Accurate Burst Research Articles

Data augmentation using SMOTE technique: Application for prediction of burst pressure of hydrocarbons pipeline using supervised machine learning models

Accurate burst pressure prediction is critical for ensuring oil and gas pipeline safety, guiding maintenance decisions, and lowering costs and risks. Traditional methods have limitations, including high experimental costs, conservative empirical models, and computationally expensive numerical algorithms. Machine learning (ML) models have supplanted traditional methods in recent years. However, small and imbalanced datasets are the big challenge to build a ML model that can generate more accurate results. Moreover, the lack of generalization in ML models trained on a dataset of pipelines with specific material grids prevents them from producing superior results on other pipeline types. First, FEA was used to make a dataset. Then, a new way to improve machine learning (ML) model generalization for burst pressure prediction is suggested: combine publicly available datasets of different pipeline specifications. In this combined dataset, some pipelines have a higher number of data samples, and some have fewer, which causes a class imbalance issue. The Synthetic Minority Oversampling Technique (SMOTE) technique was applied to address the issue of class imbalance. The performance of various ML models, Extra Trees (ET), Extreme Gradient Boosting (XGBR), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Decision Tree (DT), was evaluated to validate the model's prediction and generalization on pipelines of various material grids. Results show that all the selected ML models produced high R-squared, i.e., >0.95, on balanced data compared to the imbalance dataset. These results show that SMOTE-based augmentation is a beneficial way to fix dataset imbalance and make ML models better at predicting burst pressure in oil and gas pipelines.

Burst Speed Prediction of Impellers With Critical Strain Criteria

Abstract Accurate prediction of burst speed and critical location of a rotating disk is a great concern for turbomachinery designers and enables the reduction of test numbers required to show rotor integrity during turbo-engine certification. Existing burst methodologies mainly focused on axial disks with well-defined bore, web, and rim regions and mostly depended on correction factors that require previous test data of rotors with similar sizes and features. Furthermore, variation of radial and hoop stresses in a radial disk is less predictable than in axial disks due to the asymmetric nature of impellers, which results in uneven distribution of centrifugal and bending loads. This paper aims to explain the experimental and numerical effort dedicated to building an accurate burst methodology for impellers. Elastoplastic finite element analyses are performed by incorporating geometric and material nonlinearities. Analysis results allowed the prediction of failure locations and observation of redistribution of stresses due to material plasticity as rotor speed increases. An extensive test campaign is carried out to validate the numerical analysis. Critical strain criteria that correspond to the plastic strain of true ultimate tensile strength (UTS), are used to determine burst speeds before tests. Experimental results are compared with Robinson's average hoop stress criteria, Hallinan's criteria, and critical strain criteria to show the strong and weak sides of these methodologies for an impeller application. The numerical burst speeds and crack initiation locations associated with critical strain criteria are found to be in good agreement with the spin test results.

A parametric finite element study for determining burst strength of thin and thick-walled pressure vessels

To accurately predict the burst strength of both thin and thick-walled pressure vessels (PVs), a parametric study of PV burst strength was performed for a wide range of vessel geometries and materials using elastic-plastic finite element analysis (FEA). A valid FEA model was established through a detailed study of 2D versus 3D FEA models, the critical stress failure criterion versus the limit load criteria, and the thick-wall effect on the FEA simulations. The results show that the stresses and strains at the mean diameter, rather than outside diameter, determines a more accurate burst strength for both thin and thick-walled PVs. On this basis, a parametrized FEA script using the ABAQUS Python application programming interface (API) was used to create a large database of PV burst strengths for a variety of vessel geometries and materials, demonstrating that Python scripting is a powerful technique for performing parametric studies or generating large databases. From the FEA results, using the regression method, a new burst pressure model was developed as a function of the vessel geometry (D/t ratio) and material properties (UTS and n). As validated by a large number of full-scale burst test data, the proposed burst model can very accurately predict the burst strength for both thin and thick-walled PVs.

Burst Pressure Solutions of Thin and Thick-Walled Cylindrical Vessels

Abstract Pressure vessels (PVs) are widely used in the energy industry. Accurate burst pressure is critical to structural design and safe operation for both thin and thick-walled PVs. The traditional strength theories utilized a single-parameter material property, such as the yield stress or the ultimate tensile stress (UTS) to develop failure models for determining the yield or ultimate pressure carrying capacity in the PV design. The UTS-based Barlow formula is a typical burst pressure model developed from the Tresca strength theory that provides the basis for developing regulation rules and failure models for different industry design codes, such as ASME BPVC, ASME B31.3, and ASME B31G, among others. In order to reduce the conservatism of the Tresca strength model, ASME BPVC recently adapted failure models developed from the von Mises strength theory for the PV design and analysis. It has been commonly accepted that the burst pressure of pipelines depends on the UTS and strain hardening exponent, n, of the pipeline steel. An average shear stress yield theory was thus developed, and the Zhu-Leis solution of burst pressure was obtained as a function of UTS and n for thin-walled line pipes. Experiments showed that the Zhu-Leis solution provides an accurate, reliable prediction of burst pressure for defect-free thin-walled pipes. In order to extend the Zhu–Leis solution to thick-walled cylindrical PVs, this paper defined three new flow stresses, modified the traditional strength theories, and obtained three new burst pressure solutions that are valid for both thin and thick-walled cylindrical vessels. The proposed flow stresses are able to describe the tensile strength and the plastic flow response of PVs for a strain hardening steel. The associated strength theories were then developed in terms of the Tresca, von Mises, and Zhu-Leis yield criteria. From these new strength theories, three burst pressure solutions were obtained for thick-walled cylinders, where the von Mises solution is an upper bound prediction, the Tresca solution is a lower bound prediction, and the Zhu-Leis solution is an intermediate prediction of burst pressure for thick-walled cylinders. Finally, the proposed burst pressure solutions were evaluated and validated by two large datasets of full-scale burst tests for thick-walled tubes and for thin-walled pipes.

A new methodology for the prediction of burst pressure for API 5L X grade flawless pipelines

Accurate prediction of the burst pressure of a pipeline is critical for pipeline design and safe operation. It is usually achieved by using analytical and empirical formulae derived from theoretical, numerical and experimental methods.This study was carried out to develop a new methodology to predict the burst pressure for API 5L X grade flawless pipelines. The newly proposed methodology has been developed using finite element analysis, employing a bilinear material model using the tangent modulus approach. In this approach, the strain at ultimate tensile strength has been derived from analysis of API 5L X grade material coupon test data.A comprehensive nonlinear finite element parametric study was conducted with this bilinear material model. The results were used to derive an empirical formula for estimating the burst pressure of API 5L X grade flawless pipelines. It was found that the best agreement was achieved between the burst pressure calculated by the proposed formula and the burst test results. Therefore, it can be concluded that the burst pressure prediction empirical formula derived by following the proposed methodology can calculate an accurate burst pressure for the API 5L X grade flawless pipelines.

Deep learning identifies accurate burst locations in water distribution networks

Pipe bursts in water distribution networks lead to considerable water loss and pose risks of bacteria and pollutant contamination. Pipe burst localisation methods help water service providers repair the burst pipes and restore water supply timely and efficiently. Although methods have been reported on burst detection and localisation, there is a lack of studies on accurate localisation of a burst within a potential district by accessible meters. To address this, a novel Burst Location Identification Framework by Fully-linear DenseNet (BLIFF) is proposed. In this framework, additional pressure meters are placed at limited, optimised places for a short period (minutes to hours) to monitor system behaviour after the burst. The fully-linear DenseNet (FL-DenseNet) newly developed in this study modifies the state-of-the-art deep learning algorithm to effectively extract features in the limited pressure signals for accurate burst localisation. BLIFF was tested on a benchmark network with different parameter settings, which showed that accurate burst localisation results can be achieved even with high model uncertainties. The framework was also applied to a real-life network, in which 57 of the total 58 synthetic bursts in the potential burst district were correctly located when the top five most possible pipes are considered and among them, 37 were successfully located when considering only the top one. Only one failed because of the very small pipe diameter and remote location. Comparisons with DenseNet and the traditional fully linear neural network demonstrate that the framework can effectively narrow the potential burst district to one or several pipes with good robustness and applicability. Codes are available at https://github.com/wizard1203/waternn.

Study on Pipe Burst Detection Frame Based on Water Distribution Model and Monitoring System

This paper describes an infrastructure to detect burst events in a water distribution network, which we illustrate using the Guangzhou water distribution system (WDS). We consider three issues: The feasibility and capability of accurate detection, the layout and design of the monitoring infrastructure, and the burst event detection algorithm. Background noise is identified by analyzing the monitored data. A burst event can be accurately detected only when the impact of the burst can be differentiated from the background noise. We hypothesize that there is a minimum pipe diameter below which accurate burst detection is impossible. We found that data from at least two sensors close to the burst event are required to reduce detection errors.

Stellar populations of HII galaxies

Aims. We present a UV to mid-IR spectral energy distribution (SED) study of a large sample of SDSS DR13 HII galaxies. These galaxies are selected as starbursts (EW(Hα) > 50Å) and for their high-excitation locus in the upper left region of the BPT diagram. Their photometry was derived from the cross-matched GALEX, SDSS, UKDISS, and WISE catalogs. Methods. We used CIGALE modeling and a SED fitting routine with the parametrization of a three-burst star formation history, and a comprehensive analysis of all other model parameters. We were able to estimate the contribution of the underlying old stellar population to the observed equivalent width of Hβ, and allow for more accurate burst age determination. Results. We found that the star formation histories of HII Galaxies can be reproduced remarkably well by three major eras of star formation. In addition, the SED fitting results indicate that in all cases the current burst produces a small percent of the total stellar mass, i.e., the bulk of stellar mass in HII galaxies has been produced by the past episodes of star formation, and also indicate that at a given age the Hβ luminosity depends only on the mass of young stars favoring a universal IMF for massive stars. Most importantly, the current star formation episodes are maximum starbursts that produce stars at the highest possible rate.

Modified response surface method basis harmony search to predict the burst pressure of corroded pipelines

Abstract The accurate burst pressure prediction of pipelines with corrosion defects is important to provide a suitable design of water, oil, and gas pipes networks. Generally, the empirical burst pressure models for corroded pipelines have the narrow limitation for large-verity of steel grades. In this paper, a modified response surface model is proposed based on the novel learning procedure using harmony search algorithm to predict the burst pressure of corroded pipelines with different steel grades named as HS-MRSM. The nonlinear relation as a power and high-order polynomial functions is calibrated using improved harmony search for large experimental corroded pipes >572 in HS-MRSM model. The performances for both accuracy and agreement predictions of the HS-MRSM are compared with modified response surface method (MRSM) and existing empirical models using comparative statistics as root mean square error (RMSE), mean absolute error (MAE), the Nash-Sutcliffe Efficiency (NSE), and the Willmott index of agreement (d). The results demonstrated that the proposed HS-MRSM is significantly improved The burst pressure predictions of corroded pipelines compared to best empirical model and MRSM. Generally, the empirical models – based PCORRC format are performed the best predictions among other empirical models.

EVIDENCE FOR ENHANCED PERSISTENT EMISSION DURING SUB-EDDINGTON THERMONUCLEAR BURSTS

The standard approach for time-resolved X-ray spectral analysis of thermonuclear bursts involves subtraction of the pre-burst emission as background. This approach implicitly assumes that the persistent flux remains constant throughout the burst. We reanalyzed 332 photospheric radius expansion bursts observed from 40 sources by the Rossi X-ray Timing Explorer, introducing a multiplicative factor $f_a$ to the persistent emission contribution in our spectral fits. We found that for the majority of spectra the best-fit value of $f_a$ is significantly greater than 1, suggesting that the persistent emission typically increases during a burst. Elevated $f_a$ values were not found solely during the radius expansion interval of the burst, but were also measured in the cooling tail. The modified model results in a lower average value of the $\chi^2$ fit statistic, indicating superior spectral fits, but not yet to the level of formal statistical consistency for all the spectra. We interpret the elevated $f_a$ values as an increase of the mass accretion rate onto the neutron star during the burst, likely arising from the effects of Poynting-Robertson drag on the disk material. We measured an inverse correlation of $f_a$ with the persistent flux, consistent with theoretical models of the disc response. We suggest that this modified approach may provide more accurate burst spectral parameters, as well as offering a probe of the accretion disk structure.

Adaptive Generative Models for Digital Wireless Channels

Generative models, which can generate bursty error sequences with similar burst error statistics to those of descriptive models, have an immense impact on the wireless communications industry as they can significantly reduce the computational time of simulating wireless communication links. Adaptive generative models aim to produce any error sequences with any given signal- to-noise ratios (SNRs) by using only two reference error sequences obtained from a reference transmission system with two different SNRs. Compared with traditional generative models, this adaptive technique can further considerably reduce the computational load of generating new error sequences as there is no need to simulate the whole reference transmission system again. In this paper, reference error sequences are provided by computer simulations of a long term evolution (LTE) system. Adaptive generative models are developed from three widely used generative models, namely, the simplified Fritchman model (SFM), the Baum-Welch based hidden Markov model (BWHMM), and the deterministic process based generative model (DPBGM). It is demonstrated that the adaptive DPBGM can provide accurate burst error statistics and bit error rate (BER) performance of the LTE system, while the adaptive SFM and adaptive BWHMM fail to do so.

The Void-Identify Method of Penetration Fuze Based on Timing Control

The detonating control method of Void-identify is one of the main methods in penetration ammunition detonating control. But during the process of penetration, the accelerometer in penetration ammunition will receives many interference signal so that affects the accurate burst control. Aiming to the problems of void-identify and anti-jamming in penetration process, the paper proposes a method to identify void and eliminates double-void interference by using the high-g accelerometer and Timer, and through comparing the range and duration of G. The system can controls the burst point accurately by Matlab simulating.

EVIDENCE FOR ACCRETION RATE CHANGE DURING TYPE I X-RAY BURSTS

The standard approach for time-resolved X-ray spectral analysis of thermonuclear bursts involves subtraction of the pre-burst emission as background. This approach implicitly assumes that the persistent flux remains constant throughout the burst. We reanalyzed 332 photospheric radius expansion bursts observed from 40 sources by the Rossi X-Ray Timing Explorer, introducing a multiplicative factor fa to the persistent emission contribution in our spectral fits. We found that for the majority of spectra the best-fit value of fa is significantly greater than 1, suggesting that the persistent emission typically increases during a burst. Elevated fa values were not found solely during the radius expansion interval of the burst, but were also measured in the cooling tail. The modified model results in a lower average value of the χ2 fit statistic, indicating superior spectral fits, but not yet to the level of formal statistical consistency for all the spectra. We interpret the elevated fa values as an increase of the mass accretion rate onto the neutron star during the burst, likely arising from the effects of Poynting–Robertson drag on the disk material. We measured an inverse correlation of fa with the persistent flux, consistent with theoretical models of the disk response. We suggest that this modified approach may provide more accurate burst spectral parameters, as well as offering a probe of the accretion disk structure.

Mechanical Behavior of Internally Pressurized Copper Tube for New HVACR Applications

This paper reviews and simplifies basic theory to predict plastic strain and burst pressure of internally pressurized, thin-walled copper tube for (heating, ventilation, air conditioning, and refrigeration applications. Predictions are based upon material stress–strain data obtained from basic tensile tests. A series of pressure tests was performed at 635 to 1500 psi (4.38–10.34 MPa), and until burst, on tubes ranging from 0.625 in. (15.87 mm) to 2.125 in. (53.97 mm) in diameter. A Voce type equation is shown to provide superior correlation to tensile and instability data, such that accurate projections can be made. An assessment of the classical power-law (Ludwik–Hollomon) equation is also presented, and it did not simultaneously correlate well with stress–strain data and satisfy the Considère instability criterion in uni-axial tension. Nevertheless, its use still led to reasonably accurate burst pressure predictions due to the strain range over which it was applied. Property variation (with respect to tube size) and anisotropy were observed in the transverse and axial tube directions for 1.125 in. (28.6 mm) and 2.125 in. (54.0 mm) diameter tube. Thus, the importance of representative and accurate material data in the transverse (hoop) direction is emphasized.

Burst Detection From Multiple Data Streams: A Network-Based Approach

Modeling and detecting bursts in data streams is an important area of research with a wide range of applications. In this paper, we present a novel method to analyze and identify correlated burst patterns by considering multiple data streams that coevolve over time. The main technical contribution of our research is the use of a dynamic probabilistic network to model the dependency structures observed within these data streams. Such dependencies provide meaningful information concerning the overall system dynamics and should be explicitly integrated into the burst detection process. Using both synthetic scenarios and two real-world datasets, we compare our method with an existing burst-detection algorithm. Initial experimental results indicate that our approach allows for more balanced and accurate burst quantification.

Privately detecting bursts in streaming, distributed time series data

Surprisingly, privacy preservation in the context of streaming data has received limited attention from computer scientists. In this paper, we consider privacy preservation in the context of independently owned, distributed data streams. Specifically, we want to protect the privacy of each individual participant’s data stream while identifying bursts that exist across participant streams. We define two types of privacy breaches, data breaches and envelope breaches. In order to protect individual data, each participant transforms large subsets of the stream into small vectors that approximate the stream. These small vectors are calculated by summing coefficients of wavelet transforms at different resolutions. The participants share their vectors using bursty, self-eliminating noise. The combined participant vectors can then be used to detect bursts. We find that our approach leads to accurate burst detection results with reduced communication costs. We demonstrate these findings using both real and synthetic data.

Prediction of the failure pressure for complex corrosion defects

A new method for predicting the failure pressure of corrosion defects in pipelines has been developed. The failure pressure of a plain pipe represents an upper limit for the failure pressure of a pipe with a corrosion defect. The failure pressure of a uniform depth, infinitely long groove, where the depth is equal to the maximum depth of the corrosion defect, represents a lower limit for the failure pressure of a pipe with a natural corrosion defect. The predicted failure pressure can be calculated from these limits using the weighted depth difference (WDD) method, which accounts for the defect geometry and any interaction with adjacent defects. The WDD method has been validated using the results of 40 burst tests of pipe sections containing real corrosion defects. The results indicate that this method provides more accurate burst pressure predictions than the currently accepted corrosion defect assessment procedures.

Burst spectral measures and formant frequencies can be used to accurately discriminate place of articulation

Given an accurate burst location, four automatic measurements of the burst spectral shape and three semiautomatic formant onset measurements were used to correctly classify place of articulation of 90% of a corpus of 124 stops. At least 20 releases of each of the six English stops, in unconstrained prevocalic, preglide, or (for velar and labial stops only) pre-/r/ context, were chosen at random from the TIMIT corpus. Burst locations were marked by hand. The values of F2 and F3 20 ms later, and of ΔF2 between 20 and 50 ms later, were measured automatically, and 18% of the tokens were corrected by hand. Burst compactness, peak frequency, and mid- and high-frequency amplitude were measured automatically. In separate analyses of variance, all seven measures showed significant place effects (p<0.001). In three pairwise linear discriminants, the burst measures alone were able to classify place with 85% accuracy, showing that automatic measurements can reproduce the result of Blumstein and Stevens [J. Acoust. Soc. Am. 64 (1978)]. With seven measures, the error rate was only 10%. Most errors could be attributed to a strong labial burst and rapid formant transitions in /i/ or /r/ context, or coloration of an alveolar burst by back-cavity coupling. [Supported by a grant from NIDCD.]

γ-Ray burst observations from the UCB/LASL experiment on ISEE-3

Since the discovery of the intense bursts of γ rays by Klebesadel et al.1 in 1973, little progress has been made in the identification of the sources of the bursts. The initial observations provided directions to several sources with sufficient accuracy to establish the approximate isotropic distribution of the sources2, but not to provide a basis for association with any known objects, γ-ray burst astronomy entered a new phase in 1976 with the launch of Helios 2 and Solrad 11 containing the first experiments designed expressly for that purpose3,4. It then became possible to derive several error boxes that were very narrow in one dimension3,5. Using these data it was determined that the bursts are not associated with any type of object that has previously been recognized as a high energy astrophysical source—for example, X-ray sources. In 1978 a true interplanetary network of burst detectors was established with the launch of instruments on Pioneer Venus Orbiter6, ISEE 37–9 and Venera 11 and 1210,11. With baseline separations that can exceed 1 AU, this network provides a basis for the location of the γ burst sources with an accuracy sufficient for optical identifications. We briefly describe here one of these instruments, a joint Berkeley/Los Alamos experiment on the ISEE 3 spacecraft, and present some initial results from that experiment. These results are currently being combined with those from the other experiments to obtain accurate burst locations to be reported elsewhere.

Preliminary results from Solrad 11 γ-burst detectors

We present here temporal and 0.2-2 MeV spectral data from two gamma bursts observed on 12 June and 16 August 1976, by detectors on the Solrad 11A and 11B satellites. The 12 June burst showed evidence for structure on time scales down to approximately 10 ms throughout its lifetime, whereas the 16 August burst varied only with characteristic times longer than a few tenths of a second. A search for both slow and fast spectral variability gave negative results. Accurate absolute burst times are, however, not yet available, but since both bursts were also observed by at least one Vela satellite, positions are calculable and will be reported.