scholarly journals Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks

2021 ◽  
Author(s):  
Michael McCartney ◽  
Ushnish Sengupta ◽  
Matthew Juniper
Keyword(s):  
Dynamic Pressure ◽  
Hidden Variables ◽  
Onset Time ◽  
Rate Of Change ◽  
Operating Conditions ◽  
Detection Methods ◽  
Combustion Instabilities ◽  
Bayesian Neural Network ◽  
Operating Condition ◽  
Thermoacoustic Instability

Abstract Modern, low emission combustion systems with improved fuel-air mixing are more prone to combustion instabilities and therefore use advanced control methods to balance minimum NOx emissions and and the presence of thermoacoustic combustion instabilities. The exact operating conditions at which the system encounters an instability is uncertain because of sources of stochasticity, such as turbulent combustion, and the influence of hidden variables, such as un-measured wall temperatures or differences in machine geometry within manufacturing tolerances. Practical systems tend to be more elaborate than laboratory systems and tend to have less instrumentation, meaning that they suffer more from uncertainty induced by hidden variables. In many commercial systems, the only direct measurement of the combustor comes from a dynamic pressure sensor. In this study we train a Bayesian Neural Network (BNN) to predict the probability of onset of thermoacoustic instability at various times in the future, using only dynamic pressure measurements and the current operating condition. We show that, on a practical system, the error in the onset time predicted by the BNNs is 45% lower than the error when using the operating condition alone and more informative than the warning provided by commonly used precursor detection methods. This is demonstrated on two systems: (i) a premixed hydrogen/methane annular combustor, where the hidden variables are wall temperatures that depend on the rate of change of operating condition, and (ii) full scale prototype combustion system, where the hidden variables arise from differences between the systems.

scholarly journals Reducing Uncertainty in the Onset of Combustion Instabilities using Dynamic Pressure Information and Bayesian Neural Networks

2021 ◽  
Author(s):  
Michael McCartney ◽  
Ushnish Sengupta ◽  
Matthew Juniper
Keyword(s):  
Dynamic Pressure ◽  
Hidden Variables ◽  
Onset Time ◽  
Rate Of Change ◽  
Operating Conditions ◽  
Detection Methods ◽  
Combustion Instabilities ◽  
Operating Condition ◽  
Thermoacoustic Instability ◽  
Current Operating Condition

Modern, low emission combustion systems with improved fuel-air mixing are more prone to combustion instabilities and therefore use advanced control methods to balance minimum NOx emissions and and the presence of thermoacoustic combustion instabilities. The exact operating conditions at which the system becomes encounters an instability is uncertain because of sources of stochasticity, such as turbulent combustion, and the influence of hidden variables, such as un-measured wall temperatures or differences in machine geometry within manufacturing tolerances. Practical systems tend to be more elaborate than laboratory systems and tend to have less instrumentation, meaning that they suffer more from uncertainty induced by hidden variables. In many commercial systems, the only direct measurement of the combustor comes from a dynamic pressure sensor. In this study we train a Bayesain Neural Network (BNN) to predict the probability of onset of thermoacoustic instability at various times in the future, using only dynamic pressure measurements and the current operating condition. We show that, on a practical system, the error in the onset time predicted by the BNNs is 45% lower than the error when using the operating condition alone and more informative than the warning provided by commonly used precursor detection methods. This is demonstrated on two systems: (i) a premixed hydrogen/methane annular combustor, where the hidden variables are wall temperatures that depend on the rate of change of operating condition, and (ii) full scale prototype combustion system, where the hidden variables arise from differences between the systems.

scholarly journals Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks

2021 ◽  
Author(s):  
Michael McCartney ◽  
Ushnish Sengupta ◽  
Matthew Juniper
Keyword(s):  
Dynamic Pressure ◽  
Hidden Variables ◽  
Onset Time ◽  
Rate Of Change ◽  
Operating Conditions ◽  
Detection Methods ◽  
Combustion Instabilities ◽  
Operating Condition ◽  
Thermoacoustic Instability ◽  
Current Operating Condition

Abstract Modern, low emission combustion systems with improved fuel-air mixing are more prone to combustion instabilities and therefore use advanced control methods to balance minimum NOx emissions and and the presence of thermoacoustic combustion instabilities. The exact operating conditions at which the system encounters an instability is uncertain because of sources of stochasticity, such as turbulent combustion, and the influence of hidden variables, such as un-measured wall temperatures or differences in machine geometry within manufacturing tolerances. Practical systems tend to be more elaborate than laboratory systems and tend to have less instrumentation, meaning that they suffer more from uncertainty induced by hidden variables. In many commercial systems, the only direct measurement of the combustor comes from a dynamic pressure sensor. In this study we train a Bayesain Neural Network (BNN) to predict the probability of onset of thermoacoustic instability at various times in the future, using only dynamic pressure measurements and the current operating condition. We show that, on a practical system, the error in the onset time predicted by the BNNs is 45% lower than the error when using the operating condition alone and more informative than the warning provided by commonly used precursor detection methods. This is demonstrated on two systems: (i) a premixed hydrogen/methane annular combustor, where the hidden variables are wall temperatures that depend on the rate of change of operating condition, and (ii) full scale prototype combustion system, where the hidden variables arise from differences between the systems.

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.

scholarly journals A Comparative Study on Fault Detection Methods for Gas Turbine Combustion Systems

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 389
Author(s):  
Jinfu Liu ◽  
Zhenhua Long ◽  
Mingliang Bai ◽  
Linhai Zhu ◽  
Daren Yu
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Detection Methods ◽  
Distribution Model ◽  
Outlet Temperature ◽  
Combustion System ◽  
Comparison Results ◽  
Gas Turbine Combustion

As one of the core components of gas turbines, the combustion system operates in a high-temperature and high-pressure adverse environment, which makes it extremely prone to faults and catastrophic accidents. Therefore, it is necessary to monitor the combustion system to detect in a timely way whether its performance has deteriorated, to improve the safety and economy of gas turbine operation. However, the combustor outlet temperature is so high that conventional sensors cannot work in such a harsh environment for a long time. In practical application, temperature thermocouples distributed at the turbine outlet are used to monitor the exhaust gas temperature (EGT) to indirectly monitor the performance of the combustion system, but, the EGT is not only affected by faults but also influenced by many interference factors, such as ambient conditions, operating conditions, rotation and mixing of uneven hot gas, performance degradation of compressor, etc., which will reduce the sensitivity and reliability of fault detection. For this reason, many scholars have devoted themselves to the research of combustion system fault detection and proposed many excellent methods. However, few studies have compared these methods. This paper will introduce the main methods of combustion system fault detection and select current mainstream methods for analysis. And a circumferential temperature distribution model of gas turbine is established to simulate the EGT profile when a fault is coupled with interference factors, then use the simulation data to compare the detection results of selected methods. Besides, the comparison results are verified by the actual operation data of a gas turbine. Finally, through comparative research and mechanism analysis, the study points out a more suitable method for gas turbine combustion system fault detection and proposes possible development directions.

scholarly journals Independent and combined impact of hypoxia and acute inorganic nitrate ingestion on thermoregulatory responses to the cold

2021 ◽  
Vol 121 (4) ◽  
pp. 1207-1218
Author(s):  
Josh T. Arnold ◽  
Stephen J. Bailey ◽  
Simon G. Hodder ◽  
Naoto Fujii ◽  
Alex B. Lloyd
Keyword(s):  
Blood Flow ◽  
Cooling Rate ◽  
Skin Blood Flow ◽  
Rectal Temperature ◽  
Onset Time ◽  
Rate Of Change ◽  
Vascular Control ◽  
Delta Change ◽  
Core Cooling ◽  
The Impact

Abstract Purpose This study assessed the impact of normobaric hypoxia and acute nitrate ingestion on shivering thermogenesis, cutaneous vascular control, and thermometrics in response to cold stress. Method Eleven male volunteers underwent passive cooling at 10 °C air temperature across four conditions: (1) normoxia with placebo ingestion, (2) hypoxia (0.130 FiO2) with placebo ingestion, (3) normoxia with 13 mmol nitrate ingestion, and (4) hypoxia with nitrate ingestion. Physiological metrics were assessed as a rate of change over 45 min to determine heat loss, and at the point of shivering onset to determine the thermogenic thermoeffector threshold. Result Independently, hypoxia expedited shivering onset time (p = 0.05) due to a faster cooling rate as opposed to a change in central thermoeffector thresholds. Specifically, compared to normoxia, hypoxia increased skin blood flow (p = 0.02), leading to an increased core-cooling rate (p = 0.04) and delta change in rectal temperature (p = 0.03) over 45 min, yet the same rectal temperature at shivering onset (p = 0.9). Independently, nitrate ingestion delayed shivering onset time (p = 0.01), mediated by a change in central thermoeffector thresholds, independent of changes in peripheral heat exchange. Specifically, compared to placebo ingestion, no difference was observed in skin blood flow (p = 0.5), core-cooling rate (p = 0.5), or delta change in rectal temperature (p = 0.7) over 45 min, while nitrate reduced rectal temperature at shivering onset (p = 0.04). No interaction was observed between hypoxia and nitrate ingestion. Conclusion These data improve our understanding of how hypoxia and nitric oxide modulate cold thermoregulation.

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.

scholarly journals IpDFT-Tuned Estimation Algorithms for PMUs: Overview and Performance Comparison

2021 ◽  
Vol 11 (5) ◽  
pp. 2318
Author(s):  
David Macii ◽  
Daniel Belega ◽  
Dario Petri
Keyword(s):  
Weighted Least Squares ◽  
Performance Comparison ◽  
Rate Of Change ◽  
Parameters Estimation ◽  
Operating Conditions ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Estimation Algorithms ◽  
Class Testing ◽  
Low Pass

The Interpolated Discrete Fourier Transform (IpDFT) is one of the most popular algorithms for Phasor Measurement Units (PMUs), due to its quite low computational complexity and its good accuracy in various operating conditions. However, the basic IpDFT algorithm can be used also as a preliminary estimator of the amplitude, phase, frequency and rate of change of frequency of voltage or current AC waveforms at times synchronized to the Universal Coordinated Time (UTC). Indeed, another cascaded algorithm can be used to refine the waveform parameters estimation. In this context, the main novelty of this work is a fair and extensive performance comparison of three different state-of-the-art IpDFT-tuned estimation algorithms for PMUs. The three algorithms are: (i) the so-called corrected IpDFT (IpDFTc), which is conceived to compensate for the effect of both the image of the fundamental tone and second-order harmonic; (ii) a frequency-tuned version of the Taylor Weighted Least-Squares (TWLS) algorithm, and (iii) the frequency Down-Conversion and low-pass Filtering (DCF) technique described also in the IEEE/IEC Standard 60255-118-1:2018. The simulation results obtained in the P Class and M Class testing conditions specified in the same Standard show that the IpDFTc algorithm is generally preferable under the effect of steady-state disturbances. On the contrary, the tuned TWLS estimator is usually the best solution when dynamic changes of amplitude, phase or frequency occur. In transient conditions (i.e., under the effect of amplitude or phase steps), the IpDFTc and the tuned TWLS algorithms do not clearly outperform one another. The DCF approach generally returns the worst results. However, its actual performances heavily depend on the adopted low-pass filter.

scholarly journals Measurement of Air-Fuel Ratio Fluctuations Caused by Combustor Driven Oscillations

1998 ◽  
Author(s):  
Rajiv Mongia ◽  
Robert Dibble ◽  
Jeff Lovett
Keyword(s):  
Power Spectrum ◽  
Mole Fraction ◽  
Gas Turbines ◽  
Operating Conditions ◽  
Premixed Combustion ◽  
Pollutant Emissions ◽  
Combustion Instabilities ◽  
Pressure Oscillations ◽  
Lean Premixed Combustion ◽  
Lean Premixed

Lean premixed combustion has emerged as a method of achieving low pollutant emissions from gas turbines. A common problem of lean premixed combustion is combustion instability. As conditions inside lean premixed combustors approach the lean flammability limit, large pressure variations are encountered. As a consequence, certain desirable gas turbine operating regimes are not approachable. In minimizing these regimes, combustor designers must rely upon trial and error because combustion instabilities are not well understood (and thus difficult to model). When they occur, pressure oscillations in the combustor can induce fluctuations in fuel mole fraction that can augment the pressure oscillations (undesirable) or dampen the pressure oscillations (desirable). In this paper, we demonstrate a method for measuring the fuel mole fraction oscillations which occur in the premixing section during combustion instabilities produced in the combustor that is downstream of the premixer. The fuel mole fraction in the premixer is measured with kHz resolution by the absorption of light from a 3.39 μm He-Ne laser. A sudden expansion combustor is constructed to demonstrate this fuel mole fraction measurement technique. Under several operating conditions, we measure significant fuel mole fraction fluctuations that are caused by pressure oscillations in the combustion chamber. Since the fuel mole fraction is sampled continuously, a power spectrum is easily generated. The fuel mole fraction power spectrum clearly indicates fuel mole fraction fluctuation frequencies are the same as the pressure fluctuation frequencies under some operating conditions.

scholarly journals Analysis of Residual Current Flows in Inverter Based Energy Systems Using Machine Learning Approaches

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 582
Author(s):  
Holger Behrends ◽  
Dietmar Millinger ◽  
Werner Weihs-Sedivy ◽  
Anže Javornik ◽  
Gerold Roolfs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Residual Current ◽  
Operating Conditions ◽  
Machine Learning Algorithms ◽  
Photovoltaic System ◽  
Detection Methods ◽  
Learning Approaches ◽  
Residual Currents ◽  
Current Flows

Faults and unintended conditions in grid-connected photovoltaic systems often cause a change of the residual current. This article describes a novel machine learning based approach to detecting anomalies in the residual current of a photovoltaic system. It can be used to detect faults or critical states at an early stage and extends conventional threshold-based detection methods. For this study, a power-hardware-in-the-loop approach was carried out, in which typical faults have been injected under ideal and realistic operating conditions. The investigation shows that faults in a photovoltaic converter system cause a unique behaviour of the residual current and fault patterns can be detected and identified by using pattern recognition and variational autoencoder machine learning algorithms. In this context, it was found that the residual current is not only affected by malfunctions of the system, but also by volatile external influences. One of the main challenges here is to separate the regular residual currents caused by the interferences from those caused by faults. Compared to conventional methods, which respond to absolute changes in residual current, the two machine learning models detect faults that do not affect the absolute value of the residual current.

scholarly journals Simulation of 2-Way Fluid Structure Interaction in a 3D Model Combustor

2012 ◽  
Author(s):  
Mina Shahi ◽  
Jim B. W. Kok ◽  
P. R. Alemela
Keyword(s):  
Flue Gas ◽  
Fluid Structure Interaction ◽  
Operating Conditions ◽  
Limit Cycle Oscillation ◽  
Structural Domain ◽  
Pressure Oscillations ◽  
Fluid Structure ◽  
Thermoacoustic Instability ◽  
Structure Interaction ◽  
Computational Fluid Dynamics Analysis

The liner of a gas turbine combustor is a very flexible structure that is exposed to the pressure oscillations that occur in the combustor. These pressure oscillations can be of very high amplitude due to thermoacoustic instability, when the fluctuations of the rate of heat release and the acoustic pressure waves amplify each other. The liner structure is a dynamic mechanical system that vibrates at its eigenfrequencies and at the frequencies by which it is forced by the pressure oscillations to which it is exposed. On the other hand the liner vibrations force a displacement of the flue gas near the wall in the combustor. The displacement is very small but this acts like a distributed acoustic source which is proportional to the liner wall acceleration. Hence liner and combustor are a coupled elasto-acoustic system. When this is exposed to a limit cycle oscillation the liner may fail due to fatigue. In this paper the method and the results will be presented of the partitioned simulation of the coupled acousto-elastic system composed of the liner and the flue gas domain in the combustor. The partitioned simulation uses separate solvers for the flow domain and the structural domain, that operate in a coupled way. In this work 2-way fluid structure interaction is studied for the case of a model combustor for the operating conditions 40–60 kW with equivalence ratio of 0.625. This is done in the framework of the LIMOUSINE project. Computational fluid dynamics analysis is performed to obtain the thermal loading of the combustor liner and finite element analysis renders the temperature, stress distribution and deformation in the liner. The software used is ANSYS workbench V13.0 software, in which the information (pressure and displacement) is also exchanged between fluid and structural domain transiently.

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