Reliability and Optimal Replacement via Coefficient of Variation

1980 ◽  
Vol 102 (4) ◽  
pp. 761-768 ◽  
Author(s):  
S. M. Pandit ◽  
A. K. Shiekh
Keyword(s):  
Experimental Data ◽  
Coefficient Of Variation ◽  
Cutting Tools ◽  
Operating Conditions ◽  
Limited Data ◽  
Operating Condition ◽  
Numerical Examples ◽  
Optimal Replacement ◽  
Fatigue Failures

The paper presents an approach to obtaining reliability and optimal replacement policies at different operating conditions, based on limited data at one operating condition. Guidelines for the choice of a distribution based on the behavior of sample functions of deterioration processes such as wear and fatigue are provided. The importance of the coefficient of variation is delineated to show how it provides a dimensionless format of the distributions and optimal replacement strategies. Simple graphical solutions of the otherwise complicated optimal replacement equations are illustrated by numerical examples based on experimental data in diverse forms from the areas of cutting tools and fatigue failures.

Aerodynamic Damping Predictions Using a Linearized Navier-Stokes Analysis

1999 ◽  
Author(s):  
Daniel Hoyniak ◽  
William S. Clark
Keyword(s):  
Experimental Data ◽  
High Speed ◽  
Operating Conditions ◽  
The Other ◽  
Navier Stokes ◽  
Turbine Cascade ◽  
Blade Loading ◽  
Operating Condition ◽  
Linear Cascade ◽  
Aerodynamic Damping

A recently developed two dimensional, linearized Navier-Stokes algorithm, capable of modeling the unsteady flows encountered in turbomachinery applications, has been benchmarked and validated for use in the prediction of the aerodynamic damping. Benchmarking was accomplished by comparing numerical simulations with experimental data for two geometries. The first geometry investigated is a high turning turbine cascade. For this configuration, two different steady operating conditions were considered. The exit flow for one operating condition is subsonic whereas the exit flow for the other operating condition is supersonic. The second geometry investigated is a tip section from a high speed fan. Again, two separate steady operating conditions were examined. For this fan geometry, one operating condition falls within an experimentally observed flutter region whereas the other operating condition was observed experimentally to be flutter free. For both geometries considered, experimental measurements of the unsteady blade surface pressures were acquired for a linear cascade subjected to small amplitude torsional vibrations. Comparisons between the numerical calculations and the experimental data demonstrate the ability of the present computational model to predict accurately the steady and unsteady blade loading, and hence the aerodynamic damping, for each configuration presented.

Dynamic Data-Driven Combustor Design for Mitigation of Thermoacoustic Instabilities

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Pritthi Chattopadhyay ◽  
Sudeepta Mondal ◽  
Asok Ray ◽  
Achintya Mukhopadhyay
Keyword(s):  
Experimental Data ◽  
Design Methodology ◽  
Performance Comparison ◽  
Operating Conditions ◽  
System Stability ◽  
Data Driven ◽  
Pressure Oscillations ◽  
Dynamic Data ◽  
Operating Condition ◽  
Thermoacoustic Instabilities

A critical issue in design and operation of combustors in gas turbine engines is mitigation of thermoacoustic instabilities, because such instabilities may cause severe damage to the mechanical structure of the combustor. Hence, it is important to quantitatively assimilate the knowledge of the system conditions that would potentially lead to these instabilities. This technical brief proposes a dynamic data-driven technique for design of combustion systems by taking stability of pressure oscillations into consideration. Given appropriate experimental data at selected operating conditions, the proposed design methodology determines a mapping from a set of operating conditions to a set of quantified stability conditions for pressure oscillations. This mapping is then used as an extrapolation tool for predicting the system stability for other conditions for which experiments have not been conducted. Salient properties of the proposed design methodology are: (1) It is dynamic in the sense that no fixed model structure needs to be assumed, and a suboptimal model (under specified user-selected constraints) is identified for each operating condition. An information-theoretic measure is then used for performance comparison among different models of varying structures and/or parameters and (2) It quantifies a (statistical) confidence level in the estimate of system stability for an unobserved operating condition by using a Bayesian nonparametric technique. The proposed design methodology has been validated with experimental data of pressure time-series, acquired from a laboratory-scale lean-premixed swirl-stabilized combustor.

OPERATIONAL STATE OF BRIDGES OF UKRAINE

2019 ◽  
pp. 57-68
Author(s):  
Larysa Bodnar ◽  
Petro Koval ◽  
Sergii Stepanov ◽  
Liudmyla Panibratets
Keyword(s):  
Management System ◽  
The State ◽  
Operating Conditions ◽  
Bridge Management ◽  
Bridge Management System ◽  
Program Complex ◽  
Operating Condition ◽  
Software Complex ◽  
Automatic Mode ◽  
The Status

A significant part of Ukrainian bridges on public roads is operated for more than 30 years (94 %). At the same time, the traffic volume and the weight of vehicles has increased significantly. Insufficient level of bridges maintenance funding leads to the deterioration of their technical state. The ways to ensure reliable and safe operation of bridges are considered. The procedure for determining the predicted operational status of the elements and the bridge in general, which has a scientific novelty, is proposed. In the software complex, Analytical Expert Bridges Management System (AESUM), is a function that allows tracking the changes in the operational status of bridges both in Ukraine and in each region separately. The given algorithm of the procedure for determining the predicted state of the bridge using a degradation model is described using the Nassie-Schneidermann diagram. The model of the degradation of the bridge performance which is adopted in Ukraine as a normative one, and the algorithm for its adaptation to the AESUM program complex with the function to ensure the probabilistic predicted operating condition of the bridges in the automatic mode is presented. This makes it possible, even in case of unsatisfactory performance of surveys, to have the predicted lifetime of bridges at the required time. For each bridge element it is possible to determine the residual time of operation that will allow predict the state of the elements of the structure for a certain period of time in the future. Significant interest for specialists calls for the approaches to the development of orientated perspective plans for bridge inspection and monitoring of changes in the operational status of bridges for 2009-2018 in Ukraine. For the analysis of the state of the bridge economy, the information is available on the distribution of bridges by operating state related to the administrative significance of roads, by road categories and by materials of the structures. Determining the operating state of the bridge is an important condition for making the qualified decisions as regards its maintenance. The Analytical Expert Bridges Management System (AESUM) which is implemented in Ukraine, stores the data on the monitoring the status of bridges and performs the necessary procedures to maintain them in a reliable and safe operating condition. An important result of the work is the ability to determine the distribution of bridges on the public roads of Ukraine, according to operating conditions established in the program complex of AESUM, which is presented in accordance with the data of the current year. In conditions of limited funding and in case of unsatisfactory performance of surveys, it is possible to make the reasonable management decisions regarding the repair and the reconstruction of bridges. Keywords: bridge management system, operating condition, predicted operating condition, model of degradation, bridge survey plan, highway bridge.

Design Point for Predicting Year-Round Performance of Solar Parabolic Trough Concentrator Systems

2013 ◽  
Author(s):  
Men Wirz ◽  
Matthew Roesle ◽  
Aldo Steinfeld
Keyword(s):  
Operating Conditions ◽  
Transfer Model ◽  
Parabolic Trough ◽  
Specific System ◽  
Simple Method ◽  
Operating Condition ◽  
Average Efficiency ◽  
Design Point ◽  
Yearly Average ◽  
Solar Field

Thermal efficiencies of the solar field of two different parabolic trough concentrator (PTC) systems are evaluated for a variety of operating conditions and geographical locations, using a detailed 3D heat transfer model. Results calculated at specific design points are compared to yearly average efficiencies determined using measured direct normal solar irradiance (DNI) data as well as an empirical correlation for DNI. It is shown that the most common choices of operating conditions at which solar field performance is evaluated, such as the equinox or the summer solstice, are inadequate for predicting the yearly average efficiency of the solar field. For a specific system and location, the different design point efficiencies vary significantly and differ by as much as 11.5% from the actual yearly average values. An alternative simple method is presented of determining a representative operating condition for solar fields through weighted averages of the incident solar radiation. For all tested PTC systems and locations, the efficiency of the solar field at the representative operating condition lies within 0.3% of the yearly average efficiency. Thus, with this procedure, it is possible to accurately predict year-round performance of PTC systems using a single design point, while saving computational effort. The importance of the design point is illustrated by an optimization study of the absorber tube diameter, where different choices of operating conditions result in different predicted optimum absorber diameters.

Modeling and Experimental Validation of the Effective Bulk Modulus of a Mixture of Hydraulic Oil and Air

2014 ◽  
Vol 136 (5) ◽  
Author(s):  
Hossein Gholizadeh ◽  
Doug Bitner ◽  
Richard Burton ◽  
Greg Schoenau
Keyword(s):  
Experimental Data ◽  
Bulk Modulus ◽  
Theoretical Models ◽  
Operating Conditions ◽  
Air Bubbles ◽  
Pressure Sensitivity ◽  
Hydraulic Oil ◽  
Effective Bulk Modulus ◽  
Experimental Apparatus ◽  
Major Factors

It is well known that the presence of entrained air bubbles in hydraulic oil can significantly reduce the effective bulk modulus of hydraulic oil. The effective bulk modulus of a mixture of oil and air as pressure changes is considerably different than when the oil and air are not mixed. Theoretical models have been proposed in the literature to simulate the pressure sensitivity of the effective bulk modulus of this mixture. However, limited amounts of experimental data are available to prove the validity of the models under various operating conditions. The major factors that affect pressure sensitivity of the effective bulk modulus of the mixture are the amount of air bubbles, their size and the distribution, and rate of compression of the mixture. An experimental apparatus was designed to investigate the effect of these variables on the effective bulk modulus of the mixture. The experimental results were compared with existing theoretical models, and it was found that the theoretical models only matched the experimental data under specific conditions. The purpose of this paper is to specify the conditions in which the current theoretical models can be used to represent the real behavior of the pressure sensitivity of the effective bulk modulus of the mixture. Additionally, a new theoretical model is proposed for situations where the current models fail to truly represent the experimental data.

Performance Characterization of a Twin Scroll Volute for Turbocharging Applications

2018 ◽  
Author(s):  
Carlo Cravero ◽  
Mario La Rocca ◽  
Andrea Ottonello
Keyword(s):  
Experimental Data ◽  
Scientific Literature ◽  
Aerodynamic Performance ◽  
Operating Conditions ◽  
Automatic Design ◽  
Cfd Model ◽  
Radial Turbine ◽  
Wide Range ◽  
Partial Admission

The use of twin scroll volutes in radial turbine for turbocharging applications has several advantages over single passage volute related to the engine matching and to the overall compactness. Twin scroll volutes are of increasing interest in power unit development but the open scientific literature on their performance and modelling is still quite limited. In the present work the performance of a twin scroll volute for a turbocharger radial turbine are investigated in some detail in a wide range of operating conditions at both full and partial admission. A CFD model for the volute have been developed and preliminary validated against experimental data available for the radial turbine. Then the numerical model has been used to generate the database of solutions that have been investigated and used to extract the performance. Different parameters and indices are introduced to describe the volute aerodynamic performance in the wide range of operating conditions chosen. The above parameters can be used for volute development or matching with a given rotor or efficiently implemented in automatic design optimization strategies.

NUMERICAL ESTIMATION OF MOISTURE CONTENT IN SPRAY DRIED JUICE POWDER

2016 ◽  
Vol 78 (8-3) ◽  
Author(s):  
Aliyu Bello A. ◽  
Arshad Ahmad ◽  
Adnan Ripin ◽  
Olagoke Oladokun
Keyword(s):  
Experimental Data ◽  
Moisture Content ◽  
Diffusion Model ◽  
Process Model ◽  
Small Error ◽  
Operating Conditions ◽  
Balance Model ◽  
Pineapple Juice ◽  
Droplet Drying ◽  
Spray Dried

The moisture contents of powders is an important parameter that affects the quality and commercial value of spray dried products. The utility of predicted moisture content values from two droplet drying models were compared with experimental data for spray dried pineapple juice, using the Ranz-Marshal and its modified variants for the heat and mass transfer correlations. The droplet Diffusion model, using the Zhifu correlation, gave estimates with errors of about 8% at 165 oC, 9% at 171 oC, 26% at 179 oC and 2% at 185 oC. The Ranz-Marshal correlation also gave comparable results with this model while results using the Downing and modified Ranz-Marshall correlations widely diverged. The Energy balance model predicted completely dried juice particles, and short drying times, in contrast to the experimental data. The small error sizes of the Diffusion model improves on the wide error sizes of an earlier process model, making is useful as a first approximation choice, for spray drier design and simulation, especially for juices under comparable operating conditions.

Directionally Independent Failure Prediction of End-Milling Tools During Pocketing Maneuvers

2007 ◽  
Vol 129 (4) ◽  
pp. 770-779 ◽  
Author(s):  
Christopher A. Suprock ◽  
Joseph J. Piazza ◽  
John T. Roth
Keyword(s):  
Discrete Cosine Transform ◽  
End Milling ◽  
Cutting Tools ◽  
Primary Component ◽  
Operating Conditions ◽  
Frequency Method ◽  
Cosine Transform ◽  
Impending Fracture ◽  
Triaxial Dynamometer ◽  
Acceleration Signals

Tracking the health of cutting tools under typical wear conditions is advantageous to the speed and efficiency of manufacturing processes. Existing techniques monitor tool performance through analyzing force or acceleration signals whereby prognoses are made from a single sensor type. This work proposes to enhance the spectral output of autoregressive (AR) models by combining triaxial accelerometer and triaxial dynamometer signals. Through parallel processing of force and acceleration signals using single six degree of freedom modeling, greater spectral resolution is achieved. Two entirely independent methods of tracking the tool wear are developed and contrasted. First, using the discrete cosine transform, primary component analysis will be applied to the spectral output of each AR auto and cross spectrum (Method 1). Each discrete cosine transform of the six-dimensional spectral data is analyzed to determine the magnitude of the critical (primary) variance energy component of the respective spectrum. The eigenvalues of these selected spectral energies are then observed for trends toward failure. The second method involves monitoring the eigenvalues of the spectral matrices centered at the toothpass frequency (Method 2). The results of the two methodologies are compared. Through the use of the eigenvalue method, it is shown that, for straight and pocketing maneuvers, both methods successfully track the condition of the tool using statistical thresholding. The techniques developed in this work are shown to be robust by multiple life tests conducted on different machine platforms with different operating conditions. Both techniques successfully identify impending fracture or meltdown due to wear, providing sufficient time to remove the tools before failure is realized.

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water

2004 ◽  
Vol 50 (8) ◽  
pp. 103-110 ◽  
Author(s):  
H.K. Oh ◽  
M.J. Yu ◽  
E.M. Gwon ◽  
J.Y. Koo ◽  
S.G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Network Model ◽  
Operating Conditions ◽  
Transmembrane Pressure ◽  
Feed Water ◽  
Permeate Flux ◽  
Artificial Neural

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

Experimental and Analytical Characterization of Alternative Aviation Fuel Sprays Under Realistic Operating Conditions

2018 ◽  
Author(s):  
Andrew Corber ◽  
Nader Rizk ◽  
Wajid Ali Chishty
Keyword(s):  
Experimental Data ◽  
Alternative Fuels ◽  
Laser Sheet ◽  
National Research Council ◽  
Diagnostic System ◽  
Operating Conditions ◽  
Aviation Fuel ◽  
Injection System ◽  
Test Time

The National Jet Fuel Combustion Program (NJFCP) is an initiative, currently being led by the Office of Environment & Energy at the FAA, to streamline the ASTM jet fuels certification process for alternative aviation fuels. In order to accomplish this objective, the program has identified specific applied research tasks in several areas. The National Research Council of Canada (NRC) is contributing to the NJFCP in the areas of sprays and atomization and high altitude engine performance. This paper describes work pertaining to atomization tests using a reference injection system. The work involves characterization of the injection nozzle, comparison of sprays and atomization quality of various conventional and alternative fuels, as well as use of the experimental data to validate spray correlations. The paper also briefly explores the application viability of a new spray diagnostic system that has potential to reduce test time in characterizing sprays. Measurements were made from ambient up to 10 bar pressures in NRC’s High Pressure Spray Facility using optical diagnostics including laser diffraction, phase Doppler anemometry (PDA), LIF/Mie Imaging and laser sheet imaging to assess differences in the atomization characteristics of the test fuels. A total of nine test fluids including six NJFCP fuels and three calibration fluids were used. The experimental data was then used to validate semi-empirical models, developed through years of experience by engine OEMs and modified under NJFCP, for predicting droplet size and distribution. The work offers effective tools for developing advanced fuel injectors, and generating data that can be used to significantly enhance multi-dimensional combustor simulation capabilities.

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