Modeling and Bayesian Analysis of Time between the Breakdown of Electric Feeders

Ateeq, Kahkashan; Altaf, Saima; Aslam, Muhammad

doi:10.1155/2022/5830945

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…The sum of the heat received at the hot source (gasket terminal) Q z (0), lost by convection at Q conv , and dissipated at Q Z (L) is calculated as expressed in Equations ( 31)- (33) [38,68]. Where B b is a constant of the Bessel function that varies according to alloy material, surface to surrounding heat convection, and contact resistance, which has values between 0 to the difference between the hot and cold terminals to go to zero.…”

Section: The Virtual Suggested Teg Designmentioning

confidence: 99%

“…Therefore, the authors suggest predicting the functional change of the gasket texture caused by cracks appearing by spatiotemporal thermal filming, which can feed the metaheuristic optimization technique supported by mathematical equations to gain accurate prediction results. The tailored digital-twin simulator model [31] consists of an object (TEG and gaskets), a measuring tool (STTF), and a predicting meta-heuristic optimization algorithm, which uses some of the physical parameters related to cracks (e.g., the span, depth, and length in m × 10 −4 ) that is measured by the cracks' center propagation digitally on the gasket surface texture, as discussed by [32,33]. Research is now being performed to overcome the fundamental drawback of thermoelectric generators, which is their relatively low efficiency, and to efficiently use them in a variety of applications for recovering waste heat.…”

Section: Introductionmentioning

confidence: 99%

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Building a Digital Twin Simulator Checking the Effectiveness of TEG-ICE Integration in Reducing Fuel Consumption Using Spatiotemporal Thermal Filming Handled by Neural Network Technique

2022

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Scholars seek to recycle wasted energy to produce electricity by integrating thermoelectric generators (TEGs) with internal combustion engines (ICE), which rely on the electrical conductivity, β, of the thermal conductor strips. The TEG legs are alloyed from iron, aluminum and copper in a strip shape with specific characteristics that guarantee maximum thermo-electric transformation, which has fluctuated between a uniform, Gaussian, and exponential distribution according to the structure of the alloy. The ICE exhaust and intake gates were chosen as the TEG sides. The digital simulator twin model checks the integration efficiency through two sequential stages, beginning with recording the causes of thermal conductivity failure via filming and extracting their data by neural network procedures in the feed of the second stage, which reveal that the cracks are a major obstacle in reducing the TEG-generated power. Therefore, the interest of the second stage is predicting the cracks’ positions, Pi,j, and their intensity, QP, based on the ant colony algorithm which recruits imaging data (STTF-NN-ACO) to install the thermal conductors far away from the cracks’ positions. The proposed metaheuristic (STTF-NN-ACO) verification shows superiority in the prediction over [Mat-ACO] by 8.2% and boosts the TEGs’ efficiency by 32.21%. Moreover, increasing the total generated power by 12.15% and working hours of TEG by 20.39%, reflects reduced fuel consumption by up to 19.63%.

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Section: The Virtual Suggested Teg Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Building a Digital Twin Simulator Checking the Effectiveness of TEG-ICE Integration in Reducing Fuel Consumption Using Spatiotemporal Thermal Filming Handled by Neural Network Technique

2022

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“…In the current study, the authors aimed to deepen the understanding of the Indus River's behavior by developing a Generalized Gumbel Type 2 (GG2) statistical distribution model, using the method proposed by (Alzaatreh, Lee, and Famoye 2013) which is a powerful technique to develop a new distribution and also implemented by (Alzaatreh and Knight 2013b;Alzaatreh and Ghosh 2015;Tahir et.al 2016;Jayakumar and Babu 2017;Oguntunde et al 2018;Ateeq, Qasim and Alvi 2019;Ateeq, Altaf and Aslam 2022;Ateeq, Altaf and Aslam 2022) etc. This GG2 model was specifically designed to analyze the dataset related to the river's outflow at Tarbela during the period of 2020-2021.…”

Section: Introductionmentioning

confidence: 99%

Statistical Modeling of Indus River Outflow at Tarbela Dam using Generalized Gumbel Type 2 Distribution

Ateeq,

Qasim,

Kiran

2024

Water Resour Manage

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The Indus River, a lifeline for Pakistan, holds paramount significance for its geography, history, and economy. This research delves into a comprehensive analysis of the river's behavior by introducing a novel statistical framework. Leveraging the Gumbel Type 2 distribution and the Rayleigh distribution, a new generalized Gumbel Type 2 (GG2) distribution is derived, and used for modeling the data about the river's outflow at the Tarbela Dam during 2020-2021. Our study contributes to the understanding of the complex dynamics of the Indus River, aiding in the sustainable management of its resources. The GG2 distribution, designed for extreme value events, adept at modeling positive-valued variables, were combined to model the intricate characteristics of the river's flow. Parameters were estimated using both classical and Bayesian methods, enhancing the accuracy and reliability of our findings. The incorporation of Bayesian techniques adds robustness to our parameter estimates and allows for a more comprehensive uncertainty analysis. The results not only deepen our understanding of the river's behavior but also offer insights crucial for infrastructure planning, flood control, and resource allocation.

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Statistical Modeling of Indus River Outflow at Tarbela Dam using Generalized Gumbel Type 2 Distribution

Ateeq,

Qasim,

Kiran

2023

Preprint

Self Cite

View full text Add to dashboard Cite

The Indus River, a lifeline for Pakistan, holds paramount significance for its geography, history, and economy. This research delves into a comprehensive analysis of the river's behavior by introducing a novel statistical framework. Leveraging the Gumbel Type 2 distribution and the Rayleigh distribution, a new generalized Gumbel Type 2 (GG2) distribution is derived, and used for modeling the data about the river's outflow at the Tarbela Dam during 2020–2021. Our study contributes to the understanding of the complex dynamics of the Indus River, aiding in the sustainable management of its resources. The GG2 distribution, designed for extreme value events, adept at modeling positive-valued variables, were combined to model the intricate characteristics of the river's flow. Parameters were estimated using both classical and Bayesian methods, enhancing the accuracy and reliability of our findings. The incorporation of Bayesian techniques adds robustness to our parameter estimates and allows for a more comprehensive uncertainty analysis. The results not only deepen our understanding of the river's behavior but also offer insights crucial for infrastructure planning, flood control, and resource allocation.

show abstract

Modeling and Bayesian Analysis of Time between the Breakdown of Electric Feeders

Cited by 3 publications

References 21 publications

Building a Digital Twin Simulator Checking the Effectiveness of TEG-ICE Integration in Reducing Fuel Consumption Using Spatiotemporal Thermal Filming Handled by Neural Network Technique

Building a Digital Twin Simulator Checking the Effectiveness of TEG-ICE Integration in Reducing Fuel Consumption Using Spatiotemporal Thermal Filming Handled by Neural Network Technique

Statistical Modeling of Indus River Outflow at Tarbela Dam using Generalized Gumbel Type 2 Distribution

Statistical Modeling of Indus River Outflow at Tarbela Dam using Generalized Gumbel Type 2 Distribution

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