽  
Average Risk ◽  
Universal Method ◽  
Investment Projects ◽  
The Monte Carlo Method ◽  
The Impact

Introduction. The article deals with the modeling features in the implementation of investment projects using the Monte Carlo method. The purpose of the article is to substantiate the feasibility of using economic and mathematical models to identify the risks of investment projects in agricultural production, taking into account the randomness of factors. Results. The expediency of using this method during the analysis of projects in agriculture is determined. This type of modeling is a universal method of research and evaluation of the effectiveness of open systems, the behavior of which depends on the influence of random factors. Particular attention is paid in such cases to decisions on the implementation of investment projects. The expediency of using this method in the analysis of projects in agriculture is determined. The main characteristics of the investment project are considered: investments involve significant financial costs; investment return can be obtained in a few years; there are elements of risk and uncertainty in forecasting the results of the investment project. The algorithm of the analysis of investment projects consisting of various stages is offered. The importance of investigating the risks of investment projects in agricultural production is substantiated. It is investigated that the basis of the Monte Carlo method is a random number generator, which consists of two stages: generation of a normalized random number (uniformly distributed from 0 to 1) and conversion of a random number into an arbitrary distribution law. The task of choosing an investment project for a pig farm is proposed. The calculations revealed that the amount of the expected NPV is UAH 63,158.80 with a standard deviation of UAH 43,777.90. The coefficient of variation was 0.69, so the risk of this project is generally lower than the average risk of the investment portfolio of the farm. Conclusions. The results of the analysis obtained using the method of Monte Carlo simulation are quite simple to interpret and reflect the change of factors over a significant interval, taking into account the probabilistic nature of economic factors. Thus, this method allows the implementation of the investment project to assess the impact of uncertainty on the final result of the project.

Fabrication and Experiment of an Automatic Continuum Robot System Using Image Recognition

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yeong-min Na ◽  
Hyun-seok Lee ◽  
Jong-kyu Park
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Image Recognition ◽  
Performance Test ◽  
Skill Level ◽  
Experimental Results ◽  
Robot System ◽  
The Monte Carlo Method ◽  
Continuum Robot ◽  
Two Stages

Abstract This paper proposes a continuum robot that can be controlled automatically using image recognition. The proposed robot can operate in narrower spaces than the existing robots composed of links and joints. In addition, because it is automatically controlled through image recognition, the robot can be operated irrespective of the human controller's skill level. The manipulator is divided into two stages, with three wires connected to each stage to minimize the energy used to control the manipulator posture. The manipulator's posture is controlled by adjusting the length of the wire, similar to the relaxation and contraction of the muscles. Denavit–Hartenberg transformation and the Monte Carlo method were used to analyze the robot's kinematics and workspace. In a performance test, an experimental plate with nine targets was fabricated and the manipulator speed was adjusted to 5, 10, and 20 mm/s. Experimental results show that the manipulator was automatically controlled and reached all targets, with errors of 2.58, 3.28, and 9.18 mm.

scholarly journals Providing the environmental safety by increasing the efficiency of firefighting in unsheltered timber warehouses

2021 ◽  
pp. 123-129
Author(s):  
E. M. Hulida ◽  
I. V. Pasnak ◽  
O. E. Vasylieva ◽  
I. O. Movchan
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Mathematical Models ◽  
Combustion Products ◽  
Environmental Safety ◽  
Theoretical Research ◽  
The Monte Carlo Method ◽  
Fire Front ◽  
Free Development ◽  
The Impact

Purpose. To develop a method for reducing the impact of fires in unsheltered timber warehouses on the environmental safety by reducing the duration of free burning of timber, the speed of fire front spread, emissions of combustion products and the duration of the firefighting. Methodology. During the experimental research, the method of fractional factor experiment was used. Theoretical research was performed using optimization mathematical models. The Monte Carlo method is used to solve optimization problems. To implement this method, block diagrams of algorithms was developed, based on written corresponded computer programs. Findings. The method was developed for reducing the impact of fires in unsheltered timber warehouses on the environmental safety by reducing the duration of free development of the fire, the speed of fire front spread, the concentration of combustion products and the duration of the fire. Fire prevention measures to reduce the duration of fire and to reduce emissions of combustion products due to fires in unsheltered timber warehouses was implemented by using an automated system to determine the fire extinguishing means and forces by setting an optimization problem, applying the Monte Carlo method and developing software to solve it. Originality. The scientific novelty is the justification of ways to reduce the duration of the free development of fire and to reduce the amount of toxic emissions using optimization mathematical models. Practical value. It is possible to use the obtained results in the practical activities of fire and rescue units of the SES of Ukraine and provide environmental safety in case of fire in unsheltered timber warehouse due to the practical implementation of administrative, legal and economic methods.

Calculating the need for intensive care beds

2012 ◽  
Vol 97 (11) ◽  
pp. 943-946 ◽  
Author(s):  
Gale A Pearson ◽  
Fiona Reynolds ◽  
John Stickley
Keyword(s):  
Monte Carlo ◽  
Intensive Care ◽  
Monte Carlo Method ◽  
Average Length ◽  
Admission Rate ◽  
Final Analysis ◽  
Monte Carlo Analysis ◽  
Catchment Population ◽  
The Monte Carlo Method ◽  
Refused Admissions

AimPrompted by high refused admission rates, we sought to model demand for our 20 bed paediatric intensive care unit.MethodsWe analysed activity (admissions) and demand (admissions plus refused admissions). The recommended method for calculating the required number of intensive care beds assumes a Poisson distribution based upon the size of the local catchment population, the incidence of intensive care admission and the average length of stay. We compared it to the Monte Carlo method which would also include supra-regional referrals not otherwise accounted for but which, due to their complexity, tend to have a longer stay than average. For the new method we assigned data from randomly selected emergency admissions to the refused admissions. We then compared occupancy scenarios obtained by random sampling from the data with replacement.ResultsThere was an increase in demand for intensive care over time. Therefore, in order to provide an up-to-date model, we restricted the final analysis to data from the two most recent years (2327 admissions and 324 refused admissions). The conventional method suggested 27 beds covers 95% of the year. The Monte Carlo method showed 95% compliance with 34 beds, with seasonal variation quantified as 30 beds needed in the summer and 38 in the winter.ConclusionBoth approaches suggest that the high refused admission rate is due to insufficient capacity. The Monte Carlo analysis is based upon the total workload (including supra-regional referrals) and predicts a greater bed requirement than the current recommended approach.

scholarly journals Unbiased Monte Carlo continuum radiative transfer in optically thick regions

2020 ◽  
Vol 635 ◽  
pp. A148
Author(s):  
A. Krieger ◽  
S. Wolf
Keyword(s):  
Monte Carlo ◽  
Radiative Transfer ◽  
Optical Depth ◽  
Performance Test ◽  
Emission Spectra ◽  
Computation Time ◽  
Cumulative Distribution ◽  
The Monte Carlo Method ◽  
Deposited Energy ◽  
The Impact

Radiative transfer describes the propagation of electromagnetic radiation through an interacting medium. This process is often simulated by the use of the Monte Carlo method, which involves the probabilistic determination and tracking of simulated photon packages. In the regime of high optical depths, this approach encounters difficulties since a proper representation of the various physical processes can only be achieved by considering high numbers of simulated photon packages. As a consequence, the demand for computation time rises accordingly and thus practically puts a limit on the optical depth of models that can be simulated. Here we present a method that aims to solve the problem of high optical depths in dusty media, which relies solely on the use of unbiased Monte Carlo radiative transfer. For that end, we identified and precalculated repeatedly occuring and simulated processes, stored their outcome in a multidimensional cumulative distribution function, and immediately replaced the basic Monte Carlo transfer during a simulation by that outcome. During the precalculation, we generated emission spectra as well as deposited energy distributions of photon packages traveling from the center of a sphere to its rim. We carried out a performance test of the method to confirm its validity and gain a boost in computation speed by up to three orders of magnitude. We then applied the method to a simple model of a viscously heated circumstellar disk, and we discuss the necessity of finding a solution for the optical depth problem with regard to a proper temperature calculation. We find that the impact of an incorrect treatment of photon packages in highly optically thick regions extents even to optically thin regions, thus, changing the overall observational appearance of the disk.

scholarly journals Enterprise content management in the cloud

2013 ◽  
Vol 61 (7) ◽  
pp. 2295-2301 ◽  
Author(s):  
Jaroslava Klegová ◽  
Ivana Rábová
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Management System ◽  
Maximum Level ◽  
Content Management ◽  
Content Management System ◽  
The Monte Carlo Method ◽  
Enterprise Content Management ◽  
It Department ◽  
The Impact

At present the attention of many organizations concentrates to the Enterprise Content Management system (ECM). Unstructured content grows exponentially, and Enterprise Content Management system helps to capture, store, manage, integrate and deliver all forms of content across the company. Today, decision makers have possibility to move ECM systems to the cloud and take advantages of cloud computing. Cloud solution can provide a crucial competitive advantage. For example, it can reduce fixed IT department cost and ensure faster ECM implementation.To achieve the maximum level of benefits from implementation of ECM in the cloud it is important to understand all possibilities and actions during the implementation. In this paper, the general model of the ECM implementation in the cloud is proposed and described. The risk may relate to all aspects of the implementation, such as cost, schedule or quality. This is the reason why the introduced model places emphasize on risk. The aim of the article is to identify risks of the ECM implementation in the cloud and quantify the impact of risk. The article is focused on the Monte Carlo method. Monte Carlo method is a technique that uses random numbers and probability to solve problems. Based on interviews with an IT managers there is created an example of possible scenarios and the risk is evaluated using the Monte Carlo method.

Performance Testing of Combined Cycle Power Plant in Phased Construction

2010 ◽  
Author(s):  
Hugh Jin ◽  
Terrence B. Sullivan ◽  
Jeffrey R. Friedman
Keyword(s):  
Gas Turbines ◽  
Power Plants ◽  
Performance Test ◽  
Performance Testing ◽  
Combined Cycle ◽  
Plant Performance ◽  
Cycle Performance ◽  
Cycle Phase ◽  
Test Protocol ◽  
Testing Approach

Gas turbines in combined cycle (CC) power plants, in phased construction situations, usually operate for several months in the simple cycle (SC) mode while the steam portion of the plant is being constructed. At the time of commissioning the combined cycle phase, the gas turbines typically have accumulated a considerable number of operating hours and have possibly experienced some degradation, especially on turbines that have run on dual fuels. To determine the combined cycle new and clean performance, it is necessary to employ a phased testing approach. The phased testing approach involves testing the gas turbines when they are in new and clean condition and combining those results with the measured new and clean steam turbine cycle performance. The method of the phased testing has been introduced in ASME PTC 46 (1996) “Performance Test Code on Overall Plant Performance”. This paper will discuss in detail the test protocol, fundamental equations, corrections, and uncertainty analysis of phased testing. This paper will also discuss performance degradation and engine setting changes between the phases.

scholarly journals Measuring the Fluid Flow Velocity and Its Uncertainty Using Monte Carlo Method and Ultrasonic Technique

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Measurement Uncertainty ◽  
Flow Velocity ◽  
Analytical Method ◽  
Thermal Power ◽  
Uncertainty Budget ◽  
Combined Cycle ◽  
The Monte Carlo Method ◽  
Methods Analytical

One of the most important challenges in fluid mechanics, gas dynamics, and hydraulic machinery fields is measuring the flow velocity with high accuracy. It is more important in large systems; such as thermal power stations, large scale power generations, and combined cycle power plants. The exact estimation of the measurement uncertainty inflow velocity is extremely important in evaluating the accuracy of the measurement. This work describes the problem of estimating measurement uncertainty when there are two or more dominant components of the uncertainty budget. . Two methods, analytical and numerical methods are used to study the comparative analysis for the results of determining the expanded uncertainty of measurement using two methods: analytical method and the numerical method. The analytical method uses the law of uncertainty propagation and is based on the estimation of uncertainty values of type A and B, while the numerical technique depends on the evaluation of measured samples by the Monte Carlo method using a random number generator. The aim of this article is to show the Monte Carlo method as an alternative way to determine the distribution of individual components of the measurement uncertainty budget. Also, the measurement of liquid flow velocity by an ultrasonic method has been analyzed, which is commonly used due to high measurement accuracy and non-invasiveness. Due to the complexity of the equation defining the measured flow velocity, determining the measurement uncertainty is not an easy task.

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